Datasets:

gabrielaltay
/

pmcoa

Dataset card Data Studio Files Files and versions Community

Split (2)

train · 4.94M rows

text stringlengths 87 880k	pmid stringlengths 1 8	accession_id stringlengths 9 10	license stringclasses 2 values	last_updated stringlengths 19 19	retracted stringclasses 2 values	citation stringlengths 22 94	decoded_as stringclasses 2 values	journal stringlengths 3 48	year int32 1.95k 2.02k	doi stringlengths 3 61	oa_subset stringclasses 1 value
==== Front Int J Ment Health Addict Int J Ment Health Addict International Journal of Mental Health and Addiction 1557-1874 1557-1882 Springer US New York 957 10.1007/s11469-022-00957-0 Original Article A Longitudinal Relationship Between Mother’s Smartphone Addiction to Child’s Smartphone Addiction http://orcid.org/0000-0002-1126-9406 Jeong Kyu-Hyoung [email protected] 1 http://orcid.org/0000-0002-2644-8466 Kim Sunghee [email protected] 2 http://orcid.org/0000-0002-2566-4482 Ryu Ju Hyun [email protected] 2 http://orcid.org/0000-0003-4013-7987 Lee Seoyoon [email protected] 2 1 grid.443977.a 0000 0004 0533 259X Department of Social Welfare, Semyung University, 65 Semyung-Ro, Jecheon, 27136 Republic of Korea 2 grid.15444.30 0000 0004 0470 5454 Interdisciplinary Graduate Program in Social Welfare Policy, Yonsei University, 50 Yonsei-Ro, Seodaemun-Gu, Seoul, 03722 Republic of Korea 29 11 2022 112 29 10 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Children are more likely to become addicted as they become accustomed to using smartphones, and as they observe and imitate their parents using smartphones. This study aims to confirm longitudinally the effect of mother’s smartphone addiction on children’s smartphone addiction. Latent growth modeling was used to analyze longitudinal relationships between 3615 pairs of children and their mothers from the Korean Children and Youth Panel Survey (KCYPS) (2018–2020). As a result, both the mothers and children’s smartphone addiction significantly increased over time. The initial value of the mother’s smartphone addiction was found to have a significant effect on the child's initial value and the change rate. Moreover, children’s smartphone addiction change rate was significantly affected by the change rate of the mother’s smartphone addiction. To intervene in children’s smartphone addiction, a family-level approach, as well as parental addiction, must also be addressed, and a preventive approach should focus on those with a low risk of addiction. Keywords Smartphone addiction Mother Child Latent growth modeling ==== Body pmcIn our society, smartphones have already taken hold due to the convenience and usefulness of providing tailored programs to meet specific user needs. As a result, our lives have become more and more dependent on smartphone technology in recent years. This shift has resulted in smartphones being used for digital healthcare and online education, which have become major social functions (Alhat, 2020; Almallah & Doyle, 2020), as well as playing a role in activating social networks and alleviating loneliness (Iyengar et al., 2020; Wetzel et al., 2021). There has been much discussion regarding the negative and devastating effects of smartphones as well as their benefits. Similarly, although smartphones are claimed to have a negative relationship with mental health (Twenge et al., 2020), the relationship is still in active discussion (Twenge et al., 2020). However, the scientific evidence for the negative effects of smartphones is clearer when considering the user’s situation or personal characteristics, such as the risk of a driver’s traffic accident (Sánchez, 2006), child development, or behavior-related issues (Derevensky et al., 2019; Fecteau & Munoz, 2006; Pereira et al., 2020). Moreover, mixed results of smartphone usage in previous studies are due to the different conceptual definitions and measurement methods. Though there is no consensus definition of smartphone addiction, it is regarded as one of the behavioral addictions such as Diagnostic and Statistical Manual of Mental Disorders (DSM-5) dysfunction, withdrawal symptoms, tolerance, and compulsive behaviors (Kim, Lee, Lee, Nam & Chung, 2014). While behavioral addiction refers to an addiction to specific behaviors such as overeating, exercising, and games in a state where no substances are involved, media-related addictions, such as the Internet are sometimes classified as technology addiction, as the field of video media or technological development expands (Griffiths, 1995, 1998). Considering the conceptual definition of behavioral addiction and the smartphone addiction category, smartphone addiction cannot be self-regulating for problematic use, and it can be suggested to be defined as a state that interferes with daily activities as a result of compulsive, chronic usage. Conversely, since there are no strict standardized criteria for diagnosing smartphone addiction and reported problems with smartphone use are not synonymous with the existence of addiction (Panova et al., 2018), there are problems associated with this for using alternative terms, such as problematic smartphone use (Kuss et al., 2018) or self-reported dependence on mobile phones (Lopez-Fernandez et al., 2015). However, it is worth noting that smartphone problematic use is still associated with negative outcomes and can be understood in various contexts of users. In this point of view, the smartphone addiction prevention approach for adolescents in a society with a high smartphone addiction problem compared to other countries will have great value in solving potential problems and establishing meaningful policy agendas; one of the representative countries is South Korea. A systematic review of 31 papers conducted by Sohn et al. (2019) found that overall child addiction prevalence rates ranged between 10 and 30% in the world. However, according to a survey by the Ministry of Science and ICT (MSIT) and the National Information Society Agency (NIA) of Korea in 2022, the proportion of Korean children (ages 10 to 19) in the risk group for the dependence on the smartphones was higher (37.0%), compared with 23.3% for adults (MSIT & NIA, 2022). Furthermore, it is noteworthy that the children group showed the steepest increase compared to other age groups of 6.8% from 30.2% in the risk group in 2019 (MSIT & NIA, 2022). While children depend on their parents, they are also undergoing a developmental process to establish their own identity (Ting & Chen, 2020). As they become more accustomed to technology that allows them to communicate with their peers through their smartphones, they may be exposed to a high risk for smartphone addiction (Cha & Seo, 2018). Furthermore, because the first few years of a child’s life determine their lifelong trajectories, we should pay attention to their smartphone addiction during this period. Previous studies have shown that smartphone addiction in children neurologically causes attention deficits (Kim et al., 2019) and affects emotional inhibition and impulsivity (Chen et al., 2016). Moreover, it has been reported to cause psychological problems such as social anxiety and loneliness (Elhai et al., 2017), as well as sleep disturbances and serious disruption in daily life and social activities (Randler et al., 2016) due to compulsive use. Studies of predictive factors have been progressed as attention is taken into account when studying children’s smartphone addiction. Psychosocial factors (Lee et al., 2017), parents (Lee & Lee, 2017), and peers (Kim et al., 2018) have been reported as risk factors on smartphone addiction among children. It is particularly important to emphasize the role of parents, who have a more substantial effect on children’s behavior, such as contributing to or preventing smartphone addiction in children (Lee & Lee, 2017). Indeed, particularly, parental smartphone addiction predicts child smartphone addiction. In light of social learning theory (Bandura & Walters, 1977), the likelihood of addiction increases when children learn and imitate their parents’ smartphones usage, as children are influenced by their parents’ behavior (Kim et al., 2022). In one sense, parenting patterns including attachment or anxiety can cause children to be addicted to smartphones. For instance, when parents are stressed or depressed, they may neglect their children and loosely regulate their smartphone use (Kim et al., 2021). Although the mechanisms for smartphone addiction in children are interpreted differently by these two arguments, it is reasonable to believe that parents and children are closely connected in terms of smartphone addiction, based on the two mainstream arguments above. This relationship can be understood through the theory of imitation (Meltzoff, 2007; Meltzoff & Moore, 1977). In some empirical studies, it has been demonstrated that children can observe and mimic the behavior of their parents without any special stimulation. Moreover, parental influence on children’s smartphone addiction is also supported by mirror neuron theory (Son et al., 2021). This theory refers to the activity that the nerve cells called mirror neurons that convert the actions of others into neural signals and activate the actions of others through mirroring (Rizzolatti et al., 2010). The mirror neurons focus on observing the actions of others without making a distinction between not using the tool and tool-using behaviors; in this manner, it is possible to draw technical inferences about how to use a tool such as a smartphone by observing how others use it (Reynaud et al., 2019). However, mirror neurons interfere with reasoning about the intentions and psychological reasons underlying the observed behavior (Rizzolatti et al., 2010). Particularly in the case of children, the possibility of mirroring and activating the parent’s smartphone addiction behavior without a screening process is relatively high, as their cognitive judgment is not yet mature. Theoretically, parental smartphone addiction can be predicted as a strong risk factor for children’s smartphone addiction (Bornstein, 2012). However, in general, it is considered that mothers spend more time in contact with their children than fathers and play a central role in raising children (Collins & Russell, 1991). When the social learning theory and mirror neuron theory are applied, in spite of inferring the relative strength of mother–child interaction compared to father-child interaction in children’s smartphone addiction, smartphone-related studies (Matthes et al., 2021; Son et al., 2021) have been not distinguished between fathers and mothers, and detailed considerations on the influence of mothers were not addressed. A few studies (Kim et al., 2022; Kim et al., 2021; Lee & Lee, 2017; Song et al., 2019) have investigated the relationship between parents and children in smartphone addiction on the basis of these claims. It is difficult to confirm the evolution of addiction and the relationship over time since studies on the parent–child relationship in smartphone addiction were mostly based on cross-sectional approaches. This study, therefore, examines the longitudinal relationship between mothers’ smartphone addiction and children’s smartphone addiction within the context of Korea, which reports a relatively high level of smartphone penetration. Methods Data This study was analyzed using the Korean Children and Youth Panel Survey (KCYPS) conducted by the Korea National Youth Policy Institute. KCYPS is a representative survey of children and adolescents in Korea and their parents, providing basic data for establishing policies related to children and adolescents by establishing panel data that can comprehensively understand changes in the growth and development of children and adolescents. As of 2018, KCYPS conducted surveys on 5197 students and their parents (2607 students in 4th-grade elementary school, 2590 students in 1st-grade middle school). Three-year data from the first survey (2018–2020) and the third survey (2020) were used to analyze elementary school students (4th–6th grade), middle school students (1st–3rd grade), and their mothers. The KCYPS survey has been conducted from August to November every year. The final analysis included 3615 children (1752 elementary school students, 1863 middle school students) and 3615 mothers with no missing values in the main variables. Variables The dependent and independent variables of this study are smartphone addiction of children and mothers. According to the Korean Children and Adolescents Panel Survey, the Smartphone Addiction Proneness Scale (SAPS) developed by Kim, D. et al. (2014) was used as a reference. The smartphone addiction self-diagnosis scale is comprised of 15 items on a 4-point scale (highly disagree = 1, somewhat disagree = 2, somewhat agree = 3, highly agree = 4). Out of the 15 questions, “using a smartphone does not interfere with what I am currently doing (studying),” “I do not feel anxious even without a smartphone,” and “I do not spend a lot of time using a smartphone” were reverse-coded. The average of the items was calculated. Higher scores indicate greater smartphone addiction. In this study, Cronbach’s alpha for smartphone addiction among children was 0.884 in 2018, 0.872 in 2019, and 0.885 in 2020 and for smartphone addiction among mothers was 0.862 in 2018, 0.862 in 2019, and 0.869 in 2020. Statistical Analysis This study conducted a longitudinal analysis of mothers’ smartphone addiction and children’s smartphone addiction using the following methods and procedures. For data handling and model analysis, SPSS version 27.0 and M-plus 8.0 programs were used. We first performed a descriptive analysis to determine the characteristics of major variables. Secondly, latent growth modeling was conducted to estimate changes in smartphone addiction between mothers and children and to verify the relationship between the two. Lastly, to determine the model fit, Tucker-Lewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA) were used. Results Descriptive Statistics According to Table 1, the mother’s smartphone addiction steadily increased from an average of 1.78 points (standard deviation [SD] = 0.43) in 2018 to an average of 1.91 points (SD = 0.44) in 2020. In addition, children’s smartphone addiction showed a similar tendency; it increased from 2018 (Mean [M] = 1.92, SD = 0.50) to 2020 (M = 2.15, SD = 0.51).Table 1 Descriptive statistics Year Min Max Mean SD Mothers’ smartphone addiction 2018 1.00 3.80 1.78 .43 2019 1.00 3.47 1.88 .44 2020 1.00 3.87 1.91 .44 Children’s smartphone addiction 2018 1.00 3.87 1.92 .50 2019 1.00 3.80 2.05 .48 2020 1.00 3.80 2.15 .51 SD, standard deviation Model analysis In this study, the potential growth model was analyzed in two stages. In the first stage, the initial value and change rate of smartphone addiction in mothers and children are estimated with the analysis of unconditional model. In the second stage, using the initial value and change rate obtained in the first stage, the relationship between changes in mothers’ smartphone addiction and children’s smartphone addiction was examined. Analysis of Unconditional Model Firstly, an unconditional model analysis was conducted to understand the changes in smartphone addiction among mothers and children before proceeding with the conditional model analysis (Table 2). Through the unconditional model, the no growth model and linear growth model were analyzed in order to identify the optimal change pattern. The no growth model assumes that the smartphone addiction will not change over time, while in the linear growth model, it is based on the assumption that smartphone addiction will increase or decreases showing a constant pattern over time. The model fit criteria are CFI, TLI, and RMSEA; specifically, if the CFI and TLI are 0.9 or above (Bentler & Bonett, 1980), and the RMSEA is less than 0.1, the model is judged to be appropriate (Browne & Cudeck, 1992).Table 2 Model fit of unconditional model Model χ2 df CFI TLI RMSEA Mothers’ smartphone addiction No growth model 330.155* 4 .822 .866 .150 Linear growth model 24.386* 1 .987 .962 .068 Children’s smartphone addiction No growth model 615.739*** 4 .645 .734 .206 Linear growth model 6.556* 1 .997 .990 .039 p < .05, p < .01, p < .001 Following the analysis, the mother’s smartphone addiction (χ2 = 24.386 (p < 0.001), CFI = 0.987, TLI = 0.962, RMSEA = 0.068) and the child’s smartphone addiction (χ2 = 6.556 (p < 0.05), CFI = 0.997, TLI = 0.990, RMSEA = 0.039) showed that the linear growth model better explained the change in smartphone addiction than the no growth model; therefore, the linear growth model was selected (Table 2). According to the results of the final selected unconditional linear growth model, the average initial smartphone addiction scores for mothers and children were 1.790 (p < 0.001) and 1.924 (p < 0.001), respectively (Table 3). The rate of change was 0.067 (p < 0.001) for mothers and 0.116 (p < 0.001) for children, indicating that the rate of change in smartphone addiction in children was slightly higher. Nevertheless, both the average and the change rate of smartphone addiction among mothers and children were significant, indicating that smartphone addiction increases over time. In addition, the variance of smartphone addiction among mothers and children was found to be significant both at the initial value and at the rate of change, indicating that there is a significant difference in the initial level and rate of change of smartphone addiction between the two.Table 3 Mean and variance of initial score and rate of change of unconditional model Variables Mean Variance Correlations Estimate S.E Estimate S.E Mothers’ smartphone addiction Initial score 1.790* .007 .093* .006 − .024 Rate of change .067* .004 .014* .003 Children’s smartphone addiction Initial score 1.924* .008 .128* .008 − .182* Rate of change .116* .005 .023*** .004 p < .05, p < .01, p < .001 The correlation between the initial value of smartphone addiction and the rate of change was significantly negative for both mothers and children, and it was confirmed that the group with the higher initial value of smartphone addiction increased less than the group with the lower initial value. Analysis of Conditional Model In the conditional model analysis, we examined how the initial value and rate of change of a mother’s smartphone addiction affect the initial value and rate of change of a child’s smartphone addiction. As a result of conditional model fit analysis, it was found that the model showed appropriateness for analysis (χ2 = 211.198 (p < 0.001), CFI = 0.929, TLI = 0.917, RMSEA = 0.082). Table 4 and Fig. 1 show the relationship between the initial value and the rate of change of mother’s and children’s smartphone addiction. The initial value of the mother’s smartphone addiction was found to have a significant effect on the child’s smartphone addiction initial value (Coef. = 0.254, p < 0.001) and the change rate (Coef. = 0.109, p < 0.05). In other words, it was analyzed that the higher the mother’s smartphone addiction in the early stage, the higher the child’s smartphone addiction, and the sharp increase in children’s smartphone addiction over time.Table 4 Path between variables Path between variables Coef S.E Mothers’ smartphone addiction initial value → Children’s smartphone addiction initial value .254* .031 Mothers’ smartphone addiction initial value → Children’s smartphone addiction rate of change .109* .022 Mothers’ smartphone addiction rate of change → Children’s smartphone addiction rate of change .893* .110 p < .05, p < .01, **p < .001 Fig. 1 Result of the model analysis in this study Moreover, children’s smartphone addiction change rate was significantly affected by the change rate of the mother’s smartphone addiction (Coef. = 0.893, p < 0.01). In other words, the child’s smartphone addiction also increased sharply with the passage of time as the mother’s addiction did. Thus, the results confirmed that as mothers’ smartphone addiction gradually increased, children’s smartphone addiction gradually increased as well. Discussion The purpose of this study is to longitudinally confirm the effect of the mother’s smartphone addiction on children’s smartphone addiction. For this purpose, data from KCYPS targeting children and adolescents in Korea which has a high level of smartphone penetration was used. Latent growth modeling was used to analyze longitudinal relationships between 3615 pairs of children from elementary and middle schools and their mothers from 2018 to 2020. The main research findings derived and the main issues to be discussed are as follows. Over time, both mothers and children reported increasing levels of smartphone addiction. The average of the initial values was higher and the rate of change was higher in children than in adult mothers; therefore, it was confirmed that children’s smartphone addiction could lead to serious consequences. This is consistent with a previous study which reported that children (ages 10 to 19) had the highest percentage of smartphone addiction risk group and showed the steepest increase compared to other age groups (MSIT & NIA, 2022). This shows the seriousness of smartphone addiction among children, and at the same time shows that measures to reduce its need to be taken. These results are not only limited specifically to Korea. Globally, smartphone ownership by children has increased (OECD, 2017), and Sohn et al. (2019) reviewed studies related to problematic smartphone usage (PSU) in Europe, Asia, and the USA; 10 to 30% of the prevalence of smartphone addiction has been reported. In addition, mothers who are adult parents also showed an increase in their level of smartphone addiction longitudinally, indicating that mothers also have a risk of aggravating their addiction. Considering that the period of COVID-19 was included in the analysis, it is inevitable to not exclude the impact of the pandemic in this study. However, the seriousness of the addiction problem is being highlighted as the Internet and smartphone use rapidly increases as a result of the abrupt environmental change due to COVID-19 (Daglis, 2021). As a result, both parents and their children have increased their dependence on smartphones in this study, indicating that smartphone addiction is a problem that may exacerbate in the future due to the pandemic. As a result of examining the correlations between the initial value of smartphone addiction and the rate of change, both mothers and children were significantly negative. This means that the group with a high initial value of smartphone addiction increases less than the group with a low initial value, and the group with a low initial value of smartphone addiction increases more than the group with a high initial value. Therefore, the result of the study shows that the group with a low level of addiction in the early stages may rapidly increase their level of addiction with the passage of time. These results indicate that interventions targeting high-risk groups are important for smartphone addiction interventions, but also addressing those with low addiction levels is critical. While smartphone addiction may not seem like a serious problem now, it is necessary to take a preemptive intervention and preventive approach to prevent it from escalating. Finally, we found that a mother’s smartphone addiction had a longitudinal effect on children’s smartphone addiction. The mother’s initial value of smartphone addiction affected both the initial value and rate of change in the children’s addiction, and the mother’s change rate of smartphone addiction had a significant effect on the child’s change rate of smartphone addiction. In other words, a mother’s high smartphone addiction level predicts the child’s high smartphone addiction level as well as a rapid increase over time. If the mother’s smartphone addiction increases rapidly, the child’s smartphone addiction will also increase steeply accordingly. Indeed, according to previous studies, the effects of parents’ smartphone addiction on their children have been extensively studied (Kim et al., 2022; Lee & Lee, 2017; Son et al., 2021). In this study, this has once again been confirmed longitudinally and can be utilized as the result of this study to be in evidence to emphasize that parental smartphone addiction is an important intervention target. However, traditionally parents’ role in guiding children’s smartphone addiction was dominant (Putra et al., 2022), while there has been limited consideration of their role as primary targets of children’s smartphone addiction. This study’s findings regarding the smartphone addiction patterns of adult parents and the longitudinal impact on children indicate that adult parents should be considered an addiction subject as well as the importance of intervention. Unfortunately, however, existing studies on smartphone addiction for adults are mostly limited to college students and office workers (Olson et al., 2021), and intervention in addiction is also treated less important than children and adolescents (Kim, 2020). The longitudinal results presented in this study provide several implications for smartphone addiction-related interventions. Furthermore, the global COVID-19 pandemic situation is working as a double-edged sword of addiction, while the importance of communication via smartphones is highlighted (Yu & Liu, 2019). As a matter of fact, it is observed that among those who have experienced self-isolation, telecommuting, and online classes as a result of COVID-19, the smartphone dependence risk group and high-risk group account for a higher proportion than the general user group, and rather than the overdependence risk group, general users are utilizing smartphones as a way to control their children (MSIT & NIA, 2022). The prevention of smartphone addiction is urged more than ever, but research on prevention and treatment is lacking in comparison (Liu, 2021). This study is expected to provide a practical basis for the prevention and treatment of smartphone addiction in children. Due to the use of secondary data, this study has limitations; the number of fathers’ cases was insufficient so we could not analyze them together with their mothers. Therefore, it may be difficult to generalize as the parent’s case. In this study, fathers were not considered, although they may influence their children as primary parent as well. To determine the ultimate impact of all caregivers in the home, it is recommended to proceed in a direction that involves all of the parties involved. In addition, the last data in this study (2020) was gathered after the outbreak of COVID-19, and considering the impact of the pandemic on smartphone addiction, it is possible that this might have impacted the study. It is suggested that further study should be conducted with a long-term perspective in order to consider the impact of COVID-19 on smartphone addiction. Moreover, this study examined the effects of parental smartphone addiction on children based on previous research and theories. It is also significant to examine the reversed effect and the details of the mechanisms of how parents and children become addicted to smartphones. Follow-up studies that utilize a variety of research methods and variables for preventing and intervening in smartphone addiction are suggested to be discussed. The significance of this study lies in its ability to clarify the effects of smartphone addiction among Korean children and their mothers, who have the highest smartphone penetration rate, through a longitudinal review rather than a cross-sectional analysis. Conclusions This study shows that a mother’s smartphone addiction influenced her children’s smartphone addiction. The result of this study suggests that in order to intervene in children’s smartphone addiction, the study stresses the importance of a family-level approach, and parental addiction must also be addressed. In addition, a preventive approach should target those with a low risk of addiction. Author Contribution The study concept and design were handled by KHJ and SHK. KHJ analyzed the data. SL, SHK, and JHR interpreted the findings and drafted the manuscript. The final version of the manuscript was approved by all authors after critical review. Declarations Ethics Approval and Consent to Participate This report was exempted from approval by the institutional review boards (IRB) of the Clinical Research Ethics Committee of Semyung University (IRB number: 2022–04-001). Every participant gave a written consent prior to their participation in the study. Competing Interests The authors declare no competing interests. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Alhat S Virtual classroom: A future of education post-COVID-19 Shanlax International Journal of Education 2020 8 4 101 104 10.34293/education.v8i4.3238 Almallah YZ Doyle DJ Telehealth in the time of Corona: ‘Doctor in the house’ Internal Medicine Journal 2020 50 12 1578 1583 10.1111/imj.15108 33354885 Bandura A Walters RH Social learning theory 1977 Prentice Hall Bentler PM Bonett DG Significance tests and goodness of fit in the analysis of covariance structures Psychological Bulletin 1980 88 588 606 10.1037/0033-2909.88.3.588 Bornstein, M. H. (2012). Caregiver responsiveness and child development and learning: From theory to research to practice. In P. L. Mangione (Ed.), Infant/toddler caregiving: A guide to cognitive development and learning (2nd ed., pp. 11– 25). California Department of Education Browne MW Cudeck R Alternative ways of assessing model fit Sociological Methods & Research 1992 21 2 230 258 10.1177/0049124192021002005 Cha SS Seo BK Smartphone use and smartphone addiction in middle school students in Korea: Prevalence, social networking service, and game use Health Psychology Open 2018 5 1 2055102918755046 10.1177/2055102918755046 29435355 Chen J Liang Y Mai C Zhong X Qu C General deficit in inhibitory control of excessive smartphone users: Evidence from an event-related potential study Frontiers in Psychology 2016 7 511 10.3389/fpsyg.2016.00511 27148120 Collins WA Russell G Mother-child and father-child relationships in middle childhood and adolescence: A developmental analysis Developmental Review 1991 11 2 99 136 10.1016/0273-2297(91)90004-8 Daglis T The Increase in Addiction during COVID-19 Encyclopedia 2021 1 4 1257 1266 10.3390/encyclopedia1040095 Derevensky JL Hayman V Gilbeau L Behavioral addictions: Excessive gambling, gaming, Internet, and smartphone use among children and adolescents Pediatric Clinics 2019 66 6 1163 1182 10.1016/j.pcl.2019.08.008 31679605 Elhai, J. D., Dvorak, R. D., Levine, J. C., & Hall, B. J. (2017). Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. Journal of Affective Disorders, 207, 251–259. Fecteau JH Munoz DP Salience, relevance, and firing: A priority map for target selection Trends in Cognitive Sciences 2006 10 8 382 390 10.1016/j.tics.2006.06.011 16843702 Griffiths MD Technological addictions Clinical Psychology Forum, Division of Clinical Psychology of the British Psychological Society 1995 76 14 19 Griffiths MD Gackenbach J Internet addiction: Does it really exist? Psychology and the Internet: Intrapersonal, Interpersonal, and Transpersonal Inmplications 1998 Academic Press Iyengar K Upadhyaya GK Vaishya R Jain V COVID-19 and applications of smartphone technology in the current pandemic Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2020 14 5 733 737 10.1016/j.dsx.2020.05.033 Kim, D., Lee, Y., Lee, J., Nam, J. K., & Chung, Y. (2014). Development of Korean smartphone addiction proneness scale for youth. PloS One, 9(5), e97920. Kim HJ Min JY Min KB Lee TJ Yoo S Relationship among family environment, self-control, friendship quality, and adolescents’ smartphone addiction in South Korea: Findings from nationwide data PloS one 2018 13 2 e0190896 10.1371/journal.pone.0190896 29401496 Kim, S. G., Park, J., Kim, H. T., Pan, Z., Lee, Y., & McIntyre, R. S. (2019). The relationship between smartphone addiction and symptoms of depression, anxiety, and attention-deficit/hyperactivity in South Korean adolescents. Annals of general psychiatry, 18(1), 1–8. Kim DJ A systematic review on the intervention program of smartphone addiction Journal of the Korea Academia-Industrial cooperation Society 2020 21 3 276 288 10.5762/KAIS.2020.21.3.276 Kim B Han S Park EJ Yoo H Suh S Shin Y The relationship between mother’s smartphone addiction and children’s smartphone usage Psychiatry Investigation 2021 18 2 126 10.30773/pi.2020.03 33517617 Kim SH Jeong KH Ryu JH The effects of parental smartphone addition sub-factors on children’s smartphone addiction The Journal of Humanities and Social Sciences 2022 30 1 31 49 10.22924/jhss.30.1.202202.002 Kuss D Harkin L Kanjo E Billieux J Problematic smartphone use: Investigating contemporary experiences using a convergent design International Journal of Environmental Research and Public Health 2018 15 1 142 10.3390/ijerph15010142 29337883 Lee C Lee SJ Prevalence and predictors of smartphone addiction proneness among Korean adolescents Children and Youth Services Review 2017 77 10 17 10.1016/j.childyouth.2017.04.002 Lee H Kim JW Choi TY Risk factors for smartphone addiction in Korean adolescents: Smartphone use patterns Journal of Korean Medical Science 2017 32 10 1674 1679 10.3346/jkms.2017.32.10.1674 28875613 Liu XX A systematic review of prevention and intervention strategies for smartphone addiction in students: applicability during the COVID-19 pandemic Journal of Evidence-Based Psychotherapies 2021 21 2 3 36 10.24193/jebp.2021.2.9 Lopez-Fernandez O Short version of the Smartphone Addiction Scale adapted to Spanish and French: Towards a cross-cultural research in problematic mobile phone use Addictive Behaviors 2015 64 275 280 10.1016/j.addbeh.2015.11.013 26685805 Matthes J Thomas MF Stevic A Schmuck D Fighting over smartphones? Parents’ excessive smartphone use, lack of control over children’s use, and conflict Computers in Human Behavior 2021 116 106618 10.1016/j.chb.2020.106618 Meltzoff AN Moore MK Imitation of facial and manual gestures by human neonates Science 1977 198 75 78 10.1126/science.198.4312.75 17741897 Meltzoff AN ‘Like me’: a foundation for social cognition Developmental science 2007 10 1 126 134 10.1111/j.1467-7687.2007.00574.x 17181710 Ministry of Science and ICT(MSIT) & National Information Society Agency(NIA). (2022). 2021 Smartphone Overdependence Survey. Retrieve from https://www.nia.or.kr/site/nia_kor/ex/bbs/View.do;jsessionid=32AE282D1232C0D23BBAE173C5EE5C4C.2894582dd32606361891?cbIdx=65914&bcIdx=24288&parentSeq=24288. Accessed 3 Apr 2022 OECD PISA 2015 Results (Volume III): Students’ Well-Being 2017 OECD Publishing Olson, J. A., Sandra, D. A., Colucci, É. S., Al Bikaii, A., Chmoulevitch, D., Nahas, J., ... & Veissière, S. P. (2021). Smartphone addiction is increasing across the world: A meta-analysis of 24 countries. Computers in Human Behavior 107138 Panova T Carbonell X Is smartphone addiction really an addiction? Journal of Behavioral Addictions 2018 7 2 252 259 10.1556/2006.7.2018.49 29895183 Pereira FS Bevilacqua GG Coimbra DR Andrade A Impact of problematic smartphone use on mental health of adolescent students: Association with mood, symptoms of depression, and physical activity Cyberpsychology, Behavior, and Social Networking 2020 23 9 619 626 10.1556/2006.7.2018.49 32580574 Putra, A. R., Handayani, B., Darmawan, D., Retnowati, E., Sinambela, E. A., Ernawati, E., ... & Lestari, U. P. (2022). Relationship between parenting parenting and smartphone use for elementary school age children during the Covid 19 pandemic. Bulletin of Multi-Disciplinary Science and Applied Technology, 1(4):138-141 Randler, C., Wolfgang, L., Matt, K., Demirhan, E., Horzum, M. B., & Beşoluk, Ş. (2016). Smartphone addiction proneness in relation to sleep and morningness-eveningness in German adolescents. Journal of Behavioral Addictions, 5(3), 465–473. Reynaud E Navarro J Lesourd M Osiurak F To watch is to work: A review of neuroimaging data on tool use observation network Neuropsychology Review 2019 29 4 484 497 10.1007/s11065-019-09418-3 31664589 Rizzolatti G Sinigaglia C The functional role of the parieto-frontal mirror circuit: Interpretations and misinterpretations Nature Reviews Neuroscience 2010 11 4 264 274 10.1038/nrn2805 20216547 Sánchez. E. (2006). What effects do mobile phones have on people’s health? Copenhagen, WHO Regional Office for Europe (Health Evidence Network report; http://www.euro.who.int/document/e89486.pdf. Accessed 22 Aug 2022 Sohn SY Rees P Wildridge B Kalk NJ Carter B Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: A systematic review, meta-analysis and GRADE of the evidence BMC Psychiatry 2019 19 1 1 10 10.1186/s12888-019-2350-x 30606141 Son HG Cho HJ Jeong KH The effects of Korean parents’ smartphone addiction on Korean children’s smartphone addiction: Moderating effects of children’s gender and age International Journal of Environmental Research and Public Health 2021 18 13 6685 10.3390/ijerph18136685 34206185 Song SM Park B Kim JE Kim JE Park NS Examining the relationship between life satisfaction, smartphone addiction, and maternal parenting behavior: A south Korean example of mothers with infants Child Indicators Research 2019 12 4 1221 1241 10.1007/s12187-018-9581-0 Ting CH Chen YY Smartphone addiction Adolescent Addiction 2020 Academic Press 215 240 Twenge JM Haidt J Joiner TE Campbell WK Underestimating digital media harm Nature Human Behaviour 2020 4 4 346 348 10.1038/s41562-018-0506-1 32303719 Wetzel B Pryss R Baumeister H Edler JS Gonçalves ASO Cohrdes C “How Come You Don’t Call Me?” Smartphone Communication App Usage as an Indicator of Loneliness and Social Well-Being across the Adult Lifespan during the COVID-19 Pandemic International Journal of Environmental Research and Public Health 2021 18 12 6212 10.3390/ijerph18126212 34201322 Yu ZY Liu W A meta-analysis of relation of smartphone use to anxiety, depression and sleep quality Chinese Mental Health Journal 2019 33 12 938 943 10.3969/j.issn.1000-6729.2019.12.010	36465996	PMC9707412	NO-CC CODE	2022-12-01 23:20:23	no	Int J Ment Health Addict. 2022 Nov 29;:1-12	utf-8	Int J Ment Health Addict	2,022	10.1007/s11469-022-00957-0	oa_other
==== Front Mol Biotechnol Mol Biotechnol Molecular Biotechnology 1073-6085 1559-0305 Springer US New York 36445610 608 10.1007/s12033-022-00608-8 Original Paper Melanin Treatment Effect of Vacuoles-Zinc Oxide Nanoparticles Combined with Ascorbic Acid Jeon Gyeongchan [email protected] 1 Choi Hyojin [email protected] 1 Park Dong-Jun [email protected] 2 Nguyen Ngoc-Tu [email protected] 3 Kim Yang-Hoon [email protected] 34 http://orcid.org/0000-0001-6025-7746 Min Jiho [email protected] 1 1 grid.411545.0 0000 0004 0470 4320 Graduate School of Semiconductor and Chemical Engineering, Jeonbuk National University, 567 Baekje-Daero, Deokjin-Gu, Jeonju-Si, Jeollabuk-do 54896 Republic of Korea 2 grid.266100.3 0000 0001 2107 4242 Department of Surgery, University of California, San Diego, USA 3 grid.254229.a 0000 0000 9611 0917 Center for Ecology and Environmental Toxicology (CEET), Chungbuk National University, 1 Chungdae-Ro, Seowon-Gu, Cheongju, Chungbuk-Do 28644 South Korea 4 grid.254229.a 0000 0000 9611 0917 School of Biological Science, Chungbuk National University, Chungdae-Ro 1, Seowon-Gu, Cheongju, Chungbuk-do 28644 Republic of Korea 29 11 2022 110 27 9 2022 7 11 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Currently, ascorbic acid (AA) is widely used as a skin whitening material, but, AA, an unstable hydrophilic molecule, cannot penetrate the skin easily, due to the hydrophobic character of the stratum corneum. Therefore, we conjugated AA with hydrated zinc oxide—an inorganic matrix with positive surface charge, to improve the stability of AA. The metal-conjugated-ascorbic acid (ZnAA) was then combined with yeast vacuole through the vacuolar membrane proteins that relate to metal transportation to create an enhanced vacuole that contained ZnAA. The characteristics of vacuole with ZnAA (ZnAA_Vac) were next examined by various tests that included X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT–IR), Field emission scanning electron microscopy (FE–SEM), and energy-dispersive X-ray (EDX) analysis. Furthermore, the ability of ZnAA_Vac to degrade melanin was confirmed in both melanoma cell line B16F10, and the artificial human skin MelanoDerm. The results showed that ZnAA_Vac possessed a higher depigmenting effect than the wild-type vacuole or ascorbic acid by reducing 75% of melanin color. Interestingly, ZnAA_Vac was found to be harmless, and did not cause any cytotoxicity to the cells. Overall, ZnAA_Vac is expected to provide a robust, harmless, and effective whitening agent for the skin. Keywords Nano particles Ascorbic acid Metal conjugation Vacuole Depigmentation Human artificial skin National Research Foundation of Korea2020R1A6A1A06046235 Kim Yang-Hoon ==== Body pmcIntroduction Melanin is one of the main factors that regulate the pigmentation in human skin, besides carotenoids and hemoglobin [1]. There are two main types of melanin: pheomelanin (yellow/red soluble polymer) and epidermal melanin (black/brown insoluble polymer) [2, 3], which is the principal melanin synthesized in melanosome inside the melanocytes in the skin [4]. Even though melanin could protect the skin from the effect of DNA photodamage by absorbing and scattering UV rays, which reduces their penetration through the epidermis [5], overproduction of melanin could cause several skin pigmentation disorders, which include melasma, post-inflammatory melanoderma, and dark spot on the skin (freckles) [6]. In recent years, substantial investment has been made in the skin whitening cosmetics industry, and despite the impact of Sars-CoV-2, the global market for skin whitening products in 2020 was appraised at around 8.6 billion dollars, and by 2027, may reach 12.3 billion dollars [7]. Nevertheless, skin lightening agents could also harm human health, including inducing high blood pressure, kidney disease [8], dermatoses [9], or more seriously, cancer [10]. Thus, a novel bleaching agent is needed that could degrade melanin without adverse effects on human health. Ascorbic acid (AA) is a natural water-soluble vitamin (vitamin C) and a nutrient that is necessary for several processes in the human body [11]. First, AA is a potent reducing agent and antioxidant that decreases endothelial permeability, enhances vascular function, and weakens cellular apoptosis in the pathological phase [12]. In addition, as an antioxidant, AA is an essential factor that protects the skin from free radical damage, pollutants, toxins [13], and is involved in a collagen synthesis, which is crucial for curing injury [14]. Interestingly, AA is also a depigmenting factor that can reduce melanin by inhibiting the tyrosinase—an important enzyme involved in the melanogenesis process [15]. Most mammals could synthesize AA in the liver, but the non-functional l-gulono-gamma lactone oxidase (GULO) gene means that human can only absorb vitamin C by oral supplementation [11, 16]. In this study, we report a novel potential whitening agent that combines AA and zinc nitrate hexahydrate with yeast vacuoles that we have invented. AA is an unstable hydrophilic molecule, and cannot penetrate the skin easily, due to the hydrophobic character of the stratum corneum [17]. Therefore, the conjugation of AA with other agents, such as hydrated zinc oxide (Zn(NO3)2·6H2O)—an inorganic matrix with positive surface charge, could improve the stability of AA to overcome its limitation in the field of cosmetic [18]. Here, we developed an encapsulating approach of AA from the work of Yang et al. with a modification using yeast vacuoles [18]. Lysosome-like-vacuole in yeast was previously discovered to degrade the melanin [19, 20], and the combination of yeast vacuole and AA could strengthen the original AA to create a powerful whitening agent. In this study, a synthetic material containing ascorbic acid and zinc ion was achieved by a coprecipitation reaction. The characteristics of vacuole with ZnAA (ZnAA_Vac) were next examined by various tests that included X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT–IR), Field emission scanning electron microscopy (FE–SEM), and energy-dispersive X-ray (EDX) analysis. And, the melanin reduction test was conducted with melanoma cell line B16F10 and the three-dimensional human skin model MelanoDerm from MatTek. Interestingly, yeast vacuole is the primary site for zinc sequestration, and has two vacuolar zinc transporters Zrt3 and Zrc1, which are known to be involved in zinc’s transportation vacuole [21–23]. Therefore, zinc ion in the synthesis product would drive the conjugation zinc ion-ascorbic acid (ZnAA) to the vacuole, which technique could be used to carry drug [24, 25]. In summary, we have created a novel whitening agent that could reduce melanin using vitamin C, zinc ion, and yeast vacuoles. The nanoparticles were non-toxic, stable and suitable for human skin treatment. Materials and Methods Cell Culture and Materials B16F10 (KCLB 80080) cells were grown at 37 °C under 5% CO2. The cells were cultured in RPMI 1640 medium adding 10% NCS (Newborn calf serum, Cat No: 16010-159, GIBCO), 5 mM glutamine (Cat No: 56-85-9, Sigma Aldrich), 25 mM HEPES (Cat No: 7365-45-9, Sigma Aldrich) and 1% penicillin–streptomycin (Cat No: 15140-122, ThermoFisher). The media was exchanged every conducive day, and we treated ZnAA as the cells were grown at about 70%. Saccharomyces cerevisiae s2805 (ATCC 208280) was kindly provided by the Korea Research Institute of Bioscience and Biotechnology (KRIBB). S. cerevisiae was grown to mid-log phase in YPD medium (1% yeast extract, 2% peptone, 2% glucose) at 30 °C in a shaking incubator at 180 rpm. Encapsulation of Ascorbic Acid by Co Precipitation with Zinc Nitrate Hexahydrate (Zn(NO3)2·6H2O) 0.05 M zinc nitrate hexahydrate (Zn(NO3)2·6H2O, Cat No: 10196-18-6, Sigma Aldrich) was dissolved in distilled water (DW), then 0.1 M l-ascorbic acid (AA, Cat No: 50-81-7, Sigma Aldrich) was added in metal solution. The pH of the reaction solution was calibrated to pH 6.7 by adding NaOH with forceful stirring. To reduce the disintegration of AA and air contamination during the synthesis, nitrogen gas was flowed continuously through the reaction solvent. A white suspension appeared, and was kept at room temperature for 12 h, before being filtered and washed by DW. Finally, a vitamin C-hydrated zinc oxide hybrid was prepared, and called ZnAA [18, 26]. Figure 1 shows the synthetic process.Fig. 1 The encapsulation of ascorbic acid by coprecipitation with zinc nitrate hexahydrate (Zn(NO3)2·6H2O). Zn(NO3)2·6H2O was dissolved in distilled water, then l-ascorbic acid was added in metal solution, and the mixture was kept at room temperature for 12 h. then it was filtered and washed by DW, and freeze-dried The characteristics of ZnAA were analyzed multi-purpose high performance X-ray diffractometer (XRD, X’PERT-PRO Powder, Malvern Panalytical, England) operating at 40 kV and 30 mA with Cu–Kα radiation (λ = 1.5404) for the fixed samples on a microscope slide. Furthermore, ZnAA was characterized by Fourier transform infrared spectroscopy (FT–IR, Perkin Elmer Spectrum GX, USA). Each FT–IR spectrum was collected at a resolution of 2 cm−1 from (4000 to 600) cm−1. Vacuole Isolation from S. cerevisiae Saccharomyces cerevisiae was cultured in YPD medium at 30 °C for 24 h. Cells were harvested and mixed with 0.1 M Tris-SO4 buffer (100 mM Tris-SO4 (pH 9.4) and 10 mM dithiothreitol (DTT)). This mixture was incubated at 30 °C (90 rpm) for 10 min, followed by centrifugation at 3000 rpm for 5 min. The supernatant was discarded, and the pellet was mixed with lyticase for 1 h to make the cell membrane flexible. After that, the mixture was centrifuged at 3000 rpm for 5 min, and the pellet was washed twice with Sorbitol K + phosphate buffer. For the next step, breaking buffer (20 mM Tris–Cl (pH 7.4), 0.6 mM Sorbitol, and 1 mM phenyl methane sulfonyl fluoride (PMSF)) were mixed with the pellet, followed by ultra-sonication at 40 W for 30 min (20 s on/10 s off pulse) on ice. The mixture was centrifuged at 3000 rpm for 10 min, and the supernatant was discarded. The pellet was remixed with breaking buffer, followed by a second ultra-sonication for 20 min (10 s on/10 s off pulse). After centrifugation at 500×g for 5 min, the supernatant was collected. The remaining pellet was subjected to a second centrifugation at 20,000×g for 30 min, and the pellet containing vacuoles from cells was collected [27]. Liquid Chromatography–Mass Spectrometry (LC–MS) Saccharomyces cerevisiae was exposed individually to AA and ZnAA for 24 h. After exposing samples to the cells, the remaining AA and ZnAA in the medium would be determined. The analysis used column C18 (150 mm × 4.6 mm) which was maintained at 20 °C. The LC–MS data were collected, and processed by MassHunter software (Agilent). Field Emission Scanning Electron Microscope (FE–SEM) with Energy-Dispersive X-ray (EDX) Spectroscopy The morphology of ZnAA was observed by Field emission scanning electron microscopy (FE–SEM) (Carl Zeiss SUPRA, Germany). Samples were softened with DW, and were then prepared using the freeze-drying process [28]. While taking the image of ZnAA, electron was immediately analyzed by EDX spectroscopy. Melanin Degradation Test After being exposed to various samples, the melanoma cell line B16F10 was washed twice with Phosphate-buffered saline (PBS); after that, it was dissolved in NaOH with 10% DMSO, and heated at 80 °C for 1 h. The amount of melanin was determined by spectrophotometry at the absorbance of 400 nm [20]. Skin Whitening Test with Human Skin Model MelanoDerm MelanoDerm (MatTek Corp., Ashland, NA) was used for skin whitening test as a viable three-dimensional human skin that includes normal melanocytes and keratinocytes. The cells are derived from African–American (MEL-B), Asian (MEL-A), or white (MEL-C) donors. MEL-B tissues were used in this study and were maintained in the NMM-113 medium. The MelanoDerm tissues were fed daily with 5 mL of fresh NMM-113 and various vacuole concentrations or ZnAA_Vac for 12 days [29]. Measurement of Melanin Content Melanin content was measured as described in the previous work [29]. The skin tissue was taken out of the culture dish after 12 days, then moved in an Eppendorf tube and homogenized for 1 min in lysis buffer (20 mM Tris–Cl pH 7.4, 1 mM EDTA pH 8.8, 1 mM EGTA pH 8.5, 1% v/v Triton X-100). After homogenization, 20 μL protease K (5 mg/mL) was supplied, and the cells incubated at 45 °C overnight. The next day, 20 μL protease K was added for 4 h. Next, 50 μL 0.5 M sodium carbonate and 10 μL 30% H2O2 were added, and incubated at 80 °C for 30 min. After that, 100 μL of a 2 chloroform: 1 MtOH mixture was added, followed by centrifugation at 10,000×g for 10 min. The sample supernatant was used to determine melanin content by spectrophotometry at the absorbance of 405 nm. Cell Viability The cell viability test for MelanoDerm was described previously [30]. The tissues were washed twice by DPBS, before adding 300 μL MTT solution. All tissue samples were covered to protect from light and incubated 37 °C for 3 h. After being cleaned by sterilized gauze, the tissue samples were moved to a new plate, and then isopropanol and 0.04 M HCl were added. At the next step, the samples were stirred at 120 rpm without air in the anaerobic bag at RT for 2 h. Finally, the solutions were moved to an Eppendorf tube, and measured by ELISA plate reader on 96-well plates at the absorbance of 570 nm. Data Analysis All data were obtained from three independent samples, run simultaneously for error analysis, and the results are reported with the standard deviation. The data were analyzed using Sigma Plot (SPS, Chicago, IL. USA), and a p-value < 0.05 was considered significant. Results Characterizations of the Synthetic ZnAA The properties of the synthesis product between ascorbic acid and zinc nitrate hexahydrate were analyzed by X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT–IR). Figure 1 presents the encapsulating process of AA with zinc ion, while Fig. 2 provides the XRD patterns that show the crystalline structure through the specific diffraction peaks of ZnO (peaks (002), (110), and (201) observed at 2θ = (34.1, 58.8, and 69.2)°, respectively), as reported previously [31–35].Fig. 2 XRD patterns of synthesis material ZnAA. The ZnAA were analyzed by X-ray diffraction (XRD) spectroscopy operating at 40 kV and 30 mA with Cu–Kα radiation (λ = 1.5404) for the fixed samples on a microscope slide Figure 3 displays the characteristics of ZnAA by FT–IR and FE–SEM. Figure 3a shows that there were strong absorption bands in the ascorbic acid sample at (1686 and 1773) cm−1, which reflect the C=C and C=O stretching vibrations, respectively, while several bands from (1000 to 1500) cm−1 are related to C–O–C, CH2 scissoring, and other C–H modes, as reported previously [36, 37]. On the other hand, Fig. 3b depicts the FT–IR spectra of ZnAA in which a vibration band appears at 503 cm−1, indicating the formation of ZnO. Additionally, the peak at 3452 cm−1 shows O–H stretch, while peaks at (1397 and 1496) cm−1 are due to C–O–H bending vibrations. These FT–IR spectra results are similar to previous studies that analyzed the characteristics of ZnAA or ZnO [32, 33, 38, 39].Fig. 3 Characterizations of the synthetic ZnAA. The characteristics of ZnAA by FT–IR and FE–SEM. a FT–IR spectra of ascorbic acid; b FT–IR spectra of ZnAA; c the morphology of ZnAA by FE–SEM. ZnAA was characterized by Fourier transform infrared spectroscopy (FT–IR, Perkin Elmer Spectrum GX, USA). Each FT–IR spectrum was collected at a resolution of 2 cm−1 from (4000 to 600) cm−1. The morphology of ZnAA was observed by Field emission scanning electron microscopy (FE–SEM) (Carl Zeiss SUPRA, Germany) Morphology of ZnAA with EDX Analysis Figure 3C shows the morphology of ZnAA particle taken by FE–SEM, in which ZnAA is formed as a very tiny particle at (19–30) nm. Furthermore, Table 1 describes the ZnAA surface components using energy-dispersive X-ray (EDX) spectroscopy, where zinc ion is presented in the synthesis product, while not in the AA sample.Table 1 The EDX data of ZnAA with ascorbic acid on the surface Element Wt % AA ZnAA C 46.02 3.43 O 53.98 20.5 Zn 0 76.07 Vacuolar Targeting of ZnAA in Saccharomyces cerevisiae Saccharomyces cerevisiae was exposed individually to AA and ZnAA for 24 h. After exposing samples to the cells, to verify the existence of ZnAA in vacuole, the remaining ascorbic acid and ZnAA were measured by LC–MS. Table 2 shows that no ZnAA remained in the yeast cytosol and culture media, while (71 and 6.45) % AA remained in the media and cytosol, respectively. On the other hand, the 99.9% of ZnAA found in the vacuole affirmed that ZnAA was targeted to the vacuole as expected, while only 22.55% ascorbic acid was delivered to the vacuole. This phenomenon confirmed the role of zinc ion in the vacuolar targeting approach.Table 2 The LC–MS analysis data of AA and ZnAA after exposure to S. cerevisiae AA (%) ZnAA (%) Initial value 100 100 The ratio in media 71 N.A The ratio in cytosol 6.45 N.A Inflow ratio in vacuole 22.55 99.9 N.A. no absorbance; AA ascorbic acid; ZnAA zinc ion-conjugated-ascorbic acid Melanin Reduction in Melanocytes by ZnAA-Vacuole In this experiment, the vacuole that contained ZnAA was used to treat melanocytes for the melanin reduction test, and called ZnAA_Vac. ZnAA_Vac were isolated from S. cerevisiae, and 5% (wt/vol) vacuoles were exposed to melanocyte for 24 h. “Wild-type” vacuole was used to compare with the activity of ZnAA_Vac, while 2% ascorbic acid was the positive control, due to its depigmenting effect [40, 41]. The result in Fig. 4 shows no distinction in the melanin-reducing effect between 2% ascorbic acid, 5% vacuole, and vacuole that possessed 100 ppm ZnAA, which degraded approximatively 24% of melanin. Meanwhile, vacuole with 1000 ppm ZnAA degraded around 75.5% melanin; thus, 1000 ppm ZnAA provided a higher reducing ability. In fact, ZnAA without vacuole also showed a high effect of degrading melanin; however, it also caused high cytotoxicity that killed many cells (Fig. 4a).Fig. 4 Cell toxicity and melanin degradation test. a The cytotoxicity of ascorbic acid, vacuole and ZnAA_Vac was confirmed by MTT assay; b and, the melanin reduction test with vacuole and ZnAA_Vac. PBS was used as a negative control and ascorbic acid as a positive control. (n.s not significant; p < 0.05; *p < 0.01) Depigmentation Effect of ZnAA_Vac on Artificial Human Skin MelanoDerm The depigmentation efficiency of ZnAA_Vac depicted in Fig. 5 was observed at day 0 and day 12 of the treatment. Figure 5a presents the effect of several vacuole’s concentrations from wild-type yeast on the artificial human skin after 12 h of treatment, while Fig. 5b shows the depigmenting effect of ZnAA_Vac. After treatment with (5 and 10)% vacuole or all samples exposed to ZnAA_Vac, the color reduction is clearly evident to the naked eye. Nevertheless, to precisely assess the depigmenting effect, melanin contents were measured after 12 days of treatment.Fig. 5 Skin whitening test with human skin model MelanoDerm. Plan view observation of MelanoDerm after being exposed to a various concentrations of vacuole; b vacuoles that possessed (100, 500, and 1000) ppm of ZnAA. c Melanin amount after 12 days of treatment with several concentrations of vacuole and ZnAA_Vac. The data were analyzed using Sigma Plot, and a p-value < 0.05 was considered significant. ZnAA_Vac 100 ppm, ZnAA_Vac 500 ppm, ZnAA_Vac 1000 ppm: vacuoles that contained ZnAA at (100, 500, and 1,000) ppm, respectively Figure 5c shows that 2% ascorbic acid exhibited a high depigmenting ability by decreasing around 33% of melanin, while the vacuole's maximum effect was approximately 14% (vacuole (5 and 10)%). Regarding ZnAA_Vac, vacuole that contained 100 ppm ZnAA did not show a high depigmentation effect; however, the melanin degradation increased along with the augmentation of ZnAA dose. More precisely, ZnAA_Vac with 500 ppm ZnAA reduced 31% of melanin, while ZnAA_Vac with 1000 ppm ZnAA provided a superb ability by degrading 75% melanin after 12 days. These results were identical with the melanin reduction test in melanocytes, in which the ZnAA_Vac 1000 ppm had degraded 75.5% melanin (Fig. 4). Discussion In this study, we combine AA with yeast vacuoles that we have invented, and to improve skin penetration when used as a cosmetic, we conjugated AA with hydrated zinc oxide (Zn(NO3)2·6H2O). Through the X-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT–IR) analysis, it was confirmed that the synthetic ZnAA (zinc ion-ascorbic acid) is consistent with the previously reported properties of ZnAA or ZnO [27, 28, 34, 35]. The formation of ZnO from the chemical precipitation of zinc nitrate hexahydrate, sodium hydroxide, and ascorbic acid was commonly investigated before in which vitamin C had occupied an essential role in ZnO crystal formation [32, 33, 42]. The formation of ZnO can also be confirmed through the reduction of carbonic ion in EDX analysis. To combine ZnAA with yeast vacuoles by internalization into the vacuole, we incubated yeast with ZnAA. As mentioned before, the vacuole is the primary site for zinc sequestration, due to the two vacuolar zinc transporters Zrt3 and Zrc1; thus, the conjugation ZnAA is expected to agglomerate at the vacuole [21–23]. Recently, considering the hazardous nature of the chemical synthesis of nano particles, the current investigation is focusing on the green synthesis using eco-friendly biological extracts [43–46]. The melanin reduction test of ZnAA_Vac, the vacuole that contained ZnAA, was conducted with melanoma cell line B16F10 and the three-dimensional human skin model MelanoDerm from MatTek. the highest depigmentation effect of ZnAA_Vac in both melanoma cell lines and artificial human skin cells demonstrated our precise approach when conjugating ascorbic acid, Zn ion, and vacuole. After being conjugated with the vacuole, ZnAA became an efficient and harmless whitening agent, while individual vacuole or ascorbic acid displayed a lower effect, and ZnAA without vacuole caused high cytotoxicity that killed a lot of cells. Therefore, the encapsulation approach of ZnAA with vacuoles could provide a safer and more robust agent for melanin degradation. According to recent studies, the use of biological compounds for the synthesis of inorganic nanoparticles has shown promising results such as cost-effective and environmentally friendly compared to the conventional nanoparticles synthesis pathway [47–50]. Previously, it was reported that a combination of tyrosine, zinc and vitamin C has been shown to increase the bioavailability of vitamin C 20-times vis-a-vis using just vitamin C [51]. Like these researches, many studies using ZnO to improve the biocompatibility and stability of AA have been reported. However, in this paper, the whitening efficacy of these complex nanoparticles was verified using melanocytes and artificial skin tissue for the first time, therefore it includes novel and meaningful results can be used in the pharmaceutical and cosmetic fields. In this study, vacuole as a carrier to protect and enhance the activity of metal-conjugated-ascorbic acid was reported for the first time. Ascorbic acid synthesized with zinc ion was transported to vacuole through the vacuolar membrane protein, and then the vacuole that contained ZnAA was isolated, which we called ZnAA_Vac. ZnAA_Vac was found to be safe for the cell without cytotoxicity, and provided a considerable effect to degrade the melanin content in both melanocyte and artificial human skin cell. Conclusions In this study, vacuole as a carrier to protect and enhance the activity of metal-conjugated-ascorbic acid was reported for the first time. Ascorbic acid synthesized with zinc ion was transported to vacuole through the vacuolar membrane protein, and then the vacuole that contained ZnAA was isolated, which we called ZnAA_Vac. ZnAA_Vac was found to be safe for the cell without cytotoxicity, and provided a considerable effect to degrade the melanin content in both melanocyte and artificial human skin cells. The discovery of ZnAA_Vac could open up a new way to develop a state-of-the-art whitening agent that effectively reduce melanin, without damage to the skin, but in order to be commercialized, additional clinical and in vivo experiments must be conducted, and research on materialization is also required. Acknowledgements This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant No. 2020R1A6A1A06046235). Author Contributions GJ, HC, D-JP and N-TN have contributed equally to this work. GJ: Conceptualization, Methodology, Investigation, Visualization, Writing—original draft, Writing—review & editing, HC: Methodology, Investigation, Visualization, Writing—original draft, Writing—review & editing, DJP: Conceptualization, Investigation, Methodology, Visualization, Writing—review & editing, N-TN: Investigation, Writing—review & editing, YHK: Supervision, Writing—review & editing, JM: Supervision, Methodology, Writing—review & editing. The manuscript was written through the contributions of all authors. All authors have given approval to the final version of the manuscript. Declarations Conflict of interest Gyeongchan Jeon, Hyojin Choi, Dong-Jun Park, Ngoc-Tu Nguyen, Yang-Hoon Kim and Jiho Min declare that they have no conflict of interest. Informed Consent Informed consent was obtained from all individual participants included in the study Research Involving Human Participants and/or Animals This article does not contain any studies with human participants or animals performed by any of the authors. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Gyeongchan Jeon, Hyojin Choi, Dong-Jun Park and Ngoc-Tu Nguyen have contributed equally to this work. ==== Refs References 1. Lin JY Fisher DE Melanocyte biology and skin pigmentation Nature 2007 445 7130 843 850 10.1038/nature05660 17314970 2. Lambert MW Maddukuri S Karanfilian KM Elias ML Lambert WC The physiology of melanin deposition in health and disease Clinics in Dermatology 2019 37 5 402 417 10.1016/j.clindermatol.2019.07.013 31896398 3. Pillaiyar T Namasivayam V Manickam M Jung S-H Inhibitors of melanogenesis: An updated review Journal of medicinal chemistry 2018 61 17 7395 7418 10.1021/acs.jmedchem.7b00967 29763564 4. Karsten AE Smit JE Modeling and verification of melanin concentration on human skin type Photochemistry and photobiology 2012 88 2 469 474 10.1111/j.1751-1097.2011.01044.x 22077284 5. Brenner M Hearing VJ The protective role of melanin against UV damage in human skin Photochemistry and photobiology 2008 84 3 539 549 10.1111/j.1751-1097.2007.00226.x 18435612 6. Pillaiyar T Manickam M Namasivayam V Skin whitening agents: Medicinal chemistry perspective of tyrosinase inhibitors Journal of Enzyme Inhibition and Medicinal Chemistry 2017 32 1 403 425 10.1080/14756366.2016.1256882 28097901 7. Markets, R. A. (2020) Skin Lighteners - Global Market Trajectory & Analytics. Journal, https://www.researchandmarkets.com/reports/1056077/skin_lighteners_global_market_trajectory_and?utm_source=GNOM&utm_medium=PressRelease&utm_code=ncgpnq&utm_campaign=1416788+-%2412.3+Billion+Skin+Lighteners+Market+Outlook%2c+2027&utm_exec=joca220prd. 8. Okoye O Skin lightening and chronic kidney disease: A tale or a fact? Tropical Journal of Nephrology 2019 14 1 15 23 9. Vashi NA Patzelt N Wirya S Maymone MB Kundu RV Dermatoses caused by cultural practices: Cosmetic cultural practices Journal of the American Academy of Dermatology 2018 79 1 19 30 10.1016/j.jaad.2017.06.160 29908820 10. Burger P Landreau A Azoulay S Michel T Fernandez X Skin whitening cosmetics: Feedback and challenges in the development of natural skin lighteners Cosmetics 2016 3 4 36 10.3390/cosmetics3040036 11. van Gorkom GN Lookermans EL Van Elssen CH Bos GM The effect of vitamin C (ascorbic acid) in the treatment of patients with cancer: A systematic review Nutrients 2019 11 5 977 10.3390/nu11050977 31035414 12. Moskowitz A Andersen LW Huang DT Berg KM Grossestreuer AV Marik PE Sherwin RL Hou PC Becker LB Cocchi MN Ascorbic acid, corticosteroids, and thiamine in sepsis: A review of the biologic rationale and the present state of clinical evaluation Critical Care 2018 22 1 283 10.1186/s13054-018-2217-4 30373647 13. Chambial S Dwivedi S Shukla KK John PJ Sharma P Vitamin C in disease prevention and cure: An overview Indian Journal of Clinical Biochemistry 2013 28 4 314 328 10.1007/s12291-013-0375-3 24426232 14. Pielesz A Biniaś D Bobiński R Sarna E Paluch J Waksmańska W The role of topically applied l-ascorbic acid in ex-vivo examination of burn-injured human skin Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2017 185 279 285 10.1016/j.saa.2017.05.055 28591686 15. Serrano G Almudéver P Serrano J-M Milara J Torrens A Expósito I Cortijo J Phosphatidylcholine liposomes as carriers to improve topical ascorbic acid treatment of skin disorders Clinical, Cosmetic and Investigational Dermatology. 2015 10.2147/CCID.S90781 26719718 16. Van Gorkom GN Klein Wolterink RG Van Elssen CH Wieten L Germeraad WT Bos GM Influence of vitamin C on lymphocytes: An overview Antioxidants 2018 7 3 41 10.3390/antiox7030041 29534432 17. Al-Niaimi F Chiang NYZ Topical vitamin C and the skin: Mechanisms of action and clinical applications The Journal of Clinical and Aesthetic Dermatology 2017 10 7 14 10.3390/antiox7030041 29104718 18. Yang J-H Lee S-Y Han Y-S Park K-C Choy J-H Efficient transdermal penetration and improved stability of l-ascorbic acid encapsulated in an inorganic nanocapsule Bulletin-Korean Chemical Society 2003 24 4 499 503 10.5012/bkcs.2003.24.4.499 19. Yoon J Kim Y-H Ahn J-Y Lee H-C Oh S-J Chung B-W Min J Melanin reduction by peroxidase activity in lysosome-related organelle extracts from hen egg whites, HeLa cells, and Saccharomyces cerevisiae Molecular & Cellular Toxicology 2015 11 4 441 447 10.1007/s13273-015-0047-x 20. Park DJ Kim Y-H Min J The remarkable efficiency of melanin reduction by using functional organelle, lysosomes Journal of Nanoscience and Nanotechnology 2017 17 7 4638 4642 10.1166/jnn.2017.13786 21. Simm C Lahner B Salt D LeFurgey A Ingram P Yandell B Eide DJ Saccharomyces cerevisiae vacuole in zinc storage and intracellular zinc distribution Eukaryotic cell 2007 6 7 1166 1177 10.1128/EC.00077-07 17526722 22. Kawamata T Horie T Matsunami M Sasaki M Ohsumi Y Zinc starvation induces autophagy in yeast Journal of Biological Chemistry 2017 292 20 8520 8530 10.1074/jbc.M116.762948 28264932 23. Zhao Y-Y Cao C-L Liu Y-L Wang J Li J Li S-Y Deng Y Identification of the genetic requirements for zinc tolerance and toxicity in Saccharomyces cerevisiae G3: Genes, Genomes, Genetics 2020 10 2 479 488 10.1534/g3.119.400933 31836620 24. Gujrati V Lee M Ko Y-J Lee S Kim D Kim H Kang S Lee S Kim J Jeon H Bioengineered yeast-derived vacuoles with enhanced tissue-penetrating ability for targeted cancer therapy Proceedings of the National Academy of Sciences 2016 113 3 710 715 10.1073/pnas.1509371113 25. Choi W Heo MY Kim SY Wee J-H Kim Y-H Min J Encapsulation of daunorubicin into Saccharomyces cerevisiae-derived lysosome as drug delivery vehicles for acute myeloid leukemia (AML) treatment Journal of Biotechnology 2020 308 118 123 10.1016/j.jbiotec.2019.12.008 31846628 26. Chikere C Faisal N Lin P Fernandez C Zinc oxide nanoparticles modified-carbon paste electrode used for the electrochemical determination of Gallic acid Journal 2019 1310 012008 10.1088/1742-6596/1310/1/012008 27. Nguyen N-T Park R-M Kim Y-H Min J Detection and discrimination of Shigella sonnei and Shigella flexneri based on vacuolar responses in Saccharomyces cerevisiae Journal of biotechnology 2018 287 1 7 10.1016/j.jbiotec.2018.09.009 30261194 28. Baskin TI Orr TJ Jercinovic M Yoshida M Sample preparation for scanning electron microscopy: The surprising case of freeze drying from tertiary butanol Microscopy Today 2014 22 3 36 39 10.1017/S1551929514000522 29. Park DJ Jeon G Bang SH Kim SY Wee J-H Kim Y-H Min J Cellular lysosomes’ activity for melanin reduction on artificial skin tissue Molecular Biotechnology 2020 62 3 185 191 10.1007/s12033-019-00235-w 31894512 30. Fischer AH Jacobson KA Rose J Zeller R Hematoxylin and eosin staining of tissue and cell sections Cold Spring Harbor Protocols 2008 10.1101/pdb.prot4986 21356883 31. Yusof NAA Zain NM Pauzi N Synthesis of chitosan/zinc oxide nanoparticles stabilized by chitosan via microwave heating Bulletin of Chemical Reaction Engineering & Catalysis 2019 14 2 450 458 10.9767/bcrec.14.2.3319.450-458 32. Ghahramaninezhad M Niknam Shahrak M Facile and efficient self-template synthesis of core-coronal-shell ZnO@ ZIF-8 nanohybrid using ascorbic acid and its application for arsenic removal Inorganic Chemistry Research 2019 10.22036/icr.2019.191125.1051 33. Kumar HN Mohana NC Nuthan B Ramesha K Rakshith D Geetha N Satish S Phyto-mediated synthesis of zinc oxide nanoparticles using aqueous plant extract of Ocimum americanum and evaluation of its bioactivity SN Applied Sciences 2019 1 6 651 10.1007/s42452-019-0671-5 34. Sadollahkhani A Kazeminezhad I Lu J Nur O Hultman L Willander M Synthesis, structural characterization and photocatalytic application of ZnO@ ZnS core–shell nanoparticles RSC Advances 2014 4 70 36940 36950 10.1039/C4RA05247A 35. Cho S Jang J-W Park HJ Jung D-W Jung A Lee JS Lee K-H A method for synthesizing ZnO–carbonaceous species nanocomposites, and their conversion to quasi-single crystal mesoporous ZnO nanostructures RSC Advances 2012 2 2 566 572 10.1039/C1RA00661D 36. Ahmed I Haque A Bhattacharyya S Patra P Plaisier JR Perissinotto F Bal JK Vitamin C/stearic acid hybrid monolayer adsorption at air–water and air–solid interfaces ACS Omega 2018 3 11 15789 15798 10.1021/acsomega.8b02235 31458229 37. Panicker CY Varghese HT Philip D FT-IR, FT-Raman and SERS spectra of Vitamin C Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 2006 65 3–4 802 804 10.1016/j.saa.2005.12.044 16530470 38. Nagaraju G Prashanth S Shastri M Yathish K Anupama C Rangappa D Electrochemical heavy metal detection, photocatalytic, photoluminescence, biodiesel production and antibacterial activities of Ag–ZnO nanomaterial Materials Research Bulletin 2017 94 54 63 10.1016/j.materresbull.2017.05.043 39. Lefatshe K Muiva CM Kebaabetswe LP Extraction of nanocellulose and in-situ casting of ZnO/cellulose nanocomposite with enhanced photocatalytic and antibacterial activity Carbohydrate Polymers 2017 164 301 308 10.1016/j.carbpol.2017.02.020 28325329 40. Yussif NM Rahman ARA ElBarbary A Minimally invasive non-surgical locally injected vitamin C versus the conventional surgical depigmentation in treatment of gingival hyperpigmentation of the anterior esthetic zone: A prospective comparative study Clinical Nutrition Experimental 2019 24 54 65 10.1016/j.yclnex.2018.12.003 41. Sanadi RM Deshmukh RS The effect of vitamin C on melanin pigmentation—A systematic review Journal of Oral and Maxillofacial Pathology 2020 24 2 374 10.4103/jomfp.JOMFP_207_20 33456250 42. Cho S Jeong H Park D-H Jung S-H Kim H-J Lee K-H The effects of vitamin C on ZnO crystal formation CrystEngComm 2010 12 3 968 976 10.1039/B916750A 43. Chia SR Ahmad M Sultana S Zafar M Asif S Bokhari A Nomanbhay S Mubashir M Khoo KS Show PL Green synthesis of biodiesel from Citrus medica seed oil using green nanoparticles of copper oxide Fuel 2022 323 124285 10.1016/j.fuel.2022.124285 44. Ahmad M Asif S Klemeš JJ Mubashir M Bokhari A Sultana S Mukhtarf A Zafara M Bazmi AA Ullah S Khan MS Koyande AK Mofijur M Show PL Conversion of the toxic and hazardous Zanthoxylum armatum seed oil into methyl ester using green and recyclable silver oxide nanoparticles Fuel 2022 310 122296 10.1016/j.fuel.2021.122296 45. Karim SS Murtaza Z Farrukh S Umer MA Ali SS Younas M Show PL Future advances and challenges of nanomaterial-based technologies for electromagnetic interference-based technologies: A review Environmental Research 2022 205 112402 10.1016/j.envres.2021.112402 34838569 46. Mubashir M Ashena R Bokhari A Mukhtar A Saqib S Ali A Saidur R Khoo KS Ng HS Karimi F Karaman C Show PL Effect of process parameters over carbon-based ZIF-62 nano-rooted membrane for environmental pollutants separation Chemosphere 2022 291 133006 10.1016/j.chemosphere.2021.133006 34813846 47. Chan SS Low SS Chew KW Ling TC Rinklebe J Juan JC Ng PE Show PL Prospects and environmental sustainability of phyconanotechnology: A review on algae-mediated metal nanoparticles synthesis and mechanism Environmental Research 2022 212 113140 10.1016/j.envres.2022.113140 35314164 48. Lau ZL Low SS Ezeigwe ER Chew KW Chai WS Bhatnagar A Yap YJ Show PL A review on the diverse interactions between microalgae and nanomaterials: Growth variation, photosynthesis performance and toxicity Bioresource Technology 2022 351 127048 10.1016/j.biortech.2022.127048 35337989 49. Mubashir M Fong YY Leng CT Keong LK Jusoh N Nanocomposite catalytic membranes for energy production: Advances and challenges Handbook of Nanotechnology Applications 2021 10.1016/B978-0-12-821506-7.00010-7 50. Ali A Mubashir M Abdulrahman A Phelan PE Ultra-permeable intercalated metal-induced microporous polymer nano-dots rooted smart membrane for environmental remediation Chemosphere 2022 306 135482 10.1016/j.chemosphere.2022.135482 35780984 51. Traikovich Steven S Use of topical ascorbic acid and its effects on Photo damaged skin topography Archives of Otorhinolaryngology-Head & Neck Surgery 1999 125 10 1091 1098 10.1001/archotol.125.10.1091	36445610	PMC9707414	NO-CC CODE	2022-12-01 23:20:23	no	Mol Biotechnol. 2022 Nov 29;:1-10	utf-8	Mol Biotechnol	2,022	10.1007/s12033-022-00608-8	oa_other
==== Front J Racial Ethn Health Disparities J Racial Ethn Health Disparities Journal of Racial and Ethnic Health Disparities 2197-3792 2196-8837 Springer International Publishing Cham 36445684 1471 10.1007/s40615-022-01471-8 Article Contextual Pathways Linking Cumulative Experiences of Racial Discrimination to Black American Men’s COVID Vaccine Hesitancy http://orcid.org/0000-0002-1616-032X Curtis Michael G. [email protected] 1 http://orcid.org/0000-0002-8081-0665 Whalen Christopher C. 2 http://orcid.org/0000-0001-7981-6931 Pjesivac Ivanka 3 http://orcid.org/0000-0002-9562-5980 Kogan Steven M. 1 1 grid.213876.9 0000 0004 1936 738X Department of Human Development and Family Science, University of Georgia, 1095 College Station Road, Athens, Georgia 30602-4527 USA 2 grid.213876.9 0000 0004 1936 738X College of Public Health, University of Georgia, Athens, Georgia USA 3 grid.213876.9 0000 0004 1936 738X Grady College of Journalism & Mass Communication, University of Georgia, Athens, Georgia USA 29 11 2022 113 10 8 2022 16 11 2022 21 11 2022 © W. Montague Cobb-NMA Health Institute 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The COVID-19 pandemic has revealed and widened racialized health disparities, underscoring the impact of structural inequities and racial discrimination on COVID-19 vaccination uptake. A sizable proportion of Black American men report that they either do not plan to or are unsure about becoming vaccinated against COVID-19. The present study investigated hypotheses regarding the mechanisms by which experiences of racial discrimination are associated with Black American men’s COVID-19 vaccine hesitancy. Hypotheses were tested using structural equation modeling with 4 waves of data from 242 Black American men (aged ~ 27) living in resource-poor communities in the rural South. Study findings revealed that racial discrimination was indirectly associated with COVID-19 vaccine hesitancy via increased endorsement of COVID-19 conspiratorial beliefs. Findings also demonstrated that increased levels of ethnic identity strengthen the association between experiences of racial discrimination and COVID-19 conspiratorial beliefs. In contrast, increased levels of social support weakened the association between cumulative experiences of racial discrimination and COVID conspiratorial beliefs. Taken together, these results suggest that racial discrimination may promote conspiratorial beliefs which undermine Black American men’s willingness to be vaccinated. Future interventions aimed towards promoting vaccine uptake among Black American men may benefit from the inclusion of targeted efforts to rebuild cultural trust and increase social support. Supplementary Information The online version contains supplementary material available at 10.1007/s40615-022-01471-8. Keywords Black American Vaccine hesitancy COVID Racial discrimination Ethnic identity COVID conspiratorial beliefs http://dx.doi.org/10.13039/100000026 National Institute on Drug Abuse R01DA029488 Kogan Steven M. http://dx.doi.org/10.13039/100000027 National Institute on Alcohol Abuse and Alcoholism R01AA026623 Kogan Steven M. http://dx.doi.org/10.13039/100006108 National Center for Advancing Translational Sciences L1TR002382 Curtis Michael G. ==== Body pmcNationally, Black Americans were consistently more likely to be diagnosed, hospitalized, and die from COVID than all other racial/ethnic groups [21, 56, 76]. Prior research indicates that Black American men are particularly vulnerable to contracting COVID [22, 88]. Studies have linked this increased vulnerability to Black American men’s propensity to live in multigenerational homes and with extended kin [25], be underinsured or uninsured [20, 89], and be employed in positions that offer limited flexibility to initiate and maintain COVID-related protective behavior [45, 54]. Despite these risks, recent data suggest that Black American men are among the least likely to be vaccinated against COVID [2, 46]. Randomized clinical trials have demonstrated high levels of efficacy regarding the COVID vaccine and have showed promise in controlling the spread of the virus and reducing the severity of symptoms following infection [6, 43, 64]. However, distribution is one factor that affects vaccine effectiveness [62, 63]. Black Americans report higher rates of vaccine hesitancy, in general, and COVID vaccine hesitancy, in particular, compared to all other racial/ethnic groups in the USA [42, 60]. Vaccine hesitancy can be defined as a delay in acceptance, or downright refusal, of vaccines despite the availability of vaccine services [60]. The term covers outright refusals to vaccinate, delaying vaccines, and accepting vaccines but remaining uncertain about their use, or using certain vaccines but not others [60]. Extant studies link vaccine hesitancy to low levels of perceived proximity to COVID exposure [59], perceived severity of COVID acquisition [86], perceived impact of contracting COVID [66, 82], socioeconomic status [40], and educational attainment [81, 90]. Emerging theory and research suggest that experiences of racial discrimination may influence Black Americans’ vaccination uptake [10, 32]. Racial discrimination is a multidimensional construct with mundane experiences (i.e., everyday experiences of unfair treatment because of one’s race) being distinguishable from major acute experiences (i.e., major experiences of prejudice in domains such as employment, education, housing, and interactions with the police; [83]. Several studies have documented the impact of historical events, such as the Tuskegee Syphilis Study [16], on Black American men’s trust in health systems as well as trust in government or other institutional influences [14, 27, 44]. These studies indicate that experiences of discrimination may undermine Black American men’s trust in systems of authority that are meant to serve and protect them by demonstrating that (a) their well-being is not considered by policymakers and healthcare professions when making decisions and (b) their communities are the target of unjust medical practices and regulations [16, 27, 44]. Much of the empirical literature in this area use single time-point assessments of discrimination (e.g., [68, 84]. This represents a notable limitation as prior research indicates that the effects of racial discrimination are cumulative [1, 50]. This gap is significant as it may limit the efficacy of evidence-based culturally responsive vaccine promotion interventions. We thus investigate the link between racial discrimination young men reported experiencing over the past 3 years and their intentions to be vaccinated for COVID-19. COVID Conspiracy Beliefs Research links COVID-19 conspiracy beliefs to racial discrimination [5, 73] and to COVID vaccine hesitancy [26, 48]. Conspiracy beliefs are attempts to explain the ultimate causes of events with claims of secret plots by powerful actors [48]. Concerning COVID, conspiracy beliefs may include ideas such as the origin and spread of COVID-19 being the results of purposeful acts, or vaccines existing to serve some nefarious purpose [26]. A recent review of the antecedents and consequences of COVID-19 conspiracy beliefs suggests that the perceived lack of control borne out of experiences of contextual disenfranchisement (e.g., racial discrimination) undermine individuals’ trust in the government and other agencies of influence and increase men’s vulnerability to believing conspiracy theories in order to make sense of and better cope with stressful events [53, 75, 80]. We posit that chronic exposure to discrimination may promote conspiracy beliefs by undermining Black American men’s belief in the reliability and positive motives of societal institutions that promote public health and well-being. Our hypothesis is supported by prior research which indicates that experiences of racial discrimination may be associated with increased endorsement of COVID conspiratorial beliefs through establishment of an interrelated set of attitudes and cognitions of mistrust towards government and other agencies of influence [9, 24, 52]. Research also documents a positive association between COVID conspiratorial beliefs and COVID-19 vaccine hesitancy [5, 78]. We know of no study to date, however, that has investigated COVID conspiracy beliefs as a mediator linking experiences of racial discrimination to rural Black American men’s COVID-19 vaccine hesitancy. Moderators of the Effects of Racial Discrimination Prior research indicates that ethnic identity and social support are associated with individual differences in the consequences of racial discrimination on Black American men’s health-decision-making [28, 72, 79]. Regarding ethnic identity, evidence suggests ethnic identity moderates the effects of racial discrimination in that individuals who feel more positively about being a member of the Black American community are more aware of and sensitive to discriminatory events [69, 70]. For example, individuals with higher levels of ethnic identity were more likely to report experiencing racial discrimination and report higher levels of race-related stress [72]. Greene et al. [28] found that Black Americans who reported higher levels of ethnic identity reported increased perceived discrimination which were then associated with higher levels of psychological distress than those who reported lower levels of ethnic identity. Taken together, these data suggest that high levels of ethnic identity may amplify the influence of racial discrimination on Black American men’s COVID-19 vaccine hesitancy. Consequently, we hypothesize that high levels of ethnic identity will strengthen the relations between Black American men’s experiences of racial discrimination and (a) conspiracy beliefs, and (b) COVID vaccine hesitancy. In contrast to the potential excerabatory effects of ethnic identity, social support is expected to attenuate the detrimental effects of racial discrimination. Prior research indicates that Black American men frequently utilize social support to cope with racial discrimination [19, 67]. For example, Swim and colleagues (2003) found that Black American men reported often talking to friends and family members about a racist discriminatory event shortly after it occurred. Furthermore, Utsey and colleagues (2006) demonstrated that social support buffered the negative effects of racial discrimination, enabling individuals with high levels of support to experience less strain and cope more successfully [79]. In accordance with prior research, we hypothesize that high levels of social support would weaken the relationship between Black American men’s experiences of racial discrimination and COVID vaccine hesitancy, and COVID vaccine hesitancy, respectively. The Current Study Informed by prior investigations of Black American’s health care decision-making [29, 36, 41, 49, 85], we conducted a longitudinal study examining hypotheses regarding the mechanisms by which experiences of racial discrimination are associated with Black American men’s COVID-19 vaccine hesitancy (see Fig. 1). We hypothesized that exposure to racial discrimination would predict increases COVID-19 vaccine hesitancy indirectly via COVID-19 conspiracy beliefs. Second, we hypothesized that increase in levels of ethnic identity would exacerbate the influence of racial discrimination on COVID conspiratorial beliefs and on COVID vaccine hesitancy. Last, we hypothesized that increased levels of perceived social support to attenuate the influence of racial discrimination on COVID conspiratorial beliefs and on COVID vaccine hesitancy.Fig. 1 Summarized hypotheses Methods Participants The current analyses used data from men participating in a longitudinal study of rural Black American men’s health and well-being during young adulthood. Participants were recruited from 11 contiguous counties in rural Georgia, which were representative of areas of rural poverty in the southeastern USA [57]. Baseline eligibility criteria included (a) self-identification as African American or Black, (b) residence in a targeted county, and (c) age 19 to 22 years. The baseline assessment included 504 men (age ~ 20 years old). Assessment of racial discrimination occurred beginning with the next wave and comprises wave 1 of this analysis. At wave 1, 422 men participated (age ~ 22 years old). Approximately 1 year later, 408 men participated in wave 2 (age ~ 23 years old). Wave 3 occurred 2.5 years after the completion of wave 2; men were ~ 27 years old. The COVID-19 pandemic occurred after 242 completed wave 3 data collection, which encompassed the analytic sample used in this study. We followed up with men who had participated in wave 3 Pre-COVID approximately soliciting data on their conspiracy beliefs and vaccine hesitancy. All 242 participants who participated in wave 3 of data collection were invited to participate in wave 4; 164 participants responded. Among the participants at wave 4, 84.3% were unmarried, 38.8% lived with a romantic partner while 24.8% lived with immediate family members. Most had earned at least a high school diploma (90.9%) and were employed (73.1%). Recruitment Participants were recruited using respondent-driven sampling (RDS; [31]. Community liaisons recruited 45 “seed” participants from targeted counties to complete a baseline survey. Seed participants were members of the liaisons’ personal networks or responded to advertisements and outreach efforts in the local community. Participants were then asked to identify three other Black men in their community who qualified for participation in the study. The RDS protocols and weighing system are designed to attenuate the influence of biases common in chain-referral samples and to improve the approximation of a random sample of the target population [31]. Analyses of network data related to demographics, substance use, and other risky behavior at baseline [35] indicated that the sample evinced negligible levels of common biases observed in chain-referral samples arising from the characteristics of the initial seed participants, individual participants’ recruitment efficacy, and differences in the sizes of participants’ networks. We thus used raw data in the present analysis. Procedures Participants completed pre-COVID surveys (waves 1, 2, and 3) in their homes or at a convenient, private location in the community. Surveys were administered on a laptop computer using an audio computer-assisted self-interview protocol, which allowed participants to navigate the survey with the help of voice and video enhancements, eliminating literacy concerns. The post-COVID survey (wave 4) was completed by participants online using their own devices via the Qualtrics platform. Participants received $100 for completing the survey at each wave. Participants provided written informed consent; all study protocols were approved by the University’s Human Subjects Review Board. The study operated under a federal certificate of confidentiality issued by the US National Institute of Health. Measures Vaccine Hesitancy At W4, participants reported their delay in acceptance or refusal of vaccines despite availability of vaccine services using a 15-item measure developed by Quinn et al. [61]. This measure had two subscales: general vaccine hesitancy (6 items) and COVID vaccine hesitancy (7 items). Men responded to items using a Likert scale ranging from 0 (completely) to 3 (not at all), whereby higher scores indicated increased vaccine hesitancy. Examples of items included “how much do you trust the COVID-19 vaccine? (Reverse coded)” and “is the COVID-19 vaccine necessary?” Cronbach’s α for the measure of general vaccine hesitancy was 0.92; for the measure of COVID vaccine hesitancy, it was 0.93. Racial Discrimination In accordance with prior research, we included two indicators of racial discrimination—one for mundane experiences and one for acute experiences from W1 to W2. Mundane racial discrimination was assessed using a 9-item scale [17]. Men’s responses to the items on a scale ranged from 0 (never) to 3 (frequently) regarding their experiences of chronic discrimination in the last 6 months. Examples of items included “have you been ignored, overlooked, or not given service because of your race?” and “have you been called a name or harassed because of your race?” Items were summed to create total mundane discrimination score where higher scores were indicative of more mundane experiences of racial discrimination. Cronbach’s α were W1 = 0.86, W2 = 0.87, and W3 = 0.90. Acute experiences of racial discrimination were assessed using a 10-item measure of major experiences of racial discrimination developed by Williams et al. [83]. Men responded to items with either 0 (no) or 1 (yes) regarding their experiences of major discrimination in the last 6 months. Items included “fired from a job because you are Black” and “denied a bank loan because you are Black.” Cronbach’s α were W1 = 0.80, W2 = 0.85, and W3 = 0.81. Scores at each time point were summed to create a cumulative racial discrimination index score. COVID Conspiracy Beliefs At W4, participants reported their level of agreement to 7 statements about the COVID pandemic that reflected a conspiracy by sinister and powerful groups, often political in motivation, to explain an event or situation. The measure was developed sequentially. First, we compiled a list of potential items by conducting a review of the most popular COVID-related conspiracy theories circulating within mainstream and social media in January 2021. Next, we shared this list this with a review panel comprised of (a) experienced researchers with expertise in the study of Black Americans and conspiracy theories, (b) community partners, and (c) Black American men unrelated to the study. This review panel suggested several revisions and refinements that resulted in the final measure that was included in this study (see Appendix A). Men responded to the items on a scale from 0 (definitely not true) to 4 (definitely true). Examples of items included “COVID was created by powerful people to be a bioweapon,” and “Bill Gates developed COVID for population control.” Before testing our hypotheses, we conducted a confirmatory factor analysis, testing the unidimensionality of the 7-item scale. The single factor model fit the data as follows: χ2(10) = 9.69, p = 0.468; root mean square error of approximation (RMSEA) = 0.000 [0.000; 0.083], comparative fit index (CFI) = 1.00, and standardized root mean squared residual (SRMR) = 0.02. All factor loadings were significant, exceeded 0.67, and were in the predicted direction indicating unidimensionality in the measure. Consequently, items were summed to create a total COVID conspiratorial beliefs score. Cronbach’s α was 0.91. Ethnic Identity At W3, ethnic identity was assessed using an 11-item measure adapted from the Centrality subscale of the Multidimensional Inventory of Black Identity [71] and the Stereotype subscale of the Multi-Construct African American Identity Questionnaire [74]. This adapted measure has been used previously [18]. Participants were given a list of states and asked how strong they disagree or agree with that statement. Example items include “I feel good about Black people” and “I feel that the Black community has made valuable contributions to this society.” The response scale ranged from 1 (strongly disagree) to 4 (strongly agree). Items were summed to create total ethnic identity score where higher scores were indicative of higher levels of ethnic identity. Cronbach’s α for this measure was 0.80. Social Support At W3, social support was assessed using the 10-item version of the Social Provisions Scale developed by Cutrona and Russell [23]. Participants responded to the items on a scale ranging from 1 (strongly disagree) to 4 (strongly agree). Example items include “I feel that I do not have close personal relationships with other people,” and “Other people do not view me as competent.” Items were recoded and summed to create a total perceived social support score. Cronbach's α for this measure was 0.91. Covariates COVID Susceptibility Worry At W4, participants reported their level of COVID-related susceptibility worry using a 3-item measure. These items included (1) How worried are you about getting COVID-19? (2) How worried are you that if you got COVID-19 you would get very sick? (3) How worried are you that if you got COVID-19 you would sicken a loved one? Men responded to the items on a Likert scale ranging from 1 (not worried) to 4 (very worried). Items were summed to create a total COVID worry score. Cronbach’s α for this measure was 0.72. COVID-Related Stress At W4, participants reported their level of COVID-related stress using a 3-item measure. These items included (1) how much has the COVID-19 outbreak impacted your daily life in the past month, (2) how has your overall level of stress due to the COVID-19 outbreak, and (3) to what degree has changes related to the COVID-19 outbreak created financial problems for you. Men responded to the items on a Likert scale ranging from 1 (no impact/stress/problems) to 4 (extreme impact/stress/problems). Items were summed to create a total COVID-related stress score. Cronbach’s α for this measure was 0.87. COVID Exposure Proximity At W4, participants reported their exposure proximity COVID-19 via three items (0 = no; 1 = yes). These items included (1) whether men personally contracted COVID, (2) whether a member of participant’s friends or family contracted COVID, and (3) whether a friend or family member of participant’s died due to COVID. Items were summed to create a total COVID exposure proximity score. Demographic Information At W4, participants were asked to provide their highest level of education and employment status. Men reported their highest level of education using a single-item indicator with response options ranging from 1 (Grade 10 or below) to 6 (4-year college degree or more). Employment status was measured by a single dichotomous item (yes/no) asking whether participants were currently employed at a job where they receive a paycheck or were paid in cash. Data Analysis To investigate hypotheses regarding the mechanisms by which experiences of racial discrimination are associated with Black American men’s COVID-19 vaccine hesitancy, we first tested an indirect effects model examining the extent to which COVID-19 conspiracy beliefs mediated the association between racial discrimination and COVID-19 vaccine hesitancy. Next, we investigated the degree to which ethnic identity influenced the association between racial discrimination on COVID conspiratorial beliefs and on COVID vaccine hesitancy with the inclusion of an interaction term (racial discrimination x ethnic identity). The third and final model examined the degree to which perceived social support influenced the association between racial discrimination on COVID conspiratorial beliefs and on COVID vaccine hesitancy by replacing the interaction term included in the second model with a new interaction term (racial discrimination × perceived social support). All analyses were conducted using data from 242 men who participated in wave 3 data collection with Mplus 8 (Muthén & Muthén, 1998–2017). Retention status across wave was not associated with racial discrimination or demographic characteristics. Little’s Missing Completely at Random test, χ2(4) = 3.79, p = 0.46, suggested that missing values were missing completely at random and were unrelated to the study variables [38]. Accordingly, missing data were managed with full information maximum likelihood estimation (FIML; [39]. FIML tests hypotheses with all available data; no cases were dropped due to missing data [7, 39]. p values of 0.05 or less were considered statistically significant for the purposes of this study. The significance of indirect effects was evaluated with bootstrapping analyses with 5000 bootstrapping resamples to produce 95% confidence intervals (CIs; [13]. These intervals consider possible nonsymmetry in the distribution of estimates, which can bias p values [30]; we thus used the 95% CIs when determining the significance of indirect effects. This study used CFI values greater than or equal to 0.90, RMSEA values less than or equal to 0.08, and SRMR values less than or equal to 0.08 as being indicative of well-fitting models [8, 34]. Measures with Cronbach’s alpha levels of 0.70 or higher were deemed to have acceptable internal consistency for the purposes of this study. The chi-square test of model fit (χ2) was also reported for completeness. All covariates were included in all models. In addition to the direct paths linking the variables, covariation between all predictor variables was permitted. Additionally, residuals of the mediators and dependent variables were correlated to indicate shared unexplained variance. Moderating effects were evaluated with the creation of two interaction terms: (1) racial discrimination × ethnic identity, and (2) racial discrimination × perceived social support. The predictor and moderator variables were first standardized, and then the product terms were calculated by multiplying these standardized variables to facilitate the interpretation of slopes [4]. Simple slope analysis and the Johnson-Neyman method were conducted to verify significant interactions. The Johnson-Neyman method is a procedure for establishing regions of insignificance associated with a test of the difference between two variables at any specific point on the X continuum [33]. In the simple slope analysis, participants were divided into high and low level of groups based on the mean ± 1 standard deviation of each significant moderator. Unstandardized regression parameters for each model provided in Appendix B. Results Bivariate Analysis The means, standard deviations, and correlations for all study variables are presented in Table 1. Racial discrimination was positively correlated with increased levels of COVID conspiracy beliefs (r = 0.22, p = 0.01), COVID exposure fear (r = 0.18, p = 0.05), COVID stress (r = 0.32, p = 0.01), and COVID trauma (r = 0.20, p = 0.05). Racial discrimination was negatively correlated with increased levels of ethnic identity (r = − 0.15, p = 0.05), and perceived social support (r = − 0.31, p = 0.01). COVID vaccine hesitancy was positively correlated with increased levels of general vaccine hesitancy (r = 0.84, p = 0.01), and COVID conspiracy beliefs (r = 0.28, p = 0.01). Conversely, COVID vaccine hesitancy was negatively correlated with COVID exposure fear (r = − 0.33, p = 0.01), educational attainment (r = − 0.28, p = 0.01), and perceived social support (r = − 0.26, p = 0.01).Table 1 Descriptive statistics and two-tailed bivariate correlations among study variables Variable 1 2 3 4 5 6 7 8 9 10 11 1 COVID vaccine hesitancy 1 2 General vaccine hesitancy .84 1 3 Racial discrimination − .03 − .01 1 4 COVID conspiracy beliefs .28 .22 .22 1 5 COVID exposure fear − .33 − .34 .18* .11 1 6 COVID stress − .10 − .08 .32 .21 .46** 1 7 COVID trauma − .18* − .10 .20* .07 .22 .32 1 8 Educational attainment − .28 − .32 − .01 − .14 .07 .17* .25** 1 9 Employment status .01 − .05 − .01 − .07 − .03 − .07 .16* .23** 1 10 Ethnic identity − .14 − .08 − .15* .00 − .02 .00 .14 .09 .05 1 11 Perceived social support − .26** − .20* − .31 − .26 .07 − .04 .08 .21 .08 .29 1 M 14.35 11.52 24.93 19.08 5.46 8.66 .77 5.53 .79 40.68 30.13 SD 5.79 5.17 16.89 7.13 2.32 5.24 .89 1.98 .41 3.96 6.48 Range 0–21 0–18 0–74 7–35 3–12 1–21 0–3 1–9 0–1 25–44 10–40 p < .05; *p < .01 Indirect Effects Model Figure 2 presents the standardized results of our indirect effects model. The model fit the data as follows: χ2(3) = 7.104, p = 0.069; RMSEA = 0.075 [0.000, 0.148]; CFI = 0.981; and SRMR = 0.031. Results indicated that cumulative racial discrimination was directly associated with increased COVID conspiratorial beliefs (B = 0.101, p < 0.01), but not directly associated with Black American men’s COVID vaccine hesitancy (B = − 0.004, p = 0.762). COVID conspiratorial beliefs was directly associated with increased COVID vaccine hesitancy (B = 0.103, p < 0.01). Significant indirect effects were detected whereby racial discrimination was associated with COVID vaccine hesitancy indirectly via COVID conspiratorial beliefs (Bind = 0.010; 95% CI = 0.003, 0.027).Fig. 2 Standardized results of mediation model linking cumulative racial discrimination to COVID vaccine hesitancy among Black American men Exacerbating Effects of Ethnic Identity A second model examined whether ethnic identity moderated the associations of cumulative racial discrimination on COVID conspiratorial beliefs and on COVID vaccine hesitancy. The model fit the data as follows: χ2(11) = 28.987, p = 0.002; RMSEA = 0.08 [0.046, 0.119], CFI = 0.919, and SRMR = 0.046. Ethnic identity was not directed associated with COVID conspiracy beliefs (B = 0.149, p = 0.822) or COVID vaccine hesitancy (B = − 0.356, p = 0.135). The interaction of racial discrimination and ethnic identity on COVID vaccine hesitancy was not significant (B = − 0.191, p = 0.308). However, the interaction of racial discrimination and ethnic identity on COVID conspiracy beliefs was significant (B = 1.149, p < 0.05). Simple slope analysis indicated an increased association between racial discrimination and COVID conspiracy beliefs at high levels of ethnic identity (B = 2.389, p < 0.001). However, data failed to support a corresponding association at low levels of ethnic identity (B = 1.240, p < 0.05; see Fig. 3). Review of the Johnson-Neyman plot (Fig. 4) documented significant moderation wherein the association between racial discrimination and COVID conspiracy beliefs was significant at − 0.3 standard deviations below the average level of ethnic identity (− 0.3 SD = 39.494; see Fig. 4). This interaction evidenced significant conditional indirect effects (conditional Bind = 0.130; 95% CI = 0.019, 0.360).Fig. 3 Interaction of ethnic identity and racial discrimination on COVID conspiracy beliefs Fig. 4 Johnson-Neyman plot for the interaction of ethnic identity and racial discrimination on COVID conspiracy beliefs. Note: Bold line represents the conditional effect of racial discrimination on COVID conspiracy beliefs with dashed lines identifying 95% confidence intervals of the effect Buffering Effects of Perceived Social Support A third model examined whether perceived social support moderated the direct and indirect associations between cumulative racial discrimination and COVID conspiracy beliefs, and COVID vaccine hesitancy, respectively. The model fit the data as follows: χ2(11) = 19.540, p = 0.0521; RMSEA = 0.057 [0.000, 0.097]; CFI = 0.963; and SRMR = 0.040. Perceived social support was directly associated with decreasing COVID conspiracy beliefs (B = − 1.274, p < 0.05) but was not directed associated with COVID vaccine hesitancy (B = − 0.457, p = 0.096). The interaction of racial discrimination and perceived social support on COVID vaccine hesitancy was not significant (B = − 0.343, p = 0.123). Perceived social support did, however, moderate the association between racial discrimination and COVID conspiracy beliefs (B = 0.955, p < 0.05). Simple slope analysis indicated a diminished association between racial discrimination and COVID conspiracy beliefs at high levels of perceived social support (B = 1.939, p < 0.001). However, data failed to support a corresponding association at low levels of ethnic identity (B = 0.984, p = 0.121; see Fig. 5). Review of a Johnson-Neyman plot indicated the association between racial discrimination and COVID conspiracy beliefs was significantly affected by perceived social support at values equal to or greater than − 0.1 standard deviation below the average level of perceived social support (− 0.1 SD = 29.384; see Fig. 6). This interaction evidenced significant conditional indirect effects (conditional Bind = 0.094; 95% CI = 0.013, 0.277).Fig. 5 Interaction of perceived social support and racial discrimination on COVID conspiracy beliefs Fig. 6 Johnson-Neyman plot for the interaction of perceived social support and racial discrimination on COVID conspiracy beliefs. Note: Bold line represents the conditional effect of racial discrimination on COVID conspiracy beliefs with dashed lines identifying 95% confidence intervals of the effect Discussion The present study is among the first to investigate the pathways linking cumulative experiences of racial discrimination to COVID vaccine hesitancy among Black American men. We advanced research in this area in two ways. First, we examined if COVID conspiracy beliefs mediated the association between cumulative racial discrimination and COVID vaccine hesitancy. We found that racial discrimination was indirectly associated with COVID vaccine hesitancy via the increased endorsement of COVID conspiracy beliefs. Second, we examined the degree to which ethnic identity and perceived social support moderated the effects of cumulative racial discrimination on COVID-19 conspiracy beliefs and COVID-19 vaccine hesitancy. Our findings also indicated that when men reported increased ethnic identity, the association between racial discrimination and COVID conspiracy beliefs was strengthened. In contrast, increased levels of perceived social support weakened the association between racial discrimination and COVID conspiracy beliefs. The results of this study suggest that the endorsement of COVID conspiracy beliefs mediated the association between cumulative experiences of racial discrimination unrelated to healthcare and COVID vaccine hesitancy. These results are consistent with prior research which suggest that experiences of marginalization and disenfranchisement erode Black American men’s trust in the systems of authority associated with health care decision making [53, 80]. Our findings further indicate Black American men may adopt conspiracy beliefs to make sense of and better cope with discriminatory events. Experiences of racial discrimination may encourage Black American men to seek out narratives contrary to those being presented by the government or public health agencies to better understand their contemporary social contexts. This search for alternative narratives, coupled with historical atrocities such as the Tuskegee syphilis study, provide a compelling reason for Black Americans to believe that the US government or other agencies of influence are capable of conspiring to harm them. Regarding the COVID-19 vaccine, hesitancy and endorsement of conspiracy beliefs may be related to (a) healthcare professionals’ and stakeholders’ uncertainty concerning how to respond to the pandemic, and (b) the perceived disorganized COVID pandemic response of the government and other agencies by laypersons. This tension was compounded by a wider decline in trust of medical establishment, the politization of the healthcare system, and many state governors being at odds with the President, who in turn was at odds with his own pandemic advisory team, and the nation’s public health institutions regarding the dissemination of the vaccine [65, 87]. This confusion was further complicated by inconsistencies in governmental reporting related to the needed dosage of the vaccine to be effective, and vaccination side effects, thus undermining governmental believability and credibility [65, 87]. The results of this study, coupled with prior theoretical and empirical research, suggest that cumulative experiences of racial discrimination contributed to Black American men’s susceptibility to the endorsement of conspiracy beliefs, which were then related to increased COVID vaccine hesitancy. Consistent with study hypotheses, ethnic identity strengthened the positive association between racial discrimination and COVID conspiracy beliefs. These findings are consistent with prior research that indicated that ethnic identity magnifies the association between racial discrimination and men’s endorsement of COVID conspiracy beliefs [3, 73]. In interpreting these findings, it is important not to construe ethnic identity as a problematic feature of an individual. In fact, ethnic identity is important to an individual’s self-image and is typically associated with positive outcomes such as better mental health and higher self-esteem [51]. As such, ethnic identity may have a diverging effect on health-related outcomes among Black American men whereby individuals who are exhibiting high levels of ethnic identity may experience good mental health, such as low levels of depression and anxiety, but are at elevated risk for adverse health outcomes, such as lower medication adherence. Further research is needed to better understand the paradoxical effects of ethnic identity on the associations between Black American men’s experiences of racial discrimination, conspiracy beliefs, and various health-related outcomes. In the present study, social support weakened the association between cumulative experiences of racial discrimination and COVID conspiracy beliefs. In accordance with prior research, our findings suggest that perceived social support attenuates the effects of racial discrimination on Black American men’s trust in systems of influence [15, 55]. The attenuating effects of social support can be interpreted several ways. First, social support may reduce the downstream effects of discriminatory events by modulating individuals’ experiences of the resulting stress. Prior research indicates that individuals embedded within a socially supportive environment appraise racial stressors as less salient than those with weaker or more negative social support networks [55]. Another possible explanation for this finding may be that when Black Americans experience a discriminatory event, they cope with the stress of that event by turning to their social networks for support [58]. From this perspective, the psychosocial stress of experiencing racial discrimination does not carry forward to have long-lasting effects on men’s health decision-making. Our results, coupled with prior research, indicate that public health scholars’ efforts at prevention and intervention may be strengthened by leveraging men’s social networks to ameliorate down-stream health effects of racial discrimination. Limitations The present study used a prospective design to examine the cumulative effects of experiences of racial discrimination and COVID conspiracy beliefs on Black American men’s COVID vaccine hesitancy. Several limitations should be noted. The observational nature of the study prevents causal conclusions. Our inability to draw causal conclusions was further limited by our mediator being measured at the same time point as our outcome. A longitudinal examination of these associations for the COVID vaccine, and other adult vaccines, is warranted for future research. Furthermore, participant’s ethnic identity may be influenced by their level of perceived social support in that receiving social support from individuals of the same ethnic group may increase one’s connection to their own ethnic identity. Our finding of small correlation (r = 0.29) would support such a hypothesis; however, our limited sample size prohibited us from examining a potential three-way interaction. Future studies, with larger sample sizes, would benefit from examining this potential association. Additionally, this study focused on men living in rural communities; findings may not generalize to men living in urban settings or other regions of the USA. This limitation is less of a concern because nascent research indicates that there are not substantial differences based on rurally among the variables of interest [11, 12]. The use of self-report measures also may lead to biases related to subject recall and social desirability [37]. These limitations notwithstanding the present study provides empirical evidence of cumulative experiences of racial discrimination and COVID conspiracy beliefs influence Black American men’s vaccine beliefs. Conclusion The purpose of this study was to investigate the cumulative impact of exposure to racial discrimination on Black American men’s COVID vaccine hesitancy. Findings underscore the roles of disenfranchisement and marginalization in undermining Black American men’s trust in the government and other agencies of influence, which then carries forward to influence their health-related beliefs and decision-making. These insights may be used by public health scientists and interventionists to enhance programming aimed at promoting vaccine uptake by demonstrating the importance of rebuilding cultural trust with the Black American community, in general, and Black American men, in particular. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 15 KB) Supplementary file2 (DOCX 20 KB) Author Contribution The first author conceptualized the study, conducted statistical analyses, and wrote the first draft of the manuscript. The second and last author provided the data, contributed to the writing, and commented on drafts of the manuscript. The third author contributed to the writing and commented on drafts of the manuscript. All authors read and approved the final manuscript. Funding This work was partly supported by the National Institute on Drug Abuse under Grant R01DA029488, the National Institute on Alcohol Abuse and Alcoholism under Grant R01AA026623, and the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Numbers UL1TR002378 and TL1TR002382. The content is solely the authors’ responsibility and does not necessarily represent the official views of the National Institute on Drug Abuse, the National Institute on Alcohol Abuse and Alcoholism, the National Center for Advancing Translational Sciences of the National Institutes of Health, or the National Institutes of Health. The analysis presented was not disseminated before the creation of this article; however, this study’s dataset has been used in prior unrelated publications. Declarations Ethics Approval All study protocols were approved by the University’s Human Subjects Review Board. Consent to Participate Participants provided written informed consent before participating in this study. Competing Interests The authors declare no competing interests. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Ahmed AT, Mohammed SA, Williams DR (2007) Racial discrimination & health: pathways & evidence. Indian J Med Res 126(4), 318–327. https://www.ncbi.nlm.nih.gov/pubmed/18032807 2. Alcendor DJ Targeting COVID vaccine hesitancy in rural communities in Tennessee: implications for extending the COVID-19 pandemic in the South Vaccines (Basel) 2021 9 11 1279 10.3390/vaccines9111279 34835210 3. Allington D, McAndrew S, Moxham-Hall V, Duffy B (2021) Coronavirus conspiracy suspicions, general vaccine attitudes, trust and coronavirus information source as predictors of vaccine hesitancy among UK residents during the COVID-19 pandemic. Psychol Med 1–12. 4. Anders SL Frazier PA Frankfurt SB Variations in criterion A and PTSD rates in a community sample of women J Anxiety Disord 2011 25 2 176 184 10.1016/j.janxdis.2010.08.018 20888184 5. Andrade G Covid-19 vaccine hesitancy, conspiracist beliefs, paranoid ideation and perceived ethnic discrimination in a sample of University students in Venezuela Vaccine 2021 39 47 6837 6842 10.1016/j.vaccine.2021.10.037 34711439 6. Andrews N Stowe J Kirsebom F Toffa S Rickeard T Gallagher E Gower C Kall M Groves N O'Connell AM Simons D Blomquist PB Zaidi A Nash S Iwani Binti Abdul Aziz N Thelwall S Dabrera G Myers R Amirthalingam G LopezBernal J Covid-19 vaccine effectiveness against the omicron (B.1.1.529) variant N Engl J Med 2022 386 16 1532 1546 10.1056/NEJMoa2119451 35249272 7. Arbuckle JL (1996) Full information estimation in the presence of incomplete data. In G. A. Marcoulides & R. E. Schumacker (Eds.), Advanced structural equation modeling: Issues and techniques 243:243–277. Lawrence Erlbaum Associates, Inc. 8. Awang Z (2012) Structural equation modeling using AMOS graphic. Penerbit Universiti Teknologi MARA. 9. Ball K Lawson W Alim T Medical mistrust, conspiracy beliefs & HIV-related behavior among African Americans J Psychol Behav Sci 2013 1 1 1 7 10. Baptiste DL, Josiah NA, Alexander KA, Commodore‐Mensah Y, Wilson PR, Jacques K, Jackson D (2020) Racial discrimination in health care: an “us” problem. In: Wiley Online Library. 11. Bleich SN Findling MG Casey LS Blendon RJ Benson JM SteelFisher GK Sayde JM Miller C Discrimination in the United States: experiences of Black Americans Health Serv Res 2019 54 Suppl 2 1399 1408 10.1111/1475-6773.13220 31663124 12. Bogart LM Dong L Gandhi P Klein DJ Smith TL Ryan S Ojikutu BO COVID-19 vaccine intentions and mistrust in a national sample of Black Americans J Natl Med Assoc 2022 113 6 599 611 10.1016/j.jnma.2021.05.011 34158171 13. Bollen KA Stine R Direct and indirect effects: classical and bootstrap estimates of variability Sociol Methodol 1990 20 115 140 10.2307/271084 14. Boulware LE, Cooper LA, Ratner LE, LaVeist TA, Powe NR (2016) Race and trust in the health care system. Public Health Rep. 15. Bowleg L Burkholder GJ Massie JS Wahome R Teti M Malebranche DJ Tschann JM Racial discrimination, social support, and sexual HIV risk among Black heterosexual men AIDS Behav 2013 17 1 407 418 10.1007/s10461-012-0179-0 22437347 16. Brandon DT Isaac LA LaVeist TA The legacy of Tuskegee and trust in medical care: is Tuskegee responsible for race differences in mistrust of medical care? J Natl Med Assoc 2005 97 7 951 16080664 17. Brody GH Chen YF Kogan SM Murry VM Logan P Luo Z Linking perceived discrimination to longitudinal changes in African American mothers’ parenting practices J Marriage Fam 2008 70 2 319 331 10.1111/j.1741-3737.2008.00484.x 18. Brody GH Yu T Miller GE Chen E Discrimination, racial identity, and cytokine levels among African-American adolescents J Adolesc Health 2015 56 5 496 501 10.1016/j.jadohealth.2015.01.017 25907649 19. Brondolo E Brady Ver Halen N Pencille M Beatty D Contrada RJ Coping with racism: a selective review of the literature and a theoretical and methodological critique J Behav Med 2009 32 1 64 88 10.1007/s10865-008-9193-0 19127420 20. Caldwell JT Ford CL Wallace SP Wang MC Takahashi LM Intersection of living in a rural versus urban area and race/ethnicity in explaining access to health care in the United States Am J Public Health 2016 106 8 1463 1469 10.2105/AJPH.2016.303212 27310341 21. Centers for Disease Control and Prevention (2022) Web-based Injury Statistics Query and Reporting System (WISQARS). https://www.cdc.gov/injury/wisqars/index.html 22. Cheng KJG Sun Y Monnat SM COVID-19 death rates are higher in rural counties with larger shares of Blacks and Hispanics J Rural Health 2020 36 4 602 608 10.1111/jrh.12511 32894612 23. Cutrona CE Russell DW The provisions of social relationships and adaptation to stress Adv Pers Relatsh 1987 1 1 37 67 24. Davis J Wetherell G Henry P Social devaluation of African Americans and race-related conspiracy theories Eur J Soc Psychol 2018 48 7 999 1010 10.1002/ejsp.2531 25. Daw J Verdery AM Margolis R Kin Count(s): educational and racial differences in extended kinship in the United States Popul Dev Rev 2016 42 3 491 517 10.1111/j.1728-4457.2016.00150.x 31456597 26. Drinkwater KG Dagnall N Denovan A Walsh RS To what extent have conspiracy theories undermined COVID-19: strategic narratives? Front Commun 2021 6 576198 10.3389/fcomm.2021.576198 27. Gamble VN Under the shadow of Tuskegee: African Americans and health care Am J Public Health 1997 87 11 1773 1778 10.2105/ajph.87.11.1773 9366634 28. Greene ML Way N Pahl K Trajectories of perceived adult and peer discrimination among Black, Latino, and Asian American adolescents: patterns and psychological correlates Dev Psychol 2006 42 2 218 10.1037/0012-1649.42.2.218 16569162 29. Groom HC Zhang F Fisher AK Wortley PM Differences in adult influenza vaccine-seeking behavior: the roles of race and attitudes J Public Health Manag Pract 2014 20 2 246 250 10.1097/PHH.0b013e318298bd88 23715220 30. Hayes AF Scharkow M The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: does method really matter? Psychol Sci 2013 24 10 1918 1927 10.1177/0956797613480187 23955356 31. Heckathorn DD Respondent-driven sampling II: deriving valid population estimates from chain-referral samples of hidden populations Soc Probl 2002 49 1 11 34 10.1525/sp.2002.49.1.11 32. Jaiswal J Halkitis PN Towards a more inclusive and dynamic understanding of medical mistrust informed by science Behav Med 2019 45 2 79 85 10.1080/08964289.2019.1619511 31343962 33. Johnson PO Neyman J Tests of certain linear hypotheses and their application to some educational problems Stat Res Mem 1936 1 57 93 34. Kline RB (2015) Principles and practice of structural equation modeling. Guilford Publications. 35. Kogan SM Cho J Oshri A The influence of childhood adversity on rural Black men's sexual risk behavior Ann Behav Med 2016 50 6 813 822 10.1007/s12160-016-9807-7 27283296 36. Kolar SK Wheldon C Hernandez ND Young L Romero-Daza N Daley EM Human papillomavirus vaccine knowledge and attitudes, preventative health behaviors, and medical mistrust among a racially and ethnically diverse sample of college women J Racial Ethn Health Disparities 2015 2 1 77 85 10.1007/s40615-014-0050-2 26863245 37. Larson RB Controlling social desirability bias Int J Mark Res 2019 61 5 534 547 10.1177/1470785318805305 38. Li C Little's Test of Missing Completely at Random Stata J: Promot Commun Stat Stata 2013 13 4 795 809 10.1177/1536867x1301300407 39. Little RJ, Rubin DB (2019) Statistical analysis with missing data (Vol. 793). John Wiley & Sons. 40. Lu P-J O'Halloran A Bryan L Kennedy ED Ding H Graitcer SB Santibanez TA Meghani A Singleton JA Trends in racial/ethnic disparities in influenza vaccination coverage among adults during the 2007–08 through 2011–12 seasons Am J Infect Control 2014 42 7 763 769 10.1016/j.ajic.2014.03.021 24799120 41. Lu P-J O’Halloran A Williams WW Lindley MC Farrall S Bridges CB Racial and ethnic disparities in vaccination coverage among adult populations in the US Vaccine 2015 33 D83 D91 10.1016/j.vaccine.2015.09.031 26615174 42. Malik AA McFadden SM Elharake J Omer SB Determinants of COVID-19 vaccine acceptance in the US EClinicalMedicine 2020 26 100495 10.1016/j.eclinm.2020.100495 32838242 43. Mathieu E Ritchie H Ortiz-Ospina E Roser M Hasell J Appel C Giattino C Rodés-Guirao L A global database of COVID-19 vaccinations Nat Hum Behav 2021 5 7 947 953 10.1038/s41562-021-01122-8 33972767 44. McCallum JM Arekere DM Green BL Katz RV Rivers BM Awareness and knowledge of the US Public Health Service syphilis study at Tuskegee: implications for biomedical research J Health Care Poor Underserved 2006 17 4 716 10.1353/hpu.2006.0130 17242526 45. McConnell K, Merdjanoff AA, Burow P, Mueller T, Farrell J (2021) Rural safety net use during the Covid-19 pandemic. 46. Moore JX Gilbert KL Lively KL Laurent C Chawla R Li C Johnson R Petcu R Mehra M Spooner A Kolhe R Ledford CJW Correlates of COVID-19 vaccine hesitancy among a community sample of African Americans living in the Southern United States Vaccines (Basel) 2021 9 8 879 10.3390/vaccines9080879 34452004 47. Muthén LK, Muthén BO (1998–2017) Mplus user's guide statistical analysis with latent variables (8th ed.). Muthén & Muthén. 48. Nuzhath T, Tasnim S, Sanjwal RK, Trisha NF, Rahman M, Mahmud SF, Arman A, Chakraborty S, Hossain MM (2020) COVID-19 vaccination hesitancy, misinformation and conspiracy theories on social media: a content analysis of Twitter data. 49. Ojha RP Stallings-Smith S Flynn PM Adderson EE Offutt-Powell TN Gaur AH The impact of vaccine concerns on racial/ethnic disparities in influenza vaccine uptake among health care workers Am J Public Health 2015 105 9 e35 e41 10.2105/AJPH.2015.302736 26180953 50. Ong AD Fuller-Rowell T Burrow AL Racial discrimination and the stress process J Pers Soc Psychol 2009 96 6 1259 1271 10.1037/a0015335 19469600 51. Paradies Y Ben J Denson N Elias A Priest N Pieterse A Gupta A Kelaher M Gee G Racism as a determinant of health: a systematic review and meta-analysis PLoS One 2015 10 9 e0138511 10.1371/journal.pone.0138511 26398658 52. Parsons S Simmons W Shinhoster F Kilburn J A test of the grapevine: an empirical examination of conspiracy theories among African Americans Sociol Spectr 1999 19 2 201 222 10.1080/027321799280235 53. Pizarro JJ, Cakal H, Méndez L, Da Costa S, Zumeta LN, Gracia Leiva M, Basabe N, Navarro Carrillo G, Cazan AM, Keshavarzi S (2020) Tell me what you are like and I will tell you what you believe in: Social representations of COVID-19 in the Americas, Europe and Asia. 54. Poteat T Millett GA Nelson LE Beyrer C Understanding COVID-19 risks and vulnerabilities among black communities in America: the lethal force of syndemics Ann Epidemiol 2020 47 1 3 10.1016/j.annepidem.2020.05.004 32419765 55. Prelow HM Mosher CE Bowman MA Perceived racial discrimination, social support, and psychological adjustment among African American college students J Black Psychol 2006 32 4 442 454 10.1177/0095798406292677 56. Price-Haywood EG Burton J Fort D Seoane L Hospitalization and mortality among Black patients and White patients with Covid-19 N Engl J Med 2020 382 26 2534 2543 10.1056/NEJMsa2011686 32459916 57. Probst JC, Fozia A (2019) Social determinants of health among the rural African American population. Rural & Minority Health Research Center. https://www.sc.edu/study/colleges_schools/public_health/research/research_centers/sc_rural_health_research_center/documents/socialdeterminantsofhealthamongtheruralafricanamericanpopulation.pdf 58. Pullen E Perry B Oser C African American women's preventative care usage: the role of social support and racial experiences and attitudes Sociol Health Illn 2014 36 7 1037 1053 10.1111/1467-9566.12141 24749849 59. Qiao S, Tam CC, Li X (2020) Risk exposures, risk perceptions, negative attitudes toward general vaccination, and COVID-19 vaccine acceptance among college students in South Carolina. Am J Health Promot 08901171211028407. 60. Quinn SC Jamison A Freimuth VS An J Hancock GR Musa D Exploring racial influences on flu vaccine attitudes and behavior: results of a national survey of White and African American adults Vaccine 2017 35 8 1167 1174 10.1016/j.vaccine.2016.12.046 28126202 61. Quinn SC Jamison AM An J Hancock GR Freimuth VS Measuring vaccine hesitancy, confidence, trust and flu vaccine uptake: results of a national survey of White and African American adults Vaccine 2019 37 9 1168 1173 10.1016/j.vaccine.2019.01.033 30709722 62. Rémy V Largeron N Quilici S Carroll S The economic value of vaccination: why prevention is wealth J Market Access Health Policy 2015 3 1 29284 10.3402/jmahp.v3.29284 63. Remy V Zollner Y Heckmann U Vaccination: the cornerstone of an efficient healthcare system J Mark Access Health Policy 2015 3 1 27041 10.3402/jmahp.v3.27041 64. Rosenberg ES Dorabawila V Easton D Bauer UE Kumar J Hoen R Hoefer D Wu M Lutterloh E Conroy MB Greene D Zucker HA Covid-19 vaccine effectiveness in New York State N Engl J Med 2022 386 2 116 127 10.1056/NEJMoa2116063 34942067 65. Rutledge PE Trump, COVID-19, and the war on expertise Am Rev Public Admin 2020 50 6–7 505 511 10.1177/0275074020941683 66. Saied SM Saied EM Kabbash IA Abdo SAEF Vaccine hesitancy: beliefs and barriers associated with COVID-19 vaccination among Egyptian medical students J Med Virol 2021 93 7 4280 4291 10.1002/jmv.26910 33644891 67. Sanders Thompson VL Coping responses and the experience of discrimination 1 J Appl Soc Psychol 2006 36 5 1198 1214 10.1111/j.0021-9029.2006.00038.x 68. Savoia E Piltch-Loeb R Goldberg B Miller-Idriss C Hughes B Montrond A Kayyem J Testa MA Predictors of COVID-19 vaccine hesitancy: socio-demographics, co-morbidity, and past experience of racial discrimination Vaccines (Basel) 2021 9 7 767 10.3390/vaccines9070767 34358184 69. Sellers RM, Caldwell CH, Schmeelk-Cone KH, Zimmerman MA (2003). Racial identity, racial discrimination, perceived stress, and psychological distress among African American young adults. J Health Soc Behav 302–317. 70. Sellers RM Copeland-Linder N Martin PP Lewis RLH Racial identity matters: the relationship between racial discrimination and psychological functioning in African American adolescents J Res Adolesc 2006 16 2 187 216 10.1111/j.1532-7795.2006.00128.x 71. Sellers RM Rowley SA Chavous TM Shelton JN Smith MA Multidimensional Inventory of Black Identity: a preliminary investigation of reliability and constuct validity J Pers Soc Psychol 1997 73 4 805 10.1037/0022-3514.73.4.805 72. Sellers RM Shelton JN The role of racial identity in perceived racial discrimination J Pers Soc Psychol 2003 84 5 1079 1092 10.1037/0022-3514.84.5.1079 12757150 73. Smith AC, Woerner J, Perera R, Haeny AM, Cox JM (2021) An investigation of associations between race, ethnicity, and past experiences of discrimination with medical mistrust and COVID-19 protective strategies. J Racial Ethnic Health Disparities 1–13. 74. Smith EP Brookins CC Toward the development of an ethnic identity measure for African American youth J Black Psychol 1997 23 4 358 377 10.1177/00957984970234004 75. Stojanov A Bering JM Halberstadt J Does perceived lack of control lead to conspiracy theory beliefs? Findings from an online MTurk sample PLoS One 2020 15 8 e0237771 10.1371/journal.pone.0237771 32804940 76. Stokes EK, Zambrano LD, Anderson KN, Marder EP, Raz KM, El Burai Felix S, Tie Y, Fullerton KE (2020) Coronavirus Disease 2019 Case Surveillance - United States, January 22-May 30, 2020. MMWR Morb Mortal Wkly Rep 69(24):759–765. 10.15585/mmwr.mm6924e2 77. Swim JK Hyers LL Cohen LL Fitzgerald DC Bylsma WH African American college students’ experiences with everyday racism: characteristics of and responses to these incidents J Black Psychol 2003 29 1 38 67 10.1177/0095798402239228 78. Tomljenovic H Bubic A Erceg N It just doesn’t feel right–the relevance of emotions and intuition for parental vaccine conspiracy beliefs and vaccination uptake Psychol Health 2020 35 5 538 554 10.1080/08870446.2019.1673894 31588791 79. Utsey SO Lanier Y Williams O III Bolden M Lee A Moderator effects of cognitive ability and social support on the relation between race-related stress and quality of life in a community sample of black Americans Cultur Divers Ethnic Minor Psychol 2006 12 2 334 10.1037/1099-9809.12.2.334 16719581 80. van Mulukom V, Pummerer L, Alper S, Bai H, Cavojova V, Farias J, Kay CS, Lazarevic LB, Lobato EJ, Marinthe G (2020) Antecedents and consequences of Covid-19 conspiracy beliefs: a systematic review. PsyarXiv 10. 81. Vikram K Vanneman R Desai S Linkages between maternal education and childhood immunization in India Soc Sci Med 2012 75 2 331 339 10.1016/j.socscimed.2012.02.043 22531572 82. Wang J Jing R Lai X Zhang H Lyu Y Knoll MD Fang H Acceptance of COVID-19 vaccination during the COVID-19 pandemic in China Vaccines (Basel) 2020 8 3 482 10.3390/vaccines8030482 32867224 83. Williams DR Gonzalez HM Williams S Mohammed SA Moomal H Stein DJ Perceived discrimination, race and health in South Africa Soc Sci Med 2008 67 3 441 452 10.1016/j.socscimed.2008.03.021 18486292 84. Willis DE Andersen JA Bryant-Moore K Selig JP Long CR Felix HC Curran GM McElfish PA COVID-19 vaccine hesitancy: race/ethnicity, trust, and fear Clin Transl Sci 2021 14 6 2200 2207 10.1111/cts.13077 34213073 85. Xiao X Wong RM Vaccine hesitancy and perceived behavioral control: a meta-analysis Vaccine 2020 38 33 5131 5138 10.1016/j.vaccine.2020.04.076 32409135 86. Yang X Wei L Liu Z Promoting COVID-19 vaccination using the health belief model: does information acquisition from divergent sources make a difference? Int J Environ Res Public Health 2022 19 7 3887 10.3390/ijerph19073887 35409568 87. Yang Y Bennett L Aelst PV Blumler JG Interactive propaganda: how Fox news and Donald Trump co-produced false narratives about the COVID-19 crisis Political Communication in the Time of Coronavirus 2021 Routledge 83 100 88. Yehia BR Winegar A Fogel R Fakih M Ottenbacher A Jesser C Bufalino A Huang RH Cacchione J Association of race with mortality among patients hospitalized with Coronavirus Disease 2019 (COVID-19) at 92 US hospitals JAMA Netw Open 2020 3 8 e2018039 10.1001/jamanetworkopen.2020.18039 32809033 89. Yu Q Scribner RA Leonardi C Zhang L Park C Chen L Simonsen NR Exploring racial disparity in obesity: a mediation analysis considering geo-coded environmental factors Spat Spatiotemporal Epidemiol 2017 21 13 23 10.1016/j.sste.2017.02.001 28552184 90. Zhang S Yin Z Suraratdecha C Liu X Li Y Hills S Zhang K Chen Y Liang X Knowledge, attitudes and practices of caregivers regarding Japanese encephalitis in Shaanxi Province China Public Health 2011 125 2 79 83 10.1016/j.puhe.2010.10.011 21288546	36445684	PMC9707415	NO-CC CODE	2022-12-01 23:20:23	no	J Racial Ethn Health Disparities. 2022 Nov 29;:1-13	utf-8	J Racial Ethn Health Disparities	2,022	10.1007/s40615-022-01471-8	oa_other
==== Front Anal Bioanal Chem Anal Bioanal Chem Analytical and Bioanalytical Chemistry 1618-2642 1618-2650 Springer Berlin Heidelberg Berlin/Heidelberg 36445455 4449 10.1007/s00216-022-04449-x Review Advances in the screening of antimicrobial compounds using electrochemical biosensors: is there room for nanomaterials? Toyos-Rodríguez Celia 12 Valero-Calvo David 12 http://orcid.org/0000-0002-9600-0253 de la Escosura-Muñiz Alfredo [email protected] 12 1 grid.10863.3c 0000 0001 2164 6351 NanoBioAnalysis Group, Department of Physical and Analytical Chemistry, University of Oviedo, Julián Clavería 8, 33006 Oviedo, Spain 2 grid.10863.3c 0000 0001 2164 6351 Biotechnology Institute of Asturias, University of Oviedo, Santiago Gascon Building, 33006 Oviedo, Spain 29 11 2022 115 30 9 2022 11 11 2022 17 11 2022 © Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The abusive use of antimicrobial compounds and the associated appearance of antimicrobial resistant strains are a major threat to human health. An improved antimicrobial administration involves a faster diagnosis and detection of resistances. Antimicrobial susceptibility testing (AST) are the reference techniques for this purpose, relying mainly in the use of culture techniques. The long time required for analysis and the lack of reproducibility of these techniques have fostered the development of high-throughput AST methods, including electrochemical biosensors. In this review, recent electrochemical methods used in AST have been revised, with particular attention on those used for the evaluation of new drug candidates. The role of nanomaterials in these biosensing platforms has also been questioned, inferring that it is of minor importance compared to other applications. Graphical Abstract Keywords Electrochemical biosensors Antimicrobial compounds Antimicrobial susceptibility testing (AST) Nanomaterials Screening methods http://dx.doi.org/10.13039/100011941 Gobierno del Principado de Asturias SV-PA-21-AYUD/2021/51323 http://dx.doi.org/10.13039/501100004837 Ministerio de Ciencia e Innovación MCI-21-PID2020-115204RBI00 PRE2018-084953 PRE2021-097567 RyC-2016-20299 ==== Body pmcIntroduction The abuse in the use of antibiotic treatments both in the livestock and agricultural sectors and in human healthcare, together with a deficient treatment of antibiotic waste, has enhanced the problem of antimicrobial resistance (AMR) [1, 2]. This worldwide challenge translates into an increase in the minimal inhibitory concentrations (MIC) bacteria could tolerate, leading to an enlarged mortality and morbidity due to the prevalence of AMR superbugs. According to the European Centre for Disease Prevention and Control (ECDC), each year, more than 670,000 infections are initiated by AMR bacteria, leading to the death of over 33,000 person each year just in the European Union/European Economic Area [3] and around 700,000 worldwide. Without action, this value is expected to increase to over 10 million deaths per year by 2050 [4]. The current pandemic situation due to the SARS-CoV-2 (COVID-19 disease) has required the use of antibiotics as co-adjuvant treatments to stop more severe symptoms of this disease, what has just aggravated the AMR problem [5–8]. Resistance of bacteria against traditional antibiotic treatments (e.g., broad-spectrum β-lactam antibiotics) is generated by two main genetic mechanisms: gene mutations and horizontal gene transfer [9]. These mechanisms lead to the production of enzymes or modification in the bacteria characteristics that do not allow drug penetration, modify the antimicrobial target, or generate global changes in metabolic pathways. Hydrolytic extensive spectrum β-lactamase enzyme is an example of these resistance mechanisms. By hydrolyzing the β-lactam ring of the broad-spectrum β-lactam antibiotics, bacteria inhibit antibiotic effect by avoiding the incorporation of the β-lactam ring to bacteria cell walls through penicillin-binding proteins (PBPs), thus stopping the mechanism of action of these drugs, avoiding bacteria growth [10]. Developing resistance mechanisms is the expected consequence of the remarkable genetic plasticity of bacteria and the co-evolution of bacteria and antimicrobial compounds present in nature. However, the misuse of clinical antibiotic treatments since their discovery has favored the scaling up of acquired resistance. But, although bacteria adaptability to antibiotics is an aspect we could not modulate, there are two features on antibiotic usage in which mankind could definitively make things better. Antibiotic release to the environment is one of them [11]. Through human and domestic animal excretion, production or excess drug waste handling, or direct rivers and sea contamination, antibiotics reach the environment, favoring environmental selection [12–16]. A proper identification of contaminated environments by antibiotics is fundamental to address this problem, and in this sense, biosensing approaches, specially electrochemical biosensors, have been deeply studied as useful tools for point-of-care, rapid, and low-cost detection of antibiotics in complex samples [17–19]. However, even antibiotic detection in complex environmental samples is helpful, what it is really a challenge is to provide valuable knowledge on both the effect and the dose required of antimicrobial compounds to fight infection. Antibiotic screening approaches are used in this sense, and in this field of research, electrochemical biosensors have also played their role. However, their use specially for screening new antibiotic compounds is far to be completely exploited. In this review, a revision onto the electrochemically based antibiotic screening methods has been done, focusing on personalize medicine and those that have potential as screening platforms for new antibiotic development. A special consideration will have electrochemical sensors incorporating nanomaterials, which purpose and need will be critically evaluated. Antibiotic screening in the clinical practice Antibiotic screening is divided in two purposes, the selection of suitable treatments for antimicrobial resistant organisms and the identification of new antibiotic compounds. The first one has extended application in the clinical practice and relies on the use of antimicrobial susceptibility testing (AST). AST is defined as the identification of the susceptibility of a microorganism against a determined drug to search for resistance and provide information on the suitability of a treatment [20]. The use of AST is intended to warrant that antibiotics are prescribed properly and to construct patterned roadmaps of the antimicrobial resistant organisms present in a local area [21]. AST methods are standardized by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) and the Clinical Laboratory Standards Institute (CLSI), being necessary for a new AST method to be tested against standardized ones to be validated [22]. Main AST techniques are based on bacterial growth in solid or liquid media and rely on the diffusion or dilution of antibiotics across the media [23]. From diffusion test, disk diffusion method is the gold standard technique used in clinical microbiology laboratories. This test consists in placing a permeable disk impregnated with antibiotics on top of an agar plate seeded with the bacteria wanted to be tested [24]. After overnight inoculation, a bright clear ring is formed around the disk if the antibiotic is effective, being the diameter directly related to the bacteria susceptibility against the compound. However, a direct quantification of the MIC is just suitable for certain types of bacteria and antibiotics and implies the use of complex algorithms [25]. Differentiation between bactericidal (kills the bacteria) and bacteriostatic (suppresses the growth) effect is not possible also, as only growth inhibition is recorded [26]. Although this method is simple and cost-effective, the time required and the lack of information make it clearly improvable. However, it is still the reference technique not only in a clinical scenario but also in drug discovery [27–29]. Dilution methods in contrast are the more reliable techniques for MIC determination. Both in agar and in broth, the method consists in adding increasing and known concentrations of antibiotic to a fix concentration of inoculum, thus considering as the MIC the lowest concentration at which growth is completely inhibited by naked eye [30]. Although the simplicity of the method allows its use as antimicrobial screening platform, the irreproducibility of this quantification approach has led to a deep research on visual and colorimetric techniques that allow a more accurate MIC quantification, especially in broth dilution methods [31–34]. A combination of agar dilution and disk diffusion known as the antimicrobial gradient method is commercially available under the name Etest® and allows to determine the MIC of a compound using diffusion techniques. By impregnating a strip with different concentrations of an antimicrobial compound, the MIC could be extrapolated, in an easy-to-use approach that combines the simplicity of disk diffusion and the quantification of agar dilution. However, the cost per strip (between $2 and 3) makes their use as screening platform less cost-effective [23, 35]. Another drawback of the currently available AST methods is the lack of appropriateness for complex human samples such as blood. In this sense, EUCAST has recently moved research closer to a solution by developing a rapid antimicrobial susceptibility testing (RAST) able to detect infections in the bloodstream [36]. Moreover, four automatized equipment for AST are also approved by the FDA and available in the market: VITEK2 (bioMérieux), MicroScan WalkAway (Siemens Healthcare Diagnostics), BD Phoenix (BD Diagnostics), and Sensititre ARIS 2 × (Trek Diagnostic Systems). All of them require between 3.5 and 16 h for giving a result, apart from the time needed for pre-incubating the samples, which could last between 24 and 48 h [37]. But the use of AST methods is not limited to the identification of resistance and suitable treatments in a clinical scenario. They are also powerful tools to help in the development of alternative antibiotic compounds to substitute traditional ones, a real challenge that has been scarcely dealt. With only 43 new antibiotic compounds in development, a value that is comparably reduced considering the more than 4000 immuno-oncology drugs being researched [38], antibiotic development is the problem that pharmaceutical industry is trying to avoid. The high cost of development and reduced revenue of antibiotic commercialization has made big pharmaceutical industries leave the antibiotic development race, with only 4 top companies still in the lead. Approximately, 45% of the costs associated to antibiotic development are expended in preclinical stages, also the more risky ones [39]. Novel approximations to faster antimicrobial development pipeline have emerged to help reduce the cost associated to these initial steps. This is the case of artificial intelligence (AI), a resource deeply needed for going through the more than 1030 drug-like compounds that it is estimated that still could be discovered [40, 41]. But once synthetically discovered, the efficacy of the compounds must be evaluated, for what AST are the preferred choice. However, although there is a deep investigation on new natural and synthetic antimicrobial compounds, the need to modify the characteristic of AST assays (e.g., inoculum size, medium, and growth conditions) to achieve a proper result hinders comparison between different research works [23]. Antibiotic screening through electrochemical means The limitations of the traditional AST methods, such as the long time required or the standardization, have prompted the development of alternative screening methods that allow not only to identify resistant bacteria but also to serve as platform for new drug screening. Sensors and biosensors have stood out due to their specificity, rapid response, easy to use, portability, low cost, and suitability as point-of-care (POC) devices [42]. Biosensors are formed by two main components: a versatile recognition element (e.g., antibodies and aptamers) and the transducer that detects the recognition event and converts it in a measurable signal (e.g., electrochemical, magnetic, surface plasmon resonance, and optical) [43]. Electrochemical transduction systems have gained attention due to their simplicity, affordability, and portability, bringing POC testing to a reality [44]. Their usefulness as simple platforms have been applied to several applications including AST, ranging from the use of different types of electrodes to a variety of electrochemical techniques (Table 1). The choice of a direct or indirect detection approach for bacteria cell viability testing is one of the points that differentiate the electrochemical AST methods developed until this moment. The evaluation of AST methods in complex samples has been pointed out in those works intended to be used for resistance detection, although many works meant to be used as antibiotic screening platforms do not consider this parameter.Table 1 Current electrochemistry methods for AST detection according to the electrochemical technique used. Bacteria used, antibiotic detected, and time required are summarized in this table as the most relevant parameters in an AST sensor Electrochemical technique Bacteria Antibiotic Concentrations tested Detection time Reference LSV E. coli Penicillin and streptomycin Not specified 2 h [45] DPV E. coli and K. pneumoniae Ampicillin, kanamycin, and tetracycline 10 μg/mL 1 h [46] DPV E. coli Gentamicin sulfate 1.55 μM 90 min [47] CV E. coli Ampicillin and kanamycin 0–16 μg/mL and 0–64 μg/mL, respectively 1 h [48] Amperometric oxygen sensor E. coli, E. adecarboxylata, C. acidovorans, C. glutamicum, and S. epidermidis Tetracycline, ampicillin, and chloramphenicol 1 μg/mL, 5 μg/mL, and 5 μg/mL, respectively 8 h [49] SWV E. coli Erythromycin, amikacin, ampicillin, and cefepime 13.6 μM, 0.852 mM, 1.43 mM, and 1.04 mM, respectively 2–5 h [50] EIS and DPV S. aureus and methicillin-resistant S. aureus Amoxicillin and oxacillin 8 μg/mL < 45 min [51] EIS E. coli Streptomycin 4 μg/mL 2.5 h [52] Impedance E. coli Ampicillin 10 mg/L 1–2 h [53] EIS S. aureus Flucloxacillin 300 mg/mL 2 h [54] Impedance E. coli, S. aureus, and P. aeruginosa Ampicillin, chloramphenicol, gentamicin, and amikacin 0–128 mg/mL 4 h [55] Impedance E. coli and methicillin-resistant S. aureus Ampicillin, ciprofloxacin, erythromycin, daptomycin, gentamicin, and methicillin 0.1–100 μg/mL < 90 min [56] Capacitance E. coli and S. aureus Gentamicin, tetracycline, and ampicillin 0–50 μg/mL, 2 μg/mL, and 8 μg/mL, respectively Not specified [57] Electrical resistance E. coli, K. pneumoniae, and S. saprophyticus Ampicillin and nalidixic acid 10 mg/L and 20 mg/L, respectively 2 h [58] CV, cyclic voltammetry; DPV, differential pulse voltammetry; EIS, electrochemical impedance spectroscopy; LSV, linear sweep voltammetry; SWV, square wave voltammetry Electrochemical AST methods using redox dye labels The use of redox active dye labels to provide an indirect analytical signal has been extensive in electrochemical sensing, including, unsurprisingly, AST sensors. Using a conventional three-electrode setup, fluorescein diacetate was used as a label [45] to evaluate the susceptibility of E. coli against penicillin and streptomycin. In the presence of active bacteria, fluorescein diacetate is hydrolysed by the enzymes secreted by E. coli. The oxidation of this product is monitored through voltammetric scans, being an increase in the current associated to an increase in the bacteria growth. Using a conventional three-electrode setup, Mishra et al. [46] presented a fast-screening method to evaluate the response of bacteria to antibiotics. Using a Pt modified glass substrate as working electrode, they measured bacterial cell metabolic activity via the use of resazurin. Resazurin is a blue-colored, electroactive redox dye [59] able to penetrate bacteria cell walls and be oxidized by bacteria metabolic enzymes into a pink fluorescence resorufin product [60]. The methodology of this work is based on the incubation separately of the dye with bacteria Klebsiella pneumoniae and E. coli, reducing the dye and consequently lowering the current peak recorded through differential pulse voltammetry (DPV). However, when antibiotics such as ampicillin, kanamycin, and tetracycline are present, the reduction is inhibited due to the bacterial cell death. The use of resazurin as dye in AST methodologies is extensive. Combining resazurin with screen-printed electrodes, a platform for the electrochemical antibiotic susceptibility testing of E. coli against gentamicin sulfate was developed [47]. DPV was also used allowing the determination of antibiotic susceptibility after 90 min, including the steps of inoculation, pre-incubation, and testing. This is a considerable reduction of the assay time required compared to previous resazurin-based biosensors, and a costless and easy-to-use AST methodology that can be easily implemented in a clinical scenario. As the developed biosensor was intended to be used for the detection of urinary tract infections, artificial urine samples were used during antibiotic testing; however, no thorough matrix effects and selectivity experiments were undergone. Metabolic activity of bacteria has been used in combination with dyes attending to different parameters. A recent work by Bolotsky et al. [48] proposed a different approach for a rapid antibiotic susceptibility testing using bacteria pH changes as signal generator (Fig. 1A). Electrodeposition was performed to create a redox-active organic crystalline layer (RZx) on pyrolytic graphite sheets (PGS), used to monitor the bacterial metabolic activity. The sensors were proved with E. coli K-12 incubated with two different antibiotics: ampicillin and kanamycin. The principle relies in the fact that the RZx creates responses to the bacterial metabolism by monitoring the pH change with the cell proliferation. One of the main advantages of the sensor is that it is highly stable, being viable after 60 days storage, which can be of great interest for the point-of-care antibiotic susceptibility testing. Moreover, detection of bacterial viability has been demonstrated in spiked human blood and low-fat milk as complex solutions, with no matrix effects observed. Additionally, measurement in complex samples was compared to traditional AST methods as optical density at 600 nm measurement, for which the opacity of complex samples makes measurement impossible.Fig. 1 AST electrochemical sensors using redox dyes (A) and label-free (B, C). A Test of ampicillin and kanamycin against E. coli using a redox-active crystalline layer on pyrolytic graphite sheets (RZx-PGS) electrode. Reprinted from [48] with permission of Elsevier. B Biosensing platform with a PDMS impedimetric transducer functionalized with polyethyleneimine (PEI), polyIJN-isopropylmethacrylamide) (pNIPMAM), and E. coli for the susceptibility testing of ampicillin. Used with permission of the Royal Society of Chemistry from [53]; permission conveyed through Copyright Clearance Center, Inc. C Capacitance AST array with aptamers as recognition elements immobilized in between electrodes. Bacteria are recognized by aptamers increasing the capacitance recorded. In the presence of an antibiotic, this capacitance is decreased. Reprinted from [57] with permission of Elsevier Label-free AST methods Although labels are widely used in biosensing applications, there is an increasing interest in developing label-free biosensors. These devices are more point-of-care orientated as they involve less steps and reagents during measurement process [61]. Equally to label-based sensors, some label-free ones also rely on bacteria metabolic changes as signal generators. As example, Karasinski et al. [49] proposed the use of amperometric signals with a multi-array dissolved oxygen electrochemical sensor to monitor bacteria susceptibility. The effects of different antibiotics on the bacterial growth and their respiratory activity over time were studied. The approach is based on the addition of small concentrations of antibiotics to the bacterial medium. The presence of increasing antibiotic concentrations is directly related to the consumption of oxygen performed by the bacteria, creating a unique fingerprint for each species. Tetracycline, ampicillin, and chloramphenicol were tested by measuring the level change of oxygen against five species of bacteria: E. coli, E. adecarboxylata, Comamonas acidovorans, Corynebacterium glutamicum, and Staphylococcus epidermidis. A pattern recognition was applied using a principal component analysis (PCA), generating a template that can be used to select specific combinations and concentrations of cell/antibiotics. The direct electron transfer capacity that bacteria intrinsically present has also been exploited for cell growth viability testing in AST sensors [62]. Disposable screen-printed electrodes, modified with membranous didodecylmethylammonium bromide (DDAB), were used to estimate the susceptibility of Gram-negative Escherichia coli JM109 against well-known antibiotics such as erythromycin, amikacin, ampicillin, and cefepime [50]. The dynamic direct electron transfer capacities of E. coli were used in this work as a label-free way to monitor cell growth through cyclic voltammetry and square wave voltammetry. DDAB/E. coli biofilms, deposited on the surface of the electrode, promoted conductivity without affecting bacteria viability. In less than 5 h, they were able to confirm that cefepime, amikacin, and ampicillin inhibited cell growth while erythromycin had any effect. Alternatively, Hannah et al. [51] modified screen-printed gold electrodes with a hydrogel of agarose for bacterial growth monitoring through electrochemical impedance spectroscopy (EIS) and DPV. Drug-resistant Staphylococcus aureus was deposited on top of the hydrogel to monitor the influence of the antibiotic’s amoxicillin and oxacillin in less than 45 min, taking measurements in periods of 5 min. The electrodes showed a good resolution, being able to differentiate between bacteria growth in absence of antibiotic and under low antibiotic concentrations. In a later work [52], the same methodology was used to monitor the electrochemical growth profiles of E. coli against streptomycin, throwing differences in approximately 2.5 h. The main difference between these two protocols is the enlarged growing of bacteria among time obtained with the second method due to the prolonged integrity of the gel-modified electrode compared to the hydrogel used in the previous work. The bacterial growth profiles were monitored by EIS. EIS was also used by Brosel-Uliu et al. [53] by the immobilization of the bacteria E. coli in a three-dimensional interdigitated electrode array (3D-IDEA) modified with microgels to prevent bacteria deposition and increase reproducibility and sensitivity (Fig. 1B). This microbial sensor allows to monitor bacterial response to ampicillin by monitoring impedance fluctuations for 24 h. The 3D-IDEA sensor showed a large decrease in Rs in the first 2 h, while after the fourth hour, it remained quite stable. Abeyrathne et al. [54] used also EIS technique in a non-faradic approach for detecting the antibiotic susceptibility of S. aureus against flucloxacillin in less than 2 h using interdigitated electrodes as platforms. Electrodes were SiO2 passivated and functionalized with antibodies as recognition element. Through EIS measurements, they were able to differentiate live and dead bacteria cells while they were exposed to antibiotics due to the change of the medium conductivity as a result of the bacteria metabolic process. As next step, the modifying of the sensor for the detection of S. aureus in whole blood must be studied. For that purpose, addition of a filter paper to the proposed sensor that remove erythrocytes and neutrophils (bigger diameters respect to the bacteria) and a subsequent wash step to remove unbound cells will be investigated. Puttaswamy et al. [55] presented a rapid electrical method to determine the effect of antibiotics on bacteria using a different type of “impedance microbiology.” The bacterial metabolism shows the conductance/impedance at a single frequency since this method uses measurements at 500 different frequencies to estimate the electric charge that is stored due to the charge polarization at cell membranes of the living bacteria. It allows to track the number of living bacteria every 1 h that the measure is taken. So, the decrease in the number of bacteria that are proliferating in presence of antibiotic can be determined. For the study, different strains such as E. coli and S. aureus were tested against ampicillin and chloramphenicol while P. aeruginosa was exposed against gentamicin and amikacin in about 4 h. By taking advantage of the use of printed electrodes and impedance electrochemical measurements, Safavieh et al. [56] reported a biosensor that allows the detection of pathogens, the identification of the correct antibiotics through antibiotic susceptibility testing, and the monitoring of mutations that help to adjust the correct therapy. The biosensor presents a rapid (< 90 min), label-free, and real-time analysis via capturing the target bacteria on flexible plastic-based microchips using electrodes modified with antibodies and monitoring the impedimetric response in the presence and absence of the antibiotics after incubation for 1 h. The microchip was evaluated with E. coli and methicillin-resistant Staphylococcus aureus (MRSA) for different antibiotics such as ampicillin, erythromycin, daptomycin, ciprofloxacin, daptomycin, methicillin, and gentamicin. Also, the ability of the microchip was demonstrated with MRSA-spiked whole blood with different clinically relevant concentrations of bacteria, being able to be used in urine samples too. Another common recognition element used in biosensing to increase selectivity are aptamers [63]. The use of aptamers in AST was reported by Jo et al. [57] in a functionalized capacitance sensor that allows the monitoring of antibiotic susceptibility and the bacterial growth in real time (Fig. 1C). Due to the intrinsic high selectivity of aptamers, the bacteria can be identified withing 1 h using this sensor. The combination in the use of aptamers and electrical sensors allows the direct and rapid identification of the bacteria and the evaluation of their resistance against different antibiotics with high accuracy, sensitivity, and selectivity. In this sensor, bacteria are bounded over the sensor surface via aptamers between electrodes, acting as capacitors that are connected between parallel electrodes. For the antimicrobial susceptibility tests, different antibiotics such as gentamicin, tetracycline, and ampicillin were tested. For the culture of E. coli and S. aureus, the bacteria death is monitored by the change of capacitance when treated with antibiotics demonstrating the applicability of this sensor for rapid AST. The use of microfluidics to perform rapid drug testing has also been exploited in AST. Their feasibility and use as organ-on-a-chip platforms has facilitated their implementation in AST screening [64]. Using microfluidics, Yang et al. [58] described an ultrasensitive all-electrical measurement constituted by a set of microfluidic channels that allows the flow of a liquid bacteria sample in the device for the subsequent incubation with different antibiotics. The signal measured is the electrical resistance of the microchannels that changes in proportion to the cell viability, allowing a rapid AST within 2 h. In addition, the constant fluctuations due to the antibiotics in the electrical resistance can be related to morphological changes of the bacteria. Ampicillin and nalidixic acid, antibiotics with different action mechanisms, were evaluated against E. coli, K. pneumoniae, and S. saprophyticus. Interestingly, the electrical measurement developed in this work is suitable for a multiplexed analysis desirable for a large antibiotic screening testing. As a drawback, application in clinical test could be limited in patients with complex urine matrices, not being able to be tested in the proposed device. AST in drug screening Although AST techniques are also used for drug screening purposes, the electrochemical AST sensors developed have been predominantly focused on addressing antimicrobial resistance. However, there are just a few examples that exploit the potential of electrochemical sensors for a faster and simple screening of new drug candidates (Table 2).Table 2 Potential electrochemical AST sensors for screening new drug candidates classified according to their most relevant parameters: electrochemical technique used, bacteria detected, antibiotic tested (both type and concentration), and time required for performing the measurement Electrochemical technique Bacteria Antibiotic Concentrations tested Detection time Ref SWV P. aeruginosa Colistin sulfate 4, 16, and 100 mg/L 45 h [65] SWV P. aeruginosa Antimicrobial peptides 5–50 μM 300 s [66] SWV P. aeruginosa RA13 5–50 μM 300 s [67] RA13, reverse amide 2-aminoimidazole derivative; SWV, square wave voltammetry Combining the use of a disposable screen-printed electrode coupled to a microfluidic chamber, Webster et al. [65] monitored the antibiotic susceptibility of P. aeruginosa via electrochemical detection of the virulence factor pyocyanin (Fig. 2A). Bacteria was exposed to different concentrations of colistin sulfate as antibiotic. For the electrochemical measurement, square wave voltammetry was used as technique due to its increased sensitivity and ability to monitor the reduction peak corresponding to pyocyanin, directly correlated with a decrease in cell growth. The main disadvantage of this methodology is that the time required for completing measurements is of 45 h, comparable with the standard techniques currently used.Fig. 2 AST methods for the screening of new drug candidates. A Screen-printed electrode-based sensor covered with a microfluidic chamber with bacteria trapped on the inside. Reproduced from [65] with permission from the Royal Society of Chemistry. B Electrode based on the formation of cationic (green) and anionic (blue) polymers and P. aeruginosa biofilm (orange). After anti-biofilm exposure, the structure is compromised reducing the current recorded due to the electroactive phenazines produced by P. aeruginosa. Reprinted from [67] with permission of Elsevier Antibiotic compounds are not the only therapeutic target in antimicrobial research. Anti-biofilms have been subject of study for many pathological microorganisms as the formation of biofilms enhances multidrug tolerance. Besides, the incorporation of electrochemical scaffolds together with anti-biofilm compounds has also been confirmed to have a co-adjuvant effect in antimicrobial treatment [68]. Evaluation of new anti-biofilm forming compounds has also been done using an electrochemical sensor using ferricyanide as redox indicator. In this case, alginate, one of the main components of mucoid P. aeruginosa strain biofilms [69], was immobilized on top of a pyrolytic graphite electrode modified layer-by-layer with poly(diallyldimethylammonium) chloride and polystyrene sulfonate. Both polymers have opposite charge, which allows the retaining of alginate in the upper electrode layer [66]. By the addition of antimicrobial peptides, the alginate was broken and ferricyanide was able to go through the layers and reach the electrode, providing an increase in the oxidation current recorded. In a later work, the same group used the same type of electrodes but in this case with the immobilization of P. aeruginosa on the upper layer (Fig. 2B). Direct electrochemical reduction of phenazine compounds produced by P. aeruginosa was recorded through SWV in a label-free sensor. With this sensor, the establishment of the half maximal inhibition concentration (IC50) and half maximal effective concentration (EC50) of an anti-biofilm compound was possible in just a few minutes [67]. Nanomaterials in the screening of antibiotic compounds: are they really needed? Nanomaterials are defined as those materials that present at least one dimension in the range of 1–100 nm. Their small size provides them with outstanding properties not presented by their counterpart bulk materials [70]. In the last decades, nanomaterials have gained significant importance in multiple applications, going from medical imaging, drug delivery, food technology, cosmetics, and biomolecular electronic devices [71]. Nanomaterials have been incorporated in biosensing as they present enhanced electrical conductivity, improved biological sensing accuracy, and biocompatibility, what allows to increase sensitivities and decrease detection limits. Besides this, their high surface area also facilitates the immobilization of increased amounts of different bioreceptors [72] through an extended range of chemical reactions [73]. Nanomaterials are typically incorporated in electrochemical biosensing either as electrode modifiers or as detection labels [74]. The incorporation of nanomaterials, such as carbon-based nanomaterials, quantum dots, or metallic nanoparticles, has helped to overcome the slack electrode surface kinetics, acting as electrocatalysts or transduction systems [75]. However, the immobilization of these nanomaterials on the electrode surface is still a challenge to overcome [76]. As detection labels, nanomaterials have been explored as substitutes to traditional enzymatic labels that, although sensitive, require harsh conditions and present a low thermal stability, hindering their integration into commercial devices. To overcome these problems, nanomaterials have emerged as alternative, both alone or in combination with enzymes [77]. Again, metallic nanoparticles such as silver nanoparticles, gold nanoparticles, bimetallic nanoparticles, or zinc or cerium oxide nanoparticles stand out for their redox properties and electrocatalytic activities [78–80]. Their superior qualities have also made them ideal candidates as promising antimicrobial drugs [81, 82]. With silver nanoparticles (AgNPs) showing up for their intrinsic antimicrobial properties against known bacterial strains such as E. coli, S. aureus, or P. aeruginosa, multimetallic nanoparticles have also seem to be effective [83]. Although their use is extended in electrochemical biosensors, even for the detection of antibiotic residues [17], their application on AST is scant, being introduced in a reduced number of recent works (Table 3). The toxicity of some of these nanomaterials against microorganisms could be one of the reasons why their use is not as spread compared to other fields of research [74, 77]. However, their potential deserves a wider look onto their implementation in AST electrochemical sensing.Table 3 Nanomaterial-based electrochemical biosensors for AST classified according to the nanomaterial used and their function, the electrochemical technique used, bacteria detected, antibiotic tested and their concentration, and the time required for performing the analysis Nanomaterial Nanomaterial function Electrochemical technique Bacteria Antibiotics Concentrations tested Detection time Ref ʟ-CeONP/ITO Working electrode CV Bacillus subtilis, Escherichia coli Ciprofloxacin, cefixime, and amoxycillin 2 μg/μL 15 min [84] MWCNTs and AuNPs Enhance the sensitivity of SPCEs DPV Salmonella gallinarum Ofloxacin and penicillin 0.0625–256 μg/mL 4 h [85] Silicon nano transistors Sensor design SiNWFETs E. coli, S. saprophyticus, and S. aureus Ampicillin, cefotaxime, and ciprofloxacin 100 mg/L, 20 mg/L, and 1–4 mg/L, respectively 30 min [86] CDs Bacterial growth-monitoring sensor CV E. coli and ampicillin resistant E. coli Ampicillin 100 μg/mL 20 min [87] AgNPs-invertase complexes Inhibition of enzymatic activity PGM E. coli Colistin, spectinomycin, streptomycin, and tetracycline 0–65 μg/mL 4 h [88] Nanochannels Sensing platforms DPV S. aureus RIP, YSPWTNF-NH2 50 μg/mL 24 h [89] Nanomaterials: AgNPs-invertase complexes, silver nanoparticles-invertase complexes; CDs, carbon nanodots; ʟ-CeONP/ITO, ʟ-lysine-functionalized cerium oxide nanoparticle coated indium tin oxide; MWNCTs and AuNPs, multiwalled carbon nanotubes and gold nanoparticles; RIP, RNAIII-inhibiting peptide. Electrochemical methods: CV, cyclic voltammetry; DPV, differential pulse voltammetry; EIS, electrochemical impedance spectroscopy; LSV, linear sweep voltammetry; SWV, square wave voltammetry As example, cerium oxide nanoparticles (CeNPs) have been used in an electrochemical AST as ITO electrode modifiers [84] allowing the monitoring within 15 min by time-lapse microscopy video and electrochemistry of the susceptibility of Gram-positive Bacillus subtilis and Gram-negative E. coli against the antibiotics ciprofloxacin, cefixime, and amoxycillin. In this work, the toxicity of CeNPs against E. coli has also been considered and explored as a relevant parameter in the antimicrobial screening biosensor developed. For that purpose, bulk CeNPs, CeNPs functionalized with L-lysine (L-CeNPs), and with pluronic acid (P-CeNPs) were compared, observing that L-lysine presents a protective effect against the antimicrobial activity, allowing their use in an AST sensor. In terms of conductivity, the modification of the ITO electrode with L-CeNPs significantly increased the conductivity obtained. Carbon nanotubes (MWCNTs) and gold nanoparticles (AuNPs) have also been used as electrode modifiers in combination with resazurin as label [85]. The incorporation of these two nanomaterials combined into screen-printed carbon electrodes (SPCEs) increased the peak current recorded, what it is in correlation with the enhanced conductivity and electropolymerization that these nanomaterials present. Ofloxacin and penicillin antibiotics were tested against Salmonella gallinarum isolates by mixing bacteria, resazurin, and different concentrations of the antibiotics. DPV was selected for the detection of viable bacteria, been able to detect them above 102 CFU/mL after 1 h of incubation. The methodology was also applied in egg liquid sample obtaining a decrease of the signal sensor due to the change of the resistance of the medium, although the absolute change of current was maintained. Taking advantage of the acidification properties of bacteria while growing, an ion-selective silicon nanowire field-effect transistor (SiNWFET) sensor [86] was developed. E. coli and other pathogen species such as S. aureus and S. saprophyticus were tested in presence and absence of different antibiotics in less than 30 min. SiNWFET used in this work contained a H+-selective sensing oxide layer for a specific detection of pH changes even in media with a high ionic concentration that increase background signal. Moreover, the use of this technology also minimizes variation between sensors, what it is desirable both to perform parallel AST screening and for research transference purposes. Largely, the use of this SiNWFET allowed the detection of tiny changes on the pH even under high ionic background concentrations, providing a better sensitivity. In addition, the proposed sensor could be modified to add a pre-filtering system that allows the detection of real samples without any preparation step as pre-cultivation. In a different approach, but also considering bacteria acidification, carbon nanodots (CDs) have been used as electrochemical labels in an AST method [87] able to detect antibiotic susceptibility in 20 min (Fig. 3A). CDs have many advantages such as small size, good conductivity, low toxicity, and high solubility. Also, the economical and one-step synthesis of CDs makes them interesting materials for applications in biosensing. The sensor consisted in the encapsulation of CDs in alginate microspheres, together with E. coli (non-resistant) and E. coli + pET32 (resistant) and antibiotic concentrations. The presence of the alginate microspheres allows an enhanced 3D growing of the bacteria, better mimicking a real-case scenario. The change in the redox potential over time of the CDs due to the pH changes generated by the bacteria metabolism was recorded by cyclic voltammetry at 0 and 20 min, showing a discrimination between low bacterial counts of < 103 CFU/mL. The biocompatibility of the used microspheres with the bacteria tested was also evaluated, confirming that the bacteria growing rate was not affected by the presence of these nanomaterials in the microenvironment.Fig. 3 Nanomaterial-based AST sensors. A Using carbon nanodots as pH sensitive labels encapsulated in alginate microspheres for a better 3D bacterium growing. Reprinted from [87] with permission from Elsevier. B Using a nanochannel-based immunosensor for the detection of hyaluronidase, a virulence factor of Gram-positive bacteria, and the evaluation of a quorum sensing inhibitor (RIP). Adapted with permission from [89]. Copyright 2022 American Chemical Society Nanoparticles can also be used as encapsulating agents as they improve availability of active compounds [90]. In this line, Laibao et al. [88] proposed the use of a modified personal glucometer as a biosensor for rapid (withing 4 h) and reliable antimicrobial susceptibility testing using polyethyleneimine AgNPs (PEI-AgNPs) to encapsulate invertase complexes. Cationic PEI-AgNPs could reversibly bind the anionic enzyme invertase, inhibiting the catalytic activity by forming an electrostatic interaction between both of them. In the presence of bacteria, invertase is released from the complex, as the cationic PEI-AgNPs bind to the anionic surface of bacteria, thus releasing invertase. The enzyme is then active to convert sucrose into glucose, a change that is recorded by the glucometer. The AST of E. coli was tested with four different antibiotics (colistin, spectinomycin, streptomycin, and tetracycline) within 5 h. The PEI-AgNPs used in this case act as a detection mechanism for bacteria identification. However, a main drawback of the work is the lack of specificity of the proposed biosensor, as it responds to different bacteria which can be an issue for application in real samples. Nanopore/nanochannel-based materials have also been shown as outstanding tools for electrochemical biosensing [91, 92]. Regarding antibiotic screening, an innovative approach to monitor the effect of new quorum sensing inhibitors against S. aureus using a nanochannel-based electrochemical immunosensor has been recently reported [89]. The sensor was able to differentiate between S. aureus and P. aeruginosa by monitoring hyaluronidase detection, a virulence factor primarily secreted by Gram-positive bacteria. Additionally, the effect of anti-infective RNAIII-inhibiting peptide (RIP, YSPWTNF-NH2), a quorum sensing inhibitor, as suppressor of bacterial growth and virulence was evaluated using the developed sensor, by monitoring the decrease in the hyaluronidase secreted levels (Fig. 3B). The use of nanochannel membranes in this work facilitates the identification of biomarkers in complex samples due to their intrinsic filtering properties. Moreover, nanochannel membranes serve as platform for the immobilization of biorecognition elements, what leads to a label-free identification of the analytes of interest. In general terms, the nanomaterials used in AST methods covered both the implementation such as electrode modifiers, labels, or even encapsulating agents. And although their use is relevant in terms of the bacteria concentration that could be detected, with a low CFU/mL counting in many of the works revised, this is not translated in lower antibiotic concentrations that could be tested or even the time required for obtaining a result. It is worthy to mention that the potential toxicity of these materials and how it could affect bacterial growth is not considered in many of the works, being a parameter of paramount importance for AST methods. All considered, although the use of nanomaterials seems promising for increasing conductivity and lowering limits of detection, it is not translated into improved results for antibiotic resistance identification. However, the intrinsic characteristics of nanomaterials could be more interestingly exploited in the screening of new antimicrobial compounds and in the identification of MIC concentrations, for which reaching a lower CFU/mL count is more relevant. Commercial potential of electrochemical AST methods: barriers to overcome Antimicrobial resistance is a health challenge that research, industry, and policymakers should stop ignoring. With an increasing tendency of superbugs becoming more deadly than cancer, the need to find solutions to this problem is increasingly imminent. And these solutions go through both a control administration of existent antimicrobial drugs and the development of new ones. But the lack of fast, accurate, and easy-to-use diagnosis devices complicates them. Thus, the development of specific and rapid point-of-care devices can assist in managing this global health crisis, preventing unnecessary antibiotic administration [93]. Although some rapid infection testing are out on the market, they still lack from integration and require the extraction of invasive samples [94]. For that reason, the use of electrochemical biosensors both for the identification of antimicrobial resistance and for the development of new drugs seems to be a promising approach, and it has been seen like that by the research community. But still, the commercialization of these type of devices is far to become a reality. Technical parameters such as long-term stability of the sensors developed, matrix effects, cost, or feasibility of their use by non-specialized personnel are scarcely evaluated by research works on electrochemical sensors [63] and are also lacking in the works revised in this review. Although LOD, sensitivity, and specificity have been clearly adopted as parameters that should be optimized in biosensing, it is also important to keep in mind the above-mentioned characteristics. Moreover, cost-effectivity is also an important criterion to consider, being the low profit rate the main reason for the reduced investment of pharmaceutical industry in antibiotic development [39]. Thus, the inclusion of a deeper evaluation of these and other parameter should be promoted in research works if we want to be able to transmute laboratory results into real clinically relevant devices. Moreover, the development of multiplexed systems able to identify or evaluate more than one superbug is also desirable, as these complex infections are worsened by the presence of polymicrobial interactions [95]. This is especially relevant in the development of new antimicrobial compounds as polymicrobial infections have been directly related with an enhanced antimicrobial resistance [96]. Despite some promising leads, the way electrochemical AST methods have been developed needs to be revisited to really address market needs. Outlook The implementation of new AST methods is constantly evolving given the increasingly worrying incidence of antimicrobial resistances. However, traditional AST methods still do not solve this issue, which leads to increased research in alternative high-throughput screening strategies such as biosensors. Biosensing field has proved to be relevant in reducing antimicrobial resistance through environmental monitoring of antibiotics, with many biosensors developed in this field. The reduced complexity of environmental samples could be a fundamental factor to explain this difference. Detection of antimicrobial resistance through electrochemical means in human samples is a different matter. Although promising, as they help to reduce time and increase sensitivity compared to traditional AST methods, they still not accomplish all what it is claimed to AST methods. Cost of the final assay is not even considered in research works revised and is one of the main arguments that must be revised to point out biosensors as alternative to AST methods. This cost reduction is fundamental to improve antimicrobial screening research, being the main obstacle to overcome for pharmaceutical industry. It is also noticeable the scarcity of bacteria tested with these sensors, using E. coli as preferred choice as it is an easy-to-use bacterial model. But the reality is that genders of bacteria such as Klebsiella pneumoniae, Acinetobacter baumannii, and MRSA are predominantly associated to nosocomial infections and represent a larger burden for healthcare systems [97]. Bactericidal and bacteriostatic effects are lacked also in these works, and it is one of the main demands done to traditional AST methods. This aspect is less relevant in the identification of antibiotic resistance, but it is definitively a concern in the screening of new drug candidates. In this last field, it is surprising that the low number of electrochemical biosensors was developed. Although they have a greater potential, antimicrobial screening electrochemical sensors focused on the activity testing of new antimicrobial compounds are scarce. The lack of standardization of these methods compared to the AST techniques approved by the EUCAST and CLSI could be one of the reasons. Potentiating communication between electrochemical groups and microbiology research groups in charge of discovering new antibiotic compounds is a suitable way to promote this synergy and favor antimicrobial compounds development. Regarding the use of nanomaterials, although promising, it is still not relevant enough to make a significant difference compared to non-nanomaterial–based biosensors. However, the low CFU/mL that these methodologies can reach, open the path to their use for MIC determination. That is why their use is not unjustified, but the type of analysis to be carried out with these instruments must be modified. As we have outlined in this review, electrochemical biosensors are promising AST methods to both detect resistance in the clinical setting and to serve as screening platforms for new drug candidates, but further improvements are still required to be relevant in the combat against antimicrobial resistance. Acknowledgements C. Toyos-Rodríguez and D. Valero-Calvo thank the Spanish Ministry of Science and Innovation (MICINN) for the award of the FPI Grants PRE2018-084953 and PRE2021-097567, respectively. A. de la Escosura-Muñiz also acknowledges the MICINN for the “Ramón y Cajal” Research Fellow (RyC-2016-20299). Funding This work has been supported by the MCI-21-PID2020-115204RBI00 project from the Spanish Ministry of Science and Innovation (MICINN) and the SV-PA-21-AYUD/2021/51323 project from the Asturias Regional Government. Declarations Conflict of interest The authors declare no competing interests. Published in the topical collection Electrochemical Biosensors – Driving Personalized Medicine with guest editors Susana Campuzano Ruiz and Maria Jesus Lobo-Castañón. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Miethke M Pieroni M Weber T Brönstrup M Hammann P Halby L Arimondo PB Glaser P Aigle B Bode HB Moreira R Li Y Luzhetskyy A Medema MH Pernodet J-L Stadler M Tormo JR Genilloud O Truman AW Weissman KJ Takano E Sabatini S Stegmann E Brötz-Oesterhelt H Wohlleben W Seemann M Empting M Hirsch AKH Loretz B Lehr C-M Titz A Herrmann J Jaeger T Alt S Hesterkamp T Winterhalter M Schiefer A Pfarr K Hoerauf A Graz H Graz M Lindvall M Ramurthy S Karlén A van Dongen M Petkovic H Keller A Peyrane F Donadio S Fraisse L Piddock LJV Gilbert IH Moser HE Müller R Towards the sustainable discovery and development of new antibiotics Nat Rev Chem 2021 5 726 749 10.1038/s41570-021-00313-1 34426795 2. Ventola CL The antibiotic resistance crisis P&T 2015 40 277 283 25859123 3. WHO Regional Office for Europe/European Centre for Disease Prevention and Control (2022) Antimicrobial resistance surveillance in Europe 2022–2020 data. In: Copenhagen: WHO Regional Office for Europe; 2022. https://www.ecdc.europa.eu/en/publications-data/antimicrobial-resistance-surveillance-europe-2022-2020-data#:~:text=Antimicrobial%20resistance%20(AMR)%20remains%20a,people%20die%20as%20a%20direct. Accessed 29 Aug 2022. 4. O'Neill J. Tackling drug-resistant infections globally: final report and recommendations. 2016. https://amrreview.org/sites/default/files/160525_Final%20paper_with%20cover.pdf. Accessed 29 Aug 2022. 5. Vaughn VM Gandhi TN Petty LA Patel PK Prescott HC Malani AN Ratz D McLaughlin E Chopra V Flanders SA Empiric antibacterial therapy and community-onset bacterial coinfection in patients hospitalized with coronavirus disease 2019 (COVID-19): a multi-hospital cohort study Clin Infect Dis 2021 72 e533 e541 10.1093/cid/ciaa1239 32820807 6. Sharifipour E Shams S Esmkhani M Khodadadi J Fotouhi-Ardakani R Koohpaei A Doosti Z Golzari EJ S, Evaluation of bacterial co-infections of the respiratory tract in COVID-19 patients admitted to ICU BMC Infect Dis 2020 20 646 10.1186/s12879-020-05374-z 32873235 7. Nori P Szymczak W Puius Y Sharma A Cowman K Gialanella P Fleischner Z Corpuz M Torres-Isasiga J Bartash R Felsen U Chen V Guo Y Emerging co-pathogens: New Delhi metallo-beta-lactamase producing Enterobacterales infections in New York City COVID-19 patients Int J Antimicrob Agents 2020 56 106179 10.1016/j.ijantimicag.2020.106179 32987104 8. Getahun H Smith I Trivedi K Paulin S Balkhy HH Tackling antimicrobial resistance in the COVID-19 pandemic Bull World Health Organ 2020 98 442 442A 10.2471/BLT.20.268573 32742026 9. Munita JM, Arias CA (2016) Mechanisms of antibiotic resistance. Microbiol Spectr 4:4.2.15. 10.1128/microbiolspec.VMBF-0016-2015 10. Rice LB Mechanisms of resistance and clinical relevance of resistance to β-lactams, glycopeptides, and fluoroquinolones Mayo Clin Proc 2012 87 198 208 10.1016/j.mayocp.2011.12.003 22305032 11. Larsson DGJ Flach C-F Antibiotic resistance in the environment Nat Rev Microbiol 2022 20 257 269 10.1038/s41579-021-00649-x 34737424 12. Milaković M Vestergaard G González-Plaza JJ Petrić I Šimatović A Senta I Kublik S Schloter M Smalla K Udiković-Kolić N Pollution from azithromycin-manufacturing promotes macrolide-resistance gene propagation and induces spatial and seasonal bacterial community shifts in receiving river sediments Environ Int 2019 123 501 511 10.1016/j.envint.2018.12.050 30622075 13. Taylor P Reeder R Antibiotic use on crops in low and middle-income countries based on recommendations made by agricultural advisors CABI Agric Biosci 2020 1 1 10.1186/s43170-020-00001-y 14. Cabello FC Godfrey HP Buschmann AH Dölz HJ Aquaculture as yet another environmental gateway to the development and globalisation of antimicrobial resistance Lancet Infect Dis 2016 16 e127 e133 10.1016/S1473-3099(16)00100-6 27083976 15. Anwar M Iqbal Q Saleem F Improper disposal of unused antibiotics: an often overlooked driver of antimicrobial resistance ERATCK 2020 18 697 699 10.1080/14787210.2020.1754797 16. Wang J Chu L Wojnárovits L Takács E Occurrence and fate of antibiotics, antibiotic resistant genes (ARGs) and antibiotic resistant bacteria (ARB) in municipal wastewater treatment plant: an overview Sci Total Environ 2020 744 140997 10.1016/j.scitotenv.2020.140997 32755790 17. Sun Y Zhao J Liang L Recent development of antibiotic detection in food and environment: the combination of sensors and nanomaterials Microchim Acta 2021 188 21 10.1007/s00604-020-04671-3 18. Joshi A Kim K-H Recent advances in nanomaterial-based electrochemical detection of antibiotics: challenges and future perspectives Biosens Bioelectron 2020 153 112046 10.1016/j.bios.2020.112046 32056661 19. Hermouche L, Bendany M, Abbi K, El Hamdouni Y, Labjar N, El Mahi M, Lotfi E mostapha, Dalimi M, Dhiba D, El Hajjaji S (2021) Electrochemical sensors for tetracycline antibiotics detection based on carbon electrode materials modified by biological and chemical compounds: a review. J Environ Anal Chem 1–23 10.1080/03067319.2021.1946525 20. Giske CG Turnidge J Cantón R Kahlmeter G Update from the European Committee on Antimicrobial Susceptibility Testing (EUCAST) JCM 2022 60 e00276 e321 10.1128/jcm.00276-21 21. van Belkum A Bachmann TT Lüdke G Lisby JG Kahlmeter G Mohess A Becker K Hays JP Woodford N Mitsakakis K Moran-Gilad J Vila J Peter H Rex JH WmM D Developmental roadmap for antimicrobial susceptibility testing systems Nat Rev Microbiol 2019 17 51 62 10.1038/s41579-018-0098-9 30333569 22. Vasala A Hytönen VP Laitinen OH Modern tools for rapid diagnostics of antimicrobial resistance Front Cell Infect Microbiol 2020 10 308 10.3389/fcimb.2020.00308 32760676 23. Balouiri M Sadiki M Ibnsouda SK Methods for in vitro evaluating antimicrobial activity: a review J Pharm Anal 2016 6 71 79 10.1016/j.jpha.2015.11.005 29403965 24. Jorgensen JH Ferraro MJ Antimicrobial susceptibility testing: a review of general principles and contemporary practices CLIN INFECT DIS 2009 49 1749 1755 10.1086/647952 19857164 25. Nijs A Cartuyvels R Mewis A Peeters V Rummens JL Magerman K Comparison and evaluation of Osiris and Sirscan 2000 antimicrobial susceptibility systems in the clinical microbiology laboratory J Clin Microbiol 2003 41 3627 3630 10.1128/JCM.41.8.3627-3630.2003 12904366 26. Bernatová S Samek O Pilát Z Šerý M Ježek J Jákl P Šiler M Krzyžánek V Zemánek P Holá V Dvořáčková M Růžička F Following the mechanisms of bacteriostatic versus bactericidal action using Raman spectroscopy Molecules 2013 18 13188 13199 10.3390/molecules181113188 24284484 27. Das K Tiwari RKS Shrivastava DK Techniques for evaluation of medicinal plant products as antimicrobial agent: current methods and future trends J Med Plant Res 2010 4 104 111 10.5897/JMPR09.030 28. Lage O Ramos M Calisto R Almeida E Vasconcelos V Vicente F Current screening methodologies in drug discovery for selected human diseases Mar Drugs 2018 16 279 10.3390/md16080279 30110923 29. Sani I, Ukwuani-Kwaja AN, Abdulkadir D (2022) Antibacterial activities of plant-derived metallic nanoparticles on some selected multidrug-resistant clinical isolates. Asian J Biol Sci 15:15–26. 10.17311/ajbs.2022.15.26 30. Humphries RM Ambler J Mitchell SL Castanheira M Dingle T Hindler JA Koeth L Sei K Hardy D Zimmer B Butler-Wu S Dien Bard J Brasso B Shawar R Dingle T Humphries R Sei K Koeth L CLSI methods development and standardization working group best practices for evaluation of antimicrobial susceptibility tests J Clin Microbiol 2018 56 e01934 e2017 10.1128/JCM.01934-17 29367292 31. Vanegas D Abril-Novillo A Khachatryan A Jerves-Andrade L Peñaherrera E Cuzco N Wilches I Calle J León-Tamariz F Validation of a method of broth microdilution for the determination of antibacterial activity of essential oils BMC Res Notes 2021 14 439 10.1186/s13104-021-05838-8 34857039 32. Gun MA TanriverdiCayci Y Durupinar B Coban AY A new colorimetric method for rapid detection of antibiotic resistance in Escherichia coli isolates Jundishapur J Microbiol 2022 14 e119858 10.5812/jjm.119858 33. Philips S Van Hoecke F De Laere E Vervaeke S De Smedt R Boelens J De Geyter D Piérard D Lagrou K Comparison of two commercial colorimetric broth microdilution tests for Candida susceptibility testing: Sensititre YeastOne versus MICRONAUT-AM JoF 2021 7 356 10.3390/jof7050356 34062848 34. Gutiérrez-del-Río I López-Ibáñez S Magadán-Corpas P Fernández-Calleja L Pérez-Valero Á Tuñón-Granda M Miguélez EM Villar CJ Lombó F Terpenoids and polyphenols as natural antioxidant agents in food preservation Antioxidants 2021 10 1264 10.3390/antiox10081264 34439512 35. Hoffner SE Klintz L Olsson-Liljequist B Bolmström A Evaluation of Etest for rapid susceptibility testing of Mycobacterium chelonae and M. fortuitum J Clin Microbiol 1994 32 1846 1849 10.1128/jcm.32.8.1846-1849.1994 7989530 36. Åkerlund A, Jonasson E, Matuschek E, Serrander L, Sundqvist M, Kahlmeter G, the RAST Study Group, Dzajic E, Hansen DS, Agergaard harlotte N, Pätäri-Sampo A, Manninen R, Grönroos JO, Rasigade J-P, Salka W, Boyer PH, Lebessi E, Zapaniotis N, Petinaki E, Spiliopoulou I, Kolonitsiou F, Helgason KO, Brazil J, Riccobono E, Lo Cascio G, Maccacaro L, Kolstad H, Haukeland TS, Kellokumpu P-L, Fossum Mjøen A, Tofteland S, Harbak B, Hartzen SH, Haug Hänsgen S, Gammelsrud KW, Skolbekken U, Michalsen N, Brekken AL, Pedersen B, Guennigsman B, Lia A, Berg AK, Marco F, Pitart C, Egea P, Cortes-Cuevas JL, Machuca J, Wietzke M, Dammström M, Granström R, Corneliusson M, Skarstedt M, Frykfeldt K, Ivarsson CL, Sergejev A, Hagström S, Lidén U, Rydberg J, Ramström H, Fröding I, Petropoulos EA, Ininbergs K, Jalal S, Persson A-LS, Kamenska N, Granlund K, Smekal A-K, Hill A, Rådberg G, Heyman G, Rodriguez L, Vennberg L, Hazırolan G, Akyar I, Gülşen Altınkanat G, Kaygisiz ANS, Dzajic E, Hansen DS, Agergaard harlotte N, Pätäri-Sampo A, Manninen R, Grönroos JO, Rasigade J-P, Salka W, Boyer PH, Lebessi E, Zapaniotis N, Petinaki E, Spiliopoulou I, Kolonitsiou F, Helgason KO, Brazil J, Riccobono E, Lo Cascio G, Maccacaro L, Kolstad H, Haukeland TS, Kellokumpu P-L, Fossum Mjøen A, Tofteland S, Harbak B, Hartzen SH, Haug Hänsgen S, Gammelsrud KW, Skolbekken U, Michalsen N, Brekken AL, Pedersen B, Guennigsman B, Lia A, Berg AK, Marco F, Pitart C, Egea P, Cortes-Cuevas JL, Machuca J, Wietzke M, Dammström M, Granström R, Corneliusson M, Skarstedt M, Frykfeldt K, Ivarsson CL, Sergejev A, Hagström S, Lidén U, Rydberg J, Ramström H, Fröding I, Petropoulos EA, Ininbergs K, Jalal S, Persson A-LS, Kamenska N, Granlund K, Smekal A-K, Hill A, Rådberg G, Heyman G, Rodriguez L, Vennberg L, Hazırolan G, Akyar I, Gülşen Altınkanat G, Kaygisiz ANS (2020) EUCAST rapid antimicrobial susceptibility testing (RAST) in blood cultures: validation in 55 European laboratories. J Antimicrob Chemother 75:3230–3238 10.1093/jac/dkaa333 37. van Belkum A Burnham C-AD Rossen JWA Mallard F Rochas O Dunne WM Innovative and rapid antimicrobial susceptibility testing systems Nat Rev Microbiol 2020 18 299 311 10.1038/s41579-020-0327-x 32055026 38. The Pew Charitable Trusts. (2021) Tracking the global pipeline of antibiotics in development, March 2021. https://www.pewtrusts.org/en/research-and-analysis/issue-briefs/2021/03/tracking-the-global-pipeline-of-antibiotics-in-development. Accessed 2 Sept 2022. 39. Plackett B Why big pharma has abandoned antibiotics Nature 2020 586 S50 S50 10.1038/d41586-020-02884-3 40. Melo MCR Maasch JRMA de la Fuente-Nunez C Accelerating antibiotic discovery through artificial intelligence Commun Biol 2021 4 1050 10.1038/s42003-021-02586-0 34504303 41. Schneider G Automating drug discovery Nat Rev Drug Discov 2018 17 97 113 10.1038/nrd.2017.232 29242609 42. Anik Ü (2017) Electrochemical medical biosensors for POC applications. In: RJ Narayan (ed.), Medical biosensors for point of care (POC) applications. Duxford: Woodhead Publishing, pp 275–292. 43. Ahmadian E Samiei M Hasanzadeh A Kavetskyy T Jafari S Alipour M Salatin S Rameshrad M Sharifi S Eftekhari A Hasanzadeh M Monitoring of drug resistance towards reducing the toxicity of pharmaceutical compounds: past, present and future J Pharm Biomed 2020 186 113265 10.1016/j.jpba.2020.113265 44. Hayat A Catanante G Marty JL Current trends in nanomaterial-based amperometric biosensors Sensors (Switzerland) 2014 14 23439 23461 10.3390/s141223439 45. Menon S Vishnu N Panchapakesan SSS Kumar AS Sankaran K Unrau P Parameswaran MA Electrochemical sensing methodology for antibiogram assays J Electrochem Soc 2014 161 B3061 B3063 10.1149/2.011402jes 46. Mishra P Singh D Mishra KP Kaur G Dhull N Tomar M Gupta V Kumar B Ganju L Rapid antibiotic susceptibility testing by resazurin using thin film platinum as a bio-electrode J Microbiol Methods 2019 162 69 76 10.1016/j.mimet.2019.05.009 31103460 47. Crane B Hughes JP Rowley Neale SJ Rashid M Linton PE Banks CE Shaw KJ Rapid antibiotic susceptibility testing using resazurin bulk modified screen-printed electrochemical sensing platforms Analyst 2021 146 5574 5583 10.1039/d1an00850a 34369493 48. Bolotsky A Muralidharan R Butler D Root K Murray W Liu Z Ebrahimi A Organic redox-active crystalline layers for reagent-free electrochemical antibiotic susceptibility testing (ORACLE-AST) Biosens Bioelectron 2021 172 112615 10.1016/j.bios.2020.112615 33166804 49. Karasinski J White L Zhang Y Wang E Andreescu S Sadik OA Lavine BK Vora M Detection and identification of bacteria using antibiotic susceptibility and a multi-array electrochemical sensor with pattern recognition Biosens Bioelectron 2007 22 2643 2649 10.1016/j.bios.2006.10.037 17169547 50. Chalenko Y Shumyantseva V Ermolaeva S Archakov A Electrochemistry of Escherichia coli JM109: direct electron transfer and antibiotic resistance Biosens Bioelectron 2012 32 219 223 10.1016/j.bios.2011.12.015 22209070 51. Hannah S Addington E Alcorn D Shu W Hoskisson PA Corrigan DK Rapid antibiotic susceptibility testing using low-cost, commercially available screen-printed electrodes Biosens Bioelectron 2019 145 111696 10.1016/j.bios.2019.111696 31542679 52. Hannah S Dobrea A Lasserre P Blair EO Alcorn D Hoskisson PA Corrigan DK Development of a rapid, antimicrobial susceptibility test for E. coli based on low-cost, screen-printed electrodes Biosensors 2020 10 153 10.3390/bios10110153 33114106 53. Brosel-Oliu S Mergel O Uria N Abramova N van Rijn P Bratov A 3D impedimetric sensors as a tool for monitoring bacterial response to antibiotics Lab Chip 2019 19 1436 1447 10.1039/C8LC01220B 30882115 54. Abeyrathne C Huynh D Mcintire T Nguyen T Nasr B Zantomio D Chana G Abbot I Choong P Catton M Skafidas E Lab on a chip sensor for rapid detection and antibiotic resistance determination of Staphylococcus aureus Analyst 2016 21 1922 1929 10.1039/c5an02301g 55. Puttaswamy S (2013) Novel electrical method for the rapid determination of minimum inhibitory concentration (MIC) and assay of bactericidal/bacteriostatic activity. J Biosens Bioelectron 2 10.4172/2155-6210.S2-003 56. Safavieh M Pandya HJ Venkataraman M Thirumalaraju P Kanakasabapathy MK Singh A Prabhakar D Chug MK Shafiee H Rapid real-time antimicrobial susceptibility testing with electrical sensing on plastic microchips with printed electrodes ACS Appl Mater Interfaces 2017 9 12832 12840 10.1021/acsami.6b16571 28291334 57. Jo N Kim B Lee S-M Oh J Park IH Jin Lim K Shin J-S Yoo K-H Aptamer-functionalized capacitance sensors for real-time monitoring of bacterial growth and antibiotic susceptibility Biosens Bioelectron 2018 102 164 170 10.1016/j.bios.2017.11.010 29132052 58. Yang Y Gupta K Ekinci KL All-electrical monitoring of bacterial antibiotic susceptibility in a microfluidic device Proc Natl Acad Sci USA 2020 117 10639 10644 10.1073/pnas.1922172117 32350139 59. Rampersad SN Multiple applications of Alamar Blue as an indicator of metabolic function and cellular health in cell viability bioassays Sensors 2012 12 12347 12360 10.3390/s120912347 23112716 60. McGaw LJ, Elgorashi EE, Eloff JN (2014) Cytotoxicity of African medicinal plants against normal animal and human cells. In: Toxicological survey of African medicinal plants. Elsevier, pp 181–233. 61. Kanagavalli P Andrew C Veerapandian M Jayakumar M In-situ redox-active hybrid graphene platform for label-free electrochemical biosensor: insights from electrodeposition and electroless deposition TrAC 2021 143 116413 10.1016/j.trac.2021.116413 62. Yang Y Xu M Guo J Sun G Bacterial extracellular electron transfer in bioelectrochemical systems Process Biochem 2012 47 1707 1714 10.1016/j.procbio.2012.07.032 63. Al Mamun M Wahab YA Hossain MAM Hashem A Johan MR Electrochemical biosensors with aptamer recognition layer for the diagnosis of pathogenic bacteria: barriers to commercialization and remediation TrAC 2021 145 116458 10.1016/j.trac.2021.116458 64. Dai J Hamon M Jambovane S Microfluidics for antibiotic susceptibility and toxicity testing Bioengineering 2016 3 25 10.3390/bioengineering3040025 28952587 65. Webster TA Sismaet HJ Chan IJ Goluch ED Electrochemically monitoring the antibiotic susceptibility of Pseudomonas aeruginosa biofilms Analyst 2015 140 7195 7201 10.1039/C5AN01358E 26396994 66. Vinogradov SM Satterwhite-Warden JE Hicks RP Anderson E Hvastkovs EG Electrochemical detection of alginate penetration in immobilized layer-by-layer films by unnatural amino acid containing antimicrobial peptides Electrochim Acta 2015 186 245 252 10.1016/j.electacta.2015.10.051 67. Robb AJ Vinogradov S Danell AS Anderson E Blackledge MS Melander C Hvastkovs EG Electrochemical detection of small molecule induced Pseudomonas aeruginosa biofilm dispersion Electrochim Acta 2018 268 276 282 10.1016/j.electacta.2018.02.113 30504968 68. Sultana ST Call DR Beyenal H Eradication of Pseudomonas aeruginosa biofilms and persister cells using an electrochemical scaffold and enhanced antibiotic susceptibility NPJ Biofilms Microbiomes 2016 2 2 10.1038/s41522-016-0003-0 28649396 69. Ramsey DM Wozniak DJ Understanding the control of Pseudomonas aeruginosa alginate synthesis and the prospects for management of chronic infections in cystic fibrosis: P. aeruginosa pathogenesis Mol Microbiol 2005 56 309 322 10.1111/j.1365-2958.2005.04552.x 15813726 70. Malhotra BD, Ali MA. Nanomaterials in biosensors: fundamentals and applications. Nanomaterials for Biosensors. 2018;1:1–74. 10.1016/B978-0-323-44923-6.00001-7. 71. Anjum S Ishaque S Fatima H Farooq W Hano C Abbasi BH Anjum I Emerging applications of nanotechnology in healthcare systems: grand challenges and perspectives Pharmaceuticals 2021 14 707 10.3390/ph14080707 34451803 72. Holzinger M, Le Goff A, Cosnier S (2014) Nanomaterials for biosensing applications: a review. Front Chem 2. 10.3389/fchem.2014.00063 73. Malik P Gupta R Malik V Ameta RK Emerging nanomaterials for improved biosensing Meas Sensors 2021 16 100050 10.1016/j.measen.2021.100050 74. Toyos-Rodríguez C García-Alonso FJ de la Escosura-Muñiz A Electrochemical biosensors based on nanomaterials for early detection of Alzheimer’s disease Sensors 2020 20 4748 10.3390/s20174748 32842632 75. Baig N Sajid M Saleh TA Recent trends in nanomaterial-modified electrodes for electroanalytical applications TrAC 2019 111 47 61 10.1016/j.trac.2018.11.044 76. Ahmad R Wolfbeis OS Hahn Y-B Alshareef HN Torsi L Salama KN Deposition of nanomaterials: a crucial step in biosensor fabrication Materials Today Communications 2018 17 289 321 10.1016/j.mtcomm.2018.09.024 77. Iglesias-Mayor A Amor-Gutiérrez O Costa-García A de la Escosura-Muñiz A Nanoparticles as emerging labels in electrochemical immunosensors Sensors 2019 19 5137 10.3390/s19235137 31771201 78. Malathi S Pakrudheen I Kalkura SN Webster TJ Balasubramanian S Disposable biosensors based on metal nanoparticles Sensors International 2022 3 100169 10.1016/j.sintl.2022.100169 35252890 79. Kaushal S Nanda SS Samal S Yi DK Strategies for the development of metallic-nanoparticle-based label-free biosensors and their biomedical applications ChemBioChem 2020 21 576 600 10.1002/cbic.201900566 31634410 80. Toyos-Rodríguez C Adawy A García-Alonso FJ de la Escosura-Muñiz A Enhancing the electrocatalytic activity of palladium nanocluster tags by selective introduction of gold atoms: application for a wound infection biomarker detection Biosens Bioelectron 2022 200 113926 10.1016/j.bios.2021.113926 34990956 81. Makabenta JMV Nabawy A Li C-H Schmidt-Malan S Patel R Rotello VM Nanomaterial-based therapeutics for antibiotic-resistant bacterial infections Nat Rev Microbiol 2021 19 23 36 10.1038/s41579-020-0420-1 32814862 82. Morena AG Bassegoda A Natan M Jacobi G Banin E Tzanov T Antibacterial properties and mechanisms of action of sonoenzymatically synthesized lignin-based nanoparticles ACS Appl Mater Interfaces 2022 14 37270 37279 10.1021/acsami.2c05443 35960019 83. Basavegowda N Baek K-H Multimetallic nanoparticles as alternative antimicrobial agents: challenges and perspectives Molecules 2021 26 912 10.3390/molecules26040912 33572219 84. Rao RP Sharma S Mehrotra T Das R Kumar R Singh R Roy I Basu T Rapid electrochemical monitoring of bacterial respiration for Gram-positive and Gram-negative microbes: potential application in antimicrobial susceptibility testing Anal Chem 2020 92 4266 4274 10.1021/acs.analchem.9b04810 32050756 85. Ren Y Ji J Sun J Pi F Zhang Y Sun X Rapid detection of antibiotic resistance in Salmonella with screen printed carbon electrodes J Solid State Electrochem 2020 24 1539 1549 10.1007/s10008-020-04645-8 86. Xu X Chen S Yu Y Virtanen P Wu J Hu Q Koskiniemi S Zhang Z All-electrical antibiotic susceptibility testing within 30 min using silicon nano transistors Sens Actuators, B: Chemical 2022 357 131458 10.1016/j.snb.2022.131458 87. Das R Singh N Exploring electrochemistry of carbon nanodots and its application in noninvasive bacterial growth monitoring Biosens Bioelectron 2019 144 111640 10.1016/j.bios.2019.111640 31494506 88. Zheng L Shen Y Dong W Zheng C Zhou R Lou Y-L Rapid detection and antimicrobial susceptibility testing of pathogens using AgNPs-invertase complexes and the personal glucose meter Front Bioeng Biotechnol 2022 9 795415 10.3389/fbioe.2021.795415 35118055 89. de la Escosura-Muñiz A de la, Ivanova K, Tzanov T, Electrical evaluation of bacterial virulence factors using nanopores ACS Appl Mater Interfaces 2019 11 13140 13146 10.1021/acsami.9b02382 30888786 90. Olivieri F Castaldo R Cocca M Gentile G Lavorgna M Mesoporous silica nanoparticles as carriers of active agents for smart anticorrosive organic coatings: a critical review Nanoscale 2021 13 9091 9111 10.1039/D1NR01899J 33982729 91. de la Escosura-Muñiz A Merkoçi A Nanochannels for electrical biosensing TrAC 2016 79 134 150 10.1016/j.trac.2015.12.003 92. de la Escosura-Muñiz A Merkoçi A Nanochannels preparation and application in biosensing ACS Nano 2012 6 7556 7583 10.1021/nn301368z 22880686 93. Burnham C-AD Leeds J Nordmann P O’Grady J Patel J Diagnosing antimicrobial resistance Nat Rev Microbiol 2017 15 697 703 10.1038/nrmicro.2017.103 29021600 94. Lebovitz EE Burbelo PD Commercial multiplex technologies for the microbiological diagnosis of sepsis Mol Diagn Ther 2013 17 221 231 10.1007/s40291-013-0037-4 23636778 95. Azimi S, Lewin GR, Whiteley M (2022) The biogeography of infection revisited. Nat Rev Microbiol. 10.1038/s41579-022-00683-3 96. Yung DBY Sircombe KJ Pletzer D Friends or enemies? The complicated relationship between Pseudomonas aeruginosa and Staphylococcus aureus Mol Microbiol 2021 116 1 15 10.1111/mmi.14699 33576132 97. Christaki E Marcou M Tofarides A Antimicrobial resistance in bacteria: mechanisms, evolution, and persistence J Mol Evol 2020 88 26 40 10.1007/s00239-019-09914-3 31659373	36445455	PMC9707421	NO-CC CODE	2022-12-01 23:20:23	no	Anal Bioanal Chem. 2022 Nov 29;:1-15	utf-8	Anal Bioanal Chem	2,022	10.1007/s00216-022-04449-x	oa_other
==== Front Occup Health Sci Occup Health Sci Occupational Health Science 2367-0134 2367-0142 Springer International Publishing Cham 133 10.1007/s41542-022-00133-9 Original Research Article Isolated and Stressed? Examining the Effects of Management Communication in Alleviating Mental Health Symptoms during COVID-19 http://orcid.org/0000-0002-3360-7445 Sawhney Gargi [email protected] 1 Jimenez-Gomez Corina 13 Cook Peter 1 Albert Kristin M. 2 1 grid.252546.2 0000 0001 2297 8753 Department of Psychological Sciences, Auburn University, 205 Thach Hall, Auburn, AL 36849 USA 2 grid.255966.b 0000 0001 2229 7296 Florida Institute of Technology, Melbourne, FL USA 3 grid.15276.37 0000 0004 1936 8091 University of Florida, FL Gainesville, USA 29 11 2022 122 13 1 2022 23 10 2022 15 11 2022 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The goal of this research was to assess the role of professional isolation on mental health symptoms via stress among employees working remotely due to COVID-19. Additionally, this research explored the interactive effect of management communication on the relationship between professional isolation and stress, and stress and mental health symptoms. In Study 1, behavior analysts who were working remotely as a result of the pandemic completed assessments of professional isolation, stress, and mental health symptoms at two points in time, separated by two weeks. Study 2 replicated and extended the findings from Study 1 in a sample of remote employees recruited from Amazon’s Mechanical Turk using a three-wave design. Findings of both Study 1 and Study 2 suggested that stress mediated the relationship between professional isolation and mental health symptoms. Additionally, management communication buffered the association between stress and mental health symptoms in Study 2. Lastly, the indirect effect of professional isolation on mental health symptoms was stronger for those who received less communication from their management. The findings of these two studies expand our understanding of the mechanism and boundary condition through which professional isolation is related to mental health symptoms. Keywords Professional isolation Stress Mental health symptoms Management communication COVID-19 ==== Body pmcThe U.S. Bureau of Labor Statistics reported that 25% of American employees worked from home at least part of the time in 2017–2018 (BLS, 2019). Due to the COVID-19 pandemic this number has increased substantially. As of mid-May, 2020, 68% of American employees reported performing some or all of their work-related tasks from home (Hickman & Saad, 2020). Hence, a large proportion of the population that was not accustomed to working remotely now found themselves working from home (Wang et al., 2021). Therefore, in addition to the stressors presented in the pandemic, individuals may experience increased professional isolation. In fact, recent research identified professional isolation as a transitional challenge during the pandemic (Wang et al., 2021), which may occur due to the reduction in interactions with one’s colleagues (Cooper & Kurland, 2002). In light of the recent changes in the modality of work from on-site to remote, research on professional isolation is more urgent than ever. Although numerous studies have examined the effects of remote work on both individual and organizational outcomes (e.g., Golden et al., 2006; Vega et al., 2015), the construct of professional isolation has received considerably less research attention. This oversight is concerning because the constructs of telework and professional isolation are not synonymous. Telework refers to “the substitution of communication technology for work‐related travel, and can include paid work from home, a satellite office, a telework centre or any other work station outside of the main office for at least one day per work week” (Martin & MacDonnell, 2012, p. 603). In contrast, professional isolation has been broadly defined as the “perception of a lack of availability of support and recognition, missed opportunities for informal interactions with co-workers, and not being part of the group” (Marshall et al., 2007, p. 160) as a result of working remotely. In fact, findings of a qualitative study showed that only 57% of their telework sample experienced professional isolation, suggesting that not all teleworkers necessarily experience professional isolation (Mann et al., 2000). Considering those who teleworked previously may have chosen to work remotely and had the option to work from the premises of their employer for at least part of the week, they may not have necessarily felt disconnected from their work, or experienced stress as a result of working remotely. Measuring professional isolation directly avoids the assumption of conflation of these constructs. This study makes two contributions to the literature on professional isolation. First, we use two separate samples of employees who were required to work remotely due to the pandemic and assess the prospective effects of professional isolation on mental health symptoms via stress. Since the policies during the pandemic ceased all face-to-face interactions outside of one’s family (Matias et al., 2020), individuals mandated to work remotely may have felt isolated or disconnected from their coworkers. Therefore, for the purposes of this study, we conceptualize professional isolation as missed opportunities for interactions with ones’ coworkers. Considering that the option to perform one’s duties from their workplace may be largely unavailable to many, employees are having to learn to work remotely, which may be stressful. Prior to the COVID-19 pandemic, working from home was a luxury that was only afforded to the relatively affluent (Desilver, 2020) and, often pursued based on employee preferences (Kniffin et al., 2021). However, the pandemic has forced most of the workforce to engage in mandatory remote work. Most of the existing research on professional isolation has been conducted with employees who choose to work remotely (e.g., Golden et al., 2008), and may not generalize to employees who are now being required to work remotely as a result of the pandemic. Second, we examine the moderating effect of management communication in mitigating the association between professional isolation and stress, and stress and mental health symptoms. Management communication is defined as the degree to which employees receive adequate information regarding the functioning of the organization (Vander Elst et al., 2010). Through an open communication channel, management can notify employees of relevant policy changes with regards to working remotely, thereby alleviating stress and reducing mental health symptoms. The hypothesized model is presented in Fig. 1.Fig. 1 Hypothesized model for Study 1 and Study 2 Theoretical Framework We utilize the Conservation of Resources (COR; Hobfoll, 1989) framework to examine professional isolation as a precursor to stress and mental health symptoms among those working remotely during the COVID-19 pandemic, as well as the moderating effect of management communication. Stress is characterized by “a situation wherein job-related factors interact with a worker to change (i.e., disrupt or enhance) his or her psychological and/or physiological condition such that the person (i.e., mind–body) is forced to deviate from normal functioning (Beehr & Newman, 1978, p. 670). The COR theory postulates that individuals strive to acquire resources they value. Stress ensues due to 1) loss of resources, 2) threat to current resources, and 3) inadequate return on investment made to increase resources. Over time, one’s inability to cope with stress can result in mental health symptoms, such as feelings of anxiety, sadness, and hopelessness, among others (Hart & Cooper, 2001; Kahn & Byosiere, 1992). The COR is well situated to provide insights regarding the role of professional isolation on stress and mental health symptoms. During the current global pandemic, several organizations have asked their employees to work from home (Chang et al., 2021), which may limit their interactions with their coworkers. As a result of fewer face-to-face interactions, remote employees are likely to feel detached from their organization (Bartel et al., 2012) and experience loneliness (Wang et al., 2021). Therefore, we argue that lack of interactions with ones’ coworkers (i.e., professional isolation) may be viewed as a threat to and/or loss of social resources. Consistent with COR, the appraisal of professional isolation (i.e., threat to and/or loss of interactions with ones’ coworkers) can lead to employee stress. Consequently, the experience of stress is likely to result in strain if employees are unable to make up for those lost resources, thereby heightening their mental health symptoms. Prior research has shown that professional isolation is positively related to stress (Dussault et al., 1999), and stress is related to mental health symptoms (Sawhney et al., 2018). Taking into account this theoretical and empirical evidence, we pose the following hypothesis:Hypothesis 1: Professional isolation will exhibit an indirect positive effect on mental health symptoms through employee stress. Moderating Role of Management Communication Organizations switched their operations to a virtual modality due to the COVID-19 pandemic (Sanders et al., 2020), which may have resulted in ambiguity regarding role expectations. During crisis situations, employees often turn to their management for information, and it becomes the responsibility of the organization to effectively communicate crucial information with its employees on a timely basis so that they are able to carry out their tasks (Allen, 1992; van der Meer et al., 2017). We note that while management communication may provide information about the organization’s strategy for managing work arrangements during the pandemic, it may not fully fill the void created by the lack of interactions with ones’ coworkers. Research conducted during the pandemic has demonstrated that ineffective communication from their management is a challenge experienced by remote workers (Wang et al., 2021). Consistent with COR theory, we argue that management communication can serve as an energy resource that alleviates the effects of professional isolation on stress, and stress on mental health symptoms (Hobfoll et al., 2018). For the purposes of the current study, we define management communication as relaying timely, adequate and accurate information about working remotely (Kernan & Hanges, 2002). Management communication may facilitate a better understanding of changes in working arrangements during the pandemic. For instance, through communication, management can inform employees regarding the duration of time they will spend working remotely, as well as articulate any changes in policies and procedures, subsequently enhancing predictability of their working situation and alleviating stress. Such information may elevate employees’ perception of control by reducing ambiguity (DiFonzo & Bordia, 1998) and giving them relevant knowledge regarding when they may be able to return to work, thereby equipping them to better cope with professional isolation (Stephens & Long, 2000). Accordingly, we hypothesize the following:Hypothesis 2: Management communication will moderate the positive relationship between professional isolation and stress, such that the association between professional isolation and stress will be weaker when participants experience higher versus lower management communication. At the same time, we expect that the risk of employees’ stress during the pandemic escalating into mental health symptoms may be lower for those who receive adequate communication from their management. The COR theory’s gain paradox principle posits that the salience of resource gain increases in the face of resource loss. Stated differently, when threatened with resource loss, securing additional resources becomes critical (Hobfoll et al., 2018). In the context of the current study, when faced with stress, management communication may serve as a resource to counter the negative effects of stress on employees’ mental health. As an example, employees who receive information from their management pertaining to remote work arrangements may perceive greater control and be able to bounce back from the stress they were experiencing, thus inhibiting their mental health symptoms. Conversely, the loss spirals of those who do not receive information from their management while experiencing stress may gain both momentum and magnitude (Hobfoll et al., 2018). Consequently, we pose the following hypothesis:Hypothesis 3: Management communication will moderate the positive relationship between stress and mental health symptoms, such that the association between stress and mental health symptoms will be weaker when participants experience higher versus lower management communication. Taken together, this research presents a moderated mediation model, whereby management communication serves as a resource of the indirect association between professional isolation and mental health symptoms through stress. Employees experiencing professional isolation while working remotely may experience less stress if they receive management communication. Such communication may provide the much-needed information to employees regarding changes in work policies during the pandemic, thus enhancing their perception of control (DiFonzo & Bordia, 1998), thereby reducing the deleterious effects of stress on mental health symptoms. On the other hand, perception of professional isolation may initiate a resource loss spiral for those who do not receive communication from their management, resulting in stress and mental health symptoms (Hobfoll et al., 2018). Given the above, we propose the following hypothesis:Hypothesis 4: The indirect relationship between professional isolation and mental health symptoms through stress is moderated by management communication, such that this indirect relationship is stronger at lower levels of management communication. Overview of Study 1 and Study 2 The goal of Study 1 was to explore whether professional isolation predicted both stress and mental health symptoms in behavior analysts who were required to work remotely during the COVID-19 pandemic. Behavior analysts are professionals who provide behavioral intervention services, based on the science of behavior and learning, to address problems of social significance with the purpose of improving the quality of life of the individuals they serve (Fisher et al., 2021). The majority of their work typically is conducted face-to-face in highly interactive sessions, during which behavior analysts interact with their clients, colleagues, paraprofessionals, and other stakeholders. Over 80% of board certified registered behavioral technicians (RBT®), assistant behavior analysts (BCaBA®), and behavior analysts (BCBA®) provide clinical services to children, individuals with disabilities, and/or other vulnerable populations (Behavior Analysis Certification Board, 2021). Given the nature of the work performed by behavior analysts, they experience high rates of job burnout and work-related stress (Plantiveau et al., 2018). During the COVID-19 pandemic, behavior analysts also experienced additional sources of stress resulting from distractions, challenges posed by having to identify ways to deliver behavioral services safely, having to quickly pivot to delivering services remotely, and a work environment with greatly diminished social interaction (e.g., Behavioral Health Center of Excellence, 2020; Jimenez-Gomez et al., 2021). This drastic and sudden change may result in increased stress and expression of mental health symptoms. Hence, we considered this sample appropriate for the current study. Study 2 attempted to replicate and extend the findings from Study 1 in a more diverse working sample to delineate whether the effects of professional isolation on stress and mental health exist in employees across industries that are required to work remotely as a result of the pandemic. In particular, this study explored whether management communication serves as a resource that mitigates the effects of professional isolation on stress, and stress on mental health symptoms. Both Study 1 and Study 2 utilized a prospective design with assessments separated by two weeks. The rationale for testing these variables at different time points was twofold. First, in order to reduce attrition, we wanted to keep the length of the survey to a minimum. Second, we did not expect the measures of professional isolation, management communication, stress, or mental health symptoms to fluctuate much over the timeframe of the study. In light of these considerations, we opted to examine the relationships between our study variables using a prospective design rather than a longitudinal design. Study 1 Method Participants and Procedure The data presented in this study are from a larger study on stress and well-being of behavior analysts. None of the variables presented in this study have been published in other studies. The sample for this study comprised of 130 behavior analysts residing within the United States. On average, participants were 33.98 years of age (SD = 8.59), predominantly female (85.70%), and White (87.70%). In this sample, 75.40% participants indicated that their primary place of work was center- or clinic-based, in-school, community-based, or other prior to the pandemic. An invitation to participate in the study was sent to all members of the Behavior Analyst Certification Board in April, 2020. Prior to completing the survey, members were asked to indicate whether their employers were requiring them to work remotely, as well as the number of hours they were working at the time of the survey. Those who were employed 20 h or more per week and working remotely were retained for this study. A total of 266 members completed the survey in its entirety at Time 1. These participants were invited to complete another survey at Time 2, two weeks later. Of these, 130 participants provided complete responses. Participants who completed both surveys were entered in a raffle to win one of 25 Amazon gift cards worth $25.00. Measures Professional Isolation Five items adopted from Hawthorne (2006) were used to measure professional isolation at Time 1. Participants were instructed to indicate the extent to which they felt isolated from their coworkers during the COVID-19 pandemic. Items developed for professional isolation are presented in the Appendix. All items were measured on a 5-point Likert scale, from 1 = “Not at all” to 5 = “Extremely.” Cronbach’s alpha reliability for this scale was 0.80. Stress We used Cohen et al.’s (1983) 10-item measure to assess stress at Time 2. Participants indicated the frequency with which they experienced stress since the COVID-19 pandemic. A sample item included “Felt that you were unable to control the important things in your life.” This scale utilized a 5-point Likert scale, from 1 = “Never” to 5 = “Very frequently.” Cronbach’s alpha reliability for this scale was 0.90. Mental Health Symptoms Eleven items from the American College Health Assessment II (ACHA, 2016) were used to assess mental health symptoms at Time 2. Participants indicated the frequency with which they experienced mental health symptoms since the COVID-19 pandemic. A sample item in this measure was “Felt overwhelming anxiety.” All items were measured on a 5-point Likert scale, ranging from 1 = “Never” to 5 = “Very frequently.” Cronbach’s alpha reliability for this scale in the current study was 0.76. Study 1 Results In order to ascertain that the study variables could be distinguished from one another, we conducted confirmatory factor analysis (CFA) with robust maximum likelihood estimation (MLR) using Mplus 7.4. In line with the recommendations by Little et al. (2002), we created five parcels for stress by averaging two items per parcel. Considering the odd number of items for mental health symptoms, we created three parcels by averaging three items per parcel, and two items for the fourth parcel. We tested a three-factor model where items for professional isolation, and parcels for stress and mental health symptoms were allowed to load on their respective factors. Results suggested inadequate fit for this model, χ2 (74) = 286.23, p < 0.01, CFI = 0.77, RMSEA = 0.11, 90% C.I. of RMSEA [0.10, 0.12]. Because the initial three-factor model failed to adequately fit the data, we used the Lagrange Multiplier test (Byrne, 2006) to determine the cause of misfit. Consistent with recommendations in the literature (MacCallum et al., 1992), we allowed one error covariance between two items in the professional isolation measure to be freely estimated to enhance model fit. This respecified model demonstrated significantly better fit, χ2 (73) = 117.36, p < 0.01, CFI = 0.95, RMSEA = 0.05, 90% C.I. of RMSEA [0.03, 0.07]. Means, standard deviations, and correlations for all study variables are provided in Table 1. Correlation coefficients indicated that professional isolation was positively related to stress (r = 0.29, p < 0.01), but not mental health symptoms. Stress exhibited a positive association with mental health symptoms (r = 0.49, p < 0.01).Table 1 Means, standard deviations, correlations, and reliabilities for variables in Study 1 Variable M SD 1 2 3 1 Professional isolation (T1) 2.94 0.89 (.80) 2 Stress (T2) 2.82 0.70 .29 (.90) 3 Mental health symptoms (T2) 2.47 0.58 .13 .49 (.76) Note. N = 130. Cronbach's alphas are presented in parentheses along the diagonal. T1 = Time 1. T2 = Time 2 p < .01 Hypothesis 1 was tested using the PROCESS macro (Hayes, 2017), which allows simultaneous examination of mediation and moderation effects. Hypothesis 1 predicted that stress would mediate the relationship between professional isolation and mental health symptoms. For the purposes of testing this hypothesis, we used model 4 in the PROCESS macro, and employed a bias-corrected bootstrapping procedure with 10,000 samples to test the mediation effect. As shown in Table 2, professional isolation at Time 1 was positively related to stress at Time 2 (B = 0.23, SE = 0.07, p < 0.01). Meanwhile, stress at Time 2 predicted mental health symptoms at Time 2 (B = 0.41, SE = 0.07, p < 0.01). The indirect effect of professional isolation on Time 2 mental health symptoms was significant (effect = 0.09, SE = 0.03, 95% CI [0.03, 0.16]. These findings provide support for Hypothesis 1.Table 2 Results of the mediation analysis for study 1 Direct effects Coefficient SE LLCI ULCI Outcome variable: Stress Constant 2.16 .21 1.75 2.56 Professional isolation 0.23 .07 0.09 0.36 R2 = .08 Outcome variable: Mental health symptoms Constant 1.33 .21 0.91 1.75 Professional isolation -0.01 .05 -0.11 0.09 Stress 0.41** .07 0.28 0.54 R2 = .24 Indirect effect Effect Boot SE Boot LLCI Boot ULCI Professional isolation on Mental health symptoms 0.09 .03 0.03 0.16 Note. N = 130. p < .05; p < .01 Study 2 Method Participants and Procedure Data for Study 2 were gathered between March, 2020 through May, 2020 from 269 Amazon’s Mechanical Turk (MTurk) workers who resided in the United States. The mean age of participants was 39.34 (SD = 10.86) years, and they worked 41.43 (SD = 4.34) hours per week. Approximately 56% of participants were male. The majority of the participants were married (50%) or single (39%). With respect to ethnicity, 80% of the participants were White, followed by Asian (12%), and Black (5%). Participants were employed in various industries, including educational services, information technology, finance, and healthcare, among others, and 57.2% of our participants indicated that they had worked remotely for 15 h or fewer prior to the pandemic. Consistent with recommendations in the literature (Buhrmester et al., 2018), a qualification survey was posted on MTurk where participants indicated whether they were employed, the number of hours they were employed, and whether they were required to work remotely due to COVID-19. A total of 2,448 participants completed the qualification survey. Participants were retained if they 1) were employed, 2) worked 30 h or more per week, and 3) were required to work remotely due to COVID-19. Based on the responses to the qualification survey, 745 participants were eligible to participate in the study and were invited to complete the Time 1 survey. Of these, 369 participants that were working remotely completed the survey. Two weeks later, at Time 2, participants from Time 1 were invited to complete another survey, of which 324 remote employees responded. At Time 3, all remote participants from Time 2 were invited to complete a survey two weeks post the Time 2 survey. A total of 269 participants working remotely responded to the Time 3 survey. In order to ensure that only employees that were working remotely were retained, participants were asked to indicate whether they were working remotely at all three time points. Two attention check items were embedded in each of the three surveys. A sample attention check item was “Please select strongly disagree for this item.” Responses were only retained if participants passed all attention checks across the three surveys. Measures We assessed professional isolation and management communication at Time 1, stress at Time 2, and mental health symptoms at Time 3. We used the same measures of professional isolation and stress as in Study 1. In Study 2, the Cronbach’s alpha reliability was 0.80 for professional isolation and 0.91 for stress. Below, we provide details for measures of management communication and mental health symptoms that were administered in Study 2. Management Communication Four items were developed for this study to measure management communication (see Appendix for items). In line with the conceptualization of communication in the workplace (Kernan & Hanges, 2002), items on this scale assessed timeliness, accuracy, and adequacy of the communication received. Additionally, we added a fourth item that captured employees’ overall perceptions about being kept in the loop regarding remote working arrangements. All items were measured on a 5-point Likert scale, from 1 = “Never” to 5 = “Very frequently.” Cronbach’s alpha reliability for this scale was 0.90. Mental Health Symptoms We used the 23-item DSM-5 Self-Rated Level 1 Cross-Cutting Symptom Measure (American Psychiatric Association, 2015) to assess mental health symptoms. All items were measured on a 5-point Likert scale, from 1 = “Not at all” to 5 = “Nearly every day.” A sample item for this scale was “Feeling nervous, anxious, frightened, worried, or on edge.” Cronbach’s alpha reliability for this scale in this study was 93. Control Variable In Study 2, we controlled for household income before taxes. Response categories were: 1 = “Less than $15,000,” 2 = “$15,000—$24,999,” 3 = “$25,000—$34,999,” 4 = “$35,000—$49,999,” 5 = “$50,000—$74,999,” 6 = “$75,000—$99,999,” 7 = “$100,000—$149,999,” 8 = “$150,000—$199,999,” and 9 = “$200,000 and above.” Prior research has indicated that socioeconomic factors are correlated with employee well-being (Probst et al., 2018). Study 2 Results We used the same procedures outlined in Study 1 to conduct a CFA in Study 2. We tested a four-factor model where items for professional isolation and management communication, and parcels for stress (i.e., same five parcels that were used in Study 1) and mental health symptoms (i.e., five parcels created by averaging four items, and sixth parcel created by averaging three items) were allowed to load on their respective factors. The initial model demonstrated inadequate fit, χ2 (168) = 737.84, p < 0.01, CFI = 0.84, RMSEA = 0.09, 90% C.I. of RMSEA [0.09, 0.10]. Similar to Study 1, the Lagrange Multiplier test suggested the inclusion of an error covariance between two items in the professional isolation measure to enhance model fit. Upon including the error covariance, the model demonstrated significantly better fit, χ2 (167) = 471.65, p < 0.01, CFI = 0.91, RMSEA = 0.07, 90% C.I. of RMSEA [0.06, 0.08]. Although the assessment of the predictors (professional isolation and management communication), mediator (stress), and criterion (mental health symptoms) was temporally separated to alleviate concerns of common method variance (CMV; Podsakoff et al., 2003), we proceeded to empirically examine the degree to which CMV was present in our data. Therefore, the measurement model tested previously was re-estimated after including an uncorrelated method factor. Results suggested that not only did the measurement model with the method factor provided adequate fit, but the fit improved significantly compared to the measurement model without the method factor, Δχ2 = 49.82, p < 0.01, Δdf = 19). However, the method factor only accounted for 12.30% of the total variance, which is substantially lower than the 25% reported in the literature (Williams et al., 1989). Therefore, it is safe to conclude that while CMV was present in the data, it is unlikely to bias the results of the current study. Means, standard deviations, reliabilities, and correlations for all Study 2 variables are presented in Table 3. Bivariate correlations indicated that professional isolation was positively (r = 0.35, p < 0.01) and management communication was negatively (r = -0.24, p < 0.01) related to stress at Time 2. Stress at Time 2 was also positively associated with mental health symptoms at Time 3 (r = 0.62, p < 0.01).Table 3 Means, standard deviations, and correlations for study 2 Variable M SD 1 2 3 4 5 1 Income (T1) – – – 2 Professional isolation (T1) 2.92 0.88 –.17* (.80) 3 Management communication (T1) 4.22 0.86 .06 –.29** (.90) 4 Stress (T2) 2.40 0.83 –.15* .35 –.24 (.91) 5 Mental health symptoms (T3) 1.56 0.58 –.11 .23 –.25 .62** (.93) Note. N = 269. Cronbach's alphas are presented in parentheses along the diagonal. T1 = Time 1. T2 = Time 2. T3 = Time 3 p < .05; p < .01 Similar to Study 1, we tested Hypothesis 1 in Study 2 using Model 4 in the PROCESS macro. As indicated in Table 4, controlling for income, professional isolation at Time 1 predicted stress at Time 2 (B = 0.32, SE = 0.05, p < 0.01), and stress predicted mental health symptoms at Time 3 (B = 0.42, SE = 0.04, p < 0.01). No relationship was found between professional isolation and mental health symptoms. Hypothesis 1 was supported in Study 2 as the indirect effect of professional isolation on mental health symptoms via stress was significant (effect = 0.14, SE = 0.03, 95% CI [0.08, 0.19]).Table 4 Results of the mediation analysis for study 2 Direct effects Coefficient SE LLCI ULCI Outcome variable: Stress Constant 1.73* .25 1.24 2.22 Income –0.04 .03 –0.10 0.01 Professional isolation 0.32 .05 0.21 0.43 R2 = .13 Outcome variable: Mental health symptoms Constant 0.56 .16 0.25 0.88 Income –0.01 .01 –0.04 0.03 Professional isolation 0.01 .03 –0.06 0.07 Stress 0.42** .04 0.35 0.50 R2 = .38 Indirect effect Effect Boot SE Boot LLCI Boot ULCI Professional isolation on Mental health symptoms 0.14 .03 0.08 0.19 Note. N = 269. p < .05; p < .01 Hypothesis 2 predicted that management communication would buffer the positive relationship between professional isolation and stress, and Hypothesis 3 asserted that the interactive effect between management communication and stress would predict mental health symptoms. In order to test the mediated effects, and stage 1 and stage 2 moderated effects, we used model 58 in the PROCESS macro (see Table 5). While the interaction between management communication and stress predicted mental health symptoms (effect = -0.10, SE = 0.04, p < 0.05), management communication did not moderate the professional isolation – stress relationship. Therefore, Hypothesis 2 was not supported.Table 5 Results of the moderation mediation analysis for study 2 (Stage 1 and Stage 2 Moderation) Stress Mental Health Symptoms B SE B SE Constant 0.27 .17 1.56* .10 Income -0.05 .03 0.00 .02 Professional isolation 0.28** .06 -0.01 .03 Management communication -0.15* .06 -0.06 .03 Stress 0.42** .04 Professional isolation x management communication 0.01 .06 Stress x management communication -0.10* .04 R2 .15 .41 Note. N = 269. p < .05; p < .01 Considering that we did not find support for stage 1 moderation, we tested Hypotheses 3 and 4 using model 14 in the PROCESS macro that focused only on the second stage moderation (see Table 6). Our findings indicated that controlling for income, management communication interacted with stress to predict mental health symptoms (B = -0.10, SE = 0.04, p < 0.05). To delineate the form of interaction, we plotted the regression lines for high and low levels of management communication (i.e., 1 SD above and below the mean), consistent with recommendations by Aiken et al. (1991). The two-way interaction plotted in Fig. 2 revealed that the positive relationship between stress and mental health symptoms was significant at both high (t = 7.45, p < 0.01) and low (t = 9.66, p < 0.01) levels of management communication. However, the slope was steeper under low levels of management communication. Hence, Hypothesis 3 was supported.Table 6 Results of the moderation mediation analysis for study 2 (Stage 2 Moderation) Stress Mental Health Symptoms B SE B SE Constant –0.67* .25 1.60 .15 Income –0.05 .03 0.00 .02 Professional isolation 0.32 .05 –0.01 .03 Management communication –0.06 .03 Stress 0.42** .04 Stress x management communication –0.10* .04 R2 .13 .41 Conditional indirect effect of professional isolation on mental health symptoms Effect Bootstrap LLCI ULCI -1 SD 0.16 .03 0.10 0.23 Mean 0.13 .03 0.08 0.19 + 1 SD 0.11 .03 0.06 0.16 Index of moderated mediation –0.03 .01 –0.06 –0.01 Note. N = 269. p < .05; *p < .01 Fig. 2 Interaction between management communication at Time 1 and stress at Time 2 in predicting mental health symptoms at Time 3 in Study 2 Lastly, Hypothesis 4 predicted that the indirect relationship between professional isolation and mental health symptoms would be stronger for those who received less communication from their management. Results indicated that the conditional indirect effect of professional isolation on mental health symptoms was significant under both high (effect = 0.11, SE = 0.03, 95% CI = 0.06 to 0.16) and low (effect = 0.16, SE = 0.03, 95% CI = 0.10 to 0.23) levels of management communication. However, the effect was stronger for those who received less communication. An examination of the index of moderated mediation which provides an interval estimate that “is a direct quantification of the linear association between the indirect effect and the putative moderator of that effect” (Hayes, 2015, p. 3), indicated that the two conditional effects characterized by different values of management communication were statistically different, thereby partially supporting Hypothesis 4. Discussion With the emergence of COVID-19, working remotely may become a new reality for many. Considering that prior research suggests that at least 57% the employees who work remotely experience professional isolation (Mann et al., 2000), studies on the effects of professional isolation on employee outcomes are more urgent than ever. Although studies have demonstrated the effects of professional isolation on work-related outcomes (Bartel et al., 2012; Bentein et al., 2017; Golden et al., 2008; Mulki & Jaramillo, 2011), research on the effects of professional isolation on employee health are deficient (Bentein et al., 2017). Thus, an examination of the effects of professional isolation on stress and mental health symptoms is pertinent for those working remotely. Our findings across two samples indicate that professional isolation, which can be viewed as a threat to and/or loss of social resources, is positively associated with stress, and further predicts mental health symptoms. These results are consistent with the assertions made by the Conservation of Resources (COR; Hobfoll, 1989) framework which posits that professional isolation may threaten interpersonal resources at work, which could result in stress. Although we did not draw from uncertainty management theory (Lind & Van den Bos, 2002) in the current study, our findings lend support to this theory in the context of professional isolation. The core premise of this theory is that individuals seek predictability in their environment, and lack thereof can lead to stress. Mandating remote work during the COVID-19 pandemic may have produced feelings of relational uncertainty with respect to their standing with one’s coworkers and superiors, resulting in stress and poor mental health. Inconsistent with our hypotheses, management communication did not moderate the association between professional isolation and stress. We provide two main reasons for this lack of significance. One explanation for this finding is that other resources, such as support from one’s leader and peers may be more meaningful to those working remotely when compared to management communication relating to remote working policies. For instance, a recent study indicated that coworker support was especially important for those working remotely to be productive during the COVID-19 pandemic (Keller et al., 2020). It is also plausible that other forms of communication, such as job-related communication, may be more pertinent in reducing stress. Future research may consider exploring the interactive effects of professional isolation with various forms of support and communication in predicting stress of remote employees. Our findings also suggested that the relationship between stress and mental health symptoms can be contained with adequate management communication. In line with COR, high quality management communication may serve as an energy resource which provides knowledge regarding working arrangements, as well as when employees will likely return to work. By doing so, effective communication may remove any ambiguity surrounding work arrangements while giving employees hope to return to work. In the present research, mental health symptoms increased for those who received less versus greater management communication. Not only do these findings bolster the support for effective management communication as a resource during COVID-19, it paves a path for future intervention research to alleviate negative consequences of stress by increasing timely communication and information flow in their organization. Overall, the results of our study align with the theoretical propositions of the COR, thus bolstering its importance in the occupational stress literature. Practical Implications Our research also has practical implications that need to be highlighted. The findings of our study indicate that professional isolation is prevalent among employees who are required to work remotely during the pandemic, and is indirectly related to their mental health. Considering that much of the professional isolation is associated with employee development activities, such as interpersonal networking, informal learning, and mentoring (Cooper & Kurland, 2002), organizations can ensure that these opportunities are adequately available to employees who are being required to work remotely. The current study also substantiates the importance of management communication during a time of uncertainty. Our results indicated that the indirect effect of professional isolation on mental health symptoms was stronger for those who experienced lower management communication. These findings suggest that while organizations may not be able to fully eliminate professional isolation, they can play a pivotal role in reducing ambiguity, and consequently enhancing the mental health of remote workers by effectively communicating with its employees in a timely manner. Limitations and Directions for Future Research The findings of our study should be considered in light of its limitations. First, although we utilized a prospective design in both Study 1 and Study 2, we cannot infer causality due to the correlational nature of both studies. While the pattern of results in both studies were in line with most of our hypotheses, we cannot conclude with certainty that professional isolation results in stress or vice versa. Similarly, we cannot rule out any potential confounds or alternative explanations in our findings. Researchers may consider testing our model using longitudinal design to establish causality (Zapf et al., 1996). Our second limitation concerns the measurement of professional isolation. Although we asked participants to report the extent to which they experienced professional isolation during the pandemic, we did not capture the experience of professional isolation due to the pandemic. Furthermore, we did not capture the sources of professional isolation across the two samples. While some participants may experience professional isolation as a result of the lack of interactions with their coworkers, others may experience it because they are being ostracized. Considering that the outcomes of professional isolation may vary depending on the source of isolation, future research may consider the source as a moderator of the professional isolation – employee health relationship. Third, the conceptualization of management communication in the current study may be simplistic. Existing research has used alternative conceptualizations of communication (i.e., job-related vs. non job-related, positive vs. negative, etc.; Beehr et al., 1990) that may be relevant, but were not considered in the present research. As an example, Stephens and Long (2000) demonstrated that positive communication about work reduced the intensity of the relationship between experiencing traumatic stressors and strain. Furthermore, research suggests that frequency of communication may be equally important to consider when studying organizational communication (Bakker & Xanthopoulou, 2009). Therefore, we urge researchers to not only examine different types of communication, but also the frequency of communication as a way of mitigating stress. Fourth, we relied on a sample recruited from Amazon’s Mechanical Turk, which may be considered a limitation. However, researchers have argued that MTurk offers access to an ethnically and socioeconomically diverse sample (Casler et al., 2013) which is representative of the labor market (Michel et al., 2018). We believe that our use of the MTurk platform to recruit participants was justified since the goal of Study 2 was to replicate and extend our findings from Study 1 with a diverse working sample. Fifth, while this research utilized a validated measure of stress that has been frequently cited in the literature (Kotsou et al., 2011), we realize that this scale is contaminated as it captures perceived control, as well as one’s response to stress (Cavaiola & Stout, 2017). Future research may consider using a clean measure of stress that assesses one’s affective response to professional isolation to determine whether the findings from the current study can be replicated. Conclusion Using two independent samples, this study explored the indirect effect of professional isolation on mental health symptoms through stress in employees who were required to work remotely due to the COVID-19 pandemic. Additionally, this research assessed the moderating role of management communication on the indirect effect of professional isolation on mental health symptoms. Results suggested that stress mediated the positive relationship between professional isolation and mental health symptoms. Additionally, management communication buffered the positive association between stress and mental health symptoms and the indirect effect of professional isolation on mental health symptoms. These findings expand our understanding of the effects of professional isolation and management communication on employee well-being during a pandemic. Appendix Professional Isolation It is easy to relate to your coworkers You are isolated from your coworkers It is easy to get in touch with your coworkers You are separated from your coworkers You have coworkers to share your feeling with Management Communication The management kept me in the loop while making decisions about employees working remotely The amount of information I received about working remotely from the management was adequate The information I received from the management about working remotely was timely The information I received from the management about working remotely was accurate Author Contributions The first and second authors contributed to the study conception and design. Material preparation and data analyses were performed by the third and fourth author. The first draft of the manuscript was written by Dr. Gargi Sawhney, and all authors commented on the initial versions of the manuscript. All authors have read and approved the final manuscript. Data Availability The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Code Availability Not applicable. Declarations Ethics Approval The study was approved by the IRB prior to data collection. Conflict of Interest The authors declare that they have no conflict of interest. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Aiken LS West SG Reno RR Multiple regression: Testing and interpreting interactions 1991 Sage Allen MW Communication and organizational commitment: Perceived organizational support as a mediating factor Communication Quarterly 1992 40 4 357 367 10.1080/01463379209369852 American College Health Association (ACHA) National College Health Assessment II: Canadian Reference Group Report Fall 2016 2016 Linthicum Author American Psychiatric Association. (2015). Diagnostic and Statistical Manual-5 Self-Rated Level 1 Cross Cutting Symptom Measure-Adult. Retrieved from https://www.psychiatry.org/psychiatrists/practice/dsm/dsm-5/online-assessment-measures Ashforth BE Mael F Social identity theory and the organization Academy of Management Review 1989 14 1 20 39 10.2307/258189 Bakker AB Xanthopoulou D The crossover of daily work engagement: Test of an actor–partner interdependence model Journal of Applied Psychology 2009 94 6 1562 10.1037/a0017525 19916663 Bartel CA Wrzesniewski A Wiesenfeld BM Knowing where you stand: Physical isolation, perceived respect, and organizational identification among virtual employees Organization Science 2012 23 3 743 757 10.1287/orsc.1110.0661 Beehr TA Newman JE Job stress, employee health, and organizational effectiveness: A facet analysis, model, and literature review 1 Personnel Psychology 1978 31 4 665 699 10.1111/j.1744-6570.1978.tb02118.x Beehr TA King LA King DW Social support and occupational stress: Talking to supervisors Journal of Vocational Behavior 1990 36 1 61 81 10.1016/0001-8791(90)90015-T Behavioral Health Center for Excellence. (2020). COVID-19 FAQs for ABA Organizations. https://bhcoe.org/resources/covid-19-faqs-for-aba-providers/ Behavior Analyst Certification Board. (2021). BACB certificant data. https://www.bacb.com/bacb-certificant-data/ Bentein K Garcia A Guerrero S Herrbach O How does social isolation in a context of dirty work increase emotional exhaustion and inhibit work engagement? A process model Personnel Review 2017 46 1620 1634 10.1108/PR-09-2016-0227 Bentley TA Teo ST McLeod L Tan F Bosua R Gloet M The role of organisational support in teleworker wellbeing: A socio-technical systems approach Applied Ergonomics 2016 52 207 215 10.1016/j.apergo.2015.07.019 26360212 Brown LA Roloff ME Organizational citizenship behavior, organizational communication, and burnout: The buffering role of perceived organizational support and psychological contracts Communication Quarterly 2015 63 4 384 404 10.1080/01463373.2015.1058287 Buhrmester MD Talaifar S Gosling SD An evaluation of Amazon’s Mechanical Turk, its rapid rise, and its effective use Perspectives on Psychological Science 2018 13 149 154 10.1177/1745691617706516 29928846 Byrne BM Structural equation modeling with EQS: Basic concepts, applications, and programming 2006 2 Erlbaum Casler K Bickel L Hackett E Separate but equal? A comparison of participants and data gathered via Amazon’s MTurk, social media, and face-to-face behavioral testing Computers in Human Behavior 2013 29 2156 2160 10.1016/j.chb.2013.05.009 Cavaiola AA Stout D Other facets of workplace abuse: An exploratory study The Psychologist-Manager Journal 2017 20 4 246 266 10.1037/mgr0000060 Chang Y Chien C Shen LF Telecommuting during the coronavirus pandemic: Future time orientation as a mediator between proactive coping and perceived work productivity in two cultural samples Personality and Individual Differences 2021 171 1 6 10.1016/j.paid.2020.110508 Cohen S Kamarck T Mermelstein R A global measure of perceived stress Journal of Health and Social Behavior 1983 24 4 385 396 10.2307/2136404 6668417 Cooper CD Kurland NB Telecommuting, professional isolation, and employee development in public and private organizations Journal of Organizational Behavior 2002 23 4 511 532 10.1002/job.145 Desilver, D. (2020). Working from home was a luxury for the relatively affluent be- fore coronavirus—Not any more. World Economic Forum. Retrieved from https://www.weforum.org/agenda/2020/03/working-from-home-coronavirus-workers-future-of-work/ DiFonzo N Bordia P A tale of two corporations: Managing uncertainty during organizational change Human Resource Management 1998 37 3–4 295 303 10.1002/(SICI)1099-050X(199823/24)37:3/4<295::AID-HRM10>3.0.CO;2-3 Dussault M Deaudelin C Royer N Loiselle J Professional isolation and occupational stress in teachers Psychological Reports 1999 84 943 946 10.2466/PR0.84.3.943-946 10408217 Fisher, W. W., Groff, R. A., & Roane, H. A. (2021). In W. W. Fisher, C. C., Piazza, & H. S. Roane (Eds.). Handbook of Applied Behavior Analysis. The Guilford Press. Golden TD Veiga JF Simsek Z Telecommuting's differential impact on work-family conflict: Is there no place like home? Journal of Applied Psychology 2006 91 1340 1350 10.1037/0021-9010.91.6.1340 17100488 Golden TD Veiga JF Dino RN The impact of professional isolation on teleworker job performance and turnover intentions: Does time spent teleworking, interacting face-to-face, or having access to communication-enhancing technology matter? Journal of Applied Psychology 2008 93 6 1412 1421 10.1037/a0012722 19025257 Hart PM Cooper CL Anderson N Ones DS Sinangil HK Viswesvaran C Occupational stress: Toward a more integrated framework Handbook of industrial, work, and organizational psychology 2001 Sage 93 114 Harvey P Harris KJ Frustration-based outcomes of entitlement and the influence of supervisor communication Human Relations 2010 63 11 1639 1660 10.1177/0018726710362923 Hawthorne G Measuring social isolation in older adults: Development and initial validation of the friendship scale Social Indicators Research 2006 77 521 548 10.1007/s11205-005-7746-y Hayes AF An index and test of linear moderated mediation Multivariate Behavioral Research 2015 50 1 1 22 10.1080/00273171.2014.962683 26609740 Hayes AF Introduction to mediation, moderation, and conditional process analysis: A regression-based approach 2017 2 Guilford Publications Hickman, A., & Saad, L. (2020). Reviewing remote work in the U.S. under COVID-19. Gallup. https://news.gallup.com/poll/311375/reviewing-remote-work-covid.aspx Hobfoll SE Conservation of resources. A new attempt at conceptualizing stress American Psychologist 1989 44 513 524 10.1037/0003-066X.44.3.513 2648906 Hobfoll SE Halbesleben J Neveu JP Westman M Conservation of resources in the organizational context: The reality of resources and their consequences Annual Review of Organizational Psychology and Organizational Behavior 2018 5 103 128 10.1146/annurev-orgpsych-032117-104640 Jimenez-Gomez C Sawhney G Albert KM Impact of COVID-19 on the applied behavior analysis workforce: Comparison across remote and nonremote workers Behavior Analysis in Practice 2021 10.1007/s40617-021-00625-0 34345376 Kahn, R. L., & Byosiere, P. (1992). Stress in organizations. In M. D. Dunnette & L. M. Hough (Eds.), Handbook of industrial and organiza- tional psychology (Vol. 3, pp. 571–650). Consulting Psychologists Press. Keller, A., Knight, C., & Parker, S. (2020). Boosting job performance when working from home: Four key strategies. Society for Industrial and Organizational Psychology. https://www.siop.org/Research-Publications/Items-of-Interest/ArtMID/19366/ArticleID/4623/Boosting-Job-Performance-When-Working-from-Home-Four-Key-Strategies Kernan MC Hanges PJ Survivor reactions to reorganization: Antecedents and consequences of procedural, interpersonal, and informational justice Journal of Applied Psychology 2002 87 5 916 928 10.1037/0021-9010.87.5.916 12395816 Kniffin, K. M., Narayanan, J., Anseel, F., Antonakis, J., Ashford, S. P., Bakker, A. B., ... & Vugt, M. V. (2021). COVID-19 and the workplace: Implications, issues, and insights for future research and action. American Psychologist, 76(1), 63. Kotsou I Nelis D Grégoire J Mikolajczak M Emotional plasticity: Conditions and effects of improving emotional competence in adulthood Journal of Applied Psychology 2011 96 4 827 10.1037/a0023047 21443316 Lind EA Van den Bos K When fairness works: Toward a general theory of uncertainty management Research in Organizational Behavior 2002 24 181 223 10.1016/S0191-3085(02)24006-X Little, T. D., Cunningham, W. A., Shahar, G., & Widaman, K. F. (2002). To parcel or not to parcel: Exploring the question, weighing the merits. Structural Equation Modeling, 9(2), 151–173. Mann, S., Varey, R., & Button, W. (2000). An exploration of the emotional impact of tele‐working via computer‐mediated communication. Journal of Managerial Psychology, 668–690.10.1108/02683940010378054 Marshall GW Michaels CE Mulki JP Workplace isolation: Exploring the construct and its measurement Psychology & Marketing 2007 24 195 223 10.1002/mar.20158 Matias T Dominski FH Marks DF Human needs in COVID-19 isolation Journal of Health Psychology 2020 25 7 871 882 10.1177/1359105320925149 32375564 Martin, B. H., & MacDonnell, R. (2012). Is telework effective for organizations? A meta‐analysis of empirical research on perceptions of telework and organizational outcomes. Management Research Review. MacCallum, R. C., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: the problem of capitalization on chance. Psychological Bulletin, 111(3), 490. McCloskey DW Igbaria M Does" out of sight" mean" out of mind"? An empirical investigation of the career advancement prospects of telecommuters Information Resources Management Journal 2003 16 19 34 10.4018/irmj.2003040102 Michel JS O’Neill SK Hartman P Lorys A Amazon’s Mechanical Turk as a viable source for organizational and occupational health research Occupational Health Science 2018 2 83 98 10.1007/s41542-017-0009-x Mulki JP Jaramillo F Workplace isolation: Salespeople and supervisors in USA The International Journal of Human Resource Management 2011 22 902 923 10.1080/09585192.2011.555133 Orhan MA Rijsman JB Van Dijk GM Invisible, therefore isolated: Comparative effects of team virtuality with task virtuality on workplace isolation and work outcomes Revista De Psicología Del Trabajo y De Las Organizaciones 2016 32 2 109 122 10.1016/j.rpto.2016.02.002 Parker SK Sprigg CA Minimizing strain and maximizing learning: The role of job demands, job control, and proactive personality Journal of Applied Psychology 1999 84 6 925 939 10.1037/0021-9010.84.6.925 10639910 Plantiveau C Dounavi K Virués-Ortega J High levels of burnout among early-career board-certified behavior analysts with low collegial support in the work environment European Journal of Behavior Analysis 2018 19 195 207 10.1080/15021149.2018.1438339 Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. Probst TM Sinclair RR Sears LE Gailey NJ Black KJ Cheung JH Economic stress and well-being: Does population health context matter? Journal of Applied Psychology 2018 103 9 959 10.1037/apl0000309 29733623 Sanders K Nguyen PT Bouckenooghe D Rafferty A Schwarz G Unraveling the what and how of organizational communication to employees during COVID-19 pandemic: Adopting an attributional lens The Journal of Applied Behavioral Science 2020 56 289 293 10.1177/0021886320937026 Sawhney G Jennings KS Britt TW Sliter MT Occupational stress and mental health symptoms: Examining the moderating effect of work recovery strategies in firefighters Journal of Occupational Health Psychology 2018 23 3 443 10.1037/ocp0000091 28604019 Stephens C Long N Communication with police supervisors and peers as a buffer of work-related traumatic stress Journal of Organizational Behavior 2000 21 407 424 10.1002/(SICI)1099-1379(200006)21:4<407::AID-JOB17>3.0.CO;2-N Tajfel H Turner JC Worchel S Austin WG The social identity theory of intergroup behavior Psychology of intergroup relations 1985 2 Nelson-Hall 7 24 Thoits, P. A. (1983). Multiple identities and psychological well-being: A reformulation and test of the social isolation hypothesis. American Sociological Review, 174–187. U.S. Bureau of Labor Statistics. (2019). Economic new release: Table 1. Workers who could work at home, did work at home, and were paid for work at home, by selected characteristics, averages for the period 2017–2018. https://www.bls.gov/news.release/flex2.t01.htm van der Meer TG Verhoeven P Beentjes HW Vliegenthart R Communication in times of crisis: The stakeholder relationship under pressure Public Relations Review 2017 43 426 440 10.1016/j.pubrev.2017.02.005 Vander Elst, T., Baillien, E., De Cuyper, N., & De Witte, H. (2010). The role of organizational communication and participation in reducing job insecurity and its negative association with work-related well-being. Economic and Industrial Democracy, 31, 249–264. http://journals.sagepub.com/loi/eida Vega RP Anderson AJ Kaplan SA A within-person examination of the effects of telework Journal of Business and Psychology 2015 30 2 313 323 10.1007/s10869-014-9359-4 Wang B Liu Y Qian J Parker SK Achieving effective remote working during the COVID-19 pandemic: A work design perspective Applied Psychology 2021 70 1 16 59 10.1111/apps.12290 33230359 Williams LJ Cote JA Buckley MR Lack of method variance in self-reported affect and perceptions at work: Reality or artifact? Journal of Applied Psychology 1989 74 462 468 10.1037/0021-9010.74.3.462 Zapf D Dormann C Frese M Longitudinal studies in organizational stress research: A review of the literature with reference to methodological issues Journal of Occupational Health Psychology 1996 1 145 169 10.1037/1076-8998.1.2.145 9547043	36465153	PMC9707423	NO-CC CODE	2022-12-01 23:20:23	no	Occup Health Sci. 2022 Nov 29;:1-22	utf-8	Occup Health Sci	2,022	10.1007/s41542-022-00133-9	oa_other
==== Front J Flow Chem J Flow Chem Journal of Flow Chemistry 2062-249X 2063-0212 Springer International Publishing Cham 247 10.1007/s41981-022-00247-9 Perspectives A perspective on automated advanced continuous flow manufacturing units for the upgrading of biobased chemicals toward pharmaceuticals http://orcid.org/0000-0002-3968-8506 Kaisin Geoffroy [email protected] 1Geoffroy Kaisin After a PhD in radiochemistry and organic chemistry at the Cyclotron Research Center at the University of Liège, Geoffroy joined a startup of the University, ANMI. As Head of Chemistry, he participated to the development of a patented cold kit formulation for the labelling of PSMA-11 with gallium-68. After the acquisition of ANMI by Telix Pharmaceuticals in 2018, his research focused on new methodologies for the labelling of biomolecules with radiometals. He is now R&D chemist and co-founder at SynLock where his research interests are aimed at the automation of organic synthesis. Bovy Loïc 2Loïc Bovy Loïc holds a MSc from the Université de Liège in chemical sciences (2021). He then joined the Center for Integrated Technology and Organic Synthesis (CiTOS) to conduct a PhD. His research focuses on the upgrading of biobased building blocks and their integration in active pharmaceutical ingredients by combining both batch and flow chemistry methodologies. Joyard Yoann 1Yoann Joyard Yoann graduated from INSA Rouen as PhD in organic chemistry in 2013. He focused on the development of tracers for targeting hypoxic tumors and developed new fluorination methodologies. He carried out postdoctoral research at King’s College London in the School of Biomedical Engineering and Imaging Sciences. He worked there on the development of bimodal probes for guided surgery. Yoann also worked as research engineer for two years in a company specialized in development of continuous-flow and batch process in chemistry, before joining Optimized Radiochemical Applications (ORA) in 2017 as R&D radiochemist. He is now R&D chemist and co-founder at SynLock where his research interests are aimed at the automation of organic synthesis. Maindron Nicolas 1Nicolas Maindron Nicolas received his PhD from the University of Rouen, France, in 2012, in the field of the lanthanide-based luminescence probes. He then completed two postdoctoral positions. One at the University of Bourgogne, Dijon, France, where he designed multifunctional imaging probes and a second one at MNI (now Invicro) at New Haven, USA, where he started to work in the area of radiochemistry. He joined Optimized Radiochemical Applications (ORA) in 2016 as a R&D radiochemist. He is now R&D chemist and co-founder at SynLock where his research interests are aimed at the automation of organic synthesis. Tadino Vincent 1Vincent Tadino Vincent received his PhD, in Organic Chemistry at the University of Liège, Belgium, and completed a PostDoc research in Organic Chemistry at ISMRA in Caen, France. Vincent founded Optimized Radiochemical Applications (ORA) in 2006 and currently serves as its President and CTO. Prior to founding ORA, Vincent served as project chief, radiochemistry developer, and quality manager for 7 years in different companies active in PET radiopharmaceutical equipment and production, both in Europe and the USA. He holds, as inventor and/or as co-inventor, eleven patents and is the author of several abstracts/presentations in international congresses. He is now President and co-founder at SynLock where his research interests are aimed at theautomation of organic synthesis. Monbaliu Jean-Christophe M. 2Jean-Christophe M. Monbaliu is currently Associate Professor at the University of Liège (Belgium) and serves as Associate Editor of the Journal of Flow Chemistry. He is heading the Center for Integrated Technology and Organic Synthesis (CiTOS, www.citos.uliege.be), the first European Corning® Advanced-Flow™ reactor qualified lab. Research interests at CiTOS revolve around synthetic organic chemistry but are multidisciplinary in essence and aim at (a) designing cheaper and more efficient routes for the preparation of high value-added chemicals such as active pharmaceutical ingredients; (b) accelerating the transition from petrobased to biobased strategies and (c) developing efficient processes with a lower environmental impact. 1 SynLock SRL, Rue de la Vieille Sambre 153, B-5190 Jemeppe-sur-Sambre, Belgium 2 grid.4861.b 0000 0001 0805 7253 Center for Integrated Technology and Organic Synthesis, Research Unit MolSys, University of Liège, B-4000 Liège, Sart Tilman, Belgium 29 11 2022 115 29 8 2022 4 11 2022 © Akadémiai Kiadó 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Biomass is a renewable, almost infinite reservoir of a large diversity of highly functionalized chemicals. The conversion of biomass toward biobased platform molecules through biorefineries generally still lacks economic viability. Profitability could be enhanced through the development of new market opportunities for these biobased platform chemicals. The fine chemical industry, and more specifically the manufacturing of pharmaceuticals is one of the sectors bearing significant potential for these biobased building blocks to rapidly emerge and make a difference. There are, however, still many challenges to be dealt with before this market can thrive. Continuous flow technology and its integration for the upgrading of biobased platform molecules for the manufacturing of pharmaceuticals is foreseen as a game-changer. This perspective reflects on the main challenges relative to chemical, process, regulatory and supply chain-related burdens still to be addressed. The implementation of integrated continuous flow processes and their automation into modular units will help for tackling with these challenges. Graphical abstract Keywords Biobased platforms Flow chemistry Active Pharmaceutical Ingredient Automation ==== Body pmcIntroduction To ensure optimal economic sustainability, biorefineries should be designed as an integrated production system able to produce a wide range of versatile and valuable outputs starting from raw biomass. The two main categories of outputs are energy (under the form of fuel and heat) and chemicals intended for various applications including the food industry, fragrances, materials, bulk, pharmaceuticals and fine chemicals [1]. Biorefining processes can be very complex, and, quite unexpectedly, are often associated with much less favorable environmental metrics and economics than the refining of crude oil. For compounds also found in the conventional fossil fuel supply chain, the economics of the biomass-based process must be thoroughly optimized in order to remain competitive [2]. Primary biomass-derived (or biobased) platform chemicals are compounds obtained after the first processing step of biomass [2]. Each of these compounds can be further transformed toward a number of secondary biobased building blocks [2]. Among the various chemicals that can be sourced from processing biomass, a rather limited range have attracted considerable attention with huge potential, emerging or established markets. These biobased platforms chemicals are also classified into three categories: (a) drop-in platform chemicals, (b) smart drop-in platform chemicals and (c) dedicated platform chemicals [3]. The first category, drop-in platform chemicals, are commodity chemicals identical to an existing fossil sourced counterpart (e.g. ethylene glycol). Smart drop-in chemicals (e.g., biobased glycidol produced from glycerol 1), are commodities the biosourcing of which presents significant advantages over the petrobased supply. For example, their production process is less energy-intensive, shorter, less complex, relies on production processes involving milder conditions and lower environmental footprint chemicals or produces less harmful by-products. The last category, dedicated biobased chemicals, concerns compounds that have no direct counterpart in the petrochemical chain of value and therefore open new potential markets and products (e.g. 2,5-furanedicarboxylic acid 12) [3]. A short list of the most potent biobased platform molecules has been issued by the US Department of Energy (DoE) in 2004, emphasizing the tremendous industrial potential for 14 compounds, although this report is widely known as the “DoE Top 10” (see Fig. 1) [4]. Though the DoE list was updated in 2010 and 2016 with the addition of a few additional structures [5, 6], it is quite surprising that despite such a huge diversity and complexity, biorefineries only converge toward a very limited number of biobased platforms. While these biobased platforms are originally intended to replace petrobased building blocks in bulk and material chemistry (drop-in, smart drop-in and dedicated platforms), momentum is gained for applications targeting markets with lower volumes but higher added value, such as for the manufacturing of pharmaceuticals. Fig. 1 Extended list of top biobased platform molecules [4] as C3-C6 biobased building blocks. By order of appearance: (C3): glycerol 1, 3-hydroxypropionic acid 2; (C4): malic acid 3, aspartic acid 4, 3-hydroxybutyrolactone 5, succinic acid 6, fumaric acid 7; (C5): xylitol 8, levulinic acid 9, glutamic acid 10, itaconic acid 11; (C6): 2,5-furandicarboxylic acid 12, sorbitol 13, glucaric acid 14 The strength of the conventional fossil fuel approach relies mostly on an overall cost effectiveness associated with decades of process engineering. However, the chemicals obtained by fossil fuel exploitation are mainly hydrocarbon backbones with limited functionalizations. Heteroelements, such as oxygen and nitrogen, which are ubiquitous to the western pharmacopoeia, are indeed very rare in petrobased building blocks. They must be therefore inserted by selective chemical transformations prior to their end-uses. On the other hand, biobased platforms are by contrast very rich in heteroatoms (mostly oxygen) and therefore impose a reverse scheme with the necessary removal of heteroatoms (X = O, N) to increase their inherent C/X ratio. Primary biobased building blocks are obtained through fractionation, depolymerization and defunctionnalization of the biomass. For instance, lignocellulosic biomass is mainly composed of lignin, hemicellulose and cellulose. Their hydrolysis yields a wide array of O-containing compounds [7]. Chitin obtained from crustacean and insect shells is rich in N-containing compounds. Algae and marine biomass provide a small reservoir of S-containing molecules while an abundant source of P-containing organic backbones is still missing [8]. Concerning lignin, it has now been demonstrated that the main constituents are p-coumaryl, coniferyl and sinapyl alcohols featuring phenol, alkenes and allylic alcohol scaffolds. The lignin depolymerization method, both under batch or continuous flow conditions [9], as well as lignin’s origin (hardwood, softwood or herbaceous crops) affects the proportion of these three constituents and their derivatives, opening the doors to a variety of transformations [10, 11]. Among those transformations, Antoniotti and coworkers reported in 2007 both batch and continuous flow conditions procedures allowing the selective oxidation of a broad scope of alcohols, among which coniferyl alcohol derivatives, into the corresponding aldehydes or ketones through aerobic oxidation catalyzed by heterogenous gold nanoparticles [12]. Furthermore, the synthesis of promising inhibitors to treat cardiovascular diseases was performed by Rajagopaland coworkers in 2020. This was done through the phenol moiety of coniferyl alcohol derivatives or ferulic acid, its carboxylic acid counterparts, under the form of alpha-beta unsaturated amides, hydrazines, esters and hydroxamic acids [13]. Additionally, structure-activity relationships of the three phenolic derivatives have been investigated in 2001 by Lewis and coworkers under the form of flavonolignan and flavone scaffolds with the aim of treating multidrug resistant Staphylococcus Aureus [14]. Chitin is another complex biomacromolecule featuring a repeating N-acetyl-d-glucosamine (NAG) 15 unit (see Fig. 2), and bears great interest considering its natural nitrogen content. 15 is a key intermediate to a variety of other prominent molecules such as 3-acetamido-5-acetylfuran 16, which was Sperry’s center of interest in 2018 [15]. Indeed, the authors relied on 16 to synthesize Proximicin A 17, an alkaloid known for its anticancer properties. Additionally, NAG can be transformed into chiral 3,6-anhydro-GNF 18 and 3,6-anhydro-MNF 20, two bicyclic compounds, which are key intermediates in the synthetic route proposed by Usui and colleagues in 2010 [16]. These are engaged in the synthesis of furanodictines A 19 and B 21 which both display important neuronal differentiation properties in rats. Fig. 2 N-acetyl-d-glucosamine derivatives and some of their applications as API or API precursors [15, 16]. N-acetyl-d-glucosamine 15, 3-acetamido-5-acetylfuran 16, proximicin A 17, 3,6-anhydro-GNF 18, furanodictines A 19, 3,6-anhydro-MNF 20 and furanodictines B 21 Alongside their structural diversity, a wide range of optically active building blocks can be sourced from biobased materials. For instance, isosorbide 5-mononitrate 22 is a clinically favored substitute for isosorbide-dinitrate 23, a compound included in the World Health Organization (WHO) list of essential medicines and used as a medication against heart diseases and blood pressure issues (see Fig. 3) [17]. Fig. 3 Isorbide derivatives and continuous flow synthesis of exo-MAI [18]. Bzq: 1,4-Benzoquinone Compound 23 features an isosorbide 24 scaffold, a biobased diol building block, which is efficiently obtained by a sequence of reduction/dehydration of glucose through the intermediate formation of sorbitol. The chiral nature of isosorbide gives diverging chemical reactivity to both hydroxyl groups, although chemoselectivity remains a notable challenge. Isosorbide 5-mononitrate 22, the isomer of choice in the medical field, is obtained by nitrating the free endo-5-OH function after a selective protection through the exo-2-OH acylation. Therefore, finding a streamlined procedure towards exo-2-OH acylated products is highly relevant to the synthesis of 22. Among the most notable developments in this context, Massi and coworkers reported a tunable procedure to selectively acylate isosorbide with aldehydes through heterogeneous Nheterocyclic carbene (NHC) catalysis in batch. In a subsequent step, the authors reported the selective exo-acylation (exo-MAI) under continuous flow conditions [18]. A scope of 5 aldehydes with a limited range of molecular diversity was screened as mild acylating agents, among which, biobased furfural gave excellent results. Conversions ranging from 90 to > 95% with exo/endo ratios in the range of 4.0 to 5.3 were obtained throughout the scope. The robustness of the process was assessed with a 110 h long run, which gave consistent and steady conversion and selectivity with model substrate benzaldehyde. The authors argued their process relied on the main assets of flow chemistry to ensure strict local stoichiometry, leading to high exo/endo ratio and excellent conversion towards a key intermediate of 22. Recycling and processing of vegetable oils are also a source of interesting chemicals. Main outputs are transesterified fatty acids (typically fatty acid methyl or ethyl esters, namely, FAME or FAEE) and glycerol 1. FAME/FAEE are primarily used in the biodiesel industry and the latter is a versatile C3 platform. In 2019, Monbaliu and coworkers developed two separate compact and efficient fluidic modules allowing the upgrade of glycerol 1 into Active Pharmaceutical Ingredients (APIs) bearing a β-aminoalcohol scaffold [19]. This was achieved by activating neat glycerol 1 into its oxiranes counterparts (glycidol 25 and epichlorohydrin 26) through a chlorination/dechlorination sequence in flow (chlorination with 36 wt% aqueous HCl and dechlorination/epoxidation with aqueous NaOH). The product mixture was then subjected to a membrane liquid/liquid separation yielding epichlorohydrin 26 (in MTBE) and aqueous glycidol 25. MTBE was evaporated off-line and the resulting pure epichlorohydrin was fed to the second reactor module (see Fig. 4a). Fig. 4 End-to-end glycerol 1 upgrading towards β-aminoalcohol APIs, including propanolol 28a, naftopidil 28b and alprenolol 28c under continuous flow conditions [19]. a Upstream activation of biobased glycerol 1 into glycidol 25 and epichlorohydrin 26. b General two-step strategy (glycidol ether synthesis – aminolysis) for converting biobased epichlorohydrin 26 toward a selected range of β-aminoalcohol APIs (28a, 28b, 28c) The authors then used a glycidyl ether synthesis/aminolysis sequence under flow conditions to install the β-aminoalcohol scaffold from biobased epichlorohydrin (see general strategy and scope in Fig. 4b). In the first step, Williamson etherification between epichlorohydrin and a feed containing naphtol and t-BuOK was performed in 20 min at 70 °C. The coil effluent was subsequently engaged in the ring opening aminolysis of the corresponding glycidyl ether with an appropriate neat amine to yield a small selection of APIs, including propanolol 28a, naftopidil 28b and alprenolol 28c in good isolated yields. These selected examples show that renewable platforms are of great interest as chemical precursors for accessing APIs and to further contribute in transitioning from exclusively petrobased production schemes to biobased alternatives. Though momentum is gained in the recent literature, it is worth mentioning that there is still a lack of suitable metrics to assess the incorporation of biobased materials into high valued-added scaffolds such as APIs [17]. Such metrics could potentially complement the conventional process efficiency and environmental impact metrics, such as the Process Mass Intensity, the Atom Economy and the Environmental Factor [20] alongside the green solvent selection guides [21]. This perspective emphasizes the main challenges associated with an ambitious shift of paradigm in the pharmaceutical industry with transitioning from exclusively petrobased production schemes to increasingly biobased processes and summarizes the main assets of automated continuous flow production schemes for tackling these challenges [20–23]. Challenges Chemical and processing challenges The main technological challenges are related to production processes (biorefining) allowing the preparation of fine chemicals from biomass. When taking the full life-cycle into account [24, 25], some biorefinery processes have a detrimental environmental impact, are very expensive or are not energy efficient [26]. The processing of the wastes and byproducts can be tedious and generates additional costs [27]. For example, the processing of crustacean shells is known to generate a huge amount of chemical waste while it provides valuable N-containing building blocks [8].There is therefore still much room for improving the overall environmental footprint and the atom economy of such processes. As a matter of fact, many of the most promising biorefinery processes are still associated with intermediate Technology Readiness Levels (TRLs) [28]. Conversion processes must also be developed with improved versatility. In an ideal case scenario, biorefining should be amenable to different substrates to minimize the impact of the seasonality of some raw material causing significant production delays and shortages due to the lack of supply when the harvesting season is finished. To our knowledge, no study evaluates the impact of the nature of the initial biomass nor its harvest region on the impurity profile of the refined chemical platform. Differences could have consequences from a regulatory point of view as described in the subsequent section. One of the major challenges related to chemistry and process engineering relates to the inherent features of biobased platform molecules which bear a very high oxygen content by contrast to typical petrobased building blocks [20, 29]. This inherent major difference has a profound impact on the way chemists and chemical engineers have to design new dedicated chemistries and process conditions. A conventional petrobased production scheme is indeed engineered to access molecular diversity from hydrocarbon backbones, hence relying on specific reaction conditions for incorporating heteroelements and hence access function and added-value [20]. Emerging processes feeding on biobased platforms must be tailored to lower their high O-content, hence requiring new catalysts, chemistries and process conditions [20, 28]. These new chemistries and process conditions must be compatible with variable purity profiles (see comment below on the geographical/seasonal variability of biobased platforms) and, ideally, with unrefined biobased platforms. In the current state of the art, many of these processes are still associated with depleting metal catalysts, additives, and solvents with a significant environmental impact [29]. It is therefore critical to allocate significant research efforts to rely on low environmental footprint catalysts, solvents, additives and process technologies, especially when the increasing complexity of some biobased platforms comes along with overwhelming competing reactions [29]. Consequently, the current TRL for such biobased processes is typically around TRL 4–5 [20, 28], thus still far from any implementation at commercial scale. Solving the main chemical and processing challenges associated with the upgrading of biobased platforms is expected to stimulate creativity of Chemistry and Chemical Engineering communities for the next decades. Regulatory challenges On a broader note than just the manufacturing of APIs, the current context within the dominant economic areas is very favorable with many governmental incentives thriving with a subsequent thrust toward biobased processes. For instance in the European Union (EU), Bioeconomy has rapidly turned into a global strategy with significant funding schemes and a stable regulatory environment [30, 31]. RoadToBio (within the H2020 program) aimed at replacing 25% of the total volume of fossil-based organic chemicals with an alternative biobased feedstock by the end of 2030. In 2021, a new program (Horizon Europe) took over to stimulate innovation, to sustain the green and digital transitions, and to strengthen EU’s leadership. Similar initiatives are echoed in the US, with directives on advancing biotechnology and biomanufacturing innovation for a sustainable, safe, and secure American Bioeconomy [32, 33]. As this perspective focuses on APIs synthesis, a brief overview of the specific regulatory challenges associated with the use of biobased materials in conjunction with continuous flow manufacturing is mandatory. Regulatory agencies are aware of the importance of continuous flow manufacturing as a breakthrough technology for the future of the pharmaceutical industry. The US Food and Drug Administration (FDA) stated that “Continuous manufacturing is an emerging technology that can enable pharmaceutical modernization and deliver potential benefits to both industry and patients” as an introduction to their Draft Guidance for Industry entitled “Quality Considerations for Continuous Manufacturing” issued in February 2019 [34]. The International Council for Harmonisation (ICH) issued a draft of the guidance Q13 on “Continuous Manufacturing of Drug Substances and Drug Products” in July 2021 [35]. It is expected the European Medicine Agency (EMA) will shortly align its objectives on FDA’s. Altering synthetic pathway or changing raw material specifications (though still using the same chemical structure) for approved drugs or investigational new drugs are usually associated with a huge regulatory impact, especially if performed after the launch of a phase-III clinical study. As identical chemical entities can be produced from different sources of biomass, a risk assessment must be carried out for each of them. The impact on the product quality attributes must be evaluated. If the risk of change is low, in vitro equivalence demonstration is usually considered as sufficient. High change risk could require additional in vivo bioequivalence studies, which comes with significant additional costs and administrative burden [34]. The transposition of a batch process under continuous flow conditions also implies setting up a rigorous control strategy. Manufacturers must demonstrate that the quality of the finished product, even according to an equivalent combination of chemicals; obtained by continuous manufacturing is similar to the batch one. The system must be capable of real time monitoring with process analytical technologies (PAT) [36, 37] to ensure that it remains under a state of control during the whole production campaign, hence allowing for quality control [36, 37]. The software overlay should allow them to easily access, process and securely archive the acquired data at any time. Supply chain challenges Despite the increasing costs of fossil fuels, the biorefinery industry still lacks economic viability for many biobased platforms. This problem is identified as the main non-technical barrier to the deployment of biomass-based chemicals, especially in Europe where labor-related and energy costs are higher than anywhere else [29, 38]. In addition, there is still a need to further expand the array of secondary building blocks accessible after the transformation of the primary ones coming from the fragmentation of biomass. These high value-added products must compensate for the low incomes generated by bulk chemicals while remaining competitive towards conventional fossil fuel-sourced products [2, 39]. In order to accommodate changing input materials and to adapt to the markets demand, a change in the production paradigm is required [40]. The large-scale, delocalized and centralized units must be replaced with a network of smaller production facilities. They must be modular by design to ease the transition between different starting materials and/or finished products. The decentralization of production facilities will reduce transport-related costs and will better accommodate the associated geographical and seasonal constraints. As some biomass sources are seasonal and perishable, their processing must be done as close to the harvesting site as possible. Besides, biomass sourced raw materials are loaded with water and minerals, which come with a deleterious impact on the transportation costs. Moreover, some mineral and organic components must be moved back to the field after the processing to avoid soil depletion [1]. To minimize the need for storage capabilities for the intermediates, raw materials should be processed as high as possible in the valorization scale. Only high value-added intermediates or finished products are worth storing or transporting to another transformation unit. These inherent features of biorefining imply the development of a robust and flexible logistical system [38]. The implementation of this disruptive production paradigm also requires modular and transportable transformation plants that are environmentally and financially efficient. In conclusion, synergies should be created between the different actors of the supply chain through collaborative research programs for developing an integrated value chain for the different products obtained after treatment of the biomass. This newly developed supply chain brings together several aspects starting from biomass production and terminating to the market introduction and commercialization of high value-added products. This will stimulate further R&D efforts to develop efficient conversion and valorization process, and will spark the creativity for accessing new products. Perspectives Flow chemistry The advantages of continuous flow production technology come with invaluable assets for the development of intensified processes, with reduced environmental impact and for the development of a decentralized production network [21, 41, 42]. However, a simple transposition of a batch process to continuous conditions does not guarantee better economics and sustainability metrics; it should always come with a careful reoptimization and redefinition of the chemistry and conditions to fully benefit from the assets of flow technology [23, 43, 44]. For instance, in 2021, Kappe and coworkers published a groundbreaking approach for the synthesis of an important Remdesivir intermediate 33, the first FDA and EMA approved drug against the late COVID-19 outburst (see Fig. 5) [45]. The high demand for this drug during the pandemic highlighted the need for faster procedures towards some intermediates. The paper describes flash chemistry under continuous flow conditions, concatenating four consecutive steps within a total residence time of 8 s with an excellent 60% yield involving a biobased ribose derivative 30. Fig. 5 Flash synthesis of remdesevir intermediate 33 under continuous flow conditions [45] The most remarkable feature of this highly efficient process is obviously the extremely short combined residence time which is possible thanks to the reactivity of the engaged species. However, this efficiency and speed of execution are the result of very specific and strict conditions among which, temperature, reaction time and local stoichiometry as well as an efficient mixing and heat transfer which are some of the most attractive features of microfluidic technology. The space-time yield (STY) of the process was determined to be an astonishingly high 10.4 kg∙L1∙h1 despite the very modest size of the reactor [45]. This approach is a way to reduce the production costs by optimizing the productivity of the installation while keeping a low spatial footprint. In an industrial environment, continuous flow also provides a good answer to safety issues [46]. For example, diazo anhydrides are well-known explosives and highly carcinogenic agents. Their in situ generation under continuous flow conditions was demonstrated by Kappe and coworkers in 2015 as an elegant and efficient solution to alleviate acute hazards upon stockpiling or contact with the operator. A photochemical microfluidic reactor was developed, allowing to safely generate, handle and react this particularly hazardous substance family with non-activated arenes towards bi(hetero)aryl derivatives (see Fig. 6). Fig. 6 Chemical generator for diazo anhydrides under continuous flow conditions [47]. a General strategy for the photochemical synthesis of bi(hetero)aryl derivatives. b Application of diazo anhydrides for the preparation of dantrolene 39 (myorelaxant) and canagliflozin 41 (antidiabetic) This method supports the arylation of biobased furfural 43 with 4-nitroaniline, providing a key intermediate towards dantrolene 39, an API prescribed as myorelaxant. This arylation procedure is also amenable to the coupling of thiophene and 4-fluoroaniline towards a canagliflozin 41 (antidiabetic) intermediate (see Fig. 6). The only waste materials resulting from this elegant photochemical coupling are N2, H2O and t-BuOH, while hazardous and transient diazo anhydride has been safely generated and consumed [47]. Colacino and coworkers disclosed a solvent free method for the synthesis of dantrolene and other hydrazone-based API using continuous mechanochemistry [48]. The conversion of batch mechanochemical processes to the continuous flow paradigm is another promising emerging research area. Green-minded process chemists are highly interested in ball milling as it eliminates the need for solvents [49]. This has a direct positive impact on reducing the environmental and financial footprint of the process. Rotating or mixing mills are replaced by twin screw extruders to allows the continuous mechanochemical processing of the reagents [48, 49]. Li and coworkers showed that ball milling was suitable for the upgrade of biomass-derived materials [50]. The authors demonstrated the condensation of furfural with five different ketones, which led to the desired adducts with low to excellent yields ranging from 44.0% (MIBK as ketone counterpart) to 99.8% (cyclopentanone as ketone counterpart) using CaO as catalyst under mild process conditions (40 °C) [50]. Electrochemistry has been part of the organic chemist toolkit for numerous years now. Unfortunately, it often suffers from a series of drawbacks in batch which lead to long reaction times as well as intricate conditions and side-products. The main drawbacks of electrochemistry can be in part mitigated under continuous flow conditions, as highlighted by seminal contributions from the Noël and Kappe groups, including for the preparation of APIs and relevant scaffolds [51–58]. An exemple from Noël’s group is illustrated hereafter, where the authors provided an unprecedented continuous electrochemical protocol for upgrading biobased furfural 43 simultaneously into three industrially relevant molecules: 2(5H)-furanone 44 (polymer industry and γ-butyrolactone precursor), furfuryl alcohol 45 (resins) and hydrofuroin 46 (jet-fuel precursor) (see Fig. 7) [57, 58]. Fig. 7 Electrochemical upgrading of biobased chemicals under continuous flow conditions [57, 58] The furfural redox reaction was performed in a divided-cell flow microreactor with a key interesting aspect: varying the applied voltage (2.4 or 2.9 V) leads to different proportions of 2(5 H)-furanone 44 (46% vs. 77%) in the cathodic cell outlet, furfuryl alcohol 45 (58% vs. 20%) and hydrofuroin 46 (29% vs. 71%) in the anodic cell outlet (see Fig. 7). In other words, a tunable reactor was devised, allowing to choose which biobased derived molecules could be synthesized in larger proportions. Unfortunately, the reaction was not amenable to a 100% selectivity between furfuryl alcohol 45 or hydrofuroin 46 [57]. In another article, the same group also demonstrated the suitability of flow electrochemistry for the preparation of suitable scaffolds for APIs with C-N cross-coupling reaction between azole derivatives and arenes. The electron-driven reaction supports a scope of both azole derivatives (10 examples) and arene substrates (11 examples) with yields ranging from 20 to 98% despite a very short 10 min residence time at room temperature in the flow reactor with the only by-product of the reaction being dihydrogen [58]. In addition to having positive effects on the efficiency of electrochemically driven reactions, microfluidic reactors offer practical solutions to similar issues encountered while performing photochemical reactions. The synergistic combination of continuous flow and photochemistry was vastly documented by the Kappe and Noël groups. For instance, in a study targeting the upgrading of 5-hydroxymethyl furfural 47, Kappe and coworkers developed a photochemical flow process for the singlet oxygen-mediated preparation of butanolide 48 (see Fig. 8a) [59]. Butenolide 48 and its derivatives hold a significant role as polyester precursors while still bearing an alkene moiety, allowing for further functionalization of either the monomer or polymer. The process takes advantage of in situ generated singlet oxygen through rose bengal photo excitation to safely and neatly oxidize 47 at low temperature. A 0.5 mol% catalyst loading sufficed to activate the slight excess of oxygen into its highly oxidizing singlet state. The efficiency of the reaction conditions was then assessed by applying them to four additional derivatives bearing ester, methyl, succinimide or acetal functions, with yields ranging from poor for the acetal-bearing substrate (< 20%) to excellent (93%). Another photochemical singlet oxygen generator was reported a few years later by Monbaliu and colleagues for the preparation of methionine sulfoxide from natural amino acid methionine [61]. Fig. 8 Biobased building blocks photochemical upgrading into polymer precursors [59, 60]. a Singlet oxygen generator for the preparation of butenolide 48 [46]. b Upgrading of fumaric and itaconic acids under continuous flow photochemical conditions In 2017, Monbaliu and coworkers disclosed the photochemically-driven upgrade of plaftorms itaconic 11 and fumaric 7 acids, hence providing another illustration of the intrinsic advantages of photoflow chemistry [60]. This novel process relied on 365 nm LEDs to photochemically activate benzophenone, a radical initiator. This allows the addition of alcohol-derived radicals to the alkene substrates, forming substituted γ-butyrolactones 49 and 50, respectively (see Fig. 8b). In this process, isopropanol serves both as reagent and solvent for benzophenone and the substrates, lowering the overall footprint of the reaction. The reaction was quickly optimized by relying on in-line NMR analysis. With ideal conditions in hand, methanol and cyclohexanol were tested both for fumaric and itaconic acid in place of isopropanol. The reaction was then swiftly scaled up from a microfluidic scale to a mesofluidic Corning® Advanced-Flow™ G1 Photo Reactor resulting in an 83 g/day productivity on the model substrate (fumaric acid 7 and isopropanol). A small scope of γ-butyrolactones, including unique spiro derivatives, was documented [60]. In both electro and photochemical cases, the much larger surface to volume ratio and shorter residence time, are often associated with a significant reduction in side products. The improved yields can be ascribed to microfluidic reactor architectures; narrow channels permitting a more efficient light or current transmission to the reaction medium. Additionally, less electrolytes or photocatalysts are required and higher surface-to volume ratio ensured faster reaction rates too. Despite showing great potential for synthetic application, electrochemistry under continuous flow conditions is still a “niche” discipline, with major challenges associated to scalability yet to be addressed. As flow processes are usually faster to upscale than their batch counterparts, early R&D development in flow are quickly transposed to commercial scales, hence coming with shorter time-to-market. With an overall lower footprint than batch reactors, flow reactors also allow the development of compact production units. These production units can be adapted to sudden change in demand, either with numbering-up or scaling-out strategies with production volume easily tunable. This property can be foreseen as paramount for deploying decentralized and/or mobile production units. In 2016, Jensen, Jamison and coworkers reported the development of a compact, transportable and fully modular micro-fluidic setup for API manufacturing. Building upon a long history of impactful and groundbreaking technologies for the continuous flow manufacturing of APIs in low footprints, mobile setups, the MIT team pushed further the miniaturization of an end-to-end manufacturing unit combining upstream (chemical transformations, intermediates workups and extractions) with advanced downstream operations (purification and liquid formulation) [62]. This was made possible by the elaboration of individual multipurpose modules which could be connected based on the synthesized API. Coils of varying volumes, in-line separation units, pumps, gravity separation units as well as an in-line IR spectrometer and packed-bed columns constituted the functional blocks allowing the complete manufacturing of 4 different APIs. The coordination of the high number of pumps, IR and purification steps were made possible through automation. It means that a single operator can have a total control over the production of these four molecules, reducing the need for an abundant workforce. Additionally, concentrated or neat feedstocks were used to minimize solvent waste and maximize productivity [62]. Devising and relying on independent yet compatible modules opened the doors to the on-demand synthesis of several primordial pharmaceuticals. This modularity was taken advantage of to engineer a compact and portable (fridge-sized) mini-API factory, rendering this a potentially highly efficient tool for local drug delivery. In the original prototype, 4 APIs with different structures and pharmacological profiles were obtained with output ranging from 810 to 4500 doses per day. The 4 APIs included diphenhydramine hydrochloride (an antihistamine compound), lidocaine hydrochloride (an anaesthetic and antiarrhythmic compound), diazepam (a nervous system depressant) and fluoxetine (an antidepressant). Most notably, the prototype enabled a swift reconfiguration to swap from an API to another, in less than 30 min. In a further attempt to broaden the utility of such an approach, an additional series of 4 APIs was prepared in an updated prototype [63]. More recently, the same team combined advanced automation, robotics and artificial intelligence in a robotic platform dedicated to flow synthesis [64]. This innovative work is the first to propose a solution combining computer-aided synthesis planning (CASP), expert refined chemical recipe generation, automatic assembly of the fluidic setup by a robotic arm. The assembled system is then used to carry on the synthesis. The robotic arm can perform an adaptation of the setup while the synthesis is running. For example, changes of feedstock allow to rapidly access molecular diversity. Their proof-of-concept experiment consists of the automated synthesis of fifteen APIs or pharmaceutical intermediates. The system generated synthetic routes for each compound. As all the synthesis routes are already described in the literature, the software was not allowed to use existing pathways. Then, the automated robotic arm had to construct nine different fluidic setups as some synthesis shared common apparatus and feedstocks. The recipe generation for each step was the only human intervention in the whole process as existing databases do not contain enough information to allow using a data-driven automatic generator. The main challenge relied in the translation of batch conditions into continuous flow protocols. The system carried out automatically the synthesis of the targets compounds. For example, it produced aspirin with 91% yield and a productivity of 1.72 g/h; diazepam with 75% yield and a productivity of 638 mg/h. In order to demonstrate the advantages of fluidic path rerouting by the robotic arm during a synthesis, a scope of five different ACE inhibitors based on the quinapril scaffold was prepared. The full library was produced in 68 h with productivity ranging between 342 and 459 mg/h. Another library of four celecoxib analogs was completed in 28 h with similar productivities. Automation A sustained effort towards automation of the production unit is mandatory to reduce the cost induced by the operation of smaller production units. Moreover, for continuous manufacturing, automated production mode should ease keeping processes under a control state and reduce transient states leading to the mandatory diversion of out of specification materials. Other academic group are active in the field of organic synthesis automation. We already discussed of the work at MIT by Jensen, Jamison and coworkers. The group of Ley developed an automated system mixing continuous and batch reactors in the same integrated system controlled by a web-based interface. They demonstrated such concept in 2016 by synthesizing 5-methyl-4-propylthiophene-2-carboxylic acid, a precursor for the cancer drug candidate AZ82. For the needs of the synthetic pathway, they had to develop a glass reactor that can be temperature-regulated from − 70 °C to + 150 °C. The three-layer jacket enhanced the heat transfer and the thermal control of the reaction media, which are often ineffective using classical oil bath as a heat source. The web-based software allowed to control the whole system remotely, but also to receive analytical data in real time. The fully telescoped three-step synthesis gave the expected product with an overall yield of 30%. The fully manual procedure occurred with a slightly lower yield of 27% [65]. The modular software package they developed is also capable of multidimensional optimization. As a proof-of-concept, they made the five-dimensional optimization of an Appel reaction. The software made its self-optimization using the feedback provided by an IR spectrometer and mass spectrometry data. The software was able to find the optimal value of five experimental parameters after 30 experiments performed autonomously over 10 h [66]. In-line quality control devices and robust process analytical technology (PAT) are mandatory to comply with the regulatory obligations linked to the continuous manufacturing of APIs. The direct integration of PAT data into an integrated control software is sometimes problematic due to proprietary drivers or communication protocols. This year, Kappe and coworkers used a system composed of a thermoregulated microreactor linked to an inline NMR and an inline FTIR apparatus to collect real-time measurements the reaction media. Those data can then be processed with an appropriate chemometric model to optimize up to 7 reaction variables linked to a two-step process without any human intervention. The use of such automated process is not always possible at early stages of the development of pharmaceuticals as the quantity of precursor needed could be the limiting factor. For example, optimization of the considered two-step process required 14.3 g/h of starting material, meaning a total of 371.8 g for the 85 iterations performed over 26 h [67]. Cronin and coworkers unveiled their Chemputer in 2019. This platform is the combination of a modular robotic platform with an intuitive software overlay. The hardware part consists in modules dedicated to the four key steps of a chemical synthesis: reaction, workup, isolation and the subsequent purification. The modules are interconnected through a fluidic backbone consisting of pumps and valves allowing the transfer of chemicals between them. Thanks to a washing system, the system makes multisteps synthesis possible. As a proof of concept, they performed the autonomous synthesis of the antihistamine and mild sleep aid diphenydramine hydrochloride, the anticonvulsant rufinamide and sildenafil used to treat erectile dysfunction. The modules used were a reaction flask, a temperature regulated filtration module, a liquid-liquid separation module and a liquid evaporation module. This system, costing less than 10,000 USD, was able to produce the three APIs with yields comparable to the one obtained after a manual synthesis. In order to program the synthesis unit, they developed a language allowing the translation of physical operations performed by a trained chemist to machine-readable low-level instructions through their “Chempiler”. Operations are described by the user using a Chemical Description Language (ΧDL) allowing its use by staff without any programming skills through the user-friendly “Chemical Development Environment” (ChemIDE) interface [68]. They pushed the automation one step further by developing “Synthreader”, a program based on natural language processing technology that translates, often ambiguous, literature protocol into ΧDL instructions. This program automatically splits the protocols into a list of actions, extract the relevant process and reagent information and then translate it into unambiguous ΧDL formatted sentences. The obtained ΧDL file is then compiled to be used on the user customized setup [69]. While usable in the academic environment, these systems are not suited for an industrial use. Any commercial equipment used in the industry and especially in the pharmaceutical industry must be supplied with a sufficient documentary package to allow its qualification and validation prior use. These requirements apply for the software as well. Moreover, devices should be designed to allow an efficient and easy servicing in order to minimize the downtime due to impromptus failures or planned maintenance. Conclusion The development of automated modular units based on integrated continuous processes is a promising technology enabler for the upgrading of biobased platform chemicals into high value-added chemicals. From a chemical point of view, continuous flow offers a convenient framework to elaborate safe and versatile conversion processes with a moderate to low environmental footprint. Regarding regulatory aspects, PAT allows an accurate and responsive process control guaranteeing the compliance of the product with its specifications. However, the impact of the variability of biomass sourced material on impurity profile and byproducts should still be determined. The development of modular and automated production units will fasten the shift of the actual production paradigm to smaller decentralized manufacturing sites allowing agile supply chain management. We have to keep in mind that all those developments should be made with the pharmaceutical industry documentary standard in mind. Declarations Competing interests No funding was received to assist with the preparation of this manuscript. Yoann Joyard, Geoffroy Kaisin and Nicolas Maindron are shareholders of the Company SynLock SRL. Vincent Tadino is a shareholder and the Executive Director of the Company SynLock SRL. Loic Bovy and Jean-Christophe M. Monbaliu have no relevant financial or non-financial interests to disclose. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Holm-Nielsen JB, Ehimen EA (2014) Biorefinery plant design, engineering and process optimisation. Advances in biorefineries: biomass and waste supply chain exploitation, pp 89–111. 10.1533/9780857097385.1.89 2. Brun N Hesemann P Esposito D Expanding the biomass derived chemical space Chem Sci 2017 8 4724 4738 10.1039/c7sc00936d 28959397 3. Carus M, Dammer L, Puente A et al (2017) Bio-based drop-in, smart drop-in and dedicated chemicals. https://roadtobio.eu/uploads/news/2017_October/RoadToBio_Drop-in_paper.pdf . Accessed on 10-29-22 4. Werpy T, Petersen G (2004) Top value added chemicals from biomass. Volume I - results of screening for potential candidates from sugars and synthesis gas. 10.2172/15008859 5. Bozell JJ Petersen GR Technology development for the production of biobased products from biorefinery carbohydrates—the US Department of Energy’s “top 10” revisited Green Chem 2010 12 539 555 10.1039/b922014c 6. Biddy MJ, Scarlata C, Kinchin C (2016) Chemicals from biomass: a market assessment of bioproducts with near-term potential. https://www.nrel.gov/docs/fy16osti/65509.pdf . Accessed on 10-29-22 7. Liao Y-T, Matsagar BM, Wu KC-W (2018) Metal–Organic Framework (MOF)-derived effective solid catalysts for valorization of lignocellulosic biomass. ACS Sustain Chem Eng 6:13628–13643. 10.1021/acssuschemeng.8b03683 8. Hülsey MJ, Yang H, Yan N (2018) Sustainable routes for the synthesis of renewable heteroatom-containing chemicals. ACS Sustain Chem Eng 6:5694–5707. 10.1021/acssuschemeng.8b00612 9. Abdelaziz OY, Hulteberg CP (2020) Lignin depolymerization under continuous-flow conditions: highlights of recent developments. Chemsuschem 13:4382–4384. 10.1002/cssc.202001225 10. Zakzeski J, Bruijnincx PCA, Jongerius AL, Weckhuysen BM (2010) The catalytic valorization of lignin for the production of renewable chemicals. Chem Rev 110:3552–3599. 10.1021/cr900354u 11. Liu X, Bouxin FP, Fan J et al (2020) Recent advances in the catalytic depolymerization of lignin towards phenolic chemicals: a review. Chemsuschem 13:4296–4317. 10.1002/cssc.202001213 12. Giorgi PD, Elizarov N, Antoniotti S (2017) Selective oxidation of activated alcohols by supported gold nanoparticles under an atmospheric pressure of O2: batch and continuous-flow studies. ChemCatChem 9:1830–1836. 10.1002/cctc.201700179 13. Premkumar J, Sampath P, Sanjay R et al (2020) Synthetic guaiacol derivatives as promising myeloperoxidase inhibitors targeting atherosclerotic cardiovascular disease. ChemMedChem 15:1187–1199. 10.1002/cmdc.202000084 14. Guz NR, Stermitz FR, Johnson JB et al (2001) Flavonolignan and flavone inhibitors of a Staphylococcus a ureus multidrug resistance pump: structure – activity relationships. J Med Chem 44:261–268. 10.1021/jm0004190 15. Sadiq AD, Chen X, Yan N, Sperry J (2018) Towards the shell biorefinery: sustainable synthesis of the anticancer alkaloid proximicin A from chitin. Chemsuschem 11:532–535. 10.1002/cssc.201702356 16. Ogata M Hattori T Takeuchi R Usui T Novel and facile synthesis of furanodictines A and B based on transformation of 2-acetamido-2-deoxy-d-glucose into 3,6-anhydro hexofuranoses Carbohydr Res 2010 345 230 234 10.1016/j.carres.2009.10.007 19962133 17. World Health Organization (2021) World Health Organization model list of essential medicines – 22nd list 18. Ragno D, Leonardi C, di Carmine G et al (2021) Regiodivergent isosorbide acylation by oxidative N-Heterocyclic carbene catalysis in batch and continuous flow. ACS Sustain Chem Eng 9:8295–8305. 10.1021/acssuschemeng.1c02765 19. Morodo R Gérardy R Petit G Monbaliu JCM Continuous flow upgrading of glycerol toward oxiranes and active pharmaceutical ingredients thereof Green Chem 2019 21 4422 4433 10.1039/c9gc01819k 20. Gérardy R, Debecker DP, Estager J et al (2020) Continuous flow upgrading of selected C2–C6 platform chemicals derived from biomass. Chem Rev 120:7219–7347. 10.1021/acs.chemrev.9b00846 21. Gérardy R, Emmanuel N, Toupy T et al (2018) Continuous flow organic chemistry: successes and pitfalls at the interface with current societal challenges. Eur J Org Chem 2018:2301–2351. 10.1002/ejoc.201800149 22. Gérardy R, Morodo R, Estager J et al (2019) Sustaining the transition from a petrobased to a biobased chemical industry with flow chemistry. Top Curr Chem 377:1. 10.1007/s41061-018-0222-3 23. Monbaliu J-CM Legros J Will the next generation of chemical plants be in miniaturized flow reactors? Lab Chip 2022 10.1039/D2LC00796G 36278262 24. Lapkin AA, Yaseneva P (2017) Life cycle assessment of flow chemistry processes. In: Sustainable flow chemistry. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 249–276 25. Hessel V, Escribà-Gelonch M, Bricout J et al (2021) Quantitative sustainability assessment of flow chemistry–from simple metrics to holistic assessment. ACS Sustain Chem Eng 9:9508–9540. 10.1021/acssuschemeng.1c02501 26. Calvo-Serrano R, Guo M, Pozo C et al (2019) Biomass conversion into fuels, chemicals, or electricity? A network-based life cycle optimization approach applied to the European Union. ACS Sustain Chem Eng 7:10570–10582. 10.1021/acssuschemeng.9b01115 27. Iwaszko J, Zajemska M, Zawada A et al (2020) Vitrification of environmentally harmful by-products from biomass torrefaction process. J Clean Prod 249:119427. 10.1016/j.jclepro.2019.119427 28. Lindorfer J, Lettner M, Fazeni K et al (2019) Technical, economic and environmental assessment of biorefinery concepts 29. Muzyka C, Monbaliu J-CM (2022) Perspectives for the upgrading of bio-based vicinal diols within the developing european bioeconomy. ChemSusChem 15. 10.1002/cssc.202102391 30. Innovation for sustainable growth : a bioeconomy for Europe. https://op.europa.eu/en/publication-detail/-/publication/1f0d8515-8dc0-4435-ba53-9570e47dbd51. Accessed on 10-29-22 31. Review of the 2012 European Bioeconomy Strategy. https://ec.europa.eu/information_society/newsroom/image/document/2018-6/review_of_2012_eu_bes_2E89B85F-950B-9C84-5B426D1C24851387_49692.pdf . Accessed on 10-29-22 32. https://www.whitehouse.gov/briefing-room/presidential-actions/2022/09/12/executive-order-on-advancing-biotechnology-and-biomanufacturing-innovation-for-a-sustainable-safe-and-secure-american-bioeconomy/ . Accessed on 10-29-22 33. https://www.biopreferred.gov/BioPreferred/ . Accessed on 10-29-22 34. US Food & Drug Administration (2019) Draft guidance - quality considerations for continuous manufacturing 35. ICH Harmonised Guidelines (2021) Continuous manufacturing of drug substances and drug products Q13 - draft version 36. US Food & Drug Administration (2004) Guidance for industry - PAT — a framework for innovative pharmaceutical development, manufacturing, and quality assurance 37. Simon LL, Pataki H, Marosi G et al (2015) Assessment of recent Process Analytical Technology (PAT) trends: a multiauthor review. Org Process Res Dev 19:3–62. 10.1021/op500261y 38. European Commission (2021) EU Biorefinery Outlook to 2030 https://op.europa.eu/s/wTmR . Accessed on 10-29-22 39. Clark H, Deswarte JEI, Farmer FJ T (2009) The integration of green chemistry into future biorefineries. Biofuels Bioprod Biorefin 3:72–90. 10.1002/bbb.119 40. Kokossis AC Yang A On the use of systems technologies and a systematic approach for the synthesis and the design of future biorefineries Comput Chem Eng 2010 34 1397 1405 10.1016/j.compchemeng.2010.02.021 41. Hessel V (2009) Novel process windows - gate to maximizing process intensification via flow chemistry. Chem Eng Technol 32:1655–1681. 10.1002/ceat.200900474 42. Keil FJ Process intensification Rev Chem Eng 2018 34 135 200 10.1515/revce-2017-0085 43. Newman SG Jensen KF The role of flow in green chemistry and engineering Green Chem 2013 15 1456 10.1039/c3gc40374b 44. Kassin V-EH Gérardy R Toupy T Expedient preparation of active pharmaceutical ingredient ketamine under sustainable continuous flow conditions Green Chem 2019 21 2952 2966 10.1039/C9GC00336C 45. von Keutz T, Williams JD, Kappe CO (2021) Flash chemistry approach to organometallic C-Glycosylation for the synthesis of remdesivir. Org Process Res Dev 25:1015–1021. 10.1021/acs.oprd.1c00024 46. Chen Y, Cattoen M, Monbaliu J-CM (2021) Mitigation of chemical hazards under continuous flow conditions. In: Flow chemistry – fundamentals. De Gruyter, pp 269–312 47. Cantillo D, Mateos C, Rincon JA et al (2015) Light-induced C-H Arylation of (Hetero)arenes by in situ generated diazo anhydrides. Chem---Eur J 21:12894–12898. 10.1002/chem.201502357 48. Crawford DE, Porcheddu A, McCalmont AS et al (2020) Solvent-free, continuous synthesis of hydrazone-based active pharmaceutical ingredients by twin-screw extrusion. ACS Sustain Chem Eng 8:12230–12238. 10.1021/acssuschemeng.0c03816 49. Bolt RRA Leitch JA Jones AC Continuous flow mechanochemistry: reactive extrusion as an enabling technology in organic synthesis Chem Soc Rev 2022 51 4243 4260 10.1039/D1CS00657F 35506866 50. Xu Q, Sheng X, Li N et al (2021) Ball-milling: a productive, economical, and widely applicable method for condensation of biomass-derived aldehydes and ketones at mild temperatures. ACS Sustain Chem Eng 9:8232–8237. 10.1021/acssuschemeng.1c02270 51. Noël T, Cao Y, Laudadio G (2019) The fundamentals behind the use of flow reactors in electrochemistry. Acc Chem Res 52:2858–2869. 10.1021/acs.accounts.9b00412 52. Delparish A, Uslu A, Cao Y et al (2022) Boosting the valorization of biomass and green electrons to chemical building blocks: a study on the kinetics and mass transfer during the electrochemical conversion of HMF to FDCA in a microreactor. Chem Eng J 438:135393. 10.1016/j.cej.2022.135393 53. Ošeka M, Laudadio G, van Leest NP et al (2021) Electrochemical aziridination of internal alkenes with primary amines. Chem 7:255–266. 10.1016/j.chempr.2020.12.002 54. Cantillo D Synthesis of active pharmaceutical ingredients using electrochemical methods: keys to improve sustainability Chem Commun 2022 58 619 628 10.1039/D1CC06296D 55. Köckinger M Hanselmann P Roberge DM Sustainable electrochemical decarboxylative acetoxylation of aminoacids in batch and continuous flow Green Chem 2021 23 2382 2390 10.1039/D1GC00201E 56. Maljuric S Jud W Kappe CO Cantillo D Translating batch electrochemistry to single-pass continuous flow conditions: an organic chemist’s guide J Flow Chem 2020 10 181 190 10.1007/s41981-019-00050-z 57. Cao Y, Knijff J, Delparish A et al (2021) A divergent paired electrochemical process for the conversion of furfural using a divided-cell flow microreactor. Chemsuschem 14:590–594. 10.1002/cssc.202002833 58. Buglioni L, Beslać M, Noël T (2021) Dehydrogenative azolation of arenes in a microflow electrochemical reactor. J Org Chem 86:16195–16203. 10.1021/acs.joc.1c01409 59. Heugebaert TSA, Stevens C, Kappe CO (2015) Singlet-oxygen oxidation of 5-Hydroxymethylfurfural in continuous flow. Chemsuschem 8:1648–1651. 10.1002/cssc.201403182 60. Gérardy R, Winter M, Horn CR et al (2017) Continuous-flow preparation of γ-Butyrolactone scaffolds from renewable fumaric and itaconic acids under photosensitized conditions. Org Process Res Dev 21:2012–2017. 10.1021/acs.oprd.7b00314 61. Emmanuel N, Mendoza C, Winter M et al (2017) Scalable photocatalytic oxidation of methionine under continuous-flow conditions. Org Process Res Dev 21:1435–1438. 10.1021/acs.oprd.7b00212 62. Adamo A Beingessner RL Behnam M On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system Science (1979) 2016 352 61 67 10.1126/science.aaf1337 63. Zhang P, Weeranoppanant N, Thomas DA et al (2018) Advanced continuous flow platform for on-demand pharmaceutical manufacturing. Chem---Eur J 24:2776–2784. 10.1002/chem.201706004 64. Coley CW, Thomas DA, Lummiss JAM et al (2019) A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365:aax1566. 10.1126/science.aax1566 65. Fitzpatrick DE, Ley S (2016) Engineering chemistry: integrating batch and flow reactions on a single, automated reactor platform. React Chem Eng 1:629–635. 10.1039/c6re00160b 66. Fitzpatrick DE, Battilocchio C, Ley S (2016) A novel internet-based reaction monitoring, control and autonomous self-optimization platform for chemical synthesis. Org Process Res Dev 20:386–394. 10.1021/acs.oprd.5b00313 67. Sagmeister P, Ort FF, Jusner CE et al (2022) Autonomous multi-step and multi-objective optimization facilitated by real-time process analytics. Adv Sci 2105547:1–9. 10.1002/advs.202105547 68. Steiner S, Wolf J, Glatzel S et al (2019) Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363:aav2211. 10.1126/science.aav2211 69. Mehr SHM, Craven M, Leonov AI et al (2020) A universal system for digitization and automatic execution of the chemical synthesis literature. Science 370:101–108. 10.1126/science.abc2986	36467977	PMC9707424	NO-CC CODE	2022-12-01 23:20:23	no	J Flow Chem. 2022 Nov 29;:1-15	utf-8	J Flow Chem	2,022	10.1007/s41981-022-00247-9	oa_other
==== Front J Flow Chem J Flow Chem Journal of Flow Chemistry 2062-249X 2063-0212 Springer International Publishing Cham 247 10.1007/s41981-022-00247-9 Perspectives A perspective on automated advanced continuous flow manufacturing units for the upgrading of biobased chemicals toward pharmaceuticals http://orcid.org/0000-0002-3968-8506 Kaisin Geoffroy [email protected] 1Geoffroy Kaisin After a PhD in radiochemistry and organic chemistry at the Cyclotron Research Center at the University of Liège, Geoffroy joined a startup of the University, ANMI. As Head of Chemistry, he participated to the development of a patented cold kit formulation for the labelling of PSMA-11 with gallium-68. After the acquisition of ANMI by Telix Pharmaceuticals in 2018, his research focused on new methodologies for the labelling of biomolecules with radiometals. He is now R&D chemist and co-founder at SynLock where his research interests are aimed at the automation of organic synthesis. Bovy Loïc 2Loïc Bovy Loïc holds a MSc from the Université de Liège in chemical sciences (2021). He then joined the Center for Integrated Technology and Organic Synthesis (CiTOS) to conduct a PhD. His research focuses on the upgrading of biobased building blocks and their integration in active pharmaceutical ingredients by combining both batch and flow chemistry methodologies. Joyard Yoann 1Yoann Joyard Yoann graduated from INSA Rouen as PhD in organic chemistry in 2013. He focused on the development of tracers for targeting hypoxic tumors and developed new fluorination methodologies. He carried out postdoctoral research at King’s College London in the School of Biomedical Engineering and Imaging Sciences. He worked there on the development of bimodal probes for guided surgery. Yoann also worked as research engineer for two years in a company specialized in development of continuous-flow and batch process in chemistry, before joining Optimized Radiochemical Applications (ORA) in 2017 as R&D radiochemist. He is now R&D chemist and co-founder at SynLock where his research interests are aimed at the automation of organic synthesis. Maindron Nicolas 1Nicolas Maindron Nicolas received his PhD from the University of Rouen, France, in 2012, in the field of the lanthanide-based luminescence probes. He then completed two postdoctoral positions. One at the University of Bourgogne, Dijon, France, where he designed multifunctional imaging probes and a second one at MNI (now Invicro) at New Haven, USA, where he started to work in the area of radiochemistry. He joined Optimized Radiochemical Applications (ORA) in 2016 as a R&D radiochemist. He is now R&D chemist and co-founder at SynLock where his research interests are aimed at the automation of organic synthesis. Tadino Vincent 1Vincent Tadino Vincent received his PhD, in Organic Chemistry at the University of Liège, Belgium, and completed a PostDoc research in Organic Chemistry at ISMRA in Caen, France. Vincent founded Optimized Radiochemical Applications (ORA) in 2006 and currently serves as its President and CTO. Prior to founding ORA, Vincent served as project chief, radiochemistry developer, and quality manager for 7 years in different companies active in PET radiopharmaceutical equipment and production, both in Europe and the USA. He holds, as inventor and/or as co-inventor, eleven patents and is the author of several abstracts/presentations in international congresses. He is now President and co-founder at SynLock where his research interests are aimed at theautomation of organic synthesis. Monbaliu Jean-Christophe M. 2Jean-Christophe M. Monbaliu is currently Associate Professor at the University of Liège (Belgium) and serves as Associate Editor of the Journal of Flow Chemistry. He is heading the Center for Integrated Technology and Organic Synthesis (CiTOS, www.citos.uliege.be), the first European Corning® Advanced-Flow™ reactor qualified lab. Research interests at CiTOS revolve around synthetic organic chemistry but are multidisciplinary in essence and aim at (a) designing cheaper and more efficient routes for the preparation of high value-added chemicals such as active pharmaceutical ingredients; (b) accelerating the transition from petrobased to biobased strategies and (c) developing efficient processes with a lower environmental impact. 1 SynLock SRL, Rue de la Vieille Sambre 153, B-5190 Jemeppe-sur-Sambre, Belgium 2 grid.4861.b 0000 0001 0805 7253 Center for Integrated Technology and Organic Synthesis, Research Unit MolSys, University of Liège, B-4000 Liège, Sart Tilman, Belgium 29 11 2022 115 29 8 2022 4 11 2022 © Akadémiai Kiadó 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Biomass is a renewable, almost infinite reservoir of a large diversity of highly functionalized chemicals. The conversion of biomass toward biobased platform molecules through biorefineries generally still lacks economic viability. Profitability could be enhanced through the development of new market opportunities for these biobased platform chemicals. The fine chemical industry, and more specifically the manufacturing of pharmaceuticals is one of the sectors bearing significant potential for these biobased building blocks to rapidly emerge and make a difference. There are, however, still many challenges to be dealt with before this market can thrive. Continuous flow technology and its integration for the upgrading of biobased platform molecules for the manufacturing of pharmaceuticals is foreseen as a game-changer. This perspective reflects on the main challenges relative to chemical, process, regulatory and supply chain-related burdens still to be addressed. The implementation of integrated continuous flow processes and their automation into modular units will help for tackling with these challenges. Graphical abstract Keywords Biobased platforms Flow chemistry Active Pharmaceutical Ingredient Automation ==== Body pmcIntroduction To ensure optimal economic sustainability, biorefineries should be designed as an integrated production system able to produce a wide range of versatile and valuable outputs starting from raw biomass. The two main categories of outputs are energy (under the form of fuel and heat) and chemicals intended for various applications including the food industry, fragrances, materials, bulk, pharmaceuticals and fine chemicals [1]. Biorefining processes can be very complex, and, quite unexpectedly, are often associated with much less favorable environmental metrics and economics than the refining of crude oil. For compounds also found in the conventional fossil fuel supply chain, the economics of the biomass-based process must be thoroughly optimized in order to remain competitive [2]. Primary biomass-derived (or biobased) platform chemicals are compounds obtained after the first processing step of biomass [2]. Each of these compounds can be further transformed toward a number of secondary biobased building blocks [2]. Among the various chemicals that can be sourced from processing biomass, a rather limited range have attracted considerable attention with huge potential, emerging or established markets. These biobased platforms chemicals are also classified into three categories: (a) drop-in platform chemicals, (b) smart drop-in platform chemicals and (c) dedicated platform chemicals [3]. The first category, drop-in platform chemicals, are commodity chemicals identical to an existing fossil sourced counterpart (e.g. ethylene glycol). Smart drop-in chemicals (e.g., biobased glycidol produced from glycerol 1), are commodities the biosourcing of which presents significant advantages over the petrobased supply. For example, their production process is less energy-intensive, shorter, less complex, relies on production processes involving milder conditions and lower environmental footprint chemicals or produces less harmful by-products. The last category, dedicated biobased chemicals, concerns compounds that have no direct counterpart in the petrochemical chain of value and therefore open new potential markets and products (e.g. 2,5-furanedicarboxylic acid 12) [3]. A short list of the most potent biobased platform molecules has been issued by the US Department of Energy (DoE) in 2004, emphasizing the tremendous industrial potential for 14 compounds, although this report is widely known as the “DoE Top 10” (see Fig. 1) [4]. Though the DoE list was updated in 2010 and 2016 with the addition of a few additional structures [5, 6], it is quite surprising that despite such a huge diversity and complexity, biorefineries only converge toward a very limited number of biobased platforms. While these biobased platforms are originally intended to replace petrobased building blocks in bulk and material chemistry (drop-in, smart drop-in and dedicated platforms), momentum is gained for applications targeting markets with lower volumes but higher added value, such as for the manufacturing of pharmaceuticals. Fig. 1 Extended list of top biobased platform molecules [4] as C3-C6 biobased building blocks. By order of appearance: (C3): glycerol 1, 3-hydroxypropionic acid 2; (C4): malic acid 3, aspartic acid 4, 3-hydroxybutyrolactone 5, succinic acid 6, fumaric acid 7; (C5): xylitol 8, levulinic acid 9, glutamic acid 10, itaconic acid 11; (C6): 2,5-furandicarboxylic acid 12, sorbitol 13, glucaric acid 14 The strength of the conventional fossil fuel approach relies mostly on an overall cost effectiveness associated with decades of process engineering. However, the chemicals obtained by fossil fuel exploitation are mainly hydrocarbon backbones with limited functionalizations. Heteroelements, such as oxygen and nitrogen, which are ubiquitous to the western pharmacopoeia, are indeed very rare in petrobased building blocks. They must be therefore inserted by selective chemical transformations prior to their end-uses. On the other hand, biobased platforms are by contrast very rich in heteroatoms (mostly oxygen) and therefore impose a reverse scheme with the necessary removal of heteroatoms (X = O, N) to increase their inherent C/X ratio. Primary biobased building blocks are obtained through fractionation, depolymerization and defunctionnalization of the biomass. For instance, lignocellulosic biomass is mainly composed of lignin, hemicellulose and cellulose. Their hydrolysis yields a wide array of O-containing compounds [7]. Chitin obtained from crustacean and insect shells is rich in N-containing compounds. Algae and marine biomass provide a small reservoir of S-containing molecules while an abundant source of P-containing organic backbones is still missing [8]. Concerning lignin, it has now been demonstrated that the main constituents are p-coumaryl, coniferyl and sinapyl alcohols featuring phenol, alkenes and allylic alcohol scaffolds. The lignin depolymerization method, both under batch or continuous flow conditions [9], as well as lignin’s origin (hardwood, softwood or herbaceous crops) affects the proportion of these three constituents and their derivatives, opening the doors to a variety of transformations [10, 11]. Among those transformations, Antoniotti and coworkers reported in 2007 both batch and continuous flow conditions procedures allowing the selective oxidation of a broad scope of alcohols, among which coniferyl alcohol derivatives, into the corresponding aldehydes or ketones through aerobic oxidation catalyzed by heterogenous gold nanoparticles [12]. Furthermore, the synthesis of promising inhibitors to treat cardiovascular diseases was performed by Rajagopaland coworkers in 2020. This was done through the phenol moiety of coniferyl alcohol derivatives or ferulic acid, its carboxylic acid counterparts, under the form of alpha-beta unsaturated amides, hydrazines, esters and hydroxamic acids [13]. Additionally, structure-activity relationships of the three phenolic derivatives have been investigated in 2001 by Lewis and coworkers under the form of flavonolignan and flavone scaffolds with the aim of treating multidrug resistant Staphylococcus Aureus [14]. Chitin is another complex biomacromolecule featuring a repeating N-acetyl-d-glucosamine (NAG) 15 unit (see Fig. 2), and bears great interest considering its natural nitrogen content. 15 is a key intermediate to a variety of other prominent molecules such as 3-acetamido-5-acetylfuran 16, which was Sperry’s center of interest in 2018 [15]. Indeed, the authors relied on 16 to synthesize Proximicin A 17, an alkaloid known for its anticancer properties. Additionally, NAG can be transformed into chiral 3,6-anhydro-GNF 18 and 3,6-anhydro-MNF 20, two bicyclic compounds, which are key intermediates in the synthetic route proposed by Usui and colleagues in 2010 [16]. These are engaged in the synthesis of furanodictines A 19 and B 21 which both display important neuronal differentiation properties in rats. Fig. 2 N-acetyl-d-glucosamine derivatives and some of their applications as API or API precursors [15, 16]. N-acetyl-d-glucosamine 15, 3-acetamido-5-acetylfuran 16, proximicin A 17, 3,6-anhydro-GNF 18, furanodictines A 19, 3,6-anhydro-MNF 20 and furanodictines B 21 Alongside their structural diversity, a wide range of optically active building blocks can be sourced from biobased materials. For instance, isosorbide 5-mononitrate 22 is a clinically favored substitute for isosorbide-dinitrate 23, a compound included in the World Health Organization (WHO) list of essential medicines and used as a medication against heart diseases and blood pressure issues (see Fig. 3) [17]. Fig. 3 Isorbide derivatives and continuous flow synthesis of exo-MAI [18]. Bzq: 1,4-Benzoquinone Compound 23 features an isosorbide 24 scaffold, a biobased diol building block, which is efficiently obtained by a sequence of reduction/dehydration of glucose through the intermediate formation of sorbitol. The chiral nature of isosorbide gives diverging chemical reactivity to both hydroxyl groups, although chemoselectivity remains a notable challenge. Isosorbide 5-mononitrate 22, the isomer of choice in the medical field, is obtained by nitrating the free endo-5-OH function after a selective protection through the exo-2-OH acylation. Therefore, finding a streamlined procedure towards exo-2-OH acylated products is highly relevant to the synthesis of 22. Among the most notable developments in this context, Massi and coworkers reported a tunable procedure to selectively acylate isosorbide with aldehydes through heterogeneous Nheterocyclic carbene (NHC) catalysis in batch. In a subsequent step, the authors reported the selective exo-acylation (exo-MAI) under continuous flow conditions [18]. A scope of 5 aldehydes with a limited range of molecular diversity was screened as mild acylating agents, among which, biobased furfural gave excellent results. Conversions ranging from 90 to > 95% with exo/endo ratios in the range of 4.0 to 5.3 were obtained throughout the scope. The robustness of the process was assessed with a 110 h long run, which gave consistent and steady conversion and selectivity with model substrate benzaldehyde. The authors argued their process relied on the main assets of flow chemistry to ensure strict local stoichiometry, leading to high exo/endo ratio and excellent conversion towards a key intermediate of 22. Recycling and processing of vegetable oils are also a source of interesting chemicals. Main outputs are transesterified fatty acids (typically fatty acid methyl or ethyl esters, namely, FAME or FAEE) and glycerol 1. FAME/FAEE are primarily used in the biodiesel industry and the latter is a versatile C3 platform. In 2019, Monbaliu and coworkers developed two separate compact and efficient fluidic modules allowing the upgrade of glycerol 1 into Active Pharmaceutical Ingredients (APIs) bearing a β-aminoalcohol scaffold [19]. This was achieved by activating neat glycerol 1 into its oxiranes counterparts (glycidol 25 and epichlorohydrin 26) through a chlorination/dechlorination sequence in flow (chlorination with 36 wt% aqueous HCl and dechlorination/epoxidation with aqueous NaOH). The product mixture was then subjected to a membrane liquid/liquid separation yielding epichlorohydrin 26 (in MTBE) and aqueous glycidol 25. MTBE was evaporated off-line and the resulting pure epichlorohydrin was fed to the second reactor module (see Fig. 4a). Fig. 4 End-to-end glycerol 1 upgrading towards β-aminoalcohol APIs, including propanolol 28a, naftopidil 28b and alprenolol 28c under continuous flow conditions [19]. a Upstream activation of biobased glycerol 1 into glycidol 25 and epichlorohydrin 26. b General two-step strategy (glycidol ether synthesis – aminolysis) for converting biobased epichlorohydrin 26 toward a selected range of β-aminoalcohol APIs (28a, 28b, 28c) The authors then used a glycidyl ether synthesis/aminolysis sequence under flow conditions to install the β-aminoalcohol scaffold from biobased epichlorohydrin (see general strategy and scope in Fig. 4b). In the first step, Williamson etherification between epichlorohydrin and a feed containing naphtol and t-BuOK was performed in 20 min at 70 °C. The coil effluent was subsequently engaged in the ring opening aminolysis of the corresponding glycidyl ether with an appropriate neat amine to yield a small selection of APIs, including propanolol 28a, naftopidil 28b and alprenolol 28c in good isolated yields. These selected examples show that renewable platforms are of great interest as chemical precursors for accessing APIs and to further contribute in transitioning from exclusively petrobased production schemes to biobased alternatives. Though momentum is gained in the recent literature, it is worth mentioning that there is still a lack of suitable metrics to assess the incorporation of biobased materials into high valued-added scaffolds such as APIs [17]. Such metrics could potentially complement the conventional process efficiency and environmental impact metrics, such as the Process Mass Intensity, the Atom Economy and the Environmental Factor [20] alongside the green solvent selection guides [21]. This perspective emphasizes the main challenges associated with an ambitious shift of paradigm in the pharmaceutical industry with transitioning from exclusively petrobased production schemes to increasingly biobased processes and summarizes the main assets of automated continuous flow production schemes for tackling these challenges [20–23]. Challenges Chemical and processing challenges The main technological challenges are related to production processes (biorefining) allowing the preparation of fine chemicals from biomass. When taking the full life-cycle into account [24, 25], some biorefinery processes have a detrimental environmental impact, are very expensive or are not energy efficient [26]. The processing of the wastes and byproducts can be tedious and generates additional costs [27]. For example, the processing of crustacean shells is known to generate a huge amount of chemical waste while it provides valuable N-containing building blocks [8].There is therefore still much room for improving the overall environmental footprint and the atom economy of such processes. As a matter of fact, many of the most promising biorefinery processes are still associated with intermediate Technology Readiness Levels (TRLs) [28]. Conversion processes must also be developed with improved versatility. In an ideal case scenario, biorefining should be amenable to different substrates to minimize the impact of the seasonality of some raw material causing significant production delays and shortages due to the lack of supply when the harvesting season is finished. To our knowledge, no study evaluates the impact of the nature of the initial biomass nor its harvest region on the impurity profile of the refined chemical platform. Differences could have consequences from a regulatory point of view as described in the subsequent section. One of the major challenges related to chemistry and process engineering relates to the inherent features of biobased platform molecules which bear a very high oxygen content by contrast to typical petrobased building blocks [20, 29]. This inherent major difference has a profound impact on the way chemists and chemical engineers have to design new dedicated chemistries and process conditions. A conventional petrobased production scheme is indeed engineered to access molecular diversity from hydrocarbon backbones, hence relying on specific reaction conditions for incorporating heteroelements and hence access function and added-value [20]. Emerging processes feeding on biobased platforms must be tailored to lower their high O-content, hence requiring new catalysts, chemistries and process conditions [20, 28]. These new chemistries and process conditions must be compatible with variable purity profiles (see comment below on the geographical/seasonal variability of biobased platforms) and, ideally, with unrefined biobased platforms. In the current state of the art, many of these processes are still associated with depleting metal catalysts, additives, and solvents with a significant environmental impact [29]. It is therefore critical to allocate significant research efforts to rely on low environmental footprint catalysts, solvents, additives and process technologies, especially when the increasing complexity of some biobased platforms comes along with overwhelming competing reactions [29]. Consequently, the current TRL for such biobased processes is typically around TRL 4–5 [20, 28], thus still far from any implementation at commercial scale. Solving the main chemical and processing challenges associated with the upgrading of biobased platforms is expected to stimulate creativity of Chemistry and Chemical Engineering communities for the next decades. Regulatory challenges On a broader note than just the manufacturing of APIs, the current context within the dominant economic areas is very favorable with many governmental incentives thriving with a subsequent thrust toward biobased processes. For instance in the European Union (EU), Bioeconomy has rapidly turned into a global strategy with significant funding schemes and a stable regulatory environment [30, 31]. RoadToBio (within the H2020 program) aimed at replacing 25% of the total volume of fossil-based organic chemicals with an alternative biobased feedstock by the end of 2030. In 2021, a new program (Horizon Europe) took over to stimulate innovation, to sustain the green and digital transitions, and to strengthen EU’s leadership. Similar initiatives are echoed in the US, with directives on advancing biotechnology and biomanufacturing innovation for a sustainable, safe, and secure American Bioeconomy [32, 33]. As this perspective focuses on APIs synthesis, a brief overview of the specific regulatory challenges associated with the use of biobased materials in conjunction with continuous flow manufacturing is mandatory. Regulatory agencies are aware of the importance of continuous flow manufacturing as a breakthrough technology for the future of the pharmaceutical industry. The US Food and Drug Administration (FDA) stated that “Continuous manufacturing is an emerging technology that can enable pharmaceutical modernization and deliver potential benefits to both industry and patients” as an introduction to their Draft Guidance for Industry entitled “Quality Considerations for Continuous Manufacturing” issued in February 2019 [34]. The International Council for Harmonisation (ICH) issued a draft of the guidance Q13 on “Continuous Manufacturing of Drug Substances and Drug Products” in July 2021 [35]. It is expected the European Medicine Agency (EMA) will shortly align its objectives on FDA’s. Altering synthetic pathway or changing raw material specifications (though still using the same chemical structure) for approved drugs or investigational new drugs are usually associated with a huge regulatory impact, especially if performed after the launch of a phase-III clinical study. As identical chemical entities can be produced from different sources of biomass, a risk assessment must be carried out for each of them. The impact on the product quality attributes must be evaluated. If the risk of change is low, in vitro equivalence demonstration is usually considered as sufficient. High change risk could require additional in vivo bioequivalence studies, which comes with significant additional costs and administrative burden [34]. The transposition of a batch process under continuous flow conditions also implies setting up a rigorous control strategy. Manufacturers must demonstrate that the quality of the finished product, even according to an equivalent combination of chemicals; obtained by continuous manufacturing is similar to the batch one. The system must be capable of real time monitoring with process analytical technologies (PAT) [36, 37] to ensure that it remains under a state of control during the whole production campaign, hence allowing for quality control [36, 37]. The software overlay should allow them to easily access, process and securely archive the acquired data at any time. Supply chain challenges Despite the increasing costs of fossil fuels, the biorefinery industry still lacks economic viability for many biobased platforms. This problem is identified as the main non-technical barrier to the deployment of biomass-based chemicals, especially in Europe where labor-related and energy costs are higher than anywhere else [29, 38]. In addition, there is still a need to further expand the array of secondary building blocks accessible after the transformation of the primary ones coming from the fragmentation of biomass. These high value-added products must compensate for the low incomes generated by bulk chemicals while remaining competitive towards conventional fossil fuel-sourced products [2, 39]. In order to accommodate changing input materials and to adapt to the markets demand, a change in the production paradigm is required [40]. The large-scale, delocalized and centralized units must be replaced with a network of smaller production facilities. They must be modular by design to ease the transition between different starting materials and/or finished products. The decentralization of production facilities will reduce transport-related costs and will better accommodate the associated geographical and seasonal constraints. As some biomass sources are seasonal and perishable, their processing must be done as close to the harvesting site as possible. Besides, biomass sourced raw materials are loaded with water and minerals, which come with a deleterious impact on the transportation costs. Moreover, some mineral and organic components must be moved back to the field after the processing to avoid soil depletion [1]. To minimize the need for storage capabilities for the intermediates, raw materials should be processed as high as possible in the valorization scale. Only high value-added intermediates or finished products are worth storing or transporting to another transformation unit. These inherent features of biorefining imply the development of a robust and flexible logistical system [38]. The implementation of this disruptive production paradigm also requires modular and transportable transformation plants that are environmentally and financially efficient. In conclusion, synergies should be created between the different actors of the supply chain through collaborative research programs for developing an integrated value chain for the different products obtained after treatment of the biomass. This newly developed supply chain brings together several aspects starting from biomass production and terminating to the market introduction and commercialization of high value-added products. This will stimulate further R&D efforts to develop efficient conversion and valorization process, and will spark the creativity for accessing new products. Perspectives Flow chemistry The advantages of continuous flow production technology come with invaluable assets for the development of intensified processes, with reduced environmental impact and for the development of a decentralized production network [21, 41, 42]. However, a simple transposition of a batch process to continuous conditions does not guarantee better economics and sustainability metrics; it should always come with a careful reoptimization and redefinition of the chemistry and conditions to fully benefit from the assets of flow technology [23, 43, 44]. For instance, in 2021, Kappe and coworkers published a groundbreaking approach for the synthesis of an important Remdesivir intermediate 33, the first FDA and EMA approved drug against the late COVID-19 outburst (see Fig. 5) [45]. The high demand for this drug during the pandemic highlighted the need for faster procedures towards some intermediates. The paper describes flash chemistry under continuous flow conditions, concatenating four consecutive steps within a total residence time of 8 s with an excellent 60% yield involving a biobased ribose derivative 30. Fig. 5 Flash synthesis of remdesevir intermediate 33 under continuous flow conditions [45] The most remarkable feature of this highly efficient process is obviously the extremely short combined residence time which is possible thanks to the reactivity of the engaged species. However, this efficiency and speed of execution are the result of very specific and strict conditions among which, temperature, reaction time and local stoichiometry as well as an efficient mixing and heat transfer which are some of the most attractive features of microfluidic technology. The space-time yield (STY) of the process was determined to be an astonishingly high 10.4 kg∙L1∙h1 despite the very modest size of the reactor [45]. This approach is a way to reduce the production costs by optimizing the productivity of the installation while keeping a low spatial footprint. In an industrial environment, continuous flow also provides a good answer to safety issues [46]. For example, diazo anhydrides are well-known explosives and highly carcinogenic agents. Their in situ generation under continuous flow conditions was demonstrated by Kappe and coworkers in 2015 as an elegant and efficient solution to alleviate acute hazards upon stockpiling or contact with the operator. A photochemical microfluidic reactor was developed, allowing to safely generate, handle and react this particularly hazardous substance family with non-activated arenes towards bi(hetero)aryl derivatives (see Fig. 6). Fig. 6 Chemical generator for diazo anhydrides under continuous flow conditions [47]. a General strategy for the photochemical synthesis of bi(hetero)aryl derivatives. b Application of diazo anhydrides for the preparation of dantrolene 39 (myorelaxant) and canagliflozin 41 (antidiabetic) This method supports the arylation of biobased furfural 43 with 4-nitroaniline, providing a key intermediate towards dantrolene 39, an API prescribed as myorelaxant. This arylation procedure is also amenable to the coupling of thiophene and 4-fluoroaniline towards a canagliflozin 41 (antidiabetic) intermediate (see Fig. 6). The only waste materials resulting from this elegant photochemical coupling are N2, H2O and t-BuOH, while hazardous and transient diazo anhydride has been safely generated and consumed [47]. Colacino and coworkers disclosed a solvent free method for the synthesis of dantrolene and other hydrazone-based API using continuous mechanochemistry [48]. The conversion of batch mechanochemical processes to the continuous flow paradigm is another promising emerging research area. Green-minded process chemists are highly interested in ball milling as it eliminates the need for solvents [49]. This has a direct positive impact on reducing the environmental and financial footprint of the process. Rotating or mixing mills are replaced by twin screw extruders to allows the continuous mechanochemical processing of the reagents [48, 49]. Li and coworkers showed that ball milling was suitable for the upgrade of biomass-derived materials [50]. The authors demonstrated the condensation of furfural with five different ketones, which led to the desired adducts with low to excellent yields ranging from 44.0% (MIBK as ketone counterpart) to 99.8% (cyclopentanone as ketone counterpart) using CaO as catalyst under mild process conditions (40 °C) [50]. Electrochemistry has been part of the organic chemist toolkit for numerous years now. Unfortunately, it often suffers from a series of drawbacks in batch which lead to long reaction times as well as intricate conditions and side-products. The main drawbacks of electrochemistry can be in part mitigated under continuous flow conditions, as highlighted by seminal contributions from the Noël and Kappe groups, including for the preparation of APIs and relevant scaffolds [51–58]. An exemple from Noël’s group is illustrated hereafter, where the authors provided an unprecedented continuous electrochemical protocol for upgrading biobased furfural 43 simultaneously into three industrially relevant molecules: 2(5H)-furanone 44 (polymer industry and γ-butyrolactone precursor), furfuryl alcohol 45 (resins) and hydrofuroin 46 (jet-fuel precursor) (see Fig. 7) [57, 58]. Fig. 7 Electrochemical upgrading of biobased chemicals under continuous flow conditions [57, 58] The furfural redox reaction was performed in a divided-cell flow microreactor with a key interesting aspect: varying the applied voltage (2.4 or 2.9 V) leads to different proportions of 2(5 H)-furanone 44 (46% vs. 77%) in the cathodic cell outlet, furfuryl alcohol 45 (58% vs. 20%) and hydrofuroin 46 (29% vs. 71%) in the anodic cell outlet (see Fig. 7). In other words, a tunable reactor was devised, allowing to choose which biobased derived molecules could be synthesized in larger proportions. Unfortunately, the reaction was not amenable to a 100% selectivity between furfuryl alcohol 45 or hydrofuroin 46 [57]. In another article, the same group also demonstrated the suitability of flow electrochemistry for the preparation of suitable scaffolds for APIs with C-N cross-coupling reaction between azole derivatives and arenes. The electron-driven reaction supports a scope of both azole derivatives (10 examples) and arene substrates (11 examples) with yields ranging from 20 to 98% despite a very short 10 min residence time at room temperature in the flow reactor with the only by-product of the reaction being dihydrogen [58]. In addition to having positive effects on the efficiency of electrochemically driven reactions, microfluidic reactors offer practical solutions to similar issues encountered while performing photochemical reactions. The synergistic combination of continuous flow and photochemistry was vastly documented by the Kappe and Noël groups. For instance, in a study targeting the upgrading of 5-hydroxymethyl furfural 47, Kappe and coworkers developed a photochemical flow process for the singlet oxygen-mediated preparation of butanolide 48 (see Fig. 8a) [59]. Butenolide 48 and its derivatives hold a significant role as polyester precursors while still bearing an alkene moiety, allowing for further functionalization of either the monomer or polymer. The process takes advantage of in situ generated singlet oxygen through rose bengal photo excitation to safely and neatly oxidize 47 at low temperature. A 0.5 mol% catalyst loading sufficed to activate the slight excess of oxygen into its highly oxidizing singlet state. The efficiency of the reaction conditions was then assessed by applying them to four additional derivatives bearing ester, methyl, succinimide or acetal functions, with yields ranging from poor for the acetal-bearing substrate (< 20%) to excellent (93%). Another photochemical singlet oxygen generator was reported a few years later by Monbaliu and colleagues for the preparation of methionine sulfoxide from natural amino acid methionine [61]. Fig. 8 Biobased building blocks photochemical upgrading into polymer precursors [59, 60]. a Singlet oxygen generator for the preparation of butenolide 48 [46]. b Upgrading of fumaric and itaconic acids under continuous flow photochemical conditions In 2017, Monbaliu and coworkers disclosed the photochemically-driven upgrade of plaftorms itaconic 11 and fumaric 7 acids, hence providing another illustration of the intrinsic advantages of photoflow chemistry [60]. This novel process relied on 365 nm LEDs to photochemically activate benzophenone, a radical initiator. This allows the addition of alcohol-derived radicals to the alkene substrates, forming substituted γ-butyrolactones 49 and 50, respectively (see Fig. 8b). In this process, isopropanol serves both as reagent and solvent for benzophenone and the substrates, lowering the overall footprint of the reaction. The reaction was quickly optimized by relying on in-line NMR analysis. With ideal conditions in hand, methanol and cyclohexanol were tested both for fumaric and itaconic acid in place of isopropanol. The reaction was then swiftly scaled up from a microfluidic scale to a mesofluidic Corning® Advanced-Flow™ G1 Photo Reactor resulting in an 83 g/day productivity on the model substrate (fumaric acid 7 and isopropanol). A small scope of γ-butyrolactones, including unique spiro derivatives, was documented [60]. In both electro and photochemical cases, the much larger surface to volume ratio and shorter residence time, are often associated with a significant reduction in side products. The improved yields can be ascribed to microfluidic reactor architectures; narrow channels permitting a more efficient light or current transmission to the reaction medium. Additionally, less electrolytes or photocatalysts are required and higher surface-to volume ratio ensured faster reaction rates too. Despite showing great potential for synthetic application, electrochemistry under continuous flow conditions is still a “niche” discipline, with major challenges associated to scalability yet to be addressed. As flow processes are usually faster to upscale than their batch counterparts, early R&D development in flow are quickly transposed to commercial scales, hence coming with shorter time-to-market. With an overall lower footprint than batch reactors, flow reactors also allow the development of compact production units. These production units can be adapted to sudden change in demand, either with numbering-up or scaling-out strategies with production volume easily tunable. This property can be foreseen as paramount for deploying decentralized and/or mobile production units. In 2016, Jensen, Jamison and coworkers reported the development of a compact, transportable and fully modular micro-fluidic setup for API manufacturing. Building upon a long history of impactful and groundbreaking technologies for the continuous flow manufacturing of APIs in low footprints, mobile setups, the MIT team pushed further the miniaturization of an end-to-end manufacturing unit combining upstream (chemical transformations, intermediates workups and extractions) with advanced downstream operations (purification and liquid formulation) [62]. This was made possible by the elaboration of individual multipurpose modules which could be connected based on the synthesized API. Coils of varying volumes, in-line separation units, pumps, gravity separation units as well as an in-line IR spectrometer and packed-bed columns constituted the functional blocks allowing the complete manufacturing of 4 different APIs. The coordination of the high number of pumps, IR and purification steps were made possible through automation. It means that a single operator can have a total control over the production of these four molecules, reducing the need for an abundant workforce. Additionally, concentrated or neat feedstocks were used to minimize solvent waste and maximize productivity [62]. Devising and relying on independent yet compatible modules opened the doors to the on-demand synthesis of several primordial pharmaceuticals. This modularity was taken advantage of to engineer a compact and portable (fridge-sized) mini-API factory, rendering this a potentially highly efficient tool for local drug delivery. In the original prototype, 4 APIs with different structures and pharmacological profiles were obtained with output ranging from 810 to 4500 doses per day. The 4 APIs included diphenhydramine hydrochloride (an antihistamine compound), lidocaine hydrochloride (an anaesthetic and antiarrhythmic compound), diazepam (a nervous system depressant) and fluoxetine (an antidepressant). Most notably, the prototype enabled a swift reconfiguration to swap from an API to another, in less than 30 min. In a further attempt to broaden the utility of such an approach, an additional series of 4 APIs was prepared in an updated prototype [63]. More recently, the same team combined advanced automation, robotics and artificial intelligence in a robotic platform dedicated to flow synthesis [64]. This innovative work is the first to propose a solution combining computer-aided synthesis planning (CASP), expert refined chemical recipe generation, automatic assembly of the fluidic setup by a robotic arm. The assembled system is then used to carry on the synthesis. The robotic arm can perform an adaptation of the setup while the synthesis is running. For example, changes of feedstock allow to rapidly access molecular diversity. Their proof-of-concept experiment consists of the automated synthesis of fifteen APIs or pharmaceutical intermediates. The system generated synthetic routes for each compound. As all the synthesis routes are already described in the literature, the software was not allowed to use existing pathways. Then, the automated robotic arm had to construct nine different fluidic setups as some synthesis shared common apparatus and feedstocks. The recipe generation for each step was the only human intervention in the whole process as existing databases do not contain enough information to allow using a data-driven automatic generator. The main challenge relied in the translation of batch conditions into continuous flow protocols. The system carried out automatically the synthesis of the targets compounds. For example, it produced aspirin with 91% yield and a productivity of 1.72 g/h; diazepam with 75% yield and a productivity of 638 mg/h. In order to demonstrate the advantages of fluidic path rerouting by the robotic arm during a synthesis, a scope of five different ACE inhibitors based on the quinapril scaffold was prepared. The full library was produced in 68 h with productivity ranging between 342 and 459 mg/h. Another library of four celecoxib analogs was completed in 28 h with similar productivities. Automation A sustained effort towards automation of the production unit is mandatory to reduce the cost induced by the operation of smaller production units. Moreover, for continuous manufacturing, automated production mode should ease keeping processes under a control state and reduce transient states leading to the mandatory diversion of out of specification materials. Other academic group are active in the field of organic synthesis automation. We already discussed of the work at MIT by Jensen, Jamison and coworkers. The group of Ley developed an automated system mixing continuous and batch reactors in the same integrated system controlled by a web-based interface. They demonstrated such concept in 2016 by synthesizing 5-methyl-4-propylthiophene-2-carboxylic acid, a precursor for the cancer drug candidate AZ82. For the needs of the synthetic pathway, they had to develop a glass reactor that can be temperature-regulated from − 70 °C to + 150 °C. The three-layer jacket enhanced the heat transfer and the thermal control of the reaction media, which are often ineffective using classical oil bath as a heat source. The web-based software allowed to control the whole system remotely, but also to receive analytical data in real time. The fully telescoped three-step synthesis gave the expected product with an overall yield of 30%. The fully manual procedure occurred with a slightly lower yield of 27% [65]. The modular software package they developed is also capable of multidimensional optimization. As a proof-of-concept, they made the five-dimensional optimization of an Appel reaction. The software made its self-optimization using the feedback provided by an IR spectrometer and mass spectrometry data. The software was able to find the optimal value of five experimental parameters after 30 experiments performed autonomously over 10 h [66]. In-line quality control devices and robust process analytical technology (PAT) are mandatory to comply with the regulatory obligations linked to the continuous manufacturing of APIs. The direct integration of PAT data into an integrated control software is sometimes problematic due to proprietary drivers or communication protocols. This year, Kappe and coworkers used a system composed of a thermoregulated microreactor linked to an inline NMR and an inline FTIR apparatus to collect real-time measurements the reaction media. Those data can then be processed with an appropriate chemometric model to optimize up to 7 reaction variables linked to a two-step process without any human intervention. The use of such automated process is not always possible at early stages of the development of pharmaceuticals as the quantity of precursor needed could be the limiting factor. For example, optimization of the considered two-step process required 14.3 g/h of starting material, meaning a total of 371.8 g for the 85 iterations performed over 26 h [67]. Cronin and coworkers unveiled their Chemputer in 2019. This platform is the combination of a modular robotic platform with an intuitive software overlay. The hardware part consists in modules dedicated to the four key steps of a chemical synthesis: reaction, workup, isolation and the subsequent purification. The modules are interconnected through a fluidic backbone consisting of pumps and valves allowing the transfer of chemicals between them. Thanks to a washing system, the system makes multisteps synthesis possible. As a proof of concept, they performed the autonomous synthesis of the antihistamine and mild sleep aid diphenydramine hydrochloride, the anticonvulsant rufinamide and sildenafil used to treat erectile dysfunction. The modules used were a reaction flask, a temperature regulated filtration module, a liquid-liquid separation module and a liquid evaporation module. This system, costing less than 10,000 USD, was able to produce the three APIs with yields comparable to the one obtained after a manual synthesis. In order to program the synthesis unit, they developed a language allowing the translation of physical operations performed by a trained chemist to machine-readable low-level instructions through their “Chempiler”. Operations are described by the user using a Chemical Description Language (ΧDL) allowing its use by staff without any programming skills through the user-friendly “Chemical Development Environment” (ChemIDE) interface [68]. They pushed the automation one step further by developing “Synthreader”, a program based on natural language processing technology that translates, often ambiguous, literature protocol into ΧDL instructions. This program automatically splits the protocols into a list of actions, extract the relevant process and reagent information and then translate it into unambiguous ΧDL formatted sentences. The obtained ΧDL file is then compiled to be used on the user customized setup [69]. While usable in the academic environment, these systems are not suited for an industrial use. Any commercial equipment used in the industry and especially in the pharmaceutical industry must be supplied with a sufficient documentary package to allow its qualification and validation prior use. These requirements apply for the software as well. Moreover, devices should be designed to allow an efficient and easy servicing in order to minimize the downtime due to impromptus failures or planned maintenance. Conclusion The development of automated modular units based on integrated continuous processes is a promising technology enabler for the upgrading of biobased platform chemicals into high value-added chemicals. From a chemical point of view, continuous flow offers a convenient framework to elaborate safe and versatile conversion processes with a moderate to low environmental footprint. Regarding regulatory aspects, PAT allows an accurate and responsive process control guaranteeing the compliance of the product with its specifications. However, the impact of the variability of biomass sourced material on impurity profile and byproducts should still be determined. The development of modular and automated production units will fasten the shift of the actual production paradigm to smaller decentralized manufacturing sites allowing agile supply chain management. We have to keep in mind that all those developments should be made with the pharmaceutical industry documentary standard in mind. Declarations Competing interests No funding was received to assist with the preparation of this manuscript. Yoann Joyard, Geoffroy Kaisin and Nicolas Maindron are shareholders of the Company SynLock SRL. Vincent Tadino is a shareholder and the Executive Director of the Company SynLock SRL. Loic Bovy and Jean-Christophe M. Monbaliu have no relevant financial or non-financial interests to disclose. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Holm-Nielsen JB, Ehimen EA (2014) Biorefinery plant design, engineering and process optimisation. Advances in biorefineries: biomass and waste supply chain exploitation, pp 89–111. 10.1533/9780857097385.1.89 2. Brun N Hesemann P Esposito D Expanding the biomass derived chemical space Chem Sci 2017 8 4724 4738 10.1039/c7sc00936d 28959397 3. Carus M, Dammer L, Puente A et al (2017) Bio-based drop-in, smart drop-in and dedicated chemicals. https://roadtobio.eu/uploads/news/2017_October/RoadToBio_Drop-in_paper.pdf . Accessed on 10-29-22 4. Werpy T, Petersen G (2004) Top value added chemicals from biomass. Volume I - results of screening for potential candidates from sugars and synthesis gas. 10.2172/15008859 5. Bozell JJ Petersen GR Technology development for the production of biobased products from biorefinery carbohydrates—the US Department of Energy’s “top 10” revisited Green Chem 2010 12 539 555 10.1039/b922014c 6. Biddy MJ, Scarlata C, Kinchin C (2016) Chemicals from biomass: a market assessment of bioproducts with near-term potential. https://www.nrel.gov/docs/fy16osti/65509.pdf . Accessed on 10-29-22 7. Liao Y-T, Matsagar BM, Wu KC-W (2018) Metal–Organic Framework (MOF)-derived effective solid catalysts for valorization of lignocellulosic biomass. ACS Sustain Chem Eng 6:13628–13643. 10.1021/acssuschemeng.8b03683 8. Hülsey MJ, Yang H, Yan N (2018) Sustainable routes for the synthesis of renewable heteroatom-containing chemicals. ACS Sustain Chem Eng 6:5694–5707. 10.1021/acssuschemeng.8b00612 9. Abdelaziz OY, Hulteberg CP (2020) Lignin depolymerization under continuous-flow conditions: highlights of recent developments. Chemsuschem 13:4382–4384. 10.1002/cssc.202001225 10. Zakzeski J, Bruijnincx PCA, Jongerius AL, Weckhuysen BM (2010) The catalytic valorization of lignin for the production of renewable chemicals. Chem Rev 110:3552–3599. 10.1021/cr900354u 11. Liu X, Bouxin FP, Fan J et al (2020) Recent advances in the catalytic depolymerization of lignin towards phenolic chemicals: a review. Chemsuschem 13:4296–4317. 10.1002/cssc.202001213 12. Giorgi PD, Elizarov N, Antoniotti S (2017) Selective oxidation of activated alcohols by supported gold nanoparticles under an atmospheric pressure of O2: batch and continuous-flow studies. ChemCatChem 9:1830–1836. 10.1002/cctc.201700179 13. Premkumar J, Sampath P, Sanjay R et al (2020) Synthetic guaiacol derivatives as promising myeloperoxidase inhibitors targeting atherosclerotic cardiovascular disease. ChemMedChem 15:1187–1199. 10.1002/cmdc.202000084 14. Guz NR, Stermitz FR, Johnson JB et al (2001) Flavonolignan and flavone inhibitors of a Staphylococcus a ureus multidrug resistance pump: structure – activity relationships. J Med Chem 44:261–268. 10.1021/jm0004190 15. Sadiq AD, Chen X, Yan N, Sperry J (2018) Towards the shell biorefinery: sustainable synthesis of the anticancer alkaloid proximicin A from chitin. Chemsuschem 11:532–535. 10.1002/cssc.201702356 16. Ogata M Hattori T Takeuchi R Usui T Novel and facile synthesis of furanodictines A and B based on transformation of 2-acetamido-2-deoxy-d-glucose into 3,6-anhydro hexofuranoses Carbohydr Res 2010 345 230 234 10.1016/j.carres.2009.10.007 19962133 17. World Health Organization (2021) World Health Organization model list of essential medicines – 22nd list 18. Ragno D, Leonardi C, di Carmine G et al (2021) Regiodivergent isosorbide acylation by oxidative N-Heterocyclic carbene catalysis in batch and continuous flow. ACS Sustain Chem Eng 9:8295–8305. 10.1021/acssuschemeng.1c02765 19. Morodo R Gérardy R Petit G Monbaliu JCM Continuous flow upgrading of glycerol toward oxiranes and active pharmaceutical ingredients thereof Green Chem 2019 21 4422 4433 10.1039/c9gc01819k 20. Gérardy R, Debecker DP, Estager J et al (2020) Continuous flow upgrading of selected C2–C6 platform chemicals derived from biomass. Chem Rev 120:7219–7347. 10.1021/acs.chemrev.9b00846 21. Gérardy R, Emmanuel N, Toupy T et al (2018) Continuous flow organic chemistry: successes and pitfalls at the interface with current societal challenges. Eur J Org Chem 2018:2301–2351. 10.1002/ejoc.201800149 22. Gérardy R, Morodo R, Estager J et al (2019) Sustaining the transition from a petrobased to a biobased chemical industry with flow chemistry. Top Curr Chem 377:1. 10.1007/s41061-018-0222-3 23. Monbaliu J-CM Legros J Will the next generation of chemical plants be in miniaturized flow reactors? Lab Chip 2022 10.1039/D2LC00796G 36278262 24. Lapkin AA, Yaseneva P (2017) Life cycle assessment of flow chemistry processes. In: Sustainable flow chemistry. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 249–276 25. Hessel V, Escribà-Gelonch M, Bricout J et al (2021) Quantitative sustainability assessment of flow chemistry–from simple metrics to holistic assessment. ACS Sustain Chem Eng 9:9508–9540. 10.1021/acssuschemeng.1c02501 26. Calvo-Serrano R, Guo M, Pozo C et al (2019) Biomass conversion into fuels, chemicals, or electricity? A network-based life cycle optimization approach applied to the European Union. ACS Sustain Chem Eng 7:10570–10582. 10.1021/acssuschemeng.9b01115 27. Iwaszko J, Zajemska M, Zawada A et al (2020) Vitrification of environmentally harmful by-products from biomass torrefaction process. J Clean Prod 249:119427. 10.1016/j.jclepro.2019.119427 28. Lindorfer J, Lettner M, Fazeni K et al (2019) Technical, economic and environmental assessment of biorefinery concepts 29. Muzyka C, Monbaliu J-CM (2022) Perspectives for the upgrading of bio-based vicinal diols within the developing european bioeconomy. ChemSusChem 15. 10.1002/cssc.202102391 30. Innovation for sustainable growth : a bioeconomy for Europe. https://op.europa.eu/en/publication-detail/-/publication/1f0d8515-8dc0-4435-ba53-9570e47dbd51. Accessed on 10-29-22 31. Review of the 2012 European Bioeconomy Strategy. https://ec.europa.eu/information_society/newsroom/image/document/2018-6/review_of_2012_eu_bes_2E89B85F-950B-9C84-5B426D1C24851387_49692.pdf . Accessed on 10-29-22 32. https://www.whitehouse.gov/briefing-room/presidential-actions/2022/09/12/executive-order-on-advancing-biotechnology-and-biomanufacturing-innovation-for-a-sustainable-safe-and-secure-american-bioeconomy/ . Accessed on 10-29-22 33. https://www.biopreferred.gov/BioPreferred/ . Accessed on 10-29-22 34. US Food & Drug Administration (2019) Draft guidance - quality considerations for continuous manufacturing 35. ICH Harmonised Guidelines (2021) Continuous manufacturing of drug substances and drug products Q13 - draft version 36. US Food & Drug Administration (2004) Guidance for industry - PAT — a framework for innovative pharmaceutical development, manufacturing, and quality assurance 37. Simon LL, Pataki H, Marosi G et al (2015) Assessment of recent Process Analytical Technology (PAT) trends: a multiauthor review. Org Process Res Dev 19:3–62. 10.1021/op500261y 38. European Commission (2021) EU Biorefinery Outlook to 2030 https://op.europa.eu/s/wTmR . Accessed on 10-29-22 39. Clark H, Deswarte JEI, Farmer FJ T (2009) The integration of green chemistry into future biorefineries. Biofuels Bioprod Biorefin 3:72–90. 10.1002/bbb.119 40. Kokossis AC Yang A On the use of systems technologies and a systematic approach for the synthesis and the design of future biorefineries Comput Chem Eng 2010 34 1397 1405 10.1016/j.compchemeng.2010.02.021 41. Hessel V (2009) Novel process windows - gate to maximizing process intensification via flow chemistry. Chem Eng Technol 32:1655–1681. 10.1002/ceat.200900474 42. Keil FJ Process intensification Rev Chem Eng 2018 34 135 200 10.1515/revce-2017-0085 43. Newman SG Jensen KF The role of flow in green chemistry and engineering Green Chem 2013 15 1456 10.1039/c3gc40374b 44. Kassin V-EH Gérardy R Toupy T Expedient preparation of active pharmaceutical ingredient ketamine under sustainable continuous flow conditions Green Chem 2019 21 2952 2966 10.1039/C9GC00336C 45. von Keutz T, Williams JD, Kappe CO (2021) Flash chemistry approach to organometallic C-Glycosylation for the synthesis of remdesivir. Org Process Res Dev 25:1015–1021. 10.1021/acs.oprd.1c00024 46. Chen Y, Cattoen M, Monbaliu J-CM (2021) Mitigation of chemical hazards under continuous flow conditions. In: Flow chemistry – fundamentals. De Gruyter, pp 269–312 47. Cantillo D, Mateos C, Rincon JA et al (2015) Light-induced C-H Arylation of (Hetero)arenes by in situ generated diazo anhydrides. Chem---Eur J 21:12894–12898. 10.1002/chem.201502357 48. Crawford DE, Porcheddu A, McCalmont AS et al (2020) Solvent-free, continuous synthesis of hydrazone-based active pharmaceutical ingredients by twin-screw extrusion. ACS Sustain Chem Eng 8:12230–12238. 10.1021/acssuschemeng.0c03816 49. Bolt RRA Leitch JA Jones AC Continuous flow mechanochemistry: reactive extrusion as an enabling technology in organic synthesis Chem Soc Rev 2022 51 4243 4260 10.1039/D1CS00657F 35506866 50. Xu Q, Sheng X, Li N et al (2021) Ball-milling: a productive, economical, and widely applicable method for condensation of biomass-derived aldehydes and ketones at mild temperatures. ACS Sustain Chem Eng 9:8232–8237. 10.1021/acssuschemeng.1c02270 51. Noël T, Cao Y, Laudadio G (2019) The fundamentals behind the use of flow reactors in electrochemistry. Acc Chem Res 52:2858–2869. 10.1021/acs.accounts.9b00412 52. Delparish A, Uslu A, Cao Y et al (2022) Boosting the valorization of biomass and green electrons to chemical building blocks: a study on the kinetics and mass transfer during the electrochemical conversion of HMF to FDCA in a microreactor. Chem Eng J 438:135393. 10.1016/j.cej.2022.135393 53. Ošeka M, Laudadio G, van Leest NP et al (2021) Electrochemical aziridination of internal alkenes with primary amines. Chem 7:255–266. 10.1016/j.chempr.2020.12.002 54. Cantillo D Synthesis of active pharmaceutical ingredients using electrochemical methods: keys to improve sustainability Chem Commun 2022 58 619 628 10.1039/D1CC06296D 55. Köckinger M Hanselmann P Roberge DM Sustainable electrochemical decarboxylative acetoxylation of aminoacids in batch and continuous flow Green Chem 2021 23 2382 2390 10.1039/D1GC00201E 56. Maljuric S Jud W Kappe CO Cantillo D Translating batch electrochemistry to single-pass continuous flow conditions: an organic chemist’s guide J Flow Chem 2020 10 181 190 10.1007/s41981-019-00050-z 57. Cao Y, Knijff J, Delparish A et al (2021) A divergent paired electrochemical process for the conversion of furfural using a divided-cell flow microreactor. Chemsuschem 14:590–594. 10.1002/cssc.202002833 58. Buglioni L, Beslać M, Noël T (2021) Dehydrogenative azolation of arenes in a microflow electrochemical reactor. J Org Chem 86:16195–16203. 10.1021/acs.joc.1c01409 59. Heugebaert TSA, Stevens C, Kappe CO (2015) Singlet-oxygen oxidation of 5-Hydroxymethylfurfural in continuous flow. Chemsuschem 8:1648–1651. 10.1002/cssc.201403182 60. Gérardy R, Winter M, Horn CR et al (2017) Continuous-flow preparation of γ-Butyrolactone scaffolds from renewable fumaric and itaconic acids under photosensitized conditions. Org Process Res Dev 21:2012–2017. 10.1021/acs.oprd.7b00314 61. Emmanuel N, Mendoza C, Winter M et al (2017) Scalable photocatalytic oxidation of methionine under continuous-flow conditions. Org Process Res Dev 21:1435–1438. 10.1021/acs.oprd.7b00212 62. Adamo A Beingessner RL Behnam M On-demand continuous-flow production of pharmaceuticals in a compact, reconfigurable system Science (1979) 2016 352 61 67 10.1126/science.aaf1337 63. Zhang P, Weeranoppanant N, Thomas DA et al (2018) Advanced continuous flow platform for on-demand pharmaceutical manufacturing. Chem---Eur J 24:2776–2784. 10.1002/chem.201706004 64. Coley CW, Thomas DA, Lummiss JAM et al (2019) A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365:aax1566. 10.1126/science.aax1566 65. Fitzpatrick DE, Ley S (2016) Engineering chemistry: integrating batch and flow reactions on a single, automated reactor platform. React Chem Eng 1:629–635. 10.1039/c6re00160b 66. Fitzpatrick DE, Battilocchio C, Ley S (2016) A novel internet-based reaction monitoring, control and autonomous self-optimization platform for chemical synthesis. Org Process Res Dev 20:386–394. 10.1021/acs.oprd.5b00313 67. Sagmeister P, Ort FF, Jusner CE et al (2022) Autonomous multi-step and multi-objective optimization facilitated by real-time process analytics. Adv Sci 2105547:1–9. 10.1002/advs.202105547 68. Steiner S, Wolf J, Glatzel S et al (2019) Organic synthesis in a modular robotic system driven by a chemical programming language. Science 363:aav2211. 10.1126/science.aav2211 69. Mehr SHM, Craven M, Leonov AI et al (2020) A universal system for digitization and automatic execution of the chemical synthesis literature. Science 370:101–108. 10.1126/science.abc2986	36442067	PMC9707437	NO-CC CODE	2022-12-03 23:19:45	no	Ann Intern Med. 2022 Nov 29;:P22-0021	latin-1	Ann Intern Med	2,022	10.7326/P22-0021	oa_other
==== Front Ann Intern Med Ann Intern Med aim Annals of Internal Medicine 0003-4819 1539-3704 American College of Physicians 36442066 10.7326/M22-3283 aim-olf-M223283 Editorials 8643Antibodies3122457COVID-192553Immunity378Research quality assessment2892Systematic reviews8398Treatment guidelinesearlyCurrently Online FirstcoronavirusCoronavirus Disease 2019 (COVID-19)Living, Rapid Reviews in a Rapidly Evolving World Living, Rapid Reviews in a Rapidly Evolving World Chang Stephanie MD, MPH American College of Physicians, Washington, DC (S.C.) Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M22-3283. Corresponding Author: Stephanie Chang, MD, MPH, American College of Physicians, 25 Massachusetts Avenue #700, Washington, DC 20001; e-mail, [email protected]. 29 11 2022 29 11 2022 M22-32832022 American College of Physicians This article is made available via the PMC Open Access Subset for unrestricted re-use for research, analyses, and text and data mining through PubMed Central. Acknowledgement of the original source shall include a notice similar to the following: "© 2020 American College of Physicians. Some rights reserved. This work permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited." These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. In their article, Holmer and colleagues reported the final update of a living review that examined the duration of IgG antibody response, the role of previous SARS-CoV-2 infection to prevent reinfection, and the role of antibodies in protection from reinfection. The editorialist discusses the challenge of living reviews as new evidence emerges and the relevant clinical questions evolve. ==== Body pmcWhen the COVID-19 pandemic began, clinicians struggled to make decisions in the absence of any direct evidence. Researchers responded rapidly to fill the knowledge void with many studies of varying quality, using different interventions, cointerventions, comparators, and populations. Simultaneously, the systematic review field mobilized to organize and help collate, manage, and translate the rapid proliferation of research for on-the-ground decision makers (1, 2). In the face of a voracious need for information but an insufficient, although rapidly developing, evidence base, the living, rapid review was born. Rapid reviews, although poorly defined, emerged out of the acknowledgment that traditional systematic reviews often take more than 12 months to complete, but decisions need to be made faster (3). Previously established methods for updating reviews (4) formed the foundation of the living review. The pandemic brokered this marriage, and since 2020, Annals of Internal Medicine has since published 7 living, rapid reviews related to the COVID-19 pandemic. The publication by Holmer and colleagues (5) marks the completion of 5. In this second and final update, Holmer and colleagues (5) examine the duration of IgG antibody response, the role of previous SARS-CoV-2 infection to prevent reinfection, and the role of antibodies in protection from reinfection. The authors found low strength of evidence that antibodies are maintained for at least 12 months, although this may be less in immunocompromised groups and with later variants. They found moderate strength of evidence that prior infection with the Delta SARS-CoV-2 variant protected against symptomatic reinfection with the Omicron BA.1 and BA.2 variants (50% to 67%) and was highly protective against severe disease. There was low strength of evidence that prior infection with BA.1 or BA.2 protected against symptomatic reinfection with BA.4 or BA.5 variants for up to 5 months. However there was little evidence on the role of antibody testing to predict future reinfection risk. Updating a review is often more complicated than rerunning a search, adding more studies to an evidence table, and updating a meta-analysis. In 2016, Garner and colleagues (4) published a consensus and checklist for updating systematic reviews. That guidance suggested that the decision to update a review should start with asking whether the review addresses a current question. If the question is no longer current, the panel recommended that no further update is needed, such as when the intervention is not in use or has been superseded. In 2022, El Mikati and colleagues (6) published a framework for living practice guidelines. Once a guideline is in the living mode, authors note that each update should start with revisiting the relevance of the question and whether any modifications are needed (for example, changing the outcomes of interest) before moving on to updating the systematic evidence review and clinical practice recommendation. The review by Holmer and colleagues highlights many of the challenges of conducting living, rapid reviews not only when the evidence is rapidly evolving, but the environment and virus itself also continue to evolve. When this review began in 2020, the primary question motivating the review was whether antibodies could be used as a marker of immunity (7). Although the authors could not make this link, in the original review, they did find that most adults with SARS-CoV-2 infection developed antibodies that persisted for at least 3 months. With the advent of vaccination further confusing the measurement of antibodies, in the first update, rather than continuing down the established, but less clinically useful, path on examining evidence for antibodies as a marker of immunity, the authors turned to understanding the role of SARS-CoV-2 infection on reinfection (8). They found that before the Delta and Omicron variants, infection had strong protection against symptomatic reinfections for 7 months compared with unvaccinated, uninfected persons. This second and final update returns to update both questions about the antibody response and protection from reinfection after infection. Although both Garner and colleagues (4) and El Mikati and colleagues (6) account for the possibility of changing questions in living updates, both suggest or imply that reviewers and guideline developers restrict updates to relatively minor changes in questions. This topic exemplifies the challenges that may arise from broader changes in scope and questions. The authors of this living, rapid review could have considered that changes in the vaccination and variant landscape made the original primary question obsolete and abandoned the review and/or started a new review. Instead, they used a pragmatic approach and evolved the questions to encompass related questions on reinfection risk to remain pertinent to the decisional milieu at the time. They used what they called a SWATH review (9) where updates may focus on different parts of the overarching topic coincident with the emergence of research and interest by decision makers. Challenges in transparently conveying the linkages between the updates and the original review may be offset by increased flexibility in being able to address the question most relevant to decision making. This living, rapid review demonstrated novel methods to maintain flexibility and relevance in a rapidly changing environment. As interest and experience in living reviews increase, the systematic review field will continue to learn and experiment with different approaches to handle changing questions and methods. The COVID-19 pandemic has spurred interest in living reviews and shown how quickly questions themselves become outdated. However, interest in living reviews continues beyond COVID-19. Research, by nature, means learning and changing context; thus, the challenges around changing questions will certainly not be unique to COVID-19. This article was published at Annals.org on 29 November 2022. ==== Refs References 1. Boutron I , Chaimani A , Meerpohl JJ , et al; COVID-NMA Consortium. The COVID-NMA Project: building an evidence ecosystem for the COVID-19 pandemic [Editorial]. Ann Intern Med. 2020;173 :1015-7. [PMID: ] doi:10.7326/M20-5261 32931326 2. Rada G , Pérez D , Araya-Quintanilla F , et al. Epistemonikos: a comprehensive database of systematic reviews for health decision-making. BMC Med Res Methodol. 2020;20 :286. [PMID: ] doi:10.1186/s12874-020-01157-x 33256642 3. Hartling L, Guise JM, Kato E, et al. EPC methods: an exploration of methods and context for the production of rapid reviews. (Prepared by the Scientific Resource Center under Contract No. 290-2012-00004-C.) AHRQ Publication No. 15-EHC008-EF. Agency for Healthcare Research and Quality; 2015. Accessed at https://effectivehealthcare.ahrq.gov/products/rapid-review-production/white-paper on 14 November 2022. 4. Garner P , Hopewell S , Chandler J , et al; Panel for Updating Guidance for Systematic Reviews (PUGs). When and how to update systematic reviews: consensus and checklist. BMJ. 2016;354 :i3507. [PMID: ] doi:10.1136/bmj.i3507 27443385 5. Holmer HK, Mackey K, Fiordalisi CV, et al. Major update 2: antibody response and risk for reinfection after SARS-CoV-2 infection—final update of a living, rapid review. Ann Intern Med. 2022. [Epub ahead of print]. doi:10.7326/M22-1745 6. El Mikati IK , Khabsa J , Harb T , et al; Living Guidelines Group. A framework for the development of living practice guidelines in health care. Ann Intern Med. 2022;175 :1154-60. [PMID: ] doi:10.7326/M22-0514 35785533 7. Arkhipova-Jenkins I , Helfand M , Armstrong C , et al. Antibody response after SARS-CoV-2 infection and implications for immunity. A rapid living review. Ann Intern Med. 2021;174 :811-21. [PMID: ] doi:10.7326/M20-7547 33721517 8. Helfand M , Fiordalisi C , Wiedrick J , et al. Risk for reinfection after SARS-CoV-2. A living, rapid review for American College of Physicians practice points on the role of the antibody response in conferring immunity following SARS-CoV-2 infection. Ann Intern Med. 2022;175 :547-55. [PMID: ] doi:10.7326/M21-4245 35073157 9. Agency for Healthcare Research and Quality. Immunity after COVID-19. Accessed at https://effectivehealthcare.ahrq.gov/products/immunity-after-covid/protocol on 14 November 2022.	36442066	PMC9707438	NO-CC CODE	2022-12-03 23:19:45	no	Ann Intern Med. 2022 Nov 29;:M22-3283	utf-8	Ann Intern Med	2,022	10.7326/M22-3283	oa_other
==== Front Ann Intern Med Ann Intern Med aim Annals of Internal Medicine 0003-4819 1539-3704 American College of Physicians 36442057 10.7326/M22-3219 aim-olf-M223219 Editorials 3122457COVID-192357Health care providers3124878Infection control7426RespiratorsearlyCurrently Online FirstcoronavirusCoronavirus Disease 2019 (COVID-19)Comparative Effectiveness of Mask Type in Preventing SARS-CoV-2 in Health Care Workers: Uncertainty Persists Effectiveness of Mask Type in Preventing SARS-CoV-2 in Health Care Workers Chou Roger MD Oregon Health & Science University, Portland, Oregon Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M22-3219. Corresponding Author: Roger Chou, MD, Oregon Health & Science University, Mail Code: BICC, 3181 SW Sam Jackson Park Road, Portland, OR 97239; e-mail, [email protected]. 29 11 2022 29 11 2022 M22-32192022 American College of Physicians This article is made available via the PMC Open Access Subset for unrestricted re-use for research, analyses, and text and data mining through PubMed Central. Acknowledgement of the original source shall include a notice similar to the following: "© 2020 American College of Physicians. Some rights reserved. This work permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited." These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Two and a half years after the emergence of the COVID-19 pandemic, Loeb and colleagues reported the first randomized trial of N95 respirators versus medical masks in health care workers. The editorialist discusses the findings and highlights remaining areas of uncertainty about optimal mask type. ==== Body pmcHealth care workers have been at the forefront of the COVID-19 pandemic response and are at high risk for acquiring SARS-CoV-2 infection. Infected health care workers can transmit SARS-CoV-2 to coworkers and patients, including persons at risk for severe illness. Therefore, preventing SARS-CoV-2 infection in health care workers is essential to both maintaining an adequate health care workforce and preventing iatrogenic infections. Along with other infection control measures in the hospital setting, personal protective equipment, including masks, is a key component of strategies to prevent SARS-CoV-2 in health care workers. However, the optimal mask type remains uncertain. N95 and equivalent respirators are designed to filter out at least 95% of airborne particles that are 0.3 microns or larger, with minimal leakage when properly fitted (1). By contrast, medical masks filter less efficiently and are loose fitting, enabling leakage, but they are more comfortable, less expensive, do not require fitting, and are more widely available. An ongoing point of contention has centered around when N95 respirators are necessary. Recommendations range from routine N95 use to N95s only for settings in which aerosol-generating procedures are performed (2, 3). Evidence on the comparative effectiveness of different mask types in health care workers has been suboptimal. Until now, the only randomized trials were from the pre-COVID-19 era and indicated similar risk for influenza-like illness with N95 respirators and medical masks (4). Observational studies found that N95 respirators were associated with decreased risk for SARS-CoV-1 infection, but the studies had methodological limitations, including potential recall bias, incomplete SARS-CoV-1 exposure measurement, and residual confounding (5). The applicability to SARS-CoV-2 of observations related to transmission of other viruses is uncertain. Subsequent observational studies in the SARS-CoV-2 era also provided insufficient evidence because of methodological limitations and inconsistent findings (6). Two and a half years after the emergence of the COVID-19 pandemic, Loeb and colleagues (7) report the first randomized trial of N95 respirators versus medical masks in health care workers. This study was designed to determine if medical masks are noninferior to fit-tested N95 respirators for protecting health care workers during routine care of patients with known or suspected COVID-19. Overall, the trial found that the effects of medical masks versus N95 respirators on risk for SARS-CoV-2 infection confirmed by reverse transcriptase polymerase chain reaction were within the predefined noninferiority margin (hazard ratio, 1.14 [95% CI, 0.77 to 1.69]). A slightly higher proportion of participants randomly assigned to N95 respirators reported mask-related adverse events (primarily, discomfort or headaches). Strengths of the trial include the randomized design, pragmatic approach (for example, use of N95 respirators in participants randomly assigned to medical masks permitted when deemed necessary), and high mask type adherence. Despite evidence suggesting that outside exposures may be a stronger risk factor for COVID-19 than work exposures (8), a post hoc analysis found similar findings in participants with or without nonwork exposures. The noninferiority margin used by the trial warrants closer examination. The prespecified noninferiority margin was a 5% absolute increase in COVID-19 incidence confirmed by reverse transcriptase polymerase chain reaction. This was based on preserving an estimated 50% of the observed N95 respirator benefit versus no mask from a prior retrospective study of SARS (9) and the input of health care professionals. Yet, the 5% increase represents a potential doubling of risk with medical masks—a generous noninferiority threshold which may be unacceptable to many health workers. In fact, the finding of noninferiority in this trial was consistent with up to a relative 70% increased risk. The trial protocol also underwent several changes because of the evolution of the pandemic and availability of vaccination. These included expanded eligibility from nurses to any health care worker providing direct patient care, expansion to additional countries, permitting extended and reuse of N95 respirators, reducing follow-up from 12 to 10 weeks, expanding symptomatic criteria for COVID-19 testing, excluding previously vaccinated health care workers, continuing assessment of outcomes for 2 weeks in health care workers vaccinated during the trial, and increased sample size. These changes aimed to enhance enrollment and statistical power, maintain a pragmatic approach, and account for uptake of COVID-19 vaccinations. Although the changes do not seem to have biased findings, use of an adaptive or other flexible design could have better anticipated and addressed modifications to study methods (10). There was substantial heterogeneity in outcomes by country. A post hoc analysis stratified by country reported hazard ratios that ranged from 0.95 in Egypt (where participants were enrolled later in the pandemic and seroprevalence was high) to 2.83 in Canada (where participants were enrolled early in the pandemic and seroprevalence was low). Among the factors that could contribute to this heterogeneity are differences in vaccine types, vaccination rates, infection control measures, local transmission dynamics, and enrollment during periods when different variants were predominantly circulating. This heterogeneity warrants caution in the interpretation of the trial findings. Nearly three quarters (74% [73 of 99]) of cases occurred in Egypt, where medical masks were within the noninferiority margin (hazard ratio, 0.95 [CI, 0.60 to 1.50]). Estimates from other countries did not meet noninferiority criteria but were based on few events (range, 5 to 11) and were very imprecise, with overlapping CIs. Therefore, both the combined and site-specific findings are inconclusive. Yet, the stratified analysis enables one to judge that the trial findings are likely most applicable to an Omicron-predominant, high baseline COVID-19 seroprevalence setting. The trial had other limitations. It was not designed to evaluate the effects of mask types as source control or in effectively vaccinated health care workers. The trial was unable to assess the effect of Omicron or other variants on mask effectiveness. Some subgroup analyses were post hoc, and it was not possible to determine how the findings varied according to factors affecting SARS-CoV-2 transmission, such as ventilation, use of other infection control measures, specific health care setting, or vaccination status. Nonetheless, this trial provides the best evidence to date on comparative effectiveness of mask types in preventing SARS-CoV-2 infection in health care workers providing routine patient care. The results indicate that medical masks may be similar to N95 respirators in Omicron-era settings with high COVID-19 seroprevalence—but would not have met a more stringent noninferiority threshold (for example, 30% relative increased risk). Therefore, the results are not definitive. Decisions about mask types in health care workers should be informed by the uncertainty around the estimates and continue to account for health care worker preferences about potential tradeoffs, N95 respirator availability, and resource constraints. This article was published at Annals.org on 29 November 2022. ==== Refs References 1. Das S , Sarkar S , Das A , et al. A comprehensive review of various categories of face masks resistant to COVID-19. Clin Epidemiol Glob Health. 2021;12 :100835. [PMID: ] doi:10.1016/j.cegh.2021.100835 34368502 2. World Health Organization. WHO recommendations on mask use by health workers, in light of the Omicron variant of concern: WHO interim guidelines, 22 December 2021. Accessed at www.who.int/publications/i/item/WHO-2019-nCoV-IPC_Masks-Health_Workers-Omicron_variant-2021.1 on 21 October 2022. 3. Centers for Disease Control and Prevention. Interim infection prevention and control recommendations for healthcare personnel during the coronavirus disease 2019 (COVID-19) pandemic. Accessed at www.cdc.gov/coronavirus/2019-ncov/hcp/infection-control-recommendations.html on 21 October 2022. 4. Bartoszko JJ , Farooqi MAM , Alhazzani W , et al. Medical masks vs N95 respirators for preventing COVID-19 in healthcare workers: a systematic review and meta-analysis of randomized trials. Influenza Other Respir Viruses. 2020;14 :365-73. [PMID: ] doi:10.1111/irv.12745 32246890 5. Chou R , Dana T , Jungbauer R , et al. Masks for prevention of respiratory virus infections, including SARS-CoV-2, in health care and community settings. A living rapid review. Ann Intern Med. 2020;173 :542-55. [PMID: ] doi:10.7326/M20-3213 32579379 6. Chou R , Dana T , Jungbauer R . Update alert 8: masks for prevention of respiratory virus infections, including SARS-CoV-2, in health care and community settings [Letter]. Ann Intern Med. 2022;175 :W108-09. [PMID: ] doi:10.7326/L22-0272 35878407 7. Loeb M, Bartholomew A, Hashmi M, et al. Medical masks versus N95 respirators for preventing COVID-19 among health care workers. A randomized trial. Ann Intern Med. 29 November 2022. [Epub ahead of print]. doi:10.7326/M22-1966 8. Chou R , Dana T , Buckley DI , et al. Update alert 10: epidemiology of and risk factors for coronavirus infection in health care workers [Letter]. Ann Intern Med. 2022;175 :W8-9. [PMID: ] doi:10.7326/M21-4294 34781714 9. Loeb M , McGeer A , Henry B , et al. SARS among critical care nurses, Toronto. Emerg Infect Dis. 2004;10 :251-5. [PMID: ]15030692 10. Stallard N , Hamborg T , Parsons N , et al. Adaptive designs for confirmatory clinical trials with subgroup selection. J Biopharm Stat. 2014;24 :168-87. [PMID: ] doi:10.1080/10543406.2013.857238 24392984	36442057	PMC9707439	NO-CC CODE	2022-12-03 23:19:45	no	Ann Intern Med. 2022 Nov 29;:M22-3219	utf-8	Ann Intern Med	2,022	10.7326/M22-3219	oa_other
==== Front Ann Intern Med Ann Intern Med aim Annals of Internal Medicine 0003-4819 1539-3704 American College of Physicians 36442059 10.7326/M22-1745 aim-olf-M221745 Reviews 8643Antibodies10444Antibody production3122457COVID-193124935Omicron variant3124903Reinfection2892Systematic reviewsearlyCurrently Online FirstcoronavirusCoronavirus Disease 2019 (COVID-19)poc-eligiblePOC EligibleMajor Update 2: Antibody Response and Risk for Reinfection After SARS-CoV-2 Infection—Final Update of a Living, Rapid Review Antibody Response and Risk for Reinfection After SARS-CoV-2 Infection Holmer Haley K. PhD, MPH https://orcid.org/0000-0003-4198-8708 Mackey Katherine MD, MPP https://orcid.org/0000-0003-4749-5664 Fiordalisi Celia V. MS https://orcid.org/0000-0001-5610-1925 Helfand Mark MD, MS https://orcid.org/0000-0003-4846-9900 Scientific Resource Center for the Agency for Healthcare Research and Quality, Portland, Oregon (H.K.H., C.V.F.) VA Portland Health Care System, Portland, Oregon (K.M.) VA Portland Health Care System and Scientific Resource Center for the Agency for Healthcare Research and Quality, Portland, Oregon (M.H.) Disclaimer: The findings and conclusions in this document are those of the authors, who are responsible for its contents, and do not necessarily represent the views of the AHRQ. Therefore, no statement in this report should be construed as an official position of the AHRQ or the U.S. Department of Health and Human Services. Financial Support: By the AHRQ through the following contract: Scientific Resource Center (290-2017-00003-C). Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M22-1745. Reproducible Research Statement: Study protocol: Registered at PROSPERO (CRD42020207098) and posted to the AHRQ Effective Health Care website (4). Statistical code: Not applicable. Data set: Available at http://SRDRPLUS.AHRQ.gov. Corresponding Author: Haley K. Holmer, PhD, MPH, Portland VA Research Foundation, Scientific Resource Center, AHRQ EPC Pro, 3710 SW US Veterans Hospital Road, Portland, OR 97239; e-mail, [email protected]. Author Contributions: Conception and design: C.V. Fiordalisi, M. Helfand, H.K. Holmer, K. Mackey. Analysis and interpretation of the data: M. Helfand, H.K. Holmer, K. Mackey. Drafting of the article: C.V. Fiordalisi, M. Helfand, H.K. Holmer, K. Mackey. Critical revision of the article for important intellectual content: C.V. Fiordalisi, M. Helfand, H.K. Holmer, K. Mackey. Final approval of the article: C.V. Fiordalisi, M. Helfand, H.K. Holmer, K. Mackey. Obtaining of funding: M. Helfand. Administrative, technical, or logistic support: C.V. Fiordalisi, M. Helfand. Collection and assembly of data: C.V. Fiordalisi, M. Helfand, H.K. Holmer, K. Mackey. 29 11 2022 29 11 2022 M22-17452022 American College of Physicians This article is made available via the PMC Open Access Subset for unrestricted re-use for research, analyses, and text and data mining through PubMed Central. Acknowledgement of the original source shall include a notice similar to the following: "© 2020 American College of Physicians. Some rights reserved. This work permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited." These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. In this final update of the living, rapid review on the role of antibodies after SARS-CoV-2 infection, the authors summarize the evidence on the durability of the antibody response and the level and duration of protection from previous infection on more current variants, including the Delta and Omicron variants. Background: The durability of the antibody response after SARS-CoV-2 infection and the role of antibodies in protection against reinfection are unclear. Purpose: To synthesize evidence on the SARS-CoV-2 antibody response and reinfection risk with a focus on gaps identified in our prior reports. Data Sources: MEDLINE (Ovid), EMBASE, CINAHL, World Health Organization Research Database, and reference lists from 16 December 2021 through 8 July 2022, with surveillance through 22 August 2022. Study Selection: English-language, cohort studies evaluating IgG antibody duration at least 12 months after SARS-CoV-2 infection, the antibody response among immunocompromised adults, predictors of nonseroconversion, and reinfection risk. Data Extraction: Two investigators sequentially extracted study data and rated quality. Data Synthesis: Most adults had IgG antibodies after SARS-CoV-2 infection at time points greater than 12 months (low strength of evidence [SoE]). Although most immunocompromised adults develop antibodies, the overall proportion with antibodies is lower compared with immunocompetent adults (moderate SoE for organ transplant patients and low SoE for patients with cancer or HIV). Prior infection provided substantial, sustained protection against symptomatic reinfection with the Delta variant (high SoE) and reduced the risk for severe disease due to Omicron variants (moderate SoE). Prior infection was less protective against reinfection with Omicron overall (moderate SoE), but protection from earlier variants waned rapidly (low SoE). Limitation: Single review for abstract screening and sequential review for study selection, data abstraction, and quality assessment. Conclusion: Evidence for a sustained antibody response to SARS-CoV-2 infection is considerable for both Delta and Omicron variants. Prior infection protected against reinfection with both variants, but, for Omicron, protection was weaker and waned rapidly. This information may have limited clinical applicability as new variants emerge. Primary Funding Source: Agency for Healthcare Research and Quality. (PROSPERO: CRD42020207098) ==== Body pmcIn March 2021, we published the first version of a rapid, evolving, pragmatic review that described the antibody response in adults after an infection with SARS-CoV-2 (1, 2). In January 2022, we published a second review, meta-analysis, and data visualization (https://effectivehealthcare.ahrq.gov/products/immunity-after-covid/rapid-review) describing the risk for SARS-CoV-2 reinfection (3). Our objectives in conducting the original review were to assess the prevalence, level, and duration of the antibody response after infection; compare the risk for reinfection among those with a prior infection to persons who had never been infected; and examine the duration of protection against reinfection. We found that before the emergence of the Delta and Omicron variants, prior infection with the wild-type SARS-CoV-2 virus or the Alpha variant reduced the risk for reinfection by 80% to 97% (pooled estimate, 87% [95% CI, 84% to 90%]) compared with previously uninfected persons. Studies had a median follow-up of 8 months (range, 4 to 13 months), and protection remained above 80% for at least 7 months. There was sparse evidence on the duration of detectable antibodies beyond 6 months; whether the antibody response varied based on immunocompromised status or other factors, such as asymptomatic infection; and whether testing for SARS-CoV-2 antibodies provided clinically useful information about reinfection risk (that is, whether detectable antibodies correlated with protection). This update examines evidence gaps identified in our previous 2 versions, with a focus on the persistence of IgG antibodies for longer than 12 months after infection, whether the antibody response varies in immunocompromised persons, and characteristics of those who do not seroconvert (key question [KQ] 1). We also evaluated available evidence regarding reinfection with Delta or Omicron variants after previous infection and the relation of antibody levels, symptoms status, and age to protection against reinfection (KQ2) as well as the duration of protection in the context of Delta and Omicron variants (KQ3). Methods Our protocol for this rapid, evolving, pragmatic review was developed with the American College of Physicians, registered at PROSPERO (CRD42020207098), and posted to the Agency for Healthcare Research and Quality (AHRQ) Effective Health Care website (4). We modified the scope of this update to address gaps identified in prior versions and account for the emergence of new variants and coinciding developments in SARS-CoV-2 immunity research. Methods are described in detail in our previous reports (1–3, 5), and Supplement 1 describes specific modifications for this update, including details on searches, study selection, quality assessment, data synthesis, and grading the strength of the body of evidence. Using the same search strategies as previous reports, we conducted an updated literature search for KQ1. For KQs 2 and 3, we searched the World Health Organization's COVID-19 Research Database using the search terms reinfection and Omicron. We used Google Scholar, study-specific websites, and citation lists of all newly identified articles about reinfection to find new publications from previously included cohort studies. Articles identified in searches through 22 August 2022 were eligible for inclusion in this update. For KQs 2 and 3, we included publications that extended the results of the cohorts included in our original meta-analysis as well as newly identified retrospective or prospective cohort studies. Studies were included for KQ2 if they provided a protection estimate or data that allowed calculation of an estimate of the effect of previous infection on protection against any reinfection, symptomatic reinfection, and severe infection with the Delta or Omicron variants. Protection is calculated from the absolute risk difference (numerator) to give the proportion of reinfections prevented by previous infection (6): (risk among previously infected − risk among not previously infected) / risk among not previously infected We excluded studies that did not observe an uninfected, unvaccinated control cohort or did not present detailed adjusted or stratified results to characterize the effect of previous infection without vaccination. We also excluded publications if all data were collected before the emergence of the Delta variant and subsequent Delta wave. We prioritized publications (including preprints) that extended the length of follow-up of the cohorts included in our previous meta-analysis if they contributed new information about reinfection protection or duration. We also included new, published cohort studies that met our quality screening criteria. Test-negative case–control studies that did not extend the results of cohort studies in our original meta-analysis were not eligible for inclusion, but we examined their results to assess their concordance with the included studies. Details on study characteristics (Supplement 2), risk-of-bias assessments (Table 1 of Supplement 1), strength of evidence (SoE) (Table), and key findings (Tables 2 to 5 of Supplement 1) are provided in the supplemental materials and in the full AHRQ report (36). Table. Summary of Findings Table. Summary of Findings Data Synthesis and Analysis Evidence was synthesized qualitatively rather than quantitatively because of variability in study populations, outcomes, and the geographic distribution of circulating SARS-CoV-2 variants of concern. We assessed the SoE to describe our confidence in effect estimates as high, moderate, low, or insufficient. The assessment is based on our analysis of the study limitations, directness, precision, consistency, plausible confounding, and strength of association. Role of the Funding Source This work is based on a living, rapid review done for the AHRQ. The funding source assigned the topic and contributed to the development of the review aims and scope but was not involved in data collection, analysis, manuscript preparation, or submission. Results This update adds 29 observational studies to the evidence base (Appendix Figure). Our main findings are shown in the Table. Appendix Figure. PRISMA Preferred Reporting Items for Systematic reviews and Meta-Analyses) flow diagram. KQ = key question. Appendix Figure. PRISMA Preferred Reporting Items for Systematic reviews and Meta-Analyses) flow diagram. KQ = key question. Durability of the Antibody Response Immunoglobulin G Duration Greater Than 12 Months In our first report (2), we found that IgG may remain detectable for at least 120 days, based on the study with the longest follow-up at the time (37). For this update, 3 longitudinal studies completed during the first year of the pandemic before vaccine availability met inclusion criteria; these studies had a median follow-up of at least 12 months (range, 12.7 to 14 months) (7–9). Although a high proportion (83% to 97%) of adults had detectable IgG over the follow-up period in all 3 studies (Table 2 of Supplement 1), we have low confidence in this finding (low SoE) (Table). All studies were done early in the pandemic among adults who were mostly symptomatic during their primary infection, and we could not rule out the possibility that an asymptomatic or mild reinfection accounted for persistent antibodies. Results may not be generalizable to other settings or time periods or among adults with a mild or asymptomatic primary infection. Immunocompromised Populations In our original review, 3 observational studies provided insufficient evidence on the antibody response in immunocompromised populations. In this update, we identified 10 additional observational studies of the antibody response in immunocompromised patients compared with immunocompetent comparators: 3 studies in patients with cancer (16–18), 1 study in patients living with HIV (19), and 6 studies in patients who had undergone solid organ transplant (Table 3 of Supplement 1) (10–15). Immunoglobulin G antibodies were detected in most immunocompromised patients (≥65% at the first test after reverse transcriptase polymerase chain reaction diagnosis for all included studies, except for a single cohort study at just 15 days after infection, when IgG antibodies may not yet be detectable). However, IgG prevalence was consistently lower among immunocompromised patients compared with nonimmunocompromised control participants. We are moderately confident that most adults who are immunocompromised due to solid organ transplant develop IgG antibodies after SARS-CoV-2 infection, but the overall proportion of those who develop antibodies is lower compared with immunocompetent control participants (moderate SoE) (Table). Findings were consistent and direct, although studies were small and had methodological limitations. We have low confidence that this finding is stable for patients with cancer and persons living with HIV given fewer studies overall and study methodological limitations (low SoE) (Table). Nonseroconversion We identified 4 prospective cohort studies (20–23) comparing characteristics of patients who did not seroconvert 6 weeks after documented SARS-CoV-2 infection with those who did seroconvert, adding to the evidence from 2 cohort studies (24, 25) identified in our first report (2) (Table 4 of Supplement 1). Across these studies, the proportion of persons who did not develop antibodies ranged from 2% to 25%. Having no or few symptoms was the most consistent factor associated with nonseroconversion. Higher minimum cycle thresholds with polymerase chain reaction testing (indicating lower viral load) were associated with nonseroconversion in 2 studies (21, 23). Study methodological limitations give us low confidence in these findings (low SoE) (Table). We do not know to what extent the use of different immunoassays accounts for study variation. Moreover, participants could have been misclassified as not seroconverting depending on the timing of testing. Finally, the clinical significance of nonseroconversion is unclear. Persons who do not seroconvert after infection may still have a robust humoral response with repeated virus exposure because of immune memory (38). Magnitude and Duration of Protection From Previous Infection (KQs 2 and 3) Updates of 4 controlled, longitudinal cohort studies (26, 27, 28, 32, 34, 35, 39, 40) included in our previous meta-analysis (3) and 2 new cohort studies (29, 30, 41) contributed to estimates of protection against reinfection in the Delta and Omicron eras (Table 5 of Supplement 1). For the Delta variant, there was consistent, high-quality evidence that prior infection reduced the risk for reinfection by 80% to 97% (high SoE) (Table) (26–31). Longer follow-up for 3 of the cohorts suggested that, at least through the Delta wave, protection did not wane significantly for up to 13 months (moderate SoE) (Table) (26, 27, 29, 39). In the population-based study done in Qatar, prior infection before the emergence of the Omicron variant protected against another pre-Omicron infection by 85.5%, waning to approximately 70% by the 16th month. Compared with earlier waves, the Omicron waves were associated with an early, marked increase in the proportion of infections that were reinfections (40–43). Subsequently, cohort studies confirmed that prior infection was less protective against reinfection with the Omicron variants (BA.1, BA.2, BA.4, and BA.5) than against reinfection with Delta and older variants (moderate SoE) (Table) (26, 28, 30, 32, 34, 39, 41). Omicron BA.1 and BA.2 For Omicron BA.1 and BA.2, prior infection with the Delta variant reduced the risk for symptomatic infection by 50% to 67% (28, 31, 32, 39). Prior infection with older variants (for example, wild-type SARS-CoV-2 and the Alpha variant) was less protective against symptomatic infection (14% to 32%) and diminished more sharply over time. In the Qatar cohort, for example, protection against reinfection with Omicron BA.1 or BA.2 was higher among those with a recent Delta infection (approximately 60%) compared with all prior infections (39.8%) (39). In a Danish cohort study (28), protection against Omicron BA.1 or BA.2 was 43.1% if the previous infection occurred 3 to 6 months earlier and 22.2% if the previous infection had occurred at least 6 months earlier. Omicron BA.4 and BA.5 Additional analyses in the Qatar population provided detailed information about protection against Omicron BA.4 and BA.5. Among unvaccinated persons, a previous infection with Omicron BA.1 or BA.2 reduced the risk for any infection with Omicron BA.4 or BA.5 by at least 68.7% (CI, 64.0% to 72.9%) compared with only 27.7% (CI, 19.3% to 35.2%) if the prior infection had occurred before the emergence of the Omicron variant (35). Included studies had no information about the duration of protection against Omicron BA.4 or BA.5. Severe Disease In unvaccinated persons, protection against severe disease with Omicron BA.1 or BA.2 was 87.8% to 90% in the Qatari cohort (35, 39) and 69.8% in the Danish cohort (28). In a multivariable analysis of a large U.K. cohort, previous infection provided moderate protection against hospitalization (55%) and very high protection against death (>80%) (30). Severe disease and death from Omicron were rare in the U.K. nursing home setting, and previous infection seemed to provide some protection (33). However, in a Cleveland Clinic cohort, protection against hospitalization was lower than in other cohorts (44.4%) (32). After adjustment for age, sex, reason for testing, and vaccination status, protection against hospitalization and intensive care unit admission was reduced to 30%. The poorer results for the Cleveland Clinic cohort may be related to a lower proportion of recent (Delta or Omicron BA.1 or BA.2) infections and a higher prevalence of major comorbidities than the population-based studies. Role of Antibodies in Protection Our previous report found that seroconversion was associated with substantial protection against reinfection (3), but antibody testing to predict reinfection risk provided no additional information over the more widely used reverse transcriptase polymerase chain reaction test, and the role of antibody testing in clinical practice, if any, was uncertain. Although there is still no definitive evidence to guide practice decisions about antibody testing, studies are underway to delineate reinfection risk with infection-induced antibodies compared with vaccine-induced antibodies (44, 45). The U.K. SIREN (SARS-CoV-2 Immunity and Reinfection Evaluation) study is scheduled to complete data collection in March 2023 (46). Discussion A central question of this review has been whether a SARS-CoV-2 antibody test obtained in everyday clinical practice provides useful information about a person's future risk for infection. In this update, we found that although the antibody response to SARS-CoV-2 infection in the Omicron era remains robust, protection against reinfection was lower. The emergence of the Omicron variant, which evolved and spread despite high rates of vaccination and previous infection, has intensified interest in the capacity of SARS-CoV-2 variants to evade immune system protection. Recent infection with Delta or Omicron BA.1 or BA.2 seems to be protective against reinfection with Omicron for a few months but was lower than for previous variants and waned rapidly. Although based on relatively few studies, our findings about protection against Omicron variants are likely to be robust. First, we prioritized large, well-conducted, controlled cohort studies, most of which used consistent methods throughout the entire pandemic. Second, our findings are concordant with those of test-negative case–control studies (47–50) as well as with recent cohort studies (51, 52) and preprints (53–57) identified by surveillance. In general, these studies confirm that protection against Omicron BA.1 or BA.2 from previous infection with the Delta or earlier variants was lower and waned more rapidly over time than for previous variants and that, whereas protection against BA.4 or BA.5 from BA.1 or BA.2 infection was robust for up to 4 months, this protection may wane rapidly (54). One preprint—a meta-analysis of cohort, case-negative case–control, and cross-sectional studies—confirmed that protection against death and severe infection was generally preserved (53). The main implication of our findings about the antibody response and reinfection risk is that the presence of antibodies would be insufficient to estimate a person's degree of protection against reinfection. Although understanding population seroprevalence has important public health implications, the value of antibody testing in clinical practice remains unclear. Supplementary Material Click here for additional data file. Click here for additional data file. This article was published at Annals.org on 29 November 2022. ==== Refs References 1. Mackey K, Arkhipova-Jenkins I, Armstrong C, et al. Antibody response following SARS-CoV-2 infection and implications for immunity: a rapid living review. (Prepared by the Portland VA Research Foundation under Contract No. 290-2017-00003-C). AHRQ Publication No. 21-EHC016. Agency for Healthcare Research and Quality; 2021. doi:10.23970/AHRQEPCCOVIDIMMUNITY 2. Arkhipova-Jenkins I , Helfand M , Armstrong C , et al. Antibody response after SARS-CoV-2 infection and implications for immunity. A rapid living review. Ann Intern Med. 2021;174 :811-21. [PMID: ] doi:10.7326/M20-7547 33721517 3. Helfand M , Fiordalisi C , Wiedrick J , et al. Risk for reinfection after SARS-CoV-2. A living, rapid review for American College of Physicians Practice Points on the role of the antibody response in conferring immunity following SARS-CoV-2 infection. Ann Intern Med. 2022;175 :547-55. [PMID: ] doi:10.7326/M21-4245 35073157 4. Agency for Healthcare Research and Quality. Immunity after COVID-19. Accessed at https://effectivehealthcare.ahrq.gov/products/immunity-after-covid/protocol on 1 November 2022. 5. Helfand M, Fiordalisi C, Wiedrick J, et al. Risk of reinfection from SARS-CoV-2—an update of an antibody response following SARS-CoV-2 infection and implications for immunity: a living rapid review. (Prepared by the Scientific Resource Center under Contract No. 290-2017-0003). AHRQ Publication No. 21(22)-EHC034. Agency for Healthcare Research and Quality; 2022. doi:10.23970/AHRQEPCCOVIDIMMUNITY2 6. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Office of Workforce and Career Development. Measures of risk. In: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, Office of Workforce and Career Development, eds. Principles of Epidemiology in Public Health Practice: An Introduction to Applied Epidemiology and Biostatistics. 3rd ed. U.S. Department of Health and Human Services; 2012:3-49-50. Accessed at www.cdc.gov/csels/dsepd/ss1978/ss1978.pdf on 1 November 2022. 7. Dehgani-Mobaraki P , Zaidi AK , Yadav N , et al. Longitudinal observation of antibody responses for 14 months after SARS-CoV-2 infection. Clin Immunol. 2021;230 :108814. [PMID: ] doi:10.1016/j.clim.2021.108814 34343708 8. Haveri A, Ekström N, Solastie A, et al. Persistence of neutralizing antibodies a year after SARS-CoV-2 infection. medRxiv. Preprint posted online 16 July 2021. doi:10.1101/2021.07.13.21260426 9. Kučinskaitė-Kodzė I , Simanavičius M , Šimaitis A , et al. Persistence of SARS-CoV-2-specific antibodies for 13 months after infection. Viruses. 2021;13 :2313. [PMID: ] doi:10.3390/v13112313 34835119 10. Becchetti C , Broekhoven AGC , Dahlqvist G , et al. Humoral response to SARS-CoV-2 infection among liver transplant recipients. Gut. 2022;71 :746-56. [PMID: ] doi:10.1136/gutjnl-2021-326609 34987065 11. Caballero-Marcos A , Salcedo M , Alonso-Fernández R , et al; Spanish Society of Liver Transplantation (SETH). Changes in humoral immune response after SARS-CoV-2 infection in liver transplant recipients compared to immunocompetent patients. Am J Transplant. 2021;21 :2876-84. [PMID: ] doi:10.1111/ajt.16599 33835707 12. Søfteland JM , Gisslén M , Liljeqvist JÅ , et al. Longevity of anti-spike and anti-nucleocapsid antibodies after COVID-19 in solid organ transplant recipients compared to immunocompetent controls. Am J Transplant. 2022;22 :1245-52. [PMID: ] doi:10.1111/ajt.16909 34860447 13. Favà A , Donadeu L , Sabé N , et al. SARS-CoV-2-specific serological and functional T cell immune responses during acute and early COVID-19 convalescence in solid organ transplant patients. Am J Transplant. 2021;21 :2749-61. [PMID: ] doi:10.1111/ajt.16570 33756051 14. Favà A , Donadeu L , Jouve T , et al. A comprehensive assessment of long-term SARS-CoV-2-specific adaptive immune memory in convalescent COVID-19 solid organ transplant recipients. Kidney Int. 2022;101 :1027-38. [PMID: ] doi:10.1016/j.kint.2021.12.029 35124011 15. Caballero-Marcos A , Citores MJ , Alonso-Fernández R , et al. Decreased long-term severe acute respiratory syndrome coronavirus 2-specific humoral immunity in liver transplantation recipients 12 months after coronavirus disease 2019. Liver Transpl. 2022;28 :1039-50. [PMID: ] doi:10.1002/lt.26389 34919762 16. Agarwal A, Baghmar S, Qureshi S, et al. Persistent antibody responses to SARS-CoV-2 infection in cancer patients: a single-center retrospective observational study. Indian J Med Paediatr Oncol. 2021;42:123-9. doi:10.1055/s-0041-1733823 17. Marra A , Generali D , Zagami P , et al. Seroconversion in patients with cancer and oncology health care workers infected by SARS-CoV-2. Ann Oncol. 2021;32 :113-9. [PMID: ] doi:10.1016/j.annonc.2020.10.473 33098994 18. Cattaneo C , Cancelli V , Imberti L , et al. Production and persistence of specific antibodies in COVID-19 patients with hematologic malignancies: role of rituximab. Blood Cancer J. 2021;11 :151. [PMID: ] doi:10.1038/s41408-021-00546-9 34521813 19. Liu Y , Xiao Y , Wu S , et al. People living with HIV easily lose their immune response to SARS-CoV-2: result from a cohort of COVID-19 cases in Wuhan, China. BMC Infect Dis. 2021;21 :1029. [PMID: ] doi:10.1186/s12879-021-06723-2 34598701 20. Johannesen CK , Rezahosseini O , Gybel-Brask M , et al. Risk factors for being seronegative following SARS-CoV-2 infection in a large cohort of health care workers in Denmark. Microbiol Spectr. 2021;9 :e0090421. [PMID: ] doi:10.1128/Spectrum.00904-21 34668738 21. Masiá M , Telenti G , Fernández M , et al. SARS-CoV-2 seroconversion and viral clearance in patients hospitalized with COVID-19: viral load predicts antibody response. Open Forum Infect Dis. 2021;8 :ofab005. [PMID: ] doi:10.1093/ofid/ofab005 33614814 22. Thiruvengadam R , Chattopadhyay S , Mehdi F , et al; DBT India Consortium for COVID 19 Research. Longitudinal serology of SARS-CoV-2-infected individuals in India: a prospective cohort study. Am J Trop Med Hyg. 2021;105 :66-72. [PMID: ] doi:10.4269/ajtmh.21-0164 34003792 23. Wei J , Matthews PC , Stoesser N , et al. Anti-spike antibody response to natural SARS-CoV-2 infection in the general population. Nat Commun. 2021;12 :6250. [PMID: ] doi:10.1038/s41467-021-26479-2 34716320 24. Petersen LR , Sami S , Vuong N , et al. Lack of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a large cohort of previously infected persons. Clin Infect Dis. 2021;73 :e3066-73. [PMID: ] doi:10.1093/cid/ciaa1685 33147319 25. Staines HM , Kirwan DE , Clark DJ , et al. IgG seroconversion and pathophysiology in severe acute respiratory syndrome coronavirus 2 infection. Emerg Infect Dis. 2021;27 . [PMID: ] doi:10.3201/eid2701.203074 33256890 26. Kim P , Gordon SM , Sheehan MM , et al. Duration of severe acute respiratory syndrome coronavirus 2 natural immunity and protection against the Delta variant: a retrospective cohort study. Clin Infect Dis. 2022;75 :e185-90. [PMID: ] doi:10.1093/cid/ciab999 34864907 27. Hall V , Foulkes S , Insalata F , et al; SIREN Study Group. Protection against SARS-CoV-2 after Covid-19 vaccination and previous infection. N Engl J Med. 2022;386 :1207-20. [PMID: ] doi:10.1056/NEJMoa2118691 35172051 28. Michlmayr D , Hansen CH , Gubbels SM , et al. Observed protection against SARS-CoV-2 reinfection following a primary infection: a Danish cohort study among unvaccinated using two years of nationwide PCR-test data. Lancet Reg Health Eur. 2022;20 :100452. [PMID: ] doi:10.1016/j.lanepe.2022.100452 35791335 29. Nordström P , Ballin M , Nordström A . Risk of SARS-CoV-2 reinfection and COVID-19 hospitalisation in individuals with natural and hybrid immunity: a retrospective, total population cohort study in Sweden. Lancet Infect Dis. 2022;22 :781-90. [PMID: ] doi:10.1016/S1473-3099(22)00143-8 35366962 30. Nyberg T , Ferguson NM , Nash SG , et al; COVID-19 Genomics UK (COG-UK) consortium. Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study. Lancet. 2022;399 :1303-12. [PMID: ] doi:10.1016/S0140-6736(22)00462-7 35305296 31. Altarawneh HN , Chemaitelly H , Hasan MR , et al. Protection against the Omicron variant from previous SARS-CoV-2 infection [Letter]. N Engl J Med. 2022;386 :1288-90. [PMID: ] doi:10.1056/NEJMc2200133 35139269 32. Rothberg MB , Kim P , Shrestha NK , et al. Protection against the Omicron variant offered by previous severe acute respiratory syndrome coronavirus 2 infection: a retrospective cohort study. Clin Infect Dis. 2022;ciac604 . [PMID: ] doi:10.1093/cid/ciac604 35867678 33. Krutikov M , Stirrup O , Nacer-Laidi H , et al; COVID-19 Genomics UK consortium. Outcomes of SARS-CoV-2 omicron infection in residents of long-term care facilities in England (VIVALDI): a prospective, cohort study. Lancet Healthy Longev. 2022;3 :e347-55. [PMID: ] doi:10.1016/S2666-7568(22)00093-9 35531432 34. Altarawneh HN , Chemaitelly H , Ayoub HH , et al. Effects of previous infection and vaccination on symptomatic Omicron infections. N Engl J Med. 2022;387 :21-34. [PMID: ] doi:10.1056/NEJMoa2203965 35704396 35. Altarawneh HN , Chemaitelly H , Ayoub HH , et al. Protective effect of previous SARS-CoV-2 infection against Omicron BA.4 and BA.5 subvariants. N Engl J Med. 2022;387 :1620-22. [PMID: ] doi:10.1056/NEJMc2209306 36198139 36. Holmer HK, Mackey K, Fiordalisi CV, et al. Antibody response following SARS-CoV-2 infection and implications for immunity: final update of a rapid, living review. (Prepared by the Scientific Resource Center under Contract No. 290-2017-0003). AHRQ Publication No. 22(23)-EHC037. Agency for Healthcare Research and Quality; 2022. doi:10.23970/AHRQEPCCOVIDIMMUNITY3 37. Gudbjartsson DF , Norddahl GL , Melsted P , et al. Humoral immune response to SARS-CoV-2 in Iceland. N Engl J Med. 2020;383 :1724-34. [PMID: ] doi:10.1056/NEJMoa2026116 32871063 38. Lustig Y , Mendelson E , Mandelboim M , et al. Existence of immunological memory response in true sero-negative individuals post COVID-19 molecular diagnosis. Clin Infect Dis. 2022. [PMID: ] doi:10.1093/cid/ciac196 35271690 39. Chemaitelly H , Nagelkerke N , Ayoub HH , et al. Duration of immune protection of SARS-CoV-2 natural infection against reinfection. J Travel Med. 2022;taac109 . [PMID: ] doi:10.1093/jtm/taac109 36179099 40. UK Health Security Agency. SARS-CoV-2 variants of concern and variants under investigation in England. Technical briefing 34. Accessed at https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1050236/technical-briefing-34-14-january-2022.pdf on 1 November 2022. 41. Ferguson N, Ghani A, Cori A, et al. Report 49: growth, population distribution and immune escape of Omicron in England. Imperial College London; 2021. Accessed at 10.25561/93038 on 1 November 2022. 42. Washington State Department of Health. Reported COVID-19 reinfections in Washington State. Accessed at https://doh.wa.gov/sites/default/files/2022-02/421-024-ReportedReinfections.pdf on 1 November 2022. 43. Pulliam JRC , van Schalkwyk C , Govender N , et al. Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa. Science. 2022;376 :eabn4947. [PMID: ] doi:10.1126/science.abn4947 35289632 44. Castilla J , Lecea Ó , Martín Salas C , et al. Seroprevalence of antibodies against SARS-CoV-2 and risk of COVID-19 in Navarre, Spain, May to July 2022. Euro Surveill. 2022;27 . [PMID: ] doi:10.2807/1560-7917.ES.2022.27.33.2200619 35983774 45. Zar HJ , MacGinty R , Workman L , et al. Natural and hybrid immunity following four COVID-19 waves: a prospective cohort study of mothers in South Africa. EClinicalMedicine. 2022;53 :101655. [PMID: ] doi:10.1016/j.eclinm.2022.101655 36128333 46. Wallace S , Hall V , Charlett A , et al. Impact of prior SARS-CoV-2 infection and COVID-19 vaccination on the subsequent incidence of COVID-19: a multicentre prospective cohort study among UK healthcare workers - the SIREN (Sarscov2 Immunity & REinfection EvaluatioN) study protocol. BMJ Open. 2022;12 :e054336. [PMID: ] doi:10.1136/bmjopen-2021-054336 35768083 47. Andeweg SP , de Gier B , Eggink D , et al. Protection of COVID-19 vaccination and previous infection against Omicron BA.1, BA.2 and Delta SARS-CoV-2 infections. Nat Commun. 2022;13 :4738. [PMID: ] doi:10.1038/s41467-022-31838-8 35961956 48. Carazo S, Skowronski DM, Brisson M, et al. Protection against Omicron BA.2 reinfection conferred by primary Omicron or pre-Omicron infection with and without mRNA vaccination. medRxiv. Preprint posted online 27 June 2022. doi:10.1101/2022.06.23.22276824 49. Cerqueira-Silva T , de Araujo Oliveira V , Paixão ES , et al. Duration of protection of CoronaVac plus heterologous BNT162b2 booster in the Omicron period in Brazil. Nat Commun. 2022;13 :4154. [PMID: ] doi:10.1038/s41467-022-31839-7 35851597 50. Lind ML, Robertson A, Silva J, et al. Effectiveness of primary and booster COVID-19 mRNA vaccination against infection caused by the SARS-CoV-2 Omicron variant in people with a prior SARS-CoV-2 infection. medRxiv. Preprint posted online 20 April 2022. doi:10.1101/2022.04.19.22274056 51. Lin DY , Gu Y , Xu Y , et al. Association of primary and booster vaccination and prior infection with SARS-CoV-2 infection and severe COVID-19 outcomes. JAMA. 2022;328 :1415-26. [PMID: ] doi:10.1001/jama.2022.17876 36155617 52. Šmíd M, Berec L, Přibylová L, et al.. Protection by vaccines and previous infection against the Omicron variant of severe acute respiratory syndrome coronavirus 2. J Infect Dis. 2022;226 :1385-90. [PMID: ] doi:10.1093/infdis/jiac161 35482442 53. Bobrovitz N, Ware H, Ma X, et al. Protective effectiveness of prior SARS-CoV-2 infection and hybrid immunity against Omicron infection and severe disease: a systematic review and meta-regression. medRxiv. Preprint posted online 4 October 2022. doi:10.1101/2022.10.02.22280610 54. Malato J , Ribeiro RM , Fernandes E , et al. Rapid waning of protection induced by prior BA.1/BA.2 infection against BA.5 infection [Preprint]. med. Rxiv. 2022. [PMID: ] doi:10.1101/2022.08.16.22278820 36032977 55. Malato J, Ribeiro RM, Leite PP, et al. Risk of BA.5 infection in persons exposed to prior SARS-CoV-2 variants. medRxiv. Preprint posted online 28 July 2022. doi:10.1101/2022.07.27.22277602 56. Nadig M, Niesen MJM, Lenehan P, et al. Individuals with recent prior SARS-CoV-2 infection are at reduced risk of Omicron infection and associated hospitalization. medRxiv. Preprint posted online 15 August 2022. doi:10.1101/2022.08.10.22278641 57. Vicentini M, Venturelli F, Mancuso P, et al; The Reggio Emilia COVID-19 Working Group. Risk of SARS-CoV-2 reinfection by vaccination status, predominant variant, and time from previous infection: a cohort study in Italy. Lancet. Preprint posted online 9 June 2022. doi:10.2139/ssrn.4132329	36442059	PMC9707440	NO-CC CODE	2022-12-03 23:19:45	no	Ann Intern Med. 2022 Nov 29;:M22-1745	utf-8	Ann Intern Med	2,022	10.7326/M22-1745	oa_other
==== Front Ann Intern Med Ann Intern Med aim Annals of Internal Medicine 0003-4819 1539-3704 American College of Physicians 36442064 10.7326/M22-1966 aim-olf-M221966 Original Research 3122457COVID-192357Health care providers3282Infectious diseases98Preventive medicineearlyCurrently Online FirstcoronavirusCoronavirus Disease 2019 (COVID-19)hospitalHospital MedicinerctRandomized-Controlled Trialpoc-eligiblePOC EligibleMedical Masks Versus N95 Respirators for Preventing COVID-19 Among Health Care Workers A Randomized Trial Medical Masks Versus N95 Respirators for COVID-19 Loeb Mark MD Bartholomew Amy MScN https://orcid.org/0000-0002-4534-5964 Hashmi Madiha MD https://orcid.org/0000-0002-7332-0692 Tarhuni Wadea MD https://orcid.org/0000-0001-9729-089X Hassany Mohamed MD https://orcid.org/0000-0002-6001-8793 Youngster Ilan MD https://orcid.org/0000-0001-5233-1213 Somayaji Ranjani MD Larios Oscar MD https://orcid.org/0000-0001-6716-7938 Kim Joseph MD https://orcid.org/0000-0002-8593-6013 Missaghi Bayan MD https://orcid.org/0000-0002-2926-9383 Vayalumkal Joseph V. MD Mertz Dominik MD https://orcid.org/0000-0003-4337-1613 Chagla Zain MD Cividino Maureen MD Ali Karim MD Mansour Sarah MB BCh BAO Castellucci Lana A. MD https://orcid.org/0000-0003-1322-9368 Frenette Charles MD https://orcid.org/0000-0002-8996-0766 Parkes Leighanne MD Downing Mark MD Muller Matthew MD, PhD Glavin Verne MD Newton Jennifer BSc Hookoom Ravi BASc Leis Jerome A. MD https://orcid.org/0000-0003-2250-4894 Kinross James MD https://orcid.org/0000-0002-0427-7643 Smith Stephanie MD Borhan Sayem PhD Singh Pardeep BSc Pullenayegum Eleanor PhD https://orcid.org/0000-0003-4265-1330 Conly John MD https://orcid.org/0000-0002-3348-0157 Department of Pathology and Molecular Medicine and Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada (M.L.) Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada (A.B., J.N., P.S.) Ziauddin University, Karachi, Pakistan (M.Hashmi) University of Saskatchewan, Saskatoon, Saskatchewan, and Canadian Cardiac Research Centre, Windsor, Ontario, Canada (W.T.) National Hepatology and Tropical Medicine Research Institute, Cairo, Egypt (M.Hassany) Shamir Medical Center, Tzrifin, and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (I.Y.) Department of Medicine, University of Calgary and Alberta Health Services, Calgary, Alberta, Canada (R.S., O.L., J.Kim, B.M., J.C.) Department of Pediatrics, University of Calgary and Alberta Health Services, Calgary, Alberta, Canada (J.V.V.) Department of Medicine, McMaster University and Hamilton Health Sciences, Hamilton, Ontario, Canada (D.M., R.H.) Department of Medicine, McMaster University, and St. Joseph's Healthcare, Hamilton, Ontario, Canada (Z.C.) St. Joseph's Healthcare, Hamilton, Ontario, Canada (M.C.); Niagara Health System, Niagara, Ontario, Canada (K.A.) Montfort Hospital, Ottawa, Ontario, Canada (S.M.) Department of Medicine, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada (L.A.C.) McGill University Health Centre, Montreal, Quebec, Canada (C.F.) Jewish General Hospital, Montreal, Quebec, Canada (L.P.) Unity Health–St. Joseph's, Toronto, Ontario, Canada (M.D.) Unity Health–St. Michael's, Toronto, Ontario, Canada (M.M.) Brantford Community Health System, Brantford, Ontario, Canada (V.G.) Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada (J.A.L.) Imperial College, London, England (J.Kinross) University of Alberta Hospital, Edmonton, Alberta, Canada (S.S.) Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada (S.B.) University of Toronto, Toronto, Ontario, Canada (E.P.). Financial Support: By the Canadian Institutes of Health Research, World Health Organization, and Juravinski Research Institute. Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M22-1966. Data Sharing Statement: The following data will be made available beginning 1 July 2023 for 6 months: deidentified participant data, data dictionary ([email protected]). These data will be made available to other research groups for meta-analysis providing there is evidence to support the request for access, the process will be collaborative, and there is ethics approval for the request. Such requests will be reviewed and must be approved internally, for restricted to the analyses for which the protocol received ethics approval, after approval of a proposal that has had ethical review and with a signed data access agreement. (Restrictions: Access will be approved only after secondary analyses have been completed.) Corresponding Author: Mark Loeb, MD, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4K1, Canada; e-mail, [email protected]. Author Contributions: Conception and design: M. Loeb, D. Mertz, M. Muller, J. Newton, W. Tarhuni. Analysis and interpretation of the data: S. Borhan, J. Conly, M. Downing, C. Frenette, J. Kinross, M. Loeb, D. Mertz, B. Missaghi, M. Muller, E. Pullenayegum, P. Singh, W. Tarhuni. Drafting of the article: M. Cividino, M. Downing, M. Loeb, J. Newton. Critical revision of the article for important intellectual content: K. Ali, S. Borhan, L.A. Castellucci, J. Conly, M. Downing, C. Frenette, M. Hashmi, J. Kim, J.A. Leis, M. Loeb, D. Mertz, B. Missaghi, M. Muller, E. Pullenayegum, S. Smith, R. Somayaji, J.V. Vayalumkal, I. Youngster. Final approval of the article: K. Ali, A. Bartholomew, S. Borhan, L.A. Castellucci, Z. Chagla, M. Cividino, J. Conly, M. Downing, C. Frenette, V. Glavin, M. Hashmi, M. Hassany, R. Hookoom, J. Kim, J. Kinross, O. Larios, J.A. Leis, M. Loeb, S. Mansour, D. Mertz, B. Missaghi, M. Muller, J. Newton, L. Parkes, E. Pullenayegum, P. Singh, S. Smith, R. Somayaji, W. Tarhuni, J.V. Vayalumkal, I. Youngster. Provision of study materials or patients: K. Ali, L.A. Castellucci, J. Conly, M. Downing, C. Frenette, V. Glavin, J. Kim, J. Kinross, O. Larios, S. Mansour, B. Missaghi, M. Muller, R. Somayaji, J.V. Vayalumkal. Statistical expertise: S. Borhan, M. Loeb, E. Pullenayegum. Obtaining of funding: L.A. Castellucci, M. Loeb. Administrative, technical, or logistic support: A. Bartholomew, Z. Chagla, J. Conly, M. Downing, C. Frenette, M. Hashmi, R. Hookoom, J. Kim, J.A. Leis, B. Missaghi, J. Newton, W. Tarhuni, J.V. Vayalumkal. Collection and assembly of data: M. Cividino, M. Downing, C. Frenette, M. Hashmi, M. Hassany, O. Larios, M. Muller, J. Newton, L. Parkes, P. Singh, S. Smith, R. Somayaji, I. Youngster. 29 11 2022 29 11 2022 M22-19662022 American College of Physicians This article is made available via the PMC Open Access Subset for unrestricted re-use for research, analyses, and text and data mining through PubMed Central. Acknowledgement of the original source shall include a notice similar to the following: "© 2020 American College of Physicians. Some rights reserved. This work permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited." These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. It is uncertain if medical masks offer similar protection against COVID-19 compared with N95 respirators. This randomized trial, which enrolled participants in Canada, Israel, Pakistan, and Egypt, aimed to determine whether medical masks are noninferior to N95 respirators to prevent COVID-19 in health care workers providing routine care. Visual Abstract. Medical Masks Versus N95 Respirators for COVID-19. It is uncertain if medical masks offer similar protection against COVID-19 compared with N95 respirators. This randomized trial, which enrolled participants in Canada, Israel, Pakistan, and Egypt, aimed to determine whether medical masks are noninferior to N95 respirators to prevent COVID-19 in health care workers providing routine care. Background: It is uncertain if medical masks offer similar protection against COVID-19 compared with N95 respirators. Objective: To determine whether medical masks are noninferior to N95 respirators to prevent COVID-19 in health care workers providing routine care. Design: Multicenter, randomized, noninferiority trial. (ClinicalTrials.gov: NCT04296643). Setting: 29 health care facilities in Canada, Israel, Pakistan, and Egypt from 4 May 2020 to 29 March 2022. Participants: 1009 health care workers who provided direct care to patients with suspected or confirmed COVID-19. Intervention: Use of medical masks versus fit-tested N95 respirators for 10 weeks, plus universal masking, which was the policy implemented at each site. Measurements: The primary outcome was confirmed COVID-19 on reverse transcriptase polymerase chain reaction (RT-PCR) test. Results: In the intention-to-treat analysis, RT-PCR–confirmed COVID-19 occurred in 52 of 497 (10.46%) participants in the medical mask group versus 47 of 507 (9.27%) in the N95 respirator group (hazard ratio [HR], 1.14 [95% CI, 0.77 to 1.69]). An unplanned subgroup analysis by country found that in the medical mask group versus the N95 respirator group RT-PCR–confirmed COVID-19 occurred in 8 of 131 (6.11%) versus 3 of 135 (2.22%) in Canada (HR, 2.83 [CI, 0.75 to 10.72]), 6 of 17 (35.29%) versus 4 of 17 (23.53%) in Israel (HR, 1.54 [CI, 0.43 to 5.49]), 3 of 92 (3.26%) versus 2 of 94 (2.13%) in Pakistan (HR, 1.50 [CI, 0.25 to 8.98]), and 35 of 257 (13.62%) versus 38 of 261 (14.56%) in Egypt (HR, 0.95 [CI, 0.60 to 1.50]). There were 47 (10.8%) adverse events related to the intervention reported in the medical mask group and 59 (13.6%) in the N95 respirator group. Limitation: Potential acquisition of SARS-CoV-2 through household and community exposure, heterogeneity between countries, uncertainty in the estimates of effect, differences in self-reported adherence, differences in baseline antibodies, and between-country differences in circulating variants and vaccination. Conclusion: Among health care workers who provided routine care to patients with COVID-19, the overall estimates rule out a doubling in hazard of RT-PCR–confirmed COVID-19 for medical masks when compared with HRs of RT-PCR–confirmed COVID-19 for N95 respirators. The subgroup results varied by country, and the overall estimates may not be applicable to individual countries because of treatment effect heterogeneity. Primary Funding Source: Canadian Institutes of Health Research, World Health Organization, and Juravinski Research Institute. ==== Body pmcHealth care workers use either medical masks, also called surgical masks, or N95 respirators for the routine care of patients with COVID-19 as a component of their personal protective equipment. Medical masks are recommended by the World Health Organization for routine care (1, 2), whereas N95 respirators are recommended by the Centers for Disease Control and Prevention for the routine care of patients with COVID-19 (3–5). It is uncertain if medical masks offer similar protection against COVID-19 compared with N95 respirators (6). Observational studies report varied findings and are limited by self-reported outcomes, potential recall bias, and ecological analyses (7–14). Systematic reviews of randomized trials and observational studies of other respiratory viruses suggest similar protection (15, 16). There is concern that medical masks offer less protection because of their looser fit and that they do not filter as effectively, whereas N95 respirators are fit tested and provide greater filtration (17). There were insufficient supplies of N95 respirators globally during the pandemic, and currently there is a lack of access in low- and middle-income countries because of the high costs (18). One randomized controlled trial set in the community reported a reduction of SARS-CoV-2 with medical masks (19). It is important to determine the relative protection of medical masks compared with N95 respirators. We conducted an international pragmatic randomized controlled trial where health care workers were randomly assigned to either medical masks or N95 respirators when providing routine care to patients with suspected or confirmed COVID-19. We hypothesized that medical masks would be noninferior to N95 respirators. Methods Trial Design and Oversight This pragmatic, randomized, open-label, multicenter trial initially aimed to assess whether medical masks were noninferior to N95 respirators for protection against COVID-19 among unvaccinated nurses providing routine care to patients with suspected or confirmed COVID-19 (see the study protocol and statistical analysis plan). The evolution of the pandemic led to protocol changes (Supplement). Before trial commencement, in addition to nurses, other health care workers were made eligible to increase enrollment, and follow-up was reduced from 12 to 10 weeks to minimize loss to follow-up. As circulation of SARS-CoV-2 increased, health care workers known to have a previous laboratory-confirmed clinical diagnosis of COVID-19 at the time of enrollment were excluded. As vaccine rollout began, participants with receipt of 1 or more doses of a COVID-19 vaccine with greater than 50% efficacy for the circulating strain (for example, messenger RNA [mRNA] or vector-based COVID-19 vaccine against the original SARS-CoV-2 strain) were excluded, and sites in Israel, Pakistan, and Egypt were added to increase enrollment. Participants that received a single dose of an mRNA or vector-based COVID-19 vaccine after enrollment (with an estimated >50% efficacy against the circulating strain) were followed until 2 weeks after their first dose and then censored. The variable follow-up time led to a change to a time-to-event analysis, and a hazard ratio (HR) was used for the noninferiority margin. The trial enrolled participants in 29 health care facilities: 17 acute care hospitals in Canada, 4 acute care hospitals in Pakistan, 2 long-term care facilities in Israel (facilities where trained medical staff are always available to assist residents and where high-flow oxygen and medication via inhalation could be administered), and 6 acute care hospitals in Egypt. The study was done from 4 May 2020 to 29 March 2022. The trial was approved by the Hamilton Integrated Research Ethics Board and the institutional review boards at all participating institutions. All participants provided written informed consent. The trial was restricted to health care settings where the policy was to use medical masks while providing routine care to patients with confirmed or suspected COVID-19. A data monitoring committee provided oversight of safety considerations in the trial. Participants Health care workers who provided direct care to patients with suspected or confirmed COVID-19 in specialized COVID-19 units and in emergency departments, medical units, pediatric units, and long-term care facilities were enrolled; intensive care units were not included in the study. Health care workers were required to spend 60% or more of their time doing clinical work when enrolled. Health care workers were excluded if they did not have a valid fit test within the past 24 months or could not pass a fit test, had 1 or more high-risk comorbidities for COVID-19 (hypertension, cardiac disease, pulmonary disease, chronic kidney disease, diabetes, chronic liver disease, actively treated cancer, or immunosuppression due to illness or medications), had a previous laboratory-confirmed clinical diagnosis of COVID-19 at the time of enrollment, or had received 1 or more doses of a COVID-19 vaccine with greater than 50% efficacy for the circulating strain (for example, mRNA or vector-based COVID-19 vaccine against the original SARS-CoV-2 strain). Randomization and Blinding Trial participants were randomly assigned (1:1) to either medical masks or N95 respirators. Participants were randomly assigned centrally by a study statistician who generated the sequence using a computerized random number generator. Randomization was stratified by site in permuted blocks of 4. The randomization scheme was provided by an interactive web response system and performed centrally. Investigators were blinded to the group assignment, but it was not possible to conceal the identity of the medical mask or N95 respirator assignment to the study staff or participants. Interventions Health care workers randomly assigned to the medical mask group were instructed to use the medical mask when providing routine care to patients with COVID-19 or suspected COVID-19, which aligned with the current policy in their setting. The ASTM International certified masks were provided to the health care workers either by their health care facility or by the study (Supplement Table 1). As part of the trial protocol, health care workers could also use the N95 respirator at any time based on a point-of-care risk assessment. Health care workers randomly assigned to the N95 respirator group were instructed to use a fit-tested National Institute for Occupational Safety and Health–approved N95 respirator when providing routine care to patients with COVID-19 or suspected COVID-19. Participants were required to use the type of device they were allocated to, either a medical mask or an N95 respirator, for 10 weeks. The intervention included universal masking, which was the policy implemented at each site. This refers to the use of a mask when in the health care facility for all activities, whether patient related or not, including in workrooms, meetings, and treating persons that were not suspected or known to be positive for COVID-19. Participants were asked to report the extent to which they used the mask that they were assigned to on a weekly basis—that is, “During your last work shift, to what extent did you wear the mask you were assigned,” where the possible responses were “Always,” “Sometimes,” “Never,” or “Do not recall.” In both study groups, health care workers were required to use the N95 respirator for aerosol-generating medical procedures, as this was in keeping with their institutional policies. In keeping with local policies, eye protection, gowns, and gloves were worn when caring for patients with suspected or confirmed COVID-19. Participants were asked to discard the medical mask or N95 respirator if it became soiled or damaged or if breathing through the device became difficult. If the institutional policy was for extended use and masks were not typically removed after a patient encounter, the extended use procedure was to be followed. Outcomes The primary outcome was time to reverse transcriptase polymerase chain reaction (RT-PCR)–confirmed COVID-19. This was measured from the date of randomization until the date of procurement of a specimen that was positive by RT-PCR. Follow-up continued until the end of 10 weeks, until 2 weeks (1 incubation period) after receipt of an mRNA vaccine, or until the date of a participant withdrawal from the trial. Laboratory personnel doing COVID-19 testing were blind to treatment allocation. Testing was done at the health care facility laboratory using health care–administered nasopharyngeal swabs. Sera from participants was obtained at baseline and at the end of follow-up and then tested for spike IgG antibodies and for nucleocapsid IgG antibodies using EUROIMMUN assays. Secondary outcomes included serologic evidence of infection (done in participants who were seronegative at baseline and defined as a change from negative EUROIMMUN spike IgG and nucleocapsid IgG antibodies at baseline to positive nucleocapsid IgG antibody), acute respiratory illness (defined by fever and cough), work-related absenteeism, lower respiratory tract infection or pneumonia, intensive care admission, mechanical ventilation, or death. Laboratory-confirmed infection was defined as COVID-19 confirmed by RT-PCR in symptomatic participants or seroconversion. Participants were assessed for signs and symptoms of COVID-19 through twice-weekly automated text messages. A nasopharyngeal swab was obtained if any one the following symptoms or signs was present: fever (≥38 °C), cough, or shortness of breath, or if 2 of the following were present: fatigue, myalgia, headache, dizziness, expectoration, sore throat, diarrhea, nausea, vomiting, abdominal pain, runny nose, altered taste or smell, conjunctivitis, or painful swallowing. Adherence to the assigned medical mask or N95 respirator for routine care and to hand hygiene was measured using weekly self-reporting for all participants and external monitoring wherever feasible. Audits were done once at 3 hospitals in Pakistan and were repeated once at 2 of these hospitals within a 2-week period. They were done at 6 hospitals in Egypt where they were repeated twice at 2 hospitals and repeated once at 4 hospitals over a 4-week period. To conduct the audits of adherence to the intervention (medical mask or N95 respirator), the coordinating center randomly selected 20% of shifts at a health care facility, and during these shifts, trial participants were observed. Wearing an N95 respirator for aerosol-generating procedures was not considered during the observed audits. Reported exposures and potential exposures to COVID-19, including community and home exposure, hospital exposures, participation in aerosol-generating procedures, and hospital outbreaks (as defined by the health care facility) were measured. Participants were asked to keep diaries of signs and symptoms of respiratory illness and exposure to household and community members with respiratory illness. Cycle threshold values from patients with COVID-19, obtained while participants were on the same study units as the patients, were used to estimate viral load as a surrogate for exposure risk. Statistical Analysis The study was powered based on the primary outcome of RT-PCR–confirmed COVID-19. For a noninferiority HR of 2, a sample size of 875 participants provided 90% power at a 0.025 significance level for event rates of 10% and an actual HR of 1. The original design estimated an event rate of 5% with a noninferiority margin of 5 percentage points (that is, up to a 10% event rate would be considered noninferior). On changing the outcome from 10-week occurrence of RT-PCR–confirmed COVID-19 to time to RT-PCR–confirmed COVID-19 so as to allow for censoring due to vaccination, the original margin on the absolute effect size corresponds to a relative effect size (HR) of 2 (see the Supplement for earlier trial design sample size calculations). A final sample size of 1010 accounted for participants who could not complete 10 weeks of follow-up because of administration of mRNA vaccine as well as for withdrawals. Hazard ratios and corresponding 2-sided 95% CIs were estimated using a Cox proportional hazards model stratifying by health care facility. The analysis fulfilled the Schoenfeld residual test for the assumption of proportional hazards in Cox analysis. The cumulative incidence of RT-PCR–confirmed COVID-19 was estimated using Kaplan–Meier methods. Outcomes were analyzed on an intention-to-treat basis, defined by medical mask or N95 respirator assignment and follow-up until 10 weeks or 2 weeks after the first mRNA vaccine dose. Participants did not have to complete 10 weeks of follow-up to be included in the intention-to-treat analysis. Censoring was assumed independent of the randomized group assignment. No attempt was made to impute missing postrandomization values, and only observed values were used in the analysis. A post hoc analysis of the primary outcome with participants restricted to those seronegative at baseline was done using a Cox proportional hazards model stratifying by health care facility. For serology and overall laboratory-confirmed infection, we conducted a logistic regression analysis adjusting for site to obtain odds ratios and 95% CIs. Although subgroup analyses based on pre-Omicron variant versus Omicron variant and by universal masking were planned a priori, these analyses are not reported because of potential confounding of Omicron by country and because of the mandatory policy of universal masking for all health care facilities in the trial. A post hoc subgroup analysis was done to compare the effect of medical masks versus N95 respirators in participants with no reported exposure to household or community members with respiratory illness to those that reported at least 1 such exposure. We also conducted an unplanned subgroup analysis of the primary outcome by country. For the safety analyses, the number and percentage of participants with an adverse event according to study group are reported. For participant exposure to patients with COVID-19 or exposure to patients with suspected COVID-19, the number of exposures per week for up to 10 weeks were counted and categorized (0, 1 to 5, 6 to 10, or ≥11 exposures). The number of exposure categories per 1000 participant-days was then calculated by country and study group. Statistical analyses were done using R, version 4.2.0 (R Foundation for Statistical Computing). Role of the Funding Source The study was funded by the Canadian Institutes of Health Research, World Health Organization, and Juravinski Research Institute. The external funders of the study had no role in study design, data collection, data analysis, or data interpretation, or in writing this report. Results Between 4 May 2020 and 12 January 2022, a total of 1191 health care workers were assessed for eligibility, and 1009 were enrolled. There were 500 randomly assigned to medical masks and 509 to the N95 respirator (Figure 1). There were 268 participants from Canada, 34 from Israel, 187 from Pakistan, and 520 from Egypt. The baseline characteristics were well balanced overall and were similar within each country (Table). However, seropositivity at baseline varied by country, with few seropositive participants in Canada (2%) and a majority (81%) seropositive in Egypt (Table). Overall, there were 185 (37.5%) participants in the medical group versus 185 (37.2%) in the N95 respirator group who were seronegative at baseline—that is, had no SARS-CoV-2 spike IgG or nucleocapsid IgG antibodies at baseline. Figure 1. Trial flow diagram. ITT = intention-to-treat; mRNA = messenger RNA. * Dates of follow-up: Canada (May 2020 to May 2021), Israel (November 2020 to January 2021), Pakistan (June 2021 to December 2021), and Egypt (December 2021 to March 2022). Table. Participant Characteristics Follow-up began on 4 May 2020 and ended on 29 March 2022. Participants were enrolled from 4 May 2020 to 22 May 2021 in Canada, from 11 November 2020 to 27 January 2021 in Israel, from 24 June 2021 to 18 December 2021 in Pakistan, and from 19 December 2021 to 29 March 2022 in Egypt. The mean duration of follow-up was similar between the 2 study groups—9.06 weeks in the medical mask group and 9.03 weeks in the N95 respirator group. Five participants who were randomly assigned but never followed were excluded from analysis—3 in the medical mask group (1 was previously positive for COVID-19 on RT-PCR and 2 withdrew) and 2 in the N95 respirator group (1 was previously positive for COVID-19 on RT-PCR and 1 withdrew) (Figure 1). Of the resulting 1004, follow-up was complete (that is, full 10 weeks or 14 days after first vaccination) in 483 (97.1%) in the medical mask group and 489 (96.4%) in the N95 respirator group. The primary outcome in the intention-to-treat analysis, RT-PCR–confirmed COVID-19, occurred in 52 of 497 (10.46%) in the medial mask group versus 47 of 507 (9.27%) in the N95 respirator group (HR, 1.14 [95% CI, 0.77 to 1.69]). The proportional hazards assumption was tested for the primary outcome and was plausible. In an unplanned subgroup analysis by country, we found that in the medical mask group versus N95 respirator group, RT-PCR–confirmed COVID-19 occurred in 8 of 131 (6.11%) versus 3 of 135 (2.22%) in Canada (HR, 2.83 [CI, 0.75 to 10.72]), 6 of 17 (35.29%) versus 4 of 17 (23.53%) in Israel (HR, 1.54 [CI, 0.43 to 5.49]), 3 of 92 (3.26%) versus 2 of 94 (2.13%) in Pakistan (HR, 1.50 [CI, 0.25 to 8.98]), and 35 of 257 (13.62%) versus 38 of 261 (14.56%) in Egypt (HR, 0.95 [CI, 0.60 to 1.50]) (Figure 2). The overall cumulative incidence is shown in Figure 3 and that by country in Figure 4. Figure 2. Forest plot of the primary intention-to-treat analysis of RT-PCR–confirmed COVID-19. There were 86 of 8338 (1%) weekly surveys missing in the medical mask group and 65 of 8468 (0.8%) missing in the N95 respirator group. The subgroup analysis by country was added to show the heterogeneity of treatment effect. HR = hazard ratio; RT-PCR = reverse transcriptase polymerase chain reaction. Figure 3. Cumulative incidence of primary analysis of RT-PCR–confirmed COVID-19. RT-PCR = reverse transcriptase polymerase chain reaction. Figure 4. Cumulative incidence of primary analysis of RT-PCR–confirmed COVID-19 by country. RT-PCR = reverse transcriptase polymerase chain reaction. The secondary outcomes, which varied substantially by country, are shown in Supplement Table 2. The sensitivity analysis for RT-PCR–confirmed COVID-19 in participants who were seronegative at baseline showed within-country between-group HRs similar to those that include all participants (Supplement Figure). Pre-Omicron exposure occurred in Canada, Israel, and Pakistan, whereas Omicron exposure occurred in Egypt. This is based on dates of SARS-CoV-2 circulation given that enrollment in Egypt began on 19 December 2021, whereas enrollment from other countries ended earlier in the pandemic, with follow-up in Pakistan ending on 28 December 2021. The post hoc intention-to-treat subgroup analysis of no reported household or community exposure to respiratory illness (HR, 1.06 [CI, 0.53 to 2.11]) versus 1 or more reported household or community exposure to respiratory illness (HR, 1.08 [CI, 0.66 to 1.78]) did not show heterogeneity of treatment effect based on a test of interaction (P = 0.96) (Supplement Table 3). There were 2 participants who had serious adverse events in the medical mask group (both hospitalizations for COVID-19, where 1 had confirmed pneumonia) and 1 participant in the N95 respirator group (hospitalization for COVID-19 pneumonia). In addition, there were 3 participants (2 in the medical mask group and 1 in the N95 respirator group) who could not be safely isolated at home and were hospitalized for isolation. There were no intensive care admissions and no deaths. There were 47 (10.8%) adverse events related to the intervention reported in the medical mask group and 59 (13.6%) in the N95 respirator group (Supplement Table 4). There was 1 participant in the medical mask group and 3 in the N95 respirator group who withdrew because of discomfort or adverse events related to the device they were assigned. Exposure to patients with confirmed or suspected COVID-19, minutes of exposure to patients with COVID-19, aerosol-generating procedures, and community exposures were similar between study groups (Supplement Tables 5 to 9). Mean cycle threshold values of patients positive for COVID-19 were less than 30 in 84% of the 25 study units where these data were collected (Supplement Table 10). Ventilation in the study varied by location (Supplement Table 11). Outbreaks of COVD-19 were reported in 5 of 29 (17%) study units in Canada, in both long-term care facilities in Israel, and in all 6 acute care hospitals in Egypt (Supplement Table 12). Adherence with the assigned medical mask or N95 respirator was self-reported as “always” in 91.2% in the medical mask group versus 80.7% in the N95 respirator group and as “always” or “sometimes” in 97.7% in the medical mask group versus 94.4% in the N95 respirator group (Supplement Table 13). Of 118 participants observed in the medical mask group, 116 (98.3%) were reported by monitors to be adherent to their assigned mask—14 (100%) in Pakistan and 102 (98%) in Egypt. Of 117 observed in the N95 respirator group, 113 (96.6%) were reported to be adherent—8 (80%) in Pakistan and 105 (98%) in Egypt (Supplement Table 14). Self-reported rates of adherence to hand hygiene, eye protection, use of gowns, and use of gloves were similar between study groups (Supplement Table 13). Discussion Among health care workers who took care of patients with suspected or confirmed COVID-19, although the upper limit of the CIs of the pooled estimate for medical masks when compared with N95 respirators for preventing RT-PCR–confirmed COVID-19 was within the noninferiority margin of 2, this margin was wide, and firm conclusions about noninferiority may not be applicable given the between-country heterogeneity. The heterogeneity in the RT-PCR positivity rate, as well as the heterogeneity in baseline seropositivity by country, may be explained by many factors. Enrollment in Canada occurred early in the pandemic in acute health care facilities. In contrast, in Israel, the study was done in long-term care facilities that had substantial outbreaks. Later in the pandemic, enrollment occurred in Pakistan and Egypt, countries with a high population density, where seropositivity in participants due to previous exposure to SARS CoV-2 and receipt of vaccine was more common. Circulation of Omicron may have been a contributing factor to the high rates of RT-PCR–confirmed COVID-19 in Egypt. The observed results are consistent with a range of protection, from a 23% reduction in the HR with medical masks to a 69% risk increase. The relative protection of medical masks compared with N95 respirators varied by country. However, this finding does not seem to be explained by differences in baseline seropositivity given that a post hoc analysis of the effect of medical masks versus N95 respirators on RT-PCR–confirmed COVID-19 that was restricted to participants seronegative at baseline led to similar within-country point estimates compared with analyses that included the seropositive participants. Point estimates of the HRs for medical masks versus N95 respirators for both Israel and Pakistan were similar (HRs of 1.54 and 1.50). For Canada, the point estimate of 2.83 is suggestive of an increased risk with the medical mask, however, the absolute number of events is small. It is unclear whether lower COVID-19 rates in that setting, reducing the possibility of participants acquiring COVID-19 in the community, made such an effect more apparent. However, a post hoc subgroup analysis that compared participants with no reported household or community illness exposures to those that reported at least 1 exposure showed no heterogeneity in treatment effect and very similar effect sizes for both subgroups. It is notable that there was a close to null effect of medical masks compared with N95 respirators in Egypt, where Omicron was circulating, and from where over half of our participants were enrolled. It is possible that a higher rate of community transmission could have obscured a higher rate of infection with the medical mask versus the N95 respirator, in contrast to what was seen in Canada. It is also possible that given the high rate of exposure to patients with COVID-19 reported by health care workers in Egypt with the more transmissible Omicron, the results reflect no difference between the groups in health care acquisition of RT-PCR–confirmed COVID-19. The latter is supported by the post hoc subgroup analysis comparing participants with and without exposures to household or community illness. Differences in preexisting antibodies are another possible explanation for the difference between Canada and Egypt, although the post hoc analysis that was restricted to participants seronegative at baseline, where point estimates did not change, argues against preexisting antibodies as an explanation for differences between Canada and Egypt. These findings and those of other country-specific data should be tempered by the pitfalls of overinterpreting subgroup effects (20). Although self-reported adherence was lower in the N95 respirator group, the randomly conducted audited adherence was similar in both groups—98.3% in the medical mask group versus 96.6% in the N95 respirator group. It should be noted that the intervention included the mask policy at each site and not only the type of mask to which participants were randomly assigned. It is possible that the type of mask influenced adherence, which would be intrinsic to the pragmatic nature of the trial. We acknowledge concerns of suboptimal filtering capacity of medical masks, but the trial was done strictly in settings where the policy was use of medical masks for routine care, and no participants who were using N95 respirators were asked to use medical masks. In Pakistan and Egypt, the trial offered superior-quality medical masks and N95 respirators to participants who would otherwise not have access. High-risk participants were excluded from the study, and the data were routinely monitored by the Data Safety Committee. Furthermore, participants who believed they were at high risk during a particular exposure were allowed to use the N95 respirator if assigned to a medical mask. Some of the challenges experienced when conducting this trial included lengthy delays for ethics approvals and the establishment of contracts with sites. Implementation challenges included shipping supplies internationally and delays at customs of some of these sites, long regulatory approval delays, difficulty with procurement of N95 respirators because of supply chain issues, and delays due to the need to establish research contracts with sites. Some of the lessons learned include early onboarding of new study sites, identification of new sites through national and international public health agencies, the need for expedited ethics review and streamlined contractual processes, and early planning for design adaptation due to rollout of vaccines and new emerging variants. In conclusion, among health care workers who provided routine care to patients with COVID-19, the overall estimates rule out a doubling in hazard of RT-PCR–confirmed COVID-19 for medical masks when compared with HRs of RT-PCR–confirmed COVID-19 for N95 respirators. The subgroup results varied by country, and the overall estimates may not be applicable to individual countries because of treatment effect heterogeneity. Supplementary Material Click here for additional data file. Click here for additional data file. This article was published at Annals.org on 29 November 2022. ==== Refs References 1. World Health Organization. Rational use of personal protective equipment for coronavirus disease 2019 (COVID-19) and considerations during severe shortages: interim guidance. Accessed at www.who.int/publications/i/item/rational-use-of-personal-protective-equipment-for-coronavirus-disease-(covid-19)-and-considerations-during-severe-shortages on 24 June 2022. 2. World Health Organization. WHO recommendations on mask use by health workers, in light of the Omicron variant of concern: WHO interim guidelines, 22 December 2021. Accessed at www.who.int/publications/i/item/WHO-2019-nCoV-IPC_Masks-Health_Workers-Omicron_variant-2021.1 on 24 June 2022. 3. Janssen L , Ettinger H , Graham S , et al. The use of respirators to reduce inhalation of airborne biological agents. J Occup Environ Hyg. 2013;10 :D97-103. [PMID: ] doi:10.1080/15459624.2013.799964 23767796 4. National Institute for Occupational Safety and Health. Determination of particulate filter efficiency level for N95 series filters against solid particulates for non-powered, air-purifying respirators standard testing procedure. Accessed at www.cdc.gov/niosh/npptl/stps/pdfs/TEB-APR-STP-0059-508.pdf on 24 June 2022. 5. Centers for Disease Control and Prevention. Interim infection prevention and control recommendations for healthcare personnel during the coronavirus disease 2019 (COVID-19) pandemic. Accessed at www.cdc.gov/coronavirus/2019-ncov/hcp/infection-control-recommendations.html#print; 2021 on 24 June 2022. 6. Chou R , Dana T , Jungbauer R . Update alert 7: masks for prevention of respiratory virus infections, including SARS-CoV-2, in health care and community settings. Ann Intern Med. 2022;175 :W58-9. [PMID: ] doi:10.7326/L21-0783 35344376 7. Sims MD , Maine GN , Childers KL , et al. Coronavirus disease 2019 (COVID-19) seropositivity and asymptomatic rates in healthcare workers are associated with job function and masking. Clin Infect Dis. 2021;73 :S154-62. [PMID: ] doi:10.1093/cid/ciaa1684 33150375 8. Wilson S , Mouet A , Jeanne-Leroyer C , et al. Professional practice for COVID-19 risk reduction among health care workers: a cross-sectional study with matched case–control comparison. PLoS One. 2022;17 :e0264232. [PMID: ] doi:10.1371/journal.pone.0264232 35313328 9. Rodriguez-Lopez M , Parra B , Vergara E , et al. A case-control study of factors associated with SARS-CoV-2 infection among healthcare workers in Colombia. BMC Infect Dis. 2021;21 :878. [PMID: ] doi:10.1186/s12879-021-06581-y 34452600 10. Rosser JI , Tayyar R , Giardina R , et al. Case-control study evaluating risk factors for SARS-CoV-2 outbreak amongst healthcare personnel at a tertiary care center. Am J Infect Control. 2021;49 :1457-63. [PMID: ] doi:10.1016/j.ajic.2021.09.004 34536502 11. Haller S , Güsewell S , Egger T , et al. Impact of respirator versus surgical masks on SARS-CoV-2 acquisition in healthcare workers: a prospective multicentre cohort. Antimicrob Resist Infect Control. 2022;11 :27. [PMID: ] doi:10.1186/s13756-022-01070-6 35123572 12. Mastan S , Malik RA , Charalambous CP , et al; COVIDHipFracture Study Group. COVID-19 infection is related to differences in the use of personal protective equipment by orthopaedic specialist trainees caring for hip fracture patients during the second surge of COVID-19 in the north west of England. Eur J Orthop Surg Traumatol. 2021;31 :989-93. [PMID: ] doi:10.1007/s00590-021-03006-z 34110468 13. Schmitz D , Vos M , Stolmeijer R , et al. Association between personal protective equipment and SARS-CoV-2 infection risk in emergency department healthcare workers. Eur J Emerg Med. 2021;28 :202-9. [PMID: ] doi:10.1097/MEJ.0000000000000766 33105329 14. Li A , Slezak J , Maldonado AM , et al. SARS-CoV-2 positivity and mask utilization among health care workers. JAMA Netw Open. 2021;4 :e2114325. [PMID: ] doi:10.1001/jamanetworkopen.2021.14325 34156456 15. Jefferson T , Del Mar CB , Dooley L , et al. Physical interventions to interrupt or reduce the spread of respiratory viruses. Cochrane Database Syst Rev. 2020;11 :CD006207. [PMID: ] doi:10.1002/14651858.CD006207.pub5 33215698 16. Bartoszko JJ , Farooqi MAM , Alhazzani W , et al. Medical masks vs N95 respirators for preventing COVID-19 in healthcare workers: a systematic review and meta-analysis of randomized trials. Influenza Other Respir Viruses. 2020;14 :365-73. [PMID: ] doi:10.1111/irv.12745 32246890 17. Brooks JT , Beezhold DH , Noti JD , et al. Maximizing fit for cloth and medical procedure masks to improve performance and reduce SARS-CoV-2 transmission and exposure, 2021. MMWR Morb Mortal Wkly Rep. 2021;70 :254-7. [PMID: ] doi:10.15585/mmwr.mm7007e1 33600386 18. Boseley S. WHO warns of global shortage of face masks and protective suits. The Guardian. 7 February 2020. Accessed at www.theguardian.com/world/2020/feb/07/who-warns-global-shortage-face-masks-protective-suits-coronavirus on 24 June 2022. 19. Abaluck J , Kwong LH , Styczynski A , et al. Impact of community masking on COVID-19: a cluster-randomized trial in Bangladesh. Science. 2022;375 :eabi9069. [PMID: ] doi:10.1126/science.abi9069 34855513 20. Sun X , Ioannidis JP , Agoritsas T , et al. How to use a subgroup analysis: users' guide to the medical literature. JAMA. 2014;311 :405-11. [PMID: ] doi:10.1001/jama.2013.285063 24449319	36442064	PMC9707441	NO-CC CODE	2022-12-03 23:19:45	no	Ann Intern Med. 2022 Nov 29;:M22-1966	utf-8	Ann Intern Med	2,022	10.7326/M22-1966	oa_other
==== Front Environ Health Perspect Environ Health Perspect EHP Environmental Health Perspectives 0091-6765 1552-9924 Environmental Health Perspectives 36445295 EHP12295 10.1289/EHP12295 Invited Perspective Invited Perspective: Improved Risk Characterization for Trichloroethylene and Perchloroethylene Based on New Analyses of Glutathione Conjugation Rates https://orcid.org/0000-0003-3239-4481 Lash Lawrence H. 1 1 Department of Pharmacology, Wayne State University School of Medicine, Detroit, Michigan, USA Address correspondence to Lawrence H. Lash, Department of Pharmacology, Wayne State University School of Medicine, 540 East Canfield Ave., Detroit, MI 48201 USA. Telephone: (313) 577-0475. Email: [email protected] 29 11 2022 11 2022 130 11 11130715 10 2022 08 11 2022 15 11 2022 https://ehp.niehs.nih.gov/about-ehp/license EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. The author declares he has no conflicts of interest. Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days. Refers to https://doi.org.10.1289/EHP12006 ==== Body pmcThe halogenated solvents trichloroethylene (TCE) and perchloroethylene (PCE; also called tetrachloroethylene) are high priorities for study owing to their adverse human health effects. Certain key facts regarding TCE and PCE toxicity and metabolism are well established and have not been in dispute; these include the widespread presence of these solvents in ground- and drinking water,1–3 the classification of TCE as a known human carcinogen with the kidney and liver as primary targets,1,2,4 the classification of PCE as a probable human carcinogen with the strongest evidence for bladder cancer,1,3,5 and the prominent role of cytochrome P450s (CYPs) in mediating oxidative metabolism in both chemicals.6–9 However, uncertainty remains regarding both the activity and the toxicological importance of glutathione (GSH) conjugation of TCE and PCE in the liver and kidney in humans and rodents. Although the kidney is a recognized and accepted target organ for these two compounds,1–3,6,10,11 it is unclear whether kidney effects should be used to support derivation of noncancer toxicity values. In a study reported in this issue of Environmental Health Perspectives, Valdiviezo et al.12 performed new analyses of the metabolism of TCE and PCE by the GSH conjugation pathway. Their goal was to resolve uncertainties in quantitative data for risk assessment. Through the use of highly sensitive and recently developed analytical methods13–16 and multiple in vitro liver models, the authors derived physiologically relevant and validated estimates of GSH-mediated metabolism of TCE and PCE that enhance confidence in interspecies extrapolation and provide greater precision for risk characterizations of these environmental contaminants based on kidney-specific noncancer toxicity. The major pathways involved in the metabolism of TCE and PCE are highlighted in Figure 1. The predominant pathway (Figure 1A) is initiated by CYP-dependent oxidation and yields as major end products several chemically stable metabolites, including the haloacetic acids di- and trichloroacetic acids (DCA, TCA) and trichloroethanol (TCOH) and its glucuronide. In contrast, the other pathway (Figure 1B) is catalyzed initially by glutathione S-transferases (GSTs) to produce the GSH S-conjugates S-(1,2-dichlorovinyl)glutathione (DCVG) and S-(1,2,2-trichlorovinyl)glutathione (TCVG) from TCE and PCE, respectively. Whether DCVG and TCVG are formed in the liver or elsewhere, they are subsequently processed primarily in the kidneys to the respective cysteine S-conjugates, S-(1,2-dichlorovinyl)-l-cysteine (DCVC) and S-(1,2,2-trichlorovinyl)-l-cysteine (TCVC). As described in detail in multiple publications,7–9 the cysteine conjugates have two primary fates: a) N-acetylation to form mercapturates or b) further metabolism by either the cysteine conjugate beta-lyase or flavin-containing monooxygenase to yield reactive and chemically unstable products. Figure 1. Scheme of bioactivation pathways and adverse effects of metabolites for trichloroethylene and perchloroethylene. The scheme illustrates the overall metabolism of these two halogenated solvents by either (A) the oxidative pathway (initiated by cytochrome P450) or (B) the glutathione conjugation pathway. The scheme emphasizes the greater flux of metabolism through the oxidative pathway and the toxicological consequence of generation of metabolites by the two pathways. Note: CCBL, cysteine conjugate beta-lyase; CYP, cytochrome P450; DCA, dichloroacetic acid; DCVC, S-(1,2-dichlorovinyl)-l-cysteine; DCVG, S-(1,2-dichlorovinyl)glutathione; FMO, flavin-containing monooxygenase; GSH, glutathione; GST, GSH S-transferase; NAcDCVC, N-acetyl-S-(1,2-dichlorovinyl)-l-cysteine; NAcTCVC, N-acetyl-S-(1,2,2-trichlorovinyl)-l-cysteine; PCE, perchloroethylene; TCA, trichloroacetic acid; TCE, trichloroethylene; TCVC, S-(1,2,2-trichlorovinyl)-l-cysteine; TCVG; S-(1,2,2-trichlorovinyl)glutathione. Figure 1A is a schematic flowchart with two steps. Step 1: trichloroethylene (perchloroethylene) and cytochrome P 450 lead to haloacetic acids (trichloroacetic acid and dichloroacetic acid) and trichloroethanol (glucuronide). Step 2: Haloacetic acids (trichloroacetic acid and dichloroacetic acid) and trichloroethanol (glucuronide) lead to hepatotoxicity, liver cancer, and other nonrenal target organs. Figure 1B is a schematic flowchart with four steps. Step 1: trichloroethylene (perchloroethylene) with glutathione plus glutathione S-transferase leads to S-(1,2-dichlorovinyl)glutathione) (S-(1,2,2-trichlorovinyl)glutathione). Step 2: S-(1,2-dichlorovinyl)glutathione) (S-(1,2,2-trichlorovinyl)glutathione) leads to S-(1,2-dichlorovinyl)-L-cysteine) (S-(1,2,2-trichlorovinyl)-L-cysteine). Step 3: S-(1,2-dichlorovinyl)-L-cysteine) (S-(1,2,2-trichlorovinyl)-L-cysteine), which dissociates to N-acetyl-S-(1,2-dichlorovinyl)-L-cysteine and N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine, with cysteine conjugate beta-lyase and flavin-containing monooxygenase lead to reactive intermediates. Step 4: Reactive intermediates lead to Nephrotoxicity and Kidney Cancer. A series of studies I conducted with my colleagues8,17–20 reported rates of GSH conjugation of TCE or PCE in renal and hepatic in vitro preparations from humans, rats, and mice in the range of 1–100 nmol GSH conjugate formed/min/mg protein. In an additional study,21 my colleagues and I reported the detection of DCVG in the range of 1–50 nmol/mL blood in human volunteers exposed by inhalation for 4 h to 50 or 100 ppm TCE. In contrast to these findings, Dekant et al.22,23 and Green et al.,24,25 performing incubations in similar in vitro preparations from rat, mouse, and human liver and kidney, reported rates of GSH conjugation of TCE or PCE that were in the range of 0.1–10 nmol GSH conjugate formed/min/mg protein, or more than an order of magnitude lower than the rates we reported previously. Efforts were made back in the late 1990s to reconcile these differences by exchanging biological samples between Dr. Green’s and my labs. However, nothing ever came of these attempts to replicate measurements. Valdiviezo et al. highlighted these differences, as well as the values they themselves obtained for rates of TCE and PCE GSH conjugation, in Figure 4 of their paper.12 In the same vein as the dispute over rates of TCE and PCE GSH conjugation, Dekant’s and Henschler’s groups26–28 reported recovery of CYP-derived stable metabolites (primarily TCA and TCOH) in the urine of humans or rats exposed to TCE or PCE at levels that were two to three orders of magnitude higher than the levels of mercapturates. From these data, the authors concluded that the GSH-conjugation pathway plays an insignificant role in TCE or PCE bioactivation and toxicity, especially in humans. The problem with this interpretation is that it neglects two facts. First, mercapturates are only a portion of the metabolites generated by the GSH-conjugation pathway.26–28 Second, an unknown, but thought to be large, portion of the GSH conjugates from TCE or PCE are converted to reactive and unstable species, which in turn form covalent adducts with protein sulfhydryl groups and other cellular nucleophiles. These GSH-derived conjugates are thus difficult to isolate and quantify. Urinary mercapturates can be used to indicate exposure and the presence of a flux of metabolism through the GSH-conjugation pathway. However, they cannot provide an indication of total flux through this pathway, especially that portion of the flux that generates metabolites that are directly responsible for adverse effects in the kidneys. My colleagues and I made this argument in previous publications.1,2,6,7,9 In summary, the work by Valdiviezo et al.12 validated several in vitro liver models, particularly the micropatterned coculture model, and provided metabolism data for GSH conjugation of TCE and PCE that generally agree with those that my colleagues and I reported.8,17–21 These new findings thereby provide greater confidence in using these data for physiologically based pharmacokinetic models and human health risk assessment. ==== Refs References 1. IARC (International Agency for Research on Cancer). 2014. Trichloroethylene, tetrachloroethylene, and some other chlorinated agents. IARC Monogr Eval Carcinog Risks Hum 106 :1–512, PMID: .26214861 2. U.S. EPA (U.S. Environmental Protection Agency). 2011. Toxicological Review of Trichloroethylene: Chapter 6 (CAS No. 79-01-6): In Support of Summary Information on the Integrated Risk Information System (IRIS). EPA/635/R-09/011F. Washington, DC: U.S. EPA. 3. U.S. EPA. 2012. Toxicological Review of Tetrachloroethylene (Perchloroethylene) (CAS No. 127-18-4): In Support of Summary Information on the Integrated Risk Information System (IRIS). EPA/635/R-08/011A. Washington, DC: U.S. EPA. 4. NTP (National Toxicology Program). 1988. Toxicology and carcinogenesis studies of trichloroethylene (CAS no. 79-01-6) in four strains of rats (ACI, August, Marshall, Osborne-Mendel) (gavage studies). Natl Toxicol Program Tech Rep Ser 273 :1–299, PMID: .12748681 5. NTP. 1986. NTP toxicology and carcinogenesis studies of tetrachloroethylene (perchloroethylene) (CAS no. 127-18-4) in F344/N rats and B6C3F1 mice (inhalation studies). Natl Toxicol Program Tech Rep Ser 311 :1–197, PMID: .12748718 6. Cichocki JA, Guyton KZ, Guha N, Chiu WA, Rusyn I, Lash LH. 2016. Target organ metabolism, toxicity, and mechanisms of trichloroethylene and perchloroethylene: key similarities, differences, and data gaps. J Pharmacol Exp Ther 359 (1 ):110–123, PMID: , 10.1124/jpet.116.232629.27511820 7. Lash LH, Fisher JW, Lipscomb JC, Parker JC. 2000. Metabolism of trichloroethylene. Environ Health Perspect 108 (suppl 2 ):177–200, PMID: , 10.1289/ehp.00108s2177.10807551 8. Lash LH, Putt DA, Huang P, Hueni SE, Parker JC. 2007. Modulation of hepatic and renal metabolism and toxicity of trichloroethylene and perchloroethylene by alterations in status of cytochrome P450 and glutathione. Toxicology 235 (1–2 ):11–26, PMID: , 10.1016/j.tox.2007.03.001.17433522 9. Lash LH, Chiu WA, Guyton KZ, Rusyn II. 2014. Trichloroethylene biotransformation and its role in mutagenicity, carcinogenicity and target organ toxicity. Mutat Res Rev Mutat Res 762 :22–36, PMID: , 10.1016/j.mrrev.2014.04.003.25484616 10. Chiu WA, Jinot J, Scott CS, Makris SL, Cooper GS, Dzubow RC, et al. 2013. Human health effects of trichloroethylene: key findings and scientific issues. Environ Health Perspect 121 (3 ):303–311, PMID: , 10.1289/ehp.1205879.23249866 11. Rusyn I, Chiu WA, Lash LH, Kromhout H, Hansen J, Guyton KZ. 2014. Trichloroethylene: mechanistic, epidemiologic and other supporting evidence of carcinogenic hazard. Pharmacol Ther 141 (1 ):55–68, PMID: , 10.1016/j.pharmthera.2013.08.004.23973663 12. Valdiviezo A, Brown GE, Michell A, Trinconi C, Bodke V, Khetani SR, et al. 2022. Re-analysis of trichloroethylene and tetrachloroethylene metabolism to glutathione conjugates using human, rat and mouse liver in vitro models improves precision in risk characterization. Environ Health Perspect 130 (11 ):117009, 10.1289/EHP12006.36445294 13. Kim S, Kim D, Pollack GM, Collins LB, Rusyn I. 2009. Pharmacokinetic analysis of trichloroethylene metabolism in male B6C3F1 mice: formation and disposition of trichloroacetic acid, dichloroacetic acid, S-(1,2-dichlorovinyl)glutathione and S-(1,2-dichlorovinyl)-l-cysteine. Toxicol Appl Pharmacol 238 (1 ):90–99, PMID: , 10.1016/j.taap.2009.04.019.19409406 14. Kim S, Collins LB, Boysen G, Swenberg JA, Gold A, Ball LM, et al. 2009. Liquid chromatography electrospray ionization tandem mass spectrometry analysis method for simultaneous detection of trichloroacetic acid, dichloroacetic acid, S-(1,2-dichlorovinyl)glutathione and S-(1,2-dichlorovinyl)-L-cysteine. Toxicology 262 (3 ):230–238, PMID: , 10.1016/j.tox.2009.06.013.19549554 15. Luo YS, Cichocki JA, McDonald TJ, Rusyn I. 2017. Simultaneous detection of the tetrachloroethylene metabolites S-(1,2,2-trichlorovinyl) glutathione, S-(1,2,2-trichlorovinyl)-L-cysteine, and N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine in multiple mouse tissues via ultra-high performance liquid chromatography electrospray ionization tandem mass spectrometry. J Toxicol Environ Health A 80 (9 ):513–524, PMID: , 10.1080/15287394.2017.1330585.28696834 16. Luo YS, Furuya S, Chiu W, Rusyn I. 2018. Characterization of inter-tissue and inter-strain variability of TCE glutathione conjugation metabolites DCVG, DCVC, and NAcDCVC in the mouse. J Toxicol Environ Health A 81 (1–3 ):37–52, PMID: , 10.1080/15287394.2017.1408512.29190187 17. Lash LH, Xu Y, Elfarra AA, Duescher RJ, Parker JC. 1995. Glutathione-dependent metabolism of trichloroethylene in isolated liver and kidney cells of rats and its role in mitochondrial and cellular toxicity. Drug Metab Dispos 23 (8 ):846–853, PMID: .7493552 18. Lash LH, Qian W, Putt DA, Desai K, Elfarra AA, Sicuri AR, et al. 1998. Glutathione conjugation of perchloroethylene in rats and mice in vitro: sex-, species-, and tissue-dependent differences. Toxicol Appl Pharmacol 150 (1 ):49–57, PMID: , 10.1006/taap.1998.8402.9630452 19. Lash LH, Qian W, Putt DA, Jacobs K, Elfarra AA, Krause RJ, et al. 1998. Glutathione conjugation of trichloroethylene in rats and mice: sex-, species-, and tissue-dependent differences. Drug Metab Dispos 26 (1 ):12–19, PMID: .9443846 20. Lash LH, Lipscomb JC, Putt DA, Parker JC. 1999. Glutathione conjugation of trichloroethylene in human liver and kidney: kinetics and individual variation. Drug Metab Dispos 27 (3 ):351–359, PMID: .10064565 21. Lash LH, Putt DA, Brashear WT, Abbas R, Parker JC, Fisher JW. 1999. Identification of S-(1,2-dichlorovinyl)glutathione in the blood of human volunteers exposed to trichloroethylene. J Toxicol Environ Health A 56 (1 ):1–21, PMID: , 10.1080/009841099158204.9923751 22. Dekant W, Koob M, Henschler D. 1990. Metabolism of trichloroethene—in vivo and in vitro evidence for activation by glutathione conjugation. Chem Biol Interact 73 (1 ):89–101, PMID: , 10.1016/0009-2797(90)90110-9.2302745 23. Dekant W, Birner G, Werner M, Parker J. 1998. Glutathione conjugation of perchloroethene in subcellular fractions from rodent and human liver and kidney. Chem Biol Interact 116 (1–2 ):31–43, PMID: , 10.1016/s0009-2797(98)00077-5.9877199 24. Green T, Odum J, Nash JA, Foster JR. 1990. Perchloroethylene-induced rat-kidney tumors: an investigation of the mechanisms involved and their relevance to humans. Toxicol Appl Pharmacol 103 (1 ):77–89, PMID: , 10.1016/0041-008x(90)90264-u.1969182 25. Green T, Dow J, Ellis MK, Foster JR, Odum J. 1997. The role of glutathione conjugation in the development of kidney tumours in rats exposed to trichloroethylene. Chem Biol Interact 105 (2 ):99–117, PMID: , 10.1016/s0009-2797(97)00040-9.9251723 26. Bernauer U, Birner G, Dekant W, Henschler D. 1996. Biotransformation of trichloroethane: dose-dependent excretion of 2,2,2-trichloro-metabolites and mercapturic acids in rats and humans after inhalation. Arch Toxicol 70 (6 ):338–346, PMID: , 10.1007/s002040050283.8975632 27. Birner G, Vamvakas S, Dekant W, Henschler D. 1993. Nephrotoxic and genotoxic N-acetyl-S-dichlorovinyl-l-cysteine is a urinary metabolite after occupational 1,1,2-trichloroethene exposure in humans: implications for the risk of trichloroethane exposure. Environ Health Perspect 99 :281–284, PMID: , 10.1289/ehp.9399281.8319644 28. Völkel W, Friedewald M, Lederer E, Pähler A, Parker J, Dekant W. 1998. Biotransformation of perchloroethene: dose-dependent excretion of trichloroacetic acid, dichloroacetic acid, and N-acetyl-S-(trichlorovinyl)-l-cysteine in rats and humans after inhalation. Toxicol Appl Pharmacol 153 (1 ):20–27, PMID: , 10.1006/taap.1998.8548.9875296	36445295	PMC9707492	NO-CC CODE	2022-12-01 23:20:23	no	Environ Health Perspect. 2022 Nov 29; 130(11):111307	utf-8	Environ Health Perspect	2,022	10.1289/EHP12295	oa_other
==== Front Environ Health Perspect Environ Health Perspect EHP Environmental Health Perspectives 0091-6765 1552-9924 Environmental Health Perspectives 36445294 EHP12006 10.1289/EHP12006 Research Reanalysis of Trichloroethylene and Tetrachloroethylene Metabolism to Glutathione Conjugates Using Human, Rat, and Mouse Liver in Vitro Models to Improve Precision in Risk Characterization Valdiviezo Alan 1 2 Brown Grace E. 3 Michell Ashlin R. 3 Trinconi Cristiana M. 3 Bodke Vedant V. 3 Khetani Salman R. 3 Luo Yu-Syuan 1 2 Chiu Weihsueh A. 1 2 Rusyn Ivan 1 2 1 Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas, USA 2 Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas, USA 3 Department of Biomedical Engineering, University of Illinois Chicago, Illinois, USA Address correspondence to Ivan Rusyn, Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843 USA. Telephone: (979) 458-9866. Email: [email protected] 29 11 2022 11 2022 130 11 11700915 8 2022 16 10 2022 15 11 2022 https://ehp.niehs.nih.gov/about-ehp/license EHP is an open-access journal published with support from the National Institute of Environmental Health Sciences, National Institutes of Health. All content is public domain unless otherwise noted. Background: Both trichloroethylene (TCE) and tetrachloroethylene (PCE) are high-priority chemicals subject to numerous human health risk evaluations by a range of agencies. Metabolism of TCE and PCE determines their ultimate toxicity; important uncertainties exist in quantitative characterization of metabolism to genotoxic moieties through glutathione (GSH) conjugation and species differences therein. Objectives: This study aimed to address these uncertainties using novel in vitro liver models, interspecies comparison, and a sensitive assay for quantification of GSH conjugates of TCE and PCE, S-(1,2-dichlorovinyl)glutathione (DCVG) and S-(1,2,2-trichlorovinyl) glutathione (TCVG), respectively. Methods: Liver in vitro models used herein were suspension, 2-D culture, and micropatterned coculture (MPCC) with primary human, rat, and mouse hepatocytes, as well as human induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep). Results: We found that, although efficiency of metabolism varied among models, consistent with known differences in their metabolic capacity, formation rates of DCVG and TCVG generally followed the patterns human≥rat≥mouse, and primary hepatocytes>iHep. Data derived from MPCC were most consistent with estimates from physiologically based pharmacokinetic models calibrated to in vivo data. Discussion: For TCE, the new data provided additional empirical support for inclusion of GSH conjugation-mediated kidney effects as critical for the derivation of noncancer toxicity values. For PCE, the data reduced previous uncertainties regarding the extent of TCVG formation in humans; this information was used to update several candidate kidney-specific noncancer toxicity values. Overall, MPCC-derived data provided physiologically relevant estimates of GSH-mediated metabolism of TCE and PCE to reduce uncertainties in interspecies extrapolation that constrained previous risk evaluations, thereby increasing the precision of risk characterizations of these high-priority toxicants. https://doi.org/10.1289/EHP12006 Supplemental Material is available online (https://doi.org/10.1289/EHP12006). Salman R. Khetani is an inventor on a patent related to the human MPCC technology that has been licensed to BioIVT, Inc. for commercial distribution. Other authors declare they have nothing to disclose. Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact [email protected]. Our staff will work with you to assess and meet your accessibility needs within 3 working days. ==== Body pmcIntroduction Trichloroethylene (TCE) and tetrachloroethylene (PCE) are chlorinated solvents with a broad range of applications in industrial and other settings. TCE and PCE are used in the production of other solvents, dry cleaning of fabrics, and metal degreasing.1–3 Due to their volatility, human exposures to TCE and PCE are assumed to be mainly through inhalation (either through direct contact or vapor intrusion); both chemicals are also commonly found in ground and drinking water.4 TCE is the most commonly detected compound among contaminants that are found at National Priority List (NPL) sites in the United States, and concurrent contamination of TCE with PCE is widespread.2,5 TCE has been classified as a known human carcinogen with kidney and liver as primary targets,4,6–8 whereas PCE has been classified as a probable human carcinogen with strongest evidence for bladder cancer.4,9,10 These chemicals remain high priorities for risk assessment and risk management, especially for noncancer risks associated with chronic exposures in residences or office buildings where vapor intrusion from groundwater contamination is suspected. The United States Environmental Protection Agency (U.S. EPA) listed TCE and PCE among 10 “high-priority” existing chemical substances under Section 6 of the Toxic Substances Control Act as amended by the Frank R. Lautenberg Chemical Safety for the 21st Century Act.11 In addition, the U.S. Army Public Health Center is conducting reanalysis of the TCE data to update an occupational exposure limit (OEL) because it is a ubiquitous contaminant on military installations where both residential and office buildings may have vapor intrusion from contaminated groundwater.12 Both cancer and noncancer effects associated with TCE and PCE are known to be mediated by their metabolites.2,4,10,13,14 These compounds are metabolized through two pathways: oxidation and glutathione (GSH) conjugation.4,15–18 The major oxidative metabolites for TCE are trichloroacetic acid (TCA) and trichloroethanol (TCOH); for PCE, only TCA has been consistently detected from oxidation in both human and experimental animals.1,9,19–21 Major metabolites through GSH conjugation for TCE include S-(1,2-dichlorovinyl)glutathione (DCVG), S-(1,2-dichlorovinyl)-L-cysteine (DCVC), and N-acetyl-S-(1,2-dichlorovinyl)-L-cysteine (NAcDCVC). For PCE, GSH metabolites include S-(1,2,2-trichlorovinyl)glutathione (TCVG), S-(1,2,2-trichlorovinyl)-L-cysteine (TCVC), and N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine (NAcTCVC).22–26 Even though the metabolic flux through oxidation predominates for both TCE and PCE, their GSH conjugates are thought to be of critical importance as the metabolites derived from cysteine conjugates, formed in kidneys, are known to be highly reactive and are genotoxic.2,17 The extent of metabolism of TCE and PCE to their GSH conjugates and subsequent kidney effects has been regarded as a key uncertainty in previous risk assessments, especially because of the inconsistency among previous studies of GSH metabolism that used hepatocytes and subcellular fractions from human and rodent liver.15,17,22,24,26–30 Recent advancements in liver-based in vitro models,31 including engineered human liver models,32–34 have improved the capabilities of in vitro systems to mimic physiologically relevant metabolic function of the liver. These models also improved our ability to collect and interpret pharmacokinetic data.35–38 One liver model that showed great potential for in vitro metabolism studies is a micropatterned coculture (MPCC) system.39 The MPCC platform has been shown to maintain the viability and metabolic activity of hepatocytes from rodents and humans for several weeks.39–42 These improvements further highlight the potential for using MPCC to investigate xenobiotic metabolism, especially in cases where uncertainties are to be addressed through direct comparisons of species-specific metabolism. Thus, because there is a critical need to characterize GSH conjugation metabolism of TCE and PCE, we used several liver in vitro models, including suspension, 2-D culture, and MPCC. We used primary human hepatocytes (PHH), human induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep), primary rat hepatocytes (PRH), and primary mouse hepatocytes (PMH) and determined formation of GSH conjugation metabolites DCVG and TCVG. We compared our results with those published previously to resolve existing uncertainties and used new data to reevaluate assumptions made by the U.S. EPA in deriving noncancer candidate reference toxicity values in their most recent assessments.7,9 The results of this study provide critical new information pertaining to the utility of in vitro liver models and show that MPCC model provides physiologically relevant estimates of TCE and PCE metabolism. These data reduce interspecies extrapolation uncertainties in chemical risk evaluations and confirm the validity of kidney toxicity data for both TCE and PCE. Materials and Methods Chemicals TCE, PCE, GSH, and distilled water with 0.1% formic acid were obtained from Sigma-Aldrich. Methanol and acetonitrile were from Fisher Scientific. DCVC (purity ≥98.0%), S-(1,2-dichlorovinyl)-cysteine-13C3-15N (DCVC, purity ≥95.0%, isotopic purity ≥98.0%), DCVG (≥98.9%), and S-(1,2-dichlorovinyl)glutathione-13C2-15N (DCVG purity ≥90.0%, isotopic purity ≥98.0%) were obtained from TLC Pharmaceutical Standards. N-acetyl-S-(1,2-dichlorovinyl)-L-cysteine (NAcDCVC, purity 99.8%), N-acetyl-S-(1,2-dichlorovinyl)-cysteine-13C, d3 (NAcDCVC, purity: 97.6%, isotopic purity: 99.0%), and N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine (NAcTCVC, purity: 99.7%) were purchased from Toronto Research Chemicals. TCVC (purity: 98.4%), S-(1,2,2-trichlorovinyl)-L-cysteine-13C3-15N (TCVC, purity: 97.5%), S-(1,2,2-trichlorovinyl)glutathione (TCVG, purity: 98.9%), S-(1,2,2-trichlorovinyl)glutathione-13C2-15N (TCVG, purity: 90.4%), and N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine-13C3-15N (NAcTCVC purity: 99.0%), were synthesized by Dr. Avram Gold at the University of North Carolina at Chapel Hill as detailed previously.43 Cells Cryopreserved primary human hepatocytes (PHHs) from two different sources were used in this study. Single donor PHH (35-y-old Asian male) were obtained from Lonza (Lot No. HUM4122) and 10-donor PHH pooled sample was obtained from Gibco (Lot No. HPP1825348). Induced pluripotent stem cell (iPSC)-derived hepatocytes (iHep) were obtained from FujiFilm Cellular Dynamics International (Lot No. 103664). Primary rat (Sprague Dawley; Charles River Laboratories) hepatocytes (PRH) and primary mouse (C57Bl/6J or CD-1; Charles River Laboratories) hepatocytes (PMH) were isolated following the protocol reported by Seglen44 for rat hepatocytes and the protocol of Lee et al.45 for mouse hepatocytes; these experiments were performed in accordance with the institutional guidelines (Protocol 21-046, University of Illinois at Chicago Institutional Animal Care and Use Committee). iHep Differentiation Culture conditions for experiments with iHep were previously described.46 Briefly, iHep were seeded with plating media (DMEM/F12; Thermo Fisher) supplemented with 2% B27 supplement (Thermo Fisher), 100 nM dexamethasone, 25μg/mL gentamicin, and 20 ng/mL oncostatin M (R&D Systems) at a density of 2.5×106 cells/well on a 6-well plate, which was precoated with collagen (VWR International, LLC). Six hours after incubation at 37°C, 5% CO2, media were replaced to new plating media to remove unattached cells. Black-walled, clear-bottom, tissue cultured 96-well plates (Corning Inc.) were used for 2-D sandwich culture. This plate was precoated overnight at 37°C, 5% CO2 with 100μg/mL collagen type 1/0.02M acetic acid and rinsed 3 times with PBS before cell seeding. Cells were differentiated for 5 d with daily plating media changes. Suspension Cell Culture In vitro hepatocyte metabolism was evaluated using suspension cultures of PHH (pooled sample; Lot No. HPP1825348), iHep, PRH, and PMH (see Figure S1 for study design). In brief, cells were suspended in William’s E medium (Gibco) and adjusted to the cell concentration of 1.0×106 cells/mL. Five hundred microliters of TCE or PCE (4 mM in 1% acetone and William’s E medium) was spiked in 500 μL of the cell working stock with or without 10 mM GSH to a final chemical concentration of 2 mM in 0.5% acetone (with or without 5 mM GSH) and cell number of 5×105 cells/mL. One hundred microliters of the reaction mixture were removed subsequently at 0, 60, 120, and 240 min to individual 1.5mL Eppendorf tubes for further sample extraction and chemical analysis detailed below. 2-D Monolayer Cultures of Hepatocytes To create hepatocyte monolayers, 24- (for PMH) or 96- (for PHH and PRH) well plates were coated with rat tail collagen I (Corning). PMH were seeded at 300,000 cells/well in 24-well plates. PHH (both pooled sample and single donor sample) and PRH were seeded at 50,000 cells/well in 96-well plates (see Figure S1 for study design). All monolayer cultures were maintained in hepatocyte medium containing DMEM (Corning) supplemented with 15 mM HEPES [4-(2-hydroxyethyl)-1-piperazineethane-sulfonic acid] buffer (Corning), 1% penicillin/streptomycin, 10% bovine serum, and 2 nM glucagon (Sigma-Aldrich). PHH medium (both pooled sample and single donor sample) also contained 100 nM dexamethasone (Sigma-Aldrich) and 1% ITS+ Premix Universal Culture Supplement, which contains insulin, human transferrin, and selenous acid (Corning). PRH medium also contained 20 ng/mL epidermal growth factor, 7.5 ng/mL hydrocortisone, and 0.016μg/mL insulin. PMH medium also contained 100 nM dexamethasone and 6.25μg/mL insulin. The media was replaced in all cultures every 48 h and collected at each time point for further analysis. On day 7 of culture, monolayer cultures of PHH (both pooled and sample and single donor sample) and PRH were treated with 200× stock solution of TCE or PCE, with and without GSH in 100% acetone for the final concentration of 2 mM TCE or PCE, and 0 or 5 mM GSH, in 0.5% acetone. Media samples were collected for analytical chemistry 1 h after this treatment and fresh media without chemicals was added. Cultures were incubated for an additional 47 h when media was collected for measurements of albumin and urea (see below). Cultures of PMH were treated with chemicals on day 8 of culture and samples were collected exactly as detailed above. 2-D Sandwich Culture of iHep Differentiated iHep were collected with StemPro Accutase (Thermo Fisher) from the 6-well plate. The cell clusters were centrifuged (200×g, 3 min) and resuspended with plating media. iHep were plated in 96-well plates at a density of 1.0×105 cells/well and incubated to attach for 6 h at 37°C, 5% CO2 (see Figure S1 for study design). Media were replaced to 100μL of plating media with 0.35mg/mL Matrigel (Corning). After incubation overnight at 37°C, 5% CO2, media were exchanged to the maintenance medium (DMEM/F12, 2% B27, 100 nM dexamethasone, 25μg/mL gentamicin). Media was collected and exchanged every 48 h. On day 4 of culture, cells were treated with 200× stock solution of TCE or PCE, with and without GSH in 100% acetone for the final concentration of 2 mM TCE or PCE, and 0 or 5 mM GSH, in 0.5% acetone. Media samples were collected for analytical chemistry 1 h after this treatment and fresh media without chemicals was added. Cultures were incubated for an additional 47 h when media was collected for measurements of albumin and urea (see below). MPCC Experiments Previous studies have reported the development of the MPCC platform containing liver parenchymal cells arranged onto collagen-coated islands of experimentally optimized dimensions and subsequently surrounded by 3T3-J2 murine embryonic fibroblasts that are known to sustain robust functions in hepatocytes from numerous species.39,40 In addition to adapting the MPCC technique for primary human, rat and mouse hepatocytes, we used optimized conditions that were previously reported for iHep in MPCC.41 Briefly, rat tail collagen I was adsorbed onto tissue culture plastic 24- (for PMH) or 96- (for PHH and PRH) well plates followed by patterning lithographically via oxygen plasma treatment to create 500μm diameter circular domains spaced 1,200μm apart from center-to-center. PHHs, iHep, PRH, or PMH were seeded into the corresponding micropatterned plates in serum-free culture medium (see Figure S1 for study design). The hepatocytes preferentially attached to the circular collagen domains, leaving approximately 4,500 iHep, 5,000 PHH, and 3,200 PRH per well in 96-well plates, or 25,000 PMH per well in 24-well plates. After the circular domains were fully seeded, the cultures were rinsed to remove the unattached hepatocytes, and medium containing fetal bovine serum (FBS; Gibco) was added to the cultures overnight. Separately, 3T3-J2 murine embryonic fibroblasts (gift of Dr. Howard Green, Harvard University)47 were cultured in medium containing 10% bovine calf serum, 1% penicillin/streptomycin, and 89% DMEM. After the fibroblasts reached 85%–90% confluence, they were passaged with 0.25% trypsin/EDTA (Corning) that was neutralized with medium containing 10% serum and pelleted via centrifugation, and cells were then used for further experimentation. All 3T3-J2 murine embryonic fibroblasts were used between passage 7 and 10. After 5 d of culture for iHep or 18–24 h for PHH, PRH, and PMH, the 3T3-J2 murine embryonic fibroblasts were seeded in hepatocyte medium at a density of ∼90,000 cells/24-well or 15,000 cells/96-well to create MPCC. iHep hepatocyte medium was composed of DMEM supplemented with 1× B-27 supplement, 15 mM HEPES buffer, 1× ITS+, 1× penicillin/streptomycin, 1% bovine calf serum, 100 nM dexamethasone, 2 nM glucagon, 2.5 ng/mL oncostatin M (R&D Systems), and 10μM FPH2 small molecule (Sigma-Aldrich). Media was replaced every 48 h and collected for further analyses as detailed below. MPCC cultures of PHH (both pooled and sample and single donor sample) and PRH were treated on day 7 with 200× stock solution of TCE or PCE, with and without GSH in 100% acetone for the final concentration of 2 mM TCE or PCE, and 0 or 5 mM GSH, in 0.5% acetone. For PMH, treatments were performed on day 8. For iHep, treatments were performed on day 12 of culture. Media samples were collected for analytical chemistry 1 h after this treatment, and fresh media without chemicals was added. Cultures were incubated for additional 47 h when media was collected for measurements of albumin and urea (see below). Morphological and Functional Assessments The morphology of the monolayers and MPCC was assessed via an IX83 automated microscope (Olympus America) with a high-sensitivity 4.2MP sCMOS camera (ORCA-Flash4.0 LT+) using phase contrast objectives (10×). Urea production in culture supernatants was quantified via a colorimetric end point assay kit (Cat. No. 0580-250; Stanbio Labs). Culture supernatants were also assayed for albumin production by using a competitive enzyme-linked immunosorbent assay (human ELISA kit E80-129, rat ELISA kit E111-125, mouse ELISA kit E99-134; Bethyl Laboratories) with horseradish peroxidase detection and 3,3′,5,5′-tetramethylbenzidine (TMB; Rockland Immunochemicals) as the substrate. The absorbance measurement for both albumin and urea assays were detected on the Synergy H1 multimode plate reader (Biotech). Analytical Methods for TCE and PCE Metabolites TCE and PCE produce various metabolites through both oxidation and glutathione conjugation pathways.4,23,48 To analyze a subset of all the potential metabolites produced from TCE or PCE exposure, we used liquid chromatography–tandem mass spectrometry (LC-MS/MS). These metabolites and information on their analytical detection can be found in Table 1. Each media sample (50μL) was mixed with 100μL of chilled acetonitrile containing 0.1μM mixture of DCVG, DCVC, and NAcDCVC internal standards or 5μM mixture of TCVG, TCVC, and NAcTCVC internal standards. Next, samples were centrifuged at 12,000×g for 10 min. The supernatant was dried under vacuum using a SpeedVac (Speed SPD1010; Thermo Scientific) and reconstituted with 50μL of aqueous mobile phase prior to LC-MS/MS analyses. Table 1 TCE and PCE Metabolites evaluated in this study. Metabolite Analytical assay Ionization mode Mass transition or quantitation ion (m/z) Collision energy (eV) DCVG LC-MS/MS ESI(+) 402.0→272.9 13 402.0→169.9 † 25 402.0→133.9 37 DCVGa LC-MS/MS ESI(+) 409.0→173.9 † 25 405.0→169.9 25 DCVC LC-MS/MS ESI(+) 216.0→198.9 † 8 216.0→126.9 25 216.0→82.9 45 DCVCa LC-MS/MS ESI(+) 222.0→128.9 † 25 220.0→201.9 10 NAcDCVC LC-MS/MS ESI(+) 258.0→215.9 9 258.0→198.8 † 17 258.0→179.9 5 NAcDCVCa LC-MS/MS ESI(+) 262.0→216.9 9 262.0→198.8 † 17 TCVG LC-MS/MS ESI(+) 436.0→306.8 13 436.0→203.9 25 438.0→308.8 † 13 TCVGa LC-MS/MS ESI(+) 443.0→313.8 † 17 439.0→309.8 13 TCVC LC-MS/MS ESI(+) 251.9→234.8 † 9 249.9→160.8 21 249.9→232.8 9 TCVCa LC-MS/MS ESI(+) 255.9→237.8 † 9 255.9→162.9 21 NAcTCVC LC-MS/MS ESI(+) 291.9→249.8 † 9 291.9→232.8 17 293.9→251.8 9 NAcTCVCa LC-MS/MS ESI(+) 295.9→235.8 † 9 295.9→253.8 17 Note: DCVC, S-(1,2-dichlorovinyl)-L-cysteine; DCVG, S-(1,2-dichlorovinyl)glutathione; LC-MS/MS, liquid chromatography–tandem mass spectrometry; :NAcDCVC, N-acetyl-S-(1,2-dichlorovinyl)-L-cysteine; NAcTCVC, N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine; PCE, tetrachloroethylene; TCE, trichloroethylene; TCVC, S-(1,2,2-trichlorovinyl)-L-cysteine. a Internal standard, see “Materials and Methods” section. † Selected quantifier for analysis by LC-MS/MS analysis. Determination of TCE glutathione conjugation metabolites via LC-MS/MS was performed as previously described by Luo et al.48 In brief, analysis was performed using a 1290 Infinity II LC system and a 6470 triple-quadrupole mass spectrometer (both from Agilent Technologies). Samples (10μL) were automatically injected and chromatographed on a ZORBAX SSHD Eclipse Plus C18 column (3.0×50mm, 1.8μm; Agilent Technologies) with a guard column (2.1×5mm, 1.8μm; Agilent Technologies). Column temperature was maintained at 25°C. Initial chromatographic condition was maintained at 90% solvent A (water with 0.1% acetic acid, v/v) and 10% solvent B (methanol with 0.1% acetic acid, v/v) for 1 min, then increased to 90% solvent B by 3 min, then to 98% solvent B by 4 min, and then returned to initial condition until 7 min for sufficient equilibration prior to next run. Flow rate was set at 0.4mL/min. Metabolite levels in media were quantified by using the peak area ratios of standards to isotopically labeled internal standards in an 8-point calibration curve (0, 1.25, 2.5, 5, 12.5, 25, 50, 125 pmol). In these experiments, the detection limit for TCE GSH metabolites was 5 nM, the limit of quantitation (LOQ) was 50 nM, and the recovery of metabolites ranged from 67% to 83%. Determination of PCE glutathione conjugation metabolites via LC-MS/MS was performed as previously described by Luo et al.23 In brief, analysis was performed using 1290 Infinity II LC system and 6470 triple-quadrupole mass spectrometer (Agilent Technologies). Samples (10μL) were automatically injected and chromatographed on a ZORBAX SSHD Eclipse Plus C18 column (3.0×50mm, 1.8μm; Agilent Technologies) with a guard column (2.1×5mm, 1.8μm; Agilent Technologies). Column temperature was controlled at 25°C. For each sample analysis, initial chromatographic conditions were 80% solvent A (water with 0.1% formic acid) and 20% solvent B (methanol with 0.1% formic acid). Conditions were maintained from 0–1 min, then increased to 90% of solvent B by 3 mins, then to 98% of solvent B by 4 mins, then to 20% of solvent B by 4.2 mins. Next, 20% of solvent B was maintained until 7 mins to allow sufficient equilibration time prior to the next injection. Flow rate was maintained at 0.4mL/min. Metabolite levels in media were quantified by using the peak area ratios of standards to isotopically labeled internal standards in an 8-point calibration curve (0, 1.25, 2.5, 5, 12.5, 25, 50, 125 pmol). In these experiments, the detection limit for PCE GSH metabolites was 2 nM, the LOQ was 20 nM, and the recovery of metabolites ranged from 56% to 88%. Comparisons with Previous Studies Data on GSH conjugation metabolism of TCE and PCE has been previously measured both in vitro and in vivo, as well as incorporated into physiologically based pharmacokinetic (PBPK) models. For comparison with current study results, both in vitro and in vivo GSH conjugation rates were converted to in vitro intrinsic clearance rates in nmol/h/million hepatocytes at the tested concentration of 2 mM. For TCE, previous in vitro studies included experiments in subcellular fractions (cytosol or microsomes)28,29 and in both subcellular fractions and hepatocyte suspensions.15,17,22,30 The PBPK model for TCE18 estimated GSH conjugation rates in mice, rats, and humans based on in vivo data on TCE conjugation as well as overall mass balance.15,49–51 For PCE, previous relevant in vitro liver metabolism studies are experiments in subcellular fractions26,27 and in both subcellular fractions and hepatocyte suspensions.17,24 The PBPK model for PCE52 estimated in vivo GSH conjugation rates in mice, rats, and humans based on in vivo data on GSH conjugation metabolites in rats, as well as overall mass balance. Miscellaneous The number of replicates in each condition is listed in the legend. Statistical analyses were conducted using GraphPad Prism (version 9.4.1; GraphPad Software). Figures were prepared using GraphPad Prism, Adobe Photoshop (Adobe) and/or PowerPoint (Microsoft). Results This study used several liver in vitro models to evaluate TCE and PCE metabolism through the GSH conjugation pathway: suspension, 2-D culture, and MPCC (Figure 1 and Figure S1). Various sources of liver parenchymal cells were used including PHHs, iHep, PRH, and PMH. Cells were treated with TCE or PCE (2 mM) with and without GSH (5 mM) following the methods used in previous studies.17 Formation of GSH conjugation pathway metabolites was evaluated. These results were taken together in combination with previously reported metabolite in vitro data to facilitate liver model comparison and determine species differences. Figure 1. Schematic of TCE and PCE GSH conjugation metabolism with liver in vitro models. The diagram summarizes tissue localization of metabolic reactions and transport of TCE and PCE GSH conjugation metabolites, DCVG, and TCVG, respectively. A majority of D/TCVG formation occurs in the liver; however, formation of D/TCVG does occur, although to a lesser extent, in the kidneys. Each of the in vitro models used in this study are shown beneath the liver. Note: DCVG, S-(1,2-dichlorovinyl)-glutathione; GSH, glutathione; PCE, tetrachloroethylene; TCE, trichloroethylene; TCVG, S-(1,2,2-trichlorovinyl)glutathione. Figure 1 is a schematic flowchart with two steps. Step 1: Liver: Trichloroethylene per tetrachloroethylene with S-(1,2-dichlorovinyl)-glutathione per S-(1,2,2-trichlorovinyl) glutathione, including suspension, 2- dimensional culture, and micropatterned co-culture, each for (primary hepatocytes, induced hepatocyte-like cells) leads to alliance with blood. Step 2 Blood: Trichloroethylene per tetrachloroethylene with S-(1,2-dichlorovinyl)-glutathione per S-(1,2,2-trichlorovinyl) glutathione leads to Kidney: Trichloroethylene per tetrachloroethylene with S-(1,2-dichlorovinyl)-glutathione per S-(1,2,2-trichlorovinyl) glutathione. Hepatic Morphological and Functional Assessments in MPCC Morphology and basic function of cells in MPCC was evaluated prior to chemical treatment (Figure 2). We observed compact, concentric islands of healthy hepatocytes surrounded by 3T3-J2 fibroblasts across multiple species. In addition to morphology, albumin production and urea secretion were evaluated. Chemical treatments were performed on day 7–8 to allow for the model to reach its optimal function as demonstrated in previous studies.39,40 Following chemical treatment, supernatant media was assessed for albumin and urea at the final timepoint of day 9–10. Albumin production showed variability between single donor of PHHs, iHep, and primary rat hepatocytes. Albumin was detected at the highest levels in PRH MPCC on days 7 and 9; the amounts were approximately between 500 to 600μg/1M hep/d for the three testing conditions. In PMH MPCC, albumin production increased over the culture period from approximately 40μg/1M hep/d on day 2, to 180μg/1M hep/d on day 10. In PHH MPCC, albumin production peaked on day 7 with a range of 60 to 100μg/1M hep/d before decreasing to a range of 60 to 70μg/1M hep/d on day 9. In iHep MPCC, albumin amounts steadily increased from day 3 to day 9. The highest levels were detected on day 9 at approximately 20μg/1M hep/d for all three conditions. Estimated human liver output for albumin is 37–105μg/1M hep/d.34 Figure 2. Representative baseline morphology and hepatic functional data collected before and after chemical treatment (final concentration of 2 mM for TCE and PCE, and 5 mM for GSH, in 0.5% acetone) from different sources of hepatocytes in MPCCs with 3T3-J2 fibroblasts. (A) Primary human hepatocytes (Lot No. HUM4122), (B) induced hepatocyte-like cells (iHep), (C) primary rat hepatocytes, (D) primary mouse hepatocytes patterned onto collagen islands in culture media. Following seeding of 3T3-J2 fibroblasts, hepatic morphology was maintained in MPCCs for 7 d prior to chemical exposure. Red dashed ovals indicate hepatocyte areas. Phase contrast objectives (10×) were used to obtain the images. Normalized hepatic functional data for albumin secretion and urea synthesis are shown to the right of each corresponding MPCC hepatocyte model. The asterisk symbol indicates the day of chemical exposure in each model. No significant differences were observed among treatments at each time point, with the exception of the data for urea in primary rat hepatocytes where vehicle-treatment-designated wells had overall greater values both before and after TCE or PCE treatments (one-way ANOVA comparing vehicle with TCE+GSH and PCE+GSH). See Excel Tables S1 (for albumin) and S2 (for urea) for the raw data plotted in this Figure. Note: ANOVA, analysis of variance; GSH, glutathione; MPCC, micropatterned coculture; PCE, tetrachloroethylene; TCE, trichloroethylene. Figure 2A to 2B are sets of one stained tissue and two line graphs. The stained tissues depict the chemical treatment for trichloroethylene, tetrachloroethylene, and glutathione from different sources of hepatocytes in micropatterned co-cultures with 3 T 3-J 2 fibroblasts in primary human hepatocytes, induced hepatocyte-like cells, primary rat hepatocytes, and primary mouse hepatocytes. The line graphs, plotting albumin (microgram per 1 molar hepatocytes per day), ranging from 0 to 125 in increments of 25, 0 to 30 in increments of 10, 0 to 600 in increments of 200, and 0 to 300 in increments of 100; and urea (microgram per 1 molar hepatocytes per day), ranging from 0 to 50 in increments of 10, 0 to 10 in increments of 5, 0 to 250 in increments of 50, and 0 to 800 in increments of 200 (y-axis) across days, ranging from 3 to 9 in increments of 2 (x-axis) for vehicle, trichloroethylene plus glutathione, and tetrachloroethylene plus glutathione. Urea secretion exhibited similar trends as albumin production. The greatest amounts of urea were observed in PRH MPCC. Peak levels occurred on day 9 for all three conditions; however, wells designated for vehicle produced about 200μg/1M hep/d, whereas wells designated for TCE and PCE with GSH produced approximately 150μg/1M hep/d; however, the above differences in urea secretion across the wells were likely not due to the compounds, because trends were observed both before and after treatment with vehicle or compounds. In comparison, PMH MPCC produced approximately 300μg/1M hep/d at day 10 of culture. In PHH MPCC, urea secretion was highest on day 3 and then decreased on day 5, followed by increasing amounts on day 7 and 9. The amount of urea secreted on day 3 ranged between 30–40μg/1M hep/d. Urea secretion in iHep MPCC was lower compared to PHH and PRH MPCCs. Estimated human liver output for urea is 59–159μg/1M hep/d.34 Metabolite Formation Rates across in Vitro Models and Sources of Hepatocytes Multiple in vitro liver models and sources of hepatocytes were used in this study to characterize TCE and PCE GSH conjugation metabolism. Following chemical addition for 1 h, media samples were collected and analyzed with quantitative LC-MS/MS methods to screen for GSH conjugation metabolites. For TCE, three metabolites were screened for: DCVG, DCVC, and NAcDCVC. Similarly, for PCE we screened for TCVG, TCVC, and NAcTCVC. In this study, we detected only DCVG and TCVG, which is consistent with previous data that demonstrated a majority of DCVG and TCVG formation occurs in liver, whereas the cysteine metabolites are primarily formed and found in the kidney.14 The concentrations of DCVG and TCVG varied by in vitro model and hepatocyte source (Figure 3; left graphs). DCVG and TCVG concentrations ranged from 0.07 to 5.6μM and 0.10 to 6.6μM, respectively. Both metabolites were detected at the highest concentrations using pooled PHHs in suspension cultures; however, the lowest concentration of DCVG was reported with PMH in 2-D culture, whereas the lowest TCVG concentration occurred with PMH in MPCC. Figure 3. Comparison of DCVG and TCVG concentrations and rates of formation across different in vitro liver models and sources of hepatocytes used in this study. (A) Plotted are detected concentrations (left, mean±SD) and normalized rates of formation (right, mean±SD) for TCE GSH conjugation metabolite, DCVG. (B) Same data for PCE GSH conjugation metabolite, TCVG. The vertical red dotted lines represent the analytical method limit of quantification for each metabolite. The horizontal dotted lines separate data from different models. The following number of replicates were analyzed: n=2 for suspension cultures of PHH (pooled) and iHep; n=3 for suspension cultures of PRH and PMH; n=4 for 2-D cultures of PHH (pooled and HUM4122A), iHep, and PRH; n=3 for PMH 2-D cultures. n=3 for MPCC cultures of PHH (HUM4122A) and PMH; n=4 for MPCC cultures of PHH (pooled) and iHep; and n=5 for MPCC cultures of PRH. See Excel Tables S3 (for TCE) and S4 (for PCE) for the raw data plotted in this Figure. Note: DCVG, S-(1,2-dichlorovinyl)glutathione; GSH, glutathione; MPCC, micropatterned coculture; PCE, tetrachloroethylene; PHH, primary human hepatocytes; PMH, primary mouse hepatocytes; PRH, primary rat hepatocytes; SD, standard deviation; TCE, trichloroethylene; TCVG, S-(1,2,2-trichlorovinyl)glutathione. Figure 3A is a set of two graphs, plotting Micropatterned Co-Cultures, including primary human hepatocyte (pooled), primary human hepatocyte, 2 dimensional culture, and suspension, each includes (H U M 4122), induced hepatocyte-like cells, primary rat hepatocytes, and primary mouse hepatocytes (y-axis) across S-(1,2-dichlorovinyl)-glutathione (micromolar), ranging from 0.01 to 0.1 in increments of 0.09; 0.1 to 1 in increments of 0.9; 1 to 10 in increments of 9; and S-(1,2-dichlorovinyl)-glutathione (nanomole per hour per 1 molar hepatocytes), ranging from 0.1 to 1 in increments of 0.9; 1 to 10 in increments of 9; 10 to 100 in increments of 90 (x-axis). Figure 3B is a set of two graphs, plotting Micropatterned Co-Cultures, including primary human hepatocyte (pooled), primary human hepatocyte, 2 dimensional culture, and suspension, each includes (H U M 4122), induced hepatocyte-like cells, primary rat hepatocytes, and primary mouse hepatocytes (y-axis) across S-(1,2,2-trichlorovinyl) glutathione (micromolar), ranging from 0.01 to 0.1 in increments of 0.09; 0.1 to 1 in increments of 0.9; 1 to 10 in increments of 9; and S-(1,2,2-trichlorovinyl) glutathione (nanomole per hour per 1 molar hepatocytes), ranging from 0.1 to 1 in increments of 0.9; 1 to 10 in increments of 9; 10 to 100 in increments of 90 (x-axis). Next, to enable comparison of DCVG and TCVG formation across models and to other studies, we normalized our data by accounting for cell density and incubation volume to derive metabolite formation rates (Figure 3; right graphs). DCVG and TCVG formation rates ranged from 0.07 to 71 nmol/h/1M hep and 0.11 to 83 nmol/h/1M hep, respectively. In TCE suspension cultures, human hepatocytes created DCVG at rates 18- to 45-fold higher than PRH and 46- to 120-fold higher than PMH. Human hepatocytes in 2-D culture produced DCVG at rates 4- to 7-fold higher than PRH and 12- to 21-fold higher than PMH. PHH in MPCC yielded DCVG formation rates that were 2- to 3-fold greater than PRH and 6- to 11-fold greater than PMH. In PCE suspension cultures, human hepatocytes created TCVG at rates 77- to 186-fold higher than PRH and 128- to 277-fold higher than PMH. Human hepatocytes produced TCVG at rates equal to 7-fold higher than PRH and 3- to 25-fold higher than PMH. In MPCC cultures, iHep generated TCVG at a rate of 1.5-fold less than PRH; however, pooled PHHs produced TCVG at approximately a 2-fold higher rate than PRH. PHH and iHep in MPCC generated TCVG at rates 3- to 7-fold greater than PMH. Overall, we observed the greatest species differences in DCVG and TCVG formation in suspension cultures and least in MPCCs. Comparison of Metabolite Formation Rates with Historical Data Figure 4 compares the results of the current study with previously reported rates of GSH conjugation either measured in vitro or estimated based on PBPK modeling calibrated to in vivo data (Excel Tables S5 and S6). With respect to in vitro comparisons, in humans, for both TCE and PCE, there was a clear trend across in vitro models of suspension ≈ Lash et al.15,17,22,30 data > MPCC > 2-D > Green et al.28 and Dekant et al.29 data (Figure 4A). For rats and mice, the pattern differed with respect to suspension and Green et al. and Dekant et al. data, with Lash et al.15,17,22,30 data > MPCC > suspension > 2-D for both TCE and PCE, Green et al.28 and Dekant et al.29 similar to 2-D for TCE, and Green et al. and Dekant et al. intermediate between MPCC and 2-D for PCE (Figure 4B–C). Figure 4. GSH conjugation formation rates for TCE and PCE across various studies. (A) Box and whiskers plots are shown comparing TCE and PCE GSH conjugation metabolite formation rates using human data, (B) rat data, and (C) mouse data. In each box, the black vertical lines inside the box denote median values; boxes extend from the 25th to the 75th percentile of each group’s distribution of values; vertical extending lines indicate the minimum and maximum values. Individual values are shown as black dots. Studies that did not test the specified conditions are identified as NA. All TCE data points plotted can be found in Excel Table S3. All PCE data points plotted can be found in Excel Table S4. Note: GSH, glutathione; NA, not available; PCE, tetrachloroethylene; TCE, trichloroethylene. Figure 4A are set of two box and whiskers plots titled human, plotting This study [Micropatterned Co-Cultures, primary human hepatocyte, and induced hepatocyte-like cells; 2-D; primary human hepatocyte pooled and single; suspension; primary human hepatocyte and induced hepatocyte-like cells], Chiu and others 2009 [P B P K], Lash and others 1995, 1998, 1999, 2007 [suspension and subcellular fractions], and Green and others 1997, Dekant and others 1990 [subcellular fractions] and This study [Micropatterned Co-Cultures, primary human hepatocyte, and induced hepatocyte-like cells; 2-D; primary human hepatocyte pooled and single; suspension; primary human hepatocyte and induced hepatocyte-like cells], Chiu and Ginsberg 2011 [P B P K], Lash and others 1998, 2007, [suspension and subcellular fractions], Dekant and others, 1998 [subcellular fractions], and Green and others 1990 (y-axis) across trichloroethylene glutathione conjugation (nanomole per hour per 1 molar hepatocyte, ranging from 0.01 to 0.1 in increments of 0.09, 0.1 to 1 in increments of 0.9, 1 to 10 in increments of 9, 10 to 100 in increments of 90, 100 to 1000 in increments of 900 and tetrachloroethylene glutathione conjugation (nanomole per hour per 1 molar hepatocyte, ranging from 0.01 to 0.1 in increments of 0.09, 0.1 to 1 in increments of 0.9, 1 to 10 in increments of 9, 10 to 100 in increments of 90 (x-axis). Figure 4B are set of two box and whiskers plots titled rat, plotting This study [Micropatterned Co-Cultures, 2 D, suspension], Chiu and others 2009 [P B P K], Lash and others 1995, 1998, 1999, 2007 [suspension and subcellular fractions], and Green and others 1997, Dekant and others 1990 [subcellular fractions] and This study [Micropatterned Co-Cultures, 2 D, suspension], Chiu and Ginsberg 2011 [P B P K], Lash and others 1998, 2007, [suspension and subcellular fractions], Dekant and others, 1998 [subcellular fractions], and Green and others 1990 (y-axis) across trichloroethylene glutathione conjugation (nanomole per hour per 1 molar hepatocyte, ranging from 0.01 to 0.1 in increments of 0.09, 0.1 to 1 in increments of 0.9, 1 to 10 in increments of 9, 10 to 100 in increments of 90, 100 to 1000 in increments of 900 and tetrachloroethylene glutathione conjugation (nanomole per hour per 1 molar hepatocyte, ranging from 0.01 to 0.1 in increments of 0.09, 0.1 to 1 in increments of 0.9, 1 to 10 in increments of 9, 10 to 100 in increments of 90 (x-axis). Figures 4C are set of two box and whiskers plots titled rat, plotting This study [Micropatterned Co-Cultures, 2 D, suspension], Chiu and others 2009 [P B P K], Lash and others 1995, 1998, 1999, 2007 [suspension and subcellular fractions], and Green and others 1997, Dekant and others 1990 [subcellular fractions] and This study [Micropatterned Co-Cultures, 2 D, suspension], Chiu and Ginsberg 2011 [P B P K], Lash and others 1998, 2007, [suspension and subcellular fractions], Dekant and others, 1998 [subcellular fractions], and Green and others 1990 (y-axis) across trichloroethylene glutathione conjugation (nanomole per hour per 1 molar hepatocyte, ranging from 0.01 to 0.1 in increments of 0.09, 0.1 to 1 in increments of 0.9, 1 to 10 in increments of 9, 10 to 100 in increments of 90, 100 to 1000 in increments of 900 and tetrachloroethylene glutathione conjugation (nanomole per hour per 1 molar hepatocyte, ranging from 0.01 to 0.1 in increments of 0.09, 0.1 to 1 in increments of 0.9, 1 to 10 in increments of 9, 10 to 100 in increments of 90 (x-axis). When comparing our study results with the TCE and PCE PBPK modeling results calibrated in vivo GSH data (humans and rats for TCE, rats for PCE), the MPCC results were the most consistent, followed by the data from suspension cultures in our study. An interesting finding was that, for mice, for which the PBPK model estimated GSH conjugation rates indirectly based on mass balance, the MPCC results are close to the median PBPK-based estimates. For PCE in humans, the indirect, mass balance-based estimates of GSH conjugation from the PBPK were highly uncertain, spanning around 3,000-fold.52 All our results were more consistent with PBPK model-derived higher-end estimates, contrary to the data reported by Green et al.28 and Dekant et al.29 Discussion It is not unusual that species concordance overall, and characterization of species-specific metabolism in particular,53 are the areas where major uncertainties exist that prevent regulatory risk assessments from being as precise in their estimates of safe exposure levels as would be preferred. Although the so-called “uncertainty factors” have been used traditionally to express the degree to which default assumptions are to be used when the reliable data are lacking,54 calls have been made to use all available information, including new approach methods (NAMs) data,55 to reduce uncertainties and increase both confidence and precision in risk characterization. Indeed, development of NAMs that can help fill important information gaps in chemical safety assessments is one of the primary goals of the U.S. EPA work plan to reduce use of vertebrate animals in chemical testing.56 One type of a NAM that may assist with addressing the uncertainties in species-specific metabolism of drugs and chemicals is microphysiological systems for the liver.34 A number of such models have been developed by researchers in academia and the private sector,57 and their use in chemical risk assessment has been proposed in next-generation risk assessments.58 Although the opportunities are many, the challenges are also formidable, including the cost, complexity, and low throughput of most of the available models.38,59,60 With these considerations in mind and the desire to address the specific key gaps in GSH metabolism of TCE and PCE across species, we chose to use the MPCC model,39 together with more traditional suspension and 2-D cultures of hepatocytes,31 to enable these experiments with sufficient replication. Although conventional 2-D platforms (i.e., hepatocyte monocultures on collagen-adsorbed polystyrene or glass) decline in functionality over time, controlling homotypic interactions between hepatocytes with circular domains of empirically optimized dimensions and heterotypic interactions with supportive 3T3-J2 murine embryonic fibroblasts has been shown to induce high levels of stable functions in hepatocytes from various species, including human, rat, and mouse, as well as human iHep.33 To this end, 3T3-J2 fibroblasts have been found to express various liver developmental signals, including T-cadherin and decorin, allowing hepatocytes to maintain in vivo-like morphology, polarity, and functionality for several weeks in vitro. Furthermore, MPCCs also appear to be more consistent with metabolism estimates based on PBPK models calibrated to in vivo GSH conjugation data, providing addition empirical support for their quantitative validity. To illustrate the potential for MPCC to quantitatively impact human health risk assessment, we consider the implications of this work on reducing uncertainties in interspecies differences in metabolism characterized in the U.S. EPA IRIS Toxicological Reviews for TCE7 and PCE.9 With respect to TCE, the U.S. EPA7 used five studies in mice and rats to derive candidate RfDs61–65 and three studies to derive candidate RfCs61,62,64 (Figure 5A–B). The U.S. EPA noted7 that “There remains substantial uncertainty in the extrapolation of GSH conjugation from rodents to humans due to limitations in the available data.” The GSH conjugation data for TCE reported in the available studies are highly discordant, with the studies by Lash et al. reporting values several orders of magnitude higher than those reported by Green and Dekant et al. The U.S. EPA Science Advisory Board suggested that the discordance between these in vitro studies and PBPK model estimates constituted uncertainties in the rate of GSH conjugation in humans and thus recommended against relying on the PBPK modeling-based estimates of GSH conjugation for toxicity value derivation based on kidney end points in the U.S. EPA TCE assessment.7 As a result, the candidate RfDs and RfCs for kidney toxicity,64,65 which used the GSH conjugation predictions from the PBPK model for interspecies, intraspecies, and route-to-route extrapolation, were not considered of sufficient confidence to be used as a primary basis for the overall RfD and RfC. Instead, the final RfD=0.5μg/kg-d was based on developmental immune,63 immune,61 and fetal cardiac effects,62 and the final RfC=2 μg/m3 was based on immune61 and fetal cardiac effects.62 However, the MPCC data from our study provide independent corroboration for both the TCE PBPK model18 used by the U.S. EPA, as well as the in vitro measurements by Lash et al., increasing the confidence in the candidate RfDs of 0.3μg/kg-d64 and 0.8μg/kg-d65 and candidate RfC of 3 μg/m364 for kidney effects (Figures 5A–B). Because these are very close to the candidate RfD and RfC based on other critical end points, they further strengthen the basis for the U.S. EPA’s overall toxicity values for TCE noncancer effects by oral or inhalation exposures. Figure 5. Comparison of cRfD (panels A and C) and cRfC (panels B and D) for TCE (A,B) and PCE (C,D) with corresponding uncertainty factors. Plotted are points of departure (open diamonds) and candidate toxicity values (open circles) as well as uncertainty factors for study-specific effects as indicated by the references. The vertical red dotted line represents the final RfD or RfC values for TCE7 and PCE.9 UFA, animal to human; UFD, database; UFH, human variability; UFL, LOAEL to NOAEL. Red dashed-line rectangles identify original (as identified in U.S. EPA9) and alternative (based on the refined estimates of GSH conjugation using the data from this study) cRfD and cRfC for PCE. All data points for TCE and PCE cRfD and cRfC, including uncertainty factor values, can be found in Excel Tables S7–S9. Note: cRfC, candidate reference concentration; cRfD, candidate reference dose; LOAEL, lowest observed adverse effect level; NOAEL, no observed adverse effect level; PCE, tetrachloroethylene; TCE, trichloroethylene. Figures 5A to 5D are graphs, titled trichloroethylene candidate reference dose, trichloroethylene candidate reference concentration, tetrachloroethylene candidate reference dose, and tetrachloroethylene candidate reference concentration, plotting Renal system – rat (Woolhiser et al. 2006), Renal system – rat (N T P 1998), Immune system – mouse (Peden-Adams and others 2006), Developmental effects – mouse (Johnson and others 2003), Immune system – mouse (Keil and others 2009); Renal system – rat (N T P 1998), Developmental effects – mouse (Johnson and others 2003), Immune system – mouse (Keil and others 2009); Renal system – mouse (J I S A 1993), Alternative renal system rat (J I S A 1993), Renal system – rat (J I S A 1993), Alternative renal system human (Mutti and others 1192), Renal system – human (Mutti and others 1992), Nervous system – human (Cavalleri and others 1994), and Nervous system – human (Echeverria and others 1995; and Renal system – mouse (J I S A 1993), Alternative renal system rat (J I S A 1993), Renal system – rat (J I S A 1993), Renal system – human (Mutti and others 1992), Nervous system – human (Cavalleri and others 1994), and Nervous system – human (Echeverria and others 1995 (y-axis) across Human oral equivalent dose (milligram per kilogram per day), ranging from 0.0001 to 0.001 in increments of 0.0009, 0.001 to 0.01 in increments of 0.009, 0.01 to 0.1 in increments of 0.09, 0.1 to 1 in increments of 0.9; human equivalent concentration (parts per million), ranging from 0.0001 to 0.001 in increments of 0.0009, 0.001 to 0.01 in increments of 0.009, 0.01 to 0.1 in increments of 0.09; Human oral equivalent dose (milligram per kilogram per day), ranging from 0.001 to 0.01 in increments of 0.009, 0.01 to 0.1 in increments of 0.09, 0.1 to 1 in increments of 0.9, 1 to 10 in increments of 9; and human equivalent concentration (parts per million), ranging from 0.001 to 0.01 in increments of 0.009, 0.01 to 0.1 in increments of 0.09, 0.1 to 1 in increments of 0.9, 1 to 10 in increments of 9 (x-axis), respectively. With respect to PCE, the U.S. EPA9 used three studies in humans66–68 and a study in rats and mice69 to derive candidate RfDs and RfCs (Figures 5C–D). Based on conflicting previous in vitro data, uncertainties of many orders of magnitude were noted for PCE GSH conjugation in humans, and thus again the U.S. EPA did not rely on the PBPK modeling-based estimates of GSH conjugation for toxicity value derivation based on kidney end points in the PCE assessment.9 In particular, the PBPK model52 predictions for GSH conjugation exhibited ∼3,000-fold uncertainties in humans for this pathway, with few human in vitro data and no human in vivo data on this pathway. Because of these uncertainties, interspecies extrapolation of kidney effects68,69 was based on AUC of PCE in blood instead of a more mechanistically supported dose metric related to GSH conjugation. In this case, by analogy to the success of MPCC data for TCE being consistent with in vivo GSH conjugation data, the MPCC data can be used to substantially reduce the uncertainties from the PBPK modeling, which spanned such a wide range due to the lack of sufficient in vivo calibration data on GSH conjugation metabolites. In particular, restricting the range of PBPK model-based GSH conjugation predictions to those consistent with MPCC data (see Excel Tables S7–S9 for details), we can use the results both for interspecies extrapolation from rats to humans and route-to-route extrapolation from inhalation to oral exposure. This approach shifts the PODs based on kidney effects to lower values as follows. For the RfD, the resulting kidney-specific RfD values are 0.003mg/kg-d based on either human or rat data, which is in the range of the neurotoxicity study-specific RfDs of 0.003–0.01mg/kg-d66,67 that formed the basis of the overall RfD of 0.005mg/kg-d (Figure 5C). Similarly, for the RfC, the resulting kidney-specific RfD value is 0.006 ppm based on rat data, which is in the range of the neurotoxicity study-specific RfCs of 0.002–0.008 ppm66,67 that formed the basis of the overall RfC of 0.006 ppm (Figure 5D). Moreover, use of the MPCC to restrict the PBPK model predictions also leads to greater overall concordance between the human- and rat-based kidney-specific reference values. We note a few limitations in our study. First, our testing concentration of 2 mM for TCE and PCE is well above estimated environmental exposure levels.70,71 The estimated GSH conjugation Km values for TCE and PCE range from 0.2 to 47μM.7,52 Therefore, testing at 2 mM is expected to saturate hepatocytes and limit metabolic capacity at Vmax. We also acknowledge that our MPCC configuration does not contain other liver nonparenchymal cells (i.e., liver sinusoidal endothelial cells, hepatic stellate cells, Kupffer cells/macrophages) that are known to regulate hepatocyte functions72,73; however, in vitro cultured/passaged liver nonparenchymal cells, including liver sinusoidal endothelial cells,74 hepatic stellate cells,75 and Kupffer cells76 are not able to induce functions in primary hepatocytes or iHep in MPCC to the same level and duration as cultured 3T3-J2 fibroblasts.33 Additionally, hepatocytes are cultured on rat tail collagen I rather than other mixtures of extracellular matrix proteins found in the liver, which can be challenging to source in substantial quantities and are significantly more expensive than rat collagen I.32 Nonetheless, the MPCC model has exhibited tremendous success in drug screening and recapitulating in vivo phenomena.77–80 In summary, TCE and PCE remain high-priority substances for U.S. EPA risk evaluation and engender significant public health concern, but key uncertainties have remained with respect to GSH conjugation-mediated toxicity. Traditional in vitro systems such as hepatocyte suspensions or subcellular fractions have led to disparate results that have reduced confidence in PBPK modeling of this pathway. Here we hypothesized that MPCC, which takes advantage of advancements in engineered human liver platforms to better mimic physiologically relevant conditions, can provide in vitro data to fill these data gaps and reduce their uncertainties. Our MPCC metabolism data support higher levels of TCE and PCE GSH conjugation flux in humans in comparison with rats or mice, corroborating previous TCE PBPK modeling and reducing uncertainty in previous PCE PBPK modeling. These data thereby facilitate the inclusion of kidney-specific effects for toxicity value derivation, resulting in greater confidence in the U.S. EPA’s noncancer toxicity values for these compounds. Overall, these data suggest that MPCCs can provide physiologically relevant estimates of metabolism to reduce interspecies extrapolation uncertainties, particularly for substances whose toxicity involves bioactivation. Supplementary Material Click here for additional data file. Click here for additional data file. Click here for additional data file. Acknowledgments This work was supported, in part, by grants from the National Institute of Environmental Health Sciences (P42 ES027704) and the U.S. EPA (RD84003201 and RD84045001). The views expressed in this manuscript do not reflect those of the funding agencies. The use of specific commercial products in this work does not constitute endorsement by the funding agencies. ==== Refs References 1. Reitz RH, Gargas ML, Mendrala AL, Schumann AM. 1996. In vivo and in vitro studies of perchloroethylene metabolism for physiologically based pharmacokinetic modeling in rats, mice, and humans. Toxicol Appl Pharmacol 136 (2 ):289–306, PMID: , 10.1006/taap.1996.0036.8619237 2. Cichocki JA, Guyton KZ, Guha N, Chiu WA, Rusyn I, Lash LH. 2016. Target organ metabolism, toxicity, and mechanisms of trichloroethylene and perchloroethylene: key similarities, differences, and data gaps. J Pharmacol Exp Ther 359 (1 ):110–123, PMID: , 10.1124/jpet.116.232629.27511820 3. Guha N, Loomis D, Grosse Y, Lauby-Secretan B, El Ghissassi F, Bouvard V, et al. 2012. Carcinogenicity of trichloroethylene, tetrachloroethylene, some other chlorinated solvents, and their metabolites. Lancet Oncol 13 (12 ):1192–1193, PMID: , 10.1016/S1470-2045(12)70485-0.23323277 4. IARC (International Agency for Research on Cancer) Working Group on the Evaluation of Carcinogenic Risks to Humans. 2014. Trichloroethylene, tetrachloroethylene, and some other chlorinated agents. IARC Monogr Eval Carcinog Risks Hum 106 :1–512, PMID: .26214861 5. Fay RM, Mumtaz MM. 1996. Development of a priority list of chemical mixtures occurring at 1188 hazardous waste sites, using the HazDat database. Food Chem Toxicol 34 (11–12 ):1163–1165, PMID: , 10.1016/s0278-6915(97)00090-2.9119332 6. Rusyn I, Chiu WA, Lash LH, Kromhout H, Hansen J, Guyton KZ. 2014. Trichloroethylene: mechanistic, epidemiologic and other supporting evidence of carcinogenic hazard. Pharmacol Ther 141 (1 ):55–68, PMID: , 10.1016/j.pharmthera.2013.08.004.23973663 7. U.S. EPA (U.S. Environmental Protection Agency). 2011. Toxicological Review of Trichloroethylene (CAS No. 79-01-6): In Support of Summary Information on the Integrated Risk Information System (IRIS). Washington, DC : U.S. EPA. 8. NTP (National Toxicology Program). 2021. Report on Carcinogens, Fifteenth Edition. Research Triangle Park: U.S. Department of Health and Human Services. 9. U.S. EPA. 2012. Toxicological Review of Tetrachloroethylene (CAS No. 127-18-4): In Support of Summary Information on the Integrated Risk Information System (IRIS). Washington, DC: U.S. Environmental Protection Agency. 10. NTP. 1986. NTP toxicology and carcinogenesis studies of tetrachloroethylene (perchloroethylene) (CAS No. 127-18-4) in F344/N rats and B6C3F1 mice (inhalation studies). Natl Toxicol Program Tech Rep Ser 311 :1–197, PMID: .12748718 11. U.S. EPA. 2016. EPA Names First Chemicals for Review under New TSCA Legislation. https://19january2017snapshot.epa.gov/newsreleases/epa-names-first-chemicals-review-under-new-tsca-legislation_.html [accessed 24 November 2022]. 12. National Academies of Sciences Engineering and Medicine. 2019. Review of DOD’s Approach to Deriving an Occupational Exposure Level for Trichloroethylene. Washington, DC: The National Academies Press. 13. Richard AM, Huang R, Waidyanatha S, Shinn P, Collins BJ, Thillainadarajah I, et al. 2021. The Tox21 10K compound library: collaborative chemistry advancing toxicology. Chem Res Toxicol 34 (2 ):189–216, PMID: , 10.1021/acs.chemrestox.0c00264.33140634 14. Lash LH, Chiu WA, Guyton KZ, Rusyn I. 2014. Trichloroethylene biotransformation and its role in mutagenicity, carcinogenicity and target organ toxicity. Mutat Res Rev Mutat Res 762 :22–36, PMID: , 10.1016/j.mrrev.2014.04.003.25484616 15. Lash LH, Lipscomb JC, Putt DA, Parker JC. 1999. Glutathione conjugation of trichloroethylene in human liver and kidney: kinetics and individual variation. Drug Metab Dispos 27 (3 ):351–359, PMID: .10064565 16. Lash LH, Qian W, Putt DA, Hueni SE, Elfarra AA, Sicuri AR, et al. 2002. Renal toxicity of perchloroethylene and S-(1,2,2-trichlorovinyl)glutathione in rats and mice: sex- and species-dependent differences. Toxicol Appl Pharmacol 179 (3 ):163–171, PMID: , 10.1006/taap.2001.9358.11906246 17. Lash LH, Putt DA, Huang P, Hueni SE, Parker JC. 2007. Modulation of hepatic and renal metabolism and toxicity of trichloroethylene and perchloroethylene by alterations in status of cytochrome P450 and glutathione. Toxicology 235 (1–2 ):11–26, PMID: , 10.1016/j.tox.2007.03.001.17433522 18. Chiu WA, Okino MS, Evans MV. 2009. Characterizing uncertainty and population variability in the toxicokinetics of trichloroethylene and metabolites in mice, rats, and humans using an updated database, physiologically based pharmacokinetic (PBPK) model, and Bayesian approach. Toxicol Appl Pharmacol 241 (1 ):36–60, PMID: , 10.1016/j.taap.2009.07.032.19660485 19. Völkel W, Friedewald M, Lederer E, Pähler A, Parker J, Dekant W. 1998. Biotransformation of perchloroethene: dose-dependent excretion of trichloroacetic acid, dichloroacetic acid, and N-acetyl-S-(trichlorovinyl)-L-cysteine in rats and humans after inhalation. Toxicol Appl Pharmacol 153 (1 ):20–27, PMID: , 10.1006/taap.1998.8548.9875296 20. Lash LH, Parker JC. 2001. Hepatic and renal toxicities associated with perchloroethylene. Pharmacol Rev 53 (2 ):177–208, PMID: .11356983 21. Chiu WA, Jinot J, Scott CS, Makris SL, Cooper GS, Dzubow RC, et al. 2013. Human health effects of trichloroethylene: key findings and scientific issues. Environ Health Perspect 121 (3 ):303–311, PMID: , 10.1289/ehp.1205879.23249866 22. Lash LH, Xu Y, Elfarra AA, Duescher RJ, Parker JC. 1995. Glutathione-dependent metabolism of trichloroethylene in isolated liver and kidney cells of rats and its role in mitochondrial and cellular toxicity. Drug Metab Dispos 23 (8 ):846–853, PMID: .7493552 23. Luo YS, Cichocki JA, McDonald TJ, Rusyn I. 2017. Simultaneous detection of the tetrachloroethylene metabolites S-(1,2,2-trichlorovinyl) glutathione, S-(1,2,2-trichlorovinyl)-L-cysteine, and N-acetyl-S-(1,2,2-trichlorovinyl)-L-cysteine in multiple mouse tissues via ultra-high performance liquid chromatography electrospray ionization tandem mass spectrometry. J Toxicol Environ Health A 80 (9 ):513–524, PMID: , 10.1080/15287394.2017.1330585.28696834 24. Lash LH, Qian W, Putt DA, Desai K, Elfarra AA, Sicuri AR, et al. 1998. Glutathione conjugation of perchloroethylene in rats and mice in vitro: sex-, species-, and tissue-dependent differences. Toxicol Appl Pharmacol 150 (1 ):49–57, PMID: , 10.1006/taap.1998.8402.9630452 25. Cristofori P, Sauer AV, Trevisan A. 2015. Three common pathways of nephrotoxicity induced by halogenated alkenes. Cell Biol Toxicol 31 (1 ):1–13, PMID: , 10.1007/s10565-015-9293-x.25665826 26. Dekant W, Birner G, Werner M, Parker J. 1998. Glutathione conjugation of perchloroethene in subcellular fractions from rodent and human liver and kidney. Chem Biol Interact 116 (1–2 ):31–43, PMID: , 10.1016/s0009-2797(98)00077-5.9877199 27. Green T, Odum J, Nash JA, Foster JR. 1990. Perchloroethylene-induced rat-kidney tumors—an investigation of the mechanisms involved and their relevance to humans. Toxicol Appl Pharmacol 103 (1 ):77–89, PMID: , 10.1016/0041-008x(90)90264-u.1969182 28. Green T, Dow J, Ellis MK, Foster JR, Odum J. 1997. The role of glutathione conjugation in the development of kidney tumours in rats exposed to trichloroethylene. Chem Biol Interact 105 (2 ):99–117, PMID: , 10.1016/s0009-2797(97)00040-9.9251723 29. Dekant W, Koob M, Henschler D. 1990. Metabolism of trichloroethene–in vivo and in vitro evidence for activation by glutathione conjugation. Chem Biol Interact 73 (1 ):89–101, PMID: , 10.1016/0009-2797(90)90110-9.2302745 30. Lash LH, Qian W, Putt DA, Jacobs K, Elfarra AA, Krause RJ, et al. 1998. Glutathione conjugation of trichloroethylene in rats and mice: sex-, species-, and tissue-dependent differences. Drug Metab Dispos 26 (1 ):12–19, PMID: .9443846 31. Soldatow VY, Lecluyse EL, Griffith LG, Rusyn I. 2013. In vitro models for liver toxicity testing. Toxicol Res (Camb) 2 (1 ):23–39, PMID: , 10.1039/C2TX20051A.23495363 32. Khetani SR, Berger DR, Ballinger KR, Davidson MD, Lin C, Ware BR. 2015. Microengineered liver tissues for drug testing. J Lab Autom 20 (3 ):216–250, PMID: , 10.1177/2211068214566939.25617027 33. Monckton CP, Brown GE, Khetani SR. 2021. Latest impact of engineered human liver platforms on drug development. APL Bioeng 5 (3 ):031506, PMID: , 10.1063/5.0051765.34286173 34. Baudy AR, Otieno MA, Hewitt P, Gan J, Roth A, Keller D, et al. 2020. Liver microphysiological systems development guidelines for safety risk assessment in the pharmaceutical industry. Lab Chip 20 (2 ):215–225, PMID: , 10.1039/c9lc00768g.31799979 35. Sarkar U, Ravindra KC, Large E, Young CL, Rivera-Burgos D, Yu J, et al. 2017. Integrated assessment of diclofenac biotransformation, pharmacokinetics, and Omics-Based toxicity in a three-dimensional human liver-immunocompetent coculture system. Drug Metab Dispos 45 (7 ):855–866, PMID: , 10.1124/dmd.116.074005.28450578 36. Tsamandouras N, Kostrzewski T, Stokes CL, Griffith LG, Hughes DJ, Cirit M. 2017. Quantitative assessment of population variability in hepatic drug metabolism using a perfused three-dimensional human liver microphysiological system. J Pharmacol Exp Ther 360 (1 ):95–105, PMID: , 10.1124/jpet.116.237495.27760784 37. Sakolish C, Luo YS, Valdiviezo A, Vernetti LA, Rusyn I, Chiu WA. 2021. Prediction of hepatic drug clearance with a human microfluidic four-cell liver acinus microphysiology system. Toxicology 463 :152954, PMID: , 10.1016/j.tox.2021.152954.34543702 38. Valdiviezo A, Kato Y, Baker ES, Chiu WA, Rusyn I. 2022. Evaluation of metabolism of a defined pesticide mixture through multiple in vitro liver models. Toxics 10 (10 ):566, PMID: , 10.3390/toxics10100566.36287846 39. Khetani SR, Bhatia SN. 2008. Microscale culture of human liver cells for drug development. Nat Biotechnol 26 (1 ):120–126, PMID: , 10.1038/nbt1361.18026090 40. Ukairo O, Kanchagar C, Moore A, Shi J, Gaffney J, Aoyama S, et al. 2013. Long-term stability of primary rat hepatocytes in micropatterned cocultures. J Biochem Mol Toxicol 27 (3 ):204–212, PMID: , 10.1002/jbt.21469.23315828 41. Berger DR, Ware BR, Davidson MD, Allsup SR, Khetani SR. 2015. Enhancing the functional maturity of induced pluripotent stem cell-derived human hepatocytes by controlled presentation of cell-cell interactions in vitro. Hepatology 61 (4 ):1370–1381, PMID: , 10.1002/hep.27621.25421237 42. Ware BR, Brown GE, Soldatow VY, LeCluyse EL, Khetani SR. 2019. Long-Term engineered cultures of primary mouse hepatocytes for strain and species comparison studies during drug development. Gene Expr 19 (3 ):199–214, PMID: , 10.3727/105221619X15638857793317.31340881 43. Kim S, Collins LB, Boysen G, Swenberg JA, Gold A, Ball LM, et al. 2009. Liquid chromatography electrospray ionization tandem mass spectrometry analysis method for simultaneous detection of trichloroacetic acid, dichloroacetic acid, S-(1,2-dichlorovinyl)glutathione and S-(1,2-dichlorovinyl)-L-cysteine. Toxicology 262 (3 ):230–238, PMID: , 10.1016/j.tox.2009.06.013.19549554 44. Seglen PO. 1976. Preparation of isolated rat liver cells. Methods Cell Biol 13 :29–83, PMID: .177845 45. Lee SM, Pusec CM, Norris GH, De Jesus A, Diaz-Ruiz A, Muratalla J, et al. 2021. Hepatocyte-specific loss of PPARγ protects mice from NASH and increases the therapeutic effects of rosiglitazone in the liver. Cell Mol Gastroenterol Hepatol 11 (5 ):1291–1311, PMID: , 10.1016/j.jcmgh.2021.01.003.33444819 46. Grimm FA, Iwata Y, Sirenko O, Bittner M, Rusyn I. 2015. High-Content assay multiplexing for toxicity screening in induced pluripotent stem cell-derived cardiomyocytes and hepatocytes. Assay Drug Dev Technol 13 (9 ):529–546, PMID: , 10.1089/adt.2015.659.26539751 47. Rheinwald JG, Green H. 1975. Serial cultivation of strains of human epidermal keratinocytes: the formation of keratinizing colonies from single cells. Cell 6 (3 ):331–343, PMID: , 10.1016/S0092-8674(75)80001-8.1052771 48. Luo YS, Furuya S, Chiu W, Rusyn I. 2018. Characterization of inter-tissue and inter-strain variability of TCE glutathione conjugation metabolites DCVG, DCVC, and NAcDCVC in the mouse. J Toxicol Environ Health A 81 (1–3 ):37–52, PMID: , 10.1080/15287394.2017.1408512.29190187 49. Birner G, Vamvakas S, Dekant W, Henschler D. 1993. Nephrotoxic and genotoxic N-acetyl-S-dichlorovinyl-L-cysteine is a urinary metabolite after occupational 1,1,2-trichloroethene exposure in humans: implications for the risk of trichloroethene exposure. Environ Health Perspect 99 :281–284, PMID: , 10.1289/ehp.9399281.8319644 50. Bernauer U, Birner G, Dekant W, Henschler D. 1996. Biotransformation of trichloroethene: dose-dependent excretion of 2,2,2-trichloro-metabolites and mercapturic acids in rats and humans after inhalation. Arch Toxicol 70 (6 ):338–346, PMID: , 10.1007/s002040050283.8975632 51. Lash LH, Putt DA, Brashear WT, Abbas R, Parker JC, Fisher JW. 1999. Identification of S-(1,2-dichlorovinyl)glutathione in the blood of human volunteers exposed to trichloroethylene. J Toxicol Environ Health A 56 (1 ):1–21, PMID: , 10.1080/009841099158204.9923751 52. Chiu WA, Ginsberg GL. 2011. Development and evaluation of a harmonized physiologically based pharmacokinetic (PBPK) model for perchloroethylene toxicokinetics in mice, rats, and humans. Toxicol Appl Pharmacol 253 (3 ):203–234, PMID: , 10.1016/j.taap.2011.03.020.21466818 53. Green T. 1990. Species differences in carcinogenicity: the role of metabolism in human risk evaluation. Teratog Carcinog Mutagen 10 (2 ):103–113, PMID: , 10.1002/tcm.1770100206.1973849 54. Meek ME, Renwick A, Ohanian E, Dourson M, Lake B, Naumann BD, et al. 2002. Guidelines for application of chemical-specific adjustment factors in dose/concentration-response assessment. Toxicology 181–182 :115–120, PMID: , 10.1016/s0300-483x(02)00265-2.12505295 55. Rusyn I, Chiu WA. 2022. Decision-making with new approach methodologies: time to replace default uncertainty factors with data. Toxicol Sci 189 (1 ):148–149, PMID: , 10.1093/toxsci/kfac033.35404442 56. U.S. EPA. 2021. New Approach Methods Work Plan: Reducing Use of Vertebrate Animals in Chemical Testing. Washington, DC: U.S. EPA. 57. Messelmani T, Morisseau L, Sakai Y, Legallais C, Le Goff A, Leclerc E, et al. 2022. Liver organ-on-chip models for toxicity studies and risk assessment. Lab Chip 22 (13 ):2423–2450, PMID: , 10.1039/d2lc00307d.35694831 58. Nitsche KS, Müller I, Malcomber S, Carmichael PL, Bouwmeester H. 2022. Implementing organ-on-chip in a next-generation risk assessment of chemicals: a review. Arch Toxicol 96 (3 ):711–741, PMID: , 10.1007/s00204-022-03234-0.35103818 59. Rusyn I, Sakolish C, Kato Y, Stephan C, Vergara L, Hewitt P, et al. 2022. Microphysiological systems evaluation: experience of TEX-VAL tissue chip testing consortium. Toxicol Sci 188 (2 ):143–152, PMID: , 10.1093/toxsci/kfac061.35689632 60. Kato Y, Lim AY, Sakolish C, Valdiviezo A, Moyer HL, Hewitt P, et al. 2022. Analysis of reproducibility and robustness of OrganoPlate® 2-lane 96, a liver microphysiological system for studies of pharmacokinetics and toxicological assessment of drugs. Toxicol In Vitro 85 :105464, PMID: , 10.1016/j.tiv.2022.105464.36057418 61. Keil DE, Peden-Adams MM, Wallace S, Ruiz P, Gilkeson GS. 2009. Assessment of trichloroethylene (TCE) exposure in murine strains genetically-prone and non-prone to develop autoimmune disease. J Environ Sci Health A Tox Hazard Subst Environ Eng 44 (5 ):443–453, PMID: , 10.1080/10934520902719738.19241258 62. Johnson PD, Goldberg SJ, Mays MZ, Dawson BV. 2003. Threshold of trichloroethylene contamination in maternal drinking waters affecting fetal heart development in the rat. Environ Health Perspect 111 (3 ):289–292, PMID: , 10.1289/ehp.5125.12611656 63. Peden-Adams MM, Eudaly JG, Heesemann LM, Smythe J, Miller J, Gilkeson GS, et al. 2006. Developmental immunotoxicity of trichloroethylene (TCE): studies in B6C3F1 mice. J Environ Sci Health A Tox Hazard Subst Environ Eng 41 (3 ):249–271, PMID: , 10.1080/10934520500455289.16484062 64. NTP. 1988. Toxicology and carcinogenesis studies of trichloroethylene (CAS No. 79-01-6) in four strains of rats (ACI, August, Marshall, Osborne-Mendel) (gavage studies). Natl Toxicol Program Tech Rep Ser 273 :1–299, PMID: .12748681 65. Woolhiser M, Krieger S, Thomas J, Hotchkiss J. 2006. Trichloroethylene (TCE): Immunotoxicity potential in CD rats following a 4-week vapor inhalation exposure. Midland, MI: Dow Chemical Company. 2006(031020). 66. Echeverria D, White RF, Sampaio C. 1995. A behavioral evaluation of PCE exposure in patients and dry cleaners: a possible relationship between clinical and preclinical effects. J Occup Environ Med 37 (6 ):667–680, PMID: , 10.1097/00043764-199506000-00008.7670913 67. Cavalleri A, Gobba F, Paltrinieri M, Fantuzzi G, Righi E, Aggazzotti G. 1994. Perchloroethylene exposure can induce colour vision loss. Neurosci Lett 179 (1–2 ):162–166, PMID: , 10.1016/0304-3940(94)90959-8.7845613 68. Mutti A, Alinovi R, Bergamaschi E, Biagini C, Cavazzini S, Franchini I, et al. 1992. Nephropathies and exposure to perchloroethylene in dry-cleaners. Lancet 340 (8813 ):189–193, PMID: , 10.1016/0140-6736(92)90463-d.1353133 69. JISA (Japan Industrial Safety Association). 1993. Carcinogenicity Study of Tetrachloroethlene by Inhalation in Rats and Mice. Hadano, Japan: JISA. 70. ATSDR (Agency for Toxic Substances and Disease Registry). 1997. Toxicological Profile for Trichloroethylene (TCE). Atlanta, GA: Agency for Toxic Substances and Disease Registry, U.S. Department of Health and Human Services. 71. ATSDR. 2019. Toxicological Profile for Tetrachloroethylene. Atlanta, GA: Agency for Toxic Substances and Disease Registry, Division of Toxicology and Human Health Sciences. 72. Kmiec Z. 2001. Cooperation of liver cells in health and disease. Adv Anat Embryol Cell Biol 161 :1–151. 73. Milosevic N, Schawalder H, Maier P. 1999. Kupffer cell-mediated differential down-regulation of cytochrome P450 metabolism in rat hepatocytes. Eur J Pharmacol 368 (1 ):75–87, PMID: , 10.1016/s0014-2999(98)00988-1.10096772 74. Ware BR, Durham MJ, Monckton CP, Khetani SR. 2018. A cell culture platform to maintain long-term phenotype of primary human hepatocytes and endothelial cells. Cell Mol Gastroenterol Hepatol 5 (3 ):187–207, PMID: , 10.1016/j.jcmgh.2017.11.007.29379855 75. Davidson MD, Kukla DA, Khetani SR. 2017. Microengineered cultures containing human hepatic stellate cells and hepatocytes for drug development. Integr Biol (Camb) 9 (8 ):662–677, PMID: , 10.1039/c7ib00027h.28702667 76. Nguyen TV, Ukairo O, Khetani SR, McVay M, Kanchagar C, Seghezzi W, et al. 2015. Establishment of a hepatocyte-Kupffer cell coculture model for assessment of proinflammatory cytokine effects on metabolizing enzymes and drug transporters. Drug Metab Dispos 43 (5 ):774–785, PMID: , 10.1124/dmd.114.061317.25739975 77. March S, Ramanan V, Trehan K, Ng S, Galstian A, Gural N, et al. 2015. Micropatterned coculture of primary human hepatocytes and supportive cells for the study of hepatotropic pathogens. Nat Protoc 10 (12 ):2027–2053, PMID: , 10.1038/nprot.2015.128.26584444 78. Ware BR, Berger DR, Khetani SR. 2015. Prediction of Drug-Induced liver injury in micropatterned co-cultures containing iPSC-derived human hepatocytes. Toxicol Sci 145 (2 ):252–262, PMID: , 10.1093/toxsci/kfv048.25716675 79. Khetani SR, Kanchagar C, Ukairo O, Krzyzewski S, Moore A, Shi J, et al. 2013. Use of micropatterned cocultures to detect compounds that cause drug-induced liver injury in humans. Toxicol Sci 132 (1 ):107–117, PMID: , 10.1093/toxsci/kfs326.23152190 80. Wang WW, Khetani SR, Krzyzewski S, Duignan DB, Obach RS. 2010. Assessment of a micropatterned hepatocyte coculture system to generate major human excretory and circulating drug metabolites. Drug Metab Dispos 38 (10 ):1900–1905, PMID: , 10.1124/dmd.110.034876.20595376	36445294	PMC9707501	NO-CC CODE	2022-12-01 23:20:23	no	Environ Health Perspect. 2022 Nov 29; 130(11):117009	utf-8	Environ Health Perspect	2,022	10.1289/EHP12006	oa_other
==== Front Atención Primaria Práctica 2605-0730 2605-0730 The Author(s). Published by Elsevier España, S.L.U. S2605-0730(22)00036-0 10.1016/j.appr.2022.100159 100159 Artículo Especial Reflexiones sobre la atención primaria del siglo xxi Reflections on primary care in the 21st centuryRotaeche del Campo Rafael a⁎ Gorroñogoitia Iturbe Ana b a Grupo MBE de semFYC, Centro de salud de Alza, OSI Donostia-Osakidetza, San Sebastián, España b Unidad Docente Multiprofesional, Atención Familiar y Comunitaria, Grupo MBE de semFYC, Bizkaia, España ⁎ Autor para correspondencia. 29 11 2022 12 2022 29 11 2022 4 100159100159 21 10 2022 22 10 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. La atención primaria debe de afrontar los nuevos desafíos del siglo xxi que ya han comenzado con la pandemia de la covid-19. Desafíos que tienen que ver con una nueva realidad sociosanitaria caracterizada por un aumento de la prevalencia de la comorbilidad y fragilidad ligada al envejecimiento y al impacto de los determinantes de la salud; cambios en la población con pacientes más informados y que reclaman participar en las decisiones que afectan a su salud en una sociedad cada vez más digitalizada. En ese contexto la atención primaria debe de resolver nuevos retos como cambiar su funcionamiento con equipos más cohesionados que puedan incorporar nuevos perfiles que aporten valor y donde exista un compromiso con la docencia y la investigación. La gestión de todos estos desafíos requiere que los profesionales que trabajan en atención primaria en el siglo xxi profundicen en sus competencias mirando más allá de las consultas de su centro de salud. Competencias como la selección y el uso del mejor conocimiento, el pensamiento crítico, el uso de la comunicación para acercarse a los valores y las preferencias de los pacientes, la toma de decisiones compartida y la conciencia social. Para que todos estos cambios se puedan realizar hace falta un impulso institucional con múltiples medidas insistentemente reclamadas por los profesionales. Entre las que están, en primer lugar, una mayor inversión en personal y equipamiento, así como apostar por modelos organizativos avalados por la evidencia destinados a obtener una atención más coordinada e integrada entre la atención primaria, el hospital, la salud mental, la salud pública y los servicios sociales la utilización juiciosa de las soluciones de la e-salud o la incorporación de un área de conocimiento sobre atención primaria en la universidad. Primary care must face the new challenges of the XXI century that have already started with the Covid-19 pandemic. These challenges have to do with a new sociosanitary reality characterized by a rise of the prevalence of the comorbidity and fragility linked to the eldering and the impact of the determinants of the health. Changes in the population, one with more informed patients who demand participating in the decisions that affect their health in a society that is increasingly digitalized. In this context the primary care must solve its challenges such as changing its way of functioning in more cohesive teams that can incorporate new profiles that are really needed and that bring value and where a compromise with teaching and investigation exists. The management of these challenges requires that the professionals working in primary care in the XXI century deepen in their competences looking further than the limits of their health centres. Competences such as the selection and use of the best knowledge, the critical thought, the use of communication to approach to the values and preferences of their patients, the shared decision making and social conscience. In order to these changes to pursue, an institutional impulse is necessary, one with the, many times insistently claimed by the professionals, measures. Among which is, firstly, a bigger inversion in personnel and equipment as well as the commitment with evidently proved models intended to obtain a more coordinated and integrated attention between the primary care, the hospital, the mental health, the public health and the social services, the judicious use of the e-health solutions or the incorporation of a knowledge area of primary care in the University. Palabras clave Atención primaria Multimorbilidad Trabajo en equipo Integración Toma de decisiones Evaluación Keywords Primary care Multimorbidity Teamwork Integration Decision making Evaluation ==== Body pmcIntroducción En otros artículos de este monográfico se ha analizado en profundidad el estado actual de la atención primaria (AP) en Europa y en las diferentes autonomías de nuestro país. Estos análisis se suman a las decenas de los realizados en los últimos meses en nuestro país sobre la necesidad de fortalecer la AP. Las propuestas que se realizan en la serie de artículos de este monográfico insisten en soluciones multifactoriales que incluyen entre otras: más recursos en el personal, estructuras físicas y equipamiento, nuevas estrategias organizativas, profundizar en la actividad comunitaria, el fomento y apoyo de la investigación en AP y un cambio radical en la actitud de la universidad hacia la medicina de familia. Existe suficiente evidencia que demuestra que los sistemas de salud basados en una AP fuerte son más accesibles, equitativos y eficientes1. Por eso una verdadera asistencia sanitaria basada en la evidencia implica la implementación de los atributos de la AP comenzando por la atención longitudinal que ha demostrado que mejora la morbimortalidad de la población y consigue un mejor uso de los servicios sanitarios2. En definitiva, los responsables políticos y sanitarios deben proporcionar un marco adecuado para la práctica clínica de los médicos de familia en el siglo xxi. Transcurrido ya casi un cuarto del siglo, seguimos creyendo posible una AP que sea el desarrollo completo de la que imaginamos en el pasado y que esté preparada para afrontar los próximos desafíos (tabla 1 ), algunos de los cuales se desarrollan a continuación. Desafíos que ya han comenzado en el año 2020 con la aparición de la pandemia de covid-19.Tabla 1 Desafíos para la AP en el siglo xxi Tabla 1• Medicalización y sobrediagnóstico • Población con más información y con necesidad de participar en la toma de decisiones sobre su salud • Utilización de forma eficiente del conocimiento médico • Actividad burocrática innecesaria creciente • Aumento de la prevalencia de la multimorbilidad y de la fragilidad • Atención cada vez más subespecializada y fragmentada • Aparición de nuevas pandemias de enfermedades infecciosas • Impacto de los determinantes de salud • Digitalización e incorporación de la telemedicina a la atención clínica • Nuevo enfoque del trabajo en equipo (roles profesionales, incorporación de nuevos perfiles profesionales, autocuidado) • Continuidad en la formación de los profesionales (grado-posgrado-desarrollo profesional continuo). Atención primaria como verdadero espacio para la docencia y la investigación Determinantes sociales de la salud Los determinantes sociales de la salud (DSS) explican la mayor parte de las inequidades en salud. Las desigualdades socioeconómicas se correlacionan con la morbimortalidad en ambos sexos3 y se presentan sobre todo en las principales causas de mortalidad. La precariedad socioeconómica se relaciona, por ejemplo, con la enfermedad cardiovascular o el menor nivel educativo con la prevalencia de diabetes y mayor riesgo de hospitalización por reagudizaciones de EPOC4. En diversos estudios ecológicos en entornos urbanos, se han encontrado importantes diferencias de la salud dentro de la misma ciudad, determinadas por el territorio y una disminución de la esperanza de vida libre de discapacidad relacionada con la privación socioeconómica4. El abordaje de los DSS requiere un enfoque transversal e intersectorial que implique a todas las políticas institucionales («salud en todas las políticas») en el que la AP debe tener un papel relevante coordinada con la salud mental, la atención hospitalaria, la salud pública y los recursos sociosanitarios. En definitiva, una AP fuerte dentro de una política sanitaria donde, de verdad, se sitúe a las personas en el centro del sistema. Una nueva realidad sociosanitaria En España en el año 2050 el 35% de la población tendrá más de 65 años, lo que implica un aumento de la prevalencia de pacientes con multimorbilidad (más de 2 enfermedades crónicas), sobre todo en la población de menor nivel socioeconómico y en la que se presentan problemas de salud mental5. La prevalencia de la multimorbilidad continúa creciendo, lo mismo que los casos de demencia en el año 2050 se habrán duplicado y se acercarán a los 2 millones de personas6. La fragilidad aumenta exponencialmente en la medida que se envejece, su prevalencia en mayores de 65 años es del 18%, y es más frecuente en las mujeres7. Estas tendencias han generado un debate sobre los cambios que deben realizar los sistemas sanitarios para afrontar esta nueva realidad sociosanitaria a la que la AP no ha sido ajena8. Desde el inicio del siglo xxi han proliferado las estrategias de crónicos en nuestro país que tratan de orientar a los sistemas de salud hacia esta nueva realidad. De forma casi simultánea, y en ocasiones como consecuencia de estrategias previas de crónicos, muchos servicios de salud autonómicos hablan de la necesidad de evolucionar hacia un sistema más coordinado e integrado como respuesta a este cambio. Desde hace unos años asistimos al desarrollo de múltiples experiencias en nuestro país de integración asistencial. Dentro de los diferentes modelos, los implantados hasta ahora en nuestro país han implicado a la AP y hospitalaria de una misma zona geográfica mediante la integración administrativa en una gestión única. Muchas de ellas, incluso las más desarrolladas, como las Organizaciones Sanitarias Integradas (OSI) del País Vasco, no incluyen a la salud mental, ni la salud pública en su modelo. Todas las experiencias insisten en proponer la digitalización y el uso de las nuevas tecnologías, incluida la telemedicina, para mejorar la continuidad asistencial y la relación entre profesionales. Las estrategias de crónicos y modelos de integración siguen teniendo pendiente su evaluación en nuestro medio9. Las diferentes revisiones sistemáticas no han conseguido identificar los componentes de la integración asistencial que se asocian con mejores resultados10. En esta evaluación resulta indispensable contemplar la singularidad de las experiencias integradoras realizadas en nuestro país sobre una atención primaria debilitada por un déficit crónico de financiación y por una falta de estrategia clara. Nuevas epidemias En este nuevo siglo hemos debido hacer frente al desafío de una nueva pandemia por una enfermedad infecciosa, algo que creíamos olvidado. El sistema sanitario se empieza a recuperar del impacto de la epidemia por la covid-19 sin apenas tiempo a evaluar su actuación y por tanto sin aplicar muchas de las enseñanzas que podrían extraerse de su gestión en AP. Esta ha demostrado, pese a su situación, su capacidad para reorganizarse y adaptarse a la pandemia. En la AP se han atendido a la práctica totalidad de los pacientes infectados por la covid-19 en alguna de las fases de su enfermedad. Durante la primera ola hasta el 68,5% de los casos se atendieron exclusivamente en AP11. La AP continúa ocupando gran parte de su actividad en la gestión de la covid-19. La 7ª ola se ha caracterizado por su elevada transmisibilidad y menor morbilidad, lo que ha supuesto que prácticamente se ha gestionado en AP con una enorme carga burocrática para la gestión de la incapacidad laboral transitoria. Esta ha desbordado la capacidad administrativa de muchos centros de salud, poniendo en evidencia la necesidad de cambios radicales en la gestión de la incapacidad temporal insistentemente reclamados por los profesionales antes de la pandemia. La AP sigue participando en las sucesivas campañas de vacunación mientras que debe gestionar el impacto de la covid-19 sobre la interrupción de su propia actividad12, sobre la de los otros niveles de atención como los programas de cribado de cáncer o el aumento de las listas de espera13 y sobre la salud mental de la población. La aparición de la covid-19 persistente, que puede llegar a afectar hasta el 43% de los pacientes tras la infección aguda14, supone otro reto para el médico de familia que se enfrenta a una nueva enfermedad de la que se desconoce su etiopatogenia y con mucha incertidumbre sobre su tratamiento15. Medicalización, sobrediagnóstico y sobretratamiento La medicalización es un fenómeno creciente y preocupante por su potencial impacto negativo en la salud de la población y el riesgo que entraña para la sostenibilidad de los sistemas de salud, supone, además, un alto coste de oportunidad que impide la realización de otras actividades. Se relaciona, entre otros, con los conceptos de sobrediagnóstico y sobretratamiento. Su etiología, como la de otros problemas complejos, es multifactorial16 y con diversos agentes implicados, siendo los profesionales sanitarios, uno de los principales17. El abordaje de los problemas como el sobrediagnóstico o diagnóstico innecesario y el sobretratamiento no es sencillo, las medidas para combatir esta realidad competen a diferentes ámbitos (político, sistemas de salud, profesionales). Algunas de las soluciones que se han propuesto son16:• Formación de los profesionales: sensibilización desde el grado en el problema del sobrediagnóstico, el sobretratamiento, sus causas y consecuencias. Aprendizaje orientado al manejo y tolerancia a la incertidumbre. Habilidades en comunicación con el paciente y la familia, y la toma de decisiones compartida. • Impulsar la investigación en el sobrediagnóstico y el sobretratamiento. • Limitar al máximo o evitar las influencias capaces de sesgar la toma de decisiones diagnóstico-terapéuticas de los profesionales. • Potenciar el conocimiento veraz y sensible a las necesidades, que se ajuste a las prioridades de los enfermos y la comunidad. • Aplicación juiciosa de la medicina basada en la evidencia. • Aplicar el enfoque de la medicina mínimamente disruptiva para afrontar el sobretratamiento en los pacientes con comorbilidad, lo que supone la evaluación integral de cada persona enferma, la priorización compartida de objetivos y la elaboración de planes individualizados de cuidados y terapéuticos ajustados a sus capacidades y realidades y objetivos pactados. • Mejorar la validez de la información a los pacientes y los usuarios a través de los medios de comunicación y las redes virtuales. • Políticas sanitarias basadas en las mejores evidencias y en argumentos de bien común. • Facilitar herramientas de ayuda a la toma de decisiones compartidas (HATD) en la consulta. • Una ética de la responsabilidad para abordar la complejidad del razonamiento moral. El desafío no es solo demostrar que las ganancias superan las pérdidas, sino también mostrar cuidado y respeto por aquellos que pierden en las decisiones y reconocer aspectos que realmente importan a las personas. Nuevos equipos El cambio sociosanitario que se adivina para el resto del siglo xxi necesita también de un cambio en los profesionales sanitarios y no sanitarios de AP. Este cambio afecta a sus competencias, al trabajo en equipo, a la necesidad de nuevos perfiles y a las sinergias con otros activos de salud de la comunidad. Como se ha comentado en otros artículos de este monográfico, la falta de médicos de familia ha marcado el panorama sanitario de inicio del siglo. Aunque la ratio de médicos en España se sitúa ligeramente por encima de la media de los países de la OCDE, la universidad y los responsables sanitarios no han sido capaces de atraer a los estudiantes y a los recién licenciados hacia la AP18 y de prever con garantías el recambio generacional en la AP. Además, la AP vive asfixiada por una burocracia excesiva e innecesaria como ya se ha puesto de manifiesto en la gestión de la incapacidad temporal durante la pandemia por la covid-19. Las propuestas por profesionales y sociedades científicas para afrontar este anacronismo coinciden en las soluciones: eliminar la innecesaria gestión telemática y el refuerzo del personal administrativo19. Previo al déficit de profesionales se han desarrollado propuestas organizativas en nuestro medio sobre trabajo multidisciplinar en forma de microequipos constituidos por profesionales de medicina de familia o pediatría, de enfermería y administrativos/as. Este modelo puede facilitar la atención longitudinal, cada persona tiene a su médico/a, enfermero/a y administrativo/a de referencia, y la máxima de que cada problema lo resuelve el profesional más adecuado optimizando el tiempo de cada uno de ellos. Cuenta, además, con experiencias exitosas en nuestro país20 y resulta un tanto sorprendente que no haya tenido una mayor difusión e implantación. En estos momentos asistimos a un debate internacional sobre la efectividad de las diferentes composiciones y funcionamientos de los microequipos en la práctica clínica21. Durante los próximos años se deberá desarrollar la figura del administrativo en ciencias de la salud reconociendo a esta nueva categoría con una remuneración de acuerdo a su formación y funciones. Los equipos de atención primaria para hacer frente a esta nueva realidad sociosanitaria necesitan realizar profundos cambios en la forma de trabajar y asumir nuevas funciones:• Establecer mecanismos eficientes de identificación de las necesidades de las personas cuando contacten con el centro de salud para asignar al profesional más adecuado. • Priorizar la atención domiciliaria. • Mejorar la coordinación con los servicios sociales. • Realizar actividades comunitarias. • Fomento de la actividad física dentro de la atención a los pacientes frágiles y crónicos. • Coordinación hospitalaria para la atención a los pacientes con multimorbilidad. • Incluir en su agencia de investigación la implementación y la evaluación de los futuros cambios e innovaciones. La realización de estas y otras nuevas tareas no es factible que pueda asumirse con los perfiles y ratios de profesionales actuales. Es necesario definir y redistribuir funciones por perfiles. Por ejemplo, el personal no sanitario puede participar en actividades comunitarias mientras que el sanitario, sobre todo el de enfermería, va a necesitar dedicar más tiempo a la atención domiciliaria y a la coordinación con los servicios sociales y con el hospital. Está claro que el número de las administrativas y las enfermeras deberá aumentar y debería estudiarse la incorporación de otros perfiles como trabajadores sociales o fisioterapeutas y una colaboración más estrecha con los servicios de farmacia de AP y salud mental. Pero además de los cambios en los profesionales hay que aprovechar la tecnología y algunas de las soluciones de la e-salud1 para estas tareas. Deberíamos de poder disponer de sistemas de cita web o telefónica que canalicen de forma eficiente las demandas hacia al profesional más adecuado. De este modo, la clasificación de las demandas y por tanto el funcionamiento del equipo no dependería de si el contacto del paciente es presencial o telemático. Existen muchas propuestas para el uso de la telemedicina, entendida esta como el uso de los sistemas de telecomunicación para proporcionar asistencia sanitaria a distancia, con los pacientes con multimorbilidad. Las experiencias lideradas desde la AP en nuestro medio son escasas, aunque muestran resultados iniciales favorables22. La aparente modernidad que aporta el uso de la telemedicina puede suponer su uso irreflexivo por parte de los profesionales y los gestores sobre todo teniendo en cuenta que la demostración de la efectividad de la telemedicina necesita de evidencias más consistentes23. La posible implementación de soluciones basada en la telemedicina necesita además de evaluaciones sobre las barreras hacia su uso por parte de los profesionales y los pacientes. El médico de familia del siglo xxi y su formación La gestión de todos estos desafíos requiere que los médicos de familia y los profesionales de AP, en general, tengan una mirada más allá de su consulta y con un perfil actualizado, basado en el profesionalismo, profundizando en algunas de sus competencias. Los profesionales necesitan acceder al mejor conocimiento procedente de una investigación rigurosa adaptada a la práctica clínica de AP que pueda contribuir a resolver los problemas de su población. Pero, aunque sepan localizar las evidencias de mayor calidad y más apropiadas para cada circunstancia, necesitan de otros conocimientos y habilidades para aplicarlas a sus pacientes y el entorno (tabla 2 ).Tabla 2 Competencias del profesional de AP en el siglo xxi Tabla 2• Pericia en el diagnóstico y conocimiento clínico en profundidad • Destrezas en la selección del mejor conocimiento para resolver las cuestiones que afectan a los pacientes • Capacidad para definir y comprender los beneficios y riesgos de las diferentes alternativas • Sensibilidad y habilidades en comunicación para entender el contexto del paciente • Capacidad para obtener y comprender los valores y preferencias de los pacientes y trabajar con los pacientes en la toma de decisiones compartida • Gestión de recursos y seguridad del paciente • Pensamiento crítico y conciencia social • Atención centrada en la persona • Capacidad de trabajo en equipo • Compromiso con la docencia y la investigación El modelo de la relación médico paciente está cambiando de forma acelerada. Los pacientes están cada vez más informados y exigen participar en la toma de decisiones que afectan a su salud. Hemos pasado de un modelo de toma de decisiones paternalista (el médico decide) a un modelo compartido24. Este último requiere que el profesional sepa presentar las diferentes opciones (a favor y en contra) que afectan a la decisión compartida, exponiendo en términos sencillos y adaptados al paciente las ventajas e inconvenientes que supone el recibir una prueba de cribado mediante PSA para el cáncer de próstata a una determinada edad o recibir un tratamiento con estatinas en prevención primaria a partir de un nivel de riesgo cardiovascular determinado. Esto implica saber localizar las mejores evidencias para el problema objeto de la decisión a analizar, cuantificar los resultados a favor y en contra y realizar un proceso deliberativo respetando los valores y las preferencias del paciente. En este proceso pueden utilizarse las HATD. Existen ya iniciativas que facilitan este proceso en forma de guías de práctica clínica que nos proporcionan las mejores evidencias e incorporan HATD basadas en sus recomendaciones25. La AP, por sus componentes estratégicos y características propias, conforma un espacio educativo imprescindible en la formación de los profesionales, desde el grado hasta formación especializada y formación continuada. La medicina de familia está reconocida y desarrollada como especialidad médica, y cuenta con un área de práctica clínica y de investigación bien definida y diferente al resto de especialidades, hace más de 40 años, con la AP como su ámbito de actuación. Sin embargo, este hecho, y aunque en la actualidad en la mayoría de las facultades españolas se imparte la medicina de familia como asignatura, no se ha traducido en su consolidación académica en las estructuras universitarias en nuestro país, aún queda mucho camino por recorrer en este sentido26. Existe una descoordinación entre las competencias que se adquieren en la formación y las necesidades de las personas; sin una visión holística; los encuentros episódicos frente a un cuidado de salud continuo y una orientación hospitalaria frente a la atención primaria27; así mismo, hay una falta de continuidad entre la formación de grado y la formación especializada en medicina familiar y comunitaria. Los estudiantes adquieren muchos conocimientos muy específicos pero pocos conocimientos genéricos o esenciales y sus conocimientos son reducidos sobre los problemas más prevalentes y generales y sobre la medicina preventiva clínica28. Los modelos educativos y de atención sanitaria están interrelacionados. Un modelo centrado en el hospital (más tecnológico y focalizado en la enfermedad), u otro centrado en la AP (focalizado en el individuo, la familia y la comunidad), influyen en el sistema sanitario y en el perfil profesional, y viceversa27. Los estudiantes no conocen suficientemente el trabajo que se realiza en AP y por tanto, es difícil que se sientan atraídos hacia ella. En este sentido, la existencia del departamento propio en las facultades, junto con una experiencia académica y la práctica satisfactoria en AP, son factores favorables a la elección de medicina familiar y comunitaria como especialidad18. En la misma línea, a pesar de que los programas formativos de otras muchas especialidades contemplan como estancia formativa obligatoria la de AP, con el propósito de fortalecer la relación entre los 2 niveles asistenciales, su implantación ha sido irregular y en algunas comunidades autónomas apenas se ha desarrollado. Conflicto de intereses Los autores declaran la ausencia de conflictos de interés en relación al tema del artículo. 1 Conjunto de Tecnologías de la Información y la Comunicación (TIC) que, a modo de herramientas, se emplean en el entorno sanitario en materia de prevención, diagnóstico, tratamiento, seguimiento, así como en la gestión de la salud. ==== Refs Bibliografía 1. Kringos D.S. Boerma W.G. Hutchinson A. van der Zee J. Groenewegen P.P. The breadth of primary care: a systematic literature review of its core dimensions BMC Health Serv Res 10 1 2010 65 20226084 2. Baker R. Freeman G.K. Haggerty J.L. Bankart M.J. Nockels K.H. Primary medical care continuity and patient mortality: a systematic review Br J Gen Pract 70 698 septiembre de 2020 e600 e611 32784220 3. Esnaola S. Martín J. Calvo M. Audicana C. Aldasoro E. Elgorriaga E. Desigualdades socioeconómicas en la mortalidad por todas las causas y por las principales causas de defunción en la CAPV, 2009–2012 Vitoria Gasteiz: Departamento de Salud, Gobierno Vasco. Servicio de Estudios e Investigación Sanitaria 2017 Report No.: Departamento de Salud, Gobierno Vasco. Servicio de Estudios e Investigación Sanitaria 4. Determinantes sociales de la Salud y enfermedades prevalentes Folia Humanística. 3 de enero de 2020 [consultado 12 de octubre de 2022]; Disponible en https://revista.proeditio.com/foliahumanistica/article/view/Determinantes%20sociales%20Salud 5. Cezard G. Sullivan F. Keenan K. Understanding multimorbidity trajectories in Scotland using sequence analysis Sci Report 12 1 2022 16485 6. Ministerio de Sanidad, Consumo y Bienestar Social. Plan Integral de Alzheimer y otras Demencias (2019–2023). 2019 Ministerio de Sanidad, Consumo y Bienestar Social. 7. Ministerio de Sanidad, Consumo y Bienestar Social Actualización del documento de consenso sobre prevención de la fragilidad en la persona mayor [consultado Oct 2022] Disponible en https://www.sanidad.gob.es/profesionales/saludPublica/prevPromocion/Estrategia/EnvejecimientoSaludable_Fragilidad/docs/ActualizacionDoc_FragilidadyCaidas_personamayor.pdf 2022 8. Prados-Torres D. del Cura-González I. Prados-Torres A. Hacia un modelo de atención a la multimorbilidad en atención primaria Aten Primaria 49 5 2017 261 262 28089227 9. Minué-Lorenzo S. Fernández-Aguilar C. Visión crítica y argumentación sobre los programas de atención de la cronicidad en Atención Primaria y Comunitaria Aten Primaria 50 2 2018 114 129 29174714 10. Rocks S. Berntson D. Gil-Salmerón A. Kadu M. Ehrenberg N. Stein V. Cost and effects of integrated care: a systematic literature review and meta-analysis Eur J Health Econ 21 8 2020 1121 1211 32601992 11. Vrotsou K. Rotaeche R. Mateo-Abad M. Machón M. Vergara I. Variables associated with COVID-19 severity: an observational study of non-paediatric confirmed cases from the general population of the Basque Country, Spain BMJ Open 11 4 2021 e049066 12. Caparrós Boixés G. Suñer Soler R. Juvinyá Canal D. Reig Garcia G. El impacto de la pandemia de la COVID-19 en el control de las enfermedades crónicas en atención primaria Aten Primaria 54 1 2022 102233 13. OECD Health at a Glance 2021 [Internet] [consultado Oct 2022] Disponible en https://www.oecd-ilibrary.org/content/publication/ae3016b9-en 2021 14. Chen C. Haupert S.R. Zimmermann L. Shi X. Fritsche L.G. Mukherjee B. Global prevalence of post COVID-19 condition or long COVID: a meta-analysis and systematic review J Infect Dis 2022 jiac136 35429399 15. Veronese N. Bonica R. Cotugno S. Tulone O. Camporeale M. Smith L. Interventions for improving Long COVID-19 symptomatology: a systematic review Viruses. 14 9 2022 1863 36146672 16. Sobrediagnóstico y sobretratamiento: la visión de la Atención Primaria Aten Primaria 50 2018 86 95 1 de noviembre de. Suplemento 2 30563626 17. Meneu R. Medicalización y medicamentalización de la sociedad Revista FML 24 3 2019 4p 18. McGhie J.E. Dalmau Roig A. Florensa Puig M. Silva Ruiz P. Oñate Ferriz G. Gracia Baño E.M. Factores que influyen en la elección de la especialidad de Medicina Familiar y Comunitaria Aten Primaria 53 10 1 de diciembre de 2021 102153 34303062 19. Buendía S.C. Lozano I.N. Desburocratización de la consulta FMC. 27 4 2020 194 199 20. Arribas L. Mora J. El administrativo con el médico de familia AMF. 8 9 2012 539 540 21. Coombs C.R.H. Cohen T. Duddy C. Mahtani K.R. Roberts N. Saini A. Primary care micro-teams: a protocol for an international systematic review to describe and examine the opportunities and challenges of implementation for patients and healthcare professionals BMJ Open 12 3 2022 e052651 22. Martín-Lesende I. Orruño E. Bilbao A. Vergara I. Cairo M.C. Bayón J.C. Impact of telemonitoring home care patients with heart failure or chronic lung disease from primary care on healthcare resource use (the TELBIL study randomized controlled trial) BMC Health Serv Res 13 2013 118 23537332 23. Flodgren G. Rachas A. Farmer A.J. Inzitari M. Shepperd S. Interactive telemedicine: effects on professional practice and health care outcomes Cochrane Database Syst Rev 2015 [citado 24 de mayo de 2020];(9). Disponible en 10.1002/14651858.CD002098.pub2/full 24. Ruiz Moral R. La participación del paciente en la toma de decisiones: cuestiones controvertidas y una propuesta integradora y práctica* FMC. 18 4 2011 205 211 25. Heen A.F. Vandvik P.O. Brandt L. Achille F. Guyatt G.H. Akl E.A. Decision aids linked to evidence summaries and clinical practice guidelines: results from user-testing in clinical encounters BMC Med Inform Decis Mak 21 1 2021 202 34187484 26. López-Torres Hidalgo J.D. López-Torres Hidalgo J.D. Medicina de Familia en la Universidad sí, pero como Área de Conocimiento Revista Clínica de Medicina de Familia 11 2 2018 46 47 27. Barrios J.M. Competencias del médico del siglo XXI Un cambio necesario. Revista científica ciencias de la salud - ISSN: 2664–2891 1(2) 2019 58 73 18 de noviembre de 28. Vicente VC. El papel de la Medicina de Familia en el Grado. Clínica. 2019;(27):4–11.	0	PMC9707514	NO-CC CODE	2022-12-01 23:20:24	no	2022 Dec 29; 4:100159	utf-8	null	null	null	oa_other
==== Front Atención Primaria Práctica 2605-0730 2605-0730 The Author(s). Published by Elsevier España, S.L.U. S2605-0730(22)00040-2 10.1016/j.appr.2022.100163 100163 Artículo Especial La atención primaria del futuro: ¿qué puede aprender la atención primaria europea de las innovaciones y retos de la pandemia de COVID-19? The primary care of the future: What can primary care in Europe learn from the innovations and challenges of the COVID-19 pandemic?Vilaseca Josep M. ab⁎ Howe Amanda c a Fundación Althaia, Red Universitaria y Asistencial de Manresa, Barcelona, España b Universidad de Vic - Universidad Central de Cataluña, Cataluña, España c Norwich Medical School, University of East Anglia, Norwich, Reino Unido ⁎ Autor para correspondencia. 29 11 2022 12 2022 29 11 2022 4 100163100163 26 10 2022 31 10 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. La diversidad organizativa y financiera que caracteriza la atención primaria europea ha permitido responder extraordinariamente a la pandemia de COVID-19 con soluciones innovadoras: reorganización de la oferta de servicios, digitalización, nuevos roles profesionales e inclusión de nuevas profesiones en atención primaria, y pagos añadidos por la realización de actividades. La telemedicina ha transformado la atención al paciente. Las crisis humanitarias (inmigración y refugiados) requieren todavía nuevas soluciones en Europa. Los cuidados paliativos en atención primaria se han transformado. La novedosa educación médica virtual debe ser evaluada. La disminución de la burocracia es una clara necesidad, al igual que la coordinación con los servicios de medicina preventiva y de salud pública. La práctica en solitario versus los equipos de atención primaria, el acceso a la prescripción de ciertos fármacos, la hiperprescripción, las actividades preventivas, y las migraciones de médicos constituyen los retos del futuro para la atención primaria europea. The organisational and financial diversity that characterizes European primary care has made it possible to respond extraordinarily to the COVID-19 pandemic with innovative solutions: reorganisation of the scope of services, digitization, new professional roles and inclusion of new professions in primary care, and the use of additional fees for service. Telemedicine has transformed patient care. Humanitarian crises (immigration and refugees) still require new solutions in Europe. Palliative care in primary care has been transformed. The new virtual medical education must be evaluated. The reduction of bureaucracy is a clear need, as is coordination with preventive medicine and public health services. Solo practice versus primary care teams, access to the prescription of certain drugs, over-prescription, preventive activities, and migration of physicians constitute future challenges for European primary care. Palabras clave Atención primaria de salud Economía de la salud COVID-19 Medicina familiar y comunitaria Keywords Primary health care Health economics COVID-19 Family and community medicine ==== Body pmcLa diversidad de la atención primaria europea es una muestra de su potencial, de sus logros y de sus fracasos. Cuando hablamos de Europa podemos restringir el concepto a la Europa geográfica (¿incluyendo una parte de Turquía?) o ampliarlo a las regiones europeas de la Organización Mundial de la Salud y de la Sociedad Internacional de Médicos de Familia (WONCA). Aún en el sentido estricto, Europa presenta notables diferencias respecto a la financiación, la organización y las relaciones entre niveles asistenciales de la atención primaria. En cuanto a la financiación, los modelos básicos Beveridge (paradigma: Gran Bretaña) o Bismarck (paradigma: Alemania) condicionan por tradición el tipo de organización del trabajo. Hasta la fecha, los grandes cambios en la atención primaria se vieron a partir de la caída del Pacto de Varsovia en la Europa del Este, pasando del modelo comunista a los modelos inspirados en los sistemas sanitarios del Oeste. Pero desde entonces, una vez producidos los grandes cambios, los sistemas sanitarios se mantienen estables. Por ejemplo, en la República Checa se optó por una atención primaria con libre elección de médico de familia y libre acceso a la atención especializada1, mientras que en Eslovenia los pacientes deben acudir al médico de familia en primera instancia («gatekeeping system»)2 .Parece que no existe un modelo «superior» al resto, sino formas distintas de abordar la atención primaria. El porcentaje del PIB destinado a la sanidad es también desigual. Un reciente estudio3 muestra que la financiación de la atención primaria presenta importantes variaciones entre países: el gasto sanitario per cápita en los países de renta baja, varía desde $8 a $46 y en los países de renta media baja desde $11 hasta $120. Según la Organización y Cooperación de Desarrollo Económico4, el porcentaje del gasto total en salud destinado a la atención primaria varía entre el 18% en Polonia, el 17% en España y Estonia, y el 10% en Eslovaquia y Suiza. Un análisis del gasto sanitario en 6 países europeos (España, Portugal, Países Bajos, Alemania, Austria y Finlandia) indica que la convergencia está lejos de conseguirse actualmente5. Una atención primaria fuerte es esencial para asegurar la resiliencia de los sistemas sanitarios a los impactos como los de la pandemia y las crisis medioambientales. Los servicios de atención primaria han jugado un papel fundamental en el mantenimiento de los cuidados esenciales a la vez que han luchado contra la pandemia de COVID-196. Los servicios de atención primaria han proporcionado atención aguda para los pacientes con síntomas moderados y severos de infección por coronavirus. Asimismo han estado en la primera línea de la atención preventiva realizando la detección de contactos, el seguimiento de los mismos y la vacunación de la población. Se ha desempeñado una labor esencial en el mantenimiento de la continuidad de la atención en los pacientes con enfermedades crónicas, a la vez dando soporte a la salud mental y el bienestar social de la población. Pero esta encomiable labor ha tenido un coste muy elevado para los médicos de familia y el personal sanitario en general. En la página web de WONCA Europa se puede apreciar el obituario de los médicos de familia de sus países miembros fallecidos durante la pandemia de COVID–19. Dicha pandemia ha producido un terrible efecto en la salud y el bienestar de los médicos de familia (léase profesionales sanitarios) en toda Europa. Este desánimo que impide percibir las lecciones aprendidas y las innovaciones implementadas es un factor limitante. Percibimos que los médicos de familia europeos se sienten exhaustos después de luchar contra la pandemia de coronavirus. Más aún, otra epidemia muy grave, la epidemia de burnout en los profesionales sanitarios, es un reto de candente actualidad. Un estudio transversal7 realizado en 33 países europeos mostró que el 64,5% de los médicos de familia se consideraban en riesgo de malestar psicológico (distress) durante la pandemia de COVID-19. Los que trabajaban en centros de salud más pequeños y con poblaciones más vulnerables tenían mayor riesgo comparado con el resto. Los estudios que analizan el desgaste profesional en Europa indican que es necesario promover el bienestar de los médicos para mantener la seguridad del paciente y la atención sanitaria de alta calidad, y optimizar el rendimiento del sistema sanitario8. En la primera ola de la pandemia, la suspensión de los servicios habituales y la limitación de los contactos interpersonales produjeron cambios en los sistemas sanitarios. El número de consultas en medicina de familia por mes disminuyó entre un 10–80% en 2020 comparado con el mismo mes en 20199. Al principio de la pandemia, varios países presentaron una caída en los nuevos diagnósticos o derivaciones por enfermedades crónicas como el cáncer10. Bélgica reportó un 14% menos de nuevos casos en 2020 comparado con el mismo período de 2019. El Reino Unido mostró un descenso en derivaciones por cáncer de hasta 350.000 casos entre marzo y agosto de 2020 comparado con el mismo período en 2019. Algunos países también reportaron unas tasas menores de ingreso hospitalario por enfermedades crónicas como asma, EPOC e insuficiencia cardíaca en 202011. El impacto que ha tenido la afectación de las funciones de la atención primaria europea se verá con el tiempo, cuando podamos apreciar su impacto en indicadores epidemiológicos como las curvas de mortalidad y la esperanza de vida. Esta indeseada e indeseable alteración de la atención primaria puede poner en valor su importancia, mostrando lo que ocurre si prescindimos de ella. ¿Se han producido innovaciones casi sin ser conscientes de ello? En la 27a conferencia de WONCA Europa, donde estaban representados más de 20 países, los autores, al preparar el primer borrador de este artículo, realizamos una entrevista cualitativa informal con varios médicos de familia (líderes de opinión) de un grupo representativo de países europeos planteándoles una simple pregunta: ¿existe alguna innovación relevante en la atención primaria de tu país? A lo que los encuestados respondieron unánimemente: «ninguna». La voluntad de innovar ha sido probablemente baja entre los médicos de familia europeos. Un estudio alemán muestra que, durante la pandemia, el conocimiento en atención primaria del Fondo de Innovación Alemán era bajo. Muchos médicos de familia estaban inseguros de hasta qué punto la atención primaria se puede beneficiar del Fondo de Innovación en el largo plazo. En cuanto a la voluntad de participar en los estudios del Fondo de Innovación, las respuestas estaban divididas: los que ya habían participado en dichos proyectos (24%) expresaban un balance positivo. Sin embargo, también reportaban obstáculos y factores de estrés, como los requerimientos de documentación e intervenciones en los procesos de las consultas12. Pero no todo ha sido negativo. Las innovaciones en los países europeos han aparecido a partir de la respuesta de la atención primaria europea a la pandemia de COVID-1913. Los desarrollos se pueden categorizar en 4 grupos principales:1- La reorganización de la oferta de servicios (p.e. Francia, Islandia) cuando la atención primaria abrió consultorios móviles o realizó visitas domiciliarias para la vacunación. 2- La extensión del uso de la consulta remota a través de herramientas y sistemas digitales (p.e. Estonia, Grecia, Polonia, Turquía, Gran Bretaña), donde el uso potenciado de los servicios de telemedicina consiguió un contacto seguro con el paciente. 3- Una mayor extensión del uso de la mano de obra sanitaria en el sentido más amplio (p.e. Portugal, Irlanda, Francia) donde, además de los médicos de familia, las enfermeras, las matronas, los farmacéuticos, los técnicos de laboratorio han asumido otras tareas como la prescripción crónica de medicamentos o la vacunación de la población. 4- Pagos añadidos (p.e. Países Bajos, Alemania, Italia) para los servicios de telemedicina, los equipos médicos, y algunos servicios de los proveedores de atención primaria. Los sistemas de pago tradicionales, basados bien en el pago por servicio, bien en el pago capitativo, no eran adaptables a la pandemia. Los pagos añadidos pueden ser una buena compensación para optimizar el uso de la capacidad productiva de la atención primaria. Encontramos varios ejemplos, como la compensación financiera a los equipos de atención primaria por realizar consultas telefónicas, videoconsultas, o visitas domiciliarias, o compensaciones financieras por los costes adicionales incurridos debido a la pandemia de COVID-19 (recursos humanos, medidas de higiene o de seguridad). La mayoría de los países de la Organización y Cooperación de Desarrollo Económico potenciaron su capacidad digital para la atención primaria durante la pandemia de coronavirus y la mantuvieron después del primer choque. La transformación digital de la atención primaria permitió a los países mantener su capacidad de respuesta a las necesidades cambiantes a través de las sucesivas olas de la pandemia de COVID-19. La proporción de la población que tuvo una consulta remota con su médico de familia entre marzo y septiembre de 2020 estuvo en el rango entre casi el 80% en España, Polonia y Eslovenia hasta cerca del 20% en Francia, Bulgaria y Alemania14. Con el fin de adaptarse a las circunstancias de la pandemia, mientras el modo de las consultas de atención primaria cambiaba, algunos países como Dinamarca mostraron una estabilidad en el número global de consultas entre 2010 y 2020; incluso se incrementaron, aunque a costa de una mayor proporción de personas que tuvieron una consulta remota15. Un estudio nacional entre los médicos de familia noruegos muestra una considerable relevancia de las teleconsultas para el futuro de la planificación de la atención primaria en tiempos de ausencia de pandemia16. En el campo de los cuidados paliativos ofrecidos por la atención primaria, también se han producido nuevos desarrollos. Una encuesta realizada en el Reino Unido identifica 3 temas cualitativos para la innovación en la atención al final de la vida en la comunidad durante la pandemia de COVID-19: la pandemia de coronavirus como un catalizador del cambio en los cuidados paliativos de atención primaria; nuevas oportunidades para establecer maneras de trabajar más receptivas y tecnológicas; y factores de la pandemia que han mejorado y reforzado la colaboración interprofesional17. Algunas de las más importantes revisiones de las investigaciones sobre los cambios en atención primaria han encontrado resultados como una capacidad rápidamente creciente para responder a las necesidades de los pacientes y de la pandemia con una reducción de la burocracia y la complejidad (proporcionando a los profesionales la autonomía necesaria para actuar de forma rápida y apropiada)18. Se necesitan más esfuerzos para conseguir una atención primaria fuerte en Europa En el mundo académico, la educación médica ha sido seriamente afectada por la pandemia, amenazando tanto la productividad como la calidad. La necesidad empujó a enseñar asignaturas enteras de forma exclusivamente virtual a través de plataformas en línea. Los nuevos métodos educativos pueden ser prometedores, pero el resultado de su rápida implementación sigue siendo incierto19. Otra triste realidad ha estimulado y requiere todavía innovaciones en atención primaria. Antes, durante y después de la pandemia, han ocurrido en Europa varias crisis relacionadas con la salud de los inmigrantes y los refugiados. Los estudios sobre la respuesta de la atención primaria indican que se han producido innovaciones sobre todo centradas en el perfeccionamiento de los médicos, mientras que la implicación de otras profesiones sanitarias todavía no ha sido suficientemente estudiada20. Los aportes precoces y coordinados de los líderes profesionales de atención primaria en el Reino Unido fueron una característica de la pandemia, con asociaciones como el Royal College of General Practitioners ofreciendo recursos y boletines para médicos de familia y sus equipos en abierto y rápidamente actualizados21. Sin embargo, siguen existiendo algunas lagunas en las oportunidades para que los médicos de familia hagan aportaciones a las necesidades de salud pública y de atención social22, y debería haber más lecciones para el futuro para todos los países sobre cómo coordinar mejor estos sistemas entre múltiples proveedores para cualquier urgencia futura. Se necesita la colaboración entre países: la atención primaria europea dista de ser uniforme Cabe señalar que la Organización Mundial de la Salud ha solicitado un tratado internacional para pandemias globales para asegurar que el trabajo transfronterizo sea efectivo en cualquier pandemia futura23, y también ha validado un compromiso global para el bienestar y la protección de los trabajadores sanitarios a la luz de los roles fundamentales y riesgos personales acaecidos durante y posteriormente a la pandemia24. Esto indica que algunos de los retos afrontados en Europa van más allá de nuestra propia región, y deben ser abordados a través de sectores y fronteras. Pero el día a día en la Europa actual sigue siendo muy diferente dependiendo de las regiones que analicemos. La organización de la medicina de familia en equipos multidisciplinares, a pesar de que es una tendencia creciente, sigue lejos de ser homogénea en Europa. Grandes países como por ejemplo Alemania cuentan con un gran número de médicos de familia trabajando en solitario. Mientras en algunos países del Este de Europa los médicos de familia siguen sin poder prescribir algunos medicamentos en uso cotidiano, como por ejemplo, fármacos antidiabéticos25, en la Europa occidental la hiperprescripción sigue siendo un problema acuciante. La polimedicación asociada a la cronicidad hace que la prevención cuaternaria sea cada día más necesaria en el Occidente, mientras que el acceso a fármacos y pruebas complementarias es una de las grandes necesidades del Oriente Europeo (tabla 1 ).Tabla 1 Características de la atención primaria europea Tabla 1Características Descripción Financiación Modelo Beveridge (Reino Unido, España) Modelo Bismarck (Alemania) % del gasto sanitario en atención primaria 10% (Eslovaquia, Suiza) 17% (España, Estonia) 18% (Polonia) Burnout 64,5% de los médicos de familia europeos en riesgo Mayor riesgo en centros de salud pequeños Mayor riesgo en poblaciones deprimidas Disminucion de servicios por la COVID-19 Disminución de derivaciones (Bélgica, Reino Unido) Disminución de ingresos por enfermedades crónicas Innovaciones principales Reorganización de servicios Digitalización Nuevas profesiones o roles profesionales Pagos añadidos Prescripción Dificultades en prescribir fármacos (Europa Oriental) Hiperprescripción (Europa Occidental) La falta de médicos de familia es un problema en la mayoría de países europeos, especialmente en el Este. La especialidad de medicina de familia sigue teniendo un desarrollo desigual. Los intentos de unificar el currículum de los médicos de familia europeos, o al menos de exigir unos mínimos estándares de calidad, están todavía en proceso. El reconocimiento del título de especialista entre los diferentes países europeos sigue siendo un tema pendiente. Las reformas de la atención primaria fueron dificultadas primero por la crisis económica del año 2009 y posteriormente por la epidemia de coronavirus del 2020. Así, países tan distantes como Portugal, Rumanía, Lituania, Hungría, consiguieron un grado variable de implantación de sus respectivas reformas. La integración de servicios y la relación con los hospitales es igualmente variable26. Las actividades preventivas en atención primaria no se realizan de forma rutinaria en todos los países europeos, por lo menos no de la forma estandarizada en que se realizan en España. La prevención y la promoción de la salud se realizan de forma desigual. Incluso las inmunizaciones financiadas por el sistema sanitario público y el calendario vacunal no se han unificado en Europa. Un tema preocupante es la falta (¿o más bien fuga?) de médicos de familia. No parece existir en Europa un país que sea «receptor nato» de médicos. Más bien parece que existe un flujo de este a oeste, de sur a norte, y que a su vez los países del norte «importadores» de médicos de familia sufren también la emigración de sus propios médicos. Un ejemplo puede ser Irlanda, país que recibe (y recluta activamente) médicos españoles pero que considera perdida la batalla de la retención de sus propios médicos27. Siendo este un país desarrollado, su falta de médicos es un foco de atracción para países con un nivel socioeconómico inferior. Los motivos por los que emigran los médicos de familia están relacionados tanto con satisfacción profesional como con retribución. Las desigualdades salariales, de tareas, tecnológicas, sociales, de los médicos de familia en Europa tienen un coste que no beneficia a ningún país. Conclusión La atención primaria tiene la capacidad de contribuir significativamente en la respuesta a las crisis sanitarias presentes y futuras. Sin embargo, la atención primaria europea necesita más recursos en términos de infraestructura, recursos financieros y humanos, a la par que cuida a sus profesionales sanitarios. Es fundamental unificar y estandarizar los aspectos básicos de la medicina de familia en Europa para conseguir una atención primaria más eficiente y homogéneamente distribuida, evitando el despoblamiento de médicos de familia de las zonas rurales y de los países más pobres. Para que cada médico europeo se sienta en casa en su propio país, sin necesidad de buscar prosperidad o reconocimiento en los países vecinos. Esta atención primaria del futuro deberá contar con el apoyo de los gobernantes y de la sociedad, si realmente desean que la atención primaria sea el eje vertebrador del sistema sanitario. Agradecimientos A Candan Kendir. Conflicto de intereses Los autores declaran que no tienen ningún conflicto de intereses. ==== Refs Bibliografía 1 Holcik J. Koupilova I. Primary health care in the Czech Republic: brief history and current issues Int J Integr Care. 1 1 2000 Nov e06 10.5334/ijic.8 PMID: 16902697; PMCID: PMC1534002 16902697 2 Rotar Pavlič D. Švab I. Brinovec Pribaković R. Slovenia Kringos D.S. Boerma W.G.W. Hutchinson A. Building primary care in a changing Europe: Case studies [Internet] 2015 European Observatory on Health Systems and Policies Copenhagen (Denmark) Observatory Studies Series, No. 40 26. [Consultado 14 Sept 2022]. Disponible en https://www.ncbi.nlm.nih.gov/books/NBK459017/ 3 Hanson K. Brikci N. Erlangga D. Alebachew A. De Allegri M. Balabanova D. The Lancet Global Health Commission on financing primary health care: putting people at the centre Lancet Glob Health. 10 5 2022 May e715 e772 10.1016/S2214-109X(22)00005-5 Epub 2022 Apr 4. PMID: 35390342; PMCID: PMC9005653 35390342 4. OECD Spending on primary care: first estimates [consultado 30 Sep 2022]. Disponible en https://www.oecd.org/health/health-systems/Spending-on-Primary-Care-Policy-Brief-December-2018.pdf 2018 5 Albulescu C.T. Health care expenditure in the european union countries: new insights about the convergence process Int J Environ Res Public Health. 19 4 2022 Feb 10 1991 10.3390/ijerph19041991 PMID: 35206178; PMCID: PMC8872178 35206178 6. OECD Strengthening the frontline: How primary health care helps health systems adapt during the COVID 19 pandemic [consultado 6 Abr 2022]. Disponible en https://www.oecd.org/coronavirus/policy-responses/strengthening-the-frontline-how-primary-health-care-helps-health-systems-adapt-during-the-covid-19-pandemic-9a5ae6da/ 2021 7 Collins C. Clays E. Van Poel E. Cholewa J. Tripkovic K. Nessler K. Distress and wellbeing among general practitioners in 33 Countries during COVID-19: results from the cross-sectional PRICOV-19 study to inform health system interventions Int J Environ Res Public Health. 19 9 2022 May 6 5675 10.3390/ijerph19095675 PMID: 35565070; PMCID: PMC9101443 35565070 8 Rochfort A. Collins C. Burgers J. Emotional distress, occupational stress and burnout among Family Doctors in Europe: Monitoring and testing of interventions is required Eur J Gen Pract. 27 1 2021 Dec 271 273 10.1080/13814788.2021.1985998 PMID: 34633274; PMCID: PMC8510622 34633274 9. OECD Health at a Glance 2021: OECD Indicators 2021 10.1787/ae3016b9-en 10. Fujisawa R. Impact of the COVID-19 pandemic on cancer care in OECD countries OECD Health Working Papers) 2022 OECD Publishing Paris 10.1787/c74a5899-en 11. OECD Health at a Glance 2021: OECD Indicators 2021 10.1787/ae3016b9-en 12 Wangler J. Jansky M. Innovationsfonds und Primärversorgung – Welche Erwartungen und Erfahrungen vertreten Hausärzt*innen in Bezug auf die Teilnahme an innovativen Versorgungsmodellen? [The German Innovation Fund and primary care-What expectations and experiences do general practitioners have with regard to participating in innovative care models?] Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz 65 6 2022 Jun 697 705 German 10.1007/s00103-022-03533-y Epub 2022 Apr 27. PMID: 35476151; PMCID: PMC9132806 35476151 13. OECD Strengthening the frontline: How primary health care helps health systems adapt during the COVID 19 pandemic [consultado 6 Abr 2022]. Disponible en https://www.oecd.org/coronavirus/policy-responses/strengthening-the-frontline-how-primary-health-care-helps-health-systems-adapt-during-the-covid-19-pandemic-9a5ae6da/ 2021 14. Eurofound Living, working and COVID-19 2020 Publications Office of the European Union Luxembourg 15. OECD/European Observatory on Health Systems and Policies Denmark: Country Health Profile 2021, State of Health in the EU 2021 OECD Publishing Paris 10.1787/2dce8636-en 16 Johnsen T.M. N. B Suitability of video consultations during the COVID-19 pandemic lockdown: cross-sectional survey among Norwegian general practitioners J Med Internet Res 8 23 2021 e26433 10.2196/26433 PMID: 33465037 17 Mitchell S. Harrison M. Oliver P. Gardiner C. Chapman H. Khan D. Service change and innovation in community end-of-life care during the COVID-19 pandemic: Qualitative analysis of a nationwide primary care survey Palliat Med 36 1 2022 Jan 161 170 10.1177/02692163211049311 Epub 2021 Dec 17. PMID: 34915759; PMCID: PMC8796165 34915759 18. Baird B. What enabled rapid digital change in primary care during the Covid-19 pandemic? Feb 2021 The King's Fund [consultado 7 sept 2022]. Disponible en: kingsfund.org.uk 19 Windak A. Frese T. Hummers E. Klemenc Ketis Z. Tsukagoshi S. Vilaseca J. Academic general practice/family medicine in times of COVID-19 - Perspective of WONCA Europe Eur J Gen Pract. 26 1 2020 Dec 182 188 10.1080/13814788.2020.1855136. PMID: 33337939; PMCID: PMC7751383 33337939 20 Singer S.J. Glassman J. Glaseroff A. Joseph G.A. Jauregui A. Mulaney B. Impact of COVID-19 on primary care practice sites and their vulnerable patients Adv Health Care Manag. 6 2021 Dec 20 10.1108/S1474-823120210000020009 PMID: 34779184 21. Course: COVID-19 Resource Hub. Disponible en: rcgp.org.uk 22. Strengthening the UK primary care response to covid-19 BMJ Sept 2020 370 10.1136/bmj.m3691 23. Global leaders unite in urgent call for international pandemic treaty. Disponible en: who.int 24. Global health and care worker compact. Disponible en: who.int 25 Bucsa C. Farcas A. Iaru I. Mogosan C. Rusu A. Drug utilisation study of antidiabetic medication during 2012-2019 in Romania Int J Clin Pract. 75 11 2021 Nov e14770 10.1111/ijcp.14770 Epub 2021 Sep 8 PMID: 34473890 34473890 26 Kurpas D. Stefanicka-Wojtas D. Shpakou A. Halata D. Mohos A. Skarbaliene A. The advantages and disadvantages of integrated care implementation in Central and Eastern Europe - perspective from 9 CEE countries Int J Integr Care. 21 4 2021 Nov 8 14 10.5334/ijic.5632 PMID: 34824563; PMCID: PMC8588893 27 Taderera B.H. Doctor retention in Ireland - what it may mean for the global health workforce reform agenda comment on “doctor retention: a cross-sectional study of how ireland has been losing the battle” Int J Health Policy Manag. 10 10 2021 Oct 1 647 649 10.34172/ijhpm.2020.126 PMID: 32668895; PMCID: PMC9278536 32668895	0	PMC9707515	NO-CC CODE	2022-12-01 23:20:24	no	2022 Dec 29; 4:100163	utf-8	null	null	null	oa_other
==== Front J Am Acad Dermatol J Am Acad Dermatol Journal of the American Academy of Dermatology 0190-9622 1097-6787 Published by Elsevier on behalf of the American Academy of Dermatology, Inc. S0190-9622(22)02970-X 10.1016/j.jaad.2022.10.043 Article Human monkeypox outbreak: epidemiological data and therapeutic potential of topical cidofovir in a prospective cohort study Sobral-Costas Tristán Gabriel MD 1∗ 1 Dermatology Department, La Paz University Hospital Escudero-Tornero Rafael MD 2 2 Dermatology Department, La Paz University Hospital Servera-Negre Guillermo MD 3 3 Dermatology Department, La Paz University Hospital Bernardino Jose I. MD, PhD 4 4 HIV&Infectious Disease Unit, La Paz University Hospital, Idipaz. CIBERINFEC Arroyo Almudena Gutiérrez MD 5 5 Microbiology Department, La Paz University Hospital Díaz-Menéndez Marta MD, PhD 6 6 National Referral Centre for Tropical Diseases and International Health, La Paz University Hospital, Idipaz. CIBERINFEC Busto-Leis Jose Manuel MD 7 7 Dermatology Departament, La Paz University Hospital Álvarez Patricia Roces MD 8 8 Microbiology Department, La Paz University Hospital Pinto Pedro Herranz MD, PhD 9 9 Dermatology Department, La Paz University Hospital, Idipaz. CIBERINFEC Cudos Elena Sendagorta MD, PhD 10 10 Dermatology Department, La Paz University Hospital, Idipaz. CIBERINFEC ∗ Address: Castellana 261, 28046 Madrid, Telephone: 917277000 29 11 2022 29 11 2022 22 8 2022 4 10 2022 19 10 2022 © 2022 Published by Elsevier on behalf of the American Academy of Dermatology, Inc. 2022 Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. Background Human monkeypox has become increasingly frequent worldwide since the outbreak was first reported in May 2022. Objectives As cidofovir is effective against vaccinia and other Orthopoxvirus diseases, we hypothesize that its topical use could be an effective treatment for monkeypox skin lesions, avoiding the adverse effects of systemic administration. Methods We conducted a prospective study to collect data on the clinical and virologic course of patients with monkeypox. All patients were offered symptomatic treatment. They were also offered treatment with topical cidofovir on a compassionate use basis. 12 patients received treatment with topical cidofovir 1%, while the other received only symptomatic treatment. Prospective visits were scheduled for the collection of clinical and virological data. Results Lesions cleared quicker in the cidofovir-treated group (HR 4.572; p = 0.0039). Median time to resolution was 12 (11.5-15) and 18 (16-21) days, respectively. On day 14, PCR-positive skin lesions were detected in 10% of the cidofovir sample, compared with 62.5% of the non-treated group (p:0.019). Local adverse effects were frequent (50%), especially in the anogenital region. No systemic adverse effects were reported. Limitations The study is not a clinical trial and lacks a placebo-controlled arm. Discussion Topical cidofovir is a potentially relevant therapy in patients with skin lesions but mild systemic involvement. Reducing time to resolution could shorten isolation time and improve the cosmetic impact in areas such as the face. KEY WORDS Cidofovir monkeypox topical skin ==== Body pmcCAPSULE SUMMARY: Our study is the first to describe the use of topical cidofovir for monkeypox skin lesions, finding a shorter time to lesion resolution and negativization of viral PCR. These results encourage the development of clinical trials to demonstrate the efficacy and safety of this novel treatment. FUNDINGSOURCES: None. CONFLICTS OF INTEREST: None. CONSENT INFORMED: Consent for the publication of recognizable patient photographs or other identifiable material has been obtained by the authors and are available for submission to the journal. The study has received approval from the local ethics committee, which is available for submission to the journal The results of the study have not been previously published. In the following article it is mentioned that some of our patients were treated with cidofovir but its effect is not described.	36455826	PMC9707642	NO-CC CODE	2022-12-01 23:20:24	no	J Am Acad Dermatol. 2022 Nov 29; doi: 10.1016/j.jaad.2022.10.043	utf-8	J Am Acad Dermatol	2,022	10.1016/j.jaad.2022.10.043	oa_other
==== Front JAAD Case Rep JAAD Case Rep JAAD Case Reports 2352-5126 Published by Elsevier on behalf of the American Academy of Dermatology, Inc. This is an open access.. S2352-5126(22)00520-3 10.1016/j.jdcr.2022.11.019 Article Painful Perianal Rash in an HIV-Positive Individual Khanna Urmi MD 1∗ Toker Michelle BS 1 Wu Benedict DO PhD 1 1 - Division of Dermatology, Department of Medicine, Albert Einstein College of Medicine/Montefiore Medical Center, Bronx, New York ∗ Corresponding author: Urmi Khanna, MD 3411 Wayne Avenue Bronx, NY 10467 23 11 2022 23 11 2022 5 10 2022 12 11 2022 15 11 2022 © 2022 Published by Elsevier on behalf of the American Academy of Dermatology, Inc. This is an open access.. 2022 Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. Key words monkeypox virus proctitis anogenital vesiculopustules umbilicated ==== Body pmcFunding: None Conflicts of Interest: None Patient Consent: Consent for the publication of all patient photographs and medical information was provided by the authors at the time of article submission to the journal stating that all patients gave consent for their photographs and medical information to be published in print and online and with the understanding that this information may be publicly available.	36467278	PMC9707644	NO-CC CODE	2022-12-08 23:16:26	no	JAAD Case Rep. 2023 Jan 23; 31:115-117	utf-8	JAAD Case Rep	2,022	10.1016/j.jdcr.2022.11.019	oa_other
==== Front Lancet Infect Dis Lancet Infect Dis The Lancet. Infectious Diseases 1473-3099 1474-4457 Elsevier Ltd. S1473-3099(22)00733-2 10.1016/S1473-3099(22)00733-2 Correspondence Omicron sublineage BQ.1.1 resistance to monoclonal antibodies Arora Prerna ab Kempf Amy ab Nehlmeier Inga a Schulz Sebastian R c Jäck Hans-Martin c Pöhlmann Stefan ab Hoffmann Markus ab a Infection Biology Unit, German Primate Centre, Leibniz Institute for Primate Research, 37077 Göttingen, Germany b Faculty of Biology and Psychology, Georg-August-University Göttingen, Göttingen, Germany c Division of Molecular Immunology, Department of Internal Medicine 3, Friedrich-Alexander University of Erlangen-Nürnberg, 91054 Erlangen, Germany 18 11 2022 18 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcVaccination represents the key strategy to control the COVID-19 pandemic through induction of neutralising antibody responses and T cell-associated immunity that substantially decrease the risk of developing severe disease.1, 2 However, individuals who are immunocompromised (eg, because of comorbidities, high age, or immunosuppressive treatment) might not mount a full adaptive immune response and thus remain susceptible. For individuals at high risk, individual monoclonal antibodies (mAbs) or cocktails of mAbs are administered as prophylaxis or therapy.3, 4 All mAbs currently approved by the US Food and Drug Administration (FDA) or European Medicines Agency (EMA) target the spike (S) protein (appendix pp 1–2).5 During the course of the COVID-19 pandemic, several SARS-CoV-2 lineages evolved mutations that confer partial or full resistance against some mAbs.6, 7, 8, 9 Consequently, only few mAbs remain suitable for treatment of individuals at high risk, and only bebtelovimab shows high efficacy against multiple omicron sublineages.8 However, novel omicron sublineages have been detected, harbouring additional S protein mutations within the epitopes of bebtelovimab and other mAbs (figure A ; appendix p 11). Novel sublineages include BA.4.6 (with increasing incidence in several countries worldwide), BA.2.75.2 (with increasing incidence in India), BJ.1 (mainly observed in India and Bangladesh; notably BJ.1 is one parental lineage of the currently increasing XBB recombinant), and BQ.1.1 (with increasing incidence in the USA and Europe).Figure Extensive resistance of omicron sublineage B.Q.1.1 to neutralisation by mAbs (A) Location of mutations (blue and red) in the spike proteins of SARS-CoV-2 lineages B.1, BA.1, and BA.4–5 (which are identical at the amino acid level), BA.4.6, BA.2.75.2, BJ.1, and BQ.1.1 (numbered according to the spike protein of SARS-CoV-2 Wuhan-Hu-01). Mutations that are unique to only one of the omicron sublineages are highlighted in red and conserved mutations among omicron sublineages are indicated beneath the sequences in green. (B) Pseudovirus particles carrying the indicated S proteins were preincubated with different concentrations of single mAbs or cocktails of mAbs, before being inoculated onto Vero cells. Pseudovirus entry was analysed at 16–18 h post-inoculation, by measuring firefly luciferase activity in cell lysates, and was normalised against samples without any antibodies (0% inhibition). The EC50 was calculated by use of a non-linear regression model. Data represent the mean of three biological replicates (performed with four technical replicates). For additional information see the appendix (p 12). (C) Heatmap indicating the fold change in EC50 compared with B.1 pseudovirus particles. EC50=the concentration required for 50% of maximum inhibition. mAbs=monoclonal antibodies. Pre S1–S2=the domain between the receptor-binding domain and the S1–S2 cleavage site. S=spike. *The BA.1 spike protein contains a unique insertion at position 214 (EPE). We compared neutralisation of omicron sublineages BA.1, BA.4–5 (in which the amino acid sequence of the S protein is identical), BA.4.6, BA.2.75.2, BJ.1 and BQ.1.1 by single mAbs or mAb cocktails that are currently in clinical use, mAbs for which clinical use has been restricted or discontinued, and mAbs currently being evaluated in clinical trials. We used pseudovirus particles (pp) that represent a suitable model to investigate SARS-CoV-2 cell entry and its neutralisation.10 As we expected, pseudovirus particles bearing the BA.1 S protein (BA.1pp) were efficiently neutralised by bebtelovimab, adintrevimab, and cilgavimab–tixagevimab (50% effective concentration [EC50] <100 ng/ml), moderately neutralised by tixagevimab, romlusevimab, sotrovimab, and amubarvimab–romlusevimab (EC50 100–1000 ng/ml), and poorly neutralised by casirivimab, cilgavimab, amubarvimab, and casirivimab–imdevimab (EC50 1000–10 000 ng/ml).7 Furthermore, BA4–5pp were efficiently neutralised by bebtelovimab and cilgavimab, moderately neutralised by imdevimab and cilgavimab–tixagevimab, and poorly neutralised by amubarvimab, romlusevimab, sotrovimab, casirivimab–imdevimab, and amubarvimab–romlusevimab, in line with expectations.8 For BA.4.6pp, bebtelovimab caused efficient neutralisation, whereas poor neutralisation was noted for imdevimab, amubarvimab, casirivimab–imdevimab, cilgavimab–tixagevimab, and amubarvimab–romlusevimab. With BA.2.75.2pp, bebtelovimab caused efficient neutralisation, whereas regdanvimab and sotrovimab caused poor neutralisation. For BJ.1pp, none of the tested mAbs or mAb cocktails caused high neutralisation, whereas casirivimab, tixagevimab, sotrovimab, and cilgavimab–tixagevimab showed moderate neutralisation, and amubarvimab, casirivimab–imdevimab, and amubarvimab–romlusevimab caused poor neutralisation. Finally, none of the tested mAbs or mAb cocktails caused appreciable neutralisation of BQ.1.1pp (figure B–C; appendix p 12). Our data reveal that emerging omicron sublineages are resistant to most (ie, BA.4.6, BA.2.75.2, and BJ.1) or all (BQ.1.1) clinically used mAbs. As a consequence, in patients at high risk, treatment with mAbs alone might not provide a therapeutic benefit in regions of the globe in which BQ.1.1 is spreading, suggesting that additional treatment options (eg, paxlovid or molnupiravir) should be considered. Furthermore, novel, broadly active mAbs are urgently needed for prophylactic or therapeutic treatment, or both, in patients at high risk. This online publication has been corrected. The corrected version first appeared at thelancet.com/infection on November 29, 2022 AK, IN, and MH do contract research (testing of vaccinee sera for neutralising activity against SARS-CoV-2) for Valneva, unrelated to this Correspondence. SP does contract research (testing of vaccinee sera for neutralising activity against SARS-CoV-2) for Valneva and served as advisor for BioNTech, unrelated to Correspondence, and acknowledges funding by the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung [BMBF]; 01KI2006D, 01KI20328A, and 01KX2021), the EU project UNDINE (grant agreement number 101057100), the Ministry for Science and Culture of Lower Saxony (14–76103–184, MWK HZI COVID-19), and the German Research Foundation (DFG; PO 716/11–1 and PO 716/14–1). H-MJ received funding from BMBF (01KI2043 and NaFoUniMedCovid19-COVIM: 01KX2021), the Bavarian State Ministry for Science and the Arts and DFG through the research training groups RTG1660 and TRR130, the Bayerische Forschungsstiftung (Project CORAd), and the Kastner Foundation. All other authors declare no competing interests. Supplementary Material Supplementary appendix ==== Refs References 1 Thompson MG Yoon SK Naleway AL Association of mRNA Vaccination With Clinical and Virologic Features of COVID-19 Among US Essential and Frontline Workers JAMA 328 2022 1523 1533 36255426 2 Olson SM Newhams MM Halasa NB Effectiveness of BNT162b2 vaccine against critical COVID-19 in adolescents N Engl J Med 386 2022 713 723 35021004 3 Milam AN Doan DT Childress DT Durham SH Evaluation of monoclonal antibodies in preventing hospitalizations, emergency department visits, and mortality in high-risk COVID-19 patients J Pharm Technol 38 2022 169 173 35600282 4 Levin MJ Ustianowski A De Wit S Intramuscular AZD7442 (tixagevimab–cilgavimab) for prevention of COVID-19 N Engl J Med 386 2022 2188 2200 35443106 5 Focosi D McConnell S Casadevall A Cappello E Valdiserra G Tuccori M Monoclonal antibody therapies against SARS-CoV-2 Lancet Infect Dis 22 2022 e311 e326 35803289 6 Sheward DJ Kim C Fischbach J Omicron sublineage BA.2.75.2 exhibits extensive escape from neutralising antibodies Lancet Infect Dis 22 2022 1538 1540 36244347 7 Arora P Zhang L Krüger N SARS-CoV-2 omicron sublineages show comparable cell entry but differential neutralization by therapeutic antibodies Cell Host Microbe 30 2022 1103 11.e6 35588741 8 Arora P Kempf A Nehlmeier I Augmented neutralisation resistance of emerging omicron subvariants BA.2.12.1, BA.4, and BA.5 Lancet Infect Dis 22 2022 1117 1118 35777385 9 Cao Y Jian F Wang J Imprinted SARS-CoV-2 humoral immunity induces convergent omicron RBD evolution bioRxiv 2022 published online Oct 30 10.1101/2022.09.15.507787 (preprint) 10 Schmidt F Weisblum Y Muecksch F Measuring SARS-CoV-2 neutralizing antibody activity using pseudotyped and chimeric viruses J Exp Med 217 2020 e20201181 32692348	36410372	PMC9707647	NO-CC CODE	2022-12-01 23:20:24	no	Lancet Infect Dis. 2022 Nov 18; doi: 10.1016/S1473-3099(22)00733-2	utf-8	Lancet Infect Dis	2,022	10.1016/S1473-3099(22)00733-2	oa_other
==== Front Nefrologia (Engl Ed) Nefrologia (Engl Ed) Nefrologia 2013-2514 Sociedad Española de Nefrología. Published by Elsevier España, S.L.U. S2013-2514(22)00110-9 10.1016/j.nefroe.2022.11.005 Review Occurrence of acute kidney injury in adult patients hospitalized with COVID-19: A systematic review and meta-analysis Ocurrencia de lesión renal aguda en pacientes adultos hospitalizados con COVID-19: revisión sistemática y metanálisisPassoni Reginaldo a⁎ Lordani Tarcísio Vitor Augusto ab Peres Luis Alberto Batista cd Carvalho Ariana Rodrigues da Silva bd a Department of Nursing, Teaching Hospital of Western Paraná State University, Cascavel, Paraná, Brazil b Collegiate of Nursing, Center for Biological and Health Sciences, Western Paraná State University, Cascavel, Paraná, Brazil c Discipline of Nephrology, Undergraduate Course in Medicine, Center for Medical and Pharmaceutical Sciences, Western Paraná State University, Cascavel, Paraná, Brazil d Post-graduate Program in Biosciences and Health, Center for Biological and Health Sciences, Western Paraná State University, Cascavel, Paraná, Brazil ⁎ Corresponding author. 29 11 2022 July-August 2022 29 11 2022 42 4 404414 30 3 2021 13 9 2021 © 2021 Sociedad Española de Nefrología. Published by Elsevier España, S.L.U. 2021 Sociedad Española de Nefrología Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background and aim The knowledge about the acute kidney injury (AKI) incidence in patients with coronavirus disease 2019 (COVID-19) can help health teams to carry out a targeted care plan. This study aimed to determine the AKI incidence in patients hospitalized with COVID-19. Methods The electronic search covered research published until June 20, 2020, and included five databases, PubMed, Embase, Web of Science, Scopus, and Lilacs (Latin American and Caribbean Health Sciences Library). Eligible studies were those including data from AKI occurrence in adult patients hospitalized with COVID-19. The primary outcome was AKI incidence, and the secondary outcome assessed was the AKI mortality. Additionally, the estimated incidence of renal replacement therapy (RRT) need also was verified. Using a standardized form prepared in Microsoft Excel, data were extracted by two independents authors, regarding the description of studies, characteristics of patients and clinical data on the AKI occurrence. Results We included 30 studies in this systematic review, of which 28 were included in the meta-analysis. Data were assessed from 18.043 adult patients with COVID-19. The AKI estimate incidence overall and at the ICU was 9.2% (4.6–13.9) and 32.6% (8.5–56.6), respectively. AKI estimate incidence in the elderly patients and those with acute respiratory disease syndrome was 22.9% (−4.0–49.7) and 4.3% (1.8–6.8), respectively. Patients with secondary infection, AKI estimate incidence was 31.6% (12.3–51.0). The estimate incidence of patients that required RRT was 3.2% (1.1–5.4) and estimate AKI mortality was 50.4% (17.0–83.9). Conclusion The occurrence of AKI is frequent among adult patients hospitalized with COVID-19, and affects on average, up to 13.9% of these patients. It is believed that AKI occurs early and in parallel with lung injury. Antecedentes y objetivo El conocimiento de la incidencia de lesión renal aguda (LRA) en pacientes con enfermedad por coronavirus 2019 (COVID-19) puede ayudar a los equipos de atención médica a llevar a cabo un plan de atención específico. Este estudio tuvo como objetivo determinar la incidencia de LRA en pacientes hospitalizados con COVID-19. Métodos La búsqueda electrónica cubrió la investigación publicada hasta el 20 de junio del 2020 e incluyó 5 bases de datos: PubMed, Embase, Web of Science, Scopus y Lilacs (Biblioteca de Ciencias de la Salud de América Latina y el Caribe). Los estudios elegibles fueron aquellos que incluyeron datos sobre la aparición de LRA en pacientes adultos hospitalizados con COVID-19. El resultado primario fue la incidencia de LRA y el resultado secundario evaluado fue la mortalidad por LRA. Además, también se verificó la incidencia estimada de necesidad de terapia de reemplazo renal (TRR). Mediante un formulario estandarizado elaborado en Microsoft Excel, los datos fueron extraídos por 2 autores independientes, haciendo referencia a la descripción de los estudios, las características de los pacientes y los datos clínicos sobre la ocurrencia de LRA. Resultados En esta revisión sistemática se incluyeron 30 estudios, de los cuales 28 se incluyeron en el metaanálisis. Se evaluaron los datos de 18.043 pacientes adultos con COVID-19. La incidencia estimada de LRA en general y en la UCI fue del 9,2% (4,6-13,9) y del 32,6% (8,5-56,6), respectivamente. La incidencia estimada de LRA en pacientes ancianos y pacientes con síndrome de enfermedad respiratoria aguda fue del 22,9% (–4,0-49,7) y del 4,3% (1,8-6,8), respectivamente. En pacientes con infección secundaria, la incidencia estimada de LRA fue del 31,6% (12,3-51,0). La incidencia estimada de pacientes que requirieron TRR fue del 3,2% (1,1-5,4) y la mortalidad estimada por LRA fue del 50,4% (17,0-83,9). Conclusión La ocurrencia de LRA es frecuente en pacientes adultos hospitalizados con COVID-19 y afecta, en promedio, hasta al 13,9% de estos pacientes. Se cree que la LRA ocurre temprano y en paralelo con la lesión pulmonar. Keywords COVID-19 Acute kidney injury Incidence Mortality Continuous renal replacement therapy Meta-analysis Palabras clave COVID-19 Fracaso renal agudo Incidencia Mortalidad Tratamiento renal sustitutivo Metaanálisis ==== Body pmcIntroduction Started in December 2019, after setting in Wuhan, China, the SARS-CoV-2 infection, responsible to the illness officially nominated as coronavirus disease 2019 (COVID-19), quickly spread to other regions, affecting several countries worldwide and, at the end of the 2020, on 27 December, the global number of infected and killed individuals by COVID-19 have been over 79.2 and 1.7 million, respectively.1 Frequent manifestations of COVID-19 include characteristic symptoms of an acute respiratory illness, as fatigue and dry cough, but may also occur others signs and symptoms as dyspnea, whereas acute respiratory distress syndrome (ARDS) is the main pulmonary complication.2 As well as the lung, other organs can also be affected, such as the kidney and heart.2, 3, 4, 5, 6 Cheng et al.3 analyzed the occurrence of renal abnormalities in patients with COVID-19 from a Wuhan hospital and observed higher rate proteinuria, elevated serum creatinine and elevated blood urea nitrogen (BUN). The authors reported that the acute kidney injury (AKI) incidence was 5.1% and this complication was significantly associated with worse prognosis. In other large study, involving 5499 patients with COVID-19 from 13 hospitals in the New York metropolitan area,4 the AKI incidence was 36.6% and, just like in the Chinese study, the AKI occurrence in the North American patients also was associated with worse outcomes. In addition, the risk factors associated with AKI in this study were advanced age, comorbidities, need for ventilation and drug-associated. Several studies report the occurrence of AKI and others adverse kidney events in hospitalized patients with COVID-19.2, 3, 4, 5, 6, 7, 8, 9, 10 However, the involvement of kidney and the epidemiological burden of AKI in patients with COVID-19 is still unclear and needs to be better studied.2, 3, 4, 5 Therefore, this study aimed to determine the AKI incidence in patients hospitalized with COVID-19. The results of this systematic review will help healthcare teams to carry out a care plan with greater attention to the kidney function of patients with COVID-19. Method Study registration The report of this systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist11 and the protocol was recorded on the International Prospective Register of Systematic Reviews (PROSPERO – CRD42020187355). Eligibility criteria Inclusion criteria A systematic search was performed to identify studies that reported data from AKI occurrence in adult patients hospitalized with COVID-19. The electronic search occurred between covered research published until June 20, 2020, and included five databases, PubMed, Embase, Web of Science, Scopus, and Lilacs (Latin American and Caribbean Health Sciences Library). In addition, to cover the gray literature, we also performed a manual search by checking the list of references of the reviews found and of the eligible studies (cross-reference). The keywords used were “COVID-19” and “acute kidney injury” and “incidence” or “mortality”, as well as its entry terms. The databases and the gray literature were accessed between June 4 and 8, 2020. The search strategy and the question of this study were structured according to the acronym CoCoP (Condition, Context, Population), of which (Co) was AKI incidence as primary outcome and AKI mortality as secondary outcome; (Co) hospital context; (P) adult patients with COVID-19 (S1 File). Searches were performed in English, Portuguese, and Spanish. Data were selected in English, Portuguese, and Spanish. Data were also selected from adult (≥ 18 years) patients, hospitalized, with confirmed diagnosis of COVID-19 and that reported data of AKI occurrence and other clinical data associated. Additionally, to avoid selection bias of pairs of studies have been included that use data collected by the same working groups3, 4, 5, 7 in the same period, we verified the possibility that they were the same patients by making personal contact with the authors of the studies and we were not told that they were the same patients. Exclusion criteria Thus, studies were excluded that: (1) do not report at least the AKI incidence; (2) included data from children and adolescents; (3) with a very small sample (≤ 10 patients); (4) do not report original data, such a editorials, comments, and other reviews; (5) case reports; (6) with exclusive data from patients with chronic kidney disease (CKD) and/or kidney transplant; (7) study with duplicate data. Information sources and study selection Two independent authors (RP, TVAL) performed the search in the databases and assessed the eligibility of the studies, first reading the titles and abstract and, then reading in full the studies with potential for inclusion in this systematic review. Disagreements between the two researchers were resolved by consensus and, in the absence of consensus, a third researcher (ARSC) was consulted. In addition, the list of studies in the final selection, and included in this systematic review was also approved by a fourth researcher (LABP). Duplicate publications were excluded with the support of a reference manager.12 Data collection process Using a standardized form prepared in Microsoft Excel, data were extracted by two independents authors (RP, TVAL), regarding the description of studies, characteristics of patients and clinical data on the AKI occurrence, as described below:• Study description: code (reference number), authors, year, journal, article title, country, study type, length of data collection, number of hospitals, number of patients enrolled and number of patients in the Intensive Care Unit (ICU). • Patients’ characteristics: mean age, male gender rate, time between symptom onset and hospitalization, complications (ARDS, acute cardiac injury (ACI), secondary infection, sepsis, or shock), in-hospital mortality rate. • AKI occurrence: definition, AKI overall incidence, AKI incidence in ICU, need of Renal Replacement Therapy (RRT) and AKI mortality. Considering that incidence rates include only the first occurrence, in order to collect only incidence data, cases registered as AKI de novo were not considered. Assessment of risk of bias The risk of bias of selected studies was assessed by two independent authors (RP, TVAL), using the Checklist for Analytical Cross-Sectional Studies, a Joanna Briggs Institute's critical appraisal tool.13 The studies were considered with high, moderate, or low risk of bias when reached a positive (+) score of up to 49% (less than four positive assessments), score of 50–69% (between four and five positive assessments), and score of more than 70% (six or more positive assessments), respectively. The risk of publication bias was assessed using funnel plots. All studies meeting the inclusion criteria at the quantitative analysis were assessed for methodological quality. Statistical analysis The primary outcome was AKI incidence, being determined the estimated mean and 95% confidence interval (95% CI). The pooled AKI incidence overall was determined, as well as in the ICU and for the following different subgroups: (1) according to the geographic localization of studies; (2) in the elderly patient (with median or mean age > 60 years); (3) in the patients with early hospitalization (with median or mean of time between symptom onset and hospitalization ≤ 7 days); (4) patients with following complications: (4.1) ARDS; (4.2) ACI; (4.3) with secondary infections without sepsis/shock; (4.4) in the patients with sepsis/shock (with or without other complications). The secondary outcome assessed was the AKI mortality, also determined by the estimated mean and 95% CI. Additionally, the estimated incidence of RRT need was also verified. To identify possible factors associated with a higher AKI incidence, the estimated mean and 95% CI of AKI incidence in the patients with age ≤60 years and those with median or mean of time between symptom onset and hospitalization > 7 days also determined. In addition, it was assessed the correlation between the incidence rates of AKI and the other complications (ARDS, ACI, secondary infection and sepsis/shock). All meta-analyses and forest plots were performed in Microsoft Excel, following the step-by-step proposed by Neyeloff et al.14 The studies heterogeneity was assessed by Higgins inconsistency test (I 2)15 and those with I 2 > 50% were analyzed by random-effect model. The correlation tests were performed at software R, version 3.5.0. To perform the analyses, first, the data distribution was assessed (by the Shapiro–Wilk test) and, having found the absence of normality, Spearman correlation tests were applied. Statistically significant correlation was considered when the test p-value was < 0.05. Results Study description Initially, 462 studies were found, from which 259 were removed for being duplicated. After reading the title and abstract of the remaining articles, 75 eligible publications were read in full, from which 30 were selected to compose this systematic review for meeting the previously established criteria, of which 28 were included in the meta-analysis (Fig. 1 ), where the two excluded studies were considered outliers and their exclusion from the quantitative analysis occurred to reduce heterogeneity between studies.Fig. 1 Flow diagram of study selection, according to PRISMA checklist. All articles were published in 2020 and together included 18,043 adult patients, with COVID-19 and that were hospitalized in 629 hospitals. The majority located in China and the length of data collection was between one and 90 days (Table 1 ).Table 1 Features and description of studies selected. Table 1Reference Country Study design Length of data collection (days) Number of hospitals Patients enrolled ICU patients n (%) Wang et al.2 China Retrospective 28 1 138 36 (26.1) Cheng et al.3 China Prospective 29 1 701 73 (10.4) Hirsch et al.4 USA Retrospective multi-center 36 13 5,449 1,395 (25.6) Cheng et al.5 China Retrospective multi-center 14 3 710 … Shi et al.6 China Retrospective 21 1 416 … Richardson et al.7 USA Retrospective multi-center 35 12 5,700 373 (14.2) Guan et al.8 China Prospective multi-center 51 552 1,099 173 (15.7) Zhang et al.9 China Retrospective 23 1 645 4 (0.6) Yu et al.10 China Prospective multi-center 1 16 226 All Hong et al.16 South Korea Retrospective 90 1 98 13 (13.3) Huang et al.17 China Prospective 17 1 41 13 (31.7) Lei et al.18 China Retrospective multi-center 35 4 34 15 (44.1) Wang et al.19 China Retrospective multi-center 41 2 107 … Wang et al.20 China Retrospective 30 1 116 11 (9.5) Yang et al.21 China Retrospective 65 1 212 … Yang et al.22 China Retrospective 34 1 52 All Zhang et al.23 China Retrospective 41 1 221 55 (24.9) Zhao et al.24 China Retrospective 25 1 91 30 (33.0) Zheng et al.25 China Retrospective 42 1 34 All Zhou et al.26 China Retrospective multi-center 33 2 191 50 (26.0) Chen et al.27 China Retrospective 20 1 99 23 (23.0) Arentz et al.28 USA Retrospective 14 1 21 All Yang et al.29 China Retrospective multi-center 24 3 149 0 (0) Wan et al.30 China Retrospective 16 1 135 40 (29.6) Hu et al.31 China Retrospective 62 1 323 172 (53.3) Pei et al.32 China Retrospective 12 1 333 189 (56.8) Aggarwal et al.33 USA Retrospective 35 1 16 8 (50.) Chen et al.34 China Retrospective 46 1 274 … Deng et al.35 China Retrospective multi-center 51 2 225 … Guo et al.36 China Retrospective 31 1 187 … Descriptive summary China 25 studies; USA 4 studies; South Korea 1 study. Retrospective single-center: 18 studies; Retrospective multi-center: 8 studies; Prospective single-center: 02 studies; Prospective multi-center: 02 studies. Mean ± SD sample: 33.4 ± 18.0 days Total: 629 hospitals Total: 18,043 patients with COVID-19 Total: 3006 patients in the ICU Patients’ characteristics In eleven studies3, 10, 25, 28, 31, 33, 34 the median or mean age of enrolled patients was greater than 60 years, while the time between symptom onset and hospitalization was longer than 7 days in eight studies.3, 5, 6, 22, 25, 31, 32, 34 The ARDS, ACI, secondary infection and sepsis/shock complication is reported by 22,2, 6, 8, 9, 10, 16, 17, 18, 19, 20, 22, 23, 25, 26, 27, 28, 30, 31, 33, 34, 35, 36 19,2, 6, 10, 16, 17, 18, 19, 21, 22, 23, 24, 25, 26, 28, 30, 31, 33, 34, 35 1210, 17, 18, 19, 22, 23, 26, 27, 28, 30, 31, 33 and 15 studies,2, 8, 9, 10, 16, 17, 18, 19, 23, 26, 27, 30, 31, 34, 35 respectively (S2 File). AKI occurrence From 30 studies included in this systematic review, only two20, 29 reported that patients did not develop AKI and were not included in the meta-analysis. Among the studies that registered the occurrence of AKI, all used the KDIGO criteria for the AKI diagnosis and, by the crude analysis of the data, we verified that the overall incidence of the event was between 0.3%9 and 69%,33 while in the ICU the overall AKI incidence was between 2.9%8 and 53.2%.4 The highest rate of need of RRT was 22.7%.31 The crude mortality of patients with COVID-19 and AKI reached 100% in four studies19, 26, 34, 35 (Table 2 ).Table 2 Data from AKI occurrence reported by studies selected. Table 2Reference AKI definition AKI incidence Need of RRT N (%) AKI mortality Overall N (%) ICU N (%) Overall (%) In RRT N (%) Wang et al.2 KDIGO 5 (3.6) 3 (8.3) 2 (1.45) … … Cheng et al.3 KDIGO 36 (5.1) … … … … Hirsch et al.4 KDIGO 1993 (36.6) [Stage 1 = 927 (46.5); Stage 2 = 447 (22.4); Stage 3 = 619 (31.1)] 1060 (53.2) [Stage 1 = 320 (34.5); Stage 2 = 244 (54.6); Stage 3 = 496 (80.1)] 285 (14.3) 694 (35) … Cheng et al.5 KDIGO-sCr 22 (3.2) [Stage 1 = 8 (1.3); Stage 2 = 6 (0.8); Stage 3 = 8 (1.1)] … … … … Shi et al.6 KDIGO 8 (1.9) … 2 (0.5) … … Richardson et al.7 KDIGO-sCr 523 (22.2) … 81 (3.2) 347 (66.3) … Guan et al.8 KDIGO 6 (0.5) 5 (2.9) 9 (0.8) … … Zhang et al.9 … 2 (0.3) … 0 (0) … … Yu et al.10 KDIGO 57 (25.2) [Stage 1 = 23 (10.2); Stage 2 = 12 (5.3); Stage 3 = 22 (9.7)] 57 (25.2) 24 (10.6) … … Hong et al.16 KDIGO 9 (9.2) 8 (61.5) 3 (3.1) … … Huang et al.17 KDIGO 3 (7) 3 (23) 3 (7) … … Lei et al.18 KDIGO 2 (5.9) 2 (13.3) 1 (2.9) … … Wang et al.19 KDIGO 14 (13.1) … … 14 (100) … Wang et al.20 KDIGO 0 (0) … … … … Yang et al.21 KDIGO 28 (13.7) … … 10 (35.7) … Yang et al.22 KDIGO-sCr 15 (29) 15 (29) 9 (17) 12 (37.5) 8 (25) Zhang et al.23 … 10(4.5) 8 (14.5) 5 (2.3) … … Zhao et al.24 elevated sCr and uric acid levels 5 (5.5) 5 (16.7) 3 (3.3) … … Zheng et al.25 KDIGO 7 (20.6) 7 (20.6) 5 (14.7) … … Zhou et al.26 KDIGO 28 (15) … 10 (5) 27 (96.4) 10 (100) Chen et al.27 … 3 (3) … 9 (9) … … Arentz et al.28 KDIGO-sCr 4 (19.1) … … … … Yang et al.29 … 0 (0) … … … … Wan et al.30 … 5 (3.7) 1 (2.5) 5 (3.7) … … Hu et al.31 … 17 (5.3) 15 (8.7) 72 (22.3) … … Pei et al.32 KDIGO and KDIGO-exp KDIGO: 22 (6.6) [Stage 1 = 4 (18.2); Stage 2 = 7 (31.8); Stage 3 = 11 (50)]; KDIGO-exp: 35 (10.5) [Stage 1 = 16 (45.7); Stage 2 = 8 (22.9); Stage 3 = 11 (31.4)] 30 (15.9) … KDIGO: 20 (57.1) [Stage 1 = 4 (25); Stage 2 = 6 (75); Stage 3 = 10 (90.9)]; KDIGO-exp: 19 (86.4) [Stage 1 = 3 (75); Stage 2 = 6 (85.7); Stage 3 = 10 (90.9) … Aggarwal et al.33 … 11 (69) 3 (38) … … … Chen et al.34 KDIGO 29 (11) … 3 (1) 28 (96.6) 3 (100) Deng et al.35 … 20 (8.9) … … 20 (100) … Guo et al.36 … 18 (14.6) … … … … Footnote: KDIGO-sCr: Only the serum creatinine of KDIGO criteria; KDIGO-exp: KDIGO with expanded criteria; Legend: AKI: acute kidney injury; ICU: intensive care unit; RRT: renal replacement therapy; KDIGO: Kidney Disease Global Improving Outcomes; sCr: serum creatinine. By the meta-analysis of the assessed studies, the estimated mean and 95% CI of AKI incidence overall was 9.2% (CI 4.6–13.9%) (Fig. 2 ) while at the ICU was 32.6% (CI 8.5–56.6%) (Fig. 3 ), both with a high heterogeneity index (I 2 = 98.4% and 98.3%, respectively). According to the subgroup analysis, we verified that AKI incidence at USA, elderly patients and those with early hospitalization was 22.9% (CI −2.0–47.8%; I 2 = 90.8%) (S3 File), 22.9% (CI −4.0–49.7%; I 2 = 90.8%) (S4 File) and 11.9% (CI 5.7–18.1%; I 2 = 99.9%) (S5 File), respectively.Fig. 2 Forest plot of included studies showing the estimated overall AKI incidence in patients hospitalized with COVID-19. Fig. 3 Forest plot of included studies showing the estimated AKI ICU incidence in patients hospitalized with COVID-19. Assessing the AKI incidence in the presence of other complications, we verified that in patients with ARDS and ACI the AKI incidence was 4.3% (CI 1.8–6.8%; I 2 = 91.2%) and 9.3% (CI 1.8–16.9%; I 2 = 79.3%) (S6 File), respectively. In addition, the AKI incidence in patients with secondary infection was 31.6% (CI 12.3–51.0%; I 2 = 57.9%), while that in patients with sepsis/shock the AKI incidence was 4.7% (CI 2.1–7.4%; I 2 = 92%) (S7 File). By Spearman correlation test, we identified that ARDS and AKI occurrence present a direct and statistically significant correlation (S8 File). From 30 studies included in this systematic review 172, 4, 6, 7, 10, 16, 17, 18, 22, 23, 24, 25, 26, 27, 30, 31, 34 reported data from RRT need and, by meta-analysis we found that 3.2% (CI 1.1–5.4%) of patients with COVID-19 presented RRT need. The assessment of combined mortality of patients with COVID-19 AKI-associated made it possible to determine that the mean proportion of death was 50% (CI 17.0–83.9%) (S9 File). Risk of bias No study presented high risk of bias and 23 studies2, 8, 9, 10, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 32, 34, 35, 36 presented low risk of bias. The main methodological limitations between the studies were related to the lack of information if they identified and dealt with confounding factors (S10 File). The funnel plots (S11 and S12 Files) present the function of sample size of studies with AKI incidence, suggesting a possible risk of publication bias. Discussion This systematic review was conducted from a comprehensive search in the main biomedical databases of the world and included 30 studies published until the third week of Jun 2020, which together report data from more than 18,000 patients (table 1). By the meta-analysis from 28 studies, it was verified that 9.2% (CI 4.6–13.9%) of adult patients hospitalized with COVID-19 presented AKI (Fig. 2). Among patients that required admission to ICU the AKI incidence was substantially higher (32.6%, CI 8.5–56.6%) (Fig. 3). We verified that the meta-analysis of AKI overall and ICU incidence presented high heterogeneity and, possible causes for this may be related to recognized differences among the studies, especially, with regard to design, number of included participants and follow-up time. The number of enrolled patients varied between 16 patients in a single-center retrospective study conducted in the USA33 and 5700 enrolled in a retrospective multi-center study also conducted in the USA.7 In the research of Hirsch et al.,4 53.2% of patients with AKI were in the ICU. Yu et al.10 assessed 226 critically patients with COVID-19 in 19 ICUs from Wuhan, China, and the AKI incidence was 25.2%. In one study carried out in South Korea, AKI incidence in the ICU was 61.5%.16 From what is known so far, among the mechanisms involved in the AKI occurrence in patients with COVID-19 is the triggering of the cytokine release syndrome (CRS), also known as “cytokine storm”, with consequent endothelial damage, mitochondrial dysfunction, hypovolemia, and hypercoagulability.37, 38 Moreover, the direct viral damage, mediated by co-expression and activity of angiotensin-converting enzyme 2 (ACE2) and cellular transmembrane serine proteases (TMPRSSs), can cause kidney damage cells and apoptosis, especially, in podocytes and proximal straight tubule cells.39, 40 These multiple pathophysiological depletions can be observed in the clinical practice by identifying some renal abnormalities.3, 7, 21, 32, 37 By data analysis of 333 patients, Pei et al.32 observed that 75.4% presented renal abnormalities, being proteinuria and hematuria the most frequent. These are also the most frequent abnormalities observed in the research of Cheng et al.3 Furthermore, data from other studies in which the AKI occurrence were reported also showed that the patients had elevated levels of serum creatinine and BUN.4, 8, 16, 17, 21, 26, 27, 30, 31 We performed subgroup analyses to minimize the high heterogeneity observed in the global analysis (with all studies included) Even so, the high heterogeneity remained in the subgroup analysis. Even grouping the studies in which the patients had similar characteristics, the difference in the number of patients enrolled, possibly, was the main cause of maintaining high heterogeneity. According to geographic location of these COVID-19 cases, the AKI estimate incidence was vastly higher at USA (22.9% [CI −2.0–47.8%]) than China (3.9 [CI 1.7–6.2%]) (S3 File). This expressive difference in the AKI incidence between the two countries, could be explained at least in part, by the fact that Occidental populations have a more pronounced expression of the receptor ACE2 in podocytes and proximal straight tubule cells.39 Hirsch et al.4 identified higher AKI incidence among black (20.8%) and white (41%) patients than in Asians (8.1%). However, other epidemiological and experimental studies, including data with patients from different races are needed to confirm this preliminary evidence. In this systematic review, high AKI incidence was also found among elderly (S4 File) and patients with early hospitalization (S5 File). Hirsch et al.4 identified that advanced age is an independent risk factor for AKI, and the highest incidence of this complication on COVID-19 patients was verified in the first days of hospitalization. Yang et al.21 highlighted that elderly patients had a worse prognosis after COVID-19 infection and 14% of patients with hospitalization ≤ 7 days had essential organ injury, including AKI. Thereby, AKI incidence seems to be linked to the shorter onset time of COVID-19, given by time between symptoms onset and hospitalization. Pan et al.39 report that co-expression of the ACE2 and TMPRSS genes in kidney cells were similar to that seen in the lung and other organs. In addition, the authors highlight that the time between detection of COVID-19 in blood and AKI occurrence was around one week. Based on the findings of this study and in the literature, it is reasonable to believe that the AKI occurrence in adult patients hospitalized with COVID-19 is as early as acute respiratory failure, and the health team must be attentive to the predictive signs of AKI occurrence, as described in KDIGO (Kidney Disease: Improving Global Outcomes) guidelines for care guidance aiming the early diagnosis and treatment.3, 32, 37 AKI incidence among patients with ARDS (4.3% [CI 1.8–6.8%]) was lower than in those with ACI (9.3% [CI 1.8–16.9%] (S6 File). By the correlations test, ARDS and ACI showed a direct and statistically significant correlation of greater strength with the AKI incidence (S8 File). Shi et al.6 verified that ACI, with an incidence of 19.7%, was significantly associated with AKI (8.5%) and ARDS (58.5%) incidences. In a study performed by Guo et al.36 with patients from Wuhan, China, the authors observed that myocardial injury was significantly associated with worse outcome. In addition, the AKI and ARDS incidences were higher in patients with elevated troponin T levels, compared to those with normal levels. In a robust study from the USA, the need for mechanical ventilation (one of the ARDS consequences), as well as cardiovascular disease (including heart failure) were significant risk factors for AKI occurrence.4 In the COVID-19 infection, injury in the lung-kidney-heart triad is associated with direct viral damage due to high local expression of ACE2 receptor, resulting in organ crosstalk (as alveolar damage, renal compartment syndrome, cardiomyopathy, cardiorenal syndrome and tubular toxicity) and systemic effects. AKI estimate incidence in patients with secondary infection was 31.6% (CI 12.3–51.0%) and among those sepsis/shock was 4.7% (CI 2.1–7.4%) (S7 File). In the correlation tests, the incidence of these complications was also directly proportional to the AKI incidence (S9 File). Among selected studies, the rates of secondary infection and sepsis/shock were between 2.8%30 and 75%33 and between 0.3%9 and 65%,34 respectively. These complications result in hemodynamic instability, contributing to the increased need for RRT and the mortality rate among patients.37, 38, 39, 40 We emphasize that the studies included in this review present data collected at the beginning of the pandemic, when there were still many uncertainties about the pathophysiological mechanisms involved in the occurrence of AKI in patients with COVID-19. Thus, the possible difficulty in identifying the occurrence of AKI in critically ill patients with sepsis/shock before they died may, at least in part, justify the low incidence of AKI in patients with COVID-19 and sepsis/shock, compared to those with secondary infection. Therefore, we encourage the conduction of other review studies like this one, to identify possible changes in the current epidemiological profile of AKI in patients with COVID-19 compared to what we present in this review, which brings data from studies conducted at the beginning of the pandemic. The estimated proportion of RRT need was 3.2% (CI 1.1–5.4%) and the estimated incidence of mortality was 50.4% (CI 17.0–83.9%) (S12). Continuous RRT (CRRT) was the predominant dialysis technique reported by studies. Hu et al.31 reports that 22.3% of patients studied required CRRT and, among those that required ICU admission the CRRT rate was 26.7%. The rate of death in AKI was between 35.7%21 and 100%,19, 35 and the mortality rate was higher among patients who required CRRT.26, 34 Recommendations for practice and for research Viral tropism of SARS-CoV-2 is believed to be among the main causes of direct kidney damage. Thus, early recognition of AKI episodes in patients with COVID-19 is even more essential to avoid the rapid progression of the disease and the negative outcome. It is recommended that patients be stratified for AKI risk based on the presence of comorbidities, demographic data, and clinical conditions, establishing the monitoring of renal function from hospital admission and continuously during hospitalization. Considering the lack of specific treatment for AKI in COVID-19, instituting intensive care support is among the most favorable existing options, especially among patients with severe COVID-19.4, 37, 38, 40 Based on clinical experience, it is suggested that instituting extracorporeal therapies may assist in removing cytokines as well as can be usefulness for minimizing the organ crosstalk and systemic effects.37, 38 However, considering the high demand, the lack of structure and technological resources (dialysis machines) and human resources (specialized team in nephrology) can be a reality experienced by many countries. Thus, promoting prevention strategies and early identification of AKI, in order to prevent this complication from progressing, is the gold standard of care. To improve the quality of kidney care, the 25th Acute Disease Quality Initiative (ADQI) consensus report presents recommendations for management of AKI associated with COVID-19.41 A better understanding of the mechanisms involved in the occurrence of AKI in patients with COVID-19 is still needed. It is recommended that patients with AKI associated to COVID-19 be evaluated for the course and time of AKI. It is necessary to develop robust scientific studies with high methodological rigor, to verify whether the risk factors for AKI in COVID-19 differ from those already known, as well as to know the factors associated with the recovery of renal function in the short and long term. In addition, knowing the epidemiological burden of AKI in patients who, in addition to COVID-19, already have previous conditions of vulnerability (CKD, cancer, other infectious diseases, transplants, surgeries and others) is equally important. Limitations This systematic review and meta-analysis have some limitations. First, the most selected studies were from China and with considerable risk of methodological biases once they come from retrospective case series or observational analysis. In addition, most studies report data from a restricted sample of patients. The funnel plots (S11 and S12 Files) suggest a possible publication bias and bias related, especially to the fact that most studies were not designed to assess AKI incidence and, as consequences, the definition of AKI was unclear in most of these studies. Although KDIGO was reported as being used in several studies, information regarding rate and definition of baseline serum creatinine, urine output criteria or period of observation to define AKI were not described. In the analyses of the studies included in this systematic review we focused our efforts on identifying the incidence of AKI and, because of that, we did not collect data on risk factors for AKI occurrence. Thus, we recommend that future studies include such an analysis to better understand the epidemiology of AKI in COVID-19. Conclusions The occurrence of AKI is frequent among adult patients hospitalized with COVID-19, and affects on average, up to 13.9% of these patients. It is believed that AKI occurs early and in parallel with lung injury. The elderly population, with acute cardiac injury and secondary infection, is among the group at highest risk for AKI. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Conflict of interests The authors declare that there is no conflict of interest. Appendix A Supplementary data Appendix A Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.nefroe.2022.11.005. ==== Refs References 1 World Health Organization Coronavirus disease (COVID-2019) situation reports [Internet]. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports [accessed 3.7.20]. 2 Wang D. Hu B. Hu C. Zhu F. Liu X. Zhang J. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China JAMA 323 2020 1061 1069 10.1001/jama.2020.1585 32031570 3 Cheng Y. Luo R. Wang K. Zhang M. Wang Z. Dong L. Kidney disease is associated with in-hospital death of patients with COVID-19 Kidney Int 97 2020 829 838 10.1016/j.kint.2020.03.005 32247631 4 Hirsch J.S. Ng J.H. Ross D.W. Sharma P. Shah H.H. Barnett R.L. Acute kidney injury in patients hospitalized with COVID-19 Kidney Int 98 2020 209 218 10.1016/j.kint.2020.05.006 32416116 5 Cheng Y. Luo R. Wang K. Zhang M. Wang Z. Dong L. Kidney impairment is associated with in-hospital death of COVID19 patients medRxiv 2020 10.1101/2020.02.18.20023242 6 Shi S. Quin M. Shen B. Cai Y. Liu T. Yang F. Association of cardiac injury with mortality in hospitalized patients with COVID-19 in Wuhan China JAMA Cardiol 2020 e200950 10.1001/jamacardio.2020.0950 7 Richardson S. Hirsch J.S. Narasimhan M. Crawford J.M. McGinn T. Davidson K.W. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area JAMA 323 2020 2052 2059 10.1001/jama.2020.6775 32320003 8 Guan W.J. Ni Z.Y. Hu Y. Liang W.H. Ou C.Q. He J.X. Clinical characteristics of coronavirus disease 2019 in China N Engl J Med 382 2020 1708 1720 10.1056/NEJMoa2002032 32109013 9 Zhang X. Cai H. Hu J. Lian J. Gu J. Zhang S. Epidemiological, clinical characteristics of cases of SARS-CoV-2 infection with abnormal imaging findings Int J Infect Dis 94 2020 81 87 10.1016/j.ijid.2020.03.040 32205284 10 Yu Y. Xu D. Fu S. Zhang J. Yang X. Xu L. Patients with COVID-19 in 19 ICUs in Wuhan, China: a cross-sectional study Crit Care 24 2020 219 10.1186/s13054-020-02939-x 32410714 11 Liberati A. Altman D.G. Tetzlaff J. Mulrow C. Gotzsche P.C. Ioannidis J.P.A. The prisma statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration BMJ 339 2009 b2700 10.1136/bmj.b2700 19622552 12 Thomson Reuters Endnoteweb 2016 [Internet] Available from: https://joannabriggs.org/critical-appraisal-tools 13 The Joanna Briggs institute Critical appraisal tools: checklist for analytical cross sectional studies 2020 [Internet]. Available from: https://joannabriggs.org/critical-appraisal-tools 14 Neyeloff J.L. Fuchs S.C. Moreira L.B. Meta-analyses and Forest plots using a microsoft excel spreadsheet: step-by-step guide focusing on descriptive data analysis BMC Res Notes 5 2012 52 10.1186/1756-0500-5-52 22264277 15 Higgins J.P. Thompson S.G. Deeks J.J. Altman D.G. Measuring inconsistency in meta-analyses BMJ 327 2003 557 560 10.1136/bmj.327.7414.557 12958120 16 Hong K.S. Lee K.H. Chung J.H. Shin K.C. Choi E.Y. Jin H.J. Clinical features and outcomes of 98 patients hospitalized with SARS-CoV-2 infection in Daegu South Korea: a brief descriptive study Yonsei Med J 61 2020 431 437 10.3349/ymj.2020.61.5.431 32390367 17 Huang C. Wang Y. Li X. Ren L. Zhao J. Hu Y. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China Lancet 395 2020 497 506 10.1016/S0140-6736(20)30183-5 31986264 18 Lei S. Jiang F. Su W. Chen C. Chen J. Mei W. Clinical characteristics and outcomes of patients undergoing surgeries during the incubation period of COVID-19 infection EClinicalMedicine 21 2020 100331 10.1016/j.eclinm.2020.100331 32292899 19 Wang D. Yin Y. Hu C. Liu X. Zhang X. Zhou S. Clinical course and outcome of 107 patients infected with the novel coronavirus, SARS-CoV-2, discharged from two hospitals in Wuhan, China Crit Care 24 2020 188 10.1186/s13054-020-02895-6 32354360 20 Wang L. Li X. Chen H. Yan S. Li D. Li Y. Coronavirus disease 19 infection does not result in acute kidney injury: an analysis of 116 hospitalized patients from Wuhan, China Am J Nephrol 51 2020 343 348 10.1159/000507471 32229732 21 Yang R. Gui X. Zhang Y. Xiong Y. The role of essential organ-based comorbidities in the prognosis of COVID-19 infection patients Expert Rev Respir Med 2020 1 4 10.1080/17476348.2020.1761791 22 Yang X. Yu Y. Shu H. Xia J. Liu H. Wu Y. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan China: a single-centered, retrospective, observational study Lancet Respir Med 8 2020 475 481 10.1016/S2213-2600(20)30079-5 32105632 23 Zhang G. Hu C. Luo L. Fang F. Chen Y. Li J. Clinical features and short-term outcomes of 221 patients with COVID-19 in Wuhan, China J Clin Vrol 127 2020 104364 10.1016/j.jcv.2020.104364 24 Zhao X. Xu X. Yin H. Hu Q. Xiong T. Tang Y. Clinical characteristics of patients with 2019 coronavirus disease in a non-Wuhan area of Hubei Province China: a retrospective study BMC Infec Dis 20 2020 311 10.1186/s12879-020-05010-w 32345226 25 Zheng Y. Sun L. Xu M. Pan J. Zhang Y. Fang X. Clinical characteristics of 34 COVID-19 patients admitted to intensive care unit in Hangzhou, China J Zhejiang Univ Sci B 21 2020 378 387 10.1631/jzus.B2000174 32425003 26 Zhou F. Yu T. Du R. Fan G. Liu Y. Liu Z. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study Lancet 395 2020 1054 1062 10.1016/S0140-6736(20)30566-3 32171076 27 Chen N. Zhou M. Dong X. Qu J. Gong F. Han Y. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study Lancet 395 2020 507 513 10.1016/S0140-6736(20)30211-7 32007143 28 Arentz M. Yim E. Klaff L. Lokhandwala S. Ried F.X. Chong M. Characteristics and outcomes of 21 critically ill patients with COVID-19 in Washington State JAMA 323 2020 1612 1614 10.1001/jama.2020.4326 32191259 29 Yang W. Cao Q. Qin L. Wang X. Cheng Z. Pan A. Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): a multi-center study in Wenzhou city, Zhejiang China J Infect 80 2020 388 393 10.1016/j.jinf.2020.02.016 32112884 30 Wan S. Xiang Y. Fang W. Zheng Y. Li B. Hu Y. Clinical features and treatment of COVID-19 patients in northeast Chongqing J Med Virol 92 2020 797 806 10.1002/jmv.25783 32198776 31 Hu L. Chen S. Fu Y. Gao Z. Long H. Wang J. Risk factors associated with clinical outcomes in 323 COVID-19 hospitalized patients in Wuhan, China Clin Infect Dis 2020 ciaa539 10.1093/cid/ciaa539 32361738 32 Pei G. Zhang Z. Peng J. Liu L. Zhang C. Yu C. Renal involvement and early prognosis in patients with COVID-19 pneumonia J Am Soc Nephrol 31 2020 1157 1165 10.1681/ASN.2020030276 32345702 33 Aggarwal S. Garcia-Telles N. Aggarwal G. Lavie C. Lippi G. Henry B.M. Clinical features, laboratory characteristics, and outcomes of patients hospitalized with coronavirus disease 2019 (COVID-19): early report from the United States Diagnosis (Berl) 7 2020 91 96 10.1515/dx-2020-0046 32352401 34 Chen T. Wu D. Chen H. Yan W. Yang D. Chen G. Clinical characteristics of 113 deceased patients with coronavirus disease 2019: retrospective study BMJ 368 2020 m1091 10.1136/bmj.m1091 32217556 35 Deng Y. Liu W. Liu K. Fang Y. Shang J. Zhou L. Clinical characteristics of fatal and recovered cases of coronavirus disease 2019 in Wuhan China: a retrospective study Chin Med J (Engl) 133 2020 1261 1267 10.1097/CM9.0000000000000824 32209890 36 Guo T. Fan Y. Chen M. Wu X. Zhang L. He T. Cardiovascular implications of fatal outcomes of patients with Coronavirus Disease 2019 (COVID-19) JAMA Cardiol 5 2020 1 8 10.1001/jamacardio.2020.1017 32745165 37 Ronco C. Reis T. Kidney involvement in COVID-19 and rationale for extracorporeal therapies Nat Rev Nephrol 16 2020 308 310 10.1038/s41581-020-0284-7 32273593 38 Ronco C. Reis T. Husain-Syed F. Management of acute kidney injury in patients with COVID-19 Lancet Respir Med 8 2020 738 742 10.1016/S2213-2600(20)30229-0 32416769 39 Pan X. Xu D. Zhang H. Zhou W. Wang L. Gui X. Identification of a potential mechanism of acute kidney injury during the COVID-19 outbreak: a study based on single-cell transcriptome analysis Intensive Care Med 46 2020 1114 1116 10.1007/s00134-020-06026-1 32236644 40 Batlle D. Soler M.J. Sparks M.A. Hiremath S. South A.M. Welling P.A. Acute kidney injury in COVID-19: emerging evidence of a distinct pathophysiology J Am Soc Nephrol 31 2020 1380 1383 10.1681/ASN.2020040419 32366514 41 Nadim M.K. Forni L.G. Mehta R.L. Connor M.J. Jr. Liu K.D. Ostermann M. COVID-19-associated acute kidney injury: consensus report of the 25th Acute Disease Quality Initiative (ADQI) Workgroup Nat Rev Nephrol 16 2020 747 764 10.1038/s41581-020-00356-5 33060844	36460430	PMC9707651	NO-CC CODE	2022-12-01 23:20:24	no	Nefrologia (Engl Ed). 2022 Nov 29 July-August; 42(4):404-414	utf-8	Nefrologia (Engl Ed)	2,022	10.1016/j.nefroe.2022.11.005	oa_other
==== Front Ann Intern Med Ann Intern Med aim Annals of Internal Medicine 0003-4819 1539-3704 American College of Physicians 36442069 10.7326/P22-0023 aim-olf-P220023 Summaries for Patients 2221Antivirals3122457COVID-192882Drug therapy3124935Omicron variant2237Outpatients8398Treatment guidelinesearlyCurrently Online FirstcoronavirusCoronavirus Disease 2019 (COVID-19) Summaries for Patients are a service provided by Annals to help patients better understand the complicated and often mystifying language of modern medicine.Summaries for Patients are presented for informational purposes only. These summaries are not a substitute for advice from your own medical provider. If you have questions about this material, or need medical advice about your own health or situation, please contact your physician. The summaries may be reproduced for not-for-profit educational purposes only. Any other uses must be approved by the American College of Physicians.Summary for Patients: Outpatient Treatment of Confirmed COVID-19: Living, Rapid Practice Points From the American College of Physicians (Version 1) From: Qaseem A, Yost J, Miller MC, et al. Outpatient treatment of confirmed COVID-19: living, rapid practice points from the American College of Physicians (version 1). Ann Intern Med. 29 November 2022. [Epub ahead of print]. doi:10.7326/M22-2249 29 11 2022 29 11 2022 P22-00232022 American College of Physicians This article is made available via the PMC Open Access Subset for unrestricted re-use for research, analyses, and text and data mining through PubMed Central. Acknowledgement of the original source shall include a notice similar to the following: "© 2020 American College of Physicians. Some rights reserved. This work permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited." These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. ==== Body pmcWhat is the problem and what is known about it so far? COVID-19 is caused by a virus called SARS-CoV-2. The virus first appeared in 2019 and spread COVID-19 disease throughout the world. Various treatments are available as options for outpatient care of COVID-19. Why did the researchers do this particular study? The Scientific Medical Policy Committee (SMPC) of the American College of Physicians (ACP) developed these living, rapid practice points to summarize the best available evidence on the treatment of adults with confirmed COVID-19 in an outpatient setting. Who was studied? The population includes all adult patients diagnosed with COVID-19 in the outpatient setting regardless of whether a person received SARS-CoV-2 vaccine to prevent them from getting the disease. How was the study done? The SMPC developed these practice points according to ACP's process for the rapid development of practice points and policy on disclosure of interests and management of conflicts of interest. These practice points are based on a living, rapid review by the ACP Center for Evidence Reviews at Cochrane Austria at the University for Continuing Education Krems. What did the researchers find? ACP suggests that physicians consider treatment options depending on the health of a patient and how long a patient has had symptoms of COVID-19. Treatments to be considered are molnupiravir, nirmatrelvir–ritonavir combination therapy, or remdesivir in patients with confirmed mild to moderate COVID-19 in the outpatient setting who are within 5 days (nirmatrelvir–ritonavir), 7 days (remdesivir), or 5 to 7 days (molnupiravir) of the onset of symptoms and at high risk for progressing to severe disease. Physicians should not use azithromycin, chloroquine, hydroxychloroquine, ivermectin, nitazoxanide, lopinavir–ritonavir, casirivimab–imdevimab, regdanvimab, sotrovimab, convalescent plasma, ciclesonide, or fluvoxamine to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. What were the limitations of the study? The SARS-CoV-2 virus is changing continuously, and a new type can form known as a “variant.” COVID-19 has had many variants. The evidence informing these practice points was generated before the Omicron variant became the dominant circulating variant. Physicians should talk to their patients when making a decision about which treatment option to use. We do not know at this point if one treatment is better than another. Patients should talk to their physician about which medicines they are taking, including supplements such as vitamins. There are potential drug interactions to consider before starting a treatment against COVID-19. Remdesivir requires intravenous infusion. A patient who has tested negative for COVID-19 can test positive again, and this is known as rebound COVID-19. Rebound COVID-19 has been reported to occur with the use of nirmatrelvir–ritonavir between 2 and 8 days after initial recovery. What are the implications of the study? Three treatments are options for outpatients with mild to moderate COVID-19. The SMPC will update the practice points as more evidence becomes available. This article was published at Annals.org on 29 November 2022.	36442069	PMC9707696	NO-CC CODE	2022-12-03 23:19:45	no	Ann Intern Med. 2022 Nov 29;:P22-0023	utf-8	Ann Intern Med	2,022	10.7326/P22-0023	oa_other
==== Front Ann Intern Med Ann Intern Med aim Annals of Internal Medicine 0003-4819 1539-3704 American College of Physicians 36442058 10.7326/M22-3317 aim-olf-M223317 Editorials 3122457COVID-193282Infectious diseases2237Outpatients2892Systematic reviewsearlyCurrently Online FirstcoronavirusCoronavirus Disease 2019 (COVID-19)Ongoing Need for Clinical Trials and Contemporary End Points for Outpatient COVID-19 Clinical Trials and Contemporary End Points for Outpatient COVID-19 Lee Todd C. MD, MPH https://orcid.org/0000-0002-2267-4239 Boulware David R. MD, MPH https://orcid.org/0000-0002-4715-0060 Division of Infectious Diseases, Department of Medicine, McGill University Health Centre, Montreal, Quebec, Canada (T.C.L.) Division of Infectious Diseases and International Medicine, Department of Medicine, University of Minnesota, Minneapolis, Minnesota (D.R.B.) Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M22-3317. Corresponding Author: David R. Boulware, MD, MPH, 689 SE 23rd Avenue, Minneapolis, MN 55455; e-mail, [email protected]. 29 11 2022 29 11 2022 M22-33172022 American College of Physicians This article is made available via the PMC Open Access Subset for unrestricted re-use for research, analyses, and text and data mining through PubMed Central. Acknowledgement of the original source shall include a notice similar to the following: "© 2020 American College of Physicians. Some rights reserved. This work permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited." These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The American College of Physicians presents recommendations for the outpatient treatment of COVID-19 based on Sommer and colleagues' systematic review. The editorialists commend the authors of the recommendations and review for trying to summarize the rapidly evolving literature into clear practice points and discuss the challenges of continually updating reviews and associated recommendations as new evidence emerges and relevant outcomes evolve. ==== Body pmcIn their article, Qaseem and colleagues (1) present version 1 of the American College of Physicians' living, rapid practice points for the outpatient treatment of confirmed COVID-19. These practice points are based on a review by Sommer and colleagues (2) that included all trials on the Epistemonikos COVID-19 L·OVE Platform up to 4 April 2022. Although it is beyond the scope of this editorial to comment on the accuracy of that platform, we commend the authors for trying to summarize the rapidly evolving literature into clear practice points. They have also created a hierarchy of outcomes of interest, including death, recovery, hospitalization, and serious and nonserious adverse events. However, it is a testament to how rapidly things are changing that this living, rapid review is already more than 6 months out of date for many reasons. With respect to molnupiravir (Practice Point 1), the recommendation for use is based on 2 randomized controlled trials involving 1637 participants. However, there are many more trials involving molnupiravir outside the MOVe-OUT program (3), and up to a dozen trials remain unpublished as of November 2022. Ignoring the major issue of publication bias, the real challenge with this recommendation comes from PANORAMIC (Platform Adaptive trial of NOvel antiviRals for eArly treatMent of covid-19 In the Community [4]) from the United Kingdom. PANORAMIC recruited 25 783 participants between 8 December 2021 and 27 April 2022—more than 10 times the number included in the Annals systematic review. It showed that molnupiravir was not effective at reducing hospitalization or death due to COVID-19 in high-risk outpatients, of whom 98% were vaccinated and who primarily had Omicron variant infections. Although PANORAMIC showed a time-to-recovery benefit, this open-label study is subject to bias for assessing time to recovery. Little evidence of time-to-recovery benefit was present in the original, double-blind, placebo-controlled MOVe-OUT trial (3). It is unlikely there will ever be a larger outpatient randomized COVID-19 trial. With respect to Practice Points 2 (nirmatrelvir–ritonavir) and 3 (remdesivir), the major issue surrounds the populations that have been studied. The only published randomized controlled trials—EPIC-HR (Evaluation of Protease Inhibition for Covid-19 in High-Risk Patients [5]) (nirmatrelvir–ritonavir) and PINETREE (6) (remdesivir)—involved high-risk outpatients who were unvaccinated and were having their first COVID-19 illness, and these trials were conducted before Omicron. The standard-risk EPIC-SR trial (nirmatrelvir–ritonavir) was designed to involve low-risk unvaccinated and high-risk vaccinated patients, but EPIC-SR failed to meet the primary outcome (time to symptom improvement), and no statistical reduction in hospitalization occurred (7). To our knowledge, remdesivir has never been studied in randomized trials involving vaccinated patients, nor have monoclonal antibodies. Thus, a recommendation to use these products represents a substantial stretch from the actual evidence. In the Omicron era (and later), with natural and vaccine- or booster-derived immunity in most of the population, the effectiveness of these medicines remains unclear. The PANORAMIC platform is recruiting patients to nirmatrelvir–ritonavir at present and may provide the most insights (8). Repurposed medicines, such as fluvoxamine (Practice Point 14), are recommended against (9), and metformin (10) is not addressed. Of concern is the 2-tiered definition of hospitalization being subtly used, where more than 24 hours of acute care is an acceptable definition of “hospitalization” for the EPIC-HR and MOVe-OUT trials (3, 5) but unallowable for repurposed agents. When fluvoxamine, 100 mg twice daily, is compared using the same definition of 24 hours of acute care or hospitalization, a modest benefit exists (9). Repurposed therapies remain highly relevant for low- and middle-income countries worldwide where expensive therapies are unavailable. This editorial may feel nihilistic; however, the fact that hospitalization has become extremely uncommon compared with before vaccine availability is a testament to the successes of the public health campaigns that have fueled SARS-CoV-2 vaccination and boosting. Clinicians are still seeing COVID-19 and need guidance. Yet, the creation of guidelines may further erode the equipoise needed to perform the definitive trials that we truly require. Further, the widespread use of these agents in the absence of trials disincentivizes the manufacturers from conducting or allowing such trials. How do we move forward with generating the necessary evidence for a rational COVID-19 outpatient strategy, and how do we keep our recommendations up to date with a rapidly changing evidence base? First, we really do need evidence generated for therapeutics in persons who have been multiply vaccinated or have recovered from COVID-19. Where the idea of receiving a placebo therapy may be unpalatable to some clinicians and patients, we can use multiple active agents with placebo controls. The ACTIV-6 (Accelerating COVID-19 Therapeutic Interventions and Vaccines) platform is one example of a rigorous active and placebo-controlled randomized trial that could potentially help answer these questions; however, the ACTIV-6 trial is scheduled to end enrollment in early 2023. Second, we will clearly need to move beyond dichotomized thinking surrounding end points. With hospitalization and death now rare in outpatients, trials powered on these outcomes become completely infeasible. What kind of end points should we consider for outpatient COVID-19 trials? The same type of end points that should inform clinical decision making and health care economics moving forward. With emergency departments and urgent care centers worldwide already under tremendous pressure in fall 2022, and with influenza and other respiratory viruses further driving health care use, a good argument could be made to include these types of care in any outcome. Preventing disease progression, resulting in reductions in emergency department visits or hospitalizations, is a clinical benefit to patients and a benefit to society in 2022 and beyond. People who are sick also want to feel better faster and avoid long-term sequelae of infection. Thus, time to recovery and the prevalence of persistent symptoms or “long COVID” become key components of outcome assessments for trials. Because care-seeking behavior and perception of symptoms are subject to bias due to knowledge of treatment assignment, blinded placebo or active control is essential. With these outcomes being collected, we can avoid dichotomized thinking and arrive at a bigger picture of what a medication's effects are in the current COVID-19 era. Finally, we need a way to have evidence continually updated. The concept of living, rapid reviews is fantastically bold and innovative—but they challenge the traditional peer review and publishing model. Novel strategies to move evidence synthesis as close to publication as possible will be required. This article was published at Annals.org on 29 November 2022. ==== Refs References 1. Qaseem A, Yost J, Miller MC, et al. Outpatient treatment of confirmed COVID-19: living, rapid practice points from the American College of Physicians (version 1). Ann Intern Med. 2022. [Epub ahead of print]. doi:10.7326/M22-2249 2. Sommer I, Dobrescu A, Ledinger D, et al. Outpatient treatment of confirmed COVID-19: a living, rapid review for the American College of Physicians. Ann Intern Med. 2022. [Epub ahead of print]. doi:10.7326/M22-2202 3. Jayk Bernal A , Gomes da Silva MM , Musungaie DB , et al; MOVe-OUT Study Group. Molnupiravir for oral treatment of Covid-19 in nonhospitalized patients. N Engl J Med. 2022;386 :509-520. [PMID: ] doi:10.1056/NEJMoa2116044 34914868 4. Butler C, Hobbs R, Gbinigie O, et al. Molnupiravir plus usual care versus usual care alone as early treatment for adults with COVID-19 at increased risk of adverse outcomes (PANORAMIC): preliminary analysis from the United Kingdom randomised, controlled open-label, platform adaptive trial. SSRN. Preprint posted online 17 October 2022. doi:10.2139/ssrn.4237902 5. Hammond J , Leister-Tebbe H , Gardner A , et al; EPIC-HR Investigators. Oral nirmatrelvir for high-risk, nonhospitalized adults with Covid-19. N Engl J Med. 2022;386 :1397-1408. [PMID: ] doi:10.1056/NEJMoa2118542 35172054 6. Gottlieb RL , Vaca CE , Paredes R , et al; GS-US-540-9012 (PINETREE) Investigators. Early remdesivir to prevent progression to severe Covid-19 in outpatients. N Engl J Med. 2022;386 :305-315. [PMID: ] doi:10.1056/NEJMoa2116846 34937145 7. Pfizer. Pfizer reports additional data on PAXLOVID supporting upcoming new drug application submission to U.S. FDA. 14 June 2022. Accessed at www.pfizer.com/news/press-release/press-release-detail/pfizer-reports-additional-data-paxlovidtm-supporting on 5 October 2022. 8. University of Oxford. PANORAMIC. Accessed at www.panoramictrial.org on 5 October 2022. 9. Lee TC , Vigod S , Bortolussi-Courval É , et al. Fluvoxamine for outpatient management of COVID-19 to prevent hospitalization: a systematic review and meta-analysis. JAMA Netw Open. 2022;5 :e226269. [PMID: ] doi:10.1001/jamanetworkopen.2022.6269 35385087 10. Bramante CT , Huling JD , Tignanelli CJ , et al; COVID-OUT Trial Team. Randomized trial of metformin, ivermectin, and fluvoxamine for Covid-19. N Engl J Med. 2022;387 :599-610. [PMID: ] doi:10.1056/NEJMoa2201662 36070710	36442058	PMC9707697	NO-CC CODE	2022-12-03 23:19:45	no	Ann Intern Med. 2022 Nov 29;:M22-3317	utf-8	Ann Intern Med	2,022	10.7326/M22-3317	oa_other
==== Front Ann Intern Med Ann Intern Med aim Annals of Internal Medicine 0003-4819 1539-3704 American College of Physicians 36442061 10.7326/M22-2249 aim-olf-M222249 Clinical Guidelines 2221Antivirals3122457COVID-192882Drug therapy3124935Omicron variant2237Outpatients8398Treatment guidelinesearlyCurrently Online FirstcoronavirusCoronavirus Disease 2019 (COVID-19)acpcgACP Clinical Guidelinepoc-eligiblePOC EligibleOutpatient Treatment of Confirmed COVID-19: Living, Rapid Practice Points From the American College of Physicians (Version 1) ACP Practice Points on Outpatient Treatment of Confirmed COVID-19 Qaseem Amir MD, PhD, MHA https://orcid.org/0000-0001-6866-7985 Yost Jennifer PhD, RN https://orcid.org/0000-0002-3170-1956 Miller Matthew C. MD https://orcid.org/0000-0001-7267-4897 Andrews Rebecca MD, MS https://orcid.org/0000-0002-2658-226X Jokela Janet A. MD, MPH https://orcid.org/0000-0003-4324-4809 Forciea Mary Ann MD https://orcid.org/0000-0002-1999-1145 Abraham George M. MD, MPH https://orcid.org/0000-0003-4296-8362 Humphrey Linda L. MD, MPH Scientific Medical Policy Committee of the American College of Physicians* American College of Physicians, Philadelphia, Pennsylvania (A.Q.) American College of Physicians, Philadelphia, and Villanova University, Villanova, Pennsylvania (J.Y.) Penn Medicine, Philadelphia, Pennsylvania (M.C.M., M.A.F.) University of Connecticut, Mansfield, Connecticut (R.A.) University of Illinois College of Medicine at Urbana-Champaign, Champaign, Illinois (J.A.J.) University of Massachusetts Medical School and Saint Vincent Hospital, Worcester, Massachusetts (G.M.A.) Portland Veterans Affairs Medical Center and Oregon Health & Science University, Portland, Oregon (L.L.H.) Lee Rachael A. MD, MSPH Tschanz Mark P. DO Etxeandia-Ikobaltzeta Itziar PharmD, PhD Harrod Curtis PhD, MPH Shamliyan Tatyana MD, MS Umana Karla MPH Scientific Medical Policy Committee of the American College of Physicians Note: The practice points are meant to guide care based on the best available evidence and may not apply to all patients or individual clinical situations. They should not be used as a replacement for a clinician's judgment. Any reference to a product or process contained in a practice point is not intended as an endorsement of any specific commercial product. All practice points are considered automatically withdrawn or invalid 5 years after publication, or once an update has been issued. Financial Support: Financial support for the development of the practice points comes exclusively from the ACP operating budget. Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M22-2249. All financial and intellectual disclosures of interest were declared, and potential conflicts were discussed and managed. Drs. Akl, Dunn, Kansagara, Marcucci, and Obley were recused from authorship and voting due to moderate-level conflicts of interest (recently authored relevant publications). A record of disclosures of interest and management of conflicts is kept for each SMPC meeting and conference call and can be viewed at www.acponline.org/about-acp/who-we-are/leadership/boards-committees-councils/scientific-medical-policy-committee/disclosure-of-interests-and-conflict-of-interest-management-summary-for-scientific-medical-policy. Corresponding Author: Amir Qaseem, MD, PhD, MHA, American College of Physicians, 190 N. Independence Mall West, Philadelphia, PA 19106; e-mail, [email protected]. Author Contributions: Conception and design: I. Etxeandia-Ikobaltzeta, M.A. Forciea, J.A. Jokela, A. Qaseem, M.P. Tschanz, J. Yost. Analysis and interpretation of the data: M.A. Forciea, C. Harrod, L.L. Humphrey, J.A. Jokela, A. Qaseem, T. Shamliyan, M.P. Tschanz, K. Umana, J. Yost. Drafting of the article: G.M. Abraham, I. Etxeandia-Ikobaltzeta, J.A. Jokela, R.A. Lee, M.C. Miller, A. Qaseem, T. Shamliyan, M.P. Tschanz, J. Yost. Critical revision for important intellectual content: G.M. Abraham, R. Andrews, I. Etxeandia-Ikobaltzeta, M.A. Forciea, C. Harrod, L.L. Humphrey, J.A. Jokela, R.A. Lee, A. Qaseem, T. Shamliyan, M.P. Tschanz, J. Yost. Final approval of the article: G.M. Abraham, R. Andrews, I. Etxeandia-Ikobaltzeta, M.A. Forciea, C. Harrod, L.L. Humphrey, J.A. Jokela, R.A. Lee, M.C. Miller, A. Qaseem, T. Shamliyan, M.P. Tschanz, K. Umana, J. Yost. Statistical expertise: C. Harrod, A. Qaseem, J. Yost. Administrative, technical, or logistic support: A. Qaseem, T. Shamliyan, K. Umana, J. Yost. Collection and assembly of data: J. Yost. 29 11 2022 29 11 2022 M22-22492022 American College of Physicians This article is made available via the PMC Open Access Subset for unrestricted re-use for research, analyses, and text and data mining through PubMed Central. Acknowledgement of the original source shall include a notice similar to the following: "© 2020 American College of Physicians. Some rights reserved. This work permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited." These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The Scientific Medical Policy Committee of the American College of Physicians (ACP) developed living, rapid practice points to summarize the best available evidence on the treatment of adults with confirmed COVID-19 in an outpatient setting. Practice points are based on a living, rapid review conducted by the ACP Center for Evidence Reviews at Cochrane Austria at the University for Continuing Education Krems. Description: Strategies to manage COVID-19 in the outpatient setting continue to evolve as new data emerge on SARS-CoV-2 variants and the availability of newer treatments. The Scientific Medical Policy Committee (SMPC) of the American College of Physicians (ACP) developed these living, rapid practice points to summarize the best available evidence on the treatment of adults with confirmed COVID-19 in an outpatient setting. These practice points do not evaluate COVID-19 treatments in the inpatient setting or adjunctive COVID-19 treatments in the outpatient setting. Methods: The SMPC developed these living, rapid practice points on the basis of a living, rapid review done by the ACP Center for Evidence Reviews at Cochrane Austria at the University for Continuing Education Krems (Danube University Krems). The SMPC will maintain these practice points as living by monitoring and assessing the impact of new evidence. Practice Point 1: Consider molnupiravir to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting who are within 5 to 7 days of the onset of symptoms and at high risk for progressing to severe disease. Practice Point 2: Consider nirmatrelvir–ritonavir combination therapy to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting who are within 5 days of the onset of symptoms and at high risk for progressing to severe disease. Practice Point 3: Consider remdesivir to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting who are within 7 days of the onset of symptoms and at high risk for progressing to severe disease. Practice Point 4: Do not use azithromycin to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Practice Point 5: Do not use chloroquine or hydroxychloroquine to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Practice Point 6: Do not use ivermectin to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Practice Point 7: Do not use nitazoxanide to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Practice Point 8: Do not use lopinavir–ritonavir combination therapy to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Practice Point 9: Do not use casirivimab–imdevimab combination therapy to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting unless it is considered effective against a SARS-CoV-2 variant or subvariant locally in circulation. Practice Point 10: Do not use regdanvimab to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting unless it is considered effective against a SARS-CoV-2 variant or subvariant locally in circulation. Practice Point 11: Do not use sotrovimab to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting unless it is considered effective against a SARS-CoV-2 variant or subvariant locally in circulation. Practice Point 12: Do not use convalescent plasma to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Practice Point 13: Do not use ciclesonide to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Practice Point 14: Do not use fluvoxamine to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. ==== Body pmcThe COVID-19 pandemic continues to be a global health priority, and most COVID-19 illness occurs in the outpatient setting. Because of reductions in the risk for severe COVID-19—largely due to vaccination and the Omicron variant and subvariants (which are generally associated with less severe illness although are more highly transmissible than prior strains) (1), as well as an increase in treatment options—patients with COVID-19 are increasingly treated in the outpatient setting (2). In addition to vaccination, prevention of the development of serious illness will be the most relevant step for reducing morbidity and mortality associated with COVID-19. The Scientific Medical Policy Committee (SMPC) of the American College of Physicians (ACP) developed these living, rapid practice points to provide clinical advice based on the best available evidence about the treatment of adults with confirmed COVID-19 in the outpatient setting. Scope and Purpose The SMPC developed version 1 of these living, rapid practice points to summarize the best available evidence about the use of pharmacologic and biologic treatments of COVID-19 in the outpatient setting. These practice points do not address the use of COVID-19 treatments in the inpatient setting or adjunctive treatments of COVID-19 in the outpatient setting. Table 1 and Figures 1 and 2 summarize the current evidence. Figure 1. Evidence summary for treatment of confirmed COVID-19 in outpatient settings. * Total baseline sample sizes are reported. Analytic sample sizes might vary by outcome. CoE = certainty of evidence; RCT = randomized controlled trial. Figure 1. Evidence summary for treatment of confirmed COVID-19 in outpatient settings. * Total baseline sample sizes are reported. Analytic sample sizes might vary by outcome. CoE = certainty of evidence; RCT = randomized controlled trial. Figure 2. Evidence description. Evidence search and assessment conducted by ACP Center for Evidence Reviews at Cochrane Austria at the University for Continuing Education Krems (Danube University Krems) (31). An updated search for evidence through 4 April 2022 aimed to identify placebo RCTs evaluating selected primary treatment of persons with COVID-19 in the outpatient setting. RCT = randomized controlled trial. * See reference 30. Figure 2. Evidence description. Evidence search and assessment conducted by ACP Center for Evidence Reviews at Cochrane Austria at the University for Continuing Education Krems (Danube University Krems) (31). An updated search for evidence through 4 April 2022 aimed to identify placebo RCTs evaluating selected primary treatment of persons with COVID-19 in the outpatient setting. RCT = randomized controlled trial. * See reference 30. Table 1. Evidence Summary for Treatment of Confirmed COVID-19 in Outpatient Settings (Version 1) Table 1. Evidence Summary for Treatment of Confirmed COVID-19 in Outpatient Settings (Version 1) Population The population is all adult patients diagnosed with COVID-19 in the outpatient setting regardless of SARS-CoV-2 vaccination status. Intended Audience The intended audience for these practice points includes clinicians, patients, the public, and public health officials. Practice Points Development Process The SMPC developed these practice points according to ACP's methods for the rapid development of practice points and policy on disclosure of interests and management of conflicts of interest. The SMPC intends to maintain this topic as living. Monthly literature surveillance is planned to identify and evaluate new evidence from published randomized controlled trials on treatments of COVID-19 in the outpatient setting, and both the practice points and the living, rapid review will be periodically updated. Details of the practice points' living process, including signals for updating and retirement, can be found in ACP's methods articles (32, 33). Living, Rapid Review These practice points are based on a living, rapid review funded by ACP and done by the ACP Center for Evidence Reviews at Cochrane Austria at the University for Continuing Education Krems (Danube University Krems) to address the key questions (31). The living, rapid review searched for studies through 4 April 2022. The review included only peer-reviewed, published (preprints were excluded), placebo-controlled trials of an eligible treatment that was given to adults in an outpatient setting. The SMPC intends to maintain this topic as living. Monthly literature surveillance is planned to identify and evaluate new evidence. Surveillance through 17 August 2022 identified 6 new studies since the initial search date, which are described in the living, rapid review (31). Evidence is rapidly evolving, and studies published after the initial search date that meet inclusion criteria will be incorporated into periodic updates and future versions of both the practice points and the review. Key Question 1: What are the benefits and harms of COVID-19 treatments in symptomatic and asymptomatic adult patients with a confirmed SARS-CoV-2 infection in the outpatient setting? Key Question 1a: Do the benefits and harms vary by patient characteristics (age, gender, or comorbid conditions), type of SARS-CoV-2 variant, immunity status (prior SARS-CoV-2 infection, vaccination status, or time since infection or vaccination), symptom duration, or disease severity? Treatments Evaluated The following treatments for adults with confirmed COVID-19 in the outpatient setting were identified by the SMPC, in consultation with the ACP Center for Evidence Reviews, as those for which clinical advice was most needed to inform decision making. In practice, some treatments might be used as adjunctive therapies. However, studies were included in the living, rapid review only if the treatment was the primary treatment that patients received. • Antibiotics: azithromycin • Antiparasitics: chloroquine or hydroxychloroquine, ivermectin, and nitazoxanide • Antivirals: lopinavir–ritonavir combination therapy, molnupiravir, nirmatrelvir–ritonavir combination therapy, and remdesivir • Convalescent plasma • Corticosteroids: ciclesonide • Fluvoxamine (selective serotonin reuptake inhibitor) • Monoclonal antibodies approved by the U.S. Food and Drug Administration or European Medicines Agency as of 4 April 2022: bebtelovimab, casirivimab–imdevimab combination therapy, regdanvimab, and sotrovimab Outcomes of Interest The SMPC reviewed core outcome sets for COVID-19 (34–37) and rated the following outcomes as critical: all-cause mortality, COVID-19–specific mortality, recovery, time to recovery, hospital admissions due to COVID-19, serious adverse events, and adverse events. Overview of the Evidence The living, rapid review (31) identified 26 placebo-controlled randomized studies informing key question 1 about the benefits and harms of treatment options (4–29). Only 1 of these studies (19) informed key question 1a about variability in benefits and harms. Studies included in the review were limited to placebo-controlled trials that evaluated efficacy or how well the treatments work in controlled circumstances because no standard of care had been established for COVID-19 in the outpatient setting. Practice Points and Rationale Table 1 and Figures 1 and 2 summarize the practice points and evidence. The practice points consider the best available, appraised evidence. Outpatient treatment of COVID-19 should generally be considered only in patients with confirmed mild to moderate COVID-19. Current definitions of the categories of COVID-19 severity (asymptomatic, mild, moderate, severe, and critical) can be accessed on the website of the Centers for Disease Control and Prevention (www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum) (38). Determining the best approach to treatment of COVID-19 in the outpatient setting should be a personalized decision based on clinical judgment, discussion, and shared decision making with the patient about potential treatment benefits, harms, patient characteristics (such as risk factors, comorbid conditions, and disease severity), and patient preferences. Table 2 provides the current dosages of treatment options from the Food and Drug Administration and European Medicines Agency. Table 2. Dosages for Treatment Options* Table 2. Dosages for Treatment Options* Applicability All studies were done before the Omicron variant became the dominant circulating strain. In all of the included studies, COVID-19 was confirmed by diagnostic testing, usually a reverse transcriptase polymerase chain reaction test. Eleven of the 26 included studies excluded patients who were vaccinated (6–8, 10, 11, 13, 18, 19, 24, 27, 29), 5 excluded patients who had been previously diagnosed with COVID-19 (10, 12, 22, 25, 29), and 1 included patients if they had not been hospitalized or treated for COVID-19 (8). The duration of symptoms before study entry varied; overall, patients had had symptoms for shorter than 12 days. Only 1 of the included studies explicitly reported that patients were not required to be symptomatic for study entry (17). The way in which the included studies in the living, rapid review were done (for example, enrollment criteria and data analysis) did not allow conclusions to be drawn about how the efficacy and harms of treatment vary with such factors as patient characteristics (for example, age, gender, and comorbid conditions), SARS-CoV-2 variants and subvariants, immunity status (for example, prior SARS-CoV-2 infection, vaccination status, and time since infection or vaccination), symptom duration, and COVID-19 severity. Ongoing literature surveillance is planned to identify any relevant new studies, including those evaluating future SARS-CoV-2 variants of concern that have yet to emerge. Treatments Supported The following treatments are listed alphabetically, and the order does not imply prioritization for outpatient treatment of COVID-19. Antiviral Treatments Practice Point 1: Consider molnupiravir to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting who are within 5 to 7 days of the onset of symptoms and at high risk for progressing to severe disease. Evidence showed benefits of molnupiravir, which may reduce all-cause mortality and COVID-19–specific mortality in patients for whom treatment is initiated within 5 to 7 days of symptom onset (low certainty) compared with placebo. However, evidence showed that there is probably no difference in recovery (moderate certainty) and that there may be no difference in time to recovery or hospital admissions due to COVID-19 (low certainty). Evidence for harms showed that there may be no difference in the incidence of serious adverse events (low certainty) and that there is probably no difference in the incidence of adverse events (moderate certainty) for molnupiravir compared with placebo. The Omicron B.1.1.529 variant is expected to be susceptible to molnupiravir on the basis of currently available information (42). Practice Point 2: Consider nirmatrelvir–ritonavir combination therapy to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting who are within 5 days of the onset of symptoms and at high risk for progressing to severe disease. Evidence showed benefits of nirmatrelvir–ritonavir combination therapy, which probably reduces all-cause mortality and hospital admissions due to COVID-19 in patients for whom treatment is initiated within 5 days of symptom onset (moderate certainty) compared with placebo. Evidence for harms showed no difference in the incidence of adverse events (high certainty) between nirmatrelvir–ritonavir combination therapy and placebo. Evidence was very uncertain or lacking for other critical outcomes. The Omicron B.1.1.529 variant and its BA.2 subvariant are expected to be susceptible to nirmatrelvir–ritonavir combination therapy on the basis of currently available information (43). Rebound of COVID-19 has been reported to occur with the use of nirmatrelvir–ritonavir combination therapy between 2 and 8 days after initial recovery and is characterized by a recurrence of COVID-19 symptoms or a new positive result on a viral test after having tested negative (44). Practice Point 3: Consider remdesivir to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting who are within 7 days of the onset of symptoms and at high risk for progressing to severe disease. Evidence showed benefits of remdesivir, which may improve recovery in patients for whom treatment is initiated within 7 days of symptom onset (low certainty) compared with placebo. Evidence for harms showed that remdesivir probably does not differ from placebo in the incidence of adverse events (moderate certainty). Evidence was very uncertain or lacking for other critical outcomes. The Omicron variant and its subvariants are expected to be susceptible to remdesivir on the basis of currently available information (45). The use of remdesivir requires administration by intravenous infusion in a specialized setting (that is, an infusion center). Treatments Not Supported Antibiotics Practice Point 4: Do not use azithromycin to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Evidence showed no benefit of azithromycin, which may not differ from placebo in recovery (low certainty). Evidence for harms showed that azithromycin may increase the incidence of adverse events (low certainty) compared with placebo. Evidence was very uncertain or lacking for other critical outcomes. Antiparasitic Treatments Practice Point 5: Do not use chloroquine or hydroxychloroquine to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Evidence showed no benefit of hydroxychloroquine. Compared with placebo, hydroxychloroquine may reduce the chance that patients will recover, but there may be no difference in time to recovery or hospital admissions due to COVID-19 (low certainty). Evidence for harms showed that hydroxychloroquine may not differ from placebo in the incidence of serious adverse events or adverse events (low certainty). Evidence was very uncertain for other critical outcomes. Evidence about the efficacy of chloroquine was lacking for all critical outcomes. Practice Point 6: Do not use ivermectin to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Evidence showed no benefit of ivermectin because there is probably no difference in recovery (moderate certainty) and there may be no difference in mortality or hospital admissions due to COVID-19 (low certainty) compared with placebo. Evidence for harms showed that ivermectin probably does not differ from placebo in the incidence of adverse events (moderate certainty). Evidence was very uncertain for other critical outcomes. Practice Point 7: Do not use nitazoxanide to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Evidence showed no benefit of nitazoxanide because there is probably no difference in recovery or time to recovery (moderate certainty) and there may be no difference in hospital admissions due to COVID-19 (low certainty) compared with placebo. Evidence for harms showed that there may be no difference in the incidence of serious adverse events (low certainty) and that there is probably no difference in the incidence of adverse events (moderate certainty) for nitazoxanide compared with placebo. Practice Point 8: Do not use lopinavir–ritonavir combination therapy to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Evidence showed no benefit of lopinavir–ritonavir combination therapy, which may not differ from placebo in hospital admissions due to COVID-19 (low certainty). Evidence for harms showed that there may be no difference in the incidence of serious adverse events and that there may be an increase in adverse events (low certainty) for lopinavir–ritonavir combination therapy compared with placebo. Evidence was very uncertain or lacking for other critical outcomes. Monoclonal Antibodies Practice Point 9: Do not use casirivimab–imdevimab combination therapy to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting unless it is considered effective against a SARS-CoV-2 variant or subvariant locally in circulation. Evidence showed benefit of casirivimab–imdevimab combination therapy, which reduces time to recovery (high certainty) and probably reduces hospital admissions due to COVID-19 (moderate certainty) compared with placebo. Evidence was very uncertain or lacking for other critical outcomes, including serious adverse events and adverse events. Monoclonal antibodies target the spike protein of the virus. Hence, despite the benefits of casirivimab–imdevimab combination therapy, the efficacy of using monoclonal antibody treatment of COVID-19 varies depending on the SARS-CoV-2 variant. The Omicron variant and its subvariants have markedly reduced susceptibility to casirivimab–imdevimab combination therapy (46). Therefore, this therapy should not be used unless different SARS-CoV-2 variants or subvariants locally in circulation are considered susceptible to it. If casirivimab–imdevimab combination therapy is used, it should be used within 7 days of the onset of symptoms and only in patients who are at high risk for progressing to severe disease. Practice Point 10: Do not use regdanvimab to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting unless it is considered effective against a SARS-CoV-2 variant or subvariant locally in circulation. Evidence showed benefit of regdanvimab, which probably improves recovery (moderate certainty) compared with placebo. However, regdanvimab may not differ from placebo in time to recovery or hospital admissions due to COVID-19 (low certainty). Evidence for harms showed that there may be no difference in the incidence of adverse events for regdanvimab (low certainty) compared with placebo. Evidence was very uncertain or lacking for other critical outcomes. Monoclonal antibodies target the spike protein of the virus. Hence, despite the benefits of regdanvimab therapy, the efficacy of using monoclonal antibody treatment of COVID-19 varies depending on the SARS-CoV-2 variant. The susceptibility of the Omicron variant and its subvariants to regdanvimab is uncertain. Therefore, this therapy should not be used unless different SARS-CoV-2 variants or subvariants locally in circulation are considered susceptible to it. If regdanvimab therapy is used, it should be used within 7 days of the onset of symptoms and only in patients who are at high risk for progressing to severe disease. Practice Point 11: Do not use sotrovimab to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting unless it is considered effective against a SARS-CoV-2 variant or subvariant locally in circulation. Evidence showed benefit of sotrovimab, which may reduce hospital admissions due to COVID-19 (low certainty) compared with placebo. Evidence for harms showed that sotrovimab probably does not differ from placebo in the incidence of adverse events (moderate certainty). Evidence was very uncertain or lacking for other critical outcomes. Monoclonal antibodies target the spike protein of the virus. Hence, despite the benefits of sotrovimab therapy, the efficacy of using monoclonal antibody treatment of COVID-19 varies depending on the SARS-CoV-2 variant. The Omicron BA.2, BA.4, and BA.5 subvariants have markedly reduced susceptibility to sotrovimab therapy (46). Therefore, sotrovimab should not be used unless different SARS-CoV-2 variants or subvariants locally in circulation are considered susceptible to it. If sotrovimab therapy is used, it should be used within 7 days of the onset of symptoms and only in patients who are at high risk for progressing to severe disease. Other Treatments Practice Point 12: Do not use convalescent plasma to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Evidence showed no benefit of convalescent plasma, which may not differ from placebo in all-cause mortality or time to recovery (low certainty). Evidence for harms showed that there may be no difference in the incidence of serious adverse events (low certainty) for convalescent plasma compared with placebo. Evidence was very uncertain or lacking for other critical outcomes. Practice Point 13: Do not use ciclesonide to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Evidence showed no benefit of inhaled or intranasal ciclesonide, which may not differ from placebo in recovery (low certainty). Evidence for harms showed that there may be no difference in the incidence of serious adverse events or adverse events (low certainty) for ciclesonide compared with placebo. Evidence was very uncertain or lacking for other critical outcomes. Practice Point 14: Do not use fluvoxamine to treat patients with confirmed mild to moderate COVID-19 in the outpatient setting. Evidence showed no benefit of fluvoxamine, which may not differ from placebo in all-cause mortality or hospital admissions due to COVID-19 (low certainty). Evidence for harms showed that there may be no difference in the incidence of adverse events (low certainty) for fluvoxamine compared with placebo. Evidence was very uncertain or lacking for other critical outcomes. One study (28) evaluating the variability in benefits and harms found that fluvoxamine did not differ from placebo in hospital admissions due to COVID-19 based on age, sex, time from symptom onset, or comorbid conditions (19, 31). Clinical Considerations • These practice points do not provide clinical advice on the comparative effectiveness of the reviewed treatments. • The decision to initiate treatment of COVID-19 in the outpatient setting should be personalized and based on clinical judgment using an informed decision-making approach with the patient on potential treatment benefits, harms, patient characteristics (such as risk factors, comorbid conditions, and disease severity), and patient preferences. • Evidence on outpatient treatment of mild to moderate COVID-19 is rapidly changing as SARS-CoV-2 variants continue to emerge. • Risk stratification is an important step in the initial evaluation to decide the best approach to COVID-19 treatment in the outpatient setting. The current definition of risk factors for progression to severe COVID-19 can be accessed on the website of the Centers for Disease Control and Prevention (www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/index.html). • Do not use the suggested treatments in asymptomatic patients with confirmed COVID-19. • Before initiating outpatient treatments of COVID-19, patients should meet all treatment approval criteria, including careful consideration of potential drug interactions. • The use of remdesivir requires administration by intravenous infusion in a specialized setting (that is, an infusion center). • Rebound of COVID-19 has been reported to occur with the use of nirmatrelvir–ritonavir combination therapy between 2 and 8 days after initial recovery and is characterized by a recurrence of COVID-19 symptoms or a new positive result on a viral test after having tested negative (44). Evidence Gaps More research evaluating the efficacy of pharmacologic and biologic treatments of COVID-19 in the outpatient setting is needed, particularly as new variants emerge for which less is known about susceptibility to new and existing treatments. No placebo-controlled randomized studies evaluated the efficacy of bebtelovimab in the outpatient setting. Recovery and COVID-19–specific mortality were evaluated less frequently than other critical outcomes. Studies applying prespecified subgroup analyses are needed to assess whether the efficacy of treatments of COVID-19 used in the outpatient setting varies by patient characteristics (age, gender, or comorbid conditions), type of SARS-CoV-2 variant, immunity status (prior SARS-CoV-2 infection, vaccination status, or time since infection or vaccination), symptom duration, or disease severity. This article was published at Annals.org on 29 November 2022. * This paper, authored by Amir Qaseem, MD, PhD, MHA; Jennifer Yost, PhD, RN; Matthew C. Miller, MD; Rebecca Andrews, MD, MS; Janet A. Jokela, MD, MPH; Mary Ann Forciea, MD; George M. Abraham, MD, MPH; and Linda L. Humphrey, MD, MPH, was developed for the Scientific Medical Policy Committee of the American College of Physicians. Individuals who served on the Scientific Medical Policy Committee from initiation of the project until its approval were Linda L. Humphrey, MD, MPH† (Chair); Adam Jacob Obley, MD‡ (Vice Chair); Elie A. Akl, MD, MPH, PhD‡; Rebecca Andrews, MD, MS†; Andrew Dunn, MD, MPH‡; Mary Ann Forciea, MD†; Ray Haeme‡§; Janet A. Jokela, MD, MPH†; Devan L. Kansagara, MD, MCR‡; Rachael A. Lee, MD, MSPH†; Maura Marcucci, MD, MSc‡; Matthew C. Miller, MD†; and CDR Mark P. Tschanz, DO†. Itziar Etxeandia-Ikobaltzeta, PharmD, PhD; Curtis Harrod, PhD, MPH; Tatyana Shamliyan, MD, MS; and Karla Umana, MPH, were authors from ACP staff. Kate Carroll, MPH, was a nonauthor contributor from ACP staff. Approved by the ACP Executive Committee of the Board of Regents on behalf of the Board of Regents on 22 July 2022. † Author (participated in discussion and voting). ‡ Nonauthor contributor (participated in discussion but excluded from voting). § Nonphysician public representative. Update Alerts: These practice points are based on a literature search through 4 April 2022. There is a plan for monthly literature surveillance, and the living, rapid review along with the practice points will be periodically updated. ==== Refs References 1. Centers for Disease Control and Prevention. Variants of the virus. 11 August 2021. Accessed at www.cdc.gov/coronavirus/2019-ncov/variants/index.html on 5 October 2022. 2. World Health Organization. Strategic preparedness, readiness and response plan to end the global COVID-19 emergency in 2022. 30 March 2022. Accessed at www.who.int/publications/i/item/WHO-WHE-SPP-2022.1 on 5 October 2022. 3. Balshem H , Helfand M , Schünemann HJ , et al. GRADE guidelines: 3. Rating the quality of evidence. J Clin Epidemiol. 2011;64 :401-6. [PMID: ] doi:10.1016/j.jclinepi.2010.07.015 21208779 4. Buonfrate D , Chesini F , Martini D , et al. High-dose ivermectin for early treatment of COVID-19 (COVER study): a randomised, double-blind, multicentre, phase II, dose-finding, proof-of-concept clinical trial. Int J Antimicrob Agents. 2022;59 :106516. [PMID: ] doi:10.1016/j.ijantimicag.2021.106516 34999239 5. Chaccour C , Casellas A , Blanco-Di Matteo A , et al. The effect of early treatment with ivermectin on viral load, symptoms and humoral response in patients with non-severe COVID-19: a pilot, double-blind, placebo-controlled, randomized clinical trial. EClinicalMedicine. 2021;32 :100720. [PMID: ] doi:10.1016/j.eclinm.2020.100720 33495752 6. Ezer N , Belga S , Daneman N , et al. Inhaled and intranasal ciclesonide for the treatment of covid-19 in adult outpatients: CONTAIN phase II randomised controlled trial. BMJ. 2021;375 :e068060. [PMID: ] doi:10.1136/bmj-2021-068060 34728476 7. Fischer WA 2nd, Eron JJ Jr, Holman W, et al.. A phase 2a clinical trial of molnupiravir in patients with COVID-19 shows accelerated SARS-CoV-2 RNA clearance and elimination of infectious virus. Sci Transl Med. 2022;14 :eabl7430. [PMID: ] doi:10.1126/scitranslmed.abl7430 34941423 8. Gottlieb RL , Vaca CE , Paredes R , et al; GS-US-540-9012 (PINETREE) Investigators. Early remdesivir to prevent progression to severe Covid-19 in outpatients. N Engl J Med. 2022;386 :305-315. [PMID: ] doi:10.1056/NEJMoa2116846 34937145 9. Gupta A , Gonzalez-Rojas Y , Juarez E , et al; COMET-ICE Investigators. Effect of sotrovimab on hospitalization or death among high-risk patients with mild to moderate COVID-19: a randomized clinical trial. JAMA. 2022;327 :1236-1246. [PMID: ] doi:10.1001/jama.2022.2832 35285853 10. Hammond J , Leister-Tebbe H , Gardner A , et al; EPIC-HR Investigators. Oral nirmatrelvir for high-risk, nonhospitalized adults with Covid-19. N Engl J Med. 2022;386 :1397-1408. [PMID: ] doi:10.1056/NEJMoa2118542 35172054 11. Jayk Bernal A , Gomes da Silva MM , Musungaie DB , et al; MOVe-OUT Study Group. Molnupiravir for oral treatment of Covid-19 in nonhospitalized patients. N Engl J Med. 2022;386 :509-520. [PMID: ] doi:10.1056/NEJMoa2116044 34914868 12. Kim JY , Jang YR , Hong JH , et al. Safety, virologic efficacy, and pharmacokinetics of CT-P59, a neutralizing monoclonal antibody against SARS-CoV-2 spike receptor-binding protein: two randomized, placebo-controlled, phase I studies in healthy individuals and patients with mild SARS-CoV-2 infection. Clin Ther. 2021;43 :1706-1727. [PMID: ] doi:10.1016/j.clinthera.2021.08.009 34551869 13. Korley FK , Durkalski-Mauldin V , Yeatts SD , et al; SIREN-C3PO Investigators. Early convalescent plasma for high-risk outpatients with Covid-19. N Engl J Med. 2021;385 :1951-1960. [PMID: ] doi:10.1056/NEJMoa2103784 34407339 14. Lenze EJ , Mattar C , Zorumski CF , et al. Fluvoxamine vs placebo and clinical deterioration in outpatients with symptomatic COVID-19: a randomized clinical trial. JAMA. 2020;324 :2292-2300. [PMID: ] doi:10.1001/jama.2020.22760 33180097 15. Libster R , Pérez Marc G , Wappner D , et al; Fundación INFANT–COVID-19 Group. Early high-titer plasma therapy to prevent severe Covid-19 in older adults. N Engl J Med. 2021;384 :610-618. [PMID: ] doi:10.1056/NEJMoa2033700 33406353 16. López-Medina E , López P , Hurtado IC , et al. Effect of ivermectin on time to resolution of symptoms among adults with mild COVID-19: a randomized clinical trial. JAMA. 2021;325 :1426-1435. [PMID: ] doi:10.1001/jama.2021.3071 33662102 17. Oldenburg CE , Pinsky BA , Brogdon J , et al. Effect of oral azithromycin vs placebo on COVID-19 symptoms in outpatients with SARS-CoV-2 infection: a randomized clinical trial. JAMA. 2021;326 :490-498. [PMID: ] doi:10.1001/jama.2021.11517 34269813 18. Omrani AS , Pathan SA , Thomas SA , et al. Randomized double-blinded placebo-controlled trial of hydroxychloroquine with or without azithromycin for virologic cure of non-severe Covid-19. EClinicalMedicine. 2020;29 :100645. [PMID: ] doi:10.1016/j.eclinm.2020.100645 33251500 19. Reis G , Dos Santos Moreira-Silva EA , Silva DCM , et al; TOGETHER investigators. Effect of early treatment with fluvoxamine on risk of emergency care and hospitalisation among patients with COVID-19: the TOGETHER randomised, platform clinical trial. Lancet Glob Health. 2022;10 :e42-e51. [PMID: ] doi:10.1016/S2214-109X(21)00448-4 34717820 20. Reis G , Moreira Silva EADS , Medeiros Silva DC , et al; TOGETHER Investigators. Effect of early treatment with hydroxychloroquine or lopinavir and ritonavir on risk of hospitalization among patients with COVID-19: the TOGETHER randomized clinical trial. JAMA Netw Open. 2021;4 :e216468. [PMID: ] doi:10.1001/jamanetworkopen.2021.6468 33885775 21. Rocco PRM , Silva PL , Cruz FF , et al; SARITA-2 investigators. Early use of nitazoxanide in mild COVID-19 disease: randomised, placebo-controlled trial. Eur Respir J. 2021;58 . [PMID: ] doi:10.1183/13993003.03725-2020 33361100 22. Rossignol JF , Bardin MC , Fulgencio J , et al. A randomized double-blind placebo-controlled clinical trial of nitazoxanide for treatment of mild or moderate COVID-19. EClinicalMedicine. 2022;45 :101310. [PMID: ] doi:10.1016/j.eclinm.2022.101310 35237748 23. Schwartz I , Boesen ME , Cerchiaro G , et al; ALBERTA HOPE COVID-19 Collaborators. Assessing the efficacy and safety of hydroxychloroquine as outpatient treatment of COVID-19: a randomized controlled trial. CMAJ Open. 2021;9 :E693-E702. [PMID: ] doi:10.9778/cmajo.20210069 34145052 24. Streinu-Cercel A , Sandulescu O , Preotescu LL , et al. Efficacy and safety of regdanvimab (CT-P59): a phase 2/3 randomized, double-blind, placebo-controlled trial in outpatients with mild-to-moderate coronavirus disease 2019. Open Forum Infect Dis. 2022;9 :ofac053. [PMID: ] doi:10.1093/ofid/ofac053 35295819 25. Sullivan DJ , Gebo KA , Shoham S , et al. Early outpatient treatment for Covid-19 with convalescent plasma. N Engl J Med. 2022;386 :1700-1711. [PMID: ] doi:10.1056/NEJMoa2119657 35353960 26. Vallejos J , Zoni R , Bangher M , et al. Ivermectin to prevent hospitalizations in patients with COVID-19 (IVERCOR-COVID19) a randomized, double-blind, placebo-controlled trial. BMC Infect Dis. 2021;21 :635. [PMID: ] doi:10.1186/s12879-021-06348-5 34215210 27. Weinreich DM , Sivapalasingam S , Norton T , et al; Trial Investigators. REGEN-COV antibody combination and outcomes in outpatients with Covid-19. N Engl J Med. 2021;385 :e81. [PMID: ] doi:10.1056/NEJMoa2108163 34587383 28. Reis G , Silva EASM , Silva DCM , et al; TOGETHER Investigators. Effect of early treatment with ivermectin among patients with Covid-19. N Engl J Med. 2022;386 :1721-1731. [PMID: ] doi:10.1056/NEJMoa2115869 35353979 29. Alemany A , Millat-Martinez P , Corbacho-Monné M , et al; CONV-ERT Group. High-titre methylene blue-treated convalescent plasma as an early treatment for outpatients with COVID-19: a randomised, placebo-controlled trial. Lancet Respir Med. 2022;10 :278-288. [PMID: ] doi:10.1016/S2213-2600(21)00545-2 35150610 30. The World Bank Group. World Bank country and lending groups. Accessed at https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups on 5 October 2022. 31. Sommer I, Dobrescu A, Ledinger D, et al. Outpatient treatment of confirmed COVID-19: a living and rapid review for the American College of Physicians. Ann Intern Med. 2022. [Epub ahead of print]. doi:10.7326/M22-2202 32. Qaseem A , Yost J , Forciea MA , et al; Scientific Medical Policy Committee of the American College of Physicians. The development of living, rapid practice points: summary of methods from the Scientific Medical Policy Committee of the American College of Physicians. Ann Intern Med. 2021;174 :1126-1132. [PMID: ] doi:10.7326/M20-7641 34029483 33. Qaseem A , Wilt TJ , Forciea MA , et al; Clinical Guidelines Committee of the American College of Physicians. Disclosure of interests and management of conflicts of interest in clinical guidelines and guidance statements: methods from the Clinical Guidelines Committee of the American College of Physicians. Ann Intern Med. 2019;171 :354-361. [PMID: ] doi:10.7326/M18-3279 31426089 34. WHO Working Group on the Clinical Characterisation and Management of COVID-19 infection.. A minimal common outcome measure set for COVID-19 clinical research. Lancet Infect Dis. 2020;20 :e192-e197. [PMID: ] doi:10.1016/S1473-3099(20)30483-7 32539990 35. Jin X , Pang B , Zhang J , et al. Core outcome set for clinical trials on coronavirus disease 2019 (COS-COVID). Engineering (Beijing). 2020;6 :1147-1152. [PMID: ] doi:10.1016/j.eng.2020.03.002 32292626 36. Seligman WH , Fialho L , Sillett N , et al. Which outcomes are most important to measure in patients with COVID-19 and how and when should these be measured? Development of an international standard set of outcomes measures for clinical use in patients with COVID-19: a report of the International Consortium for Health Outcomes Measurement (ICHOM) COVID-19 Working Group. BMJ Open. 2021;11 :e051065. [PMID: ] doi:10.1136/bmjopen-2021-051065 34782342 37. Tong A , Elliott JH , Azevedo LC , et al; COVID-19-Core Outcomes Set (COS) Workshop Investigators. Core outcomes set for trials in people with coronavirus disease 2019. Crit Care Med. 2020;48 :1622-1635. [PMID: ] doi:10.1097/CCM.0000000000004585 32804792 38. National Institutes of Health. Clinical spectrum of SARS-CoV-2 infection. Updated 26 September 2022. Accessed at www.covid19treatmentguidelines.nih.gov/overview/clinical-spectrum on 5 October 2022. 39. U.S. Food and Drug Administration. Fact sheet for healthcare providers: emergency use authorization for Lagevrio (molnupiravir) capsules. Accessed at www.fda.gov/media/155054/download on 5 October 2022. 40. U.S. Food and Drug Administration. Fact sheet for healthcare providers: emergency use authorization for Paxlovid. Accessed at www.fda.gov/media/155050/download on 5 October 2022. 41. Gilead. Highlights of prescribing information. Accessed at www.gilead.com/-/media/files/pdfs/medicines/covid-19/veklury/veklury_pi.pdf on 5 October 2022. 42. National Institutes of Health. Molnupiravir. Updated 26 September 2022. Accessed at www.covid19treatmentguidelines.nih.gov/therapies/antiviral-therapy/molnupiravir on 5 October 2022. 43. National Institutes of Health. Ritonavir-boosted nirmatrelvir (Paxlovid). Updated 26 September 2022. Accessed at www.covid19treatmentguidelines.nih.gov/therapies/antiviral-therapy/ritonavir-boosted-nirmatrelvir--paxlovid- on 5 October 2022. 44. Centers for Disease Control and Prevention. COVID-19 rebound after Paxlovid treatment. 24 May 2022. Accessed at https://emergency.cdc.gov/han/2022/pdf/CDC_HAN_467.pdf on 5 October 2022. 45. National Institutes of Health. Remdesivir. Updated 8 August 2022. Accessed at www.covid19treatmentguidelines.nih.gov/therapies/antiviral-therapy/remdesivir on 5 October 2022 46. National Institutes of Health. Anti-SARS-CoV-2 monoclonal antibodies. Updated 8 August 2022. Accessed at www.covid19treatmentguidelines.nih.gov/therapies/anti-sars-cov-2-antibody-products/anti-sars-cov-2-monoclonal-antibodies on 5 October 2022.	36442061	PMC9707698	NO-CC CODE	2022-12-03 23:19:46	no	Ann Intern Med. 2022 Nov 29;:M22-2249	utf-8	Ann Intern Med	2,022	10.7326/M22-2249	oa_other
==== Front Vaccine Vaccine Vaccine 0264-410X 1873-2518 Elsevier Ltd. S0264-410X(22)01333-0 10.1016/j.vaccine.2022.10.056 Article A randomized phase 3 trial to assess the immunogenicity and safety of 3 consecutively produced lots of freeze-dried MVA-BN® vaccine in healthy adults Turner Overton Edgar a Schmidt Darja b Vidojkovic Sanja b Menius Erika c Nopora Katrin b Maclennan Jane b Weidenthaler Heinz b⁎ a Division of Infectious Diseases, University of Alabama at Birmingham, Birmingham, AL, United States b Bavarian Nordic GmbH, Fraunhoferstrasse 13, 82152 Martinsried, Germany c Bavarian Nordic Inc., 1005 Slater Road, Suite 101, Durham, NC 27703, United States ⁎ Corresponding author. 29 11 2022 29 11 2022 7 4 2022 24 10 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. Since vaccination remains the only effective protection against orthopox virus-induced diseases such as smallpox or monkeypox, the strategic use and stockpiling of these vaccines remains of significant public health importance. The approved liquid-frozen formulation of Bavarian Nordic's Modified Vaccinia Ankara (MVA-BN) smallpox vaccine has specific cold-chain requirements, while the freeze-dried (FD) formulation of this vaccine provides more flexibility in terms of storage conditions and shelf life. In this randomized phase 3 trial, the immunogenicity and safety of 3 consecutively manufactured lots of the FD MVA-BN vaccine was evaluated. A total of 1129 healthy adults were randomized to 3 treatment groups (lots 1 to 3) and received 2 vaccinations 4 weeks apart. For both neutralizing and total antibodies, a robust increase of geometric mean titer (GMT) was observed across all lot groups 2 weeks following the second vaccination, comparable to published data. For the primary results, the ratios of the neutralizing antibody GMTs between the lot group pairs ranged from 0.936 to 1.115, with confidence ratios well within the pre-specified margin of equivalence. Results for total antibodies were similar. In addition, seroconversion rates were high across the 3 lots, ranging between 99.1 % and 99.7 %. No safety concerns were identified; particularly, no inflammatory cardiac disorders were detected. The most common local solicited adverse events (AEs) reported across lot groups were injection site pain (87.2%) and erythema (73.2%), while the most common general solicited adverse events were myalgia, fatigue, and headache in 40.6% to 45.5% of all participants, with no meaningful differences among the lot groups. No related serious AEs were reported. In conclusion, the data demonstrate consistent and robust immunogenicity and safety results with a freeze-dried formulation of MVA-BN. Clinical Trial Registry Number: NCT03699124. Keywords Vaccine Smallpox Monkeypox Orthopoxvirus Freeze-dried MVA-BN Modified vaccinia Ankara Lot consistency ==== Body pmc1 Introduction While the efforts of global vaccination campaigns using vaccinia-based vaccines succeeded in eradicating smallpox in 1980 [1], [2], the strategic use and storage of these vaccines is of continued public health importance. Large national vaccine stockpiles are maintained by countries around the world as a means of protecting against the intentional release of smallpox virus in an act of bioterrorism [3], [4]. Smallpox vaccination also is recommended for individuals at risk for occupational exposure, including certain healthcare professionals, laboratory workers [5] and military personnel [6]. Additionally, protection against other related orthopoxviruses, such as monkeypox, can be provided through vaccinia-based smallpox vaccines [5], [7]. This use of smallpox vaccine remains highly relevant because human monkeypox infections appear to have increased over the past decade, with the potential for human-to-human spread [8], [9], [10], [11]. In response to monkeypox, vaccinia-based vaccines have been administered as part of a clinical trial in areas of Africa where the virus is endemic [12] and in countries, like the United Kingdom, where travelers have presented with monkeypox disease [13], [14] and transmitted it to others upon their return [15]. Bavarian Nordic A/S produces a smallpox vaccine that is based upon the highly attenuated modified vaccinia Ankara strain (MVA-BN). As of January 2022, the liquid-frozen formulation of MVA-BN is registered in Europe for active immunization against smallpox in adults (tradename: IMVANEX®), in the US for the prevention of smallpox and monkeypox disease in adults at high risk for infection (tradename: JYNNEOS®) and in Canada for active immunization against smallpox, monkeypox, and related orthopoxvirus infection and disease (tradename: IMVAMUNE®). MVA-BN has been attenuated to the point of being unable to replicate in mammals and has a superior safety profile compared to other replication-competent vaccinia-based smallpox vaccines. Such other live vaccines (e.g., ACAM2000) are associated with increased incidences of acute myo-/pericarditis (∼1:200) and cardiac-related adverse events such as dyspnea at rest (∼1:100) [16], [17], [18], [19], [20]. In contrast, no confirmed cases of inflammatory cardiac disease have been observed following administration of MVA-BN to nearly 8000 clinical trial participants at the time of US licensure in 2019 [21], [22]. Also, unlike other approved smallpox vaccines, MVA-BN has a favorable safety profile for individuals with atopic dermatitis [23], [24] and immunodeficiency [25], [26], [27]. Thus, the MVA-BN vaccine addresses a number of safety concerns that limit the use of previous generations of smallpox vaccines [21], [22]. To enhance both the long-term storage and the ease of distribution of smallpox vaccine stockpiles, a freeze-dried (FD) formulation of MVA-BN was developed. Previous trials using MVA-BN have demonstrated noninferior immune responses and comparable safety profiles between lyophilized and liquid frozen formulations [28], [29]. Assessments of the long-term stability of the freeze-dried formulation (FD MVA-BN) are ongoing. In this report, FD MVA-BN is further characterized by assessing the consistency of immunogenicity across 3 consecutively produced vaccine lots. This phase 3 lot-to-lot consistency trial not only provides valuable insight into the reliability of the freeze-dried formulation manufacturing process but also allows for further characterization of the safety and reactogenicity of FD MVA-BN in over 1000 clinical trial participants. 2 Methods 2.1 Trial design This was a randomized, double-blind, multicenter, phase 3 trial conducted at 12 sites in the United States over an approximately 1-year period, ending in 2020. All trial-related procedures were conducted in accordance with the provisions of the Declaration of Helsinki and approved by the relevant institutional review boards (IRBs) at each site. The primary objective of the trial was to show the consistency of neutralizing antibody immune responses to 3 consecutively produced lots of FD MVA-BN. The secondary objectives were to assess uncommon adverse reactions, with a focus on cardiac signs and symptoms indicating myo-/pericarditis, and to collect additional humoral immune response data. It was planned that approximately 1110 adults would be randomized (1:1:1) to receive treatment with 1 of 3 FD MVA-BN production lots (Lot Groups 1, 2, and 3) using an automated randomization system stratified by clinical trial site. Participants in each Lot Group received 1 injection at Week 0 and again at Week 4. 2.2 Participants Healthy men and women between 18 and 45 years of age, without a medical history of autoimmune or coronary heart disease, were eligible if they had a body mass index (BMI) ≥ 18.5 and < 35 kg/m2, an electrocardiogram (ECG) without clinically significant findings, hematology and chemistry laboratory values within prespecified normal limits, total bilirubin levels ≤ 1.5 times the upper limit of normal in the absence of evidence of significant liver disease, and no prior smallpox vaccination. Participants with an immediate family member who experienced an onset of ischemic heart disease prior to 50 years of age were also excluded, along with participants who had abused alcohol or illicit drugs within 6 months of screening. Women of childbearing potential were instructed to use an acceptable method of contraception, could not be actively breastfeeding, and were required to have a negative pregnancy test at screening and on each vaccination day. 2.3 Vaccine MVA-BN is a highly attenuated, purified, live, vaccinia-based vaccine [30]. The FD MVA-BN bulk drug substance was produced at Bavarian Nordic A/S (Kvistgård, Denmark) according to GMP standards, and the final drug product was filled, formulated, and labeled by IDT Biologika GmbH (Dessau-Rosslau, Germany). The freeze-dried vaccine was provided as lyophilized aliquots with a nominal virus titer of 1 × 108 Inf.U/0.5 mL dose. Clinical site personnel reconstituted each aliquot with 0.65 mL of water for injection (WFI) and then administered 0.5 mL subcutaneously in the upper (deltoid) region of the subject's nondominant arm. Participants in each lot group received both of their injections from the same batch (C00020, C00021, and C00022 for Lot Groups 1, 2, and 3, respectively). FD MVA-BN was shipped and stored between –25 °C and –15 °C (-13°F to +5°F) and WFI was shipped separately at 20 °C to 25 °C (68°F to 77°F) and then stored between 15 °C and 30 °C (59°F to 86°F) at the clinical site prior to use. 2.4 Immunogenicity assessments Immunogenicity parameters to assess lot-to-lot consistency included total and neutralizing antibody GMTs; seroconversion rates; the ratio, or consistency, of GMTs between group lot pairs; and a correlation analysis between total and neutralizing antibody titers in each lot group. Total serum antibodies were measured using a vaccinia-specific enzyme-linked immunosorbent assay (ELISA), and neutralizing antibodies were measured using a vaccinia-specific plaque reduction neutralization test (PRNT). Samples for these assessments were drawn at baseline (Week 0) and 2 weeks after the second FD MVA-BN vaccination (Week 6). This postvaccination assessment timepoint was chosen because peak antibody titers are consistently observed 2 weeks following the second vaccination in individuals who have not been previously vaccinated against smallpox [24], [26], [31], [32], [33]. The PRNT and ELISA antigens were Western Reserve and MVA, respectively. Both methods were validated and were performed as previously described for a prior phase 2 trial [26] with the following modifications: For the ELISA, an optical density cut-off value of 0.35 was used, and for the PRNT, the neutralization was performed in Dulbecco's modified Eagle's medium/0.1 % human serum albumin. The lower limits of quantification (LLOQs) for the ELISA and PRNT assays were 200 and 20, respectively. 2.5 Safety assessments Assessments of solicited and unsolicited adverse events were used to characterize the overall safety and reactogenicity of FD MVA-BN and to make comparisons across lot groups. Solicited adverse events constituted a set of pre-defined, expected local reactions (erythema, swelling, pruritus, induration and pain) as well as general events (elevated body temperature, headache, chills, myalgia, nausea and fatigue) listed on a memory aid. This memory aid was provided to participants for 2 solicitation periods of 8 days each, which included each vaccination day and the week that followed. Intensity of solicited adverse events was graded according to prespecified criteria defined for each local and general event. All local solicited events were considered related to trial vaccine, while relatedness of general events was assessed by the investigator. Unsolicited events were collected from the day the first vaccine was administered (at Week 0) until 4 weeks following the last vaccination (overall vaccination period) and consisted of any adverse event that was either not listed on the memory aid or had occurred outside the 8–day solicitation periods. Any unsolicited adverse events ongoing 4 weeks following the last vaccination were followed until resolution or until the 6-month follow-up visit. Both the intensity of the event and its relationship to the trial vaccine were assessed by the investigator. Any SAEs or adverse events of special interest (AESIs) experienced during the 6-month follow–up period were also collected. As a precaution, AESIs in this trial were defined as any: (1) cardiac symptoms, (2) clinically significant ECG changes, or (3) troponin I values that were above the upper limit of normal and developed since the first vaccination. Participants developing an AESI were to return for physical and cardiac examinations or further diagnostic tests, if clinically indicated, and were followed up until resolution or stabilization, or until the end of the 6-month follow-up period. Safety hematology and chemistry laboratory tests were performed at screening and 2 weeks after each vaccination, and—if clinically indicated—at any other visit. 2.6 Statistical methods All statistical analyses were performed using SAS 9.4 (SAS-Institute, Cary, NC, USA). A simulation was performed to estimate the required number of analyzable participants per group based on several underlying assumptions. A two-sided 95 % significance level was used, and assumptions of within- and between-lot variability were made based on prior data. An equivalence margin (Δ), within which the difference between lots would be considered equivalent, of ± 0.301 on the log10 scale (a 2-fold difference) was assumed. An analyzable sample size of 315 participants in each group was calculated to yield a power of at least 90 % to show equivalence for all 3 FD MVA-BN lot groups. In order to account for a dropout rate of about 15 %, observed in previous MVA-BN trials, a total of 370 participants was planned for each group. Analyses of immunogenicity endpoints were based on the per protocol set (PPS), which included those who received all vaccinations and adhered to the protocol without major deviations with the potential to substantially affect the immunogenicity results. Geometric mean titers (GMTs), or the antilogarithms of the means of the log10 titer transformations, were calculated for neutralizing antibodies (measured by PRNT) and total antibodies (measured by ELISA) at baseline and 2 weeks following the second vaccination. For titers that were below the limit of quantification (LLOQ), a value of half the LLOQ was assigned for calculation purposes. The primary analysis is presented as GMT ratios between lot groups and their 95 % confidence intervals (CIs). The primary endpoint of equivalence between any 2 lot groups was defined as a CI around the ratio of the GMTs, measured 2 weeks after the second vaccination, that was within the prespecified margin of equivalence of 0.5 to 2. The secondary outcome of total antibody titers was analyzed using the same method of comparison and likewise presented as GMT ratios and confidence intervals. A sensitivity analysis for the primary outcome was repeated on the Full Analysis Set (FAS) using multiple imputation (MI) for missing data, assuming titer values were missing at random and lognormally distributed. Year of birth, sex, and race were used as predictors. MI was used to create 100 complete sets of results that accounted for the random variability in titer values. The log-transformed differences and associated standard errors were combined over the imputations and then back-transformed to the original scale. Seroconversion in this trial was defined as either the appearance of antibody levels ≥ LLOQ for participants who had a titer level below LLOQ at baseline, or a doubling (or more) of the antibody titer compared to baseline for participants who had a titer ≥ LLOQ at baseline. Analyses of safety endpoints were based on the FAS, comprised of all randomized participants who received at least 1 vaccination. Safety data were summarized descriptively, and unsolicited adverse events were coded using the Medical Dictionary for Regulatory Activities, version 22. 3 Results 3.1 Participant Demographics and characteristics A total of 1129 participants were randomized, with 377 participants in Lot Group 1, 375 participants in Lot Group 2, and 377 participants in Lot Group 3 (Fig. 1 ). All randomized participants received the first vaccination and were included in the FAS for evaluation of safety. Across lot groups, the majority of participants also received the second vaccination (92.8 % to 95.2 %), with participant-elected withdrawal being the most common reason for having an incomplete immunization schedule. Very few participants did not receive the second vaccination due to an adverse event. Of those who received both vaccinations, the most common reason for exclusion from the PPS immunogenicity analyses was not having a serum sample collected 2 weeks following the second vaccination (at Week 6). However, most participants across lot groups were included in the immunogenicity analyses (86.7 % to 88.3 %) and attended the follow-up visit (91.5 % to 92.3 %).Fig. 1 Subject Disposition (All Participants) Abbreviations: FAS = full analysis set; I/E = inclusion/exclusion criteria; PPS = per protocol set. Note: Participants excluded on account of timing were either vaccinated or had serum draws at timepoints substantially outside the timeframe specified in the protocol. a All randomized participants received the first dose of trial vaccine and were included in the FAS for the purpose of evaluating safety. b Some participants may have been excluded from the PPS for more than one reason; thus, the individual reason counts may add up to be more than the total number of participants excluded. The demographic and baseline medical history characteristics across lot groups was comparable (Table1 ). Overall, the median age of participants was 30.0 years, with 52.6 % of all volunteers falling in the age range of > 18 to 30 years, and 55.8 % of participants were female. Most participants were either of White (77.9 %) or Black or African American (15.2 %) race, and predominantly not Hispanic or Latino (93.1 %) ethnicity. Overall, in this generally healthy adult population, the most common medical history conditions were anxiety (17.2 %), seasonal allergy (17.0 %), depression (16.3 %), and history of drug hypersensitivity (13.8 %). No participants had a known history of receiving a smallpox vaccine or a poxvirus-based vaccine or had a typical vaccinia scar.Table 1 Demographics and Baseline Characteristics (Full Analysis Set). Characteristic Statistic Lot Group 1 (N = 377) Lot Group 2 (N = 375) Lot Group 3 (N = 377) Overall (N = 1129) Age at Informed Consent (years) Mean (SD) 30.7 (7.31) 30.6 (7.29) 30.7 (7.50) 30.7 (7.36) Min, Max 18, 45 18, 45 18, 45 18, 45 Age Group (years), n (%) 18 to 30 203 (53.8) 196 (52.3) 195 (51.7) 594 (52.6) > 30 to 45 174 (46.2) 179 (47.7) 182 (48.3) 535 (47.4) Sex, n (%) Female 210 (55.7) 213 (56.8) 207 (54.9) 630 (55.8) Male 167 (44.3) 162 (43.2) 170 (45.1) 499 (44.2) Body Mass Index (kg/m2) Mean (SD) 26.56 (4.367) 26.48 (4.476) 26.36 (4.389) 26.47 (4.408) Min, Max 18.5, 34.9 18.5, 34.8 18.5, 35.1 18.5, 35.1 Race, n (%) White 292 (77.5) 295 (78.7) 293 (77.7) 880 (77.9) Black or African American 58 (15.4) 55 (14.7) 59 (15.6) 172 (15.2) Asian 17 (4.5) 13 (3.5) 13 (3.4) 43 (3.8) Other 7 (1.9) 9 (2.4) 8 (2.1) 24 (2.1) American Indian or Alaska Native 2 (0.5) 2 (0.5) 4 (1.1) 8 (0.7) Pacific Islandera 1 (0.3) 1 (0.3) 0 2 (0.2) Ethnicity, n (%) Not Hispanic or Latino 348 (92.3) 354 (94.4) 349 (92.6) 1051 (93.1) Hispanic or Latino 28 (7.4) 20 (5.3) 25 (6.6) 73 (6.5) Not Reported 1 (0.3) 1 (0.3) 3 (0.8) 5 (0.4) Abbreviations: BMI = body mass index; BMI = body mass index; SD = standard deviation; N = number of participants in the specified group; n = number of participants within a specified group (N); %, percentage based on N. a Including Native Hawaiian. 3.2 Immunogenicity results As expected in an unvaccinated population, most participants did not have detectable vaccinia-specific neutralizing and/or total antibody levels at baseline (97.8 % and 99.5 %, respectively). Those with neutralizing or total antibody levels at or above the LLOQ were roughly evenly distributed across lot groups (Table2 ).Table 2 Antibody Titers and Ratios Between Groups at 2 Weeks After the Second Vaccination (Per Protocol Set). Lot Group 1 (N = 327) Lot Group 2 (N = 331) Lot Group 3 (N = 330) Neutralizing Antibodies (Assessed by PRNT) Baseline, n 326 331 330 <LLOQ, n (%) 321 (98.5) 322 (97.3) 322 (97.6) 2 Weeks After Second Vaccination (Visit 4), n 327 331 330 GMT 252.7 269.9 242.0 95 % CI [LCL, UCL] [231.3, 276.0] [243.2, 299.7] [219.5, 266.8] GMT Ratio Compared to Group 3 1.044 1.115 95 % CI [LCL, UCL] [0.915, 1.191] [0.967, 1.287] Equivalence Meta Yes Yes GMT Ratio Compared to Group 2 0.936 95 % CI [LCL, UCL] [0.816, 1.073] Equivalence Met a Yes Total Antibodies (Assessed by ELISA) Baseline, n 326 331 330 <LLOQ, n (%) 324 (99.4) 329 (99.4) 329 (99.7) 2 Weeks After Second Vaccination (Visit 4), n 327 331 330 GMT 1222.0 1311.0 1226.1 95 % CI [LCL, UCL] [1123.3, 1329.4] [1195.7, 1437.5] [1118.1, 1344.6] GMT Ratio compared to Group 3 0.997 1.069 95 % CI [LCL, UCL] [0.880, 1.129] [0.939, 1.218] GMT Ratio compared to Group 2 0.932 95 % CI [LCL, UCL] [0.823, 1.056] Abbreviations: CI = Confidence Interval; ELISA = Enzyme-linked Immunosorbent Assay; LCL = Lower Confidence Limit; LLOQ = Lower Limit of Quantitation; N = number of subjects in the PPS in the specified group; n = number of subjects with available titer values; PRNT = Plaque Reduction Neutralization Test; UCL = Upper Confidence Limit. Note: Geometric means were calculated using the mean of the log10 transformed titer values, with corresponding 95% CIs based on a t-test. Note: Antibody titers below the LLOQ were given a value of half of the LLOQ. The LLOQ was 20 for PRNT and 200 for ELISA. a Equivalence (only assessed for neutralizing antibodies) was met if the LCL > 1/2 and UCL < 2 for PRNT. Two weeks following the second vaccination (at Week 6), neutralizing antibody GMTs had increased from non-detectable to 252.7 for Lot Group 1, 269.9 for Lot Group 2, and 242.0 for Lot Group 3. The ratios of GMTs between lot group combinations ranged between 0.936 and 1.115, with 95 % confidence limits ranging between 0.816 and 1.287. Since the CIs of the neutralizing antibody GMT ratios all fell within the prespecified interval of 0.5 to 2.0, the lot groups were considered equivalent, and the primary endpoint of the trial was met. The sensitivity analysis on the FAS using multiple imputation to compensate for missing values yielded ratios of neutralizing antibody GMTs between the lot groups that were closer to 1 than the results for the PPS. Values ranged between 0.947 and 1.095, and all CIs were within the interval of 0.5 to 2.0. Similar findings were observed for total antibodies, with a response observed 2 weeks following the second vaccination of GMTs ranging from 1222.0 to 1311.0 across lot groups. Although equivalence was not formally assessed for this secondary endpoint, the ratios of GMTs between lot groups and their 95 % CIs were in the same range as the primary endpoint (Table2). Seroconversion rates 2 weeks following the second vaccination were above 98.0 % for both neutralizing and total antibodies in all groups, with no statistically significant differences among the 3 lot groups for neutralizing and total antibodies (p = 0.7102 and p = 0.6916, respectively) (Table3 ). Total and neutralizing antibody levels were highly correlated within each of the lot groups (Fig. 2 ). Pearson correlation coefficients (r values) ranged between 0.656 and 0.657, with p-values < 0.0001 for each lot group and overall.Table 3 Seroconversion Rates 2 Weeks Following the Second Vaccination (Per Protocol Set). Sampling Time Point Lot Group 1 (N = 327) Lot Group 2 (N = 331) Lot Group 3 (N = 330) Neutralizing Antibodies (Assessed by PRNT), n 326 331 330 Seroconversion, n (%) 325 (99.7) 328 (99.1) 327 (99.1) 95 % CI [LCL, UCL] [98.3, 100.0] [97.4, 99.8] [97.4, 99.8] p-valuea 0.7102 Total Antibodies (Assessed by ELISA), n 326 331 330 Seroconversion, n (%) 323 (99.1) 325 (98.2) 326 (98.8) 95 % CI [LCL, UCL] [97.3, 99.8] [96.1, 99.3] [96.9, 99.7] p-valuea 0.6916 Abbreviations: CI = Confidence Interval; ELISA = Enzyme-linked Immunosorbent Assay; LCL = Lower Confidence Limit; LLOQ = Lower Limit of Quantitation; N = number of subjects in the specified group; n = number of subjects with available titer values; PRNT = Plaque Reduction Neutralization Test; UCL = Upper Confidence Limit. Seroconversion was defined as either the appearance of antibody titers ≥ LLOQ for subjects with a titer below LLOQ at baseline, or a doubling (or more) of the antibody titer compared to the baseline titer for subjects with a titer equal or above the LLOQ at baseline. The LLOQ for PRNT was 20 and 200 for ELISA. 95% CI: Clopper-Pearson 95% 2-sided confidence intervals for the proportion of seroconverted subjects. a Comparison of Lot Groups 1 to 3 using Freeman-Halton exact test. Fig. 2 Correlation Between Total and Neutralizing Antibodies (Per Protocol Set) Abbreviations: CI = confidence interval; ELISA = Enzyme-Linked Immunosorbent Assay; PRNT = Plaque Reduction Neutralization Test. Notes: Values below the LLOQ for each method were imputed to half the value of the LLOQ. Pearson correlation coefficient (r) was calculated on the log10 titers. 3.3 Safety results Local solicited AEs were experienced by 91.2 % of all participants. (Table4 ). The most common local solicited AEs were injection site pain and injection site erythema reported for 87.2 % and 73.2 % of all participants, respectively (Table6). Across all local solicited AEs categories, <12.0 % were of Grade 3 intensity, with the longest mean duration being 15.6 days for injection site induration and all other events having mean durations ranging from 4.6 to 6.9 days. Across all local solicited AEs categories, the mean duration was longer following the first vaccination (4.7 to 18.4 days) compared to after the second vaccination (3.7 to 5.5 days). Most prominently, the mean duration for injection site induration after the first vaccination (18.4 days) was longer compared to after the second vaccination (5.5 days).Table 4 Summary of Solicited and Unsolicited Adverse Events for the Overall Vaccination Period (Full Analysis Set). Lot Group 1 (N = 377) n (%) [events] Lot Group 2 (N = 375) n (%) [events] Lot Group 3 (N = 377) n (%) [events] Overall (N = 1129) n (%) [events] Unsolicited AEs 88 (23.3) [1 4 3] 110 (29.3) [1 7 2] 98 (26.0) [1 6 2] 296 (26.2) [4 7 7] Relateda 33 (8.8) [58] 38 (10.1) [54] 37 (9.8) [48] 108 (9.6) [1 6 0] Grade ≥ 3 6 (1.6) [7] 8 (2.1) [10] 7 (1.9) [10] 21 (1.9) [27] Grade ≥ 3 Related 2 (0.5) [2] 0 1 (0.3) [1] 3 (0.3) [3] Led to Withdrawal from Second Vaccination 6 (1.6) [11] 4 (1.1) [5] 2 (0.5) [7] 12 (1.1) [23] Led to Trial Withdrawal 0 0 0 0 Fatal 0 1 (0.3) [1] 0 1 (0.1) [1] Local Solicited AEsb 338 (89.7) [1967] 348 (92.8) [2082] 344 (91.2) [2025] 1030 (91.2) [6074] Led to Vaccine Deferral 1 (0.3) [1] 0 1 (0.3) [1] 2 (0.2) [2] Led to Vaccine Discontinuation 0 0 0 0 General Solicited AEsc 253 (67.1) [8 1 1] 267 (71.2) [9 1 2] 266 (70.6) [9 1 5] 786 (69.6) [2638] Relateda 246 (65.3) [7 7 3] 256 (68.3) [8 4 9] 252 (66.8) [8 3 4] 754 (66.8) [2456] Led to Vaccine Deferral 0 0 3 (0.8) [3] 3 (0.3) [3] Led to Vaccine Discontinuation 0 0 0 0 Serious Adverse Events 1 (0.3) [1] 3 (0.8) [3] 1 (0.3) [1] 5 (0.4) [5] Relateda 0 0 0 0 Grade ≥ 3 0 2 (0.5) [2] 1 (0.3) [1] 3 (0.3) [3] Cardiac AEs of Special Interestd 2 (0.5) [2] 3 (0.8) [3] 1 (0.3) [3] 6 (0.5) [8] Relateda 0 1 (0.3) [1] 0 1 (0.1) [1] Grade ≥ 3 1 (0.3) [1] 0 0 1 (0.1) [1] Abbreviations: AE = Adverse Event; ECG = Electrocardiogram; N = number of subjects in the specified group; n = number of subjects reporting an AE in the specified category. Note: This summary includes all AEs, SAEs, AESIs, Grade ≥ 3 AEs, and fatalities reported during the overall vaccination period only. The overall vaccination period is the time from first vaccination through the second vaccination + 35 days or the date of the last visit, whichever is later. Adverse events are presented separately for the two vaccination periods and additionally for the follow-up period in the Supplemental Materials. a Related AEs were either considered at least possibly related to trial vaccine by the investigator or had missing information pertaining to relatedness. b Local solicited AEs included redness, swelling, induration, pruritus, and pain within 8 days after each vaccination. All local solicited AEs were considered related to trial vaccine, as defined in the protocol. c General solicited AEs included pyrexia, headache, myalgia, chills, nausea, and fatigue within 8 days after each vaccination. General solicited AEs were considered related when the investigator considered them at least possibly related to vaccination or when information on relatedness was missing. d Cardiac AEs of special interest were defined as any cardiac sign or symptom that developed after the first vaccination, including ECG changes determined to be clinically significant and cardiac enzyme troponin I levels above the upper limit of normal. These were the only AEs of special interest defined in this trial. General solicited AEs were experienced by 69.6 % of all participants during the overall vaccination period, with nearly all events considered related to trial vaccine (Table4). The most common general solicited AEs were myalgia, fatigue, and headache in 40.6 % to 45.5 % of all participants (Table6). Across all general solicited AEs categories, <4.0 % were of Grade 3 intensity, with mean durations between 2.0 and 4.1 days. The mean duration of general solicited AEs was slightly longer after the first vaccination (2.0 to 4.3 days) compared to after the second vaccination (1.8 to 3.2 days). Unsolicited AEs were reported for 26.2 % of all participants during the overall vaccination period (the time from the first vaccine dose until the last visit or 35 days after the second vaccine dose, whichever is later), with 9.6 % experiencing events considered at least possibly related to the vaccine by the investigator (Table4). Grade 3 and higher unsolicited AEs were reported for 1.9 % of participants, with 0.3 % having events of this intensity deemed related to trial vaccine. Overall, the most common unsolicited adverse events were upper respiratory tract infection (5.8 %), injection site nodule (2.9 %), and an increase in blood potassium (2.4 %). All other adverse events were experienced by 1.3 % or fewer of the overall trial population (Table5 ). A slightly higher proportion of all participants reported unsolicited AEs after the first vaccination (18.1 %) as compared to after the second vaccination (13.5 %).Table 5 Most Common Unsolicited Adverse Events for the Overall Vaccination Period (Full Analysis Set). Preferred Term Lot Group 1 (N = 377) n (%) [events] Lot Group 2 (N = 375) n (%) [events] Lot Group 3 (N = 377) n (%) [events] Overall (N = 1129) n (%) [events] Upper respiratory tract infection 19 (5.0) [19] 25 (6.7) [26] 21 (5.6) [23] 65 (5.8) [68] Injection site nodule 11 (2.9) [11] 10 (2.7) [10] 12 (3.2) [12] 33 (2.9) [33] Blood potassium increased 6 (1.6) [6] 11 (2.9) [11] 10 (2.7) [10] 27 (2.4) [27] Injection site discoloration 3 (0.8) [3] 5 (1.3) [5] 7 (1.9) [7] 15 (1.3) [15] Injection site bruising 5 (1.3) [5] 4 (1.1) [4] 3 (0.8) [3] 12 (1.1) [12] Urinary tract infection 1 (0.3) [1] 4 (1.1) [4] 4 (1.1) [4] 9 (0.8) [9] Vaccination site bruising 2 (0.5) [2] 3 (0.8) [4] 2 (0.5) [2] 7 (0.6) [8] Anxiety 2 (0.5) [2] 2 (0.5) [2] 2 (0.5) [3] 6 (0.5) [7] Ligament sprain 4 (1.1) [4] 1 (0.3) [1] 1 (0.3) [1] 6 (0.5) [6] Pyrexia 0 3 (0.8) [3] 3 (0.8) [3] 6 (0.5) [6] Abbreviations: N = number of subjects in the specified group; n = number of subjects reporting an adverse event in the specified Preferred Term category. Table 6 Summary of Solicited Adverse Events (Full Analysis Set). Lot Group 1 (N = 377) n (%) Lot Group 2 (N = 375) n (%) Lot Group 3 (N = 377) n (%) Overall (N = 1129) n (%) Local Solicited Adverse Events Injection Site Pain, n (%) 325 (86.2) 330 (88.0) 329 (87.3) 984 (87.2) Grade 3, n (%) 46 (12.2) 45 (12.0) 43 (11.4) 134 (11.9) Mean Duration, days [SD] 6.9 [4.41] 7.1 [4.44] 6.7 [3.64] 6.9 [4.18] Injection Site Erythemaa, n (%) 271 (71.9) 283 (75.5) 272 (72.1) 826 (73.2) Grade 3, n (%) 44 (11.7) 42 (11.2) 29 (7.7) 115 (10.2) Mean Duration, days [SD] 7.1 [6.53] 6.5 [5.06] 6.6 [5.39] 6.7 [5.68] Injection Site Swellinga, n (%) 215 (57.0) 233 (62.1) 236 (62.6) 684 (60.6) Grade 3, n (%) 17 (4.5) 25 (6.7) 12 (3.2) 54 (4.8) Mean Duration, days [SD] 6.7 [6.47] 6.0 [5.04] 5.9 [4.52] 6.2 [5.38] Injection Site Indurationa, n (%) 215 (57.0) 226 (60.3) 229 (60.7) 670 (59.3) Grade 3, n (%) 11 (2.9) 8 (2.1) 6 (1.6) 25 (2.2) Mean Duration, days [SD] 18.0 [24.04] 13.9 [13.12] 15.0 [15.01] 15.6 [17.94] Injection Site Pruritus, n (%) 210 (55.7) 230 (61.3) 198 (52.5) 638 (56.5) Grade 3, n (%) 10 (2.7) 7 (1.9) 10 (2.7) 27 (2.4) Mean Duration, days [SD] 5.0 [5.70] 4.4 [3.24] 4.4 [3.87] 4.6 [4.37] General Solicited Adverse Events Myalgia, n (%) 166 (44.0) 173 (46.1) 175 (46.4) 514 (45.5) Grade 3, n (%) 13 (3.4) 10 (2.7) 20 (5.3) 43 (3.8) Mean Duration, days [SD] 4.1 [2.80] 4.0 [3.27] 4.1 [3.30] 4.1 [3.13] Fatigue, n (%) 140 (37.1) 171 (45.6) 171 (45.4) 482 (42.7) Grade 3, n (%) 15 (4.0) 13 (3.5) 14 (3.7) 42 (3.7) Mean Duration, days [SD] 3.9 [5.16] 3.1 [2.70] 3.6 [3.57] 3.5 [3.86] Headache, n (%) 146 (38.7) 157 (41.9) 155 (41.1) 458 (40.6) Grade 3, n (%) 14 (3.7) 11 (2.9) 10 (2.7) 35 (3.1) Mean Duration, days [SD] 3.2 [2.85] 3.1 [2.49] 3.2 [3.59] 3.2 [3.01] Nausea, n (%) 75 (19.9) 81 (21.6) 78 (20.7) 234 (20.7) Grade 3, n (%) 6 (1.6) 6 (1.6) 6 (1.6) 18 (1.6) Mean Duration, days [SD] 2.6 [2.98] 2.7 [2.76] 2.7 [2.50] 2.6 [2.74] Body Temperature Increasedb, n (%) 47 (12.5) 60 (16.0) 63 (16.7) 170 (15.1) Grade 3, n (%) 1 (0.3) 4 (1.1) 1 (0.3) 6 (0.5) Mean Duration, days [SD] 2.3 [1.87] 1.9 [1.91] 1.9 [1.92] 2.0 [1.90] Chills, n (%) 58 (15.4) 54 (14.4) 57 (15.1) 169 (15.0) Grade 3, n (%) 5 (1.3) 4 (1.1) 6 (1.6) 15 (1.3) Mean Duration, days [SD] 2.3 [2.01] 2.3 [2.71] 2.3 [2.19] 2.3 [2.30] Abbreviations: N = number of participants in the specified group; n = number of participants reporting an AE in the specified category. a For injection site erythema, swelling, and induration, the intensity was graded based on the maximum diameter: Grade 0 (none): 0 mm, Grade 1: < 30 mm, Grade 2: ≥ 30 to < 100 mm, Grade 3: ≥ 100 mm. b For body temperature increased, intensity was graded as: Grade 0 (none): < 99.5⁰F (or < 37.5⁰C), Grade 1: ≥ 99.5⁰F to < 100.4⁰F (or ≥ 37.5°C to < 38.0°C), Grade 2: ≥ 100.4⁰F to < 102.2⁰F (or ≥ 38.0⁰C to < 39.0⁰C), Grade 3: ≥ 102.2⁰F to < 104.0⁰F (or ≥ 39.0⁰C to < 40.0⁰C), Grade 4: ≥ 104.0⁰F (or ≥ 40.0⁰C). A total of 9 serious AEs (SAEs) were experienced by 9 participants (0.8 %) across the 3 lots, 5 (0.4 %) during the overall vaccination period (Table4) and 4 (0.4 %) during the follow-up period (Supplemental Materials), with none considered related to trial vaccine. Those SAEs that occurred during the overall vaccination period included events of depression, colitis, foot deformity, and alcoholic pancreatitis. Also, a 44-year-old male with a medical history of asthma died of unknown causes 28 days after the first vaccination. During the 6-month follow-up period, SAEs included groin abscess, panic attack, and spontaneous abortion, and a 37-year-old male died of unknown causes 167 days after the last vaccination. All non-fatal SAEs had resolved by the time of the last follow-up assessment. One pregnancy occurred approximately 7 days after a participant received a second vaccination and resulted in a live birth. A total of 8 cardiac-related AEs of special interest (AESIs) were experienced during the overall vaccination period by 6 participants (0.5 % across the 3 lots) (Table4). Importantly, no inflammatory cardiac disorders were observed. One participant with a medical history of dyspnea, allergic rhinitis, and heart murmur experienced a Grade 2 event of exertional dyspnea 6 days after receiving the first vaccination. The participant had not reported any symptoms of dyspnea at screening. While the investigator assessed this event as possibly related to trial vaccine, the sponsor considered the relationship unlikely. Concomitant medication and medical history suggested that exertional dyspnea had been a repeated symptom for this subject, and the 6-day period between vaccination and respiratory symptom onset did not clearly suggest a causal relationship. All other cardiac-related AESIs were considered unrelated to trial vaccine by both the investigator and the sponsor. The outcomes of all AESIs were reported as resolved, with the exception of one case of supraventricular extrasystoles in a participant who was lost to follow-up. Withdrawal from the second vaccination occurred due to unsolicited AEs in only 1.1 % of all participants, with no participants withdrawing from the second vaccination on account of solicited AEs (Table4). No AEs led to withdrawal from the trial, and there were no meaningful differences in solicited or unsolicited AEs across lot groups. 4 Discussion Vaccines stand as one of the greatest achievements of modern medicine to improve public health. Even so, vaccine instability remains a challenge during both development and distribution, particularly for vaccines using live, attenuated virus [34], [35]. Though lyophilization is a primary method for improving vaccine stability, freeze-drying live vaccines generally remains complex, but affords greater long-term stability and facilitates a longer product shelf life [35]. Fortunately, lyophilization of live, replicating poxvirus vaccines was achieved over a century ago and then improved in the 1900s. The resulting vaccines (e.g., Dryvax, Lancy-Vaxina) were used in the successful eradication of smallpox, and the World Health Assembly declared the world free of smallpox in 1980. These live vaccines were then replaced by cell-cultured poxvirus vaccines (ACAM2000, CCSV), which still contained replicating virus. The cell-cultured vaccines were found to have similar safety concerns [36], [37], including, as previously noted, the occurrence of acute myocarditis and pericarditis, as well as the potential for local replication and onward transmission. MVA-BN was developed as a safer, nonreplicating vaccine in a liquid-frozen formulation administered by subcutaneous injection, rather than by skin scarification with a bifurcated needle. In 2008, the US Strategic National Stockpile added MVA-BN to its reserves of essential medical supplies to strengthen national security and protect US interests in the event of a smallpox outbreak. Early development of MVA-BN also included a freeze-dried formulation. For the post-eradication world, lyophilized poxvirus vaccines provide potential advantages for long-term storage of stockpiled doses and for their distribution in the event of an emergency. Given this and the safety profile that was observed with liquid-frozen MVA-BN, the freeze-dried formulation was recently targeted for further development and testing. The shelf life of the current FD MVA-BN vaccine is not yet known, nor is the extent of the need for cold chain management, as stability assessments are ongoing. Shelf life of stockpiled vaccine likely is optimized by storage at −20 °C. However, the titer of the freeze-dried formulation has remained above the minimum specification level at 2 °C to 8 °C for 5 years, certainly supporting the possibility to ship and/or store the vaccine under refrigerated conditions before use in the event of an emergency. While enhancing long-term storage and easing cold-chain requirements of vaccine stockpiles is of great value to public health initiatives [34], [38], demonstrating the consistency and robustness of the production process is an important step towards the future licensing of this FD MVA-BN formulation. This phase 3 trial investigating lot-to-lot consistency of the vaccine with regard to immunogenicity provided such evidence. For neutralizing antibody responses, there was no statistical difference in the titers induced by 3 consecutively produced lots of FD MVA-BN 2 weeks after the last vaccination, and seroconversion was nearly complete across groups, ranging between 99.1 % and 99.7 %. Comparison of the two MVA-BN formulations was undertaken in 2 previous trials, which demonstrated that the humoral response to FD MVA-BN is noninferior to the LF formulation of the vaccine [28], [29]. The immunogenicity findings in this trial not only demonstrate consistency among the lots used for this trial, but also consistency with the growing body of evidence for the humoral response induced by MVA–BN, regardless of formulation. Previous studies have repeatedly shown that MVA-BN induces peak humoral responses 2 weeks following the second vaccination [24], [26], [31], [32], [33]. If one compares the peak responses across trials, including this trial, total antibody titers were reliably in the 3 log10 range, at which protection against infection is provided. There was some variability in the neutralizing antibody titers across different trials, but this variability is largely accounted for by modifications to the PRNT methods used in different trials. Therefore, while this trial demonstrates that high antibody titers are consistently generated across multiple production lots of FD MVA-BN, it also adds to the evidence that lyophilization does not appear to compromise the immunogenicity of the vaccine. The favorable safety profile of MVA-BN is further supported by the findings of this trial, with no vaccine-related serious adverse events. Importantly, no cardiac inflammatory disorders were reported in any participants vaccinated with FD MVA-BN, in marked contrast to what has been observed with replicating smallpox vaccines [16], [17], [18]. As with other MVA-BN trials, the most commonly reported adverse events after administration of FD MVA-BN were solicited local and general reactions that were mostly mild to moderate in nature and self-limiting. These events also were evenly distributed across the 3 lot groups with regard to frequency, intensity, and duration. Similarly, unsolicited adverse events were evenly distributed across lot groups, with no apparent clusters of events affecting specific body systems. Therefore, these results demonstrate consistency across production lots in terms of reactogenicity as well as immunogenicity. 5 Conclusions The results of this clinical trial show consistent immunogenicity for FD MVA-BN, with statistically equivalent neutralizing antibody titers 2 weeks after the second vaccination across multiple lot groups. Both neutralizing and total antibody responses were robust and similar to those observed in previous clinical trials. Safety and reactogenicity of FD MVA-BN were also comparable across lot groups and consistent with the known safety profile of MVA-BN. The summary of this evidence supports the appropriateness of FD MVA-BN as an alternative to liquid-frozen vaccine and demonstrates the reliability of the manufacturing process. Further information on long-term stability could confirm its advantages for stockpiling and emergency distribution. Disclosures. Edgar Turner Overton: None. Darja Schmidt, Sanja Vidojkovic, Erika Menius, Katrin Nopora, Jane Maclennan, Heinz Weidenthaler: Employees of Bavarian Nordic. Declarations. The trial protocol was reviewed and approved by the relevant IRBs/ECs prior to its start and throughout its conduct, and subjects provided written informed consent prior to participation in the trial. The trial was conducted according to the principles of the Declaration of Helsinki and was performed in compliance with Good Clinical Practices. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary material The following are the Supplementary data to this article:Supplementary data 1 Acknowledgments We thank clinical operations and analysis colleagues for their support during the conduct of the trial, including: Eva Kreitmeir, Tracie Pickler, DeShara Eley-Abdullah, Cindy Handelsman, Teresa Perschy, Andrea Bentz, Rainer Richter, Monika Flür, Nicole Baedeker (all of Bavarian Nordic), Erin Reagoso and Cindy Dukes (of ICON clinical research organization), all of the clinical trial site investigators and personnel; the members of the data safety monitoring board: Frank V. Sonnenburg, Harish Doppalapudi and Herwig Kollaritsch; and also Jacqueline Powell, Barbara Martin, Thomas Meyer and Liddy Chen (of Bavarian Nordic) for medical writing assistance. Funding. The study was funded through a contract with the US Biomedical Advanced Research and Development Authority (HHSO100201700019C). Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.vaccine.2022.10.056. ==== Refs References 1 Breman J.G. Arita I. The confirmation and maintenance of smallpox eradication N England J Med 303 22 1980 1263 1273 10.1056/nejm198011273032204 6252467 2 Fenner F. Smallpox: emergence, global spread, and eradication Hist Philos Life Sci 15 3 1993 397 420 7529932 3 Mayr A. Smallpox vaccination and bioterrorism with pox viruses Comp Immunol Microbiol Infect Dis 26 5–6 2003 423 430 10.1016/S0147-9571(03)00025-0 12818626 4 Artenstein A.W. Grabenstein J.D. Smallpox vaccines for biodefense: need and feasibility Expert Rev Vaccines 7 8 2008 1225 1237 10.1586/14760584.7.8.1225 18844596 5 Petersen B.W. Harms T.J. Reynolds M.G. Harrison L.H. Use of Vaccinia Virus Smallpox Vaccine in Laboratory and Health Care Personnel at Risk for Occupational Exposure to Orthopoxviruses - Recommendations of the Advisory Committee on Immunization Practices (ACIP), 2015 MMWR Morb Mortal Wkly Rep 65 2016 257 262 10.15585/mmwr.mm6510a2 26985679 6 Department of Defense. Update to clinical policy for the Department of Definse Smallpox Vaccination Program (memorandum), https://health.mil/Reference-Center/Policies/2008/04/01/Update-to-Clinical-Policy-for-the-Department-of-Defense-Smallpox-Vaccination-Program [accessed 19 Oct 2021]. 7 McCollum A.M. Damon I.K. Human monkeypox Clin Infect Dis 58 2 2014 260 267 10.1093/cid/cit703 24158414 8 Durski K.N. McCollum A.M. Nakazawa Y. Petersen B.W. Reynolds M.G. Briand S. Emergence of Monkeypox - West and Central Africa, 1970–2017 MMWR Morb Mortal Wkly Rep 67 10 2018 306 310 29543790 9 Reed K.D. Melski J.W. Graham M.B. Regnery R.L. Sotir M.J. Wegner M.V. The detection of monkeypox in humans in the Western Hemisphere N England J Med 350 4 2004 342 350 14736926 10 Petersen E. Abubakar I. Ihekweazu C. Heymann D. Ntoumi F. Blumberg L. Monkeypox - Enhancing public health preparedness for an emerging lethal human zoonotic epidemic threat in the wake of the smallpox post-eradication era Int J Infect Dis 78 2019 78 84 30453097 11 Bunge EM, Hoet B, Chen L, et al. The changing epidemiology of human monkeypox—a potential threat? A systematic review. PLoS Negl Trop Dis In press. 12 Petersen B.W. Kabamba J. McCollum A.M. Lushima R.S. Wemakoy E.O. Muyembe Tamfum J.-J. Vaccinating against monkeypox in the Democratic Republic of the Congo Antiviral Res 162 2019 171 177 30445121 13 Vaughan A. Aarons E. Astbury J. Balasegaram S. Beadsworth M. Beck C.R. Two cases of monkeypox imported to the United Kingdom, September 2018 Euro Surveill 23 38 2018 10.2807/1560-7917.ES.2018.23.38.1800509 14 Erez N. Achdout H. Milrot E. Schwartz Y. Wiener-Well Y. Paran N. Diagnosis of Imported Monkeypox, Israel, 2018 Emerg Infect Dis 25 5 2019 980 983 30848724 15 Vaughan A. Aarons E. Astbury J. Brooks T. Chand M. Flegg P. Human-to-Human Transmission of Monkeypox Virus, United Kingdom, October 2018 Emerg Infect Dis 26 4 2020 782 785 32023204 16 Cassimatis D.C. Atwood J.E. Engler R.M. Linz P.E. Grabenstein J.D. Vernalis M.N. Smallpox vaccination and myopericarditis: a clinical review J Am Coll Cardiol 43 9 2004 1503 1510 10.1016/j.jacc.2003.11.053 15120802 17 Engler RJ, Nelson MR, Collins LC, Jr., et al. A prospective study of the incidence of myocarditis/pericarditis and new onset cardiac symptoms following smallpox and influenza vaccination. PloS one 2015;10(3):e0118283. DOI: 10.1371/journal.pone.0118283. 18 Su J.R. McNeil M.M. Welsh K.J. Marquez P.L. Ng C. Yan M. Myopericarditis after vaccination, Vaccine Adverse Event Reporting System (VAERS), 1990–2018 Vaccine 39 5 2021 839 845 33422381 19 Eckart R.E. Love S.S. Atwood J.E. Incidence and follow-up of inflammatory cardiac complications after smallpox vaccination J Am Coll Cardiol 44 1 2004 201 205 10.1016/j.jacc.2004.05.004 15234435 20 Lin A.H. Phan H.-A. Barthel R.V. Maisel A.S. Crum-Cianflone N.F. Maves R.C. Myopericarditis and pericarditis in the deployed military member: a retrospective series Mil Med 178 1 2013 18 20 23356113 21 Bavarian Nordic. JYNNEOS Prescribing Information. 1 ed, 2019. 22 Volkmann A. Williamson A.L. Weidenthaler H. The Brighton Collaboration standardized template for collection of key information for risk/benefit assessment of a Modified Vaccinia Ankara (MVA) vaccine platform Vaccine 39 22 2020 3067 3080 10.1016/j.vaccine.2020.08.050 33077299 23 Greenberg R.N. Hurley M.Y. Dinh D.V. A Multicenter, Open-Label, Controlled Phase II Study to Evaluate Safety and Immunogenicity of MVA Smallpox Vaccine (IMVAMUNE) in 18–40 Year Old Subjects with Diagnosed Atopic Dermatitis PLoS ONE 10 10 2015 e0138348 26439129 24 von Sonnenburg F. Perona P. Darsow U. Safety and immunogenicity of modified vaccinia Ankara as a smallpox vaccine in people with atopic dermatitis Vaccine 32 43 2014 5696 5702 10.1016/j.vaccine.2014.08.022 25149431 25 Greenberg R.N. Overton E.T. Haas D.W. Safety, immunogenicity, and surrogate markers of clinical efficacy for modified vaccinia Ankara as a smallpox vaccine in HIV-infected subjects J Infect Dis 207 5 2013 749 758 10.1093/infdis/jis753 23225902 26 Overton E.T. Stapleton J. Frank I. Safety and immunogenicity of Modified Vaccinia Ankara-Bavarian Nordic smallpox vaccine in vaccinia-naive and experienced human immunodeficiency virus-infected individuals: an open-label, controlled clinical phase II trial. Open Forum Infect Dis 2(2):ofv040 2015 10.1093/ofid/ofv040 27 Overton E.T. Lawrence S.J. Stapleton J.T. A randomized phase II trial to compare safety and immunogenicity of the MVA-BN smallpox vaccine at various doses in adults with a history of AIDS Vaccine 38 11 2020 2600 2607 10.1016/j.vaccine.2020.01.058 32057574 28 NCT01668537. A phase II trial to compare a liquid-frozen and a freeze-dried formulation of IMVAMUNE (MVA-BN) smallpox vaccine in vaccinia-naïve healthy subjects, https://clinicaltrials.gov/ct2/show/results/NCT01668537?term=POX-MVA&cond=Smallpox+%28Variola%29&draw=2&rank=6&view=results [accessed 01 Nov 2021]. 29 Frey S.E. Wald A. Edupuganti S. Comparison of lyophilized versus liquid modified vaccinia Ankara (MVA) formulations and subcutaneous versus intradermal routes of administration in healthy vaccinia-naïve subjects Vaccine 33 39 2015 5225 5234 10.1016/j.vaccine.2015.06.075 26143613 30 Suter M. Meisinger-Henschel C. Tzatzaris M. Modified vaccinia Ankara strains with identical coding sequences actually represent complex mixtures of viruses that determine the biological properties of each strain Vaccine 27 52 2009 7442 7450 10.1016/j.vaccine.2009.05.095 19539582 31 Frey S.E. Winokur P.L. Hill H. Goll J.B. Chaplin P. Belshe R.B. Phase II randomized, double-blinded comparison of a single high dose (5×10(8) TCID50) of modified vaccinia Ankara compared to a standard dose (1×10(8) TCID50) in healthy vaccinia-naïve individuals Vaccine 32 23 2014 2732 2739 10.1016/j.vaccine.2014.02.043 24607004 32 von Krempelhuber A. Vollmar J. Pokorny R. A randomized, double-blind, dose-finding Phase II study to evaluate immunogenicity and safety of the third generation smallpox vaccine candidate IMVAMUNE Vaccine 28 5 2010 1209 1216 10.1016/j.vaccine.2009.11.030 19944151 33 Vollmar J. Arndtz N. Eckl K.M. Safety and immunogenicity of IMVAMUNE, a promising candidate as a third generation smallpox vaccine Vaccine 24 12 2006 2065 2070 10.1016/j.vaccine.2005.11.022 16337719 34 Kumru O.S. Joshi S.B. Smith D.E. Middaugh C.R. Prusik T. Volkin D.B. Vaccine instability in the cold chain: mechanisms, analysis and formulation strategies Biologicals : journal of the International Association of Biological Standardization 42 5 2014 237 259 10.1016/j.biologicals.2014.05.007 24996452 35 Hansen L.J.J. Daoussi R. Vervaet C. Remon J.P. De Beer T.R.M. Freeze-drying of live virus vaccines: A review Vaccine 33 42 2015 5507 5519 10.1016/j.vaccine.2015.08.085 26364685 36 Sanchez-Sampedro L. Perdiguero B. Mejias-Perez E. Garcia-Arriaza J. Di Pilato M. Esteban M. The evolution of poxvirus vaccines Viruses 7 4 2015 1726 1803 10.3390/v7041726 25853483 37 Tack D.M. Karem K.L. Montgomery J.R. Unintentional transfer of vaccinia virus associated with smallpox vaccines: ACAM2000((R)) compared with Dryvax((R)) Hum Vaccin Immunother 9 7 2013 1489 1496 10.4161/hv.24319 23571177 38 Lloyd J. Cheyne J. The origins of the vaccine cold chain and a glimpse of the future Vaccine 35 17 2017 2115 2120 10.1016/j.vaccine.2016.11.097 28364918	36460535	PMC9707699	NO-CC CODE	2022-12-01 23:20:25	no	Vaccine. 2022 Nov 29; doi: 10.1016/j.vaccine.2022.10.056	utf-8	Vaccine	2,022	10.1016/j.vaccine.2022.10.056	oa_other
==== Front J Gen Intern Med J Gen Intern Med Journal of General Internal Medicine 0884-8734 1525-1497 Springer International Publishing Cham 7904 10.1007/s11606-022-07904-8 Concise Research Report Understanding of and Trust in the Centers for Disease Control and Prevention’s Revised COVID-19 Isolation and Quarantine Guidance Among US Adults Dexter Joseph P. Ph.D. 1 http://orcid.org/0000-0002-4149-7119 Mishra Vishala M.B.B.S. [email protected] 2 1 grid.38142.3c 000000041936754X Data Science Initiative and Department of Human Evolutionary Biology, Harvard University, Cambridge, MA USA 2 grid.26009.3d 0000 0004 1936 7961 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC USA 29 11 2022 14 25 6 2022 26 10 2022 © The Author(s), under exclusive licence to Society of General Internal Medicine 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Poynter Institutehttp://dx.doi.org/10.13039/100018077 Harvard Data Science Initiative, Harvard University ==== Body pmcINTRODUCTION On December 27, 2021, the Centers for Disease Control and Prevention (CDC) announced changes to their guidance for individuals who are exposed to or test positive for COVID-19.1 The revised recommendations have prompted widespread discussion of both the scientific rationale and communication strategy, including criticism from the American Medical Association.2 In this survey study, we assessed understanding of and trust in the CDC’s initial public statement about the new guidance among US adults.1 METHODS We administered the online survey to 603 participants recruited through Prolific between January 5 and 6, 2022. A copy of the survey instrument is available at https://osf.io/gwhfe/. The cohort was assembled using nonprobability convenience sampling of US adults, with quotas chosen to match 2019 US Census data on age (18–24, 25–39, or ≥40 years old), race (not white or white), ethnicity (not Hispanic/Latinx or Hispanic/Latinx), and education (no bachelor’s degree or bachelor’s degree); quotas were rounded to include at least one participant in every group. The study was approved by Harvard University’s Committee on the Use of Human Subjects, and participants, who were paid $2.00, provided informed consent electronically before beginning the survey. Associations between participant characteristics and passage comprehension were assessed using ordinal logistic regression implemented with the Python package statsmodels (version 0.13.0.dev0). RESULTS The demographic characteristics of the participants are listed in Table 1. Participants answered comprehension questions about the application of the isolation and quarantine guidelines to hypothetical scenarios, based on either a vaccination history specified in the question (“scenario” questions) or their own history (“personal” questions). One hundred fifty (25%) participants correctly answered all 4 scenario questions, and 180 (30%) participants correctly answered all 4 personal questions (Table 1). In an ordinal logistic regression analysis also considering age, gender, ethnicity, race, education, political partisanship, and geography, being unvaccinated for COVID-19 (OR, 0.63; 95% CI, 0.49–0.80; P < .001) and not having received a booster (OR, 0.75; 95% CI, 0.61–0.92; P = .005) were negatively associated with the number of correct responses to the personal questions. Table 1 Characteristics and Responses of Participants Who Completed the Online Survey About Change in CDC Isolation and Quarantine Guidance Characteristic Participants, % (N = 603) 2019 US census %a Age, y 18–24 70 (12) 12 25–39 163 (27) 27 ≥40 370 (61) 62 Genderb Female 329 (55) Male 269 (45) Non-binary, transgender, or other 14 (2) Ethnicity Hispanic or Latinx 77 (13) 16 Raceb Asian 45 (7) Black or African American 78 (13) White 480 (80) 78 Otherc 9 (1) Education High school diploma or lower 125 (21) Some college or associate’s degree 279 (46) Bachelor’s degree or higher 199 (33) 33 Political partisanship Democratic (including leaners) 290 (48) Republican (including leaners)d 142 (24) Independent or othere 171 (28) Geography Urban areaf 159 (26) Suburban area 320 (53) Rural area 124 (21) COVID-19 vaccination history Unvaccinatedg 133 (22) Partially or fully vaccinated, no boosterh 199 (33) Fully vaccinated, booster 271 (45) Comprehension score (scenarios)i 4 150 (25) 3 188 (31) 2 161 (27) 0–1 104 (17) Comprehension score (personal)i 4 180 (30) 3 231 (38) 2 127 (21) 0–1 65 (11) Self-reported impressions of passagej Accurate and should be trusted 276 (46) High-quality evidence 269 (45) Clear and easy to read 396 (66) aPercentage of US population based on 2019 Census data for characteristics used in quota sampling. bParticipants could select more than one option. cIncludes participants who selected “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” or “Another option not listed here.” dIdentifying as a Republican was negatively associated with the number of correct responses to both the scenario (OR, 0.74; 95% CI, 0.59–0.93; P = .009) and personal comprehension questions (OR, 0.73; 95% CI, 0.58–0.92; P = .008). eIdentifying as an Independent was negatively associated with the number of correct responses to the scenario questions (OR, 0.75; 95% CI, 0.61–0.93; P = .007) fLiving in a self-described urban area was negatively associated with the number of correct responses to the scenario comprehension questions (OR, 0.69; 95% CI, 0.53–0.90; P = .006) gBeing unvaccinated for COVID-19 was negatively associated with the number of correct responses to the personal questions (OR, 0.63; 95% CI, 0.49–0.80; P < .001) hNot having received a booster was negatively associated with the number of correct responses to the personal questions (OR, 0.75; 95% CI, 0.61–0.92; P = .005) iNumber of participants who gave the indicated number of correct answers to these questions jNumber of participants who answered “Strongly agree” or “Agree” about each description on a 6-point Likert scale The CDC web page stated that vaccination “decreases the risk of severe disease, hospitalization, and death from COVID-19” but gave quantitative information only for effectiveness against infection. When participants were asked to estimate the effectiveness against hospitalization from COVID-19, the modal response was 30–39% without a booster (139 [23%] participants; Fig. 1) and 70–79% with a booster (177 [29%] participants; Fig. 1), corresponding to the stated numbers for effectiveness against infection (35% and 75%, respectively). A majority of participants estimated that vaccination is less than 90% effective against death from COVID-19 both without (437 [72%] participants) and with a booster (342 [57%]; Fig. 1). Figure 1 Self-reported attitude changes and estimated vaccine effectiveness against hospitalization or death from COVID-19. The graphs show (A) estimated effectiveness of a COVID-19 vaccine without a booster against hospitalization (orange bars) or death (gray bars), (B) estimated effectiveness of a COVID-19 vaccine with a booster against hospitalization (orange bars) or death (gray bars), and (C) the percentage of respondents who expressed changes in attitude before and after the release of the revised guidance in response to three counterfactual questions. Participants were asked about their current attitudes towards the CDC’s COVID-19 guidance, as well as what their attitudes had been before the announcement, using the nonrandomized counterfactual format of Graham and Coppock.3 In response to these questions, 158 (26%) participants indicated that the change in guidance lowered their overall trust of the CDC’s recommendations (Fig. 1). One hundred sixty-seven (28%) participants expressed reduced confidence that the agency relies on the best scientific evidence, and 265 (44%) said they now think it is more likely that economic factors influence CDC guidance (Fig. 1). DISCUSSION Public health messaging about Omicron and subsequent SARS-CoV-2 variants must balance speed, clarity, and responsiveness to rapid scientific changes.2,4 In a survey of a representative sample of US adults, comprehension testing of the CDC’s revised guidance revealed widespread gaps in understanding. The negative association of comprehension scores with vaccination status suggests the recommendations may be least accessible to individuals at greatest risk of infection. When asked counterfactual questions, many participants expressed reduced trust in CDC recommendations about COVID-19 and a stronger belief that the agency’s guidance is influenced by economic considerations. Participants also underestimated the protectiveness of COVID-19 vaccines against hospitalization and death; providing specific numbers might have reduced variability in risk perception.5 This omission is notable in light of evidence that highlighting protection against death may reduce COVID-19 vaccine hesitancy.6 Limitations of the study include that it was conducted only online, such that individuals without internet access were not sampled, and the use of a nonprobability sample, which limits generalizability to the US population as a whole. Joseph P. Dexter, Ph.D. Vishala Mishra, M.B.B.S. Funding This work was supported by a CoronaVirusFacts Alliance Grant from the Poynter Institute and a Harvard Data Science Fellowship. Declarations Ethics Approval Approval was obtained from Harvard University’s Committee on the Use of Human Subjects on January 22, 2021. Conflict of Interest The authors declare that they do not have a conflict of interest. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Centers for Disease Control and Prevention. CDC updates and shortens recommended isolation and quarantine period for general population. Published December 27, 2021. https://www.cdc.gov/media/releases/2021/s1227-isolation-quarantine-guidance.html. Accessed 27 Jan 2022. 2. Harmon GE. AMA: CDC quarantine and isolation guidance is confusing, counterproductive. Published January 5, 2022. https://www.ama-assn.org/press-center/press-releases/ama-cdc-quarantine-and-isolation-guidance-confusing-counterproductive. Accessed 27 Jan 2022. 3. Graham MH, Coppock A. Asking about attitude change. Public Opin Q. 2021;85(1):28-53. 10.1093/poq/nfab009. 4. Klein WMP, Boutté AK, Brake AK, et al. Leveraging risk communication science across US federal agencies. Nat Hum Behav. 2021;5:411-413. 10.1038/s41562-021-01081-0. 5. Wood S, Schulman K. Beyond politics—Promoting Covid-19 vaccination in the United States. N Engl J Med. 2021;384(7):e23. 10.1056/NEJMms2033790. 6. Merkley E, Loewen PJ. Assessment of communication strategies for mitigating COVID-19 vaccine-specific hesitancy in Canada. JAMA Netw Open. 2021;4(9):e2126635. 10.1001/jamanetworkopen.2021.26635.	36447065	PMC9707849	NO-CC CODE	2022-12-01 23:20:27	no	J Gen Intern Med. 2022 Nov 29;:1-4	utf-8	J Gen Intern Med	2,022	10.1007/s11606-022-07904-8	oa_other
==== Front Int J Clin Pharm Int J Clin Pharm International Journal of Clinical Pharmacy 2210-7703 2210-7711 Springer International Publishing Cham 1520 10.1007/s11096-022-01520-6 Review Article A systematic review and pooled prevalence of burnout in pharmacists Dee Jodie Dhuhaibawi Nabaa http://orcid.org/0000-0003-3344-6888 Hayden John C. [email protected] grid.4912.e 0000 0004 0488 7120 RCSI University of Medicine and Health Sciences, Dublin, Ireland 29 11 2022 110 18 8 2022 5 11 2022 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Background Burnout is an occupational phenomenon caused by ineffectively managed work-related stress. Burnout is common among healthcare professionals and has the capacity to compromise patient care, but is not well characterised in pharmacists. Aim This systematic review aimed to establish the prevalence of burnout among pharmacists, and its associated risk factors. Method A systematic search of Embase, PubMed, CINAHL and PsychInfo was carried out. Studies were included using the following eligibility criteria; original research investigating burnout prevalence in pharmacists in patient-facing roles in any jurisdiction, using any validated burnout measurement instrument. No language or date barriers were set. Data were extracted by the first author and accuracy checked by co-authors. A pooled prevalence was estimated, and narrative synthesis provided. Results Burnout prevalence data were extracted from 19 articles involving 11,306 pharmacist participants across eight countries. More than half (51%) of pharmacists were experiencing burnout. Associated risk factors included longer working hours, less professional experience, high patient and prescription volumes, excessive workload and poor work/life balance. The COVID-19 pandemic has negatively impacted pharmacist burnout and resilience. Involvement in education and training and access to burnout management resources were associated with lower rates of burnout, but burnout intervention effectiveness is unknown. Conclusion Burnout remains high among pharmacists and may negatively affect the quality of patient care. There is significant heterogeneity pertaining to the definition and assessment of burnout and there remains a need to identify and evaluate effective individual and organisational burnout interventions. Supplementary Information The online version contains supplementary material available at 10.1007/s11096-022-01520-6. Keywords Burnout Pharmacists Professional wellbeing Systematic review ==== Body pmcImpact statements Over half of pharmacists surveyed report experiencing professional burnout. The Covid-19 pandemic has worsened reported burnout but these levels appear to have stabilised, albeit at high levels. Organisation level changes to workload and work type are required to retain pharmacists and ensure continued patient safety. Introduction Burnout is a term used to describe the psychological response to work related stress, presenting as emotional exhaustion, increased levels of depersonalisation and cynicism and reduced feeling of personal accomplishment or efficacy [1]. The World Health Organisation’s definition describes it as an occupational phenomenon that occurs when chronic stress is ineffectively managed, although it is not considered a medical illness [2]. There has been an explosion of interest in researching burnout [3]. This has led to the development of multiple burnout assessment questionnaires, such as the Maslach Burnout Inventory (MBI) [4], the Copenhagen Burnout inventory (CBI) [5] and the Oldenburg Burnout Inventory (OBI) [6]. Burnout rates are high among healthcare professionals [3] and have been associated with reduction in productivity [7], increased job turnover [7] and reduced psychological and physical wellbeing [8]. The subsequent negative impact on patient care is significant, resulting in medication errors, reduced quality of care and fatalities [7, 9, 10]. The pharmacy profession is not as extensively studied as other professions such as medicine and teachers [3]. One previous systematic review reported a prevalence ranging from 10–61% of pharmacists, but this estimate range was based on just five studies, all from the United States, and based on pre-Covid-19 studies only [11]. This is of significance as the role of the pharmacist in healthcare provision has evolved at varying pace worldwide, in some cases catalysed by pandemic-related changes. More evolved pharmacy systems now include use of pharmacist’s full scope of practice including prescribing [12] and vaccine administration [13]. With a growing ageing population worldwide, multimorbidity and polypharmacy is now commonplace and pharmacist-directed care and medicines management are becoming increasingly complex [12]. Pharmacists play a vital role in the safe and efficient use of medicines, continuity of supply amid regular medicines shortages, compounding of medicines and medicines information [14]. In addition, patient-facing pharmacists, especially those in a community setting, are a substantially utilised resource by the public, due to pharmacies’ convenience, accessibility and free services, resulting in high patient and prescription volumes, frequent clinical consultation and patient education. This increase in workload and responsibility, coupled with the additional pressure of a pandemic on health services may impact pharmacists’ physical and psychological wellbeing and rates of burnout [14]. Aim This systematic review aimed to establish the prevalence of burnout among pharmacists, and its associated risk factors. Method This review was carried out in accordance with the PRISMA 2020 guidance [15]. A search for articles investigating burnout in pharmacists was conducted on the 27th February 2022 on EBSCO (which encompasses the databases PubMed, CINAHL and PsychInfo 1982-present), Embase (1982-present) and ERIC (2003-present). A bespoke search strategy was developed with keyword searches with MeSH terms applied in the database search of EMBASE, ERIC and EBSCO (See Supplementary Appendix 1). Results were not limited to English language to allow broad article inclusion. One existing systematic review investigating burnout in pharmacists [11] was found during the search and the articles in this review were additionally screened for inclusion. Citation tracking was also performed. Inclusion criteria Original studies investigating burnout in pharmacists in patient-facing roles in any jurisdiction, using any validated burnout measurement instrument were eligible for inclusion in this review. The most commonly used validated instruments include Maslach burnout Inventory, Oldenburg Burnout Inventory and Copenhagen Burnout Inventory. Details of the instruments used were extracted. Studies were excluded if they did not report burnout related outcomes or investigated other healthcare or pharmacy professionals for which pharmacist-specific data could not be extracted, or were qualitative in nature. Studies of pharmacists in non-patient facing roles were also excluded as these are typically heterogeneous roles, and results would be difficult to generalise with traditional pharmacist roles. Conference proceedings were excluded as they offer limited data, are generally not peer reviewed and may duplicate published work. The search results were exported into Zotero® software and screened to remove duplicate articles. A title and abstract screen was performed by two authors independently, and the articles selected were reviewed at full text by two authors. Authors liaised with each other if clarity was needed on an article’s eligibility for inclusion. Data were tabulated using Excel®. Results from included studies were described in a narrative synthesis. Heterogeneity of prevalence estimates was observed between burnout assessment tools. A minority of studies also used the CBI which reported prevalence by burnout type. As a result, a pooled prevalence estimate was restricted to the MBI and SMBM. The pooled estimate and CI were calculated assuming a random effects model via the Metaprop command in STATA (Stata Statistical Software: Release 16. StataCorp LLC, College Station, TX). Quality assessment A quality assessment of the studies was carried out applying a modified Newcastle–Ottawa quality assessment scale (See Supplementary Appendix 2) and each was ranked as good, fair or poor quality with a maximum possible score of 8. Common risks of bias were also assessed. Results Study characteristics Nineteen articles [16–34] were deemed eligible for inclusion and selected for data extraction. Figure 1 details the PRISMA flow diagram. All of the studies were cross-sectional, carried out across eight different countries, the majority from the USA (n = 11/19), published from 1990 up to 2022, with a total of 11,306 pharmacist participants in patient-facing work environments. Nine studies [17–20, 23, 24, 27, 28, 34] investigated pharmacists across multiple professional settings, six studies [21, 26, 28, 31–33] measured burnout in hospital pharmacists and four studies [16, 22, 25, 29] investigated community pharmacists. The number of participants in each study ranged from 116–2231. Study characteristics are summarised in Table 1.Fig. 1 Preferred reporting items for systematic reviews and meta analyses (PRISMA) flow diagram Table 1 Characteristics of Studies included for Systematic Review Author Location Year(s) of survey Study design Sample Size (n =) Professional Setting Age (years): mean or [median] % Female Burnout assessment instrument Outcome definition Overall burnout prevalence estimate (%) Alameddine et al. [16] Lebanon 2020–2021 Cross-sectional 459 Community NRa 60 CBI CBI score > 50 Personal burnout 56.7 work related burnout 58.2 client related burnout 57 Golbach et al. [17] USA 2020 Cross-sectional 550 Mixed 34 74 MBI-HSS MBI-EE ≥ 27 OR MBI-DP ≥ 10 MBI-PA ≤ 33 61.8 Santos et al. [18] Portugal 2020 Cross-sectional 1246 Mixed NRa 85.5 MBI-HSS EE at z = M + (SD × 0.5) DP/CY at z = M + (SD × 1.25) PA at z = M + (SD × 0.10) 7.3 Jones et al. [19] USA 2020 Cross-sectional 484 Mixed 41.96 71.5 Pro.QOL-BO, CF/STS subscales Pro.QOL (BO + CF + STS) 23–41 = moderate (BO + CF + STS) > 42 = high 65.3 (moderate-high score) 47 (self-reported) Tan et al. [20] Singapore 2020 Cross-sectional 702 Mixed 33 73.7 MBI-HSS MBI-EE ≥ 27 MBI-DP ≥ 11 61.5 Weichel et al. [21] Canada 2019 Cross-sectional 270 Hospital NRa 77 MBI-HSS (MP) MBI-EE ≥ 27 MBI-DP ≥ 10 61.1 Youssef et al. [22] Lebanon 2021 Cross-sectional 387 Community NRa 53.7 CBI CBI score ≥ 50 Personal burnout 77.8 work related burnout 76.8 client related burnout 89.7 Ball et al. [23] USA 2018 Cross-sectional 193 Mixed 37 62 MBI-HSS MBI-EE ≥ 27 ORMBI-DP ≥ 10 ORMBI-PA ≤ 33 64 Kang et al. [24] USA 2018 Cross-sectional 357 Mixed NRa 70 MBI-HSS (MP) MBI-EE ≥ 27 ORMBI-DP ≥ 10 55.5 Patel et al. [25] USA NR Cross-sectional 411 Community 45.3 70.3 MBI-HSS MBI-EE ≥ 27 ORMBI-DP ≥ 10 OR^MBI-PA ≤ 33 74.9 Rozycki et al. [26] USA 2018–2019 Cross-sectional 116 Hospital 32.6 66.4 MBI-HSS (MP) MBI-EE ≥ 27 ORMBI-DP ≥ 10 ORMBI-PA ≤ 33 69.8 Skrupky et al. [27] USA 2019 Cross-sectional 2231 Mixed NRa 71.3 WBI (2 questions from MBI) High score (not specified) onMBI-EE ORMBI-DP 59.1 Smith et al. [28] USA 2020 Cross-sectional 221 Hospital NR NR MBI-HSS (MP) MBI-EE ≥ 27 ORMBI-DP ≥ 10 60 Protano et al. [29] Italy NR Cross-sectional 469 Community 42.6 74.2 SMBM SMBM ≥ 4.40 10.5 Durham et al. [30] USA 2016 Cross-sectional 329 Mixed NRa 66.9 MBI-HSS MBI-EE ≥ 27 ORMBI-DP ≥ 10 ORMBI-PA ≤ 33 53.2 Jones et al. [31] USA 2016 Cross-sectional 974 Hospital 35 69.5 MBI (NS) MBI-EE ≥ 27 ORMBI-DP ≥ 10 61.2 Higuchi et al. [32] Japan NR Cross-sectional 380 Hospital 37.3 60.4 Pro.QOL-BO, CF/STS subscales Pro.QOL-BO > 26Pro.QOL-CF/STS > 16 49.2 Muir & Bortoletto, [33] Australia 2005 Cross-sectional 266 Hospital 42.6 73 MBI (NS) NR 5 Lahoz & Mason, [34] USA NR Cross-sectional 1261 Mixed 41.3 29.2 MBI (NS) NR 51.9 BO, Burnout; CF, Compassion fatigue; CBI, Copenhagen Burnout Inventory; DP, Depersonalisation; EE, Emotional exhaustion; MBI, Maslach Burnout Inventory; MBI-HSS, MBI-Human Services Survey; MBI-HSS (MP), MBI-HSS Medical Professionals; NR, Not reported; NS, Not specified; PA, Personal accomplishment; Pro. QOL, Professional Quality of Life Scale; STS, Secondary traumatic stress; SMBM, Shirom-Melamed Burnout Measure; WBI, Wellbeing Index aReported age ranges Assessment of burnout Fourteen of the studies [17, 18, 20, 21, 23, 24, 26–31, 33, 34] (73.7%) measured burnout using a version of the MBI. Two studies [16, 22] used the CBI and one study [29] used the Shirom-Melamed Burnout Measure (SMBM). The remaining two studies [19, 32] used the Professional Quality of Life Scale. The MBI and SMBM measure burnout across three similar domains; the CBI domains are personal burnout, work-related burnout and client-related burnout. The Professional Quality of Life subscales are burnout, secondary traumatic stress, compassion fatigue and compassion satisfaction [19]. The Wellbeing Index is a simple questionnaire without subscales, with higher scores indicating higher likelihood of burnout, among other measured parameters such as fatigue and anxiety [27]. Prevalence of burnout Burnout prevalence estimates ranged from 5 to 75%. Three studies [18, 29, 33] reported burnout rates of 10% or lower, while the remainder of the studies reported estimates of 49% or higher. Pooled prevalence was calculated from 17 studies [17–21, 23–34] giving an overall prevalence estimate of 51% (95% CI 38–65%), shown in Fig. 2. The remaining two studies [16, 22] utilised the CBI and reported that 56.7% and 77.8% of community pharmacist participants had personal burnout, 58.2% and 76.8% had work-related burnout and 57% and 89.7% had client-related burnout, respectively, with neither study reporting an overall burnout prevalence estimate. Burnout prevalence estimates among hospital pharmacists ranged from 5 to 70%. Overtime, burnout prevalence has increased and stabilised at a high level since 2020, when Covid-19 was declared a pandemic, with ten studies [17–21, 23–28] in this time period reporting burnout rates of 55% or higher. High rates of burnout were consistent across studies, geographies and professional setting.Fig. 2 Pooled prevalence estimate of Burnout in Pharmacists. ES = estimate of prevalence (per study) with associated confidence interval and is calculated using STATA® metaprop command. The hatched line represents the pooled mean estimate to allow for comparison with individual contributing studies Two [18, 33] of the three studies reporting the lowest rates of burnout among pharmacists had strict defining criteria by scoring high across all domains of the MBI, in comparison to all other studies using the MBI which generally classified a participant as experiencing or at risk of burnout if they scored high in one domain. The remaining study [29] with a low burnout rate used the SMBM and defined 10.5% of participants as having “clinically relevant” levels of burnout based on overall SMBM index scores across all domains, so this prevalence may be lower for the same reason. Risk factors Risk factors that increased the likelihood of burnout were varied, some being transient, cultural or jurisdictional, such as difficult economic circumstances [16], health care reform [31] or the impact of the COVID-19 pandemic [17, 22]. Two of the studies identified female gender as a risk factor [24, 34], however, the majority of the collective participants were female, and male pharmacists are likely to be under-represented. The most frequently mentioned risk factors are listed in Table 2. Those that were experiencing burnout were more likely to have made a medication-related error [17, 27] or were more likely to leave their current employment [16, 17, 20, 26, 27]. Factors associated with a lower rate or protective effect against burnout were involvement in patient and peer education and training [28], time away from work, social interactions and hobbies [21], having burnout management resources and being a tutor to a pharmacy student [21, 24], although having too many students was identified as a possible risk factor in one study [30]. One study reported that those who were aware of or had accessed wellness programs or burnout resources provided by their employer had lower rates of burnout [24].Table 2 Most common risk factors associated with Burnout • Working full time/longer hours worked per week [16, 19, 21, 23, 24, 28] • Younger age/less professional experience [17, 21, 24, 25, 32, 33] • High prescription/patient volumes [24, 25, 27, 29] • Increased workload [15, 17, 18, 20] • Poor work/life balance [18, 20, 19] • Too many non-clinical/administrative duties [16, 26, 30] • Inadequate administrative/teaching time [26, 30] • Additional professional/leadership role [24, 27] • Lack of burnout management resources or unaware of resources available [16, 24] • Lack of appreciation by colleagues for professional contributions [20, 30] Quality assessment results Results of application of the quality assessment tool are shown in Table 3. Studies that were ranked as fair or poor quality was primarily due to incomplete or unreported statistical tests, followed by low response rates. There was a high risk of selection, performance and detection bias for all studies as there was no random sampling or blinding of outcome assessors. Both response and non-response bias was also present in all studies as none can definitively deduce that those who did not respond did not have burnout symptoms, coupled with low response rates in most studies. It could be argued that those with burnout are more likely to participate in such studies and conversely, less likely to participate due to burnout itself. This possibly represents an underestimation of burnout in the literature.Table 3 Quality assessment of included studies using modified Newcastle–Ottawa Quality Assessment Scale Study Representativeness of the Sample Sample size Non-respondents Ascertainment of the exposure Assessment of outcome Statistical Test Total score (max. 8) Quality rank Alameddine et al. [16] * * 0 ** * * 6 Good Golbach et al. [17] * 0 0 ** * * 5 Good Santos et al. [18] * * 0 ** * * 6 Good Jones et al. [19] * 0 0 ** * 0 4 Fair Tan et al. [20] * * 0 ** * * 6 Good Weichel et al. [21] * 0 0 ** * * 5 Good Youssef et al. [22] * * 0 ** * * 6 Good Ball et al. [23] * 0 0 ** * * 5 Good Kang et al. [24] * 0 0 ** * * 5 Good Patel et al. [25] * 0 0 ** * 0 4 Fair Rozycki et al. [26] * * 0 ** * 0 5 Good Skrupky et al. [27] * * 0 ** * * 6 Good Smith et al. [28] * 0 0 ** * * 5 Good Protano et al. [29] * 0 0 ** * * 5 Good Durham et al. [30] * 0 0 ** * 0 4 Fair Jones et al. [19] * 0 0 ** * * 5 Good Higuchi et al. [32] * * 0 ** * * 6 Good Muir & Bortoletto, [33] * 0 0 ** * 0 4 Fair Lahoz & Mason, [34] * 0 0 ** * 0 4 Fair 0 No point awarded, 1 point awarded, *2 points awarded Discussion Statement of key findings More than half of pharmacists surveyed were defined as experiencing burnout, with prevalence estimates ranging from 5 to 75%, and in the last three years this has increased and plateaued at almost 60%. While prevalence estimates are limited by assessment instrument and study design challenges, these results give a picture of a significant workforce wellbeing problem within the pharmacy profession. With more than half of pooled respondents reporting feelings of burnout across published studies, organisational factors, occupational stressors and personal resilience must all be examined. Most of the risk factors associated with burnout were modifiable and workload related. Burnout was also associated with higher risk of dispensing errors and leaving the pharmacy profession [16, 17, 20, 26, 27], which could significantly impact current and future quality of patient care. The COVID-19 pandemic was also identified in some studies as a contributing factor to developing burnout [16–19, 22]. There was a significant heterogeneity in the data. Varying levels of burnout could be due to practice setting differences, as well as jurisdictional differences in professional roles. Differences in burnout prevalence also reflect individual factors, such as participants’ perception of burnout and subjective experiences. Jones [31] reported a burnout prevalence based on Pro.QOL scores of 65.3%, yet participants’ self-reported burnout was 47%. These findings suggest a lack of awareness of what burnout is, but could also reflect a work culture where stress is considered a normal part of professional life. Both could possibly contribute to burnout over time, whereby pharmacists continue to work in stressful environments without protest. Strengths and weaknesses To the investigators’ knowledge, this is the second systematic review examining burnout in pharmacists. However, this review is an up to date insight into pharmacist burnout as the previous review [11] was undertaken before the COVID-19 pandemic. There are several limitations to this systematic review. Only observational studies were included, which are at high risk of bias and do not identify causation. The data presented stems from only eight countries, a disproportionate amount representing the USA (n = 11/19 studies). There is also a disproportionate amount of studies published in the last three years (13/19 studies) making it difficult to assess prevalence over time. Interpretation The primary reason for variance in prevalence among the studies reviewed is inconsistency in both the definition and measurement of burnout. The three studies [18, 29, 33] with the lowest prevalence estimates defined burnout as scoring high across all subscales of the burnout assessment tools used. The authors of the MBI no longer recommend using previously published cut-off scores, which were removed in 2016 from the most recent edition of the MBI manual [35] as they lack validity and now recommend using burnout profiles to establish burnout patterns. The benefits of burnout profiles are that they acknowledge the varied and subjective nature of burnout among individuals, giving a more holistic view. However, only one study [18] utilised this approach. It found that the most affected domain of the MBI was personal accomplishment at 50.1%, which is inconsistent with the rest of the studies using the traditional cut-off score method, which most found this domain being the least affected, with several studies excluding it completely. In a recent systematic review investigating the validity of five different burnout measures [36], the CBI and OBI had the most robust validity and reliability for measuring burnout. However, all of these questionnaires have their own strengths and limitations and the development of a diagnostic standard is needed [36]. The impact of burnout The most concerning consequence of burnout is that those experiencing burnout are more likely to leave the profession completely [3, 8]. This has a subsequent negative impact in multiple ways; on healthcare teams, as pharmacists leave taking valuable skills and experience with them and on patients, potentially compromising quality of care and influencing the frequency of medication errors [17, 37]. This is possibly cyclical, as it could also be argued that quality of care provided by those with burnout is compromised, potentially causing errors. Staff shortages and lack of resources are a frequent concern and increased staff turnover can also potentially impact patient care. Burnout can negatively affect mental health and wellbeing and is associated with conditions such as depression and anxiety [1, 37]. There is a financial impact to burnout as there are costs involved in absence, staff turnover, recruitment and potentially individual costs, such as loss of income due to absence or treatment of associated co-morbidities. Chronic heavy workload and poor organisational structure and work culture are often cited as important drivers of burnout [8, 37, 38] and in order to reduce the impact of burnout, these factors need to be addressed by pharmacy owners and policymakers to retain pharmacists and foster a work environment conducive to pharmacists’ wellbeing. Impact of COVID-19 pandemic Several studies were published during the COVID-19 pandemic and cited concerns of pharmacists that included lack of confidence providing care to those with COVID-19, become infected or passing infection to family members [18, 22]. It was reported that the pandemic had increased pharmacist workload [16], increased work hours or that self-reported burnout was specifically related to or impacted by the pandemic [17, 19]. Overall, the perceived threat of COVID-19 was associated with higher levels of burnout. Conversely, accepting COVID-19 risks altruistically and education and training on COVID-19 were associated with lower levels of burnout [18, 22]. Pharmacists are often the first point of contact for patients and the pandemic has impacted issues such as personal protective equipment provision, medication shortages, increased patient volumes and providing the public with evidence-based COVID-related information, all contributing further to workload burden and negatively affecting pharmacists’ mental health and wellbeing [14, 39]. This impact introduces the concept of “syndemics” [40], whereby the epidemic of burnout and its existing stressors has been exacerbated by the pandemic, creating additional workload and new stressors, such as vaccine rollout, virtual consultations and medication delivery services amidst regular staff shortages [41], reflecting the need to provide additional support to healthcare professionals in public health emergencies [42]. One study found that personal resiliency requires workplace support, but changes like improved scheduling and mandatory breaks, confidence using technology, specialised staff and task focused environments were positively associated with resiliency and pharmacists’ ability to adapt to novel situations [43]. Further research Burnout is high among pharmacists and strategies to overcome and manage burnout are essential. The nature of the pharmacist role has a large amount of responsibility and heavy workload reflected in the risk factors listed in Table 2. Lack of resources is a consistent issue, whether it be understaffing, contributing to higher workload and increased working hours, or administrative work and additional roles draining pharmacist resources, or a lack of burnout management resources. It may be prudent for healthcare organisations to examine their organisational structure and how this contributes to burnout among employees and implement changes to address shortcomings. Newly qualified pharmacists need to be protected by additional systems-level support to retain them in the profession, as younger age and less professional experience are risk factors themselves. Therefore, the concept of resilience and managing potential burnout needs to start during pharmacy education, but this is not commonplace. Weichel [21] investigated burnout education in pharmacy schools in Canada and found that 90% of them did not have burnout prevention addressed in their curricula. Of the studies that considered the availability of burnout management resources [17, 23–25, 30], a large proportion of participants claimed they had either no access to or were unaware of any burnout support provided. It would appear that healthcare organisations that have support in place need to create awareness of their availability and encourage pharmacists in distress to utilise such supports. Another issue is identifying the most effective burnout interventions. There have been several systematic reviews investigating intervetnions in physicians [44–46] with mixed results. Most of the published research is individual focused, with mindfulness techniques, cognitive behavioural therapy and self-care causing short term or small reductions in burnout [44, 45]. Organisational changes, such as reductions in workload, changes to working hours and improved organisation communication may have more meaningful reductions in burnout, or may boost the effect of individual interventions [46, 47], but much of the existing evidence base is of low quality, lacks long term follow up and there is a dearth of investigation in pharmacists. Pharmacy leadership bodies have online resources for managing resilience and wellbeing but their effectiveness and how often they are utilised by pharmacists is understudied. Conclusion Approximately half of pharmacists are experiencing burnout globally which has the potential to negatively impact patient care. There is a clear increase in the amount of research investigating burnout among pharmacists in the last five years. There is a need for longitudinal studies to account for any transient contributors, like COVID-19. Burnout awareness and management techniques should be addressed within healthcare organisations should provide wellness programmes and support to those at risk of and/or experiencing symptoms of burnout, as well as continuously evaluate their effectiveness and how its organisational structure and work culture affects burnout. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 14 kb) Funding No specific funding was received. Conflicts of interest The authors have no conflicts of interest to declare. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Bianchi R Schonfeld IS Laurent E Is it time to consider the “burnout syndrome” a distinct illness? Front Public Health 2015 8 3 158 2. World Health Organization. ICD-11 for mortality and morbidity statistics (2018). Available from: https://icd.who.int/browse11/l-m/en#/http://id.who.int/icd/entity/129180281 Accessed 01 Nov 2022. 3. Haase KK Addressing burnout in clinical pharmacy: What can we learn from other health care disciplines? Pharmacother 2020 3 3 645 654 4. Maslach C, Schaufeli W. Historical and conceptual development of burnout. Professional burnout: Recent developments in theory and research. USA; 1993 p. 1–6. Report No.: 12. 5. Kristensen TS Borritz M Villadsen E The copenhagen burnout inventory: a new tool for the assessment of burnout Work Stress 2005 19 192 207 10.1080/02678370500297720 6. Halbesleben JR Demerouti E The construct validity of an alternative measure of burnout: Investigating the english translation of the oldenburg burnout inventory Work Stress 2005 19 3 208 220 10.1080/02678370500340728 7. Hagemann TM Reed BN Bradley BA Burnout among clinical pharmacists: Causes, interventions, and a call to action Pharmacother 2020 3 4 832 842 8. Royal Pharmaceutical Society, Workforce Wellbeing. Available from: https://www.rpharms.com/recognition/all-our-campaigns/workforce-wellbeing Accessed 01 Nov 2022. 9. Shanafelt TD Balch CM Bechamps G burnout and medical errors among american surgeons Ann Surg 2010 251 995 1000 10.1097/SLA.0b013e3181bfdab3 19934755 10. Hall LH Johnson J Watt I healthcare staff wellbeing, burnout, and patient safety: a systematic review PLoS ONE 2016 11 7 e0159015 10.1371/journal.pone.0159015 27391946 11. McQuade BM Reed BN DiDomenico RJ Feeling the burn? A systematic review of burnout in pharmacists Pharmacother 2020 3 3 663 675 12. Bourne RS Baqir W Onatade R Pharmacist independent prescribing in secondary care: opportunities and challenges Int J Clin Pharm 2016 38 1 1 6 10.1007/s11096-015-0226-9 26613738 13. Poudel A Lau ETL Deldot M Pharmacist role in vaccination: Evidence and challenges Vaccine 2019 37 5939 5945 10.1016/j.vaccine.2019.08.060 31474520 14. Hayden JC Parkin R The challenges of COVID-19 for community pharmacists and opportunities for the future Ir J Psychol Med 2020 37 3 198 203 10.1017/ipm.2020.52 32434603 15. Page MJ Moher D Bossuyt PM PRISMA 2020 explanation and elaboration: updated guidance and exemplars for reporting systematic reviews BMJ 2021 29 372 n160 10.1136/bmj.n160 16. Alameddine M Bou-Karroum K Hijazi MA A national study on the resilience of community pharmacists in Lebanon: a cross-sectional survey J Pharm Policy Pract 2022 15 1 1 10.1186/s40545-022-00406-2 34983680 17. Golbach AP, McCullough KB, Soefje SA et al. Evaluation of burnout in a national sample of hematology-oncology pharmacists. JCO Oncol Pract. 2021;OP2100471 18. Santos PMD, Silva CRD, Costa D et al. Burnout in the pharmaceutical activity: the impact of COVID-19. Front Psychiatry. 2022:2389. 19. Jones AM Clark JS Mohammad RA Burnout and secondary traumatic stress in health-system pharmacists during the COVID-19 pandemic Am J Health Syst Pharm 2021 78 9 818 824 10.1093/ajhp/zxab051 33580662 20. Tan YZ Chong JJ Chew LST Burnout and resilience among pharmacists: A Singapore study Pharmacother 2022 5 1 75 84 21. Weichel C Lee JS Lee JY Burnout among hospital pharmacists: prevalence, self-awareness, and preventive programs in pharmacy school curricula Can J Hosp Pharm 2021 74 4 309 316 34602618 22. Youssef D Youssef J Hassan H Prevalence and risk factors of burnout among Lebanese community pharmacists in the era of COVID-19 pandemic: results from the first national cross-sectional survey J Pharm Policy Pract 2021 14 1 12 10.1186/s40545-021-00393-w 33397497 23. Ball AM Schultheis J Lee HJ Evidence of burnout in critical care pharmacists Am J Health Syst Pharm 2020 77 790 796 10.1093/ajhp/zxaa043 32280966 24. Kang K Absher R Granko RP Evaluation of burnout among hospital and health-system pharmacists in North Carolina Am J Health Syst Pharm 2020 77 441 448 10.1093/ajhp/zxz339 31950988 25. Patel SK Kelm MJ Bush PW Prevalence and risk factors of burnout in community pharmacists J Am Pharm Assoc 2021 61 145 150 10.1016/j.japh.2020.09.022 26. Rozycki E Bilhimer M Bridgeman P Evaluation of burnout among emergency medicine pharmacists Pharmacother 2020 3 8 1423 1433 27. Skrupky LP West CP Shanafelt T Ability of the Well-Being Index to identify pharmacists in distress J Am Pharm Assoc 2020 60 906 914.e2 10.1016/j.japh.2020.06.015 28. Smith SE Slaughter AA Butler SA Examination of critical care pharmacist work activities and burnout Pharmacother 2021 4 5 554 569 29. Protano C De Sio S Cammalleri V A Cross-Sectional Study on Prevalence and Predictors of Burnout among a Sample of Pharmacists Employed in Pharmacies in Central Italy Biomed Res Int 2019 24 2019 8590430 30. Durham ME Bush PW Ball AM Evidence of burnout in health-system pharmacists Am J Health Syst Pharm 2018 75 S93 100 10.2146/ajhp170818 30333113 31. Jones GM Roe NA Louden L Factors associated with burnout among US hospital clinical pharmacy practitioners: results of a nationwide pilot survey Hosp Pharm 2017 52 742 751 10.1177/0018578717732339 29276254 32. Higuchi Y Inagaki M Koyama T A cross-sectional study of psychological distress, burnout, and the associated risk factors in hospital pharmacists in Japan BMC Public Health 2016 8 16 534 10.1186/s12889-016-3208-5 33. Muir PR Bortoletto DA Burnout among Australian hospital pharmacists J Pharm Pract Res 2007 37 3 187 189 10.1002/j.2055-2335.2007.tb00740.x 34. Lahoz MR, Mason HL. Burnout among pharmacists. Am Pharm. 1990; NS30(8):28–32 35. Maslach Burnout Toolkit for Medical Personnel - Mind Garden [Internet]. [cited 2022 Jun 19]. Available from: https://www.mindgarden.com/329-maslach-burnout-toolkit-for-medical-personnel#horizontalTab3 Accessed 01 Nov 2022. 36. Shoman Y Marca SC Bianchi R Psychometric properties of burnout measures: a systematic review Epidemiol Psychiatr Sci 2021 30 e8 10.1017/S2045796020001134 33436137 37. House of Commons 2021 Workforce burnout and resilience in the NHS and social care. Available at: https://committees.parliament.uk/work/494/workforce-burnout-and-resilience-in-the-nhs-and-social-care/ Accessed 01 Nov 2022. 38. Chui MA Look KA Mott DA The association of subjective workload dimensions on quality of care and pharmacist quality of work life Res Social Adm Pharm 2014 10 2 328 340 10.1016/j.sapharm.2013.05.007 23791360 39. Elbeddini A, Wen CX, Tayefehchamani Y et al. Mental health issues impacting pharmacists during COVID-19. J Pharm Policy Pract [Internet]. 2020;13(1). 40. Lemke MK Apostolopoulos Y Sönmez S Syndemic frameworks to understand the effects of COVID-19 on commercial driver stress, health, and safety J Transp Health 2020 1 18 100877 10.1016/j.jth.2020.100877 41. Connelly D. Work-related stress: the hidden pandemic in pharmacy [Internet]. The Pharmaceutical Journal. Available from: https://pharmaceutical-journal.com/article/feature/work-related-stress-the-hidden-pandemic-in-pharmacy Accessed 01 Nov 2022. 42. Missouridou E, Mangoulia P, Pavlou V et al. Wounded healers during the COVID-19 syndemic: compassion fatigue and compassion satisfaction among nursing care providers in Greece. Perspect Psychiatr Care. 2021. 10.1111/ppc.12946 43. Austin Z Gregory P Resilience in the time of pandemic: the experience of community pharmacists during COVID-19 Res Social Adm Pharm 2021 17 1 1867 1875 10.1016/j.sapharm.2020.05.027 32499160 44. Murray M Murray L Donnelly M Systematic review of interventions to improve the psychological well-being of general practitioners BMC Prim Care 2016 17 1 36 45. Regehr C Glancy D Pitts A Interventions to reduce the consequences of stress in physicians: a review and meta-analysis J Nerv Ment Dis 2014 202 353 359 10.1097/NMD.0000000000000130 24727721 46. West CP Dyrbye LN Erwin PJ Interventions to prevent and reduce physician burnout: a systematic review and meta-analysis Lancet 2016 388 2272 2281 10.1016/S0140-6736(16)31279-X 27692469 47. Panagioti M Panagopoulou E Bower P Controlled Interventions to Reduce Burnout in Physicians: A Systematic Review and Meta-analysis JAMA Intern Med 2017 177 195 205 10.1001/jamainternmed.2016.7674 27918798	36446993	PMC9707850	NO-CC CODE	2022-12-01 23:20:27	no	Int J Clin Pharm. 2022 Nov 29;:1-10	utf-8	Int J Clin Pharm	2,022	10.1007/s11096-022-01520-6	oa_other
==== Front Qual Quant Qual Quant Quality & Quantity 0033-5177 1573-7845 Springer Netherlands Dordrecht 1587 10.1007/s11135-022-01587-3 Article Biased, wrong and counterfeited evidences published during the COVID-19 pandemic, a systematic review of retracted COVID-19 papers Capodici Angelo 12 Salussolia Aurelia 1 http://orcid.org/0000-0001-8288-0563 Sanmarchi Francesco [email protected] 1 Gori Davide 1 Golinelli Davide 1 1 grid.6292.f 0000 0004 1757 1758 Department of Biomedical and Neuromotor Sciences (DIBINEM), Alma Mater Studiorum - Università di Bologna, Via San Giacomo 12, 40126 Bologna, Italy 2 grid.168010.e 0000000419368956 Department of Medicine (Biomedical Informatics), Stanford University - School of Medicine, Stanford, CA USA 29 11 2022 133 15 11 2022 © The Author(s), under exclusive licence to Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. In 2020 COVID-19 led to an unprecedented stream of papers being submitted to journals. Scientists and physicians all around the globe were in need for information about this new disease. In this climate, many articles were accepted after extremely fast peer-reviews to provide the scientific community with the latest discoveries and knowledge. Unfortunately, this also led to articles retraction due to authors’ misconduct or errors in methodology and/or conclusions. The aim of this study is to investigate the number and characteristics of retracted papers, and to explore the main causes that led to retraction. We conducted a systematic review on retracted articles, using PubMed as data source. Our inclusion criteria were the following: English-language retracted articles that reported original data, results, opinions or hypotheses on COVID-19 and Sars-CoV-2. Twenty-seven retracted articles were identified, mainly reporting observational studies and opinion pieces. Many articles published during the first year of the pandemic have been retracted, mainly due to the authors' scientific misconduct. Duplications, plagiarism, frauds and absence of consent, were the main reasons for retractions. In modern medicine, researchers are required to publish frequently, and, especially during situations like the COVID-19 pandemic, when articles were rapidly published, gaps in peer-reviews system and in the path to scientific publication arose. Keywords Retracted Frauds COVID-19 Peer-review Systematic review Misconduct ==== Body pmcIntroduction In present times, the creation of sound scientific evidence in healthcare lies within the Evidence-Based Medicine (EBM) framework; scholars trust their peers to review their work, and, if it is found to be rigorous and unbiased, the process ends with publication (Boetto et al. 2020). The COVID-19 pandemic had a significant effect on scientific literature and practice in underlining their greatest strengths by promoting interprofessional collaborations and sharing ideas between multiple sites (Hynicka et al. 2022; Pawłowicz-Szlarska et al. 2022; Lincoln et al. 2022). These vivid collaborations sparked by the pandemic outbreak, united with globalization and easy online access, led to an enormous stream of research papers: to understand the scale of this phenomenon, Dimension Database estimates well over 870,000 research papers published about COVID-19, from 198 countries, in 2020 and 2021 alone, as compared to 109,000 papers regarding Seasonal Influenza published between 2015 and 2019 (Dimension Database 2022). In 2020 a great deal -around 33%- of academic papers about COVID-19 were disseminated as pre-prints, which could be published online without the need for peer review (Franser et al. 2021; Watson 2022), and this contributed to the infodemic which altered mass media information (Palayew et al. 2020; Pian et al. 2021; Corinti et al. 2022). These preprints were eventually screened by peers, but the vast amount of information needing peer-review led to decreased review times for COVID-19 papers, mainly at the expense of other topics (Else 2020). The overwhelming need for quick peer-reviews, combined with the necessity for focused healthcare workers, made the peer-review and publication process more vulnerable to errors, and amplified the risk of lacking transparency and reproducibility in studies, and therefore of frauds and misconducts (Publications Office of the EU 2020). Publishing timeframes have also become increasingly tight during the pandemic, due to expedited review and fast-tracks specifically suited for COVID-19 papers (Bagdasarian et al. 2020; Benjamens et al. 2021; Schonhaut et al. 2022). As a result, as Yeo-Teh and al. described in their work (Yeo-Teh et al. 2019), the retraction rate spiked to alarming heights, having more than tripled from a pre-pandemic retraction rate regarding viruses and epidemics of 0.021% to a COVID-19 related retraction rate of 0.074%. This worrying pattern is reaffirmed in comparing present times retraction rates with past ones, as retractions were most commonly related to either Basic Life Sciences or Technology, rather than medicine, and, furthermore, the number of retractions per journal did not change since 1756–2019 (Vuong et al. 2020). In this scenario, a noteworthy number of authors acted mistakenly or sometimes even maliciously to exploit this “opportunity” by submitting papers of dubious scientific value, duplicating their submission to various journals, plagiarizing others’ work, or plainly tampering data to fit their assumption. On the other hand, other authors simply made honest mistakes or suffered from unreliable data; at this date Retraction Watch has listed 253 COVID-19 related, retracted, papers (Retraction Watch 2022). Although peer review is designed to promptly assess methodology and conclusion flaws, it is not yet fully equipped to deal rapidly with shady behaviors, especially during times of high demand of information, such as the COVID-19 pandemic. All the aforementioned issues had serious consequences, reducing trust in science and medicine, fueling misinformation and contributing to the infodemic. Aim of the study We reviewed the COVID-19 related scientific literature to investigate the number and characteristics of retracted papers, and to explore the main causes or motivations that led to retraction. Methods Search strategy and selection criteria We conducted a systematic review of the scientific literature, following the Preferred Reporting Items for Systematic Reviews (PRISMA) approach (Moher et al. 2009), to identify COVID-19-related quantitative and qualitative studies of different designs published during the pandemic and then retracted for any cause. The initial search was implemented on March 1, 2022 and was limited to the timespan between December 1, 2019 and December 31, 2021. The search query consisted of terms considered pertinent by the authors to review the literature on retracted COVID-19 articles. We searched for publications on Pubmed using the following search string: “(retracted publication[pt]) AND ((covid) OR (coronavirus) OR (Sars-c))”. We included English-language retracted articles that reported original data, results, opinions or hypotheses on COVID-19 and Sars-CoV-2. We excluded studies that did not focus on COVID-19 as the primary subject of the study or considered it as a background factor only. Data extraction Data was extracted by four independent reviewers (AC, AS, FS, DGor), and disagreement on extracted data was discussed with one independent tiebreaker (DGol). Descriptive variables extracted from each article and journal were: publishing journal, journal’s 3-year impact factor, first author affiliation’s country (categorized in high-income, middle-high income, middle-low-income and low-income countries (The World Bank 2021)), study type, data availability, analyzed population, sample size, aim, results, retraction statement, who first requested retraction, subscription or open access, funding and timestamps of publication and retraction (i.e., time between date of publication and date of retraction). Articles’ lifespan and the time difference between the timestamps were extracted from the article information or full-text. Whenever the exact day was not provided and the article reported only the month, we referred to the 15th of the same month as the corresponding date. The following categories were assigned to retraction statements: Process, Misconduct and Others. Each of these categories had specific reasons for retraction, “Process” included: “Approval issue”, which implied problems regarding the use of data, “Error”, which implied problems regarding the methodology of the study and “Not reproducible”, which implied the non-reproducibility of the study due to inaccessible data. “Misconduct” comprehended all mischievous behaviors by authors: fraud, plagiarism and duplication of the article. Finally, the label “Other” was used to categorize all those retraction statements which did not fall in the previous labels, in this category we found two specific reasons: “not first-hand account”, “premature conclusions”, “redundant”, “out of journal scope”, “publisher error” and “not specified”. Results Of the initial 69 records identified, 66 full-text articles matched our inclusion criteria and were assessed and included in the review (Fig. 1, Table 1). 38 studies were published in 2020 and 28 in 2021. Studies' characteristics are summarized in Table 1. Although we did not limit our search to specific study types, the majority of retracted articles were either observational studies (n = 34, 52%) or opinion papers (n = 11, 17%) (Fig. 2 and Table 1), followed by reviews articles (n = 9, 14%), experimental (n = 7, 11%), meta-analysis (n = 4, 6%), study protocol (n = 1, 1%). Three studies were excluded during the screening process (Zellmer 2021; Hill et al. 2021a, b; Liu et al. 2020) since COVID-19 was not the main focus of the paper. The included studies came from all regions of the world, with 46 (60%) of the articles drafted in high-income countries (Table 1).Fig. 1 PRISMA flowchart Table 1 Included articles’ summary Title Country Journal 3-year IF Study type Retraction label Specific reasons for retraction OA article Retraction requested/accepted by authors Journal type Δt retraction (days) Racial disparity amongst stroke patients during the coronavirus disease 2019 pandemic USA Cureus 0.00 Observational Process Approval issue Yes No Open 125 Effects of the covid-19 pandemic on stroke patients USA Cureus 0.00 Observational Process Approval issue Yes No Open 142 Noteworthy neurological manifestations associated with covid-19 infection USA Cureus 0.00 Observational Misconduct Fraud Yes No Open 245 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection mimicking as pulmonary tuberculosis in an inmate USA Cureus 0.00 Observational Process Error Yes Yes Open 452 Severe dengue with multisystem inflammatory syndrome in children due to COVID-19: A Co-infection case series Bangladesh Cureus 0.00 Observational Process Error Yes No Open 4 Smoker, former smoker and COVID-19: nicotine does not protect against SARS-CoV-2 Spain Arch Bronconeumol 0.69 Observational Other NS Yes No Subscription/open 4 Covid-19, suicide, and femicide: rapid research using google search phrases New Zealand J Gen Psychol 0.75 Review Process Error No Yes Subscription/open 117 Basic demographic parameters help predict outcomes in patients hospitalized With COVID-19 during the first wave of infection in West Texas USA J Prim Care Community Health 0.96 Observational Process Approval issue Yes Yes Open 256 A retrospective analysis and comparison of prisoners and community-based patients with COVID-19 requiring intensive care during the first phase of the pandemic in West Texas USA J Prim Care Community Health 0.96 Observational Process Approval issue Yes Yes Open 321 Clinical characteristics and blood test results in covid-19 patients China Ann Clin Lab Sci 1.02 Observational Process Error No Yes Subscription 61 Retracted: facemasks in the covid-19 era: a health hypothesis USA Med Hypotheses 1.46 Review Other Premature Conclusions No No Subscription/open 171 Retracted: 5 g technology and induction of coronavirus in skin cells Italy J Biol Regul Homeost Agents 1.47 Opinion Misconduct Fraud No No Subscription 37 Retracted: A study of potential SARS-CoV-2 antiviral drugs and preliminary research of their molecular mechanism, based on Anti-SARS-CoV drug screening and molecular dynamics simulation China J Comput Biol 1.48 Experimental Other Premature conclusions Yes Yes Subscription/open 226 WITHDRAWN: health risk assessment and health management of urban residents facing epidemic pneumonia China Work 1.51 Observational Misconduct Fraud Yes No Subscription/open 107 Convalescent plasma therapy in covid 19: every dark cloud has a silver lining India J Anaesthesiol Clin Pharmacol 1.74 Opinion Misconduct Duplication Yes No Subscription/open 94 Noninvasive versus invasive ventilation: one modality cannot fit all during covid-19 outbreak India Korean J Anesthesiol 1.79 Review Misconduct Plagiarism Yes No Open 68 Efficacy and safety of acupuncture therapy for asymptomatic infection of covid-19: a protocol for systematic review and meta-analysis China Medicine (Baltimore) 1.87 Study protocol Misconduct Duplication Yes No Open 161 Human immunodeficiency virus (HIV) and outcomes from coronavirus disease 2019 (COVID-19) pneumonia: A Meta-Analysis and Meta-Regression Indonesia AIDS res hum retroviruses 1.91 Meta-analysis Misconduct Plagiarism No No Subscription/open 125 Withdrawn: a mechanistic analysis placental intravascular thrombus formation in covid-19 patients USA Ann diagn pathol 1.92 In vitro Misconduct Duplication Yes No Subscription/open 58 Impact of the COVID-19 pandemic on stroke epidemiology and clinical stroke practice in the US USA J Stroke cerebrovasc dis 1.95 Observational Process Approval Issue Yes No Subscription/open 165 Mental health burden for the public affected by the covid-19 outbreak in china: who will be the high-risk group? China Psychol health med 2.00 Observational Misconduct Duplication Yes No Subscription/open 192 Vitritis and outer retinal abnormalities in a patient with COVID-19 Brazil Ocul immunol inflamm 2.02 Observational Process Error No No Subscription/open 366 Implementation of a telemedicine service during COVID-19 pandemic in Pakistan Pakistan Int J Clin Pract 2.04 Experimental Process Error Yes Yes Subscription/open 147 Clinical characteristics and outcomes of patients with COVID-19 pneumonia admitted to an intensive care unit in Faisalabad, Pakistan Pakistan Int J Clin Pract 2.04 Observational Misconduct Fraud Yes Yes Subscription/open 201 Neutrophil/lymphocyte ratio-A marker of COVID-19 pneumonia severity Pakistan Int J Clin Pract 2.04 Observational Misconduct Fraud Yes Yes Subscription/open 401 Obesity and mortality of covid-19. Meta-analysis UK Obes Res Clin Pract 2.17 Meta-analysis Process Error Yes Yes Subscription/open 212 COVID-19 admissions calculators – revisited Malta Early Hum Dev 2.18 Observational Other Premature conclusions Yes Yes Subscription/open 424 COVID-19 admissions calculators: general population and paediatric cohort Malta Early Hum Dev 2.18 Observational Other Premature conclusions Yes Yes Subscription/open 433 Unknown unknowns—COVID-19 and potential global mortality Malta Early Hum Dev 2.18 Observational Other Premature conclusions Yes Yes Subscription/open 437 Clinical sequelae of the novel coronavirus: does covid-19 infection predispose patients to cancer? USA Future Oncol 2.22 Review Misconduct Plagiarism Yes No Subscription/open 189 RETRACTED: Biopsychosocial intersections of social/affective touch and psychiatry: Implications of 'touch hunger' during COVID-19 India Int J Soc Psychiatry 2.24 Review Misconduct Fraud Yes No Subscription/open 275 A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell Egypt J Nanopart Res 2.25 Observational Other Out of journal scope Yes No Subscription/open 303 Coronavirus disease-2019: a brief compilation of facts India J Oral Maxillofac Pathol 2.27 Opinion Misconduct Plagiarism Yes No Subscription/open 53 Efficacy of favipiravir in COVID-19 treatment: a multi-center randomized study Egypt Arch Virol 2.44 Experimental Process Error Yes No Subscription/open 301 Anal swab as a potentially optimal specimen for sars-cov-2 detection to evaluate hospital discharge of covid-19 patients China Future Microbiol 2.51 Observational Misconduct Absence of consent by patients Yes No Subscription/open 242 Clinical and scientific rationale for the "MATH + " hospital treatment protocol for COVID-19 USA J Intensive Care Med 2.51 Review Process Error Yes No Subscription/open 329 Chinese mental health burden during the covid-19 pandemic China Asian J Psychiatr 2.53 Observational Misconduct Duplication Yes No Subscription/open 217 Covid-19 and potential global mortality – revisited Malta Early Hum Dev 2.53 Opinion Other Premature Conclusions Yes Yes Subscription/open 330 The mechanisms of action of ivermectin against SARS-CoV-2-an extensive review Italy J Antibiot (Tokyo) 2.69 Review Process Error Yes No Subscription/open 104 SARS-CoV-2 vaccination and antibody testing in immunosuppressed populations: you can't tell the players without a scorecard [RETRACTED] USA Transplantation 2.83 Opinion Misconduct Duplication Yes No Subscription/open 63 Meta-analysis of randomized trials of ivermectin to treat SARS-CoV-2 infection UK Open Forum Infect Dis 3.02 Meta-analysis Process Error Yes Yes Open 214 Methylene blue photochemical treatment as a reliable SARS-CoV-2 plasma virus inactivation method for blood safety and convalescent plasma therapy for COVID-19 China BMC Infect Dis 3.02 Experimental Process Error Yes No Open 86 Lung ultrasound score in establishing the timing of intubation in covid-19 interstitial pneumonia: a preliminary retrospective observational study China PLoS One 3.21 Observational Misconduct Duplication Yes No Open 119 Tracking COVID-19 vaccine hesitancy and logistical challenges: a machine learning approach Canada PLoS One 3.21 Observational Process Approval Issue Yes Yes Open 50 Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China China BMC Public Health 3.36 Observational Misconduct Fraud Yes No Open 76 Comment on an article: "Osteoporosis in the age of COVID-19 patients" Bosnia and Herzegovina Osteoporos Int 3.59 Opinion Other Redundant Yes No Subscription/open 174 A meta-analysis of granulocyte–macrophage colony-stimulating factor (GM-CSF) antibody treatment for COVID-19 patients China Ther Adv Chronic Dis 4.42 Meta-analysis Process Error Yes No Open 93 Retracted: The safety of COVID-19 vaccinations-we should rethink the policy Poland Vaccines 4.42 Observational Process Error Yes No Open 8 Phytotherapeutic options for the treatment of covid-19: a concise viewpoint Pakistan Phytother Res 4.55 Opinion Misconduct Plagiarism Yes Yes Subscription/open 132 Safety and efficacy of favipiravir versus hydroxychloroquine in management of COVID-19: A randomised controlled trial Egypt Sci Rep 4.60 Experimental Misconduct Fraud No No Subscription/open 171 Exploring the potential effect of COVID-19 on an endangered great ape Denmark Sci Rep 4.60 Observational Process Error Yes Yes Subscription/open 96 GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest India Sci Rep 4.60 Observational Process Error Yes No Subscription/open 229 Stay-at-home policy is a case of exception fallacy: an internet-based ecological study Brazil Sci Rep 4.60 Observational Process Error Yes No Subscription/open 284 Retracted: no deleterious effect of lockdown due to covid-19 pandemic on glycaemic control, measured by glucose monitoring, in adults with type 1 diabetes Spain Diabetes Technol Ther 4.80 Observational Process Approval issue Yes No Subscription/open 76 Effects of a single dose of ivermectin on viral and clinical outcomes in asymptomatic SARS-CoV-2 infected subjects: a pilot clinical trial in Lebanon Lebanon Viruses 5.02 Experimental Process Error Yes Yes Open 153 Rationale and criteria for a COVID-19 model framework Italy Viruses 5.02 Review Misconduct Plagiarism Yes No Open 23 Intersectionality and inequalities in medical risk for severe COVID-19 in the Canadian longitudinal study on aging Canada Gerontologist 5.27 Observational Process Approval issue Yes Yes Subscription/open 120 Analyzing pre-pandemic patterns of contacts is partly inappropriate to explain the current COVID-19 situation in Germany Germany Lancet Reg Health Eur 6.37 Review Other Publisher error Yes No Open 28 Retracted article: sars-cov-2 infects t lymphocytes through its spike protein-mediated membrane fusion China Cell Mol Immunol 6.76 In vitro Process Error Yes Yes Subscription/open 94 Will the extraction of COVID-19 from wastewater help flatten the curve? South Africa Chemosphere 7.18 Observational Misconduct Duplication Yes No Subscription/open 231 Effectiveness of surgical and cotton masks in blocking sars-cov-2: a controlled comparison in 4 patients South Korea Ann Intern Med 9.79 Opinion Process Error Yes No Subscription/open 57 Chinese medical staff request international medical assistance in fighting against covid-19 China Lancet Glob Health 22.28 Opinion Other Not first hand account Yes No Subscription/open 3 Family planning in COVID-19 times: access for all Kenya Lancet Glob Health 22.28 Opinion Process Error Yes Yes Subscription/open 38 Cardiovascular disease, drug therapy, and mortality in covid-19 USA N Engl J Med 40.27 Observational Process Not reproducible Yes No Subscription/open 34 Chloroquine or hydroxychloroquine for covid-19: why might they be hazardous? France Lancet 47.90 Opinion Process Not reproducible Yes Yes Subscription/open 48 Retracted: hydroxychloroquine or chloroquine with or without a macrolide for treatment of covid-19: a multinational registry analysis USA Lancet 47.90 Observational Process Not reproducible Yes No Subscription/open 14 Fig. 2 Number of publications by study type The most frequent label attributed to the articles’ retraction was “process” (32, 48%), followed by “misconduct” (23, 35%). As for retraction’s specific reasons, “Authors’ Error” (21, 32%), “Approval Issue” (8, 12%), “Fraud” (8, 12%) and “Duplication” (8, 12%) were the most frequent ones (Fig. 3). The articles’ retraction followed authors’ self-report in 35% (23) of the included records. Most of the retracted articles (45, 68%) were submitted to hybrid journals, whereas 19 (29%) were submitted to open-access journals and 2 (3%) to subscription-only journals. Interestingly, 91% (60) of the included articles were published with the open access model. The median journals’ impact factor was 2.352 (Range: 0—47.9).Fig. 3 Number of publications by retraction’s specific reason The median number of days that occurred between publication and retraction was 137 (Range: 3—452). This timespan differed among retractions’ specific reasons: articles with the “premature conclusions” label were the ones retracted after the longest period, with a mean of 337 days. The fastest retraction occurred for the study labeled as “Not first hand account” (3 days). As for study type, the 34 meta-Observational studies were those with the longest mean lifespan by study type (mean = 191). On the other hand, the 11 opinion papers were the ones with the shorter timespan (mean = 94 days) (Fig. 4).Fig. 4 Time to retraction by A study type and B retraction’s specific reason For reference, the entirety of the selected papers can be found in Table 1, and their citations are as follows: Ghanchi et al. 2020a, b; Elkhouly et al. 2020; An et al. 2020; Mulvey et al. 2020; Huang et al. 2021; Sun et al. 2020; Huang et al. 2020; Lu et al. 2020; Beato-Víbora 2020; Mehra et al. 2020a, b; Mehra et al. 2020a, b; Walach et al. 2021; Dutta et al. 2021; Friedlich et al. 2021; Ali et al. 2020a, b; Lin 2021; Ali et al. 2020a, b; Khalifa et al. 2020; Gul et al. 2021; Victor 2020a, b; Victor 2020a, b; Colchero et al. 2021; Saha et al. 2021; Ma et al. 2021; Imran et al. 2021; Akbar et al. 2020; Ferdous et al. 2021; Jiménez-Ruiz et al. 2021; Savaris et al. 2021; Grech 2020a, b; Zago Filho et al. 2020; Atangana et al. 2021; Mao et al. 2021; Fioranelli et al. 2020; Deokar et al. 2020; Temmerman 2021; Ibrahimagić et al. 2021; Saxena 2020; Grech 2020a, b; Din et al. 2020; Bae et al. 2020; Zeng et al. 2020; Funck-Brentano et al. 2020; Woodle et al. 2021; Standish 2021; Vainshelboim 2021; Singh 2020; Hays 2020; Kampf 2022; Kory et al. 2021; Messina et al. 2021; Banerjee et al. 2021; Zaidi et al. 2022; Wang et al. 2020; Jin et al. 2021; Zhao et al. 2020; Samaha et al. 2021; Dabbous et al. 2021; Nagra et al. 2021; Dabbous et al. 2021; Hussain et al. 2020; Hariyanto et al. 2019; Hill et al. 2021a, b; Guan et al. 2021; Huang et al. 2020. Discussion In this systematic literature review, we sought and described the characteristics of the COVID-19 related retracted papers that were accepted in impacted journals after the peer review process during the first two years of the COVID-19 pandemic (2020–2021). We found that observational studies were the most retracted ones, followed by opinion papers and reviews; this could be a consequence of the epidemic outbreak, since in a brief period of time it was not possible to properly design and publish results from Randomized Controlled Trials (RCTs) or methodologically sound prospective studies, while observational retrospective studies can be set up and conducted more rapidly, being, nonetheless, at higher risk of several types of bias and pitfalls (Viswanathan et al. 2013; Grimes et al. 2002). We found misconduct and process-related issues as the most prominent cause of paper retraction. As an example of observational study that got retracted due to misconduct reasons, Sun M, et al. (Sun et al. 2020), in their article “Anal swab as a potentially optimal specimen for SARS-CoV-2 detection to evaluate hospital discharge of COVID-19 patients” did not acquire patients’ consent prior to publication in order to conduct their study. Even though these issues have always been there for the scientific path to publication, a stress test such as COVID-19 amplified their detrimental effects. An interesting peer-review process’ pitfall can be described recalling one attempt at meta-analysis by Hussain et Al. (Hussain et al. 2020). The retraction statement goes: “This article has been retracted at the request of the authors due to inadvertent errors (such as in the calculation of odds ratio for age and naming patients’ groups in the plots) that unfortunately passed unnoticed during the extremely rapid review and publication process at the peak of the COVID-19 pandemic”. This meta-analysis wanted to assess how mortality by COVID-19 was influenced by obesity, and it was based on data from the first four months of the pandemic. This retracted study demonstrates the importance of relying on sound scientific methods and rigorous peer-reviews. Furthermore, one study supported the non-effectiveness of both surgical and cotton masks in preventing the dissemination of SARS-CoV-2 from patients with COVID-19 (Bae et al. 2020). It is possible that this type of publications can have a negative indirect effect on health policy decisions and therefore on the health of the population. Notably, Impact-Factor (IF) was extremely heterogeneous, and some articles found their way to high IF journals, as was the case with the two articles published in the New England Journal of Medicine and The Lancet, by Dr. Mandeep R. Mehra et Al., who utilized an unfit database taking part in the “Surgisphere Scandal” (Baker et al. 2020). Articles published in high IF journals were checked and retracted relatively quickly, while articles published in journals with lower IFs took much more time to be community checked and retracted. A possible explanation for this is that journals with higher IF have more visibility and are therefore read by a higher number of experts. As a result, poor quality articles are more easily spotted and forced to retraction. Overall, journals’ IF was found to be relatively high and with high variance. This means IF cannot always be considered as a guarantee for peer-review quality, as various authors have stated in their work (Vrabel 2019; Paulus et al. 2018; Juyal et al. 2019). As for retracted papers’ geographic distribution, we found that the Asian continent was the most afflicted. This represents a direct consequence of one key factor: the urgent need for information within the epicenter of the pandemic, which required observational studies to be preferred during the first months of the pandemic, even though these are more easily tampered in their data and afflicted with errors in their method (Viswanathan et al. 2013; Grimes et al. 2002). In order to address these issues, publishers, editors and other stakeholders should focus on several aspects of the path to scientific publication: improving the process by granting adequate peer-review times (Barnabic et al. 2022; COVID-19 Report 2022; Improving pandemic preparedness and management 2020); using open data when possible or making it always available upon reasonable requests (Hynicka et al. 2022). It could also be beneficial to use distributed ledger technologies (eg, blockchains) in order to tamper proof data and its analysis or machine learning to double check data (Boetto et al. 2020). Study limitations This review presents various limitations. First, we may have missed other studies since we used only one database. Secondly, information about specific reasons for article retraction are only available in the article retraction statement, therefore the comprehensiveness of retraction motivations could not be as complete as it should. Although these limitations exist, to the best of our knowledge no other studies on the subject have been published yet. This study therefore starts a much-needed discussion on the topic of peer reviewed scientific literature during times in which the need for accurate new information is in high demand. Finally, in reading this review, a healthy dose of discretion is advised, since we do not suggest the academic situation was much better or worse before the pandemic, but only analyze the effects COVID-19 has had on the scientific path to publication. Conclusions In a modern scientific community in which “publish or perish” is still mandatory and h-index is considered a sound metric to value scholars, quantity is frequently prioritized over quality. Although peer-reviewers usually defend the scientific community's integrity, the publication process is not perfect and it can miss its target, as reported here about the COVID-19 pandemic. Open access and fast track paths to publication are useful tools but might represent a menace if left unchecked since the former can be an incentive to accept manuscripts, and the latter can lead to lower quality threshold and reduced attention by peer-reviewers. It is reasonable to assume the retracted articles found in this review are just the tip of an iceberg of dubious-value publications pre- and during the pandemic. During times of great stress, such as the SARS-CoV-2 pandemic, this should be considered a menace for the integrity of modern science as well as for the well-being of the global community since unchecked facts and fake evidence are the ground on which fake news thrive. Author contributions AC had the idea, contributed to data extraction and to drafting the manuscript; AS contributed to data extraction and to drafting the manuscript; FS data extraction and analyzed the data; DG contributed to data extraction and to drafting the manuscript; DG contributed to drafting the manuscript and coordinated the project. All authors read and approved the final manuscript. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Declarations Competing interests The authors have no relevant financial or non-financial interests to disclose. The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Italian or the United States government, nor of the institutions the authors are affiliated with. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Akbar, H., Kahloon, R., Akbar, S., Kahloon, A.; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection mimicking as pulmonary tuberculosis in an inmate. Cureus. 12(6):e8464 (2020). doi: 10.7759/cureus.8464. Retraction in: Cureus. 2021 Aug 31;13(8):r34 Ali, K., Rao, S., Dennis, J., Berdine, G., Test, V., Nugent, K.: Basic demographic parameters help predict outcomes in patients hospitalized with covid-19 during the first wave of infection in West Texas. J Prim Care Community Health. 11:2150132720970717 (2020a). doi: 10.1177/2150132720970717. Retraction in: J Prim Care Community Health. 2021 Jan-Dec;12:21501327211033367 Ali, K., Rao, S., Berdine, G, Test, V., Nugent, K.: A retrospective analysis and comparison of prisoners and community-based patients with COVID-19 requiring intensive care during the first phase of the pandemic in West Texas. J. Prim. Care Community Health. 11:2150132720954687 (2020b). doi: 10.1177/2150132720954687. Retraction in: J Prim Care Community Health. 2021 Jan-Dec;12:21501327211033367 An, X.S., Li, X.Y., Shang, F.T., Yang, S.F., Zhao, J.Y., Yang, X.Z., Wang, H.G.: Clinical characteristics and blood test results in COVID-19 patients. Ann. Clin. Lab. Sci.;50(3):299–307 (2020). Retraction in: Ann Clin Lab Sci. 2020 Jul;50(4):560 Atangana, E., Oberholster, P.J., Turton, A.R.: Will the extraction of COVID-19 from wastewater help flatten the curve? Chemosphere. 271:129429 (2021). doi: 10.1016/j.chemosphere.2020.129429. Epub 2021 Jan 5. Retraction in: Chemosphere. 2021 Nov;283:131992 Bae, S., Kim, M.C., Kim, J.Y., Cha, H.H., Lim, J.S., Jung, J., Kim, M.J., Oh, D.K., Lee, M.K., Choi, S.H., Sung, M., Hong, S.B., Chung, J.W., Kim, S.H.: Effectiveness of surgical and cotton masks in blocking SARS-CoV-2: a controlled comparison in 4 patients. Ann. Intern. Med. 173(1):W22-W23 (2020). doi: 10.7326/M20-1342. Epub 2020 Apr 6. Retraction in: Ann Intern Med. 2020 Jun 2 Bagdasarian N Cross GB Fisher D Rapid publications risk the integrity of science in the era of COVID-19 BMC Med. 2020 18 1 192 10.1186/s12916-020-01650-6.PMID:32586327;PMCID:PMC7315694 32586327 Baker N Van Noorden R Maxmen A Coronapod: the surgisphere scandal that rocked coronavirus drug research Nature. 2020 10.1038/d41586-020-01790-y Banerjee, D., Vasquez, V., Pecchio, M., Hegde, M.L., Ks, Jagannatha, R., Rao, T.S.: RETRACTED: Biopsychosocial intersections of social/affective touch and psychiatry: Implications of 'touch hunger' during COVID-19. Int J Soc Psychiatry. 20764021997485 (2021). doi: 10.1177/0020764021997485. Epub ahead of print. Retraction in: Int J Soc Psychiatry. 2021 Nov 26;:207640211063062 Barnabic C Rambob I Berman BM Interprofessional culinary medicine training enhanced nutrition knowledge, nutrition counseling confidence, and interprofessional experience J. Integr. Complement. Med. 2022 10.1089/jicm.2022.0573 Beato-Víbora, P.I., RETRACTED: No deleterious effect of lockdown due to COVID-19 pandemic on glycaemic control, measured by glucose monitoring, in adults with type 1 diabetes. Diabetes Technol Ther. (2020) doi: 10.1089/dia.2020.0184. Epub ahead of print. Retraction in: Diabetes Technol. Ther. 2020 Aug;22(8):643. PMID: 32396400. Benjamens S Pol RA de Meijer VE Peer review during demanding times: maintain rigorous standards Scientometrics 2021 10.1007/s11192-021-04007-9 Boetto, E., Golinelli, D., Carullo, G., et al. Frauds in scientific research and how to possibly overcome them. J. Med. Eth. Published Online First: 06 October 2020. 10.1136/medethics-2020-106639 Colchero, F., Eckardt, W., Stoinski, T.: Exploring the potential effect of COVID-19 on an endangered great ape. Sci. Rep. 11(1):20715 (2021). doi: 10.1038/s41598-021-00061-8. Retraction in: Sci Rep. 2022 Jan 25;12(1):1624 Corinti F Pontillo D Giansanti D COVID-19 and the infodemic: an overview of the role and impact of social media, the evolution of medical knowledge, and emerging problems Healthc. (basel). 2022 10.3390/healthcare10040732 COVID-19 Report: publications, clinical trials, funding - data obtained on August 9, 2022, from Digital Science’s Dimensions platform, https://covid-19.dimensions.ai Dabbous, H.M., Abd-Elsalam, S., El-Sayed, M.H., Sherief, A.F., Ebeid, F.F.S., El Ghafar, M.S.A., Soliman, S., Elbahnasawy, M., Badawi, R., Tageldin, M.A.: Efficacy of favipiravir in COVID-19 treatment: a multi-center randomized study. Arch Virol. 166(3):949–954. (2021). doi: 10.1007/s00705-021-04956-9. Epub 2021 Jan 25. Retraction in: Arch Virol. 2022 Jan;167(1):277 Dabbous, H.M., El-Sayed, M.H., El Assal, G., Elghazaly, H., Ebeid, F.F.S., Sherief, A.F., Elgaafary, M., Fawzy, E., Hassany, S.M., Riad, A.R., TagelDin, M.A.: Safety and efficacy of favipiravir versus hydroxychloroquine in management of COVID-19: A randomised controlled trial. Sci. Rep. 11(1):7282 (2021). doi: 10.1038/s41598-021-85227-0. Retraction in: Sci Rep. 2021 Sep 18;11(1):18983 Deokar, K., Chawla, G., Shadrach, B.J., Dutt, N.: Convalescent plasma therapy in COVID 19: every dark cloud has a silver lining. J. Anaesthesiol Clin. Pharmacol. 36(3):419–423 (2020). doi: 10.4103/joacp.JOACP_523_20. Epub 2020 Oct 16. Retraction in: J Anaesthesiol Clin Pharmacol. 2020 Oct-Dec;36(4):573 Din, M., Ali, F., Waris, A., Zia, F., Ali, M.: Phytotherapeutic options for the treatment of COVID-19: A concise viewpoint. Phytother Res. 34(10):2431–2437 (2020). doi: 10.1002/ptr.6786. Epub 2020 Aug 20. Retraction in: Phytother Res. 2021 Jan;35(1):542 Dutta, S., Kumar, A., Dutta, M., Walsh, C.: Tracking COVID-19 vaccine hesitancy and logistical challenges: a machine learning approach. PLoS One. 2021 Jun 2;16(6):e0252332. doi: 10.1371/journal.pone.0252332. Retraction in: PLoS One. 2021 Jul 22;16(7):e0255347 Elkhouly, A., Kaplan, A.C.: Noteworthy neurological manifestations associated with COVID-19 infection. Cureus.;12(7):e8992 (2020). doi: 10.7759/cureus.8992. Retraction in: Cureus. 5 Mar 2021;13(3):r26 Else H How a torrent of COVID science changed research publishing - in seven charts Nature 2020 588 7839 553 10.1038/d41586-020-03564-y 33328621 Ferdous, A., Hossain, M.M., Afrin, M.: Severe dengue with multisystem inflammatory syndrome in children due to COVID-19: a co-infection case series. Cureus. 13(11):e19516 (2021). doi: 10.7759/cureus.19516. Retraction in: Cureus. 2021 Nov 16;13(11):r35 Fioranelli, M., Sepehri, A., Roccia, M.G., Jafferany, M., Olisova, O.Y., Lomonosov, K.M., Lotti, T.: RETRACTED: 5G Technology and induction of coronavirus in skin cells. J Biol Regul Homeost Agents. (2020). 10.23812/20-269-E-4. doi: 10.23812/20-269-E-4. Epub ahead of print. Retraction in: J Biol Regul Homeost Agents. 2020 Jul 16;34(4) Fraser N Brierley L Dey G The evolving role of preprints in the dissemination of COVID-19 research and their impact on the science communication landscape PLoS Biol. 2021 19 4 e3000959 10.1371/journal.pbio.3000959 33798194 Friedlich, D., Newman, T., Bricker, S.: Impact of the COVID-19 pandemic on stroke epidemiology and clinical stroke practice in the US. J Stroke Cerebrovasc Dis. 30(4):105639 (2021). doi: 10.1016/j.jstrokecerebrovasdis.2021.105639. Epub 2021 Jan 26. Retraction in: J Stroke Cerebrovasc Dis. 2021 Aug;30(8):105982 Funck-Brentano, C., Salem, J.E.: Chloroquine or hydroxychloroquine for COVID-19: why might they be hazardous? Lancet. S0140–6736(20)31174–0 (2020). doi: 10.1016/S0140-6736(20)31174-0. Epub ahead of print. Retraction in: Lancet. 2020 Jul 18;396(10245):e2-e3 Ghanchi, H., Takayanagi, A., Savla, P., Hariri, O.R., Tayag, E.C., Schiraldi, M., Jorgensen, L., Miulli DE.: Effects of the COVID-19 pandemic on stroke patients. Cureus.;12(8):e9995 (2020a). doi: 10.7759/cureus.9995. Retraction in: Cureus. 2021 Jan 13;13(1):r22 Ghanchi, H., Patchana, T., Wiginton, J., 4th, Browne, J.D., Ohno, A., Farahmandian, R., Duong, J., Cortez DE Miulli, V.: Racial disparity amongst stroke patients during the coronavirus disease 2019 pandemic Cureus. 12 9 e10369 (2020b). 10.7759/cureus.10369. Retraction in: Cureus. 2021 Jan 13;13(1):r21 Grech, V.: Unknown unknowns - COVID-19 and potential global mortality. Early Hum Dev. 144:105026 (2020a). doi: 10.1016/j.earlhumdev.2020.105026 Retraction in: Early Hum Dev. 2021 Aug;159:105377 Grech, V.: COVID-19 and potential global mortality - Revisited. Early Hum Dev. 144:105054 (2020b). doi: 10.1016/j.earlhumdev.2020b105054. Epub 2020b Apr 30. Retraction in: Early Hum Dev. 2021 Mar 26;156:105341 Grimes DA Schulz KF Bias and causal associations in observational research Lancet 2002 359 9302 248 252 10.1016/S0140-6736(02)07451-2 11812579 Guan, J.T., Wang, W.J., Jin, D., Mou, X.Y., Lei, S.S., Xu, Z.H.: A meta-analysis of granulocyte-macrophage colony-stimulating factor (GM-CSF) antibody treatment for COVID-19 patients. Ther Adv Chronic Dis. 12:20406223211039699 (2021). doi: 10.1177/20406223211039699. Retraction in: Ther Adv Chronic Dis. 2021 Nov 21;12:20406223211056430 Gul, N., Usman, U., Ahmed, U., Ali, M., Shaukat, A., Imran, M.M.: Clinical characteristics and outcomes of patients with COVID-19 pneumonia admitted to an intensive care unit in Faisalabad, Pakistan. Int. J. Clin. Pract. 75(6):e14152 (2021). doi: 10.1111/ijcp.14152. Epub 2021 Mar 25. Retraction in: Int. J. Clin. Pract. 2021 Dec;75(12):e14926 Hariyanto, T.I., Putri, C., Frinka, P., Louisa, J., Lugito, N.P.H., Kurniawan, A.: Human immunodeficiency virus (HIV) and outcomes from coronavirus disease 2019 (COVID-19) pneumonia: a meta-analysis and meta-regression. AIDS Res Hum Retroviruses. (2021). 10.1089/AID.2020.0307. Epub ahead of print. Retraction in: AIDS Res Hum Retroviruses. 2021 Jun;37(6):503 Hays, P.: Clinical sequelae of the novel coronavirus: does COVID-19 infection predispose patients to cancer? Future Oncol. 16(20):1463–1474 (2020). doi: 10.2217/fon-2020-0300. Epub 2020 May 27. Retraction in: Future Oncol. 2020 Dec;16(35):3015 Hill, A., Garratt, A., Levi, J., Falconer, J., Ellis, L., McCann, K., Pilkington, V., Qavi, A., Wang, J., Wentzel, H.: Meta-analysis of Randomized Trials of Ivermectin to Treat SARS-CoV-2 Infection. Open Forum Infect Dis. 8(11):ofab358 (2021a). doi: 10.1093/ofid/ofab358. Retraction in: Open Forum Infect Dis. 2022 Feb 05;9(3):ofac056 Hill, A., Garratt, A., Levi, J., Falconer, J., Ellis, L., McCann, K., Pilkington, V., Qavi, A., Wang, J., Wentzel, H.: Erratum: expression of concern: "meta-analysis of randomized trials of ivermectin to treat SARS-CoV-2 Infection". Open Forum Infect Dis. 8(8):ofab394 (2021b). doi: 10.1093/ofid/ofab394 Huang, Y., Zhao, N.: Chinese mental health burden during the COVID-19 pandemic. Asian J Psychiatr. 51:102052 (2020). doi: 10.1016/j.ajp.2020.102052. Epub 2020 Apr 14. Retraction in: Asian J Psychiatr. 2020 Dec;54:102482 Huang, Y., Zhao, N.: Mental health burden for the public affected by the COVID-19 outbreak in China: Who will be the high-risk group?. Psychol. Health. Med.;26(1):23–34 (2021). doi:10.1080/13548506.2020.1754438. Epub 2020 Apr 14. Retraction in: Psychol Health Med. 2020 Oct 23;:1 Huang, S., Wang, S., Li, G., Wang, M., Yu, W., Shao, G., Zhang, J., Yang, D.: Efficacy and safety of acupuncture therapy for asymptomatic infection of COVID-19: A protocol for systematic review and meta-analysis. Medicine (Baltimore). 99(41):e22697 (2020). doi: 10.1097/MD.0000000000022697. Retraction in: Medicine (Baltimore). 2021 Mar 19;100(11):e25227 Hussain, A., Mahawar, K., Xia, Z., Yang, W., El-Hasani, S.: Obesity and mortality of COVID-19. Meta-analysis. Obes. Res. Clin. Pract. 14(4):295–300 (2020). doi: 10.1016/j.orcp.2020.07.002. Epub 2020 Jul 9. Retraction in: Obes Res Clin Pract. 2021 Jan-Feb;15(1):100. Hynicka, L.M., Piedrahita, G., Barnabic, C., Rambob, I., Berman, B.M., D'Adamo, C.R. Interprofessional culinary medicine training enhanced nutrition knowledge, nutrition counseling confidence, and interprofessional experience. J Integr Complement Med. 28(10):811–820 (2022). 10.1089/jicm.2022.0573 Ibrahimagić, O.Ć., Vujadinović, A., Ercegović, Z., Kunić, S., Smajlović, D., Dostović, Z.: Comment on an article: "Osteoporosis in the age of COVID-19 patients". Osteoporos Int. 32(4):783–784 (2021). doi: 10.1007/s00198-020-05607-6. Epub 2021 Jan 21. Retraction in: Osteoporos Int. 2021 Jul 14 Improving pandemic preparedness and management - publications office of the eu - group of chief scientific advisors to the European commission, European Group on Ethics in Science and New Technologies (EGE), Special advisor to President Ursula von der Leyen on the response to the coronavirus and COVID-19 – (2020). Imran, M.M., Ahmad, U., Usman, U., Ali, M., Shaukat, A., Gul, N.: Neutrophil/lymphocyte ratio-A marker of COVID-19 pneumonia severity. Int. J. Clin. Pract. 75(4):e13698 (2021). doi: 10.1111/ijcp.13698. Epub 2021 Jan 12. Retraction in: Int J Clin Pract. 2021 Dec;75(12):e14927 Jiménez-Ruiz, C.A., López-Padilla, D., Alonso-Arroyo, A., Aleixandre-Benavent, R., Solano-Reina, S., de Granda-Orive, J.I.: Smoker, former smoker and COVID-19: nicotine does not protect against SARS-CoV-2. Arch Bronconeumol (Engl Ed). 2021 Mar 16. doi: 10.1016/j.arbr.2021.03.012 Jin, C., Yu, B., Zhang, J., Wu, H., Zhou, X., Yao, H., Liu, F., Lu, X., Cheng, L., Jiang, M., Wu, N.: Methylene blue photochemical treatment as a reliable SARS-CoV-2 plasma virus inactivation method for blood safety and convalescent plasma therapy for COVID-19. BMC Infect Dis. 21(1):357 (2021). doi: 10.1186/s12879-021-05993-0. Retraction in: BMC Infect Dis. 2021 Jul 9;21(1):672 Juyal, D., Thawani, V., Sayana, A., Pal, S.: Impact factor: mutation, manipulation, and distortion. J Family Med Prim Care. 15;8(11):3475-3479 (2019). doi: 10.4103/jfmpc.jfmpc_515_19 Kampf, G.: Analyzing pre-pandemic patterns of contacts is partly inappropriate to explain the current COVID-19 situation in Germany. Lancet Reg Health Eur. 12:100290 (2022). doi: 10.1016/j.lanepe.2021.100290. Epub 2021 Dec 13. Retraction in: Lancet Reg Health Eur. 2022 Jan;12:100298. Erratum in: Lancet Reg Health Eur. 2022 Jan;12:100298. Khalifa, N.E.M., Taha, M.H.N., Manogaran, G., Loey, M.: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell. J. Nanopart. Res.;22(11):313 (2020). doi: 10.1007/s11051-020-05041-z. Epub 2020 Oct 17. Retraction in: J Nanopart Res. 2021;23(8):184 Kory, P., Meduri, G.U., Iglesias, J., Varon, J., Marik, P.E.: Clinical and scientific rationale for the "MATH+" hospital treatment protocol for COVID-19. J. Intensive Care Med. 36(2):135–156 (2021). doi: 10.1177/0885066620973585. Epub 2020 Dec 15. Retraction in: J Intensive Care Med. 2021 Nov 9;:8850666211049062. Lin, S.L.: Intersectionality and inequalities in medical risk for severe COVID-19 in the Canadian Longitudinal study on aging. Gerontologist. 13;61(5):e245 (2021). doi: 10.1093/geront/gnaa143. Retraction in: Gerontologist. 2021 Jul 13;61(5):e244 Lincoln M Gabr A Kennedy C Collaboration, supervision and patient safety in the era of COVID-19: an analysis of medical wards and ICU Ir. J. Med. Sci. 2022 191 3 1085 1087 10.1007/s11845-021-02693-1 34218409 Liu, Y., Jiang, T.T., Shi, T.Y., Liu, Y.N., Liu, X.M., Xu, G.J., Li, F.L., Wang, Y.L., Wu, X.Y.: WITHDRAWN: The effectiveness of diaphragmatic breathing relaxation training for improving sleep quality among nursing staff during the COVID-19 outbreak: a before and after study. Sleep Med X. 2:100026 (2020). doi: 10.1016/j.sleepx.2020.100026. Retraction in: Sleep Med X. 2020 Dec;2:100030 Lu, X., Zhang, M., Qian, A., Tang, L., Xu, S.: Lung ultrasound score in establishing the timing of intubation in COVID-19 interstitial pneumonia: a preliminary retrospective observational study. PLoS One. 3;15(9):e0238679 (2020). doi: 10.1371/journal.pone.0238679. Retraction in: PLoS One. 2020 Dec 31;15(12):e0245032 Ma, R., Wu, H., Deng, Z.: Identify and measure the degree of over-prevention behaviors in the post-COVID-19 era in China. BMC Public Health. 21(1):1743 (2021). doi: 10.1186/s12889-021-11823-4. Retraction in: BMC Public Health. 2021 Dec 10;21(1):2244 Mao, K.: WITHDRAWN: Health risk assessment and health management of urban residents facing epidemic pneumonia. Work. (2021). doi: 10.3233/WOR-205367. Epub ahead of print. Retraction in: Work. 2021;70(2):673–674 Mehra, M.R., Desai, S.S., Ruschitzka, F., Patel, A.N.: RETRACTED: Hydroxychloroquine or chloroquine with or without a macrolide for treatment of COVID-19: a multinational registry analysis. Lancet. 22:S0140–6736(20)31180–6 (2020a). doi: 10.1016/S0140-6736(20)31180-6. Epub ahead of print. Retraction in: Lancet. 2020a Jun 5;:null. Erratum in: Lancet. 2020a May 30 Mehra MR, Desai SS, Kuy S, Henry TD, Patel AN. Cardiovascular Disease, Drug Therapy, and Mortality in Covid-19. N Engl J Med. 2020b Jun 18;382(25):e102. doi:10.1056/NEJMoa2007621. Epub 2020b May 1. Retraction in: N Engl J Med. 2020b Jun 4;: PMID: 32356626; PMCID: PMC7206931. Messina, F., Montaldo, C., Abbate, I., Antonioli, M., Bordoni, V., Matusali, G., Sacchi, A., Giombini, E., Fimia, G.M., Piacentini, M., Capobianchi, M.R., Lauria, F.N., Ippolito, G.: On behalf of covid-scoping review working group. rationale and criteria for a COVID-19 model framework. Viruses. 13(7):1309 (2021). doi: 10.3390/v13071309. Retraction in: Viruses. 2021 Jul 29;13(8) Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., PRISMA Group.: Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. Aug 18;151(4):264–9, W64 (2009) Mulvey, J.J., Magro, C.M., Ma, L.X., Nuovo, G.J., Baergen, R.N.: WITHDRAWN: A mechanistic analysis placental intravascular thrombus formation in COVID-19 patients. Ann. Diagn. Pathol. 46:151529 (2020). doi: 10.1016/j.anndiagpath.2020.151529. Retraction in: Ann. Diagn. Pathol. 2020 Jun 22;:151550 Nagra, M.H., Ehsan, S., Ahmad, U., Ali, M., Hussain, H.A., Bakar, A.: Implementation of a telemedicine service during COVID-19 pandemic in Pakistan. Int J Clin Pract. 75(8):e14310 (2021). doi: 10.1111/ijcp.14310. Epub 2021 May 18. Retraction in: Int J Clin Pract. 2021 Dec;75(12):e14925. Palayew A Norgaard O Safreed-Harmon K Pandemic publishing poses a new COVID-19 challenge Nat. Hum. Behav. 2020 4 666 669 10.1038/s41562-020-0911-0 32576981 Paulus FM Cruz N Krach S The Impact Factor Fallacy. Front Psychol. 2018 20 9 1487 10.3389/fpsyg.2018.01487 Pawłowicz-Szlarska E Sawościan M Lipińska K Interprofessional collaboration in the renal care settings: experiences in the COVID-19 era Adv. Clin. Exp. Med. Off. Organ Wroc. Med. Univ. 2022 31 7 749 755 10.17219/acem/146777 Pian W Chi J Ma F The causes, impacts and countermeasures of COVID-19 "infodemic": a systematic review using narrative synthesis Inf. Process Manag. 2021 10.1016/j.ipm.2021.102713 Saha, P., Mukherjee, D., Singh, P.K., Ahmadian, A., Ferrara, M., Sarkar, R.: GraphCovidNet: A graph neural network based model for detecting COVID-19 from CT scans and X-rays of chest. Sci Rep. 11(1):8304 (2021). doi: 10.1038/s41598-021-87523-1. Retraction in: Sci Rep. 2021 Nov 30;11(1):23451 Samaha, A.A., Mouawia, H., Fawaz, M., Hassan, H., Salami, A., Bazzal, A.A., Saab, H.B., Al-Wakeel, M., Alsaabi, A., Chouman, M., Moussawi, M.A., Ayoub, H., Raad, A., Hajjeh, O., Eid, A.H., Raad, H.: Effects of a single dose of ivermectin on viral and clinical outcomes in asymptomatic SARS-CoV-2 infected subjects: a pilot clinical trial in Lebanon. Viruses. 26;13(6):989 (2021). doi: 10.3390/v13060989. Retraction in: Viruses. 2021 Oct 26;13(11) Savaris, R.F., Pumi, G., Dalzochio, J., Kunst, R.: Stay-at-home policy is a case of exception fallacy: an internet-based ecological study. Sci Rep. 11(1):5313 (2021). doi: 10.1038/s41598-021-84092-1. Retraction in: Sci Rep. 2021 Dec 14;11(1):24172 Saxena, S.: Coronavirus disease-2019: a brief compilation of facts. J Oral Maxillofac Pathol. 24(1):5–7 (2020). doi: 10.4103/jomfp.JOMFP_137_20. Epub 2020 May 8. Retraction in: J Oral Maxillofac Pathol. 2020 May-Aug;24(2):407 Schonhaut L Costa-Roldan I Oppenheimer I Scientific publication speed and retractions of COVID-19 pandemic original articles Revista Panamericana de Salud Publica Pan Am. J. Public Health. 2022 10.26633/RPSP.2022.25 Singh, A.: Noninvasive versus invasive ventilation: one modality cannot fit all during COVID-19 outbreak. Korean J. Anesthesiol. 73(4):359–360 (2020). doi: 10.4097/kja.20227. Epub 2020 Jul 8. Retraction in: Korean J Anesthesiol. 2020 Oct;73(5):468 Standish, K.: COVID-19, suicide, and femicide: Rapid research using google search phrases. J Gen Psychol. 148(3):305–326 (2021). doi: 10.1080/00221309.2021.1874863. Epub 2021 Jan 22. Retraction in: J Gen Psychol. 2021 May 19;:1 Sun, M., Guo, D., Zhang, J., Zhang, J., Teng, H.F., Xia, J., Liu, P., Ge, Q.X., Wang, M.Y.: Anal swab as a potentially optimal specimen for SARS-CoV-2 detection to evaluate hospital discharge of COVID-19 patients. Future Microbiol.15:1101–1107 (2020). doi: 10.2217/fmb-2020-0090. Epub 2020 Aug 14. Retraction in: Future Microbiol. 2021 May;16:537 Temmerman, M.: Family planning in COVID-19 times: access for all. Lancet Glob Health. 9(6):e728-e729 (2021). doi: 10.1016/S2214-109X(21)00231-X. Retraction in: Lancet Glob Health. 2021 Jun 25;:null The World Bank, World Bank Country and Lending Groups (2021), Retrieved from: https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lending-groups Vainshelboim, B.: Retracted: facemasks in the COVID-19 era: a health hypothesis. Med. Hypotheses. 146:110411 (2021). doi: 10.1016/j.mehy.2020.110411. Epub 2020 Nov 22. Retraction in: Med Hypotheses. 2021 May 12;:110601 Victor, G.: COVID-19 admissions calculators - revisited. Early Hum. Dev.;144:105053 (2020a). doi: 10.1016/j.earlhumdev.2020a105053. Epub 2020a Apr 25. Retraction in: Early Hum Dev. 2021 Jun 23;159:105412 Victor, G.: COVID-19 admissions calculators: General population and paediatric cohort. Early Hum. Dev.;145:105043 (2020b). doi: 10.1016/j.earlhumdev.2020b105043. Epub 2020b Apr 10. Retraction in: Early Hum Dev. 2021 Jun 17;159:105410 Viswanathan, M., Berkman, N.D., Dryden, D.M., Hartling, L.: Assessing risk of bias and confounding in observational studies of interventions or exposures: further development of the RTI item bank [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US); 2013 Aug. Report No.: 13-EHC106-EF Vrabel M Beyond the Impact Factor Oncol Nurs Forum. 2019 46 2 143 145 10.1188/19.ONF.143-145 30767957 Vuong QH La VP Ho MT Characteristics of retracted articles based on retraction data from online sources through February 2019 Sci Ed. 2020 10.6087/kcse.187 Walach, H., Klement, R.J., Aukema, W.: Retracted: the safety of covid-19 vaccinations-we should rethink the policy. Vaccines (Basel). 24;9(7):693 (2021). doi: 10.3390/vaccines9070693. Retraction in: Vaccines (Basel). 2021 Jul 02;9(7):null Wang, X., Xu, W., Hu, G., Xia, S., Sun, Z., Liu, Z., Xie, Y., Zhang, R., Jiang, S., Lu, L.: RETRACTED ARTICLE: SARS-CoV-2 infects T lymphocytes through its spike protein-mediated membrane fusion. Cell. Mol. Immunol. 1–3 (2020). doi:10.1038/s41423-020-0424-9. Epub ahead of print. Retraction in: Cell Mol Immunol. 2020 Aug;17(8):894. Retraction watch, retracted coronavirus (COVID-19) papers, https://retractionwatch.com/retracted-coronavirus-covid-19-papers/. Accessed on 9 August 2022 Watson C Rise of the preprint: how rapid data sharing during COVID-19 has changed science forever Nat. Med. 2022 28 2 5 10.1038/s41591-021-01654-6 35031791 Woodle, E.S., Gebel, H.M., Montgomery, R.A., Maltzman, J.S.: SARS-CoV-2 vaccination and antibody testing in immunosuppressed populations: you can't tell the players without a scorecard [RETRACTED]. Transplantation. (2021). doi: 10.1097/TP.0000000000003882. Epub ahead of print. Retraction in: Transplantation. 2021 Sep 1;105(9):e113 Yeo-Teh NSL Tang BL An alarming retraction rate for scientific publications on coronavirus disease COVID-19 Account. Res. 2019 10.1080/08989621.2020.1782203 Zago Filho, L.A., Lima, L.H., Melo, G.B., Zett, C., Farah, M.E.: Vitritis and outer retinal abnormalities in a patient with COVID-19. Ocul. Immunol. Inflamm. 28(8):1298–1300 (2020). doi: 10.1080/09273948.2020.1821898. Epub 2020 Oct 6. Retraction in: Ocul Immunol Inflamm. 2021 Jul 4;29(5):1035 Zaidi, A.K., Dehgani-Mobaraki, P.: The mechanisms of action of ivermectin against SARS-CoV-2-an extensive review. J. Antibiot. (Tokyo). 75(2):60–71 (2022). doi: 10.1038/s41429-021-00491-6. Epub 2021 Dec 21. Retraction in: J Antibiot (Tokyo). 2022 Feb;75(2):122. Zellmer, W.A.: How pharmacists can help heal democracy in America. Am J Health Syst Pharm. 78(6):525-526 (2021). doi: 10.1093/ajhp/zxaa435. Retraction in: Am. J. Health Syst Pharm. 2021 Jan 20;:null Zeng, Y., Zhen, Y.: Chinese medical staff request international medical assistance in fighting against COVID-19. Lancet Glob Health. S2214–109X(20)30065–6 (2020). doi: 10.1016/S2214-109X(20)30065-6. Epub ahead of print. Retraction in: Lancet Glob Health. 2020 Aug;8(8):e995 Zhao, X., Liu, R., Miao, Z., Ye, N., Lu, W.: Retracted:a study of potential SARS-CoV-2 antiviral drugs and preliminary research of their molecular mechanism, based on Anti-SARS-CoV drug screening and molecular dynamics simulation. J. Comput. Biol. (2020). doi: 10.1089/cmb.2020.0112. Epub ahead of print. Retraction in: J Comput Biol. 2021 Jul;28(7):744.	36466994	PMC9707851	NO-CC CODE	2022-12-01 23:20:27	no	Qual Quant. 2022 Nov 29;:1-33	utf-8	Qual Quant	2,022	10.1007/s11135-022-01587-3	oa_other
==== Front Comput Inform Nurs Comput Inform Nurs CIN Computers, Informatics, Nursing 1538-2931 1538-9774 Lippincott Williams & Wilkins 35234699 CIN_210194 10.1097/CIN.0000000000000884 00007 3 Features Content and Usability Validation of an Intelligent Virtual Conversation Assistant Used for Virtual Triage During the COVID-19 Pandemic in Brazil de Campos Filho Amadeu Sá PhD [email protected] Vasconcelos Cursino José Ricardo MSc [email protected] do Nascimento José William Araújo MSc de Souza Rafael Roque PhD [email protected] da Silva Lima Roque Geicianfran MSc [email protected] de Souza Cavalcanti Andréia Roque MSc [email protected] Author Affiliations: Telehealth Center of the Clinical Hospital of the Federal University of Pernambuco (Dr de Campos Filho and Vasconcelos Cursino), Recife, Brazil; Medical Science Center, Federal University of Pernambuco (Dr de Campos Filho), Recife, Brazil; Unissaomiguel University Center (Vasconcelos Cursino), Recife, Brazil; Centro de Informática, Universidade Federal de Pernambuco - Cin UFPE (Dr do Nascimento, and Dra da Silva Lima Roque), Recife, Brazil; Imune Research Lab (Dr do Nascimento, Dr de Souza, Dra da Silva Lima Roque, and Dra de Souza Cavalcanti), João Pessoa, Brazil; Catholic University of Pernambuco (Dr de Souza), Recife, Brazil; and ABC Medical School (Dra de Souza Cavalcanti), São Paulo, Brazil. Corresponding author: José William Araújo do Nascimento, MSc, Centro de Informática da Universidade Federal de Pernambuco (Cin-UFPE), Av. Jorn. Aníbal Fernandes, Recife, PE 50740-560, Brazil ([email protected]). 11 2022 02 3 2022 02 3 2022 40 11 779785 Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved. 2022 Lippincott Williams & Wilkins This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. This study aimed to describe the development process, content validation, and usability of a COVID-19 screening system incorporated into a chatbot-type intelligent virtual assistant (CoronaBot). This is a methodological research carried out in three phases. The first corresponded to the development of the flowchart and content of the virtual assistant, the second phase consisted of the implementation of the content in chatbot, and the third phase consisted of content validation. Data analysis was performed by agreement rate, content validity index, and kappa statistical test. Finally, in the third phase, the chatbot's usability was analyzed using the System Usability Scale, by 10 users. The CoronaBot content presented domains with agreement rate above 87.5%, and its items referring to symptomatological scores and interface screens had values of content validity index with a mean of 0.96, kappa test with values from 0.70 to 0.76, and interspecialist agreement of 1.00, demonstrating excellence of prototype content. The global usability score was 80.1. The script developed and incorporated into the chatbot prototype achieved a satisfactory level of content validity. The usability of the chatbot was considered good, adding to the credibility of the device. KEY WORDS Artificial intelligence COVID Pandemic Telemedicine ==== Body pmcCOVID-19 is a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), generating great global impact, still with alarming numbers of contagion in the world, specifically in Brazil.1 The World Health Organization announced COVID-19 as a pandemic on March 11, 2020, so that by February 2021, the virus had already spread to more than 100 million people worldwide, causing more than 2 million deaths, with 10 million of the cases and over 220,000 of the deaths in Brazil.2–4 Due to the rapid spread of SARS-CoV-2, the accelerated increase in the number of infections in Brazil, and the large flow of people seeking health units, SARS-CoV-2 became a concern in urgency and emergency networks, especially when suspected cases with non-specific symptoms arrive in the same period,5 which will overload the health units' care capacity. Thus, strategic resources that support the use of technology have shown great potential in the screening, diagnosis, and management of infections, besides being useful in the screening of suspected cases, which can serve as an effective instrument in the control of various diseases with high potential for dissemination.6 In this perspective, technological systems based on artificial intelligence and natural language processing provide great advantages to the entire population. These include rapid processing of data seeking a more accurate answer to decision making and by expediting the acquisition of answers to health questions and even for the guidance of treatments. Moreover, these technological resources understand the text informed by the patient by anticipating common doubts, often excluding the need to go to health units, and talking to a patient by text or audio in a humanized way.7 One of these technologies is the chatbot or intelligent conversation agent, robots that mimic human interaction and are able to replicate behaviors and perform various health actions, such as providing information to patients, assessing health status, and reviewing care plans, being available 24 hours a day.8 The use of chatbots to screen users with symptoms of COVID-19 can help health professionals and managers to control the demand of patients seeking hospital care. Thus, it avoids the unnecessary collapse of the health system and decreases the risk of contagion of people who do not present symptoms of this infection.7 This study aims to describe the process of content development and validation of a classification system for screening COVID-19 incorporated in a chatbot called CoronaBot. METHODS Study Design Initially, to develop the approach method to be used by CoronaBot, it was necessary to establish the classification method that would be used. For this purpose, the Manchester protocol was used, whose categorization is based on the symptoms present in the patient.9 According to the authors, when the number of classification points is observed, the protocols that use a 5-point classification, when compared with those of 3 points, stand out for a better and more reliable evaluation.9 Since the Manchester protocol did not meet the symptom screening scenarios of COVID-19, it was necessary to adapt it to the classification system used by the Brazilian Ministry of Health. After analyzing the available symptoms for each stage of the classification, it was possible to notice that the symptomatological scoring (SS) scheme would be the best method for the association with CoronaBot. As for the theoretical aspects of information technology used in the construction of CoronaBot, natural language processing was used to understand text dialogues with artificial intelligence algorithms. In this way, the conversation agent was developed on Google's Dialogflow platform and its structure was based on dialog flows that can lead to a specific or branched path of questions and answers, similar to a flowchart.10 To feed the flow, questions and answers were added to perform the virtual screening of COVID-19 symptoms among users. Steps for CoronaBot's Content Validation After implementing the content in CoronaBot, its content validation was performed, through a panel of specialists composed of 15 health professionals. Six physicians and nine nurses with notorious experience in scientific research and clinical practice were directly or indirectly linked to the treatment of COVID-19. These professionals were selected upon applying the expert selection instrument used by Teles et al11 (2014) based on degree, research, and clinical practice. The CoronaBot evaluation questionnaire was based on the model proposed by Coluci et al12 (2015), adapted by the researchers, with a 4-point Likert response scale, namely, (1) not relevant, (2) little relevant, (3) relevant, and (4) very relevant. It was divided into two sections: (1) evaluation of domains and (2) evaluation of the items. The domains of CoronaBot refer to the thematic axes present in the virtual assistant, whereas the items of the domains are its functional requirements, which are the functions that the system should provide, that is, how it should react to the user's inputs.13 CoronaBot content analysis consisted of the evaluation of three major domains: (1) score scale; (2) total symptom score for COVID-19 virtual screening; and (3) conversation agent interface (screens). Domain 1 had one item evaluated by the specialists: the scale of the score of symptoms and risk factors of COVID-19. Domain 2, in turn, was evaluated by its six items, namely, item 1: non-suspected asymptomatic cases; item 2: non-suspected symptomatic cases; item 3: suspected cases with mild symptoms; item 4: suspected cases with moderate symptoms; item 5: suspected case with alarming symptoms; and item 6: serious suspected case. Domain 3 was evaluated in its nine items, which represent CoronaBot's screens as exemplified in Figure 1. FIGURE 1 CoronaBot interface. A, Home screen. B, Screening for symptoms of COVID-19. C, Screening for COVID-19 risk factors. After the specialists' analysis, the agreement rate of the evaluations was calculated, considering results greater than or equal to 90% as adequate. In the second stage, experts should evaluate each item and picture individually concerning their clarity, relevance, and representativeness for the construct underlying the study, according to the theoretical definitions of the construct itself and its domains. Usability Test The evaluated CoronaBot randomly recruited 10 Brazilian users from the northeast region of Brazil with suspected new coronavirus infection to test the usability of the system. Individuals aged 18 years or older with access to digital technology were included in the tests. Those users with marked functional dependence and cognitive deficits or difficulties that made it impossible to handle a smartphone or computer were excluded from the tests. After a brief videoconference demonstration of the system to the study participants, an e-mail was sent containing the CoronaBot access link; Google Forms link containing the consent form; a questionnaire on personal characteristics including age, sex, and education; and the System Usability Scale (SUS) questionnaire. The data were collected using the SUS, an instrument containing 10 questions that aim to measure the usability of various products and services. As for its scoring scale, the SUS produces a single number. To calculate the score, first, the score of each item is summed. These contribute on a scale of 0 to 5. For items 1, 3, 5, 7, and 9, the individual score is the score received minus 1. For items 2, 4, 6, 8, and 10, the contribution is minus 5. The sum of all scores is multiplied by 2.5, and thus, the total SUS value is obtained.14 After scoring and calculating the score, it is possible to make the classification of the evaluated system: 20.5, worst imaginable; 21 to 38.5, poor; 39 to 52.5, average; 53 to 73.5, good; 74 to 85.5, excellent; and 86 to 100, best imaginable.15 Participants were also asked to answer how long it took them to interact with CoronaBot and what their level of satisfaction was with the system, using an analog scale from 0 to 10. Data Analysis To analyze the data from the content validation of CoronaBot, the content validity index (CVI) and the kappa (κ) index were used, after the domains and items of the virtual assistant were judged by the experts. The CVI measures the proportion or percentage of judges who are in agreement about certain aspects of the virtual assistant and its items, and allows for the analysis of each item individually and the prototype as a whole.16 To evaluate the whole prototype using the CVI, the form used in this study was the average of the item values calculated separately. In this way, all the CVIs calculated separately were summed and divided by the number of items of the virtual assistant.17 To stipulate the acceptable level of agreement among the experts, the recommended CVI value of at least 0.75 was established, and an agreement of at least 80% among the experts was considered to serve as a criterion for deciding on the relevance and/or acceptance of the CoronaBot items.16 In addition to this analysis, the interrater agreement was verified, which analyzes the agreement of the experts among the items evaluated through the ratio between the number of items with CVI values above 0.80 by the total number of CoronaBot items.18 To verify the experts' level of agreement and consistency level regarding the CoronaBot items, the κ index was calculated using the Online Kappa Calculator.19 κ Values above 0.74 were considered excellent, those between 0.60 and 0.74 were considered good, those from 0.40 to 0.59 were considered regular, and those below 0.40 were considered insufficient to be adopted.20 Regarding the data analysis of usability with users, the data obtained through the SUS questionnaire were described in two sections: (1) socio-demographic variables of the participants and (2) questions regarding the SUS, with evaluation of each of the 10 questions. The Kruskal-Wallis test (95% confidence interval and 5% significance, P < .05) was used to assess the effect of user type on quality indicators, using IBM SPSS Statistics version 23.0 (IBM Inc., Armonk, NY, USA). Ethical Criteria This study was approved by the institutional review board of the Federal University of Pernambuco, each participant voluntarily decided to participate in this research, and written consent was obtained. RESULTS Content Development of CoronaBot Script CoronaBot was designed to virtually screen users who believe they have symptoms of COVID-19 and provide them with the appropriate procedures for each case. For this screening, the SS scheme was used, which concerns the sum of the values obtained due to the attribution of values to each symptom present in the patient and to each risk factor that the patient has (Symptomatological Score = Symptoms + Risk Factors). The scale of symptom scoring and risk factors was defined based on the combinatorial analysis of several scenarios where various symptoms and risk situations were simulated. To perform the simulation, some scenarios were defined: first, the patient's profile was defined based on a combination of two to four factors (Age + SAH + DM + Obesity + Presence of Chronic Disease). As a result of this combination, each patient obtains a value referring to his/her health condition (Risk Factors). The preparation of base score defined the second stage according to the definition of the three forms of presentation of COVID-19.21 To obtain results that are more consistent with reality, the scores were reevaluated, in both the area of risk factors and symptoms. To perform this stage, other chatbots that work in the same area were evaluated, revealing the score lag. Simulations were performed with different scores but following the same patient profiles, resulting in discovering an ideal score. Figure 2 exemplifies the difference between pre- and post-reassessment scores. FIGURE 2 Comparison of values referring to symptoms and comorbidities before and after reassessment. With the reassessment of the scores, it is possible that the total sum of SS described by the patient when interacting with CoronaBot will be more coherent and reliable, thus giving rise to the patient's classification, which will subsequently influence the message and action passed at the end of the dialog with the conversational agent. For a better understanding, Figure 3 describes the mentioned method. FIGURE 3 Scheme for obtaining the CoronaBot symptom score and classification method. CoronaBot interacts with the user through natural language processing, establishing a conversation with the user and performing the screening according to the symptoms presented, risk factors, and contact with suspected or confirmed cases of COVID-19. Below are the main guidelines provided to users based on the SS and the presentation of the text on CoronaBot: Non-suspected asymptomatic (SS: 0–1) or symptomatic (SS: 2–5): orientation of preventive methods of COVID-19 and maintenance of social distancing. Suspected with mild symptoms (SS: 6–10): social isolation and other protective measures are oriented. Suspected with moderate symptoms (SS: 11–15) or with alarming symptoms (SS: 16–20): social isolation and directs the user to a teleconsultation with health professionals of the Telehealth Center of the Clinical Hospital of the Federal University of Pernambuco (Recife, Brazil). Suspected with severe symptoms (SS: +21): to seek an emergency medical service immediately, already offering an option for direct connection to the mobile emergency care service (SAMU-192). CoronaBot's Content Validation The participants of this step were 15 specialists (physicians and nurses) working in the research and clinical conduct of COVID-19; these professionals were, on average, 45 years old, and 75% have over 11 years of clinical and research experience in the area of practice. The first evaluation stage consisted of the analysis of the three major CoronaBot domains, revealing that all were considered adequate, relevant, and representative; domains 1 and 2 presented an agreement rate of 87.5%, suggesting improvements in the content present in their items. Some experts also suggested leaving the chatbot with more common words, since the conversation agent will be used by people of all social classes. One point also addressed by the evaluators were concerned with some of the comorbidities, suggesting changes to some terms and scores, the puerperium time, and the addition of close-to-positive COVID contact. Also, on the agreement rate of the CoronaBot domains, domain 3 obtained a 100% result, without any suggestions for improvement. In the second evaluation stage, the experts analyzed the items present in domain 1 (scoring scale), domain 2 (symptomatological score for virtual screening of COVID-19), and domain 3 (CoronaBot screens). As shown in Table 1, 12 of the 16 CoronaBot items were considered “excellent” and only four items related to domain 2 had a CVI of 0.87 and a κ of 0.70, being interpreted as “good” contents. Table 1 Content Validity Index per Item, by Scale and Agreement Between CoronaBot, Brazil, 2021 Evaluations S-CVI κ Interpretation Items/Domain Domain 1 Item 1: COVID-19 symptom scoring scale and risk factors 1.00 0.76 Excellent Domain 2 Item 1: asymptomatic non-suspected case (conduct) 0.87 0.70 Good Item 2: symptomatic non-suspected case (conduct) 0.87 0.70 Good Item 3: suspected case with mild symptoms (conduct) 0.87 0.70 Good Item 4: suspected case with mild symptoms (conduct) 1.00 0.76 Excellent Item 5: suspected case with alarming symptoms (conduct) 0.87 0.70 Good Item 6: suspected case with severe symptoms (conduct) 1.00 0.76 Excellent Domain 3 Item 1: home screen 1.00 0.76 Excellent Item 2: home menu 1.00 0.76 Excellent Item 3: explanation of CoronaBot's objectives 1.00 0.76 Excellent Item 4: initial symptom assessment 1.00 0.76 Excellent Item 5: screening for various symptoms 1.00 0.76 Excellent Item 6: other screening criteria 1.00 0.76 Excellent Item 7: screening for risk factors 1.00 0.76 Excellent Item 8: identification of contact with suspected or diagnosed persons 1.00 0.76 Excellent Item 9: CoronaBot guidelines through symptomatic scoring 1.00 0.76 Excellent S-CVI/Avea 0.96 — — IRA 1.00 — — Abbreviation: IRA, interrater agreement. aContent validity index by scale and average level. When analyzing the content validation by average, an excellent value (0.96) was verified, so that the interrater agreement, that is, the agreement of reliability among the experts, obtained a value of 1.00, indicating high agreement. Usability Test Ten users with suspected new coronavirus infection participated in CoronaBot usability tests. The most frequent age group was 20 to 30 years, with a minimum age of 20 years and a maximum age of 65 years (mean [SD], 27.5 [15.2] years). The male sex prevailed with 7 of the 10 respondents (70%). Eight users had a high level of education. All participants took an average of up to 5 minutes of interaction with CoronaBot. When asked about overall satisfaction with the chatbot, using an analog scale from 0 to 10, an average of 9.13 was found, indicating excellent satisfaction. Regarding the usability questionnaire, SUS, all participants completed the questions. The average total score was 80.1, with a standard deviation of 10.2, a minimum value of 45.5, and a maximum value of 100. When analyzed by the Kruskal-Wallis test, the association of the SUS score with the sociodemographic variables was not statistically significant (Table 2). Table 2 Quality Indicators and SUS Global Score Quality Indicators Average ± SD H a P Learning 14.2 ± 2.16 0.66 .118 Efficiency 12.1 ± 1.26 1.05 .017 Memory capacity 3.86 ± 1.25 0.91 .045 Error minimization 2.24 ± 0.63 0.66 .167 Satisfaction 8.87 ± 1.88 0.37 .766 SUS global score 80.1 ± 10.2 0.74 .077 aKruskal-Wallis test. DISCUSSION COVID-19 has challenged health professionals and researchers in various aspects, from an early and accurate diagnosis to the most usual and effective treatment. This has caused wear in several spheres, especially in Brazil, where the disease continues with alarming numbers, mainly due to the lack of efficient public policies and also the low level of social isolation of the population.22–24 The evaluation of CoronaBot content was essential to develop the device, because it verified the association between abstract concepts (COVID-19 screening) and observable indicators (screening of symptoms and screening of risk factors), to analyze the extent to which the items created represent the construct, that is, its relevance. Regarding the selection of appropriate experts to make up the panel, the experience and qualification of its members should be considered. The experts' classification system used allowed selecting a sample composed of qualified professionals in the subject, attributing greater precision, accuracy, and quality to the evaluations made, as observed by the high correlation between the results. Still, with the objective of verifying the consistency between the absolute value of the experts' evaluations, the interrater agreement was calculated, which showed a very strong agreement for the evaluated instrument. Applications of concordance statistics between evaluators for Likert scales are abundant in research and practice, having as one of their applications the evaluation of panel interviews and any other approach to collect systematic observations.25 The assessment of agreement among specialists, whether as a primary or secondary component of the study, is common in several health disciplines where the use of evaluators or observers as a measurement method is prevalent. The literature stresses that the concept of ARI is fundamental for both the design and evaluation of research instruments, since it measures the extent to which different judges assign the same precise value to each item being judged.26 In this way, the CoronaBot content was considered excellent by experts, making the virtual assistant an additional public utility instrument to complement the current contact identification and tracking applications to improve the tracking of COVID-19 transmission in the community. To identify possible biases in the interface of the developed prototype, a usability test was performed. Initially used in the area of software engineering, the concept of usability is fully applicable to the development of interactive health instruments, as it is not enough to create and validate an instrument without evaluating its user interface. This is essential; otherwise, it cannot be used in clinical practice due to difficulties such as learning the methods involved, memorizing its functioning, or knowing how to issue possible results. These system evaluation tests are becoming increasingly essential before making the prototype available to the end user. Prior to applicability checks in a real-life context, this provides a technical foundation on which users become familiar with the potential of mobile technology. This allows users to provide richer feedback on functional requirements and use cases.27 The SUS instrument used in this research was efficient to assess the usability of the CoronaBot through the user's perception, being classified as a good usability instrument according to the SUS score. When considering the representative values of quality, it was possible to verify that the CoronaBot presents the five usability components: ease of learning, efficiency of use, ease of recall, low error rate, and subjective satisfaction. Similar usability data can be observed in the development of other prototypes in the healthcare field.28 In the scientific literature, intelligent virtual assistants like chatbots can be observed in aid of the COVID-19 pandemic. These actions performed by chatbots are configured as extremely important, as they help health professionals in decision making, alleviating stressed health systems.29 Researchers in Japan developed a chatbot-based healthcare system called COvid-19: Operation for Personalized Empowerment to Render smart prevention And care seeking (COOPERA) developed through the LINE application. This is the first real-time system used to diagnose and monitor COVID-19 in Japan.30 The chatbot asks participants questions about personal information, preventive actions, and non-specific symptoms related to COVID-19 and its duration.30 CONCLUSION CoronaBot, a chatbot, was developed to perform virtual screening of users with COVID-19 symptoms and screening of suspicious individuals. The content of CoronaBot obtained a satisfactory level of content validity and good usability, giving greater credibility to the device. Thus, it is possible to use this chatbot to screen patients with suspected COVID-19, thus being possible to guide users to the most appropriate approach for their case. Use in conjunction with the CoronaBot online service platforms is ideal due to the following factors: low maintenance, high precision in the results, and easy handling by users, among others. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. ==== Refs References 1 World Health Organization (WHO). Coronavirus Disease 2019 (COVID-19): Situation Report-78. Geneva, Switzerland: WHO; 2020. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200407-sitrep-78-covid-19.pdf?sfvrsn=bc43e1b_2 2 World Health Organization (WHO). Clinical Management of Severe Acute Respiratory Infection (SARI) When Covid-19 Disease Is Suspected. Interim Guidance. Geneva, Switzerland: WHO; 2020. https://apps.who.int/iris/handle/10665/331446 3 World Health Organization (WHO). Painel do WHO coronavirus disease (COVID-19). 2021. https://covid19.who.int/ 4 Brazil Ministry of Health. Coronavirus panel. 2021. https://covid.saude.gov.br/ 5 Levenfus I Ullmann E Battegay E Schuurmans MM . Triage tool for suspected COVID-19 patients in the emergency room: AIFELL score. The Brazilian Journal of Infectious Diseases. 2020;24 (5 ): 458–461. doi:10.1016/j.bjid.2020.07.003.32828735 6 Ossemane EB Moon TD Were MC Heitman E . Ethical issues in the use of SMS messaging in HIV care and treatment in low- and middle-income countries: case examples from Mozambique. Journal of the American Medical Informatics Association. 2018;25 (4 ): 423–427. doi:10.1093/jamia/ocx123.29088384 7 Munsch N Martin A Gruarin S , . Diagnostic accuracy of web-based COVID-19 symptom checkers: comparison study. Journal of Medical Internet Research. 2020;22 (10 ): e21299. doi:10.2196/21299.33001828 8 Zhou S Bickmore T Paasche-Orlow M Jack B . Agent-user concordance and satisfaction with a virtual hospital discharge nurse. Paper presented at: International Conference on Intelligent Virtual Agents; 2014: 528–541. doi: 10.1007/978-3-319-09767-1_63. 9 Júnior WC Torres BLB Rausch MCP . Manchester risk classification system: comparing models. 2014. Brazilian Risk Classification Group Web site. http://gbcr.org.br/public/uploads/filemanager/source/53457bf080903.pdf 10 Dialogflow. Dialogflow: build natural and rich conversational experiences. 2019. https://dialogflow.com 11 Teles LM Oliveira AS Campos FC , . Development and validating an educational booklet for childbirth companions. Revista da Escola de Enfermagem da U.S.P. 2014;48 (6 ): 977–984. doi:10.1590/S0080-623420140000700003.25626495 12 Coluci MZ Alexandre NM Milani D . Construction of measurement instruments in the area of health. Ciênca & Saúde Coletiva. 2015;20 (3 ): 925–936. doi:10.1590/1413-81232015203.04332013. 13 Pressman R . Software Engineering: A Professional Approach. 8th ed. McGrawHill, Bookman; 2016:940. 14 Bangor A Kortum P Miller J . An empirical evaluation of the system usability scale. International Journal of Human Computer Interaction. 2008;24 (6 ): 574–594. doi:10.1080/10447310802205776. 15 Bangor A Kortum P Miller J . Determining what individual SUS scores mean: adding an adjective rating scale. Journal of Usability Studies. 2009;4 : 114–123. 16 Alexandre NM Coluci MZ . Content validity in the development and adaptation processes of measurement instruments. Ciênca & Saúde Coletiva. 2011;16 (7 ): 3061–3068. doi:10.1590/S1413-81232011000800006. 17 Polit DF Beck CT . The content validity index: are you sure you know what's being reported? Critique and recommendations. Research in Nursing & Health. 2006;29 (5 ): 489–497. doi:10.1002/nur.20147.16977646 18 Gisev N Bell JS Chen TF . Interrater agreement and interrater reliability: key concepts, approaches, and applications. Research in Social & Administrative Pharmacy. 2013;9 (3 ): 330–338. doi:10.1016/j.sapharm.2012.04.004.22695215 19 Randolph JJ . Online kappa calculator. 2008. http://justusrandolph.net/kappa/ 20 Perroca MG Gaidzinski RR . Assessing the inter-rater reliability of an instrument for the classification of patients—kappa's quotient. Revista da Escola de Enfermagem da U.S.P. 2003;37 (1 ): 72–80. doi:10.1590/S0080-62342003000100009. 21 Rockwell KL Gilroy AS . Incorporating telemedicine as part of COVID-19 outbreak response systems. The American Journal of Managed Care. 2020;26 (4 ): 147–148. doi:10.37765/ajmc.2020.42784.32270980 22 Judson TJ Odisho AY Young JJ , . Implementation of a digital chatbot to screen health system employees during the COVID-19 pandemic. Journal of the American Medical Informatics Association. 2020;27 (9 ): 1450–1455. doi:10.1093/jamia/ocaa130.32531066 23 Ohannessian R Duong TA Odone A . Global telemedicine implementation and integration within health systems to fight the COVID-19 pandemic: a call to action. JMIR Public Health and Surveillance. 2020;6 (2 ): e18810. doi:10.2196/18810.32238336 24 Brazil. Ministry of Health. Guidelines for Managing Patients With COVID-19. Brasilia, Brazil; 2020. https://portalarquivos.saude.gov.br/images/pdf/2020/June/18/Covid19-Orientac--o--esManejoPacientes.pdf 25 O'Neill TA . An overview of interrater agreement on Likert scales for researchers and practitioners. Frontiers in Psychology. 2017;8 : 777. doi:10.3389/fpsyg.2017.00777.28553257 26 Shweta B Bajpai R Chaturvedi HK . Evaluation of inter-rater agreement and inter-rater reliability for observational data: an overview of concepts and methods. Journal of the Indian Academy of Applied Psychology. 2015;41 (3 ): 20–27. http://www.jiaap.org.in/Listing_Detail/Logo/273614a1-a76f-4d64-9221-3609cafcc24a.pdf 27 Epalte K Tomsone S Vetra A Bērziņa G . Patient experience using digital therapy “Vigo” for stroke patient recovery: a qualitative descriptive study. Disability and Rehabilitation. Assistive Technology. 2020;15 : 1–10. doi:10.1080/17483107.2020.1839794.30132353 28 da Silva Lima Roque G Roque de Souza R Araújo do Nascimento JW de Campos Filho AS de Melo Queiroz SR Ramos Vieira Santos IC . Content validation and usability of a chatbot of guidelines for wound dressing. International Journal of Medical Informatics. 2021;151 : 104473. doi:10.1016/j.ijmedinf.2021.104473.33964703 29 Battineni G Chintalapudi N Amenta F . AI chatbot design during an epidemic like the novel coronavirus. Healthcare. 2020;8 (2 ): 154. doi:10.3390/healthcare8020154.32503298 30 Yoneoka D Kawashima T Tanoue Y , . Early SNS-based monitoring system for the covid-19 outbreak in Japan: a population-level observational study. Journal of Epidemiology. 2020;30 (8 ): 362–370. doi:10.2188/jea.je20200150.32475884	35234699	PMC9707853	NO-CC CODE	2022-12-02 23:21:20	no	Comput Inform Nurs. 2022 Mar 2; 40(11):779-785	utf-8	Comput Inform Nurs	2,022	10.1097/CIN.0000000000000884	oa_other
==== Front Comput Inform Nurs Comput Inform Nurs CIN Computers, Informatics, Nursing 1538-2931 1538-9774 Lippincott Williams & Wilkins 35234702 CIN_210195 10.1097/CIN.0000000000000883 00004 3 Features Digital Health Literacy in Patients With Heart Failure in Times of Pandemic Rodríguez Parrado Indira Yuselfy RN [email protected] Achury Saldaña Diana Marcela MSN Author Affiliations: Clínica Palermo (Ms Rodríguez Parrado); and School of Nursing, Pontificia Universidad Javeriana (Mrs Achury Saldaña), Bogota, Colombia. Corresponding author: Diana Marcela Achury Saldaña, MSN, School of Nursing, Pontificia Universidad Javeriana, 138th Street, Bogota, Colombia 110911 ([email protected]). 11 2022 02 3 2022 02 3 2022 40 11 754762 Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved. 2022 Lippincott Williams & Wilkins This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. This study sought to determine the effect of a digital health literacy program regarding knowledge and skills in the use of digital resources related to self-care and health empowerment for patients with heart failure. A cross-sectional pilot study was conducted before and after the program in patients (n = 28) with heart failure at a tertiary care center. Both a knowledge test and the Health Empowerment Scale were used with a Cronbach's α of 0.89. The information was processed using the statistical software Restudio, which allowed us to make a descriptive and inferential analysis. Seventy-five percent of the participants were men with an average age of 68 years, 60.7% had elementary schooling, 71.4% had preserved ejection fraction, and 57.6% had a family member as a caregiver. A statistically significant change (P < .005) was found in the level of empowerment and the knowledge and skills in the use of digital resources applied in health. The results showed that the digital health literacy program is a cost-effective intervention that nursing professionals must integrate into continuity of care, not only in pandemic times but also in a permanent and standardized manner. An empowered patient with knowledge and skills in the use of digital resources is a patient with the ability to decide, satisfy needs, and solve problems, with critical thinking and control over their health. KEY WORDS Digital resources Health literacy Heart failure Pandemic Patient CMECME ==== Body pmcHeart failure (HF) has become a public health issue, since it is one of the main causes of morbidity and mortality in the population worldwide.1 This population becomes one of the most vulnerable—in this day and age considering the COVID-19 pandemic—as they have an increased risk of contagion and complications associated with their base pathology.2 Therefore, it is necessary to make every effort to keep face-to-face medical appointments to a minimum, when not essential. For this reason, nursing professionals must promote educational processes and integrate instruments to teach, reinforce, improve, and evaluate—on a constant basis—the skills for self-care, seeking to achieve a high level of empowerment in the management of the disease in the patient.3 The acquisition of the educational information must be framed in health literacy, which is defined as the capacity of an individual to obtain, process, and grasp data and basic services of health so that they can make appropriate health decisions.4 To obtain data, adapt them, and make decisions, digital health literacy becomes an educational instrument with a high potential to increase empowerment in patients with HF. Digital health literacy is an extension of health literacy, but in the context of technology. It also uses the same operative definition, but again—as mentioned—in the context of technology. Technological solutions have the potential to either promote health literacy or become an obstacle.5 This literacy is understood as the set of skills, knowledge, and attitudes that a person needs to perform functionally in their daily activities with regard to the information and communication technologies (ICT).6 Nonetheless, this definition should not be reduced to technical competencies only, since its possibilities range from sharing opinions to creating high-level knowledge. All this is possible thanks to the great availability of instruments of a social and collaborative nature that have greatly impacted health education.2,6 Digital solutions offer the opportunity for the individual to be an active participant in their health. Digital solutions will provide a more centered approach in the person. In this approach, the individual will have more control on their health and a greater access to their data, while staying connected to their medical attention team.7 Digital health literacy has an enormous educational potential for patients with HF. In times of pandemic, it helps to promote the development of skills to achieve problem-solving, solution proposals, and decision-making processes regarding self-care through the ability to understand, evaluate, use, and transform digital objects, processes, and systems. In addition, it promotes the deployment of three interdependent dimensions: knowledge, ways of thinking, and capacity to act.8 The implementation of digital health literacy as an educational instrument for patients generates a series of benefits reflected in the reduction of the number of hospitalizations and emergency calls.9 Despite these benefits, at an international level, there is evidence that older adults reject the adoption of ICT for health management and some others believe that it is a temporary phenomenon that will soon be over.10 According to Seifert et al,11 there are eight characteristics that can allow and restrict the use of technology: the ability to learn how to use technology, the patient's skills, the patient's knowledge, security measures, perceived autonomy, the nature of the patient's responsibilities, the adaptation to home environment, and the maintenance of a person's professional and social life. Additionally, aging and the presence of other diseases and physical disabilities complicate the adoption of digital health resources. In Colombia, this panorama is very similar. Carrillo-González et al12 evidenced that there is a low level of knowledge and access to ICT in patients with chronic diseases: only 35.9% of men and 27.6% of women present optimal levels of knowledge and mastery of digital resources. Moreover, the decline in physical and mental abilities that inevitably arrive over the years13 is another obstacle to overcome; hence, that is the importance of using a simple, dynamic, and participatory method during the application of digital instruments. It is worth mentioning that family support is a key factor in this whole process, since it constitutes an important source of psychological stability for the patient. Furthermore, it enables relatives to guide the patient to participate in self-care activities and effectively cope with any complication derived from their disease.14 Numerous studies revealed that patients do not retain the information provided by nurses about self-care procedures necessary to avoid decompensation, and many of them walk away feeling confused about the information provided. Only 1 in 10 patients with HF can be expected to master their self-care, which creates the necessity to re-educate patients in a constant way and to do a follow-up.15,16 There is an urgent need to recognize that ICT can favor re-education processes.17 For this reason, it is necessary for nursing professionals to consider the use of ICT as an effective educational support system, seeking to empower patients to fully participate in their health decisions, adequately informed via online health resources led by professionals.18,19 Thus, the objective of this research study was to determine the effect of a digital health literacy program on knowledge and skills in the use of digital resources regarding self-care and health empowerment in patients with HF. METHODS Design A cross-sectional pilot study was performed—before and after the program—in patients diagnosed with HF, who attended an outpatient consultation in a tertiary institution. Procedure First, the fourth-level health institution was contacted to request authorization to access the research population. Then, the participants were selected, and it was made sure that they met the established inclusion criteria. The inclusion criteria were male or female patients over 18 years of age, diagnosed with HF, who were linked to an outpatient care program, who had a portable device with Internet connectivity, and who had a caregiver. Sample/Participants The sample size corresponded to the entire population with the restriction criteria (n = 28 subjects). A digital health literacy program was implemented. Description of the Program The program was developed by a nurse trained in the use of ICT. During eight sessions, patients were provided with a theoretical component on ICT applied to health—the use of technological resources such as e-mail, chats, blogs, forums, and applications—and a practical component, where each of the technological resources seen in self-care were integrated. The frequency of the sessions was weekly, with an intensity of 2 hours. The methodology used in the program had a motivational, participatory, and playful approach (see Figure 1), where the family and/or caregivers were included to support the patient in the incorporation of technological resources. FIGURE 1 Model for the implementation of digital health literacy regarding self-care in patients diagnosed with HF. The motivational component was developed thanks to health coaching, where the aim was to empower patients in their ability to recognize that they could use and integrate technological resources in their self-care through the establishment of goals, fully developing self-confidence and breaking down fears or barriers—often self-imposed—that prevented the use of these resources. An individual and personalized process was developed, in which the patient was the focus of the learning process, whereas the professional was the facilitator, who accompanied the patient in the achievement of goals, never imposing his or her methods or beliefs. Gamification instruments were used in the development and evaluation of the sessions, which is characterized by the integration of game dynamics that help to enhance the motivation of patients. Gamification was based on applying game mechanics to non-game contexts—in this case, in the development of the theoretical component to generate a bit of fun in the participants during the activities developed in each session. In addition to generating motivational and cognitive benefits, these games managed to transform the idea of the use of technological resources into an attractive challenge that was worth continuing.20 On the other hand, teaching back was a way of confirming whether patients had understood the information delivered. In each session, understanding was evaluated by asking about the use of the technological resources. In case there were doubts, the explanation would be given one more time using a simple language, emphasizing on one to three key points so that patients could apply this knowledge to their own healthcare (see Figure 1).19 Data Collection Technique To collect the information, a test on knowledge about the digital resources applied to their health and the Health Empowerment Scale were used. These were administered before and after the end of the program. The test on knowledge about digital resources was designed by the researchers, based on different theoretical references that link the use, employment, and utilization of ICT to the quality of life dimension.21,22 It consisted of nine questions with a Likert-type scale of 1 (always) to 4 (never). It was reviewed by two experts in digital literacy and two health professionals who are leaders in HF programs. A pilot test was conducted to determine the clarity and understanding of the questions. It has a Cronbach's α of 0.85. Regarding the Health Empowerment Scale, it consists of eight dimensions that enable the assessment of self-control, self-efficacy, problem solving, psychosocial coping, stress management, social support, self-motivation, and decision-making. It has a 5-point Likert-type scale (1, strongly disagree; 5, strongly agree). The higher the score, the better the empowerment. It has been validated in the Colombian population and has a Cronbach's α of 0.89.23 Ethical Considerations The study was based on international ethical considerations, such as the Declaration of Helsinki, which establishes the scientific, technical, and administrative standards for health research. This study was approved by the Institutional Ethics and Research Committee. Data Analysis To analyze the information, a descriptive analysis of data was carried out, in which measures of central tendency and standard deviation were calculated for the quantitative variables. The questions of the test on knowledge about digital resources corresponded to ordinal qualitative variables. For these variables, percentage distribution measures and frequency tables were calculated. Subsequently, an inferential analysis was carried out so as to compare the scores of the scales applied before and after the program through the non-parametric Mann-Whitney U test for paired data, where all the assumptions for the performance of the test were previously verified. Statistical significance was taken into account with a P value < .05 and a confidence level of 95%. The information was processed using the statistical software Restudio. RESULTS It was observed that most of the patients were men, with an average age of 67 years, with a standard deviation of ±12.9 years, with elementary schooling, whose occupation was homemaking. All of them had a relative as a caregiver, either a son or a daughter. Regarding the clinical data, it was found that the most predominant etiology was the ischemic one, with a preserved left ventricular ejection fraction and a functional class I according to the New York Heart Association (Table 1). Table 1 Description of Sociodemographic and Clinical Variables Variables N = 28 n % Age, mean ± SD, y 67.3 ± 12.9 Sex Male 21 75 Female 7 25 Total 28 100 Schooling Elementary 17 60.7 High school 6 21.4 Other 5 17.8 Total 28 100 Occupation Homemaking 14 50 Freelance 9 32 Pensioner 4 14 Employee 1 3.5 Total 28 100 Caregiver Children 16 57.6 Spouse 7 25 Other 5 17.8 Total 28 100 Diagnosis Ischemic 15 53.5 Valvular 8 28.6 Hypertension 3 10.7 Other 2 7.1 Total 28 100 Ejection fraction Greater than 40% 20 71.4% Less than 40% 8 28.5 Total 28 100 Functional class I 12 42.8 II 10 35.7 III 6 21.4 IV 0 0 V 0 0 Total 28 100 It was noticed that after the intervention, the proportion of the use of smartphones increased, as well as the use of social networks, which generated a decrease in the use of classic/basic cell phones and landlines. According to the dimensions “technological resource” and “time per day”—despite noticing changes in frequencies—no statistically significant differences were found in the vast majority, except for the variable “does not spend time” in “time per day” (see Table 2). Table 2 Results of Questionnaire on the Use of Information Technologies Before and After the Program, Most Used Resource and Time Spent Item Before After P n % n % 1. Is there any support you can rely on from a relative or caregiver when using technological resources? Always 22 78.6 17 60.7 .39 Often 3 10.7 10 35.7 Sometimes 1 3.6 1 3.6 Never 2 7.1 0 0 Total 28 100 28 100 2. Do you feel comfortable when asking your caregiver or relative for help with the use of technological resources? Always 15 53.6 11 39.3 .62 Often 8 28.6 15 53.6 Sometimes 4 14.3 1 3.6 Never 1 3.6 1 3.6 Total 28 100 28 100 3. Do you have sufficient knowledge and skills for the use of Internet, chat, forums, and health applications? Always 4 14.3 3 10.7 .003 Often 3 10.7 6 21.4 Sometimes 4 14.3 17 60.7 Never 17 60.7 2 7.1 Total 28 100 28 100 4. How often do you try to resolve doubts about your illness using the Internet? Always 2 7.1 2 7.1 .001 Often 2 7.1 16 57.1 Sometimes 6 21.4 9 32.1 Never 18 64.3 1 3.6 Total 28 100 28 100 5. Do your family and friends encourage you to use the Internet, applications, forums, and blogs to improve your self-care? Always 2 7.1 8 28.6 .001 Often 5 17.9 14 50 Sometimes 10 35.7 6 21.4 Never 11 39.3 0 0 Total 28 100 28 100 6. Are you motivated to receive training to improve self-care through the Internet, chat, apps, forums, and blogs? Always 22 78.6 24 85.7 .11 Often 3 10.7 1 3.6 Sometimes 0 0 3 10.7 Never 3 10.7 0 0 Total 28 100 28 100 7. Do you feel safe and confident when accessing to the Internet, chats, applications, forums, and health blogs? Always 10 35.7 4 14.3 .4 Often 9 32.1 14 50 Sometimes 6 21.4 10 35.7 Never 3 10.7 0 0 Technological resource Smartphone 10 34.5 12 44.8 .5 Social networks 0 0 1 3.4 .7 Classic/basic cell phone 13 48.3 12 41.4 .03 Landline 5 17.2 3 10.3 .07 Total 28 100 28 100 Time per day Does not spend time 10 35.7 3 10.7 .01 1 h 10 35.7 14 50 .5 Between 2 and 3 h 4 14.3 4 14.3 1.0 More than 3 h 4 14.3 7 25 .5 Total 28 100 28 100 Questions related to the level of knowledge and skills for digital resources (P = .003), to resolving doubts when using the Internet (P = .001), and to motivation by family and friends in the use of digital resources (P = .001) showed a statistically significant change after the digital health literacy program (see Table 2). As shown in Table 3, it was found that the level of empowerment improved significantly (P = .01) after the digital health literacy program. A positive change was observed in the dimensions that emphasized the level of self-control to change certain aspects that generated dissatisfaction regarding their self-care, as well as in troubleshooting, decision-making, and motivation to make changes in behavior. Table 3 General Results and by Dimensions in the Level of Empowerment General Level of Empowerment n Average P a Before 28 27.6 .001 After 28 38.2 Empowerment Dimensions Before After P Self-control 3 4 .002 Self-efficacy 3 4 .001 Troubleshooting 3 5 .001 Coping 5 4 .09 Stress management 5 4 .09 Social support 3 5 .001 Self-motivation 4 5 .001 Decision-making 3.5 4 .001 aMann-Whitney U test. DISCUSSION Regarding the socio-demographic profile, the data of the present study showed that the majority of the participants were men, with an average age of 67 years, with a standard deviation of ±12.9 years, with elementary education, whose occupation was homemaking. All of them had a caregiver, which was a son or a daughter. Jones22 and Serrani-Azcurra23 have considered that age and schooling can influence knowledge and skills in the use of ICT, given that patients with lower literacy levels do not seek to have smartphones or use the Internet, especially for health reasons. On the other hand, both the cognitive deficit and the low self-efficacy associated with an advanced age significantly reduce adults' ability to use technology. Nevertheless, as long as teaching-learning methodologies that increase their motivation are integrated, greater access and participation will be achieved. Various authors—such as Smith and Magnani24 and Vaportzis et al25—highlight that there are three motivations to use technology: connecting with others, learning new information, and integrating the caregiver into learning. The results showed that the effect of the digital health literacy program generated a statistically significant change in the level of knowledge and skills of patients regarding ICT and in the level of empowerment of the patient concerning the disease. It was observed that after the intervention, the proportion of the use of smartphones increased, as well as the use of social networks, which generated a decrease in the use of classic/basic cell phones and landlines. According to Sims et al,26 educating patients in digital resources applied to health expands the target audience, which allows the inclusion of patients to this modality, adapts the instruments depending on the patient, delivers information in a more timely manner, standardizes the message, and allows patients to deepen their care and knowledge using smartphones and social networks through digital health literacy. Resources can be delivered more efficiently and instruments can be more sustainable, as patients increase their skill in using these technologies. With regard to the amount of time spent per day on technologies, there was a noticeable increase. This result coincides with Halvorsen et al27 who found that the association of digital resources with a chronic disease facilitates its adoption by patients, which increases the amount of time they invest in these. An improvement was also identified in the level of knowledge and skill in the use of specific resources such as e-mail, applications, and chats. This aspect agrees with that mentioned by Gordon and Crouch,28 who state that for technologies to be used, they must satisfy the patients' needs, adapting to the activities that are most likely to be performed in their day-to-day life. They must also be useful and easy to use to be accepted. According to Kuerbis et al,29 active use of e-mail, Internet, and social networks by patients can improve access to care, improve patient education, facilitate detection programs, and increase adherence to treatment plans. Another aspect that improved was related to the resolution of health questions using the Internet. Authors such as Fausset et al30 and Zhang et al31 highlight that digital health literacy allows to find, understand, evaluate, and access information through electronic sources or online—to address health-related concerns—but it is also important to educate the patient about the limitations of health information on the Internet, to ensure that patients can critically evaluate and collaborate effectively with healthcare professionals in the context of decision-making related to their health. Likewise, an increase in the patient's motivation was found by family and friends regarding the use of digital resources. This result agrees with Silver32 and Featherall et al33 who mention that a more explicit and determined participation of relatives as caregivers in digital information resources about health could improve clinical quality and patient safety by increasing the transparency, precision, and exhaustiveness of the information about the patient's health in all care settings, increasing the motivation of patients to use these types of resources. Some aspects of the program that could have favored these results were the motivational approach—centered on coaching as a starting point—in which it was possible to identify the limitations and the degree of confidence that patients had in the use of technological resources, through questions and active listening. From this exploration throughout the sessions, patients were shown how they could transform these limitations and change their behaviors, establishing weekly goals to apply what had been learned during the sessions, and identifying obstacles and looking for solutions. In this way, self-confidence was encouraged. The treatment of patients with HF is complex, since changes in their lifestyle are required. Improving these patients' self-care is a process that requires, mainly, a change of behavior and a change in the motivation of each person. Health interventions become necessary to help the patient in this transition and maintenance of healthy lifestyle habits.34,35 From this perspective, the behavioral approach—through coaching—that was used as the methodology in the development of the program's sessions made it possible to help the individual in the process of changing their behavior, encouraging commitment through a convincing and encouraging approach. Another component integrated into the literacy program that contributed to the results was the teach-back technique and gamification. Teach-back would be applied in all educational sessions. Guiding questions were elaborated on the topics developed to determine what patients had understood. Some of the examples of the questions asked were “Can you tell me what we discussed today?” or “What can you tell your wife/husband about using the apps?” If the person responded with an incorrect explanation or seemed to have a gap in understanding, the nurse could identify what information was necessary to be repeated or clarified. The cycle continued until the patient responded correctly. In this way, comprehension is assessed and healthcare professionals can identify an educational strategy that almost all people commonly understand. This tool allowed those patients with low literacy levels to actively participate and information to be reiterated. Tran et al36 show that the teach-back method is an intervention that has shown to be effective in improving retention and understanding of information, since it allows healthcare providers to ask patients to explain, in their own words, the information that has been discussed. If the patient cannot remember or has difficulty understanding the information, the provider can identify specific misunderstandings or deficiencies and re-explain the concept, allowing to encompass the cognitive and social skills that influence an individual's ability to promote and maintain good health through the effective understanding and application of health information. The gamification approach to the literacy program included games that had challenges and rewards (eg, points, achievement badges, and leaderboards), which contributed to knowledge acquisition, increased motivation, and a competitive spirit in favor of their learning. This approach made the theoretical component more enjoyable, promoted the development of positive social relationships, and fostered a feeling of integration with their peers. Sardi et al37 affirm that empirical evidence is just emerging to support gamification in health. Through games, the information provided is reaffirmed and motivation is increased. With respect to empowerment, an increase was identified after the intervention, which was evidenced in a change in the level of self-control to modify certain aspects of their self-care, related to hydrosaline restriction, weight control, and management of alarm signs and symptoms. This increase was also evident in both the resolution of problems and the decision-making skill to make changes in conduct. The literacy program was a tool to increase patients' empowerment, since it allowed the acquisition of motivations and skills that patients can use to improve their participation in decision-making and, thus, improve their control in their relationship with professionals. This goes beyond the fact of simply informing the patient and requires a process of motivation so that actions can be understood. There is growing evidence about the fact that effective patient empowerment is considered a key factor for patients to assume co-responsibility in their care and for them to increase their self-management and self-efficacy regarding their health, which helps them achieve better health conditions. Not only does it improve patients' quality of life, it also alleviates the impact of morbidity on people's lives and limits the demands on health and social care services.38 It is important to note that a low level of literacy may limit information retention and, therefore, be reflected in a lower awareness of the disease. High levels of empowerment derived from literacy favor patients with HF to have a better quality of life, as evidenced in the improvement of their functional capacity, adequate mental health, symptom control, and easiness in the process of social, labor, and family reincorporation. On the other hand, as there is an increase in the patient's self-control, positive coping is developed. The same occurs with the problem-resolution capacity and achieved decision-making processes. Self-control is considered a key factor to improve adhesion to treatment regimens and to guarantee the efficient use of primary health resources.8 The results obtained in this study showed that digital health literacy has a strong impact on empowerment and patient self-care regarding their pathology. It also encourages exploring new training possibilities through digital resources. Nursing professionals must integrate and standardize digital health literacy in educational processes in a transversal manner, not limited to times of pandemic. It should be considered as a fundamental instrument to promote more active, informed, and educated patients. Nonetheless, it is a challenge since innovative methodologies must be permanently incorporated to increase access and acceptance in the use of ICT. The incorporation of digital health literacy settings in clinical practice will lead to an increase of the coverage of patients who benefit from these digital resources and will minimize the physical, psychosocial, and economic impact derived from the lack of adherence to treatment and the lack of involvement in their care. Certain limitation that was identified was the use of nonparametric statistics due to the weakness of statistical power, as well as the use of a small sample for convenience. As authors, we will continue advancing with the conduct of more studies under this research phenomenon, to generalize results. Finally, it is suggested to continue with this type of study with a larger sample size and with a control group to minimize selection biases. CONCLUSIONS Technological literacy is a complementary instrument in the educational processes that should be integrated by nursing professionals in the continuity of care, in a permanent and standardized manner. Digital health literacy provides the patient with capacities for their empowerment and self-management of their disease. Digital health literacy also allows patients to feel supported through technologies and socialize the achievements with people who go through the same situation. An empowered patient with knowledge and skills in the use of digital resources is a patient with the ability to decide, satisfy needs, and solve problems, with critical thinking and control over their health. The project was funded by the call 850—Call for the Strengthening of CTeI Projects in Medical and Health Sciences with Young Talent and Regional Impact. The authors have disclosed that they have no significant relationships with, or financial interest in, any commercial companies pertaining to this article. ==== Refs References 1 Comín-Colet J Enjuanes C Lupón J Cainzos-Achirica M Badosa N Verdú JM . Transitions of care between acute and chronic heart failure: critical steps in the design of a multidisciplinary care model for the prevention of rehospitalization. Revista Española de Cardiología (English Edition). 2016;69 (10 ): 951–961. doi:10.1016/j.rec.2016.05.001. 2 DeFilippis EM Reza N Donald E Givertz MM Lindenfeld J Jessup M . Considerations for heart failure care during the COVID-19 pandemic. JACC: Heart Failure. 2020;8 (8 ): 681–691. doi:10.1016/j.jchf.2020.05.006.32493638 3 Irani E Moore SE Hickman RL Dolansky MA Josephson RA Hughes JW . The contribution of living arrangements, social support, and self-efficacy to self-management behaviors among individuals with heart failure: a path analysis. The Journal of Cardiovascular Nursing. 2019;34 (4 ): 319–326. doi:10.1097/JCN.0000000000000581.31058704 4 Sewell J . Informatics and Nursing: Opportunity and Challenges. 6th ed. Lippincott Williams & Wilkins; 2018. 5 Dunn P Hazzard E . Technology approaches to digital health literacy. International Journal of Cardiology. 2019;293 : 294–296. doi:10.1016/j.ijcard.2019.06.039.31350037 6 Manganello J Gerstner G Pergolino K Graham Y Falisi A Strogatz D . The relationship of health literacy with use of digital technology for health information: implications for public health practice. Journal of Public Health Management and Practice. 2017;23 (4 ): 380–387. doi:10.1097/PHH.0000000000000366.26672402 7 Conard S . Best practices in digital health literacy. International Journal of Cardiology. 2019;292 : 277–279. doi:10.1016/j.ijcard.2019.05.070.31230937 8 Park C Otobo E Ullman J , . Impact on readmission reduction among heart failure patients using digital health monitoring: feasibility and adoptability study. JMIR Medical Informatics. 2019;7 (4 ): e13353. doi:10.2196/13353.31730039 9 Hsu WC . The effect of age on electronic health literacy: mixed-method study. JMIR Human Factors. 2019;6 (2 ): e11480. doi:10.2196/11480.31066696 10 Blok M van Ingen E de Boer AH Slootman M . The use of information and communication technologies by older people with cognitive impairments: from barriers to benefits. Computers in Human Behavior. 2020;104 : 106173. doi:10.1016/j.chb.2019.106173. 11 Seifert A Cotten SR Xie B . A double burden of exclusion? Digital and social exclusion of older adults in times of COVID-19. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences. 2021;76 (3 ): e99–e103. doi:10.1093/geronb/gbaa098.32672332 12 Carrillo-González GM Chaparro-Diaz L Sánchez-Herrera B . Conocimiento y acceso a las TIC en personas con enfermedad crónica y cuidadores familiares en Colombia. Enfermería Global. 2014;13 (2 ): 123–134. http://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S1695-61412014000200006 13 Wildenbos GA Peute L Jaspers M . Aging barriers influencing mobile health usability for older adults: a literature based framework (MOLD-US). International Journal of Medical Informatics. 2018;114 : 66–75. doi:10.1016/j.ijmedinf.2018.03.012.29673606 14 Allida S Du H Xu X , . mHealth education interventions in heart failure. The Cochrane Database of Systematic Reviews. 2020;7 (7 ): CD011845. doi:10.1002/14651858.CD011845.pub2.32613635 15 Jaarsma T Hill L Bayes-Genis A , . Self-care of heart failure patients: practical management recommendations from the Heart Failure Association of the European Society of Cardiology. European Journal of Heart Failure. 2021;23 (1 ): 157–174. doi:10.1002/ejhf.2008.32945600 16 Deza MPP . Improving Nursing Knowledge of Heart Failure Discharge Self-Care Instructions [dissertation]. Walden University; 2020:8696. https://scholarworks.waldenu.edu/dissertations/8696 17 Mackert M Mabry-Flynn A Champlin S Donovan EE Pounders K . Health literacy and health information technology adoption: the potential for a new digital divide. Journal of Medical Internet Research. 2016;18 (10 ): e264. doi:10.2196/jmir.6349.27702738 18 Dunn P Conard S . Digital health literacy in cardiovascular research. International Journal of Cardiology. 2018;269 : 274–275. doi:10.1016/j.ijcard.2018.07.011.29996976 19 Yen PH Leasure AR . Use and effectiveness of the teach-back method in patient education and health outcomes. Federal Practitioner. 2019;36 (6 ): 284–289. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6590951/ 31258322 20 Johnson D Deterding S Kuhn KA Staneva A Stoyanov S Hides L . Gamification for health and wellbeing: a systematic review of the literature. Internet Interventions. 2016;6 : 89–106. doi:10.1016/j.invent.2016.10.002.30135818 21 Martínez-Alcalá C Rosales-Lagarde A Alonso-Lavernia M , . Digital inclusion in older adults: a comparison between face-to-face and blended digital literacy workshops. Frontiers in ICT. 2018;5 : 21. doi:10.3389/fict.2018.00021. 22 Jones R . Development of a questionnaire and cross-sectional survey of patient eHealth readiness and eHealth inequalities. Medicine 2.0. 2013;2 (2 ): –e9. doi:10.2196/med20.2559. 23 Serrani-Azcurra DJL . Escala de empoderamiento sobre la salud para adultos mayores. Adaptación al español y análisis psicométrico. Colombia Médica. 2014;45 (4 ): 179–185. http://www.scielo.org.co/scielo.php?script=sci_abstract&pid=S1657-95342014000400007&lng=en&nrm=iso&tlng=es 25767307 24 Smith B Magnani JW . New technologies, new disparities: the intersection of electronic health and digital health literacy. International Journal of Cardiology. 2019;292 : 280–282. doi:10.1016/j.ijcard.2019.05.066.31171391 25 Vaportzis E Clausen MG Gow AJ . Older adults perceptions of technology and barriers to interacting with tablet computers: a focus group study. Frontiers in Psychology. 2017;8 : 1687. doi:10.3389/fpsyg.2017.01687.29071004 26 Sims T Reed AE Carr DC . Information and communication technology use is related to higher well-being among the oldest-old. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences. 2017;72 (5 ): 761–770. doi:10.1093/geronb/gbw130.27702839 27 Halvorsen K Dihle A Hansen C , . Empowerment in healthcare: a thematic synthesis and critical discussion of concept analyses of empowerment. Patient Education and Counseling. 2020;103 (7 ): 1263–1271. doi:10.1016/j.pec.2020.02.017.32164960 28 Gordon NP Crouch E . Digital information technology use and patient preferences for internet-based health education modalities: cross-sectional survey study of middle-aged and older adults with chronic health conditions. JMIR Aging. 2019;2 (1 ): e12243. doi:10.2196/12243.31518291 29 Kuerbis A Mulliken A Muench F Moore AA Gardner D . Older adults and mobile technology: factors that enhance and inhibit utilization in the context of behavioral health. Mental Health and Addiction Research. 2017;2 (2 ): 1–11. doi:10.15761/MHAR.1000136. 30 Fausset CB Mitzner TL Price CE Jones BD Fain WB Rogers WA . Older adults' use of and attitudes toward activity monitoring technologies. Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. 2013;57 (1 ): 1683–1687. doi:10.1177/1541931213571374.31263349 31 Zhang X Hailu B Tabor DC , . Role of health information technology in addressing health disparities: patient, clinician, and system perspectives. Medical Care. 2019;57 (Suppl 6 2 ): S115–S120. doi:10.1097/MLR.0000000000001092.31095049 32 Silver MP . Patient perspectives on online health information and communication with doctors: a qualitative study of patients 50 years old and over. Journal of Medical Internet Research. 2015;17 (1 ): e19. doi:10.2196/jmir.3588.25586865 33 Featherall J Lapin B Chaitoff A Havele SA Thompson N Katzan I . Characterization of patient interest in provider-based consumer health information technology: survey study. Journal of Medical Internet Research. 2018;20 (4 ): e128. doi:10.2196/jmir.7766.29674312 34 Calvillo J Román I Roa LM . How technology is empowering patients? A literature review. Health Expectations. 2015;18 (5 ): 643–652. doi:10.1111/hex.12089.23711169 35 Wang T Xiao D Dong Y Goossens RHM . Development of a design strategy for playful products of older adults. The Design Journal. 2021;24 (4 ): 525–545. doi:10.1080/14606925.2021.1912903. 36 Tran S Bennett G Richmond J , . 'Teach-back' is a simple communication tool that improves disease knowledge in people with chronic hepatitis B—a pilot randomized controlled study. BMC Public Health. 2019;19 (1 ): 1355. doi:10.1186/s12889-019-7658-4.31647001 37 Sardi L Idri A Fernández-Alemán JL . A systematic review of gamification in e-health. Journal of Biomedical Informatics. 2017;71 : 31–48. doi:10.1016/j.jbi.2017.05.011.28536062 38 Kärner Köhler A Tingström P Jaarsma T Nilsson S . Patient empowerment and general self-efficacy in patients with coronary heart disease: a cross-sectional study. BMC Family Practice. 2018;19 (1 ): 76. doi:10.1186/s12875-018-0749-.29843619	35234702	PMC9707854	NO-CC CODE	2022-12-02 23:21:20	no	Comput Inform Nurs. 2022 Mar 2; 40(11):754-762	utf-8	Comput Inform Nurs	2,022	10.1097/CIN.0000000000000883	oa_other
==== Front Resour Policy Resour Policy Resources Policy 0301-4207 1873-7641 Elsevier Ltd. S0301-4207(22)00608-0 10.1016/j.resourpol.2022.103165 103165 Article Natural resources volatility and causal associations for BRICS countries: Evidence from Covid-19 data Cao Yanyan a Xiang Shihui b∗ a School of Economic Management, Daqing Normal University, Heilongjiang, China b Chinese Graduate School, Panyapiwat Institute of Management, Nonthaburi, Thailand ∗ Corresponding author. 29 11 2022 1 2023 29 11 2022 80 103165103165 30 12 2021 15 10 2022 22 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Natural resource price volatility has been a major concern in recent time, especially during the COVID 19 period. Although several empirical research have looked into the oil and natural resources prices nexus with economic growth, but, our study makes a significant contribution to the present literature by estimating the long run natural resource price volatility influence on economic growth as well as the causal associations between volatility of the prices of natural resources and economic growth for BRICS economies over 1995–2020 period. To conduct empirical estimation, the study has used new and advanced (CUP-FM) continuously updated fully modified and continuously updated bias-corrected (CUP-BC) estimators for long term influences of the natural resources prices and (Dumitrescu and Hurlin, 2012) heterogeneous test for panel causality for the estimation of the causal relationship between the variables. The results provide clear evidences about the negative influence of volatility in natural resources prices, whereas positive impact of gas and oil rents on economic growth or economic performance of the BRICS economies. Moreover, bidirectional causal association is also revealed from our empirical findings to exist between economic growth and price volatility of natural resources. The findings of our study are robust to various policy implementations. It is recommended to reduce the reliance of natural resources as well as the adoption of short run and long run natural resource hedging policies to mitigate the detrimental impacts of price volatility of natural resources on economic growth and environment. Keywords Volatility in natural resources prices Natural gas rents Oil rents Economic growth BRICS economies ==== Body pmc1 Introduction Our world has encountered several challenges since the beginning of the 21st century, including the international financial crisis of 2007–2008 and the contagious pandemic of the Covid-19 among others, where the former disrupted all social, environmental and economic operations. This pandemic has rapidly spread across all economies, first in developed countries and now in developing countries. Because their health services and health conditions are worse to those in rich nations, and because their macroeconomic indicators are unable to survive such a long-run turbulence in socioeconomic terms, emerging economies are unquestionably at a disadvantage during this era. (DEMİRCAN ÇAKAR et al., 2021). Besides impacting other sectors drastically, natural resource volatility is substantially contributed by global financial and economic uncertainty, which could be critical to macroeconomic and microeconomic growth: expenditures at household level, business revenues, and the whole country (B. Lin and Bai, 2021). The world has recently experienced two significant shocks: (a) the Covid 19 epidemic and (b) a decline in prices of the world's most valuable natural resource, i,e., oil (Sharif et al., 2020). The combination of these two problems will almost certainly lead to a prolonged economic downturn, dragging the biggest economy in the world (the United States) and other nations into another recession. (Sun and Wang, 2021). The association amongst natural resource volatility and economic growth is significant because it has the potential to alter macroeconomic stability and a country's level of welfare. For example, there are several ways in which a rise in the prices of natural resource commodity, particularly gas and oil costs, can affect output (Yıldırım and Öztürk, 2014). At first, the natural gas and oil price shocks dampen overall demand because they produce a redistribution of wealth from oil exporters to oil importers. Second, the company's ability to invest in new equipment and employees may be hampered by the rise in oil and gas costs. Energy efficiency measures are taken because of the ever-increasing cost of oil and gas. (Bing and Ting, 2021). Furthermore, this low level of consumption of energy occurs before a rise in unemployment as a result of real wager declines (Zhang, 2021). As a result, lowering real wages increases unemployment and lowers the country's real GDP. A reduction in oil prices, on the other side, lowers costs of production, increase economic activity and fosters growth (Narayan et al., 2014). Due to increased future returns, this drop would dramatically raise stock market values. The recent drop in natural gas, oil and natural resources, on the other hand, is mostly due to the Covid-19 epidemic. The current pandemic and ensuing world lockdowns in social and economic terms lead to reduction of global aggregate demand and disruptions in supply networks (Prabheesh et al., 2020). According to reports, a substantial drop in oil consumption due to the lockdown environment has resulted in severe drop of oil prices in the world market, with prices falling from 61 US$ in January to 12 US$ in April 2020. The Covid-19 crisis has caused volatility in natural gas, oil and many natural resources, such as gold and minerals (Hordofa et al., 2021). As a result, in recent era, policymakers and governments have increased their concerns about the influence of price volatility of natural resources on economic growth and sustainability. Studies empirically examined the negative influence of price volatility of natural resources on an economy's performance in this regard (Guan et al., 2021), (Atil et al., 2020), (Chien et al., 2021), (Khan et al., 2020). The primary focus is on the volatility of oil prices because it is one of the most trading commodities on the world. Therefore, empirical studies show that volatility in oil prices has an adverse influence on economic growth (Y. Lin et al., 2020) (Gkillas et al., 2020), (Nonejad, 2020). However, several studies have investigated the link between natural resource uncertainty and economic prosperity. There is still a big hole in the research about the connection between the price of natural resources and economic development. In instance, the aforementioned research experimentally investigated oil price volatility and its link to the stock market but ignored the impact of natural resource price volatility in the expansion process. The primary purpose of this research is to bring the issue to the attention of policymakers and governments. The policy implications and results of this study may help governments overcome the volatility of natural resource prices and the problems it causes for economic expansion. The development of economies may also be affected by variables like oil and natural gas rents and energy inflation in addition to the ebb and flow of natural resource prices. The rents from the sale of natural gas and oil have been demonstrated in several studies to have a significant impact on economic growth. (Adedoyin et al., 2020). Therefore, the objective of the present study is to empirically estimate the causal relations between the volatilities of natural resources prices, natural gas and oil rents on the BRICS economies’ economic growth. This study is important since it is one of the few that empirically examined the specified variables while tackling the Covid-19 pandemic, especially for the BRICS economies. The current study, on the other hand, looked at the impact of price volatility of natural resources in natural resources over the pandemic period. Although, substantial literature is present on economic growth and natural resources prior to the Covid 19 pandemic, our analysis is unique and has three-fold addition to the previous literature. To begin with, this is one of the first studies to take into account price volatility of natural resource and economic performance using a large dataset that spans the Covid-19 outbreak period. Nonetheless, recent investigations by (Ma et al., 2021; Sun and Wang, 2021) and others have looked at the relationship of price volatility of natural resources with economic performance. These investigations, however, simply investigated for a causal link between these variables. Our study goes a step further by giving the evidence for long run impact of each independent variable as well as the causal relationship between these variables with economic growth, which is a significant contribution to the literature. As a result, the study gives evidence, particularly from the standpoint of the BRICS economies (Jiao and Liu, 2021). Second, the study used the recent dataset, which encompasses two economic critical situations or crises i.e., the economic crisis globally in 2008, and current Covid 19 pandemic of 2020. Oil prices hit an all time high rate of 145.18 $ per barrel in 2008, July, during the current pandemic era, oil prices plummeted to a new all-time low of −37.63 $ per barrel. Moreover, during the pandemic, crude oil prices are also falling (Guan et al., 2021) and provided policy recommendations for reducing the uncertainty of natural resource prices and performance of an economy. Therefore, this study is significant one since it is one of the few studies to provide empirical estimations as well as policy recommendations in the Covid 19 period. Regarding empirical estimation, our study is the among a few studies that applied second generation panel estimation techniques including (Pesaran, 2007) unit root test and (Westerlund and Edgerton, 2008) panel test for cointegration. Furthermore, our research is the first to use the continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC) estimation methods to estimate the long-term influence of price volatility of natural resources, giving it sufficient superiority over other empirical estimations due to its properties of attending the issue of cross-sectional dependency arising from global unobserved stochastic trends (Samadi and Rad, 2013)., endogeniety, autocorrelation, heteroskedasticity, and fractional integration (Ahmed and Le, 2021). The outcomes of our study are believed to benefit future policy practitioners, researchers, institutions, students, academics concerned with the price volatility of natural resources and growth from the perspective of BRICS economies. The remainder of our study is arranged in a way that section 2 presents a comprehensive review of the past literature on the relationship between prices of natural resource and growth. The estimation technique and model specification are given in Section 3. Section 4 gives the interpretation of the empirical findings. Finally, in Section 5, the briefly concludes the study and policy recommendations regarding the empirical results is presented. 2 Literature review The existing literature provide mixed studies regarding natural resource price-growth nexus. Some of the studies investigated oil price and economic growth relationships (B. Lin and Bai, 2021), (Tahar et al., 2021), (Monye Michael and Omogbiya Shulammite, 2020), (B. Lin and Bai, 2021), some studies investigated gas rents or prices and total natural resource price’ relationship with growth (Guan et al., 2021), (Shahbaz et al., 2019), (Hayat and Tahir, 2021), (Etokakpan et al., 2020). The researchers have estimated this association both in pre as well as post covid era. For instance, Guan et al. (2021) analyzed the panel data of countries having abundance of natural resources over the period 2000–2020 to examine the effects of price volatility of natural resource on e-growth. The findings of the study from study from ARDL and PMG estimations concluded that economic growth was significantly reduced by volatility of natural resources considerably in the long-run. It was found that Covid-19 and global financial crisis had a much greater impact on the crude oil price than on the gold market. Tahar et al. (2021) employed ARDL model to reveal the long run and short run symmetric effects of price volatility of natural resource commodity prices with economic performance in commodity dependent countries. Their empirical estimation revealed that boom effect (current commodities) of 2004–2014 period had significant variation from past phenomena that illustrated the learning impacts gained from the previous experiences. Furthermore, the non linear ARDL estimations demonstrated that commodity price shocks had asymmetric effects. Before pandemic outbreak of Covid 19, taking Canada as the case study, similarly (Bashar et al., 2013), studied the link between uncertainity in oil price and macroeconomy by applying the structural VAR estimation. According to their primary findings, shocks in oil prices did not influence the aggregate level of output. But these oil price uncertainities had significant contributions to the output level variations. Recenly in the era of COVID-19 taking USA economy into consideration, Sharif et al. (2020) examined the same relationship i.e., oil economic and oil price uncertainty and many other related macroeconomic variables. Their results using a wavelet-based estimate method showed that variables varied throughout time. On the other hand, the Covid 19 epidemic had a greater effect on the unpredictability of the US economy. The researchers also said that the primary market in the United States that shown both greater and lower frequency across data was oil. Atil et al. (2020) studied the relationship between finance and growth whlie examining effect of oil prices over the period from 1972 to 2017 in Pakistan. The study applied the long run co variability estimation and found that natural resources had a promoting impact on financial development. But the oil prices affected financial development negatively in the country. Albulescu (2020) examined the oil prices and uncertainity of economic policy in the USA by analyzing the daily data from 21 Jan to 13 March2020. The authors applied the ARDL estimation model and the results of the study reported that increase in the deaths and cases of Covid 19 had no affect on uncertainity in economic policy in the United States. Oil prices, on the other hand, had a detrimental impact on the uncertainity of economic policy in the Covid 19 era. Applying second generation panel estimation methodology, Shahbaz et al. (2019) found that natural resources had significant and positive impact on economic growth of resource abundant countries. However, resource dependence had a negative impact on the economic growth from 1980 to 2015. However, the other study by Shahbaz et al. (2019) analyzed the USA economy and found opposite results. The authors found that the resource curse hypothesis was valid in the USA. Capitalization and oil prices, on the other hand, help the region's economy thrive. Similarly, Hayat and Tahir (2021) estimated the data of three resource rich countries over 1960–2016 period. By applying the ARDL methodology, the study found a crucial role of natural resources in economic development, but volatility in natural resources prices had an adverse affect on economic growth in Saudi Arabia, Oman and UAE. On the contrary, Rahim et al. (2021) studied the impact of rents of natural resources on e-growth over 1990 to 2019 period. According to the author's findings, natural resource rents considerably hinder economic growth. Human capital development, on the other hand, could be crucial in enhancing the favourable influence of on economic growth by natural resources. Similarly, by studying the Nigerian economy, Monye Michael and Omogbiya Shulammite (2020) analyzed the primary data of 320 respondents sample and estimated a negative and significantly negative association of oil prices with economic growth. In case of Pakistan, Chien et al. (2021) analyzed oil prices (crude) volatility association with economic growth over the period of 1980–2018. The results of the ARDL calculation led to the conclusion that the economy as a whole was negatively impacted by the rise in oil prices. Only the areas of transportation and communication saw an improvement. Ma et al. (2021) researched the causal relationship between prices of natural resources with economic growth in China both in post and pre Covid 19 era from 01, January 2019, to 01, April 2021. Their study by employing the wavelet coherence approaches, wavelet power spectrum and frequency domain causality tests revealed that prices of natural resource commodities were more volatile than performance of the economy especially during China's Covid 19 climax period. The wavelet coherence method, on the other hand, showed that there was a two directional causal relationship of the prices of natural resource commodity with economic performance at different time periods and frequencies. Applying the same estimation techniques for global data, Sun and Wang (2021) studied the nexus of price volatility in natural resource commodities with economic performance from 01, Jan 2019, to 01, July 2021. According to their findings, only prices of natural resource were vulnerable, but in-vulnerability was indicated for the economic performance globally. Moreover, these two variables exhibited no short run or long-run causal linkage in the wavelet coherence technique. Analyzing the data for Algeria over 1970–2012 period, Benramdane (2017) studied the how price volatility of oil impacted economic growth by employing the VAR model. The findings of this study show that the adverse consequences of volatility in oil price on growth outweigh the beneficial advantages of the oil price boom. it was concluded that the “resource curse” enigma in Algeria was driven by price volatility of oil rather than its abundance. In case of G-7 countries, Hordofa et al. (2021) evaluated how different natural resource rents for example natural gas, energy, oil rents affected the performance in economic terms over 1990 to 2020 period. Economic performance was found to be declined during and post COVID-19 pandemic. Natural resource rents, such as oil and gas, were found to aid boost economic performance in this study. Furthermore, the G7 economies' economic performance was hindered by the structural break imposed by COVID-19 for the year 2019 (Etokakpan et al., 2020). scrutinized the data for Malaysia over 1980 to 2014 period by applying cointegration and Granger causality test and contegration approach. According to the estimated results, natural gas, at one side, helped in the growth of the economy but it also contributed to environmental damages on the other hand (Katoka and Dostal, 2021). analyzed international prices of commodities, natural resources and economic performance in the countries of Sub Saharan Africa over the period 1990 to 2019. Natural resources promoted economic expansion, using the random coefficient estimate. The results also show that nations with plenty of natural resources that prioritise commodity exports do much better than others. Using the panel for 5 ASEAN countries and applying the ARDL technique, Rosnawintang et al. (2021) studied the relation of volatility of oil prices with economic growth over 1995 to 2018 period. The study found that volatility in oil price had a detrimental impact on economic growth only in the short run. Although there is a large body of empirical research on the impact of natural resource prices, gas prices, oil prices, on the growth or economic performance. However, the relationship between these factors is under researched in the BRICS region. Moreover, to the author's best knowledge, there have been no attempts to empirically examine and understand the relationship of price volatility of natural resource price with economic growth by applying most novel estimations techniques of CUP FM and CUP-BC. Hence our study is poineering one in these aspects and is going to be a significant contribution in the literature. 3 Empirical methodology 3.1 Model specification and data In order to achieve the said objective, the study uses GDP as dependent variable to measure the economic growth. According to (Hordofa et al., 2021), GDP is a well known measure of the performance of an economy considering many and economic factors and indicators such as investment, consumption, revenue, transaction and many other. As a result, GDP is an appropriate measurement for expressing economic performance. volatility of natural resources price is meaured by total natural resource rent (TR). Other independent variables include oil rents (OR), natural gas rents (GR). Data of all of the aforementioned variables spans over 1995 to 2020 and is gathered from World Development Indicators (WDI, 2020). The study takes BRICS economies into consideration-a five countries group, namely: Russia, Brazil, China, South Africa and India. The BRICS economies were selected because they have united to achieve a number of economic and development objectives. These nations' economy primarily aim to promote security, stability, and peace. Because of this, every change in policy in one economy might have an effect on another. On the other hand, any policy effort that involves the whole BRICS group may have a stronger influence on the remaining developing as well as advanced nations. These factors lead to the use of the BRICS economies as a case study. Hence the model in its functional form is given asGDP = f (TR, OR, GR) Where TR = total natural resource rent, OR = oil rent, GR = gas rent. The econometric functional form of the model is given as(1) GDPit=α0+β1TRit+β2ORit+β3GRit+εit Where subscript i = cross section and t = time. 3.1.1 Econometric techniques 3.1.1.1 Cross sectional dependence (CSD) testing In order to estimate our empirical model, the study uses panel data approaches to account for CSD. When CSD is neglected, panel data estimations reveal significant size distortions and biased results, according to (Pesaran, 2006). Therefore, before performing preliminary tests for the estimation of the parameters, CSD is examined first. To determine whether CSD exists or not, we use the Langrange Multiplier test proposed by (Breusch and Pagan, 1980), and Scaled LM and CD test proposed by Pesaran. The above tests compare the H0 of “no CSD” to the H1 of the “presence of CSD". In the next step of the analysis, unit root test and long run cointegration test are employed because it is compulsory to decide whether the data is stationary or unit root, as non-stationary data highlight the issue of false regression (Pesaran, 2007). proposed CADF (augmented ADF) test for unit root/stationarity that takes CSD into account. The CIPS (cross-sectional IPS) statistic is generated using the arithmetic averages of CADF data individually calculated for each member of the panel. The H0 of CIPS test states that the series is non-stationary i.e., is having unitroot problem. The unit root analysis findings will indicate that series can either be level stationary, i.e., I (0) or the first difference stationary i.e., I (1). Conventional OLS method is used to estimate coefficients if the series is level stationary. If the series has a unit-root, on the other hand, the presence of the long run co integration association should be confirmed before the coefficient calculations of the coefficients (Hatemi-j, 2008). For this estimation, the study applies (Westerlund and Edgerton, 2008) method for the estimation of the long run co-integration among variables. This co-integration algorithm produces samples and two statistics by using LM bootstrap co-integration technique. The significance of this technique originates from its null hypothesis, which states the presence of long-run co-integration and solves the variable heterogeneity. The test statistics are given as:(viii) LMφ(i)=Tφˆi(rˆi/σˆi) (ix) LMτ(i)=φˆi/SE(φˆi) Here, φˆi is the φi approximation against σˆi standard error, and rˆ2i denotes the long run estimated variance of mit, φi(L)=1−ΣφijLj denotes a scalar polynomial with L lag length, and ρi represents the factor loading parameters vector. The level shifts and regime shifts that represent the structural breaks are accounted for in these data (Umer et al., 2020). The long run parameters are computed after the cointegration relationship has been established. To accomplish it, our study applies the CUP-FM and the CUP-BC estimators, proposed by (Bai and Kao, 2006), (Bai et al., 2009). To begin (Bai and Kao, 2006), employed eq. (2) to investigate correlations between units by inserting common components in matrix form.(2) hit=ci+γ′mit+eit where, hit is the panel's dependent variable, i represents unit and t shows time period. constant term and coefficient matrix are represented by c and γ. Matrix of explanatory variables and respective error term are denoted by mit and eit, respectively, factor loadings and unobserved factors (ft) in series are separated into two sections as in eq. (3) (3) mit=mi,t−1+uit,eit=λi′fi+ηit Secondly, FMOLS (fully modified ordinary least squares) estimator was used by (Bai and Kao, 2006) that (Phillips and Hansen, 1990) proposed to spot the common factors existence by eq (4) γˆFMOLS=[∑i=1N∑i=1T(mit−m‾i)(mit−m‾i)′]x (4) [∑i=1N[∑t=1T(mit−m‾i)hˆit+−T(Δˆeu+Δˆuf+λi′)]] After estimating coefficients (γ) through equation. (1) in the initial step, until convergence is achieved, estimations are resumed utilizing residuals from each preceding step. CUP-FM estimator is the name given to this repeated procedure. Bai et al. (2009) afterward changed the procedure in equation. (2) as in following equation (5) (5) hit=ci+γ′mit+λi′fi+eit Moreover, Bai et al. (2009) made direct corrections in biases in the estimations. a bias-corrected estimate is also created by them that is updated constantly until convergence is achieved. The (CUP-BC) estimator is the name of this approach. By completing Monte Carlo simulations, Bai et al. (2009) showed that the CUP-FM and CUP-BC clearly be better than traditional estimators in all circumstances. These estimators are resilient in the presence of I (0) and I (1) factors and regressors as well, and they are robust against independent factors and endogeneity problems (Bai et al., 2009). Through causality test, the study explores the possible bi-directional relationship between economic growth and volatility in natural resource prices at the last of the empirical estimations. To this goal, the causality test that (Dumitrescu and Hurlin, 2012) proposed, is used to uncover plausible bidirectional causality between economic growth and price volatility of natural resources, taking CSD into account. The H0 of the test implies “absence of the causal relationship among variables.” 4 Results and discussion Table 1 provides descriptive statistics for the research variables, including mean, standard deviation, minimum and maximum values. The mean value of GDP is the greatest while the mean value of GR is the lowest among all variables. The results show that GR has the lowest variability around the mean whereas TR has the largest. Additionally, the Jarque-Bera Test's J-B statistics show that the data set is normal since the null hypothesis of data normality, H0, cannot be rejected.Table 1 Descriptive statistics analysis. Table 1Variables Mean Minimum value Maximum value Standard. Deviation J-B Stats GDP 2.23 1.760 1.467 2.24 1.337 TR 5.62 0.004 14.50 5.62 3.568 OR 2.55 0.25 14.50 2.55 2.074 GR 0.765 0.006 8.67 0.77 3.018 Source: Author's own Estimation *, and * denote 1, 5 and 10 percent significance level respectively. Moreover, correlation statistics among variables are given in Table 2 below. It is revealed that GDP only has negative association with OR and TR. All other variables are found to be positively correlated with each other. Furthermore, the correlation among variables is also less than 0.8 which shows that there is no issue of multicollinearity among the variables.Table-2 Correlation test. Table-2Variables OR TR GR GDP OR 1 TR 0.1023 1 GR 0.6136 0.0568 1 GDP −0.0345 −0.1925 0.1781 1 Source: Author Estimation Our empirical estimation firstly begin by estimation of CSD in panel series because ignoring the issue of CSD leads to erroneous findings. For this purpose we applied three different CSD tests namely Bruesh-Pagan LM, Pesaran Scaled LM and Pesaran CD tests. Table 3 provides us the estimations of these three tests. According to the findings we can reject H0 of cross sectional independence. Hence it is proved that CSD is present in our data.Table-3 Results of CSD tests. Table-3Variables Breusch-Pagan LM Pesaran Scaled LM Pesaran CD GDP 424.027a 33.516a 10.109a TR 310.358a 49.220a 13.950a OR 637.039a 40.025a 7.089a GR 729.082a 66.360a 24.397a Note. ** 5% significance value. * 10% significance value. a 1% significance value. After the confirmation of the CSD, second step of the analysis involves unit root testing of the data because stationarity of the data is an important as it helps in the adoption of the proper estimation both for short and long run. For this estimation, we applied CIPS and CADF tests proposed by Pesaran (2007) and Table 4 below gives us the results. From the results of both tests, it is clearly evident that all of the variables are level unit root, however they are stationary at their first difference.Table-4 CADF and CIPS Results for unit root test. Table-4Variables CIPS CADF Level 1st Difference Level 1st Difference GDP −1.029 −4.302a −1.258 −5.063a OR −1.698 −3.652a −1.630 −4.652a GR −1.029 −6.352a −1.057 −6.352a TR −0.392 −5.024a −2.169 −3.102a Note. ** 5% significance level. * 10% significance level. a 1% significance level. Long run cointegration relationship estimation follows the unit root testing. For this purpose we applied (Westerlund and Edgerton, 2008) coimtegration test and its results are given in Table 5 below. The H0 of the test states that no cointegration exists in the presence of various panel data problems such as CSD, serial correlation and structural break. The test findings reject the H0 and endorses that long run cointegration exists between TR,OR, GR and GDP.Table-5 Findings of westerlund and edgerton Co-integration test. Table-5Model without Shift Mean Shift Regime Shift Test Stat prob-value Test Stat prob-value Test Stat prob-value LMτ −4.409 0.000 −6.987 0.000 −5.882 0.000 LMφ −2.919 0.000 −4.249 0.000 −5.908 0.000 Note: Maximum five factors are used to run the model. Table 6 shows (Westerlund and Edgerton, 2008) test results in the existence of the structural breaks. It is necessary to explain the significant value of regime shift. The variables GDP, TR, GR and OR are found to be co-integrated because several key structural breaks occurred locally, regionally and globally, such as Asian crises, RMB exchange rate reforms declared in China (August 2015), 2001's mild recession and financial crises over 2007–2008 period.Table 6 Structural breaks of Westerlund and Edgerton (2008). Table 6Economies No Shift Mean Shift Regime Shift China 2005 2009 2013 India 2013 2000 2018 Brazil 1998 2009 2013 Russia 2012 2007 2019 South Africa 2006 2013 2013 After all these preliminary estimations, now we proceed to the long run coefficient estimations through CUP-FM and CUP-BC techniques. Table 7 below provides us the estimates for these two approaches. It is clearly indicated from the results that all of the variables are statistically significant and either have positive or negative impact on economic growth in BRICS economies. Specifically, TR is found to decrease the GDP or economic growth in the BRICS economies. For each unit increase in TR, GDP decline by 0.58 units in CUP-FM and 0.48 units in CUP-BC. Thus, our findings suggest that natural resource rent volatility is harmful to economic performance of the studied economies. The existing findings of (Hordofa et al., 2021), (Tahar et al., 2021), (Monye Michael and Omogbiya Shulammite, 2020), (B. Lin and Bai, 2021) second our findings that natural resources prices and the volatility in these prices affect any region or country’ economic growth. A possible explanation of this effect can be that social and economic lock down conditions in all economies during current COVID 19 period cause the reduction in industrial production and economic activities all over the world. This situation caused significant reduction in the demand for natural gas, oil as well as many other resources around the world. As a result, lowering energy demand during the Covid-19 era would lead natural resource rents to fluctuate. This fluctuation in natural resource rents may have an impact on countries' economic success.Table 7 CUP -BC and CUP-FM test results. Table 7Variables CUP FM CUP BC Coeff t-stat Coeff t-stat GR 0.255a 4.035 0.289a 3.868 TR −0.587a 3.152 −0.487a 5.027 OR 0.439a 5.863 0.233a 5.190 Note. ** 5% significance value. * 10% significance value. a 1% significance value. However, gas rents and oil rents have positive impact on economic growth in BRICS countries. For a unit increase in GR, GDP increases by 0.255 units in CUP-FM and by 0.289 units in CUP-BC. Similary OR are found to increase GDP by 0.439 units in CUP-FM and by0.233 units in CUP-BC respectively. Gas resources and oil resources have stimulating effect on economic growth through supplying the resources and energy necessary in the production or manufacturing processes that boost economic growth of the group of the economies. our findings are varified by a number of previous studies including (Pérez and Claveria, 2020), (Hayat and Tahir, 2021), (Wen et al., 2022). (Wen et al., 2022), Etokakpan et al. (2020), Galadima and Aminu (2020) and Topcu et al. (2020). In addition to long-run coefficient estimations, our study looks into the causality associations between all the variables in consideration. For this, Dumitrescu and Hurlin (2012) heterogeneity granger test for panel causality test is used in our study and Table-8 shows the estimated findings. The results show us that factors and the BRICS nations' economic development are related in both directions. The regional economic growth is therefore significantly influenced by OR, TR, and GR. On the other hand, it has been shown that OR, TR, and NR in the research region are significantly impacted by economic growth. The analysis produced highly statistically significant findings at the 1% level, strong enough to disprove the hypothesis that there is no causal relationship between the variables under examination (H0). Rather, it is argued that there is a bidirectional causal relationship exists between the studied variables and the BRICS countries' economic growth. As a result, policies aimed at OR, TR, GR should be compatible to address economic growth, as observed estimates imply that these variables significantly impact the economic performance. As a result, policies aimed at OR, TR, GR, should also address economic performance, as empirical estimates imply that these variables can have a significant impact on economic performance. In contrast with the study of (Rafindadi and Ozturk, 2015), our study is consistant with the study of (Wen et al., 2022), (Magazzino et al., 2021) and (Hordofa et al., 2021) who found bidirectional causality between economic growth and natural gas rents.Table-8 Dumitrescu and Hurlin (2012) heterogeneous causality test Results. Table-8H0 Stats Prob value. TR does'nt homogenously cause GDP 17.029 0.000 GDP does' nt homogenously cause TR 18.830 0.000 GR does'nt homogenously cause GDP 24.665 0.000 GDP does' nt homogenously cause GR 14.535 0.000 OR does'nt homogenously cause GDP 17.552 0.000 GDP does' nt homogenously cause OR 25.30 0.000 Source: Author's Estimation 4.1 Conclusion and policy recommendations Our world has undergone several changes over the last three decades because of the oil price climb of 2003, global financial crisis over 2007–08, outbreak of Covid-19 pandemic and many others. All of these incidents have had a significant impact on global consumption and production patterns. Because of the recent global epidemic, academics and policymakers have paid increased attention to volatility of natural resource price and economic performance. In this sense, it is critical to look into the volatility of natural resource price and economic growth of both developing and developed economies in the pandemic of Covid-19. Furthermore, every country's locked-down economy lowers economic and industrial activities. This reduces the requirement for oil, natural gas, and other natural resources considerably around the globe. As a result, decreasing energy demand during the Covid-19 period will lead natural resource rents to fluctuate. This natural resource rents volatility may have an impact on countries' economic progress. In this regard, the current study investigates the causal association between volatility of natural resources and economic growth for BRICS economies over 1995–2020 period. The study has applied several panel data econometric techniques such as the Pesaran (2007) CD test, Pesaran Sclaled LM test and Bruesh-Pagan LM test for the CSD testing, the Pesaran (2007) CADF and CIPS unit root test, Westerlund and Edgerton (2008) test for the estimation of long run cointegration among panel memebers. In terms of the effect of explanatory variables on economic growth, the long-run estimates validity was also assessed in this study. For this, most proper long-run estimations that is the CUP-FM and CUP-BC is applied in our study. The outcomes of these methodologies show that rents of natural oil and gas have a considerable impact on economic performance, whereas total natural resource rents exert a negative impact on BRICS countries' economic growth. Furthermore, the Granger panel causality test by (Dumitrescu and Hurlin, 2012) shows a bidirectional causal relationship between the study variables. Specifically, GR, TR, OR granger causes GDP, and a feedback effect has also been observed for these variables. This means that any movement in the explanatory variable(s) will have a big impact on the outcome variable and vice versa. On the basis of our empirical findings, a few practical policies are recommended for the policymakers that necesitate the immediate implementation in this covid-19 pandemic to accommodate volatility of natural resources and growth. Firstly, the heavy reliance on oil, natural gas, other natural resouces must be condensed by acquiring environmentally friendly and innovative technologies to reduce its negative effect on economic growth. It will contribute to economic growth and satisfy the needs of consumers. Moreover, natural resource hedging, for example, could be useful in reducing volatility in natural resource prices. As a result, policies that incorporate natural resource hedging in both the long and short term must be updated. Furthermore, price ceiling and price freezing regulations may aid in maintaining natural resource rents' favourable contribution to economic performance. Furthermore, research and development spending might be increased, assisting in the transition of the dependency of natural resource to efficient energy sources. This would lead to long-term development for both the environment and the economy. Author statement We have submitted the revision of our article entitled’’ Natural Resources Volatility and Causal Associations for BRICS countries: Evidence from Covid-19 Data’’ All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certifies that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the resource policy Journal. Data availability Data will be made available on request. ==== Refs References Adedoyin F.F. Gumede M.I. Bekun F.V. Etokakpan M.U. Balsalobre-Lorente D. Modelling coal rent, economic growth and CO2 emissions: does regulatory quality matter in BRICS economies? Sci. Total Environ. 710 2020 136284 Ahmed Z. Le H.P. Linking Information Communication Technology, trade globalization index, and CO 2 emissions: evidence from advanced panel techniques Environ. Sci. Pollut. Control Ser. 28 7 2021 8770 8781 Albulescu C. Do COVID-19 and Crude Oil Prices Drive the US Economic Policy Uncertainty? 2020 07591 arXiv preprint arXiv:2003 Atil A. Nawaz K. Lahiani A. Roubaud D. Are natural resources a blessing or a curse for financial development in Pakistan? The importance of oil prices, economic growth and economic globalization Resour. Pol. 67 2020 101683 Bai J. Kao C. On the estimation and inference of a panel cointegration model with cross-sectional dependence Contrib. Econ. Anal. 274 2006 3 30 Bai J. Kao C. Ng S. Panel cointegration with global stochastic trends J. Econom. 149 1 2009 82 99 Bashar O.H. Wadud I.M. Ahmed H.J.A. Oil price uncertainty, monetary policy and the macroeconomy: the Canadian perspective Econ. Modell. 35 2013 249 259 Benramdane A. Oil price volatility and economic growth in Algeria Energy Sources B Energy Econ. Plann. 12 4 2017 338 343 Bing C. Ting Z. Heterogeneous bio technology data mining algorithm of Internet of things based on Spark artificial intelligence architecture J. Commerc. Biotechnol. 26 4 2021 130 138 10.5912/jcb1040 Breusch T.S. Pagan A.R. The Lagrange multiplier test and its applications to model specification in econometrics Rev. Econ. Stud. 47 1 1980 239 253 Chien F. Chau K.Y. Jalees T. Zhang Y. Nguyen V.C. Baloch Z.A. Crude oil price volatility and economic growth: mediating role of macroeconomic indicators Singapore Econ. Rev. 2021 1 25 DEMİRCAN Çakar N. GEDİKLİ A. Erdoğan S. The global economic hibernation: macroeconomic indicators and health management policies Duzce Medical Journal 23 2021 Dumitrescu E.-I. Hurlin C. Testing for Granger non-causality in heterogeneous panels Econ. Modell. 29 4 2012 1450 1460 Etokakpan M.U. Solarin S.A. Yorucu V. Bekun F.V. Sarkodie S.A. Modeling natural gas consumption, capital formation, globalization, CO2 emissions and economic growth nexus in Malaysia: fresh evidence from combined cointegration and causality analysis Energy Strategy Rev. 31 2020 100526 Galadima M.D. Aminu A.W. Nonlinear unit root and nonlinear causality in natural gas-economic growth nexus: evidence from Nigeria Energy 190 2020 116415 Gkillas K. Gupta R. Pierdzioch C. Forecasting realized oil-price volatility: the role of financial stress and asymmetric loss J. Int. Money Finance 104 2020 102137 Guan L. Zhang W.-W. Ahmad F. Naqvi B. The volatility of natural resource prices and its impact on the economic growth for natural resource-dependent economies: a comparison of oil and gold dependent economies Resour. Pol. 72 2021 102125 Hatemi-j A. Tests for cointegration with two unknown regime shifts with an application to financial market integration Empir. Econ. 35 3 2008 497 505 Hayat A. Tahir M. Natural resources volatility and economic growth: evidence from the resource-rich region J. Risk Financ. Manag. 14 2 2021 84 Hordofa T.T. Liying S. Mughal N. Arif A. Vu H.M. Kaur P. Natural resources rents and economic performance: post-COVID-19 era for G7 countries Resour. Pol. 2021 102441 Jiao J. Liu X. Modeling and analysis of the impact of technological innovation on corporate governance performance based on Econometrics J. Commerc. Biotechnol. 26 4 2021 10.5912/jcb1053 Katoka B. Dostal J.M. Natural resources, international commodity prices and economic performance in sub-saharan Africa (1990–2019) J. Afr. Econ. 2021 Khan Z. Hussain M. Shahbaz M. Yang S. Jiao Z. Natural resource abundance, technological innovation, and human capital nexus with financial development: a case study of China Resour. Pol. 65 2020 101585 Lin B. Bai R. Oil prices and economic policy uncertainty: evidence from global, oil importers, and exporters' perspective Res. Int. Bus. Finance 56 2021 101357 Lin Y. Xiao Y. Li F. Forecasting crude oil price volatility via a HM-EGARCH model Energy Econ. 87 2020 104693 Ma Q. Zhang M. Ali S. Kirikkaleli D. Khan Z. Natural resources commodity prices volatility and economic performance: evidence from China pre and post COVID-19 Resour. Pol. 74 2021 102338 Magazzino C. Mele M. Schneider N. A D2C Algorithm on the natural gas consumption and economic growth: challenges faced by Germany and Japan Energy 219 2021 119586 Monye Michael C. Omogbiya Shulammite O. Effect of oil prices on the NIGERIAN economic growth Journal 6 9 2020 Narayan P.K. Sharma S. Poon W.C. Westerlund J. Do oil prices predict economic growth? New global evidence Energy Econ. 41 2014 137 146 Nonejad N. Crude oil price volatility and short-term predictability of the real US GDP growth rate Econ. Lett. 186 2020 108527 Pérez C. Claveria O. Natural resources and human development: evidence from mineral-dependent African countries using exploratory graphical analysis Resour. Pol. 65 2020 101535 Pesaran M.H. Estimation and inference in large heterogeneous panels with a multifactor error structure Econometrica 74 4 2006 967 1012 Pesaran M.H. A simple panel unit root test in the presence of cross-section dependence J. Appl. Econom. 22 2 2007 265 312 Phillips P.C. Hansen B.E. Statistical inference in instrumental variables regression with I (1) processes Rev. Econ. Stud. 57 1 1990 99 125 Prabheesh K. Padhan R. Garg B. COVID-19 and the oil price–stock market nexus: evidence from net oil-importing countries Energy Res.Lett. 1 2 2020 13745 Rafindadi A.A. Ozturk I. Natural gas consumption and economic growth nexus: is the 10th Malaysian plan attainable within the limits of its resource? Renew. Sustain. Energy Rev. 49 2015 1221 1232 Rahim S. Murshed M. Umarbeyli S. Kirikkaleli D. Ahmad M. Tufail M. Wahab S. Do natural resources abundance and human capital development promote economic growth? A study on the resource curse hypothesis in Next Eleven countries Res. Environ. Sustain. 4 2021 100018 Rosnawintang R. Tajuddin T. Adam P. Pasrun Y.P. Effects of crude oil prices volatility, the internet and inflation on economic growth in ASEAN-5 countries: a panel autoregressive distributed lag approach Int. J. Energy Econ. Pol. 11 1 2021 15 Samadi A. Rad E.H. Determinants of healthcare expenditure in Economic Cooperation Organization (ECO) countries: evidence from panel cointegration tests Int. J. Health Pol. Manag. 1 1 2013 63 Shahbaz M. Destek M.A. Okumus I. Sinha A. An empirical note on comparison between resource abundance and resource dependence in resource abundant countries Resour. Pol. 60 2019 47 55 Sharif A. Aloui C. Yarovaya L. COVID-19 pandemic, oil prices, stock market, geopolitical risk and policy uncertainty nexus in the US economy: fresh evidence from the wavelet-based approach Int. Rev. Financ. Anal. 70 2020 101496 Sun L. Wang Y. Global economic performance and natural resources commodity prices volatility: evidence from pre and post COVID-19 era Resour. Pol. 74 2021 102393 Tahar M.B. Slimane S.B. Houfi M.A. Commodity prices and economic growth in commodity-dependent countries: new evidence from nonlinear and asymmetric analysis Resour. Pol. 72 2021 102043 Topcu E. Altinoz B. Aslan A. Global evidence from the link between economic growth, natural resources, energy consumption, and gross capital formation Resour. Pol. 66 2020 101622 Umer S. Buhari D. Avik S. Zeeshan F. Does Export Product Diversification Help to Reduce Energy Demand: Exploring the Contextual Evidences from the Newly Industrialized Countries 2020 Wen J. Mughal N. Kashif M. Jain V. Meza C.S.R. Cong P.T. Volatility in natural resources prices and economic performance: evidence from BRICS economies Resour. Pol. 75 2022 102472 Westerlund J. Edgerton D.L. A simple test for cointegration in dependent panels with structural breaks Oxf. Bull. Econ. Stat. 70 5 2008 665 704 Yıldırım E. Öztürk Z. Oil price and industrial production in G7 countries: evidence from the asymmetric and non-asymmetric causality tests Proced. Soc. Beha. Sci. 143 2014 1020 1024 Zhang W. Research on innovation strategies and best practices model of higher education of biotechnology based on hierarchical ordered probity J. Commerc. Biotechnol. 26 3 2021 10.5912/jcb1047	36465834	PMC9707950	NO-CC CODE	2022-12-01 23:20:27	no	Resour Policy. 2023 Jan 29; 80:103165	utf-8	Resour Policy	2,022	10.1016/j.resourpol.2022.103165	oa_other
==== Front Diagn Microbiol Infect Dis Diagn Microbiol Infect Dis Diagnostic Microbiology and Infectious Disease 0732-8893 1879-0070 Elsevier Inc. S0732-8893(22)00224-3 10.1016/j.diagmicrobio.2022.115860 115860 Original Article Analytical performance of rapid nucleic acid detection assays and routine RT-qPCR assays for detection of SARS-CoV-2 in Shanghai, China in 2022 Jiang Min ab# Chen Weiqin a# Chen Yong a# Chen Jia a Zhang Yue a Yin Hongmei a Li Yi c⁎⁎ Liu Weiwei a⁎ a Department of Laboratory Medicine, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China b Department of Laboratory Medicine and Central Laboratory, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China c Department of Nephropathy, LongHua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China ⁎ Corresponding author: Tel.: +86-131-6264-0870; fax: 021-64385700-9401. ⁎⁎ Corresponding author: Tel.: +86-135-0178-8925; fax: 021-64385700-6430. # These authors contributed equally to this work. 12 11 2022 2 2023 12 11 2022 105 2 115860115860 2 8 2022 8 11 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Diagnostic accuracy of COVID-19 varies among different assays. In this study, the analytical performance of 1 rapid nucleic acid detection assay (Coyote assay) and 2 routine RT-qPCR assays (BioGerm assay and DaAn assay) was evaluated, using 1196 clinical samples. Disagreement in the results of 2 paired targets occurred in all 3 assays. The Coyote assay failed to detect 15 samples, and the DaAn assay failed to detect 5 samples. The Cohen's kappa coefficient was 0.970 between the BioGerm and DaAn assays, 0.907 between the Coyote and BioGerm assays, and 0.936 between the Coyote and DaAn assays. The positive percent agreement, and negative percent agreement of the Coyote assay were 84.04%, and 100%, respectively. Our study revealed that the results of the Coyote, BioGerm, and DaAn assays were highly consistent, which provided reference for the application of these assays for diagnosis of COVID-19. Keywords COVID-19 SARS-CoV-2 RT-qPCR rapid nucleic acid detection analytical performance Abbreviations Cq, quantification cycle LOD, limit of detection NAD, nucleic acid detection NPA, negative percent agreement NPV, negative predictive value PPA, positive percent agreement PPV, positive predictive value ==== Body pmc1 Introduction The COVID-19 pandemic caused by SARS-CoV-2 has created enormous burdens to national healthcare systems worldwide and resulted in unrecoverable damage in terms of massive loss of life. The majority of patients infected with SARS-CoV-2 develop mild respiratory symptoms, including fever and cough [1]. SARS-CoV-2 is more likely to affect older individuals and those with cardiovascular disease, chronic lung disease, diabetes, and obesity, resulting in more severe symptoms, including acute respiratory distress syndrome and multi-organ failure [2]. As of May 24, 2022, there have been more than 523 million confirmed cases of COVID-19 globally, resulting in 6.2 million deaths [3]. Accurate diagnosis of infected individuals followed by appropriate quarantine is critical to interrupt the spread of SARS-CoV-2. Comprehensive diagnosis of COVID-19 requires the combination of clinical manifestations and laboratory examination targeting the pathogen SARS-CoV-2. As the proportion of mild and asymptomatic infection keeps increasing, it is challenging to distinguish infected from healthy individuals. Various immunoassays have been developed for detection of specific serum antibodies against SARS-CoV-2. However, the window period of antibody generation impairs diagnostic efficiency in the early stage of infection [4,5]. Therefore, nucleic acid detection (NAD) based on RT-qPCR is recommended as the gold standard for COVID-19 diagnosis, given the high sensitivity and specificity [6]. However, detection of SARS-CoV-2 RNA by RT-qPCR is limited due to the requirement of certified clinical laboratories and qualified technicians for conventional NAD, the time from sample collection to obtaining results, especially for emergency cases, and the continuous evolution of SARS-CoV-2. Genetic mutations affect not only the pathogenic characteristics of viruses, but also the performance of commercial NAD assays. The currently circulating variant Omicron is the most mutated form of SARS-CoV-2, and the BA.2 lineage is the main pathogen of the COVID-19 pandemic in the Shanghai region in the spring of 2022 [7,8]. The Shanghai Municipal Health Commission confirmed 57,819 infections and 586 deaths associated with the Omicron variant from Feb 26 to May 24, 2022, in addition to more than 590,000 asymptomatic infections [9]. Hence, extensive application of assays for rapid detection of SARS-CoV-2 with equivalent diagnostic efficiency of RT-qPCR analysis is warranted. Although currently available commercial rapid NAD assays and RT-qPCR mainly target the relatively conserved open reading frame 1ab gene (ORF1ab), the nucleocapsid (N) protein gene, or the envelop (E) protein gene, rather than the spike (S) protein, a mutation hotspot of the Omicron variant, their diagnostic efficiency should be adequately assessed for detection of Omicron considering its current dominance [7,10]. Therefore, the aim of the present study was to compare the limit of detection (LOD), reproducibility, and agreement of the rapid NAD assay (Coyote assay) to those of the routine NAD assays (BioGerm and DaAn assays) for testing clinical samples collected from patients with suspected infection with the Omicron variant. 2 Materials and methods 2.1 Preparation of samples and reference standard material A total of 1196 nasopharyngeal samples collected from individuals with suspected or diagnosed SARS-CoV-2 infections from March 26 to May 12, 2022 were selected for this study. Reference standard material containing heat-inactivated SARS-CoV-2 RNA was purchased from Shanghai BioGerm Medical Technology Co., Ltd. (Shanghai, China). All reference standard material were diluted to appropriate concentrations prior to testing in parallel with the Coyote assay (Coyote Bioscience Co. Ltd., Beijing, China), BioGerm assay (Shanghai BioGerm Medical Technology Co., Ltd.), and DaAn assay (DaAn Gene Co., Ltd., Guangzhou, China). The assays results were interpreted in accordance with the instructions provided by the respective manufacturers. 2.2 Coyote assay Nasopharyngeal swabs were fully eluted in preservation solution and a 15 μL aliquot was mixed with 15 μL of respiratory tract specimen treatment reagent in a centrifuge tube. SARS-CoV-2 RNA was amplified using a DirectDetect™ SARS-CoV-2 Detection Kit (PCR-Fluorescence Probe) (Coyote Bioscience Co., Ltd.) with the FlashDetect™ Flash 20 Nucleic Acid Fast Detection System (Coyote Bioscience Co., Ltd.). Each 52 μL PCR reaction volume contained 15 μL of the above-mentioned mixture, 35 μL of PCR reagent, and 2 μL of Coyote flash enhance buffer. The PCR amplification protocol included 1 cycle at 42 °C for 3 minutes followed by 15 preamplification cycles at 96 °C for 3 seconds and 55 °C for 5 seconds, and 30 amplification cycles at 96 °C for 3 seconds and 55 °C for 10 seconds. A period of approximately 30 minutes was required to obtain results from the Coyote assay targeting ORF1ab and the N. The detailed result interpretation criteria are shown in Supplementary Table 1. 2.3 BioGerm assay The nucleic acids of SARS-COV-2 were extracted utilizing the BioGerm Nucleic Acid Extraction and Purification kit (magnetic beads method) in accordance with the manufacturer's instructions. Briefly, 200 μL of each sample was added to a 1.5 mL reaction tube containing 500 μL of extraction buffer, 15 μL of protease K, and 4 μL of magnetic beads. After binding, the magnetic beads were removed, and the purified RNA was eluted and amplified using a Novel Coronavirus (2019-nCoV) NAD Kit (PCR-Fluorescence Probing) (Shanghai BioGerm Medical Biotechnology Co., Ltd.). Each 25 μL PCR reaction volume contained 5 μL of purified RNA. The PCR amplification protocol included 1 cycle at 50 °C for 10 minutes, followed by 1 cycle at 95 °C for 5 minutes and 45 cycles at 95 °C for 10 seconds and 60 °C for 40 seconds. A period of approximately 95 minutes was required to obtain results from the BioGerm assay targeting ORF1ab and the N. The detailed result interpretation criteria are shown in Supplementary Table 1. 2.4 DaAn assay RNA extraction was conducted using the DaAn Gene Nucleic Acid Extraction Kit (DaAn Gene Co., Ltd.). Briefly, 200 μL of each sample were added to 250 μL of nucleic acid extraction and purification reagent. The nucleic acid was eluted with 50 μL of elution buffer. The RNA was amplified with the Novel Coronavirus (2019-nCoV) Real Time Multiplex RT-PCR kit (DaAn Gene Co., Ltd.). Each 25 μL PCR reaction volume contained 5 μL of purified RNA. The PCR amplification protocol included 1 cycle at 50°C for 15 min, followed by 1 cycle at 95 °C for 15 minutes and 45 cycles at 94 °C for 15 seconds and 55 °C for 45 seconds. A period of approximately 60 minutes was required to obtain results from the DaAn assay targeting ORF1ab and the N. The detailed result interpretation criteria are shown in Supplementary Table 1. 2.5 Verification of the LOD and reproducibility of the 3 assays To verify the LOD of 3 selected assays, the reference material was diluted to concentrations equal to or below the LODs declared by the respective manufacturers. Each dilution was tested 5 times per day in a single run for 3 consecutive days. Positive detection rates at each concentration were calculated to estimate the LOD. Verification of intra- and inter-assay reproducibility was conducted at concentrations higher than the calculated LOD. Of 5 replicates applied in the intra-assay stage, 4 was included in the inter-assay stage. Negative controls were tested by each assay at the same time. Positive and negative detection rates and coefficients of variation were calculated to determine the reproducibility of the 3 assays. 2.6 Agreement among the 3 assays In total, 1196 clinical samples were tested. Based on the agreement of results of a single NAD assay or across all 3 assays, the samples were classified into different groups. The detailed detection results and quantification cycle (Cq) values were rigorously recorded for deeper analysis. The Cohen's kappa coefficient was calculated to further estimate the consistency among the BioGerm, DaAn, and Coyote assays. The positive percent agreement, negative percent agreement, positive predictive value, and negative predictive value of the Coyote assay were calculated when 2 routine NAD assays were taken as reference methods. 2.7 Statistical analysis The final results of 3 assays were collected and analyzed. The Cohen's kappa coefficient, 95% confidence interval (CI), and quartile of Cq values were calculated using IBM SPSS Statistics for Windows, version 22.0. (IBM Corporation, Armonk, NY). The distribution of Cq values was analyzed using GraphPad Prism 8.0 software (GraphPad Software, Inc., San Diego, CA). 3 Results 3.1 Comparison of the LOD of 3 assays The claimed LOD was 400 copies/mL for the Coyote assay and 500 copies/mL for the BioGerm and DaAn assays. Thus, the reference standard material was diluted from 2000 to 400 copies/mL for the Coyote assay, from 106 to 500 copies/mL for the BioGerm assay, and from 5 × 105 to 500 copies/mL for the DaAn assay. At the designated concentrations, all 3 NAD assays accurately detected ORF1ab and the N. The assays showed substandard positive detection rates at lower concentrations, so the LODs verified in our study were consistent with the claimed LODs. 3.2 Comparison of the reproducibility of 3 assays Reference standard material was diluted to 2000 copies/mL to verify the reproducibility. The positive detection rates of all 3 assays were 100% (Supplementary Table 2). To be detailed, the intra-assay CV of the Coyote assay for detecting ORF1ab and N was 0.835% and 1.301%, and the inter-assay CV was 1.114% and 2.134%. The intra-assay CV of the BioGerm assay for detecting ORF1ab and N was 0.958% and 0.663%, and the inter-assay CV was 1.035% and 1.095%. The intra-assay CV of the DaAn assay for detecting ORF1ab and N was 0.921% and 0.585%, and the inter-assay CV was 2.791% and 2.750%. The CV values were all ≤5%, and the coincidence rate of negative controls was 100%, which met the requirements declared by the manufacturer. 3.3 Agreement within each assay Clinical testing was conducted using 1196 samples collected from March 26 to May 12, 2022. For each NAD assay, the results were classified as positive (2-targets positive), partially positive (1-target positive), or negative (Table 1 ). The DaAn assay yielded the highest percentage of positive results (82/1196; 6.86%), followed by the BioGerm assay (79/1196; 6.60%) and Coyote assay (77/1196; 6.52%). The BioGerm assay (15/1196; 1.34%) was more likely to yield partially positive results as compared to the DaAn assay (7/1196; 0.59%) and Coyote assay (2/1196; 0.17%). In most cases, partially positive results were caused by failure of ORF1ab.Table 1 Agreement within each NAD assay. Table 1 Coyote Coyote ORF1ab Coyote N BioGerm BioGerm ORF1ab BioGerm N DaAn DaAn ORF1ab DaAn N Positive 77 (6.52%) 77 77 79 (6.60%) 79 79 82 (6.86%) 82 82 Partially positive 2 (0.17%) 0 2 15 (1.34%) 0 15 7 (0.59%) 1 6 Negative 1117 (93.32%) 1117 1117 1102 (92.06%) 1102 1102 1107 (92.56%) 1107 1107 Total 1196 (100%) 1196 (100%) 1196 (100%) NAD = nucleic acid detection. The results of analysis of the Cq values are presented in Fig. 1 A. For positive detection of ORF1ab and the N, the Cq values were 6.42 to 28.09 and 4.85 to 25.10 for the Coyote assay, 16.74 to 40.00 and 15.26 to 38.44 for the BioGerm assay, and 9.30 to 29.61 and 8.07 to 29.42 for the DaAn assay, respectively. For partially positive samples, the positive marker tended to be close to the assays' cut-off value.Fig. 1 Comparison of Cq ranges of each target among different groups. (A) Comparison of Cq ranges among samples with positive and partially positive results for paired targets. (B) Comparison of Cq ranges among samples with similar and nonsimilar results of the BioGerm and DaAN assays. Data are presented with quartiles. Cq = quantification cycle. Fig 1 Next, the degree of disagreement in the results within the assay in selected Cq value ranges was investigated (Table 2 ). Those Cq value ranges were chosen because the low value was the lowest Cq value where a disagreement was seen and the upper value was the cut-off Cq value. Specifically, 25% of samples with Cq values of 20 to 27 for the Coyote N, 83% of samples with Cq values of 35 to 40 for the BioGerm N, and 40% samples with Cq values of 25 to 30 for the DaAn N yielded negative results for ORF1ab. Only 1 (9%) of 11 samples with Cq values of 25 to 30 for DaAn ORF1ab yielded negative results for the N (data not shown).Table 2 Association between partially positive results and Cq values around the cut-off values. Table 2Assay Cq of N Total Positive for ORF1ab Negative for ORF1ab Coyote 20−27 8 6 (75%) 2 (25%) BioGerm 35−40 18 3 (17%) 15 (83%) DaAn 25−30 15 9 (60%) 6 (40%) Cq = quantification cycle. 3.4 Agreement among the 3 assays Based on the agreement of the results among the 3 assays for the same sample, the 1196 samples were divided into 4 groups (Table 3 ). The 3 assays yielded the same results for 1181 (98.75%) samples (group A, 79 samples with positive results; group B, 1102 samples with negative results). The results of the remaining 15 (1.25%) samples differed among the assays (i.e., positive with the routine NAD assays but negative with the rapid NAD assay). Among these 15 samples, 10 (66.7%) yielded positive results with the BioGerm and DaAn assays (group C), while 5 (33.3%) yielded positive results with only the BioGerm assay (group D). All three assays were more likely to yield positive results for both ORF1ab and the N in group A. For the samples with differing results in groups C and D, the BioGerm and DaAn assays tended to yield partially positive results.Table 3 Agreement among the Coyote, BioGerm, and DaAn assays. Table 3 Coyote ORF1ab Coyote N BioGerm ORF1ab BioGerm N DaAn ORF1ab DaAn N n Agreement: n = 1181 (98.75%) Group A: Positive for all assays + + + + + + 76 + + - + + + 1 - + - + - + 1 - + - + + - 1 Group B: Negative for all assays - - - - - - 1102 Disagreement: n = 15(1.25%) Group C: Positive for 2 routine NAD assays - - + + + + 2 - - + + - + 1 - - - + + + 3 - - - + - + 4 Group D: Positive for 1 routine NAD assay - - - + - - 5 NAD = nucleic acid detection. The range of Cq values for both ORF1ab and N differed among groups A, C, and D (Fig. 1B). The results for ORF1ab and the N were either negative or positive with Cq values concentrated around the cut-off values in groups C and D. The Cq values were characteristically lower in group A. Hence, the Cq values of each target in the different groups were further stratified (Supplementary Table 3), which revealed distinct distributions across the targets or groups. For group A, the Cq values of ORF1ab and the N were concentrated at 5 to 25 and 5 to 25 with the Coyote assay, 15 to 35 and 15 to 35 with the BioGerm assay, and 15 to 35 and 10 to 30, respectively, with the DaAn assay. For group C, the Cq values of the N were concentrated at 35 to 40 with the BioGerm assay and 25 to 30 with the DaAn assay. For group D, the Cq values of the N were concentrated at 35 to 40 with the BioGerm assay. Cohen's kappa coefficient was calculated to further evaluate the agreement among the three assays (Supplementary Table 4). Agreement was highest between the BioGerm and DaAn assays (κ = 0.970, 95% CI [0.945, 0.995]). The results of the Coyote assay were more in agreement with those of the DaAn assay (κ = 0.936, 95% CI [0.897, 0.975]) than the BioGerm assay (κ = 0.907, 95% CI [0.860, 0.954]). 3.5 Clinical performance of the coyote assay Next, the clinical performance of the Coyote assay was compared to that of the DaAn and BioGerm assays as reference methods. The 94 samples that yielded positive results with either the BioGerm or DaAn assay were defined as “true positives,” while the 1102 samples that yielded negative results with both assays were defined as “true negatives.” As shown in Supplementary Table 5, among the true positive samples, the Coyote assay yielded positive results for 79 and negative results for 15, thus the results of 1117 samples were classified as negative (positive percent agreement = 84.04%; negative percent agreement = 100%; positive predictive value = 100%; negative predictive value = 98.66%). 4 Discussion More than 60 mutations have been identified in the Omicron variant, significantly changing the pathogenic characteristics of SARS-CoV-2 and thus bringing new challenges to countries [11]. A meta-analysis revealed that the pooled proportion of nonsevere disease (97.9%) and asymptomatic infection (25.5%) among Omicron-positive individuals were significantly higher than those of Delta-positive individuals [12]. Moreover, compared to the Delta variant, the transmissibility of Omicron is 3.31-fold higher [13]. Consequently, the difficulty of diagnosing Omicron infection according to clinical manifestations was increased and the requirement of the efficiency of diagnostic methods was enhanced. The emergent outbreak of COVID-19 drove the development of NAD assays over a relatively short period, and the evolution of the virus may have led to poorer performance [14]. Variations in the primer- and/or probe-binding regions may result in failure to detect SARS-CoV-2 RNA. The C26340U mutation in the SARS-CoV-2 genome was associated with the failure of E gene target of the cobas® SARS-CoV-2 test (Roche Diagnostics GmbH, Mannheim, Germany) [15]. Several single-point mutations, including C29200T, C29200A, and C29197T, have been associated with the failure of N gene target of the Xpert® Xpress assay (Cepheid, Sunnyvale, CA, USA) [16], [17], [18], [19]. Partial ORF1ab gene failure was once described in the BA.2.12.1 lineage of the Omicron, which contained ORF1ab synonymous mutations C11674T and T15009C [20]. Holland et al. reported that C636T, T651C, 641∆6, and A638G mutations in the Delta lineage could result in N gene target failure of the TaqPath COVID-19 Combo Kit [21]. As well, the 21765 to 21770 genomic deletion (spike ∆69−70) of the Alpha variant led to S gene target failure of this kit [22]. S gene target failure was a hallmark of the Alpha variant, and was observed in the BA.1 lineage of Omicron, but not the BA.2 lineage [20]. It can be seen that SARS-Cov-2 variants differently affected the efficiency of diagnostic assays. Given these factors, the performance and application of NAD assays in clinical use must be further evaluated. Since the discovery of SARS-CoV-2, multiple in vitro diagnostic assays have been approved for clinical use by the National Medical Products Administration of China. In the present study, the performance of three NAD assays targeting ORF1ab and the N of SARS-CoV-2 was compared. Overall, ORF1ab was more likely to go undetected, leading to discrepancies in the results of 2 targets of the same assay (Table 1). Similarly, Gdoura et al. reported that 94.9% of the discrepancy in the results of the DaAn assay were caused by a single positive amplification of the N when detecting the Alpha variant [23]. On the contrary, Wang et al. identified more mutations to the targets of probes and/or primers based on the N versus the E and RdRp sequences of the ORF1ab fragment [24]. Thus, target failure varied according to different mutations and SARS-CoV-2 variants. Interestingly, almost all samples partially positive with the DaAn assay were partially positive with BioGerm assay, while samples that tested partially positive with the BioGerm assay could be positive for both ORF1ab and the N with the DaAn assay. Nevertheless, it was uncertain whether DaAn ORF1ab possessed better performance or it was variability in detecting samples with low viral load, reflected by Cq values around the cut-off value. There were notable inconsistencies in the results across the assays (Table 3). Most samples in group A yielded positive results for both targets. Of note, samples that yielded negative results with the BioGerm assay overlapped those in group B, indicating an NPV of 100% with the BioGerm assay. Group D included 5 samples that yielded positive results with the BioGerm assay only. From this perspective, the BioGerm assay was more sensitive for testing of the N gene of the Omicron variant as compared to the DaAn assay. Despite several inconsistencies, the results of the Coyote, BioGerm, and DaAn assays showed considerable agreement, as confirmed by Cohen's kappa coefficient, which ranged from 0.907 to 0.970. Besides, the Coyote assay showed considerable sensitivity and specificity. Taken together, the three assays demonstrated equivalent analytical performance for diagnosis of COVID-19. For detection of diluted reference standard material, the Coyote assay was more sensitive, as the verified LOD was 400 copies/mL, lower than that of the BioGerm and DaAn assays. For detection of clinical samples containing Omicron, the Coyote assay yielded several false negative results. The decreased sensitivity may be associated with variables of detection assays, including master mix components, the polymerase enzyme used, primer design, and cycling conditions. It is also worth noting that the first samples allocated to group C and D were isolated on April 21 and 25, 2022, respectively, as daily new asymptomatic infections in Shanghai began to decline. However, the sample size in this study was relatively small and the sequencing of isolated RNA was infeasible owing to the quarantine policy, which limited the explanation of above-mentioned phenomenons. Future large-scale studies with standard methods, such as digital PCR and genome sequencing, are warranted to further investigate the causes of contradictory results. In conclusion, there was considerable agreement between the results of the selected rapid NAD assay and routine RT-qPCR assays. The time required for the rapid NAD assays is significantly shorter, suggesting advantages for screening of large populations. Considering the possibility of false negative results, rapid NAD assays would better serve as a complement to routine RT-qPCR and the results of rapid NAD assays should be interpreted with caution. Declaration of competing interest The authors declare that they have no known competing interests. Appendix Supplementary materials Image, application 1 Image, application 2 Funding This study was supported by the grant from Shanghai Municipal Health Leading Talent Plan of Shanghai Municipal Health Commission (2022LJ021), the grant from Special Clinical Research Project of Shanghai Municipal Health Commission (202140147) and the grant of Scientific and Technological Innovation Action Plan-Medical Innovation Special Research Project from Science and Technology Commission of Shanghai Municipality (Grant No. 22Y11902900). Authors’ contributions Min Jiang: Formal analysis, Data Curation, Writing - Original Draft, Writing - Review & Editing. Weiqin Chen: Methodology, Validation, Data Curation, Writing - Review & Editing. Yong Chen: Methodology, Validation, Data Curation, Writing - Review & Editing. Jia Chen: Investigation, Data Curation. Yue Zhang: Investigation, Data Curation. Hongmei Yin: Investigation, Data Curation. Yi Li: Conceptualization, Supervision. Weiwei Liu: Conceptualization, Writing - Review & Editing, Supervision. Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.diagmicrobio.2022.115860. ==== Refs References 1 Zhou H Ni WJ Huang W Wang Z Cai M Sun YC Advances in pathogenesis, progression, potential targets and targeted therapeutic strategies in SARS-CoV-2-Induced COVID-19 Front Immunol 13 2022 834942 2 Gandhi RT Lynch JB Del Rio C Mild or moderate Covid-19 N Engl J Med 383 18 2020 1757 1766 32329974 3 World Health Organization. WHO Coronavirus (COVID-19) Dashboard \| WHO Coronavirus (COVID-19) dashboard with vaccination data. May 25, 2022]; Available from: https://covid19.who.int/. 4 Krammer F Simon V Serology assays to manage COVID-19 Science 368 6495 2020 1060 1061 32414781 5 Conklin SE Martin K Manabe YC Schmidt HA Miller J Keruly M Evaluation of serological SARS-CoV-2 lateral flow assays for rapid point-of-care testing J Clin Microbiol 59 2 2021 e02020-20 6 Yin H Tong Z Shen C Xu X Ma H Wu Z Micro-PCR chip-based multifunctional ultrafast SARS-CoV-2 detection platform Lab Chip 22 14 2022 2671 2681 35543190 7 Jawad B Adhikari P Podgornik R Ching WY Binding interactions between receptor-binding domain of spike protein and human angiotensin converting enzyme-2 in omicron variant J Phys Chem Lett 13 17 2022 3915 3921 35481766 8 Zhang X Zhang W Chen S Shanghai's life-saving efforts against the current omicron wave of the COVID-19 pandemic Lancet 399 10340 2022 2011 2012 35533708 9 Shanghai Municipal Health Commission. Novel Coronavirus Pneumonia Epidemic Prevention and Control_Shanghai Municipal Health Commission [25th May 2022]. Available from: http://wsjkw.sh.gov.cn/yqfk2020/. 10 Corman VM Landt O Kaiser M Molenkamp R Meijer A Chu DK Detection of 2019 novel coronavirus (2019-nCoV) by real-time RT-PCR Euro Surveill 25 3 2020 2000045 11 Alkhatib M Salpini R Carioti L Ambrosio FA D’Anna S Duca L Update on SARS-CoV-2 omicron variant of concern and its peculiar mutational profile Microbiol Spectr 10 2 2022 e0273221 12 Yu W Guo Y Zhang S Kong Y Shen Z Zhang J Proportion of asymptomatic infection and nonsevere disease caused by SARS-CoV-2 Omicron variant: a systematic review and analysis J Med Virol 94 12 2022 5790 5801 35961786 13 Bálint G Vörös-Horváth B Széchenyi A Omicron: increased transmissibility and decreased pathogenicity Signal Transduct Target Ther 7 1 2022 151 35525870 14 Lu Y Li L Ren S Liu X Zhang L Li W Comparison of the diagnostic efficacy between two PCR test kits for SARS-CoV-2 nucleic acid detection J Clin Lab Anal 34 10 2020 e23554 15 Artesi M Bontems S Göbbels P Franckh M Maes P Boreux R A recurrent mutation at position 26340 of SARS-CoV-2 is associated with failure of the E gene quantitative reverse transcription-PCR utilized in a commercial dual-target diagnostic assay J Clin Microbiol 58 10 2020 e01598-20 16 Miller S Lee T Merritt A Pryce T Levy A Speers D Single-point mutations in the N gene of SARS-CoV-2 adversely impact detection by a commercial dual target diagnostic assay Microbiol Spectr 9 3 2021 e0149421 17 Hasan MR Sundararaju S Manickam C Mirza F Al-Hail H Lorenz S A novel point mutation in the N gene of SARS-CoV-2 May affect the detection of the virus by reverse transcription-quantitative PCR J Clin Microbiol 59 4 2021 e03278-20 18 Rhoads DD Plunkett D Nakitandwe J Dempsey A Tu ZJ Procop GW Endemic SARS-CoV-2 polymorphisms can cause a higher diagnostic target failure rate than estimated by aggregate global sequencing data J Clin Microbiol 59 8 2021 e0091321 19 Ziegler K Steininger P Ziegler R Steinmann J Korn K Ensser A SARS-CoV-2 samples may escape detection because of a single point mutation in the N gene Euro Surveill 25 39 2020 2001650 20 Rodino KG Peaper DR Kelly BJ Bushman F Marques A Adhikari H Partial ORF1ab gene target failure with omicron BA.2.12.1 J Clin Microbiol 60 6 2022 e0060022 21 Holland SC Bains A Holland LA Smith MF Sullins RA Mellor NJ SARS-CoV-2 delta variant n gene mutations reduce sensitivity to the TaqPath COVID-19 multiplex molecular diagnostic assay Viruses 14 6 2022 1316 35746787 22 Borges V Sousa C Menezes L Gonçalves AM Picão M Almeida JP Tracking SARS-CoV-2 lineage B.1.1.7 dissemination: insights from nationwide spike gene target failure (SGTF) and spike gene late detection (SGTL) data, Portugal, week 49 2020 to week 3 2021 Euro Surveill 26 10 2021 2100131 23 Gdoura M Abouda I Mrad M Ben Dhifallah I Belaiba Z Fares W SARS-CoV2 RT-PCR assays: In vitro comparison of 4 WHO approved protocols on clinical specimens and its implications for real laboratory practice through variant emergence Virol J 19 1 2022 54 35346227 24 Wang R Hozumi Y Yin C Wei GW Mutations on COVID-19 diagnostic targets Genomics 112 6 2020 5204 5213 32966857	36459887	PMC9708047	NO-CC CODE	2022-12-01 23:20:27	no	Diagn Microbiol Infect Dis. 2023 Feb 12; 105(2):115860	utf-8	Diagn Microbiol Infect Dis	2,022	10.1016/j.diagmicrobio.2022.115860	oa_other
==== Front Cogn Behav Neurol Cogn Behav Neurol WNN Cognitive and Behavioral Neurology 1543-3633 1543-3641 Lippincott Williams & Wilkins Hagerstown, MD 36178498 10.1097/WNN.0000000000000319 00005 3 Opinion Consciousness as a Memory System Budson Andrew E. MD †[email protected] Richman Kenneth A. PhD ‡[email protected] Kensinger Elizabeth A. PhD [email protected] § Center for Translational Cognitive Neuroscience, Veterans Affairs Boston Healthcare System, Boston, Massachusetts † Alzheimer’s Disease Research Center, Boston University, Boston, Massachusetts ‡ Center for Health Humanities, Massachusetts College of Pharmacy and Health Sciences, Boston, Massachusetts § Psychology and Neuroscience Department, Boston College, Boston, Massachusetts Correspondence: Andrew E. Budson, MD, VA Boston Healthcare System, 150 South Huntington Ave, 10B-67, Boston, Massachusetts 02130 (email: [email protected]). 12 2022 03 10 2022 35 4 263297 21 4 2022 3 5 2022 2022 Written work prepared by employees of the Federal Government as part of their official duties is, under the U.S. Copyright Act, a “work of the United States Government” for which copyright protection under Title 17 of the United States Code is not available. As such, copyright does not extend to the contributions of employees of the Federal Government. We suggest that there is confusion between why consciousness developed and what additional functions, through continued evolution, it has co-opted. Consider episodic memory. If we believe that episodic memory evolved solely to accurately represent past events, it seems like a terrible system—prone to forgetting and false memories. However, if we believe that episodic memory developed to flexibly and creatively combine and rearrange memories of prior events in order to plan for the future, then it is quite a good system. We argue that consciousness originally developed as part of the episodic memory system—quite likely the part needed to accomplish that flexible recombining of information. We posit further that consciousness was subsequently co-opted to produce other functions that are not directly relevant to memory per se, such as problem-solving, abstract thinking, and language. We suggest that this theory is compatible with many phenomena, such as the slow speed and the after-the-fact order of consciousness, that cannot be explained well by other theories. We believe that our theory may have profound implications for understanding intentional action and consciousness in general. Moreover, we suggest that episodic memory and its associated memory systems of sensory, working, and semantic memory as a whole ought to be considered together as the conscious memory system in that they, together, give rise to the phenomenon of consciousness. Lastly, we suggest that the cerebral cortex is the part of the brain that makes consciousness possible, and that every cortical region contributes to this conscious memory system. Key Words: consciousness episodic memory cerebral cortex neural correlates of consciousness SDCT OPEN-ACCESSTRUE ==== Body pmcAD Alzheimer disease BIS bispectral index ERP event-related potential PCI perturbation complexity index TMS transcranial magnetic stimulation When we consider consciousness, a number of questions naturally arise. Why did consciousness develop? What is consciousness good for? If consciousness developed to help us plan and act for the future, why is consciousness so difficult to control? Why is mindfulness so hard? And for that matter, if our actions are under our conscious control, why is dieting (and resisting other urges) so difficult for most of us? Why does it appear that we are observers, peering out through our eyes at the world while sitting in the proverbial Cartesian theater? Why do we speak, in William James’s words, of a “stream of consciousness”? Can we perform complicated activities (such as driving) without being consciously aware of it? Are animals conscious (and if so, which ones)? Are there developmental, neurologic, or psychiatric disorders that are actually disorders of consciousness? There have, of course, been many answers to these questions over the last 2500 years. We hope to provide new answers to these and a number of related questions in this paper. DEFINITIONS Before we attempt to answer these questions, we should clarify what we mean when we use the word consciousness. For the most part, we mean what William James (1890) meant when he used the term: our own personal experience of perceiving, thinking, emoting, and acting. Self-consciousness, that is, being conscious of our selves as thinking entities (à la Descartes), would certainly be included in what we mean, but only a small part of it. Similarly, the various so-called levels of consciousness, as measured by the Glasgow Coma Scale (Teasdale and Jennett, 1974), including conscious, confused, delirious, somnolent, obtunded, stuporous, and comatose (Posner et al, 2019), would be included in our use. Any understanding of consciousness should also be consistent with the four basic properties that arise from studying its phenomenology: intentionality, unity, selectivity, and transience (Schacter et al, 2019). Consciousness is directed toward an object; it is about something (ie, intentionality). We experience consciousness as unified, as one experience, rather than as separate experiences of sights, sounds, smells, thoughts, feelings, and so on (ie, unity). We have the capacity to be conscious of some things and not others (ie, selectivity). Objects of consciousness are there transiently; the contents of consciousness tend to change (ie, transience). Defining postdictive effects will be important for understanding our theory of consciousness. As counterintuitive as it sounds, in postdictive effects, a later stimulus can affect the perception of an earlier stimulus, or earlier and later stimuli can mutually affect each other (Herzog et al, 2020; Michel and Doerig, 2021; Sergent, 2018). We wish to keep the commonsense use of the word conscious when we say, “I was so wrapped up in my thoughts that, driving without being conscious of where I was going, I found myself sitting in the empty parking lot at work, despite intending to drive to the post office.” We also want to retain the idea of having unconscious awareness, such as when we see our friend, are sure that something is different about him, but it takes us a minute to figure out that he got a new haircut. We would like to separate this idea of unconscious awareness, which may contain a conscious feeling (such as familiarity) short of a full conscious experience, from unconscious knowledge, which may influence our actions without any awareness (such as priming). Block (2011) and others have promulgated the idea of two separate aspects of consciousness. Phenomenal consciousness is what it is like for us to have an experience. Access consciousness is when representations are made available to cognitive processing. PROBLEMS James (1890) famously used the phrase “stream of consciousness” as a metaphor to describe the intuitive feeling that there is not only a “now” but also “downstream” events that have occurred in the past and “upstream” events that will come to pass. Why does consciousness feel this way when we know that the brain is actually processing massive amounts of information in parallel? And does consciousness flow linearly with time, as this metaphor implies? In fact, postdictive and other order effects have demonstrated that, at timescales <500 ms, consciousness does not flow linearly with time (Herzog et al, 2020; Michel and Doerig, 2021). Conscious awareness often occurs in the wrong order (ie, after, rather than before or with, the perception, decision, or action) (Sergent, 2018), and conscious sensations are sometimes referred backward in time (Hodinott-Hill et al, 2002; Libet et al, 1979). Consciousness is also too slow to guide many split-second decisions and actions that occur routinely when playing sports or musical instruments (Blackmore, 2017). Experiments performed with individuals who have experienced a brain injury have demonstrated that consciousness is not necessary to perform a number of activities that we usually think require conscious awareness (Weiskrantz et al, 1974). Lastly, mindfulness is hard, which suggests that controlling our conscious thoughts is not easy to do—something that is quite odd if the purpose of consciousness is to enable us to control our thoughts and actions. We will review these problems briefly here, along with how these problems lead us to the largest problem—the purpose of consciousness. Order Problems: Consciousness After the Perception, Decision, Action There are many examples in which the consciousness associated with a perception, decision, or action seems to occur only after the physiological perception, decision, or action has actually occurred. This order is incompatible with the idea that perceptions, decisions, and actions are only possible when conscious awareness and thought are present. Tolling Bells and Cocktail Parties An oft-mentioned example is from Exner, quoted by James (1890) in The Principles of Psychology (and often attributed to James): “Impressions to which we are inattentive leave so brief an image in the memory that it is usually overlooked. When deeply absorbed, we do not hear the clock strike. But our attention may awake after the striking has ceased, and we may then count off the strokes.” The problem here is how we can become conscious of something after it has occurred. Were we conscious of it at the time that it occurred, or not? Another common example, which almost everyone has experienced at a cocktail party, is that we hear our name spoken, our attention is suddenly focused, and we can then recall the earlier part of the sentence in which our name was mentioned (Blackmore, 2017). How is it that our consciousness can work backward to perceive the earlier part of the sentence that we were not paying attention to? Block (2011) and others might argue that the simple answer to these two order problems is that we were phenomenally conscious of the sounds of the clock and the earlier part of the sentence, but that our access to consciousness arose only later. Although attractive, we believe that this is not the best explanation for this phenomenon. Postdictive Effects Below we review several of the many experimentally created postdictive effects; see Sergent (2018), Herzog et al (2020), and Michel and Doerig (2021) for comprehensive reviews of these effects and their implications for theories of consciousness. Cutaneous Rabbits. In the cutaneous rabbit illusion, we hold out one arm while looking the other way. The experimenter then taps quickly at precisely equal intervals with equal pressure five times at our wrist, thrice near our elbow, and twice near our shoulder. This produces the odd sensation as if a little rabbit were running up our arm—not three separate groups of taps (Geldard and Sherrick, 1972). There are Bayesian models that can closely replicate this illusion and thus in some sense explain it (Goldreich, 2007), but what is not explained is how the brain knows where to put the intervening taps running up the forearm from the wrist to the elbow before the elbow taps have occurred. If we think about consciousness in the ordinary sense, it simply makes no sense. Blackmore (2017, p. 40) stated the problem very well when she said:If you stick to the natural idea that any tap (say the fourth one) must either have been conscious or unconscious (in the stream or not), then you get into a big muddle. For example, you might have to say that the third tap was consciously experienced at its correct place (i.e. on the wrist), but then later, after the sixth tap occurred, this memory was wiped out and replaced with the conscious experience of it happening half way between wrist and elbow. If you don’t like this idea, you might prefer to say that consciousness was held up for some time—waiting for all the taps to come in before deciding where to place each one. In this case, the fourth tap remained unconscious until after the sixth tap occurred, and was then referred back in time so as to be put in its correct place in the stream of consciousness. In this illusion, consciousness simply fails to capture what is happening as it happens. Color Phi Illusion. In his book, Consciousness Explained, Dennett (1991) discusses extensively the color phi illusion. In this illusion, a viewer watches a blue dot at the top of a frame, followed by a blank screen, and then a red dot at the bottom of the frame, all in quick succession. The viewer then reports two odd things. First, the viewer experiences a sensation of motion, as if the first spot were moving downward; second, the viewer believes that the spot changes color abruptly and in the middle of its illusory path (Kolers and von Grünau, 1976). The problem, of course, is that it makes no sense for the viewer to consciously experience the downward motion or the color change before the second dot is consciously perceived. How (and why) do these phenomena happen? Keuninckx and Cleeremans (2021, p. 1) recently suggested that the color phi illusion may simply be related to “inherent dynamical and nonlinear sensory processing in the brain” and not related to consciousness, per se. This is an interesting idea that we discuss later in the context of our theory. Color Fusion Effects. In this illusion, when a red disk is presented for 40 ms, the viewer sees a red disk. Yet, when a red disk is presented for 40 ms followed by a green disk presented for 40 ms in the same location, the viewer sees a single yellow disk (Pilz et al, 2013). Why does our conscious perception fuse the two colors together? How does the later presentation of the green disk interfere with the prior perception of the red disk? Illusory and Invisible Audiovisual Rabbits. Postdictive effects can be crossmodal. In one experiment, three flashes are presented, each paired with a sound. When the sounds are repeated but the central flash is omitted, an illusory flash is perceived. Conversely, when all three flashes are present but the central sound is absent, the central flash is not perceived (Stiles et al, 2018). Why do these postdictive illusions happen? Transcranial Magnetic Stimulation. Postdictive effects can be produced by brain stimulation. In one ingenious experiment, transcranial magnetic stimulation (TMS) pulses to the occipital cortex at various time intervals from 20 to 370 ms after stimuli were flashed altered how the stimuli were perceived. Thus, the TMS pulse itself acted as a postdictive stimulus, which had different effects depending on when the TMS pulse was applied (Scharnowski et al, 2009). How can brain stimulation several hundred ms after a stimulus alter the perception? Motor Cortex First, Conscious Decision to Move Second In one of the most convincing examples of this problem of consciousness occurring after decisions and actions, Dennett (1991) described an experiment that was performed by the neurosurgeon W. Grey Walter in 1963. Patients who had electrodes implanted in their motor cortex were set up with a slide projector and were told that they could advance the slides by pressing the button on a controller. However, the controller was fake—it was not connected to the projector. What advanced the slides was actually a signal from the implanted electrodes. The patients experienced that the slide projector was anticipating their decisions. As Dennett (1991, p. 167) described, “They reported that just as they were ‘about to’ push the button, but before they had actually decided to do so, the projector would advance the slide—and they would find themselves pressing the button with the worry that it was going to advance the slide twice!” The commonsense view of consciousness tells us that the conscious decision to act precedes and causes the action itself. How do we explain this strange phenomenon in which motor actions occur before the conscious decisions to take these actions? How could the effect precede the cause? Conscious Sensations Referred Backward in Time In addition to order problems, there are also situations where conscious sensations are referred backward in time. Chronostasis The stopped-clock illusion is one example of the multisensory illusion of chronostasis (Hodinott-Hill et al, 2002). In this illusion, the viewer makes a saccade to a clock with a second hand. As in all saccades, the perception of the visual information during the saccade is masked in order to prevent the viewer from experiencing motion blur. After the saccade is completed, the viewer focuses on the clock. The viewer’s experience is that the clock seems to be taking more than 1 second for the second hand to move. The explanation is that the sensory information of the image of the clock that the eyes receives after the fixation is projected backward in time to fill in the time period when the viewer was making the saccade (Thilo and Walsh, 2002). But, how can a sensation be projected backward in time? Stimulation of the Hand Versus the Somatosensory Cortex Following up on a prior experiment in which they produced conscious sensation by stimulating the cortex directly (Libet et al, 1964), Libet et al (1979) produced the conscious experience of a tingle in a participant’s hand by either stimulating the back of the hand or stimulating the contralateral somatosensory cortex of the brain directly. If we assume that the conscious experience is related to the somatosensory cortex receiving the stimulation—because the impulses initiated in the hand need to travel from the hand through the wrist, forearm (radius or ulnar nerve), arm (brachial nerve), shoulder (brachial plexus), neck (spinal nerves and cord), and head (including the brain stem, internal capsule, and corona radiata)—we would expect that stimulation of the cortex would be noticed more quickly than stimulation of the hand. Two surprising results were found. First, in each case, it took a long time, ∼500 ms, from stimulus onset until conscious experience. Second, as Libet and colleagues (1979, p. 222) wrote: “After delayed neuronal adequacy is achieved, there is a subjective referral of the sensory experience backwards in time so as to coincide with this initial ‘time-marker.’” What does it mean for a conscious experience to be referred “backwards in time”? Many scientists and philosophers have provided explanations for these results and the 1979 conclusions by Libet and colleagues (Churchland, 1981; Dennett, 1991). We have no evidence that any of them are wrong; we simply think that our theory provides a more parsimonious explanation of these observations. As we will see, backward referral in time does not pose a challenge for our theory. Timing Problems: Consciousness Is too Slow The experiment by Libet and colleagues (1979) raises another problem of consciousness that Blackmore (2017) stated explicitly: Consciousness is too slow. Recall that Libet and colleagues (1979) discovered that it took ∼500 ms from stimulus onset until the conscious experience occurred. Blackmore (2017) reminded us just how very long that amount of time actually is from a neurophysiological perspective, where impulses travel at speeds up to 100 m/seconds. If it takes 500 ms (long enough for an impulse to travel up to 50 m) for conscious experience to occur, then consciousness is too slow to be playing an active, controlling role in many activities, including playing sports and making music. It is estimated that professional baseball players need to decide whether to swing at a pitch within 125 ms after it has left the pitcher’s hand, and the ball crosses the plate within 300–400 ms after it has left the pitcher’s hand (Science Non Fiction, 2016). Ordinary reaction time measured by clicking a mouse in response to an auditory, tactile, or combined auditory/tactile stimulation produced speeds in one study ranging from ∼210 to 320 ms for most people, but was as fast as 100–210 ms in trained musicians (Landry and Champoux, 2017). How can we be consciously in charge of our actions if our actions are occurring much faster than our conscious thoughts? Lesion Patients Individuals with neurologic disorders may help us to understand consciousness. Some individuals with brain lesions are unable to consciously perform a task but, when asked to do the task unconsciously—guess or just perform the task without thinking about it—they are able to do it. Visual Apperceptive Agnosia In a study by Ganel and Goodale (2019), a patient with visual apperceptive agnosia was unable to consciously perceive and report the size or shape of objects, yet she was able to correctly scale her grip to pick them up. She was also able to accurately perform actions, such as putting cards through slots of different angles, despite not being able to consciously perceive or report the angles. How is this performance without conscious awareness possible? Moreover, this dissociation between inaccurate visual perception and accurate grip scaling can be produced in individuals with normal vision as well (Aglioti et al, 1995; Chen et al, 2015); again, how is this possible if our actions are consciously controlled? Blindsight Blindsight causes problems for consciousness similar to those observed in patients with visual apperceptive agnosia (Kentridge et al, 2008; Poppel et al, 1973; Weiskrantz et al, 1974). Individuals with blindsight are cortically blind in at least one hemifield; cannot consciously see objects in that hemifield; and yet perform above chance when they are asked to guess, point, or otherwise act based on a visual stimulus to their blind field. Explanations of the physiological phenomenon include preserved cortical islands; dissociation between a dorsal, action-related unconscious stream and a ventral, perception-related conscious stream (Brogaard, 2011); and visual information reaching subcortical structures such as the superior colliculi and lateral geniculate nuclei. These physiological explanations, however, still fail to explain the phenomena regardless of its biological underpinnings. How is it possible to accurately point at objects and perform similar tasks when such objects cannot be consciously visualized? Mindfulness Mindfulness is a problem because it is hard. Anyone who has tried to practice mindfulness knows that it is difficult. But if, as a commonsense view of consciousness would have it, our consciousness (ie, access consciousness) evolved to allow us to perform high-level abstract reasoning using language, logic, visuospatial abilities, or other cognitive capacities in order to enable us to carry out intentional actions, then controlling our thoughts should be easy. So why is mindfulness so hard? Why is it so difficult for us to control our conscious thoughts? Lack of Apparent Causal Role for Consciousness The idea that it is difficult to control our thoughts, and that individuals with brain lesions and no relevant conscious perception can still perform tasks that we intuitively feel must require consciousness, leads to the interesting—and for most people, uncomfortable—thought that perhaps consciousness is epiphenomenal. Many researchers (eg, Chalmers, 2010) have made this point, arguing that there could be philosophical zombies who act as if they are conscious but actually are not. Some biological traits will survive through natural selection only incidentally, because they arose in association with an adaptive trait (Gould and Lewontin, 1979). Is consciousness like that, merely epiphenomenal? Perhaps consciousness appears to be epiphenomenal only because we are looking in the wrong place to find its causal role. Could consciousness be epiphenomenal with respect to perceptions, decisions, and actions, but play an important causal role in some other function? How Does Consciousness Contribute to Evolutionary Success? To review, consciousness often occurs after the perception, decision, and action have taken place, in part because consciousness is too slow to participate in many real-time events. Consciousness fools us in many different ways, creating visual and tactile illusions. We know from individuals with various brain lesions that a variety of judgments and actions can occur without conscious perception, suggesting that although consciousness is commonly present, it is not necessary to carry out at least some types of tasks. Lastly, consciousness is difficult to control—something that seems very odd if it evolved to allow us to carry out the complex reasoning that is necessary for intentional action. All of these problems lead us to one of the most important questions regarding consciousness: What does consciousness do? How does consciousness contribute to the evolutionary success of human beings? CONSCIOUSNESS AS A MEMORY SYSTEM The problems discussed thus far demonstrate just how many difficulties there are if we simply take at face value the idea that the role of consciousness is to enable our perceptions, decisions, and actions to occur. In this setting, we are ready to explain our theory that consciousness is, at its core, a memory system. Consciousness Is Part of the Episodic Memory System Tulving (1985) described episodic memory as the set of processes that allow us to mentally time-travel and to re-experience a past moment in time. To be able to do that, we must first take information that comes in through our sensory stores and our working memory and then create a mental representation of a moment in time; this is the process of encoding. If we want that representation to be accessible later, we must store it in some durable form; this is the process of consolidation. And, when we want to later reflect on that moment in time, we must engage retrieval processes to do so. We argue that one function of consciousness—and more importantly, what it initially developed to do—is to allow for each of these phases of episodic memory. Consciousness binds elements of an experience together, allowing for the creation of a memory trace that can include multisensory details. Over time, consciousness provides a medium in which these memory traces can be replayed—a mechanism that is key to their successful storage. This idea has been hinted at or suggested in some form for decades (Dafni-Merom and Arzy, 2020). In 1985, Tulving (p. 2) wrote, “Remembering is a conscious experience. To remember an event means to be consciously aware now of something that happened on an earlier occasion.” The author further explained that different memory systems are characterized by different kinds of consciousness: procedural memory by anoetic (nonknowing) consciousness, semantic memory by noetic (knowing) consciousness, and episodic memory by autonoetic (self-knowing) consciousness. In 1995, Moscovitch (p. 1341) wrote, “Consciousness is an inherent property of the memory trace, being bound to it along with other aspects of the experienced event by the hippocampus and structures related to it. … With respect to remembering, and perhaps with respect to no other function, consciousness is also an inherent property of the very object of our apprehension.” However, as these examples show, the relationship between episodic memory and consciousness is normally explained as the other way around, that episodic memories bind conscious experiences together, not—as we are suggesting—that the conscious experience is the process of remembering. Cleeremans (2011) also suggested in his radical plasticity thesis that learning and memory are necessary for consciousness to develop. In his account, the brain “learns” to be conscious by continuously attempting to predict the consequences of its actions on both its self and the outside world. This activity produces meta-representations that, when combined with the emotional value associated with them, produce conscious experience. Thus, although learning, memory, and plasticity are necessary for consciousness in Cleeremans’s thesis, his views are clearly quite different from our theory. Episodic memory is now understood to have value not just for its ability to represent the past, but also for its utility in allowing the use of past experiences to increase understanding of the present moment and to make predictions about future occurrences (Schacter et al, 2007; Suddendorf and Corballis, 2007). At retrieval, these traces of episodic events can be flexibly and creatively combined and rearranged in consciousness to allow anticipation, future planning, and intentional action. If we view consciousness as having developed as a critical part of the episodic memory system, we believe that all of the questions that we asked can be answered, and that all of the problems that we raised can be solved—or they simply cease to look like problems. To elaborate, our theory of consciousness rejects the idea that consciousness initially evolved in order to allow us to make sense of the world and act accordingly, and then, at some later point, episodic memory developed to store such conscious representations. Our theory is that consciousness developed with the evolution of episodic memory simply—and powerfully—to enable the phenomena of remembering. We view the fact that our ability to imagine things in consciousness is constrained by and related to our episodic memory (James, 1890; Moulton and Kosslyn, 2009) as another piece of evidence supporting the idea that consciousness evolved as part of episodic memory. Today, consciousness certainly participates in functions that we do not generally associate with episodic memory, such as problem-solving, abstract reasoning, and language. We argue that such functions developed later in evolutionary history, after consciousness was already functioning to furnish the content of episodic memory representations. Consciousness, Sensory Memory, Working Memory, Episodic Memory, and Semantic Memory Are Part of the Same System At this point, we would like to clarify the distinction between consciousness and working memory—the ability to keep information in mind and manipulate it. Our theory is entirely consistent with the ideas put forth by Baddeley, Hitch, and others regarding the phonological loop, visuospatial sketchpad, central executive, and episodic buffer (Repovs and Baddeley, 2006). Moreover, we would argue—along with many others—that although we can split sensory, working, episodic, and semantic memory into separate systems, in the healthy brain, these systems function seamlessly together as a single system (Renoult et al, 2019; Repovs and Baddeley, 2006). Information of which we are consciously aware either from our senses, via sensory memory, or from long-term memory stores comes into working memory. For example, let’s say that we are walking in our neighborhood when we hear a bark. This auditory information is present in our sensory memory for mere seconds, but it is long enough for us to transfer it to our working memory. Once in our working memory, this auditory information acts as a cue that triggers the retrieval of an episodic memory from last week—when the dog that makes that particular bark chased us to the edge of its property! Now that the sensory information of the bark and last week’s experience of being chased are together in our working memory, it is not difficult to imagine the future: The dog is likely to chase us again. Without waiting for the dog to chase us, we quickly cross the street. So we have sensory memory, working memory, and episodic memory helping us sense, remember, imagine, and act. But, where does consciousness come in? We argue that we hear the bark when we are consciously aware of it. We consciously remember last week’s chase when we retrieve the elements of the prior experience and build a conscious representation of that prior experience in working memory. Without effort, we consciously imagine the dog chasing us again. And—either consciously with deliberation or automatically and unconsciously without deliberation—we walk across the street. In this example, we view consciousness as being necessary for the sensory memory perceptions, retrieval of episodic memories, and imagination of the future that may have led to our action of crossing the street. We view consciousness as an integral part of sensory memory, working memory, and episodic memory, and in that way, we view all of these elements as part of the same memory system. Now, we will note that we do not really need consciousness for the action we performed of crossing the street. In fact, we might correctly note that we do not need episodic memory either. We just need operant conditioning to hear the bark and walk across the street. This is one reason we chose this particular example. There are two points we would like to make here. First, this is yet another example where, at least at a superficial level, consciousness does not seem to be necessary. So why is consciousness present? We now have an explanation that we did not have when we first discussed this problem earlier: Consciousness is necessary to remember this event. Could consciousness be epiphenomenal in relation to the dog barking and our walking across the street? Yes. But, it could not be epiphenomenal in relation to our forming and retrieving an episodic memory; thus, we argue that consciousness is essential. Second, we believe that our conscious episodic memory record of the dog barking is critically important in many situations. One is when the stimuli and setting are not similar enough to trigger the conditioning response. Another is when the conditioning response is triggered but the situation requires us to choose actions different from what we are conditioned to do. In both of these situations, the ability to consciously retrieve different memories and to imagine different scenarios is critical for making decisions that could have evolutionary implications, as pointed out by both Schacter et al (2007) and Suddendorf and Corballis (2007). In fact, individuals with impaired episodic memory show impairment in their ability to learn from their prior errors despite showing near-normal implicit memory (Baddeley and Wilson, 1994), again demonstrating that conscious episodic memory is critical for learning that leads to flexible future performance. Now, it would certainly be useful to be able to generalize our experience with this particular dog to other dogs, so that, if we are taking a shortcut through a different neighbor’s yard and their dog begins to bark, we will know that we should move swiftly to get off their property even before the dog starts chasing us. This ability to form general facts from specific events stored in episodic memory is, of course, one way to define semantic memory. Here, we would simply like to make the point (that many others have as well [eg, Janssen et al, 2022; Renoult et al, 2019]) that although we can view semantic memory as a separate memory system, we can certainly think of it as being part of the same system as episodic memory. Our View Differs From Baars’s Global Workspace Theory Next, we would like to comment on the global workspace theory of consciousness that was proposed by Baars (2005) and expanded on by others (eg, Gaillard et al, 2009). To explain this theory, Baars (2005, p. 46) used a metaphor of a theater in which consciousness “resembles a bright spot on the stage of immediate memory, directed there by a spotlight of attention under executive guidance.” Although our theory of consciousness is mostly consistent with global workspace theory, the major difference is that our theory adds the original purpose of consciousness: to allow us to store prior experiences in episodic memory. Baars (2005) outlined six theoretical claims; our comments on these claims are as follows. In claim 1, Baars suggested that conscious perception enables access to widespread brain sources, which we agree with. However, he continued, stating that compared with conscious perception, unconscious sensory processing is more limited, which we do not believe has been proven or disproven; our suspicion is that it is not more limited than conscious perception. Claim 2 is that conscious processes enable working memory and that there is no evidence for unconscious working memory. We agree that part of the definition of working memory is that it is conscious. Claim 3 begins by stating that consciousness enables episodic and other forms of explicit learning, which we certainly agree with. However, it then continues to state that conscious events also enable implicit and skill learning, which we believe is only partly true. It is true that many instances of skill learning and other forms of implicit memory are enabled or facilitated by conscious events, such as taking a tennis lesson or practicing the viola. There are, however, a number of instances of implicit learning, such as priming, in which the learning occurs without consciousness. Claim 4 of Baars’s (2005) claims starts by stating that conscious perceptual feedback facilitates voluntary control over motor functions. We believe that this is sometimes true—such as when we are working on improving our backhand or our vibrato—but note that, in our view, the vast majority of motor actions are unconscious, and thus the perceptual feedback will also be unconscious. Claim 4 continues by speculating that conscious perceptual feedback enables voluntary control over neuronal populations and perhaps single neurons. On the one hand, if we are consciously changing our behavior, there will necessarily be changes in the brain; this seems obvious unless one is a dualist. On the other hand, whether it is single neurons or assemblies of neurons that are changed is an empirical matter that future experiments can resolve. Claim 5 is that attention can be directed by conscious thought but can also be captured by relevant stimuli (such as a name, the need to use the toilet, or a fire-alarm signal), which then becomes the contents of consciousness. We certainly agree with this claim. Claim 6 has phenomenological and brain components. The phenomenological claim is that consciousness enables access to the observing self through executive interpreters. This claim makes intuitive sense, although it seems to evoke a homunculus sitting in the Cartesian theater. Claim 6 goes on to state that this process involves the parietal and prefrontal cortex; we agree that these brain regions are certainly important, and we will discuss them in more detail later. Lastly, we reiterate that, in addition to these differences between global workspace theory and our theory, the major distinction is that our theory adds that the original purpose of consciousness was (and a major purpose still is) to facilitate the encoding, storage, retrieval, and flexible recombination of prior events using episodic memory and its related memory systems. Attention Is Necessary but not Sufficient for Conscious Awareness Insofar as what we attend to determines which contents are engaged in working memory and thus have the potential for being stored as episodic memory, we believe that what we are conscious of (ie, the object of consciousness) depends on what we are attending to (ie, the object of attention). Therefore, we believe that attention is necessary for conscious awareness. Attention is not, however, sufficient for consciousness, as has been demonstrated through many experiments that have manipulated both voluntary (endogenous) and external, reflexive (exogenous) attention and produced a change in performance and/or reaction time without conscious perception (Hsieh et al, 2011; Kentridge et al, 2008; Zhang et al, 2012). For overviews, see Breitmeyer (2015) and Breitmeyer et al (2015). There are also examples where attention to one stimulus can cause other stimuli to disappear or be absent from conscious perception. The Troxler fading effect shows that attention that is directed to one area of a visual scene can cause effects of either fading or intensifying to other areas of the scene, which may be due to microsaccades (Alexander et al, 2021). In motion-induced blindness, relevant targets may disappear when they are viewed against a moving background (Thomas et al, 2017). In change blindness (Andermane et al, 2019), inattentional blindness (Hutchinson et al, 2021, and the attention blink (Sy et al, 2021), even highly incongruent stimuli—such as someone in a gorilla costume walking through a basketball court (Simons, 2010)—can be invisible to conscious perception. We believe that all of these phenomena are consistent (or, at least, not inconsistent) with our theory, which we will refer to as our memory theory of consciousness. To reiterate, attention is necessary, but not sufficient, for stimuli to enter working memory. Therefore, stimuli that are unattended to will neither be consciously perceived nor remembered using working or episodic memory. If these unconsciously perceived stimuli are learned, it occurs with unconscious memory processes, such as priming. Our Theory Is Consistent With the System 1 (Unconscious) and System 2 (Conscious) Distinction Our memory theory of consciousness is fully consistent with the distinction that Kahneman and Tversky (Kahneman, 2011; see also Carruthers, 2015) made between the slow, effortful, logical, calculating, conscious System 2 and the fast, automatic, stereotypic, unconscious System 1. Our theory would simply add that conscious System 2 was made possible by the original purpose of consciousness—to be the contents of episodic memory. From the Contents of Memory to Problem-solving and Abstract Reasoning How did consciousness move from being solely the contents of episodic memory to being involved with problem-solving, abstract reasoning, and the other abilities made possible by System 2? We speculate that consciousness evolved and became involved in these other abilities due to consciousness being a key element of the episodic memory system’s function of flexibly and creatively combining episodic memories to imagine the future. We envision an early stage of this type of combination that would simply allow us to predict the future. For example, episodic memories of finding a delicious berry near a specific cave each autumn might combine with another episodic memory of being chased by a bear near that cave. The outcome is that we are able to predict that if we go to pick the berries, we might end up being chased by a bear. In addition to merely envisioning how the future might unfold, at some later point, this conscious memory creative recombination process envisioned two or more possible future outcomes. In one future, when we go to pick the berries, we are chased by the bear, whereas in another future, we are not chased. Once consciousness is able to compare two possible futures, problem-solving comes in when we think about what we can do to help bring about the future that we want and to avoid the future that we do not. For example, memories of ways to determine whether an animal is in its lair or not may come to mind. Other memories may remind us that a bear can only chase one person at a time. Comparing these types of memory retrievals—that perhaps we may now refer to as thoughts—can allow a plan to develop. Problem-solving in consciousness/working memory via episodic memory retrieval is now taking place. Once problem-solving is occurring, it is a small step to conscious abstract reasoning. As multiple events stored in episodic memory generalize into semantic memory, abstractions automatically occur. Episodic memories of individual dogs, bears, and rabbits allow for the general semantic memory categories of dog, bear, and rabbit to be formed. A more abstract semantic memory category of animal can then emerge and be contrasted with the abstract semantic memory category of plant, and so on. Language Much has been written about the development of language that will not be repeated here (eg, Pinker, 1994). Succinctly, we believe that language developed from a conjunction of consciousness and semantic memory. One of the things that makes language an interesting case, however, is that although we can certainly speak with full conscious awareness and deliberation, it is our observation that we can also speak unconsciously, without thinking about it. We will return to this important concept in a later section. For now, we simply want to introduce the idea that just because a function developed with consciousness does not mean that it must be present only with consciousness. Conscious Perception as a Memory At this point in our paper, one might be willing to accept our theory that consciousness evolved as part of the episodic memory system but say, “So what? How does this explanation help us understand consciousness (or, for that matter, episodic memory)?” If we believe that consciousness evolved as part of the episodic memory system, as a critical part of that system that allows us to store prior experiences in memory and retrieve them so that that the memories of these experiences can be flexibly and creatively combined to allow future planning and intentional action, then there is no reason that consciousness needs to operate in real time. If consciousness is a system for memory encoding and retrieval—and not direct action—there is no reason that it cannot function properly with a small delay. We would argue, in fact, that we do not consciously perceive events directly in real time. We perceive the world as a memory. In other words, technically, we are not consciously perceiving anything directly; we are actually experiencing a memory of a perception. We suggest that we experience the world by remembering sensory memories. Moreover, most of the time, we are not experiencing these bottom–up sensory memory processes by themselves. We experience sensory memory processes influenced by top–down episodic and/or semantic memory processes, such that the percept that is consciously perceived is a mashup between the bottom–up sensory memory processes and the top–down episodic and sematic memory processes. Postdictive Effects Explained Our memory theory of consciousness can now explain postdictive effects. For example, Sergent et al (2013) showed that not only does cuing a stimulus before its presentation improve conscious perception of the stimulus, but cuing after the stimulus presentation can as well. Sergent and colleagues concluded that (p. 154):the initial sensory processing associated with a stimulus can occur preconsciously, because its conscious or nonconscious fate can change drastically beyond this phase. Conscious perception would thus relate to secondary amplification of preconscious information held in sensory areas. … this secondary amplification does not have to be a direct consequence of the initial processing of the stimulus itself, but can be triggered by a subsequent and independent event. Our memory theory of consciousness is both consistent with this explanation and can allow us to understand it better. Attention is drawn to the unconscious perception by the poststimulus cue, and we then experience the conscious perception in the same way that we experience all conscious perceptions—by remembering it. The Conscious Memory of Unconscious Decisions and Actions If we are willing to consider that we—at least as conscious selves—do not perceive the world directly but rather remember it, then we are ready to explain conscious decisions and actions. Our theory is simply that the brain processes that decide and act are unconscious. Our conscious decisions and conscious actions are actually memories of those unconscious decisions and actions. We believe that this explanation—that decisions and actions are fundamentally occurring through unconscious brain processes—is consistent with an evolutionary perspective that would argue that there is no single conscious decision-making system in the brain. Instead, there are various processes that are unconsciously engaged to make specific decisions, such as when to eat, sleep, avoid, approach, grasp, release, and so on (Cisek, 2019). We do think, however, that unconscious brain processes will sometimes engage the conscious memory system to facilitate optimal decision-making and performance in certain situations. To explain these notions further, we will provide an example using Kahneman and Tversky’s System 1 and System 2 processing (Kahneman, 2011; see also Carruthers, 2015). System 1 Decisions and Actions Let’s first consider System 1 decisions. These are the fast, automatic, unconscious decisions that require little or no thought or effort. Let’s say that we are working, perhaps engrossed in thought writing a paper on our computer. We suddenly decide, “I’d like a glass of water.” Before we finish typing the paragraph we are working on, and still thinking about the sentence that comes next, we rise, walk into the kitchen, open the cabinet door, reach inside, grasp a glass, pull it out, close the cabinet door, hold the glass under the faucet, turn the cold water on, watch the glass fill, turn the water off, raise the glass to our lips, tilt the end upward, take a sip, and, while holding the glass steady so as not to spill, walk back to our computer, set the glass down, sit down, and get back to work. We would argue that our decision to get a glass of water could well have been unconsciously initiated and acted out, as could every part of the sequence. In fact, we believe that almost all of the time when we walk or grasp an object, completely unconscious brain processes carry out these actions (consistent with Aglioti et al, 1995; Chen et al, 2015; and Cisek, 2019). Insofar as we are consciously aware of what we are doing, we suggest that this conscious awareness is a memory of this decision and action. On this account, consciousness is epiphenomenal with respect to the decisions and actions, but not epiphenomenal in general because it plays an important role via the episodic memory system. System 2 Decisions and Actions Now let’s consider some analogous System 2 decisions and actions. Perhaps, instead of working on our computer, we are participating in a dangerous Hunger Games-like activity. We are thirsty, and we can see the cool spring up ahead. But to reach it, we need to either make our way across a bubbling lava field with floating rocks that we have to step on or run across a grassy meadow filled with poisonous snakes and spiders. We carefully consider our options. In the end, we decide that we will have a better chance jumping from floating rock to floating rock across the lava. We carefully step on one rock, get our balance, and wait for another to float nearby. We time our jump perfectly and land in a crouch, distributing our weight. We continue this way until we reach the spring. We can see the glass we need, but to get it, we need to carefully reach our hand through razor-sharp barbed wire. We contort and angle our hand and fingers to reach the glass and delicately pull it back through. We fill our glass from the stream and drink down the precious liquid. Here, we have a series of decisions and actions that need to be thoughtfully and carefully carried out. Instead of acting automatically (perhaps while thinking about something else), we have to consciously consider and fully attend to each of these decisions and actions. However, we believe that the actual decisions and actions themselves are made and carried out by our unconscious self, and that we experience the conscious decision being made or action taking place only after the fact. Unconscious Perceivers, Decision Makers, and Actors Another way of saying this is that it is the System 1 unconscious parts of our brain that actually perceive, make decisions, and act (consistent with Cisek, 2019), and the System 2 conscious parts that provide an additional layer of information that our unconscious brain can use (or not) to make decisions and act accordingly. System 2 uses consciousness, and thus all of the explicit memory systems, to review what we know about lava flows, snakes, and spiders (from semantic memory); how well we did the last time we had to jump from rock to rock (from episodic memory); and how, by counting in our head and watching the floating rock (using working memory), we might be able to time a jump perfectly. The Conscious Memory System Thus far, we have sometimes been using the term episodic memory in its standard definition (ie, memory for prior events) and sometimes as shorthand for all of the explicit memory systems: working memory, episodic memory, and semantic memory. Because we believe that all of these explicit memory systems are truly part of one system—the explicit or conscious memory system—going forward, we will use the terms episodic memory and episodic memory system just in their narrow senses and the terms conscious memory and conscious memory system in this broader sense, referring to all explicit memory systems. Challenges for Our Theory If we are correct that consciousness and explicit forms of memory are all part of the same system, then there should not be examples of explicit memory that are unconscious. We could immediately argue that there cannot be examples of unconscious explicit memory, as that would be the same as saying that there are examples of unconscious conscious memory. Nonetheless, it has been shown through carefully conducted experiments that unconscious processes similar to episodic memory, using similar anatomical networks, have enabled participants to perform inferences that would usually require conscious awareness to perform (eg, Schneider et al, 2021). In addition, other carefully conducted experiments have shown unconscious (ie, guessing) above-chance performance on delayed-response working memory tasks that would usually require keeping information consciously in mind (eg, Trübutschek et al, 2017). Do these experimental results mean that our memory theory of consciousness must be false? Although we readily admit that such experimental results are problematic for our theory and need to be explained, we would argue that they do not negate it, for the reasons presented in the next two sections. Unconscious Episodic Memory? In the Schneider et al (2021) study, participants were exposed to either weak masking of stimuli that allowed for conscious processing or strong masking of stimuli that necessitated unconscious, subliminal processing. Conscious processing led to an improvement in reported accuracy as well as a reduction in reaction time. Unconscious processing led to a reduction in reaction time but did not improve accuracy, which was at chance. Further, this reduction in reaction time for unconscious processing was only observed in “intuitive” decision makers who habitually responded according to their instincts (using System 1), and not in “deliberative” decision makers who preferred relying on consciously accessible knowledge (using System 2). The fMRI experiments that showed the neuroanatomical correlates of this unconscious processing were conducted exclusively with intuitive decision makers. Our first comment is that it is possible that even the strongly masked stimuli were minimally or partially conscious because it is difficult to exclude this possibility in experiments of this type (eg, Holender, 1986; Timmermans and Cleeremans, 2015). Thus, one explanation of these results is that the strongly masked stimuli engaged the episodic memory system because the stimuli are minimally or partially conscious for some individuals. Our second comment is that if we agree that the strongly masked stimuli are processed unconsciously, it is possible that they still do engage the episodic memory system, but only partially, and not strongly enough for a full, true, conscious episodic memory to be formed. Support for this view comes from the fact that these strongly masked stimuli produced a change in reaction time, but not a change in accuracy. Thus, we would argue that although the strongly masked stimuli unconsciously activated the episodic memory network and produced a change in reaction time, there was no change in accuracy, no true episodic memory was formed, and therefore, there was no requirement for consciousness. Unconscious Working Memory? In the Trübutschek et al (2017) study, participants identified the location of visual stimuli after a delay. Stimuli were rated by the participants on a 1 (unseen) to 4 (clearly seen) scale. The participants were instructed to guess at the location even if they were unable to see the stimulus. Behaviorally, the participants performed greater than chance on both the seen and the unseen trials. Magnetoencephalographic data were also obtained in order to determine if the same or different neural mechanisms were used by the participants when identifying the seen (correct) trials versus the unseen (correct) trials, with the hypothesis that if different neural mechanisms were engaged, accuracy on the unseen (correct) trials was not simply due to the participants misclassifying trials as unseen that were, in actuality, glimpsed. The magnetoencephalographic data clearly showed two different neural mechanisms. Trübutschek and colleagues (2017) discovered that conscious and nonconscious working memory used different brain mechanisms, and that nonconscious working memory used an “activity-silent mechanism” based on slowly decaying calcium-mediated synaptic weights. The authors then postulated that perhaps this activity-silent mechanism underlies both conscious and nonconscious working memory, which they supported with modeling (but not empirical data). Our comment on this study is simply that there are many examples of unconscious processing that leads to a future change in behavior or performance; priming and procedural memory being two. Thus, although we do not dispute the findings that there may be an activity-silent mechanism that supports unconscious processing of a spatial delayed response task, we would suggest that calling this processing nonconscious working memory may not be using the best nomenclature. ANSWERS AND SOLUTIONS Let’s now review the many previously inexplicable findings discussed earlier and consider how our memory theory of consciousness can provide explanations for each, along with some additional inferences. Order Problems: Consciousness After the Perception, Decision, Action If the contents of our consciousness (ie, what we are consciously aware of) are a memory of the perception, decision, and action, then there is no difficulty with consciousness occurring after the perception, decision, and action. Tolling Bells and Cocktail Parties Thus, there is no difficulty with our being able to count off the strokes of the clock even though we did not pay attention to them until the last chime; our conscious awareness is always a memory of the chimes. Similarly, it is not surprising that when we hear our name at a cocktail party, that we can hear the earlier part of the sentence; we are remembering it, just as we do with all of our other experiences. This feature of our awareness just becomes more apparent to us when we reflect on certain types of situations. Motor Cortex First, Conscious Decision to Move Second The experiment conducted by W. Grey Walter now makes sense as well. In normal individuals (without electrodes implanted in their brain) who are controlling a slide show with a carousel and a push-button controller, their experience is that they make a conscious decision to advance the slide, consciously use their thumb to push the button, and then, the slide advances. However, what actually occurs is that the individuals make an unconscious decision to advance the slide—then consciously remember that unconscious decision, unconsciously use their thumb to push the button—then consciously remember that unconscious action, and then the slide advances. The conscious memory for the decision and action are “time-stamped” by the brain to occur not only in the proper order, but also at the proper time such that it appears that this conscious decision and conscious action coincided with the unconscious decision and unconscious action even though the conscious memory of these events was actually experienced after the events themselves. This is not strange or mysterious: It is the nature of memory that remembered events are referred to a previous time. In the case of the patients with electrodes implanted in their motor cortex, the patients unconsciously decide to advance the slide—then consciously remember that decision. They unconsciously decide to use their thumb to push the button, which creates the motor cortex readiness potential that triggers the slide to advance, the slide begins to advance, they consciously remember the slide beginning to advance, then consciously remember pushing the button, generating this feeling of the slide projector anticipating their decisions. In an experiment related to motor movement, Libet (1985) compared the time in which participants consciously decided to move their wrist (determined by participants noting the time they made their decision on a special clock) with the measured readiness potential of this voluntary action at the scalp. The readiness potential has been interpreted as representing the final stages of planning preparation for movement. What was startling was that Libet (1985) found that the readiness potential preceded the voluntary decision by ∼350 ms. The author concluded that the initiation of a spontaneous voluntary act “begins unconsciously.” We did not introduce this important experiment before now because it is quite controversial for at least two reasons. The first is that not everyone who has tried to replicate the experiment has been able to do so, although some have (eg, Vinding et al, 2014), which should set that issue to rest. The second is that Schurger et al (2012) conducted a terrific set of related experiments and analyses that they suggest explains the readiness potential not as representing the final stages of motor preparation, but rather as representing spontaneous subthreshold fluctuations in neuronal activity. We do not disagree with the work done by Schurger and colleagues (2012), as their explanation may be the correct one for this phenomenon. We would simply like to point out that our memory theory of consciousness makes Libet’s (1985) initial interpretation comprehensible: Decisions and actions are initiated unconsciously and then we experience the conscious memory of those decisions and actions. If we believe Libet’s (1985) interpretation, and these motor results are generalizable, our conscious memory for our decisions and actions may occur ∼350 ms after the decisions and actions are unconsciously initiated. The Incredible Slowness of Consciousness We can now understand why it does not matter that consciousness is too slow for the real-time decisions and actions of athletes, musicians, and others who need to react quickly. All decisions and actions are occurring unconsciously. Our conscious memory of these decisions and actions occurs later. Conscious Sensations Referred Backward in Time Stimulation of the Hand Versus the Somatosensory Cortex Our memory theory of consciousness also helps us to understand some of Libet and colleagues’ (1979) other experimental results and how these results are informative regarding the timing of consciousness. Recall that the authors found that it took ∼500 ms of cortical stimulation before conscious experience of a tingle occurred. If the stimulation was <500 ms, the participants did not report feeling anything. Libet and colleagues (1979, p. 222) referred to this extended time as “the neuronal adequacy for consciousness.” They explained that we are not aware of this delay because events are referred backward in time after neuronal adequacy has been achieved. The idea is that when we feel a touch on our arm, the impulses travel through our peripheral nerves, spinal cord, and brain until they reach the somatosensory cortex. If the stimulus activates our cortex for at least 500 ms, we consciously feel the touch, and it is referred backward in time such that we do not notice any delay in the conscious perception (Blackmore, 2017; Dennett, 1991; Libet et al, 1979). The first point to make here is that our new understanding of consciousness—that we do not perceive events directly but only remember them—makes it easy to understand this finding of Libet and colleagues (1979): Once the 500-ms threshold for conscious sensation is crossed, we can simply remember the sensory memory of the touch. It is not even surprising that it is referred backward in time—again, it is fundamental to memory that it allows us to experience events that occurred earlier. The second point that Libet and colleagues’ (1979) experimental results could suggest is that 500 ms is the amount of time that our conscious perceptions are delayed. In other words, if this result from these authors is generalizable to other conscious sensory experiences, then our conscious perception of the world may be delayed by half a second—although referred backward in time so that we do not notice the delay. Chronostasis The problem of the stopped clock illusion is that the final fixation of the clock is projected backward in time to fill in the time period when we were making the saccade. This is no longer a problem because our visual perceptions are not directly experienced; they are consciously remembered. In other words, we are consciously perceiving a memory that is delayed ∼500 ms. Thus, a top–down process in our visual system projects the final fixation backward in time in order to fill in what would have been the missing perceptual experience while the saccade was taking place. Problem solved. Postdictive Effects Because conscious perception is a memory, likely delayed by ∼500 ms and referred backward in time, we now have an easy and comprehensible explanation of postdictive effects. Rabbits In the case of the cutaneous rabbit, there are five taps at the wrist, three near the elbow, and two at the shoulder, yet we consciously experience intervening taps as well. Again, because sensations are consciously remembered (500 ms later) rather than consciously experienced, it is not a problem for some top–down process to interpose, backward in time, intervening taps between our wrist and elbow and between our elbow and shoulder such that we consciously perceive a little rabbit running up our arm. An analogous explanation can explain the illusory and invisible audiovisual rabbits. Color Fusion Effects Color fusion effects can be explained in a similar fashion. When a red disk is presented for 40 ms by itself, there is the obligatory delay, and then we consciously perceive the red disk by remembering it. When the red disk is followed by the green disk in the same location, the colors are fused together in our sensory memory (because they are occurring in the same time window) and, after the 500-ms delay, we perceive the fused yellow image by remembering it. TMS Pulses It should no longer be surprising that a TMS pulse between 20 and 370 ms can induce postdictive effects. Because conscious perception is delayed, if there is disruption to the visual stream during the delay period, it can alter conscious perception of the prior stimulus just like a physical stimulus can. Color Phi Illusion Similarly, we can now explain why it is that when we watch the color phi illusion and see a blue dot at the top of a frame followed by a blank screen, and then a red dot at the bottom of the frame, we consciously experience the blue dot traveling down and changing color before we see the red dot. Again, a top–down process has interposed, backward in time, the intervening dots and the color change into our conscious perception—easy to do because that conscious perception is actually a memory. What about the suggestion by Keuninckx and Cleeremans (2021, p. 1) that the color phi illusion may simply be related to “inherent dynamical and nonlinear sensory processing in the brain” and not related to consciousness, per se? We would argue that our work helps to elucidate one part of this phenomenon (how events are referred backward in time), while theirs helps to elucidate another (why there is a sense of motion and color switch before the second stimulus is seen). Creating Uniformity: Remembering the Gist Our memory theory of consciousness may also help to explain why we do not notice that our peripheral vision is grayscale or that our vision is filled with the blobs and stripes related to our fixations and saccades with black areas in between. Although here we admit that we may be pushing the explanatory power of our theory past its limits, one possible explanation relates to the type of information that our conscious memory system remembers. In general, we tend to remember the general concept, idea, or gist of the information (Reyna and Brainerd, 1995). Because we now understand that we do not directly consciously perceive visual information—we remember it—we may be remembering the gist of the visual scene in the same way that we might remember the gist of a conversation, movie, or list of items. If consciousness does not have the function of capturing our occurrent sensory input, then these features of our visual experiences are not problematic. Our view of conscious perception is thus consistent with that espoused by Cohen et al (2016). Using an understanding of perception that arises from the field of visual ensembles and summary statistics, they described how observers can extract the gist of the scene with just a few fixations. Does Conscious Perception Overflow Working Memory? Block (2011) and others have suggested that phenomenally conscious perception has a rich content with a greater capacity that “overflows” the more limited access consciousness of perception. Evidence supporting this overflow idea comes from the Sperling (1960) paradigm, in which an array of letters (eg, in a 3×4 grid) is briefly shown. Participants generally report seeing all (or almost all) of the letters, yet they can report only three to four letters. Powerfully, however, when they are cued to report the letters from any row, they are able to recall three to four letters in that row, suggesting that all 12 of the letters were potentially accessible. Our memory theory of consciousness would explain that the full array of items is present briefly in unconscious perceptual processes, although we only experience the conscious perception of the items that our attention is drawn to by the poststimulus cue, as only after our attention is drawn to one row of letters are those items transferred into sensory memory and then into working memory. This explanation is analogous to the one we previously discussed for Sergent and colleagues (2013). Kentridge (2013, p. R71) noted that Sergent and colleagues’ result “does not necessarily invalidate the distinction between access and phenomenal consciousness, but it does lend weight to the alternative, and perhaps simpler, position that consciousness is just consciousness.” We agree with Kentridge (2013) and Cohen et al (2016) that although distinguishing between phenomenal and access consciousness appears to be a useful distinction, it leads to strange situations in which we can have a phenomenally conscious experience that our conscious mind does not have access to. This could be rephrased to say that there can be phenomenally conscious experiences that are unconscious—an idea that does not make much sense to us. We believe that such distinctions have been postulated to solve some of the problems of consciousness—problems that we believe our memory theory of consciousness can explain without the need to invoke phenomenal and access consciousness. Mindfulness We can now understand why it is difficult to control our thoughts when we practice mindfulness. Our theory is that consciousness did not develop in order for us to perform high-level abstract reasoning using language, logic, visuospatial abilities, or other cognitive capacities in order to allow us to carry out intentional actions. Instead, consciousness developed in order for us to remember events and information, as well as to creatively and flexibly recombine those events and information. We speculate that much of this remembering—and even the recombination of the remembered events and information—can occur without volitional control over consciousness. In other words, we may consciously perceive events and, at a later time, consciously imagine the recombination of elements of those events, even if the recombination is being directed by unconscious processes. As anyone knows who has practiced mindfulness, it can take great effort to sustain conscious attention to a single object—because, we argue, that is not what consciousness developed to do. If we consider mindfulness from our new perspective, we might imagine the following processes occurring. A thought is generated unconsciously, perhaps of a meeting we are anticipating later today. Our attention is captured by this thought. Depending on the goals of our mindfulness session, we may be content to simply be metacognitively aware of this thought, observing with some detachment the different emotions the thought produces (eg, excitement that the meeting might go well as well as anxiety that it might go poorly). Or, we may attempt to nudge our awareness from the meeting to our breath as we try to consciously attend to air going in and out of our nostrils. The Subjective Experience of Consciousness One of the most exciting consequences of our memory theory of consciousness is how it might possibly explain certain aspects of the subjective experience of consciousness. Stream of Consciousness We all feel that James’s (1890) metaphor of a stream of consciousness is intuitively correct, with the momentary now where we are standing in the river, past events flowing progressively downstream, and future upstream events that are going to occur rushing toward us. Part of the power of this metaphor is that it is fairly linear. Yet, we know that the brain is processing a massive amount of information in parallel. Why do we experience events serially instead of in the parallel manner that the brain processes them? We would argue that it is because it is a property of our conscious memory system to remember—and thus to consciously experience—events serially in time. Once we decouple real-time sensory input from consciousness, we no longer need the two to be processed by the brain in the same way. We also believe that our memory theory of consciousness explains, at least on one level, why events are bundled together over time such that they seem continuous. Again, we would argue that it is part of the neural architecture of our conscious memory system to store and retrieve events that are bundled together and time-stamped in a certain order. In the Cartesian Theater We sometimes have the intuitive experience that we, as conscious selves, are sitting inside our head—in the proverbial Cartesian theater—peering out at the world through our eyes, as if we are watching a movie. We believe that this feeling is present because we are not experiencing our perceptions directly; we are experiencing a memory of our perceptions. Who is this homunculus sitting in the Cartesian theater? It is our conscious self remembering our perceptions, decisions, and actions. Why did it develop this way? We believe that it developed this way to allow the flexible recombination of prior events and information through imagination, like a movie director moving scenes around on a storyboard. Do we now have to deal with an infinite regress of homunculi sitting in Cartesian theaters? We would argue not. We have one conscious self. Our conscious self is sitting in the theater, mostly passively, watching memories of experiences. The various parts of our unconscious self are processing information in parallel, sometimes paying attention to what is going on with the conscious self but mostly ignoring it. There is only one conscious homunculus. It stops right there. Consistent With Higher-order Theories Higher-order theories of consciousness claim that a mere first-order representation (eg, being presented with a red stimulus) would only lead to consciousness if we are in some way aware of having that experience (ie, being aware that we are seeing red). These theories also claim that conscious experiences involve some type of minimal inner awareness of one’s ongoing mental functioning due to the first-order representation being monitored or represented by a relevant higher-order representation (Brown et al, 2019). Although our memory theory of consciousness clearly differs from higher-order theories of consciousness in many ways, we believe that our theory is consistent with the idea that we are not conscious of the first-order brain mechanisms that process the presentation of a red stimulus in front of our eyes, and that we only become conscious of this stimulus when (sitting in the Cartesian theater) we experience the memory of its perception. Thus, we believe that our memory theory can explain this intuitive sense that we do not experience first-order representations directly, but only indirectly. Higher-order theories would say that consciousness is experiencing higher-order representations of the first-order representations, whereas our memory theory would say that consciousness is remembering the first-order representations. Resonant Metaphors Our memory theory of consciousness helps explain why some metaphors resonate with us intuitively. Now that we understand why the Cartesian theater feels natural, we can understand why Plato’s allegory of the cave resonates with us. In a very real sense, we do not perceive the real world directly; we only perceive shadows or reflections of the real world through memory. Similarly, now that we understand how both the feeling of the theater and the serial appearance of reality are created by our conscious memory system, we can understand why the two metaphors of the movie The Matrix are so powerful: the thought that we are not truly experiencing the world directly, and that the world is actually made up of massive parallel processing streams of data. Horse and Rider We would like to introduce one additional metaphor here that captures some of how we think about the conscious and unconscious self. Imagine our brain as a horse and rider together. Our unconscious, System 1 self is the horse, which is in control of the moment-to-moment journey we are taking. Just as we do not need to provide detailed instructions to a horse on how to cross a rocky field or jump over a short wall, neither do we need to provide detailed instructions to our unconscious as to how to carry a cup full of hot coffee across the room and down a flight of stairs—we just need to look at the cup and our unconscious self does the rest. Our conscious, System 2 self is the human rider, who is mostly just going for a ride. The rider can, of course, provide either moment-to-moment or more general, overall instructions to the horse, and the horse is usually happy to oblige. How does this interaction between the conscious rider and the unconscious horse happen? Metaphorically, the rider says a few words, tugs gently on the reins, or squeezes their legs to let the horse know which way the rider wants to go. Approximately 500 ms later, the rider can then sense whether the horse has, indeed, made the desired decision and moved in the desired direction. Of course, there are sometimes conflicts—such as when the horse wants to go down the easy path but the rider wants to travel up the mountain. How is it that our System 2 rider is able to inform careful, considered decisions? Well, it is sunny out there in the Plains, and so the rider always wears a special pair of sunglasses. These sunglasses have a high-tech video screen built into them. In fact, these sunglasses have no lenses, only the built-in screen—a screen that has a delay of ∼500 ms. In other words, the rider is always perceiving the world by looking at the screen, which shows the visual world a little bit after the fact. But these special sunglasses allow a variety of amazing options: the rider can either (a) look out at the world (via sensory memory) 500 ms after it has passed by; (b) access prior autobiographical episodic memories using an active, creative, memory-building process; (c) access prior semantic information; (d) keep information on the screen so that it can be manipulated in working memory; or (e) use a combination of these features to flexibly, creatively imagine possible future outcomes. When faced with a difficult problem, the rider can use all of these tools to come up with one or more possible solutions, which the rider then communicates to the horse, and the horse makes the actual decision—which may or may not be the same as what the rider suggested. Note that because the rider is always perceiving the world 500 ms after the fact, the rider depends on the horse to make decisions without the rider’s input whenever quick, System 1 decisions are needed. Oh, and there is just one other issue about these special sunglasses that needs to be mentioned: The images come from the horse. The horse is in control of the videos that the sunglasses are showing the rider. We can think of this part of the metaphor as explaining the 500-ms delay that the rider experiences when perceiving the world. But the horse is also in charge of whether the rider views the world, prior autobiographical episodic memories, semantic information, working memory, or imagination. As always, the rider can communicate to the horse which images they wish to see, but the horse does not always comply, either because it cannot or because it wishes to show the rider another image. This is why mindfulness is so hard; the horse has a mind of its own. To summarize this metaphor, we consider the rider (with their sunglasses) to be the conscious memory system—remembering rather than directly perceiving, deciding, and acting. We believe that this conscious memory system was always involved in providing information that could be used by the unconscious brain to make decisions that are informed by past events and information. Through continued evolution, we believe that the conscious memory system developed additional capacities in humans as described above. By contrast, the horse represents all of the unconscious brain processes. Limitation of These Explanations of the Subjective Experience of Consciousness We wish to clearly state that we are well aware that our so-called explanations of the subjective experience of consciousness do not even begin to get at the hard problem of consciousness—how a collection of neurons and supporting brain tissue produces subjective experience. We are, however, hopeful that our slightly increased understanding of the phenomenology of subjective experience of consciousness can help other researchers look in the right locations and do the right experiments to tackle the hard problem. Our suggestion to them is to focus on the conscious memory system. Lesion Patients Our memory theory of consciousness makes it easy to understand how individuals with visual apperceptive agnosia and individuals with blindsight can frequently respond correctly to visual tasks despite lacking conscious awareness of visual objects and other stimuli. Although the visual aspects of their conscious memory system are not functioning properly, such that their sensory and working memory are impaired—meaning that perceptions do not enter consciousness—their unconscious self can still perceive stimuli and respond accordingly. The Causal Role for Consciousness and How Consciousness Contributes to Evolutionary Success We have made our case that consciousness did not develop to play a direct causal role in decisions and actions, and instead developed as part of the conscious memory system. However, as implied by our horse and rider metaphor, we believe that, in modern human beings—and probably many other animals as well—consciousness is essential to make good decisions and take proper actions. Consciousness enables System 2 (the rider) to use working memory as well as all prior autobiographical and semantic information to inform important decisions. Consciousness is thus tremendously important for evolutionary success: Without consciousness, System 2 decisions could not be made. If we had no consciousness, we could still make decisions, but they would always be fast, System 1 (horse only) decisions. Consciousness allows us to make slow, carefully considered System 2 decisions. We will discuss these issues in more detail later on when we discuss the implications of our memory theory of consciousness. Consciousness Is not Epiphenomenal Although we have argued that conscious memory is important for evolution, we have not yet discussed whether the actual subjective experience of consciousness is epiphenomenal. Our belief is that subjective experience is an inherent property of the conscious memory system, and that to say that we can have one without the other would be analogous to saying that we can have molecular motion without heat. Just as ½MV2 = 3/2 KT, we believe that use of the conscious memory system produces subjective experience. As mentioned earlier, we do not have the answer to this hard problem of consciousness, but we are hopeful that our theory will move the field toward finding that answer. NEUROANATOMICAL CORRELATES AND DISORDERS OF CONSCIOUSNESS As might be expected from our experience in philosophy, experimental psychology, cognitive neuroscience, and neurology, our hypothesis of the neuroanatomical correlates of consciousness relates to brain regions and structures and not to underlying cells, cellular assemblies, or neural oscillations (eg, Lou et al, 2017). We understand that cellular and molecular microstructures may be crucial to gaining a full understanding of the neurophysiologic basis of consciousness, including the hard problem. Again, we hope that this discussion of what we consider to be key brain regions and structures will help others to dive deeper and achieve a more complete understanding of the hard problem. Because much of our discussion of the possible neuroanatomical correlates of consciousness is related to individuals with various brain disorders, we will consider these two topics together. Other Theories of the Neuroanatomical Correlates of Consciousness There are currently four major theories that make predictions regarding the neural correlates of consciousness: recurrent processing theory (Lamme, 2015, 2018), global neuronal workspace theory (Mashour et al, 2020), integrated information theory (Tononi et al, 2016), and higher-order thought theory (Brown et al, 2019). Some of these theories specifically address the hard problem, whereas others, like ours, only try to point the way toward possible solutions. As pointed out by Yaron et al (2022), each of these four theories emphasizes largely different cortical regions as being critical for consciousness. Recurrent Processing Theory Recurrent processing theory suggests that conscious processing depends on horizontal connections and recurrent loops between lower- and higher-brain regions that are extended in time and space and involve changes mediated by NMDA-dependent feedback activations (Lamme, 2015). Posterior cortical regions associated with visual processing of information are emphasized, with the prefrontal cortex contributing to, but not essential for, conscious processing (Lamme, 2018). Global Neuronal Workspace Theory Arising out of the global workspace theory (Baars, 2005; described earlier), global neuronal workspace theory proposes that conscious processes occur when information in specialized processors enters a large-scale reverberant brain-scale network of high-level cortical areas linked by long-distance re-entrant loops and becomes ignited, allowing global access by other specialized processors (Mashour et al, 2020). The parietal and prefrontal cortical areas are critical for routing information between other cortical processors. At the neuronal level, large pyramidal cells in cortical layers II/III and V play key roles in the neuroanatomical correlates of consciousness. Integrated Information Theory Integrated information theory attempts to directly address the hard problem by starting from the essential phenomenal properties of experience and infers postulates about the characteristics that are required of its physical substrate (Tononi et al, 2016). It also provides a mathematical quantity of integrated information that yields a measure of the degree of consciousness of any system. The occipital and parietal lobes are considered to be critical and sufficient for conscious experience. Higher-order Theories As introduced earlier, higher-order theories postulate that a first-order representation, such as awareness of a rose, would not be sufficient for conscious experience to arise. An organism must be in some way aware of itself as being in a first-order state in order to be conscious of it. Proponents of higher-order theories consider the prefrontal cortex to be important for conscious perception (Brown et al, 2019). Neuroanatomical Correlates of the Conscious Memory System Having reviewed some of the leading theories that suggest which brain regions are important for consciousness, we will now state our hypothesis, which we will then work to support. Because we contend that the conscious memory system supports both consciousness and all forms of explicit memory, we hypothesize that the neuroanatomical correlates of consciousness are the neuroanatomical structures that are involved in all forms of explicit memory. Which structures are involved in explicit memory? The hippocampus should certainly be included along with related structures, such as the neighboring entorhinal and perirhinal cortex, as well as Papez’s circuit, including the fornix, mamillary bodies, and anterior nucleus of the thalamus. We would also argue that the cerebral cortex is necessary for explicit memory. We might immediately think of the inferolateral temporal cortex for semantic memory, and then perhaps the frontal and parietal cortex for working memory, including regions that are involved with the default mode network, such as medial prefrontal cortex, posterior cingulate cortex, precuneus, and angular gyrus (eg, Zheng et al, 2021). But, we take the view espoused by Murray et al (2020, p. 2) in their book, The Evolutionary Road to Human Memory. They argue that the entire cerebral cortex is important for memory, writing:In our opinion, every cortical area contributes to memory, each in a specialized way. As our ancestors traveled along their evolutionary trajectories, cortical areas accumulated over time; and, in each instance, this happened for the same fundamental reason: to transcend problems and exploit opportunities that these animals faced in their time and place. The Evolutionary Road to Human Memory (Murray et al, 2020) provides a wonderful summary of the animal, human brain lesion, and neuroimaging studies that support the view that all cortical structures are not only involved with, but are critical for, a specific type of memory that is needed for a specific type of task, whether it be navigating the wilderness, distinguishing the sounds of prey and predators, or recognizing faces. We contend not only that every cortical region contributes to memory, but that they each also contribute to a specific domain of conscious awareness. For example, we believe that the following speculations are likely correct:Visual areas in the occipital cortex are necessary for visual consciousness, including visual imagery. The auditory cortex in the superior temporal cortex is necessary for auditory consciousness. The parietal cortex (particularly of the nondominant hemisphere) is necessary for conscious awareness of space (particularly on the contralateral side). The primary somatosensory cortex in the postcentral gyrus of the parietal lobe is necessary for certain subdomains of sensory consciousness such as graphesthesia (identification of numbers or letters written on the skin) and stereognosis (identification of objects by touch). The primary motor cortex in the precentral gyrus of the frontal lobe is necessary for conscious awareness of the fine motor movements that are used to play musical instruments, thread needles, and perform other delicate tasks. The frontal eye fields are necessary for conscious awareness of eye movements. Broca’s area is necessary for conscious awareness of our own speech, whether vocalized or just “in our head.” The insular cortex is necessary for conscious awareness of our body. The prefrontal cortex areas that facilitate complex thought, working memory, problem-solving, and judgment are necessary for the conscious awareness that comes with these higher-level abilities. The work of Gazzaniga (2015) and others with split-brain patients has demonstrated that when the corpus callosum is severed, the individual may be left with two separate consciousnesses in one brain that, seemingly, work together just fine without any apparent functional difficulty or conflict (or, at least, no more internal conflict than any of us have with our unsplit brain from time-to-time). We believe that something analogous occurs not only with the left and right hemispheres, but also with each region of the cortex. We further suggest that each region of the cortex is autonomously conscious, and its island of consciousness is not dependent on any central executive or other region. That is, the minimally sufficient cortical region needed for conscious awareness may be any cortical region, whether it be a sensory region enabling conscious perception, a motor region enabling conscious movement, or an association region enabling some type of multimodal thought. We believe that this must be the case because, as far as we are aware, there is no single cortical region (unilateral or bilateral) that, when removed, renders the individual unconscious. Thus, we contend that the visual conscious awareness in the occipital cortex is independent of the auditory consciousness in the superior temporal cortex, and both are independent of the conscious awareness and guidance of motor movements occurring in the frontal cortex. What is the evidence for this hypothesis that any cortical region may be autonomously conscious? In large part, it comes from the experience of working with several thousand patients who have had strokes or neurodegenerative diseases affecting every part of their cortex, and is supported by the human patient literature. It is well known that damage to subcortical structures that are part of the reticular activating system (such as portions of the midbrain and thalamus) can lead to unconsciousness (eg, Kinney et al, 1994), but, again, we know of no cortical regions that lead to unconsciousness when they are damaged. Indeed, it might be that some of these subcortical reticular activating system structures (such as the thalami) act as a hub, switching between different conscious cortical regions. The literature supporting our view that (a) disruption of no cortical region (even widespread frontal or occipital/parietal regions) leads to unconsciousness but (b) virtually all cortical regions contribute to consciousness has been well reviewed by the opposing front versus back cortical theories of consciousness (Boly et al, 2017; Michel and Morales, 2020; Odegaard et al, 2017). We believe that our theory can reconcile those views espoused by the opposing front versus back camps. For example, if posterior brain regions are critical for consciousness, as suggested by recurrent processing theory and integrated information theory, how is it that patients with posterior cortical atrophy or bilateral occipital and parietal strokes are not unconscious? These patients most certainly have deficits of conscious awareness, but we would never say that they are unconscious. Similarly, if the prefrontal cortex is critical for consciousness, as suggested by global neuronal workspace theory and higher-order theories, what about patients with behavioral variant frontotemporal dementia or bilateral frontal strokes? There is no doubt that the consciousness in these patients is impaired, but we would not say that they are the physical manifestation of unconscious philosophical zombies appearing to have a conscious inner world when they actually have none. We believe that they could consciously experience the beauty of a sunset as well as anyone else. Which group of patients is not conscious (in addition to those with reticular activating system damage)? We believe that it is individuals with either widespread or diffuse cortical dysfunction. These awake-but-not-conscious individuals are described as having an encephalopathy or delirium. Exactly how large would a cortical region need to be in order to support some form of independent consciousness? We do not know the answer, but we will suggest some methods to address that question in Future Directions. Note, however, that we are not saying that the only function of the cortex is to provide conscious awareness for specific modalities. The cortex certainly performs much additional work that is solely involved in the unconscious processing of information. We will also point out that saying that the entire cortex is involved in consciousness is not the same as saying that the entire brain is involved in consciousness. For those readers who think of the cortex as being more or less synonymous with the brain, please note that although the cerebral cortex comprises 82% of the mass of the human brain, it contains only 16 billion (19%) of the 86 billion neurons that are in the brain (Herculano-Houzel, 2009). We now turn to a brief review of some of the major brain regions and structures that are involved in the conscious memory system, along with some of the relevant neurologic disorders that impair consciousness in one way or another. Hippocampus, Related Structures, and Individuals With Amnesia When considering the hippocampus, we typically also consider both neighboring medial temporal lobe structures such as the entorhinal and perirhinal cortex, as well as anatomically connected structures such as the fornix, mamillary bodies, and anterior nucleus of the thalamus. Whether because of neurodegenerative disease, infection, inflammation, stroke, seizure, or surgery, the cognitive effects of damage to the hippocampus and related structures are well known. Disruption of episodic memory invariably occurs, leading to anterograde and some retrograde amnesia. Consciousness, at least in the ordinary sense of the term, does not appear to be disrupted by damage to the hippocampus and related structures. Individuals with such damage can certainly consciously experience many perceptions, decisions, and actions. However, individuals with damage to the hippocampus and related structures do show impairment in several aspects of cognition that we would argue are related to consciousness. First, individuals with hippocampal damage typically lose the ability to consciously perceive subtle differences in visual scenes, and individuals with neighboring perirhinal cortex damage typically lose the ability to consciously perceive subtle differences in faces (Mundy et al, 2013). Second, these individuals show reduced ability to imagine the future (Addis et al, 2009) and therefore to plan for it. Third, by losing the ability to form new episodic memories, these individuals are impaired in their ability to update their sense of self. We consider the ability to imagine the future and to update one’s sense of self to be important aspects of consciousness. Lastly, we speculate that consciousness would almost certainly be impaired if the hippocampus and related structures were completely absent from birth. Although studies have shown that individuals with perinatal damage to some regions of the hippocampus develop normal semantic memory and do not show any obvious impairments in consciousness (Elward and Vargha-Khadem, 2018), we believe that consciousness would not be normal in individuals with complete absence or complete dysfunction of the hippocampus and related structures since birth. Occipital Cortex, Visual System, Anton Syndrome, Blindsight We described previously how individuals with damage to the occipital cortex leading to visual apperceptive agnosia or hemifield blindness can sometimes perform tasks unconsciously that depend on vision despite the fact that they cannot perform the task with conscious awareness. We believe that these cases support the idea that it is the visual cortex in the occipital lobes that provides conscious awareness of vision. Individuals with damage to their entire occipital cortex bilaterally, leading to complete cortical blindness, may deny that they are blind (Anton or Anton-Babinski syndrome), which is a form of anosognosia or unawareness of their deficit (Das and Naqvi, 2022). We believe that this syndrome is one example of a general phenomenon whereby the brain region that generates a specific aspect of consciousness—in this case, visual perception and visual imagery—is the same part that is responsible for the awareness of whether that aspect of consciousness is present, absent, or distorted. Our prediction would be that Anton syndrome would not be present if the damage to the visual system affected pathways before the occipital lobes (such as the eyes, optic nerves, tracts, radiations, or lateral geniculate nuclei). Thus, we believe that the phenomenon of Anton syndrome supports the idea of visual consciousness being present in the occipital cortex. Parietal Cortex, Spatial Neglect, and the “Aha” Moment of Recollection The parietal lobe plays an important role in attention to, or awareness of, one side of the world. Although both parietal lobes contribute to awareness of both the left and the right side, damage to the parietal lobe of the language-dominant hemisphere (usually left) typically produces a mild and temporary loss of awareness (or neglect) of the contralateral right side, whereas damage to the nondominant (usually right) parietal lobe typically produces prominent and sometimes permanent neglect of the contralateral left side (Mesulam, 1999). Neglect most commonly occurs for sensory stimuli that are localized in space, such as visual and tactile stimuli. Individuals with neglect may not notice food on the left half of their dinner plate, may find it difficult to pay attention to someone who is speaking to them on their left, and may not notice a touch to the left side of their body. What is particularly striking about individuals with right parietal lobe damage and left neglect is the fact that they are typically completely unaware that the left side of the world is missing. As in Anton syndrome, individuals with neglect have anosognosia and are unaware of their deficit. We believe that parietal damage-induced neglect supports the idea of spatial awareness (consciousness) of one side of the world being present in the parietal cortex, particularly of the nondominant hemisphere. Other parietal lobe functions may also be relevant for consciousness. With the exception of the retrosplenial cortex, damage to the parietal lobes is not known to impair episodic memory. Yet virtually every recognition memory task evaluated with fMRI or event-related potentials (ERPs) produces activation of the parietal cortex (Simons et al, 2008). How are we to reconcile this discrepancy? Our answer is that the so-called parietal old–new effect (in which previously seen or old items show greater parietal activation compared with novel or new items) is part of the neural correlate of the conscious awareness that an item has been seen before. It is that aha moment when we consciously think, “Yes, I remember that” (Ally et al, 2008). Frontal Cortex, Motor System, and Corticobasal Syndrome Regarding consciousness and the frontal cortex, we begin by noting that neglect also occurs for movements and activities after damage to the supplementary motor cortex (Laplane and Degos, 1983). Individuals with this type of damage might abandon washing, shaving, or brushing their hair on one side. Similarly, individuals with damage to the frontal eye fields typically show a form of neglect in that they experience difficulty moving their eyes voluntarily to the opposite side. We believe that these motor forms of neglect support the idea that the frontal lobes are important for the conscious control of movements and activities. Some individuals with brain lesions experience a bizarre delusion in which they do not recognize or believe that their paralyzed limb cannot move (ie, anosognosia) or even that it is part of their body (ie, somatoparaphrenia). Brain regions implicated in somatoparaphrenia include portions of the insula and the middle and inferior frontal gyri (Gandola et al, 2012). Thus, conscious awareness of movement, and even awareness that a body part is one’s own, may be related to proper cortical functioning. Some individuals with corticobasal syndrome experience alien limb phenomena, in which an affected limb appears to move on its own—without conscious control. We have seen patients in which the usually useless limb may rise and make simple movements. When the patient is performing tasks that would typically require two hands, such as tying shoelaces, the useless limb can sometimes be cajoled into helping, which it then may do easily and automatically (A.E.B. observation). Although individuals with corticobasal syndrome show involvement in the basal ganglia in addition to the cortex, atrophy and/or hypometabolism of the frontal and parietal cortex is prominent and generally detectable on brain imaging studies. This phenomenon supports the idea of the importance of the cortex in conscious motor control. Individuals with behavioral variant frontotemporal dementia show atrophy and/or hypometabolism of various regions of the frontal cortex, leading to behavioral problems. Many of these individuals demonstrate utilization behavior, which occurs when individuals see a tool and automatically start to use it. An individual may pick up a pair of scissors and begin cutting or a pen and begin writing. We view utilization behavior as an example of damage to the frontal cortex, leading to impaired conscious control of behavior but preserved unconscious actions. There are many other examples of damage to the frontal lobes leading to a loss of conscious control of behavior (eg, Phineas Gage [Damasio et al, 1994]), which we will not review here. Apathy is a very common symptom in individuals with a variety of damage to the prefrontal cortex. Here, we would argue that some of the so-called higher frontal lobe functions, such as planning and abstract reasoning, are impaired because conscious awareness and control of such functions has been impaired. Not consciously realizing that one should be planning and acting leads to apathy. Temporal Lobes, Auditory Cortex, Inferolateral Temporal Cortex, Names, Words, and Meaning Important aspects of the temporal lobe cortex include parts of the insula, the auditory cortex, and the vast store of information that comprises semantic memory. Individuals with damage to the bilateral auditory cortex lose conscious awareness of sounds but still may react to them, which is a phenomenon that is considered analogous to blindsight (Cavinato et al, 2012). Animal studies have shown experimentally that damage to the bilateral auditory cortex does not necessarily change behaviors in response to sounds (Floody et al, 2010). Thus, we have evidence that the auditory cortex is involved in the conscious awareness of sounds in a manner that is consistent with our hypothesis. Evidence from both imaging and individuals with brain lesions suggests that the inferolateral temporal cortex is critical for conscious awareness of the names of people, animals, and tools (Damasio et al, 1996). Moreover, although unilateral left-sided damage may impair access to just the names of such items, bilateral damage may lead to a complete loss of knowledge of animals, plants, and man-made objects. Individuals with neurodegenerative disease, including semantic dementia and Alzheimer disease (AD), frequently experience this loss of knowledge in the later stages—not knowing what a rabbit, pumpkin, or remote control is—which, we would argue, is the loss of a form of awareness of, or consciousness of, the items. In addition, even very early on in the course of their disease, once the concept of an item is completely lost from semantic memory, individuals with these semantic memory deficits are not consciously aware that anything is wrong—they just believe that they may never have encountered the missing items and therefore have no knowledge of them. Properties of the Cortex That Support Consciousness We hope that we have made our case that each cortical area contributes to specific conscious awareness related to the function of that cortical area, and that destruction of any cortical area will disrupt or abolish the domain of consciousness that was supported by that area without disrupting other domains of consciousness. Our hypothesis that all cortical regions contribute to consciousness is consistent with a recent neuroimaging study that compared conscious versus unconscious object recognition and found widespread areas of cortical involvement during conscious object recognition (Levinson et al, 2021). Our anatomical hypothesis also provides one way to reconcile the different predictions made by the four major theories of consciousness regarding which cortical regions are most important (see Yaron et al, 2022, for review), arguing that they all are, but none is critical. We are not implying that subcortical structures are not necessary for consciousness—they most certainly are—but they are not sufficient for consciousness. Furthermore, subcortical structures do not contain the necessary neuronal architecture that allows for phenomenology or qualia of consciousness to occur. Although we consider cerebral cortex architecture as the unit of neuronal assemblies that allows consciousness to occur, exactly how consciousness arises from the cortex is unclear. We will leave it to others to determine whether consciousness is related to the spindle neurons (also known as von Economo neurons) found in layer V, thick-tufted pyramidal neurons in layer VB, large pyramidal cells in cortical layer II/III, other neuronal types, or none of these. It may be that assemblies of neurons are the unit of consciousness, and that looking at single cells in order to understand consciousness is similar to studying the nature of quarks inside the neutrons of iron atoms in order to understand why iron filings are attracted to magnets. Thus, it may be that concepts such as the perturbation complexity index (PCI) that evaluate the integrity of brain networks will be key to understanding consciousness (Koch et al, 2016). Supposing that assemblies of neurons in the cortex are the unit of consciousness, how many layers of the cortex are required? Is only a 6-layered cortex conscious, or is a 5-, 4-, or 3-layered cortex sufficient? Can a 3-layered allocortex produce some “low-level” perceptual or emotional consciousness, whereas a 6-layered neocortex is required for higher-level self-consciousness and abstract reasoning? Because our memory theory of consciousness is that consciousness developed as part of the conscious memory system, which includes hippocampally based episodic memory, we speculate that some conscious awareness is present in even the allocortex. Whatever the correct answer, we would argue that our lack of understanding of exactly how the cortex produces consciousness does not prevent us from using this hypothesis of the involvement of the cortex in consciousness to discuss some implications, which we will do now. IMPLICATIONS Our memory theory of consciousness, with regions of the cerebral cortex as the fundamental units that allow for conscious awareness, leads to a number of implications regarding which animals are conscious, which neurologic and psychiatric disorders may impair consciousness, and how the conscious mind and unconscious brain work together day by day, minute by minute, and second by second. These implications, in turn, raise ethical implications, which we will also briefly discuss. Animal Minds In the prior section, we speculated that conscious awareness of basic perceptions and emotions may be present with a 3- or 4-layered allocortex, whereas conscious abilities such as problem-solving may require a 6-layered neocortex. Consciousness in Mammals Because all mammals have a neocortex (Kaas, 2019), we argue that all mammals are conscious. Paralleling their differences in cortical structure, we believe that the consciousness of mice differs in its complexity from that of a dog, which differs from that of a chimpanzee, which differs from that of a human. For example, Pine et al (2021, p. 701) reviewed some of the changes in the neuroanatomy between nonhuman and human primates, such as the expansion of homotypical association areas and the hippocampus and how these expansions are related to “(i) a subjective sense of participating in and re-experiencing remembered events; and (ii) a limitless capacity to imagine details of future events.” Although we certainly agree that humans developed these capacities to a greater extent than all other species, we also believe that all mammals have some conscious awareness of (ie, memory for) perceptions, decisions, and actions. See Carruthers (2015) for a similar argument that is also based on neuroanatomical homology. Does this mean that all mammals have human-like conscious memory abilities to mentally time-travel? Suddendorf and Corballis (2007, p. 307) argued that there is little evidence to suggest that nonhuman mammals have developed this mental time-traveling ability, at least in the way that humans do, stating, “We maintain that the data so far continue to suggest that mental time travel is unique to humans.” Although we do not contest their statement, we would still argue that nonhuman mammals possess some form of conscious memory that allows them to have some conscious awareness that provides them with evolutionary advantages, such as being able to determine temporal order (Eichenbaum et al, 2005). What about the mirror self-recognition test? In this test, animals have a spot of color applied to their head when they are anesthetized or otherwise unaware of its application, in a location that they can only see in a mirror, such as their forehead. If the animal looks in the mirror and reaches for the spot or tries to rub the spot off, then we know that the animal recognizes itself in the mirror. If not, we presume that the animal does not realize that it is its self in the mirror. Although a number of mammals have been shown to pass this test, including the four great apes, bottlenose dolphins, and Asian elephants (de Waal, 2019), most other mammals have not, including our feline and canine companions (although dogs can pass an odor version of the test [Horowitz, 2017]). We believe that this test tells us something about visual perception and the recognition of self-consciousness, but we would argue that just because an animal fails the mirror test does not mean that it has no conscious awareness of any type. Consciousness in Nonmammalian Species What about consciousness in other vertebrates such as birds, lizards, amphibians, and fish? Magpies have, in fact, been shown to pass the mirror self-recognition test (Prior et al, 2008), as has a species of fish (Kohda et al, 2019). Does this mean that certain bird and fish species are self-conscious? de Waal (2019) argued that passing the mirror test indicates at least some rudimentary self-awareness; however, one researcher suggested that we should consider all the data that shed light on the cognitive capacities of a species before drawing conclusions on its self-awareness (or lack thereof) (Vonk, 2020). Our theory would be aligned with de Waal’s (2019), that consciousness and self-awareness among species are on a continuum. Based on both experimental work and the brain anatomy of vertebrates, we believe that there is evidence that most vertebrates have at least some rudimentary conscious memory system because they have some form of a hippocampus (or a brain structure analogous to it) and some cortex (or analogous structure). It follows logically that those vertebrates that have the requisite anatomy for the conscious memory system would experience at least some conscious awareness, although it might be little more than perceptions and/or emotions. Some researchers have, in fact, argued that at least one nonmammalian vertebrate species, the California scrub-jay, can use its memory to spontaneously plan for the future without reference to its current motivation—something that had previously been thought to be a uniquely human ability (Raby et al, 2007; see also Carruthers, 2015). Our theory is also consistent with many of the ideas put forth by Ginsburg and Jablonka (2021), who go even further than we do. They suggested that “unlimited associative learning” can be considered a marker of minimal consciousness. They noted that such learning is present not only in almost all vertebrates, but also in octopods, squid, cuttlefish, honeybees, and cockroaches. Ethical Implications of Conscious Vertebrates We are not vegetarians and do not want to imply that the inevitable ethical conclusions that stem from our theory mean that we should all become vegetarians (or at least, nonvertebrate consumers) in order to avoid harming conscious animals. However, we would argue that we as societies and individuals should consider the ethical implications of consuming cows, pigs, chickens, and other vertebrates that we argue have forms of conscious awareness. Disorders of Consciousness Now that we have a better understanding of both the phenomenology of consciousness and the neuroanatomical structures needed for it, we can speculate that a number of psychiatric, neurologic, and developmental brain disorders may be disorders of consciousness. Strokes We reviewed earlier how strokes that damage certain areas of the cerebral cortex result in specific impairments in consciousness that are related to the functions of those particular areas. Here, we will simply add that strokes in the subcortical white matter of the corona radiata may also cause impairments in consciousness by disconnecting cortical regions from one another, leading to, for example, Wernicke aphasia (Mesulam et al, 2015) or alexia without agraphia (Geschwind, 1965). In brief, strokes that affect cortical and/or subcortical white matter frequently impair one or more domains of consciousness, the conscious memory system, and the ability to use previously learned information to problem-solve and plan for the future. Delirium Individuals with delirium (also known as an acute confusional state or encephalopathy) show an inability to carry out a coherent stream of thought or action (Lipowski, 1989). We suggest that these individuals manifest a primary disorder of consciousness. As expected, they are also unable to properly store, retrieve, or use conscious memory for goal-directed activity. Individuals with delirium can (and often do) engage in a variety of activities, including running, undressing, eating, and performing other complex activities (Lipowski, 1989). We suggest that such activity is performed without conscious awareness or control, and it is because of this lack of conscious awareness and control that the activities result in no episodic memories being formed and essentially no complex goals being achieved. (Individuals may undress because they are warm or drink because they are thirsty, but these types of basic goals are the only ones that can be achieved.) Delirium may result from cortical dysfunction caused by metabolic disturbances (such as elevated calcium) or the inflammatory processes that accompany systemic infections (such as pneumonia) (Wilson et al, 2020). Delirium is more common when the cerebral cortex has already been damaged by strokes or degenerative diseases (such as AD). AD and Other Cortical Dementias We contend that whether or not all cortical dementias start with involvement of the subcortical structures (such as the substantia nigra of the midbrain in Parkinson disease dementia), these dementias eventually impair one or more domains of consciousness due to their involvement of the cortical regions. Cortical dementias include AD; Lewy body dementia (including Parkinson disease dementia and dementia with Lewy bodies); progressive supranuclear palsy; corticobasal degeneration; and the frontotemporal lobar degenerations that may lead to behavioral variant frontotemporal dementia, primary progressive aphasia, or progressive amnestic dysfunction, depending on where in the cortex the pathology starts. We believe that cortical dementias impair different domains of consciousness depending on which cortical regions they disrupt, just as strokes do. In addition, the pathology in cortical dementias spreads throughout the brain, often affecting both adjacent cortical regions and regions that are neuronally connected. For example, AD’s pathology spreads throughout the default mode network, which consists of the hippocampus; related medial temporal lobe structures; anterior and lateral temporal cortex; medial prefrontal cortex; and posterior cingulate, precuneus, and angular gyrus in parietal lobes (Buckner et al, 2008). As cortical dementias spread and the pathology involves more than one cortical region, we believe that more than one domain of consciousness becomes impaired. Once several cortical regions are involved, a phenomenon known as sundowning becomes more likely to occur. Most cases of sundowning are a temporary and periodic state of delirium. Over time, most cortical dementias spread to most cortical areas of the brain, which we believe causes impairment in most domains of consciousness. Ultimately, the individual with cortical dementia spends more time in a delirious, confused—and, we would argue, unconscious—state, with few periods of conscious lucidity. Migrainous Phenomena Individuals with classic migraines experience auras that are most commonly visual but may also cause numbness, speech or language difficulties, weakness, or confusion. Although migraine auras are not completely understood, it is clear that they are associated with cortical spreading depression (also known as spreading depolarization), in which there is a spreading of electrophysiological hyperactivity over the cortex at a velocity of several millimeters per minute followed by spreading inhibition. Phenomenologically, the most common experience is that of positive visual phenomena (often described as scintillating zigzag lines or fortifications) followed by a lack of vision or scotoma. Both the scintillations and the scotoma progress over minutes and generally resolve in under an hour. Relevant here is the fact that cortical spreading depression causes disturbances of conscious perception and sometimes other conscious abilities. Epilepsy Similar to individuals with migraine auras, individuals with epilepsy may consciously experience an “aura” of positive phenomena, such as flashes of light or tingling and numbness, which usually spreads over seconds. Following the positive phenomenon, a postictal loss of function may occur (sometimes called a Todd paresis when it is related to motor function in a limb). In addition to visual and tactile, auras may also be olfactory or gustatory, or they may cause simple or complex motor movements (such as lip smacking or bicycling movements). Seizure auras have been shown to be focal seizures that occur in some individuals before the seizure becomes generalized. Once the seizure becomes generalized, the individual loses consciousness. Again, relevant here is that the temporary disruption of cortical activity may cause focal or general disturbances of consciousness, depending on the extent of the cortical disruption. Dissociative Disorders Individuals with dissociative identity disorder have two or more distinct and relatively enduring personality states (American Psychiatric Association, 2013). This disorder generally develops in childhood due to prolonged and severe physical, sexual, and/or emotional abuse. We speculate that individuals with this disorder may have two or more separate consciousnesses, each with its own memories, that the other consciousnesses may or may not have access to. If we are correct, it may be an example in which psychological factors can cause a disorder of consciousness. There is evidence from the literature, such as cases reported by James (1890), that not only can episodic memories be separated between identities, but semantic memories can as well. In one case, James (1890) described that when the individual moved from one identity to another, her prior knowledge base was not accessible and she had to learn (or relearn) information that her other self knew. Another related disorder is dissociative amnesia, in which an experienced or witnessed traumatic event—so emotionally laden that it would typically not be forgotten—cannot be remembered. We believe that this disorder may be considered a more focal case of the conscious memory system being fractured such that one or more explicit memories cannot be retrieved. Depersonalization–derealization disorder includes symptoms of feelings of detachment from one’s mental or bodily processes or one’s surroundings. It is thought to be caused by early childhood emotional abuse and neglect. Triggers of depersonalization symptoms may include significant stress, panic attacks, and drug use. We speculate that, at least in some cases, this disorder may be a manifestation of a disruption of consciousness. Schizophrenia Schizophrenia consists of positive symptoms of psychosis, such as hallucinations, delusions, and disorganized thinking, and negative symptoms of social withdrawal, decreased emotional expression, and apathy. Although all modern theories of schizophrenia state that it is a disorder of the brain resulting from interactions between individuals’ genetic makeup and their environment, the specific biological pathophysiology has not been determined. We believe that schizophrenia may represent a disorder of consciousness, with the hallucinations (usually of voices) being a symptom of disrupted and possibly fragmented consciousness (Tordjman et al, 2019). This idea is supported by the observation that individuals with schizophrenia show impaired conscious access but intact unconscious processing of perceptual stimuli (Berkovitch et al, 2017). Consistent with our memory theory of consciousness, memory deficits are common in individuals with this disorder (Avery et al, 2020). Autism The autism spectrum includes individuals of highly varied capacities, from those who are nonverbal and function at a cognitive level of <2 years, to those who would have previously been diagnosed with Asperger syndrome and may show superior intellectual abilities. For the purposes of this paper, our brief discussion of autism will be restricted to individuals with high needs, focusing on nonverbal individuals with significant impairment and intellectual abilities less than those of a typical 2-year-old child. Autism may be caused by several underlying brain differences, leading to several different phenotypes. In individuals with severe classic Kanner autism, there appear to be what we would consider severe deficits in the conscious memory system. Regarding memory, these individuals tend to learn in stereotyped ways, not in the typical episodic-memory-leading-to-semantic-memory manner. Education of these individuals therefore often involves operant conditioning (eg, applied behavior analysis) to produce learning. Problem-solving and responses to conflict and discomfort appear to us to predominantly use System 1 unconscious processes. There is little evidence (at least to the observer) for conscious, thoughtful, deliberative System 2 processing in some of these individuals. The unfortunate result for many is that they learn, via operant conditioning, maladaptive behaviors such as aggression and self-injury because those behaviors have previously provided them with an escape from activities that they did not like (Oliver and Richards, 2015). We therefore speculate that individuals with severe classic Kanner autism have a disorder of the conscious memory system that manifests as impairments in both memory and consciousness. This speculation is supported by pathology studies showing that the cerebral cortical architecture is disrupted in these individuals (Courchesne et al, 2011). Other clinicians and researchers have also speculated that autism may be related to deficits in consciousness (Tordjman et al, 2019). However, this speculation should not be misconstrued as suggesting that autistic people are somehow permanently unconscious or incapable of human experience. There is also interesting anecdotal evidence that some individuals with Kanner autism appear to be able to process information in parallel fashion more easily than typical individuals; for example, some desire to listen to music and watch an unrelated movie at the same time (A.E.B. observation). This comfort with parallel processing may be related to a lack of serial one-thing-at-a-time thinking that is imposed by consciousness. Lastly, we will note that just as no two neurotypical individuals are the same in their capacities, neither are two individuals with severe classic Kanner autism, such that this discussion may seem consistent with the capacities and limitations of some individuals and inconsistent with the capacities and limitations of others. Most of our Decisions and Actions Are Unconscious, Although we Think They Are Otherwise One of the most radical implications of our memory theory of consciousness is that although we generally have the subjective experience that most of our decisions and actions are consciously controlled and few occur automatically and unconsciously, we argue that it is the other way around—few of our decisions and actions are consciously controlled, and most occur automatically and unconsciously. Unconscious Routines Let’s take our morning routine, for example. We believe that we are acting primarily unconsciously as we get out of bed, walk to the bathroom, flip on the light, use the toilet, brush our teeth (including all the steps such as wet our toothbrush, put on toothpaste, move the brush across our teeth, rinse our toothbrush and mouth, put the brush back), shower (including all the separate steps), dry ourselves, dress, fix our hair, and so on. It would only be if something were unusual—such as we needed to dress for a job interview or a snowstorm—that there would be much conscious deliberation. In fact, not only did we not make conscious decisions to guide our actions, but we likely were also only partially aware of our morning routine. There may have been moments when we attended to what we were doing, allowing us to form episodic memories of those moments. At other times, however, our mind was free to use our conscious memory system to review what happened yesterday, reminisce about a prior event, or imagine what will likely happen in our 9 AM meeting. If these latter thoughts were occurring during the time that we were showering, instead of remembering the steps of our shower, we would remember the topics that we were consciously reviewing, reminiscing, and imagining. But, even if we were perceptually aware of each step of our shower, that does not mean that we were consciously controlling those steps; our awareness simply allowed us to remember each step. Conscious or Unconscious Decision? It may not be controversial to say that we typically go through our morning routine unconsciously, and that we would only remember whatever we were paying attention to at the time. But, now we arrive at work, drink some coffee, and walk to our meeting. We are a little nervous because we need to explain to our boss why our team did not meet its quarterly target, and we are going to have to explain that it is because our colleague, Biff, who is also in the conference room, did not do his part. To our surprise, as we launch into our explanation, we hear ourselves taking responsibility for the team’s shortfall and not blaming Biff. As we are leaving the meeting, we consider what just happened. We end up consciously thinking that we said what we said because it probably was not really Biff’s fault, mistakes can happen to anyone, and, after all, we were leading this team, so we were ultimately responsible. We nod our head as we think, “Yes, that’s what happened,” and turn to other work. However, we speculate that what actually occurred is that as we started to speak, we were also unconsciously processing in parallel the facial expressions, body language, and eye movements of everyone in the room—particularly those of Biff and our boss. Our unconscious perceptions of Biff's slightly red and determined face led us to unconsciously realize that he was not going to quietly accept that it was his fault; he would likely deny responsibility and blame us instead. Our boss’s body language—mimicking Biff’s posture and leaning toward him slightly, signifying that our boss was going to side with Biff—was also unconsciously processed. Our unconscious System 1 quickly made the decision to change tactics and say it was our fault—regardless of the truth or of what our conscious System 2 had planned to say. (Remember that we believe that unconscious brain processes make the final decisions—the rider can indicate to the horse where they wish to go, but in the end, the horse decides.) This scenario raises the questions of (a) why we did not realize what actually happened in the conference room and (b) why we came up with an alternative not-quite-right explanation. We argue that the answer to the first question is that we did not realize what happened because we were not consciously aware of it at the time, and thus we could not consciously consider it then nor could we use our episodic memory (part of the conscious memory system) to remember it. (We can, of course, train ourselves to notice subtle facial expressions, body language, and eye movements, which we could then be consciously aware of and remember.) The second question can best be answered by considering an experiment that was performed by Gazzaniga (2015) and his colleagues with one of his split-brain patients. The Conscious Interpreter An individual who had undergone a callosotomy to separate his left and right hemispheres in order to reduce the frequency of disabling seizures was shown separate images in his left and right visual fields. The individual’s task was to use each hand to point to a card that corresponded to the visual image he saw in each visual field. His right visual field, projecting to the verbal left hemisphere, was shown a chicken foot, and his right hand (controlled by the left hemisphere) pointed to a picture of a chicken. His left visual field, projecting to the nonverbal right hemisphere, was shown a snowy scene, and his left hand (controlled by the right hemisphere) pointed to a picture of a shovel. So far so good. But, when the individual was asked why he was pointing to the shovel, his response was, “You’re going to need a shovel to clean out the chicken coop.” From experiments like this one, Gazzaniga and colleagues came up with the explanation that our left hemisphere acts as an interpreter, explaining to others—and our conscious selves—why we do things (Gazzaniga, 2015). It is not only individuals with split brains, however, who may not be aware of why they act in a certain way. In their classic paper, “Telling More Than We Can Know,” Nisbett and Wilson (1977) reviewed evidence that any of us may be unaware of stimuli that influence our responses. This idea was more recently articulated by Carruthers (2011), who argued that we do not have any more access to most of our own thoughts than we do to anyone else’s, and that we must use the same tools that we would use to discern how others think to discern our own thoughts. These experiments by Gazzaniga (2015) and Nisbett and Wilson (1977) make it clear that what we consciously explain to others—and our selves—may not always be the true explanation. Similarly, confabulation by individuals with memory impairment, such as those with Korsakoff syndrome, may simply represent the conscious interpreter doing its best to make sense of the world with partial and outdated information. To review, we believe that unconscious brain processes are ultimately responsible for our decisions and actions. If we consider our decisions and actions, most of the time we will have a ready explanation for them—and most of the time, our explanation will be correct. But sometimes, as these examples demonstrate, the explanation we tell our selves will be incorrect. This phenomenon is likely one reason why people use the psychological defense mechanism of rationalization—in this case, because they are not consciously aware of the truth, but only of the narrative that their conscious mind creates. Willpower and Difficulty Controlling Behavior Perhaps some of the most obvious examples that we do not have direct conscious control over our decisions and actions relates to willpower and the ability to control behavior. We have all had the occasional experience of finding it difficult to control our behavior. For example, we may have promised our selves that we are going to have just one spoonful of chocolate ice cream but, the next thing we know, the entire container is empty. While we were eating it, we may have said to our selves, “I shouldn’t be doing this. I should put the spoon down and put the container back in the freezer.” In this situation, it appeared that we were simply watching our selves eat the ice cream and that we had little control over our actions. Our memory theory of consciousness helps us understand why it sometimes appears that we have little or no control over our own actions—because it is actually our unconscious brain processes that are in control. We now also understand why it appears that we are merely watching our selves perform actions that we do not consciously wish to do—because our conscious self is not participating directly; it watches memories of our actions (via our special video screen sunglasses as we are riding on our unconscious horse or via the Cartesian theater, depending on which metaphor is preferred). In fact, our memory theory of consciousness suggests that the sense that we are watching our selves act is a more accurate depiction of reality than the sense that we are consciously engaged in decision and action. How the Conscious Mind Can Influence Decisions and Actions Just because we argue that it is unconscious brain processes that ultimately decide and act does not mean that we believe that our conscious mind cannot influence those decisions and actions. Because conscious and unconscious processes are both occurring in the brain, it should not be surprising or mysterious that they interact, even if we do not understand the brain physiology underlying those processes and interactions. As Kahneman described so eloquently in his work with Tversky, we frequently have fast, unconscious System 1 “gut instinct” choices competing with slow, conscious System 2 logical choices (Kahneman, 2011). Luckily, for both the individual and the community, the conscious System 2 logical choices often win out for important choices, and the best decision is made. We would simply argue that, when that happens, the conscious System 2 “convinces” the unconscious System 1 to make the rational choice. The conscious memory system then remembers that the desired decision was made and thinks that it made the decision itself—even though it just remembered the decision that was actually made unconsciously. (The rider succeeds in cajoling the horse to go up the steeper path in order to avoid the dangers on what appears to be the easier route. The rider thinks that they made the decision, but where the rider goes is always up to the horse.) Free Will We would like to make several points about free will. First, just because our decisions and actions are ultimately made unconsciously does not mean that we do not have free will—or, at least, not any more than if we made our decisions and actions consciously. The implications regarding determinism are no different whether decisions are initiated consciously or unconsciously. Second, as we just discussed, our conscious mind can cajole our unconscious self into making the best decisions in particular instances and can change the tendencies of our unconscious self over time. Third, if major life decisions are made slowly, over minutes, hours, days, or longer, these important decisions will almost certainly have input from both our conscious mind and our unconscious brain processes. Lastly, our memory theory of consciousness makes no predictions about whether our decisions and actions are determined on a microscopic scale, so on that point, we will not comment other than to say that we believe that we must act as if we have free will. Who Are We? One important issue that we have been skirting around is who, exactly, are we? Are we our conscious mind, our unconscious brain processes, both, or neither? The obvious answer is the correct one: We are both, of course. However, we would argue that the elements of our personality, our character—our innate tendencies of how we interact with the world—are determined by our unconscious brain processes, which we can call our unconscious self. Our conscious mind, on the other hand, is the part of us that does the conscious thinking, remembering, feeling, and calculating, and carries out other conscious processes. Note that our unconscious self certainly has different parts or aspects (Cisek, 2019). One part may crave chocolate ice cream while another part may wish to look svelte in the mirror. Understandably, our conscious mind believes that we are primarily our conscious self, as our conscious mind spends most or all of each day alone with its self. From the perspective of everyone else interacting with us, however, we are primarily our unconscious self. Do not forget: We believe that most of our decisions and actions occur automatically without conscious input, and only a few have an element of conscious control. This means that, most of the time, the world is interacting with our unconscious self. Thus, we experience self and other as a mix of conscious and unconscious perceptions and actions. We almost certainly overestimate how much of the mix is conscious. We Can Change Who We Are Just because a large part of who we are is our unconscious self does not mean that we cannot change and improve our selves over time. As educators and clinicians, we most certainly believe that people can grow, learn, and change for the better. For example, perhaps we have noticed that we have a tendency toward selfishness. In contemplating this tendency along the lines we have discussed in this paper, we realize that it stems from one aspect of our unconscious self. Our conscious self, as well as other aspects of our unconscious self, may feel embarrassed about this tendency. To create a change, the parts of our unconscious self that desire a change can use conscious System 2 processes to create a conscious plan that may include both large actions in the world (such as sharing our time or money with charitable organizations) and small, everyday acts of selflessness (such as being considerate to those around us and helping strangers in need). If these actions are rewarded by positive feelings, our nonconscious memory systems (such as operant conditioning) will reinforce the actions such that they become part of our character—that is, our unconscious self. This ability to change our character—the way we tend to interact with the world—is relevant to moral responsibility. If we find that our character tends toward bad behavior and we can change our character, then we are responsible for making ourselves into a better person. For instance, a parent may have an obligation to develop the (unconscious) tendency to engage in caring behaviors toward their children (cf. Björnsson and Brülde, 2017). The memory theory of consciousness points us toward a mixed type of ethics that includes a healthy dose of virtue ethics. Most of our actions arise from our unconscious self, such that the proper object of moral evaluation is character, in line with the virtue ethics that are familiar from Aristotle. Other actions will be guided by conscious deliberation. Each of these deliberate actions will be subject to moral evaluation individually, which is consistent with deontological (“rule-based” or “duty-bound”) or consequentialist (“outcome-based”) ethics. We Can Control Our Behaviors and Actions In the same way that we can change who we are by consciously deciding to change our unconscious self, we firmly believe that we can control our actions. Perhaps this is obvious now that we have explained that we are both our conscious and our unconscious selves. But, even if we are just considering our conscious self, we believe that our conscious self can cajole and convince our unconscious self to make the decisions and take the actions we desire. In fact, we hope that the viewpoint regarding consciousness expounded on in this paper will help everyone (including educators and clinicians) understand that in order to change our actions, we need to change both our conscious and our unconscious selves. Furthermore, to change our unconscious self may require the use of nonconscious training such as operant conditioning, skill learning, and the like. For example, to train our unconscious self to put our keys in the same place each day so we will not spend hours hunting around the house for them, we initially need to set up conscious reminders until procedural memory takes over and our unconscious is trained (ie, until it becomes a habit). Returning to our one spoonful of chocolate ice cream example, for most people, it is not enough to simply consciously desire to stop after eating one spoonful using willpower. Instead, we will need to use our conscious mind to set up systems to either reduce the temptation of eating the entire container (perhaps dividing the container into one-spoonful amounts each on a plastic spoon, with one in front and the others in the back of the freezer) or reward our unconscious self for stopping after one spoonful with something greater than the reward that the chocolate ice cream itself would produce. The way our memory theory of consciousness relates to behavior can enable us to describe heroic actions from a new vantage point. Some actions seem heroic to onlookers but come easily to the individual because of training or experience. For instance, running into a burning building to save a child is “just part of the job” for a firefighter. Other actions can be called heroic because they require overriding our System 1, unconscious character. We know that this type of action is possible. Indeed, the underdog characters in popular books and movies are often portrayed as overcoming certain unconscious or characterological tendencies (such as trepidation or cowardice) in just this way. Neville Longbottom from the Harry Potter books is an example. Consistent With the Principles of Cognitive Behavioral Therapy Everything we are suggesting in the ways in which our conscious mind can influence and train our unconscious self is consistent with the principles of cognitive behavioral therapy. Cognitive behavioral therapy explicitly recognizes that psychological problems are commonly based on both “unhelpful ways of [conscious] thinking” and “[unconscious] learned patterns of behavior” (American Psychological Association, 2017, text in brackets added). Moreover, cognitive behavioral therapy works both to change conscious thinking patterns and to use role play to change patterns of behavior. We believe that cognitive behavioral therapy is a successful therapy because it directly addresses both the conscious mind and the unconscious self, using techniques that each can understand. Understanding Mindfulness Therapy We can also understand mindfulness therapy in this context. By focusing attention on a process that usually occurs without our noticing, mindfulness can bring what is usually unconscious to the attention of the conscious self. That allows System 2 deliberate processing, deliberate evaluation, and deliberate changes in how we respond. To achieve the individual’s goals for the therapy, the new responses can remain in the domain of the conscious self or be adopted by the unconscious System 1 self. Ethical Decisions and Actions It follows from our discussion that an ethical decision or action may stem entirely from our unconscious self, or from both our conscious and unconscious selves. If we are in a horrible situation, we may jump in front of our child to save them from a bullet or other attack. Such actions would typically be automatic and too fast for conscious thought and deliberation. We make many small ethical decisions each week using our fast System 1 unconscious processing. At other times, we engage in slow, deliberate, System 2 conscious thinking to determine the appropriate ethical course of action; perhaps stepping down from a committee in protest if we strongly disagree with their decision, despite the loss of prestige and revenue that it would entail. Another example would be consciously choosing a less remunerative profession based on the amount of good it could produce in the world when certain aspects of our unconscious self would have chosen a more remunerative profession based on the amount of money or other comforts it could provide. Such consciously informed decisions would, of course, require our conscious mind to cajole and convince our unconscious self to make the actual decision. Teaching Ethical Decisions and Actions Our theory that all decisions and actions are ultimately made by the unconscious self has implications for the pedagogical methods that we use to teach ethics to our children and others in society. Teaching the theory behind ethics and ethical decisions and actions is helpful but not sufficient, as such theories will only inform the learners’ conscious minds and not their unconscious selves. Just as learning a new procedural skill first requires conscious memorization of a sequence and then practice of the actual motor/procedural skill so that the learning occurs in the unconscious self, so should ethical education involve not only conscious thinking and memorization, but also the visceral, emotional, empathetic learning that is acquired by doing. For example, children can participate in role-playing various actions and situations in order to feel the emotional consequences that different actions engender. These ideas speak to the importance of ethical approaches involving fictional and autobiographical narrative insofar as these approaches support the integration of conscious rules with unconscious responses. These ideas also suggest that we may want to examine the ethics curricula in professional training (such as in medical, nursing, education, and law schools) so that we avoid a one-and-done, isolated ethics course in favor of an ethics-across-the-curriculum model. Understanding Hurtful Actions If, as we argue, decisions and actions are made unconsciously, then we must work together as a society to use this understanding to reduce the occurrence of hurtful acts through pedagogical methods, such as role-playing. We should not excuse those people who commit hurtful acts, but we should also have greater sympathy for them because we understand that they may be acting on unconscious impulses. Nonetheless, in order to live together in society, individuals need to learn to control their unconscious selves and thus their behavior through cognitive behavioral therapy and other ethical methods. (Although the horse determines where it will go, the rider needs to learn to tame and control the horse.) Developing Conscious Control of the Unconscious Mind: The Importance of Mindfulness Mindfulness has recently been touted as the key to everything in our society from avoiding burnout to achieving enlightenment. Initially, we raised the issue of why mindfulness is hard, and then we provided our explanation that mindfulness is hard because our conscious mind did not develop to be in control of our thoughts; rather, the unconscious parallel-processing brain developed our conscious mind simply as a necessary part of our conscious memory system. That is, we argue that our conscious mind developed as a tool to be used by our unconscious brain to help it predict the future and make decisions accordingly. It is for this reason that we believe that it is difficult for us to consciously control our conscious thoughts—because normally, our unconscious brain is in control of our conscious thoughts! If, however, we wish to achieve more control over our decisions and actions, it is helpful to have more control over our conscious thoughts. Once we have control over our conscious thoughts, we can use our thoughts to cajole and convince our unconscious self to make the decisions we consciously desire, whether that is eating only one spoonful of chocolate ice cream, leaving work problems at work when we come home to our family, or stopping the flow of thoughts that run through our mind when we are trying to sleep. Because mindfulness is one method of improving our ability to consciously control our thoughts, we believe that practicing mindfulness can help us control our decisions, behaviors, and actions as well. Unlock the Power of the Unconscious Self Sometimes we have the experience that someone asks us a question and, without thinking, we start to answer, only to be surprised at the words that leave our mouths. In this circumstance, we have stepped back and reflected, “Oh, I guess that is what I think about that. …” We believe that this situation is an example of how we may learn what our unconscious self thinks about an issue, and it is just one way that we can use our unconscious brain processes to our advantage. Put the Unconscious to Work Most people have had the experience of trying to come up with a name or an answer to a question and, after the conversation has finished, the name or answer then pops into consciousness. We can actually task our unconscious to work on a problem, and our unconscious brain may (or may not) help us solve it. By simply saying to our selves, “OK, I need to recall that piece of information,” and then turning to other matters, we may find the information coming to us spontaneously later that day, often within minutes. Another method we use is to review some of the work that we need to do the following day just before sleep. For example, before sleep, we will generally review the last section of writing we have done as well as our notes or our plan of what we will be writing the following day. Based on the work of Sanders, Beeman, Paller, and others (Paller et al, 2021; Sanders et al, 2019), we assume that this little bedtime preparation facilitates our unconscious brain processes while we are sleeping. In any event, when we sit down at our computer the next morning, the writing proceeds easily. Bring Unconscious Processes to Conscious Awareness Sometimes we are in a meeting and notice that a person appears scared, angry, sad, excited, or happy. We are all fairly good at detecting basic emotions in ourselves and others using either unconscious or conscious processes (Calvi et al, 2020; Ochsner et al, 2004; Smith and Lane, 2015). What about more complex states such as bluffing, lying, manipulating, seducing, protecting, defending, and supporting? These can be a bit more difficult to discern consciously, but we are aware of many of them some of the time at an unconscious level. We can train ourselves to bring these states that other people are exhibiting to our consciousness by consciously analyzing the individual’s tone of voice, body language, eye contact, and others. We can also tap into our unconscious perception by noticing how the person makes us feel. Do we feel sorry for them? Do we like or dislike them? Are we afraid of them? Do they make us angry? Do they make us smile—and if so, why? By analyzing our reactions to people and events, we can bring at least some of our unconscious perceptions to consciousness. Stereotype Threat and Other Unconscious Biases One of the most important reasons to bring unconscious thoughts and emotions to conscious awareness is to reduce the effects of stereotype threat and other unconscious biases that we are all susceptible to. Stereotypes and biases have been shown to impact the careers of women and minorities (Grewal et al, 2013), in addition to having a detrimental effect on many other aspects of society. Education and training have been shown to increase awareness of these issues and provide concrete steps that individuals and institutions can follow to reduce the harmful effects of stereotypes and unconscious biases (Grewal et al, 2013; Rodriguez et al, 2021). Conversations We sometimes have the experience that it is hard to get a word in edgewise in a conversation, as if we are half a second behind, while at other times it is easy and effortless to contribute to the discussion. We believe that, at least in part, it depends on whether we are responding consciously, with the built-in perceptual delay of approximately half a second, or unconsciously, which has no delay. If this speculation is correct, the more consciously determined we are to make a specific point in the conversation, the more likely we are to have trouble doing it because we will be experiencing the perceptual delay. We will be more able to break into the conversation if we relax and let our reactions and responses be more spontaneous. Unconscious Talking? Can our language be controlled by unconscious brain processes? Or do speaking and writing always involve not only conscious awareness but conscious participation in such activities? We hinted at aspects of these questions previously, discussing how we can sometimes be surprised by the words that come out of our mouths. This example alone is perhaps sufficient to make the claim that unconscious brain processes can control our language—how else could our conscious mind be surprised at what we just said? Another example relates to when we are giving a lecture that we have given many times before. Here, we have noticed that two related and interesting phenomena sometimes occur. The first is that we often enter a mode where we, as our conscious selves, can watch and listen to our selves talk on what appears to be automatic pilot—we at least do not feel as if we need to use any conscious effort to make the words come out of our mouths. It is as if we are not only in the Cartesian theater watching the world go by, but we are also able to see our selves on the stage! The second is that, during such a time when we can step back and watch our selves lecture, we sometimes get distracted, start thinking of something completely different, and do not pay attention to our selves lecturing. When our conscious awareness returns to our lecture, we find that we cannot remember what point we have just made, and whether we should be making the point that is on the slide on the screen or have already done that and should move on to the next slide. Thus, speaking without conscious control or even awareness is not only possible, but probably common. As with other perceptions, decisions, and actions, if we are not consciously aware of our speaking, we will not be able to remember what we have just said. In the Zone: Playing Sports and Musical Instruments As discussed earlier, reaction times are often too fast in sports and music to be consciously controlled. Thus, many athletes and musicians perform using their unconscious System 1 brain processes. In fact, many athletes and musicians perform much better when they use only their unconscious processes to control their behavior. This unconscious performance is often referred to as being “in the zone,” which the Oxford English Dictionary defines as “a state of perfect concentration leading to optimum mental or physical performance.” We suggest that being “in the zone” means that unconscious brain processes are given full rein to make the right, often precise or creative, choices much faster and more accurately than would be possible with conscious control. This level of unconscious expertise is, of course, only possible when these unconscious brain processes have been trained through thousands of hours of practice. But, once that training has taken place, the unconscious brain processes can be let loose! Conscious control is as likely to interfere with performance as it is to help. We are not saying that conscious processes have no role in sports or music. Some of the best athletes, such as the tennis champions Serena Williams and Roger Federer, are known to consciously modify their strategy when their current approach is not producing success. Similarly, some of the most creative music, such as that from Miles Davis and Lady Gaga, appears to result from the interaction of conscious and unconscious brain processes working together. Art and Insight Many—perhaps most—creative endeavors, from painters to novelists, likely result from the combination of conscious and unconscious brain processes working together. Additionally, the appreciation of beauty and other aesthetics in any sensory modality (vision, hearing, smell, taste, touch) may be driven primarily by unconscious properties, which may be why aesthetic appreciation is so difficult to describe using words. We suspect that creative insights in other fields, whether psychology, biology, chemistry, physics, mathematics, and others, often arise from unconscious processes in a brain that has been prepared by consciously struggling with the same or related problems for some time. As Louis Pasteur famously said, “Dans les champs de l’observation le hasard ne favorise que les esprits prepares,” which is often colloquially translated as “Chance favors the prepared mind.” Such scientific insights that likely arose through the combination of conscious and unconscious processes include those of Alice Ball, Rachel Carson, Marie Curie, Marie Daly, Jennifer Doudna, Albert Einstein, Jane Goodall, August Kekulé, Ada Lovelace, Isaac Newton, and many others. Some of the insights of these people might be because they are somehow able to see the world more the way it actually is, rather than the way the conscious mind suggests that it is. In other words, some of these individuals may have had more access to their unconscious brain processes. Hume, for example, asserted that the self is “nothing but a bundle or collection of different perceptions” (Hume, 1978, p. 252), and that we can have no idea of any subject to which these perceptions belong. It is simply that we “bundle” these experiences together that they appear continuous. It is possible that some of Hume’s (1978) insights may be related to his glimpsing some of his unconscious brain processes. FUTURE DIRECTIONS Here, we outline some possible methods that can be used to test different aspects of our memory theory of consciousness. We will first discuss methods that may be able to prove or disprove the theory that consciousness developed as a memory system, and then methods that examine the idea that each region of the cerebral cortex is sufficient to make one aspect of consciousness possible. Methods to Evaluate Consciousness as a Memory System We have argued that consciousness developed as part of a memory system and that all explicit forms of memory (sensory, working, episodic, and semantic) are part of that unified system. One line of investigation would therefore be to examine whether consciousness is truly bound up with, and an integral part of, these forms of memory. Are there situations in which explicit memory can exist in the absence of conscious awareness? Conversely, can there be any type of conscious awareness without involvement of one of these forms of explicit memory? Are there correlations (or not) between explicit memory performance and conscious perception abilities? Are there correlations between subjective measures of explicit memory and subjective measures of consciousness? Such experiments would provide important insights into this issue. Experimental Paradigms Experiments that are similar to those performed by Schneider and colleagues (2021), which attempted to show engagement of the episodic memory system with and without conscious awareness by using different types of visual masking, may be one fruitful line of research. Other paradigms that contrast conscious versus unconscious processing of stimuli may also be useful (for reviews, see Breitmeyer, 2015, and Timmermans and Cleeremans, 2015). The key consideration in all of these paradigms would be the relationship between conscious awareness of, and explicit memory for, the stimulus. These include paradigms that use change blindness (eg, Simons, 2010), attentional blink (eg, Sy et al, 2021), visual crowding, continuous flash suppression, perceptual fading, motion-induced blindness, reversible figures, binocular rivalry, perceptual filling in (eg, Davidson et al, 2020), and visual perception of degraded objects (eg, Levinson et al, 2021). False memory paradigms may also be useful (Nichols and Loftus, 2019; Schacter and Dodson, 2001); these experiments often produce a vivid, confident, conscious recollection of an item or experience that never occurred (Sikora-Wachowicz et al, 2019). Visual imagery experiments, in which a conscious image is produced in the absence of a percept, may also be useful (eg, Moulton and Kosslyn, 2009). Lastly, experimental paradigms involving sleep and memory may be particularly useful (eg, Paller et al, 2021), as dreams appear to be one area in which consciousness and memory may not be co-occurring. Subjective Measures Subjective measures have been used to fractionate memory into different components, such as the remember–know (Tulving, 1999) and recollection–familiarity distinction (Yonelinas, 1994). Subjective measures ask participants to use introspection (as in the case of remember–know) or to provide confidence judgments. Confidence judgments have been successfully used to create receiver operating characteristic curves to estimate the degree to which participants use recollection and familiarity during a task (Yonelinas, 1994). Process-dissociation procedures can also be used to separate components of memory (eg, automatic vs intentional use [Jacoby, 1991]). Subjective measures have also been used to evaluate conscious awareness (for review, see Timmermans and Cleeremans, 2015). These measures include the perceptual awareness scale (Ramsøy and Overgaard, 2004), in which participants rate stimuli as glimpsed, almost clear, or clear; the continuous visual analog scale (Sergent and Dehaene, 2004), in which participants rate stimuli on a continuum from not seen to maximally visible; the rule awareness scale (Wierzchoń et al, 2012), in which participants rate how aware they are of a rule; postdecision wagering (Persaud et al, 2007), whereby participants wager money on their response; confidence ratings (Dienes et al, 1995), whereby participants rate their conscious awareness; and a feeling of warmth (Metcalfe, 1986), which is similar to confidence ratings but lends itself to a more intuitive response. Methods to Evaluate Order and Timing Issues Around Consciousness Regarding issues of the order and timing of consciousness, investigations of chronostasis (eg, Melcher et al, 2020) and postdictive effects (for reviews, see Herzog et al, 2020; Michel and Doerig, 2021; and Sergent, 2018) would likely be informative. Such paradigms include the cutaneous rabbit illusion (Geldard and Sherrick, 1972), illusory and invisible audiovisual rabbits (Stiles et al, 2018), color fusion effects (Pilz et al, 2013), the color phi illusion (Keuninckx and Cleeremans, 2021), the sequential metacontrast paradigm (Otto et al, 2006), rapid serial visual presentation (Akyürek and Wolff, 2016), and cues that are presented after a faint Gabor grating (Sergent et al, 2013). Key in these paradigms would be the relationship between explicit memory and the conscious postdictive experience. Disrupting Consciousness and Memory Another line of research that may be fruitful would be to disrupt either explicit memory or conscious awareness and evaluate whether the other is also disrupted. For example, will medications that are known to impair explicit memory (eg, anticholinergics, benzodiazepines) impair consciousness as well? Will medications that are known to impair consciousness (eg, sedatives, anesthetics) impair memory as well? Will a TMS pulse that disrupts explicit memory also disrupt at least one aspect of consciousness and vice versa? Neural Signals of Consciousness and Memory Although currently there is no consensus as to which physiological markers correlate with conscious experience, one can still evaluate whether such markers correlate with both one domain of conscious awareness and explicit memory (rather than correlating with one but not the other). Putative markers of consciousness—such as EEG activity and its derived components, ERPs—may be of particular interest. Methods to Evaluate the Theory That the Cerebral Cortex Is Where Consciousness Occurs We have argued that not only do all areas of the cerebral cortex contribute to consciousness, but that each region of the cortex may be autonomously conscious of its own particular aspect of consciousness. This theory is contrary to those of a number of other researchers, some of whom have suggested that the frontal lobes are the minimally sufficient regions necessary for consciousness (Brown et al, 2019; Murray et al, 2020) and others who have suggested that the parietal and occipital cortex are the regions that are minimally sufficient for consciousness (Lamme, 2015; Lee et al, 2019; Tononi et al, 2016). Methods for evaluating the anatomical aspect of our theory of consciousness and that of other theories can include studies of individuals with brain lesions or dementia, studies of simulated lesions using TMS, brain imaging studies that provide anatomical localization using fMRI or PET, brain imaging studies that provide information about the timing and physiology of brain processes using EEG and ERPs, derived EEG measures such as the bispectral index (BIS) that is used by anesthesiologists, and other derived measures such as the PCI. Different Domains of Consciousness May Have Different Neural Correlates Because we believe that different regions of the cortex can be autonomously conscious and that the consciousness awareness (the conscious phenomena or qualia) provided by each region will be different, there is no reason that the physiological markers of these different brain regions need to be the same. Therefore, from our perspective, the task is to look for physiologic markers of conscious versus unconscious processes for each cognitive domain (eg, conscious visual awareness vs unconscious visual sensation) rather than conscious versus unconscious processes of the brain as a whole. Even within a cognitive domain, different experimental paradigms may lead to different conclusions regarding the neural correlates of consciousness due to the specifics of the paradigm and how consciousness is measured and reported (eg, Cohen et al, 2020). If this idea is correct, one fruitful project would be to try to separate different domains of conscious awareness (eg, visual, auditory, olfactory, gustatory, tactile, etc.) and perhaps also differences within a domain (eg, visual perceptual fading, visual perceptual filling in, and visual perception of degraded objects, etc.) and then try to determine the neural correlate of consciousness for each. Some scientists and philosophers would argue that amodal thoughts (ie, abstract thoughts completely separate from sensation) can also be conscious and should be added to this list, whereas other scientists and philosophers would argue that that is not possible (Carruthers, 2015). Fixed and Temporary Brain Lesions One straightforward way to evaluate whether our cortical hypothesis of consciousness is correct is to study individuals with brain lesions resulting from strokes or surgical resections. The advantages of studying such individuals include that their lesions are generally constant over time and, using structural MRI combined with measures of white matter integrity (such as diffusion tensor imaging), the anatomical extent of the lesions and their connections can be readily ascertained. Studies of individuals who happen to have symmetric bilateral lesions may be particularly informative. Such individuals can then be evaluated for different domains of consciousness via structured interviews, questionnaires, and experimental paradigms. A similar approach can use TMS to produce transient brain lesions in various cortical areas. Advantages include that lesions can be produced on demand and, when combined with structural MRI for localization, in the precise area that is wished. Lesions can also be bilateral. Disadvantages include that these lesions are transient and that deep brain structures, such as the thalamus, hippocampus, and insular cortex, are difficult to stimulate. Another promising approach to ascertaining the anatomical regions associated with certain aspects of consciousness is to combine these methods by evaluating an individual with a known, unilateral, fixed brain lesion and then using TMS to temporarily inactivate the reciprocal cortex on the opposite hemisphere. In all of these situations, one of the key questions is whether there are any bilateral cortical lesions that produce complete unconsciousness. Our theory would predict that bilateral lesions would impair specific domains of consciousness, but not other domains of consciousness. The hypotheses of several other researchers (eg, Boly et al, 2017; Michel and Morales, 2020; Odegaard et al, 2017) predict that all domains of consciousness will be impaired when certain bilateral cortical regions are not functioning. Dementia as Bilateral and Multifocal Brain Lesions Although one does not frequently think of individuals with dementia as helpful for addressing issues of focal brain dysfunction, this approach has several advantages. First, many dementias affect brain regions bilaterally. Although the impact of the disease is not generally identical between the two hemispheres, it is often roughly equivalent. For example, individuals with AD may show roughly symmetric bilateral atrophy of the hippocampi, anterior temporal lobes, and parietal lobes. Individuals with posterior cortical atrophy may show roughly symmetric atrophy of the parietal and occipital lobes. Some individuals with behavioral variant frontotemporal dementia show roughly symmetric atrophy of the frontal lobes. But, the real opportunity of studying individuals with cortical dementias comes when the precise degree of atrophy is measured by cortical thickness, for example, using structural MRI. When the degree of atrophy is measured along with consciousness-related variables including structured interviews, questionnaires, experimental paradigms, and potentially EEG, ERP, BIS, PCI, and other measures, which cortical areas are important for specific domains of consciousness can be ascertained. This approach has been used successfully in parcellating different aspects of memory (Wolk et al, 2011). fMRI and PET fMRI and FDG (fluorodeoxyglucose) PET studies are the standard tools that are used to evaluate the function of various brain regions. Such tools have been used to evaluate consciousness and can yield valuable insights when they are combined with appropriate experimental paradigms. Paradigms that contrast conscious versus unconscious perception are key (eg, Levinson et al, 2021; for reviews see Mashour et al, 2020, and Michel and Morales, 2020). Similar paradigms can be used to evaluate other domains of consciousness by contrasting conscious versus unconscious perception in auditory, olfactory, tactile, and perhaps even gustatory modalities. New paradigms can be developed to evaluate conscious versus unconscious perceptions, decisions, and actions. Taken together, such experiments will enable our hypothesis that all cortical regions participate in their own domain of consciousness to be tested. EEG and Derived EEG Measures Such as the BIS, ERPs, and PCI Although a scalp EEG does not provide good spatial localization, it has the advantage of measuring the electrical activity of the cerebral cortex. Raw EEG data, such as the proportion of different EEG rhythms, is already used to determine various levels of consciousness, related both to sleep and to coma stages. Derived EEG measures, such as the BIS, are used by anesthesiologists to measure the depth of anesthesia and reduce intraoperative awareness. Both raw and derived EEG measures can therefore provide a measurement of consciousness that may be informative regarding our and others’ hypotheses about various aspects of consciousness. ERPs are averages of the EEG signal time locked to specific auditory or visual stimuli. ERPs can provide information regarding the precise timing of events. ERPs have also been used to provide information regarding the cortical activity of a variety of different conscious and unconscious brain processes, including those related to attention, perception, working memory, episodic memory, and language. We believe that important insights can be gained when ERPs are measured in experimental paradigms that evaluate conscious versus unconscious brain processes. Global neuronal workspace theory hypothesizes that consciousness can be measured by a late positive ERP component that occurs ∼300 ms after a stimulus, which is often called a P300 or P3 (Mashour et al, 2020). This component is thought to be the same as the old–new parietal effect that in memory research is often termed the late positive component. Using this ERP component to detect conscious awareness is consistent with studies of conscious awareness of remembering (Ally et al, 2008), and with the ∼300-ms delay suggested by our memory theory of consciousness. Other researchers, however, have suggested that the P3b tracks what observers are reporting—not what they are perceiving (Cohen et al, 2020). Regardless of the debate regarding this particular ERP component, we believe that ERPs will be helpful in the search for neural correlates of consciousness. We believe it is likely that different ERP components will reflect different domains of consciousness—one for the conscious perception and one for the action of reporting. PCI can be thought of as a special type of ERP, one that is triggered by TMS pulses rather than by auditory or visual stimuli. Additionally, rather than simply averaging the resultant EEG signal to produce the ERP, the EEG data are evaluated regarding its integration (a measure of how wide the perturbation of the TMS pulses spread) and its information (a measure of how compressed its spatiotemporal pattern of activity can be). A single number is produced. PCI has been shown to be able to reliably differentiate individuals who are awake from those whose consciousness is diminished due to sleep, anesthesia, or coma (Casali et al, 2013). Evaluating MRI, FDG PET, EEG, BIS, ERP, and PCI in Individuals who May Exhibit Impaired Consciousness Lastly, if we are correct that there are individuals who show impairments of consciousness (as described in Disorders of Consciousness), then the prediction naturally follows that measuring aspects of their consciousness using neuroimaging, neurophysiological, and computational derived techniques should yield differences between these individuals and those with normal consciousness. One approach to answering the question of how large a cortical region would need to be in order to support independent consciousness would be to use structural MRI techniques to measure cortical areas in individuals who demonstrate phenomenologically impaired consciousness of a particular modality (such as visual consciousness) due to a brain lesion, degenerative disease, or other pathology. The relevant structural maps and total cortical volumes in these individuals could be compared with those of other individuals who also experienced damage to the relevant brain regions but have intact consciousness in that modality. CONCLUSIONS AND LIMITATIONS Our central claim is that consciousness is essentially and originally part of explicit memory. We experience the world progressing serially because our conscious memory system creates a linear, coherent stream of experiences from our unconscious, parallel brain processes. We believe that our memory theory of consciousness is useful (and perhaps correct) because it helps explain phenomena that have been recognized as long-standing puzzles for previous theories, such as postdictive effects. We have shown how our memory theory of consciousness helps us understand clinical syndromes, experimental studies, and everyday experiences. We have also hypothesized that regions of the entire cerebral cortex are the functional units that make consciousness possible. We are hopeful that this paper provides the framework for a fruitful line of theoretical, observational, and experimental work that can prove or disprove each of the hypotheses that we have put forth. As is implicit in that statement, we are well aware that many—perhaps even most—of the hypotheses that we are proposing may turn out to be incorrect. What we are confident of, however, is that research using the methodologies that we and other researchers have outlined to test our hypotheses will move forward the cognitive neuropsychology, experimental psychology, and cognitive neuroscience of consciousness studies, bringing us closer to understanding the fundamental nature and anatomical basis of consciousness. In addition to the possibility that some of our hypotheses are wrong, we must also acknowledge that our hypotheses only discussed several small aspects of consciousness and ignored many of the most important parts of any complete theory of consciousness, such as the so-called hard problem (how a collection of biological material can produce the subjective experience that we call consciousness). Nonetheless, by careful observation and well-designed experiments, examining this memory theory of consciousness may move us toward a time in the future when such questions will seem quaint, similar to questions about what constitutes the life force that living beings have or how light travels through the ether. Supplementary Material ACKNOWLEDGMENTS The authors thank Ken Paller, PhD (Northwestern University) and the many anonymous reviewers of prior versions of this manuscript for their insightful and helpful comments. For individuals who are interested in the origins of this memory theory of consciousness, please see the supplementary digital content (http://links.lww.com/CBN/A119). Supported in part by a P30 grant (AG072978) from the National Institutes of Health to A.E.B. and a grant (BCS-1823795) from the National Science Foundation to E.A.K. The authors declare no conflicts of interest. Supplemental digital content is available for this article. Direct URL citations are provided in the HTML and PDF versions of this article on the journal's website, www.cogbehavneurol.com. ==== Refs REFERENCES Addis DR Sacchetti DC Ally BA . 2009. Episodic simulation of future events is impaired in mild Alzheimer’s disease. Neuropsychologia. 47 :2660–2671. doi:10.1016/j.neuropsychologia.2009.05.018 19497331 Aglioti S DeSouza JF Goodale MA . 1995. Size-contrast illusions deceive the eye but not the hand. Curr Biol. 5 :679–685. doi:10.1016/s0960-9822(95)00133-3 7552179 Akyürek EG Wolff MJ . 2016. Extended temporal integration in rapid serial visual presentation: attentional control at Lag 1 and beyond. Acta Psychol (Amst). 168 :50–64. doi:10.1016/j.actpsy.2016.04.009 27155801 Alexander RG Venkatakrishnan A Chanovas J . 2021. Microsaccades mediate perceptual alternations in Monet’s “Impression, sunrise.”. Sci Rep. 11 :3612. doi:10.1038/s41598-021-82222-3 33574386 Ally BA Simons JS McKeever JD . 2008. Parietal contributions to recollection: electrophysiological evidence from aging and patients with parietal lesions. Neuropsychologia. 46 :1800–1812. doi:10.1016/j.neuropsychologia.2008.02.026 18402990 American Psychiatric Association. 2013. Diagnostic and Statistical Manual of Mental Disorders, 5th ed. Arlington, Virginia: American Psychiatric Association. American Psychological Association. 2017. What is cognitive behavioral therapy? Accessed July 5, 2021. https://www.apa.org/ptsd-guideline/patients-and-families/cognitive-behavioral Andermane N Bosten JM Seth AK . 2019. Individual differences in change blindness are predicted by the strength and stability of visual representations. Neurosci Conscious. 2019 :niy010. doi:10.1093/nc/niy010 30697440 Avery SN McHugo M Armstrong K . 2020. Habituation during encoding: a new approach to the evaluation of memory deficits in schizophrenia. Schizophr Res. 223 :179–185. doi:10.1016/j.schres.2020.07.007 32736836 Baars BJ . 2005. Global workspace theory of consciousness: toward a cognitive neuroscience of human experience. Prog Brain Res. 150 :45–53. doi:10.1016/S0079-6123(05)50004-9 16186014 Baddeley A Wilson BA . 1994. When implicit learning fails: amnesia and the problem of error elimination. Neuropsychologia. 32 :53–68. doi:10.1016/0028-3932(94)90068-x 8818154 Berkovitch L Dehaene S Gaillard R . 2017. Disruption of conscious access in schizophrenia. Trends Cogn Sci. 21 :878–892. doi:10.1016/j.tics.2017.08.006 28967533 Björnsson G Brülde B Hens K Cutas D Horstkötter D . 2017. Normative responsibilities: Structure and sources. Parental Responsibility in the Context of Neuroscience and Genetics. Cham, Switzerland: Springer International; 13–33. Blackmore S . 2017. Consciousness: A Very Short Introduction, 2nd ed. Oxford, UK: Oxford University Press. Block N . 2011. Perceptual consciousness overflows cognitive access. Trends Cogn Sci. 15 :567–575. doi:10.1016/j.tics.2011.11.001 22078929 Boly M Massimini M Tsuchiya N . 2017. Are the neural correlates of consciousness in the front or in the back of the cerebral cortex? clinical and neuroimaging evidence. J Neurosci. 37 :9603–9613. doi:10.1523/JNEUROSCI.3218-16.2017 28978697 Breitmeyer BG . 2015. Psychophysical “blinding” methods reveal a functional hierarchy of unconscious visual processing. Conscious Cogn. 35 :234–250. doi:10.1016/j.concog.2015.01.012 25704454 Breitmeyer BG Kiefer M Niedeggen M . 2015. Exploring the visual (un)conscious. Conscious Cogn. 35 :178–184. doi:10.1016/j.concog.2015.04.021 26066985 Brogaard B . 2011. Conscious vision for action versus unconscious vision for action. Cogn Sci. 35 :1076–1104. doi:10.1111/j.1551-6709.2011.01171.x 21790744 Brown R Lau H LeDoux JE . 2019. Understanding the higher-order approach to consciousness. Trends Cogn Sci. 23 :754–768. doi:10.1016/j.tics.2019.06.009 31375408 Buckner RL Andrews-Hanna JR Schacter DL . 2008. The brain’s default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci. 1124 :1–38. doi:10.1196/annals.1440.011 18400922 Calvi E Quassolo U Massaia M . 2020. The scent of emotions: a systematic review of human intra- and interspecific chemical communication of emotions. Brain and Behav. 10 :e01585. doi:10.1002/brb3.1585 Carruthers P . 2015. The Centered Mind: What the Science of Working Memory Shows Us About the Nature of Human Thought. Oxford, UK: Oxford University Press. Carruthers P . 2011. The Opacity of Mind: An Integrative Theory of Self-Knowledge. Oxford, UK: Oxford University Press. Casali AG Gosseries O Rosanova M . 2013. A theoretically based index of consciousness independent of sensory processing and behavior. Sci Transl Med. 5 :198ra105. doi:10.1126/scitranslmed.3006294 Cavinato M Rigon J Volpato C . 2012. Preservation of auditory P300-like potentials in cortical deafness. PLoS One. 7 :e29909. doi:10.1371/journal.pone.0029909 22272260 Chalmers DJ . 2010. The Character of Consciousness. Oxford, UK: Oxford University Press. Chen J Jayawardena S Goodale MA . 2015. The effects of shape crowding on grasping. J Vis. 15 :6. doi:10.1167/15.3.6 Churchland PS . 1981. The timing of sensations: reply to Libet. Philos Sci. 48 :492–497. doi:10.1086/289013 Cisek P . 2019. Resynthesizing behavior through phylogenetic refinement. Atten Percept Psychophys. 81 :2265–2287. doi:10.3758/s13414-019-01760-1 31161495 Cleeremans A . 2011. The radical plasticity thesis: how the brain learns to be conscious. Front Psychol. 2 :86. doi:10.3389/fpsyg.2011.00086 21687455 Cohen MA Dennett DC Kanwisher N . 2016. What is the bandwidth of perceptual experience. Trends Cogn Sci. 20 :324–335. doi:10.1016/j.tics.2016.03.006 27105668 Cohen MA Ortego K Kyroudis A . 2020. Distinguishing the neural correlates of perceptual awareness and post-perceptual processing. J Neurosci. 40 :4925–4935. doi:10.1523/JNEUROSCI.0120-20.2020 32409620 Courchesne E Mouton PR Calhoun ME . 2011. Neuron number and size in prefrontal cortex of children with autism. JAMA. 306 :2001–2010. doi:10.1001/jama.2011.1638 22068992 Dafni-Merom A Arzy S . 2020. The radiation of autonoetic consciousness in cognitive neuroscience: a functional neuroanatomy perspective. Neuropsychologia. 143 :107477. doi:10.1016/j.neuropsychologia.2020.107477 32360475 Damasio H Grabowski T Frank R . 1994. The return of Phineas Gage: clues about the brain from the skull of a famous patient. Science. 264 :1102–1105. doi:10.1126/science.8178168 8178168 Damasio H Grabowski TJ Tranel D . 1996. A neural basis for lexical retrieval. Nature. 380 :499–505. doi:10.1038/380499a0 8606767 Das JM Naqvi IA . 2022. Anton syndrome. StatPearls [Internet]. Treasure Island, Florida: StatPearls. Davidson MJ Graafsma IL Tsuchiya N . 2020. A multiple-response frequency-tagging paradigm measures graded changes in consciousness during perceptual filling-in. Neurosci Conscious. 2020 :niaa002. doi:10.1093/nc/niaa002 32296545 de Waal FB . 2019. Fish, mirrors, and a gradualist perspective on self-awareness. PLoS Biol. 17 :e3000112. doi:10.1371/journal.pbio.3000112 30730875 Dennett DC . 1991. Consciousness Explained. Boston, Massachusetts: Little, Brown and Company. Dienes Z Altmann GT Kwan L . 1995. Unconscious knowledge of artificial grammars is applied strategically. J Exp Psychol Learn Mem Cog. 21 :1322–1338. doi:10.1037/0278-7393.21.5.1322 Eichenbaum H Fortin NJ Ergorul C . 2005. Episodic recollection in animals: “If it walks like a duck and quacks like a duck.…” Learn Motiv. 36 :190–207. doi:10.1016/j.lmot.2005.02.006 Elward RL Vargha-Khadem F . 2018. Semantic memory in developmental amnesia. Neurosci Letts. 680 :23–30. doi:10.1016/j.neulet.2018.04.040 29715544 Floody OR Ouda L Porter BA . 2010. Effects of damage to auditory cortex on the discrimination of speech sounds by rats. Physiol Behav. 101 :260–268. doi:10.1016/j.physbeh.2010.05.009 20580729 Gaillard R Dehaene S Adam C . 2009. Converging intracranial markers of conscious access. PLoS Biol. 7 :e61. doi:10.1371/journal.pbio.1000061 19296722 Gandola M Invernizzi P Sedda A . 2012. An anatomical account of somatoparaphrenia. Cortex. 48 :1165–1178. doi:10.1016/j.cortex.2011.06.012 21774922 Ganel T Goodale MA . 2019. Still holding after all these years: an action-perception dissociation in patient DF. Neuropsychologia. 128 :249–254. doi:10.1016/j.neuropsychologia.2017.09.016 28951167 Gazzaniga MS . 2015. Tales From Both Sides of the Brain: A Life in Neuroscience. New York, New York: Ecco/HarperCollins. Geldard FA Sherrick CE . 1972. The cutaneous “rabbit”: a perceptual illusion. Science. 178 :178–179. doi:10.1126/science.178.4057.178 5076909 Geschwind N . 1965. Disconnexion syndromes in animals and man. I Brain. 88 :237–294. doi:10.1093/brain/88.2.237 5318481 Ginsburg S Jablonka E . 2021. Evolutionary transitions in learning and cognition. Philos Trans R Soc Lond B Biol Sci. 376 :20190766. doi:10.1098/rstb.2019.0766 33550955 Goldreich D . 2007. A Bayesian perceptual model replicates the cutaneous rabbit and other tactile spatiotemporal illusions. PLoS One. 2 :e333. doi:10.1371/journal.pone.0000333 17389923 Gould SJ Lewontin RC . 1979. The spandrels of San Marco and the Panglossian paradigm: a critique of the adaptationist programme. Proc R Soc Lond B Biol Sci. 205 :581–598. doi:10.1098/rspb.1979.0086 42062 Grewal D Ku MC Girod SC Roberts LW . 2013. How to recognize and address unconscious bias. The Academic Medicine Handbook: A Guide to Achievement and Fulfillment for Academic Faculty. New York, New York: Springer; 405–412. Herculano-Houzel S . 2009. The human brain in numbers: a linearly scaled-up primate brain. Front Hum Neurosci. 3 :31. doi:10.3389/neuro.09.031.2009 19915731 Herzog MH Drissi-Daoudi L Doerig A . 2020. All in good time: long-lasting postdictive effects reveal discrete perception. Trends Cogn Sci. 24 :826–837. doi:10.1016/j.tics.2020.07.001 32893140 Hodinott-Hill I Thilo KV Cowey A . 2002. Auditory chronostasis: hanging on the telephone. Curr Biol. 12 :1779–1781. doi:10.1016/S0960-9822(02)01219-8 12401174 Holender D . 1986. Semantic activation without conscious identification in dichotic listening, parafoveal vision, and visual masking: a survey and appraisal. Behav Brain Sci. 9 :1–23. doi:10.1017/S0140525X00021269 Horowitz A . 2017. Smelling themselves: dogs investigate their own odours longer when modified in an “olfactory mirror” test. Behav Processes. 143 :17–24. doi:10.1016/j.beproc.2017.08.001 28797909 Hsieh PJ Colas JT Kanwisher N . 2011. Pop-out without awareness: unseen feature singletons capture attention only when top–down attention is available. Psychol Sci. 22 :1220–1226. doi:10.1177/0956797611419302 21852451 Hume D . 1978. A Treatise of Human Nature, 2nd ed. Oxford, UK: Oxford University Press. Hutchinson BT Pammer K Jack B . 2021. Pre-stimulus alpha predicts inattentional blindness. Conscious Cogn. 87 :103034. doi:10.1016/j.concog.2020.103034 33296852 Jacoby LL . 1991. A process-dissociation framework: separating automatic from intentional uses of memory. J Mem Lang. 30 :513–541. doi:10.1016/0749-596X(91)90025-F James W . 1890. The Principles of Psychology. New York, New York: Henry Holt and Company. doi:10.1037/11059-000 Janssen J Oudman E Irish M . 2022. Exploring episodic and semantic contributions to past and future thinking performance in Korsakoff’s syndrome. Mem Cognit. 50 :630–640. doi:10.3758/s13421-021-01262-2 Kaas JH . 2019. The origin and evolution of neocortex: from early mammals to modern humans. Prog Brain Res. 250 :61–81. doi:10.1016/bs.pbr.2019.03.017 31703909 Kahneman D . 2011. Thinking, Fast and Slow. New York, New York: Farrar, Straus & Giroux. Kentridge RW . 2013. Visual attention: bringing the unseen past into view. Curr Biol. 23 :R69–R71. doi:10.1016/j.cub.2012.11.056 23347942 Kentridge RW Nijboer TC Heywood CA . 2008. Attended but unseen: visual attention is not sufficient for visual awareness. Neuropsychologia. 46 :864–869. doi:10.1016/j.neuropsychologia.2007.11.036 18237752 Keuninckx L Cleeremans A . 2021. The color phi phenomenon: not so special, after all. PLoS Comput Biol. 17 :e1009344. doi:10.1371/journal.pcbi.1009344 34478441 Kinney HC Korein J Panigrahy A . 1994. Neuropathological findings in the brain of Karen Ann Quinlan. The role of the thalamus in the persistent vegetative state. N Engl J Med. 330 :1469–1475. doi:10.1056/NEJM199405263302101 8164698 Koch C Massimini M Boly M . 2016. Neural correlates of consciousness: progress and problems. Nat Rev Neurosci. 17 :307–321. doi:10.1038/nrn.2016.22 27094080 Kohda M Hotta T Takeyama T . 2019. If a fish can pass the mark test, what are the implications for consciousness and self-awareness testing in animals? PLoS Biol. 17 :e3000021. doi:10.1371/journal.pbio.3000021 30730878 Kolers PA von Grünau M . 1976. Shape and color in apparent motion. Vision Res. 16 :329–335. doi:10.1016/0042-6989(76)90192-9 941407 Lamme V . 2015. The crack of dawn: perceptual functions and neural mechanisms that mark the transition from unconscious processing to conscious vision. Open MIND. 22 (T ). doi:10.15502/9783958570092 Lamme VA . 2018. Challenges for theories of consciousness: seeing or knowing, the missing ingredient and how to deal with panpsychism. Philos Trans R Soc Lond B Biol Sci. 373 :20170344. doi:10.1098/rstb.2017.0344 30061458 Landry SP Champoux F . 2017. Musicians react faster and are better multisensory integrators. Brain Cogn. 111 :156–162. doi:10.1016/j.bandc.2016.12.001 27978450 Laplane D Degos JD . 1983. Motor neglect. J Neurol Neurosurg Psychiatry. 46 :152–158. doi:10.1136/jnnp.46.2.152 6842219 Lee M Baird B Gosseries O . 2019. Connectivity differences between consciousness and unconsciousness in non-rapid eye movement sleep: a TMS–EEG study. Sci Rep. 9 :5175. doi:10.1038/s41598-019-41274-2 30914674 Levinson M Podvalny E Baete SH . 2021. Cortical and subcortical signatures of conscious object recognition. Nat Commun. 12 :2930. doi:10.1038/s41467-021-23266-x 34006884 Libet B . 1985. Unconscious cerebral initiative and the role of conscious will in voluntary action. Behav Brain Sci. 8 :529–539. doi:10.1017/S0140525X00044903 Libet B Alberts WW Wright EW Jr . 1964. Production of threshold levels of conscious sensation by electrical stimulation of human somatosensory cortex. J Neurophysiol. 27 :546–578. doi:10.1152/jn.1964.27.4.546 14194958 Libet B Wright EW Jr Feinstein B . 1979. Subjective referral of the timing for a conscious sensory experience: a functional role for the somatosensory specific projection system in man. Brain. 102 :193–224. doi:10.1093/brain/102.1.193 427530 Lipowski ZJ . 1989. Delirium in the elderly patient. N Engl J Med. 320 :578–582. doi:10.1056/NEJM198903023200907 2644535 Lou HC Changeux JP Rosenstand A . 2017. Towards a cognitive neuroscience of self-awareness. Neurosci Biobehav Rev. 83 :765–773. doi:10.1016/j.neubiorev.2016.04.004 27079562 Mashour GA Roelfsema P Changeux JP . 2020. Conscious processing and the global neuronal workspace hypothesis. Neuron. 105 :776–798. doi:10.1016/j.neuron.2020.01.026 32135090 Melcher D Kumar D Srinivasan N . 2020. The role of action intentionality and effector in the subjective expansion of temporal duration after saccadic eye movements. Sci Rep. 10 :16922. doi:10.1038/s41598-020-73830-6 33037289 Mesulam MM . 1999. Spatial attention and neglect: parietal, frontal and cingulate contributions to the mental representation and attentional targeting of salient extrapersonal events. Philos Trans R Soc Lond B Biol Sci. 354 :1325–1346. doi:10.1098/rstb.1999.0482 10466154 Mesulam MM Thompson CK Weintraub S . 2015. The Wernicke conundrum and the anatomy of language comprehension in primary progressive aphasia. Brain. 138 :2423–2437. doi:10.1093/brain/awv154 26112340 Metcalfe J . 1986. Premonitions of insight predict impending error. J Exp Psychol Learn Mem Cogn. 12 :623–634. doi:10.1037/0278-7393.12.4.623 Michel M Doerig A . 2021. A new empirical challenge for local theories of consciousness. Mind Lang. Published online March 17. doi:10.1111/mila.12319 Michel M Morales J . 2020. Minority reports: consciousness and the prefrontal cortex. Mind Lang. 35 :493–513. doi:10.1111/mila.12264 Moscovitch M Gazzaniga MS . 1995. Models of consciousness and memory. The Cognitive Neurosciences. Cambridge, Massachusetts: MIT Press; 1341–1356. Moulton ST Kosslyn SM . 2009. Imagining predictions: mental imagery as mental emulation. Philos Trans R Soc Lond B Biol Sci. 364 :1273–1280. doi:10.1098/rstb.2008.0314 19528008 Mundy ME Downing PE Dwyer DM . 2013. A critical role for the hippocampus and perirhinal cortex in perceptual learning of scenes and faces: complementary findings from amnesia and fMRI. J Neurosci. 33 :10490–10502. doi:10.1523/JNEUROSCI.2958-12.2013 23785161 Murray EA Wise SP Baldwin MKL . 2020. The Evolutionary Road to Human Memory. Oxford, UK: Oxford University Press. doi:10.1093/oso/9780198828051.001.0001 Nichols RM Loftus EF . 2019. Who is susceptible in three false memory tasks. Memory. 27 :962–984. doi:10.1080/09658211.2019.1611862 31046606 Nisbett RE Wilson TD . 1977. Telling more than we can know: verbal reports on mental processes. Psychol Rev. 84 :231–259. doi:10.1037/0033-295X.84.3.231 Ochsner KN Knierim K Ludlow DH . 2004. Reflecting upon feelings: an fMRI study of neural systems supporting the attribution of emotion to self and other. J Cogn Neurosci. 16 :1746–1772. doi:10.1162/0898929042947829 15701226 Odegaard B Knight RT Lau H . 2017. Should a few null findings falsify prefrontal theories of conscious perception. J Neurosci. 37 :9593–9602. doi:10.1523/JNEUROSCI.3217-16.2017 28978696 Oliver C Richards C . 2015. Practitioner review: self-injurious behaviour in children with developmental delay. J Child Psychol Psychiatry. 56 :1042–1054. doi:10.1111/jcpp.12425 25916173 Otto TU Ögmen H Herzog MH . 2006. The flight path of the phoenix—the visible trace of invisible elements in human vision. J Vis. 6 :1079–1086. doi:10.1167/6.10.7 17132079 Paller KA Creery JD Schechtman E . 2021. Memory and sleep: how sleep cognition can change the waking mind for the better. Annu Rev Psychol. 72 :123–150. doi:10.1146/annurev-psych-010419-050815 32946325 Persaud N McLeod P Cowey A . 2007. Post-decision wagering objectively measures awareness. Nat Neurosci. 10 :257–261. doi:10.1038/nn1840 17237774 Pilz KS Zimmermann C Scholz J . 2013. Long-lasting visual integration of form, motion, and color as revealed by visual masking. J Vis. 13 :12. doi:10.1167/13.10.12 Pine DS Wise SP Murray EA . 2021. Evolution, emotion, and episodic engagement. Am J Psychiatry. 178 :701–714. doi:10.1176/appi.ajp.2020.20081187 34080889 Pinker S . 1994. The Language Instinct: How the Mind Creates Language. New York, New York: William Morrow. Poppel E Held R Frost D . 1973. Leter: residual visual function after brain wounds involving the central visual pathways in man. Nature. 243 :295–296. doi:10.1038/243295a0 4774871 Posner JB Saper CB Schiff ND . 2019. Plum and Posner’s Diagnosis and Treatment of Stupor and Coma, 5th ed. Oxford, UK: Oxford University Press. Prior H Schwarz A Güntürkün O . 2008. Mirror-induced behavior in the magpie (pica pica): evidence of self-recognition. PLoS Biol. 6 :e202. doi:10.1371/journal.pbio.0060202 18715117 Raby CR Alexis DM Dickinson A . 2007. Planning for the future by western scrub-jays. Nature. 445 :919–921. doi:10.1038/nature05575 17314979 Ramsøy TZ Overgaard M . 2004. Introspection and subliminal perception. Phenomenol Cogn Sci. 3 :1–23. doi:10.1023/B:PHEN.0000041900.30172.e8 Renoult L Irish M Moscovitch M . 2019. From knowing to remembering: the semantic–episodic distinction. Trends Cogn Sci. 23 :1041–1057. doi:10.1016/j.tics.2019.09.008 31672430 Repovs G Baddeley A . 2006. The multi-component model of working memory: explorations in experimental cognitive psychology. Neuroscience. 139 :5–21. doi:10.1016/j.neuroscience.2005.12.061 16517088 Reyna VF Brainerd CJ . 1995. Fuzzy-trace theory: an interim synthesis. Learn Individ Differ. 7 :1–75. doi:10.1016/1041-6080(95)90031-4 Rodriguez N Kintzer E List J . 2021. Implicit bias recognition and management: tailored instruction for faculty. J Natl Med Assoc. 113 :566–575. doi:10.1016/j.jnma.2021.05.003 34140145 Sanders KE Osburn S Paller KA . 2019. Targeted memory reactivation during sleep improves next-day problem solving. Psychol Sci. 30 :1616–1624. doi:10.1177/0956797619873344 31603738 Schacter DL Dodson CS . 2001. Misattribution, false recognition and the sins of memory. Philos Trans R Soc Lond B Biol Sci. 356 :1385–1393. doi:10.1098/rstb.2001.0938 11571030 Schacter DL Addis DR Buckner RL . 2007. Remembering the past to imagine the future: the prospective brain. Nat Rev Neurosci. 8 :657–661. doi:10.1038/nrn2213 17700624 Schacter DL Gilbert DT Nock MK . 2019. Psychology, 5th ed. New York, New York: Worth. Scharnowski F Rüter J Jolij J . 2009. Long-lasting modulation of feature integration by transcranial magnetic stimulation. J Vis. 9 :1.1–10. doi:10.1167/9.6.1 19761316 Schneider E Züst MA Wuethrich S . 2021. Larger capacity for unconscious versus conscious episodic memory. Curr Biol. 31 :3551–3563.e9. doi:10.1016/j.cub.2021.06.012 34256016 Schurger A Sitt JD Dehaene S . 2012. An accumulator model for spontaneous neural activity prior to self-initiated movement. Proc Natl Acad Sci USA. 109 :E2904–E2913. doi:10.1073/pnas.1210467109 22869750 Science Non Fiction. 2016. Hitting a fastball requires more than just quick reactions. Accessed May 30, 2021. https://sciencenonfiction.org/2016/05/23/hitting-a-fastball-requires-more-than-just-quick-reactions/comment-page-1/ Sergent C . 2018. The offline stream of conscious representations. Philos Trans R Soc Lond B Biol Sci. 373 :20170349. doi:10.1098/rstb.2017.0349 30061463 Sergent C Dehaene S . 2004. Is consciousness a gradual phenomenon? Evidence for an all-or-none bifurcation during the attentional blink. Psychol Sci. 15 :720–728. doi:10.1111/j.0956-7976.2004.00748.x 15482443 Sergent C Wyart V Babo-Rebelo M . 2013. Cueing attention after the stimulus is gone can retrospectively trigger conscious perception. Curr Biol. 23 :150–155. doi:10.1016/j.cub.2012.11.047 23246406 Sikora-Wachowicz B Lewandowska K Keresztes A . 2019. False recognition in short-term memory—age-differences in confidence. Front Psychol. 10 :2785. doi:10.3389/fpsyg.2019.02785 31920832 Simons DJ . 2010. Monkeying around with the gorillas in our midst: familiarity with an inattentional–blindness task does not improve the detection of unexpected events. Iperception. 1 :3–6. doi:10.1068/i0386 23397479 Simons JS Peers PV Hwang DY . 2008. Is the parietal lobe necessary for recollection in humans. Neuropsychologia. 46 :1185–1191. doi:10.1016/j.neuropsychologia.2007.07.024 17850832 Smith R Lane RD . 2015. The neural basis of one’s own conscious and unconscious emotional states. Neurosci Biobehav Rev. 57 :1–29. doi:10.1016/j.neubiorev.2015.08.003 26363579 Sperling G . 1960. The information available in brief visual presentations. Psychol Monogr. 74 :1–29. doi:10.1037/h0093759 Stiles NRB Li M Levitan CA . 2018. What you saw is what you will hear: two new illusions with audiovisual postdictive effects. PLoS One. 13 :e0204217. doi:10.1371/journal.pone.0204217 30281629 Suddendorf T Corballis MC . 2007. The evolution of foresight: What is mental time travel, and is it unique to humans. Behav Brain Sci. 30 :299–351. doi:10.1017/S0140525X07001975 17963565 Sy JL Miao HY Marois R . 2021. Conscious perception can be both graded and discrete. J Exp Psychol Gen. 150 :1461–1475. doi:10.1037/xge0001009 33539134 Teasdale G Jennett B . 1974. Assessment of coma and impaired consciousness. A practical scale. Lancet. 2 :81–84. doi:10.1016/s0140-6736(74)91639-0 4136544 Thilo KV Walsh V . 2002. Vision: when the clock appears to stop. Curr Biol. 12 :R135–R137. doi:10.1016/S0960-9822(02)00707-8 11864586 Thomas V Davidson M Zakavi P . 2017. Simulated forward and backward self motion, based on realistic parameters, causes motion induced blindness. Sci Rep. 7 :9767. doi:10.1038/s41598-017-09424-6 28851914 Timmermans B Cleeremans A Overgaard M . 2015. How can we measure awareness? An overview of current methods. Behavioral Methods in Consciousness Research. Oxford, UK: Oxford University Press; 21–46. Tononi G Boly M Massimini M . 2016. Integrated information theory: from consciousness to its physical substrate. Nat Rev Neurosci. 17 :450–461. doi:10.1038/nrn.2016.44 27225071 Tordjman S Celume MP Denis L . 2019. Reframing schizophrenia and autism as bodily self-consciousness disorders leading to a deficit of theory of mind and empathy with social communication impairments. Neurosci Biobehav Rev. 103 :401–413. doi:10.1016/j.neubiorev.2019.04.007 31029711 Trübutschek D Marti S Ojeda A . 2017. A theory of working memory without consciousness or sustained activity. eLife. 6 :e23871. doi:10.7554/eLife.23871 28718763 Tulving E . 1985. Memory and consciousness. Can Psychol. 26 :1–12. doi:10.1037/h0080017 Tulving E . 1999. Memory, Consciousness, and the Brain: The Tallinn Conference. New York, New York: Psychology. Vinding MC Jensen M Overgaard M . 2014. Distinct electrophysiological potentials for intention in action and prior intention for action. Cortex. 50 :86–99. doi:10.1016/j.cortex.2013.09.001 24113032 Vonk J . 2020. A fish eye view of the mirror test. Learn Behav. 48 :193–194. doi:10.3758/s13420-019-00385-6 31209801 Weiskrantz L Warrington EK Sanders MD . 1974. Visual capacity in the hemianopic field following a restricted occipital ablation. Brain. 97 :709–728. doi:10.1093/brain/97.1.709 4434190 Wierzchoń M Asanowicz D Paulewicz B . 2012. Subjective measures of consciousness in artificial grammar learning task. Conscious Cogn. 21 :1141–1153. doi:10.1016/j.concog.2012.05.012 22728143 Wilson JE Mart MF Cunningham C . 2020. Delirium. Nat Rev Dis Primers. 6 :90. doi:10.1038/s41572-020-00223-4 33184265 Wolk DA Dickerson BC , Alzheimer’s Disease Neuroimaging Initiative. 2011. Fractionating verbal episodic memory in Alzheimer’s disease. NeuroImage. 54 :1530–1539. doi:10.1016/j.neuroimage.2010.09.005 20832485 Yaron I Melloni L Pitts M . 2022. The ConTraSt database or analysing and comparing empirical studies of consciousness theories. Nat Hum Behav. 6 :593–604. doi:10.1038/s41562-021-01284-5 35190711 Yonelinas AP . 1994. Receiver-operating characteristics in recognition memory: evidence for a dual-process model. J Exp Psychol Learn Mem Cogn. 20 :1341–1354. doi:10.1037//0278-7393.20.6.1341 7983467 Zhang X Zhaoping L Zhou T . 2012. Neural activities in v1 create a bottom–up saliency map. Neuron. 73 :183–192. doi:10.1016/j.neuron.2011.10.035 22243756 Zheng A Montez DF Marek S . 2021. Parallel hippocampal–parietal circuits for self- and goal-oriented processing. Proc Natl Acad Sci USA. 118 :e2101743118. doi:10.1073/pnas.2101743118 34404728	36178498	PMC9708083	NO-CC CODE	2022-12-07 23:18:50	no	Cogn Behav Neurol. 2022 Oct 3; 35(4):263-297	utf-8	Cogn Behav Neurol	2,022	10.1097/WNN.0000000000000319	oa_other
==== Front Lancet Infect Dis Lancet Infect Dis The Lancet. Infectious Diseases 1473-3099 1474-4457 Elsevier Ltd. S1473-3099(22)00804-0 10.1016/S1473-3099(22)00804-0 Corrections Correction to Lancet Infect Dis 2022; published online Nov 18. https://doi.org/10.1016/S1473-3099(22)00733-2 29 11 2022 29 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcArora P, Kempf A, Nehlmeier I, et al. Omicron sublineage BQ.1.1 resistance to monoclonal antibodies. Lancet Infect Dis 2022; published online Nov 18. https://doi.org/10.1016/S1473-3099(22)00733-2—In this Correspondence, in the figure the number above the fifth column under Pre S1–S2 should have read 679. This correction has been made to the online version as of Nov 29, 2022, and will be made to the printed version.	36460026	PMC9708087	NO-CC CODE	2022-12-01 23:20:28	no	Lancet Infect Dis. 2022 Nov 29; doi: 10.1016/S1473-3099(22)00804-0	utf-8	Lancet Infect Dis	2,022	10.1016/S1473-3099(22)00804-0	oa_other
==== Front Lancet Respir Med Lancet Respir Med The Lancet. Respiratory Medicine 2213-2600 2213-2619 The Author(s). Published by Elsevier Ltd. S2213-2600(22)00360-5 10.1016/S2213-2600(22)00360-5 Articles Pregnancy outcomes after SARS-CoV-2 infection in periods dominated by delta and omicron variants in Scotland: a population-based cohort study Stock Sarah J Prof PhD a†* Moore Emily PhD c† Calvert Clara PhD ad Carruthers Jade BSc c Denny Cheryl MPH c Donaghy Jack MRes c Hillman Sam BSc a Hopcroft Lisa E M PhD ce Hopkins Leanne MSc c Goulding Anna MSc c Lindsay Laura MSc c McLaughlin Terry BSc c Taylor Bob PhD c Auyeung Bonnie PhD b Katikireddi Srinivasa Vittal Prof PhD cf McCowan Colin Prof PhD g Ritchie Lewis D Prof MD h Rudan Igor Prof PhD a Simpson Colin R Prof PhD ci Robertson Chris Prof PhD cj Sheikh Aziz Prof MD a Wood Rachael PhD ac a Usher Institute, University of Edinburgh, Edinburgh b Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh c Public Health Scotland, Edinburgh, UK d Faculty of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK e Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK f MRC/CSO Social & Public Health Sciences Unit, University of Glasgow, Glasgow, UK g School of Medicine, University of St Andrews, St Andrews, UK h Academic Primary Care, University of Aberdeen, Aberdeen, UK i School of Health, Wellington Faculty of Health, Victoria University of Wellington, Wellington, New Zealand j Department of Mathematics and Statistics, University of Strathclyde, Glasgow, UK * Correspondence to: Prof Sarah J Stock, Usher Institute, University of Edinburgh, Edinburgh EH16 4UX, UK † Contributed equally 7 10 2022 12 2022 7 10 2022 10 12 11291136 © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Evidence suggests that the SARS-CoV-2 omicron (B.1·1.529) is associated with lower risks of adverse outcomes than the delta (B.1.617.2) variant among the general population. However, little is known about outcomes after omicron infection in pregnancy. We aimed to assess and compare short-term pregnancy outcomes after SARS-CoV-2 delta and omicron infection in pregnancy. Methods We did a national population-based cohort study of women who had SARS-CoV-2 infection in pregnancy between May 17, 2021, and Jan 31, 2022. The primary maternal outcome was admission to critical care within 21 days of infection or death within 28 days of date of infection. Pregnancy outcomes were preterm birth and stillbirth within 28 days of infection. Neonatal outcomes were death within 28 days of birth, and low Apgar score (<7 of 10, for babies born at term) or neonatal SARS-CoV-2 infection in births occurring within 28 days of maternal infection. We used periods when variants were dominant in the general Scottish population, based on 50% or more of cases being S-gene positive (delta variant, from May 17 to Dec 14, 2021) or S-gene negative (omicron variant, from Dec 15, 2021, to Jan 31, 2022) as surrogates for variant infections. Analyses used logistic regression, adjusting for maternal age, deprivation quintile, ethnicity, weeks of gestation, and vaccination status. Sensitivity analyses included restricting the analysis to those with first confirmed SARS-CoV-2 infection and using periods when delta or omicron had 90% or more predominance. Findings Between May 17, 2021, and Jan 31, 2022, there were 9923 SARS-CoV-2 infections in 9823 pregnancies, in 9817 women in Scotland. Compared with infections in the delta-dominant period, SARS-CoV-2 infections in pregnancy in the omicron-dominant period were associated with lower maternal critical care admission risk (0·3% [13 of 4968] vs 1·8% [89 of 4955]; adjusted odds ratio 0·25, 95% CI 0·14–0·44) and lower preterm birth within 28 days of infection (1·8% [37 of 2048] vs 4·2% [98 of 2338]; 0·57, 95% CI 0·38–0·87). There were no maternal deaths within 28 days of infection. Estimates of low Apgar scores were imprecise due to low numbers (5 [1·2%] of 423 with omicron vs 11 [2·1%] of 528 with delta, adjusted odds ratio 0·72, 0·23–2·32). There were fewer stillbirths in the omicron-dominant period than in the delta-dominant period (4·3 [2 of 462] per 1000 births vs 20·3 [13 of 639] per 1000) and no neonatal deaths during the omicron-dominant period (0 [0 of 460] per 1000 births vs 6·3 [4 of 626] per 1000 births), thus numbers were too small to support adjusted analyses. Rates of neonatal infection were low in births within 28 days of maternal SARS-CoV-2 infection, with 11 cases of neonatal SARS-CoV-2 in the delta-dominant period, and 1 case in the omicron-dominant period. Of the 15 stillbirths, 12 occurred in women who had not received two or more doses of COVID-19 vaccination at the time of SARS-CoV-2 infection in pregnancy. All 12 cases of neonatal SARS-CoV-2 infection occurred in women who had not received two or more doses of vaccine at the time of maternal infection. Findings in sensitivity analyses were similar to those in the main analyses. Interpretation Pregnant women infected with SARS-CoV-2 were substantially less likely to have a preterm birth or maternal critical care admission during the omicron-dominant period than during the delta-dominant period. Funding Wellcome Trust, Tommy's charity, Medical Research Council, UK Research and Innovation, Health Data Research UK, National Core Studies—Data and Connectivity, Public Health Scotland, Scottish Government Health and Social Care, Scottish Government Chief Scientist Office, National Research Scotland. ==== Body pmcIntroduction The SARS-CoV-2 omicron (B.1·1.529) variant rapidly became dominant in Scotland in December, 2021, as it has done in most other high-income countries.1 Emerging evidence suggests that omicron is associated with lower risks of adverse outcomes than previous variants, particularly delta (B.1.617.2), among the general population.2, 3 However, little is known about outcomes after omicron infection in pregnancy, although there have been reports of less severe disease with the omicron variant when compared with the delta variant in a selected population of pregnant women attending prenatal care.4 Research in context Evidence before this study SARS-CoV-2 infection in pregnancy is associated with an increased risk of complications for women (critical care admission and preterm birth) and babies (stillbirth and neonatal mortality). Observational data suggest that the delta (B.1.617.2) variant is associated with particularly severe outcomes, but scarce data exists on the effects of the omicron (B.1.1.529) variant, which is currently dominant in many settings, in pregnancy. We searched PubMed for observational studies including pregnant women exposed to SARS-CoV-2 as of June 8, 2022, with the search terms pregnan* and COVID-19 or SARS-CoV-2, and Delta or Omicron or B.1.1.529 or B.1.617.2, with no language restrictions. A single study from the USA reported less severe maternal disease with omicron variant than with delta in a population of pregnant women attending prenatal care (2641 women). A small study in two maternity units, one in Turkey and one in the UK, found that outcomes of pregnant women admitted to hospital with omicron infection (n=77) were similar to those of pregnant women admitted during the pre-delta period, but the statistical power was low. We searched medRxiv for relevant preprint articles from Jan 01 to June 15, 2022, using the same search terms. We found a preprint study examining outcomes in 1561 women hospitalised with a positive SARS-CoV-2 PCR test up to 7 days before admission or during admission up to 2 days after giving birth, during the period when omicron was dominant. The authors found that the risk of severe respiratory disease among unvaccinated pregnant women admitted with symptomatic SARS-CoV-2 infection during the omicron-dominant period was similar to that observed during the period when the wildtype variant was dominant. Added value of this study To our knowledge, our study is the first to report population-level data of pregnant women exposed to the omicron variant, compared with delta, including those in early pregnancy. We found that the risk of maternal critical care, and COVID-19-related critical care, associated with SARS-CoV-2 infection in pregnancy during the omicron-dominant period was lower than that during the delta-dominant period. We found that SARS-CoV-2 infection in pregnancy during the omicron-dominant period was associated with substantially lower rates of preterm birth in the month after infection, compared with infection during the delta-dominant period. The numbers of stillbirths and neonatal deaths after SARS-CoV-2 infection during the omicron-dominant period were low. Implications of all the available evidence Our national population-based study shows that SARS-CoV-2 infection in pregnancy during the omicron-dominant period was associated with a lower risk of short-term maternal and perinatal adverse outcomes than infection during the delta-dominant period. Pregnant women, health-care professionals, and policy makers should be aware of this information as it can inform mitigation measures, such as guidance on partners attending hospital and provision of assisted reproduction treatments for unvaccinated women. Further studies are needed to assess longer-term pregnancy outcomes and rare outcomes such as stillbirth. Understanding the impact of omicron and any future variants in pregnancy is important to inform appropriate public health measures to prevent infection in pregnant populations. Updated information is also crucial to inform policy around maternity and neonatal care provision, for example, for guidance on partners attending hospital and provision of assisted reproduction treatments for unvaccinated women.5, 6 In this study, we aimed to assess and compare short-term pregnancy-related outcomes after SARS-CoV-2 infection in pregnancy during omicron-dominant and delta-dominant periods, using whole population data from a national cohort.7, 8 Methods Study design and population For this population-based cohort study, we followed a pre-published protocol and statistical analysis plan with methods described in detail previously,7, 8, 9 and we report results according to STROBE guidelines. Briefly, pregnant women were identified for inclusion through the COVID-19 in Pregnancy in Scotland Study (COPS) database.7, 8 COPS is a substudy of EAVE II (Early Pandemic Evaluation and Enhanced Surveillance of COVID-19).10 COPS comprises a dynamic database based on linkage of routine electronic health records, which includes all women aged 11–55 years at the time of conception who were known to be pregnant in Scotland from Jan 1, 2015, to the present date. For the analyses presented here, we included the cohort of women who had SARS-CoV-2 infection in pregnancy during the period between May 17, 2021 (the start of the period in which the delta variant was dominant in Scotland), and Jan 31, 2022. We examined maternal and pregnancy outcomes occurring up to 28 days after SARS-CoV-2 infection in pregnancy. For liveborn babies born within 28 days of maternal infection, we also examined the babies' outcomes up to the end of the neonatal period (up to 28 days of life). To allow for adequate follow-up and outcome ascertainment from health-care records,7, 8 we limited cases to those occurring up to Jan 31, 2022, given source data latency of up to 3 months.8 COPS has ethical approval from the National Research Ethics Service Committee, South East Scotland 02 (REC 12/SS/0201: SA 2) and information governance approval from the Public Benefit and Privacy Panel for Health and Social Care (2021–0116); written consent from participants was not required or obtained. All data were housed within Public Health Scotland and accessed only by approved researchers. Exposures SARS-CoV-2 infection in pregnancy was generally defined as infection diagnosed at any point from the date of conception (2+0 weeks of gestation) to the date the pregnancy ended inclusive (censoring infections occurring at 44+0 weeks of gestation or more, because it was very likely that these women would have completed their pregnancy, but the end of pregnancy record had not yet been received by Public Health Scotland). More specific exposure periods for individual outcomes are described in the appendix (p 2). The date of first positive sample collection was taken as the date of onset of the first episode of SARS-CoV-2 infection. Subsequent episodes were recorded if a positive sample was taken 90 days or more after a first positive sample. Throughout the study period, confirmed infections (maternal or neonatal) were determined by a positive RT-PCR test. From Jan 6, 2022, onwards, confirmed infections were also determined by a positive lateral flow device (provided it was not followed by a negative RT-PCR within 48 h).11 We used period of infection to designate SARS-CoV-2 variants. We defined two study exposure periods indicating times during which delta or omicron variants were dominant. On the basis of the S gene status of positive RT-PCR samples taken from the general population in Scotland (available for 95% of RT-PCR tests during the study period), we defined the delta period as May 17, 2021, to Dec 14, 2021, (>50% of samples were S-gene positive) and the omicron period as Dec 15, 2021, to Jan 31, 2022 (>50% of samples were S-gene negative; appendix p 10). We chose a 50% threshold to define dominance to align to other studies of SARS-CoV-2 variants in pregnancy.4, 12 We tested the impact of using this cutoff in a prespecified sensitivity analysis of infections during periods when 90% of SARS-CoV-2 infections were S-gene positive (delta; June 19, 2021, to Dec 7, 2021) or after the point when 90% of SARS-CoV-2 infections became S-gene negative again (omicron; Dec 27, 2021, to Jan 31, 2022). We were unable to directly use viral sequencing data for our analyses as this was only done on a small proportion (about 20%) of all population tests in Scotland.13 Outcomes Outcome definitions, data sources, and relevant exposure periods are summarised in the appendix (p 2). The primary maternal outcome was admission to critical care or maternal death. Critical care admission was defined as any admission to critical care in which the date of onset of infection occurred during a critical care admission or within the 21 days before admission, using critical care discharge records from the Scottish Intensive Care Society Audit Group (SICSAG). Completed admissions to all intensive care units and general (non-obstetric) high-dependency units across Scotland were included. Completed admissions to the seven obstetric high-dependency units that contribute data to SICSAG (collectively covering about 60% of deliveries in Scotland) were also included. Maternal death was defined as any death within 28 days of the date of infection. We also examined the more stringent outcome of admission to critical care in which COVID-19 was recorded as a primary or secondary diagnostic code (but not coded as an incidental infection), or in which COVID-19 was recorded as the primary cause of maternal death. Pregnancy outcomes included stillbirths within 28 days of infection, and preterm birth (20+0 weeks to 36+6 weeks of gestation) in livebirths within 28 days of infection. We used a broader definition of stillbirth than the one normally used in the UK, including late fetal losses from 20+0 weeks to 23+6 weeks of gestation, as well as statutorily registerable stillbirths from 24+0 weeks onwards. This definition aligns to one used in other countries such as the USA and Australia.14 Late fetal losses share similar causes with stillbirths that occur after 24 weeks of gestation and are included in stakeholder developed core outcome sets for stillbirth prevention.15 Neonatal outcomes examined among liveborn babies born within 28 days of maternal infection included neonatal death (death from any cause within 28 days of birth), low 5-min Apgar score (<7 of 10), and confirmed neonatal infection with SARS-CoV-2 within 28 days of birth in livebirths within 28 days of maternal infection. Examination of Apgar score was restricted to babies born at term (≥37+0 weeks of gestation) as preterm birth is recognised to be strongly associated with low Apgar scores.16 Covariates We included the following potentially confounding variables within our models: maternal age, deprivation quintile (based on the Scottish index of multiple deprivation17), maternal ethnicity (White; South Asian; Black, Caribbean, or African; mixed or other ethnic group, and unknown), week of gestation at time of infection, and vaccination status at time of infection (categorised as unvaccinated [no COVID-19 vaccination before the date of onset of SARS-CoV-2 infection, or with one dose of vaccination ≤21 days before the date of onset]; one dose [one dose of vaccination >21 days before the date of onset of infection, or two doses with the second dose ≤14 days before the date of onset]; two doses [two doses with the second dose >14 days before infection, or three doses with the third dose ≤14 days before the date of onset]; or three or more doses [three or more doses, with the third dose >14 days before infection]). In a post-hoc additional analysis, we also included clinical vulnerability status (ascertained from the national shielding list maintained by Public Health Scotland for extreme clinical vulnerability18 and general practitioner records for clinical vulnerability19) and smoking status. We were not able to adjust for body-mass index (BMI; which is ascertained from completed birth records) because of imbalances in missing data due to a higher proportion of ongoing pregnancies in the omicron-dominant period. We did not adjust for previous SARS-CoV-2 infection, but we explored the effect of previous infection in a pre-specified sensitivity analysis restricting the cohort to women who had not had a previous confirmed SARS-CoV-2 infection recorded within national records. Statistical analysis Monthly infection rates were calculated as the number of pregnant women infected during the month divided by the number of women with ongoing pregnancies at the start of the month of interest. Infection rates were stratified by trimester at the time of infection. We compared maternal, pregnancy, and neonatal outcomes in the delta and omicron pregnant cohorts using logistic regression, with adjustments for maternal age, deprivation, ethnicity, vaccination status at the time of infection, and gestation at the time of infection (except for in the analysis of Apgar score, as this outcome was only considered in babies born at term and thus was restricted to infections ≥33+0 weeks of gestation). Clustering was used to account for pregnancies with multiple babies (as babies from the same pregnancy are more likely to have the same outcome as each other). Odds ratios (ORs) with 95% CIs were produced for each comparison. In post-hoc additional analyses, we explored a potential interaction between SARS-CoV-2 variant and vaccination status at the time of infection by including an interaction term between these predictors within the models examining critical care admission or death and preterm birth. In response to a suggestion from reviewers on submission of the manuscript to this journal, we also did two additional post-hoc analyses for the outcomes of any critical care admission or death, critical care admission or death due to COVID-19, and preterm birth. Firstly, we restricted the analysis to women who were unvaccinated at the time of infection in pregnancy (ie, had not received a first dose of a COVID-19 vaccine >21 days before infection). Secondly, we included additional covariates in the model to adjust for any potential confounding effect from clinical vulnerability or smoking status. Analyses were done in R, version 3.6.1. We used the package named survey (version 4.1-1) for conditional logistic regression to account for clustering. The code is available online. Role of the funding source The funders had no role in study design, data collection, data analysis, data interpretation, or writing of the report. Results Between May 17, 2021, and Jan 31, 2022, there were 9923 SARS-CoV-2 infections in 9823 pregnancies, in 9817 women in Scotland. Disregarding May and December (the months in which delta was dominant for only part of the month), the monthly infection rate during the delta-dominant period ranged from 1710 per 100 000 pregnant women (November, 2021) to 2658 per 100 000 pregnant women (September, 2021). In the omicron-dominant period (January, 2022) the infection rate was 11 011 per 100 000 pregnancies. The number and rate of SARS-CoV-2 infections confirmed by RT-PCR in pregnant women in Scotland over the study period are shown in figure 1 .Figure 1 SARS-CoV-2 infection in women who were pregnant from May, 2021, to January, 2022 (A) Number of infections. (B) Infection rate per 100 000 pregnant women. In the delta-dominant period, two women were infected in two different pregnancies and eight women were infected twice during the same pregnancy (4955 infections in 4947 pregnancies, in 4945 women). In the much shorter omicron-dominant period, there were no recorded repeat infections (4968 infections in 4968 pregnancies, in 4968 women). However, 96 of the women infected in the omicron-dominant period had been previously infected in pregnancy during the delta-dominant period. Maternal demographics of pregnant women infected with SARS-CoV-2 were similar over the two time periods (table ). Of note, a higher proportion of women with SARS-CoV-2 infection in pregnancy in the omicron-dominant period had received one or more doses of COVID-19 vaccine than in the delta-dominant period, which reflects the rollout of the vaccination programme.Table Characteristics of pregnant women with SARS-CoV-2 infection during pregnancy included in the study Delta-dominant period Omicron-dominant period Infections 4955 4968 Trimester 1 (2+0 to 13+6 weeks of gestation) 1579 (31·9% [30·3–33·4]) 1869 (37·6% [36·1–39·2]) Trimester 2 (14+0 to 27+6 weeks gestation) 1870 (37·7% [36·2–39·3]) 1724 (34·7% [33·2–36·3]) Trimester 3 (≥28+0 weeks gestation) 1506 (30·4% [28·9–32·0]) 1375 (27·7% [26·1–29·2]) Age group, years ≤19 224 (4·5% [3·0–6·0]) 194 (3·9% [2·4–5·4]) 20–24 836 (16·9% [15·4–18·3]) 802 (16·1% [14·7–17·6]) 25–29 1478 (29·8% [28·4–31·3]) 1495 (30·1% [28·6–31·6]) 30–34 1521 (30·7% [29·2–32·2]) 1579 (31·8% [30·3–33·3]) 35–39 747 (15·1% [13·6–16·6]) 754 (15·2% [13·7–16·7]) ≥40 149 (3·0% [1·5–4·5]) 144 (2·9% [1·4–4·4]) Missing 0 0 SIMD quintile 1 (most deprived) 1330 (26·8% [25·4–28·3]) 1241 (25·0% [23·6–26·4]) 2 1155 (23·3% [21·9–24·8]) 1058 (21·3% [19·9–22·8]) 3 892 (18·0% [16·6–19·5]) 891 (17·9% [16·5–19·4]) 4 880 (17·8% [16·3–19·2]) 977 (19·7% [18·2–21·1]) 5 (least deprived) 691 (13·9% [12·5–15·4]) 781 (15·7% [14·3–17·2]) Missing or unknown 7 (0·1% [0–1·6]) 20 (0·4% [0–1·9]) Ethnicity White 4535 (91·5% [90·8–92·2]) 4532 (91·2% [90·5–92·0]) South Asian 133 (2·7% [2·0–3·4]) 129 (2·6% [1·9–3·3]) Black, Caribbean, or African 61 (1·2% [0·5–1·9]) 80 (1·6% [0·9–2·3]) Other or mixed ethnicity 140 (2·8% [2·1–3·5]) 121 (2·4% [1·7–3·2]) Missing or unknown 86 (1·7% [1·0–2·4]) 106 (2·1% [1·4–2·9]) Vaccination status at time of infection Unvaccinated 3006 (60·7% [59·3–62·1]) 1227 (24·7% [23·2–26·2]) One dose 720 (14·5% [13·1–16·0]) 385 (7·7% [6·3–9·2]) Two doses 1198 (24·2% [22·8–25·6]) 2375 (47·8% [46·3–49·3]) Three or more doses 31 (0·6% [0–2·1]) 981 (19·7% [18·3–21·2]) Data are n (% [95% CI]). The delta (B.1.617.2)-dominant period was defined as the period between May 17, 2021, and Dec 14, 2021. The omicron (B.1.1.529)-dominant period was defined as the period between Dec 15, 2021, and Jan 31, 2022. SIMD=Scottish index of multiple deprivation. We observed one maternal death after COVID-19 in pregnancy in the delta-dominant period. However, this occurred more than 28 days after the first SARS-CoV-2-positive test, and thus it is not included in our outcome definition. Therefore, all models for critical care admission or death included only critical care admissions. Compared with the delta-dominant period, SARS-CoV-2 infection in pregnancy during the omicron-dominant period was associated with a reduction in any critical care admission (0·3% [13 of 4968] vs 1·8% [89 of 4955]; adjusted OR 0·25, 95% CI 0·14–0·44) and a reduction in critical care admission with COVID-19 (0·2% [9 of 4968] vs 1·3% [64 of 4955]; 0·29, 0·14–0·60; figure 2 ). There were no maternal deaths within 28 days of infection.Figure 2 Crude and adjusted ORs for all outcomes “Within 28 days” means within 28 days of a positive maternal RT-PCR test. OR=odds ratio. There were 4386 livebirths occurring within 28 days of SARS-CoV-2 infection between 20 and 36+6 weeks of gestation. Infections in the omicron-dominant period were also associated with a reduction in preterm birth within 28 days of infection at 20+0 to 36+6 weeks of gestation (1·8% [37 of 2048] vs 4·2% [98 of 2338]; 0·57, 95% CI 0·38–0·87) compared with infections in the delta-dominant period. Full models for critical care admission and preterm birth are shown in the appendix (p 3). There was imprecision surrounding estimates of low Apgar scores due to low numbers (5 [1·2%] of 423 with omicron vs 11 [2·1%] of 528 with delta; adjusted OR 0·72, 95% CI 0·23–2·32). We observed fewer stillbirths in the omicron-dominant period versus delta-dominant period (4·3 [2 of 462] per 1000 births vs 20·3 [13 of 639] per 1000; crude OR 0·21, 95% CI 0·05–0·95) and no neonatal deaths during the omicron-dominant period (0 [0 of 460] per 1000 births vs 6·3 [4 of 626] per 1000 births), so numbers were too small to support adjusted analyses. Rates of neonatal infection were low in births within 28 days of maternal SARS-CoV-2 infection, with 11 cases of neonatal SARS-CoV-2 after maternal infection in the delta-dominant period (rate of 17·6 per 1000 livebirths within 28 days of maternal infection), and one case of neonatal SARS-CoV-2 after maternal infection in the omicron-dominant period (2·1 per 1000 livebirths). Regarding the sensitivity analyses restricted to women with infections at periods with 90% or more of delta and omicron predominance (appendix pp 4, 11), including women with first infections only (appendix pp 5, 12), or including women who were unvaccinated at the time of SARS-CoV-2 infection in pregnancy (appendix pp 6, 13), the findings were similar to those in the main analyses, although numbers were lower, and thus 95% CIs were wider around estimates. The inclusion of smoking and clinical vulnerability status in models did not materially change the results (appendix pp 7–8, 14). The inclusion of a term for an interaction between variant and vaccination status at the time of infection was not significant (appendix p 9), although it was imprecisely estimated. Discussion We have used whole population data from Scotland to show for the first time, to our knowledge, that the risk of preterm birth in the 28 days after SARS-CoV-2 infection in pregnancy was substantially lower in the omicron-dominant period compared with the delta-dominant period. We also show that the risk of maternal critical care admission was lower after infection in the omicron-dominant period than in the delta-dominant period. These risk reductions persisted after adjustment for maternal age, deprivation, ethnicity, vaccination status, and gestation at the time of infection and were robust to sensitivity analyses. These data suggest that, as in non-pregnant adults, the omicron variant is less commonly associated with severe COVID-19 than the delta variant.13 We saw some evidence of a reduced risk of stillbirth and neonatal deaths in association with infection in the omicron-dominant period compared with the delta-dominant period. However, the numbers of deaths in the omicron-dominant period were very small, and thus we were unable to meaningfully adjust for potential confounding factors in our comparisons. Only three of 15 stillbirths, and no neonatal infections occurred in women who had two or more doses of vaccine at the time of maternal infection. This supports evidence that maternal COVID-19 vaccination is protective against severe pregnancy and neonatal outcomes.9 Our findings on critical care admission after SARS-CoV-2 infection in whole pregnancy and whole population data substantiate those from a 2022 study looking at outcomes of pregnant women with COVID-19 attending prenatal care at a single centre in the USA.4 Another USA hospital-based study found higher rates of asymptomatic COVID-19 in pregnant women admitted to hospital who were COVID-19-positive in the omicron-dominant period versus delta-dominant period, with lower rates of maternal respiratory support use. However, analyses were unadjusted and vaccination rates were higher in women admitted in the omicron-dominant period than in the delta-dominant period.20 We found that the reduction in maternal critical care admissions in association with SARS-CoV-2 infection in the omicron-dominant period persisted even when only first SARS-CoV-2 infections were considered or only unvaccinated women were included in analyses. This suggests that differences were not due to maternal immunity to SARS-CoV-2 and are most likely to be associated with the variant. The key strengths of our study are that we used whole population data, with a prespecified protocol and analysis plan. Our study also had some limitations that need to be considered. Firstly, we did not have sequencing for all samples, so we used the dominant variant in the wider population at the time of SARS-CoV-2 infection as a proxy for variant identification. Secondly, at the time of analysis, the omicron-dominant period was relatively short. We restricted our study period to infections occurring up to the end of January, 2022, to ensure that sufficient time was allowed to reliably ascertain outcome events after infections occurring across the study period.8 Thirdly, we had an insufficient sample size for the adjusted analysis of rare, but important outcomes such as stillbirth and neonatal death. Fourthly, there is the potential for unmeasured or residual confounding. We specified a priori a small number of covariates within models, given that we anticipated a low number of women and babies with outcomes of interest. We adjusted for potential biases arising from group imbalances in maternal age, ethnicity, deprivation quintile, vaccination status, and week of gestation at the time of infection, which are important potential confounders.21 In a post-hoc additional analysis including smoking status and comorbidities, the results were similar to those of the main analysis. However, we could not account for all potential confounders. For example, BMI is ascertained from completed maternity hospital discharge records generated at the end of pregnancy. We were unable to include BMI in our models because of imbalances in missing data, as there were a higher proportion of ongoing pregnancies in the omicron-dominant period. We were also unable to account for seasonal variation in outcomes. Finally, it is possible that there was a change in pregnancy outcomes more generally over the periods studied, independent of SARS-CoV-2 variant circulation. However, this seems unlikely given no such change has been seen in routine surveillance data for pregnancy outcomes such as preterm birth, stillbirth, and Apgar score.22 In conclusion, our data suggest that SARS-CoV-2 infection in pregnancy during the omicron-dominant period was associated with reduced risk of complications compared with the delta-dominant period, with substantially decreased risk for critical care admission and preterm birth. Future work should capture pregnancy outcomes with a longer timeframe than 28 days, and meta-analyses with data from other sources is important to determine effects on rarer outcomes such as stillbirth. Additionally, the development and validation of prediction models to identify women at highest risk of complications from SARS-CoV-2 infection would be clinically useful when sufficient size cohorts allow adequately powered analyses. Data sharing Aggregate data files of infections among pregnant women are available online (https://www.opendata.nhs.scot/organization/health_protection). Patient-level data underlying this article cannot be shared publicly due to data protection and confidentiality requirements. Public Health Scotland and the Chief Medical Officer for Scotland are the data holders for the data used in this study. Data can be made available to approved researchers for analysis after securing relevant permissions from the data holders through the Public Benefit and Privacy Panel. Enquiries regarding data availability should be directed to [email protected]. Declaration of interests AS and CR were members of the Scottish Government's COVID-19 Advisory Group and are members of the New and Emerging Respiratory Virus Threats Advisory Group risk stratification subgroup and the Scottish Government's Committee on Pandemic Preparedness (unremunerated roles). CR is a member of the Scientific Pandemic Influenza Group on Modelling (unremunerated role). AS is a member of AstraZeneca's Thrombotic Thrombocytopenic Advisory Group (unremunerated role). SVK was co-chair of Scottish Government's Expert Reference Group on Ethnicity and COVID-19 (unremunerated role). All other authors declare no competing interests. Supplementary Material Supplementary appendix Acknowledgments Our thanks to the EAVE II Patient Advisory Group and Sands charity for their support. COPS is a substudy of EAVE II, which is funded by the Medical Research Council (MR/R008345/1) with the support of BREATHE—The Health Data Research Hub for Respiratory Health (MC_PC_19004), which is funded through the UK Research and Innovation Industrial Strategy Challenge Fund and delivered through Health Data Research UK. Additional support has been provided through Public Health Scotland and Scottish Government DG Health and Social Care and the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation. COPS has received additional funding from Tommy's charity. SJS is funded by a Wellcome Trust Clinical Career Development Fellowship (209560/Z/17/Z). SVK acknowledges funding from a NRS Senior Clinical Fellowship (SCAF/15/02), the Medical Research Council (MC_UU_00022/2), and the Scottish Government Chief Scientist Office (SPHSU17). Contributors SJS, CC, RW, CR, and AS conceived and designed the study. SJS, EM, CR, and RW drafted the protocol. JC, CD, JD, LEMH, LH, AG, LL, TM, and EM prepared the dataset for analysis. EM, LL, and JC accessed and verified the data and performed data analysis. SJS and EM prepared the first draft of the manuscript. All authors provided critical input to drafts of the manuscript. SJS, CC, RW, and AS gave final approval of the version to be published. SJS and RW acts as guarantors for the study. All authors had the final responsibility to submit for publication. ==== Refs References 1 Sheikh A Kerr S Woolhouse M Severity of omicron variant of concern and effectiveness of vaccine boosters against symptomatic disease in Scotland (EAVE II): a national cohort study with nested test-negative design Lancet Infect Dis 22 2022 959 966 35468332 2 Nyberg T Ferguson NM Nash SG Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study Lancet 399 2022 1303 1312 35305296 3 Madhi SA Kwatra G Myers JE Population immunity and COVID-19 severity with omicron variant in South Africa N Engl J Med 386 2022 1314 1326 35196424 4 Adhikari EH MacDonald L SoRelle JA Morse J Pruszynski J Spong CY COVID-19 cases and disease severity in pregnancy and neonatal positivity associated with delta (B.1.617.2) and omicron (B.1.1.529) variant predominance JAMA 327 2022 1500 1502 35325015 5 Scottish Government Coronavirus (COVID-19): fertility treatment for unvaccinated patients https://www.gov.scot/publications/coronavirus-covid-19-fertility-treatment-for-unvaccinated-patients/ 2022 6 Black M Farre A Gray NM Kynn M Gavine AAS Perinatal experiences during the COVID-19 pandemic in Scotland: exploring the impact of changes in maternity services on women and staff 2022 Public Heath Scotland Edinburgh 7 Stock SJ McAllister D Vasileiou E COVID-19 in Pregnancy in Scotland (COPS): protocol for an observational study using linked Scottish national data BMJ Open 10 2020 e042813 8 Stock SJ Carruthers J Denny C Cohort profile: the COVID-19 in Pregnancy in Scotland (COPS) dynamic cohort of pregnant women to assess effects of viral and vaccine exposures on pregnancy Int J Epidemiol 2022 published online Jan 3. 10.1093/ije/dyab243 9 Stock SJ Carruthers J Calvert C SARS-CoV-2 infection and COVID-19 vaccination rates in pregnant women in Scotland Nat Med 28 2022 504 512 35027756 10 Mulholland RH Vasileiou E Simpson CR Cohort profile: early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II) database Int J Epidemiol 50 2021 1064 1074 34089614 11 Public Health Scotland Further changes to COVID-19 reporting https://publichealthscotland.scot/news/2022/january/further-changes-to-covid-19-reporting/ 2022 12 Vousden N Ramakrishnan R Bunch K Severity of maternal infection and perinatal outcomes during periods of SARS-CoV-2 wildtype, alpha, and delta variant dominance in the UK: prospective cohort study BMJ Med 1 2022 e000053 13 Kerr S, Robertson C, Hillman S, Grange Z, Sullivan C, Sheikh A. Severity of BA.2 variant and vaccine effectiveness against symptomatic disease in Scotland. Lancet Reg Health Eur (in press). 14 Reinebrant HE Leisher SH Coory M Making stillbirths visible: a systematic review of globally reported causes of stillbirth BJOG 125 2018 212 224 29193794 15 Kim BV Aromataris EC Middleton P Development of a core outcome set for interventions to prevent stillbirth Aust N Z J Obstet Gynaecol 61 2021 658 666 34060072 16 Iliodromiti S Mackay DF Smith GC Pell JP Nelson SM Apgar score and the risk of cause-specific infant mortality: a population-based cohort study Lancet 384 2014 1749 1755 25236409 17 Scottish Government Scottish index of multiple deprivation https://www.gov.scot/collections/scottish-index-of-multiple-deprivation-2020/ 18 Public Health Scotland Search criteria for highest risk patients for inclusion to the shielding list https://hpspubsrepo.blob.core.windows.net/hps-website/nss/3008/documents/1_covid-19-search-criteria-highest-risk-patients.pdf 2021 19 Clift AK Coupland CAC Keogh RH Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study BMJ 371 2020 m3731 20 Seasely AR Blanchard CT Arora N Maternal and perinatal outcomes associated with the omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection Obstet Gynecol 140 2022 262 265 35852277 21 Fell DB Dimitris MC Hutcheon JA Guidance for design and analysis of observational studies of fetal and newborn outcomes following COVID-19 vaccination during pregnancy Vaccine 39 2021 1882 1886 33715900 22 Public Health Scotland COVID-19 wider impacts on the health care system Available at https://scotland.shinyapps.io/phs-covid-wider-impact/	36216011	PMC9708088	NO-CC CODE	2022-12-06 23:15:32	no	Lancet Respir Med. 2022 Dec 7; 10(12):1129-1136	utf-8	Lancet Respir Med	2,022	10.1016/S2213-2600(22)00360-5	oa_other
==== Front Lancet Respir Med Lancet Respir Med The Lancet. Respiratory Medicine 2213-2600 2213-2619 Published by Elsevier Ltd. S2213-2600(22)00393-9 10.1016/S2213-2600(22)00393-9 Review Respiratory system mechanics, gas exchange, and outcomes in mechanically ventilated patients with COVID-19-related acute respiratory distress syndrome: a systematic review and meta-analysis Reddy Mallikarjuna Ponnapa FCICM ab* Subramaniam Ashwin FCICM bcde Chua Clara bd Ling Ryan Ruiyang g Anstey Christopher FCICM ij Ramanathan Kollengode MD fh Slutsky Arthur S Prof MD kl Shekar Kiran Prof PhD imno a Department of Intensive Care Medicine, Calvary Hospital, Canberra, ACT, Australia b Department of Intensive Care Medicine, Peninsula Health, Frankston, VIC, Australia c Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, Monash University, Clayton, VIC, Australia d Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC, Australia e Peninsula Clinical School, Monash University, Clayton, VIC, Australia f Department of Surgery, National University of Singapore, Singapore g Yong Loo Lin School of Medicine, National University of Singapore, Singapore h Cardiothoracic Intensive Care Unit, National University Heart Centre, National University Hospital, Singapore i Prince Charles Hospital Northside Clinical Unit, Faculty of Medicine University of Queensland, Brisbane, QLD, Australia j School of Medicine and Dentistry, Griffith University, Gold Coast, QLD, Australia k Keenan Research Centre for Biomedical Science, Li Ka Shing Knowledge Institute, St Michael's Hospital, Unity Health Toronto, Toronto, ON, Canada l Department of Medicine and Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada m Department of Intensive Care Medicine, Bond University, Gold Coast, QLD, Australia n Adult Intensive Care Services and Critical Care Research Group, the Prince Charles Hospital, Brisbane, QLD, Australia o Department of Intensive Care Medicine, Queensland University of Technology, Brisbane, QLD, Australia * Correspondence to: Dr Mallikarjuna Ponnapa Reddy, Department of Intensive Care Medicine, Calvary Hospital, Canberra ACT 2617, Australia 3 11 2022 12 2022 3 11 2022 10 12 11781188 Crown Copyright © 2022 Published by Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The association of respiratory mechanics, particularly respiratory system static compliance (CRS), with severity of hypoxaemia in patients with COVID-19-related acute respiratory distress syndrome (ARDS) has been widely debated, with some studies reporting distinct ARDS phenotypes based on CRS. Ascertaining whether such phenotypes exist is important, because they might indicate the need for ventilation strategies that differ from those used in patients with ARDS due to other causes. In a systematic review and meta-analysis of studies published between Dec 1, 2019, and March 14, 2022, we evaluated respiratory system mechanics, ventilator parameters, gas exchange parameters, and clinical outcomes in patients with COVID-19-related ARDS. Among 11 356 patients in 37 studies, mean reported CRS, measured close to the time of endotracheal intubation, was 35·8 mL/cm H2O (95% CI 33·9–37·8; I2=96·9%, τ2=32·6). Pooled mean CRS was normally distributed. Increasing ARDS severity (assessed by PaO2/FiO2 ratio as mild, moderate, or severe) was associated with decreasing CRS. We found no evidence for distinct CRS-based clinical phenotypes in patients with COVID-19-related ARDS, and we therefore conclude that no change in conventional lung-protective ventilation strategies is warranted. Future studies should explore the personalisation of mechanical ventilation strategies according to factors including respiratory system mechanics and haemodynamic status in patients with ARDS. ==== Body pmcIntroduction Acute respiratory distress syndrome (ARDS) is a complex clinical syndrome with a variety of aetiologies, including infectious and non-infectious precipitating factors. ARDS is characterised by a combination of acute onset, hypoxaemia (ratio of partial pressure of oxygen in arterial blood [PaO2] to fractional inspired oxygen [FiO2] of ≤300 mm Hg), and bilateral pulmonary opacities on chest x-ray or CT that are not fully explained by cardiac failure or volume overload.1 ARDS severity is classified on the basis of the PaO2/FiO2 ratio as mild (>200 to 300 mm Hg), moderate (>100 to 200 mm Hg), or severe (≤100 mm Hg).1 Patients with ARDS usually have low respiratory system compliance, but this factor was excluded from the Berlin definition of ARDS because respiratory mechanics added little predictive value.1 The current protocolised approach to treating ARDS has attracted much scrutiny, especially during the COVID-19 pandemic, with calls being made for a more personalised approach.2 There was an initial suggestion, on the basis of small observational studies of patients with COVID-19-related ARDS, that the severity of hypoxaemia was out of proportion to impairment in respiratory system mechanics.3, 4 Despite meeting the criteria for moderate-to-severe ARDS, these patients were found to have relatively normal respiratory system compliance, and shunt fraction—ie, the percentage of cardiac output circulating through the lungs that is not completely oxygenated—that was out of proportion to non-aerated lung fraction. If this finding proved to be generalisable to the large population of patients with COVID-19-related ARDS, it could have implications for ventilation management, as suggested by others.3, 5, 6 However, these early observations have been challenged by several researchers and commentators.4, 7, 8, 9 It has also been proposed that severe hypoxaemia and high respiratory drive in patients with COVID-19-related and non-COVID-19-related ARDS with otherwise preserved respiratory system static compliance (CRS)—ie, respiratory system compliance when there is no airflow—could mediate ARDS progression via patient self-inflicted lung injury (P-SILI).2, 10 P-SILI refers to the lung injury that can develop from intense inspiratory effort. Although the existence of different phenotypes in patients with COVID-19-related ARDS based on CRS values seems plausible, measured CRS is intricately linked to extent of the aerated lung at the time of measurement, which depends on the timing of endotracheal intubation and invasive mechanical ventilation. Multiple small observational studies have not been able to identify such distinct phenotypes of CRS and reported a unimodal distribution of CRS in COVID-19-related ARDS.8, 11, 12 It is possible that patients with hypoxaemia who were intubated early and placed on mechanical ventilation during the first months of the pandemic, when less invasive respiratory supports were discouraged, could have exhibited high CRS. Subsequently, greater uptake of less invasive supports, less proactive endotracheal intubation, and the advent of disease-modifying therapies, such as corticosteroids, might all have affected the occurrence of the high-CRS phenotype. A 2021 study that used dual-energy CT showed that, in critically ill patients with severe COVID-19-related ARDS, oxygenation impairment and the need for invasive mechanical ventilation were associated with a loss of lung aeration, greater shunt fraction, and the extent of ventilation–perfusion mismatch, which indicates a potential loss of hypoxic vasoconstriction.13 Patients with COVID-19-related ARDS could have varying degrees of CRS, recruitability, and hypoxaemia depending on the extent of ventilation–perfusion mismatch. Key messages • Acute respiratory distress syndrome (ARDS) is typically associated with reduced respiratory system compliance due to loss of surfactant, flooded alveoli, and compressive atelectasis • Reduced lung compliance in ARDS is usually a marker of decreased lung volume, and hence an indication that tidal volume should be reduced to prevent over-distension of healthy lung units; substantially reduced compliance in ARDS is associated with a high risk of lung injury from sheer strain, barotrauma, or volutrauma; lung-protective ventilation with high positive end-expiratory pressure and low tidal volume in patients with poor lung compliance has been shown to decrease lung injury • In the first months of the pandemic, when less invasive strategies for respiratory support were discouraged and disease-modifying therapies such as corticosteroids were not in use, some reports suggested that patients with COVID-19-related ARDS had higher respiratory system static compliance (CRS) measured soon after intubation than that reported for ARDS due to other causes, with potential implications for ventilation management • The suggestion that severity of hypoxaemia is out of proportion to impairment in respiratory system mechanics in patients with COVID-19-related ARDS was not borne out in our systematic review and meta-analysis, in which CRS measured soon after the initiation of invasive mechanical ventilation in patients with COVID-19-related ARDS was normally distributed and similar to CRS reported in clinical trials that recruited patients with conventional ARDS; we did not find evidence for distinct CRS-based clinical phenotypes in patients with COVID-19-related ARDS • Reported CRS in patients with COVID-19-related ARDS decreased as the severity of ARDS increased (assessed by PaO2/FiO2 ratio); positive end-expiratory pressure and tidal volume showed a positive association with CRS, whereas plateau pressure was negatively associated with CRS • Our study suggests that ventilatory strategies used in patients with non-COVID-19-related ARDS should be used in patients with COVID-19-related ARDS until there is evidence to the contrary; future research should focus on how best to individualise ventilatory strategy to the patient's specific respiratory mechanics and haemodynamic status In view of uncertainty about respiratory system mechanics and phenotypes of CRS in patients with COVID-19-related ARDS—and their potential implications for ventilation management and outcomes—we aimed to characterise and evaluate basic respiratory system mechanics, ventilator parameters, gas exchange parameters, and clinical outcomes in a systematic review and meta-analysis of studies of patients with COVID-19-related ARDS. We used the earliest measures of respiratory variables taken (hours to days) after the initiation of invasive mechanical ventilation, because delayed findings would be influenced by disease progression and interventions or therapies administered to patients.9, 11 We discuss the implications of our findings for the management of individuals with COVID-19-related ARDS. Methods The study was conducted and the findings reported in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement (appendix pp 2–3).14 Study selection We carried out a literature search of MEDLINE through PubMed, Embase via Ovid, and the COVID-19 living systematic review on March 14, 2022. Living systematic reviews have been used during the Zika virus epidemic15 and during the COVID-19 pandemic.16 The key search terms “acute respiratory distress syndrome”; “intubate” or “mechanical ventilation”; and “coronavirus” were combined using the Boolean operators “OR” within search strings and using “AND” across search strings (appendix p 11). We included several study types published during the COVID-19 pandemic period (from Dec 1, 2019, to March 14, 2022), including prospective cohort studies, case series with at least five patients, and studies of unspecified design. Studies were included if they involved adult patients (≥18 years) who received mechanical ventilation and if ventilator parameters were reported soon (hours to days) after the initiation of invasive mechanical ventilation. Studies were excluded if they did not present CRS data. Studies with a selected group of patients (eg, patients with moderate or severe ARDS or patients with obesity) or with patients who required special interventions (eg, prone positioning or inhaled nitric oxide) were excluded from the primary analysis. In the case of overlapping patient data (eg, overlapping enrolment period or same hospital) across two or more studies, we included the study with the largest sample size. Data extraction and quality assessment We extracted data for the included studies using a pre-specified datasheet (appendix p 12). Variables in the pre-specified datasheet included study characteristics, demographic data, clinical characteristics, interventions, and outcomes. For each selected observational study, the risk of bias was evaluated by two authors (MPR and CC) independently, using the five domains (selection, sample size, comparability, compliance reporting method, and quality of statistics; appendix p 13) of the modified Newcastle-Ottawa Scale (mNOS), which has a range of scores from 2 to 5.17 The mNOS was used because there were no control groups in the included studies. Certainty of evidence was assessed by four main factors (risk of bias, inconsistency, indirectness, and imprecision)18 using the Grading of Recommendations Assessment, Development and Evaluations (GRADE) approach. The certainty of the evidence was rated from high (ie, we are very confident that the true effect lies close to that of the effect estimate) to very low (ie, we have very little confidence in the effect estimate: the true effect is likely to be substantially different; appendix pp 14–15).18, 19 The screening of studies, data collection, and risk-of-bias assessment were conducted independently and in duplicate by MPR and CC. Conflicts were resolved by consensus or by ASu. Data synthesis Given the substantial heterogeneity in patient demographics and treatment modalities for COVID-19, an inverse-variance random-effects meta-analysis was conducted. When pooling studies for meta-analysis of proportions, a distribution-free random-effects model was applied, using the standard method proposed by DerSimonian and Laird.20 Furthermore, the popular two-step arc-sine transformation described by Freeman and Tukey21 was applied to yield better final approximations of the normal distribution. We assessed the normal distribution of studies using a Shapiro-Wilk test. 95% CIs were computed using the Clopper-Pearson method.22 We assessed the possibility of publication bias via visual inspection of the funnel plot and Egger's test, and corrected for small-study effects using the random-effects trim-and-fill (R 0 estimator) procedure. We performed two sensitivity analyses. Firstly, we excluded studies with higher risks of bias (defined as an mNOS score of <3). Secondly, we repeated the primary meta-analysis using the fixed-effects model. Survival outcomes are presented as pooled proportions, and continuous outcomes are presented as pooled means, each with their respective 95% CI. To explore potential sources of heterogeneity and to examine potential prognostically relevant covariates, we conducted subgroup analyses on the basis of ARDS severity (PaO2/FiO2 ratio of >200–300 mm Hg, >100 to 200 mm Hg, or ≤100 mm Hg), presence of hypercarbia (partial pressure of carbon dioxide in arterial blood [PaCO2] >45 mm Hg vs ≤45 mm Hg) and value of positive end-expiratory pressure (PEEP; >15 cm H2O vs ≤15 cm H2O). The random-effects χ2 test was used to assess differences between subgroups. We also conducted a summary-level meta-regression to investigate the effect of demographic factors, disease factors, and intervention factors on CRS. Demographic factors included mean BMI, average date of patient enrolment (defined as the midpoint between the first and last dates of patient enrolment in the study), disease factors (assessed by PaO2/FiO2 ratio and PaCO2), and intervention factors (PEEP, plateau pressure, driving pressure, and tidal volume [VT]). Subsequent regression results were reported as regression slopes (β) and 95% CIs. We then pooled the following mortality rates: 28-day mortality, intensive care unit [ICU] mortality, in-hospital mortality, and cumulative mortality (defined as mortality at the latest point of recording). We also pooled the ICU length of stay, the hospital length of stay, and ventilator-free days. For continuous variables, means and SDs were derived from the aggregate data as per Luo and colleagues23 and Wan and colleagues.24 Because statistical heterogeneity can be overestimated by I 2 statistics in observational studies, we assessed interstudy variability as part of the assessment of the certainty of evidence outlined by the GRADE approach.25, 26 A post-hoc sensitivity analysis was done on the basis of individual study sample size (≤100 vs >100). All statistical analyses were done with R (version 4.0.2). A nominal p value of less than 0·05 was considered to be statistically significant in our study. The study protocol is registered with PROSPERO, CRD42020226124. Results Of the 3416 references published until March 14, 2022, we assessed 1311 full-text articles for eligibility. We identified 12 095 patients receiving invasive ventilation for COVID-19-related ARDS from 51 observational studies that met our inclusion criteria. Of these studies, 37 unselected ARDS studies, involving a total of 11 356 patients (8861 male patients and 2495 female patients), were used for the primary analysis (figure 1 ). Among 51 studies of COVID-19-related ARDS, only nine had an mNOS score of less than 3; the others had an mNOS score that was 3 or more (23 studies had scores of 3, 11 studies had scores of 4, and eight studies had scores of 5; appendix p 13). The population was predominantly male (9268 [78·9%] of 11 742 patients; 45 studies). The mean BMI of patients was 25·9 kg/m2 (SD 2·45, range 25·3–34·7 kg/m2; appendix pp 18–20). GRADE assessments are illustrated in the appendix (pp 14–15). Ventilator parameters and respiratory variables are shown in the appendix (pp 21–23). Variations in CRS, PaCO2, VT, and PEEP are also shown in the appendix (p 9).Figure 1 Study selection ARDS=acute respiratory distress syndrome. ECMO=extracorporeal membrane oxygenation. Studies were excluded because they were not about patients with COVID-19. The pooled reported CRS in patients from studies of unselected COVID-19-related ARDS was 35·8 mL/cm H2O (95% CI 33·9–37·8; I 2=96·9%, τ2=32·6; high certainty; figure 2 ).27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62 In these studies, CRS dispersion was not significantly different from a normal distribution (Shapiro-Wilk test p=0·92; figure 3 ). The pooled reported CRS in all patients from studies of selected and unselected COVID-19-related ARDS was 34·7 mL/cm H2O (32·8–36·6; 97·2%, 43·6; high certainty; appendix p 5). CRS of patients remained similar (33·9 mL/cm H2O, 32·0–35·9; 98·0%, 37·1) after excluding nine studies with mNOS scores of less than 3. Visual inspection did not reveal any significant asymmetry of the funnel plot (Egger's test p=0·95; appendix p 6). Furthermore, correction of small-study effects using the R 0 estimator procedure did not change the pooled estimate of CRS (34·7 mL/cm H2O, 32·8–36·6).Figure 2 Forest plot showing pooled CRS from studies of unselected patients with COVID-19-related ARDS Data from 37 unselected ARDS studies, with a total of 11 356 patients, are shown. ARDS=acute respiratory distress syndrome. CRS=respiratory system static compliance. Figure 3 Kernel density plot showing the distribution of CRS means CRS means in 37 unselected ARDS studies were normally distributed (Shapiro-Wilk test p=0·92). Pooled mean CRS was 35·8 mL/cm H2O (vertical dashed line). ARDS=acute respiratory distress syndrome. CRS=respiratory system static compliance. The overall pooled mean PaO2/FiO2 ratio of patients in unselected studies was 149·1 mm Hg (95% CI 135·4–162·9; 34 studies). Increasing severity of COVID-19-related ARDS, assessed by PaO2/FiO2, was associated with reduced CRS (pinteraction<0·0001): CRS progressively decreased from 39·3 mL/cm H2O (95% CI 36·6–42·0) in patients with mild ARDS, to 34·9 mL/cm H2O (32·8–36·9) in patients with moderate ARDS, and 27·3 mL/cm H2O (23·3–31·2) in patients with severe ARDS (table 1 , figure 4 ).63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75 There was no significant association between CRS and PaCO2 (31·5 [95% CI 27·7–35·4] in patients with PaCO2 >45 mm Hg vs 36·5 [33·3–39·6] in those with PaCO2 ≤45 mm Hg; pinteraction=0·052; table 1) or between CRS and PEEP (35·2 [95% CI 27·6–42·8] in patients with PEEP >15 cm H2O vs 34·2 [32·3–36·2] in those with PEEP ≤15 cm H2O; pinteraction=0·80; table 1). Further details of the association between CRS and PEEP are provided in the appendix (pp 8–9).Table 1 Interaction of CRS with the severity of ARDS, the presence of hypercarbia, and the value of PEEP Number of studies Number of patients Pooled estimate of CRS, mL/cm H2O (95% CI) Heterogeneity and between-studies variance PaO2/FiO2 ratio (pinteraction<0·0001) >200 to 300 mm Hg 3 154 39·3 (36·6–42·0) I2=43·4%, τ2=2·4 >100 to 200 mm Hg 38 11 142 34·9 (32·8–36·9) I2=97·0%, τ2=37·9 ≤100 mm Hg 6 471 27·3 (23·3–31·2) I2=84·3%, τ2=17·7 PaCO2 (pinteraction=0·052) >45 mm Hg 16 2814 31·5 (27·7–35·4) I2=98·2%, τ2=56·6 ≤45 mm Hg 13 7738 36·5 (33·3–39·6) I2=97·8%, τ2=31·7 PEEP (pinteraction=0·80) >15 cm H2O 7 410 35·2 (27·6–42·8) I2=92·3%, τ2=95·5 ≤15 cm H2O 41 11 685 34·2 (32·3–36·2) I2=97·6%, τ2=37·9 Severity of ARDS was assessed using the PaO2/FiO2 ratio (mild, >200 to 300 mm Hg; moderate, >100 to 200 mm Hg; or severe, ≤100 mm Hg). The presence of hypercarbia was assessed using PaCO2 (>45 mm Hg vs ≤45 mm Hg). PEEP values were high or low (>15 cm H2O vs ≤15 cm H2O) compared with patients with mild ARDS (PaO2/FiO2 ratio >200 to 300 mm Hg). ARDS=acute respiratory distress syndrome. CRS=respiratory system static compliance. FiO2=fractional inspired oxygen. PaCO2=partial pressure of carbon dioxide in arterial blood. PaO2=partial pressure of oxygen in arterial blood. PEEP=positive end-expiratory pressure. Figure 4 Forest plot comparing mean CRS of each study with the severity of ARDS Data from 47 of the 51 studies of patients with COVID-19-related ARDS are shown, with severity of ARDS assessed by the mean PaO2/FiO2 ratio of each study. ARDS=acute respiratory distress syndrome. CRS=respiratory system static compliance. FiO2=fractional inspired oxygen. PaO2=partial pressure of oxygen in arterial blood. The complex interactions of CRS with PaO2/FiO2 ratio and PEEP are shown in figure 5A ; the interactions of CRS with VT and PEEP are shown in figure 5B. In patients with moderate-to-severe ARDS (PaO2/FiO2 ratio ≤200 mm Hg), increased PEEP was associated with higher compliance than in patients with mild ARDS (PaO2/FiO2 ratio >200 to 300 mm Hg).Figure 5 Three-dimensional plots showing the complex interactions between CRS and related variables (A) Interactions between CRS, PaO2/FiO2 ratio, and PEEP. As oxygenation worsened (ie, PaO2/FiO2 ratio decreased), CRS also decreased. As PEEP increased, CRS also increased. (B) Interactions between CRS, VT, and PEEP. CRS increased with an increase in VT or PEEP. CRS=respiratory system static compliance. FiO2=fractional inspired oxygen. PaO2=partial pressure of oxygen in arterial blood. PEEP=positive end-expiratory pressure. VT=tidal volume. In univariable meta-regression analyses, there was a statistically significant positive correlation between CRS (the dependent variable) and PaO2/FiO2 ratio (β=0·06, 95% CI 0·02 to 0·11), between CRS and PEEP (0·88, 0·07 to 1·68), and between CRS and VT (6·28, 3·37 to 9·19). CRS had a statistically significant negative correlation with driving pressure (–2·49, –3·12 to –1·86), with plateau pressure (–0·77, –1·53 to –0·02), and with PaCO2 (–0·39, –0·71 to –0·07). There was no significant correlation between CRS and BMI (–0·02, –1·09 to 1·05) or between CRS and average date of patient enrolment (–0·005, –0·04 to 0·03). The results of the meta-regression analyses are presented in table 2 and in the appendix (p 9).Table 2 Univariable meta-regression analysis with CRS as the dependent variable Number of studies Regression coefficient, β (95% CI) p value PaO2/FiO2ratio (mm Hg) 47 0·06 (0·02 to 0·11) 0·0070 PaCO2(mm Hg) 29 −0·39 (−0·71 to −0·07) 0·018 PEEP (cm H2O) 48 0·88 (0·07 to 1·68) 0·033 VT (mL) 31 6·28 (3·37 to 9·19) <0·0001 Driving pressure (cm H2O) 36 −2·49 (−3·12 to −1·86) <0·0001 Plateau pressure (cm H2O) 36 −0·77 (−1·53 to −0·02) 0·045 BMI (kg/m2) 30 −0·02 (−1·09 to 1·05) 0·97 Average date of patient enrolment (days from Jan 1, 2020) 47 −0·005 (−0·04 to 0·03) 0·76 * Average date of patient enrolment was calculated by taking the midpoint of the first and final dates of patient enrolment. ARDS=acute respiratory distress syndrome. CRS=respiratory system static compliance. FiO2=fractional inspired oxygen. PaCO2=partial pressure of carbon dioxide in arterial blood. PaO2=partial pressure of oxygen in arterial blood. PEEP=positive end-expiratory pressure. VT=tidal volume. The pooled mean reported ICU mortality was 35·7% (95% CI 29·4–42·2; I 2=97%, τ2=41·1; moderate certainty; 27 studies), whereas the pooled in-hospital mortality was 39·1% (32·6–45·8; moderate certainty; 14 studies). The overall pooled 28-day mortality was 43·2% (32·6–54·1; moderate certainty; 13 studies). Pooled cumulative mortality was 40·3% (35·1–45·6; moderate certainty; 43 studies). CRS had no significant correlation with cumulative morality (β=–0·005, 95% CI –0·01 to 0·002). The pooled mean ICU length of stay was 19 days (95% CI 15–22; high certainty; 25 studies), whereas the pooled mean hospital length of stay was 28 days (23–34; high certainty; 13 studies). The pooled mean ventilator-free days (up to day 28) was 5·2 days (2·9–7·6; high certainty; seven studies). The appendix shows the results for secondary outcomes (p 24). Post-hoc sensitivity analysis conducted on the basis of the individual study sample size (≤100 patients in 31 studies vs >100 patients in 20 studies) found no difference in CRS (34·6 mL/cm H2O [95% CI 31·8–37·3; I 2=91·9%, τ2=55·5] for sample ≤100 vs 35·0 mL/cm H2O [32·4–37·5; 98·5%, 32·5] for sample >100; appendix p 29). Another post-hoc sensitivity analysis using the fixed-effects model yielded a value that was similar to that of the random-effects model (CRS 34·1 mL/cm H2O, 33·9–34·3; 97·0%, 43·6; appendix p 10). Discussion In this systematic review and meta-analysis, we evaluated respiratory mechanics, gas exchange, and outcomes in a very large group of mechanically ventilated patients with COVID-19-related ARDS. Our main findings, based on data from 11 356 patients in 37 unselected studies, were as follows: pooled mean CRS, measured close to the time of endotracheal intubation, was normally distributed; CRS decreased progressively with increasing severity of ARDS (assessed by decreasing PaO2/FiO2 ratio); higher PEEP and higher VT were associated with greater CRS; and higher plateau pressure and driving pressure were associated with lower CRS. Respiratory system mechanics in COVID-19-related ARDS We found that pooled mean CRS, measured close to the time of the initiation of invasive mechanical ventilation, was 35·8 mL/cm H2O (95% CI 33·9–37·8) across 37 unselected studies and the reported mean CRS across the 51 studies ranged from 22·7 to 54·3 mL/cm H2O. In comparison, the reported mean CRS in landmark ARDS clinical trials published between 2004 and 2017 that included patients with all severities of ARDS, or moderate-to-severe ARDS, ranged from 23·2 to 41·0 mL/cm H2O.76, 77, 78, 79, 80, 81, 82, 83, 84 These ARDS trials are useful comparators because they applied lung-protective ventilation strategies (ie, low VT and moderate-to-high PEEP) similar to those applied in COVID-19 studies, given that such a strategy is now standard practice. In this setting, studies of patients with COVID-19-related ARDS that reported higher CRS than is typically seen in patients with ARDS due to other causes might have included patients who were treated with an early intubation strategy at the beginning of the COVID-19 pandemic. Non-invasive ventilation and high-flow oxygen ventilation were not preferentially used during the first months of the pandemic due to concerns of viral aerosolisation and resultant safety concerns for health-care workers. As such, patients were intubated at the early stages of the disease process, when CRS was still preserved. In addition, we found that the mean PaO2/FiO2 ratio of patients from unselected COVID-19-related ARDS studies, obtained at the time of CRS measurements, was 149·1 mm Hg (95% CI 135·4–162·9; range 95·3–271·8). In comparison, the range of mean PaO2/FiO2 ratios in non-COVID-19-related ARDS clinical trials was 100–200 mm Hg.76, 77, 78, 79, 80, 81, 82, 83, 84 The range of mean VT values in COVID-19-related ARDS studies was 5·6–6·4 mL/kg, compared with a mean VT range of 5·8–8·6 mL/kg in ARDS clinical trials (appendix pp 31–33). The mean PEEP range in COVID-19 ARDS studies was 6·0–20·1 cm H2O, compared with a range of 7·5–16·1 cm H2O in ARDS clinical trials.76, 77, 78, 79, 80, 81, 82, 83, 84 These data indicate that the suggested existence of distinct CRS-based phenotypes in patients with COVID-19-related ARDS could simply have been based on observations from small datasets during an early phase of the pandemic. Reduction in lung compliance in ARDS is attributed to a decrease in aerated lung volume due to fluid-filled alveoli from inflammatory oedema, decreased surfactant function, and atelectasis. We believe that patients with COVID-19 also have similar pathophysiological changes. However, in addition to the timing of intubation, ARDS severity, and the ventilation strategy applied, factors such as viral mutations, vaccination uptake, and increased use of disease-modifying drugs as the pandemic progressed could all contribute to the measures obtained and could not be controlled for in our analysis. The association of higher PEEP with greater CRS might be related to lung recruitment. There is an ongoing debate about optimal PEEP and lung tissue available for recruitment in COVID-19-related ARDS, and about the routine application of low VT in all patients with ARDS, regardless of respiratory mechanics. Many have argued that the routine application of higher PEEP and low VT in all patients with COVID-19-related ARDS, regardless of their respiratory mechanics, could be detrimental.85, 86 Greater CRS was seen in patients who received higher PEEP, higher VT, or a combination of both. Application of higher PEEP in patients with relatively high CRS might have been driven partly by the severity of hypoxaemia, which suggests a possible dissociation between gas exchange and mechanics. These observations, in a small group of patients, might have led to the CRS-based phenotype hypothesis. It is possible that hypoxaemia and CRS could at times be decoupled, albeit transiently, in patients with ARDS, and this observation might not be specific to COVID-19-related ARDS. Similarly, the higher VT found in patients with greater CRS might indicate that clinicians applied both VT and PEEP—based on an assessment of respiratory mechanics—to maintain safe driving and plateau pressures, instead of undertaking a routine low-VT, high-PEEP approach. Establishing whether either of these approaches was independently detrimental is beyond the scope of our analysis given the scarcity of individual patient data. Higher driving pressures seen in patients with lower CRS, although expected, are challenging to decipher further in the absence of individual patient data and measurements over time. Clinicians might have further optimised PEEP and VT to reduce driving pressure in the patients most severely affected by COVID-19-related ARDS. Personalised ventilation management in ARDS The COVID-19 pandemic has re-kindled the concept that mechanical ventilation should be personalised for every patient according to several factors, including a patient's CRS, PEEP, overall respiratory system mechanics, acid-base balance, and haemodynamic status. Studies during the initial stages of the COVID-19 pandemic suggested the existence of different ARDS phenotypes with either normal CRS 5, 62, 87 or very low CRS.88 Subsequent studies addressed the existence of these COVID-19-related ARDS phenotypes, but the studies were limited by relatively small sample sizes.8, 9, 12 Early in the pandemic, many guidelines across the globe recommended early intubation to minimise the risk of SARS-CoV-2 infection among health-care workers.89, 90, 91, 92 As such, many patients might have been intubated and mechanically ventilated early; alternatively, they might have faced unnecessary delays owing to resource constraints. Either of these scenarios could have influenced measurements of CRS and might have led to potential artifactual clinical phenotypes. Mechanical ventilation supports should be tailored to patient physiology rather than to a pathological condition. Making changes to conventional ventilatory strategies or adopting newer strategies on the basis of scarce evidence in emerging infectious respiratory pathologies can be deleterious to patients. Future studies should explore personalised strategies, which might be informed by multivariable in silico modelling of the dynamic variables related to respiratory mechanics and gas exchange.93 These strategies could be augmented by cardiopulmonary computational models and artificial intelligence, which might provide safer ventilatory approaches in patients with ARDS of any aetiology. Ideally, these approaches would be tested in clinical trials prior to widespread use. Study strengths and limitations Our systematic review and meta-analysis had many strengths. To our knowledge, this is the first systematic review evaluating respiratory characteristics and mechanics of patients with COVID-19-related ARDS. Rigorous search criteria were used, and appropriate statistical analyses were done to evaluate the outcomes. Of note, 42 of 51 COVID-19-related ARDS studies were of acceptable quality, with an mNOS score that was greater than 3. In addition, our meta-analysis summarised data from more than 2 years across the COVID-19 pandemic. Through this analysis, we can derive a broader, longitudinal view of how respiratory mechanics change in patients who receive mechanical ventilation for COVID-19, which other studies have not been able to investigate. Furthermore, appropriate sensitivity analyses were conducted, and the GRADE approach was used to rate the certainty of the evidence, allowing us to communicate our findings in an efficient and standardised manner. However, some limitations of our study need to be acknowledged. First, there was substantial heterogeneity, not only in patient management with regard to mechanical ventilation, but also in the way and time of reporting of the analysed variables in the included studies. Variable effects of the pandemic on different countries, and variable resource availability during the COVID-19 era, might understandably have further influenced the timing of commencement of invasive mechanical ventilation and, therefore, might have affected measured CRS. Nonetheless, the subgroup and meta-regression analyses were able to identify several covariates (eg, PaO2/FiO2 ratio, PEEP, PaCO2, VT, plateau pressure, and driving pressure) that might have influenced CRS, explaining the possible sources of heterogeneity in our analysis. Second, there was wide variation in sample size among studies, ranging from 11 to 2635 patients. This heterogeneity, especially in small studies, could lead to selection bias and could increase the risk of random error.94, 95 However, our sensitivity analysis showed that the study sample size and fixed-effects model did not affect the derived mean CRS. Third, deriving the mean (SD) CRS from studies that reported CRS as median (IQR) using validated methods, despite being a well described practice, might have influenced the distribution pattern of CRS. Fourth, we did not have individual patient data, which would have provided us with a richer analysis, allowing us to investigate patient-level variability and possible factors related to mean CRS. In addition, the absence of individual patient data meant that we could not precisely delineate the association of an individual patient's mechanics with their clinical and functional outcomes. We performed subgroup and meta-regression analyses to look for potential sources of heterogeneity and prognostically relevant study-level covariates. Finally, we did not have large datasets with approximate physiology to comment on phenotypes. Despite these limitations, our analysis was a comprehensive analysis of large sample size and provided an estimate of CRS with a relatively narrow confidence interval. Conclusions and future directions In this large systematic review and meta-analysis, the pooled CRS in patients with COVID-19-related ARDS, measured close to the time of the initiation of invasive mechanical ventilation, was normally distributed. We did not observe any distinct CRS-based clinical phenotypes in patients with COVID-19-related ARDS. Furthermore, the association of greater CRS with higher PEEP or VT, or both, indicates that clinicians might have applied either of these ventilator settings on the basis of respiratory mechanics, instead of using a routine low-VT, high-PEEP approach. Similarly, the association of higher plateau pressure with lower CRS also supports the utility of plateau pressure as a guide to PEEP and VT optimisation in patients with COVID-19-related ARDS. Future studies that use patient-level data should explore the complex inter-relationship and trajectory of respiratory system mechanics, gas exchange, and control of breathing to assess the effect of these factors on clinical outcomes in patients with COVID-19-related ARDS, with a view to developing a personalised and safe approach to ventilation management. Declaration of interests We declare no competing interests. Supplementary Material Supplementary appendix Acknowledgments We thank Suei Nee Wong from the Medical Resource Team at the National University of Singapore Libraries for her input on the search criteria. We acknowledge research support from Metro North Hospital and Health Service and the Canadian Institutes of Health Research. No funding was received for this study. Contributors MPR, ASu, and KS designed the study. MPR, CC, and ASu contributed to the search strategy, screening of articles, data collection, and risk-of-bias assessment. RRL, CA, MPR, CC, ASu, KR, ASS, and KS contributed to the data analysis and interpretation. RRL, CA, MPR, and CC created the tables and figures. MPR drafted the manuscript. ASu, CC, RRL, CA, KR, ASS, and KS contributed to the critical revision of the manuscript for intellectually important content. All authors provided critical conceptual input, interpreted results of the data analysis, and read and approved the final draft of the manuscript. MPR and CC accessed and verified the data. All authors were responsible for the decision to submit the manuscript for publication. ==== Refs References 1 The ARDS Definition Task Force Acute respiratory distress syndrome: the Berlin Definition JAMA 307 2012 2526 2533 22797452 2 Gattinoni L Marini JJ Isn't it time to abandon ARDS? The COVID-19 lesson Crit Care 25 2021 326 34488807 3 Chiumello D Busana M Coppola S Physiological and quantitative CT-scan characterization of COVID-19 and typical ARDS: a matched cohort study Intensive Care Med 46 2020 2187 2196 33089348 4 Goligher EC Ranieri VM Slutsky AS Is severe COVID-19 pneumonia a typical or atypical form of ARDS? And does it matter? Intensive Care Med 47 2021 83 85 33237346 5 Gattinoni L Chiumello D Caironi P COVID-19 pneumonia: different respiratory treatments for different phenotypes? Intensive Care Med 46 2020 1099 1102 32291463 6 Marini JJ Gattinoni L Management of COVID-19 respiratory distress JAMA 323 2020 2329 2330 32329799 7 Estenssoro E Loudet CI Ríos FG Clinical characteristics and outcomes of invasively ventilated patients with COVID-19 in Argentina (SATICOVID): a prospective, multicentre cohort study Lancet Respir Med 9 2021 989 998 34224674 8 Grasselli G Tonetti T Protti A Pathophysiology of COVID-19-associated acute respiratory distress syndrome: a multicentre prospective observational study Lancet Respir Med 8 2020 1201 1208 32861276 9 Bos LDJ Sjoding M Sinha P Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts Lancet Respir Med 9 2021 1377 1386 34653374 10 Brochard L Slutsky A Pesenti A Mechanical ventilation to minimize progression of lung injury in acute respiratory failure Am J Respir Crit Care Med 195 2017 438 442 27626833 11 Ferrando C Suarez-Sipmann F Mellado-Artigas R Clinical features, ventilatory management, and outcome of ARDS caused by COVID-19 are similar to other causes of ARDS Intensive Care Med 46 2020 2200 2211 32728965 12 Vandenbunder B Ehrmann S Piagnerelli M Static compliance of the respiratory system in COVID-19 related ARDS: an international multicenter study Crit Care 25 2021 52 33557868 13 Ball L Robba C Herrmann J Lung distribution of gas and blood volume in critically ill COVID-19 patients: a quantitative dual-energy computed tomography study Crit Care 25 2021 214 34154635 14 Moher D Liberati A Tetzlaff J Altman DG Group P Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement BMJ 339 2009 b2535 15 Counotte MJ Egli-Gany D Riesen M Zika virus infection as a cause of congenital brain abnormalities and Guillain-Barre syndrome: from systematic review to living systematic review F1000Res 7 2018 196 30631437 16 Lim ZJ Subramaniam A Reddy MP Case fatality rates for COVID-19 patients requiring invasive mechanical ventilation: a meta-analysis Am J Respir Crit Care Med 203 2020 54 66 17 Mata DA Ramos MA Bansal N Prevalence of depression and depressive symptoms among resident physicians: a systematic review and meta-analysis JAMA 314 2015 2373 2383 26647259 18 Balshem H Helfand M Schünemann HJ GRADE guidelines: 3. Rating the quality of evidence J Clin Epidemiol 64 2011 401 406 21208779 19 Schünemann H Brożek J Guyatt G Oxman A GRADE handbook https://gdt.gradepro.org/app/handbook/handbook.html#h.9rdbelsnu4iy 2013 20 DerSimonian R Laird N Meta-analysis in clinical trials Control Clin Trials 7 1986 177 188 3802833 21 Miller JJ The inverse of the Freeman-Tukey double arcsine transformation Am Stat 32 1978 138 22 Clopper CJ Pearson ES The use of confidence or fiducial limits illustrated in the case of the binomial Biometrika 26 1934 404 413 23 Luo D Wan X Liu J Tong T Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range Stat Methods Med Res 27 2018 1785 1805 27683581 24 Wan X Wang W Liu J Tong T Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range BMC Med Res Methodol 14 2014 135 25524443 25 Guyatt GH Oxman AD Kunz R GRADE guidelines: 7. Rating the quality of evidence—inconsistency J Clin Epidemiol 64 2011 1294 1302 21803546 26 Iorio A Spencer FA Falavigna M Use of GRADE for assessment of evidence about prognosis: rating confidence in estimates of event rates in broad categories of patients BMJ 350 2015 h870 25775931 27 Pan C Lu C She X Evaluation of positive end-expiratory pressure strategies in patients with coronavirus disease 2019-induced acute respiratory distress syndrome Front Med (Lausanne) 8 2021 637747 28 Chaudhary S Lo KB Matta A Azmaiparashvili Z Benzaquen S Patarroyo-Aponte G Ventilator mechanics and outcomes in critically ill patients with COVID-19 infection Chest 158 2020 A627 29 Lemmers DHL Abu Hilal M Bnà C Pneumomediastinum and subcutaneous emphysema in COVID-19: barotrauma or lung frailty? ERJ Open Res 6 2020 00385-2020. 30 Cummings MJ Baldwin MR Abrams D Epidemiology, clinical course, and outcomes of critically ill adults with COVID-19 in New York City: a prospective cohort study Lancet 395 2020 1763 1770 32442528 31 Mahida RY Chotalia M Alderman J Characterisation and outcomes of ARDS secondary to pneumonia in patients with and without SARS-CoV-2: a single-centre experience BMJ Open Respir Res 7 2020 e000731 32 Schenck EJ Hoffman K Goyal P Respiratory mechanics and gas exchange in COVID-19-associated respiratory failure Ann Am Thorac Soc 17 2020 1158 1161 32432896 33 Chew SY Lee YS Ghimiray D Tan CK Chua GS Characteristics and outcomes of COVID-19 patients with respiratory failure admitted to a “pandemic ready” intensive care unit—lessons from Singapore Ann Acad Med Singap 49 2020 434 448 33000106 34 Yildirim M Kesikli B Talan L Determination of serum zinc and copper levels and their relation with mortality in critically ill patients Intensive Care Med Exp 9 2020 001419 35 Ibarra-Estrada M García-Salas Y Mireles-Cabodevila E Use of airway pressure release ventilation in patients with acute respiratory failure due to coronavirus disease 2019: results of a single-center randomized controlled trial Crit Care Med 50 2021 586 594 36 Baedorf Kassis E Schaefer MS Maley JH Transpulmonary pressure measurements and lung mechanics in patients with early ARDS and SARS-CoV-2 J Crit Care 63 2021 106 112 33676795 37 Caravita S Baratto C Di Marco F Haemodynamic characteristics of COVID-19 patients with acute respiratory distress syndrome requiring mechanical ventilation. An invasive assessment using right heart catheterization Eur J Heart Fail 22 2020 2228 2237 33200458 38 Ferreira JC Ho YL Besen BAMP Protective ventilation and outcomes of critically ill patients with COVID-19: a cohort study Ann Intensive Care 11 2021 92 34097145 39 Lemyze M Courageux N Maladobry T Implications of obesity for the management of severe coronavirus disease 2019 pneumonia Crit Care Med 48 2020 e761 e767 32452889 40 COVID-ICU Group on behalf of the REVA Network and the COVID-ICU Investigators Clinical characteristics and day-90 outcomes of 4244 critically ill adults with COVID-19: a prospective cohort study Intensive Care Med 47 2021 60 73 33211135 41 Schavemaker R Schultz MJ Lagrand WK van Slobbe-Bijlsma ER Serpa Neto A Paulus F Associations of body mass index with ventilation management and clinical outcomes in invasively ventilated patients with ARDS related to COVID-19-insights from the PRoVENT-COVID study J Clin Med 10 2021 1176 42 Bain W Yang H Shah FA COVID-19 versus non-COVID-19 acute respiratory distress syndrome: comparison of demographics, physiologic parameters, inflammatory biomarkers, and clinical outcomes Ann Am Thorac Soc 18 2021 1202 1210 33544045 43 Bavishi AA Mylvaganam RJ Agarwal R Avery RJ Cuttica MJ Timing of intubation in coronavirus disease 2019: a study of ventilator mechanics, imaging, findings, and outcomes Crit Care Explor 3 2021 e0415 34079946 44 Beloncle F Studer A Seegers V Longitudinal changes in compliance, oxygenation and ventilatory ratio in COVID-19 versus non-COVID-19 pulmonary acute respiratory distress syndrome Crit Care 25 2021 248 34266454 45 Wolf M Alladina J Navarrete-Welton A Obesity and critical illness in COVID-19: respiratory pathophysiology Obesity (Silver Spring) 29 2021 870 878 33533193 46 Puah SH Cove ME Phua J Association between lung compliance phenotypes and mortality in COVID-19 patients with acute respiratory distress syndrome Ann Acad Med Singap 50 2021 686 694 34625756 47 Carboni Bisso I Huespe I Lockhart C Clinical characteristics of critically ill patients with COVID-19 Medicina (B Aires) 81 2021 527 535 34453793 48 Torres A Motos A Riera J The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients Crit Care 25 2021 331 34517881 49 Diehl JL Peron N Chocron R Respiratory mechanics and gas exchanges in the early course of COVID-19 ARDS: a hypothesis-generating study Ann Intensive Care 10 2020 95 32676824 50 Gamberini L Tonetti T Spadaro S Factors influencing liberation from mechanical ventilation in coronavirus disease 2019: multicenter observational study in fifteen Italian ICUs J Intensive Care 8 2020 80 33078076 51 Dupuis C Bouadma L de Montmollin E Association between early invasive mechanical ventilation and day-60 mortality in acute hypoxemic respiratory failure related to coronavirus disease-2019 pneumonia Crit Care Explor 3 2021 e0329 33521646 52 Blot M Jacquier M Aho Glele LS CXCL10 could drive longer duration of mechanical ventilation during COVID-19 ARDS Crit Care 24 2020 632 33138839 53 Dreher M Kersten A Bickenbach J The characteristics of 50 hospitalized COVID-19 patients with and without ARDS Dtsch Arztebl Int 117 2020 271 278 32519944 54 Zorbas JS Ho KM Litton E Wibrow B Fysh E Anstey MH Airway pressure release ventilation in mechanically ventilated patients with COVID-19: a multicenter observational study Acute Crit Care 36 2021 143 150 33940775 55 Gibot S Conrad M Courte G Cravoisy A Positive end-expiratory pressure setting in COVID-19-related acute respiratory distress syndrome: comparison between electrical impedance tomography, PEEP/FiO2 tables, and transpulmonary pressure Front Med (Lausanne) 8 2021 720920 56 Abu Sayf A Ouellette D Fadel R Mechanical ventilation and COVID-19: a case-control analysis of clinical characteristics, lung mechanics, and mortality Chest 3 2021 e0377 57 Langer T Brioni M Guzzardella A Prone position in intubated, mechanically ventilated patients with COVID-19: a multi-centric study of more than 1000 patients Crit Care 25 2021 128 33823862 58 Bastos GAN Azambuja AZ Polanczyk CA Clinical characteristics and predictors of mechanical ventilation in patients with COVID-19 hospitalized in southern Brazil Rev Bras Ter Intensiva 32 2020 487 492 33263703 59 Lenka J Chhabria MS Sharma N Clinical characteristics and outcomes of critically ill patients with COVID-19 in a tertiary community hospital in upstate New York J Community Hosp Intern Med Perspect 10 2020 491 500 33194116 60 Fogagnolo A Grasso S Dres M Focus on renal blood flow in mechanically ventilated patients with SARS-CoV-2: a prospective pilot study J Clin Monit Comput 36 2021 1 7 34665392 61 Boscolo A Sella N Lorenzoni G Static compliance and driving pressure are associated with ICU mortality in intubated COVID-19 ARDS Crit Care 25 2021 263 34321047 62 Gattinoni L Quintel M Marini JJ “Less is more” in mechanical ventilation Intensive Care Med 46 2020 780 782 32162029 63 Bagate F Tuffet S Masi P Rescue therapy with inhaled nitric oxide and almitrine in COVID-19 patients with severe acute respiratory distress syndrome Ann Intensive Care 10 2020 151 33150525 64 Diaz RA Graf J Zambrano JM Extracorporeal membrane oxygenation for COVID-19-associated severe acute respiratory distress syndrome in Chile: a nationwide incidence and cohort study Am J Respir Crit Care Med 204 2021 34 43 33823118 65 Fanelli V Giani M Grasselli G Extracorporeal membrane oxygenation for COVID-19 and influenza H1N1 associated acute respiratory distress syndrome: a multicenter retrospective cohort study Crit Care 26 2022 34 35123562 66 Jozwiak M Chiche JD Charpentier J Use of venovenous extracorporeal membrane oxygenation in critically-ill patients with COVID-19 Front Med (Lausanne) 7 2020 614569 67 Li J Fink JB Augustynovich AE Mirza S Kallet RH Dhand R Effects of inhaled epoprostenol and prone positioning in intubated coronavirus disease 2019 patients with refractory hypoxemia Crit Care Explor 2 2020 e0307 33354678 68 Bell J William Pike C Kreisel C Sonti R Cobb N Predicting impact of prone position on oxygenation in mechanically ventilated patients with COVID-19 J Intensive Care Med 37 2022 883 889 35195460 69 Giraud R Legouis D Assouline B Timing of VV-ECMO therapy implementation influences prognosis of COVID-19 patients Physiol Rep 9 2021 e14715 70 Laghlam D Rahoual G Malvy J Estagnasié P Brusset A Squara P Use of almitrine and inhaled nitric oxide in ARDS due to COVID-19 Front Med (Lausanne) 8 2021 655763 71 Lang CN Zotzmann V Schmid B Intensive care resources and 60-day survival of critically-ill COVID-19 patients Cureus 13 2021 e13210 72 Mittermaier M Pickerodt P Kurth F Evaluation of PEEP and prone positioning in early COVID-19 ARDS EClinicalMedicine 28 2020 100579 73 Park J Lee HY Lee J Lee SM Effect of prone positioning on oxygenation and static respiratory system compliance in COVID-19 ARDS vs. non-COVID ARDS Respir Res 22 2021 220 34362368 74 Roldán R Rodriguez S Barriga F Sequential lateral positioning as a new lung recruitment maneuver: an exploratory study in early mechanically ventilated Covid-19 ARDS patients Ann Intensive Care 12 2022 13 35150355 75 Weiss TT Cerda F Scott JB Prone positioning for patients intubated for severe acute respiratory distress syndrome (ARDS) secondary to COVID-19: a retrospective observational cohort study Br J Anaesth 126 2021 48 55 33158500 76 Brower RG Lanken PN MacIntyre N Higher versus lower positive end-expiratory pressures in patients with the acute respiratory distress syndrome N Engl J Med 351 2004 327 336 15269312 77 Cavalcanti AB Suzumura É A Laranjeira LN Effect of lung recruitment and titrated positive end-expiratory pressure (PEEP) vs low peep on mortality in patients with acute respiratory distress syndrome: a randomized clinical trial JAMA 318 2017 1335 1345 28973363 78 Guerin C Gaillard S Lemasson S Effects of systematic prone positioning in hypoxemic acute respiratory failurea randomized controlled trial JAMA 292 2004 2379 2387 15547166 79 Guérin C Reignier J Richard JC Prone positioning in severe acute respiratory distress syndrome N Engl J Med 368 2013 2159 2168 23688302 80 Hodgson CL Cooper DJ Arabi Y Maximal recruitment open lung ventilation in acute respiratory distress syndrome (PHARLAP). A phase II, multicenter randomized controlled clinical trial Am J Respir Crit Care Med 200 2019 1363 1372 31356105 81 Steinberg KP Hudson LD Goodman RB Efficacy and safety of corticosteroids for persistent acute respiratory distress syndrome N Engl J Med 354 2006 1671 1684 16625008 82 Mancebo J Fernández R Blanch L A multicenter trial of prolonged prone ventilation in severe acute respiratory distress syndrome Am J Respir Crit Care Med 173 2006 1233 1239 16556697 83 Mercat A Richard JC Vielle B Positive end-expiratory pressure setting in adults with acute lung injury and acute respiratory distress syndrome: a randomized controlled trial JAMA 299 2008 646 655 18270353 84 Papazian L Forel JM Gacouin A Neuromuscular blockers in early acute respiratory distress syndrome N Engl J Med 363 2010 1107 1116 20843245 85 Pelosi P Ball L Barbas CSV Personalized mechanical ventilation in acute respiratory distress syndrome Crit Care 25 2021 250 34271958 86 Gattinoni L Coppola S Cressoni M Busana M Rossi S Chiumello D COVID-19 does not lead to a “typical” acute respiratory distress syndrome Am J Respir Crit Care Med 201 2020 1299 1300 32228035 87 Tobin MJ Basing respiratory management of COVID-19 on physiological principles Am J Respir Crit Care Med 201 2020 1319 1320 32281885 88 Pan C Chen L Lu C Lung recruitability in COVID-19-associated acute respiratory distress syndrome: a single-center observational study Am J Respir Crit Care Med 201 2020 1294 1297 32200645 89 Zuo M-Z Huang Y-G Ma W-H Expert recommendations for tracheal intubation in critically ill patients with novel coronavirus disease 2019 Chin Med Sci J 35 2020 105 109 32102726 90 Cook TM El-Boghdadly K McGuire B McNarry AF Patel A Higgs A Consensus guidelines for managing the airway in patients with COVID-19: guidelines from the Difficult Airway Society, the Association of Anaesthetists the Intensive Care Society, the Faculty of Intensive Care Medicine and the Royal College of Anaesthetists Anaesthesia 75 2020 785 799 32221970 91 Brown CA 3rd Mosier JM Carlson JN Gibbs MA Pragmatic recommendations for intubating critically ill patients with suspected COVID-19 J Am Coll Emerg Physicians Open 1 2020 80 84 32427182 92 Brewster DJ Chrimes N Do TB Consensus statement: Safe Airway Society principles of airway management and tracheal intubation specific to the COVID-19 adult patient group Med J Aust 212 2020 472 481 32356900 93 Das A Saffaran S Chikhani M In silico modeling of Coronavirus disease 2019 acute respiratory distress syndrome: pathophysiologic insights and potential management implications Crit Care Explor 2 2020 e0202 32984832 94 Pannucci CJ Wilkins EG Identifying and avoiding bias in research Plast Reconstr Surg 126 2010 619 625 20679844 95 Tripepi G Jager KJ Dekker FW Zoccali C selection bias and information bias in clinical research Nephron Clin Pract 115 2010 c94 c99 20407272	36335956	PMC9708089	NO-CC CODE	2022-12-01 23:20:28	no	Lancet Respir Med. 2022 Dec 3; 10(12):1178-1188	utf-8	Lancet Respir Med	2,022	10.1016/S2213-2600(22)00393-9	oa_other
==== Front Lancet HIV Lancet HIV The Lancet. HIV 2405-4704 2352-3018 Published by Elsevier Ltd. S2352-3018(22)00298-3 10.1016/S2352-3018(22)00298-3 Profile BCN Checkpoint—sexual health services in Barcelona Kirby Tony 13 10 2022 12 2022 13 10 2022 9 12 e826e826 © 2022 Published by Elsevier Ltd. 2022 Elsevier Ltd Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. ==== Body pmcBarcelona (BCN) Checkpoint existed as a community organisation for men who have sex with men (MSM) living with HIV before it became a community HIV-testing centre. Cofounders Michael Meulbroek, from the Netherlands, and his husband Ferran Pujol, from Barcelona, Spain, saw the need for community-based testing and opened the centre in 2006. Checkpoint clinics have since opened elsewhere in Spain and Europe, including Athens, Greece, and Lisbon, Portugal. “We wanted the centre to be run by gay men who themselves were living with HIV and could talk positively about sexuality and how they were managing their condition”, explains Meulbroek. BCN Checkpoint became the first clinic in Spain to use new-generation rapid HIV tests, with trained counsellors on hand to manage any diagnoses. The centre has accelerated from 1000 tests per year to the current figure of around 15 000. “Anyone testing positive is referred to a named doctor or nurse at their local HIV hospital service for prescription of antiretroviral medication and will receive further support from us if needed”, says Meulbroek. In 2023, the centre hopes to begin prescribing antiretrovirals for people living with HIV and is already able to prescribe pre-exposure prophylaxis (PrEP) to around 2500 MSM and transgender men and women across Barcelona. Enhanced funding means that since 2015, the centre has been able to offer syphilis and hepatitis C screening, and, since 2019, has been able to increase capacity further so that clients can request full sexual health screening. Pep Coll is the chief physician at BCN Checkpoint and is a consultant in infectious diseases at Germans Trias i Pujol Hospital, Badalona, Spain. On top of seeing and treating patients, he oversees the centre's PrEP programme and says that “as more resources become available, we hope to increase [the number of people accessing PrEP] even further”. BCN Checkpoint aims to detect HIV early, and 60% of positive diagnoses are made within 3 months of infection. Barcelona, like other major European centres, has seen new HIV infections drop sharply thanks to PrEP, treatment as prevention, contact tracing, and detecting infections early in those at high risk. Compared with five or more infections per week in 2015, now BCN Checkpoint can see zero in a week. At the time of writing, BCN Checkpoint is at the forefront of battling the new monkeypox outbreak that affects MSM and often involves a history of group sex and multiple partners over an extended period, combined with recreational drug use (chemsex). In June and July, around ten patients each day were presenting at BCN Checkpoint with monkeypox symptoms, such as anal pain and discharge, fever, and one-to-several genital lesions. Numbers have dropped slightly, partly because the centre and others across Spain are administering vaccines. However, so far this is not close to enough, with only 700 vaccine doses received by BCN Checkpoint in its first delivery. The centre's team is under huge pressure as each case must be dealt with in personal protective equipment and diverts staff from routine STI treatment and surveillance work. Cases must also all be reported to Spain's central ministry of health. Coll believes that “far more vaccines and contact tracing will be key to bringing monkeypox cases down to manageable levels”. The team and two other Spanish centres has published a study of early cases in The Lancet. Coll is also overseeing BCN Checkpoint's involvement in a number of clinical trials, including DISCOVER for PrEP, the MOSAICO HIV vaccine trial, studies on alternative antibiotics for both syphilis and gonorrhoea, a human papillomavirus vaccine study, and work investigating how many HIV-negative MSM are becoming infected with hepatitis C. Today, a large part of BCN Checkpoint's resources are dedicated to helping MSM and trans people having problems with chemsex. The problem was first noticed in 2015, when more and more visitors to BCN Checkpoint talked about addiction to methamphetamine (also known as crystal meth or tina) and other party drugs such as GBL/GBH (γ-butyrolactone/γ-hydroxybutyric acid) and mephedrone. BCN Checkpoint set up a dedicated service to help counsel people about chemsex. In 2015, just 30 people were being treated for serious chemsex addiction, but today that number has increased to 170. During this period, the proportion of MSM practising chemsex has doubled, from 5% to 10%, and the average age of first chemsex experience has dropped from around 30 years to the early 20s. Antonio Gata is the psychologist at BCN Checkpoint who leads the chemsex support services, having initially worked at the centre as a volunteer doing rapid HIV tests. “Visitors to BCN Checkpoint are asked if they are having any chemsex issues, and, if so, an appointment is provided immediately, or no later than 3 days, so we do not lose contact. In therapy, we explore the person's family and work situations, and the issues around loneliness and lack of intimacy that are common to many engaging in chemsex”, explains Gata. “Some MSM are in a deep crisis, feeling alone and isolated. Despite all our interactions through social media today, we have maybe forgotten how to practise intimacy and trust in our relationships. All of these things are connected.”	36243016	PMC9708091	NO-CC CODE	2022-12-01 23:20:28	no	Lancet HIV. 2022 Dec 13; 9(12):e826	utf-8	Lancet HIV	2,022	10.1016/S2352-3018(22)00298-3	oa_other
==== Front J Psychosom Res J Psychosom Res Journal of Psychosomatic Research 0022-3999 1879-1360 The Authors. Published by Elsevier Inc. S0022-3999(22)00389-0 10.1016/j.jpsychores.2022.111104 111104 Article Psychological factors associated with reporting side effects following COVID-19 vaccination: A prospective cohort study (CoVAccS – Wave 3) Smith Louise E. ab⁎ Sim Julius c Sherman Susan M. d Amlôt Richard be Cutts Megan d Dasch Hannah f Sevdalis Nick f Rubin G. James ab a Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, Weston Education Centre, King’s College London, London SE5 9RJ, UK b NIHR Health Protection Research Unit in Emergency Preparedness and Response, Weston Education Centre, King’s College London, London SE5 9RJ, UK c School of Medicine, David Weatherall Building, University Road, Keele University, Staffordshire, ST5 5BG, UK d School of Psychology, Dorothy Hodgkin Building, Keele University, Staffordshire, ST5 5BG, UK e UK Health Security Agency, Chief Scientific Officer’s Group, 17 Smith Square, London, SW1P 3HX, UK f Centre for Implementation Science, NIHR ARC South London, King’s College London, Institute of Psychiatry, Psychology & Neuroscience, De Crespigny Park, London SE5 8AF, UK ⁎ Corresponding author at: Department of Psychological Medicine, Weston Education Centre, Cutcombe Road, London, SE5 9RJ, UK. 30 11 2022 1 2023 30 11 2022 164 111104111104 7 9 2022 24 11 2022 26 11 2022 © 2022 The Authors 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objective To investigate symptom reporting following the first and second COVID-19 vaccine doses, attribution of symptoms to the vaccine, and factors associated with symptom reporting. Methods Prospective cohort study (T1: 13–15 January 2021, T2: 4–15 October 2021). Participants were aged 18 years or older, living in the UK. Personal, clinical, and psychological factors were investigated at T1. Symptoms were reported at T2. We used logistic regression analyses to investigate associations. Results After the first COVID-19 vaccine dose, 74.1% (95% CI 71.4% to 76.7%, n = 762/1028) of participants reported at least one injection-site symptom, while 65.0% (95% CI 62.0% to 67.9%, n = 669/1029) reported at least one other (non-injection-site) symptom. Symptom reporting was associated with being a woman and younger. After the second dose, 52.9% (95% CI 49.8% to 56.0%, n = 532/1005) of participants reported at least one injection-site symptom and 43.7% (95% CI 40.7% to 46.8%, n = 440/1006) reported at least one other (non-injection-site) symptom. Symptom reporting was associated with having reported symptoms after the first dose, having an illness that put one at higher risk of COVID-19 (non-injection-site symptoms only), and not believing that one had enough information about COVID-19 to make an informed decision about vaccination (injection-site symptoms only). Conclusions Women and younger people were more likely to report symptoms from vaccination. People who had reported symptoms from previous doses were also more likely to report symptoms subsequently, although symptom reporting following the second vaccine was lower than following the first vaccine. Few psychological factors were associated with symptom reporting. Keywords Adverse effects COVID-19 Immunization Psychological factors Symptom perception ==== Body pmc1 Introduction Side effects can occur after taking a medication, including being vaccinated. While some side effects may be due to the pharmacological action of the drug, others may arise from the so-called ‘nocebo effect’, a phenomenon whereby the expectation that symptoms will develop becomes self-fulfilling. [1] The role of expectation in later perception of side effects has been well-documented for medications [2,3] and vaccinations. [4] Psychosocial factors may contribute to the nocebo effect; for example, seeing or hearing that a vaccine causes side effects, [5] or holding more negative beliefs about medications. [6,7] Clinical trial data for UK approved COVID-19 vaccines (AstraZeneca, Pfizer-BioNTech, Moderna) indicate that side-effect reporting is lower in older adults. [[8], [9], [10]] However, the association with vaccine dose is less clear cut, with different patterns emerging for the different vaccines. For the AstraZeneca vaccine, side-effect reporting was lower after the second dose than after the first. [8] For the Pfizer-BioNTech vaccine, there was no difference in the percentage of people experiencing local reactions following the first and second dose. [9,11] One UK study investigating symptom reporting on an app (over 627,000 vaccinated app users) found that people who had the Pfizer-BioNTech vaccine reported more systemic effects (e.g. fatigue and headache) after the second dose, compared to the first. [12] The pattern for reporting systemic reactions differed by previous SARS-CoV-2 infection, with those with evidence of previous infection reporting more systemic reactions after the first dose and those with no evidence of previous infection reporting more systemic reactions after the second dose. [11] Reported adverse effects for the Moderna vaccine were more severe following the second than following the first dose. [10] Research investigating symptom reporting following vaccination for COVID-19 has focused on the sociodemographic factors associated with symptom reporting. One US survey (over 19,000 respondents) found that reporting adverse effects was associated with being younger, female, Asian ethnicity (compared to white), having had SARS-CoV-2 before, and it being the second vaccine dose (Moderna and Pfizer-BioNTech). [13] A study of UK app users (over 627,000 respondents) found that women, younger people, and those with previous SARS-CoV-2 infection were more likely to report symptoms (local and systemic, both AstraZeneca and Pfizer-BioNTech), but found no clear trend with vaccine type, dose or comorbidity. [12] Fewer studies have investigated the association between psychological factors and COVID-19 vaccine side-effect reporting. Where they have, studies have focused on the influence of seeing or hearing about symptom reporting in others. For example, one study found that seeing more social media posts about COVID-19 vaccine side effects and severity of impressions from news stories and personal contacts were associated with later experiencing side effects. [14] Another study found that following the reporting of severe adverse events in the media, reporting to the national Centre for Adverse Reactions Monitoring (New Zealand) for effects mentioned in the media increased, whereas there was no change in the reporting of adverse events that were not specifically mentioned. [15] One study, investigating other psychological variables, found evidence for an association between COVID-19 vaccine side-effect reports and higher side-effect expectations, greater worry about COVID-19, and depressive symptoms. [16] At the start of the COVID-19 vaccine rollout in the UK (January 2021), we conducted an online cross-sectional survey investigating perceptions about COVID-19 and vaccination, vaccination intention and side-effect expectations. [17] We found that only 9% of participants thought that side effects were likely (58% judged them uncertain, 33% judged them unlikely), while clinical trial data indicated that rates experienced were substantially higher (injection-site symptoms up to 89%, non-injection-site symptoms up to 70%). Higher expectations that one would experience side effects from a COVID-19 vaccine were associated with older age, being clinically extremely vulnerable to COVID-19, being afraid of needles, perceiving lower social norms for COVID-19 vaccination, lower perceived necessity and safety of COVID-19 vaccination, and not thinking that one had enough information about COVID-19 vaccination or illness. In this study we used results from a follow-up survey, conducted in October 2021, after all UK adults had been offered two doses of the vaccine, [18] to investigate prevalence of injection- and non-injection-site symptoms following COVID-19 vaccination and their attribution to the vaccine. We investigated associations between symptom reporting (injection- and non-injection-site) and personal, clinical, psychological, and contextual factors following the first and second COVID-19 vaccine doses separately. 2 Methods 2.1 Design Prospective cohort study conducted at two timepoints, with participants who completed the first survey (T1, 13–15 January 2021, n = 1500) also completing the second survey (T2, 4–15 October 2021, n = 1148, response rate 76.5%). For more details on the study design, see Smith et al. [18] Results of analyses investigating factors associated with side-effect expectations (measured at T1) have been published elsewhere. [17] 2.2 Participants People were eligible for the study if they lived in the UK, were aged 18 years or older, and had not completed our previous survey (conducted in July 2020) due to similarities in questionnaire materials. [19] Participants were recruited to the study from Prolific's online research panel (people who have signed up to take part in online surveys). Quota sampling was used, based on age, sex, and ethnicity, so that participant characteristics in these respects were similar to those of the UK population. Consent was provided before starting the survey. Participants were paid £2 per survey upon completion. For this study, we excluded people who reported having been vaccinated for COVID-19 at T1 (n = 30). As the outcome measures are symptom reporting following vaccination, only participants who reported that they had been vaccinated for COVID-19 (one or two doses) were selected (first vaccine dose, n = 1034; second vaccine dose, n = 1009). 2.3 Measures Survey materials are available online. [20,21] 2.3.1 Outcome measures We measured symptom reporting and attribution to the vaccine at T2 using items based on the Side Effect Attribution Scale. [22] Participants were asked if they experienced any symptoms (from a list of thirteen: seven injection-site symptoms, six non-injection-site symptoms; symptoms based on Menni et al. [12]) in the seven days after they received a COVID-19 vaccine. We asked participants about each symptom on a six-point scale (“no”, “yes, but definitely not a side effect”, “yes, but probably not a side effect”, “yes, but unsure whether a side effect”, “yes, and probably a side effect” and “yes, and definitely a side effect”). Participants were categorized as having injection-site symptoms if they reported experiencing any of the seven injection-site symptoms after vaccination. Participants were categorized as having non-injection-site symptoms if they reported experiencing any of the six non-injection-site symptoms after vaccination. We asked about participants' first and second dose of the vaccine separately. 2.3.2 Personal and clinical factors Participants were asked for their age, gender, and ethnicity at T1. Participants were also asked whether they thought they had previously had COVID-19 or currently had it at T1. Answers were recoded into a binary variable (“definitely” and “probably” had it or have it now vs “definitely” and “probably” not had it and do not have it now; we coded “don't know” and “prefer not to say” as missing). At T2, participants were asked whether they had a chronic illness. We recoded this into a binary variable indicating whether or not the participant was at high risk for COVID-19. 2.3.3 Psychological and contextual factors At T1, participants were asked about their COVID-19 vaccination beliefs and attitudes using a series of seventeen items rated on an eleven-point (0−10) scale anchored at ‘strongly disagree’ (0) and ‘strongly agree’ (10). In previous analyses, these items were subjected to a principal components analysis, resulting in five components relating to COVID-19 vaccination beliefs. [23] Participants were also asked whether they were afraid of needles on the same 0–10 scale. Side-effect expectations were measured at T1 with a single item asking how likely participants thought they were to get side effects from a COVID-19 vaccine on a 0–10 scale anchored at ‘extremely unlikely’ (0) and ‘extremely likely’ (10). 2.4 Ethics Ethical approval for this study was granted by Keele University's Research Ethics Committee (reference: PS-200129). 2.5 Sample size Our achieved sample size of 1005 for the logistic regression analysis was sufficient to avoid overfitting of a model with 14 estimated parameters and an assumed outcome prevalence of 70%, using a conservative event-to-predictor ratio of 20:1. [24] 2.6 Analysis Responses to side-effect questions were not forced; there was, therefore, a small amount of missing data for individual symptoms (first vaccine dose, up to 0.6%, n = 6/1034; second vaccine dose, up to 0.4%, n = 4/1009). Reporting and attribution of symptoms was investigated following the first and second COVID-19 vaccine doses separately. We recoded symptom reporting into a binary variable (symptom not reported, vs one or more symptom reported). For reported symptoms, we also recoded symptom attribution into three categories (definitely not, probably not; unsure; probably, definitely). We categorized symptoms into two categories: i) injection-site symptoms (pain or tenderness where the injection was, redness where the injection was, swelling where the injection was, itch where the injection was, warmth where the injection was, bruising where the injection was, other symptom[s] where the injection was), and ii) non-injection-site symptoms (diarrhoea, headache, joint or muscle pain, high temperature/fever, nausea, fatigue). For each vaccine dose, we then created two further variables indicating whether the participant had experienced any injection-site symptoms (no injection-site symptoms reported, vs injection-site symptom reported) or any non-injection-site symptoms (no non-injection-site symptom reported, vs non-injection-site symptom reported) respectively. We did the same for attribution (of symptoms to vaccine). Symptom reporting and attribution of individual symptoms, and injection- and non-injection-site symptoms, are reported descriptively. A chi-squared test was used to investigate whether there was an association between reporting injection-site symptoms and non-injection-site symptoms. We investigated factors associated with reporting of injection-site and non-injection-site symptoms separately, using binary logistic regression. Explanatory variables (except for at risk for COVID-19) were measured at T1, while the outcomes (symptom reporting) were reported at T2. Explanatory variables were entered into the logistic regression model in two blocks, selected a priori based on previous analyses. [18,23] We entered personal and clinical characteristics into the first block: age, gender, ethnicity, at risk for COVID-19, think or had COVID-19 previously or currently, and vaccine brand. Psychological and contextual factors were entered into the second block: fear of needles, four principal vaccine components (social norms relating to vaccination, perceived necessity of vaccination, perceived safety of the vaccine, adequacy of information about the vaccine; associated with side-effect expectations in previous analyses [17]) and side-effect expectations. In analyses investigating symptom reporting following the second vaccine dose we also included a single item in a third block: symptom reporting following the first vaccine. The Nagelkerke (pseudo-) R 2 was used to investigate the predictive strength of the regression models; this statistic can take values between 0 and 1. Statistical significance was set at p ≤ .05. We did not conduct analyses of associations with symptom attribution owing to the very small number of symptoms that were not attributed to vaccination, in relation to both the first and the second vaccine. 3 Results 3.1 Participant characteristics After excluding those who had been vaccinated at T1, and those who had not been vaccinated, preferred not to say, or did not know if they had been vaccinated at T2, 1034 participants were included in analyses of symptom reporting. Just over half (52.1%) were female, most (86.9%) were white, and the mean age was 48.7 years (Table 1 ). The most commonly reported vaccine received was AstraZeneca, followed by Pfizer-BioNTech and Moderna.Table 1 Participant characteristics. Table 1 n (%) Vaccinated at T2 Two doses 1009 (89.8) One dose 25 (2.2) Not vaccinated a 85 (7.6) Prefer not to say a 4 (0.4) Don't know a 1 (0.1) Total included in analyses 1034 (100.0) Gender Female 539 (52.1) Male 492 (47.6) Non-binary 2 (0.2) Prefer to self-describe 1 (0.1) Ethnicity White 899 (86.9) Other ethnic groups 131 (12.7) Prefer not to say 4 (0.4) Age Range 18 to 80 years M = 48.7, SD = 15.1 Vaccine received AstraZeneca 597 (57.7) Pfizer-BioNTech 395 (38.2) Moderna 36 (3.5) Janssen 1 (0.1) Vaccine not listed 3 (0.3) Don't know 2 (0.2) a Not included in analyses of symptom reporting as participants had to be vaccinated by definition. 3.2 Symptom reporting and attribution to COVID-19 vaccination The most common injection-site symptom reported following the first and second doses of a COVID-19 vaccine was pain or tenderness where the injection was (Table 2 ). The most common non-injection-site symptoms were fatigue, joint or muscle pain, and headache.Table 2 Symptom reporting and attribution to COVID-19 vaccination for the first and second vaccine dose separately. Table 2 Did you experience this symptom and if so, do you think it was a side effect of the COVID-19 vaccine? a First vaccine, n (%) b Second vaccine, n (%) b n No Yes; definitely or probably not a side effect Yes; unsure whether a side effect Yes; probably or definitely a side effect n No Yes; definitely or probably not a side effect Yes; unsure whether a side effect Yes; probably or definitely a side effect Injection-site symptoms Pain or tenderness where the injection was 1032 323 (31.3) 48 (4.7) 28 (2.7) 633 (61.3) 1009 519 (51.4) 35 (3.5) 19 (1.9) 422 (43.2) Redness where the injection was 1030 811 (78.7) 19 (1.8) 12 (1.2) 188 (18.3) 1007 873 (86.7) 13 (1.3) 6 (0.6) 115 (11.4) Swelling where the injection was 1030 854 (82.9) 11 (1.1) 9 (0.9) 156 (15.1) 1006 892 (88.7) 8 (0.8) 7 (0.7) 99 (9.8) Itch where the injection was 1029 907 (88.1) 18 (1.7) 7 (0.7) 97 (9.4) 1006 936 (93.0) 8 (0.8) 8 (0.8) 54 (5.4) Warmth where the injection was 1030 822 (79.8) 17 (1.7) 17 (1.7) 174 (16.9) 1006 892 (88.7) 8 (0.8) 6 (0.6) 100 (9.9) Bruising where the injection was 1029 909 (88.3) 11 (1.1) 6 (0.6) 103 (10.0) 1006 936 (93.0) 4 (0.4) 4 (0.4) 62 (6.2) Other symptom(s) where the injection was 1028 1007 (98.0) 5 (0.5) 3 (0.3) 13 (1.3) 1005 995 (99.0) 3 (0.3) 2 (0.2) 5 (0.5) Any injection-site symptom 1028 266 (25.9) 59 (5.7) 32 (3.1) 671 (65.3) 1005 473 (47.1) 42 (4.2) 20 (2.0) 470 (46.8) Non-injection-site symptoms Diarrhoea 1029 999 (97.1) 10 (1.0) 8 (0.8) 12 (1.2) 1006 987 (98.1) 9 (0.9) 3 (0.3) 7 (0.7) Headache 1030 651 (63.2) 42 (4.1) 74 (7.2) 263 (25.5) 1006 789 (78.4) 27 (2.7) 42 (4.2) 148 (14.7) Joint or muscle pain 1033 585 (56.6) 33 (3.2) 57 (5.5) 358 (34.7) 1007 737 (73.2) 26 (2.6) 30 (3.0) 214 (21.3) High temperature/fever 1030 830 (80.6) 6 (0.6) 20 (1.9) 174 (16.9) 1006 918 (91.3) 5 (0.5) 10 (1.0) 73 (7.3) Nausea 1030 910 (88.3) 12 (1.2) 18 (1.7) 90 (8.7) 1006 943 (93.7) 15 (1.5) 7 (0.7) 41 (4.1) Fatigue 1031 542 (52.6) 50 (4.8) 83 (8.1) 356 (34.5) 1009 693 (68.7) 44 (4.4) 48 (4.8) 224 (22.2) Any non-injection-site symptom 1029 360 (35.0) 52 (5.1) 83 (8.1) 534 (51.9) 1006 566 (56.3) 48 (4.8) 45 (4.5) 347 (34.5) a Responses to these items were not forced, therefore n values for individual symptoms reported vary slightly. b Where percentages do not add to 100, this is due to rounding. Following the first dose of the vaccine, 74.1% (95% CI 71.4% to 76.7%, n = 762/1028) of participants reported experiencing at least one injection-site symptom. Of these, 88.1% (95% CI 85.6% to 90.2%, n = 671/762) attributed at least one symptom experienced to the vaccine. 65.0% (95% CI 62.0% to 67.9%, n = 669/1029) reported experiencing at least one other (non-injection-site) symptom. Of these, 79.8% (95% CI 76.6% to 82.7%, n = 534/669) attributed at least one symptom experienced to the vaccine. Following the second dose of the vaccine, 52.9% (95% CI 49.8% to 56.0%, n = 532/1005) of participants reported experiencing at least one injection-site symptom. Of these, 88.3% (95% CI 85.3% to 90.8%, n = 470/532) attributed at least one symptom experienced to the vaccine. 43.7% (95% CI 40.7% to 46.8%, n = 440/1006) reported experiencing at least one other (non-injection-site) symptom. Of these, 78.9% (95% CI 74.8% to 82.4%, n = 347/440) attributed at least one symptom experienced to the vaccine. There was a significant difference in reporting of injection-site and non-injection-site symptoms for both vaccines, with 354/1028 (34.4%) of participants reporting one type of symptom but not the other for the first vaccine (χ 2 1 = 40.9, p < .001) and a corresponding figure of 367/1005 (36.5%) for the second vaccine dose (χ 2 1 = 78.7, p < .001; Table 3 ).Table 3 Number of people reporting injection-site and non-injection-site symptoms after the first and second vaccine dose. Table 3 First vaccine, n = 1028 Second vaccine, n = 1005 Non-injection-site symptoms, n (%) Non-injection-site symptoms, n (%) Not reported Reported Not reported Reported Injection-site symptoms Not reported 136 (37.8) 130 (19.5) 336 (59.4) 137 (31.2) Reported 224 (62.2) 538 (80.5) 230 (40.6) 302 (68.8) Total 360 (100.0) 668 (100.0) 566 (100.0) 439 (100.0) 3.3 Associations between symptom reporting and personal, clinical, psychological, and contextual factors Results reported are from the full regression model. Results from block 1 alone are presented in Appendix A. Descriptive statistics relating to variables in the regression models are presented in Table 4 . Missing data in the regression models are due to missing values for individual variables.Table 4 Participant characteristics, in subgroups according to dose and symptom reporting. Data are n (%) except where indicated otherwise. Table 4 First vaccine, n (%) Second vaccine, n (%) Injection-site symptoms Non-injection-site symptoms Injection-site symptoms Non-injection-site symptoms Not reported n = 234 Reported n = 666 Not reported n = 234 Reported n = 666 Not reported n = 419 Reported n = 463 Not reported n = 501 Reported n = 382 Age (years); mean (SD) 53.4 (14.4) 47.1 (15.2) 52.7 (14.9) 46.6 (15.0) 52.0 (14.4) 46.3 (15.3) 51.6 (14.1) 45.5 (15.7) Gender Female 82 (17.5) 386 (82.5) 143 (30.5) 326 (69.5) 192 (41.6) 269 (58.4) 242 (52.4) 220 (47.6) Male 152 (35.2) 280 (64.8) 170 (39.4) 262 (60.6) 227 (53.9) 194 (46.1) 259 (61.5) 162 (38.5) Ethnicity White 209 (26.4) 582 (73.6) 284 (35.9) 508 (64.1) 377 (48.7) 397 (51.3) 451 (58.2) 324 (41.8) Other ethnic groups 25 (22.9) 84 (77.1) 29 (26.6) 80 (73.4) 42 (38.9) 66 (61.1) 50 (46.3) 58 (53.7) At risk for COVID-19 No 187 (25.4) 548 (74.6) 244 (33.2) 491 (66.8) 341 (47.4) 379 (52.6) 412 (57.2) 308 (42.8) Yes 47 (28.5) 118 (71.5) 69 (41.6) 97 (58.4) 78 (48.1) 84 (51.9) 89 (54.6) 74 (45.4) Think previously or currently had COVID-19 No 205 (26.8) 559 (73.2) 277 (36.3) 487 (63.7) 369 (49.1) 383 (50.9) 444 (59.0) 308 (41.0) Yes 29 (21.3) 107 (78.7) 36 (26.3) 101 (73.7) 50 (38.5) 80 (61.5) 57 (43.5) 74 (56.5) Vaccine brand Pfizer-BioNTech 64 (17.9) 293 (82.1) 142 (39.8) 215 (60.2) 128 (36.9) 219 (63.1) 176 (50.7) 171 (49.3) AstraZeneca 168 (32.4) 350 (67.6) 164 (31.6) 355 (68.4) 287 (56.1) 225 (43.9) 322 (62.8) 191 (37.2) Moderna 2 (8.0) 23 (92.0) 7 (28.0) 18 (72.0) 4 (17.4) 19 (82.6) 3 (13.0) 20 (87.0) I am afraid of needles (0–10, strongly disagree to strongly agree); mean (SD) 2.2 (3.1) 2.8 (3.3) 2.3 (3.2) 2.8 (3.3) 2.4 (3.2) 2.8 (3.4) 2.5 (3.3) 2.8 (3.3) How likely to get side effects from vaccine? (0–10, strongly disagree to strongly agree); mean (SD) 3.6 (2.4) 3.7 (2.3) 3.6 (2.4) 3.7 (2.3) 3.5 (2.3) 3.8 (2.3) 3.5 (2.3) 3.8 (2.3) Symptoms reported after first vaccine dose No – – – – 207 (89.2) 25 (10.8) 266 (86.6) 41 (13.4) Yes – – – – 212 (32.6) 438 (67.4) 235 (40.8) 341 (59.2) SD = standard deviation. 3.3.1 First vaccine dose After the first vaccine dose, reporting of injection-site and of non-injection-site symptoms was in each case associated with being female and younger (Table 5 ). Reporting symptoms at either site was also associated with vaccine brand. Examination of Table 4 reveals that reporting was highest for the Moderna vaccine, at 92.0% and 72.0% for vaccine-site and non-vaccine-site symptoms respectively, though these estimates are imprecise in view of the small number of cases in this category. Reporting was somewhat higher for the Pfizer-BioNTech vaccine than for the AstraZeneca vaccine for injection-site symptoms, but the reverse was the case for non-injection-site symptoms.Table 5 Results of the full logistic regression models analysing associations with symptom reporting following the first dose of a COVID-19 vaccination. Parameter estimates relate to the full model containing all explanatory variables (injection-site symptoms, n = 900, 13.0% missing data; non-injection-site symptoms, n = 901, 12.9% missing data). For continuous variables, the adjusted odds ratios represent the change in likelihood of side effects for a one-unit increase in the predictor variable, apart from age, where an increase of one-unit represents an increase by decade. Table 5 Predictor variable Level Injection-site symptoms, n = 900 Non-injection-site symptoms, n = 901 Adjusted odds ratio (95% CI) for reporting symptoms a p value Adjusted odds ratio (95% CI) for reporting symptoms a p value Block 1 – personal and clinical characteristics Age Decade 0.785 (0.691 to 0.893) <0.001⁎ 0.722 (0.644 to 0.810) <0.001⁎ Gender Female Reference – Reference – Male 0.356 (0.257 to 0.492) <0.001⁎ 0.656 (0.492 to 0.875) 0.004⁎ Ethnicity White Reference – Reference – Other ethnic groups 0.954 (0.566 to 1.608) 0.860 1.129 (0.699 to 1.822) 0.620 At risk for COVID-19 No Reference – Reference – Yes 0.965 (0.643 to 1.446) 0.861 0.854 (0.589 to 1.240) 0.408 Think previously or currently had COVID-19 No Reference – Reference – Yes 0.874 (0.542 to 1.410) 0.582 1.251 (0.811 to 1.929) 0.311 Vaccine brand 0.002 ⁎ <0.001⁎ Pfizer-BioNTech Reference Reference AstraZeneca 0.514 (0.361 to 0.732) 2.066 (1.506 to 2.835) Moderna 2.029 (0.454 to 9.075) 1.132 (0.448 to 2.858) Block 2 – psychological and contextual factors I am afraid of needles 0 (strongly disagree) to 10 (strongly agree) 1.043 (0.991 to 1.098) 0.108 1.020 (0.974 to 1.067) 0.407 COVID-19 vaccination beliefs component 1: social norms regarding COVID-19 vaccination – 1.070 (0.874 to 1.309) 0.514 0.966 (0.806 to 1.158) 0.706 COVID-19 vaccination beliefs component 2: the necessity of vaccination – 1.083 (0.906 to 1.294) 0.383 1.059 (0.900 to 1.246) 0.491 COVID-19 vaccination beliefs component 3: safety of the vaccine – 0.983 (0.801 to 1.206) 0.869 1.065 (0.885 to 1.282) 0.502 COVID-19 vaccination beliefs component 4: adequacy of information about vaccination – 0.973 (0.813 to 1.166) 0.770 0.960 (0.814 to 1.133) 0.632 How likely do you think it is that you would get side effects from a coronavirus vaccine? 0 (extremely unlikely) to 10 (extremely likely) 1.003 (0.929 to 1.084) 0.936 1.000 (0.931 to 1.075) 0.991 Nagelkerke R2 For 1st block = 0.134 For 1st block = 0.096 For full model = 0.140 For full model = 0.099 CI = confidence interval a Adjusting for all other variables. ⁎ p ≤ .05. The regression model for injection-site symptoms had greater predictive power (Nagelkerke R 2 = 0.140) than that for non-injection-site-symptoms (Nagelkerke R 2 = 0.099). In both cases, the addition of psychological and contextual factors in the second block produced only a small increase in the R 2 value over that derived from the personal and clinical variables in the first block. 3.3.2 Second vaccine dose In respect of both injection-site and non-injection-site symptoms, those having reported symptoms after the first dose were much more likely to do so after the second dose (Table 6 ). Reporting injection-site and non-injection-site symptoms after the second vaccine dose was associated with vaccine brand, in relation to both types of symptoms. As in the case of first-dose symptoms, the highest rate reported was for the Moderna vaccine (82.6% and 87.0% for injection-site and non-injection-site symptoms, respectively; Table 4). The rate was higher for the Pfizer-BioNTech than for the AstraZeneca vaccine in both cases. Reporting non-injection-site symptoms was associated with having an illness that put one at higher risk of COVID-19. Reporting injection-site symptoms was associated with not believing that one had enough information about COVID-19 (illness and vaccination) to make an informed decision about vaccination.Table 6 Results of the full logistic regression models analysing associations with symptom reporting following the second dose of a COVID-19 vaccination. Parameter estimates relate to the full model containing all explanatory variables (injection-site symptoms, n = 882, 14.7% missing data; non-injection-site symptoms, n = 883, 14.6% missing data). For continuous variables, the adjusted odds ratios represent the change in likelihood of side effects for a one-unit increase in the predictor variable, apart from age, where it an increase of one-unit represents an increase by decade. Table 6 Predictor variable Level Injection-site symptoms, n = 882 Non-injection-site symptoms, n = 883 Adjusted odds ratio (95% CI) for reporting symptoms a p value Adjusted odds ratio (95% CI) for reporting symptoms a p value Block 1 – personal and clinical characteristics Age Decade 0.947 (0.837 to 1.070) 0.383 0.942 (0.833 to 1.066) 0.343 Gender Female Reference – Reference – Male 0.845 (0.613 to 1.166) 0.305 0.776 (0.567 to 1.063) 0.114 Ethnicity White Reference – Reference – Other ethnic groups 1.232 (0.738 to 2.056) 0.425 1.221 (0.752 to 1.981) 0.420 At risk for COVID-19 No Reference – Reference – Yes 1.291 (0.851 to 1.960) 0.229 1.737 (1.144 to 2.635) 0.010⁎ Think previously or currently had COVID-19 No Reference – Reference – Yes 1.234 (0.781 to 1.950) 0.368 1.341 (0.865 to 2.080) 0.190 Vaccine brand 0.007⁎ <0.001⁎ Pfizer-BioNTech Reference Reference AstraZeneca 0.627 (0.446 to 0.883) 0.483 (0.339 to 0.688) Moderna 2.332 (0.671 to 8.108) 8.593 (2.182 to 33.837) Block 2 – psychological and contextual factors I am afraid of needles (0–10) 0 (strongly disagree) to 10 (strongly agree) 1.005 (0.957 to 1.055) 0.838 0.997 (0.950 to 1.047) 0.920 COVID-19 vaccination beliefs component 1: social norms regarding COVID-19 vaccination – 0.895 (0.730 to 1.097) 0.285 0.869 (0.712 to 1.060) 0.167 COVID-19 vaccination beliefs component 2: the necessity of vaccination – 0.915 (0.762 to 1.099) 0.344 0.886 (0.739 to 1.063) 0.193 COVID-19 vaccination beliefs component 3: safety of the vaccine – 0.943 (0.770 to 1.155) 0.572 0.958 (0.785 to 1.169) 0.670 COVID-19 vaccination beliefs component 4: adequacy of information about vaccination – 0.780 (0.648 to 0.939) 0.009⁎ 0.989 (0.828 to 1.181) 0.898 How likely do you think it is that you would get side effects from a coronavirus vaccine? 0 (extremely unlikely) to 10 (extremely likely) 1.020 (0.943 to 1.103) 0.626 1.051 (0.973 to 1.135) 0.209 Block 3 – previous symptoms Symptoms reported after first vaccine dose b No Reference – Reference – Yes 15.424 (9.724 to 24.467) <0.001⁎ 11.243 (7.489 to 16.878) <0.001⁎ Nagelkerke R2 For 1st block = 0.102 For 1st block = 0.100 For 1st & 2nd block = 0.116 For 1st & 2nd block = 0.106 For full model = 0.361 For full model = 0.338 CI = confidence interval a Adjusting for all other variables. b Injection-site symptoms reported after first vaccine dose included in model investigating reporting of injection-site symptoms. Non-injection-site symptoms reported after first vaccine dose included in model investigating reporting of non-injection-site symptoms. ⁎ p ≤ .05. The predictive power of the regression models for injection-site and non-injection-site symptoms was similar (Nagelkerke R 2 of 0.361 and 0.338, respectively). The higher R 2 values than in the regression models for the first dose is largely due to the marked effect of reporting of symptoms related to the first dose; before this predictor was added in the third block, the R 2 values (0.115 and 0.105) were not markedly different from those in the regression models for first-dose symptom reporting. 4 Discussion We found that 74% of people included in this study reported injection-site symptoms and 65% reported non-injection-site symptoms following their first COVID-19 vaccine dose. Following the second vaccine dose, 53% reported injection-site and 44% reported non-injection-site symptoms. Clinical trial data indicate that side-effect reporting is lower for the second dose of the AstraZeneca vaccine, [8] which most of our participants (58%) reported receiving. Rates of commonly reported injection-site and non-injection-site symptoms (fatigue, headache, fever) were within the range of those seen in clinical trial data. [9,12,25] Most people attributed at least one of the symptoms reported after vaccination to the vaccine. In line with previous research, we found that younger people were more likely to report vaccine side effects. [[8], [9], [10]] Women were also more likely to report symptoms, as in other studies investigating symptom reporting following COVID-19 vaccination. [12,13] Higher rates of symptoms are consistently perceived by females than by males in studies investigating symptom reporting. [26,27] Though most research points to an association between female sex and symptom perception, a recent comprehensive systematic review of factors associated with the nocebo response found little evidence for a gender effect. [3] We found no evidence for an association between previous or current SARS-CoV-2 infection and symptom reporting, contrary to previous research. [12,13] This may have been due to smaller sample sizes and wording of the item used to include current infection. Personal and clinical characteristics contributed little to the predictive strength of the regression models in this study. Few psychological and contextual factors were significantly associated with symptom reporting following COVID-19 vaccination, except for prior symptom experience. This variable likely drove the additional predictive power (increase in Nagelkerke R 2 from 0.116 to 0.361 for injection-site symptoms and from 0.106 to 0.338 for non-injection-site symptoms) when added to personal and clinical characteristics in the regression models of symptom reporting following the second vaccine dose. An important implication for policy, however, is that high uptake of the second COVID-19 vaccine dose suggests that previously experiencing symptoms from the first vaccine did not influence uptake of the second vaccine dose. This contrasts with review findings that fear of side effects is one of the most common reasons for vaccine refusal. [28,29] However, one study suggests that perceived severity of, and worry about, side effects, rather than mere perception of side effects, may affect future uptake. [30] Other possible reasons that uptake of the second COVID-19 vaccine dose was high include the emphasis on vaccination in the media and public discourse about the pandemic. Alternatively, people may have perceived side effects as evidence that the vaccine is “working”, increasing motivation to have a second dose. [31] One mechanism through which previous symptom experience may feed into later symptom perception is expectation. Symptom expectation is strongly associated with the nocebo effect and later symptom perception. [3,4,32] However, in this study, we did not find a statistically significant association between side-effect expectations at the start of the vaccine rollout in the UK and later symptom reporting. One reason for this may be that most participants were unsure whether they would experience symptoms from a COVID-19 vaccine at T1. [17] We investigated factors associated with side-effect expectations in our T1 survey, [17] as well as others theoretically associated with nocebo reporting [3] and side-effect expectations. [33] However, we found few associations with symptom reporting, and in particular few associations with psychological factors. This may be due to the long period of time between measurement of psychological factors (January 2021) and symptom reporting (October 2021) and the fact that each explanatory variable was adjusted for all of the other variables in the statistical model. For example, we found no evidence for an association between perceived vaccine safety and side-effect reporting. In the UK, there was a media flurry around vaccine safety in April 2021, when news broke that the AstraZeneca vaccine may have been linked to unusual blood clots with low platelets. [34] This occurred after we had measured the psychological factors used as predictors in this study, and is likely to have affected perceptions. Studies have found that media reporting is associated with symptom reporting from the COVID-19 vaccine. [14,15] Investigation of possible associations of the influence of media and social media on symptom reporting was outside the scope of the study. Other explanations are also possible. Biological factors may play a stronger role in incidence of symptoms following COVID-19 vaccination than psychological factors. Relevant psychological factors may not have been measured. Alternatively, the influence of psychological factors previously found to be associated with symptom reporting and attribution may have been attenuated in this unique pandemic situation, where emphasis was repeatedly placed on vaccination as a “route out of the pandemic”. [35,36] Strengths of this study include its large sample size and consequent power to detect small effects. Limitations include that our outcomes (symptom reporting after the first and second COVID-19 vaccines) were measured in October 2021. While the second wave of data collection was timed to coincide with when all UK adults had been offered both vaccine doses, thus avoiding systematic biases within the data, some participants may have completed their vaccine schedule some months before. Recall for symptoms can fade quickly. [37] Therefore symptom reporting, especially for the first vaccine dose (generally given 12 weeks before the second vaccine dose) may have been affected by recall bias. We were unable to investigate factors associated with symptom attribution, as very few people did not attribute their symptoms to the vaccine (first and second vaccine dose). We were unable to investigate whether reporting side effects following the first vaccine dose affected uptake of the second dose due to small numbers. As so few people reported only having one dose (2%, n = 25/1034), we assume there was no impact. This is supported by other research finding that experiencing side effects from the initial course of the COVID-19 vaccine (two doses) did not affect intention to receive a booster dose. [38] Few people reported receiving a Moderna vaccine and so confidence intervals are wider for these analyses, and no hypothesis tests were performed in respect of comparisons between the three vaccines in view of the disparity in the size of these subgroups. There may be some experience of adverse events from vaccination in those infected with SARS-CoV-2 when vaccinated. [10] Our question measuring previous SARS-CoV-2 infection asked whether participants had previously had, or currently had, COVID-19 and was asked at T1. Therefore, some participants may have been infected with SARS-CoV-2 after having completed the T1 questionnaire, but before receiving their first vaccine, whom we were not able to identify. In conclusion, in this study, more people reported injection-site symptoms than non-injection-site symptoms. Symptoms were more likely to be reported following the first compared to the second vaccine dose. Approximately 90% of people reporting symptoms attributed them to the vaccine. Women and younger people were more likely to report symptoms, in line with clinical trial data. The factor most strongly associated with symptom reporting following the second vaccine dose was reporting symptoms from the first vaccine. However, few people had only had one vaccine, suggesting that perception of side effects did not deter people from having their second vaccine. Few psychological factors were associated with side-effect reporting, possibly due to the long time period between waves of data collection (January and October 2021). Sources of funding Data collection was funded by a Keele University Faculty of Natural Sciences Research Development award to SS, JS and NS, and a King’s COVID Appeal Fund award granted jointly to LS, GJR, RA, NS, SS and JS. NS is supported by the 10.13039/501100000272 National Institute for Health Research (NIHR) Applied Research Collaboration (ARC) South London at King's College Hospital NHS Foundation Trust. NS is a member of King's Improvement Science, which offers co-funding to the NIHR ARC South London and is funded by King's Health Partners (Guy's and St Thomas' NHS Foundation Trust, King's College Hospital NHS Foundation Trust, King's College London and South London and Maudsley NHS Foundation Trust), and the Guy's and St Thomas' Foundation. LS, RA and GJR are funded by the National Institute for Health and Care Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response, a partnership between the UK Health Security Agency, King's College London and the University of East Anglia. The views expressed are those of the authors and not necessarily those of the NIHR, UK Health Security Agency, the charities or the Department of Health and Social Care. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. Declaration of Competing Interest All authors have completed the Unified Competing Interest form at http://www.icmje.org/coi_disclosure.pdf and declare that data collection was funded by a Keele University Faculty of Natural Sciences Research Development award to SS, JS and NS, and a King’s COVID Appeal Fund award granted jointly to LS, GJR, RA, NS, SS and JS. NS is the director of the London Safety and Training Solutions Ltd., which offers training in patient safety, implementation solutions and human factors to healthcare organizations and the pharmaceutical industry; RA is employed by the UK Health Security Agency. At the time of writing GJR is acting as an expert witness in an unrelated case involving Bayer PLC. LS helped prepare documents for the testimony. The other authors have no conflicts of interest to declare. Appendix A Appendix Table A.1 Results of the logistic regression models for block one (personal and clinical characteristics only) analysing associations with symptom reporting following the first dose of a COVID-19 vaccination. For continuous variables, the adjusted odds ratios (aORs) represent the change in likelihood of side effects for a one-unit increase in the predictor variable, apart from age, where it an increase of one-unit represents an increase by decade. Table A.1 Predictor variable Level Injection-site symptoms, n = 900 Non-injection-site symptoms, n = 901 Adjusted odds ratio (95% CI) for reporting symptoms a p value Adjusted odds ratio (95% CI) for reporting symptoms a p value Block 1 – personal and clinical characteristics Age Decade 0.778 (0.687 to 0.881) <0.001⁎ 0.714 (0.639 to 0.798) <0.001⁎ Gender Female Reference – Reference – Male 0.359 (0.260 to 0.495) <0.001⁎ 0.665 (0.499 to 0.885) 0.005⁎ Ethnicity White Reference – Reference – Other ethnic groups 0.944 (0.563 to 1.583) 0.827 1.129 (0.702 to 1.816) 0.617 At risk for COVID-19 No Reference – Reference – Yes 0.963 (0.646 to 1.435) 0.852 0.853 (0.591 to 1.232) 0.398 Think previously or currently had COVID-19 No Reference – Reference – Yes 0.881 (0.548 to 1.418) 0.603 1.242 (0.807 to 1.911) 0.325 Vaccine brand 0.001⁎ <0.001⁎ Pfizer-BioNTech Reference Reference AstraZeneca 0.530 (0.374 to 0.752) 2.073 (1.514 to 2.837) Moderna 2.059 (0.462 to 9.176) 1.135 (0.451 to 2.853) a Adjusting for age, gender, ethnicity, being at risk for COVID-19, think or had COVID-19 previously or currently, and vaccine brand. ⁎ p ≤ .05. Table A.2 Results of the logistic regression models for block one (personal and clinical characteristics only) analysing associations with symptom reporting following the second dose of a COVID-19 vaccination. For continuous variables, the adjusted odds ratios (aORs) represent the change in likelihood of side effects for a one-unit increase in the predictor variable, apart from age, where it an increase of one-unit represents an increase by decade. Table A.2 Predictor variable Level Injection-site symptoms, n = 882 Non-injection-site symptoms, n = 883 Adjusted odds ratio (95% CI) for reporting symptoms a p value Adjusted odds ratio (95% CI) for reporting symptoms a p value Block 1 – personal and clinical characteristics Age Decade 0.844 (0.760 to 0.937) 0.001⁎ 0.813 (0.733 to 0.903) <0.001⁎ Gender Female Reference – Reference – Male 0.579 (0.439 to 0.764) <0.001⁎ 0.666 (0.504 to 0.880) 0.004⁎ Ethnicity White Reference – Reference – Black and minority ethnic 1.219 (0.782 to 1.900) 0.382 1.276 (0.826 to 1.969) 0.272 At risk for COVID-19 No Reference – Reference – Yes 1.138 (0.795 to 1.628) 0.481 1.395 (0.974 to 1.998) 0.069 Think previously or currently had COVID-19 No Reference – Reference – Yes 1.153 (0.769 to 1.727) 0.492 1.429 (0.962 to 2.121) 0.08 to 0.077 Vaccine brand <0.001⁎ 0.002⁎ Pfizer-BioNTech Reference Reference AstraZeneca 0.534 (0.396 to 0.727) 0.759 (0.563 to 1.025) Moderna 2.471 (0.809 to 7.545) 6.044 (1.739 to 21.006) a Adjusting for age, gender, ethnicity, being at risk for COVID-19, think or had COVID-19 previously or currently, and vaccine brand. ⁎ p ≤ .05. ==== Refs References 1 Barsky A.J. Saintfort R. Rogers M.P. Borus J.F. Nonspecific medication side effects and the nocebo phenomenon JAMA 287 5 2002 622 627 10.1001/jama.287.5.622 11829702 2 Faasse K. Petrie K.J. The nocebo effect: patient expectations and medication side effects Postgrad. Med. J. 89 1055 2013 540 546 10.1136/postgradmedj-2012-131730 23842213 3 Webster R.K. Weinman J. Rubin G.J. A systematic review of factors that contribute to nocebo effects Health Psychol. 35 12 2016 1334 1355 10.1037/hea0000416 27657801 4 Smith L.E. Weinman J. Amlot R. Yiend J. Rubin G.J. Parental expectation of side effects following vaccination is self-fulfilling: a prospective cohort study Ann. Behav. Med. 53 3 2019 267 282 10.1093/abm/kay040 29868792 5 Faasse K. Porsius J.T. Faasse J. Martin L.R. Bad news: the influence of news coverage and Google searches on Gardasil adverse event reporting Vaccine 35 49 Pt B 2017 6872 6878 10.1016/j.vaccine.2017.10.004 29128382 6 Nestoriuc Y. Orav E.J. Liang M.H. Horne R. Barsky A.J. Prediction of nonspecific side effects in rheumatoid arthritis patients by beliefs about medicines Arthritis Care Res. 62 6 2010 791 799 10.1002/acr.20160 7 Cooper V. Metcalf L. Versnel J. Upton J. Walker S. Patient-reported side effects, concerns and adherence to corticosteroid treatment for asthma, and comparison with physician estimates of side-effect prevalence: a UK-wide, cross-sectional study NPJ Prim. Care Respir. Med. 2015 2515026 10.1038/npjpcrm.2015.26 8 Voysey M. Clemens S.A.C. Madhi S.A. Weckx L.Y. Folegatti P.M. Safety and efficacy of the ChAdOx1 nCoV-19 vaccine (AZD1222) against SARS-CoV-2: an interim analysis of four randomised controlled trials in Brazil, South Africa, and the UK Lancet 397 10269 2021 99 111 10.1016/S0140-6736(20)32661-1 33306989 9 Polack F.P. Thomas S.J. Kitchin N. Absalon J. Gurtman A. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine N. Engl. J. Med. 383 27 2020 2603 2615 10.1056/NEJMoa2034577 33301246 10 Baden L.R. El Sahly H.M. Essink B. Kotloff K. Frey S. Efficacy and safety of the mRNA-1273 SARS-CoV-2 vaccine N. Engl. J. Med. 384 5 2021 403 416 10.1056/NEJMoa2035389 33378609 11 Thomas S.J. Moreira E.D. Jr. Kitchin N. Absalon J. Gurtman A. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine through 6 months N. Engl. J. Med. 385 19 2021 1761 1773 10.1056/NEJMoa2110345 34525277 12 Menni C. Klaser K. May A. Polidori L. Capdevila J. Vaccine side-effects and SARS-CoV-2 infection after vaccination in users of the COVID symptom study app in the UK: a prospective observational study Lancet Infect. Dis. 21 7 2021 939 949 10.1016/S1473-3099(21)00224-3 33930320 13 Beatty A.L. Peyser N.D. Butcher X.E. Cocohoba J.M. Lin F. Analysis of COVID-19 vaccine type and adverse effects following vaccination JAMA Netw. Open 4 12 2021 e2140364 10.1001/jamanetworkopen.2021.40364 14 Clemens K.S. Faasse K. Tan W. Colagiuri B. Colloca L. Social communication pathways to COVID-19 vaccine side-effect expectations and experience J. Psychosom. Res. 2023 164111081 10.1016/j.jpsychores.2022.111081 15 MacKrill K. Impact of media coverage on side effect reports from the COVID-19 vaccine J. Psychosom. Res. 2023 164111093 10.1016/j.jpsychores.2022.111093 16 Geers A.L. Clemens K.S. Faasse K. Colagiuri B. Webster R. Psychosocial factors predict COVID-19 vaccine side effects Psychother. Psychosom. 91 2 2022 136 138 10.1159/000519853 34736267 17 Smith L.E. Sim J. Amlot R. Cutts M. Dasch H. Side-effect expectations from COVID-19 vaccination: findings from a nationally representative cross-sectional survey (CoVAccS - wave 2) J. Psychosom. Res. 2021 152110679 10.1016/j.jpsychores.2021.110679 18 Smith L.E. Sim J. Cutts M. Dasch H. Amlôt R. Psychosocial factors affecting COVID-19 vaccine uptake in the UK: a prospective cohort study (CoVAccS – wave 3) medRxiv [pre-print] 2022 10.1101/2022.03.25.22272954 19 Sherman S.M. Smith L.E. Sim J. Amlot R. Cutts M. COVID-19 vaccination intention in the UK: results from the COVID-19 vaccination acceptability study (CoVAccS), a nationally representative cross-sectional survey Hum. Vaccin. Immunother. 17 6 2021 1612 1621 10.1080/21645515.2020.1846397 33242386 20 Open Science Framework COVID-19 vaccination acceptability in the UK January 2021 (CoVAccS - Wave 2) https://osf.io/bdprf/ 2021 21 Open Science Framework COVID-19 vaccination - factors affecting uptake in the UK (CoVAccS wave 3) https://osf.io/tehg8/ 2022 22 MacKrill K. Webster R. Rubin G.J. Witthoft M. Silvester C. When symptoms become side effects: development of the side effect attribution scale (SEAS) J. Psychosom. Res. 2021 141110340 10.1016/j.jpsychores.2020.110340 23 Sherman S.M. Sim J. Cutts M. Dasch H. Amlot R. COVID-19 vaccination acceptability in the UK at the start of the vaccination programme: a nationally representative cross-sectional survey (CoVAccS - wave 2) Public Health 2022 2021 2029 10.1016/j.puhe.2021.10.008 36333806 24 Peduzzi P. Concato J. Kemper E. Holford T.R. Feinstein A.R. A simulation study of the number of events per variable in logistic regression analysis J. Clin. Epidemiol. 49 12 1996 1373 1379 10.1016/s0895-4356(96)00236-3 8970487 25 Folegatti P.M. Ewer K.J. Aley P.K. Angus B. Becker S. Safety and immunogenicity of the ChAdOx1 nCoV-19 vaccine against SARS-CoV-2: a preliminary report of a phase 1/2, single-blind, randomised controlled trial Lancet 396 10249 2020 467 478 10.1016/S0140-6736(20)31604-4 32702298 26 van Wijk C.M. Kolk A.M. Sex differences in physical symptoms: the contribution of symptom perception theory Soc. Sci. Med. 45 2 1997 231 246 10.1016/s0277-9536(96)00340-1 9225411 27 Barsky A.J. Peekna H.M. Borus J.F. Somatic symptom reporting in women and men J. Gen. Intern. Med. 16 4 2001 266 275 11318929 28 Smith L.E. Amlot R. Weinman J. Yiend J. Rubin G.J. A systematic review of factors affecting vaccine uptake in young children Vaccine 35 45 2017 6059 6069 10.1016/j.vaccine.2017.09.046 28974409 29 Bish A. Yardley L. Nicoll A. Michie S. Factors associated with uptake of vaccination against pandemic influenza: a systematic review Vaccine 29 38 2011 6472 6484 10.1016/j.vaccine.2011.06.107 21756960 30 Smith L.E. Amlôt R. Weinman J. Yiend J. Rubin G.J. Why do parents not re-vaccinate their child for influenza? A prospective cohort study Vaccine 38 27 2020 4230 4235 10.1016/j.vaccine.2020.04.029 32386745 31 Leibowitz K.A. Howe L.C. Crum A.J. Changing mindsets about side effects BMJ Open 11 2 2021 e040134 10.1136/bmjopen-2020-040134 32 Rheker J. Winkler A. Doering B.K. Rief W. Learning to experience side effects after antidepressant intake - results from a randomized, controlled, double-blind study Psychopharmacology 234 3 2017 329 338 10.1007/s00213-016-4466-8 27807605 33 Smith L.E. Webster R.K. Rubin G.J. A systematic review of factors associated with side-effect expectations from medical interventions Health Expect. 23 4 2020 731 758 10.1111/hex.13059 32282119 34 European Medicines Agency AstraZeneca’s COVID-19 vaccine: EMA Finds Possible Link to Very Rare Cases of Unusual Blood Clots with Low Blood Platelets https://www.ema.europa.eu/en/news/astrazenecas-covid-19-vaccine-ema-finds-possible-link-very-rare-cases-unusual-blood-clots-low-blood 2021 35 Prime Minister's Office Prime Minister Issues Vaccine Call to Arms Ahead of Winter 2021 Available from: https://www.gov.uk/government/news/prime-minister-issues-vaccine-call-to-arms-ahead-of-winter 36 Boris Johnson and @BorisJohnson The Vaccine is Our Best Route Out of this Pandemic and We Must All Do Our Part by Taking the […] [Twitter] 2021 30 March 2021 Available from: https://twitter.com/borisjohnson/status/1376796581970710529?lang=id 37 Feikin D.R. Audi A. Olack B. Bigogo G.M. Polyak C. Evaluation of the optimal recall period for disease symptoms in home-based morbidity surveillance in rural and urban Kenya Int. J. Epidemiol. 39 2 2010 450 458 10.1093/ije/dyp374 20089695 38 Geers A.L. Clemens K.S. Colagiuri B. Jason E. Colloca L. Do side effects to the primary COVID-19 vaccine reduce intentions for a COVID-19 vaccine booster? Ann. Behav. Med. 2022 10.1093/abm/kaac027	36495757	PMC9708101	NO-CC CODE	2022-12-07 23:16:30	no	J Psychosom Res. 2023 Jan 30; 164:111104	utf-8	J Psychosom Res	2,022	10.1016/j.jpsychores.2022.111104	oa_other
==== Front Appl Anim Behav Sci Appl Anim Behav Sci Applied Animal Behaviour Science 0168-1591 0168-1591 Elsevier B.V. S0168-1591(22)00267-2 10.1016/j.applanim.2022.105807 105807 Article Preface for the special issue of the 54th international congress of the ISAE Campbell Dana L.M. Agriculture and Food, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Armidale, NSW, Australia Kamboj Madan Lal Indian Council of Agricultural Research (ICAR) - National Dairy Research Institute, Karnal, Haryana, India Singh Vijay Pal Council of Scientific and Industrial Research-Institute of Genomics and Integrative Biology (CSIR-IGIB), Delhi, India, Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, India Descovich Kris School of Veterinary Science, The University of Queensland, Gatton, QLD, Australia 30 11 2022 30 11 2022 105807© 2022 Elsevier B.V. All rights reserved. 2022 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcIn 2021, the International Society for Applied Ethology (ISAE) held their 54th international congress for the second time virtually, due to the need to cancel the proposed in-person event in Bangalore, India in the face of continued COVID-19 related travel restrictions. The theme for this congress was 'Developing animal behaviour and welfare: Real solutions for real problems'. The collection of published papers highlights the value of applied ethological research to improve animal welfare, advancing our standards of care and human-animal working relationships. One of the first steps in any animal related research, is to gain ethical approval for the study. However, the guidelines around what is required for approval are not always equal across countries, regions, and institutions. The ISAE has a set of ethical guidelines to be adhered to when submitting research to the society’s journal (Applied Animal Behaviour Science), but there is still room for improvements. This was the focus of Olsson et al. (2022) who detailed a summary of workshops held during the congress on ethics in animal research internationally. The questions resulting from this workshop highlight the continued need for discussions on this critical topic. Similarly, Rodenburg et al. (2022), also summarised the results of a congress workshop focussed on identifying solutions to common problems in laying hens that are now increasingly housed cage-free. One clear outcome from this workshop was that solutions do exist, and many producers successfully house their hens cage-free, but greater collaboration is needed between all stakeholders to ensure research outcomes are communicated to the end-users. An example of some of the continued research around understanding the behaviour of cage-free broiler breeders and laying hens was presented by Candelotto et al. (2022). This paper described data cleaning methods to optimise sensor-based automatically generated movement tracking data. Humans have close relationships with domestic dogs, but these relationships can be both positive and negative. Stray and free roaming dogs are a public health concern with potential to spread zoonotic diseases. Casaca et al. (2022) discussed the implementation of a rehabilitation and training program for unsocialised dogs that could lead to successful rehoming and adoption. Lee et al. (2022) developed an ethogram that can be used to objectively assess human-dog interaction in animal-assisted humane-education programs. This tool can be used to support good welfare for dogs involved in assistance activities. Finally, Juge et al. (2022) conducted a systematic review to summarise the evidence for the use of canine olfaction in disease detection. Their assessment indicated there may be potential for dogs to be used for this purpose, but current literature is inconsistent with a need for more testing under real-world conditions. Cattle are a key livestock species across many regions of the world with continuing research to improve the way we house and manage them. To be able to improve animal welfare, we first must have accurate methods of assessing it which was the focus of Racciatti et al. (2022) who described the development of a practical welfare assessment protocol specific to Argentine feedlots. Environmental enrichment is a strategy for moving towards positive welfare in animal housing systems. In pre-weaned calves, Zhang et al. (2022) demonstrated that provision of physical enrichment along with pair housing before weaning resulted in more positive outcomes than either management practice alone with physical enrichments showing some impact on cognitive performance. The cognitive abilities of dairy cattle in relation to housing system design was the focus of a review by Nawroth and Rørvang (2022). In this paper, three potential areas for future study were discussed including cow-calf information transfer, fear attenuation, and cognition around the human-cattle relationship. With some animals in our care, there are management procedures that can be painful, and research has been conducted to determine ways to minimise this suffering. Lou et al. (2022) reported on a pilot study to evaluate the effectiveness of a CO2 laser which caused less tissue damage, inflammation and pain in comparison to the conventional method of pliers. In cattle, Thomas et al. (2022) assessed pain responses from digital dermatitis lesions using locomotion scores, mechanical nociceptive thresholds, and thermal imaging. Lesions decreased cattle tolerance to pressure and increased maximum foot temperature, and these measures may help to improve pain assessment from this disease. As the articles in the Special Issue are published when accepted, they appear in different volumes. The articles of this Special Issue appeared over issues 249–255 and are listed here with their DOI. ==== Refs References Candelotto L. Grethen K.J. Montalcini C.M. Toscano M.J. Gómez Y. Tracking performance in poultry is affected by data cleaning method and housing system Appl. Anim. Behav. Sci. 249 2022 105597 10.1016/j.applanim.2022.105597 Casaca M. Morello G.M. Magalhães T. Olsson I.A.S. Vieira de Castro A.C. Is there hope beyond fear? Effects of social rehabilitation on unsocialised stray dogs Appl. Anim. Behav. Sci. 253 2022 105671 10.1016/j.applanim.2022.105671 Juge A.E. Foster M.F. Daigle C.L. Canine olfaction as a disease detection technology: a systematic review Appl. Anim. Behav. Sci. 253 2022 105664 10.1016/j.applanim.2022.105664 Lee C.Y. Ngai J.T.K. Chau K.K.Y. Yu R.W.M. Wong P.W.C. Development of a pilot human-canine ethogram for an animal-assisted education programme in primary schools – a case study Appl. Anim. Behav. Sci. 255 2022 105725 10.1016/j.applanim.2022.105725 Lou M.E. Kleinhenz M.D. Schroeder R. Lechtenberg K. Montgomery S. Coetzee J.F. Viscardi A.V. Evaluating the utility of a CO2 surgical laser for piglet tail docking to reduce behavioral and physiological indicators of pain and to improve wound healing: A pilot study Appl. Anim. Behav. Sci. 254 2022 105720 10.1016/j.applanim.2022.105720 Nawroth C. Rørvang M.V. Opportunities (and challenges) in dairy cattle cognition research: a key area needed to design future high welfare housing systems Appl. Anim. Behav. Sci. 255 2022 105727 10.1016/j.applanim.2022.105727 Olsson I.A.S. Nielsen B.L. Camerlink I. Pongrácz P. Golledge H.D.R. Chou J.-Y. Ceballos M.C. Whittaker A.L. An international perspective on ethics approval in animal behaviour and welfare research Appl. Anim. Behav. Sci. 253 2022 105658 10.1016/j.applanim.2022.105658 Racciatti D.S. Bottegal D.N. Aguilar N.M. Menichelli M.L. Soteras T. Zimerman M. Cancino A.K. Marcoppido G.A. Blanco-Penedo I. Lloveras J.P. Langman L.E. Development of a welfare assessment protocol for practical application in Argentine feedlots Appl. Anim. Behav. Sci. 253 2022 105662 10.1016/j.applanim.2022.105662 Rodenburg T.B. Giersberg M.F. Petarsan P. Shields S. Freeing the hens: workshop outcomes for applying ethology to the development of cage-free housing systems in the commercial egg industry Appl. Anim. Behav. Sci. 251 2022 105629 10.1016/j.applanim.2022.105629 Thomas A.D. Orsel K. Cortés J.A. Pajor E.A. Objective determination and quantification of pain and inflammation associated with digital dermatitis in feedlot cattle Appl. Anim. Behav. Sci. 253 2022 105684 10.1016/j.applanim.2022.105684 Zhang C. Juniper D.T. Meagher R.K. Effects of physical enrichment and pair housing before weaning on growth, behaviour and cognitive ability of calves after weaning and regrouping Appl. Anim. Behav. Sci. 249 2022 105606 10.1016/j.applanim.2022.105606	36468081	PMC9708102	NO-CC CODE	2022-12-02 23:17:11	no	Appl Anim Behav Sci. 2022 Nov 30;:105807	utf-8	Appl Anim Behav Sci	2,022	10.1016/j.applanim.2022.105807	oa_other
==== Front Int J Disaster Risk Reduct Int J Disaster Risk Reduct International Journal of Disaster Risk Reduction 2212-4209 Elsevier Ltd. S2212-4209(22)00691-4 10.1016/j.ijdrr.2022.103472 103472 Article The impact of paid social Q&A on panic buying and digital hoarding at the stage of coexistence with COVID-19: The moderating role of sensitivity to pain of payment Wang Yajuan abcd Ding Austin Shijun e Xu Chonghuan abcd∗ a School of Business Administration, Zhejiang Gongshang University, Hangzhou, China b Modern Business Research Center, Zhejiang Gongshang University, Hangzhou, China c Zheshang Research Institute, Zhejiang Gongshang University, Hangzhou, China d Institute of Consumer Behavior and Digital Marketing, Zhejiang Gongshang University, Hangzhou, China e Sobey School of Business, Saint Mary's University, Nova Scotia, Canada ∗ Corresponding author. School of Business Administration, Zhejiang Gongshang University, Hangzhou, China. 30 11 2022 1 2023 30 11 2022 84 103472103472 3 8 2022 22 11 2022 28 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The worldwide disaster caused by COVID-19 and its variants has changed the behavior and psychology of consumers. Panic buying and hoarding of various commodities continue to emerge in our daily life. Meanwhile, many scholars have focused on the causes of panic buying and hoarding of physical products like daily necessities and food during the outbreak of COVID-19. In fact, the phenomenon of panic buying and digital hoarding of paid social Q&A and other digital content products is very prominent, both in the outbreak period of COVID-19 epidemic and the current coexistence stage. However, the existing literature lacks empirical research to explore this phenomenon, and the psychological mechanism behind it has not been clearly revealed. Therefore, at the current stage of coexistence with COVID-19, based on the SOBC framework, we developed a theoretical model and explored the causes of panic buying and digital hoarding in paid social Q&A. The data collected from 863 paid social Q&A users in China are empirically tested. The results show that the characteristics of paid social Q&A (usefulness, ease of use, professionalism and value) can cause emotional contagion among platform users, activate their willingness to pay, and finally lead to digital hoarding and panic buying behavior of COVID-19 co-existence stage. In addition, the sensitivity to pain of payment moderates the relationship between emotional contagion and willingness to pay. Compared with the spendthrifts, the tightwads are more willing to pay. The conclusions will have positive significance for improving the retail service of digital content platform and promoting the consumption of digital content. Keywords Paid social Q&A Panic buying Digital hoarding Emotional contagion Sensitivity to pain of payment ==== Body pmc1 Introduction COVID-19 and its mutants (such as Omicron, Delta, lambda, etc.) have caused worldwide economic and medical damage, which has brought fear, panic and uncertainty to people nearly around the world. Panic buying has become one of the typical characteristics of consumer behavior during and aftermath of COVID-19. For example, people snapped up toilet paper, important medicines, masks, liquid soaps, gloves, groceries, and food in the United States, Britain, Australia, Portugal, South Korea, and China during COVID-19 [1,2,28]. In addition to the panic buying and hoarding behaviors caused by the fear of shortage of physical products, people's mental pressure and unemployment worries caused by the epidemic have also significantly increased their consumption of digital content products [3]. Different from panic buying and hoarding of physical products, panic buying of digital content products is generally manifested in the immediate payment and participation of users online. With the content perishability of digital content products, users’ digital hoarding behaviors are generally more manifested in collecting a large number of blogger materials or contents in their favorites on the digital content platform [4]. For example, paid social Q&A, a typical digital content product in China, was also one of the targets of hoarding and panic buying during COVID-19, about 90% of Chinese online learners have purchased paid knowledge products, and the most frequently purchased products are column subscriptions, paid lectures and paid questions and answers1. In addition, some bloggers specialized in career, education, skill improvement and other columns has been favorited by users as many as 470,000 times on the Zhihu social Q&A platform. Therefore, panic buying and hoarding behaviors of both physical products and digital content products have become a global phenomenon. Many existing literatures have studied the panic buying and hoarding behaviors of daily necessities, medicines and food during the period of COVID-19 [[11], [12], [13], [14], [15], [16]]. Scholars believe that individual psychological factors [[5], [6], [7]] and social psychological factors are the main factors triggering people panic buying and hoarding food during the epidemic [[8], [9], [10]]. The COVID-19 is known as “the deadliest plague of the century” [17], it damages not only the material world of human beings, but also the spiritual world. However, there is still a lack of discussion to systematically explore the antecedents, consequences and processes of panic buying and digital hoarding behaviors of digital content products related to human spiritual world in the current literature. With the development of digital media, the global consumption of digital content has shown a significant growth trend in recent years [18]. As a typical digital content product, paid social Q&A is an innovative application in the Internet field [19]. For example, Quora, Pearl, Stack Exchange, Zhihu and Himalaya are typical social Q&A platforms. During the prevalence of COVID-19 and its mutants, the global offline real economy suffered heavy losses. On the contrary, on many digital content platforms in China, like Zhihu, Weibo and Himalaya, the sales of paid products increased tremendously, more than half of Chinese users have bought knowledge paid products, among which the growth characteristics of young and highly educated people are obvious. Does consumer strong desire for social Q&A stem from the fear of accidental unemployment [[20], [21], [22]], layoffs, salary cuts, loan cuts and bankruptcy affected by COVID-19? Or from the fear of quarantine, COVID-19 infection and death [23]? Perhaps people want to seek information through paid social Q&A to explore possible epidemic survival solutions. A Previous study has shown that operators of paid social Q&A websites try to encourage participants to share their knowledge or promote reciprocity from the extrinsic incentive by using virtual scores [24]. Unlike free social Q&A, paid social Q&A require questioners to pay. Other onlookers who are curious about the answers also need to pay [25]. In addition, sharing the income paid by audience for paid social Q&A products is the main factor to encourage questioners and respondents to contribute content, improve participation and promote content purchase [26]. Therefore, questioners and respondents have the opportunity to get additional monetary rewards, their knowledge sharing behavior and social contact are also stimulated [27]. Therefore, we have to think about a question: is the “recover” of paid social Q&A due to the sharing mechanism (the mechanism for the questioner and the respondent to share the expenses from the paid audiences), the virtual scores incentive, “COVID-19 boom” or paid social Q&A characteristics? Moreover, because digital content products are inherently perishable, that is, the degree to which users' interest and attention in content are attenuated with time. High-perishable content only attracts users at specific time points. Once it expires, the value, attention and usefulness of some paid social Q&A content will drop sharply [26]. Meanwhile, different from the free mode, one problem that can't be ignored about paid social Q&A is the user's willingness to pay. From the type of payers, as users of paid social Q&A-Tightwads and Spendthrifts, which group shows stronger willingness to pay? Consequently, the answers to these questions above are crucial for the development of digital content platform enterprises, social Q&A products, and user conception about content consumption in a long run [29]. However, there is still a lack of deeper research on these issues in the theoretical field. This study has three novel contributions:(1) It is a challenge to define the panic buying and digital hoarding of digital content products in the coexistence stage with COVID-19. This study tries to make an original conceptual contribution, and it is the first empirical study by using the SOBC framework to conceptualize panic buying and digital hoarding in social paid Q&A. To some extent, this study makes up for the blank of the current literature on panic buying and digital hoarding of digital content products. (2) According to the Emotional Contagion Theory, with the popularity of social media, emotions break through the limitations of time and space, spread synchronously with information as the carrier, and eventually spread to the whole social network [30]. This diffusion process will infect individuals who have originally no emotional tendency, and the transmission speed and scope are much greater than the traditional group emotional contagion [31]. In addition, the pain of payment theory suggests that individuals with different sensitivity to pain of payment (tightwad vs. spendthrift) will show different behavior under the same stimulation [32,33]. The purpose of this study is to identify and test the differences in emotional contagion of users with different sensitivity to payment pain when faced with some characteristics of paid social Q&A, and to explore the differences in their willingness to pay based on the above theory. (3) This study confirms the psychological mechanism of panic buying and digital hoarding of paid social Q&A users, which can make up for the current literature vacancy. At the current stage of coexistence with COVID-19, the findings can help users with different sensitivity to pain of payment to evaluate and adjust their digital content consumption control ability, and also help digital content platform enterprises to master the core needs of different types of users, enhance user stickiness and achieve sustainable development in the coexistence stage. Other parts of this study are structured as follows: Firstly, based on the SOBC framework, this paper reviews the relevant literature on paid social Q&A characteristics, emotional contagion, sensitivity to pain of payment, panic buying and digital hoarding respectively; Secondly, based on the SOBC framework, the research hypotheses and research models are proposed; Thirdly, we specify the process of research design, data collection and questionnaire design; Finally, we make an empirical test on the research hypothesis and discuss the research results. Finally, this study makes an empirical analysis of the research hypothesis, and discusses the research results. The last part is theoretical contribution, practical suggestions, future research prospects and limitations. 2 Literature review and hypothetical development 2.1 SOBC framework The SOBC (Stimulus-Organism-Behavior-Consequence) framework is used to explain the complex mechanism of human behavior [34], which can provide the foundation for the conceptualization of behavioral consequences. Relevant studies have adopt the SOBC framework to study the antecedents or mechanisms in the consumption field [[35], [36], [37]]. 2.1.1 Stimulus in the SOBC framework (S): Paid social Q&A characteristics Panic buying and digital hoarding of paid social Q&A can be understood as the consequences triggered by external stimulus, that is, external stimulus (such as some paid social Q&A characteristics) can start user emotional contagion mechanism (such as language-mediated association and perspective taking) at the internal psychological level of consumers, and then drive behavioral responses (such as willingness to pay), which further lead to certain consequences (such as digital hoarding and panic buying). Therefore, this study believes that the SOBC framework [34] can provide a suitable framework for analyzing the antecedents and consequences of panic buying and digital hoarding of paid social Q&A. Since digital content is served through online and mobile platforms, its characteristics can be considered from the information system quality characteristics and the content quality characteristics [38]. Paid social Q&A is a typical digital content product, so its characteristics also include the information system quality characteristics and the content quality characteristics. Previous studies have shown that dissatisfaction with information quality and system quality prompted users to give up free social Q&A, while satisfaction with the two factors prompted them to choose paid social Q&A [25,39]. Specially, the information system quality characteristics have a positive impact on perceived user interests and satisfaction, which can further affect the continuous intention of users to consume and provide information in information exchange virtual communities [40]. The information system quality characteristics of paid social Q&A mainly include usefulness and ease of use [38]. Usefulness indicates the degree to which users trust using information systems to improve business performance; Ease of use mainly refers to the ease of learning of the system and the relax of service use. Moreover, in paid online environment, users show higher expectations for content quality [41], so the content quality characteristics of paid social Q&A mainly include professional [42] and value [43]. Professional refers to the quality of content created by users; Value refers to user money burden and income perception when using new technologies and services. Accordingly, we take usefulness, ease of use, professional and value as the stimulus (S) in the SOBC framework. 2.1.2 Organism in the SOBC framework (O): Emotional contagion Emotional contagion means that the self-emotions may be affected by others' emotions through conscious or unconscious ways [44]. From the perspective of unconscious way, some studies believe that emotional contagion is primitive, emotional imitation formed unconsciously in which individuals learn others' language and other information independently, and finally achieve emotional unification and express similar emotions [45]. From the perspective of conscious way, believes that emotional contagion is a kind of emotional cognition with conscious involvement, which is controlled by individual advanced cognition [46]. The cognitive mechanisms of conscious emotional contagion mainly include language-mediated association and perspective taking. Language-mediated association mainly refers to inducing the perceiver to imagine the same situation by describing the situation of others through language, which further conduces the same emotional experience [46]. Perspective taking generally refers to “viewing the world from others' eyes”, which is a psychological process in which individuals imagine or speculate others' views and attitudes from others or their situations [47]. Further research shows that social media, which contains emotional content, is becoming an important carrier of online public opinion. Users are vulnerable to emotional contagion when reading these contents [31]. For example, the emotions expressed on Facebook can produce a large-scale contagion effect through social networks [48]. Under the threat of COVID-19 outbreak, using social media alone may not be closely related to abnormal mental health consequences [49]. On the contrary, it may be more critical to ponder the content of interaction with COVID-19. Therefore, in this study, emotional contagion is used to express the psychological process (including conscious and unconscious ways) between external stimulus and behavioral intention, which mainly refers to the emotional contagion process formed by users after being stimulated by paid social Q&A characteristics and triggering behavioral intention. For example, at the current stage of coexistence with COVID-19, questions and answers on unemployment prevention, skill improvement or health protection flooded into the paid social Q&A platforms, which trigger users’ willingness to pay of paid social Q&A through emotional contagion among platform users. Therefore, this study takes emotional contagion as the organism (O) in the SOBC framework. 2.1.3 Behavior in the SOBC framework (B): Willingness to pay Willingness to pay refers to the possibility that consumers want to buy goods or services [50], mainly represents the amount people are willing to pay for consumption products or services they want to obtain [51]. This concept is usually used to measure consumers’ perception and evaluation of products or services. The willingness to pay for social Q&A refers to the willingness of users to pay voluntarily for answers on social Q&A platforms [52,53]. Previous studies have shown that the diversity of answers, credibility, cognition of questions, acceptable price and expectation of potential benefits constitute the motivation of users to pay for paid social Q&A [41]. In addition, based on the Pain of Payment Theory, consumers will generate pain of payment in the consumption process, which plays an important role in restraining consumption [54]. Specially, lower pain of paying will spur consumption willingness [33,55]. Individuals with strong sensitivity to pay of payment are the tightwad, while individuals with less sensitive to pay of payment are the spendthrift [32]. At the current stage of coexistence with COVID-19, both the spendthrift and the tightwad users are affected by emotional contagion from the perspective of the sensitivity to pain of payment of paid social Q&A. But which type of users show stronger willingness to pay, and are more easily to digital hoarding and panic buying? The existing literature is still lack of discussion on these issues. Therefore, this study takes willingness to pay as behavior (B) in the SOBC framework. 2.1.4 Consequence in the SOBC framework (C): Panic buying and digital hoarding Panic buying is a herding behavior with a large number of products purchase after a disaster, which mainly occurs when consumers are expected to suffer a disaster and lack of resources [56]. COVID-19 brings a global catastrophic emergency, which has caused psychological and social stress to individuals [57,60]. When consumers see others hoarding goods on social media which can be seen as a collective behavior, they may even take the same approach. People worried about the shortage of food and daily necessities are 7.5% likely to take panic buying. These consumers think that buying more than what they need can bring security, and they take it as a way to relieve negative emotions [61]. Therefore, at the current stage of coexistence with COVID-19, panic buying has been taking place both offline and online. At the current stage of coexistence with COVID-19, the concurrent abnormal consumption phenomenon is the hoarding behaviors both offline and online. Hoarding originates from the psychological adaptation mechanism in the process of evolution- “fear” [64]. Different from the hoarding behaviors of basic daily necessities and food, digital hoarding, like keeping useless data for a long time or unwilling to delete the accumulated data for fear of being useful in the future [65] refers to the accumulation of digital files at the cost of individual reducing target retrieval ability, which ultimately leads to individual stress and confusion [66]. Knowledge is vital to improve self-worth and gain social status in the future. Information is an important source for acquiring knowledge as a “currency”. Individuals hoard important information to maintain self-advantages [67]. Therefore, digital hoarding is related with people needs to satisfy their security sense, certainty and control. At the current stage of coexistence with COVID-19, users take digital hoarding behaviors to resist the harmful effects of COVID-19. To sum up, paid social Q&A panic buying can be understood as user immediate payment and participation in paid social Q&A in this study. User digital hoarding behaviors are generally more manifested in collecting a large number of blogger materials or content in the user favorites on the digital content platform. At the current stage of coexistence with COVID-19, behavioral intentions and consequences are triggered through emotional contagion. In addition, panic buying and digital hoarding of paid social Q&A are generally separated and asynchronous. Panic buying of paid social Q&A may not be accompanied by digital hoarding of relevant content. Panic buying and digital hoarding are manipulated as two independent dependent variables in this paper. Therefore, this study takes panic buying and digital hoarding as the consequences (C) in the SOBC framework. 2.2 Hypothesis development and research model 2.2.1 S–O: The relationship between paid social Q&A characteristics and emotional contagion The information system quality characteristics (usefulness and ease of use) and the content quality characteristics (professional and value) of paid social Q&A are the main stimulus factors at the current stage of coexistence with COVID-19. Because of unemployment and intensified competition caused by COVID-19, the information system quality characteristics and the content quality characteristics of paid social Q&A may cause emotional contagion among users. In our study paid social Q&A characteristics include usefulness, ease of use, professional and value. Usefulness refers to the extent to which users believe that using information systems can improve their business performance [[67], [68], [69]]. At the current stage of coexistence with COVID-19, the usefulness of paid social Q&A refers to the extent to which users perceive that the use of paid social Q&A services is valuable to themselves and beneficial to their work and life. Ease of use refers to the characteristics of easy operation and easy learning of systems and services [67,68]. At the current stage of coexistence with COVID-19, the ease of use of paid social Q&A refers to user perceived ease of use in the paid social Q&A service system and the service. Professional refers to user subjective perception of content quality and disseminator quality [42]. At the current stage of coexistence with COVID-19, the professional of paid social Q&A refers to user subjective cognition of the professionalism, authority of the questioners and respondents of paid social Q&A as well as the quality of the content they create. Price is regarded as the monetary sacrifice for acquiring products [70]. Apart from the perception of price, knowledge consumers also receive additional value and benefits from the communication between knowledge content creators and consumers due to the benefit sharing mechanism (the mechanism by which questioners and respondents can share the expenses of paying users) on the social Q&A platform [71]. At the current stage of coexistence with COVID-19, the value of paid social Q&A refers to the perceived money payment, benefits and interaction when users browse content or participate in paid social Q&A, that is, a trade-off between perceived gains and perceived losses [50]. In addition, recent studies have confirmed that emotions can be transmitted digitally [30,72]. Therefore, the professional level of COVID-19 content information in paid social Q&A can affect user emotional response. At the current stage of coexistence with COVID-19, the “ask” and “answer” on the paid social Q&A platform contain massive content with the keyword of “COVID-19”, which is generated by user questions, answers and discussions about “COVID-19”. Once users find the questions, answers or discussions are professional in paid social Q&A, and can be useful to themselves, they will generate language-mediated association: imagine the same situation by describing others’ situations in language, and further induce the same emotional experience [46]. If the questioner thinks that using the Q&A service is valuable, he will be more willing to pay for these questions [73]. At the current stage of coexistence with COVID-19, when browsing, reading and participating in paid social Q&A, users are exposed to a large number of contents about pandemic health protection, death, infection, unemployment, and economic recession. The views and emotions of some opinion leaders, industry experts and members are easy to trigger emotional imitation and emotional cognition through social Q&A [31]. At the same time, users also generate active perspective taking when they view the questions, discussions or answers in the paid social Q&A useful, accurate or convenient to themselves. Perspective taking is a psychological process. Individuals imagine or speculate the views and attitudes of others from the situation of others or others [47], which generally refers to “viewing the world from the eyes of others”. At the current stage of coexistence with COVID-19, taking others views as cognitive evidence through paid social Q&A, users spread emotions in social media, in which language-mediated association and active perspective taking are the main mechanisms of emotional contagion. Thus, the following hypotheses are proposed:H1a The stronger level of the Usefulness in the information system quality characteristics of the paid social Q&A is, the stronger level of the emotional contagion will users get. H1b The stronger level of the Ease of Use in the information system quality characteristics of the paid social Q&A is, the stronger level of the emotional contagion will users get. H2a The stronger level of the Professional in the content quality characteristics of the paid social Q&A is, the stronger level of the emotional contagion will users get. H2b The stronger level of the Value in the content quality characteristics of the paid social Q&A is, the stronger level of the emotional contagion will users get. 2.2.2 O–B: The relationship between emotional contagion and willingness to pay According to the intergroup emotion theory (IET), individual emotional response is affected by the emotional response of other members in group interaction. When the core group members or opinion leaders appear in the media, most other members of the group are vulnerable to the emotions of these typical members, and imitate their emotions [74]. At the current stage of coexistence with COVID-19, ordinary individual has limited understanding and cognition of COVID-19. Professional opinions, answers, views and research judgments on safety threats, unemployment and economic stress caused by COVID-19 and how to protect them is crucial. Therefore, ordinary people will seek professional and authoritative answers from opinion leaders through paid social questions and answers. Cost, financial benefits, self-improvement and other factors are important antecedents of perceived value of social Q&A [46], and perceived value also affects the questioner’ s willingness to pay [75]. From the perspective of emotional priming, this study explores how paid social Q&A characteristics trigger user willingness to pay through emotional contagion. The difference in user emotional contagion stems from the stimulus difference in paid social Q&A characteristics, which also affects their willingness to pay. At the current stage of coexistence with COVID-19, emotion is closely related to consumption [76], which also enhances consumer willingness to pay. Platforms like Facebook, Twitter, Instagram or specific Internet forums are important channels for people sharing views, personal experiences, happy events, worries or fears. This is reflected in the sharp increase in terms related to COVID-19 on these channels, which has reached millions by March 2020 [77]. On social media, when consumers find others snapping up and hoarding goods which can be seen as a collective behavior, they will even adopt the same approach, so their willingness to pay increase. Thus, the hypothesis is proposed as below:H3 The stronger level of the emotional contagion users get, the stronger level of the willingness to pay will users show. 2.2.3 B–C: The relationship between willingness to pay and panic buying and digital hoarding In the field of consumer behavior, fear appeal has a significant relationship with online purchase [78]. At the current stage of coexistence with COVID-19, people consulted, exchanged, discussed various topics about the epidemic on paid social Q&A posts, and sought the best answers to decline the fear brought by COVID-19. Relevant studies also show that panic buying is significantly related to more frequent shopping and spending more money [12,58,59]. At the same time, during the second wave of COVID-19, Apple pay or Google pay was used by 13% of the generation Z, compared with only 1% of the Generation X and the Generation Y [3]. Therefore, the stronger level of the willingness to pay for social Q&A services (especially mobile payment), the more they spend, the higher purchase frequency, the easier users will trigger panic buying behavior for digital content products at the current stage of coexistence with COVID-19. In addition, the reasons for digital hoarding may be stem from the “fear of infection” [62] and “fear of economic recession”, fear of death [63] and unemployment [79]. To improve competitiveness and obtain the career security sense, the behavior of hoarding digital information is exacerbated. This collection provides convenience for users to pay for Q&A consultation in the future. Furthermore, the reason why the paid social Q&A platform designs a collection function is the expectation of users paying for the digital hoards in their favorites in the future. Therefore, the digital hoarding of social Q&A products is related to user willingness to pay. The digital hoarding of social Q&A products has its particularity, especially refers to the collection behavior of Q&A posts, bloggers’ personal data, etc. On the social Q&A platform. As long as participating in paid social Q&A in the future, user may pay to the blogger and show their willingness to pay. More studies have shown that the reason for user digital hoarding may be to take the stored information as a form of evidence to protect against possible threats in the future [80]. Therefore, digital hoarding is not only an “emotional storage” to solve psychological needs, but also an “instrumental storage” to obtain security sense [81]. At the current stage of coexistence with COVID-19, digital hoarding further aggravates user anxiety, intensifies involution, and exerts serious harm to user psychological resources. The stronger level of the willingness to pay users show, the easier it will be to trigger digital hoarding. Thus, the following hypotheses are proposed:H4a The stronger level of the willingness to pay users show, the easier it will be to trigger panic buying. H4b The stronger level of the willingness to pay users show, the easier it will be to trigger digital hoarding. 2.2.4 Individual differences in sensitivity to pain of payment Pain of payment is an emotional response that consumers experience when making payment behavior [82], refers to the psychological pain that an individual feels when people separate from money [54], which is a kind of psychological pain that is different from physiological pain [33,83]. When an individual feel too much or too little pain in paying in his consumption behavior, it may lead to two psychological problems: compulsive shopping and compulsive non-shopping [54]. Pain of payment is not only a situational factor, but also an individual difference variable. Different individuals have different sensitivities to pain of payment. Some people are more sensitive to pain than others [86]. A study divided consumers into two basic types based on the sensitivity to pain of payment: those who feel intense pain when spending money and spend less than what they need are the tightwad; those who feel slight pain when spending money and spend more than what they need are the spendthrift [32]. The consumption influence of differences in pain of payment is moderated by the framework effect. Specially, the tightwad are more sensitive to information, while the spendthrift people are less sensitive to pain of payment [86]. In addition to the difference of the sensitivity of individual consumers, individual difference in pain of payment also depends on different payment methods. The feeling of separation between people and money is different under different ways of payment (cash payment vs. virtual currency payment). People who choose cash payment can intuitively feel the loss of money [82], as a result, the pain of cash payment is more intense [[83], [84], [85]]. While new payment methods (such as credit-card payment and mobile payment) make people lose the realistic sense of monetary separation in transactions [87], and reduce their pain of payment. When paying virtual currency, consumers feel less pain [82]. Different from previous studies, we concentrate more on the differences in the willingness to pay of users with different sensitivity to pain of payment (tightwad vs. spendthrift) after getting emotion contagion from paid social Q&A in the mobile payment environment at the current stage of coexistence with COVID-19. This study holds that even under the condition of the same mobile payment, there are differences in payment pain of individual consumers. The sensitivity of pain of payment provides a reference framework for distinguishing the degree of emotional contagion and their consumption control ability of different users when using and experiencing paid social Q&A at the current stage of coexistence with COVID-19. More precisely, the spendthrift may be more likely to ignore the stimulus from paid social Q&A. They are less sensitive to the positive or negative questions and answers in paid social Q&A, and their degree of emotional contagion is lower. As a result, their willingness to pay is weaker. On the contrary, the tightwad pays more attention to the content presented in the paid social Q&A. They are more sensitive to group emotions, and their degree of emotional contagion is higher. As a result, their willingness to pay is stronger. Previous studies have used the sensitivity of pain of payment as the moderate variable to study the impact of payment methods on pain of payment [86,88]. Thus, the hypothesis is proposed as below:H5 The sensitivity of pain of payment (tightwad vs. spendthrift) moderates the relationship between emotional contagion from paid social Q&A and user willingness to pay. Specially, compared with the spendthrift, the tightwad are more susceptible to emotional contagion, and their willingness to pay is stronger. Based on the SOBC framework, the intergroup emotion theory and the pain of payment theory, we propose a theoretical model (see Fig. 1 ). At the current stage of coexistence with COVID-19, paid social Q&A characteristics (the usefulness, ease of use, professional and value) are extracted from the two dimensions of information system quality and content quality in this study. We further study the process that these characteristics trigger user willingness to pay through emotional contagion, leading to digital hoarding and panic buying. Besides, we explore the moderating role of sensitivity to pain of payment (tightwad vs. spendthrift) between the relationship of emotional contagion and willingness to pay.Fig. 1 The Research model. Fig. 1 3 Method 3.1 Participants and procedures Our research background is based on Zhihu (www.zhihu.com) paid social Q&A products launched on August 2018. More than 150,000 Zhihu respondents have provided personalized and scene-based solutions to consultants through one-on-one consultation. At the same time, these public consultations were paid by millions of Zhihu users to attend in 1 yuan RMB. In May 2021, the paid social Q&A products in Zhihu launched the voice/video consultation function. Compared with the traditional graphic consultation service, the respondent and the consultant can chat face to face, get more immediate feedback, describe and solve more complicated problems, and build a closer emotional connection and trust relationship between the respondent and the consultant. At present, in the live broadcast room in Zhihu, the answering host can attach his own paid consultation portal, and viewers with personalized questions can initiate “one-to-one” consultation to the answering host through the paid consultation portal displayed by the answering host in the live broadcast. By April 2022, the monthly average number of active users in Zhihu reached 101.6 million, up by 19.4% year-on-year, the number of paid members was 6.89 million, up 72.8% year-on-year, and the payment rate was 6.8%2. This study aims to investigate user willingness to pay, panic buying and digital hoarding behaviors at the stage of coexistence with COVID-19 from the perspective of emotional contagion. Therefore, the proper respondents should have real browsing, payment and collection experience of paid social Q&A at the stage of coexistence with COVID-19. On April 22, 2020, the World Health Organization said at a press conference in Geneva that COVID-19 will coexist with mankind for a long time. This study took this point in time as the starting time of “the coexistence period between COVID-19 and human beings”. We have established the user database of social Q&A platform in cooperation with our professional technology company, and screened out the users (n = 1972) who used, paid and collected experiences in Zhihu from December 2020 to March 2022, and randomly selected 1022 users from the sample pool. Then, we invited them to participate in the survey by email on April 15th, 2022. This time span is chosen because these users had snapped up and hoarded digital content products during the coexistence period in COVID-19, which is consistent with the purpose of our research, and the conclusions drawn from this batch of data are more reliable and accurate. In the invitation email, we inserted a network survey link of “WJX” (https://www.wjx.cn). By providing rewards to the interviewed users (we paid each participant $5 as a reward), we invited them to click on the link to enter the “WJX” platform to fill in the questionnaire. The survey was completed on May 28, 2022. A total of 987 questionnaires were issued, eliminating 124 questionnaires with incomplete items. Finally, 863 valid samples were obtained, with an effective return rate of 87.4%. In addition, we screened out the data of tightwads and spendthrifts from 863 respondents through Tightwad-Spendthrift Scale to test their sensitivity to pain of payment, which was used in the final analysis of this study. The final valid sample (n = 863) consisted of 357 females (41.37%) and 506 males (58.63%). The sample had a mean age of 33.62 years (range 18–49). Education was assessed through three options, and participants were asked to make a choice: 14.72% selected “High school or below”, 64.19% selected “Bachelor degree”, 21.09% selected “Master degree and above”, and no one left the question blank. Monthly income was assessed through four options, 12.51% selected “＄1000 and below”, 29.43% selected “＄1000 -＄2000”, 36.97% selected “＄2000 -＄3000”, 21.09%selected “＄3000 and above”. 97.57% of the respondents reported that they were influenced by COVID-19, and 97.91% respondents used mobile payment. Paid social Q&A form was assessed through three options, and participants were asked to make a choice: 50.41% selected “Image-text”, 29.78% selected “Voice”, 19.81% selected “Video”, and no one left the question blank. In addition, among the final valid sample, 429 samples are tightwads and 434 samples are spendthrifts after the sensitivity to pain of payment test (see Table 1 ). We assured the participants that their responses were confidential and anonymous and would be used only for research.Table 1 Respondents’ demographic profile (N = 863). Table 1Characteristics All Sensitivity to Pain of Payment Tightwads Spendthrifts N % N % N % Gender Male 506 58.63 232 45.85 274 54.15 Female 357 41.37 202 56.58 155 43.42 Age 18–29 336 38.93 125 37.21 211 62.79 30–39 323 37.43 246 76.16 77 23.84 40–49 204 23.64 86 42.16 118 57.84 Education High school and below 127 14.72 51 40.16 76 59.84 Bachelor degree 554 64.19 322 58.12 232 41.88 Master degree and above 182 21.09 134 73.63 48 26.37 Monthly income ＄1000 and below 108 12.51 45 41.67 63 58.33 ＄1000 -＄2000 254 29.43 112 44.09 142 55.91 ＄2000 -＄3000 319 36.97 130 40.75 189 59.25 ＄3000 and above 182 21.09 101 55.49 81 44.51 Influence by COVID-19 Yes. 842 97.57 412 48.93 430 51.07 No. 21 2.43 5 23.81 16 76.19 Mobile payment use Yes. 845 97.91 472 55.86 373 44.14 No. 18 2.09 3 16.67 15 83.33 Paid social Q&A form Image-text 435 50.41 322 74.02 113 25.98 Voice 257 29.78 51 19.84 206 80.16 Video 171 19.81 45 26.31 126 73.68 Total 863 100 429 100 434 100 3.2 Variable measurement We conducted a survey in Chinese. According to the suggestions of scholars and experts, we have adjusted and translated the items and language descriptions of the initial scale, and made a pretest in 80 samples, and verified the translated scale, finally forming a measurement scale that conforms to the understanding habits of the respondents in China. Items utilize were a 5-point Likert Scale from “strongly disagree” (1) to “strongly agree” (5). Table 2 lists the measurement items of variables.Table 2 Constructs and measurement items used in this study. Table 2Variables Measurement Items Usefulness(UF) (Davis, 1989; Moores, 2012; Cheng and Liu, 2017) UF1 The use of paid social Q&A improves my work and life efficiency at the stage of coexistence with COVID-19. UF2 The use of paid social Q&A makes me feel control over my work and life at the stage of coexistence with COVID-19. UF3 The use of paid social Q&A makes it easier for me to understand, know and predict COVID-19, which saves my time. UF4 The use of paid social Q&A makes it easier for me to improve my knowledge of COVID-19 health protection and relieve my psychological stress. Easy of Use(EU) (Davis, 1989; Moores, 2012) EU1 I feel it is easy to use the paid social Q&A service system at the stage of coexistence with COVID-19. EU2 I feel it is convenient to use the paid social Q&A service system at the stage of coexistence with COVID-19. EU3 It is clear and easy for me to understand the interactive operation of the paid social Q&A service system at the stage of coexistence with COVID-19. EU4 It is easy for me to remember how to use the paid social Q&A service system to ask questions or give answers at the stage of coexistence with COVID-19. Professional(PF) (Du and Xu, 2018) PF1 The respondents of the paid social Q&A are professional at the stage of coexistence with COVID-19. PF2 The respondents of the paid social Q&A are authoritative at the stage of coexistence with COVID-19. PF3 The content created by the questioners and respondents of the paid social Q&A is professional in the aftermath of COVID-19. PF4 The content created by the questioners and respondents of the paid social Q&A is authoritative in the aftermath of COVID-19. Value(VL) (Kim et al., 2012) VL1 It is valuable for questioners to pay directly for questions rather than spending a lot of time looking for answers from free Q&A at the stage of coexistence with COVID-19. VL2 It is valuable for questioners to pay directly for questions rather than spending a lot of efforts looking for answers from free Q&A at the stage of coexistence with COVID-19. VL3 The questioner can get additional benefits by paid Q&A. I think it is valuable. VL4 Through output of content, respondents can gain support and recognition from users, and gain corresponding benefits. I think it is valuable. Emotional Contagion(EC) (Davis, 1980b) EC1 In the paid social Q&A, when I see warm and touching questions and answers (such as encouraging words) related to COVID-19, I will be touched. EC2 In the paid social Q&A, when I see sad and melancholy content related to COVID-19, I almost always feel sympathy for the characters. EC3 In the paid social Q&A, when I see someone's experience of being hurt under the influence of COVID-19, I feel sad and want to help them. EC4 In the paid social Q&A, when I don't agree or like someone's questions or answers about COVID-19, I usually try to “put myself in his shoes” for a while. EC5 In the paid social Q&A, I will try to imagine COVID-19 from the perspective of my netizens, so as to better understand my netizens. Willingness to Pay(WTP) (Zhang et al., 2019) WTP1 When the answer's heterogeneous resources on paid social Q&A, I am willing to pay at the stage of coexistence with COVID-19. WTP2 When more credible answers exist on paid social Q&A, I am willing to pay at the stage of coexistence with COVID-19. WTP3 When the question I am interested in, or urgent or very important, appears on paid social Q&A, I am willing to pay at the stage of coexistence with COVID-19. WTP4 When the price is affordable on paid social Q&A, I am willing to payat the stage of coexistence with COVID-19. WTP5 When the expecting potential revenue exist on paid social Q&A, I am willing to pay at the stage of coexistence with COVID-19. Panic Buying(PB) Lins and Aquino (2020) PB1 The panic about COVID-19 made me spend more on paid social Q&A. PB2 One way to reduce the uncertainty is to ensure that there are enough paid social Q&A products I need on social Q&A platform at the stage of coexistence with COVID-19. PB3 When I think that paid social Q&A may be time-limited, I will panic, so I will pay for a large number of social Q&A products at the stage of coexistence with COVID-19. Digital Hoarding(DH) (Neave et al., 2019) DH1 If I delete certain files in my paid social Q&A platform favorites, I feel apprehensive about it afterwards at the stage of coexistence with COVID-19. DH2 I resist having to delete certain files in my paid social Q&A platform favorites at the stage of coexistence with COVID-19. DH3 Deleting certain files in my paid social Q&A platform favorites would be like losing part of myself at the stage of coexistence with COVID-19. 3.2.1 Usefulness The Usefulness Scale is a four-item, self-report inventory that was created for the present study by adapting from Refs. [[67], [68], [69]]. The subjects were asked to complete the scale, based on their true feelings. Higher scores reflect a higher level of usefulness of information system quality characteristics of paid social Q&A. This measure was translated to Chinese and found to have good reliability and validity. The confirmatory factor analysis (CFA) indicated a good construct validity (χ2/df = 2.37, RMSEA = 0.06, CFI = 0.97, and TLI = 0.98). In our study, Cronbach's alpha was 0.883. 3.2.2 Easy of use The Easy of Use Scale is a four-item, self-report inventory that was created for the present study by adapting from Refs. [67,68]. The subjects were asked to complete the scale, based on their true feelings. Higher scores reflect a higher level of easy of use of information system quality characteristics of paid social Q&A. This measure was translated to Chinese and found to have good reliability and validity. The confirmatory factor analysis (CFA) indicated a good construct validity (χ2/df = 1.86, RMSEA = 0.05, CFI = 0.94, and TLI = 0.95). In our study, Cronbach's alpha was 0.875. 3.2.3 Professional The Professional Scale is a four-item, self-report inventory that was created for the present study by adapting from Ref. [42]. Higher scores reflect a higher level of professional of content quality characteristics of paid social Q&A. The scale has been used in Chinese university students with good reliability and validity [42]. In our study, Cronbach's alpha was 0.881. 3.2.4 Value The Value Scale is a four-item, self-report inventory that was created for the present study by adapting from Ref. [43]. The subjects were asked to complete the scale, based on their true feelings. Higher scores reflect a higher level of Value of content quality characteristics of paid social Q&A. This measure was translated to Chinese and found to have good reliability and validity. The confirmatory factor analysis (CFA) indicated a good construct validity (χ2/df = 2.33, RMSEA = 0.06, CFI = 0.97, and TLI = 0.98). In our study, Cronbach's alpha was 0.868. 3.2.5 Emotional contagion The Emotional Contagion Scale is a five-item, self-report inventory that was created for the present study by adapting from two sub-scales (Empathic Concern Scale and Perspective-taking Scale) of Interpersonal Reactivity Index (IRI) which was developed by Ref. [89]. The subjects were asked to complete the scale, based on their true feelings. Higher scores reflect a higher level of emotional contagion effected by paid social Q&A characteristics. This measure was translated to Chinese and found to have good reliability and validity. The confirmatory factor analysis (CFA) indicated a good construct validity (χ2/df = 2.28, RMSEA = 0.05, CFI = 0.96, and TLI = 0.95). In our study, Cronbach's alpha was 0.912. 3.2.6 Willingness to pay The Willingness to Pay Scale is a five-item, self-report inventory that was created for the present study by adapting from Ref. [41]. The subjects were asked to complete the scale, based on their true feelings. Higher scores reflect a higher level of willingness to pay for paid social Q&A. The scale has been used in Chinese university students with good reliability and validity [41]. In our study, Cronbach's alpha was 0.876. 3.2.7 Panic buying The Panic Buying Scale is a three-item, self-report inventory that was created for the present study by adapting from Ref. [90]. The subjects were asked to complete the scale, based on their true feelings. Higher scores reflect a higher level of panic buying for paid social Q&A. This measure was translated to Chinese and found to have good reliability and validity. The confirmatory factor analysis (CFA) indicated a good construct validity (χ2/df = 2.12, RMSEA = 0.08, CFI = 0.92, and TLI = 0.93). In our study, Cronbach's alpha was 0.865. 3.2.8 Digital hoarding The Digital Hoarding Scale is a three-item, self-report inventory that was created for the present study by adapting from Ref. [91]. Higher scores reflect a higher level of digital hoarding for paid social Q&A. This measure was translated to Chinese and found to have good reliability and validity. The confirmatory factor analysis (CFA) indicated a good construct validity (χ2/df = 2.16, RMSEA = 0.07, CFI = 0.94, and TLI = 0.95). In our study, Cronbach's alpha was 0.871. 3.2.9 Sensitivity to pain of payment Tightwad-Spendthrift Scale [32] was used to measure sensitivity to pain of payment in this study. This scale comprises four items that measure respondents' spending habits on their usual shopping trips. The subjects were asked to complete the scale, based on their true feelings. A higher score on this scale indicates that the respondent experiences more pain of payment and is a tightwads (Cronbach's alpha = 0.852), this measure was translated to Chinese and found to have good reliability and validity. The confirmatory factor analysis (CFA) indicated a good construct validity (χ2/df = 1.87, RMSEA = 0.07, CFI = 0.96, and TLI = 0.97). A lower score on this scale indicates that the respondent experiences less pain of payment and is a spendthrift (Cronbach's alpha = 0.844), this measure was translated to Chinese and found to have good reliability and validity. The confirmatory factor analysis (CFA) indicated a good construct validity (χ2/df = 2.24, RMSEA = 0.08, CFI = 0.95, and TLI = 0.96). 3.2.10 Control variables Several demographic variables are included in this study, because they will affect consumption decisions and willingness to pay. We controlled several variables, such as gender, age, education, monthly income, influence by COVID-19, mobile payment use and paid social Q&A form. These variables may affect the relationship among variables in this study. 4 Data analysis SPSS 22.0 and Amos 26.0 were used for data analysis and testing. Exploratory and confirmatory factor analyses were performed to confirm the validity of each item of the variables, and the reliability of each variable was measured using Cronbach's alpha. Six demographic variables were included in this study which are Gender, Age, Education, Monthly income, Influence by COVID-19 and Mobile payment use. After cleaning the data, the total numbers of respondents included in this study analysis were 863. We have tested the normality of the data, and the bivariate correlation of multiple comparisons has been corrected. Analyses revealed that there was no significant impact of these demographic variables on the main model variables. 4.1 Descriptive statistics and correlation analysis Table 3 depicts the Mean, Standard Deviation and Correlations among the study variables. Usefulness (M = 3.263, SD = 0.424), easy of use (M = 3.348, SD = 0.336), professional (M = 4.114, SD = 0.517), value (M = 3.179, SD = 0.427), emotional contagion (M = 3.237, SD = 0.328), willingness to pay (M = 3.192, SD = 0.518), panic buying (M = 4.112, SD = 0.418), digital hoarding (M = 3.266, SD = 0.422), and sensitivity to pain of payment (M = 3.132, SD = 0.326). Correlation analysis showed that usefulness (r = 0.226, p < 0.001), easy of use (r = 0.139, p < 0.01), professional (r = 0.215, p < 0.01) and value (r = 0.187, p < 0.01) were significantly positively correlated with emotional contagion. Emotional contagion was significantly positively correlated with willingness to pay (r = 0.217, p < 0.05). Willingness to pay was significantly positively correlated with panic buying (r = 0.164, p < 0.05) and digital hoarding (r = 0.112, p < 0.05). The results above preliminary support the subsequent regression analysis.Table 3 Descriptive Statistics and Correlations among variables. Table 3Variables Mean SD 1 2 3 4 5 6 7 8 1-UF 3.263 0.424 – 2-EU 3.348 0.336 0.329* – 3-PF 4.114 0.517 0.238 0.321 – 4-VL 3.179 0.427 0.332 0.225*** 0.234* – 5-EC 3.237 0.328 0.226* 0.139 0.215 0.187 – 6-WTP 3.192 0.518 0.147* 0.224 0.112 0.145* 0.217* – 7-PB 4.112 0.418 0.124* 0.138* 0.126* 0.212 0.215 0.164* – 8-DH 3.266 0.422 0.136* 0.213* 0.221* 0.138* 0.124** 0.112* 0.109* – 9-SPP 3.225 0.338 −0.109 0.215* 0.226** 0.213 0.195 0.213 0.118* −0.215* Note: N = 863, UF, Usefulness; EU, Easy of Use; PF, Professional; VL,Value; EC, Emotional Contagion; WTP, Willingness to Pay; PB, Panic Buying; DH, Digital Hoarding; SPP, Sensitivity to Pain of Payment. p < 0.05, p < 0.01, *p < 0.001. The existence of common method bias in the data set was tested by using the Harman's one-factor test. The items of all eight factors (e.g., usefulness, easy of use, professional, value, emotional contagion, willingness to pay, panic buying, and digital hoarding) were all combined into a single factor and compared with that of the eight-factor model. The goodness of fit indices of the one-factor model (χ2 = 1776.32, df = 484, p < 0.01, RMSEA = 0.16, CFI = 0.65, TLI = 0.63, SRMR = 0.08) were significantly poorer than those of the eight-factor model (χ2 = 789.82, df = 475, p < 0.01, RMSEA = 0.05, CFI = 0.92, TLI = 0.91, SRMR = 0.05) suggesting that common method bias is not a serious concern in our data set. The test results show that the considered study items explained 28.33% of the variance when extracted as a single factor, less than the suggested threshold of 50% [92]. 4.2 Confirmatory factor analysis Data on paid social Q&A characteristics (usefulness, easy of use, professional, and value), emotional contagion, willingness to pay, panic buying, digital hoarding, and sensitivity to pain of payment were collected at one time, therefore it was necessary to conduct Confirmatory Factor Analysis (CFA) to compare different models. The fit of the nine-factor model was then compared with two alternative model. The seven-factor model (one in which usefulness was combined with easy of use, and professional was combined with value) was estimated in this study. Another model (six-factor) was also estimated, in which paid social Q&A characteristics (usefulness, easy of use, professional, and value) were combined on one factor, emotional contagion remained as a second factor, willingness to pay remained as a third factor, panic buying remained as a forth factor, digital hoarding remained as a fifth factor, and sensitivity to pain of payment remained as a sixth factor. The fit indices of these alternative models were weak compared to the nine-factor model, providing support for the distinctiveness of the model used in this study. CFA results revealed that nine factors structure provided a better fit (χ2 = 387, df = 198, CFI = 0.94, GFI = 0.92, IFI = 0.93, RMSEA = 0.04, SRMR = 0.05) as compared to the alternative models (see Table 4 ).Table 4 Descriptive Statistics and Correlations among variables. Table 4Model χ2 Df CFI GFI IFI RMSEA SRMR Nine-factor model 387 198 0.94 0.92 0.93 0.04 0.05 Seven-factor model 533 235 0.88 0.89 0.89 0.09 0.07 Six-factor model 552 238 0.86 0.85 0.85 0.12 0.08 4.3 Structural model The model fit indices revealed that the model had a good fit with Comparative Fit Index (CFI) = 0.93, Incremental Fit Index (IFI) = 0.92, and Standard Root Mean Square Residual (SRMR) = 0.05, and Root Mean Square Error of Approximation (RMSEA) = 0.06. The results of the structural model are presented in Fig. 2 . Usefulness (β = 0.227, p < 0.01), easy of use (β = 0.315, p < 0.01), professional (β = 0.118, p < 0.01), and value (β = 0.338, p < 0.001) all had positive influences on emotional contagion, thus supporting H1a, H1b, H2a, and H2b. Emotional contagion positively influenced willingness to pay (β = 0.219, p < 0.01), supporting H3. Willingness to pay were shown to have positive effects on panic buying (β = 0.125, p < 0.05) and digital hoarding (β = 0.119, p < 0.01), supporting H4a and H4b.Fig. 2 Structural analysis results. Note: Standardized coefficients. Numbers in parentheses are standard errors. p < 0.05, p < 0.01, p < 0.001. Fig. 2 In conclusion, this study identified usefulness, easy of use, professional and value as important characteristics that motivate consumers to be attracted by paid social Q&A. In addition, consumers’ emotions are susceptible to infection by these four characteristics of paid social Q&A, which also effectively increases their willingness to pay, and leads to digital hoarding and panic buying. 4.4 Moderation analysis Analysis revealed (see Table 5 ) that control variables (e.g., gender, age, education, monthly income, and influence by COVID-19 and mobile payment use) did not explained any significant amount of variance in the willingness to pay (Step 1). Step 2 shows that the direct effect of emotional contagion was significant on willingness to pay (β = 0.427, p < 0.01). Step 3 shows the result of adding the interaction term (emotional contagion × sensitivity to pain of payment). As shown in Table 5 (step 3), the interactions (emotional contagion × sensitivity to pain of payment) was found significant for advertising attitudes (β = 0.125, p < 0.001). This analysis produced a significant main effect and a significant interaction effect (See Fig. 3 ).Table 5 Results for main effect and moderated regression analyses. Table 5Predictors Willingness to Pay β ΔR2 Step1: Control Variables Gender 0.013 Age −0.042 Education −0.083 Monthly Income 0.052 Influence by COVID-19 0.114 Mobile Payment Use 0.025 Step2: Main Effects Emotional Contagion 0.427* Sensitivity to Pain of Payment −0.068 0.285 Step3: Interaction Terms Emotional Contagion × Sensitivity to Pain of Payment 0.125* 0.018 Note: p < 0.01; ***p < 0.001. Fig. 3 The Moderating effect of Sensitivity to Pain of Payment on the relationship between EC and WTP. Fig. 3 To further reveal the interaction effect, a simple slope test was performed. As shown in Fig. 3, the results show that when low sensitivity to pain of payment (Spendthrift) (below one standard deviation), the influence of emotional contagion (EC) on willingness to pay (WTP) decreases, and the positive relationship between emotional contagion (EC) and willingness to pay (WTP) is not significant (β = 0.26, ns.); When high sensitivity to pain of payment (Tightwad) (above one standard deviation), the positive relationship between emotional contagion (EC) and willingness to pay (WTP) is significant (β = 0.43, p < 0.001), that is, Tightwad is more sensitive to the changes of emotional contagion. Thus, those high in sensitivity to pain of payment (Tightwads) will show more positive willingness to pay toward paid social Q&A. H5 is supported. 5 Discussion and conclusion 5.1 Discussion Firstly, results show that at the current stage of coexistence with COVID-19, the Usefulness (H1a) and Ease of Use (H1b) of information system quality characteristics positively affect user emotional contagion. At the same time, the Professional (H2a) and Value (H2b) of content quality characteristics positively affect user emotional contagion. This consequence means that the emotions of paid social Q&A users (including questioners, respondents and participants) can easily be transmitted to others on paid social Q&A platform which is highly useful and easy to use. Besides, emotional contagion can also easy to occur in paid social Q&A posts and communities with strong professional and valuable content. This results better confirm that the reason for the “resurgence” of paid social Q&A is not “COVID-19 boom”, the sharing mechanism [26] or the virtual scores incentive [24], but the information system quality characteristics and the content quality characteristics. Secondly, results also show that user emotional contagion positively affect their willingness to pay (H3). The determination of H3 confirms the preconditions for willingness to pay of paid social Q&A users (including questioners, respondents and participants). At the current stage of coexistence with COVID-19, they inquired, communicated and interacted in paid social Q&A platforms, posts and communities. Emotions are transmitted and infected among users through the mechanism of language-mediated association and active perspective taking. Significantly different from the free social Q&A, “payment” is a necessary operation. Therefore, the stronger degree of emotional contagion in paid social Q&A user experience, the stronger degree of the willingness to pay he will possess. For example, at the current stage of coexistence with COVID-19, others' situation described by the language in social Q&A induces other users to imagine the same situation, thus causing the same emotional experience [46]. When seeing the warm and touching questions and answers related to COVID-19 in the social Q&A (like words encouraging active response to the epidemic), users can be easily moved; when seeing sad and tearful contents (like people die or separate affected by the epidemic), users can easily feel sympathy for the roles in the contents, so they are more willing to pay and participate in it. Our research also confirmed that users can “observe the world from others’ eyes” through paid social Q&A platform [47]. They can find that they are not alone through paid social Q&A as other people around the world are suffering from more arduous difficulties. Thirdly, the hypotheses proposed in this study on the relationship between users’ willingness to pay for social Q&A services and panic buying (H4a) and digital hoarding (H4b) are also supported by the empirical results. Understanding the reasons for consumer panic buying and hoarding behaviors at the current stage of coexistence with COVID-19 can be useful to reduce negative impacts, control escalation and ensure preparedness for future consumption behavior [61]. Our research results show that at the current stage of coexistence with COVID-19 consumers not only panic buying or hoard physical products (like toilet paper, masks, antiseptic solution, rice, etc.), but also knowledge payment digital products. For example, during the period of COVID-19, 63.1% of Chinese consumers have purchased content products. The sales of paid social Q&A increase tremendously, which indirectly reflects that in a populous country, like in China, the economic and competitive pressure caused by COVID-19 have been further intensified. Emotions like fear and panic spread through various knowledge payment platforms. People are easily infected by the emotions of others. They are afraid of being laid off, so they generate the willingness to pay. They seek answers and helps by panic buying or hoarding social Q&A products to alleviate their fear and anxiety. Previous studies have shown that the panic buying behavior for physical goods is significantly stronger in wealthier geographical regions [14]. Unlike the supply chain shortage triggered by panic buying and hoarding for physical products, paid social Q&A products with its replicability rarely cause knowledge supply chain problems. We believe that during the epidemic, user panic buying and digital hoarding for paid social Q&A also have certain positive effects to some extent, which can help people better reduce their “fear of infection” and “fear of economic recession”, reduce their fear of death and unemployment, and even get improve their job security perception from the employment advice of paid social Q&A. However, the appropriateness of user panic buying and digital hoarding of social Q&A products is still worth to study further, as excessive panic buying and digital hoarding of knowledge paid products also do serious harm to user psychological resources. Besides, consumers can generate pain of payment in the consumption process, which plays an important role in restraining consumption [54]. Specially, lower level of pain of payment may strengthen consumer willingness to pay [33]. Previous studies on pain of payment have confirmed that digital payment produces lower pain than cash payment, which makes consumer willingness to pay stronger, that is, “the less pain, the more buying” [85,87,88]. The results of this study confirm the moderating effect of sensitivity to pain of payment (tightwad vs. spendthrift) (H5). Compared with the spendthrift, the tightwad are more likely to be infected by the emotions of other users in paid social Q&A, and their willingness to pay is also stronger. That is to say, users who are more sensitive to pain of payment are more susceptible to emotional contagion and their willingness to pay for paid social Q&A are stronger. The analysis of moderating effect showed with the increase of emotional contagion degree, users who have high level of sensitivity to pain of payment, their willingness to pay will increase. However, with the increase of emotional contagion degree, users who have low level of sensitivity to pain of payment maintains, their willingness to pay unchanged. One of the possible reason is that at the current stage of coexistence with COVID-19, even in the same digital payment environment, users with high sensitivity to pain of payment are more easier to get emotional contagion and are more willing to pay, that is, “the more pain, the more buying”. Our results extend the theoretical boundary of the research on pain of payment in the digital payment environment to a certain extent (that is, expanding the research paradigm from “the less pain, the more buying” to “the more pain, the more buying”). 5.2 Conclusion This study investigated the paid social Q&A characteristics (the usefulness, ease of use, professional and value) affect user willingness to pay through emotional contagion among platform users, and then lead to panic buying and digital hoarding at the current stage of coexistence with COVID-19. The SOBC framework was used for theoretical analysis. Because it helps to link the stimulus factor–paid social Q&A characteristics (the usefulness, ease of use, professional and value) with user internal psychological processes, which is reflected in user willingness to pay of paid social Q&A and their further panic buying and digital hoarding behaviors. Specifically, by collecting and analyzing 863 survey data of Chinese users, five research conclusions are obtained in this study: (1) the stronger level of the usefulness and ease of use in the information system quality characteristics is, the stronger level of the emotional contagion the users suffer; (2) the stronger level of the Professional and Value the content quality characteristics is, the stronger level of the emotional contagion the users suffer; (3) user emotional contagion is positively related to the willingness to pay in paid social Q&A; (4) the stronger level of users' willingness to pay for social Q&A, the more likely trigger the users’ panic buying and digital hoarding; (5) The moderating analysis shows that sensitivity to pain of payment moderates the relationship between the emotional contagion received by users on paid social Q&A platforms and their willingness to pay. Compared with the spendthrift, the tightwad are more susceptible to emotional contagion, and their willingness to pay is stronger. Finally, gender, age, education, monthly income, influence by COVID-19 and mobile payment use have no confounding effect on dependent variables. Therefore, this study has theoretical and practical significance to some extent. We confirm the psychological mechanism of panic buying and digital hoarding of paid social Q&A services, which makes up for current literature gap. At the same time, our findings can help different users (tightwad vs. spendthrift) to evaluate and adjust their digital content consumption control ability in the face of disasters. Besides, our findings also helps digital content platform enterprises master the core needs of different types of users in the disaster situation, enhance user stickiness, and achieve sustainable development. 5.3 Theoretical contributions This research makes some novelty and pioneer theoretical contributions to the research of panic buying of digital content products. At the current stage of coexistence with COVID-19, it is more appropriate for us to study consumer panic buying behavior from the perspective of emotional contagion. At the same time, this study also broke through the limitation of existing researches, combining panic buying, digital hoarding, emotional contagion and the paid social Q&A characteristics. We find that there is a significant positive correlation between the characteristics of paid social question and answer and emotional contagion. At the same time, there is a significant positive correlation between emotional contagion and panic buying, while emotional contagion is also positively correlated with digital hoarding. The findings not only broaden the research scope of paid social Q&A characteristics and panic buying, but also further promote the development of research in the field of digital content consumption. The first outstanding contribution in this research is to investigate the incentives of panic buying and digital hoarding behaviors for paid social Q&A products from the perspective of emotional contagion, which expands the scope of existing research. Base on the SOBC model, this study provides new evidence for the argument that consumers' emotional contagion can significantly influence their buying behavior, and expands the research on the antecedents of panic buying. We believe that the essence of this process of emotional infection through paid social Q&A platform is a digital emotional contagion. Digital emotional contagion should be understood as the mediated emotional contagion. As its intermediary, the goal of digital media companies is to expose individuals to stronger emotions with higher frequency [72], thus bringing more consumption. A previous survey evidence from Japan suggest that it has both bright and dark sides of social media usage during the COVID-19 [93]. Similar, will the sales promotion of the social Q&A products based on digital emotional contagion deepen the “involution” and cause more anxiety and psychological damage to users? Or will it help users resist the psychological stress caused by disasters such as COVID-19? These are still important issues worth exploring in academic circles and digital media companies. Our research provides an exploratory idea for further research. Secondly, this study has important theoretical guidance for the practical application of the SOBC model in the paid social Q&A user group. The results show that the information system quality characteristics and the content quality characteristics of paid social Q&A can affect the probability of user emotional contagion, and trigger their willingness to pay, which in turn will cause panic buying and digital hoarding behaviors. This result not only expands the research of the influence brought by panic buying, but also further expands the study on the antecedents of emotional contagion. As billions of people were forced to stay at home at the current stage of coexistence with COVID-19, the number of users of paid social Q&A increased dramatically. In this unique environment, our research further confirms that the paid social Q&A characteristics play an important role in triggering consumer individual emotions and spreading group emotions. Thirdly, this study firstly introduce the conception of sensitivity to pain of payment as the moderator in the field of paid social Q&A, and find that the positive relationship between emotional contagion and user willingness to pay for paid social Q&A is moderated by the sensitivity to pain of payment. The sensitivity to pain of payment provides a reference framework for distinguishing different consumer (tightwad vs. spendthrift) possibility of emotional contagion at the current stage of coexistence with COVID-19 and their ability of consumption control when facing panic situations (whether panic buying and digital hoarding will occur). The conclusion enriches the coping mechanism to prevent panic buying and digital hoarding. Finally, different from previous studies on panic buying for consumer general daily necessities at the current stage of coexistence with COVID-19, we explore the impact of paid social Q&A products on panic buying and digital hoarding through the digital content industries. We believe that user panic buying and digital hoarding behaviors for social Q&A also has certain positive effects, which can help people better reduce their “fear of infection” and “fear of economic recession”, reduce their fear of death and unemployment, and improve their sense of job security. However, the appropriateness of user panic buying and digital hoarding of social Q&A services is still a problem worthy for deeper study. 5.4 Managerial implications The COVID-19 has caused great obstacles to the global economy, resulting in serious economic recession, companies and industries closing and unemployment rate rising [94]. Therefore, the current research on panic buying is of great value for current global marketing and enterprise decision-making [95]. Reviewing previous studies, digital content payment has become a new consumption trend in China especially in recent years. The results of the study have important practical significance for corporate decision makers, marketing managers of the social Q&A platform, consumers, media and government departments. Firstly, this study shows that the paid social Q&A characteristics are positively related to emotional contagion. Emotional contagion can easily trigger user willingness to pay, and ultimately lead to panic buying and digital hoarding. Therefore, it is necessary for the enterprise decision makers and marketing managers of the social Q&A platform to understand the emotional perception and needs of users at the current stage of coexistence with COVID-19, deeply cultivate high-quality and healthy content, prevent false news and bad advertising, reduce the spread of panic emotions, and guide consumers to consume rationally, so as to effectively improve the development performance of the paid social Q&A products. Secondly, this study also obtained an interesting but very important finding: sensitivity to pain of payment moderates the relationship between emotional contagion and willingness to pay. Users with higher level of sensitivity to pain of payment are more vulnerable to emotional contagion, stimulate their willingness to pay, and eventually trigger panic buying and digital hoarding. At the current stage of coexistence with COVID-19, even in the same digital payment environment, user sensitivity to pain of payment is different. Users with stronger level of pain of payment (the tightwad) are more likely to get emotional contagion through paid social Q&A, and are more likely to generate their willingness to pay, that is, “the more pain, the more buying”. In this sense, the sensitivity to pain of payment promotes the consumption behavior of digital content to a certain extent. This interesting discovery is a new interpretation of the traditional research viewpoint that “pain payment can inhibit consumer behavior” [54] under the background of Internet. Finally, paid social Q&A platform enterprises and government departments can take some stabilization measures to reduce panic, which may effectively reduce user panic buying and digital hoarding behaviors. 5.5 Limitation and future research This study has made timely and important theoretical and practical contributions to the field of consumer behavior by investigating the potential impact of the paid social Q&A characteristics at the current stage of coexistence with COVID-19 on panic buying and digital hoarding behaviors. However, there are still some limitations. Firstly, this study only explored the role of a single cognitive and emotional mechanism (emotional contagion) in the relationship between the paid social Q&A characteristics and panic buying. However, as situational simulations, the paid social Q&A characteristics may also stimulate other cognitive and emotional factors (such as self-construction and prosocial motivation) that may affect panic buying. Besides, only one individual factor is discussed as a moderator. Future research can further explore other cognitive affective factors (such as Openness of social Q&A platform and user absorption capacity) that effect the relationship between the paid social Q&A characteristics and panic buying by establishing a structural equation model. Secondly, this study adopts the cross-sectional survey method, which can only study the concurrency relationship between variables. And the data is collected in China, even if the relevant data in China are typical. The data collection adopts the self-report questionnaire method, and the respondents may make some concealment of the real information. Also, the research itself is affected by many unexpected factors, such as the interference of the respondents’ answering environment and emotional fluctuations, which will affect the research results. For future research, relevant data from multiple countries can be used for cross-national and cross-cultural comparative research. Besides various measurement methods can be used, such as eye movement experiments and case analysis to measure variables more accurately, and longitudinal tracking or situational experiments to analyze the deeper causal relationship between variables. 6 Note 1. In 2020, Iimedia Consulting reported on the research and analysis of the operation and development of China’ s knowledge payment industry and user behavior in 2020, saying that during the prevention and control of novel coronavirus epidemic, the offline entity business was greatly impacted, while the knowledge payment industry revealed many development opportunities. About 90% of online learning users have purchased paid knowledge products, and the most frequently purchased products are column subscriptions, paid courses and paidsocial Q&A. (Source: https://www.iimedia.cn/c460/69261.html). 2. In 2022, Zhihu put forward the strategy of “ecology first”, striving to build a high-quality commercial ecology that matches the pace of community development, and making efforts to supply high-quality content by encouraging creators. As of March 31, 2022, the cumulative amount of content in Zhihu reached 523 million pieces, with the quarterly average daily activity of high-level creators increasing by more than 45%, and the daily average content creation increasing by more than 125%. (Source:https://xw.qq.com/amphtml/20220525A0AL5106). Funding details This work was supported by the 10.13039/501100012456 National Social Science Foundation of China (Grant No.22BGL318). Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Yajuan Wang ([email protected]) is a Senior Lecturer at the School of Business Administration, Zhejiang Gongshang University. She is also the Head of Experimental Psychology Course at Zhejiang Gongshang University. She holds a doctorate in management and postdoctoral experience from Dongbei University of Finance and Economics. Before joining the academic community, she worked as a Senior User Experience Designer in the field of User Experience, and provided consulting for Digital Content Platform Design and Marketing Decisions of Enterprises. Her main research interests are Knowledge Payment, UGC, Social Media Marketing and Digital Content Marketing. Her research has been published in Journal of Management Science, Academic Exchange, Chinese Journal of Management Science and Frontiers in Psychology. Austin Shijun Ding ([email protected]) is a Lecturer of Computer Science at the Sobey School of Business at Saint Mary's University. He was a research assistant with the Department of Finance, Information Systems, and Management Science, from Jan 3rd, 2013 to April 30th, 2017. He is currently an active member of the Canadian Union of Public Employees (CUPE), 3912. His current research interests include Agile Manifesto, Business Analytics, Data Mining, and Knowledge Management. Chonghuan Xu ([email protected]) is an Associate Professor at the School of Business Administration, Zhejiang Gongshang University. He was a Visiting Fellow with the Department of Information System, City University of Hong Kong, from March 1, 2019 to August 31, 2019. His current research interests include Electronic Commerce, Consumer Behavior, Marketing Communication, and Personalized Recommendation. His work has been published in Information Processing & Management, Knowledge-based systems, Electronic Commerce Research and Applications, Multimedia tools and applications, Human-centric Computing and Information Sciences, Psychology Research and Behavior Management and Frontiers in Public Health. Data availability Data will be made available on request. ==== Refs References 1 Arafat S.M.Y. Kar S.K. Menon V. Marthoenis M. Marthoenis M. Sharma P. Alradie-Mohamed A. Mukherjee S. Kaliamoorthy S. Kabir R. Media portrayal of panic buying: a content analysis of online news portals Global Psychiatry 3 2020 249 254 2 Wang X. Shi W. Yuen K.F. A synthesised review of pandemic-driven consumer behaviours and applied theories: towards a unified framework Int. J. Disaster Risk Reduc. 77 2022 103104 3 Petra J. Petra K. Digital consumer behaviour and eCommerce trends during the COVID-19 crisis Int. Adv. Econ. Res. 27 2021 83 85 4 Choy V. Perishable information in an increasingly mobile world New Rev. Inf. Netw. 16 2 2011 141 150 5 Arafat S.M.Y. Kar S.K. Menon V. Alradie-Mohamed A. Mukherjee S. Kaliamoorthy S. Kabir R. Responsible factors of panic buying: an observation from online media reports Front. Public Health 8 2020 1 6 32117846 6 Bermes A. Information overload and fake news sharing: a transactional stress perspective exploring the mitigating role of consumers' resilience during COVID-19 J. Retailing Consum. Serv. 61 2021 102555 7 Singh G. Aiyub A.S. Greig T. Naidu S. Sewak A. Sharma S. Exploring panic buying behaviour during the COVID-19 pandemic: a developing country perspective Int. J. Emerg. Mark. 2021 In press 8 Yuen K.F. Leong J.Z.E. Wong Y.D. Wang X. Panic buying during COVID-19:Survival psychology and needs perspectives in deprived environments Int. J. Disaster Risk Reduc. 62 2021 102421 9 Chua G. Yuen K.F. Wang X. Wong Y.D. The determinants of panic buying during COVID-19 Int. J. Environ. Res. Publ. Health 18 6 2021 3247 10 Bentall R.P. Lloyd A. Bennett K. Mckay R. Mason L. Murphy J. McBride O. Hartman T.K. Gibson-Miller J. Levita L. Pandemic buying: testing a psychological model of over-purchasing and panic buying using data from the United Kingdom and the republic of Ireland during the early phase of the COVID-19 pandemic PLoS One 16 1 2021 e0246339 11 Im H. Kim N.L. Lee H.K. Why did (some) consumers buy toilet papers? A cross-cultural examination of panic buying as a maladaptive coping response to COVID-19 J. Consum. Aff. 56 1 2021 391 431 12 David J. Visvalingam S. Norberg M.M. Why did all the toilet paper disappear? distinguishing between panic buying and hoarding during COVID-19 Psychiatr. Res. 303 2021 114062 13 O'Connell M. de Paula Á. Smith K. Preparing for a pandemic: spending dynamics and panic buying during the COVID-19 first wave Fisc. Stud. 42 2021 249 264 34176997 14 Elek P. Bíró A. Fadgyas-Freyler P. Income gradient of pharmaceutical panic buying at the outbreak of the COVID-19 pandemic Health Econ. 30 9 2021 2312 2320 34218496 15 Arafat S.M.Y. Kumar S.K. Sex during Pandemic: panic buying of sex toys during COVID-19 lockdown Journal of Psycho sexual Health 3 1 2021 175 177 16 Lehberger M. Kleih A.K. Sparke K. Panic buying in times of coronavirus (COVID-19): extending the theory of planned behavior to understand the stockpiling of nonperishable food in Germany Appetite 161 2021 105118 17 Ezat S. Fatemeh G. Mina N. Mohammad H.R. Perception risk, preventive behaviors and assessing the relationship between their various dimensions: a cross-sectional study in the COVID-19 peak period Int. J. Disaster Risk Reduc. 77 2022 103093 18 Li J. Zhang S. Wang W. Wang Y. Research on the influencing factors of user's online knowledge consumption behavior Procedia Comput. Sci. 122 2017 174 179 19 Li Z. Detlor B. Catherine E. Connelly sharing knowledge in social Q&A sites: the unintended consequences of extrinsic motivation J. Manag. Inf. Syst. 33 1 2016 70 100 20 Fancourt D. Steptoe A. Bu F. Trajectories of anxiety and depressive symptoms during enforced isolation due to COVID-19 in England: a longitudinal observational study Lancet Psychiatr. 8 2 2021 141 149 21 Mazza C. Ricci E. Biondi S. Colasanti M. Ferracuti S. Napoli C. Roma P. A nationwide survey of psychological distress among Italian people during the COVID-19 pandemic: immediate psychological responses and associated factors Int. J. Environ. Res. Publ. Health 17 9 2020 3165 22 Xiong J. Lipsitz O. Nasri F. Lui L.M.W. Gill H. Phan L. McIntyre R.S. Impact of COVID-19 pandemic on mental health in the general population: a systematic review J. Affect. Disord. 277 2020 55 64 32799105 23 Shi L. Lu Z. Que J. Huang X. Liu L. Ran M. Lu L. Prevalence of and risk factors associated with mental health symptoms among the general population in China during the coronavirus disease 2019 pandemic JAMA Netw. Open 3 7 2020 11381 24 Zhao L. Detlor B. Connelly C.E. Sharing knowledge in social Q&A sites: the unintended consequences of extrinsic motivation J. Manag. Inf. Syst. 33 1 2016 70 100 25 Liu Z. Zhao Y.C. Chen S. Song S. Hansen P. Zhu Q. Exploring askers' switching from free to paid social Q&A services: a perspective on the push-pull-mooring framework Inf. Process. Manag. 58 1 2021 102396 26 Ye H. Yang X.P. Wang X.W. Stratopoulos T.C. Monetization of digital content: drivers of revenue on Q&A platforms J. Manag. Inf. Syst. 38 2 2021 457 483 27 Kuang L. Huang N. Hong Y. Yan Z. Spillover effects of financial incentives on non-incentivized user engagement: evidence from an online knowledge exchange platform J. Manag. Inf. Syst. 36 1 2019 289 320 28 Islam T. Pitafi A.H. Arya V. Wang Y. Naeem A. Mubarik S. Liang X.B. Panic buying in the COVID-19 pandemic: a multi-country examination J. Retailing Consum. Serv. 59 2021 102357 29 Yu L. Chen Z. Yao P. Liu H. A study on the factors influencing users' online knowledge paying-behavior based on the UTAUT model Journal of Theoretical and Applied Electronic Commerce Research 16 5 2021 1768 1790 30 Ferrara E. Yang Z. Measuring emotional contagion in social media PLoS One 10 11 2015 e0142390 31 Gao X. Liu Z. Cai Y. Liu T. Wang J. Research on emotional contagion model in social media Chinese Journal of Applied Psychology 25 5 2019 372 384 32 Rick S.I. Cryder C.E. Loewenstein G. Tightwads and spendthrifts J. Consum. Res. 3 6 2008 767 782 33 Mazar N. Plassmann H. Robitaille N. Linder A. The origin of the pain of paying Ahluwalia R. Chartrand T.L. Ratner R.K. NA-Advances in Consumer Research vol. 39 2011 Duluth, America: Association for Consumer Research 147 148 34 Davis T.R.V. Luthans F. A social learning approach to organizational behavior Acad. Manag. Rev. 5 2 1980 281 290 35 Kumar S. Talwar S. Krishnan S. Kaur P. Dhir A. Purchasing natural personal care products in the era of fake news? the moderation effect of brand trust J. Retailing Consum. Serv. 63 2021 102668 36 Talwar S. Jabeen F. Tandon A. Sakashita M. Dhir A. What drives willingness to purchase and stated buying behavior toward organic food? A Stimulus - organism - Behavior - consequence (SOBC) perspective J. Clean. Prod. 293 2021 125882 37 Bigne E. Chatzipanagiotou K. Ruiz C. Pictorial content, sequence of conflicting online reviews and consumer decision-making: the stimulus-organism-response model revisited J. Bus. Res. 115 2020 403 416 38 Jing J. Lu C. A study on factors affecting Chinese users' willing to pay for online paid knowledge contents: focusing on theory of planned behavior Journal of Digital Convergence 18 2 2020 151 162 39 Shah C. Radford M.L. Connaway L.S. Choi E. Kitzie V. How much change do you get from 40$? analyzing and addressing failed questions on social Q&A Proceedings of the Association for Information Science and Technology 49 1 2012 1 10 40 Zheng Y.M. Zhao K. Stylianou A. The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: an empirical investigation Decis. Support Syst. 56 2013 513 524 41 Zhang F. Hu Q. Fang X. Why pay? An empirical study of paid-for SQA sites in China Online Inf. Rev. 43 7 2019 1302 1315 42 Du Z.T. Xu J.H. From demand to experience: impact factors of users' online knowledge payment behavior Journal. Commun. 25 10 2018 19 39 43 Kim H.W. Xu Y. Gupta S. Which is more important in Internet shopping, perceived price or trust? Electron. Commer. Res. Appl. 11 3 2012 241 252 44 Hsee C.K. Hatfield E. Chemtob C. Assessments of the emotional States of others: conscious judgments versus emotional contagion J. Soc. Clin. Psychol. 11 2 1992 119 128 45 Du J.G. Fan X.C. Feng T.J. Multiple emotional contagions in service encounters J. Acad. Market. Sci. 39 3 2011 449 466 46 Hoffman M.L. How automatic and representational is empathy, and why BBS (Behav. Brain. Sci.) 25 1 2002 38 39 47 Galinsky A.D. Ku G. Wang C.S. Perspective taking and self-other overlap: fostering social bonds and facilitating social coordination Group Process. Intergr. Relat. 8 2 2005 109 124 48 Kramer A.D.I. Guillory J.E. Hancock J.T. Experimental evidence of massive-scale emotional contagion through social networks Proc. Natl. Acad. Sci. U. S. A. 111 24 2014 8788 8790 24889601 49 M.G.Wheaton, A.Prikhidko, G.R.Messner, Is fear of COVID-19 contagious? The effects of emotion contagion and social media use on anxiety in response to the coronavirus pandemic, Front. Psychol.. 11 (2021),1-9. 50 Zeithaml V.A. Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence J. Market. 52 3 1988 2 22 51 Hanemann W.M. Willingness to pay and willingness to accept:How much can they differ? Am. Econ. Rev. 81 3 1991 635 647 52 Jan S.T. Wang C. Zhang Q. Wang G. Pay-per-question: towards targeted Q&A with payments Proceedings of the 2018 ACM Conference on Supporting Groupwork 2018 1 11 53 ZhangJiang S. Xiao Y. Cheng Y. Global challenges and developmental lessons in the knowledge sharing economy J. Global Inf. Technol. Manag. 21 3 2018 167 171 54 Zellermayer O. The Pain of paying:Doctoral Dissertation, Department of Social and Decision Sciences 1996 Carnegie Mellon University 55 Shah A.M. What's Pain Got to Do with it? How the Pain of Payment Influences Our Choices and Our Relationships 2015 Duke University Dissertation 56 Singh C.K. Rakshit P. A critical analysis to comprehend panic buying behaviour of Mumbaikar’ s in COVID-19 era Studies in Indian Place Names 40 69 2020 44 51 57 Van B.J.J. Baicker K. Boggio P.S. Capraro V. Cichocka A. Cikara M. Willer R. Using social and behavioural science to support COVID-19 pandemic response Nat. Human Behav. 4 5 2020 460 471 32355299 58 Sharma P. Kar S.K. Menon V. Charanya R. Srijeeta M. Angi A.M. Marthoenis M. Russell K. Arafat S.M.Y. Panic buying: is it a normal social construct? The Anatolian Journal of Family Medicine 3 3 2020 270 271 59 Arafat S.M.Y. Singh R. Menon V. Sivasubramanian M. Kabir R. Panic buying research: a systematic review of systematic reviews, South East Asia J. Med. Sci. 6 1 2022 1 11 60 Mamun M.A. Ullah I. COVID-19 suicides in Pakistan, dying off not COVID-19 fear but poverty? The forthcoming economic challenges for a developing country Brain Behav. Immun. 87 2020 163 166 32407859 61 Li X. Zhou Y.S. Wong Y.D. Wang X.Q. Yuen K.F. What influences panic buying behaviour? A model based on dual-system theory and stimulus-organism-response framework Int. J. Disaster Risk Reduc. 64 2021 102484 62 Campos P.O. Mélo L.B. Souza J.C.V. Santana P.N. Matte J. Costa M.F. Consumer fear and healthy eating during COVID-19 pandemic Market. Intell. Plann. 40 2021 227 241 63 Siddiqi U.I. Akhtar N. Islam T. Restaurant hygiene attributes and consumers' fear of COVID-19: does psychological distress matter? J. Retailing Consum. Serv. 67 2022 102972 64 Tolin D.F. Frost R.O. Steketee G. Gray K.D. Fitch K.E. The economic and social burden of compulsive hoarding Psychiatr. Res. 160 2 2008 200 211 65 Gormley C.J. Gormley S.J. Data hoarding and information clutter: the impact on cost, life span of data, effectiveness, sharing, productivity and knowledge management culture Issues in Information Systems 13 2 2012 90 95 66 van Bennekom M.J. Blom R.M. Vulink N. Denys D. A case of digital hoarding BMJ Case Rep. 2015 2015 bcr2015210814 67 Davis F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology MIS Q. 13 1989 319 340 68 Moores T.T. Towards an integrated model of IT acceptance in healthcare Decis. Support Syst. 53 3 2012 507 516 69 Cheng X.Y. Liu K.F. A study on the factors influencing user’ s willing to pay for mobile reading Research on Library Science 16 2017 87 96 70 Reibstein D.J. What attracts customers to online stores and what keeps them coming back? J. Acad. Market. Sci. 30 4 2002 465 473 71 Cai S. Luo Q. Xin F. Fang B. What drives the sales of paid knowledge products? a two-phase approach Inf. Manag. 57 5 2020 103264 72 Goldenberg A. Gross J.J. Digital emotion contagion Trends Cognit. Sci. 24 4 2020 316 328 32160568 73 Zhao Y.C. Peng X. Liu Z. Song S. Hansen P. Factors that affect asker’ s pay intention in trilateral payment‐based social Q&A platforms: from a benefit and cost perspective Journal of the Association for Information Science and Technology 71 5 2020 516 528 74 Maitner A.T. Mackie D.M. Smith E.R. Evidence for the regulatory function of intergroup emotion: emotional consequences of implemented or impeded intergroup action tendencies J. Exp. Soc. Psychol. 42 6 2006 720 728 75 Hsiao K.L. Why interment users are willing to pay for social networking services Online Inf. Rev. 35 5 2011 770 788 76 Abosedra S. Laopodis N.T. Fakih A. Dynamics and asymmetries between consumer sentiment and consumption in pre- and during- COVID-19 time: evidence from the US J. Econ. Asymmetries 24 2021 e00227 77 Wiederhold B.K. Using social media to our advantage: alleviating anxiety during a pandemic Cyberpsychol., Behav. Soc. Netw. 23 4 2020 197 198 32207630 78 Eger L. Komárková L. Egerová D. Miík M. The effect of COVID-19 on consumer shopping behaviour: generational cohort perspective J. Retailing Consum. Serv. 61 2021 102542 79 Ahmadi I. Habel J. Jia M. Wei S. Consumer stockpiling under the impact of a global disaster: the evolution of affective and cognitive motives J. Bus. Res. 142 2022 56 71 80 Sweeten G. Sillence E. Neave N. Digital hoarding behaviours: underlying motivations and potential negative consequences Comput. Hum. Behav. 85 2018 54 60 81 Fritze M.P. Eisingerich A.B. Benkenstein M. Digital transformation and possession attachment: examining the endowment effect for consumers' relationships with hedonic and utilitarian digital service technologies Electron. Commer. Res. 19 2 2019 311 337 82 Raghubir P. Srivastava J. Monopoly money: the effect of payment coupling and form on spending behavior J. Exp. Psychol. 14 3 2008 213 225 83 Guo T. Zhong S. Wang X. Li G. Does product display quantity increase purchase intention? The mediation of diminished pain of payment J. Res. Indian Med. 16 1 2022 101 117 84 Moore A. Taylor M. Time to cut up those debit cards? Effect of payment mode on willingness to spend J. Consum. Pol. 34 4 2011 415 422 85 Helion C. Gilovich T. Gift cards and mental accounting: green-lighting hedonic spending J. Behav. Decis. Making 27 4 2014 386 393 86 Thomas M. Desai K.K. Seenivasan A.S. How credit card payments increase unhealthy food purchases: visceral regulation of vices J. Consum. Res. 38 1 2011 126 139 87 Kamleitner B. Erki B. Payment method and perceptions of ownership Market. Lett. 24 1 2013 57 69 88 Shah A.M. Noah E. Bettman J.R. Chartrand T.L. Paper or plastic? how we pay influences post-transaction connection J. Consum. Res. 42 5 2016 699 708 89 Davis M.H. A multidimensional approach to individual differences in empathy J. Pers. Soc. Psychol. 10 1980 85 90 Lins S. Aquino S. Development and initial psychometric properties of a panic buying scale during COVID-19 pandemic Heliyon 6 9 2020 e04746 91 Neave N. Briggs P. Mckellar K. Sillence E. Digital hoarding behaviours: measurement and evaluation Comput. Hum. Behav. 96 2019 72 77 92 Podsakoff P.M. MacKenzie S.B. Podsakoff N.P. Sources of method bias in social science research and recommendations on how to control it Social Science Electronic Publishing 63 1 2012 539 569 93 Cato S. Iida T. Ishida K. Ito A. Katsumata H. McElwain K. Shoji M. The bright and dark sides of social media usage during the COVID-19 pandemic: survey evidence from Japan Int. J. Disaster Risk Reduc. 54 2021 102034 94 Loxton M. Truskett R. Scarf B. Sindone L. Baldry G. Zhao Y. Consumer behaviour during crises: preliminary research on how coronavirus has manifested consumer panic buying, herd mentality, changing discretionary spending and the role of the media in influencing behaviour J. Risk Financ. Manag. 13 8 2020 166 95 Yuen K.F. Tan L.S. Wong Y.D. Wang X. Social determinants of panic buying behaviour amidst covid-19 pandemic: the role of perceived scarcity and anticipated regret J. Retailing Consum. Serv. 66 2022 102948	36465701	PMC9708103	NO-CC CODE	2022-12-06 23:15:54	no	Int J Disaster Risk Reduct. 2023 Jan 30; 84:103472	utf-8	Int J Disaster Risk Reduct	2,022	10.1016/j.ijdrr.2022.103472	oa_other
==== Front J Infect Public Health J Infect Public Health Journal of Infection and Public Health 1876-0341 1876-035X The Authors. Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. S1876-0341(22)00323-9 10.1016/j.jiph.2022.11.028 Original Article Primary and Booster Vaccination in Reducing Severe Clinical Outcomes Associated with Omicron Naïve Infection Hsu Chen-Yang ab Chang Jung-Chen cd Chen Sam Li-Shen e Chang Hao-Hsiang f Lin Abbie Ting-Yu g Yen Amy Ming-Feng e⁎1 Chen Hsiu-Hsi g⁎⁎1 a Master of Public Health Program, College of Public Health, National Taiwan University, Taipei, Taiwan b Daichung Hospital, Miaoli, Taiwan c School of Nursing, College of Medicine, National Taiwan University, Taipei, Taiwan d Department of Nursing, National Taiwan University Hospital, Taipei,Taiwan e School of Oral Hygiene, College of Oral Medicine, Taipei Medical University, Taipei, Taiwan f Department of Family Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan g Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan ⁎ Correspondence to: 250 Wu-Hsing Street, Taipei city, Taiwan 110. Fax: +886-2-2739-8004 ⁎⁎ Correspondence to: Room 533, No. 17, Hsuchow Road, Taipei, 100, Taiwan. Fax: +886-2-23587707 1 The authors contributed equally to this work 30 11 2022 30 11 2022 12 10 2022 21 11 2022 24 11 2022 © 2022 The Authors. Published by Elsevier Ltd on behalf of King Saud Bin Abdulaziz University for Health Sciences. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Little is known about long-term effectiveness of COVID-19 vaccine in reducing severity and deaths associated with Omicron VOC not perturbed by prior infection and independent of oral anti-viral therapy and non-pharmaceutical (NPI). Methods A retrospective observational cohort study was applied to Taiwan community during the unprecedent large-scale outbreaks of Omicron BA.2 between April and August, 2022. Primary vaccination since March, 2021 and booster vaccination since January, 2022 were offered on population level. Oral Anti-viral therapy was also offered as of mid-May 2022. The population-based effectiveness of vaccination in reducing the risk of moderate and severe cases of and death from Omicron BA.2 with the consideration of NPI and oral anti-viral therapy were assessed by using Bayesian hierarchical models. Results The risks of three clinical outcomes associated with Omicron VOC infection were lowest for booster vaccination, followed by primary vaccination, and highest for incomplete vaccination with the consistent trends of being at increased risk for three outcomes from the young people aged 12 years or below until the elderly people aged 75 years or older with 7 age groups. Before the period using oral anti-viral therapy, complete primary vaccination with the duration more than 9 months before outbreaks conferred the statistically significant 47% (23-64%) reduction of death, 48% (30-61%) of severe disease, and 46% (95% CI: 37-54%) of moderate disease after adjusting for 10-20% independent effect of NPI. The benefits of booster vaccination within three months were further enhanced to 76% (95% CI: 67-86%), 74% (95% CI: 67-80%), and 61% (95% CI: 56-65%) for three corresponding outcomes. The additional effectiveness of oral anti-viral therapy in reducing moderate disease was 13% for the booster group and 5.8% for primary vaccination. Conclusions We corroborated population effectiveness of primary vaccination and its booster vaccination, independent of oral anti-viral therapy and NPI, in reducing severe clinical outcomes associated with Omicron BA.2 naïve infection population. Keywords COVID-19 Vaccine effectiveness Omicron-naïve oral antiviral therapy ==== Body pmcIntroduction COVID-19 pandemic has evolved with SARS-CoV-2 variants accompanied with various mitigation strategies adopted by relevant containment measures when health decision-makers are faced with the interplay between the emerging variants and available containment measures mainly including NPI, vaccine, and anti-viral therapy. During the wild type and D614G period, NPIs combined with RT-PCR testing is the only mitigation strategy for containing the epidemic of COVID-19. When SARS-CoV-2 variants has further evolved into the emerging variant of concern (VOCs) the discovery and the delivery of vaccine has grown parallelly in chronological order. Health authority would have much cherished full vaccination as the mitigation strategy for lifting NPI in order to return to pre-pandemic life [1]. Unfortunately, as most of vaccines have been developed before the emergence of VOCs the efficacy of mRNA and vector-based RNA vaccines have been challenged by the waning immunity due to the evasiveness of immunity beginning from Delta VOC until the recent Omicron VOC [2], [3], [4], [5]. There are several studies that have reported the reduced effectiveness of vaccine in protecting symptomatic infection during the Omicron VOC period [2], [3]. Rapid testing, booster vaccination, and personal strategy of NPI such as masking and social distancing are mandatory [1], [6]. Evaluation of effectiveness of vaccine in different countries and different periods plays an important role in a better understanding whether and how the effectiveness of vaccine has been attenuated due to the waning immunity of first generation of vaccine. These also include the effectiveness of vaccine in reducing moderate and severe cases and death from COVID-19, which are particularly important for the recent Omicron pandemic [1], [7], [8]. It should be noted that most countries worldwide have had a series of COVID-19 outbreaks from the wild type or D614G until Delta VOC evaluating the effectiveness of vaccination in reducing the severity and deaths may be affected by the possible T-cell mediated memory immune response rusting from antecedent infections [1], [8], [9], [10]. The purified effectiveness at population level as a result of full-dose and booster vaccination still remain elusive. In Taiwan, as there has not been a large-scale community-acquired outbreak before the emergence of Omicron BA.2 [11], the Taiwanese cohort together with naïve Omicron infection provides an opportunity of evaluating pure effectiveness of primary and booster vaccination in reducing the severity of and death from COVID-19 without being largely confounded by prior infection resulting from the antecedent large-scale community-acquired outbreaks. The aim of this study was to estimate the effectiveness of booster and full-dose vaccination in reducing moderate and severe disease and death from COVID-19 Omicron by using population-based vaccination program in Taiwan where naïve Omicron infection hit the underlying community residents whom had been offered with various kinds of vaccines, albeit dominated by m-RNA types, four-fifths for full-dose and two-thirds for booster vaccination. Materials and Methods Study Design and Subjects On the basis of 23 million residents dwelling in Taiwan, a prospective cohort for assessing vaccine effectiveness was first formed and classified by vaccination status, including incomplete vaccination (the unvaccinated and the vaccinated with one dose), the full primary vaccinated (two-dose), and the booster group before the confirmation as an COVID-19 case between April and August in 2022. This cohort was followed over time to ascertain the following main outcomes including moderate and severe cases, and deaths among confirmed cases during community-acquired outbreak of Omicron VOCs from 20 April up to 13 August, 2022. Fig. 1 shows the framework of study design and study subjects by vaccination status and the frequencies of the main outcomes of each category.Fig. 1 Study flowchart for assessing vaccine effectiveness in reducing the severity of and deaths from COVID-19 disease spectrum. Mod: moderate, Sev: severe. Fig. 1 As we are interested in population-based effectiveness of vaccination but such an evaluation would be also affected by the operation of NPIs on population level, two-stage level confounding would be considered. At first level, we made allowance for age distribution of exposure (vaccination status) and outcomes (moderate and severe COVID-19, and death) because age is representative of the main confounding for population effectiveness affected by transmission and evasive immunity. Age would be adjusted in the following statistical models for removing such confounding effects. The second level influence of population effectiveness of vaccine may be conferred by certain NPIs such as facial masking, social distancing, and personal hygiene which have been still in operation in Taiwan. To make allowance for such an influence from NPIs, age-specific infection rates of confirmed cases are taken into account in the model. The estimates regarding the effect size of population-based effectiveness of vaccination based on the first-level model were compared with those based on the two-stage level model. So doing enables us to estimate the effectiveness of NPIs in preventing the spectrum of COVID-19 disease spectrum. Collecting the Information on COVID-19 Disease Spectrum The study period covering April to August, 2022, by when the major community-acquired outbreak took place with the Omicron BA.2 as the dominant strain responsible for more than 96% of cases in Taiwan [12]. The information on vaccination status (incomplete, full-dose, and booster dose as defined above), age, and disease severity (moderate, severe, and death) were retrieved from the digital COVID-19 surveillance platform maintained and reported on a daily basis by Central Epidemic Command Center (CECC), Taiwan and Taiwan Centre for Disease Control.12 The disease severity for COVID-19 patients was categorized by using the TCDC clinical guideline.13 Moderate COVID-19 patients were defined for subjects with any of the presentations including SpO2 <94% on room air, respiratory rate > 30 breaths/min, PaO2/FiO2≤300, or lung infiltration > 50% on plain film and requiring low flow oxygenation supplement. Severe COVID-19 disease was defined for patients with any of the presentations including lung infiltration > 50%, PaO2/FiO2 ≤ 300 and requiring high flow oxygenation therapy provided by mask or non-invasive positive pressure ventilator or mechanical ventilation or extracorporeal membrane oxygenation (ECMO) as treatment modality. Patients with clinical evidence of organ failure or shock were categorized as having severe COVID-19 disease [13]. Statistical Analysis The outcomes of COVID-19 disease spectrum including moderate and severe disease status and death were treated as ordered categorical data. A Bayesian cumulative logistic regression model was applied to assess the vaccine effectiveness in preventing the spectrum of COVID-19 disease [14]. Fig. 2 (a) shows the Bayesian Directed Acyclic Graphic (DAG) model taking into account the characteristics of vaccination status (Vaccine [k, i]) and age group (Age [k, i]) at individual level and the heterogeneity in disease transmission across the periods of outbreak (α [k]) on the risk (π[k, i]) of having multiple outcomes of COVID-19 disease spectrum (Moderate [k, i], Severe [k, i], and Death [k, i]). This can be specified by the random intercept model,(1) logit(P(Y[k,i] ≤ r)= αr[k]+βr vaccine× Vaccine[k, i] + βr age× Age[k, i], αr[k] ~ Normal (α0r, σα2), r =1(moderate), 2 (severe), 3(death), Fig. 2 Two stage model for assessing the effectiveness of vaccination against COVID-19 spectrum (moderate, severe, and death) taking into account the effect of age and NPI. (a) Bayesian random intercept model for vaccine effectiveness against COVID-19 spectrum adjusting for age logit(P(Y [k,i] ≤ r)= αr[k]+βr vaccine× Vaccine [k, i] + βage r× Age [k, i], αr[k] ~ Normal (α0r, σα2) (b) Bayesian random intercept and random slope model for vaccine effectiveness against COVID-19 spectrum considering NPI effect captured by age-specific infection rate logit(P(Y [k, i] ≤ r)= αr[k]+βr vaccine× Vaccine [k, i], αr[k] = α0r[k]+ βr NPI[k]× Age-specific infection rate [k], αr[k] ~ Normal (α0r, σα2) βr NPI[k] ~ Normal(β0r, σβ2). Fig. 2 where Y[k,i] represents the outcomes of COVID-19 disease spectrum for subject i during period k. The risk for the occurrence of each type of COVID-19 disease (πr) is derived by(2) πr = P(Y[k, i]=r) = P(Y≤ r)-P(Y≤ r-1). The vaccine effectiveness in averting COVID-19 disease in the form of moderate (r =1), severe (r =2), and death (r =3) are thus captured by regression coefficients β1 vaccine, β2 vaccine, and β3 vaccine, respectively. The relative risk of being susceptible to moderate and severe COVID-19 and death from COVID-19 adjusting for seven age groups (adjusted relative risk, aRR) can be derived by taking the exponent of the three corresponding estimated regression coefficients, namely exp(β1 vaccine), exp(β2 vaccine), and exp(β3 vaccine), respectively. By further categorizing the vaccination status into primary series and booster vaccination, the corresponding aRRs can be derived for each of clinical outcome in both primary series and booster vaccination without the consideration of NPIs in the Eq. (1) and with the consideration of NPIs captured by the age-specific infection rate in the Eq. (3) in a similar manner. The effectiveness adjusting for seven age groups was derived by (1-aRR)×100%. The third column of Table A.1 in the Supplementary material (random intercept model) shows the estimated regression coefficients derived by using the Bayesian DAG model (Fig. 2 (a)) without considering NPI. Here we show how to derive aRR and effectiveness adjusting for seven age groups. Taking the booster vaccination for example, the estimated results of aRR can be derived by exp(β1 boost = -1.78) = 0.17 for moderate disease, exp(β2 boost = -1.84) = 0.16 for severe disease, and exp (β3 boost = -1.85) = 0.16 for death from Omicron. The corresponding effectiveness (1-aRR) adjusting for seven age groups are thus 84%, 84%, and 83%, respectively. Fig. 2(b) shows the DAG model for evaluating the vaccine effectiveness with the consideration of NPIs captured by age-specific infection rate as a period-level covariate (random slope). Following this rational, the regression form of the random intercept and random slope model is written as(3) logit(P(Y[k, i] ≤ r)= αr[k]+βr vaccine× Vaccine[k, i], αr[k] = α0r[k]+ βr NPI[k]× Age-specific infection rate[k], αr[k] ~ Normal (α0r, σα2) βr NPI[k] ~ Normal (β0r, σβ2). The regression coefficient βr vaccine thus represents the effectiveness of vaccination in preventing the rth COVID-19 disease spectrum after separating the effect of NPIs during period k with its effect captured by βr NPI. The effectiveness of NPIs in averting moderate and severe COVID-19 and death can thus be separated from that of vaccination. Note that the final column of Table A.1 in the Supplementary material (random intercept and slope model) shows the estimated regression coefficients derived by using the Bayesian DAG model (Fig. 2 (b)) with considering NPI. By comparing the estimated results on vaccination effectiveness (βr vaccine) between the model with (the Eq. (3)) and without considering NPI (the Eq. (1)), the proportion of risk reduction attributable to NPI can be derived. Regarding the univariate analysis derived by using the simplified model containing only vaccination status or seven age groups, the relative risk (RR) of having three COVID-19 outcomes (RR) was derived from the transformed regression coefficients for both vaccination status and seven age groups and the crude effectiveness of vaccination was derived by (1-RR)×100% in a manner similar to the aRR. A Bayesian Markov chain Monte Carlo (MCMC) method was used for estimating the parameters of Bayesian random intercept model and Bayesian random intercept and random slope model mentioned above. Non-informative priors (Normal (0, 106)) were used for regression coefficients (β) and squared variance parameters (σα and σβ) with log transformation. The block-wise Metropolis-Hasting sampling algorithm with a burn-in iteration of 5,000 followed by 50,000 iterations with a thinning interval of 10 were applied to derive 5,000 posterior samples of parameters [15], [16], [17]. The mean and the 95% credible interval (CI) of regression coefficients and the effectiveness of vaccination can be derived on the basis of these 5000 posterior samples on the parameters of interest. The mean value of each regression coefficient was derived by the average of 5000 posterior samples. The corresponding 95% credible interval was obtained from 2.5th and 97.5th of 5000 posterior samples. The transformed regression coefficients to get aRR and the adjusted effectiveness of vaccination and age groups can be also derived [14], [18]. The aggregated data used for evaluation were retrieved from the digital COVID-19 surveillance platform reported by Central Epidemic Command Center (CECC), Taiwan and Taiwan Centre for Disease Control daily for the purpose epidemic surveillance. As such an aggregated data was made available to the public, this study thus required no approval from Institutional Review Board. Results During the study period between April 20 and August 13, 2022, a total of 4787319 COVID-19 cases were reported, including 4756775 (99%) asymptomatic and mild cases and 30544 (0.6%) moderate or severe cases caused by Omicron VOC. Of the 30544 COVID-19 cases, there were 14264 (47%) moderate cases, 7734 (25%) severe cases, and 8546 (28%) deaths. Fig. 3 shows age-specific risks of COVID-19 disease spectrum by vaccination status in Taiwan between 20 April and 13 August, 2022. Regardless of moderate (Fig. 3 (a)), severe (Fig. 3 (b)) and death from COVID-19 (Fig. 3 (c)), the risk was lowest for booster vaccination (green line), followed by primary vaccination (yellowish line), and highest for incomplete vaccination (red line) with the consistent trends that the risks associated with three outcomes increased with advancing age from the young group aged 12 years or below until the old group aged 75 years or older with 7 age groups. The detailed frequencies on the distribution of moderate and severe COVID-19 disease, and death by vaccination status (incomplete vaccination, primary series, and booster vaccination) as a function of seven age groups from the young group aged 12 years or below until the old group aged 75 years or older are listed in the Table A.2 in Supplementary material. Following the study design depicted in Fig. 1, the numbers of moderate, severe, and death were 7177, 4093, and 4524 for the incompletely vaccinated group, 1859, 1021, and 1113 for the fully vaccinated group, and 5228, 2620, and 2909 for the booster group, respectively. The risks of three outcomes were similar between the fully vaccinated and the booster group whereas both vaccinated groups were lower than the incompletely vaccinated group (Table A.2 in the Supplementary material). The numbers of weekly reported COVID-19 infections by age group through the study period are listed in Table A.3 in the Supplementary material, which would be further used as the proxy of NPIs in the two-stage hierarchical model specified above.Fig. 3 Age-specific Risk (per 100,000) of COVID-19 disease spectrum by vaccination status in Taiwan between 20 April and 13 August, 2022. (a) Moderate COVID-19 (b) Severe COVID-19 (c) Death from COVID-19 Primary series and booster vaccination were combined for the age group of 12-17 years as few subjects have received booster vaccination as of the end of study period in the light of nationwide vaccination schedule. Bar denotes 95% confidence interval of the estimated risk. Fig. 3 Table 1 shows the estimated results on the crude relative risk of being moderate, severe and dead related to COVID-19 disease for full and booster vaccination as opposed to incomplete vaccination adjusting for the reported infections by seven age groups from the young subjects aged 12 years or below until the elderly aged 75 years or older. The effectiveness of booster vaccination (1-RR) conferred the highest reduction of three outcomes, ranging from 84% reduction for being dead (relative risk (RR): 0.156, 95% CI: 0.149-0.164)) and severe cases (RR: 0.156, 95% CI: 0.151-0.162), and 84% for moderate cases (RR=0.165, 95% CI: 0.161-0.169). The corresponding estimates for the primary series group were 71% (RR=0.29 95% CI: 0.27-0.31) for being dead, 71% for severe case (RR=0.29, 95% CI: 0.28-0.30), and 71% for moderate case (RR=0.29, 95% CI: 0.28-0.30), respectively. Regarding children aged 12 years or below who were lacking of the eligible vaccine during the study period, it was expected that the risk of being moderate or severe was higher than the adolescents in the adjacent 12-17 age group. From 18 years of age onwards, the risk of being three outcomes increased with advancing age, particularly in two old age groups, 65-74 and >=75 years.Table 1 Estimated results on the relative risk of COVID-19 disease spectrum (moderate, severe, and death) by vaccination status and age group. Table 1 Moderate Severe Death RR 95% CI RR 95% CI RR 95% CI Vaccination status Incomplete Ref Ref Ref Booster dose 0.165 (0.161, 0.169) 0.156 (0.151, 0.162) 0.156 (0.149, 0.164) Primary series 0.29 (0.28, 0.30) 0.29 (0.28, 0.30) 0.29 (0.27, 0.30) Age <12 0.009 (0.008, 0.011) 0.006 (0.005, 0.007) 0.001 (0.001, 0.003) 12-17 0.004 (0.003, 0.005) 0.002 (0.001, 0.003) 0.001 (0.000, 0.002) 18-29 0.007 (0.006, 0.008) 0.005 (0.004, 0.006) 0.003 (0.002, 0.004) 30-49 0.019 (0.018, 0.020) 0.017 (0.016, 0.018) 0.014 (0.013, 0.016) 50-64 0.056 (0.054, 0.058) 0.048 (0.046, 0.051) 0.043 (0.041, 0.046) 65-74 0.175 (0.170, 0.180) 0.16 (0.15, 0.17) 0.15 (0.14, 0.16) >=75 Ref Ref Ref Overall Effectiveness of Vaccination in Reducing the Severity of and Death from COVID-19 Table A.4 shows the aRR for the effectiveness of vaccination in reducing moderate and severe disease of and death from Omicron VOC. After adjusting for age, booster vaccination conferred the statistically significant reduction in the risk of death from and severe and moderate disease of Omicron by 84% (aRR: 0.16, 95% CI: 0.15-0.17), 84% (aRR: 0.159, 95% CI: 0.153-0.164), and 83% (aRR: 0.168, 95% CI: 0.164-0.173), respectively. The corresponding figures for the primary vaccination group were 65% (aRR: 0.35, 95% CI: 0.32-0.37) for being death, 65% (aRR: 0.35, 95% CI: 0.33-0.37) for severe disease, and 65% (aRR: 0.36, 95% CI: 0.34-0.37) for moderate disease, respectively. Consistent with the crude result, the two old age groups (65-74 years and >=75 years) were at increased risks for the severity of and death from COVID-19. Effectiveness of Vaccination in Reducing the Severity of and Death from COVID-19 Adjusting for NPIs Table 2 shows the estimated results on the effectiveness of vaccine with the consideration of NPIs captured by the proxy of age-specific infection rate for each week. Recall that the final column of Table A.1 in the Supplementary material shows the estimated results on the parameters derived from the Bayesian random intercept and random slope model sketched by the DAG in Fig. 2 (b).Table 2 Estimated results on vaccine efficacy adjusting for age-specific infection rate. Table 2(a) All period from April to August, 2022 Vaccination status Moderate Severe Death aRR 95% CI aRR 95% CI aRR 95% CI Vaccine Effectiveness Incomplete Ref Ref Ref Booster dose 0.275 (0.268, 0.282) 0.26 (0.25, 0.27) 0.26 (0.25, 0.27) Primary series 0.48 (0.46, 0.50) 0.48 (0.46, 0.50) 0.47 (0.44, 0.50) Proportion of risk reduction attributable to NPI (%) Incomplete Ref Ref Ref Booster dose 12.8 (12.5, 13.2) 12.1 (11.6, 12.6) 12.3 (11.6, 13.0) Primary series 19.4 (18.6, 20.4) 19 (18, 21) 19 (18, 21) (b)Periods without oral anti-viral agents (before May 13, 2022) Vaccination status Moderate Severe Death aRR 95% CI aRR 95% CI aRR 95% CI Vaccine Effectiveness Incomplete Ref Ref Ref Booster dose 0.39 (0.35, 0.44) 0.26 (0.20, 0.33) 0.24 (0.17, 0.34) Primary series 0.54 (0.46, 0.63) 0.52 (0.39, 0.70) 0.53 (0.36, 0.77) Proportion of risk reduction attributable to NPI (%) Incomplete Ref Ref Ref Booster dose 20 (17, 23) 12 (9, 16) 11 (8, 17) Primary series 21 (17, 28) 20 (14, 35) 21 (12, 45) (c)Periods with oral anti-viral agents (after May 13, 2022) Vaccination status Moderate Severe Death aRR 95% CI aRR 95% CI aRR 95% CI Vaccine Effectiveness Incomplete Ref Ref Ref Booster dose 0.27 (0.26, 0.28) 0.26 (0.25, 0.27) 0.26 (0.25, 0.27) Fully vaccinated 0.48 (0.46, 0.50) 0.47 (0.45, 0.50) 0.47 (0.44, 0.50) Proportion of risk reduction attributable to NPI (%) Incomplete Ref Ref Ref Booster dose 12.6 (12.3, 13.0) 12.0 (11.6, 12.4) 12.1 (11.6, 12.8) Fully vaccinated 20 (18, 21) 19 (18, 21) 19 (17, 22) Random intercept (σα): 0.80 (95% CI: 0.48-1.15) Random slope (σβ): 0.50 (95% CI: 0.17-0.86) Random intercept (σα): 1.16 (95% CI: 0.18-2.87) Random slope (σβ): 0.41 (95% CI: 0.14-1.17) Random intercept (σα): 0.17 (95% CI: 0.14-0.24) Random slope (σβ): 0.41 (95% CI: 0.14-0.76) The estimates were derived by using the data for the entire study period from April to August, 2022 (Table 2 (a)), for the period without oral anti-viral therapy (Table 2 (b)), and that with oral anti-viral therapy (Table 2 (c)). After considering the protection from NPI, the booster vaccination and the primary series were attenuated to slightly lower effectiveness in reducing three sequels of Omicron VOC infection. The booster vaccination conferred 74% (aRR: 0.26, 95% CI: 0.25-0.27), 74% (aRR: 0.26, 95% CI: 0.25-0.27), and 73% (aRR: 0.275, 95% CI: 0.268-0.282) reduction for being death, severe disease, and moderate disease from Omicron infection, respectively (Table 2 (a)). The corresponding figures for the primary series were estimated as 53% (aRR: 0.47, 95% CI: 0.44-0.50) for being deaths, 52% (aRR: 0.47, 95% CI: 0.46-0.50) for severe disease, and 52% (aRR: 0.48, 95% CI: 0.47-0.50) for moderate disease, respectively (Table 2 (a)). Using the overall protective effect (Table A.1 in the Supplementary material) as the comparator, the proportions of the risk reduction in terms of three sequels of COVID-19 attributable to NPI were around 12% (lower panel of Table 2 (a)). Since May 13, 2022, oral anti-viral therapy was available for infected subjects who were eligible to indication [19]. To further purified the effectiveness in preventing moderate and severe disease and death resulting from Omicron infection, we further assess the impact of booster vaccination and primary series by using data collected from the period before (Table 2 (b)) and after (Table 2 (c)) oral anti-viral therapy was available. While the effectiveness in preventing severe disease and death remained close in two periods for booster vaccination, the administration of oral anti-viral therapies resulted in a protective effect of 74 (aRR: 0.27, 95% CI: 0.26-0.28, Table 2 (c)), corresponding to a 12% higher effectiveness compared to the period without oral anti-viral therapy (61%, aRR: 0.39, 95% CI: 0.35-0.44, Table 2 (b)). Regarding the effectiveness of primary series, the effectiveness was increased with the use of oral anti-viral therapies by 6.4%, 4.7, and 5.9% for death, severe, and moderate COVID-19, respectively. Discussion Effectiveness of Mass Vaccination in Reducing Severe Cases and Deaths in Omicron Naïve Infection While a body of evidence has shown the effectiveness of reducing in severity and death related to various SARS-CoV-2 variants including the wild type/D614G [20], [21], Alpha VOC (20-22), Beta [22], [23], Gamma [24], Delta [25], [26], [27], and Omicron [27], [28], [29], [30], the recent studies have focused on whether and how prior infections may affect the effectiveness of mass vaccination [7], [31], [32], [33], [34]. Considering prior infection is particularly important when population-based effectiveness of vaccination against the recent Omicron VOC infection needs to be evaluated as most of countries worldwide have seen incessant large-scale community-acquired outbreaks before Omicron VOC infection [1], [7], [8]. To tackle this issue, the current study evaluated pure effectiveness of vaccination in reducing severity and death pertaining to Omicron COVID-19 by better utilizing the Omicron naïve infection data derived from Taiwan where there had been modest community-acquired outbreaks before the emerging Omicron VOC. Using such an Omicron BA.2 naïve infection data, we proved substantial starkly effectiveness of mass vaccination in reducing more than 80% reduction of moderate, severe, and deaths related to Omicron BA.2 naïve infection. Even after making allowance for NPI effectiveness, mass vaccination still led to at least 70% reduction of three severe outcomes of COVID-19 Omicron BA.2. These findings have significant implications for containing the emerging Omicron VOC infection. As the first-generation vaccines may not be effective in protection from Omicron VOC infection because of its high transmissibility and easiness to escape immune response but they are very effective in reducing severe cases and deaths from COVID-19 Omicron VOC. Therefore, the main mitigation strategy should target at scaling up vaccination rather than the restricted NPI strategy for precluding the folk people from returning to pre-pandemic life. In addition to the effectiveness against severe COVID-19 outcomes, adverse effects of vaccination may attenuate the uptake of population-based vaccination program and lead to vaccine hesitancy. Recent cohort studies show that the majority of adverse effects of COVID-19 vaccination were focal reaction such as fatigue, headache and local pain with transient nature [35], [36] without involving a significant increase in the risk of adverse events of special interest or mortality among the vaccinated population [37]. This empirical evidence, together with the long-term effectiveness in reducing the risk of severe COVID-19 outcomes as demonstrated in our study, provide reassurance for mass vaccination program against COVID-19. Given the unprecedent nature of COVID-19 vaccines in terms of the novel technology deployed and the time scale of development to global use of these newly manufactured products, a network for post marketing surveillance for vaccine effectiveness and its related adverse effects is warranted. T-cell Mediated Immunity Inducted by COVID-19 Vaccine Taiwan residents were provided with primary series since March, 2021 and booster vaccination since January, 2022 [15]. As the vaccination schedule were completed in four-fifth for primary series by December in 2021 and in two-thirds for booster dose by May, 2022 in Taiwan, our result derived from such an Omicron naïve cohort following up until August 2022 demonstrated the long-term effectiveness of vaccination in averting disease progression. In the early phase of COVID-19 pandemic in 2020, the potential of vaccination in triggering the persistent germinal centre response resulting in memory B cell production associated with permanent immune response and hence protection from being infected by SARS-CoV-2 has been reported [38], [39]. However, the waning in the protective effect of vaccination and the emergence of VOCs with the characteristic of immune escape render the booster vaccination required for maintaining the immunity at population level [1], [2], [3], [4], [5], [40], [41], [42]. Recent studies showed that the history of infection and reinfection by VOCs, together with the vaccines received, shape the landscape of immunity and resistance to disease severity and death following infection [10], [32], largely due to T cell mediated immune response in modulating the inflammatory response associated with disease progression [8], [9], [10], [43], [44]. While this T cell mediated process led to the benefit of reducing the severity of and death from current Omicron VOCs outbreak, it also hampered the evaluation of protective effectiveness conferred by vaccination [7], [8]. Our analysis using Omicron-naïve Taiwan population thus provides an unique opportunity to separate pure immunity as a result of mass vaccination from that resulting from prior infection. The function of T cell immunity in modulating inflammation process also explained why the risk of severe disease and death from COVID-19 increased with advancing age. The reasons may be that the loss regarding the balance between pro-inflammatory and anti-inflammatory pathway mainly associated with helper T cell and type I interferon pathway predisposes the elder population to severe clinical outcomes of COVID-19. This is further aggravated by the compromised physiological conditions including the alternation of ACE2 receptor expression and alveolar macrophages composition, both of which render the elder population vulnerable to the risk of severe disease and highly desirable for booster vaccination [45], [46]. Evaluating Effectiveness of Vaccination, NPI, and oral anti-viral therapy with Bayesian Hierarchical Model To the best of our knowledge, this is the first time to evaluate the effectiveness of mass vaccination adjusting for NPI and making allowance for oral anti-viral therapy with hierarchical Bayesian multinomial model. The novelty is two-fold. First, the influences of both vaccination and NPI on the reduction of moderate and severe cases, and deaths are operated at different levels. The former is exerted through individual level whereas the latter is operated mainly through policy-making at population level. The hierarchical models as shown in Fig. 2 are tailored for such a purpose. Age-specific infection rate in contrast to vaccination and age that were placed at individual level was incorporated into the second population level in different periods in order to capture the specific effectiveness of NPI. Second, Bayesian hierarchical model with the random intercept as shown in Fig. 2 (a) was first to capture the heterogeneity of the risk of three outcomes beyond the explanation of age and vaccination across three periods, which may include the restrictiveness of NPI and the gradual adaptation of replacing RT-PCR test with the rapid test both of which may affect the reported age- specific infection rates, further leading to age-specific risk of three outcomes. The use of random slope and random intercept model in Fig. 2 (b) further captured the effectiveness of NPI and residual confounding for the risk of three outcomes. By using the approach of Bayesian Network analysis, Sinclair et al. assessed the balance between the protective effectiveness of one to three doses of BNT162B2 vaccination and the risk of vaccine-associated myocarditis allowing for vaccine coverage, vaccine effectiveness, age groups, and sex during Delta VOC dominant period in Australia [47]. Informed by the inputs derived from the CoRiCal [48], the evidence from Sinclair et al. support the decision on vaccination for all age groups to reduce the COVID-19 mortality. Our results regarding the effectiveness of booster and primary vaccination against the severe clinical outcomes during Omicron outbreak can provide the supplementary and the updated inputs for the Bayesian Network analysis to support an informed decision-making with Bayesian underpinning. Limitations Although to Omicron BA.2 naïve infection population data provides an opportunity for assessing the pure population-based effectiveness of vaccination the generalizability of our finding to other SARS-CoV-2 variants is therefore limited. Second, our major goal is to evaluate the effectiveness of vaccination on population level such a finding may not be directly applied to individual level without considering other important personal correlates such as co-morbidity [49]. However, the weakness of lacking of individual trait may still can be strengthened by using Bayesian hierarchical model to capture such an individual heterogeneity. Third, unlike primary vaccination with 9 months of follow-up, the effectiveness of booster vaccination was within three months. Long duration of booster vaccination needs to be verified. In conclusion, the present study demonstrated population effectiveness of primary vaccination and its booster vaccination, independent of oral anti-viral therapy and NPI, in reducing severe clinical outcomes associated with Omicron BA.2 naïve infection population. Funding This study was funded by Ministry of Science and Technology, Taiwan (MOST 111-2321-B-002-017; MOST 111-2118-M-002-004-MY2; MOST 111-2118-M-038-002-MY2; MOST 111-2118-M-038-001-MY2). CRediT authorship contribution statement Conceptualization: C.Y. Hsu, A.M.F. Yen, and H.H. Chen. Study design: S.L.S. Chen, A.M.F. Yen, and H.H. Chen. Methodology: C.Y. Hsu, S.L.S. Chen, A.M.F. Yen, and H.H. Chen. Data retrieval and management: A.T.Y. Lin, H.H. Chang, J.C. Chan, and A.M.F. Yen. Statistical analysis: A.T.Y. Lin and A.M.F. Yen. Computer programming: S.L.S. Chen, A.M.F. Yen, and A.T.Y. Lin. Drafting of the article: C.Y. Hsu, A.T.Y. Lin and A.M.F. Yen. Critical revision of the article for important intellectual content: H.H. Chang, J.C. Chan, and H.H. Chen. Final approval of the article: C.Y Hsu, J.C. Chan, S.L.S. Chen, H.H. Chang. A.T.Y. Lin, A.M.F. Yen, and H.H. Chen. Obtaining of funding: S.L.S Chen, A.M.F. Yen, and H.H. Chen. Administrative, technical, or logistic support: A.T.Y. Lin, S.L.S. Chen, and A.M.F. Yen All authors agreed the findings and provided input on the revision of the manuscript. Competing interests Authors declare no competing interests Acknowledgements N/A Appendix A Supplementary material Supplementary material Data and code availability Data and code of analysis are available from the corresponding authors (Professor Chen and Professor Yen) on request. Appendix A Supplementary data associated with this article can be found in the online version at doi:10.1016/j.jiph.2022.11.028. ==== Refs References 1 Koelle K. Martin M.A. Antia R. Lopman B. Dean N.E. The changing epidemiology of SARS-CoV-2 Science 375 2022 1116 1121 10.1126/science.abm4915 35271324 2 Castillo M.S. Khaoua H. Courtejoie N. Vaccine-induced and naturally-acquired protection against Omicron and Delta symptomatic infection and severe COVID-19 outcomes, France, December 2021 to January 2022 Eurosurveillance 27 2022 2200250 10.2807/1560-7917.ES.2022.27.16.2200250 3 Andrews N. Stowe J. Kirsebom F. Toffa S. Rickeard T. Gallagher E. Covid-19 vaccine effectiveness against the Omicron (B. 1.1. 529) variant N Engl J Med 386 2022 1532 1546 10.1056/NEJMoa2119451 35249272 4 Ssentongo P. Ssentongo A.E. Voleti N. Groff D. Sun A. Ba D.M. SARS-CoV-2 vaccine effectiveness against infection, symptomatic and severe COVID-19: a systematic review and meta-analysis BMC Infect Dis 22 2022 1 2 10.1186/s12879-022-07418-y 34983406 5 Feikin D.R. Higdon M.M. Abu-Raddad L.J. Andrews N. Araos R. Goldberg Y. Duration of effectiveness of vaccines against SARS-CoV-2 infection and COVID-19 disease: results of a systematic review and meta-regression Lancet. 399 2022 924 944 10.1016/S0140-6736(22)00152-0 35202601 6 Layton A.T. Sadria M. Understanding the dynamics of SARS-CoV-2 variants of concern in Ontario, Canada: A modeling study Sci Rep 12 2022 1 6 10.1038/s41598-022-06159-x 34992227 7 Bhattacharyya R.P. Hanage W.P. Challenges in inferring intrinsic severity of the SARS-CoV-2 Omicron variant N Engl J Med 386 2022 e14 10.1056/NEJMp2119682 8 Barouch D.H. Covid-19 Vaccines—Immunity, Variants, Boosters N Engl J Med 387 2022 1011 1020 10.1056/NEJMra2206573 36044620 9 Wherry E.J. Barouch D.H. T cell immunity to COVID-19 vaccines Science 377 2022 821 822 10.1126/science.add2897 35981045 10 Reynolds C.J. Pade C. Gibbons J.M. Immune boosting by B. 1.1. 529 (Omicron) depends on previous SARS-CoV-2 exposure Science 377 2022 eabq1841 10.1126/science.abq1841 35699621 11 Yen A.M. Chen T.H. Chang W.J. Lin T.Y. Jen G.H. Hsu C.Y. Epidemic surveillance models for containing the spread of SARS-CoV-2 variants: Taiwan experience medRxiv 2021 10.1101/2021.10.19.21265107 12 Taiwan Centers for Disease Control. Taiwan national infectious disease statistics system. Accessed at 〈https://data.cdc.gov.tw/dataset/aagsdctable-day-19cov〉 on August 25, 2022. 13 Taiwan Centers for Disease Control. Treatment Guidelines for SARS-CoV-2 Infection. Available at 〈https://www.cdc.gov.tw/File/Get/3Uk4nSt0rUljIOh7czgiWw〉. Accessed on August 25, 2022. 14 Hsu C.Y. Yen A.M. Chen L.S. Chen H.H. Analysis of household data on influenza epidemic with Bayesian hierarchical model Math Biosci 261 2015 13 26 10.1016/j.mbs.2014.11.006 25484132 15 Metropolis N. Rosenbluth A.W. Rosenbluth M.N. Teller A.H. Teller E. Equation of state calculations by fast computing machines J Chem Phys 21 1953 1087 1092 10.1063/1.1699114 16 Hastings W.K. Monte Carlo sampling methods using Markov chains and their applications Biometrika 57 1970 97 109 10.2307/2334940 17 Lunn D.J. Thomas A. Best N. Spiegelhalter D. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility Stat Comput 10 2000 325 337 10.1023/A:1008929526011 18 Armitage P. Berry G. Matthews J.N. Statistical methods in medical research Ch 16.4 2008 Apr 15 John Wiley & Sons 10.1002/9780470773666 19 TMHW, Ministry of Health and Welfare, Taiwan. Crucial policies for combating COVID-19. Accessed at 〈https://covid19.mohw.gov.tw/ch/sp-timeline0-205.html?fbclid=IwAR1eeqICUPFQdPuQkG7z3hIKYAwDaaLnigzqHR6xFXOVlZn0BcXT0dNhfkI〉 on August 30, 2022. 20 Tregoning J.S. Flight K.E. Higham S.L. Wang Z. Pierce B.F. Progress of the COVID-19 vaccine effort: viruses, vaccines and variants versus efficacy, effectiveness and escape Nat Rev Immunol 21 2021 626 636 10.1038/s41577-021-00592-1 34373623 21 Chung H. He S. Nasreen S. Sundaram M.E. Buchan S.A. Wilson S.E. Effectiveness of BNT162b2 and mRNA-1273 covid-19 vaccines against symptomatic SARS-CoV-2 infection and severe covid-19 outcomes in Ontario, Canada: test negative design study BMJ 374 2021 n1943 10.1136/bmj.n1943 34417165 22 Nasreen S. Chung H. He S. Brown K.A. Gubbay J.B. Buchan S.A. Effectiveness of COVID-19 vaccines against symptomatic SARS-CoV-2 infection and severe outcomes with variants of concern in Ontario Nat Microbiol 7 2022 379 385 10.1038/s41564-021-01053-0 35132198 23 [22] Chemaitelly H. Yassine H.M. Benslimane F.M. Al Khatib H.A. Tang P. Hasan M.R. mRNA-1273 COVID-19 vaccine effectiveness against the B. 1.1. 7 and B. 1.351 variants and severe COVID-19 disease in Qatar Nat Med 27 2021 1614 1621 10.1038/s41591-021-01446-y 34244681 24 Hitchings M.D. Ranzani O.T. Dorion M. D’Agostini T.L. de Paula R.C. de Paula O.F. Effectiveness of ChAdOx1 vaccine in older adults during SARS-CoV-2 Gamma variant circulation in São Paulo Nat Commun 12 2021 1 8 10.1038/s41467-021-26459-6 33397941 25 Barda N. Dagan N. Cohen C. Hernán M.A. Lipsitch M. Kohane I.S. Effectiveness of a third dose of the BNT162b2 mRNA COVID-19 vaccine for preventing severe outcomes in Israel: an observational study Lancet. 398 2021 2093 2100 10.1016/S0140-6736(21)02249-2 34756184 26 Cerqueira-Silva T. Andrews J.R. Boaventura V.S. Ranzani O.T. Oliveira V.D. Paixão E.S. Effectiveness of CoronaVac, ChAdOx1 nCoV-19, BNT162b2, and Ad26. COV2. S among individuals with previous SARS-CoV-2 infection in Brazil: a test-negative, case-control study Lancet Infect Dis 22 2022 791 801 10.1016/S1473-3099(22)00140-2 35366959 27 Butt A.A. Talisa V.B. Shaikh O.S. Omer S.B. Mayr F.B. Relative Vaccine Effectiveness of a SARS-CoV-2 mRNA Vaccine Booster Dose Against the Omicron Variant Clin Infect Dis 2022 ciac328 10.1093/cid/ciac328 35511586 28 Glatman-Freedman A. Bromberg M. Hershkovitz Y. Sefty H. Kaufman Z. Dichtiar R. Effectiveness of BNT162b2 vaccine booster against SARS-CoV-2 infection and breakthrough complications, Israel Emerg Infect Dis 28 2022 948 956 10.3201/eid2805.220141 35361311 29 Abu-Raddad L.J. Chemaitelly H. Ayoub H.H. AlMukdad S. Yassine H.M. Al-Khatib H.A. Effect of mRNA vaccine boosters against SARS-CoV-2 omicron infection in Qatar N Engl J Med 386 2022 1804 1816 10.1056/NEJMoa2200797 35263534 30 Sheikh A. Kerr S. Woolhouse M. Severity of omicron variant of concern and effectiveness of vaccine boosters against symptomatic disease in Scotland (EAVE II): a national cohort study with nested test-negative design Lancet Infect Dis 22 7 2022 959 966 10.1016/S1473-309900141-4 35468332 31 Crotty S. Hybrid immunity Science. 372 6549 2021 1392 1393 10.1126/science.abj225 32 Suryawanshi R. Ott M. SARS-CoV-2 hybrid immunity: silver bullet or silver lining Nat Rev Immunol 9 2022 1 2 10.1038/s41577-022-00771-8 33 Carazo S. Skowronski D.M. Brisson M. Sauvageau C. Brousseau N. Gilca R. Estimated Protection of Prior SARS-CoV-2 Infection Against Reinfection With the Omicron Variant Among Messenger RNA–Vaccinated and Nonvaccinated Individuals in Quebec e2236670-e2236670 Canada. JAMA Netw Open 5 10 2022 10.1001/jamanetworkopen.2022.3667 34 Carazo S. Skowronski D.M. Brisson M. Barkati S. Sauvageau C. Brousseau N. Protection against omicron (B. 1.1. 529) BA. 2 reinfection conferred by primary omicron BA. 1 or pre-omicron SARS-CoV-2 infection among health-care workers with and without mRNA vaccination: a test-negative case-control study 000578-3 Lancet Infect Dis S1473-3099 22 2022 10.1016/S1473-3099(22)00578-3 35 Amanzio M. Mitsikostas D.D. Giovannelli F. Bartoli M. Cipriani G.E. Brown W.A. Adverse events of active and placebo groups in SARS-CoV-2 vaccine randomized trials: A systematic review Lancet Reg Health Eur 12 2022 100253 10.1016/j.lanepe.2021.100253 36 Rogers A. Rooke E. Morant S. Guthrie G. Doney A. Duncan A. Adverse events and overall health and well-being after COVID-19 vaccination: interim results from the VAC4COVID cohort safety study BMJ open 12 6 2022 e060583 10.1136/bmjopen-2021-060583 37 Wong C.K. Lau K.T. Xiong X. Au I.C. Lai F.T. Wan E.Y. Adverse events of special interest and mortality following vaccination with mRNA (BNT162b2) and inactivated (CoronaVac) SARS-CoV-2 vaccines in Hong Kong: A retrospective study PLoS Med 19 6 2022 e1004018 10.1371/journal.pmed.1004018 38 Turner J.S. O’Halloran J.A. Kalaidina E. Kim W. Schmitz A.J. Zhou J.Q. SARS-CoV-2 mRNA vaccines induce persistent human germinal centre responses Nature 596 2021 109 113 10.1038/s41586-021-03738-2 34182569 39 Turner J.S. Kim W. Kalaidina E. Goss C.W. Rauseo A.M. Schmitz A.J. SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans Nature 595 2021 421 425 10.1038/s41586-021-03647-4 34030176 40 Tao K. Tzou P.L. Nouhin J. Gupta R.K. de Oliveira T. Kosakovsky Pond S.L. The biological and clinical significance of emerging SARS-CoV-2 variants Nat Rev Genet 22 2021 757 773 10.1038/s41576-021-00408-x 34535792 41 Monge S. Rojas-Benedicto A. Olmedo C. Mazagatos C. Sierra M.J. Limia A. Effectiveness of mRNA vaccine boosters against infection with the SARS-CoV-2 omicron (B. 1.1. 529) variant in Spain: a nationwide cohort study Lancet Infect Dis 22 9 2022 1313 1320 10.1016/S1473-3099(22)00292-4 35658998 42 Regev-Yochay G. Gonen T. Gilboa M. Mandelboim M. Indenbaum V. Amit S. Efficacy of a fourth dose of COVID-19 mRNA vaccine against omicron N Engl J Med 386 14 2022 1377 1380 10.1056/NEJMc2202542 35297591 43 Moss P. The T cell immune response against SARS-CoV-2 Nature immunology 23 2022 186 193 10.1038/s41590-021-01122-w 35105982 44 GeurtsvanKessel C.H. Geers D. Schmitz K.S. Mykytyn A.Z. Lamers M.M. Bogers S. Divergent SARS-CoV-2 Omicron–reactive T and B cell responses in COVID-19 vaccine recipients Sci immunol 7 69 2022 eabo2202 10.1126/sciimmunol.abo2202 35113647 45 Meftahi G.H. Jangravi Z. Sahraei H. Bahari Z. The possible pathophysiology mechanism of cytokine storm in elderly adults with COVID-19 infection: the contribution of “inflame-aging” Inflamm Res 69 9 2020 825 839 10.1007/s00011-020-01372-8 32529477 46 Chen S.T. Park M.D. Del Valle D.M. Buckup M. Tabachnikova A. Thompson R.C. A shift in lung macrophage composition is associated with COVID-19 severity and recovery Sci Transl Med 14 662 2022 eabn5168 10.1126/scitranslmed.abn5168 36103512 47 Sinclair J.E. Mayfield H.J. Short K.R. Brown S.J. Puranik R. Mengersen K. A Bayesian network analysis quantifying risks versus benefits of the Pfizer COVID-19 vaccine in Australia NPJ Vaccines 7 1 2022 11 93 10.1038/s41541-022-00517-6 35953502 48 Immunisation Coalition. CoRiCal: Covid Risk Calculator. Accessed at 〈https://corical.immunisationcoalition.org.au/〉 on November 22, 2022. 49 Havers F.P. Pham H. Taylor C.A. Whitaker M. Patel K. Anglin O. COVID-19-Associated Hospitalizations Among Vaccinated and Unvaccinated Adults 18 Years or Older in 13 US States, January 2021 to April 2022 (in press) JAMA Intern Med 2022 e224299 10.1001/jamainternmed.2022.4299	36470007	PMC9708104	NO-CC CODE	2022-12-02 23:17:33	no	J Infect Public Health. 2023 Jan 30; 16(1):55-63	utf-8	J Infect Public Health	2,022	10.1016/j.jiph.2022.11.028	oa_other
==== Front Reg Stud Mar Sci Reg Stud Mar Sci Regional Studies in Marine Science 2352-4855 Elsevier B.V. S2352-4855(22)00344-9 10.1016/j.rsma.2022.102749 102749 Article Assessing the impact of the Covid-19 pandemic on the financial and economic structure performance of Turkish sea freight transport sector Erol Sercan Surmene Faculty of Marine Sciences, Karadeniz Technical University, Trabzon, Turkiye 30 11 2022 30 11 2022 10274925 10 2022 21 11 2022 22 11 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The impact of Covid-19 on the global economy’s functioning and the long-term growth of supply chains was first reported in Türkiye in March 2020. The purpose of this study is to determine whether the Covid-19 pandemic has had an impact on the financial and economic structure performance of Turkish sea freight transport sector, as well as how the sector has adapted to the new reality brought on by Covid-19. In this regard, the consolidated financial statements of 997 companies for the year 2020 were studied using ratio analysis, and the results were compared to the financial outputs of the 2008 global financial crisis and the European sovereign dept crisis. As a result of the study, it was determined that the sector’s response to crises in different time periods varied. In order to avoid debt default during the pandemic, the capital structure restricted short-term resources while increasing long-term resources. Despite the pandemic conditions, it has been noticed that there are no barriers to accessing money market instruments in the sector, that the working capital structure has been enhanced, and that a balanced financing plan has been established to ensure the continuity of cash flows. This is the first study that analyses the sector as a whole, reveals the financial and economic repercussions of the pandemic on the sector, and compares these effects to those of recent financial crises. In addition, authorities of the maritime transport industry in other countries will find this helpful research for conducting comparative analyses, and the findings can be generalised. Graphical abstract Keywords Covid-19 Sea freight transport Ratio analyses Financial structure policy Blue growth ==== Body pmc1 Introduction The maritime transport industry, the primary provider of the global logistics network, is regarded as one of the industries with high potential in terms of the blue growth strategy (Fernández-Macho et al., 2015, Tijan et al., 2021). However, it is a highly vulnerable and risky sector with respect to unexpected events such as global economic developments, freight price volatility, financial crises, political events, and diseases (Stopford, 2009, Lun et al., 2010, Tatenhove, 2021, Yin et al., 2022). With the outbreak of Covid-19, the worldwide maritime transportation industry that accounts for 90% of the international trade and supply of cargo presents new and unprecedented impacts on the maritime transportation-related industry (Narasimha et al., 2021, Charłampowicz, 2021, Tatenhove, 2021, Tijan et al., 2021, Koyuncu et al., 2021). Specifically, the public health measures adopted to prevent the spread of the Covid-19 pandemic resulted in a sharp decline in consumer expenditure and a suspension of face-to-face services, including travel, entertainment, and catering (Notteboom et al., 2021, Remes et al., 2021, Hossain, 2021, Chen et al., 2022, Pan and He, 2022, Downey et al., 2022). In this condition, global demand and supply have decreased by nearly 60%, and 41% of global exports have been affected by the pandemic (Baldwin and Mauro, 2020, Narasimha et al., 2021). The supply–demand imbalance creates deterioration in the cash flow balance, a decline in profitability, and negative consequences on the ability to pay debts (Stopford, 2009, Hoffmann, 2010, Narasimha et al., 2021, Li et al., 2022). Therefore, the maritime trade volume decreased by 3.8% in 2020 to 10.6 billion metric tons due to the Covid-19 pandemic (UNCTAD, 2021). The Baltic Dry Index (BDI) hit the absolute bottom in the previous four years in May 2020, down 89.1% from September 2019 (Kamal Md. et al., 2021). Any sudden drop in global demand has an immediate impact on the maritime transportation-related industry and may alter corporate strategies or even market structures (Notteboom and Pallis, 2021). In this context, the strategic decision that the authorities of the maritime transportation-related industries should make in response to unexpected events such as the Covid-19 pandemic is to determine the most suitable economic and financial structure and to provide the financial methods and tools necessary to support this structure (Erol and Dursun, 2015). The most appropriate tools for analysing the economic and financial structure are the balance sheet and income statement, while strategic decisions are determined by comparing the business’s strengths and weaknesses using a variety of financial ratios and indicators (Engle, 2012, Erol and Dursun, 2012). Otherwise, the financially risky nature of the business threatens the sustainability of the sector’s stakeholders (Syriopoulos, 2007, Gong et al., 2013). When the literature is examined, it is seen that studies related to maritime transport are mainly associated with cruise, port and maritime operations. Examining these research reveals that they concentrate mostly on the effects of the pandemic on transport volume and freight capacity, the impact on seafarers’ lives aboard maritime vessels, seaport terminals, marine tourism, and the cruise industry (Loske, 2020, Zheng et al., 2020, Syriopoulos et al., 2020, Depellegrin et al., 2020, Xu et al., 2021, Tatenhove, 2021, Narasimha et al., 2021, Cengiz and Turan, 2021, Menhat et al., 2021, Liu and Chang, 2020, Mankowska et al., 2021, Koyuncu et al., 2021, Zhou et al., 2022, Chen et al., 2022, Guerrero et al., 2022, Li et al., 2022) etc. However, studies revealing the impact of Covid-19 on the financial performance of shipping have been rather limited. In their study, Kamal Md. et al. (2021) analysed how the Covid-19 pandemic affected maritime transportation industry stocks traded on the New York Stock Exchange (NYSE). In this context, they reported that the stocks achieved their lowest minimum levels (−20.73%) since the World Health Organization (WHO) stated that Covid-19 is a global pandemic and the United States imposed a travel restriction on 26 European nations. In their study, Notteboom and Pallis (2021) compared the financial crisis of 2008–2009 to the Covid 19 pandemic and evaluated the consequences of these crises on global container transportation and ports. The study found that although the financial crisis and Covid-19 appear to have similar effects, these effects do not exhibit similar patterns between ports and marine networks (Notteboom and Pallis, 2021). Over and above, to date, there has been no quantitative study in maritime-related disciplines in Türkiye to analyze the impact of the Covid-19 crisis on the financial performance of sea freight water transport. The aim of this study is to assess the impact of the Covid-19 pandemic on the financial and economic structure performance of the sea freight transport sector in Türkiye and how the sector adapts to the new reality brought by Covid-19. The Central Bank of the Republic of Türkiye’s published consolidated financial statements were utilised for this purpose. In this regard, the consolidated balance sheet and income statement of 997 enterprises during the Covid-19 crisis were studied using ratio analysis. The results were compared to financial outputs throughout the 2008 global financial crisis (GFC) and the European sovereign debt crisis (ESDC). The study contributes to the existing literature since it is the first to examine the industry holistically, reveal the financial and economic impact of the pandemic on the sector, and compare these impacts to those of prior financial crises. In addition, the data set and findings of this research may be used to do comparative analyses of businesses operating in various countries According to ISL (2021) statistics, Türkiye controls a fleet with a capacity of 28,989,000 DWT and 1,492 ships, placing it fifteenth in the World sea trade fleet ranking (1000 GT and above) (ISL, 2021). Consequently, the results and recommendations of this study can serve as a guide for the authorities of maritime-related industries in various countries to determine the optimal economic and financial structure and to develop financial policies to support this structure in the face of unexpected events such as the Covid-19 pandemic. 2 Data set The data set used in the study was obtained from the Central Bank of the Republic of Türkiye (CBRT). The data represents the consolidated income statements and balance sheets of 997 enterprises in the “A-502-Sea and coastal freight water transport” sector for the year 2020, as registered in the CBRT system under the H-50 Maritime Transport main sector. Table 1 displays the scale distribution of these companies. According to Table 1, the majority of the companies included in the study are micro and small businesses. There are 439 joint-stock companies, 553 limited liability companies, 3 cooperatives, and 2 other companies, and 60.1% of all companies are micro-sized. Medium-sized companies, with 4,608 employees, have the greatest employment rate among the organisations that employ a total of 14,528 individuals. Furthermore, large-scale enterprises, which account for 50.4% of the sector’s total net sales of 26.5 billion ₤ in 2020, have the largest net sales. In the sector with a total asset value of 40.9 billion ₤, total equity is 14.1 billion ₤.Table 1 Scale distribution of sea freight transport campanies (CBRT, 2022). Scale Number of % Number of Net sales Total assets Equity resources companies employees (000) % (000) % (000) % Micro 559 60.1% 1129 476,502.4 1.8% 9,177,972.8 22.4% 4,899,518,4 34.6% Small 286 28.7% 4288 4,665,977.7 17.6% 6,664,608.5 16.3% 2,444,087,4 17.2% Medium 93 9.3% 4608 8,023,491.3 30.2% 9,756,317.1 23.8% 4,382,220,4 30.9% Large 19 1.9% 4503 13,392,059.8 50.4% 15,358,122.7 37.5% 2,452,527,6 17.3% Total 997 14.528 26.558.031,2 40.957.021,2 14.178.353,8 The first financial accounts provided during the Covid-19 pandemic process, on the other hand, date from 2020 and were released in September 2021. In this study, in order to measure the impact of the pandemic, emphasis was placed on 2020 data, from which ratios were calculated. However, the sector’s consolidated balance sheet (Table 2) and income statement (Table 3) for the period 2009–2020 were analysed in order to explain the pandemic’s impact on the sector more clearly. Table 2 Consolidated balance sheet of the sector from 2009 to 2020 in Türkiye (CBRT, 2022).. Balance sheet (try thousands) 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 I-current assets 3,071,325.6 3,489,565.8 3,856,540.2 4,615,553.9 5,500,420.3 5,489,266.0 5,691,275.1 6,181,611.8 7,716,418.0 10,074,873.6 11,783,292.4 14,505,945.1 II- Fixed assets 13,187,483.9 13,275,071.4 15,930,199.2 14,523,197.2 15,214,562.3 16,549,615.3 17,384,966.8 18,238,688.2 19,446,820.8 22,745,373.5 24,792,071.9 26,451,076.1 Total assets 16,258,809.5 16,764,637.2 19,786,739.4 19,138,751.1 20,714,982.6 22,038,881.3 23,076,241.9 24,420,300.0 27,163,238.8 32,820,247.1 36,575,364.3 40,957,021.2 I- Short-term liabilities 2,760,309.7 2,951,528.4 4,127,214.4 4,293,098.4 4,739,620.6 4,822,452.0 5,452,547.6 6,286,469.1 7,531,570.5 9,214,002.4 11,997,434.7 11,871,923.5 A- Financial Liabilities 1,011,630.9 966,898.6 1,091,298.7 1,203,872.2 1,155,383.0 1,147,495.9 1,283,712.1 1,752,995.0 1,974,394.9 2,585,702.2 2,842,741.8 2,203,726.9 B- Trade Debts 719,746.7 790,951.6 1,224,809.3 1,148,663.7 1,298,245.2 1,391,668.3 1,635,238.2 1,804,222.8 2,216,074.0 2,817,347.8 4,507,556.5 3,633,493.1 C- Other Short-Term Debts 728,453.4 903,914.0 1,409,827.3 1,378,669.1 1,426,577.3 1,635,722.9 1,730,997.4 1,961,515.3 2,550,263.0 2,587,331.5 2,604,048.2 3,764,314.0 D- Advances Received 178,390.1 145,966.2 175,660.9 344,633.7 567,297.2 327,066.1 402,638.6 347,284.4 267,182.6 423,415.9 1,207,626.6 1,278,495.1 E- Remunerations Spread Over Years 87.9 4,972.0 0.0 201.1 493.7 1,669.6 49,299.4 14,011.2 35,459.5 55,335.7 63,589.8 10,613.5 F- Taxes and Other Liabilities Payable 50,607.0 65,988.5 86,372.6 89,992.5 106,910.9 121,872.0 112,188.3 150,555.8 160,801.3 238,077.4 220,056.0 278,620.9 G- Provisions for Liabilities and Charges 16,470.3 9,984.4 16,614.9 26,986.0 21,529.2 22,629.2 18,666.2 28,936.4 53,050.6 42,922.0 47,161.3 50,799.7 H- Defer.Inc.& Accr.Exp.for the Next Months 54,711.9 62,804.4 122,459.5 99,881.9 162,946.4 173,308.0 219,711.2 226,916.7 274,128.3 463,172.9 498,538.9 650,903.3 I- Other Short-Term Liabilities 211.5 48.7 171.3 198.0 237.6 1,020.0 96.3 31.5 216.4 697.0 6,115.6 957.0 II- Long-term liabilities 4,610,263.4 4,863,878.9 7,114,418.7 5,503,500.5 7,020,490.7 6,667,616.2 7,168,111.7 7,799,448.7 8,576,481.0 11,319,132.5 11,492,711.5 14,906,743.9 A- Financial Liabilities 4,439,153.5 4,699,773.5 6,894,158.8 5,279,039.4 6,727,008.1 6,280,802.6 6,720,781.0 7,198,506.9 7,967,355.2 10,728,501.6 10,777,934.6 13,316,566.3 B- Trade Debts 40,155.0 19,950.6 50,780.7 56,028.4 52,827.2 55,516.6 50,842.0 39,954.9 57,517.6 68,138.0 54,419.1 288,649.1 C- Other Long-Term Debts 116,598.5 132,705.7 156,256.3 139,598.1 198,658.6 229,088.6 291,420.3 451,017.1 373,738.1 351,661.0 232,204.7 463,934.5 D-Advances Received 4,243.8 53.6 1.0 479.0 4,576.5 73,442.6 75,743.5 86,024.1 78,267.6 26,897.2 323,956.1 743,513.4 E- Provisions for Liabilities and Charges 5,119.0 4,984.8 5,788.6 7,912.0 9,103.0 9,778.6 12,054.9 13,069.7 15,479.0 34,777.7 43,373.5 57,420.8 F- Defer.Inc.& Accr.Exp.for the Next Yrs. 4,249.9 6,345.3 7,367.9 20,405.3 28,317.3 18,987.2 17,270.0 10,857.9 84,123.5 109,157.1 60,823.6 36,659.3 G- Other Long-Term Liabilities 743.8 65.4 65.4 38.3 0.0 0.0 0.0 18.0 0.0 0.0 0.0 0.4 III- Shareholders equity 8,888,236.4 8,949,229.9 8,545,106.3 9,342,152.2 8,954,871.4 10,548,813.2 10,455,582.6 10,334,382,2 11,055,187.2 12,287,112.2 13,085,218.1 14,178,353.8 Total liabilities 16,258,809.5 16,764,637.2 19,786,739.4 19,138,751.1 20,714,982.6 22,038,881.3 23,076,241.9 24,420,300,0 27,163,238.8 32,820,247.1 36,575,364.3 40,957,021.2 Number of companies 960 971 984 1.021 1.039 1.030 1.002 1.008 1.020 1.021 1.006 997 Table 3 Consolidated income statement of the Sector from 2009 to 2020 in Türkiye (CBRT, 2022). Income statement (try thousands) 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 A-GROSS SALES 4,367,573.0 5,301,844.6 6,708,520.5 7,134,650.2 7,278,480.9 8,659,407.7 9,186,065.0 9,471,957.7 12,628,522.6 19,402,832.6 22,164,749.4 27,424,783.8 B-DEDUCTIONS FROM SALES (-) 22,406.2 35,515.8 76,615.7 44,076.3 39,722.0 80,547.9 165,159.2 184,390.9 265,236.9 507,290.6 668,558.0 866,752.6 C-Net sales 4,345,166.7 5,266,328.8 6,631,904.7 7,090,573.9 7,238,758.9 8,578,859.8 9,020,905.8 9,287,566.8 12,363,285.7 18,895,542.0 21,496,191.4 26,558,031.2 D-COST OF GOODS SOLD (-) 3,845,158.0 4,472,116.1 5,772,843.9 6,268,304.6 6,406,212.3 7,361,539.6 7,493,205.2 7,816,319.0 10,343,992.0 15,717,796.5 18,172,894.7 22,151,404.9 GROSS PROFIT OR LOSS 500,008.7 794,212.7 859,060.8 822,269.3 832,546.6 1,217,320.2 1,527,700.6 1,471,247.8 2,019,293.6 3,177,745.5 3,323,296.6 4,406,626.3 E-Operating ExpenseES (-) 418,031.8 425,624.9 459,468.7 520,701.8 579,828.6 614,246.5 688,577.7 738,510.4 815,277.3 1,099,560.1 1,181,149.6 1,417,316.5 OPERATING PROFIT OR LOSS 81,976.9 368,587.8 399,592.1 301,567.4 252,718.0 603,073.7 839,122.9 732,737.4 1,204,016.3 2,078,185.3 2,142,147.0 2,989,309.8 F-INCOME FROM OTHER OPERATIONS 1,156,815.3 873,866.6 911,763.3 1,504,371.7 1,131,011.9 1,721,498.8 1,739,907.5 1,837,001.5 2,774,345.9 8,455,120.6 6,857,115.6 8,380,716.7 G-EXPENSES FROM OTHER OPERATIONS (-) 579,607.1 515,679.4 872,369.5 467,845.0 927,850.1 852,422.5 1,257,252.0 1,507,704.7 1,753,168.3 6,501,839.0 5,463,698.9 6,935,423.0 H-FINANCING EXPENSES (-) 558,863.2 453,688.1 958,022.7 457,489.5 1,094,245.0 792,096.1 1,347,219.3 1,181,060.5 1,464,715.3 3,329,048.1 2,240,564.4 4,172,346.0 Profit before extraordinary items 100,322.0 273,086.8 (519,036.8) 880,604.6 (638,365.2) 680,053.9 (25,440.9) (119,026.3) 760,478.6 702,418.8 1,294,999.3 262,257.6 I-EXTRAORDINARY INCOME AND PROFITS 253,817.2 215,018.4 309,422.0 191,884.3 366,605.7 286,317.9 288,396.3 354,947.5 355,102.2 639,688.8 1,187,704.8 808,105.5 J-EXTRA ORDINARY EXPENSES AND LOSSES (-) 107,371.7 262,816.6 619,471.2 235,044.7 196,573.0 428,960.9 258,587.8 546,324.9 283,282.5 372,687.5 324,197.5 449,697.5 Profit or loss before taxes 246,767.5 225,288.6 (829,085.9) 837,444.2 (468,332.5) 537,410.9 4,367.5 (310,403.7) 832,298.3 969,420.2 2,158,506.6 620,665.6 K-PROVISIONS FOR INC.TAX & OTH.LIAB.TO GOV. 16,985.7 14,416.8 27,436.9 36,542.7 40,366.5 31,119.5 35,115.0 46,312.5 58,320.9 149,752.4 126,813.3 172,239.6 Net profit or loss for the financial year 229,781.8 210,871.8 (856,522.8) 800,901.5 (508,699.0) 506,291.4 (30,747.4) (356,716.2) 773,977.3 819,667.8 2,031,693.3 448,425.9 Number of companies 960 971 984 1.021 1.039 1.030 1.002 1.008 1.020 1.021 1.006 997 3 Method Theoretically, there are different measures to assess the financial and economic performance of companies and sectors. The method of the study is the ratio analysis method, which is one of the financial analysis methods used in financial structure analysis. The term ratio analysis was first used in 1908 by William M. Rosendale in his article “Credit Department methods” (Horrigan, 1968, Sarıkamış, 2007). Compared to other estimations methods, ratio analysis is a more simple and predictive method used by investors, managers, financiers, and credit institutions to assess a company’s financial status (Horrigan, 1968, Erol and Dursun, 2012, Engle, 2012). So, the ratio analysis method in the marine industry is commonly used for financial and economic performance (Engle, 2012, Erol and Dursun, 2012, Wolf et al., 2016, Lee and Yang, 2018, Rahman Md. et al., 2020). Ratios employed in financial analysis enable a horizontal and vertical analysis of the profit and loss accounts of a company’s balance sheet and establish various correlations between balance sheet items. Using these ratio relations, the liquidity (Fluidity) ratios, financing structure ratios, activity ratios, profitability ratios, and growth and capital market performance ratios of any company are calculated and interpreted (Sarıkamış, 2007, Engle, 2012, Erol and Dursun, 2015, Erol, 2022). Various types of indicators can be used to measure company or industry performance. So, the ratio analysis is classified into 5 broad groups in Table 4 below. On the other hand, in order to evaluate the results obtained in the ratio analysis in a healthy way, the calculated financial ratios:Table 4 Financial ratio formulas (Solongo, 2017, Erol, 2022). Group Ratio Formula Liquidity ratios Current Ratio Current asset/current liabilities Acid Test Ratio (Current asset-inventory)/current liabilities Cash Ratio Cash & Cash equivalents/ Current Liabilities Financial leverage ratios Debt Ratio Total Foreign Assets/ Total Asset Ratio Times Interest Earned EBIT/ Interest expense Short Term Debt Ratio Short term debt/Total asset Long Term Debt Ratio Long term debt/Total asset Interest Coverage Ratio EBITDA∗∗∗/ Interest Expense Turnover ratios Stock Turnover Ratio COGS/Average stock Total Asset Turnover Sales/Total assets Receivable Turnover Ratio Sales/Average receivable Payable Turnover Ratio Cost of Goods Sold/Average payables Profitability ratios Gross Profit Margin (Sales-COGS∗)/Sales Operating Profit Margin EBIT∗∗/Sales Net Profit Margin Net income/Sales Solvency ratios Return on Equity (ROE) Net Income/Shareholder’s Equity Return onAsset (ROA) Net Profit/total asset • should be compared with the company’s rates in the previous operating period, • should be compared with the ratios or sector averages of other companies in the same sector, • should be compared with the rates found as a result of experience (Erol and Dursun, 2012). 4 Result and discussion The ratios calculated using the sector’s consolidated balance sheet and income statement are presented in Table 5, Table 7. Table 5 reveals that the relevant sector’s Current Ratio, Acid-Test Ratio, and Cash Ratio for 2020 are all 1.88, 1.34, and 0.35, respectively. Furthermore, as compared to the previous year, these three ratios climbed by 32.8%, 23.6%, and 48.5% in 2020, respectively. The financing of ready value reserves, on the other hand, was done using permanent capital. In other words, the consolidated balance sheet in Table 2 shows that the share of permanent capital in total liabilities is 72%, while the share of fixed assets in total assets is 65%. This demonstrates that current assets are financed with a share of permanent capital; as a result, during the pandemic period, a balanced financing approach (Heffernan, 1996) was used to mitigate the danger of short-term default risk. In other words, the maritime freight transport sector has had a working capital and ready value structure that has allowed it to pay its debts in the short term during the pandemic.Table 5 Liquidity and financial structure ratios (CBRT, 2022). Q, The weighted mean of the individual ratios by total assets N.of Firmsa Pre-Covid N.of Firmsa During-Covid Annual change 2019 Q 2020 Q A- Liquidity ratios 1-Current Ratio (%) 844 142.1 863 188.6 32.8% 2-Quick (Acid-Test) Ratio (%) 840 108.6 850 134.2 23.6% 3-Cash Ratio (%) 833 24.1 821 35.8 48.5% 4-Inventories/ Current Assets (%) 528 18.4 536 14.5 −21.5% 5-Inventories/ Total Assets (%) 493 5.2 498 4.6 −12.4% 6-Inventory Dependency Ratio (%) 855 75.9 843 64.2 −15.4% 7-Short-Term Receivables/ Current Assets (%) 820 48.9 815 42.5 −13.1% 8-Short-Term Receivables/ Total Assets (%) 820 18.1 816 19.4 7.2% B- Ratios of financial position 1-Total Loans/ Total Assets (Leverage Ratio) (%) 905 63.0 884 63.4 0.6% 2-Shareholders Equity/ Total Assets (%) 917 37.0 899 36.6 −1.0% 3-Shareholders Equity/ Total Loans (%) 842 117.7 835 93.2 −20.8% 4-Short-Term Liabilities/ Total Liabilities (%) 912 32.2 889 28.0 −13.0% 5-Long-Term Liabilities/Total Liabilities (%) 294 38.4 339 43.6 13.4% 6-Long-Term Liabilities/ Long-Term Liabilities and Shareholders Equity (%) 294 57.1 331 52.8 −7.4% 7-Tangible Fixed Assets (Net)/ Shareholders Equity (%) 651 59.0 639 52.1 −11.7% 8-Tangible Fixed Assets (Net)/ Long-Term Liabilities (%) 256 149.5 259 127.8 −14.5% 9-Fixed Assets/ Total Loans (%) 744 109.0 740 91.1 −16.5% 10-Fixed Assets/ Shareholders Equity (%) 749 137.8 759 122.0 −11.4% 11-Fixed Assets/ Long-Term Liabilities and Shareholders Equity (%) 768 97.3 778 80.6 −17.2% 12-Bank Loans/ Total Assets (%) 368 48.5 342 52.0 7.3% 13-Short-Term Bank Loans/ Short-Term Liabilities (%) 298 31.2 280 34.0 9.0% 14-Bank Loans/ Total Loans (%) 378 64.1 350 64.7 1.1% 15-Current Assets/ Total Assets (%) 989 32.2 980 35.4 9.9% 16-Tangible Fixed Assets (Net)/ Total Assets (%) 732 40.7 728 35.4 −12.9% a Companies with zero numerator or zero denominator are excluded. On the other side, the debt/asset ratio, also known as the financial leverage ratio (Leverage Ratio), indicates what percentage of assets are funded with foreign resources, with a value of 0.50 being ideal. If this ratio surpasses 0.60, it indicates that the company is under debt stress with the increased risk (Erol and Dursun, 2012). In this perspective, the relevant sector’s Leverage Ratio in 2020 was 63.4%. There was no significant change in this ratio when compared to the prior year. Additionally, Shareholders Equity/ Total Assets (%) in 2020 was 36.6%. Besides, the ratio of shareholders’ equity to total loans (%), which must have a theoretical value of 1 and demonstrates a company’s financial independence, was 0.93 in 2020. Therefore, analyzing the sector’s financing structure reveals that the sector is under pressure from lenders and is at risk of loan repayment failure. On the other hand, a high solvency (debt to equity ratio) indicates that there is a risk in terms of economic sustainability. Additionally, in the sector where financial leverage and risk are structurally high, Short-Term Liabilities/Total Liabilities (%) declined by 13% in the period following the outbreak, while Long-Term Liabilities/Total Liabilities (%) grew by 13.4%. Moreover, when the change in the other financial structure ratios in Table 5 is compared to the previous year, the fact that the proportion of fixed assets in the financing structure has decreased while the proportion of current assets has increased indicates that the sector prefers to hold more liquidity. This scenario can be interpreted as the reduction of short-term liabilities in 2020 to avoid default owing to potential cash flow problems caused by the pandemic effect, while assets are financed by a continuous capital rise. This has led to a significant increase in borrowing costs. Based on Table 3’s calculated statistics, financial expenditures increased by 86% in 2020 compared to the previous year. On the other hand, the Bank Loans/Total Foreign Assets Ratio was 52% in 2020, while the Bank Loans/Total Foreign Loans Ratio was 64.7%. In comparison to the previous year, these two ratios have also increased. Therefore, it is evident that the Turkish maritime freight transport sector has difficulty gaining access to capital market instruments, and most activities are financed by bank loans. In fact, bank loans are primarily utilised in maritime transportation (Haralambides, 2004, Alizadeh and ve Nomikos, 2009, Stopford, 2009). However, unexpected events such as covid 19 pandemic has supported that shipping companies must take precautionary measures against financial risks and design steadier steps for sustanable financial management (Erol, 2017, Xu et al., 2022). Indeed, in their analysis with applications in portfolio management under Covid-19, Meng et al. (2022) noted that after the outbreak, the hedge ratio is rised sharply. But due to limited usage of capital market instruments in Türkiye, it is impossible to optimise the financial structure, which significantly impacts financial leverage and solvency. In addition, the trends of a number of ratios between 2009 and 2020 are investigated in Fig. 1 to clarify the influence of the Covid-19 pandemic on the Turkish sea freight water transport’s financial and economic structure. Fig. 1 shows the financial structure ratios in Turkish sea freight transport fluctuating from 2008 and 2020, and the global, regional, and national crises that occurred during this time had an impact on the industry. As a result of the 2008 global financial crisis, the cash-to-debt ratio decreased by 53% in 2009, from 1.69 to 0.80. During the European sovereign debt crisis (ESDC), which peaked between 2010 and 2012, the cash-to-debt ratio decreased in a similar manner. However, the cash ratio grew after the failed coup attempt in 2016 and the pandemic. This circumstance suggests that the working capital has been enhanced to ensure the continuity of cash flows during the pandemic, and that access to the money markets is not restricted. In contrast, the leverage ratio tended to rise between 2008 and 2020, but the Covid-19 pandemic had no significant impact on this ratio. In addition, the ratio of shareholders’ equity to total assets, which has been declining since 2014, continued to fall during the post-pandemic period. Consequently, when the period 2008–2020 is viewed as a whole, it has been observed that the Covid-19 pandemic did not produce a major change in the debt/equity structure of the industry. Also, Table 6 compares the effects of the 2008 global financial crisis (GFC) and the Covid-19 pandemic on sector ratios, two crises with the most catastrophic impact on the maritime transport sector.Fig. 1 Some financial and economic structure ratios for 2009–2020. Table 6 reveals that during the Global Financial Crisis in 2008, the sector financed its assets with equity capital at a higher rate and maintained the same level of activity with less debt investment. Access to the money markets was severely restricted during this time period, when many banks experienced serious losses and were close to failing. Therefore, Shareholders Equity/ Total Assets (%) increased by 16% in 2009 compared to the previous year, whilst the leverage ratio declined by 15% during the same year. During the pandemic, however, the sector is observed to have had little trouble gaining access to money market instruments, while the ratio of shareholders’ equity to total assets declined.Table 6 A comparison of GFC and the Covid-19 pandemic in terms of financial and economic structure ratios. Financial and economic structure ratios Global Financial Crisis The Covid-19 pandemic 2008 2009 Change 2019 2020 Change Cash ratio (%) 169.2 80.3 −53% 142.1 188.6 33% Total loans/ Total assets (Leverage ratio) (%) 52.8 45.1 −15% 63.0 63.4 1% Long-term liabilities/Total liabilities (%) 40.3 37.5 −7% 38.4 43.6 13% Shareholders equity/ Total assets (%) 47.1 54.9 16% 37.0 36.6 −1% Return on assets, on the other hand, can be used as a measure for solvency (Rahman Md. et al., 2020). In this context, another critical issue that must be addressed is whether the companies in the sector generate enough value to cover their debts. Turnover Ratios and Profitability Ratios are included in Table 7 below. In Table 8, all turnover rates except for those of shareholders equity have increased. In 2020, Shareholders Equity Turnover declined by 102% compared to the previous year, reaching 0.04. This demonstrates that equity capital is not adequately employed throughout the pandemic period. Moreover, among the turnover ratios, Tangible Fixed Assets Turnover is the one with the greatest increase. The rise in this ratio, which reached 8.2 in 2020, can be attributed to the expansion of the liquidity structure.Table 7 Turnover ratios and profitability ratios (CBRT, 2022). Q, The arithmetic mean of the individual ratios N.of Firmsa Pre-Covid N.of Firmsa During-Covid Annual change 2019 Q 2020 Q C- Turnover ratios 1-Receivables Turnover (Times) 517 9.9 490 11.3 14.4% 2-Working Capital Turnover (Times) 654 2.1 626 2.2 1.7% 3-Net Working Capital Turnover (Times) 581 1.9 549 2.2 10.6% 4-Tangible Fixed Assets Turnover (Times) 515 5.4 507 8.2 50.7% 5-Fixed Assets Turnover (Times) 556 3.7 545 4.9 32.2% 6-Shareholders Equity Turnover (Times) 591 2.2 572 0.04 −102.2% 7-Total Assets Turnover (Times) 652 0.7 620 0.8 3.3% D- Profitability ratios 1-Ratios relating profit to capital a)Net Profit/ Shareholders Equity (%) (ROE) 880 10.0 890 20.6 106.1% b) Profit Before Tax/ Shareholders Equity (%) 887 19.2 892 21.5 12.0% c) Profit Before Interest and Tax/ Total Liabilities (%) 813 16.7 809 16.3 −2.0% d)Net Profit/ Total Assets (%) (ROA) 868 5.1 861 1.2 −77.0% e) Operating Profit/ Total Assets-Financial Fixed Assets (%) 878 6.0 869 7.5 26.3% f) Reserves from Retained Earnings/ Total Assets (%) 435 6.4 419 7.5 17.6% 2-Ratios relating profit to sales a) Operating Profit/ Net sales (%) 598 18.6 587 21.8 16.9% b) Gross Profit/ Net sales (%) 670 27.3 636 32.9 20.3% c)Net Profit/ Net sales (%) 577 15.7 557 2.0 −87.3% d)Cost of Goods Sold/ Net sales (%) 571 74.6 563 70.5 −5.5% e) Operating Expense/ Net sales (%) 595 7.2 563 6.8 −6.2% f) Interest Expenses/ Net sales (%) 383 5.0 373 4.1 −17.7% 3-Ratios relating profit to financial obligations 1-Interest Coverage Ratios a) Profit Before Interest and Tax/ Interest Expenses (%) 371 247.8 358 654.7 164.2% b)Net Profit and Interest Expenses/ Interest Expenses (%) 369 225.7 354 571.9 153.4% a Companies with zero numerator or zero denominator are excluded. During the pandemic, however, return on equity (ROE) increased by 106% compared to the previous year, reaching 20,6%. When the opportunity cost is considered, the CBRT 1-Year Term TL Deposit interest rate of 16.79% in 2020 can be understood as a high earning power (ROE: 20.6%) of the sector’s equity despite the Covid-19 pandemic conditions. Nevertheless, the high ROE results from the increased financial leverage (Leverage Ratio). In other words, since the majority of the sector’s assets are financed by debt, the same activities can be carried out with less equity investment, and the same operating profitability can be maintained, which increases ROE. On the other hand, the high Leverage Ratio combined with the revenue loss caused by the reduction in global demand (Baldwin and Mauro, 2020, Remes et al., 2021, Narasimha et al., 2021) would increase the financing expenses, which will have a negative effect on the net profit. In this situation, return on assets (ROA) was 1.2% and Net Profit/ Net Sales (net profit margin) was 2% in 2020. Compared to the previous year, these ratios declined by 77% and 87.33%, respectively. When ROE, ROA, and net profit margin are considered altogether, a rise in the borrowing rate degrades the sector’s financial structure while also raising borrowing costs. As a result, improving return on equity via financial leverage is not a long-term strategy. Based on the explanations provided above, the companies in the Turkish sea freight transport sector were unable to generate enough value to pay their debts due to the high leverage ratio, and the negative impact of this condition on profitability. Fig. 2 examines the trends of several ratios between 2009 and 2020 in order to more clearly demonstrate the impact of the Covid-19 pandemic on the profitability rates of the maritime freight transport sector. Fig. 2 reveals that the Turkish maritime transport sector’s net profit margin varied far more dramatically during the ESDC period, which is the first remarkable observation. This upsurge is significantly more severe than the GFC and the Covid-19 Pandemic. In 2009, the net profit margin declined by 43% compared to the previous year, and by 253% in 2013 compared to the previous year [= (10,5−(−16,1))/10,5]. As shown in Table 8, the decline in the net profit margin in 2020 was only 87%. Similar volatility applies to both ROA and ROE. Comparing the effects of the GFC and the Covid-19 pandemic on sector ratios in Table 8, net profit margin and ROA were significantly more severely affected during the pandemic period, excluding ROE, which increased due to financial leverage.Fig. 2 Profitability ratios trend from 2008 to 2020. In fact, Notteboom and Pallis (2021) indicate in their study that market participants employ strategic behaviours differently throughout the GFC and Covid-19 eras, resulting in distinct outcomes (Notteboom and Pallis, 2021). In this perspective, the impact of Covid-19 on trade has been greater than the 2008 financial crisis, according to a report by UNCTAD (Kamal Md. et al., 2021). Although Narasimha et al. (2021) identified the Covid-19 pandemic as the sector’s most devastating effect after World War II, Fig. 2 demonstrates that the effects of ESDC on the Turkish maritime sector were more severe.Table 8 A comparison of GFC and the Covid-19 pandemic in terms of profitability ratios. Profitability ratios Global Financial Crisis The Covid-19 pandemic 2008 2009 Change 2019 2020 Change Net profit/ Net sales (%) 10.9 6.2 −43% 15.7 2.0 −87% Net profit/ Total assets (%) ROA 3.5 1.4 −60% 5.1 1.2 −77% Net profit/ Shareholders equity (%) ROE 7.4 1.4 −81% 10.0 20.6 106% 5 Conclusion and suggestions After experiencing unexpected events such as financial crises, political events, and pandemics in the past and present, sector participants have had to establish policies to ensure operational flexibility and financial fluency in managerial effectiveness in order to keep up with the changing environmental conditions. Determining the optimal capital structure and providing the financial processes and instruments to support it should be among the fundamental business strategies. This study was conducted in an attempt to reveal the financial and economic consequences of the Covid-19 pandemic on the Turkish sea freight transport sector, as well as the sector’s financial responses to the pandemic. In this regard, the first consolidated balance sheet/income statement of 997 enterprises, made during the pandemic, was studied by ratio analysis. The results were then compared to the financial outputs during the GFC and ESDC periods. In this context, the outcomes of the sector’s 2020 financial and economic structure analysis and the consequences of the pandemic are detailed below. During the pandemic, a balanced financing strategy was established to ensure the continuity of cash flows and to minimize the risk of short-term default, and an increase in working capital was noted. In this respect, the Current Ratio, Acid-Test Ratio, and Cash Ratio all increased by 32.8%, 23.6%, and 48.5%, respectively, during the pandemic period compared to the previous year. The sector with a significant financial leverage is under pressure from lenders. On the other hand, while the ratio of short-term resources declined in the capital structure in 2020, the ratio of long-term resources increased to prevent default. This circumstance substantially boosted borrowing costs, and financing expenses increased by 86% during the pandemic period compared to the prior year. Furthermore, when the sector’s financing structure is studied, it is observed that the operations are mostly supported by bank loans, implying a lack of access to/use of capital market instruments. This is a significant danger in terms of debt default in depressed conditions that cause chain crises such as the Covid-19 pandemic. For this reason, the sector, which is largely comprised of small and medium-sized enterprizes, should have access to capital market instruments through mergers and the listing of companies on the stock exchange. Thus, in a sector with such a highly dynamic and changing operating environment, companies can use capital market instruments more effectively and economically by diversifying their portfolios of financial processes and instruments. The debt-to-equity ratio in the capital structure has not changed much over the last three years despite the pandemic, regardless of the fact that one of the most significant responses of the sector throughout the pandemic period has been to extend the loan from short-term to long-term. Alternatively, during the GFC period (in 2009), when access to financial money markets was extremely limited, Shareholders Equity/ Total Assets increased by 16%, and the leverage ratio decreased by 15%, whereas during the pandemic period, the sector had no difficulty gaining access to financial market instruments and Shareholders Equity/ Total Assets was observed to decrease. In other words, the industry had no difficulty gaining access to funding during the course of the pandemic. Regarding opportunity cost, the financial profitability (ROE) of 20.6% in 2020 indicates that, despite the Covid-19 pandemic, the earning capacity of equity capital in the industry remains strong. The high return on equity is, however, due to the high leverage ratio, and increasing return on equity through financial leverage is not a sustainable strategy. Furthermore, when the profitability ratios and Financial Structure Ratios are combined, it is clear that the relevant sector’s companies’ capacity to pay their debts is insufficient. Despite the Covid-19 outbreak, which was considered the industry’s most devastating effect since World War II, the consequences of ESDC on the Turkish maritime sector were seen as being more devastating. In 2009, the net profit margin declined by 43% compared to the previous year, and by 253% in 2013 compared to the previous year. However, during the pandemic, the net profit margin dropped by only 87%. As a result, it has been noted that the effects of crises on the sector in different time periods vary. This study’s findings and recommendations can serve as a guide for authorities in maritime-related sectors in various countries to determine the optimal economic and financial structure and develop financial policies to support this structure in the face of unexpected events such as the Covid-19 pandemic. Moreover, companies operating in these countries can use this research for comparative analysis. 6 Glossary - TL (₤) represents the Turkish Lira. In 2022, the exchange rate with the USD and the EUR is 18.57 ₤ and 18.12₤, respectively (www.tcmb.gov.tr, 22.10.2022). - Central Bank of the Republic of Türkiye (CBRT) - Firms with zero numerator and denominator are excluded when calculating the ratios. - Q, Weighted average of the ratios of companies in the sector according to assets - EBIT: Earnings before interest and taxes - GFC: Global financial crisis - COGS: Costs of goods sold - ESDC: European sovereign debt crisis - EBITDA: EBIT + depreciation & amortization - CBRT: The Central Bank of the Republic of Türkiye - GT: Gross tonnage - DWT: Deadweight tonnage - NYSE: New York Stock Exchange - BDI: The Baltic dry index - ROE: Return on equity - ROA: Return on equity assets Uncited References Ito et al. (2020) Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability No data was used for the research described in the article. Acknowledgments The author would like to thank Seda ALTUNTAŞ for redacting the English language. ==== Refs References Alizadeh A.H. ve Nomikos N.K. Shipping Derivatives and Risk Management 2009 Faculty of Finance, Cass Business School, City University London Baldwin R. Mauro B.W. Economics in the time of COVID-19 A Centre for Economic Policy Research Press VoxEU.Org eBook 2020 CEPR Press CBRT Company accounts statistics for 2009–2020 2022 https://www3.tcmb.gov.tr/sektor/#/en/H/502/sea-and-coastal-freight-water-transport, (Access date: 02.05.2022) Cengiz H. Turan E. Business impact of covid-19 pandemic on global maritime industry J. Nav. Sci. Eng. 17 1 2021 43 75 https://dergipark.org.tr/en/pub/jnse/issue/61654/851475 Charłampowicz j. Maritime container terminal service quality in the face of COVID-19 outbreak Pomorstvo-Sci. J. Marit. Res. 35 1 2021 93 99 Chen Q. Ge Y.E. Lau Y.Y. Dulebenets M.A. Sun X. Kawasaki T. Mellalou A. Tao X. Effects of COVID-19 on passenger shipping activities and emissions: empirical analysis of passenger ships in Danish waters Marit. Policy Manag. 2022 10.1080/03088839.2021.2021595 Depellegrin D. Bastianini M. Fadini A. Menegon S. The effects of COVID-19 induced lockdown measures on maritime settings of a coastal region Sci. Total Environ. 740 10 2020 1 7 10.1016/j.scitotenv.2020.140123 Downey L. Fonzone A. Fountas G. Semple T. The impact of COVID-19 on future public transport use in Scotland’ Transp. Res. A 2022 10.1016/j.tra.2022.06.005 Engle, C.R., 2012. Assessing the financial position of an aquaculture business: Using balance sheets. SRAC Publication, No: 4401. Erol S. Hedging against bunker price fluctuations by using derivative products Int. J. E-Navig. Marit. Econ. 8 2017 11 19 Erol S. Financial and economic impacts of the COVID-19 pandemic on aquaculture in Türkiye and financial policy recommendations Mar. Policy 146 2022 105313 10.1016/j.marpol.2022.105313 Erol S. Dursun A. Financial structure analysis of the companies in the business of the maritime transportation sector Atatürk Univ. J. Soc. Sci. Inst. 16 3 2012 367 382 Erol S. Dursun A. Financial risks and hedging in maritime transportation Dokuz Eylül Univ. Marit. Faculty J. 7 2 2015 172 201 10.18613/deudfd.68524 Fernández-Macho J. Murillas A. Ansuategi A. Escapa M. Gallastegui C. González P. Prellezo R. Virto J. Measuring themaritim eeconomy: Spainin the European Atlantic Arc’ Mar. Policy 60 2015 49 61 10.1016/j.marpol.2015.05.010 Gong S.X. Ye H.Q. ve Zeng Y.Y. Impacts of the recent financial crisis on ship financing in Hong Kong: A research note Marit. Policy Manag. 40 1 2013 1 9 Guerrero D. Letrouit L. Pais-Montes C. The container transport system during Covid-19: An analysis through the prism of complex networks Transp. Policy 115 2022 113 125 10.1016/j.tranpol.2021.10.021 Haralambides, E.H., 2004. Determinants of price and price stability in liner shipping. In: Workshop on the Industrial Organization of Shipping and Ports National University of Singapore, Singapore. Heffernan S. Modern Banking in Theory and Practice 1996 Wiley New York Hoffmann J. Shipping out of the economic crisis Brown J. World Aff. XVI II 2010 121 130 Horrigan J.O. A short history of financial ratio analysis Account. Rev. 42 2 1968 284 294 Hossain M. The effect of the Covid-19 on sharing economy activities J. Clean. Prod. 280 2021 124782 10.1016/j.jclepro.2020.124782 ISL ISL Shipping Statistics and Market Review 2021 2021 Institute of Shipping Economics and Logistics (ISL) Bremen, Germany 65, 3 Ito H. Hanaoka S. Kawasaki T. The cruise industry and the COVID-19 outbreak Transp. Res. Interdiscip. Perspect. 5 2020 100136 10.1016/j.trip.2020.100136 2590–1982 Kamal Md. R. Chowdhury M.A.F. Md. Hosain M. Stock market reactions of maritime shipping industry in the time of COVID-19 pandemic crisis: an empirical investigation Marit. Policy Manag. 2021 10.1080/03088839.2021.1954255 Koyuncu K. Tavacioğlu L. Gökmen N. Arican U.Ç. Forecasting COVID-19 impact on RWI/ISL container throughput index by using SARIMA models Marit. Policy Manag. 48 1096 2021 8 1108 10.1080/03088839.2021.1876937 Lee P.T.W. Yang Z. Multi-criteria decision making in maritime studies and logistics applications and cases International Series in Operations Research & Management Science (ISOR, Volume 260) 2018 Springer International Publishing AG Switzerland Li Y. Yin M. Khan K. Su C.W. The impact of COVID-19 on shipping freights: asymmetric multifractality analysis Marit. Policy Manag. 2022 10.1080/03088839.2022.2081372 Liu X. Chang Y.C. An emergency responding mechanism for cruise epidemic prevention—taking COVID-19 as an example Mar. Policy 119 2020 104093 10.1016/j.marpol.2020.104093 Loske D. The impact of covid-19 on transport volume and freight capacity dynamics: an empirical analysis in german food retail logistics Transp. Res. Interdiscip. Perspect. 6 2020 100165 10.1016/j.trip.2020.100165 Lun Y.H. Lai K.H. ve Cheng T.C.E. Shipping and Logistics Management 2010 Springer London Dordrecht Heidelberg New York Mankowska M. Plucinski M. Kotowska I. Filina-Dawidowicz L. Seaports during the COVID-19 pandemic: The terminal operators’ tactical responses to disruptions in maritime supply chains Energies 14 4 2021 339 10.3390/en14144339 Meng B. Chen S. Yang M. Kuang H. Spillover effects between the carbon and linear shipping markets under COVID-19: A time-varying frequency-domain analysis with applications in portfolio management Ocean Coast. Manag. 229 2022 106351 10.1016/j.ocecoaman.2022.106351 Menhat M. Zaideen M.I.M.M. Yusuf Y. Salleh N.H.M. Zamri M.A. Jeevan J. The impact of Covid-19 pandemic: A review on maritime sectors in Malaysia Ocean Coast. Manag. 209 2021 105638 10.1016/j.ocecoaman.2021.105638 Narasimha P.T. Jena P.R. Majhi R. Impact of COVID-19 on the Indian seaport transportation and maritime supply chain Transp. Policy 110 2021 2021 191 203 10.1016/j.tranpol.2021.05.011 Notteboom T. Pallis T. IAPH-WPSP Port Economic Impact Barometer One Year Report: A Survey-Based Analysis of the Impact of COVID-19 on World Ports in the Period 2020 to 2021 2021 International Association of Ports and Harbors Antwerp Notteboom T. Pallis T. Rodrigue J.P. Disruptions and resilience in global container shipping and ports: the COVID-19 pandemic versus the 2008–2009 financial crisis Marit. Econ. Logist. 23 2021 179 210 10.1057/s41278-020-00180-5 Pan Y. He S.Y. Analyzing COVID-19’s impact on the travel mobility of various social groups in China’s Greater Bay Area via mobile phone big data Transp. Res. A 159 2022 2022 263 281 10.1016/j.tra.2022.03.015 Rahman Md. T. Nielsen R. Khan Md. A. Isaac A.Y. Impact of management practices and managerial ability on the financial performance of aquaculture farms in Bangladesh Aquac. Econ. Manag. 24 1 2020 79 101 10.1080/13657305.2019.1647578 Remes J. Manyika J. Smit S. Kohli S. Fabius V. Sundiatu D.F. Nakaliuzhnyi A. The Consumer Demand Recovery and Lasting Effects of COVID-19 2021 McKinsey & Company Sarıkamış C. Development of ratio analyze J. Account. Finance 33 2007 44 45 Solongo G. Financial resources management for SME’S of Mongolia Int. Bus. Res. 10 6 2017 145 163 Stopford M. Maritime Economics, London and New York 2009 Routledge Syriopoulos T.C. Financing greek shipping: Modern instruments, methods and markets Marit. Transp.: Greek Paradigm Res. Transp. Econ. 21 2007 171 219 Syriopoulos T. Tsatsaronis M. Gorila M. The global cruise industry: financial performance evaluation Res. Transp. Bus. Manag. 2020 100558 10.1016/j.rtbm.2020.100558 Tatenhove Jan P.M. COVID-19 and European maritime futures: different pathways to deal with the pandemic’ Marit. Stud. 20 2021 63 74 10.1007/s40152-021-00216-3 35300183 Tijan E. Jović M. Hadžić A.P. Achieving blue economy goals by implementing digital technologies in the maritime transport sector Sci. J. Marit. Res. 35 2021 241 247 10.31217/p.35.2.6 UNCTAD Review of Maritime Transport 2021: Sales No. E.21.11.D.21 2021 United Nations publication New York and Geneva Wolf C. Stephenson M. Knoblauch W. Novakovic A. Dairy farm financial performance: Firm, year, and size effects Agric. Finance Rev. 76 4 2016 532 543 10.1108/AFR-02-2016-0009 Xu L. Yang S. Chen J. Jia S. The effect of COVID-19 pandemic on port performance: evidence from China Ocean Coast Manag. 209 2021 105660 10.1016/j.ocecoaman.2021.105660 0964–5691 Xu L. Zou Z. Zhou S. The influence of COVID-19 epidemic on BDI volatility: An evidence from GARCH-MIDAS model Ocean Coast. Manag. 229 2022 106330 10.1016/j.ocecoaman.2022.106330 Yin H. Chen C. Xiao Y. Mohsin M. Liu Z. A study on the assessment of risk management performance in maritime start-ups: evidence from China Marit. Policy Manag. 2022 10.1080/03088839.2022.2028028 Zheng R. Xu Y. Wang W. Ning G. Bi Y. Spatial transmission of COVID-19 via public and private transportation in China Trav. Med. Infect. Dis. 34 2020 101626 10.1016/j.tmaid.2020.101626 Zhou X. Jing D. Dai L. Wang Y. Guo S. Hu H. Evaluating the economic impacts of COVID-19 pandemic on shipping and port industry: A case study of the port of Shanghai Ocean Coast. Manag. 230 2022 106339 10.1016/j.ocecoaman.2022.106339	36465688	PMC9708105	NO-CC CODE	2022-12-08 23:16:22	no	Reg Stud Mar Sci. 2023 Jan 30; 57:102749	utf-8	Reg Stud Mar Sci	2,022	10.1016/j.rsma.2022.102749	oa_other
==== Front Dialogues in Health 2772-6533 2772-6533 The Author. Published by Elsevier Inc. S2772-6533(22)00087-9 10.1016/j.dialog.2022.100087 100087 Article In the midst of a pandemic, more introverted individuals may have a mortality advantage Glei Dana A. ⁎ Weinstein Maxine Center for Population and Health, Georgetown University, 312 Healy Hall, 37th and O Streets, NW, Washington, DC 20057-1197, USA ⁎ Corresponding author at: 5985 San Aleso Court, Santa Rosa, CA 95409-3912, USA. 30 11 2022 12 2023 30 11 2022 2 100087100087 26 7 2022 10 11 2022 27 11 2022 © 2022 The Authors 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Purpose We investigated whether the relationship between extroversion and mortality changed during the COVID-19 pandemic. Methods Midlife Americans were surveyed in 1995–96 with mortality follow-up through December 31, 2020. We used a Cox model to estimate age-specific mortality controlling for sex, race/ethnicity, the period trend in mortality, an indicator for the pandemic period (Mar-Dec 2020), extroversion, and an interaction between extroversion and the pandemic indicator. Results Prior to the pandemic, extroversion was associated with somewhat lower mortality (HR = 0.93 per SD, 95% CI 0.88–0.97), but the relationship reversed during the pandemic. Extroversion was associated with greater pandemic-related excess mortality (HR = 1.29 per SD, 95% CI 1.002–1.67). That is, compared with persons who were more introverted, those who were highly extroverted suffered a bigger increase in mortality during the pandemic relative to pre-pandemic mortality levels. Conclusions The slight mortality advantage enjoyed by more extroverted Americans prior to the pandemic disappeared during the first 10 months of the COVID-19 pandemic. We suspect that the mortality benefit of introversion during the pandemic is largely a result of reduced exposure to the risk of infection, but it may also derive in part from the ability of more introverted individuals to adapt more easily to reduced social interaction without engaging in self-destructive behavior (e.g., drug and alcohol abuse). Graphical abstract Unlabelled Image Keywords Mortality Extroversion Introversion COVID-19 Pandemic United States Abbreviations CI, Confidence interval HR, Hazard ratio MIDUS, Midlife in the United States NDI, National Death Index SD, Standard deviation ==== Body pmc1 Introduction Under normal circumstances, people who are more extroverted (i.e., those energized by social situations [1]) may enjoy lower mortality than more introverted individuals (i.e., who prefer less socially stimulating environments [1]) [[2], [3], [4]]. The health-behavior model of personality suggests that the main mechanism through which personality affects mortality is by influencing one's propensity to adopt health-promoting behaviors while avoiding harmful behaviors [5,6]. For example, extroversion could improve survival by enhancing social relationships [[7], [8], [9]]. Yet, extroversion could become a detriment during an airborne pandemic that thrives on human contact. Social life changed abruptly when COVID-19 was declared a global pandemic. In the interest of reducing contagion, social interaction was severely curtailed when offices, non-essential businesses, and schools were closed, while large social gatherings were canceled. We suspected that individuals who were more introverted were more willing to limit social activities and avoid large gatherings of people, thereby lowering risk of exposure to SARS-CoV-2. Apart from exposure-to-risk, we thought people who were more introverted may have been better-equipped to cope with reduced social interaction while maintaining healthy behaviors without resorting to risk-taking behavior such as substance abuse. If so, we hypothesized that more introverted people would have experienced fewer adverse consequences of the pandemic than their more extroverted counterparts. To our knowledge, no one has evaluated the effect of extroversion on excess mortality during the pandemic, although there has been considerable attention paid to subjective feelings of loneliness, anxiety, and depression. Most of those studies suggest that extroversion was associated with bigger increases in loneliness [10,11] and greater deterioration in mental health during the pandemic [12,13]. Similarly, we expected that more extroverted individuals experienced more excess mortality during the pandemic than those who were more introverted. In this paper, we use data for midlife Americans surveyed in 1995–96 with mortality follow-up through December 31, 2020 to investigate whether the association between extroversion and mortality in the US changed during the COVID-19 pandemic. A priori, we hypothesized that there would be an inverse association between extroversion and mortality during the pre-pandemic period (1995-Feb 2020), but the relationship would be reversed during the pandemic (March-Dec 2020). That is, we anticipated that excess mortality during the pandemic would be greater for individuals who were more extroverted than for those who were highly introverted. 2 Materials and methods The data came from the Midlife in the United States (MIDUS) study, which surveyed Americans in 1995–96 with mortality follow-up through December 31, 2020 (see Appendix A for details). Among the 6325 respondents who completed the mail-in self-administered questionnaires at baseline, 1767 (27.9%) died by December 31, 2020. Personality was measured at Wave 1 using the standardized questionnaire for the “Big Five” taxonomy of personality [14]. We included potential confounders that may affect personality and are known to be associated with mortality: sex, age, and race/ethnicity. Table S1 shows descriptive statistics for all analysis variables. We used standard practices of multiple imputation to handle missing data [15,16]. A Cox model was used to model age-specific mortality with a robust variance estimator to correct for family-level clustering. Model 1 adjusted for age (as the time metric), sex, race/ethnicity, calendar year (i.e., to capture period mortality decline), a dichotomous indicator (P) for the pandemic period, extroversion (E), and an interaction between the extroversion score and the pandemic indicator. The pandemic indicator represents the extent to which mortality after March 2020 differed from the expected level of mortality in the absence of a pandemic after accounting for cohort aging and period mortality decline. A hazard ratio (HR) greater than 1.0 implies excess mortality during the pandemic (i.e., mortality was higher than expected based on the pre-pandemic mortality linear trend), whereas a value less than 1.0 indicates that mortality was lower than expected. Excess mortality includes deaths resulting directly from COVID-19 (whether recorded as such or not) as well as potential increases in mortality from other causes indirectly affected by the pandemic. To ease interpretation, we reparameterized the model to include two interaction effects for extroversion rather than a main and an interaction effect. The first interaction (E × (1 − P)) represents the effect of extroversion during the pre-pandemic period; it is the same as the main effect in a standard specification. For this interaction, we expected a hazard ratio less than 1.0, indicating that individuals who scored higher on extroversion experienced lower mortality prior to the pandemic than those who were more introverted. The second interaction (E × P) represents the effect of extroversion during the pandemic period; the coefficient for this interaction equals the sum of the main effect and the interaction effect from the standard specification. For this interaction, we expected a hazard ratio greater than 1.0, implying that those who scored higher on extroversion experienced higher mortality during the pandemic than their more introverted counterparts. Model 2 further adjusts for the main effect of conscientiousness, which is the personality trait previously reported to be most strongly and consistently associated with mortality [2,3,[17], [18], [19]]. We might expect conscientious individuals to exhibit greater compliance with public health orders to socially distance, wear a mask in higher-risk settings, accept vaccination, and stay up-to-date with appropriate boosters. As expected, conscientiousness conferred a mortality advantage even before the pandemic. In auxiliary models, we tested an interaction between conscientiousness and the pandemic indicator, but found no evidence that the effect of conscientiousness differed significantly between the pre-pandemic and pandemic period. That is, conscientiousness continued to be associated with lower mortality throughout the period, but there was no indication that the mortality advantage increased during the pandemic. In Model 3, we added the other three personality traits (i.e., neuroticism, openness, and agreeableness). 3 Results Prior to the pandemic, extroversion was associated with somewhat lower mortality (HR = 0.93 per SD, 95% CI 0.88–0.97; Table S2, Model 1). In contrast, the effect of extroversion reversed during the pandemic: more extroverted individuals appeared to suffer higher mortality than their introverted counterparts, although the effect was not significant (HR = 1.20 per SD, 95% CI 0.93–1.54). Given the relatively small number of deaths during March–December 2020 (N = 79, 14 of which were reported to have resulted from COVID-19), the confidence intervals are very wide for the pandemic period. Nonetheless, the results indicate that extroversion was associated with greater pandemic-related excess mortality (HR = 1.20/0.93 = 1.29 per SD, 95% CI 1.002–1.67); that is, compared with those who scored higher on introversion, people who were more extroverted suffered a bigger increase in mortality during the pandemic relative to their pre-pandemic mortality levels. After adjusting for conscientiousness (Model 2), the difference in the effect of extroversion between pandemic vs. pre-pandemic periods was only marginally significant (HR = 1.24/0.97 = 1.28, p ∼ 0.057, 95% CI 0.99–1.65). After adjusting for the other three personality traits (Model 3), the effect of extroversion prior to the pandemic was somewhat stronger, whereas the effect during the pandemic was somewhat weaker. Nonetheless, the association between extroversion and excess mortality remained unchanged (HR = 1.19/0.93 = 1.28, 95% CI 0.99–1.66). To better demonstrate how the effect of extroversion changed during the pandemic, we computed the hazard ratios associated with selected levels of the extroversion score based on Model 3 (Fig. 1 ). Compared with someone who scored at the mean level of extroversion, mortality rates prior to the pandemic were 10% lower for a person who was very extroverted (i.e., maximum score, which comprised 12% of the sample), while the rates were 12% higher for someone who was very introverted (i.e., 11th percentile). However, the mortality advantage for more extroverted Americans disappeared during the COVID-19 pandemic. Although the differences are not significant (because of limited statistical power), the pattern of results suggests that, if anything, very extroverted individuals suffered higher mortality during the pandemic than those who were very introverted. Relative to those who scored at the mean level of extroversion, mortality rates during the pandemic appeared to be higher for very extroverted individuals (HR = 1.15, 95% CI 0.77–1.72) and lower for those who were very introverted (HR = 0.70, 95% CI 0.43–1.14).Fig. 1 Fully-adjusted hazard ratios for mortality by level of extroversion and time period. Based on a Cox model that uses age as the time metric and adjusts for sex, race/ethnicity, the linear period trend in mortality decline (prior to the pandemic), the main effects for all 5 personality traits, a dichotomous indicator for the pandemic period (March–December 2020), and an interaction between the extroversion score and the pandemic indicator (Table S2, Model 3). The error bars represent the 95% confidence intervals. A substantial fraction (12%) of the sample scored the maximum value on extroversion (4, which is 1.43 SD above the mean); we defined this group as “very extroverted.” However, fewer than 0.1% of the sample scored the minimum value (1, which is 3.92 SD below the mean). To be more symmetric with the definition of “very extroverted” (i.e., those scoring at least 1.43 SD above the mean), we defined “very introverted” to represent those scoring 1.43 SD below the mean (which corresponds with the 11th percentile of the distribution). The other values show here were chosen to be as close as possible to the 25th, 50th, and 75th percentiles of the distribution. Fig. 1 When we translated the estimated mortality rates into survival ratios before vs. during the pandemic (Fig. 2 ), we found that the percentage expected to survive from age 25 to 85 fell 9 percentage points for someone who was very extroverted (from 57% to 48%), whereas it increased 15 percentage points (from 49% to 64%) for their very introverted counterparts. Thus, survival of highly extroverted individuals during the pandemic was comparable to those who were highly introverted prior to the pandemic, whereas very introverted people had even better survival during the pandemic than highly extroverted individuals prior to the pandemic.Fig. 2 Estimated percentage surviving from age 25 to 85 by level of extroversion and time period. Estimates are based on Model 3 (Table S2) where the year is set to 2020, the dichotomous indicator for period is set to either pre-pandemic (January–February) or pandemic (March–December), and the extroversion score is fixed at the 11th percentile (i.e. scored 2.4 out of 4, which we defined as very introverted) or the top 12% of the distribution (i.e. scored 4 out of 4, which we defined as very extroverted). All other covariates (i.e. sex race/ethnicity and the other four personality traits) are fixed at the mean for the sample. Fig. 2 4 Discussion As hypothesized, our results suggest that more extroverted people suffered higher excess mortality during the pandemic than their more introverted counterparts. We cannot say yet whether that pattern continued into 2021–22. The answer will have to wait until further mortality follow-up data become available. Modern society is culturally biased toward extroverts, but a culture that favors extroversion and individualism is not the best prescription for surviving a pandemic. The mortality benefit of introversion during the pandemic was likely a result of reduced exposure to the risk of infection. Persons who were more introverted may have been more willing to limit social activities, practice social distancing, and avoid large social gatherings, which would have made them less prone to deaths resulting directly from COVID-19. Some of the benefit may also derive from the ability of more introverted individuals to adapt more easily to reduced social interaction. If they were less likely than highly extroverted persons to succumb to depression, anxiety, and/or loneliness during the pandemic as previous studies suggest [[10], [11], [12], [13]], it could have suppressed mortality from other causes indirectly affected by the pandemic. If psychological distress contributed to excess mortality, we would expect to find an increase in suicide—the ultimate “death of despair” (a term commonly used to refer to deaths resulting from suicides and drug- and alcohol-related causes [20]). Yet, there is little evidence that suicide rates increased during the pandemic. In fact, contrary to many predictions, suicide mortality was significantly lower than expected throughout March–December 2020 [21], although there appears to have been a small increase among Americans aged 25–34 [22]. In contrast, other so-called “deaths of despair” increased dramatically in the US during the pandemic. Between 2019 and 2020, alcohol-related deaths increased 25% [23], while drug overdoses grew 30%, and in particular, deaths involving synthetic opioids such as fentanyl rose 55% [24]. If highly extroverted people were more likely than their more introverted counterparts to succumb to substance abuse—perhaps because of difficulty coping with the stressors imposed by the pandemic—it could have led to more excess mortality from external causes. There are several limitations to this study, First, mortality during 2020 is almost certainly under-estimated. Deaths during 2020 were based on an early release file for the National Death Index (NDI), which according to the National Center for Health Statistics, accounted for only about 95% of all recorded US deaths in 2020 at the time of the NDI search [25]. Second, the MIDUS sampling frame excluded the institutionalized population, who suffered especially high mortality early during the early stages of the pandemic. Thus, mortality among the MIDUS cohort is likely to be lower than pandemic-related mortality for the population as a whole. Third, we have no information about the degree to which MIDUS participants complied with public health orders during the pandemic and whether it differed by personality. Nor do we have any information about self-destructive behaviors (e.g., alcohol and drug abuse) during the pandemic. Finally, the MIDUS sample under-represents minorities, who suffered higher mortality during the pandemic. 5 Conclusion Our results suggest that the slight mortality advantage enjoyed by more extroverted people under normal circumstances disappeared during the first 10 months of the COVID-19 pandemic. Some would say that highly introverted people have been training for a pandemic their whole lives. Research data for this article The original data used for this analysis are publicly available from ICPSR (https://www.icpsr.umich.edu/web/NACDA/series/203) or from the MIDUS portal (https://midus.colectica.org/). The data from Wave 1 of MIDUS can be downloaded from https://www.icpsr.umich.edu/web/NACDA/studies/2760. The most recent mortality followup for the original cohort can be downloaded from https://midus.colectica.org/item/midus.wisc.edu/0cf8bc9a-1daa-437b-9603-c02320a03fee. Funding This work was supported by the 10.13039/100000049 National Institute on Aging [grant numbers P01 AG020166, U19AG051426] and 10.13039/100005688 the Graduate School of Arts and Sciences, 10.13039/100008064 Georgetown University . The funders played no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary data Supplementary material Image 1 Acknowledgments We are grateful to Kristen Harknett, Samuel H. Preston, Jonathan Mormino, and Jennifer Dowd for suggestions on an earlier draft of this manuscript. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.dialog.2022.100087. ==== Refs References 1 Cain S. Quiet: The power of introverts in a world that can’t stop talking 2012 Broadway Paperbacks New York 2 Chapman B.P. Elliot A. Sutin A. Terraciano A. Zelinski E. Schaie W. Mortality risk associated with personality facets of the big five and interpersonal Circumplex across three aging cohorts Psychosom Med 82 2020 64 73 10.1097/PSY.0000000000000756 31688676 3 Graham E.K. Rutsohn J.P. Turiano N.A. Bendayan R. Batterham P.J. Gerstorf D. Personality predicts mortality risk: an integrative data analysis of 15 international longitudinal studies J Res Pers 70 2017 174 186 10.1016/j.jrp.2017.07.005 29230075 4 Wilson R.S. Krueger K.R. Gu L. Bienias J.L. Mendes de Leon C.F. Evans D.A. Neuroticism, extraversion, and mortality in a defined population of older persons Psychosom Med 67 2005 841 845 10.1097/01.psy.0000190615.20656.83 16314587 5 Friedman H.S. Long-term relations of personality and health: dynamisms, mechanisms, tropisms J Pers 68 2000 1089 1107 10.1111/1467-6494.00127 11130733 6 Smith T.W. Personality as risk and resilience in physical health Curr Dir Psychol Sci 15 2006 227 231 10.1111/j.1467-8721.2006.00441.x 7 Berkman L.F. Glass T. Brissette I. Seeman T.E. From social integration to health: Durkheim in the new millennium Soc Sci Med 51 2000 843 857 10.1016/s0277-9536(00)00065-4 10972429 8 Roberts B.W. Kuncel N.R. Shiner R. Caspi A. Goldberg L.R. The power of personality Perspect Psychol Sci 2 2007 313 345 10.1111/j.1745-6916.2007.00047.x 26151971 9 Holt-Lunstad J. Smith T.B. Layton J.B. Social relationships and mortality risk: a meta-analytic review PLoS Med 7 2010 e1000316 10.1371/journal.pmed.1000316 10 Entringer T.M. Gosling S.D. Loneliness during a Nationwide lockdown and the moderating effect of extroversion. Social psychological and personality Science 2021 10.1177/19485506211037871 19485506211037870 11 Folk D. Okabe-Miyamoto K. Dunn E. Lyubomirsky S. Did social connection decline during the first wave of COVID-19?: The role of extraversion Collabra: Psychol 6 2020 37 10.1525/collabra.365 12 Proto E. Zhang A. COVID-19 and mental health of individuals with different personalities Proc Natl Acad Sci 118 2021 e2109282118 10.1073/pnas.2109282118 13 Rettew D.C. McGinnis E.W. Copeland W. Nardone H.Y. Bai Y. Rettew J. Personality trait predictors of adjustment during the COVID pandemic among college students PLoS One 16 2021 e0248895 10.1371/journal.pone.0248895 14 John O.P. The “big five” factor taxonomy: Dimensions of personality in the natural language and in questionnaires Handbook of personality: Theory and research 1990 The Guilford Press New York, NY, US 66 100 15 Rubin D.B. Multiple imputation after 18+ years (with discussion) J Am Stat Assoc 91 1996 473 489 16 Schafer J.L. Multiple imputation: a primer StatMethods MedRes 8 1999 3 15 17 Iwasa H. Masui Y. Gondo Y. Inagaki H. Kawaai C. Suzuki T. Personality and all-cause mortality among older adults dwelling in a Japanese community: a five-year population-based prospective cohort study Am J Geriatr Psychiatry 16 2008 399 405 10.1097/JGP.0b013e3181662ac9 18403571 18 Jokela M. Batty G.D. Nyberg S.T. Virtanen M. Nabi H. Singh-Manoux A. Personality and all-cause mortality: individual-participant meta-analysis of 3,947 deaths in 76,150 adults Am J Epidemiol 178 2013 667 675 10.1093/aje/kwt170 23911610 19 Weiss A. Costa P.T.J. Domain and facet personality predictors of all-cause mortality among Medicare patients aged 65 to 100 Psychosom Med 67 2005 724 733 10.1097/01.psy.0000181272.58103.18 16204430 20 Case A. Deaton A. Mortality and morbidity in the 21st century Brook Pap Econ Act 2017 2017 397 476 21 Glei D.A. The U.S. Midlife mortality crisis continues: excess cause-specific mortality during 2020 Am J Epidemiol 191 2022 1677 1686 10.1093/aje/kwac055 35333293 22 Ehlman D.C. Changes in suicide rates — United States, 2019 and 2020 MMWR Morb Mortal Wkly Rep 2022 71 10.15585/mmwr.mm7108a5 23 White A.M. Castle I.-J.P. Powell P.A. Hingson R.W. Koob G.F. Alcohol-related deaths during the COVID-19 pandemic JAMA 327 2022 1704 1706 10.1001/jama.2022.4308 35302593 24 National Institute on Drug Abuse Overdose Death Rates 2022 National Institute on Drug Abuse https://nida.nih.gov/drug-topics/trends-statistics/overdose-death-rates [accessed May 3, 2022] 25 Ryff C. Almeida D. Ayanian J. Binkley N. Carr D.S. Coe C. Documentation of mortality statistics and cause of death codes for core (non-refresher) MIDUS and Milwaukee samples 2022 https://midus-study.github.io/public-documentation/Mortality/Core/MIDUS_Core_DocumentationOfMortality_20220316.pdf [accessed March 17, 2022]	0	PMC9708106	NO-CC CODE	2022-12-07 23:16:29	no	2023 Dec 30; 2:100087	utf-8	Dialogues Health	2,022	10.1016/j.dialog.2022.100087	oa_other
==== Front J Infect J Infect The Journal of Infection 0163-4453 1532-2742 The British Infection Association. Published by Elsevier Ltd. S0163-4453(22)00685-5 10.1016/j.jinf.2022.11.023 Letter to the Editor Changes in the prevalence of respiratory pathogens in children due to the COVID-19 pandemic: a systematic review and meta-analysis Tang Yuyi 1 Dang Xiangyang 1 Lv Meng 2 Norris Susan-L 3 Chen Yaolong 4567 Ren Luo 18⁎⁎# Liu Enmei 1⁎# 1 Department of Respiratory Medicine, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation base of Child development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China 2 Chevidence Lab of Child and Adolescent Health, Chongqing, China 3 Oregon Health & Science University, Portland, Oregon, USA 4 Research Unit of Evidence-Based Evaluation and Guidelines, Chinese Academy of Medical Sciences (2021RU017), School of Basic Medical Sciences, Lanzhou University, Lanzhou, China 5 Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, China 6 Chinese GRADE Centre, Lanzhou, China 7 Lanzhou University, An Affiliate of the Cochrane China Network, Lanzhou, China 8 Pediatric Research Institute, Children's Hospital of Chongqing Medical University, Chongqing, China ⁎ Corresponding author: Dr. Enmei Liu, Children's Hospital of Chongqing Medical University, Department of Respiratory, China, Tel: +86-13368070773 ⁎⁎ Corresponding authors: Luo Ren, Department of Respiratory Medicine, Pediatric Research Institute, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child development and Critical Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China # Luo Ren and Enmei Liu contributed equally to this study. 30 11 2022 30 11 2022 25 11 2022 © 2022 The British Infection Association. Published by Elsevier Ltd. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. ==== Body pmcDear Editor We read with interest the study by Han et al., which identified changes in the detection rates of respiratory viruses among children during the coronavirus disease 2019 (COVID-19) pandemic in Hangzhou, China.1 We congratulate Han et al for their important work, however, the study was single-centered and focused on only four viruses. Therefore, we conducted a systematic review and meta-analysis to assess and compare the global prevalence of respiratory pathogens in children with acute respiratory tract infections (ARTIs) before and during the COVID-19 pandemic to examine how COVID-19 may have altered the spectrum of respiratory pathogens in children. We searched PubMed, Web of Science, Embase, Cochrane library, China National Knowledge Infrastructure, Wanfang Data, and China Biology Medicine disc from inception to May 4, 2022 (Supplementary Material 1). We also searched preprint servers (medRxiv, bioRxiv and SSRN) and Google Scholar, as well as the reference lists of included studies. We included observational studies that reported the detection rate of one or more respiratory viruses (respiratory syncytial virus [RSV], influenza virus [IFV], human adenovirus [HAdV], human rhinovirus [HRV], parainfluenza virus [PIV], human bocavirus [HBoV], human metapneumovirus [HMPV], human coronavirus [HCoV]) or atypical pathogens (mycoplasma pneumoniae [MP], chlamydia pneumoniae [CP]) in children (aged <18 years old) with ARTIs. The included study either focused only on detection during the pandemic period, or compared rates before and during COVID-19. The definitions of before and during COVID-19 were adapted according to the authors’ description, which followed the local outbreak time. Risk of bias assessment was performed using the Joanna Briggs Institute Prevalence Critical Appraisal Tool.2 Freeman-Tukey double arcsine transformation and DerSimonian and Laird random-effects models were used to calculate the pooled prevalence for each pathogen and the Mann-Whitney test to compare the differences in prevalence before and during the COVID-19 pandemic.3 , 4 Data before COVID-19 were extracted from studies that compared prevalence before and during COVID-19, and data during COVID-19 were extracted from studies that focused only on the COVID-19 period as well as studies that compared rates before and during COVID-19. Statistical analyses were performed using Stata 15.0 and SPSS 22.0 (Stata “meta-prop” command to achieve pooled prevalence). We reported this review according to PRISMA guidelines.5 The protocol was registered in PROSPERO (CRD42022315842). A total of 42 studies with 589,074 children were included (Supplementary Material 2). Overall, the studies were carried out in 11 countries in four WHO regions, plus an additional study which was conducted in four countries in Latin America. Thirty-three studies were performed in low- and middle-income countries (LMIC). Twelve studies focused only during the COVID-19 pandemic, while 30 studies compared before and during COVID-19. The period before COVID-19 covered from September 2010 to February 2020, and the period during COVID-19 covered from January 2020 to December 2021. Thirty-five studies were rated as low risk of bias and the remaining seven had moderate risk of bias (Supplementary Material 3). MP, HRV and RSV were the three most detected pathogens before and during COVID-19. HRV (23.4%, 95%CI 17.8-29.6) ranked first during the pandemic. The prevalence of most pathogens decreased compared with that before COVID-19, with statistically significant decreases in RSV, IFV, IFV-A, HAdV, and PIV. An increase in the prevalence was observed for HRV and HCoV, but the changes were not statistically significant (Fig. 1 ; Supplementary Material 4). Substantial heterogeneity was detected in overall prevalence. Sensitivity analysis found reductions of heterogeneity for HCoV and CP before COVID-19 when including studies with low risk of bias and studies with sample sizes of 101 to 10,000, respectively (Supplementary Material 5). Funnel plots and Egger's test suggested possible publication bias for RSV (p=0.001) before COVID-19 (Supplementary Material 6-7).Fig. 1 Overview of the prevalence of respiratory pathogens in children with ARTIs before and during the COVID-19 pandemic. * p<0.05 after comparing the prevalence of each pathogen before and during the COVID-19 pandemic by Mann-Whitney test Fig 1 Subgroup analysis showed that the prevalence of most pathogens in high-income countries (HIC) was lower than that in LMIC during COVID-19, while the prevalence of HRV was as high as 43.3% (95%CI 19.1-69.3) in Europe, which may be related to economic and resource differences. In LMIC, the availability and implementation of non-pharmaceutical interventions (NPIs) and virus testing may be inadequate. Besides, differences in the prevalence of each pathogen existed among population sources, specimens, ARTIs types, and detection methods (Supplementary Material 8). Our study suggests an overall downward epidemiological shift during COVID-19, and NPIs may be a major contributor to this phenomenon, especially for enveloped viruses, as they are known to be more sensitive to environmental determinants. Other explanations could be changes in care-seeking behavior, particularly for children with mild symptoms, as well as virus-virus interactions. Of note, HRV increased and became the most frequent pathogen during COVID-19. We believe that this is possibly the result of its absence of envelope, small diameter, tremendous genetic diversity (>160 serotypes), and epidemiological inverse correlation with IFV (virus interference).6 , 7 However, at the time of writing this paper, there was a sharp rise of IFV activity in southern China.8 Similarly, an atypical peak of RSV has been observed in many countries earlier in 2020-2021.9 In our study, we also noticed that the prevalence of most pathogens in China after September 2020 was higher than before September 2020, likely corresponding to the relaxation of NPIs. These phenomena may be related to what is known as “immunity debt”,10 which reminds us the need to recognize NPIs as a double-edged sword, and highlights the importance of active and continuous epidemiological surveillance and timely adjustment of immunization strategies. The limitations of this review include high level of heterogeneity among studies; inconsistent stratification of included studies, hence the analyses of some subgroups were not sufficiently detailed; the possible underestimation of the prevalence before COVID-19, as we extracted pre-COVID-19 data from studies that compared before and during COVID-19; and the pooled prevalence during the pandemic corresponds to a wide time range, hence some subtle changes such as initial suppression and subsequent resurgence may have been averaged out. This study is the first to systematically summarize the changes in the etiology for childhood ARTIs during COVID-19. However, hopefully, with further containment of the pandemic, the prevalence may gradually return to the pre-COVID-19 patterns. Further research is needed to elucidate how different components and intensities of NPIs relate to pathogen transmission, as well as the mechanism of virus-virus interactions, to better prepare us for any future respiratory virus pandemics. Contributors EL and LR proposed the concept of the systematic review. YT and XD did the literature search, reviewed studies for inclusion, and extracted and checked the data, with assistance from LR. YT did the data analyses and wrote the first draft of the manuscript. YC, SLN, ML provided methodological advice. EL, LR, YC, and SLN critically reviewed the manuscript. All authors approved the final manuscript as submitted. Declaration of Competing Interest We declare no competing interests. Appendix Supplementary materials Image, application 1 Acknowledgments This work was supported by the Innovation Program for Chongqing's Overseas Returnees (2021059) Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jinf.2022.11.023. ==== Refs References 1 Han X Xu P Wang H Mao J Ye Q. Incident changes in the prevalence of respiratory virus among children during COVID-19 pandemic in Hangzhou, China J Infect 84 4 2022 579 613 10.1016/j.jinf.2022.01.007 2 Munn Z Moola S Lisy K Riitano D Tufanaru C. Methodological guidance for systematic reviews of observational epidemiological studies reporting prevalence and cumulative incidence data Int J Evid Based Healthc 13 3 2015 147 153 10.1097/XEB.0000000000000054 26317388 3 Barendregt JJ Doi SA Lee YY Norman RE Vos T. Meta-analysis of prevalence J Epidemiol Community Health 67 11 2013 974 978 10.1136/jech-2013-203104 23963506 4 DerSimonian R Laird N. Meta-analysis in clinical trials Control Clin Trials 7 3 1986 177 188 10.1016/0197-2456(86)90046-2 3802833 5 Moher D Liberati A Tetzlaff J Altman DG Group PRISMA Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement PLoS Med 6 7 2009 e1000097 10.1371/journal.pmed.1000097 6 Nickbakhsh S Mair C Matthews L Virus-virus interactions impact the population dynamics of influenza and the common cold Proc Natl Acad Sci U S A 116 52 2019 27142 27150 10.1073/pnas.1911083116 31843887 7 Wu A Mihaylova VT Landry ML Foxman EF. Interference between rhinovirus and influenza A virus: a clinical data analysis and experimental infection study Lancet Microbe 1 6 2020 e254 e262 10.1016/s2666-5247(20)30114-2 33103132 8 Chinese National Influenza Center, Weekly Influenza Surveillance Report in China, https://ivdc.chinacdc.cn/cnic/zyzx/lgzb/(accessed Oct 1, 2022). 9 Williams TC Sinha I Barr IG Zambon M. Transmission of paediatric respiratory syncytial virus and influenza in the wake of the COVID-19 pandemic Euro Surveill 26 29 2021 2100186 10.2807/1560-7917.ES.2021.26.29.2100186 10 Hatter L Eathorne A Hills T Bruce P Beasley R. Respiratory syncytial virus: paying the immunity debt with interest Lancet Child Adolesc Health 5 12 2021 e44 e45 10.1016/S2352-4642(21)00333-3 34695374	36460171	PMC9708107	NO-CC CODE	2022-12-08 23:16:12	no	J Infect. 2022 Nov 30; doi: 10.1016/j.jinf.2022.11.023	utf-8	J Infect	2,022	10.1016/j.jinf.2022.11.023	oa_other
==== Front Intelligent Systems with Applications 2667-3053 2667-3053 The Authors. Published by Elsevier Ltd. S2667-3053(22)00097-7 10.1016/j.iswa.2022.200160 200160 Article Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans Nasir Nida a⁎ Kansal Afreen b Barneih Feras a Al-Shaltone Omar a Bonny Talal ac Al-Shabi Mohammad ad Al Shammaa Ahmed e a Research Institute of Science and Engineering, University of Sharjah, Sharjah, UAE b Department of Statistics, London School of Economics and Political Science, London, UK c College of Computing and Informatics, University of Sharjah, Sharjah, UAE d College of Engineering, University of Sharjah, Sharjah, UAE e Khorfakkan University, Khorfakkan, UAE ⁎ Corresponding author. 30 11 2022 2 2023 30 11 2022 17 200160200160 11 8 2022 21 11 2022 27 11 2022 © 2022 The Authors. Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. COVID pandemic across the world and the emergence of new variants have intensified the need to identify COVID-19 cases quickly and efficiently. In this paper, a novel dual-mode multi-modal approach is presented to detect a covid patient. This has been done using the combination of image of the chest X-ray/CT scan and the clinical notes provided with the scan. Data augmentation techniques are used to extrapolate the dataset. Five different types of image and text models have been employed, including transfer learning. The binary cross entropy loss function and the adam optimizer are used to compile all of these models. The multi-modal is also tried out with existing pre-trained models such as: VGG16, ResNet50, InceptionResNetV2 and MobileNetV2. The final multi-modal gives an accuracy of 97.8% on the testing data. The study provides a different approach to identifying COVID-19 cases using just the scan images and the corresponding notes. Keywords Machine learning Transfer learning Adam optimiser Binary cross entropy loss Data augmentation ==== Body pmc1 Introduction Late in December 2019, in Wuhan, China, the COVID-19 illness caused by the SARS-CoV-2 coronavirus made its initial appearance (Phan, 2020). All ages, including kids and teenagers, are susceptible to COVID-19 infection, which can lead to life-threatening consequences. As of April 11, 2022, the World Health Organization reported that there had been over 500 million confirmed cases of COVID-19, resulting in 6,250,000 fatalities. The SARS-CoV-2 virus can transmit through direct touch or through droplets coughed or sneezed out. When COVID-19 affects the respiratory system, it can result in severe pneumonia, which can lead to death (De Miranda & Teixeira, 2020). To detect the SARS-Cov-2, the Reverse transcription polymerase chain reaction (RT-PCR) test is used. This test is relatively complicated and produces less consistent results (Kucirka, Lauer, Laeyendecker, Boon, & Lessler, 2020). Radiography examination by radiologists is an alternative method to visually detect COVID-19 viral infection. However, detecting the infection from X-ray images is challenging and requires a high level of expertise. Clinical diagnosis of X-ray and CT images by radiologists yields an accuracy of 75% (Satia, Bashagha, Bibi, Ahmed, Mellor, Zaman, 2013, Wong, Lam, Fong, Leung, Chin, Lo, et al., 2020). Therefore, a quick and more precise method is needed to aid physicians in identifying COVID-19 symptoms. In the past few years, deep learning (DL) have been widely used in the medical field in detecting area such as hypertension detection (Nasir et al., 2021), diabetic retinopathy detection (Nasir et al., 2022b), epileptic seizure detection (Barneih et al., 2022), sleep apnea detection (Qatmh et al., 2022) and image object detection and image classification (Woźniak, Siłka, & Wieczorek, 2021). In the COVID-19 pandemic, artificial intelligence has been extensively used in areas such as diagnosis, social control, surveillance public health and controlling the COVID-19 patients. To alleviate the significant strain on limited medical resources caused by the COVID-19 pandemic, the most important measures to control the pandemic’s spread are rapid diagnosis, accurate prediction, enhanced monitoring, and effective treatments. Many review articles on the subject have been published. However, the findings of these studies are inconclusive, and there is little research systematically assessing the application of AI for COVID-19 in accordance with PRISMA, with the majority of them focusing on aspects such as diagnosis or treatment. Researchers have made significant contributions to the anti-COVID-19 campaign, and the number of COVID-19-related AI models in the literature is rapidly increasing. Artificial intelligence models that have been properly trained can ensure accurate and rapid diagnosis or assist doctors in streamlining the diagnosis and reducing manual labor. By using training data, AI models could detect patients at higher risk, characterize the epidemiology of COVID-19, and model disease transmission. Artificial intelligence-based methods, such as repurposing existing drugs, screening targets as vaccines based on the potential mutation model to SARS-CoV-2, and screening compounds as potential vaccine adjuvants, could aid in the discovery of novel drugs and vaccines. A unique retinal blood vessel categorization approachsis suggested in Dash et al. (2022), this article recommends a combination model of a directed filter and a matched filter for improving atypical retinal images with weak vascular contrasts. This paper proposes a multi-modal approach to detect whether a patient is COVID-19 positive or not. Along with using the images of the CT scan/X-ray of the patient, the notes that have been jotted down by the doctor/nurse are also considered for the final prediction, which has resulted in better performance and efficient detection of COVID-19 cases. Specific keywords that can only be associated with COVID-19 are very helpful in detection. Along with this, even the problem of small size datasets is resolved by using various data augmentation techniques to increase the number of observations in the data to get results that reflect the real world scenario in terms of covid - non covid cases imbalance. In this paper, we explore the solution to the problem in coherence to the following contributions:1. Concatenation of a text and Image model to predict COVID. 2. Comparison of Augmentation results: a) with No-Augmentation, b) with Augmentation on whole data, and c) with Augmentation on training data only. 3. Comparing the performance of benchmark CNNs and Proposed Multi-Modal Approach in classifying the X-ray scans (along with three Image Models). The novelty of the study of Dual-mode (Text and Image) multi-Model for covid detection. This study will help as a precautionary step towards various ailment detection. The binary cross entropy loss function and the Adam optimizer are used to compile all of these models. The model is trained using the default batch size of 32 and the early stopping criterion and model checkpoint callbacks. 2 Literature review Using artificial intelligence (AI) and machine learning (ML) techniques, many researchers developed models to diagnose COVID-19 cases from chest X-ray and CT imaging. El Asnaoui & Chawki (2021) detected and classified COVID-19 cases using seven different deep learning models i.e. ResNet50, DenseNet201, MobileNetV2, InceptionResNetV2, InceptionV3, VGG16 and VGG19. The overall accuracy was 82.80%, with InceptionResNetV2 achieving the highest accuracy of 92.18%. Wang, Lin, & Wong (2020a), proposed COVID-Net, which is a CNN used for detecting COVID-19 from X-ray images. The network was trained using the COVIDx dataset, which consists of 13,975 chest X-ray images. The model achieved a testing accuracy of 91%. Authors in Horry et al. (2020), compared different CNN models and then chose and optimized a VGG19 model. Using OpenCV library, they pre-processed the images by applying histogram equalization followed by texture enhancement. Their model was able to detect COVID-19 using chest X-ray images, CT scans and ultrasound with an accuracy of 86%, 84% and 100% respectively. Zhang et al. (2020) proposed COVID19XrayNet which is a deep learning based model that detects COVID-19 from X-ray images. The model is based on ResNet32 with two layers i.e., smoothing layer and feature extraction layer. The model achieved better results than the original ResNet32 with an accuracy of 91.92%. Authors in Ismael & Şengür (2021) proposed Support Vector Machine (SVM) for COVID19 classification and several pretrained CNN models i.e. VGG16, VGG19, ResNet18, ResNet50 and ResNet101 for feature extraction to achieve an accuracy of 94.7%. The dataset used consists of 380 normal and COVID-19 chest X-ray images. Hemdan et al. Hemdan, Shouman, & Karar (2020), proposed COVIDX-Net which a deep learning framework dedicated to detect COVID-19 using X-ray images. Authors composed a comparative study of other deep learning models including InceptionResNetV2, InceptionV3, VGG19, ResNetV2, Xception and MobileNetV2. Their study showed that VGG19 and DenseNet19 achieved the highest accuracy of 90%. Authors in Maghdid et al. (2021) combined simple CNNs (single convolution layer followed by batch normalization, rectified linear unit (ReLU) with two fully-connected layer and AlexNet model. The proposed model achieved an accuracy of 94%. Authors in Hall, Paul, Goldgof, & Goldgof (2020) used transfer learning strategy with VGG16. Moreover they used data augmentation to increase the size of the dataset achieving an accuracy of 96.1%. A comparison of state-of-the-art studies has been done in discussion section which compares results of proposed study with existing studies. 3 Methodology This section discusses the dataset, models and their architectures, and proposed methodology. The dataset has been gone through data augmentation and text analysis. Moreover, the model architectures discussed along with a basic CNN architecture are VGG16, Resnet50, MobileNetV2 and InceptionResnetV2. 3.1 Dataset description The data used in this article is a public dataset made available by Cohen, Morrison, & Dao (2020a); Cohen et al. (2020c). The data contains images of the chest X-rays and CT scans done on patients who either tested positive for COVID-19 or were suspected of having COVID-19 or other viral/bacterial pneumonia. Along with the images, metadata is made available which contains information about the patient like their sex, age, clinical notes and other additional notes associated with the scan. The 2 columns of the data - clinical notes and other notes are combined into a single column by string concatenation. The data consists of 535 images. Out of these 535 images, clinical notes associated are only available for 485 of them and information about sex and age for only 483 and 440, respectively. The missing values of age are imputed with the mean while the missing values for the sex are filled with the mode. The missing clinical notes are filled with an empty string. The problem of classification in this paper is converted to a binary classification problem, with all other labels except for covid categorized as “Non-Covid”. This results in an imbalanced data consisting of 342 covid cases and remaining 193 as non-covid cases. The data is split into the training, validation and test datasets where the training data is 85%, validation data 10% and testing data 5% of the original data. This is done via random sampling.The noise and blurring of image has been fixed during the pre-processing stage, using denoising function. 3.1.1 Data augmentation Given the imbalanced nature of the dataset, data augmentation techniques are implemented to make the data balanced and the results compared with the imbalanced data. These data augmentation methods are applied to both the text and the images. The augmentation is done for the texts and images associated to the non-covid patients and is done in two ways, once on the whole data and once just on the training data. The augmentation is done such that the data still remains unbalanced, but with higher number of non-covid cases. So for each non-covid case in the data, two more augmented observations are added. For the text data, the augmentation is done in 2 ways - by replacing certain number of random words with their synonyms and randomly swapping words within the text. The number of words to be replaced and swapped is chosen as 15 and the resulting text is the augmented text. For images as well, two types of augmentation is done - rotation and decreasing brightness. Since dealing with medical images, inversion of the images is not possible. The augmented images are rotated by an angle of 20 degrees. Data augmentation is done twice, once on the whole dataset and once only on the training dataset. An example of the augmented images is shown in Fig. 1 .Fig. 1 Image augmentation : left: Original, middle: Rotated, right: Decreased Brightness. Fig. 1 3.1.2 Text analysis The clinical notes are analyzed by plotting word clouds and top 20 uni-grams, bi-grams and tri-grams. Word clouds are a visual representation that is often used to visualize text data. It breaks down the texts into words and plots the words with varying sizes and colors that represent it’s frequency in the data. A word which is much bigger in size in the word cloud is said to be most frequently occurring word in the data while smaller sized words are less frequent. Uni-grams refer to single words alone. Bi-grams refer to pairs of words together while tri-grams refer to groups of 3 words together. All possible such combinations are taken and the most frequently occurring groups of words are then plotted. 3.2 K-fold cross validation Three different situations are tested to get the best results - when there is no data augmentation, data augmentation only on the training data and data augmentation on the whole data. Out of these three, the best model is chosen and then K-fold cross validation is performed to test for the validity of results since the data is small and data splitting is done randomly. For this, K is chosen as 10. For each of the 10 iterations, the model was run keeping one fold as testing and the remaining as training data. While running the model, validation data size is chosen as 30% of the training data. 3.3 Model architectures The state-of-the-art pre-trained networks included in the Keras core library have consistently outperformed Convolutional Neural Networks on the ImageNet challenge. These networks also show a strong ability to generalize to images outside of the ImageNet dataset using transfer learning techniques such as feature extraction and fine-tuning. Four used CNN architectures are discussed below: 3.3.1 VGG16 model The most distinctive aspect of VGG16 is that it focused on having convolution layers of 3×3 filter with stride one instead of a bunch of hyper-parameters and always utilized the same padding and maxpool layer of 2×2 filter with stride two. Convolution and max pool layers are arranged in this manner throughout the entire architecture. two fully connected layers and a softmax are included as its final features. The 16 in VGG16 stands for the number of weighted layers, which are 16. This network has around 138 million parameters, making it fairly huge (Simonyan & Zisserman, 2014). 3.3.2 ResNet50 model The introduction of ResNet or residual networks, which are made up of Residual Blocks, has alleviated the problem of training very deep networks. The difference is that there is a direct connection that skips some layers in between (this may vary depending on the model). This connection is known as the ‘skip connection,’ and it is at the heart of residual blocks. Because of this skip connection, the layer’s output is no longer the same. Without this skip connection, the input ‘×’ is multiplied by the layer weights before being multiplied by a bias term. This term is then passed through the activation function, f(), and the result is H(x)=f(x). With the addition of the skip connection, the output is now H(x)=f(x)+x. This method appears to have a minor flaw when the dimensions of the input differ from those of the output, which can occur with convolutional and pooling layers. When the dimensions of f(x) differ from those of x, one of two approaches can be taken: the skip connection is padded with extra zero entries to increase its dimensions. To match the dimension, the projection method is used, which is accomplished by adding 11 convolutional layers to the input. In this case, the result is H(x)=f(x)+w1.x. In this case, we add an extra parameter w1, whereas in the first approach, no extra parameter is added. The skip connections in ResNet solve the problem of vanishing gradient in deep neural networks by allowing the gradient to flow through an alternate shortcut path. Another way that these connections help is by allowing the model to learn the identity functions, which ensures that the higher layer performs at least as well as, if not better than, the lower layer (He, Zhang, Ren, & Sun, 2016). 3.3.3 MobileNetV2 model In MobileNetV2, there are two different kinds of blocks. A residual block with a stride of one and another one with a stride of two for downsizing. Both sorts of blocks have an 11 convolution with ReLU6 layer as their first layer. A depth wise convolution makes up the second layer, and a further 11 convolutions with no non-linearity make up the third layer. Deep networks are said to only have the power of a linear classifier on the non-zero volume portion of the output domain if ReLU is applied once more (Sandler, Howard, Zhu, Zhmoginov, & Chen, 2018). 3.3.4 InceptionResNetV2 model The Inception-ResNet-v2 convolutional neural network was trained on over a million images from the ImageNet database. Images may be categorized into 1000 different object categories using the 164-layer network, including the keyboard, mouse, pencil, and numerous animals. The network has therefore learned in-depth feature representations for a wide range of images. The network outputs a list of estimated class probabilities after receiving a 299 by 299 picture as input. It is made by merging the Residual connection and the Inception structure. In the Inception-Resnet block, multiple convolutional filters of various sizes are merged with residual connections. In addition to avoiding the deterioration problem brought on by deep structures, using residual connections speeds up training. Fig. 5 depicts the fundamental network architecture of Inception-Resnet-v2 (Mahdianpari, Salehi, Rezaee, Mohammadimanesh, & Zhang, 2018). 3.4 Proposed methodology Two different kinds of model architectures are tried out for the classification problem. In the first, only the images are considered for the basis of classification. Three kinds of basic CNN architectures are implemented for this purpose. In the first image model (Model 1), only one 2D convolutional neural network layer with 16 filters, kernel size (3,3), stride of length 1 and padding of type same is used due to the small size of the data and to avoid over-fitting. This layer is then followed by a max pooling layer with pool size (2,2) and stride length two. The output is then flattened and passed onto a dense layer with 64 units and activation function ReLU and kernel initializer he uniform. This is completed by the final output Dense layer with one unit and activation function sigmoid. The second image model (Model 2) follows the same architecture but the difference being that the convolutional layer has 32 filters and the Dense layer has 128 units. The third image model (Model 3)is made more deep by including 3 groups of a convolutional 2D layer, batch normalization, max pooling and a dropout layer, each with increasing number of units, 16,32 and 64 and dropout percentage 0.2,0.25 and 0.3. This is then passed to a Dense layer with 100 units and finally the output layer. The structure of all the three models are shown in Figs. 6 and 7. All these models are compiled using the binary cross entropy loss function and Adam optimizer. The early stopping criterion and model checkpoint callbacks are used and the model is trained using the default batch size of 32. The best model is saved, wherein best is defined as the model having the highest validation accuracy, and that is used to make predictions for the test data. The image models alone don’t perform very well, and to improve this, a second model architecture is considered, where even the clinical notes associated with the patients’ scan is passed as input. A multi-modal approach is utilized to incorporate both the images and text in the input. The images are passed to a separate image model and the text is passed into a text model. The outputs of both these models are concatenated and then passed into a final model which gives the resulting prediction. The model architecture is shown in Fig. 8. The text is converted to a numeric vector before being passed to the model. Since we are dealing with medical data and few keywords in the notes make a lot of difference in diagnosis, each document is converted to a vector by counting the frequency of each word in the text. For example, if we consider 2 sentences - I like an apple and bananas and He ate an apple, then both the sentences can be converted to a numeric vector in the following way - All the words from all documents are considered and the vector is formed such that each value in the vector represents the frequency of that corresponding word in the sentence. So the vector form for these 2 sentences are shown in Table 1 . This is done for all the texts in the data. As a pre-processing step, the stopwords from the texts are removed. Stopwords refer to all those words that are very frequently used in a sentence but offer no contextual information. These include words like and, the, I, am, etc. Along with these, terns frequently used in medical text like patient, doctor, dr, etc. are also removed. The words are converted to lower case as well for easier implementation. Once each text is converted to the document feature vectors, these are passed as input to the text model. Although there are more sophisticated text models build like recurrent neural networks and BERT models, for the proposed study, the Bag Of Words approach is used. This is done due to the importance given to specific keywords in the clinical notes written by the nurse/doctor that can help identify symptoms of COVID-19. Since the main focus is on those keywords and it’s frequency in the text, BoW model has been used instead of RNNs and other NLP models. To test this hypothesis, an LSTM model is also used to see whether it performs better than the BoW approach or not.Table 1 Document feature vectors. Table 1Documents I like an apple and bananas he ate Sentence 1 1 1 1 1 1 1 0 0 Sentence 2 0 0 1 1 0 0 1 1 The text model is created as a simple 2 layer deep neural network. Both of the dense layers have 64 units. The output of the final layer is passed on as input to the concatenation layer and the final model. The image model used is the third image model used described formerly. The final concatenated model is just one dense layer with 16 units and activation function ReLU. This is followed by a dropout layer and then the final output layer. The model again is compiled using the binary cross entropy loss function and Adam optimizer. The same set of callbacks are used to get the best model. For the LSTM model, the texts are tokenized and padded to create all vectors of the same length. The maximum length of a vector is 451. The LSTM text model is created using an embeddings later and an LSTM layer. The number of output units in the embeddings layer is 10 while the number of units in the LSTM layer is 16. Four pre-trained models are also tried out as a replacement for the custom build image model. The models tried out were - ResNet 50, InceptionResNetV2, MobileNetV2 and VGG16. The text model and final model, along with the compiling and training conditions, are kept the same. This is done only on the augmented training data. For all the models trained, the best model is obtained and tested on the testing data. The learning diagnostic curves are plotted for all the models’ history - training and validation loss plotted with number of epochs and the training and validation accuracy with number of epochs. Using the best model, predictions on the test data are obtained and the confusion matrix and ROC curve is plotted. The full methodology for the multi-modal is described in Fig. 9. 4 Results In the notes associated with covid cases, as shown in the word clouds in Fig. 10, the most frequently used words are chest, bilateral, fever, cough, day, history. The very frequently used pair of words are chest radiography, dry cough, shortness breath, oxygen saturation, pleural effusion. The frequently used groups of 3 words are normal range elevated, polymerase chain reaction, fever dry cough. The same for non-covid cases are night, chest, left, lung, pneumonia, normal, upper lobe, lower lobe, left lung, middle lobe, weight loss and right upper lobe, left lower lobe, anteroposterior radiograph obtained, human immunodeficiency virus. The covid cases presented were noted for the commonly occurring symptoms - fever, cough among others. The top uni-grams, bi-grams and tri-grams are shown in Fig. 11, Fig. 12, Fig. 13. The image models on their own don’t perform very well when only the images are considered for classification. Considering the first image model, it performs poorly when there is no data augmentation or when data augmentation is done only on the training data. It fails to identify many covid cases and misclassifies them as non-covid, resulting in high number of false negatives. The learning curves and results are shown in Fig. 14. When the loss vs. epochs curve is inspected, it can be seen that the validation loss is little higher than the training loss with there is no data augmentation and when the data augmentation is done only on the training data. Since data augmentation is done only on the training data, the balance between the classes is different in the validation/testing data, hence resulting in a higher loss and lower accuracy. But addition of more data has resulted in the validation loss and accuracy to be more stable across epochs as compared to when there was no data augmentation done. Even the area under the ROC curve is highest for the case when the class imbalance is consistent across the training, validation and testing datasets. The second image model doesn’t perform very well either giving low accuracy, especially when data augmentation is done only on the training data. This model suffers from the problem of high false positives. Many patients are termed as covid positive despite being negative. The results for this model is shown in Fig. 15. The performance of the second model is much more unstable compared to the first model. The validation loss and accuracy is highly erratic as the model is trained for more epochs. The area under the ROC curve is much lesser too than that of the first model. Consistent with the first model, performance is more unstable in the case of no augmented data compared to when there was additional augmented data added. The third image model has the lowest performance among all of them, which can be attributed to over-fitting due to a small size dataset. There is very high misclassification of covid patient as not having COVID-19, resulting in a high number of false negatives. The learning curves and results are shown in Fig. 16. The model shows similar unstable behavior as the previous model owing to over-fitting. The model also makes more errors in classification as compared to the previous 2 models. The same can be said for the behavior of the ROC curve. The third image model is used as the image model for the multi-modal classification. Since it’s a complex architecture, the text model is made to be very simple. The multi-modal approach performs very well in classifying the patient as covid positive or not. In all 3 cases, just one case is misclassified. The graphs associated with this model are shown in Fig. 17. The validation and training accuracy reach almost 100% with much more consistent values of loss across epochs, although, there seems to be slight overfitting when the model is trained for more number of epochs. The ROC curve when data augmentation is done on the whole data is almost perfect, giving an area under the curve value of 0.98. The addition of the text has shot up the performance of the models, than what was obtained by just using the images. Finally, the results of using the pre-trained models are summarized in Table 2 and the learning curves are shown in Fig. 18. The ResNet50 and InceptionResNetV2 model perform better than the other 2. When MobileNetV2 is used, the performance on the validation data is extremely poor. This can be attributed to the complex structure of the model. When using ResNet50, the validation and training loss are perfectly stable, with few ups and downs in the validation accuracy. When using InceptionResNetV2, the model starts to overfit which can be seen from the sharp upward rising spike in the validation loss and slightly falling validation loss. But this and the ResNet50 model result in only 1 false positive, with all other cases classified correctly. The VGG16 model shows signs of overfitting from the beginning, with an upward rising validation loss curve and a downward validation accuracy curve.Table 2 Performance metrics. Table 2Models Accuracy Sensitivity Specificity Precision F1 Score Model 1 No Data Aug 70.37% 72.22% 66.67% 81.25% 76.47% Data Aug (Whole) 91.30% 90.91% 91.67% 90.91% 90.91 Data Aug (Training) 70.37% 66.67% 77.78% 85.71% 75.00% Model 2 No Data Aug 76.92% 83.33% 62.50% 83.33% 83.33% Data Aug (Whole) 86.96% 90.91% 83.33% 83.33% 86.96% Data Aug (Training) 66.67% 72.22% 55.56% 76.47% 74.29% Model 3 No Data Aug 62.96% 61.11% 66.67% 78.57% 67.75% Data Aug (Whole) 91.30% 81.82% 100% 100% 90.00% Data Aug (Training) 55.56% 44.44% 77.78% 80.00% 57.14% Multi-Model No Data Aug 96.30% 100% 88.89% 94.74% 97.30% Data Aug (Whole) 97.83% 95.45% 100% 100% 97.67% Data Aug (Training) 96.30% 100% 91.74% 88.89% 94.12% Transfer Learning MobileNetV2 70.37% 66.67% 77.78% 85.71% 75.00% ResNet50 96.30% 100% 88.89% 94.74% 97.30% InceptionResNetV2 96.30% 100% 88.89% 94.74% 97.30% VGG16 92.59% 94.44% 88.89% 94.44% 94.44% The results of the K-Fold cross-validation on the data is summarised in Fig. 19. The average of all the accuracies for each fold is 85.1% and the standard deviation is 14.17%, which accounts to nearly 10 observations being misclassified out of 92, which is the size of the testing data. The results of the LSTM text model is shown in Fig. 20. The testing accuracy obtained is 88.89% with 3 data points misclassified. 5 Discussion The models are trained on both kinds of data, completely augmented data and augmented training data. This helps us to give us models in both scenarios, when there are a lot of covid cases and during the time when there are less cases. When the augmentation is done on the whole data, the validation and testing data are imbalanced but the majority of cases are non-covid. When the augmentation is done only on the training data, the validation and testing data are also imbalanced but now, the majority are covid cases. In both these cases, the models perform well and only one case is misclassified. 5.1 Comparison with other studies This section offers an important evaluation of deep learning algorithm for detecting COVID-19 positive cases for some related papers as shown in Table 3 , moreover, a compared study from other similar deep learning approaches with our proposed model that was done. And a discussion table (Table 3) has been created to evaluate our model in terms of others. According to Table 3, the majority of the datasets contained a small quantity of data (limited pictures for training and testing) to create and improve their model. Another notable fact is that the authors’ most prevalent techniques for model creation were based on VGG and ResNet. In this paper, authors employed VGG16, Resnet50, MobileNetV2, and InceptionResnetV2 to create the model faster and more reliably so that it may be used as a real-time evaluation tool. All the models of transfer learning are standard and therefore when compared to other studies, their conditions and parameters are same.Table 3 Comparison with other studies. Table 3Ref. Dataset used Methods/Models Results Description Sahinbas and Catak (2021) COVID-19 X-ray images + collected 50 positive and 50 negative CNN Accuracy in VGG16 got the highest percentage which equals to 80% Images scaled to 256*22 and later augmented by flipping and different angles. Study presented five pretrained deep CNN models, including VGG16, VGG19, ResNet, DenseNet, and InceptionV3, for transfer learning implementing X-ray images. Ohata et al. (2020) “1394 Chest X-ray Images (Pneumonia) with data augmentation (Kermany et al., 2018) CNN, MLP, and SVM SVM got the highest accuracy of 98.5% Used CNNs to extract features, then using the transfer learning approach and categorizing these features with consolidated machine learning methods. Apostolopoulos and Mpesiana (2020) 1427 X-ray images from Cohen et al. (2020d) without data augmentation CNN Highest accuracy of 96.78% Assessed the effectiveness of CNN designs created in recent years for medical image classification. Shaik and Cherukuri (2022) 2483 images for SARS-CoV-2 where 1252 of them is diagnosed with the virus (Soares et al., 2020) Dataset for COVID-CT (Zhao et al., 2020) which contains 349 COVID-19 CT and 463 non-COVID-19 images from 216 patients CNN Highest accuracy for SARS-CoV-2 = 98.99, Highest accuracy for COVID-CT = 93.33 The study is to present an effective ensemble strategy for identifying SARS-CoV-2 infection in chest CT scan images. Wang et al. (2020b) ImageNet dataset, the number of the dataset is 18,567 with using data augmentation ResNet101 and ResNet152 Accuracy = 96.1% Their approach attempts to transfer learning, integrate models, and categorize chest X-ray pictures into three categories: normal, COVID-19, and viral pneumonia. Phankokkruad (2020) COVID-19 research challenge dataset that contains 323 images without data augmentation (Cohen et al., 2020b) CNN Highest accuracy of 97.19% CNN model with Xception outperforms the VGG16 and Inception-ResNet-V2 models in terms of accuracy. [This Work] Image + text Dataset VGG 16, Resnet 50, MobileNet V2 and Inception-Resnet V2 multi-modal results in a 97.8% accuracy Multi-Modal approach with data augmentation methods are applied to both the text and the images. 5.2 Tradeoffs of performance metrics To improve precision, the model’s parameters and hyperparameters can be changed. While adjusting, you may notice that higher precision generally leads to lower recall, and higher recall leads to lower accuracy. Similarly, the recall value of any machine learning model can be altered by adjusting multiple parameters or hyperparameters. A higher or lower recall for any model has a specific meaning: With a high recall, the majority of positive instances (TP + FN) will be identified (TP). As a result, the number of FP measurements increases while overall accuracy decreases. Assume, however, that the outcome is low recall.In that case, it indicates that there were many FNs (should have been positive but labeled negative), which means that if the results find a positive example, there is a better chance that it is a true positive. Furthermore, while F1 is less intuitive than accuracy, it is usually more advantageous, particularly when the class distribution is unequal. Accuracy improves when the cost of false positives and false negatives is the same. If the cost of false positives and false negatives is significantly different, both Precision and Recall should be considered. Furthermore, recall and sensitivity are inversely proportional. Susceptible tests yield more positive results in patients who are sick, whereas precise tests reveal no illness in patients who do not have a finding. Sensitivity and specificity should always be considered concurrently to provide a complete diagnosis. Furthermore, accuracy is a good quality measure when datasets are symmetric and the values of false-positive and false-negatives are nearly similar. As a result, other parameters play an important role in determining the performance of a model. 5.3 Behaviour of models used Each VGG block is made up of 2D Convolution and Max Pooling layers, as shown in Fig. 2 . As the number of layers in CNN increases, so does the model’s ability to fit more complex functions. As a result, more layers promise improved performance. This is not to be confused with an Artificial Neural Network (ANN), where increasing the number of layers does not always result in improved performance. The backpropagation algorithm is used to update the weights of a neural network, which makes minor changes to each weight in order to reduce the model’s loss. It updates each weight so that it moves in the direction of the decreasing loss.This is simply the gradient of this weight as determined by the chain rule. However, as the gradient flows backward to the initial layers, the value grows with each local gradient. As a result, the gradient becomes smaller and smaller, resulting in very small changes to the initial layers. As a result, the training time is significantly increased. If the local gradient equals one, the problem is solved.Fig. 2 VGG16 model architecture (Simonyan and Zisserman, 2014). Fig. 2 This is where ResNet comes in, as it accomplishes this via the identity function. As a result, as the gradient is back-propagated, its value does not decrease because the local gradient is 1.Deep residual networks (ResNets), such as the popular ResNet-50 model, are another type of 50-layer deep convolutional neural network architecture (CNN), as seen in Fig. 3 . A residual neural network converts a plain network into its residual network counterpart by inserting shortcut connections. ResNets are less complex than VGGNets because they have fewer filters. The vanishing gradient problem is not permitted in ResNet. The skip connections function as gradient superhighways, allowing the gradient to flow freely. This is also one of the main reasons why ResNet comes in different versions such as ResNet50, ResNet101, and ResNet152.Fig. 3 ResNet50 model architecture (Ji et al., 2019). Fig. 3 Inception was designed to reduce the computational burden of deep neural nets while achieving cutting-edge performance. Because the computational efficiency decreases as the network grows deeper, the authors of Inception were interested in finding a way to scale up neural nets without increasing computational cost. Fig. 5 shows the InceptionResNetV2 model architecture. While Inception is concerned with computational cost, ResNet is concerned with computational accuracy. In theory, deeper networks should outperform shallower networks, but in practice, deeper networks outperformed shallower networks due to an optimization problem rather than overfitting. In short, the deeper the network, the more difficult it is to optimize. To achieve higher accuracy, computer vision networks are becoming deeper and more complicated.Deeper networks, on the other hand, come at the expense of size and speed. The object detection task must be able to be performed on a computationally limited platform in real-world applications such as an autonomous vehicle or robotic visions. MobileNet, a network for embedded vision applications and mobile devices, was created to address this issue. The idea behind MobileNet is to build lighter deep neural networks by using depthwise separable convolutions. The convolution kernel or filter is applied to all of the channels of the input image in a regular convolutional layer by doing a weighted sum of the input pixels with the filter and then sliding to the next input pixels across the images. Only the first layer of MobileNet employs this regular convolution. The depthwise separable convolutions are the next layers, which are a combination of the depthwise and pointwise convolutions. The depthwise convolution convolutions each channel independently. If the image has three channels, the output image will also have three channels. The input channels are filtered using this depthwise convolution.The pointwise convolution follows, which is similar to regular convolution but with a 1x1 filter. The goal of pointwise convolution is to combine the depthwise convolution output channels to create new features. As a result, the computational work required is less than that of regular convolutional networks. The model architecture is shown in Fig. 4 . MobileNet outperforms other cutting-edge convolutional neural networks such as VGG16, VGG19, ResNet50, InceptionV3, and Xception. MobileNets are thin deep neural networks that are ideal for mobile and embedded vision applications. It uses depthwise separable convolutions in a streamlined architecture and employs two simple global hyperparameters to efficiently trade off accuracy versus latency. MobileNet could be used for object detection, fine-grain classification, face recognition, large-scale geolocation, and other applications.Fig. 4 MobileNetV2 model architecture (Seidaliyeva et al., 2020). Fig. 4 Fig. 5 Compressed InceptionResNetV2 model architecture (Mahdianpari et al., 2018). Fig. 5 Fig. 6 Different layers of: a) image model 1, and b) image model 2. Fig. 6 Fig. 7 Different layers of image model 3. Fig. 7 Fig. 8 Multi-modal architecture. Fig. 8 Fig. 9 Metholodogy. Fig. 9 Fig. 10 Word clouds of clinical notes. Fig. 10 Fig. 11 Top uni-grams of clinical notes. Fig. 11 Fig. 12 Top bi-grams of clinical notes. Fig. 12 Fig. 13 Top tri-grams of clinical notes. Fig. 13 Fig. 14 Image model 1 results [Top-Bottom: No Data Aug, Data Aug (Whole), Data Aug (Training)]. Fig. 14 Fig. 15 Image model 2 results [Top-Bottom: No Data Aug, Data Aug (Whole), Data Aug (Training)]. Fig. 15 Fig. 16 Image model 3 results [Top-Bottom: No Data Aug, Data Aug (Whole), Data Aug (Training)]. Fig. 16 Fig. 17 Multi-modal results [Top-Bottom: No Data Aug, Data Aug (Whole), Data Aug (Training)]. Fig. 17 Fig. 18 Transfer learning [Top-Bottom: MobileNetV2, ResNet50, InceptionResNetV2, VGG16]. Fig. 18 Fig. 19 K-fold cross validation results. Fig. 19 Fig. 20 Confusion matrix and diagnostic curves of LSTM + image model. Fig. 20 The following are the benefits of using MobileNet over other cutting-edge deep learning models. It reduced network size to 17MB and parameter count to 4.2 million. It is more performant and useful for mobile applications. It has a convolutional neural network with a low latency. Advantages always have some drawbacks, and with MobileNet, it’s the accuracy. Even though MobileNet is smaller, has fewer parameters, and performs faster, it is less accurate than other cutting-edge networks. ResNet models reduce training time while increasing accuracy by not activating all neurons in every epoch. Furthermore, the model employs a clever strategy for improving model training performance by learning the feature once and then not attempting to learn it again; instead, it focuses on learning additional features. While VGG significantly improved speed and accuracy by introducing pretrained models and increasing model depth. The model’s nonlinearity increased as the number of layers with smaller kernels increased. Unlike Inception v1 to v3, the Inception-ResNet-v2 model makes use of residual networks to improve the accuracy and convergence speed of the original model. The LSTM based text model performs poorly as compared to the BoW approach text model with reduction in accuracy. This can be attributed to the fact that usually, attention mechanism don’t work very well with clinical data, as also shown by the study conducted by researchers in Korea (Kim et al., 2020). Keywords play a more important role and hence, a more simple text model in this case performs better. 6 Conclusion and future outlook A multi-modal approach is presented in this paper to classify a patient as covid positive or negative using the image of the chest X-ray/CT scan and the clinical notes provided with the scan. Data augmentation techniques are used to overcome the problem of small data sets, and they have been shown to improve model performance. The multi-modal is also compared to previously trained models. The final multi-modal results in a 97.8 percent accuracy on the testing data, with only one data point misclassified. The study takes a unique approach to identifying COVID-19 cases by relying solely on scan images and corresponding notes. This research can benefit all researchers working in this field around the world. The limitation of the study is the size of the dataset, in future a big data (comprises of text and image data both) can be generated and used. This study’s future scope cannot be limited to hardware implementation, hybrid classification, etc. Applications can be expanded to include other types of medical data with additional classifiers, neural networks, and other AI and data techniques (Nasir et al., 2022a). CRediT authorship contribution statement Nida Nasir: Conceptualization, Methodology, Software, Writing – original draft. Afreen Kansal: Conceptualization, Methodology, Software, Writing – original draft. Feras Barneih: Investigation, Methodology, Validation, Writing – original draft. Omar Al-Shaltone: Investigation, Methodology, Validation, Writing – original draft. Talal Bonny: Supervision, Writing – review & editing. Mohammad Al-Shabi: Supervision, Writing – review & editing. Ahmed Al Shammaa: Funding acquisition, Project administration. Declaration of Competing Interest Authors declare that they have no conflict of interest. Data Availability Data will be made available on request. Acknowledgment We would like to extend sincere thanks to the University of Sharjah and its Research Institute of Science and Engineering (RISE) especially to the Bio-Sensing Research Group for supporting this work. ==== Refs References Apostolopoulos I.D. Mpesiana T.A. COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks Physical and Engineering Sciences in Medicine 43 2 2020 635 640 32524445 Barneih F. Nasir N. Alshaltone O. Qatmah M. Bonny T. Al Shabi M. Artificial neural network model using short-term Fourier transform for epilepsy seizure detection 2022 advances in science and engineering technology international conferences (ASET) 2022 IEEE 1 5 Cohen, J. P., Morrison, P., & Dao, L. (2020a). COVID-19 image data collection. arXiv:2003.11597 https://github.com/ieee8023/covid-chestxray-dataset. Cohen, J. P., Morrison, P., & Dao, L. (2020b). COVID-19 image data collection. 10.48550/ARXIV.2003.11597. Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020c). COVID-19 image data collection: Prospective predictions are the future. arXiv:2006.11988 https://github.com/ieee8023/covid-chestxray-dataset. Cohen, J. P., Morrison, P., Dao, L., Roth, K., Duong, T. Q., & Ghassemi, M. (2020d). COVID-19 image data collection: Prospective predictions are the future. arXiv:2006.11988 https://github.com/ieee8023/covid-chestxray-dataset. Dash S. Verma S. Bevinakoppa S. Wozniak M. Shafi J. Ijaz M.F. Guidance image-based enhanced matched filter with modified thresholding for blood vessel extraction Symmetry 14 2 2022 194 De Miranda A.S. Teixeira A.L. Coronavirus disease-2019 conundrum: Ras blockade and geriatric-associated neuropsychiatric disorders Frontiers in Medicine 7 2020 515 32850927 El Asnaoui K. Chawki Y. Using X-ray images and deep learning for automated detection of coronavirus disease Journal of Biomolecular Structure and Dynamics 39 10 2021 3615 3626 32397844 Hall, L. O., Paul, R., Goldgof, D. B., & Goldgof, G. M. (2020). Finding COVID-19 from chest X-rays using deep learning on a small dataset. arXiv preprint arXiv:2004.02060. He K. Zhang X. Ren S. Sun J. Deep residual learning for image recognition Proceedings of the ieee conference on computer vision and pattern recognition 2016 770 778 Hemdan, E. E.-D., Shouman, M. A., & Karar, M. E. (2020). COVIDX-Net: A framework of deep learning classifiers to diagnose COVID-19 in X-ray images. arXiv preprint arXiv:2003.11055. Horry M.J. Chakraborty S. Paul M. Ulhaq A. Pradhan B. Saha M. COVID-19 detection through transfer learning using multimodal imaging data IEEE Access 8 2020 149808 149824 34931154 Ismael A.M. Şengür A. Deep learning approaches for COVID-19 detection based on chest X-ray images Expert Systems with Applications 164 2021 114054 33013005 Ji Q. Huang J. He W. Sun Y. Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images Algorithms 12 3 2019 51 Kermany D.S. Goldbaum M. Cai W. Valentim C.C. Liang H. Baxter S.L. Identifying medical diagnoses and treatable diseases by image-based deep learning Cell 172 5 2018 1122 1131 29474911 Kim J. Lee S. Hwang E. Ryu K.S. Jeong H. Lee J.W. Limitations of deep learning attention mechanisms in clinical research: Empirical case study based on the korean diabetic disease setting Journal of Medical Internet Research 22 12 2020 e18418 33325832 Kucirka L.M. Lauer S.A. Laeyendecker O. Boon D. Lessler J. Variation in false-negative rate of reverse transcriptase polymerase chain reaction–based SARS-CoV-2 tests by time since exposure Annals of Internal Medicine 173 4 2020 262 267 32422057 Maghdid H.S. Asaad A.T. Ghafoor K.Z. Sadiq A.S. Mirjalili S. Khan M.K. Diagnosing COVID-19 pneumonia from X-ray and CTimages using deep learning and transfer learning algorithms Multimodal image exploitation and learning 2021 vol. 11734 2021 SPIE 99 110 Mahdianpari M. Salehi B. Rezaee M. Mohammadimanesh F. Zhang Y. Very deep convolutional neural networks for complex land cover mapping using multispectral remote sensing imagery Remote Sensing 10 7 2018 1119 Nasir N. Alshaltone O. Barneih F. Al-Shabi M. Bonny T. Al-Shammaa A. Hypertension classification using machine learning-part I 2021 14th international conference on developments in esystems engineering (DESE) 2021 IEEE 464 468 Nasir N. Kansal A. Alshaltone O. Barneih F. Sameer M. Shanableh A. Water quality classification using machine learning algorithms Journal of Water Process Engineering 48 2022 102920 Nasir N. Oswald P. Alshaltone O. Barneih F. Al Shabi M. Al-Shammaa A. Deep DR: Detection of diabetic retinopathy using a convolutional neural network 2022 advances in science and engineering technology international conferences (ASET) 2022 IEEE 1 5 Ohata E.F. Bezerra G.M. das Chagas J.V.S. Neto A.V.L. Albuquerque A.B. de Albuquerque V.H.C. Automatic detection of COVID-19 infection using chest X-ray images through transfer learning IEEE/CAA Journal of Automatica Sinica 8 1 2020 239 248 Phan T. Novel coronavirus: From discovery to clinical diagnostics Infection, Genetics and Evolution 79 2020 104211 Phankokkruad M. COVID-19 pneumonia detection in chest X-ray images using transfer learning of convolutional neural networks Proceedings of the 3rd international conference on data science and information technology 2020 147 152 Qatmh M. Bonny T. Barneih F. Alshaltone O. Nasir N. Al-Shabi M. Sleep apnea detection based on ECG signals using discrete wavelet transform and artificial neural network 2022 advances in science and engineering technology international conferences (ASET) 2022 IEEE 1 5 Sahinbas K. Catak F.O. Transfer learning-based convolutional neural network for COVID-19 detection with X-ray images Data science for COVID-19 2021 Elsevier 451 466 Sandler M. Howard A. Zhu M. Zhmoginov A. Chen L.-C. Mobilenetv2: Inverted residuals and linear bottlenecks Proceedings of the ieee conference on computer vision and pattern recognition 2018 4510 4520 Satia I. Bashagha S. Bibi A. Ahmed R. Mellor S. Zaman F. Assessing the accuracy and certainty in interpreting chest X-rays in the medical division Clinical Medicine 13 4 2013 349 23908502 Seidaliyeva U. Akhmetov D. Ilipbayeva L. Matson E.T. Real-time and accurate drone detection in a video with a static background Sensors 20 14 2020 3856 32664365 Shaik N.S. Cherukuri T.K. Transfer learning based novel ensemble classifier for COVID-19 detection from chest CT-scans Computers in Biology and Medicine 141 2022 105127 34915332 Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. Soares, E., Angelov, P., Biaso, S., Froes, M. H., & Abe, D. K. (2020). SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification. MedRxiv. Wang L. Lin Z.Q. Wong A. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images Scientific Reports 10 1 2020 1 12 31913322 Wang N. Liu H. Xu C. Deep learning for the detection of COVID-19 using transfer learning and model integration 2020 IEEE 10th international conference on electronics information and emergency communication (ICEIEC) 2020 IEEE 281 284 Wong H.Y.F. Lam H.Y.S. Fong A.H.-T. Leung S.T. Chin T.W.-Y. Lo C.S.Y. Frequency and distribution of chest radiographic findings in COVID-19 positive patients Radiology 296 2 2020 E72 E78 32216717 Woźniak M. Siłka J. Wieczorek M. Deep neural network correlation learning mechanism for CT brain tumor detection Neural Computing and Applications 05841 2021 1 16 Zhang R. Guo Z. Sun Y. Lu Q. Xu Z. Yao Z. COVID19XrayNet: A two-step transfer learning model for the COVID-19 detecting problem based on a limited number of chest X-ray images Interdisciplinary Sciences: Computational Life Sciences 12 4 2020 555 565 32959234 Zhao J. Zhang Y. He X. Xie P. Covid-ct-dataset: a ct scan dataset about covid-19 arXiv preprint arXiv:2003.13865 490 2020	0	PMC9708108	NO-CC CODE	2022-12-06 23:15:43	no	2023 Feb 30; 17:200160	utf-8	null	null	null	oa_other
==== Front Intensive Care Med Intensive Care Med Intensive Care Medicine 0342-4642 1432-1238 Springer Berlin Heidelberg Berlin/Heidelberg 36446853 6941 10.1007/s00134-022-06941-5 Letter Association between COVID-19 pandemic and mental disorders in spouses of intensive care unit patients http://orcid.org/0000-0001-8544-2569 Ohbe Hiroyuki [email protected] 1 http://orcid.org/0000-0002-5880-2968 Goto Tadahiro 12 http://orcid.org/0000-0001-6480-3388 Okada Akira 3 http://orcid.org/0000-0002-6017-469X Yasunaga Hideo 1 1 grid.26999.3d 0000 0001 2151 536X Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 1130033 Japan 2 TXP Medical Co. Ltd., 7-3-1-252 Hongo, Bunkyo-Ku, Tokyo, 1138454 Japan 3 grid.26999.3d 0000 0001 2151 536X Department of Prevention of Diabetes and Lifestyle-Related Diseases, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-Ku, Tokyo, 1130033 Japan 29 11 2022 13 15 11 2022 © Springer-Verlag GmbH Germany, part of Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. ==== Body pmcDear Editor, With the coronavirus disease 2019 (COVID-19) pandemic, families of intensive care unit (ICU) patients witnessed practice changes, such as the prohibition of hospital visits and reduced opportunities to see attending physicians [1]. Such changes might affect the mental status of a patient’s family, but previous studies were limited by the lack of controls [2, 3]. Moreover, it remains unclear whether the increased mental disorders were due to the COVID-19 pandemic itself or COVID-19 pandemic-related changes in ICU care for a family. Therefore, we aimed to examine the impact of COVID-19 pandemic-related changes in ICU care for the family on the mental status of spouses of ICU patients using a controlled interrupted time series design. We used data on married couples from a large commercially available Japanese administrative database from DeSC Healthcare Inc. Tokyo, Japan, from April 1, 2019 to February 28, 2021. The study period was split into before and during the COVID-19 pandemic on April 1, 2020. Using the risk set matched-pair sampling method, we created matched pairs (1 case:10 controls) with cases being the spouse of a patient admitted to ICU and controls being spouses (matched by birth month and year, sex, and medical insurance status) of individuals who had not experienced ICU admission on the date of ICU admission for the case before and after April 1, 2020 (before COVID-19 and during COVID-19). The main outcome was the incidence of mental disorders within 90 days of the date of ICU admission for the cases. We conducted a controlled interrupted time series analysis that involved both before and during the COVID-19 pandemic comparison and comparison between spouses of ICU patients and matched controls [4]. Details are available in Supplemental Material. Among 483,768 eligible spouses, we identified 5524 (1.1%) patients who were admitted to the ICU, and 5190 cases matched with 51,603 controls. Incidence of mental disorders within 90 days of the date of ICU admission for the cases was observed in 17.9% and 21.5% of the cases before and during the COVID-19 pandemic, respectively, and in 17% and 17.5% of the controls, respectively. The controlled interrupted time series analysis showed an upward level change in the cases compared with the controls during the COVID-19 pandemic (+ 4.5%, 95% confidence intervals + 0.01% to + 8.98%) (Fig. 1).Fig. 1 Change in the incidence of mental disorders before and during the COVID-19 pandemic in spouses of ICU patients and matched controls. Points represent the mean percentage of incidence of mental disorders within 90 days of the date of ICU admission for the cases in each month. Solid and dashed lines represent the predicted outcomes using controlled interrupted time-series analysis. April 2019 through March 2020 was the period before the COVID-19 pandemic, and April 2020 through February 2021 was the period during the COVID-19 pandemic. The red points and red solid lines represent the spouses of the ICU patient group and the blue points and blue dashed lines represent the matched control group. ICU intensive care unit; COVID-19 coronavirus disease 2019 To our knowledge, this study is the first that examines the association between mental disorders in spouses of ICU patients and the COVID-19 pandemic by using a before-after design and by establishing an appropriate control. Although the present study had limitations (Supplemental Material), the controlled interrupted time series analyses results can provide strong evidence on public health events and rank second to randomized controlled designs in terms of its ability to control for bias [4]. Therefore, the observed increase in mental disorders among spouses of ICU patients calls for further studies to develop effective strategies, such as addressing barriers to care for family members of ICU patients using emerging technologies or investigating the health policy effectiveness of reopening ICU [5]. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 214 KB) Author contributions HO conceived the study idea. HO and TG designed the study. HO processed the corrected data. HO and HY analyzed the data. All authors interpreted the data. HO and TG wrote the initial draft of the manuscript. All authors revised the manuscript for intellectual content and approved the final version. HO was the guarantor of this study. The corresponding author attests that all listed authors meet the authorship criteria and that no others meeting the criteria have been omitted. Funding None. Data availability The datasets analyzed in the current study are not publicly available because of contracts with the hospitals providing data to the database. All codes used in the current study are available from the corresponding author on reasonable request. Declarations Conflicts of interest The authors report no personal conflicts of interest pertaining to this work. Ethical approval The Institutional Review Board of The University of Tokyo approved this study. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Consent to participate Because the data were anonymized before the researchers received them, the requirement for informed consent was waived by the Institutional Review Board of The University of Tokyo. Consent to publish All authors approved the final version of the manuscript submitted for publication. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Aziz S Arabi YM Alhazzani W Managing ICU surge during the COVID-19 crisis: rapid guidelines Intensive Care Med 2020 46 1303 1325 10.1007/s00134-020-06092-5 32514598 2. Heesakkers H van der Hoeven JG Corsten S Mental health symptoms in family members of COVID-19 ICU survivors 3 and 12 months after ICU admission: a multicentre prospective cohort study Intensive Care Med 2022 48 322 331 10.1007/s00134-021-06615-8 35103824 3. Rose L Cook A Onwumere J Psychological distress and morbidity of family members experiencing virtual visiting in intensive care during COVID-19: an observational cohort study Intensive Care Med 2022 48 1156 1164 10.1007/s00134-022-06824-9 35913640 4. Lopez Bernal J Cummins S Gasparrini A The use of controls in interrupted time series studies of public health interventions Int J Epidemiol 2018 47 2082 2093 10.1093/ije/dyy135 29982445 5. Azoulay E Kentish-Barnes N A 5-point strategy for improved connection with relatives of critically ill patients with COVID-19 Lancet Respir Med 2020 8 e52 10.1016/S2213-2600(20)30223-X 32380024	36446853	PMC9708118	NO-CC CODE	2022-12-01 23:20:30	no	Intensive Care Med. 2022 Nov 29;:1-3	utf-8	Intensive Care Med	2,022	10.1007/s00134-022-06941-5	oa_other
==== Front Pediatr Radiol Pediatr Radiol Pediatric Radiology 0301-0449 1432-1998 Springer Berlin Heidelberg Berlin/Heidelberg 36447051 5547 10.1007/s00247-022-05547-9 Original Article Gender disparity in academic advancement: exploring differences among adult and pediatric radiologists Schilling Samantha M. 1 Trout Andrew T. 234 Ayyala Rama S. [email protected] 23 1 grid.24827.3b 0000 0001 2179 9593 University of Cincinnati College of Medicine, Cincinnati, OH USA 2 grid.239573.9 0000 0000 9025 8099 Department of Radiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnett Ave, Cincinnati, OH 45229 USA 3 grid.24827.3b 0000 0001 2179 9593 Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH USA 4 grid.239573.9 0000 0000 9025 8099 Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH USA 29 11 2022 16 29 5 2022 13 10 2022 8 11 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Background Gender imbalance in research output and academic rank in academic radiology is well-documented and long-standing. Less is known regarding this imbalance among pediatric radiologists. Objective To characterize gender differences for academic rank and scholarly productivity of pediatric radiologists relative to adult radiologists. Materials and methods During summer 2021, faculty data for the top 10 U.S. News & World Report ranked adult radiology programs and the top 12 largest pediatric hospital radiology departments were collected. Information regarding self-reported gender, age, years of practice and academic rank was accessed from institutional websites and public provider databases. The h-index and the number of publications were acquired via Scopus. Group comparisons were performed using Mann–Whitney and chi-square tests. Results Three hundred and sixty-four (160 women) pediatric and 1,170 (468 women) adult radiologists were included. Compared to adult radiologists, there were significantly fewer pediatric radiologists in advanced ranks (associate or full professor) (P = 0.024), driven by differences between male (P = 0.033) but not female radiologists (P = 0.67). Among pediatric radiologists, there was no significant difference in years in practice (P = 0.29) between males and females. There also was no significant difference in academic rank by gender (P = 0.37), different from adult radiology where men outnumber women in advanced ranks (P < 0.001). Male pediatric radiologists displayed higher academic productivity (h-index: 9.0 vs. 7.0; P = 0.01 and number of publications: 31 vs. 18; P = 0.003) than their female colleagues. Conclusion Academic pediatric radiology seems to have more equitable academic advancement than academic adult radiology. Despite similar time in the workforce, academic output among female pediatric radiologists lags that of their male colleagues. Keywords Academic advancement Adult Gender Pediatric radiology Publications ==== Body pmcIntroduction Gender diversity in workplace leadership and management contributes to enhanced resourcefulness, improved efficiency and higher collective intelligence among teams [1]. In the medical field, physicians are often responsible for leading health care teams. Medical school admissions have made strides toward equal gender representation and, in 2020, the majority of graduating medical students were women [2]. Nonetheless, radiology remains a male-dominated workforce. More specifically, women have accounted for less than 27% of radiologic residency positions over the last 12 years [3], which has translated to a workforce consisting of 23% female radiologists as of 2019 [4]. Upon graduation from medical school, women are more likely than men to pursue a career in academia [5, 6], an environment that values collaboration and productivity. Despite this, the unequal promotion of women within academic medicine in general has been widely demonstrated [5–7] and the yearly proportion of women in advanced academic ranks and in leadership positions decreased between 2006 and 2017 [8]. Similar trends exist in academic radiology where women account for 34% of full-time faculty members and only held 26% of full professor positions in a 2021 analysis [9]. Continuing to work on these trends can be important to improve diversity in academic department, which can help produce higher quality research, with bigger impact and more citations [10–12]. Although a number of factors influence professional advancement in academic medicine, one important factor is scholarly productivity and research output [13]. While female-first and senior authorship in medical imaging journals has gradually risen between 1978 and 2013 [14], women in academic radiology still lag men in these important publication metrics [15], a trend exacerbated by the coronavirus disease 2019 (COVID-19) pandemic [15, 16]. A recent study of authorship trends in Pediatric Radiology inclusive of the COVID-19 pandemic showed female senior authorship was significantly lower in the early phase of the pandemic compared to the previous year [17]. Academic pediatric radiology is one of the few subspecialties in radiology with a more equal gender distribution among full-time faculty members with women comprising around 45% of the workforce [9, 18]. However, gender trends of academic advancement and scholarly productivity in this subspecialty are less well understood. Understanding these trends in academic pediatric radiology and how they differ from academic adult radiology presents an opportunity to define pathways toward gender equity in academic radiology. Therefore, the purpose of this study is to characterize gender differences for academic rank and scholarly productivity of pediatric radiologists relative to adult radiologists. Materials and methods Following review by the local Institutional Review Board, this study was determined to be exempt as all data were collected from publicly available sources. Between July and August 2021, information was gathered for the 2021 top 10 U.S. News & World Report ranked adult radiology programs [19] and the top 12 largest pediatric hospital radiology departments (by number of faculty) were determined via personal correspondence from the Society of Chiefs of Radiology at Children’s Hospitals (SCORCH) in 2022 (Table 1). A larger cohort of pediatric hospitals was included in analysis as a means to improve the power of this study due to the smaller sample sizes of faculty within pediatric radiology departments. Each included institution had an Accreditation Council for Graduate Medical Education (ACGME)-accredited training program in diagnostic radiology at the time of data collection. Emeritus professors, adjunct faculty, visiting radiologists and volunteer professors were excluded from the final faculty sample as were residents or fellows listed as in training. Otherwise, each faculty radiologist holding a Doctor of Medicine (MD), Doctor of Osteopathic Medicine (DO) or an international equivalent medical degree who appeared on the institutional websites of hospitals meeting inclusion criteria were included.Table 1 Included pediatric and adult radiology departments Pediatric radiology departments Adult radiology departments Boston Children’s Hospital Duke University Children’s Hospital of Colorado Johns Hopkins Children’s Hospital of Philadelphia Massachusetts General Hospital Cincinnati Children’s Hospital Medical Center Mayo Clinic (Rochester) Lucile Packard Children’s Hospital New York University Lurie Children’s Hospital Stanford University Mercy Children’s Hospital University of California – San Francisco Nationwide Children’s Hospital University of Michigan Seattle Children’s Hospital University of Pennsylvania Texas Children’s Hospital Washington University University of Pittsburgh Children’s Hospital University of Texas Southwestern Children’s Hospital Information pertaining to each faculty member within the adult and pediatric radiology cohorts was accessed by one author (S.M.S., a medical student) from institutional websites, Doximity (www.doximity.com) and the Centers for Medicare and Medicaid Services (CMS) National Plan and Provider Enumeration System (NPPES) National Provider Identifier (NPI) registry. Collected data included self-reported gender, age, years of practice and academic rank. Years of practice were defined as the number of years since completing their most recent fellowship at the time of data collection. Individuals were further categorized as early career (≤ 10 years), mid-career (11–20 years) and late career (≥ 21 years) based on years of practice. Academic rank was classified as instructor, assistant professor, associate professor or professor. If a faculty member held multiple titles within different clinical or academic departments, only the position held within the radiology department was included for analysis. Any radiologist with an affiliation at both a large university hospital and a children’s hospital, such as a radiologist with an affiliation at both Stanford University and Lucile Packard Children’s Hospital, was only included in the pediatric radiology cohort. Accuracy of the information was verified with spot checking by the senior author (R.S.A., a pediatric radiologist with 8 years of experience). Scholarly productivity was defined as academic output, which can be measured by the h-index and number of publications per individual. The authorship database maintained through Scopus (Elsevier, Amsterdam, Netherlands) was used to gather metrics on scholarly activity. The author profile of all radiologists in the sample was retrieved through the Scopus search function to view the h-index and number of publications. If a radiologist had more than one author profile, Scopus’ merge author function was employed to obtain a combined count of the author’s metrics of interest. Statistical analysis was performed using GraphPad Prism (v9.3.1; GraphPad Software, LLC, San Diego, CA). Descriptive statistics including counts, percentages, medians and interquartile ranges (IQR) were used to summarize the study sample. Group comparisons were achieved with Mann–Whitney tests for continuous variables and chi-squared tests for categorical variables. Mixed effects modeling was used to test for interactions between genders and years in practice as predictors of academic productivity. A P-value of < 0.05 was considered statistically significant for all inference testing. Results Three hundred and sixty-four (44% [n = 160] women) pediatric radiologists were included in the final sample. Women comprised the majority only among instructors (n = 10/18, 56%), while occupying less than 50% of roles in other academic ranks, a difference compared to men that was not statistically significant (P = 0.37) (Table 2). Male pediatric radiologists had significantly higher h-indexes (P < 0.02) and numbers of publications (P < 0.01) compared to their female counterparts (Table 2). There was no significant difference in age (P = 0.74) or years in practice (P = 0.29) between male and female pediatric radiologists (Table 2).Table 2 Characteristics of pediatric radiologists Total Female Male P-value Age (years) 48 (42, 55) 46.5 (42, 55.75) 0.74 Years in practice 14 (8, 20) 12 (7, 20) 0.29 Academic rank (n, %) 0.37 Instructor 18 10 (56%) 8 (44%) Assistant 179 84 (47%) 95 (53%) Associate 103 42 (41%) 61 (59%) Professor 64 24 (38%) 40 (62%) Academic productivity H-index 7 (3, 14.75) 9 (4, 20) 0.01 Total publications 18 (6, 50.5) 31 (11, 75) 0.03 P-values reflect comparisons between men and women Results are presented as medians and interquartile ranges or as counts and percentages Across the 10 adult radiology departments analyzed, 1,170 (36% [n = 421] women) radiologists were included in the final sample. Men significantly outnumbered women in all academic ranks (P < 0.001), holding 68% of instructor roles, 58% of assistant professor roles, 65% of associate professor roles and 73% of professor roles (Table 3). Male adult radiologists had significantly higher h-indexes (P < 0.0001) and numbers of publications (P < 0.0001) compared to their female counterparts (Table 3). There was no significant difference in age (P = 0.07) or years in practice (P = 0.44) between male and female adult radiologists (Table 3).Table 3 Characteristics of adult radiologists Total Female Male P-value Age (years) 47 (39, 57) 48 (41, 59) 0.07 Years in practice 14 (5, 23) 13 (6, 24) 0.44 Academic rank (n, %) 0.0004 Instructor 68 22 (32%) 46 (68%) Assistant 529 222 (42%) 307 (58%) Associate 283 98 (35%) 185 (65%) Professor 290 79 (27%) 211 (73%) Academic productivity H-index 8 (3, 16) 15 (6, 29) < 0.0001 Total publications 20 (6, 51.5) 45 (15, 108.8) < 0.0001 P-values reflect comparisons between men and women Results are presented as medians and interquartile ranges or as counts and percentages There was no significant difference in median age (pediatric: 47 [IQR: 42, 55] years; adult: 47 [IQR: 40, 58] years; P = 0.84) or median years in practice (pediatric: 13 [IQR: 7, 20] years; adult: 13 [IQR: 6, 24] years; P = 0.60) between pediatric and adult radiologists. However, compared to adult radiologists, there were significantly fewer pediatric radiologists in advanced ranks (associate or full professor) (167/364 vs. 573/1,170; P = 0.02). This difference was significant for men (101/204 vs. 396/749; P = 0.03) but not for women (66/160 vs. 177/421; P = 0.67). There was no statistically significant difference among women in pediatric or adult groups for publications or h-index (P = 0.25 and P = 0.11, respectively). Among pediatric radiologists, time in career and male gender were each statistically significant independent factors associated with more publications (P < 0.0001, P = 0.001), higher h-index (P < 0.0001, P = 0.0032), and advanced rank (associate or full professor) (P < 0.0001, P = 0.007), respectively. However, when gender and years in practice were assessed together, there was no statistically significant effect on publications (P = 0.66), h-index (P = 0.81) or advanced rank (P = 0.65). Discussion Diverse teams foster collaboration and innovation [1, 20, 21], yet academic radiology has historically struggled with a gender imbalance in staffing and promotion [9, 22]. As a subspecialty, pediatric radiology has more equal gender representation among radiologists than other radiology subspecialties [9, 18]. How this interacts with academic rank and scholarly productivity metrics relevant to academic rank is not well understood. In our sample that included the largest academic pediatric radiology departments and highest ranked adult radiology departments, the proportion of female faculty members was relatively similar between pediatric and adult radiology departments (44% and 36%, respectively). However, women had more equal representation in advanced ranks in pediatric radiology departments than in adult departments where women were significantly underrepresented across all ranks, but particularly in the advanced ranks. Despite this, women in both pediatric and adult radiology departments displayed lower academic productivity compared to their male counterparts. It is well known that gender discrepancies exist in adult radiology departments. The most recent workforce survey by the American College of Radiology from 2019 showed an enduring predominance of male and no significant change in the proportion of female faculty (23%) since 2012 [4]. In our sample of the top 10 U.S. News & World Report ranked adult academic radiology departments, the gender gap appears less wide than previously reported [9, 22], a discrepancy that may relate to sample differences or could represent real improvement in equitable gender advancement efforts. Possible sources of improved gender balance in academic radiology include more women graduating medical school [2] alongside the greater preference women have for choosing to pursue an academic career relative to men [5, 6]. Early participation in research, professional goals and interests, and work-life balance are all thought to contribute to this decision to enter academia [9, 23, 24]. Although our sample demonstrates a similar proportion of women in academic adult radiology departments to previous work, our results continue to show disproportionately low representation of women in advanced academic ranks in adult radiology departments, particularly the full professor rank (73% men). This finding aligns with a 2016 study showing that only 16% of women in adult academic radiology held the title of full professor compared to 26% of men [25] and a more recent study in 2021 reporting that only 26% of all full professor titles in adult academic radiology belonged to women [9]. These findings all suggest that while the gender gap may be narrowing for representation in academic radiology faculty, the gender gap in academic advancement has not yet caught up. Pediatric radiology is one of the radiologic subspecialties with more equal gender distribution among full-time radiologists [18], a trend that holds true in academic pediatric radiology as shown in our study (44% women) and previous studies (45–46% women) [9, 26]. Despite this, previous studies suggest there is a gender gap in academic rank in academic pediatric radiology departments, mirroring adult radiology departments. Specifically, a 2020 study of pediatric radiologists in the United States and Canada showed a minority of senior faculty members (associate professors and full professors) to be women (34% and 29%, respectively) [26]. Among assistant professors in the aforementioned study, however, women held a small majority (55%) [26]. In our sample, women were the minority in all advanced ranks, but there was no statistically significant difference in rank by gender. This discrepancy in results may speak to differences in sample populations given that our study did not include Canadian radiology departments. Alternatively, this might signify a recent movement toward more equitable promoting practices, particularly in the largest academic pediatric radiology departments in the United States. While such a shift would indicate progress, work remains to achieve and promote equity in academic advancement. Disproportionate career development between genders in academia is thought to be multifactorial with perhaps inconsistent standards even within individual institutions [27]. Earlier work has acknowledged scholarly productivity levels, faculty-chosen track, a lack of oversight in promotion procedures, poor retention of women, an unequal burden of family responsibilities and imbalanced resource allocation as possible causes of gender inequity in promotion [5, 6, 25, 27]. When comparing pediatric to adult radiology departments, our results show lower frequencies of advanced academic rank among pediatric compared to adult radiologists despite no difference in age or years in practice. This appears to be driven by a significant difference between male radiologists, but not female radiologists. This suggests two things: 1) male adult radiologists appear to progress to advanced ranks faster than male pediatric radiologists; and 2) female radiologists are equally underrepresented at advanced ranks in both adult and pediatric departments despite no difference in age or years in practice from their male counterparts. While the cause of the overall lower frequency of pediatric associate or full professors is unknown, one possible explanation is that pediatric radiologists are not afforded, or are not seeking, promotion in their departments at the same rate as their adult radiology counterparts. In academic medicine, scholarly productivity is the factor that tends to have the greatest influence on promotion [9, 13]. Our results show that women in academic pediatric and adult radiology lag their male colleagues in scholarly productivity as indicated by lower h-indices and a smaller number of publications in peer-reviewed imaging journals. This is despite no significant difference in age or years in practice between genders, which is concordant with previous reports [9, 14, 15, 17]. Our study was not designed to define the causes for this discrepancy, but there are a number of barriers known to limit scholarly achievement and therefore hinder the opportunity for promotion. Female radiologists have been shown to be less likely to receive National Institutes of Health grants compared to men and, compounding the inequity, the amount awarded to women was lower compared to their male counterparts [28, 29]. Additionally, faculty members with mentors tend to have a higher number of publications [30], yet women in medicine often lack access to mentors [31]. Lastly, childcare responsibilities and work-life balance preferences may impact research output and have been identified as barriers to professional development by women in academic radiology [32]. This study is not without limitations. First, faculty track (tenure versus nontenure; educator versus academic), part-time status and length of employment at each faculty member’s current institution (versus overall years in practice) were not included in analyses as these data were not consistently publicly available. Similarly, institution-specific promotion guidelines/criteria are also not publicly available and could not be included in analyses. Second, although all data were obtained over a 2-month period, the majority of institutional websites did not display when faculty lists had been updated making the data in this study dependent on the frequency of individual departmental updating practices. Furthermore, given that Scopus is continuously updated according to journal publishing, author h-indices and publication counts will not be identical if a similar study were to use the Scopus database in the future. Third, if an individual had changed their name, such as after marriage, so that it differed from — or was not merged with — an earlier Scopus profile, their scholarly productivity metrics would have been underestimated. Fourth, the calculation of a faculty member’s total years of practice did not account for any career pauses or disruptions, such as obtaining advanced degrees or parental/medical leave, which would lead to an overestimation of this metric. Finally, it is not possible to know the degree of similarity or difference between institutions within this study sample in terms of characteristics that might be relevant to scholarly productivity of faculty. Conclusion Although academic pediatric radiology appears to have more equitable advancement to higher academic rank between genders as compared to adult radiology, women still hold a minority of senior academic ranks overall. Women in pediatric radiology also continue to have lower academic output relative to men despite similar time in practice. These findings show a continued need for initiatives that both support women in academic radiology and remove barriers to productivity and promotion, in addition to a critical assessment of promotion criteria differences among pediatric and adult radiology departments. Declarations Conflicts of interest None Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Dwyer S Richard OC Chadwick K Gender diversity in management and firm performance: the influence of growth orientation and organizational culture J Bus Res 2003 56 1009 1019 10.1016/S0148-2963(01)00329-0 2. Association of American Medical Colleges (2021) Total U.S. medical school enrollment by race/ethnicity and sex, 2017–2018 through 2021–2022. Washington, DC 3. Adham S, Rybicki FJ, Mahoney MC et al (2022) Analysis of gender disparity in US and Canadian radiology residency programs. Curr Probl Diagn Radiol 51:21–24 4. Bender CE Bansal S Wolfman D Parikh JR 2019 ACR Commission on Human Resources Workforce Survey J Am Coll Radiol 2020 17 673 675 10.1016/j.jacr.2020.01.012 32068008 5. Nonnemaker L Women physicians in academic medicine — new insights from cohort studies N Engl J Med 2000 342 399 405 10.1056/NEJM200002103420606 10666431 6. Richter KP Clark L Wick JA Women physicians and promotion in academic medicine N Engl J Med 2020 383 2148 2157 10.1056/NEJMsa1916935 33252871 7. Jena AB Khullar D Ho O Sex differences in academic rank in US medical schools in 2014 JAMA 2015 314 1149 1158 10.1001/jama.2015.10680 26372584 8. Niu BT Nicolaou S Levine D Trends in gender and racial profiles of US academic radiology faculty J Am Coll Radiol 2020 17 1337 1343 10.1016/j.jacr.2020.03.019 32305422 9. Goswami AK Kokabi N Khaja MS Academic radiology in the United States: defining gender disparities in faculty leadership and academic rank Acad Radiol 2021 29 714 725 10.1016/j.acra.2021.05.016 34176728 10. Campbell LG Mehtani S Dozier ME Rinehart J Gender-heterogeneous working groups produce higher quality science PLoS ONE 2013 8 e79147 10.1371/journal.pone.0079147 24205372 11. Freeman RB Huang W Collaboration: strength in diversity Nature 2014 513 305 10.1038/513305a 25230634 12. National Research Council Enhancing the effectiveness of team science 2015 Washington, DC National Academies Press 13. Rice DB Raffoul H Ioannidis JPA Moher D Academic criteria for promotion and tenure in biomedical sciences faculties: cross sectional analysis of international sample of universities BMJ 2020 369 m2081 10.1136/bmj.m2081 32586791 14. Piper CL Scheel JR Lee CI Forman HP Gender trends in radiology authorship: a 35-year analysis AJR Am J Roentgenol 2015 206 3 7 10.2214/AJR.15.15116 15. Quak E Girault G Thenint MA Author gender inequality in medical imaging journals and the COVID-19 pandemic Radiology 2021 300 E301 E307 10.1148/radiol.2021204417 33724061 16. Mogensen MA Lee CI Carlos RC The impact of the COVID-19 pandemic on journal scholarly activity among female contributors J Am Coll Radiol 2021 18 1044 1047 10.1016/j.jacr.2021.01.011 33640339 17. Ayyala RS Trout AT Gender trends in authorship of Pediatric Radiology publications and impact of the COVID-19 pandemic Pediatr Radiol 2021 52 868 873 10.1007/s00247-021-05213-6 34671821 18. Pfeifer CM Gokli A Reid JR Advancing from gender equity to women in leadership in pediatric radiology Pediatric Radiol 2020 50 631 633 10.1007/s00247-020-04645-w 19. No authors listed (2021) Best radiology programs. In: U.S. News & World Report. Accessed 26 Jun 2021 20. Østergaard CR Timmermans B Kristinsson K Does a different view create something new? The effect of employee diversity on innovation Res Policy 2011 40 500 509 10.1016/j.respol.2010.11.004 21. Williams AW Chabris CF Pentland A Evidence for a collective intelligence factor in the performance of human groups Science 2010 330 686 688 10.1126/science.1193147 20929725 22. Grimm LJ Ngo J Pisano ED Yoon S Men (and women) in academic radiology: how can we reduce the gender discrepancy? AJR Am J Roentgenol 2016 206 678 680 10.2214/AJR.15.15277 27003048 23. Edmunds LD Ovseiko PV Shepperd S Why do women choose or reject careers in academic medicine? A narrative review of empirical evidence Lancet 2016 388 2948 2958 10.1016/S0140-6736(15)01091-0 27105721 24. Andriole DA Jeffe DB Hageman HL Variables associated with full-time faculty appointment among contemporary U.S. Medical school graduates: implications for academic medicine workforce diversity Acad Med 2010 85 1250 1257 10.1097/ACM.0b013e3181e10159 20592523 25. Kapoor N Blumenthal DM Smith SE Gender differences in academic rank of radiologists in U.S. medical schools Radiology 2016 283 140 147 10.1148/radiol.2016160950 27768540 26. Counter WB Khurshid K Jalal S Gender differences among academic pediatric radiology faculty in the United States and Canada Acad Radiol 2020 27 575 581 10.1016/j.acra.2019.06.011 31371209 27. Murphy M Callander JK Dohan D Grandis JR Women’s experiences of promotion and tenure in academic medicine and potential implications for gender disparities in career advancement: a qualitative analysis JAMA Netw Open 2021 4 e2125843 e2125843 10.1001/jamanetworkopen.2021.25843 34542616 28. Jutras M Malekafzali L Jung S National Institutes of Health: gender differences in radiology funding Acad Radiol 2020 29 748 754 10.1016/j.acra.2020.08.004 32893113 29. Franceschi AM Rosenkrantz AB Patterns of recent National Institutes of Health (NIH) funding to diagnostic radiology departments: analysis using the NIH RePORTER System Acad Radiol 2017 24 1162 1168 10.1016/j.acra.2017.02.018 28528855 30. Sambunjak D Straus SE Marušić A Mentoring in academic medicine: a systematic review JAMA 2006 296 1103 1115 10.1001/jama.296.9.1103 16954490 31. Bredella MA Fessell D Thrall JH Mentorship in academic radiology: why it matters Insights Imaging 2019 10 107 10.1186/s13244-019-0799-2 31728762 32. Piltch-Loeb R Rosenkrantz AB Merdjanoff AA Identifying barriers and facilitators of success for female radiology researchers: an analysis of in-depth interviews with nationally recognized leaders of the field J Am Coll Radiol 2020 17 1344 1351 10.1016/j.jacr.2020.03.020 32305421	36447051	PMC9708121	NO-CC CODE	2022-12-01 23:20:30	no	Pediatr Radiol. 2022 Nov 29;:1-6	utf-8	Pediatr Radiol	2,022	10.1007/s00247-022-05547-9	oa_other
==== Front Pediatr Radiol Pediatr Radiol Pediatric Radiology 0301-0449 1432-1998 Springer Berlin Heidelberg Berlin/Heidelberg 36447051 5547 10.1007/s00247-022-05547-9 Original Article Gender disparity in academic advancement: exploring differences among adult and pediatric radiologists Schilling Samantha M. 1 Trout Andrew T. 234 Ayyala Rama S. [email protected] 23 1 grid.24827.3b 0000 0001 2179 9593 University of Cincinnati College of Medicine, Cincinnati, OH USA 2 grid.239573.9 0000 0000 9025 8099 Department of Radiology, Cincinnati Children’s Hospital Medical Center, 3333 Burnett Ave, Cincinnati, OH 45229 USA 3 grid.24827.3b 0000 0001 2179 9593 Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH USA 4 grid.239573.9 0000 0000 9025 8099 Department of Pediatrics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH USA 29 11 2022 16 29 5 2022 13 10 2022 8 11 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Background Gender imbalance in research output and academic rank in academic radiology is well-documented and long-standing. Less is known regarding this imbalance among pediatric radiologists. Objective To characterize gender differences for academic rank and scholarly productivity of pediatric radiologists relative to adult radiologists. Materials and methods During summer 2021, faculty data for the top 10 U.S. News & World Report ranked adult radiology programs and the top 12 largest pediatric hospital radiology departments were collected. Information regarding self-reported gender, age, years of practice and academic rank was accessed from institutional websites and public provider databases. The h-index and the number of publications were acquired via Scopus. Group comparisons were performed using Mann–Whitney and chi-square tests. Results Three hundred and sixty-four (160 women) pediatric and 1,170 (468 women) adult radiologists were included. Compared to adult radiologists, there were significantly fewer pediatric radiologists in advanced ranks (associate or full professor) (P = 0.024), driven by differences between male (P = 0.033) but not female radiologists (P = 0.67). Among pediatric radiologists, there was no significant difference in years in practice (P = 0.29) between males and females. There also was no significant difference in academic rank by gender (P = 0.37), different from adult radiology where men outnumber women in advanced ranks (P < 0.001). Male pediatric radiologists displayed higher academic productivity (h-index: 9.0 vs. 7.0; P = 0.01 and number of publications: 31 vs. 18; P = 0.003) than their female colleagues. Conclusion Academic pediatric radiology seems to have more equitable academic advancement than academic adult radiology. Despite similar time in the workforce, academic output among female pediatric radiologists lags that of their male colleagues. Keywords Academic advancement Adult Gender Pediatric radiology Publications ==== Body pmcIntroduction Gender diversity in workplace leadership and management contributes to enhanced resourcefulness, improved efficiency and higher collective intelligence among teams [1]. In the medical field, physicians are often responsible for leading health care teams. Medical school admissions have made strides toward equal gender representation and, in 2020, the majority of graduating medical students were women [2]. Nonetheless, radiology remains a male-dominated workforce. More specifically, women have accounted for less than 27% of radiologic residency positions over the last 12 years [3], which has translated to a workforce consisting of 23% female radiologists as of 2019 [4]. Upon graduation from medical school, women are more likely than men to pursue a career in academia [5, 6], an environment that values collaboration and productivity. Despite this, the unequal promotion of women within academic medicine in general has been widely demonstrated [5–7] and the yearly proportion of women in advanced academic ranks and in leadership positions decreased between 2006 and 2017 [8]. Similar trends exist in academic radiology where women account for 34% of full-time faculty members and only held 26% of full professor positions in a 2021 analysis [9]. Continuing to work on these trends can be important to improve diversity in academic department, which can help produce higher quality research, with bigger impact and more citations [10–12]. Although a number of factors influence professional advancement in academic medicine, one important factor is scholarly productivity and research output [13]. While female-first and senior authorship in medical imaging journals has gradually risen between 1978 and 2013 [14], women in academic radiology still lag men in these important publication metrics [15], a trend exacerbated by the coronavirus disease 2019 (COVID-19) pandemic [15, 16]. A recent study of authorship trends in Pediatric Radiology inclusive of the COVID-19 pandemic showed female senior authorship was significantly lower in the early phase of the pandemic compared to the previous year [17]. Academic pediatric radiology is one of the few subspecialties in radiology with a more equal gender distribution among full-time faculty members with women comprising around 45% of the workforce [9, 18]. However, gender trends of academic advancement and scholarly productivity in this subspecialty are less well understood. Understanding these trends in academic pediatric radiology and how they differ from academic adult radiology presents an opportunity to define pathways toward gender equity in academic radiology. Therefore, the purpose of this study is to characterize gender differences for academic rank and scholarly productivity of pediatric radiologists relative to adult radiologists. Materials and methods Following review by the local Institutional Review Board, this study was determined to be exempt as all data were collected from publicly available sources. Between July and August 2021, information was gathered for the 2021 top 10 U.S. News & World Report ranked adult radiology programs [19] and the top 12 largest pediatric hospital radiology departments (by number of faculty) were determined via personal correspondence from the Society of Chiefs of Radiology at Children’s Hospitals (SCORCH) in 2022 (Table 1). A larger cohort of pediatric hospitals was included in analysis as a means to improve the power of this study due to the smaller sample sizes of faculty within pediatric radiology departments. Each included institution had an Accreditation Council for Graduate Medical Education (ACGME)-accredited training program in diagnostic radiology at the time of data collection. Emeritus professors, adjunct faculty, visiting radiologists and volunteer professors were excluded from the final faculty sample as were residents or fellows listed as in training. Otherwise, each faculty radiologist holding a Doctor of Medicine (MD), Doctor of Osteopathic Medicine (DO) or an international equivalent medical degree who appeared on the institutional websites of hospitals meeting inclusion criteria were included.Table 1 Included pediatric and adult radiology departments Pediatric radiology departments Adult radiology departments Boston Children’s Hospital Duke University Children’s Hospital of Colorado Johns Hopkins Children’s Hospital of Philadelphia Massachusetts General Hospital Cincinnati Children’s Hospital Medical Center Mayo Clinic (Rochester) Lucile Packard Children’s Hospital New York University Lurie Children’s Hospital Stanford University Mercy Children’s Hospital University of California – San Francisco Nationwide Children’s Hospital University of Michigan Seattle Children’s Hospital University of Pennsylvania Texas Children’s Hospital Washington University University of Pittsburgh Children’s Hospital University of Texas Southwestern Children’s Hospital Information pertaining to each faculty member within the adult and pediatric radiology cohorts was accessed by one author (S.M.S., a medical student) from institutional websites, Doximity (www.doximity.com) and the Centers for Medicare and Medicaid Services (CMS) National Plan and Provider Enumeration System (NPPES) National Provider Identifier (NPI) registry. Collected data included self-reported gender, age, years of practice and academic rank. Years of practice were defined as the number of years since completing their most recent fellowship at the time of data collection. Individuals were further categorized as early career (≤ 10 years), mid-career (11–20 years) and late career (≥ 21 years) based on years of practice. Academic rank was classified as instructor, assistant professor, associate professor or professor. If a faculty member held multiple titles within different clinical or academic departments, only the position held within the radiology department was included for analysis. Any radiologist with an affiliation at both a large university hospital and a children’s hospital, such as a radiologist with an affiliation at both Stanford University and Lucile Packard Children’s Hospital, was only included in the pediatric radiology cohort. Accuracy of the information was verified with spot checking by the senior author (R.S.A., a pediatric radiologist with 8 years of experience). Scholarly productivity was defined as academic output, which can be measured by the h-index and number of publications per individual. The authorship database maintained through Scopus (Elsevier, Amsterdam, Netherlands) was used to gather metrics on scholarly activity. The author profile of all radiologists in the sample was retrieved through the Scopus search function to view the h-index and number of publications. If a radiologist had more than one author profile, Scopus’ merge author function was employed to obtain a combined count of the author’s metrics of interest. Statistical analysis was performed using GraphPad Prism (v9.3.1; GraphPad Software, LLC, San Diego, CA). Descriptive statistics including counts, percentages, medians and interquartile ranges (IQR) were used to summarize the study sample. Group comparisons were achieved with Mann–Whitney tests for continuous variables and chi-squared tests for categorical variables. Mixed effects modeling was used to test for interactions between genders and years in practice as predictors of academic productivity. A P-value of < 0.05 was considered statistically significant for all inference testing. Results Three hundred and sixty-four (44% [n = 160] women) pediatric radiologists were included in the final sample. Women comprised the majority only among instructors (n = 10/18, 56%), while occupying less than 50% of roles in other academic ranks, a difference compared to men that was not statistically significant (P = 0.37) (Table 2). Male pediatric radiologists had significantly higher h-indexes (P < 0.02) and numbers of publications (P < 0.01) compared to their female counterparts (Table 2). There was no significant difference in age (P = 0.74) or years in practice (P = 0.29) between male and female pediatric radiologists (Table 2).Table 2 Characteristics of pediatric radiologists Total Female Male P-value Age (years) 48 (42, 55) 46.5 (42, 55.75) 0.74 Years in practice 14 (8, 20) 12 (7, 20) 0.29 Academic rank (n, %) 0.37 Instructor 18 10 (56%) 8 (44%) Assistant 179 84 (47%) 95 (53%) Associate 103 42 (41%) 61 (59%) Professor 64 24 (38%) 40 (62%) Academic productivity H-index 7 (3, 14.75) 9 (4, 20) 0.01 Total publications 18 (6, 50.5) 31 (11, 75) 0.03 P-values reflect comparisons between men and women Results are presented as medians and interquartile ranges or as counts and percentages Across the 10 adult radiology departments analyzed, 1,170 (36% [n = 421] women) radiologists were included in the final sample. Men significantly outnumbered women in all academic ranks (P < 0.001), holding 68% of instructor roles, 58% of assistant professor roles, 65% of associate professor roles and 73% of professor roles (Table 3). Male adult radiologists had significantly higher h-indexes (P < 0.0001) and numbers of publications (P < 0.0001) compared to their female counterparts (Table 3). There was no significant difference in age (P = 0.07) or years in practice (P = 0.44) between male and female adult radiologists (Table 3).Table 3 Characteristics of adult radiologists Total Female Male P-value Age (years) 47 (39, 57) 48 (41, 59) 0.07 Years in practice 14 (5, 23) 13 (6, 24) 0.44 Academic rank (n, %) 0.0004 Instructor 68 22 (32%) 46 (68%) Assistant 529 222 (42%) 307 (58%) Associate 283 98 (35%) 185 (65%) Professor 290 79 (27%) 211 (73%) Academic productivity H-index 8 (3, 16) 15 (6, 29) < 0.0001 Total publications 20 (6, 51.5) 45 (15, 108.8) < 0.0001 P-values reflect comparisons between men and women Results are presented as medians and interquartile ranges or as counts and percentages There was no significant difference in median age (pediatric: 47 [IQR: 42, 55] years; adult: 47 [IQR: 40, 58] years; P = 0.84) or median years in practice (pediatric: 13 [IQR: 7, 20] years; adult: 13 [IQR: 6, 24] years; P = 0.60) between pediatric and adult radiologists. However, compared to adult radiologists, there were significantly fewer pediatric radiologists in advanced ranks (associate or full professor) (167/364 vs. 573/1,170; P = 0.02). This difference was significant for men (101/204 vs. 396/749; P = 0.03) but not for women (66/160 vs. 177/421; P = 0.67). There was no statistically significant difference among women in pediatric or adult groups for publications or h-index (P = 0.25 and P = 0.11, respectively). Among pediatric radiologists, time in career and male gender were each statistically significant independent factors associated with more publications (P < 0.0001, P = 0.001), higher h-index (P < 0.0001, P = 0.0032), and advanced rank (associate or full professor) (P < 0.0001, P = 0.007), respectively. However, when gender and years in practice were assessed together, there was no statistically significant effect on publications (P = 0.66), h-index (P = 0.81) or advanced rank (P = 0.65). Discussion Diverse teams foster collaboration and innovation [1, 20, 21], yet academic radiology has historically struggled with a gender imbalance in staffing and promotion [9, 22]. As a subspecialty, pediatric radiology has more equal gender representation among radiologists than other radiology subspecialties [9, 18]. How this interacts with academic rank and scholarly productivity metrics relevant to academic rank is not well understood. In our sample that included the largest academic pediatric radiology departments and highest ranked adult radiology departments, the proportion of female faculty members was relatively similar between pediatric and adult radiology departments (44% and 36%, respectively). However, women had more equal representation in advanced ranks in pediatric radiology departments than in adult departments where women were significantly underrepresented across all ranks, but particularly in the advanced ranks. Despite this, women in both pediatric and adult radiology departments displayed lower academic productivity compared to their male counterparts. It is well known that gender discrepancies exist in adult radiology departments. The most recent workforce survey by the American College of Radiology from 2019 showed an enduring predominance of male and no significant change in the proportion of female faculty (23%) since 2012 [4]. In our sample of the top 10 U.S. News & World Report ranked adult academic radiology departments, the gender gap appears less wide than previously reported [9, 22], a discrepancy that may relate to sample differences or could represent real improvement in equitable gender advancement efforts. Possible sources of improved gender balance in academic radiology include more women graduating medical school [2] alongside the greater preference women have for choosing to pursue an academic career relative to men [5, 6]. Early participation in research, professional goals and interests, and work-life balance are all thought to contribute to this decision to enter academia [9, 23, 24]. Although our sample demonstrates a similar proportion of women in academic adult radiology departments to previous work, our results continue to show disproportionately low representation of women in advanced academic ranks in adult radiology departments, particularly the full professor rank (73% men). This finding aligns with a 2016 study showing that only 16% of women in adult academic radiology held the title of full professor compared to 26% of men [25] and a more recent study in 2021 reporting that only 26% of all full professor titles in adult academic radiology belonged to women [9]. These findings all suggest that while the gender gap may be narrowing for representation in academic radiology faculty, the gender gap in academic advancement has not yet caught up. Pediatric radiology is one of the radiologic subspecialties with more equal gender distribution among full-time radiologists [18], a trend that holds true in academic pediatric radiology as shown in our study (44% women) and previous studies (45–46% women) [9, 26]. Despite this, previous studies suggest there is a gender gap in academic rank in academic pediatric radiology departments, mirroring adult radiology departments. Specifically, a 2020 study of pediatric radiologists in the United States and Canada showed a minority of senior faculty members (associate professors and full professors) to be women (34% and 29%, respectively) [26]. Among assistant professors in the aforementioned study, however, women held a small majority (55%) [26]. In our sample, women were the minority in all advanced ranks, but there was no statistically significant difference in rank by gender. This discrepancy in results may speak to differences in sample populations given that our study did not include Canadian radiology departments. Alternatively, this might signify a recent movement toward more equitable promoting practices, particularly in the largest academic pediatric radiology departments in the United States. While such a shift would indicate progress, work remains to achieve and promote equity in academic advancement. Disproportionate career development between genders in academia is thought to be multifactorial with perhaps inconsistent standards even within individual institutions [27]. Earlier work has acknowledged scholarly productivity levels, faculty-chosen track, a lack of oversight in promotion procedures, poor retention of women, an unequal burden of family responsibilities and imbalanced resource allocation as possible causes of gender inequity in promotion [5, 6, 25, 27]. When comparing pediatric to adult radiology departments, our results show lower frequencies of advanced academic rank among pediatric compared to adult radiologists despite no difference in age or years in practice. This appears to be driven by a significant difference between male radiologists, but not female radiologists. This suggests two things: 1) male adult radiologists appear to progress to advanced ranks faster than male pediatric radiologists; and 2) female radiologists are equally underrepresented at advanced ranks in both adult and pediatric departments despite no difference in age or years in practice from their male counterparts. While the cause of the overall lower frequency of pediatric associate or full professors is unknown, one possible explanation is that pediatric radiologists are not afforded, or are not seeking, promotion in their departments at the same rate as their adult radiology counterparts. In academic medicine, scholarly productivity is the factor that tends to have the greatest influence on promotion [9, 13]. Our results show that women in academic pediatric and adult radiology lag their male colleagues in scholarly productivity as indicated by lower h-indices and a smaller number of publications in peer-reviewed imaging journals. This is despite no significant difference in age or years in practice between genders, which is concordant with previous reports [9, 14, 15, 17]. Our study was not designed to define the causes for this discrepancy, but there are a number of barriers known to limit scholarly achievement and therefore hinder the opportunity for promotion. Female radiologists have been shown to be less likely to receive National Institutes of Health grants compared to men and, compounding the inequity, the amount awarded to women was lower compared to their male counterparts [28, 29]. Additionally, faculty members with mentors tend to have a higher number of publications [30], yet women in medicine often lack access to mentors [31]. Lastly, childcare responsibilities and work-life balance preferences may impact research output and have been identified as barriers to professional development by women in academic radiology [32]. This study is not without limitations. First, faculty track (tenure versus nontenure; educator versus academic), part-time status and length of employment at each faculty member’s current institution (versus overall years in practice) were not included in analyses as these data were not consistently publicly available. Similarly, institution-specific promotion guidelines/criteria are also not publicly available and could not be included in analyses. Second, although all data were obtained over a 2-month period, the majority of institutional websites did not display when faculty lists had been updated making the data in this study dependent on the frequency of individual departmental updating practices. Furthermore, given that Scopus is continuously updated according to journal publishing, author h-indices and publication counts will not be identical if a similar study were to use the Scopus database in the future. Third, if an individual had changed their name, such as after marriage, so that it differed from — or was not merged with — an earlier Scopus profile, their scholarly productivity metrics would have been underestimated. Fourth, the calculation of a faculty member’s total years of practice did not account for any career pauses or disruptions, such as obtaining advanced degrees or parental/medical leave, which would lead to an overestimation of this metric. Finally, it is not possible to know the degree of similarity or difference between institutions within this study sample in terms of characteristics that might be relevant to scholarly productivity of faculty. Conclusion Although academic pediatric radiology appears to have more equitable advancement to higher academic rank between genders as compared to adult radiology, women still hold a minority of senior academic ranks overall. Women in pediatric radiology also continue to have lower academic output relative to men despite similar time in practice. These findings show a continued need for initiatives that both support women in academic radiology and remove barriers to productivity and promotion, in addition to a critical assessment of promotion criteria differences among pediatric and adult radiology departments. Declarations Conflicts of interest None Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Dwyer S Richard OC Chadwick K Gender diversity in management and firm performance: the influence of growth orientation and organizational culture J Bus Res 2003 56 1009 1019 10.1016/S0148-2963(01)00329-0 2. Association of American Medical Colleges (2021) Total U.S. medical school enrollment by race/ethnicity and sex, 2017–2018 through 2021–2022. Washington, DC 3. Adham S, Rybicki FJ, Mahoney MC et al (2022) Analysis of gender disparity in US and Canadian radiology residency programs. Curr Probl Diagn Radiol 51:21–24 4. Bender CE Bansal S Wolfman D Parikh JR 2019 ACR Commission on Human Resources Workforce Survey J Am Coll Radiol 2020 17 673 675 10.1016/j.jacr.2020.01.012 32068008 5. Nonnemaker L Women physicians in academic medicine — new insights from cohort studies N Engl J Med 2000 342 399 405 10.1056/NEJM200002103420606 10666431 6. Richter KP Clark L Wick JA Women physicians and promotion in academic medicine N Engl J Med 2020 383 2148 2157 10.1056/NEJMsa1916935 33252871 7. Jena AB Khullar D Ho O Sex differences in academic rank in US medical schools in 2014 JAMA 2015 314 1149 1158 10.1001/jama.2015.10680 26372584 8. Niu BT Nicolaou S Levine D Trends in gender and racial profiles of US academic radiology faculty J Am Coll Radiol 2020 17 1337 1343 10.1016/j.jacr.2020.03.019 32305422 9. Goswami AK Kokabi N Khaja MS Academic radiology in the United States: defining gender disparities in faculty leadership and academic rank Acad Radiol 2021 29 714 725 10.1016/j.acra.2021.05.016 34176728 10. Campbell LG Mehtani S Dozier ME Rinehart J Gender-heterogeneous working groups produce higher quality science PLoS ONE 2013 8 e79147 10.1371/journal.pone.0079147 24205372 11. Freeman RB Huang W Collaboration: strength in diversity Nature 2014 513 305 10.1038/513305a 25230634 12. National Research Council Enhancing the effectiveness of team science 2015 Washington, DC National Academies Press 13. Rice DB Raffoul H Ioannidis JPA Moher D Academic criteria for promotion and tenure in biomedical sciences faculties: cross sectional analysis of international sample of universities BMJ 2020 369 m2081 10.1136/bmj.m2081 32586791 14. Piper CL Scheel JR Lee CI Forman HP Gender trends in radiology authorship: a 35-year analysis AJR Am J Roentgenol 2015 206 3 7 10.2214/AJR.15.15116 15. Quak E Girault G Thenint MA Author gender inequality in medical imaging journals and the COVID-19 pandemic Radiology 2021 300 E301 E307 10.1148/radiol.2021204417 33724061 16. Mogensen MA Lee CI Carlos RC The impact of the COVID-19 pandemic on journal scholarly activity among female contributors J Am Coll Radiol 2021 18 1044 1047 10.1016/j.jacr.2021.01.011 33640339 17. Ayyala RS Trout AT Gender trends in authorship of Pediatric Radiology publications and impact of the COVID-19 pandemic Pediatr Radiol 2021 52 868 873 10.1007/s00247-021-05213-6 34671821 18. Pfeifer CM Gokli A Reid JR Advancing from gender equity to women in leadership in pediatric radiology Pediatric Radiol 2020 50 631 633 10.1007/s00247-020-04645-w 19. No authors listed (2021) Best radiology programs. In: U.S. News & World Report. Accessed 26 Jun 2021 20. Østergaard CR Timmermans B Kristinsson K Does a different view create something new? The effect of employee diversity on innovation Res Policy 2011 40 500 509 10.1016/j.respol.2010.11.004 21. Williams AW Chabris CF Pentland A Evidence for a collective intelligence factor in the performance of human groups Science 2010 330 686 688 10.1126/science.1193147 20929725 22. Grimm LJ Ngo J Pisano ED Yoon S Men (and women) in academic radiology: how can we reduce the gender discrepancy? AJR Am J Roentgenol 2016 206 678 680 10.2214/AJR.15.15277 27003048 23. Edmunds LD Ovseiko PV Shepperd S Why do women choose or reject careers in academic medicine? A narrative review of empirical evidence Lancet 2016 388 2948 2958 10.1016/S0140-6736(15)01091-0 27105721 24. Andriole DA Jeffe DB Hageman HL Variables associated with full-time faculty appointment among contemporary U.S. Medical school graduates: implications for academic medicine workforce diversity Acad Med 2010 85 1250 1257 10.1097/ACM.0b013e3181e10159 20592523 25. Kapoor N Blumenthal DM Smith SE Gender differences in academic rank of radiologists in U.S. medical schools Radiology 2016 283 140 147 10.1148/radiol.2016160950 27768540 26. Counter WB Khurshid K Jalal S Gender differences among academic pediatric radiology faculty in the United States and Canada Acad Radiol 2020 27 575 581 10.1016/j.acra.2019.06.011 31371209 27. Murphy M Callander JK Dohan D Grandis JR Women’s experiences of promotion and tenure in academic medicine and potential implications for gender disparities in career advancement: a qualitative analysis JAMA Netw Open 2021 4 e2125843 e2125843 10.1001/jamanetworkopen.2021.25843 34542616 28. Jutras M Malekafzali L Jung S National Institutes of Health: gender differences in radiology funding Acad Radiol 2020 29 748 754 10.1016/j.acra.2020.08.004 32893113 29. Franceschi AM Rosenkrantz AB Patterns of recent National Institutes of Health (NIH) funding to diagnostic radiology departments: analysis using the NIH RePORTER System Acad Radiol 2017 24 1162 1168 10.1016/j.acra.2017.02.018 28528855 30. Sambunjak D Straus SE Marušić A Mentoring in academic medicine: a systematic review JAMA 2006 296 1103 1115 10.1001/jama.296.9.1103 16954490 31. Bredella MA Fessell D Thrall JH Mentorship in academic radiology: why it matters Insights Imaging 2019 10 107 10.1186/s13244-019-0799-2 31728762 32. Piltch-Loeb R Rosenkrantz AB Merdjanoff AA Identifying barriers and facilitators of success for female radiology researchers: an analysis of in-depth interviews with nationally recognized leaders of the field J Am Coll Radiol 2020 17 1344 1351 10.1016/j.jacr.2020.03.020 32305421	0	PMC9708123	NO-CC CODE	2022-12-01 23:20:28	no	Control Manag Rev. 2022 Nov 30; 66(8):28-37	latin-1	null	null	null	oa_other
==== Front Arch Orthop Trauma Surg Arch Orthop Trauma Surg Archives of Orthopaedic and Trauma Surgery 0936-8051 1434-3916 Springer Berlin Heidelberg Berlin/Heidelberg 36447057 4712 10.1007/s00402-022-04712-x Hip Arthroplasty Periprosthetic femoral fractures around the original cemented polished triple-tapered C-stem femoral implant: a consecutive series of 500 primary total hip arthroplasties with an average follow-up of 15 years http://orcid.org/0000-0002-2807-1965 Baryeh Kwaku [email protected] 1 Wang Chao [email protected] 2 Sochart David H. [email protected] 34 1 grid.461588.6 0000 0004 0399 2500 Postgraduate Medical Education, West Middlesex University Hospital, Twickenham Road, Islewoth, TW7 6AF Middlesex UK 2 grid.15538.3a 0000 0001 0536 3773 Department of Statistics, Kingston University, River House, 53-57 High Street, Kingston upon Thames, KT1 1LQ Surrey UK 3 The Academic Unit, South West London Elective Orthopaedic Centre, Dorking Road, Epsom, KT18 7EG UK 4 grid.8752.8 0000 0004 0460 5971 The School of Health and Society, University of Salford, Prestwood Road, Salford, M50 2EQ Manchester UK 29 11 2022 18 16 10 2022 22 11 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Introduction The true incidence of periprosthetic femoral fracture (PFF) around cemented polished taper-slip implants remains largely unknown. Registries usually only capture PFFs that result in revision, missing those managed non-operatively or treated by open reduction and internal fixation (ORIF). This study reports the long-term rate of PFF with the original triple-tapered C-stem femoral implant. Materials and methods A prospective review of a consecutive series of 500 primary total hip arthroplasties (THAs) performed at a single centre between March 2000 and December 2005, with average follow-up of 15 years (12–19 years). Results There were 500 consecutive THAs in 455 patients. Seven PFFs (1.4%) occurred in seven patients at an average of 7.9 years (range 2–11.5) from the primary arthroplasty. Five PFFs were managed by ORIF, one Vancouver B3 fracture was revised for a loose implant and one patient was treated non-operatively. Average age at primary operation was 74 years (67–87) and BMI averaged 27.3 (22–31). There was no typical fracture pattern and no statistically significant associations with patient demographics (age, gender, BMI, diagnosis) or prosthetic details (size, offset, alignment, cement mantle, subsidence). Survivorship to the occurrence of PFF was 99% (97.3–99.6%) at 10 years and 97.8% (95.5–99.0%) at 15. Conclusion A PFF rate of 1.4% at an average follow-up of 15 years represents the true incidence of PFF with the use of the original triple-tapered C-Stem femoral implant, similar to that of published Exeter series (1.85%) but lower than the CPT (3.3%). Keywords Periprosthetic femoral fracture Cemented Polished Taper-slip Hip arthroplasty ==== Body pmcIntroduction Periprosthetic femoral fracture (PFF) is an infrequent, yet potentially devastating complication of total hip arthroplasty (THA), which is associated with poorer functional outcomes, significant morbidity and an increased overall 12-month mortality of 11% [1]. In 2020, it was reported that patients admitted to hospital with PFF were at increased risk of developing post-operative COVID-19 infection [2]. Increased life expectancy and the longevity of physically active adults into older age [3] have led to a projected increased demand for primary THA in the UK of 134% by 2030 [4]. Coinciding with this, an estimated increased incidence of PFF of 4.6% every decade until 2045 has also been predicted [5]. The management of PFF is complex and can result in further costly re-operations, prolonged rehabilitation and persisting dysfunction [6, 7]. The true incidence of PFF remains unknown, but a prevalence of 0.1–4% has previously been estimated [3, 7–9]. National joint registries, except the Swedish Hip Arthroplasty Registry, record only those PFFs which necessitate revision, failing to capture those managed non-operatively or by ORIF [10, 11]. Uncemented implants are associated with an increased rate of PFF [12, 13] but it is less clear what impact the design specifics of cemented stems have on the incidence. The two categories of cemented stems are the taper-slip (force-closed) and the composite beam (shape-closed). Taper-slip stems provide excellent long-term results [14–16], and their use dominates the hip arthroplasty market in the United Kingdom [17]. A statistically significant increased risk of PFF for taper-slip stems compared to composite beams has been reported [18, 19], despite which there remain few published series addressing the issue and reporting the long-term results of taper-slip stems [14, 15, 20, 21] with only one previous publication on the long-term results of the original C-stem [16]. The aim of this study was to determine the true long-term incidence of PFF in a prospective cohort of 500 consecutive cemented polished triple-tapered original C-stem femoral implants. Materials and methods Data were collected prospectively on 500 consecutive primary THAs in 455 patients performed between March 2000 and December 2005. Ethical approval for this study was not required. The original cemented polished triple-tapered C-stem featured a 9/10 rather than the later 12/14 trunnion and was used in all cases, having the same dimensions as the later C-stem AMT, other than the extended shoulder (both DePuy International, Leeds, UK). All operations were performed at a single centre under the care of four Orthopaedic Consultants, in laminar flow operating theatres. A posterior surgical approach was used with a stay suture in the short external rotators to protect the sciatic nerve. A box chisel was used to access the piriform fossa, then a blunt-ended tapered reamer before sequential broaching of the canal to obtain a cement mantle of at least 2 mm. Trial reduction was performed to assess leg length and stability, before a cement restrictor of appropriate size was inserted, and the canal prepared with pulse lavage. A third generation cementing technique was used, with vacuum-mixed Palacos-R Bone Cement (Heraeus GmbH, Hanau, Germany) containing Gentamicin inserted in retrograde fashion with a cement gun. The cement was constantly pressurised prior to the insertion of the femoral prosthesis with a hollow polymethyl-methacrylate (PMMA) tip centraliser, which was then held until the cement had set. The prosthesis was then reduced, and stability, leg length and offset are re-assessed before closure of the short external rotators and capsule as a single layer with loop PDS, but without trans-osseous sutures. Outpatient review began at six weeks, then continued annually for five years and every second year thereafter. Plain radiographs were performed prior to discharge, then at twelve months and each clinical review thereafter. Antero-posterior radiographs of the pelvis were taken using a standardised technique, with the X-ray centred over the symphysis pubis and the patellae pointing upwards. Femoral component alignment was measured with respect to the long axis of the femur (neutral being within five degrees), and the cement mantle was graded using the Barrack system [22]. Subsidence was measured using the Fowler technique [23] and PFF was categorised using the Vancouver Classification [24, 25]. Statistical analysis Descriptive statistics were presented for relevant variables at THA level by fracture outcome (PFF or not). For continuous variables, statistics such as mean, median, standard deviation (SD), first and third quantiles (Q1, Q3), and number of observations were calculated. For categorical variables, count and percentage of each category were presented. To test the difference in the variables between the fracture and non-fracture groups, t tests were performed for continuous variables and Fisher’s exact test for categorical variables. Survival analysis was performed with the end point as time to PFF or to the latest follow-up (if no PFF), and Kaplan–Meier survival estimates were plotted for the entire cohort. In addition, a series of Cox regression model was used to explore variables associated with PFF. All statistical analyses were conducted using Stata (StataCorp LLC, Texas, USA). Results There were 500 consecutive primary THAs in 455 patients, with 282 females (62%) and 173 males (38%). Average age at surgery was 68.8 years (range 23–92), and average BMI was 29 (range 18–42). The most common indication for surgery was primary osteoarthritis (80.6%, Table 1).Table 1 Pre-operative diagnosis Pre-operative diagnosis Number % Osteoarthritis 403 80.60 AVN 51 10.20 Rheumatoid Arthritis 19 3.80 NOF 7 1.40 Paget's Disease 4 0.80 DDH 3 0.60 Other 13 2.60 During follow-up, 244 (54%) patients died (265 THA, 53%), 23 patients (5%) with 25 THA (5%) declined further follow-up [8 moved out of region (10 THA), 15 due to poor health (15 THA)], with only three further patients (0.7%) with three THA (0.6%) being lost to follow-up. Fourteen femoral implants (2.8%) in thirteen patients (2.9%) were revised [one late sepsis, three aseptic loosening (one with a PFF), 10 during revision of a loose acetabular component]. Of the remaining 172 patients (37.8%) with 193 THA (38.6%), 13 (2.9%; 13 THA, 2.6%) residing in care homes declined radiological follow-up. These patients underwent a telephone consultation to confirm that they remained satisfied, had not suffered any complications and consented to a review of their medical records and radiological images, none of which subsequently demonstrated any PFF. This left a total of 180 THA (36%) in 159 patients (34.9%) with complete clinical and radiological follow-up (Fig. 1).Fig. 1 Flow chart diagram of patients detailing follow-up High offset femoral stems were used in 288 cases (58%), with the average combined femoral offset (stem plus head) for the entire series being 44.1 mm (range 35–54 mm, Table 2).Table 2 Femoral stem sizes Femoral stem size Number % 1 26 5.20 2 67 13.40 HO2 80 16.00 3 65 13.00 HO3 102 20.40 4 39 7.80 HO4 80 16.00 5 4 0.80 HO5 26 5.20 6 7 1.40 7 4 0.80 HO high offset stem Seven PFFs occurred in seven patients (1.4%, Table 3), with a mean time from operation of 7.9 years (2–11.5). There was one Vancouver Type A fracture, three Type B1, two Type B2 and one Type B3 fracture. None were distal to the tip of the implant (Vancouver C).Table 3 Fracture type and management, patient, prosthesis and radiological findings Patient Vancouver Mx Time Age Gender BMI Side Dx Stem Offset Align Barrack Subs-12 1 A ORIF 133 68 M 25 R OA HO3 47 VAR B 0.5 2 B1 CON 24 79 M 31 L OA 2 41 N A 0.5 3 B1 ORIF 84 78 F 29 R OA 4 42 N A 1 4 B1 ORIF 64 87 F 23 R OA HO2 45 N A 1 5 B2 ORIF 138 67 M 30 R OA HO4 52 N B 0.5 6 B2 ORIF 88 71 F 31 L OA HO2 42 N B 2 7 B3 REV 137 68 F 22 L OA 4 45 VAL A 1 Mx how the fracture was managed. ORIF is open reduction and internal fixation, Con is conservative management, Rev is revision, Dx pre-operative diagnosis, OA is osteoarthritis, Time number of months until fracture, Align is alignment of the stem, Var is varus, N is neutral, Val is valgus, Barrack is the grading of the cement mantle, Offset is the combined offset of the stem plus the head, Subs-12 is subsidence at 12 months The mean age at time of surgery was 74 years (67–87) in the PFF group compared to 68.6 years (23–92) in the non-fracture group, which was not statistically significant (p = 0.187). There were four fractures in female patients and three in males, with four being right sided and three left. The average BMI was 27.3 (22–31) in the PFF group compared to 28.6 (18–42), and the pre-operative diagnosis in all PFF cases was osteoarthritis (Table 3). Femoral prosthesis alignment was neutral in five cases, varus in one and valgus in one, with cement mantle quality being Barrack Grade A in three cases and Grade B in four. Prosthetic offset (stem plus head) averaged 45.1 (41–52) in the fracture group compared to 44.1 (35–54), with subsidence of the femoral component at 12 months averaging 0.9 mm in both groups. Distal femoral cortical hypertrophy (DFCH) occurred in six cases (0.12%), none of whom suffered a PFF. Five PFFs were managed by ORIF, one B2 fracture was treated non-operatively as the patient was unfit for surgery and the B3 fracture underwent revision for aseptic loosening of the stem (Table 3). There were no subsequent re-operations in any of these patients, four of whom died at an average of 16.5 months (range 3 to 36) from the date of fracture. Statistical analysis of age, gender, pre-operative diagnosis, operated side, BMI, implant size, prosthetic offset (stem plus head), Barrack classification and femoral alignment demonstrated no statistical significance between the fracture and non-fracture groups (Table 4). A series of Cox regression models was performed with variables in Table 4 as covariates. Because the sample size was small (only seven PFF), the model was restricted to include a single continuous or dummy variable. It was found that none of the variables had a statistically significant association with PFF.Table 4 Summary of statistical analysis Variables Non-fracture (N = 493) Fracture (N = 7) p value Age 0.187 Mean (SD) 68.61 (10.73) 74.00 (7.53) Median (Q1, Q3) 70.0 (64.0, 75.0) 71.0 (68.0, 79.0) N (% Non-missing) 493 (100.0%) 7 (100.0%) BMI 0.468 Mean (SD) 28.61 (4.78) 27.29 (3.86) Median (Q1, Q3) 29.0 (25.0, 32.0) 29.0 (23.0, 31.0) N (% Non-missing) 380 (77.1%) 7 (100.0%) Offset 0.537 Mean (SD) 44.13 (4.31) 45.14 (3.89) Median (Q1, Q3) 44.0 (41.0, 48.0) 45.0 (42.0, 47.0) N (% Non-missing) 493 (100.0%) 7 (100.0%) Side 1.000 Right 273 (55.4%) 4 (57.1%) Left 220 (44.6%) 3 (42.9%) Gender 0.704 Female 318 (64.5%) 4 (57.1%) Male 175 (35.5%) 3 (42.9%) Femoral Stem Size 0.685 1 26 (5.3%) 0 (0.0%) 2 66 (13.4%) 1 (14.3%) HO2 78 (15.8%) 2 (28.6%) 3 65 (13.2%) 0 (0.0%) HO3 101 (20.5%) 1 (14.3%) 4 37 (7.5%) 2 (28.6%) HO4 79 (16.0%) 1 (14.3%) 5 4 (0.8%) 0 (0.0%) HO5 26 (5.3%) 0 (0.0%) 6 7 (1.4%) 0 (0.0%) 7 4 (0.8%) 0 (0.0%) Barack classification 0.340 A 326 (66.1%) 3 (42.9%) B 149 (30.2%) 4 (57.1%) D 12 (2.4%) 0 (0.0%) Unknown* 6 (1.2%) 0 (0.0%) 0.686 Alignment Neutral 335 (68.0%) 5 (71.4%) Right 106 (21.5%) 1 (14.3%) Left 46 (9.3%) 1 (14.3%) Unknown* 6 (1.2%) 0 (0.0%) T tests used for continuous variables and Fisher’s exact test used for categorical variables HO high offset stem *Unknown: six patients died before 12-month follow-up radiographs were obtained Kaplan–Meier survivorship, with PFF as the end point, was 99.0% (292 THA at risk, 97.3–99.6%) at 10 years and 97.8% (114 THA at risk, 95.5–99.0%) at 15 years (Fig. 2).Fig. 2 Kaplan–Meier survival curve for entire cohort Discussion The PFF rate was 1.4% in a consecutive cohort of 500 cemented polished triple-tapered C-stem femoral implants, using third generation cementing and Palacos-R + G bone cement, with long-term follow-up averaging 15 years. There was no typical fracture pattern or statistically significant associations with patient demographic or prosthetic details. The Exeter (Stryker, New Jersey, USA) and CPT stems (Zimmer, Warsaw, Indiana, USA) are double-tapered femoral implants and comprise in excess of 75% of the UK market share [17]. The C-stem has a third taper, running from lateral to medial, for proximal loading of the calcar to reduce negative bone remodelling in the long term [16] and only six cases (0.12%) in the current series developed DFCH confirming that this was being achieved. Force-closed femoral implants achieve stability by means of controlled subsidence within the cement mantle, acting as a wedge and generating hoop stresses in the cement-bone construct [10]. The polished implant surface allows micromotion at the implant–cement interface without abrasion, facilitating controlled subsidence due to the visco-elastic property of bone cement called creep, which is non-recoverable deformation under load. PFF in taper-slip implants is typically caused by a low-velocity rotational injury with forced axial loading [10], and it has been postulated that the wedge shape of the prosthesis, which is not fixed within the cement mantle, will transmit momentarily increased hoop stresses at the cement–bone interface leading to an increased risk of PFF compared to composite beam stems, which are fixed within the cement mantle [19]. The Vancouver classification system guides optimum management of PFF for both cemented and uncemented prostheses [24, 25] and has been integrated into the unified classification system, to characterise periprosthetic fractures around any joint [26]. Due to the complexity relating specifically to polished taper-slip implants, Maggs et al. recently advocated a sub-classification of B2 fractures distinguishing between those in which the cement–bone interface is well fixed and those in which it is loose, as this determines the definitive management [10]. National joint registries now provide the majority of arthroplasty outcome data, but with the exception of the Swedish Hip Arthroplasty Registry, capture only those patients in whom a complication has necessitated revision surgery [10, 11]. In the case of PFF, this will not include fractures treated by ORIF or non-operatively due to patient frailty, and in a recent study of 539 PFFs, 23% (122 PFFs) were managed non-operatively, 31% (169 PFFs) by ORIF alone and 46% (246 PFFs) by ‘revision and/or fixation’ [27]. Registries therefore underestimate the incidence of PFF [1, 8, 10] but inconsistencies can also occur with the revision data itself [28]. A recent study assessing risk factors for PFF compared the German Arthroplasty Registry data to insurance record ICD codes, discovering a 13.7% discrepancy with regards to PFF being the actual cause of revision [29]. In the current study, only the single patient with the Vancouver B3 PFF, which underwent revision, would have been captured by the National Joint Registry (NJR). An inaccurate PFF incidence of 0.2% would therefore have been estimated, as opposed to the actual rate of 1.4%, with an average follow-up of 15 years. Where registry data are lacking, well-conducted single, or multi-centre, case series can give insight into the true rates and management of PFF. Due to their proportion of market share, the Exeter and CPT stems constitute the majority of the reported series assessing the risk and rates of PFF with taper-slip designs. Mahon et al. reviewed 829 Exeter V40 stems reporting a PFF rate resulting in revision of 0.36%, with a mean follow-up of 12.4 years [21] and Petheram et al. reported a PFF rate resulting in revision of 0.78% in a series of 382 Exeter Universal stems with an average follow-up of 22.4 years [14]. Westerman et al. reviewed the first 540 Exeter V40 stems performed at their centre in the two years following its introduction, reporting a PFF rate of 1.85% at a mean follow-up of 12.4 years [20]. The CPT stem is similar in design to the Exeter, but has a wider shoulder. One study of 191 CPT stems with a mean follow-up of 15.9 years reported only one PFF (0.52%) leading to revision, which occurred at five years [15, 30], however, another reported a PFF rate of 3.34% in a series of 1403 hips, with a mean follow-up of only 4 years [7]. In an observational cohort study, Mohammed et al. compared PFF rates at a single centre during the transition from the standard use of a CPT stem to the Lubinus SP2 composite beam. At two years, the CPT group had sustained 18 PFFs (3.31%) and the Lubinus group only two (0.37%) [6]. The latter two studies had a limited duration of follow-up, and in the current series, the fractures occurred at an average of 7.9 years, with only one during the first four years, consistent with the 7.6 years reported in a large study in 2022 [31]. In a registry-based study, Palan et al. reported incidences of PFF, based only on revision, of 0.12% for the Exeter V40 stem, 0.14% for the C-stem and 0.46% for the CPT, which, as expected, were markedly lower than in the cohort studies [19]. This study also postulated that the CPT’s higher PFF rate may be down to having a larger, broader shoulder than both the Exeter and the C-Stem [19]. In a biomechanical study, Erdhart et al. compared the periprosthetic fracture patterns around the CPT and the C-Stem. Ten double-tapered CPT stems and 10 triple-tapered C-stems were cemented into synthetic femurs and subjected to axial compression. There were seven Vancouver B fractures in the CPT constructs and three Vancouver C. In all ten C-stem constructs, the fractures occurred at the tip of the implant with the cement mantle remaining intact, suggesting there is less harmful strain produced to the cement mantle in torsion than in other designs [32]. There was, however, no typical fracture pattern in the PFF cohort in the current study (Table 3). The only previously published long-term series with the original C-stem included 621 arthroplasties performed using trochanteric osteotomy. At a mean follow-up of 13 years, there were no instances of PFF, but fractures of the femoral prosthesis occurred in two cases [16]. There were no cases of femoral prosthesis fracture in the current study. The strength of the current study is that data were collected prospectively, with only three patients (0.66%) being lost to follow-up, two of them after 10 years, allowing an accurate determination of the outcome of almost every THA. One limitation germane to all longitudinal studies is the number of patients who will inevitably die during the follow-up period, which, in the current study, was 244 patients (54%) with 265 THA (53%) at an average follow-up of 15 years (Table 5).Table 5 Summary of studies Author Implant name Manufacturer Average follow-up (yrs) PFFs, hips (n) PFF % Time to PFF (yrs) Average age (yrs) Comments Westerman et al. [20] Exeter V40 Stryker 12.4 10 of 540 1.85 10.9 67.7 Six PPFs were cause for stem revision (1.11%) Mahon et al. [21] Exeter V40 Stryker 12.3 3 of 829 0.36 6.9 67.8 Only details PFF as cause of revision Petheram et al. [14] Exeter Universal Stryker 22.4 3 of 382 0.78 – 66.3 Only details PFF as cause of revision Yates et al. [30], Burston et al. [15] CPT Zimmer 10 then 15 1 of 191 0.52 5 64.9 Both papers report on same cohort at 10 and 15 years, respectively Palan et al. [19] Exeter V40 Stryker 3.8 182 of 146,409 0.12 – 72 Registry Data—Only details PFF as caused of revision Registry data based on revision CPT Zimmer " 111 of 24,300 0.46 – 73 " Charnley DePuy " 15 of 20,182 0.07 – 73 " C-Stem DePuy " 21 of 15,113 0.14 – 71 " Broden et al. [7] CPT Zimmer 4.0 47 of 1403 3.35 7 months 82 Elderly cohort—mean age 82 years Mohammed et al. [6] CPT Zimmer 2.0 18 of 543 3.31 2 months 82 Follow-up only to two years Lubinus SP2 Waldermar Link 2.0 2 of 534 0.37 " " " Conclusion The incidence of PFF in this prospective cohort of 500 THAs using the original cemented polished triple-tapered C-stem femoral implant was 1.4% after 15 years of follow-up. This is similar to the PFF rates reported for the polished double-tapered Exeter V40 but lower than for the CPT. With an increased incidence of PFF predicted over the next three decades, a more detailed knowledge of the risk profile for specific implant designs is required. This could be achieved either by expanding the minimum data set for National Joint Registries to include all PFFs managed by any means or alternatively, by widening the scope of National Hip Fracture Databases to include PFFs in a similar way that femoral shaft and distal femoral fractures have recently been included in the Best Practice Tariff in the United Kingdom. Large long-term single-, or multi-centre, studies of individual prostheses would remain of great value, as they include more detailed demographic and radiological analysis, in order to augment the currently limited body of knowledge on this subject. Acknowledgements The senior author (DHS) would like to thank previous consultant colleagues, research fellows and registrars at North Manchester General Hospital, where this study was performed, for their contributions, as well as Mrs. M. Austin and Miss D. Hudson who maintained the high rates of follow-up throughout the study period. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data availability The authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials. Declarations Conflict of interest The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article. Ethical approval Ethical approval for this study was not required. Informed consent Not applicable. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Carli AV Negus JJ Haddad FS Periprosthetic femoral fractures and trying to avoid them Bone Joint J 2017 99-B 50 59 10.1302/0301-620X.99B1.BJJ-2016-0220.R1 28042119 2. Clement ND Hall AJ Makaram NS Robinson PG Patton RFL Moran M IMPACT-Restart: the influence of COVID-19 on postoperative mortality and risk factors associated with SARS-CoV-2 infection after orthopaedic and trauma surgery Bone Joint J 2020 102-B 1774 1781 10.1302/0301-620X.102B12.BJJ-2020-1395.R2 33249904 3. Cook RE Jenkins PJ Walmsley PJ Patton JT Robinson CM Risk factors for periprosthetic fractures of the hip: a survivorship analysis Clin Orthop Relat Res 2008 466 1652 1656 10.1007/s11999-008-0289-1 18470576 4. Patel A Pavlou G Mújica-Mota RE Toms AD The epidemiology of revision total knee and hip arthroplasty in England and Wales Bone Joint J 2015 97-B 1076 1081 10.1302/0301-620X.97B8.35170 26224824 5. Pivec R Issa K Kapadia BV Cherian JJ Maheshwari AV Bonutti PM Incidence and future projections of periprosthetic femoral fracture following primary total hip arthroplasty: an analysis of international registry data J Long Term Eff Med Implants 2015 10.1615/JLongTermEffMedImplants.2015012625 26852635 6. Mohammed J Mukka S Hedbeck CJ Chammout G Gordon M Sköldenberg O Reduced periprosthetic fracture rate when changing from a tapered polished stem to an anatomical stem for cemented hip arthroplasty: an observational prospective cohort study with a follow-up of 2 years Acta Orthop 2019 90 427 432 10.1080/17453674.2019.1624339 31154885 7. Brodén C Mukka S Muren O Eisler T Boden H Stark A High risk of early periprosthetic fractures after primary hip arthroplasty in elderly patients using a cemented, tapered, polished stem: An observational, prospective cohort study on 1,403 hips with 47 fractures after mean follow-up time of 4 years Acta Orthop 2015 86 169 174 10.3109/17453674.2014.971388 25280133 8. Baryeh K Sochart DH Post-operative peri-prosthetic fracture rates following the use of cemented polished taper-slip stems for primary total hip arthroplasty: a systematic review Arch Orthop Trauma Surg 2022 10.1007/s00402-021-04302-3 35048171 9. Lindahl H Malchau H Herberts P Garellick G Periprosthetic femoral fractures: classification and demographics of 1049 periprosthetic femoral fractures from the Swedish National Hip Arthroplasty Register J Arthroplasty 2005 20 857 865 10.1016/j.arth.2005.02.001 16230235 10. Maggs JL Swanton E Whitehouse SL Howell JR Timperley AJ Hubble MJW B2 or not B2? That is the question: a review of periprosthetic fractures around cemented taper-slip femoral components Bone Joint J 2020 103-B 71 78 10.1302/0301-620X.103B1.BJJ-2020-0163.R1 11. Chatziagorou G Lindahl H Kärrholm J Surgical treatment of Vancouver type B periprosthetic femoral fractures Bone Joint J 2019 101-B 1447 1458 10.1302/0301-620X.101B11.BJJ-2019-0480.R2 31674248 12. Abdel MP Watts CD Houdek MT Lewallen DG Berry DJ Epidemiology of periprosthetic fracture of the femur in 32 644 primary total hip arthroplasties: a 40-year experience Bone Jt J 2016 98B 461 467 10.1302/0301-620X.98B4.37201 13. Springer BD Etkin CD Shores PB Gioe TJ Lewallen DG Bozic KJ Perioperative periprosthetic femur fractures are strongly correlated with fixation method: an analysis from the American Joint Replacement Registry J Arthroplasty 2019 34 S352 S354 10.1016/j.arth.2019.02.004 30852066 14. Petheram TG Whitehouse SL Kazi HA Hubble MJW Timperley AJ Wilson MJ The Exeter Universal cemented femoral stem at 20 to 25 years Bone Joint J 2016 98-B 1441 1449 10.1302/0301-620X.98B11.37668 27803218 15. Burston BJ Barnett AJ Amirfeyz R Yates PJ Bannister GC Clinical and radiological results of the collarless polished tapered stem at 15 years follow-up J Bone Joint Surg Br 2012 94 889 894 10.1302/0301-620X.94B7.28799 22733941 16. Purbach B Kay PR Siney PD Fleming PA Wroblewski BM The C-stem in clinical practice: fifteen-year follow-up of a triple tapered polished cemented stem J Arthroplasty 2013 28 1367 1371 10.1016/j.arth.2012.10.030 23528555 17. National Joint Registry for England Wales Northern Ireland and the Isle of Man. NJR 18th Annual Report 2021. Natl Jt Regist 2021 18. Kazi HA Whitehouse SL Howell JR Timperley AJ Not all cemented hips are the same: a register-based (NJR) comparison of taper-slip and composite beam femoral stems Acta Orthop 2019 90 214 219 10.1080/17453674.2019.1582680 30838914 19. Palan J Smith MC Gregg P Mellon S Kulkarni A Tucker K The influence of cemented femoral stem choice on the incidence of revision for periprosthetic fracture after primary total hip arthroplasty: an analysis of national joint registry data Bone Jt J 2016 98-B 1347 1354 10.1302/0301-620X.98B10.36534 20. Westerman RW Whitehouse SL Hubble MJW Timperley AJ Howell JR Wilson MJ The Exeter V40 cemented femoral component at a minimum 10-year follow-up Bone Jt J 2018 100B 1002 1009 10.1302/0301-620X.100B8.BJJ-2017-1535.R1 21. Mahon J McCarthy CJ Sheridan GA Cashman JP O’Byrne JM Kenny P Outcomes of the Exeter V40 cemented femoral stem at a minimum of ten years in a non-designer centre Bone Jt Open 2020 1 743 748 10.1302/2633-1462.112.BJO-2020-0163.R1 33367281 22. Barrack RL Mulroy RD Harris WH Improved cementing techniques and femoral component loosening in young patients with hip arthroplasty. A 12-year radiographic review J Bone Joint Surg Br 1992 74-B 385 389 10.1302/0301-620X.74B3.1587883 23. Fowler JL Gie GA Lee AJ Ling RS Experience with the Exeter total hip replacement since 1970 Orthop Clin North Am 1988 19 477 489 3269211 24. Duncan CP Masri BA Fractures of the femur after hip replacement Instr Course Lect 1995 44 293 304 7797866 25. Masri BA Meek RMD Duncan CP Periprosthetic fractures evaluation and treatment Clin Orthop Relat Res 2004 10.1097/00003086-200403000-00012 15057082 26. Duncan CP Haddad FS The Unified Classification System (UCS): improving our understanding of periprosthetic fractures Bone Jt J 2014 10.1302/0301-620X.96B6.34040 27. The COMPOSE Study Team Management and outcomes of femoral periprosthetic fractures at the hip Bone Joint J 2022 104-B 997 1008 10.1302/0301-620X.104B8.BJJ-2021-1682.R1 35909379 28. Afzal I Radha S Smoljanović T Stafford GH Twyman R Field RE Validation of revision data for total hip and knee replacements undertaken at a high volume orthopaedic centre against data held on the National Joint Registry J Orthop Surg Res 2019 14 318 10.1186/s13018-019-1304-9 31601231 29. Konow T Baetz J Melsheimer O Grimberg A Morlock M Factors influencing periprosthetic femoral fracture risk Bone Joint J 2021 103-B 650 658 10.1302/0301-620X.103B4.BJJ-2020-1046.R2 33789487 30. Yates PJ Burston BJ Whitley E Bannister GC Collarless polished tapered stem: Clinical and radiological results at a minimum of ten years’ follow-up J Bone Jt Surg Ser B 2008 90 16 22 10.1302/0301-620X.90B1.19546 31. The COMPOSE Study Team Epidemiology and characteristics of femoral periprosthetic fractures Bone Joint J 2022 104-B 987 996 10.1302/0301-620X.104B8.BJJ-2021-1681.R1 35909377 32. Erhardt JB Khoo PP Stoffel KK Yates PJ Periprosthetic fractures around polished collarless cemented stems: the effect of stem design on fracture pattern HIP Int 2013 23 459 464 10.5301/hipint.5000052 23813164	36447057	PMC9708125	NO-CC CODE	2022-12-01 23:20:30	no	Arch Orthop Trauma Surg. 2022 Nov 29;:1-8	utf-8	Arch Orthop Trauma Surg	2,022	10.1007/s00402-022-04712-x	oa_other
==== Front Control Manag Rev Controlling & Management Review 2195-8262 2195-8270 Springer Fachmedien Wiesbaden Wiesbaden 1004 10.1007/s12176-022-1004-x Schwerpunkt Gemeinnützige Krankenhäuser effizienter steuern Weiß Andreas Andreas Weiß ist Prokurist für Controlling, Finanzen und Qualitätsmanagement im Klinikum Leverkusen, Geschäftsführer von MVZ Leverkusen und MediLev sowie Vorstandsmitglied des Deutschen Vereins für Krankenhaus-Controlling e.V. E-Mail: [email protected] grid.419829.f 0000 0004 0559 5293 Klinikum Leverkusen, Leverkusen, Deutschland 30 11 2022 2022 66 8 3846 © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. issue-copyright-statement© Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2022 ==== Body pmcEine seit Jahren sinkende Auslastung und dazu die Corona-Pandemie erhöhen auch den Druck auf gemeinnützige Häuser, Controllinginstrumente auszubauen. Die Standards und Muster des Deutschen Vereins für Krankenhaus-Controlling e. V. (DVKC) sehen eine Steuerung bis auf Ebene der einzelnen Fachabteilungen vor. Krankenhäuser in Deutschland, die sich in öffentlicher, freigemeinnütziger oder privater Trägerschaft befinden und bis auf die privaten Häuser keine Gewinnerzielungsabsicht verfolgen, stehen aktuell vor großen wirtschaftlichen Herausforderungen. Seit 2016 sinken bereits die Fallzahlen, und mit Beginn der COVID-19-Pandemie in 2020 sind die Fallzahlen gegenüber 2019 noch einmal massiv um rund zehn Prozent eingebrochen. Die durch die Bundesregierung geleisteten Ausgleichszahlungen wurden im Laufe des ersten Halbjahres 2022 eingestellt. Entscheidungen zu einer Fortführung der Ausgleichszahlungen sind angesichts der aktuellen Haushaltslage ungewiss. Bereits für 2021 haben 60 Prozent der Krankenhäuser in Deutschland ein negatives Jahresergebnis erwartet. Und jedes zweite Haus rechnet laut dem Deutschen Krankenhausinstitut mit einer weiteren Verschlechterung seiner Situation. Gleichzeitig mehren sich die Stimmen von Experten, dass die Fallzahlen wegen einer allgemeinen Krankenhausskepsis und der politisch geförderten Ambulantisierung auf einem niedrigeren Niveau verharren könnten. Vor diesem Hintergrund ist es zwingend erforderlich, dass sich auch die gemeinnützigen Krankenhäuser den Fragen einer besseren Steuerung ihrer Kosten und einer Bereinigung ihres Leistungsportfolios stellen. Zum Verständnis der notwendigen Steuerungsinstrumente sind grundlegende Kenntnisse über die Krankenhausfinanzierung unabdingbar. Grundsätze der Krankenhausfinanzierung Die Finanzierung der Krankenhäuser in Deutschland ist in der Sozialgesetzgebung geregelt und unterliegt der Maxime der Beitragssatzstabilität. Damit soll sichergestellt werden, dass die Ausgaben der Krankenkassen nicht schneller steigen als deren Einnahmen aus den Beiträgen der Versicherten. Im dual angelegten Finanzierungssystem werden die Betriebskosten der Krankenhäuser im Wesentlichen durch die Krankenkassen finanziert und die Investitionen durch die Bundesländer. Durch die ab 2003 schrittweise erfolgte Einführung einer pauschalierten Finanzierung der Krankenhausaufenthalte wurde das bis dahin vorherrschende Selbstkostendeckungsprinzip aufgegeben. Das System der "Diagnosis Related Groups" (DRGs, deutsch: Diagnosebezogene Fallgruppen) wurde aus Australien übernommen und auf deutsche Verhältnisse angepasst. Abrechnung nach Fallpauschalen Zielsetzungen des neuen Systems sollten sein, die im internationalen Vergleich ausgedehnten Verweildauern der Krankenhauspatienten zu reduzieren, ein Wettbewerbsdenken der Krankenhäuser zu entwickeln sowie Transparenz und Qualität der erbrachten Leistungen zu steigern. Bis dahin hatten die Krankenhäuser den überwiegenden Teil ihres Umsatzes durch mit Patienten belegte Betten erzielt, unabhängig von Schweregrad und Komplexität der Behandlung. Nun sollte das Geld der Leistung folgen (vergleiche Eisenmenger 2021, S. 9 f.). Die nach dem neuen System durch die Krankenhäuser abzurechnenden Fallpauschalen werden jährlich vom Institut für das Entgeltsystem im Krankenhaus (InEK) auf der Basis der Kostendaten von rund 300 Krankenhäusern kalkuliert. Im aktuellen Fallpauschalenkatalog sind rund 1.300 DRGs enthalten. Jeder DRG ist ein Relativgewicht zugeordnet, das die Kostenrelation zu den anderen DRGs angibt. Das Produkt aus Relativgewicht und dem auf Landesebene festgelegten Landesbasisfallwert ergibt im Wesentlichen den Betrag, den das Krankenhaus für eine Behandlung mit der Krankenkasse abrechnen darf. Beispielsweise können Krankenhäuser 2022 für eine Blinddarmentfernung nach der gängigen DRG G07B in Nordrhein-Westfalen 1,64 * 3.825,28 Euro = 6.273,46 Euro in Rechnung stellen. Die Rahmenbedingungen und mit ihnen die wirtschaftliche Lage der Krankenhäuser dürften sich weiter verschlechtern. Die Abrechnung der Fallpauschalen setzt eine Verhandlung von Leistungsbudgets zwischen den Krankenkassen und dem einzelnen Krankenhaus voraus. Gesetzlich vorgeschrieben ist, dass jedes Krankenhaus mit den Krankenkassen, deren Versicherten hauptsächlich im jeweiligen Krankenhaus behandelt werden, jährlich die Menge und Art der Behandlungen vereinbart. Über das Budget des Vorjahres hinausgehend neu verhandelte Leistungen werden in der Regel für drei Jahre mit einem sogenannten Fixkostendegressionsabschlag von 35 Prozent belegt, das heißt, der eigentlich im Fallpauschalenkatalog vorgesehene Betrag wird entsprechend reduziert. Damit soll die Mengenentwicklung begrenzt werden. "Die Entwicklung und Nutzung einer Bereichsergebnisrechnung ist in Krankenhäusern zwar eine Herausforderung, allerdings existenziell wichtig." Diese Regulatorik zwingt Krankenhäuser dazu, ihr Verhalten an die vorgegebenen Erlöse anzupassen. Mengenanpassungen sind angesichts sprungfixer Kostengrenzen (vor allem hinsichtlich der Personalkapazität) nur in einem behutsamen Maße möglich, weil die mit einem Fixkostendegressionsanschlag belegten Fälle sicher nur hinsichtlich der Steigerung der variablen Kosten finanziert sind. Finanzierung von Investitionen und Mieten In die Kalkulation der DRGs gehen Aufwendungen für die Finanzierung von Investitionen und Mieten nicht ein. Die gesetzlichen Regelungen sehen vor, dass die Bundesländer die Krankenhauslandschaft planen und die Investitionen der Krankenhäuser sicherstellen. Jedoch reichen die hierfür zur Verfügung gestellten Mittel nicht aus. Infolgedessen besteht ein erheblicher Investitionsstau. Die Deutsche Krankenhausgesellschaft (DKG) stellte dazu im vergangenen Jahr fest: "Dem vom InEK berechneten jährlichem Investitionsbedarf der Krankenhäuser in Höhe von derzeit sieben Milliarden Euro […] stehen tatsächliche Investitionen von drei Milliarden Euro gegenüber. Der bereits aufgelaufene Investitionsstau ist immens." (DKG 2021) Deshalb müssen Krankenhäuser die notwendigen Investitionen zu einem erheblichen Teil aus Mitteln finanzieren, die eigentlich für die Betriebskosten vorgesehen sind. Insofern liegt es auf der Hand, dass auch gemeinnützige Krankenhäuser gezwungen sind, Überschüsse zu erwirtschaften und hierfür die Unterstützung durch ein effizientes und gutes Controlling benötigen. Im Verhältnis zu den Häusern der privaten Betreiber fallen Ergebniserwartungen der gemeinnützigen Häuser allerdings geringer aus, weil die Interessen ihrer Kapitalgeber nicht an Überschüssen orientiert sind. Geeignetes Berichtswesen aufbauen Eine gezielte Planung und Steuerung der Ergebnisse sind also auch für die gemeinnützigen Krankenhäuser notwendig. Dies verlangt entsprechende Transparenz, die über ein geeignetes Berichtswesen zu realisieren ist. Basisinstrumentarium ist der Akkord aus Kostenarten-, Kostenstellen- und Kostenträgerrechnung. Die erforderlichen Ergebnisrechnungen beziehen sich auf die Einrichtungs- beziehungsweise Konzernebene, die Bereichsebene und die Ebene der Leistungsgruppen. Es besteht ein großer Druck, Kosten anzupassen und das Leistungsportfolio zu straffen. Welche Ergebnisse hat ein Haus erzielt? Krankenhäuser sind nach der Krankenhaus-Buchführungsverordnung (KHBV) unabhängig von der Rechtsform verpflichtet, einen Jahresabschluss nach kaufmännischen Grundsätzen vorzulegen, der aus Bilanz, Gewinn-und-Verlust-Rechnung (GuV) und Anhang einschließlich Anlagennachweis besteht. Die KHBV gibt eine Gliederung für die GuV vor. In der Regel berichten die Krankenhäuser in monatlichen oder vierteljährlichen Abständen über die aktuelle Situation. Die Berichte auf der Konzern- und/oder Einrichtungsebene sind an die Aufsichtsgremien, die Konzernleitung sowie die Leitungsebene der Krankenhausunternehmen gerichtet. Weitere Adressaten sind die kreditgebenden Banken. Die Berichte sollen, ergänzt um aktuelle Steuerungsmaßnahmen, nach außen für Vertrauen in die Unternehmensführung sorgen und die Investitionsfähigkeit nachweisen. Nach innen soll Transparenz über die aktuelle Lage erzeugt, die Notwendigkeit noch durchzusetzender Maßnahmen begründet sowie der Erfolg bereits umgesetzter Maßnahmen aufgezeigt werden. Das als Anlage 2 in der KHBV enthaltene Gliederungsschema der GuV ist sehr feingranular aufgebaut. Nicht berücksichtigt sind Investitions- und Finanzergebnis sowie die Ausweisung von Sondereffekten durch ein neutrales Ergebnis. Insofern ist eine nach diesem Muster erstellte GuV nur begrenzt brauchbar, um die Finanzierungsfähigkeit eines Krankenhauses und die Wirksamkeit eingeleiteter Maßnahmen zur Verbesserung der Wirtschaftlichkeit zu beurteilen. Eine durch das Bundesministerium für Gesundheit geförderte Arbeitsgruppe des Deutschen Vereins für Krankenhaus-Controlling e. V. (DVKC) hat deshalb "ein Schema entwickelt, das die Finanzierungsfähigkeit nachweist und den Unternehmenserfolg aus der gewöhnlichen operativen Geschäftstätigkeit klar herausarbeitet" (vergleiche Maier/Weiß/Heitmann 2016, S. 843). Das Schema sieht den EBITDAR (earnings before interest, taxes, depreciation, amortization or rent costs) als nachhaltiges Betriebsergebnis und zentrale Ergebnisgröße vor (vergleiche Tabelle 1). Damit wird die Betrachtung des bislang häufig verwendeten bereinigten EBITDAs weiterentwickelt, um die auch im Gesundheitswesen zunehmende Anwendung von Finanzierungsinstrumenten wie Leasing zur Finanzierung von Anlagevermögen zu berücksichtigen. Fördermittel sind an dieser Stelle im Schema noch nicht eingeflossen, wodurch auch ein Vergleich von Einrichtungen hinsichtlich ihrer Investitionsfähigkeit möglich wird. Mit dem Schema sollen die im Krankenhausbereich häufig voneinander abweichenden Rechenschemata der Ertragslage standardisiert werden, um eine Vergleichbarkeit der Krankenhäuser untereinander zumindest hinsichtlich der umsatzbezogenen Margen zu ermöglichen. Auf der Konzern- und Einrichtungsebene ist so der Erfolg des Unternehmens erklärbar. Betriebliche Erträge − betriebliche Aufwendungen = nachhaltiger EBITDAR + Fördermittel = EBITDAR gefördert − Mieten − Leasing = EBITDA gefördert + Finanzergebnis + neutrales Ergebnis = EBT gefördert − Steuern = EAT gefördert (entspricht Jahresergebnis) Quelle: DVKC 2021 Welche Abteilung leistet welchen Beitrag? Krankenhäuser verfügen abhängig von ihrer Größe und Leistungsfähigkeit über eine ganze Reihe von Abteilungen mit eigenen Leistungen, Prozessen und Märkten. Um die einzelnen Beiträge zum Unternehmensergebnis zu beurteilen und zu steuern, sollten auch Krankhäuser kostenrechnerische Instrumente einsetzen (vergleiche Weber 2022, S. 222). In der jährlich erscheinenden Studie zum Krankenhauscontrolling in Deutschland wurde allerdings wiederholt festgestellt, dass nur rund die Hälfte der Krankenhäuser regelmäßig das Instrument einer Bereichsergebnisrechnung nutzt (vergleiche Crasselt/Wacker 2022). Die Entwicklung und Nutzung einer Bereichsergebnisrechnung ist in Krankenhäusern schon wegen der in der Regel relativ geringen Ausstattung mit Controllingpersonal zwar eine Herausforderung, allerdings aus den beschriebenen Gründen existenziell wichtig. Der DVKC hat deshalb entschieden, einen Standard zur mehrstufigen Bereichsergebnisrechnung zu entwickeln, um Krankenhäuser bei dem Thema zu unterstützen (vergleiche Tabelle 2). Damit soll der individuelle Entwicklungsaufwand für die Controller in den Krankenhäusern gering gehalten und gleichzeitig ein fundiertes und sowohl wissenschaftlich als auch in der Praxis evaluiertes Instrument zur Verfügung gestellt werden. Mittlerweile führt eine Reihe von Krankenhäusern das Modell unter Begleitung des DVKCs ein. + Erlöse aus externer und interner Leistungserbringung − Personal- und Sachkosten = Bereichsergebnis, Stufe 1 (BES1) − Kosten diagnostischer und/oder therapeutischer Leistungen = Bereichsergebnis, Stufe 2 (BES2) − Kosten sonstiger patienten- und/oder fachabteilungsbezogener Leistungen = Bereichsergebnis, Stufe 3 (BES3) − Kosten der Infrastruktur = Bereichsergebnis, Stufe 4 (BES4) + Fördermittel, Zuschüsse, Spenden = Bereichsergebnis, Stufe 5 (BES5) − Kostenumlage für sonstige Verwaltungsbereiche = Bereichsergebnis, Stufe 6 (BES6) +/− neutrales Ergebnis, Finanzergebnis, Ertragssteuern = Bereichsergebnis, Stufe 7 (BES7) Quelle: DVKC 2021 Gemeinnützige Krankenhäuser müssen ihr etriebswirtschaftliches Instrumentarium dringend erweitern. Der Standard sieht eine nach unten abnehmende Beeinflussbarkeit in den einzelnen Ergebnisstufen vor. Die Einrichtung kann frei definieren, auf welcher Ebene der einzelne Bereich zu steuern ist. In CS 200 werden sachlogisch zusammenhängende Sachverhalte unabhängig von der buchhalterischen Logik zusammengeführt. So werden beispielsweise extern bezogene Laborleistungen mit den im eigenen Haus erstellten gemeinsam dargestellt. Zu der Methodik der Erlösverteilung bei abteilungsübergreifend behandelten Patienten gibt der Standard ebenfalls Vorgaben. Weiterhin sind marktnahe Verrechnungspreise vorgesehen, um Gewinnverschiebungen zwischen den Bereichen möglichst auszuschließen. Wegen der Standardisierung des Berechnungsverfahrens besteht die Möglichkeit, Quervergleiche mit gleichartigen Abteilungen anderer Einrichtungen durchzuführen. Die Bereichsleiter erhalten so ein zuverlässiges Instrument, das auch hohen Ansprüchen an eine methodische Qualität genügt. Deshalb ist zu erwarten, dass mit der Nutzung dieses Standards mehr über mögliche Ansatzpunkte zur Verbesserung der Ergebnisse als wie bisher über deren Berechnungsmethode diskutiert wird (nähere Informationen zur Funktionsweise des Standards und der Pilotphase bei Maier/Weiß 2021; Crasselt/Wacker 2022). Wie laufen die einzelnen Leistungen einer Fachabteilung? Der einzelne Bereich, zum Beispiel die Klinik für Kardiologie eines Krankenhauses, erbringt eine ganze Reihe unterschiedlicher Leistungen. Das können im Falle der Kardiologie beispielsweise Linksherzkatheter-Untersuchungen oder Ablationen sein. Um die Aussage zur Wirtschaftlichkeit der Klinik noch differenzierter zu betrachten, hat es sich bewährt, Portfolioanalysen durchzuführen. Dazu ist es notwendig, die Leistungen nach medizinischen Kriterien zu clustern, die eine Steuerung durch die verantwortlichen Mediziner unterstützen. Beispielsweise ist das von der DRG Research Group entwickelte System der Klinischen Leistungsgruppen (KLG) für diese Zwecke nutzbar. Alle Behandlungsfälle beziehungsweise deren Ergebnisse aus der Fallkostenrechnung werden genau einer KLG zugeordnet. Die jeweiligen Fallkosten werden mit aus den Abrechnungserlösen entwickelten Zielkosten verglichen, um so die Wirtschaftlichkeit je Fall und je KLG zu ermitteln. Aus den Ergebnissen lassen sich Handlungsoptionen zur Kostenanpassung oder Mengenentwicklung ableiten. Eine Mengensteigerung, um beispielsweise Skaleneffekte zu nutzen, wird sich nur realisieren lassen, wenn das dafür erforderliche Marktpotenzial besteht. Insofern ist es sinnvoll, neben der Wirtschaftlichkeit auch die Marktsituation zu analysieren. Der Gesundheitsmarkt ist weit überwiegend lokal oder regional geprägt. Im ersten Schritt ist das eigene Einzugsgebiet zu definieren. Das lässt sich mit Bordmitteln aus den Patientendaten bewerkstelligen, weil je Patient der Wohnort nebst Postleitzahl im Krankenhausinformationssystem abgespeichert wird. Die Krankenhaushäufigkeit lässt sich aus den Landes- und Bundesstatistiken in Bezug auf Krankheiten und Prozeduren ermitteln und mit den Bevölkerungsdaten der Postleitzahlenbezirke korrelieren, um eine erwartete Patientenanzahl zu definieren. Das Marktpotenzial ergibt sich aus der Subtraktion der selbst behandelten Patienten von der erwarteten Patientenzahl. Zur Beurteilung, ob ein Markt lohnend erscheint, sind außerdem die eigene und die Stärke der jeweiligen Mitbewerber einzuschätzen. Daneben sind noch weitere Faktoren einzubeziehen, die in der Zukunft auf die Marktattraktivität einwirken, wie zum Beispiel ein etwaiges Potenzial zur Ambulantisierung der jeweiligen Leistung. Die Ergebnisse lassen sich in einem Portfolio der Leistungsgruppen je Klinik darstellen (vergleiche Abbildung 1). Ohne unterstützendes Change Management ist die Gefahr des Scheiterns groß. In der Vierfeldertafel sind Primärhandlungen als Standard hinterlegt:Marktpotenzial hoch, Wirtschaftlichkeit gering: Wirtschaftlichkeit optimieren, dann Marktabschöpfung steigern, sonst Reduktion prüfen. Marktpotenzial hoch, Wirtschaftlichkeit hoch: Marktabschöpfung ausbauen. Marktpotenzial gering, Wirtschaftlichkeit hoch: Leistungsmenge halten. Marktpotenzial gering, Wirtschaftlichkeit gering: Wirtschaftlichkeit optimieren, sonst Reduktion prüfen. Die Ergebnisse der Portfolioanalyse beantworten wichtige Fragen zur Entwicklung der strategischen Ausrichtung des Krankenhauses: Was sind meine Gewinnbringer? Wo erziele ich Verluste? Wo liegen unsere zukünftigen Chancen? Bei diesen Überlegungen sind Aspekte des Versorgungsauftrages und eine Reihe weiterer Rahmenbedingungen zu beachten. Ausblick und Schlussbetrachtung Ein im April 2022 veröffentlichtes Gutachten beschreibt ein ganz erhebliches Ambulantisierungspotenzial unter den derzeit stationär behandelten Patienten und empfiehlt eine Verdopplung der bisher im Katalog für ambulantes Operieren vorgesehenen Leistungen (IGES 2022). Das erhöht für Krankenhäuser weiter den Druck, die Kostensituation stringenter zu steuern und gleichzeitig ihr Leistungsportfolio zu bereinigen. Hierfür braucht es entsprechende Change-Management-Projekte, die durch die oberste Leitung entschieden und permanent unterstützt werden. Denn auch wenn der Handlungsdruck groß ist und die prekäre Situation spätestens seit der Corona-Pandemie nicht an den Mitarbeitern vorbeigegangen sein dürfte, stellt die Einführung der beschriebenen Instrumente oder ein Relaunch derselben einen tiefen Eingriff in die Unternehmenskultur dar. Im Zweifel kann schon das bloße Erzeugen von Transparenz als bedrohliche Veränderung wirken. Neben der Einbindung der obersten Leitung müssen Meinungsführer gewonnen werden. Gegebenenfalls ist auch externe Unterstützung erforderlich, um die Arbeitslast des Controllings zu reduzieren und die vorhandene Expertise zu verbreitern. Die Feststellung eines Gefühls der Change-befördernden Dringlichkeit (Sense of Urgency) dürfte im Krankenhaus angesichts der aktuellen Entwicklungen der Rahmenbedingungen hingegen sehr leichtfallen. Die geplante strukturelle Neuordnung der Krankenhauslandschaft könnte die Krankenhäuser bei ihren Steuerungsvorhaben unterstützen. Ansätze einer detaillierteren Krankenhausplanung bestehen in Nordrhein-Westfalen. Auch der Koalitionsvertrag 2021 auf der Bundesebene sieht eine diesbezügliche Veränderung der Politik vor. Vermutlich wird der Weg der Regulatorik eher langsam sein. Schnell wirken allerdings die jetzt schon etablierten Strukturvoraussetzungen für die abrechnungsrelevanten Prozedurenschlüssel und Richtlinien des gemeinsamen Bundesausschusses, die vom Medizinischen Dienst streng kontrolliert werden. Die Handlungsnotwendigkeit für die Krankenhäuser ist also äußerst dringlich. Das dort vorhandene Controllinginstrumentarium ist vielfach als "ausbaufähig" zu bezeichnen. Den Krankenhäusern kann nur geraten werden, ihre Instrumente im oben beschriebenen Sinne möglichst schnell auszubauen. Literatur Crasselt, N./Wacker, F. (2022): Überwindbare Hürden, in: KU Gesundheitsmanagement, 05/2022, S. 17-19. DKG (2021): Positionen der Deutschen Krankenhausgesellschaft für die 20. Legislaturperiode des Deutschen Bundestags, https://go.sn.pub/KGeocB (letzter Abruf: 21.07.2022). DVKC (2021): Standard CS 100, www.stacog.de/standard-100 (letzter Abruf: 21.07.2022). DVKC (2021): Standard CS 200, www.stacog.de/standard-cs-200 (letzter Abruf: 21.07.2022). Eisenmenger, N. (2021): Das aG-DRG-System, 1. Auflage, Backnang. Hentze, J./Kehres, E./Maier, B. (2022): Kosten- und Leistungsrechnung in Krankenhäusern, 6. Auflage, Stuttgart. IGES (2022): Ambulantisierung: Gutachten nennt 2.500 neue AOP-Leistungen, https://go.sn.pub/eO8vse (letzter Abruf: 21.07.2022). Maier, B./Weiß, A. (2021): Start für den CS 200, in: f & w 01/2021, S. 66-69. Maier, B./Weiß, A./Heitmann, C. (2016): Ein Quantum Transparenz, in: f&w 09/2016, S. 842-845. Weiß, A. (2012): Weg mit Rasenmäher und Gießkanne, in: KU Gesundheitsmanagement, 10/2012, S. 48-51. Zusammenfassung Das Finanzierungssystem der Krankenhäuser macht es erforderlich, dass auch gemeinnützige Krankenhäuser Überschüsse erwirtschaften und hierbei vom Controlling unterstützt werden. Effektive Steuerungsinstrumente sind vor allem die Methode einer mehrstufigen Bereichsergebnisrechnung sowie die Markt-Wirtschaftlichkeits-Portfolioanalyse. Wegen des tiefen Eingriffs in die Kultur der Häuser braucht es für die erfolgreiche Implementierung der Steuerungsinstrumente wirkungsvolle Change-Management-Projekte. Springer Professional Gesundheitswesen Kümpel, T./Schlenkrich, K./Heupel, T. (Hrsg.): Controlling & Innovation 2022 - Gesundheitswesen, Wiesbaden. https://go.sn.pub/lwADiT Lachmann, M./Rüsch, S./Wenger, F. (2022): Controlling im Gesundheitswesen, in: Becker, W./Ulrich, P. (Hrsg.): Handbuch Controlling, 2. Auflage, S. 489-511. https://go.sn.pub/wTSk8f	0	PMC9708127	NO-CC CODE	2022-12-01 23:20:28	no	Control Manag Rev. 2022 Nov 30; 66(8):38-46	utf-8	null	null	null	oa_other
==== Front Spine Deform Spine Deform Spine Deformity 2212-134X 2212-1358 Springer International Publishing Cham 36447049 622 10.1007/s43390-022-00622-5 Case Series The evolving stall rate of magnetically controlled growing rods beyond 2 years follow-up http://orcid.org/0000-0002-3553-2889 Shaw K. Aaron 1 Bassett Paul 2 Ramo Brandon A. 1 McClung Anna 1 Thornberg David 1 Jamnik Adam 1 Jo Chan-Hee 1 Johnston Charlie E. 1 McIntosh Amy L. [email protected] 1 1 Department of Pediatric Orthopaedic Surgery, Scottish Rite for Children Hospital, Dallas, TX USA 2 grid.414376.3 0000 0004 0441 5326 Department of Pediatric Orthopaedic Surgery, Eastern Maine Medical Center, Bangor, ME USA 29 11 2022 17 3 6 2022 17 11 2022 © The Author(s), under exclusive licence to Scoliosis Research Society 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Purpose Magnetically controlled growing rods (MCGR) have become the dominant distraction-based implant for the treatment of early onset scoliosis (EOS). Recent studies, however, have demonstrated rising rates of implant failure beyond short-term follow-up. We sought to evaluate a single-center experience with MCGR for the treatment of EOS to define the rate of MCGR failure to lengthen, termed implant stall, over time. Methods A single-center, retrospective review was conducted identifying children with EOS undergoing primary MCGR implantation. The primary endpoint was the occurrence of implant stalling, defined as a failure of the MCGR to lengthen on three consecutive attempted lengthening sessions with minimum of 2 years follow-up. Clinical and radiographic variables were collected and compared between lengthening and stalled MCGRs. A Kaplan–Meier survival analysis was conducted to assess implant stalling over time. Results A total of 48 children met inclusion criteria (mean age 6.3 ± 1.8 years, 64.6% female). After a mean 56.9 months (range of 27 to 90 months) follow-up, 25 (48%) of children experienced implant stalling at a mean of 26.0 ± 14.1 months post-implantation. Kaplan–Meier survival analysis demonstrated that only 50% of MCGR continue to successfully lengthen at 2 years post-implantation, decreasing to < 20% at 4 years post-implantation. Conclusion Only 50% of MCGR continue to successfully lengthen 2 years post-implantation, dropping dramatically to < 20% at 4 years, adding to the available knowledge regarding the long-term viability and cost-effectiveness of MCGR in the management of EOS. Further research is needed to validate these findings. Keywords Magnetically controlled growing rods Early onset scoliosis Failure Stall ==== Body pmcIntroduction Early onset scoliosis (EOS) represents a particularly challenging condition were clinicians are charged with controlling the spinal and chest wall deformity while allowing the child’s thorax to continue to grow. The magnetically−controlled growing rod (MCGR) system was introduced in the United States in 2014 as a revolutionary treatment approach that obviated the need for surgical lengthening of the growing rod, using instead an external remote controller to drive the lengthening of the spinal rod through the magnetic actuator [1]. Early reports assessing outcomes 2 years following implantation demonstrated high rates of success in managing these complex deformities with substantially lower complication rates compared to traditional growing rods (TGR) [1, 2]. However, more recent series using small sample sizes have assessed outcomes beyond 2 year’s follow-up, showing that upwards of 52% of MCGR’s fail to lengthen [3–5]. As such, we sought to quantify our institutional experience with MCGR since their initial utilization in 2014. Specifically, we sought to focus upon the premature failure of MCGR to lengthen during subsequent lengthening sessions. We defined this phenomenon as implant stalling. Specifically, a stall occurred when the MCGR failed to lengthen after 3 consecutive attempted lengthening sessions separated by 4-month intervals. Our hypothesis was that a substantial percentage of MCGRs would experience implant stalling beyond 2 years post implantation. Methods After obtaining institutional review board approval, a single-center, retrospective review was performed to identify all children with EOS undergoing surgical intervention between January 2014 and December 2020. Inclusion criteria consisted of children undergoing primary MCGR implantation with subsequent lengthening procedures performed at the study institution, and who had a minimum of 2-years clinical follow-up. Children were excluded if they had their implantation or subsequent lengthening procedures performed at an outside hospital, had < 2-year clinical follow-up, or underwent MCGR conversion from traditional growing rods. Clinical and radiographic variables were collected, consisting of patient demographic variables as well as deformity parameters. Clinical variables analyzed included preoperative age at MCGR placement, height, weight, and diagnosis, and occurrence of any prior surgical treatment with subsequent conversion to MCGR. Intraoperative data included implant size, and number of vertebral levels spanned by the construct. Duration of lengthening and total attempted lengthenings until final treatment or stall were also recorded. Actuator length achieved at final follow-up was measured radiographically, as well as the extent of maximal actuator length based upon the maximal expansion of a 70 or 90 mm actuator. The primary endpoint for this study was to characterize the occurrence and time to occurrence of MCGR stalling. We defined implant stalling as a failure of the MCGR to lengthen after 3 consecutive lengthening sessions spaced at 4-month intervals. Failure to lengthen was diagnosed based on radiographic appearance of the actuator. Our institutional protocol is to lengthen MCGR implants at 4-month intervals. Implants that failed to lengthen were maintained on an every 4-month lengthening schedule prior to reattempting to lengthen. Time to stall was recorded as the time from implantation to the first occurrence of failure in MCGR lengthening. Data was collected at three time-intervals: preoperative, first erect post-operative, and most recent follow-up or at the occurrence of stalling. Final follow-up variables included terminal deformity parameters, height gained, and unplanned return to operating room (UPROR). Statistical analysis Statistical analysis was performed using IBM SPSS 27 Software (IBM Corp, Armonk, NY) and SAS 9.4 (SAS Institute, Cary, NC). Descriptive statistics were generated. Clinical and radiographic variables were first examined for normality with the Shapiro–Wilk test. Univariate analysis was performed to identify variables associated with implant stalling using Student’s T tests and Mann–Whitney tests for two-group comparison were used as appropriate. Additionally, a Kaplan–Meier survival curve was created for the study endpoint of implant stalling from time of MCGR implantation. Statistical significance was pre-determined at P < 0.05. Results Over the study period, a total of 67 children were treated with MCGR for EOS by one of four pediatric orthopaedic surgeons. Of these, 19 children were excluded (7 converted to MCGR from TGR/VEPTR, 6 underwent MCGR implantation at an outside hospital, 5 had undergone only one lengthening session, and one child was deceased) leaving 48 children for study inclusion (mean age 6.3 ± 1.8 years, 64.6% female). Primary EOS diagnoses consisted of 16 idiopathic, 14 syndromic, 14 neuromuscular, and 4 congenital patients. Children were followed for a mean 56.9 months ± 17.4 (range of 27 to 90 months). A summary of preoperative patient demographics is provided in Table 1.Table 1 Summary of patient and radiographic variable for children undergoing MCGR implantation for EOS Preoperative variable Valuea Range Patient age (year) 6.3 ± 1.8 2.9–9.4 Weight (kg) 18.6 ± 5.7 11–41.6 Height (cm) 108.1 ± 14.2 78–139.4 Body mass index 15.5 ± 2.1 11.8–23.5 Preoperative coronal cobb (°) 68.2 ± 15.4 30.7–100.0 Preoperative T2–T12 kyphosis (°) 43.2 ± 16.9 8.3–78.5 Preoperative sagittal balance (mm) 20.5 ± 33.1 − 71 to 91 Preoperative coronal balance (mm) 6.9 ± 21.4 − 41 to 60 Independently ambulatory 64.6% (N = 31) Vertebral levels spanned 10.0 ± 1.3 6 to 13 levels aValues are listed as mean ± standard deviation for continuous variables and percentage for dichotomous variables Children began with a mean preoperative coronal Cobb of 68.2 ± 15.4° (range 27.4°–100°) and improved to a mean postoperative coronal Cobb at most recent follow-up of 52.2 ± 17.1° (range 15.9 to 96.0°), resulting in a mean 16.0(± 2.2)° deformity improvement. Implant stall occurred in 23 children (48.0%), at a mean of 26.0 ± 14.1 months (range 4–58.9 months). These implants underwent a mean 7.4 (± 3.8, range 0 to 18) lengthening sessions with these children achieving a mean gain of 17.0.2 (± 8.9)mm in actuator length and a mean total of 35.8% (± 20.3%) of maximal actuator length prior to implant stalling. Curve etiology did not influence the development of MCGR stalling (P = 0.2). Children with implant stalling were not more likely than children without stalling to undergo revision surgery (Stall:69.6%% (N = 16/23) vs 60.0% (N = 15/25); P = 0.489) or implant removal (Stall:56.5% (N = 13/23) vs 76.0% (N = 19/25); P = 0.153). Univariate analysis for continuous variables associated with MCGR stalling failed to identify any significant patient or deformity factor, Table 2. Children with stalled implants did not experience any significant difference in coronal deformity magnitude or correction at any time point.Table 2 Summary of patient and deformities variables between children who experienced MCGR stalling and those that continued to successfully lengthen Variable Functioning MCGR Stalled MCGR T-test N Mean StDev N Mean StDev Age at MCGR implantation (year) 25.0 6.0 1.8 23.0 6.5 1.9 0.4 Preoperative height (cm) 25.0 104.7 14.5 23.0 111.7 13.2 0.1 Preoperative weight (kg) 25.0 17.2 4.4 23.0 20.1 6.6 0.1 Preoperative BMI 25.0 15.3 1.8 23.0 15.7 2.5 0.5 Preoperative coronal cobb (°) 25.0 70.8 15.2 23.0 65.3 15.3 0.2 Preoperative coronal balance (mm) 25.0 8.2 21.2 23.0 5.5 21.9 0.7 Preoperative T2–5 Kyphosis (°) 25.0 16.2 12.4 23.0 12.5 11.9 0.3 Preoperative T2–12 Kyphosis (°) 25.0 44.8 19.0 23.0 41.4 14.6 0.5 Preoperative sagittal balance (mm) 25.0 21.9 35.6 23.0 19.0 30.9 0.8 Follow-up since surgery (months) 25.0 39.1 13.1 23.0 47.5 19.1 0.1 Final coronal cobb (°) 25.0 58.6 17.9 23.0 55.1 17.2 0.5 Coronal cobb correction post implantation (°) 25.0 27.0 18.0 23.0 27.3 18.4 1.0 Coronal cobb correction post-implantation to final (°) 25.0 17.2 22.3 23.0 14.7 22.5 0.7 The Kaplan–Meier survival analysis from MCGR stalling is depicted in Fig. 1. At 2-year follow-up, approximately 50% of MCGR’s continue to successfully lengthen. However, between 2 and 4 years, the development of implant stalling increased precipitously with only 15% of MCGR’s continuing to successfully lengthen at 4-year follow-up.Fig. 1 Depiction of Kaplan–Meier survival curve for successful MCGR lengthening prior to implant stalling Complications Thirty-two out of 48 patients (66.7% of the studied cohort) experienced an unplanned return to operating room (UPROR). In all, there were 55 unplanned reoperations, accounting for 1.12 (± 1.1) reoperations per UPROR patient. The most common reoperation was MCGR revision for rod failure/stall, accounting for 49% of the total reoperations (N = 27/55 reoperations in 22 children), followed by anchor revision for loosening (8 reoperations in 6 children) and irrigation and debridement (I&D) for a surgical site infection (9 reoperations in 6 children; 5/6 with neuromuscular EOS and 1 syndromic EOS). An additional 4 children underwent 4 reoperations for combined indications (e.g. rod revision with anchor revision and/or I&D), and 23 children were converted to posterior spinal fusion. The patients that experienced UPROR demonstrated no significant differences in deformity parameters at any point but did exhibit significantly longer follow-up than the uncomplicated patients, Table 3.Table 3 Summary of comparison of variables between patients who did and did not undergo unplanned return to the operating room (UPROR) Levels spanned—right Levels spanned—left Total # of lengthenings Immediate post-op cobb (°) Immediate cobb correction (°) Follow-up (months) UPROR patients (N = 32) 10.0 ± 1.2 10.1 ± 1.3 7.8 ± 3.6 (0–14) 40.8 ± 12.8 27.5 ± 15.0 60.2 ± 16.5 Uncomplicated patients (N = 16) 9.6 ± 1.8 9.8 ± 1.5 8.6 ± 3.1 (5–18) 41.5 ± 14.3 26.4 ± 23.5 50.4 ± 18 P value 0.5 0.6 0.4 0.9 0.8 0.08 UPROR , unplanned return to operating room, Post-op, post-operative Discussion Magnetically controlled growing rods have been recognized as a promising technology in the treatment of EOS which allow for implant lengthening without repeated general anesthetic exposures or surgical incisions as required in TGR. However, previous studies have raised concern with regard to the longevity of MCGR implants for functional spinal lengthening [3, 4, 6]. In this retrospective, single-center review of 48 children undergoing primary MCGR treatment, we identified that MCGR implants have unreliable long-term functionality, with 50% of children demonstrating MCGR stalling at 2 years post-implantation, increasing precipitously to > 80% at 4 years. Andras et al. [7] stated following an initial minimal lengthening episode, subsequent lengthening attempts should be pursued as 91% of those patients successfully lengthened after one or two subsequent attempts. In our patient cohort, once the MCGR began to stall, subsequent attempts did not result in successful lengthening. Since the introduction of MCGR implants for EOS in 2014, this treatment approach has been largely promoted as the magic bullet for the management of EOS. Previous studies have shown that MCGR implants have the ability to provide comparable curve correction as compared with TGR but with significantly fewer surgical procedures [1, 8]. However, with the introduction of any new technology, there are also new mechanisms of failure, and the failure of implant distraction, termed implant stalling, is exclusive to MCGR. MCGR stalling has been recognized as the most common surgeon-reported reason for implant removal [9, 10], with failure of distraction occurring in upwards of 40% of treated children [2–4]. However, this has been shown to vary from as low as approximately 10% of children at 2-years post-implantation [2] to as high as 48% at more than 3 years post-implantation [4]. In the current study, we found a time dependent association between MCGR stalling and follow-up post-implantation, ranging from 50% at 2 years post-implantation to > 80% at 4 years. Given the significant amount of stalling that was experienced within this cohort in addition to the high UPROR rate, a valid concern arises regarding longevity and cost-effectiveness of the implant. The mechanistic reason for MCGR stalling is not well understood in the literature. A postulate is that the magnetic actuator generates an insufficient force to continue to lengthen the spine over time with successive lengthening sessions resulting in decrease yield or complete failure to lengthen [9, 11, 12]. This “law of diminishing returns” has been proposed to represent progressive stiffness or even auto-fusion of spanned spinal segments over the treatment course [11], as has been reported in the TGR literature [13, 14]. However, it has also been reported that this diminished distraction gain is rather a progressive failure of magnetic actuator that is reversed with MCGR exchange [12]. Explant analysis of MCGR implants have identified that duration of implantation is directly correlated with the extent of lengthening with implanted MCGRs of greater duration being significantly less likely to be functional at the time of explant [6]. Force testing of explanted implants further supports this explanation with force production being negatively correlated with the duration of treatment [10]. Regardless of mechanistic understanding for the stalling phenomena, it does have direct implications on the cost-effectiveness of MCGR treatment. Previous studies have investigated the time points to cost neutrality for MCGR in the treatment of EOS compared with TGR, estimated from 3 to 6 years [15, 16]. However, these cost analyses are based upon certain assumptions regarding the functional longevity of the MCGR implants, the complication rate, and implant costs which may not correctly represent real-world circumstances. Only 2 studies to date have reviewed actual costs of care for children with EOS treated with MCGR, with varying results. Harshavardhana et al. [17] reported on the payer costs of 9 children treated with MCGR, finding that when compared with previous reported payer costs of TGR, MCGR were at least 40% more cost effective than TGR. However, Oetgen et al. [18] reported on the hospital charges and payer reimbursements for 16 children treated with MCGR, compared to 21 children with TGR. MCGR treatment resulted in significantly higher charges than TGR despite received statistically similar average percentage reimbursements (MCGR: 43% vs TGR: 46%). The charge difference in this study largely represented implant cost differences (MCGR: $31,621 vs TGR: $8966) [18], which were significantly higher than implant cost estimated utilized in previous cost-analysis studies [15, 16]. Luhmann et al. [19] performed a cost analysis of MCGR with implant costs more closely representing the values reported by Oetgen et al. [18] and found that MCGR did not meet cost neutrality in comparison to TGR after 6 years of simulated care, further calling into question the cost effectiveness of MCGR at their current price point. Although concerning, it is important to recognize that MCGR implants continue to carry the advantage of avoiding surgical construct lengthening and the potential developmental concerns associated with repeated general anesthesia exposures at young age [20]. Additionally, the current study indicated that despite the high rate of stalling, there were no differences in coronal plane deformity or the rate of revision surgery due to implant stalling. This study has several inherent limitations which warrant consideration. As a retrospective review, there are inherent biases with the presented data. The inclusion criteria for our cohort dictate that each patient have undergone at least 2 lengthening sessions after implantation, resulting in some patients with only 6 months of follow up information after post implantation. As identified in the Kaplan–Meier, implant survival from stalling drops precipitously with time post-implantation. As such, our data may be skewed toward under-reporting implant stalling for patients early in their treatment course with the potential to experience complications and/or experience implant stalling later in their post-implantation follow-up. However, the mean follow-up was not statistically difference between stalled and functioning implant cohorts. Additionally, our data represents a heterogenous population of patients with various indications for MCGR implantation. Given the mixed etiologies represented in the patient cohort, there are some significant limitations when performing radiographic measurements of non-ambulatory and neuromuscular children sagittal and coronal balance. These patients often require assistance during these radiographs to maintain upright posture in either the sitting or standing position that can significantly influence these measures. The etiology breakdown also has direct implication on the UPROR data. The SSI rate, 12.5% (N = 6/48) in the current study is higher than previous reports using only MCGR implants, reported as low as 3% by Suresh et al. [21] at a mean of 13.1 months following implantation in a series of 992 EOS patients in the PSSG database. All SSI in the current series occurred in neuromuscular (N = 5/6 SSI patients, 4/5 nonambulatory) and syndromic patients (N = 1/6). These etiology cohorts accounted for 62.5% (N = 30/48) of the reported patient cohort which, given their known increased risk for SSI [22], may result in the SSI rate being non-representative. Additionally, 67% of the patients in the current cohort experienced UPROR. This data aligns with studies reporting long-term follow-up in children treated with MCGR. Lebel et al. [23] reported on 47 MCGR patients followed to MCGR graduation, reporting complication development in 66% of patients with 45% experiencing UPROR. This is further supported by Cheung et al. [3] who reported a 70% complication rate for 40% of their cohort experiencing an UPROR for rod distraction failure at a mean of 6-year follow-up and Tahir et al. [8] who reported a 43.8% UPROR rate in children followed to MCGR graduation. Furthermore, Tahir et al. [8] reported no difference in UPROR for children treated with MCGR as compared with children treated with TGR when followed to definitive fusion. These findings are in direct contradiction to early-term follow-up studies reported a significant reduction in UPROR rates for MGCR treated patients [1] emphasizing the importance of long-term follow-up. Finally, this study includes patients treated during the COVID-19 pandemic, which has been shown to complicate and delay patient care in various categories of care as well as post-surgical care. However, this study represents one of the largest single-center studies on primary MCGR with results extending beyond 2 years of clinical follow-up, with all surgeries performed by one of four pediatric orthopedic fellowship trained surgeons practicing at a high-volume specialty center. The determination of implant stall was verified utilizing multiple radiographs over 3 attempted lengthening sessions. We understand there has been recent literature published suggesting that the stall phenomenon can be overcome by repeated attempts at lengthening, however our study is in direct contradiction to this proposal as no implant that stalled regained the ability to lengthen with subsequent attempts [7]. Interestingly, implant stall was not found to be associated with an increased rate of UPROR (Stall: 69.6%% (N = 16/23) vs 60.0% (N = 15/25); P = 0.489). However, many children who experienced MCGR stalling undergo a period of observation, especially though approaching the maturity necessary for a definitive spinal fusion. As such, the UPROR data in the Stall cohort may be biased toward under-reporting UPROR. In conclusion, this study identified a high rate of implant stalling with the utilization of MCGR for the management of EOS. Only 50% of implants were functionating 2 years post-implantation, a value which continued to drop precipitously to < 20% at 4 years. This data expands the available literature on several assumptions regarding MCGR longevity in cost-effectiveness modeling for EOS treatment. Further research is needed to confirm these findings as well as to re-evaluate the cost-effectiveness of MCGR treatment in light of these high rates of implant failure and reoperation. Author contribution KAS: data analysis, data interpretation, manuscript drafting, manuscript approval, and accountable. PB: study design, data analysis, data interpretation, manuscript approval, and accountable. BAR: study design, data analysis, manuscript editing, manuscript approval, and accountable. AM: data analysis, data interpretation, manuscript editing, manuscript approval, and accountable. DT: data analysis, data interpretation, manuscript editing, manuscript approval, and accountable. AJ: data collection, data analysis, manuscript editing, manuscript approval, and accountable. C-HJ: study design, data analysis, statistical analysis, manuscript editing, manuscript approval, and accountable. CEJ: study design, data interpretation, manuscript editing, manuscript approval, and accountable. ALM: study design, data analysis, manuscript editing, manuscript approval, and accountable. Funding No funding was received for this work. Data availability Data is maintained by the author’s institution and is available upon request. Declarations Conflict of interest Dr. Shaw is a committee member for NASS and AAOS; Dr. Bassett reports nothing to disclose; Dr. Ramo reports receiving publishing royalties from Saunders/MosbyElsevier, Ms. McClung reports nothing to disclose; Mr. Thornberg reports nothing to disclose, Mr. Jamnik reports nothing to disclose; Dr. Jo reports nothing to disclose; Dr. Johnston reports receiving publishing royalties from Saunders/MosbyElsevier, IP royalties from Medtronic, is a board/committee member for GSSG, POSNA, and SRS, and editorial board member for Jounral of Children’s Orthopaedics; Dr. McIntosh reports being a paid speaker for Nuvasive. Ethical approval Approved by IRB: #052011-039. Informed consent Not applicable. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Akbarnia BA Pawelek JB Cheung KM Traditional growing rods versus magnetically controlled growing rods for the surgical treatment of early-onset scoliosis: a case-matched 2-year study Spine Deform 2014 2 6 493 497 10.1016/j.jspd.2014.09.050 27927412 2. Saarinen AJ Sponseller PD Andras LM Matched comparison of magnetically controlled growing rods with traditional growing rods in severe early-onset scoliosis of ≥90°: an interim report on outcomes 2 years after treatment J Bone Joint Surg Am 2022 104 1 41 48 10.2106/JBJS.20.02108 34644282 3. Cheung JPY Yiu K Kwan K Cheung KMC Mean 6-year follow-up of magnetically controlled growing rod patients with early onset scoliosis: a glimpse of what happens to graduates Neurosurgery 2019 84 5 1112 1123 10.1093/neuros/nyy270 30102378 4. Welborn MC Bouton D Outcomes of MCGR at > 3 year average follow-up in severe scoliosis: who undergoes elective revision vs UPROR? Spine Deform 2022 10 2 457 463 10.1007/s43390-021-00424-1 34648137 5. Dragsted C Fruergaard S Jain MJ Distraction-to-stall versus targeted distraction in magnetically controlled growing rods J Pediatr Orthop 2020 40 9 e811 e817 10.1097/BPO.0000000000001585 32398627 6. Rushton PRP Smith SL Kandemir G Spinal lengthening with magnetically controlled growing rods: data from the largest series of explanted devices Spine (Phila Pa 1976) 2020 45 3 170 176 10.1097/BRS.0000000000003215 31513114 7. Andras LM Siddiqui AA Nazareth A Illingworth KD Gupta P Vitale MG Smith JT Skaggs DL Minimal lengthening episodes in magnetically controlled growing rod patients often resolve with subsequent lengthening attempts Pediatrics 2020 146 405 406 8. Tahir M Mehta D Sandhu C Jones M Gardner A Mehta JS A comparison of the post-fusion outcome of patients with early-onset scoliosis treated with traditional and magnetically controlled growing rods Bone Joint J. 2022 104-b 2 257 264 10.1302/0301-620X.104B2.BJJ-2021-1198.R1 35094579 9. Agarwal A Kelkar A Garg Agarwal A Jayaswal D Jayaswal A Shendge V Device-related complications associated with MAGEC rod usage for distraction-based correction of scoliosis Spine Surg Related Res 2020 4 2 148 151 10.22603/ssrr.2019-0041 10. Rushton PRP Smith SL Forbes L Bowey AJ Gibson MJ Joyce TJ Force testing of explanted magnetically controlled growing rods Spine (Phila Pa 1976) 2019 44 4 233 239 10.1097/BRS.0000000000002806 30044365 11. Ahmad A Subramanian T Panteliadis P Wilson-Macdonald J Rothenfluh DA Nnadi C Quantifying the 'law of diminishing returns' in magnetically controlled growing rods Bone Joint J. 2017 99-b 12 1658 1664 10.1302/0301-620X.99B12.BJJ-2017-0402.R2 29212690 12. Cheung JPY Bow C Cheung KMC "Law of temporary diminishing distraction gains": the phenomenon of temporary diminished distraction lengths with magnetically controlled growing rods that is reverted with rod exchange Global Spine J 2022 12 2 221 228 10.1177/2192568220948475 32799681 13. Noordeen HM Shah SA Elsebaie HB Garrido E Farooq N Al-Mukhtar M In vivo distraction force and length measurements of growing rods: which factors influence the ability to lengthen? Spine (Phila Pa 1976) 2011 36 26 2299 2303 10.1097/BRS.0b013e31821b8e16 21494191 14. Sankar WN Skaggs DL Yazici M Lengthening of dual growing rods and the law of diminishing returns Spine (Phila Pa 1976) 2011 36 10 806 809 10.1097/BRS.0b013e318214d78f 21336236 15. Su AW Milbrandt TA Larson AN Magnetic expansion control system achieves cost savings compared to traditional growth rods: an economic analysis model Spine (Phila Pa 1976) 2015 40 23 1851 1856 10.1097/BRS.0000000000001077 26230537 16. Polly DW Jr Ackerman SJ Schneider K Pawelek JB Akbarnia BA Cost analysis of magnetically controlled growing rods compared with traditional growing rods for early-onset scoliosis in the US: an integrated health care delivery system perspective Clinicoecon Outcomes Res 2016 8 457 465 10.2147/CEOR.S113633 27695352 17. Harshavardhana NS Noordeen MHH Dormans JP Cost analysis of magnet-driven growing rods for early-onset scoliosis at 5 years Spine (Phila Pa 1976) 2019 44 1 60 67 10.1097/BRS.0000000000002738 29894450 18. Oetgen ME McNulty EM Matthews AL Cost-effectiveness of magnetically controlled growing rods: who really benefits? Spine Deform 2019 7 3 501 504 10.1016/j.jspd.2018.09.066 31053322 19. Luhmann SJ McAughey EM Ackerman SJ Bumpass DB McCarthy RE Cost analysis of a growth guidance system compared with traditional and magnetically controlled growing rods for early-onset scoliosis: a US-based integrated health care delivery system perspective Clinicoecon Outcomes Res 2018 10 179 187 10.2147/CEOR.S152892 29588607 20. Hu D Flick RP Zaccariello MJ Association between exposure of young children to procedures requiring general anesthesia and learning and behavioral outcomes in a population-based birth cohort Anesthesiology 2017 127 2 227 240 10.1097/ALN.0000000000001735 28609302 21. Suresh KV Marrache M Gomez J Li Y Sponseller PD Can magnetically controlled growing rods be successfully salvaged after deep surgical site infection? Spine Deform 2022 10 4 919 923 10.1007/s43390-022-00472-1 35084718 22. Cahill PJ Mahmoud MA MacAlpine EM Tatad AM Campbell RM Flynn JM Correlation between surgical site infection and classification of early onset scoliosis (C-EOS) in patients managed by rib-based distraction instrumentation Spine Deform 2020 8 4 787 792 10.1007/s43390-020-00103-7 32232746 23. Lebel DE Rocos B Helenius I Magnetically controlled growing rods graduation: deformity control with high complication rate Spine (Phila Pa 1976) 2021 46 20 E1105-e12 10.1097/BRS.0000000000004044 34559751	36447049	PMC9708129	NO-CC CODE	2022-12-01 23:20:30	no	Spine Deform. 2022 Nov 29;:1-7	utf-8	Spine Deform	2,022	10.1007/s43390-022-00622-5	oa_other
==== Front Acad Psychiatry Acad Psychiatry Academic Psychiatry 1042-9670 1545-7230 Springer International Publishing Cham 36447069 1732 10.1007/s40596-022-01732-y Educational Case Report Food4Thought: a Medical Trainee–Led, Remotely Delivered Nutrition Outreach Program for Individuals with Serious Mental Illness Cheung Amy 1 Dutta Pooja 1 Kovic Yumi 2 Stojcevski Marko 1 http://orcid.org/0000-0003-4299-8924 Fan Xiaoduo [email protected] 1 1 UMass Chan Medical School, Worcester, MA USA 2 grid.15276.37 0000 0004 1936 8091 University of Florida College of Medicine, Gainesville, FL USA 29 11 2022 15 10 6 2022 10 11 2022 © The Author(s), under exclusive licence to American Association of Chairs of Departments of Psychiatry, American Association of Directors of Psychiatric Residency Training, Association for Academic Psychiatry and Association of Directors of Medical Student Education in Psychiatry 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. ==== Body pmcPeople with serious mental illness (SMI), such as those with a schizophrenia spectrum disorder, bipolar disorder, or major depressive disorder, are highly susceptible to medical comorbidities. Cardiovascular disease (CVD) is the leading cause of premature death in this population and has been attributed to several factors including obesity, smoking, and lifestyle behaviors [1–4]. Among them, suboptimal diet is an important and modifiable contributor to poor cardiometabolic health. Culinary medicine and nutritional psychiatry are growing fields that explore the relationship between food and personal and mental health, respectively [5]. As of 2016, more than ten medical schools in the USA have adopted culinary medicine curricula to educate rising physicians about the integration of eating behaviors with the health care goals of patients [6, 7]. Diet modification programs have been shown to improve symptoms of depression [8, 9] and bipolar disorder [10], suggesting the potential mental health benefit of such interventions [11]. We present Food4Thought, a virtual nutrition outreach program for community members with SMI and staff that support them at a community mental health agency. The program followed the community participatory model which promotes community-academic collaborations to ensure program content is relevant and meaningful [12, 13]. The goals of our initiative were to evaluate the feasibility and potential benefit of Food4Thought and provide an opportunity for medical trainees to develop a public health intervention program. Program Development and Evaluation The Food4Thought program was crafted as an educational collaboration between the Community Intervention Program (CIP) at UMass Chan Medical School and Genesis Club, a non-profit clubhouse that utilizes a person-centered approach to support the recovery of people with mental illness in the Greater Worcester Region of Massachusetts. CIP is a community outreach initiative led by medical students and psychiatry residents and supported by UMass MIND research employees and volunteers. The Healthy Living initiative within CIP aims to promote lifestyle behaviors in the SMI population. In partnership with the Genesis Club kitchen unit, three medical students and one psychiatry resident within the Healthy Living team designed the program under the advisement of an attending psychiatrist and a registered dietitian. Additional medical trainees joined to help prepare materials and lead sessions. For this paper, “participants” refer to Genesis Club members with SMI and staff and “facilitators” refer to individuals from the CIP Healthy Living team. The UMass Chan Medical School IRB determined that the Food4Thought program was not research involving human subjects. The program was administered through the HIPAA-compliant Zoom platform and advertised via flyers posted within the clubhouse and email through the Genesis Club listserv. A remote format was chosen in response to the shift in virtual care delivery during the COVID-19 pandemic. First, three medical students and a psychiatry resident facilitated a Listening & Informational session open to all clubhouse members and staff to gather their perspectives on lifestyle programming and brainstorm topics of interest. Following this session, participants completed a survey about preferred learning modalities. Enrollment was capped at twelve participants set by Genesis Club based on their ability to fund ingredients. The first program comprised three modules with each module containing an educational session and kitchen skills session. Each session lasted 1 h and was led by a medical student. Educational sessions consisted of 30–40 min of a slide presentation with group discussion. Prior to each kitchen skills session, participants picked up their recipe and ingredient package at Genesis Club. Participants used their own kitchen equipment in their homes. The lead medical student provided recipe instructions while simultaneously preparing the meal. Each module was separated by two weeks to provide time for facilitators to prepare materials. Surveys with 4- or 5-point Likert scales (1 = strongly disagree, 4 or 5 = strongly agree) were delivered after each module to assess thoughts on and motivation for healthy eating. A second Listening & Informational session was held 1 week after the completion of the program to allow participants and facilitators to reflect on the experience. Those who attended at least one module of the program were asked to complete a final survey which included questions for future program improvement. A second Food4Thought program was again open to all Genesis Club members and staff and closely followed the format of the first program: first Listening & Informational session, four two-part modules, and second Listening & Informational session. Each session was separated by 1 week. Using a hybrid approach, participants with SMI had the option to work together with staff participants at the Genesis Club kitchen to prepare the recipe(s). A survey using 5-point Likert scales was administered following the first and second Listening & Informational sessions to evaluate nutrition knowledge and attitudes toward the effect of food on health. No surveys were provided after each module to reduce respondent fatigue. The second Listening & Informational session of the first program and the entire second program were recorded with participant consent. The audio was transcribed, and representative quotes were excerpted. Medical trainees who participated in the program and were not involved in the writing of this report were asked to complete a survey including 5-point Likert scales of their experience as program facilitators. Program Results Twelve individuals from Genesis Club attended the first Listening & Informational session of the launch program. An overview of the program is described in Table 1. Nutritional Psychiatry, Mindful Eating, and Cooking Healthy on a Budget were chosen by the facilitators and Genesis Club kitchen unit as topics based on feedback. From the survey responses (n = 10), participants preferred materials to be presented as informational videos, PowerPoint presentations, and small-group discussion. Selected recipes were inspired by meals already served at the clubhouse and the module theme. For example, a multi-component meal was chosen for Mindful Eating to allow participants to practice mindful eating principles with foods of various flavors and textures. Module-specific survey response rates of participants who attended either the educational or kitchen skill sessions were 42% (5 respondents/12 participants), 50% (5/10), and 100% (8/8), respectively. Ten of the twelve participants attended at least four of the six total sessions across the three modules. In all post-module surveys, all participants strongly agreed or agreed that “I am confident I can make changes in my everyday diet” and “Nutritious meals can taste good too.” In the second Listening & Informational session, participants (n = 8) discussed lessons learned during the program and challenges to eat healthier including easy access to unhealthy foods. One participant noted that they learned to think about “more than just food groups… [but also] think colors” when choosing foods with different nutrients. In the final survey provided after this session, participants (n = 8) expressed their preference for the virtual format and additional module topics.Table 1 Objectives of the nutrition outreach program Module Objective Meal prepared* First iteration of Food4Thought 1: Nutritional psychiatry Learn about the connection between diet and mood Learn about foods that contain antioxidants and anti-inflammatory properties Understand the concept of MyPlate with a clinical dietician Beef and cheese tostadas 2: Mindful eating Understand the concept of mindfulness and how it relates to healthy eating Understand how to use mindfulness to build awareness around food choices and hunger Learn how to apply a hunger scale to eat mindfully “Breakfast for dinner” (yogurt-berry parfait, sausage-egg scramble, and apple cinnamon toast) 3: Cooking healthy on a budget Learn tips to save money while grocery shopping Understand how to shop smart and meal prep Reinforce budget-friendly cooking within the context of MyPlate with a clinical dietician One-pot Thai chicken curry Second iteration of Food4Thought 1: Food, mind, and body Understand the impact of food on physical health Learn about concepts in nutritional psychiatry covered in the first program Beef and cheese tostadas 2: Cooking healthy on a budget Learn tips to save money while grocery shopping Understand how to shop smart and meal prep Reinforce budget-friendly cooking within the context of MyPlate with a clinical dietician One-pot Thai chicken curry 3: Mindful eating Understand the concept of mindfulness and how it relates to healthy eating Understand how to use mindfulness to build awareness around food choices and hunger Learn how to apply a hunger scale to eat mindfully “Breakfast for dinner” (tofu scramble, chocolate baked oats) 4: Food as medicine Learn about vitamins and nutrients in food Understand the impact of the microbiome on overall health Understand the side effects of psychiatric medications and their effect on physical health Mediterranean-inspired quinoa salad All meals were able to be modified with protein substitutes as preferred by participants The second program incorporated feedback from the first program. Twelve Genesis Club members and staff attended the first Listening & Informational session. The module topics were expanded to Food, Mind, and Body (Module 1), Cooking Healthy on a Budget (Module 2), Mindful Eating (Module 3), and Food as Medicine (Module 4) (Table 1). Module-specific attendance of participants who attended either the educational or kitchen skills sessions was twelve, ten, nine, and nine, respectively. Ten of the twelve participants attended at least five of the eight total sessions. During the second Listening & Informational session, participants (n = 5) discussed their relationships with their SMI and food. One participant stated “My mind interacts with my belly. Since I have schizophrenia, my appetite changes during the meal.” Others commented on the benefits of digital technologies to maintain their social connections and reduce anxiety of meeting in person. Seven participants completed both pre- and post-program surveys. No statistically significant changes in knowledge and attitudes surrounding healthy eating behaviors were observed; however, there was a trend for “The foods I eat can affect my mood” (p = 0.10, paired t test) (Table 2).Table 2 Summary of pre- and post-program survey results from the second Food4Thought program Pre-program Mean (SD) Post-program Mean (SD) p value I am motivated to discuss the effects of my medications with my doctor 4.0 (1.2) 3.9 (1.2) > 0.05 I am concerned about the impact of medications on my weight 3.7 (1.5) 3.3 (1.9) > 0.05 It is important to pay attention to how food makes me feel while eating 4.4 (0.5) 4.4 (0.5) > 0.05 I check the nutrition label of the food I am eating 2.9 (0.9) 3.3 (1.0) > 0.05 I think about how hungry or full I am before eating 3.1 (1.1) 3.7 (0.5) 0.17 The foods I eat can affect my physical function 4.3 (1.1) 4.1 (0.7) > 0.05 The foods I eat can affect my mood 3.9 (1.1) 4.4 (0.5) 0.10 *Each question was assigned on a 5-point Likert scale of strength of agreement response (from 1 = strongly disagree to 5 = strongly agree). Seven participants completed both surveys. No pre-post-program results were collected in the first program Four medical trainee facilitators (three medical students and one psychiatry resident) completed the post-program survey. One survey respondent commented “while discussing and sharing healthy food habits, I was able to significantly improve my own.” Another facilitator noted the importance of continuing a hybrid model to directly assist participants who may have challenges with cooking during kitchen skills sessions. All medical student respondents “strongly agree” that community-based programs should be part of the medical education curriculum. Interpretation of Program Findings The remotely delivered Food4Thought program was well-received by participants. The feasibility of our program can be attributed to the open exchange of ideas between the CIP Healthy Living team and Genesis Club during its development. The first Listening & Informational session was critical in forming an alliance with the community-based partner. The virtual format was feasible and allowed both facilitators and participants to easily sign onto sessions in their own homes or the Genesis Club kitchen, encouraging equitable access to nutrition support. Participants also received teachings from a clinical dietician who helped develop the recipes and materials used in the program. Several limitations of the program are apparent. The small number of participants and facilitators in the program may not reflect the overall attitudes of individuals with SMI or medical trainees, respectively. No statistical differences were observed in the pre-post program survey in the second program, which may have been impacted by the small sample size of the participants. People with SMI who do not have access to or require education on the use of digital technologies as well as kitchen space will have more difficulty participating in the program [14, 15]. Reflections on Academic-Community Partnerships Food4Thought used the community participatory model to recognize identities and strengths within the community, build on existing resources, and attain a balance between research and action [16–18]. After the program, the Genesis Club kitchen unit changed and expanded their menu to include more culturally varied and vegetarian meals. Printouts of visual aids used during the program were placed in the kitchen and dining area to reinforce information and skills learned in the program. These discernible changes demonstrate how nutrition knowledge can be transported from an academic to community setting supporting individuals with SMI. Implications for Medical Education Remote delivery of Food4Thought allowed medical trainees to directly interact with individuals with SMI and better understand their attitudes around and challenges to eating healthier. Medical trainees had the opportunity to engage community partners and develop a public health intervention program to serve one of the most vulnerable populations in our society. We hope our experience encourages more medical trainees to be involved in similar endeavors, which could be an important part of the medical education curriculum. Data Availability The data that support the findings of this study are available on request from the corresponding author. Declarations Disclosures XF has received research support from Alkermes, Janssen, Otsuka, Roche, Lundbeck, Avanir, and Boehringer Ingelheim. XF serves on the PCORI Advisory Panel on Healthcare Delivery and Disparities Research, and the Data and Safety Monitoring Board for Northwell Health/The Zucker Hillside Hospital. In addition, XF holds a patent on “Combination Treatment for Neuropsychiatric Disorders” (USPTO Patent No. 11,331,319). YK has received compensation from ScholarRx. The other authors declare no conflict of interest. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Rossom RC Hooker SA O'Connor PJ Crain AL Sperl-Hillen JM Cardiovascular risk for patients with and without schizophrenia, schizoaffective disorder, or bipolar disorder J Am Heart Assoc 2022 11 6 e021444 10.1161/JAHA.121.021444 35261265 2. Daumit GL Dalcin AT Dickerson FB Miller ER Evins AE Cather C Effect of a comprehensive cardiovascular risk reduction intervention in persons with serious mental illness: a randomized clinical trial JAMA Netw Open 2020 3 6 e207247 10.1001/jamanetworkopen.2020.7247 32530472 3. Nordentoft M Wahlbeck K Hallgren J Westman J Osby U Alinaghizadeh H Excess mortality, causes of death and life expectancy in 270,770 patients with recent onset of mental disorders in Denmark, Finland and Sweden PLoS ONE 2013 8 1 e55176 10.1371/journal.pone.0055176 23372832 4. Teasdale SB Ward PB Samaras K Firth J Stubbs B Tripodi E Dietary intake of people with severe mental illness: systematic review and meta-analysis Br J Psychiatry 2019 214 5 251 259 10.1192/bjp.2019.20 30784395 5. Marx W Moseley G Berk M Jacka F Nutritional psychiatry: the present state of the evidence Proc Nutr Soc 2017 76 4 427 436 10.1017/S0029665117002026 28942748 6. La Puma J What is culinary medicine and what does it do? Popul Health Manag 2016 19 1 1 3 10.1089/pop.2015.0003 26035069 7. Monlezun DJ Kasprowicz E Tosh KW Nix J Urday P Tice D Medical school-based teaching kitchen improves HbA1c, blood pressure, and cholesterol for patients with type 2 diabetes: results from a novel randomized controlled trial Diabetes Res Clin Pract 2015 109 2 420 426 10.1016/j.diabres.2015.05.007 26002686 8. Jacka FN O'Neil A Opie R Itsiopoulos C Cotton S Mohebbi M A randomised controlled trial of dietary improvement for adults with major depression (the 'SMILES' trial) BMC Med 2017 15 1 23 10.1186/s12916-017-0791-y 28137247 9. Francis HM Stevenson RJ Chambers JR Gupta D Newey B Lim CK A brief diet intervention can reduce symptoms of depression in young adults – a randomised controlled trial PLoS ONE 2019 14 10 e0222768 10.1371/journal.pone.0222768 31596866 10. Saunders EFH Mukherjee D Myers T Wasserman E Hameed A Bassappa Krishnamurthy V Adjunctive dietary intervention for bipolar disorder: a randomized, controlled, parallel-group, modified double-blinded trial of a high n-3 plus low n-6 diet Bipolar Disord 2022 24 2 171 184 10.1111/bdi.13112 34218509 11. Clark A Bezyak J Testerman N Individuals with severe mental illnesses have improved eating behaviors and cooking skills after attending a 6-week nutrition cooking class Psychiatr Rehabil J 2015 38 3 276 278 10.1037/prj0000112 25559077 12. Israel BA Schulz AJ Parker EA Becker AB Review of community-based research: assessing partnership approaches to improve public health Annu Rev Public Health 1998 19 173 202 10.1146/annurev.publhealth.19.1.173 9611617 13. Lincoln AK Borg R Delman J Developing a community-based participatory research model to engage transition age youth using mental health service in research Fam Community Health 2015 38 1 87 97 10.1097/FCH.0000000000000054 25423247 14. Young AS Cohen AN Niv N Nowlin-Finch N Oberman RS Olmos-Ochoa TT Mobile phone and smartphone use by people with serious mental illness Psychiatr Serv 2020 71 3 280 283 10.1176/appi.ps.201900203 31744429 15. Barre LK Ferron JC Davis KE Whitley R Healthy eating in persons with serious mental illnesses: understanding and barriers Psychiatr Rehabil J 2011 34 4 304 310 10.2975/34.4.2011.304.310 21459746 16. Holkup PA Tripp-Reimer T Salois EM Weinert C Community-based participatory research: an approach to intervention research with a Native American community ANS Adv Nurs Sci 2004 27 3 162 175 10.1097/00012272-200407000-00002 15455579 17. Agenor M Qureshi F Johnson K Kacevich S McAdams-Mahmoud A Curlin J Developing a community-academic partnership to promote mental health among underserved preadolescent girls in Boston Massachusetts Prog Community Health Partnersh 2018 12 3 321 328 10.1353/cpr.2018.0056 30581175 18. Mullins CD Wingate LT Edwards HA Tofade T Wutoh A Transitioning from learning healthcare systems to learning health care communities J Comp Eff Res 2018 7 6 603 614 10.2217/cer-2017-0105 29478331	36447069	PMC9708131	NO-CC CODE	2022-12-01 23:20:30	no	Acad Psychiatry. 2022 Nov 29;:1-5	utf-8	Acad Psychiatry	2,022	10.1007/s40596-022-01732-y	oa_other
==== Front Opt Spectrosc Opt Spectrosc Optics and Spectroscopy 0030-400X 1562-6911 Pleiades Publishing Moscow 2930 10.1134/S0030400X22080057 Article Photoplethysmographic Imaging of Hemodynamics and Two-Dimensional Oximetry Volkov I. Yu. [email protected] Sagaidachnyi A. A. [email protected] Fomin A. V. [email protected] grid.446088.6 0000 0001 2179 0417 Saratov State University, 410012 Saratov, Russia 29 11 2022 118 29 12 2021 30 1 2022 4 2 2022 © Pleiades Publishing, Ltd. 2022, ISSN 0030-400X, Optics and Spectroscopy, 2022. © Pleiades Publishing, Ltd., 2022.Russian Text © The Author(s), 2022, published in Izvestiya Saratovskogo Universiteta. Novaya Seriya. Seriya: Fizika, 2022, Vol. 22, No. 1, pp. 15–45. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The review of recent papers devoted to actively developing methods of photoplethysmographic imaging (the PPGI) of blood volume pulsations in vessels and non-contact two-dimensional oximetry on the surface of a human body has been carried out. The physical fundamentals and technical aspects of the PPGI and oximetry have been considered. The manifold of the physiological parameters available for the analysis by the PPGI method has been shown. The prospects of the PPGI technology have been discussed. The possibilities of non-contact determination of blood oxygen saturation SpO2 (pulse saturation O2) have been described. The relevance of remote determination of the level of oxygenation in connection with the spread of a new coronavirus infection SARS-CoV-2 (COVID-19) has been emphasized. Most of the works under consideration cover the period 2010–2021. Keywords: photoplethysmography photoplethysmographic imaging remote photoplethysmography im-aging photoplethysmography oxygenation oximetry saturation blood oxygen saturation heart rate SAR-S‑CoV-2 COVID-19 ==== Body pmcINTRODUCTION This work is devoted to the analysis of modern works in a relatively new rapidly developing field of two-dimensional photoplethysmographic imaging of hemodynamic phenomena. In the Russian-language literature, the method of photoplethysmographic imaging (the PPGI) has several synonymous names, which corresponds to the English-language names: “remote photoplethysmography” (rPPG) and “imaging photoplethysmography” (iPPG) or “photoplethysmographic imaging” (the PPGI). In the text of this review, we will use a single name: the PPGI-imaging, consonant with the name of the techniques similar in the problem being solved to such as, e.g., laser Doppler imaging, speckle contrast imaging, and thermographic imaging, which are actively used to study the processes of regulation of peripheral hemodynamics. The wide spread of the photoplethysmography method is facilitated by its high relevance in the development and design of the wearable devices that control the frequency and variability of the heart rhythm. Currently, most sports watches and bracelets determine heart rate parameters precisely on the basis of the analysis of the photoplethysmographic signal using software processing of the signal reflected from the LED-photodiode optocoupler. As far as we know, this work is the first attempt to generalize the results of research on the PPGI and two-dimensional non-contact oximetry in the Russian-language scientific literature. The existing review [1] discusses the results of work on the PPGI of the face without describing two-dimensional oximetry. Therefore, it seems relevant to review modern achievements in the field of the PPGI as a method characterized by high spatial resolution, informativity in terms of the diagnostics based on the registration of a complex of optical, biomechanical, and physiological properties of a biological object, and being comparatively simple and inexpensive in hardware and software implementation. The review structure is constructed in such a way that the method of classical contact photoplethysmography is briefly considered first and then the method of remote two-dimensional the PPGI, as a newer direction of its development. The paper describes the physical basis of the formation of the PPGI signal; the technical aspects of the method (the choice of the emitter spectrum, the camera type, and the area of interest); the physiological parameters determined by the PPGI method; and the possibilities of non-contact oximetry based on the PPGI technology are considered. In conclusion, the deductions on the main directions of the considered problems of the PPGI and oximetry are formulated. Thus, the main purpose of this review is to summarize the results of the modern foreign and Russian scientific works related to the development of the non-contact oximetry and the PPGI of hemodynamic phenomena on the surface of living objects with a description of the basic physical, technical, and physiological aspects of the methods. Most of the works under consideration were completed during the period 2010–2021. The authors did not set the task of an exhaustive description of all aspects of the PPGI, so the review can rather be considered as an introduction to this subject area. From the Contact Photoplethysmography to the PPGI Before the invention of the photoplethysmography as a method of measuring volumetric blood filling in different parts of the body, various designs of the mechanical plethysmographs were used (based on the Greek plethysmos—magnification + grapho—to write, to depict). The first work by A. Hertzman, which describes a photoelectric method for measuring blood pressure, dates back to 1938 [2]. By illuminating a section of tissue with red and infrared light, which after reflection or passage, was recorded by a phototransformer, the authors found its pulsations, which were associated with the pulsating nature of blood flow in microcirculatory vessels. Since blood has optical properties different from those of the surrounding tissue, this ultimately causes a change (modulation) in the intensity of transmitted or reflected light. Initially, the described method was called “photoelectric plethysmography,” but the name “photoplethysmography” became established over time. The further studies have confirmed that the photoplethysmographic (PPGI) signal is linearly related to changes in blood volume in microcirculatory vessels [3]. Depending on the locations of the radiation source and receiver, there are two main types of PPGI sensors: (1) a “transmission” one (transmission sensor) when the source and receiver are located on opposite sides of the object of study, e.g., of a finger; (2) a “reflection” one (reflective sensor) when the source and receiver are located on the same side of the object of study. Progress in the improvement of the PPGI sensors of these types is described in the review [4]. In early 1975, Aoyagi et al. proposed a new noninvasive method for determining arterial blood oxygen saturation based on photoplethysmography [5], which was called pulse oximetry. The pulse oximetry method made it possible to simultaneously assess the pulse rate and the level of oxygen saturation in the blood (SpO2—saturation pulse O2, pulse saturation O2), as a result of which it gained popularity because of its low cost, simplicity, and practicality. In the early 1990s, the pulse oximetry became a mandatory procedure during anesthesia monitoring according to international standards [6]. The first studies indicating the possibility of non-contact photoplethysmographic imaging of the skin blood flow using a video camera were started in 1996 [7]. Later, in 2000, the authors presented one of the first the PPGI systems based on a charge-coupled device (CCD) and demonstrated its applicability for assessing local changes in tissue blood filling [8]. Since then, the number of publications related to the PPGI methods has been rapidly increasing every year. So, according to the Google Scholar search engine (https://scholar.google.ru/) at the end of 2021, the number of publications with the keyword “plethysmography” over the past 6 years exceeds the number of similar publications over the previous 100 years (Fig. 1a), and the number of publications with the keywords “imaging photoplethysmography” and “remote plethysmography” over the past 3 years exceeds the number of similar publications over the previous 10 years. At the same time, the relative weight of the number of the publications on the topic of photoplethysmographic imaging in the total number of the publications devoted to the photoplethysmography has been steadily increasing every year over the past 10 years (Fig. 1b), which indicates an increase in the relevance of the photoplethysmographic imaging. Fig. 1. Growth in the number of publications with the keywords “imaging photoplethysmography” and “photoplethysmography”: (a) absolute number of publications and (b) percentage of publications with the keywords “imaging photoplethysmography” to the total number of publications with the keyword “photoplethysmography”. The first measurements of the PPGI signal were made in 2007 and 2008 by Takano [9] and Verkruysse [10] using a standard camera in the face area. The authors proposed a method that detects fluctuations in the complexion from the set of predefined areas of interest. This method was used for the monochromatic [9] and color images [10]. At the same time, the PPGI signal was formed by simply averaging the intensity of the pixels that make up the area of interest. In 2011, A. Kamshilin and colleagues presented the results of the PPGI in each pixel of the video frame, which provided a high spatial resolution of the method [11, 12]. At the same time, the methods of the non-contact determination of SpO2-oxygen saturation of blood using a video camera have been developing since 2005 [13, 14]. Currently, the diagnostic capabilities of the methods of the contact and non-contact PPGIs are expanding and include not only the assessment of the heart rate, determination of the vascular wall properties, the level of blood oxygen saturation [15–19], but also the determination of the pulse wave propagation time, as well as its spatial distribution over the skin surface [20, 21]. PHYSICAL FOUNDATIONS OF THE PHOTOPLETHYSMOGRAPHY METHOD AND ALTERNATIVE IDEAS ABOUT THE REASONS FOR THE FORMATION OF A PHOTOPLETHYSMOGRAM In accordance with the modern most common concepts, the PPGI signal is formed as follows: part of the incident radiation of a LED or laser penetrates into biological tissue, undergoing many acts of absorption and scattering, reaches the tissue vascular layer, in which it is partially absorbed by blood (mainly erythrocytes) and partially returns from the depth of the tissue to the surface and is detected by a photodiode or a video camera as a PPGI variable signal [22–24]. As a result, the change in the PPGI signal intensity is associated both with a change in volumetric blood filling and with the absorption and scattering of light by the tissue structures It is considered in most works on PPGI that the pulsating variable part of the PPGI signal (or the “AC” component) is determined by the volume variation associated with the pulse, whereas the non-pulsating slowly changing part of the PPGI signal (or the “DC” component) is determined by a slow change in the blood filling of the venous and arterial vessels, as well as changes in the intercellular fluid volume and other physiological processes affecting the biological tissue optical properties. The growing interest in PPG applications and in particular in the PPGI has raised certain questions among researchers related to the physical mechanisms of the formation of the PPGI signal [25, 26]. The factors potentially influencing the PPGI signal shape such as: changes in blood volume, movement of the blood vessel wall, and orientation of erythrocytes, were considered [27–30]. Traditionally, it is believed that the PPGI signal mainly depends on changes in the blood volume in the vessels. However, there have been disagreements on this issue relatively recently [31–33]. For example, in [32, 34], the authors assumed that pulse fluctuations of the transmural arterial pressure in the larger arteries mechanically deform the dermal tissue structure, which leads to periodic changes in the density of the capillaries in the dermis papillary layer and the light scattering coefficient of the skin layers. Thus, the formation of the PPGI signal can be caused precisely by the mechanical compression of the tissue by the blood vessels. This assumption explains why it is possible to obtain an PPGI signal even in the case when optical radiation does not penetrate to the depth of the location of the blood vessels. The depth of the light penetration into the skin for the PPGI in the reflection mode is really of little importance [35], while there are ambiguities in its definition [36]. When performing the PPGI, it is necessary to consider that the depth of the penetration of LED radiation into the skin will be less than when using a contact reflection sensor with the same type of emitters since the power density of the radiation incident on the skin surface decreases. The PPGI with simultaneous regulation of pulse oscillations at wavelengths of 530 and 810 nm showed the possibility of determining the blood perfusion from two functionally and morphologically different layers of the cutaneous microcirculatory bloodstream: the superficial subpapillary plexus and the deeper plexus at the junction of the dermis and hypodermis [37]. The study [38] shows that the difference in the geometry of the contact and non-contact methods does not affect the possibility of the SpO2 calibration based on the camera. A deep understanding of the origin of the PPGI wave form measured distantly is necessary to establish an unambiguous connection of this signal with the variations in the arterial blood volume in the blood vessels and cardiac contractions and to exclude the influence of other physiological factors. Analyzing the skin spectral properties, it can be concluded that the use of the red and near-infrared radiation in PPGI studies makes it possible to obtain information about the hemodynamics in the blood vessels, while the effect of pulsation in certain areas on the skin surface manifests itself in the form of small mechanical vibrations, i.e., it is a ballistographic effect [33]. These effects can have global and local origin. The global ballistographic effects can be associated, e.g., with the movement of the head caused by the release of blood into the aorta and the local effects, e.g., with the skin surface slope because of the passage of a large artery under the measurement area. The systematic studies of the PPGI signal origin have shown that ballistographic effects occur mainly when using inhomogeneous and non-orthogonal illumination (incident light is not perpendicular to the skin surface). The degree of influence of the global and local effects on the resulting PPGI signal is difficult to quantify. The ballistographic effects differ significantly from the effects of the fluctuations in blood volume. They also produce a pulsating signal, but the phase of the resulting signal and its morphology can differ from the signals that arise because of the effects of fluctuations in blood volume. Averaging of the areas influenced by the various types of effects can lead to distortion of the resulting PPGI signal. The latter is of particular importance when determining the level of oxygen saturation in the blood when only the change in blood volume should be considered, but not the ballistic effect. Therefore, it is recommended to carefully select the skin surface areas for the oximetry in such a way as to exclude from consideration the areas that perform mechanical vibrations under the influence of the cardiac contractions and the pulse wave propagating through the vessels. TECHNICAL ASPECTS OF THE PPGI The Principle of Visual Data Formation in the PPGI The initial data in the PPGI method are two-dimensional matrices of the values proportional to the intensity of the light reflected from various objects of the background and the object being measured itself and, in the end, incident on the camera matrix (Fig. 2). The penetration depth of the radiation used to illuminate the object will depend on its wavelength, so the type of the PPGI images can be significantly different when using the sources of different spectral composition, e.g., the green and red ones (Figs. 3a and 3b). A change in the living object reflective properties over time, e.g., with a variation in the skin volumetric blood filling, leads to a corresponding change in the matrix data from the camera. As a result, a set of two-dimensional matrices evolving over time, i.e., an array of three-dimensional data, is formed over a selected period of time. The signal from each element of the two-dimensional matrix recorded for a long time can be considered as a one-dimensional time series, which is mathematically processed by the spectral or statistical analysis methods in most cases. As a result, each image pixel corresponds to a statistical, spectral, or other numerical parameter, each value of which corresponds to the color (shade) of the palette (pseudo-palette). The use of a pseudo-palette makes it possible to transform an image from shades of gray into a color image with a certain color gradient of the transition of the values from the minimum to the maximum ones, which increases the visibility of the PPGI results. Fig. 2. Hardware implementation of the PPG imaging method: (a) scheme for recording a signal from the skin surface (the components of the radiation in the green and red regions of the spectrum backscattered and reflected from blood and biological tissue are shown) and (b) an example of a ring illuminator for PPG imaging and determination of the level of oxygenation (the central wavelength of the LEDs is 660 nm (red) and 530 nm (green)) (the combination of red and green LEDs alternates on/off with a continuous operation of the camera). Fig. 3. PPG imaging of the palmar part of the hand: (a) image of the object using LEDs with a central wavelength of 530 nm (green), (b) image of the object when using LEDs with a central wavelength of 660 nm (red), (c) imaging of the pulsation amplitude in each image pixel, and (d) imaging of the amplitude of pulsations averaged within square areas of 10 × 10 pixels. The spectral processing of photoplethysmographic data using a fast Fourier transform either from each individual pixel or from a group of pixels (zones of interest) is most common. To determine the heart rate, the spectral components corresponding to the cardiac range of 0.5–2 Hz can be distinguished in the amplitude spectrum. The frequency at which the maximum amplitude of the spectral components is detected will correspond to the frequency of heart rate (HR) averaged over the measurement time. Such a method can be implemented either with spatial averaging of the determined heart rate values over the object entire surface or with averaging of the object surface in selected areas characterized by a high signal-to-noise ratio. Another common method of the visualization of PPGI data is the construction of a pulsation amplitude map, implemented by calculating the power of the spectral components of the image intensity fluctuations corresponding to the blood pulsation in the vessels in each image pixel (or a group of them). As a result, the imaging parameter in such images is the spatial distribution of the pulsation amplitude either in each pixel (Fig. 3c) or averaged over a group of pixels (Fig. 3d). In general, the pulsation amplitude map can indicate the areas of the object surface that are the most informative from the point of view of the possibility of determining the heart rate. An important aspect of the high-quality PPGI is the reduction of the mechanical movements of the object during shooting both using the special experimental techniques and the image processing algorithms that compensate for the movements and track the displacements of the object contours from frame to frame. Selection of the Emitter Spectrum Area It was noted in early works [10, 39] that the pulsation amplitudes of the green channel signal of the color camera (RGB camera) are the largest in comparison with those of the red and blue ones. This is due to the fact that oxyhemoglobin and deoxyhemoglobin of the blood absorb the radiation from the green region more intensively than those from the red and blue ones (Figs. 3a and 3b demonstrate the difference in the images obtained when the skin is illuminated by the radiation from the green and red regions of the spectrum). For this reason, an increase in blood volume in the vessels leads to a more significant decrease in the reflected component in the green channel compared with those the red and blue ones. Thus, the greatest variation of the pulsation signal is observed in the color image green channel, therefore, the digital frame green channel is used in most works on the PPGI using natural light and an RGB camera to determine the pulse and the green lighting is used in the case of a monochrome camera. While the sources in the red and near infra-red spectrum are most often used for tissue probing in the contact PPGI, the use of the radiation from the green spectral region gives the highest signal-to-noise ratio for the PPGI despite the fact that red and near infrared radiation penetrate deeper into the tissue. Using the blue region of the spectrum for the illumination gives a high noise level [39]. Using Different Types of Cameras As a rule, the systems based on either a monochrome camera with external illumination (more often green) or systems based on an RGB camera have found the greatest use for the PPGI. To avoid shadows on the object, they provide high intensity and uniformity of the illumination. For this purpose, an annular LED illuminator mounted on the camera lens so that their optical axes are aligned is often used (Fig. 2b) [40, 41]. The use of a polarizing filter on the camera makes it possible to reduce the effect of optical interference on the PPGI signal [42]. In the last 5 years, the possibilities of using budget RGB cameras and webcams (as varieties of RGB cameras with, as a rule, lower resolution and shooting speed) for non-contact heart rate monitoring have been intensively investigated. Compared to the PPGI system based on a monochrome camera, these systems have a low cost and ease of implementation since external lighting is often used for the ambient illumination [10, 43, 44]. The use of the PPGI signal normalization algorithm makes it possible to eliminate the influence of the ambient light intensity instability on the measurement results [45]. When using the data from the RGB cameras, the signal is divided into R‑ (red), G- (green), and B- (blue) channels, which are analyzed independently making it possible to examine a body part in three different wavelength ranges. The use of the three channels expands the variety of the algorithms for extracting pulse oscillations and ways to reduce motion artifacts on the basis of a combination or independent analysis of R, G, and B components. Some rarer types of artifacts, e.g., the irregularity of the speed of shooting video frames and the influence of the width of the time window for averaging heart rate are considered in [46]. Selection of the Measurement Area of Pulse Oscillations on the Human Body Surface In early studies, the area of the wrist or fingers, which were fixed to reduce motion artifacts since they were the ones that introduced significant interference, was used [13, 39, 47]., It was shown later that using a monochrome [9] and RGB camera [10] makes it possible to determine the heart rate from the face area. To measure the pulse on the face, it is recommended to use the cheeks, forehead, and chin or the entire face area [48, 49]. At the same time, recording data from the forehead area leads to the oscillations with the highest amplitude [10, 50, 51]. The area around the lips can also be used to record the pulse [52]. The advantage of using the face area to determine the pulse is that this area is always open. In addition, the algorithms for identifying informative areas on the face that reduce movement artifacts, e.g., the Viola–Jones object detection method [53] or algorithms that use Haar signs [54] have now been developed while it is necessary to manually isolate informative areas on other parts of the body [55]. Defining the Area of Interest When manually selecting the region of interest and its size [11, 49, 56], the resulting PPGI signal is usually determined by averaging the values from the group of pixels that make up this area. The disadvantages of this method include: firstly, the influence of a subjective factor, because it is difficult to ensure the reproducibility of the location of the area because of the object micro-movements and secondly, the problem of choosing the optimal size of the area of interest when averaging over small spatial areas is that the signal will be more stepwise and when averaging over a large area, the resulting signal can include both informative and uninformative pixels or the pixels that give a signal of the motion artifacts or spatial reflection. Testing of the existing automatic algorithms for identifying the area of interest, e.g., such as the Viola–Jones or Lucas–Canade face detectors, shows that these algorithms can not accurately track the selected area when the face is rotated. The only best solution to these problems does not yet exist, so various compromise options are offered. A method for determining the informative pixels using the simultaneous analysis of the entire face area and automatic detection of the areas of interest with the highest pulse amplitude and using adaptive matrices [57] or variations in the brightness of the areas [58] is proposed. Earlier, the same authors proposed using a continuous wavelet transform to identify the pixels with a pronounced pulse signal [59]. The concept of “super pixels” is proposed for the automatic selection and tracking of the most informative sections [60–63]. The nonparametric Bayesian algorithm can be used for image segmentation and the autoregressive models can be used to remove artifacts associated with light flickering [64]. A stochastic method for selecting points from the cheek region is proposed to estimate the PPGI waveform using the Bayesian approach [65]. One of the modern approaches for automatic identification of areas of interest is the use of convolutional neural networks [66]. ASSESSMENT OF THE HUMAN BODY MAIN PHYSIOLOGICAL PARAMETERS BY THE PPGI METHOD Extraction of the Pulse Signal from the PPGI Data Heart rate is one of the most important markers of the health of the cardiovascular system, the control of which is vital for some patients, so the issue of continuous and non-contact heart rate monitoring remains relevant [67]. A relatively high signal-to-noise ratio in the cardiac oscillation spectral region (0.5–2 Hz), which makes it possible to isolate a useful signal against the background of optical interference and video camera matrix noise, contributes to the increased interest in non-contact determination of heart rate parameters. Contactless heart rate monitoring using the PPGI has already had success in clinical application, e.g., for bedside monitoring of infants and premature infants [68], as well as for monitoring sleep disorders. After the signal have been recorded, as a rule, preprocessing of frames is performed in order to isolate the pulse wave, for which band-pass signal filtering [69], Fourier filtering [49], continuous wavelet filtering [59], or adaptive filtering techniques [70] are used as standard. Another approach is to purposefully isolate a weak pulse signal using the Eulerian amplification algorithm [71, 72]. The authors in [56] proposed to minimize the influence of motion artifacts and highlights in the original signal by removing the trend and separating the RGB data using independent analysis of the R, G, and B components. This method is based on the separation of a multidimensional signal into its independent initial components, while it is assumed that the initial signals are statistically independent of each other and are not Gaussian. The CHROM method, in which the RGB data vectors are combined into two orthogonal signals, normalizing the color channel and reducing the level of interference associated with the highlights on the skin surface, has been developed on the basis of the selection of the main components [18, 56, 73–75]. A normalized RGB space with an orthogonal plane of the signal tone is used in other works [69, 76]. It is possible to exclude the components that are not related to the pulsations of the RGB data caused by the heart rate and the passage of a pulse wave through the vessels on the basis of the analysis of RGB data in such a space. In addition to the use of various signal filtering methods and algorithms, the use of neural networks is also becoming increasingly popular for determining the heart rate. For this purpose, the deep or shallow learning is used. The input data for this method are either the original video recording of the signal (spatial-temporal data) or preprocessed one-dimensional data from the selected area. The deep learning approach was used for the first time in [48], in which the data was pre-processed by the method of selecting the main components and transmitted to the neural network. Later, more advanced deep learning neural networks, in which the input data was the pre-selected signals in the time domain [77–79], as well as neural networks where the original video recording is used as the input signal [80], appeared. In a number of studies, the influence of various external conditions and interference was modeled in order to assess the degree of distortion of the PPGI data and ways to minimize them [81, 82]. As a response to the change in the current epidemiological situation associated with the spread of the new coronavirus infection SARS-CoV-2, there are works in which the pulse is determined for people in a mask [83], while the works in which the issue of the minimum required area on the face necessary to determine the pulse with a high degree of certainty was studied [84]. Two-Dimensional Mapping of the Pulsation Amplitudes In addition to the task of extracting the heart rate signal using the video recording, the spatial mapping of the pulse wave signal amplitudes on the body selected part is also an urgent task. The laser speckle contrast imaging [85] and laser Doppler imaging devices are commercially available for measuring blood perfusion maps [86]. The limitations of such devices include the need for complex measurement protocols and the high cost of hardware and software. The PPGI method does not have these disadvantages, therefore, it can take in the future a firm place as a method of two-dimensional mapping of peripheral hemodynamics parameters. The first algorithms for constructing a spatial distribution map of the amplitude of blood flow fluctuations included splitting the original image into separate regions. Next, a three-dimensional matrix of size x × y × z was formed with the subsequent execution of the Fourier transform (see Section 2.1). One of the first who used the two-dimensional imaging of blood flow fluctuations were the authors [13]. When performing visualization, the spatial resolution of this method decreases in inverse proportion to the size of the square within which the data is averaged. A new method for obtaining the PPGI images with a high spatial resolution equal to the resolution of the camera used was presented [11]. This method is based on the synchronous amplification of recorded video frames (coherent de-modulation method). It is shown that the method can be used to study the regulation of peripheral blood circulation [87]. To form a reference signal, it is recommended to use an area of 1 cm2 or more [88]. The possibility of the PPGI and determination of the heart rate in newborns under room lighting has been demonstrated [89]. Another approach for constructing perfusion maps is the multisensory PulseCam method, in which the signal from each pixel is compared with the reference PPGI signal from the finger sensor. Thus, the method also allows obtaining perfusion maps with a spatial resolution equal to the full resolution of the camera matrix [90, 91]. The coherent demodulation method and the multisensory method use a reference pulse signal, which in the first method is obtained by averaging the signals from the pixels in the selected area and in the second method, – using an independent PPGI sensor. A high noise level is recorded in each individual pixel, so the reference signal is used to increase the signal-to-noise ratio. In [92], the PPGI unit with a green backlight was used to photograph the face and detect systemic scleroderma. The results of the study of the healthy and sick subjects revealed that the asymmetry of the pulse amplitude on the face is manifested in systemic scleroderma, which is clearly demonstrated by the presented two-dimensional images of amplitude maps. Also, the two-dimensional PPGI demonstrates an increase in blood flow at local heating [93]. The possibilities of the PPGI in the study of cerebral microcirculation of the rat brain were demonstrated [94]. The authors used in experiments an anesthetized rat in which an autopsy of the cranium was previously performed without damage to the meninges. In response to painful stimulation, an increase in the pulse amplitude was recorded in various areas of the rat’s open brain. Further studies have demonstrated the possibilities of the PPGI of pulsation amplitude and pulse wave delay time for the analysis of cerebral blood flow during open brain surgery. It was found that this procedure well visualizes changes in the cerebral blood supply caused by surgical intervention [95]. A decrease in the uniformity of the pulsation amplitude maps in migraine was demonstrated [96]. The PPGI data can be used in conjunction with the electrocardiography to measure the pulse wave propagation time and its spatial distribution, e.g., in the facial region [20, 97]. For three-dimensional mapping of pulse amplitudes, an alternating illumination at several wavelengths, e.g., 660 and 880 nm is used, which makes it possible to visualize the distribution of the pulse wave signal along the depth [41]. Low-Frequency Rhythms of the PPGI Signal Oscillations The relevance of the study of the photoplethysmogram low-frequency oscillation rhythms of less than 0.5 Hz is due to the fact that the corresponding spectral components can characterize changes in the vascular tone determined by the influence of various physiological mechanisms of regulation such as: endothelial, neurogenic, myogenic, and respiratory. Thus, e.g., a decrease in the amplitude of the PPGI signal spectral components in the neurogenic, myogenic, and respiratory ranges was found in a group of patients with diabetes mellitus [98]. In [99], a decrease in the amplitude of the vasomotion with a frequency of about 0.1 Hz in the presence of allergy was recorded with the PPGI. The analysis of the PPGI low-frequency component demonstrated the informativeness in the study of the respiratory waves [100] and vasomotion [101–103]. The high informativeness of the PPGI low-frequency region for determining the functions of the sympathetic nerves and the sympathetic part of the autonomic nervous system is emphasized [104]. It is shown that the hemodynamics slow fluctuations in the frequency range from 0.003 to 0.04 Hz have a high level of correlation of more than 0.9 with a very low-frequency component of the spectrum of the heart rate variability over most of the observation interval. The possibility of using the PPGI system to detect changes in a person’s psychoemotional and energy-deficient states is discussed [105, 106]. Devices are being designed and methods are being developed for the PPGI diagnostics of the disorders of the low-frequency rhythms of the peripheral vascular tone regulation [107, 108]. Recent studies found a high phase coherence of the PPGI signal low-frequency component and the pressure with the heart rate variability low-frequency component [109, 110]. There is a significant phase synchronization of the PPGI signal and the laser Doppler flowmetry (LDF) signal in the low-frequency range (0.0095–0.1 Hz) [111]. A high coherence of the PPGI signal phases between the arm and leg is recorded especially in the endothelial range [112] despite the fact that this range corresponds to a local rather than a central mechanism of vascular tone regulation. Setting a constant breathing rhythm of 0.04, 0.1, and 0.25 Hz led to an increase in the phase synchronization of the PPGI and LDF signals [113, 114]. The results in [112] indicate that the PPGI signal low-frequency part is influenced or interrelated with the heart rate variability and respiratory rate. At the same time, as shown in [115, 116], the PPGI signal low-frequency part largely causes fluctuations in the skin temperature of the extremities and can be converted into temperature using a heat wave model and a low-pass filter [117]. A recent study [118] established a significant correlation between the signals of laser speckle contrast and the PPGI. The results presented above were obtained mainly using contact photoplethysmography. Therefore, the photoplethysmographic imaging of the low-frequency rhythms of the volume blood filling oscillations in the endothelial, myogenic, neurogenic, and respiratory ranges (Fig. 4) seems relevant from the point of view of medical diagnostics and in the future can find confirmation of its results using laser Doppler flowmetry, infrared thermography, and speckle contrast imaging [99, 118]. Fig. 4. Photoplethysmographic imaging of low-frequency rhythms of blood flow oscillations (wavelength of illumination 530 nm) [119, 120]. Figure 4 shows examples of the PPGI of the amplitude of low-frequency oscillations of the reflected signal of the green region in the endothelial, neurogenic, myogenic, and respiratory frequency ranges from 0.005 to 0.5 Hz. The presented maps demonstrate the heterogeneity of the spatial distribution of the oscillation amplitudes and a similar topography of the “islands” on the left and right hands with the maximum amplitudes in the lateral region of the phalanges of the fingers and the tenar zones. Other Physiological Indicators 3.4.1. Respiratory rate. The measurement of the respiratory rate in most works on the PPGI is considered as an additional procedure to the measurement of pulse, e.g., the determination of the heart rate and the frequency and depth of respiration of newborns [49, 56, 68]. 3.4.2. Heart rate variability (HRV). The method of non-contact assessment of the heart rate variability based on the PPGI using a webcam was first introduced in 2011 [49] and confirmed in 2013 [121]. The disadvantages of this method include the low sampling rate of the webcam video image, which limits the accuracy of measuring the time intervals between systolic peaks [121, 122]. The cardiac rhythm variability was also assessed using the PPGI in the process of monitoring the procedures of electroneurostimulation [123]. The zones located near the nose and the bridge of the nose are highly informative from the point of view of the possibilities of determining the heart rate when using a webcam [124]. 3.4.3. Vascular disorders and allergies. The ability of the PPGI to characterize vascular skin lesions, e.g., wine stains, was demonstrated in [41, 125]. Other authors demonstrated the use of the PPGI to assess the skin allergic reactions to the use of antihistamines [126] and to assess mechanical injuries [127]. the PPGI of the near-surface arteries and veins is implemented for the skin of varying degrees of pigmentation using color and monochrome cameras and an illuminator operating alternately at two wavelengths [128]. 3.4.4. Merging of photoplethysmographic imaging and infrared thermography methods. Since the considered PPGI method provides information about the hemodynamic processes on the skin surface, it is of interest to compare it with alternative methods of hemodynamic imaging primarily such as laser Doppler imaging, speckle contrast imaging, and infrared thermography. Currently, there are only a few works in this aria, e.g., [118]. At the same time, in our opinion, it looks promising not only to compare the results of the PPGI and other hemodynamic imaging techniques as independent methods, but also to detect the possibilities of their fusion when the whole becomes larger than the sum of its parts and the so-called emergence manifests itself, e.g., the fusion of the PPGI and infrared thermography methods. Considering the found relationships between the PPGI signal fluctuations and the fluctuations in skin temperature [115, 116], it is possible to calibrate the PPGI data expressing them not in relative units, as is usually done, but in absolute units related to the temperature measured in degrees by the calibrated thermal imaging cameras. The fusion of the PPGI and thermographic imaging methods can also make it possible to determine the skin thermophysical properties by the delay time of the temperature signal spectral components relative to those of the PPGI signal [116] and thus detect and monitor therapy of, e.g., trophic lesions on the skin surface. Currently, the PPGI and thermography methods in most cases are considered to a greater extent as independent ways of obtaining information about the living object properties [129, 130]. Various schemes of the mutual arrangement of the thermal imaging camera and the camera for the PPGI are proposed (Fig. 5) [131]. Fig. 5. Options for combining infrared thermal imaging and PPG imaging methods: (a) combining fields of view, (b) stereoscopic alignment, and (c) use of a glass plate as a splitter for a CCD-camera and as a mirror for the IR rays of a thermal imager (adapted from [131]). Noteworthy is [132], in which a significant number of calculations related to statistical and spectral processing of the dynamic thermograms and two-dimensional photoplethysmograms was performed in order to build a map of the informative features of the object of study. The main idea of this work was to study the spatial-temporal and spectral characteristics of images. The dynamics of the signal in each pixel of the images formed a time series, which at the processing stage was subjected either to the calculation of the signal statistical characteristics in a given pixel (standard deviation, mathematical expectation, coefficients of asymmetry, kurtosis, etc.) or spectral processing using the Fourier transform. As a result, the video image was compressed for the selected time interval into a single frame, which is a map of the spatial distribution of statistical or spectral features characteristic of this time window. Next, the spatial characteristics of the constructed maps such as the contrast of the selected area of interest or the intersection of histograms (the sum of the minimum column of the matching ones of the model histogram and the histogram of the analyzed image area) were evaluated. A “similarity map” was built on the basis of the results of the analysis of the intersection of the histograms. Thus, first, the processing of the data from each pixel in the time domain was performed and the maps of statistical or spectral features were constructed, and then the features of the spatial distribution of such maps were evaluated, i.e., a spatial-temporal approach to the processing of the PPGI frames and dynamic thermograms was implemented. As a result, it was found that the determination of the spatial contrast of the sign map makes it possible to identify the informative areas a pulsating component of the photoplethysmogram and an area with a noise component characteristic of the background or uninformative areas of interest. The intersection of the histograms and the similarity map can be used to segment parts of an object and separate it from the background. At the same time, the maps based on the PPGI data are more suitable for highlighting the silhouette of a living object and the dynamic infrared image data is more suitable for highlighting its contour. Nevertheless, despite the considerable number of the calculations carried out, so far the advantages of merging the two technologies of the PPGI and thermographic imaging have not been demonstrated, since the results of processing the corresponding images were considered independently. NON-CONTACT DETERMINATION OF BLOOD OXYGEN SATURATION LEVEL The First Mention of Non-Contact Oximetry Over the past 15 years, various approaches have been developing to implement non-contact oximetry or to determine the level of saturation of blood with oxygen using a video camera. In most cases, SpO2, which is pulse oxygen saturation of blood, is determined. The first results, which describes a method for recording pulse oscillations using a monochrome CMOS camera and sequential shooting at several wavelengths (660, 810, and 940 nm) in order to calculate the SpO2 level, were published in 2005 [13]. In the same year, using synchronous activation of a monochrome CMOS camera at each switching of LEDs with wavelengths of 880 and 760 nm, the first numerical results of the level of oxygenation from the finger region were obtained, however, the values of the oxygenation level were lower than those in the pulse oximeter readings [47]. Later, as a result of the study of the blood samples with different oxygen content in vitro, a calibration curve was constructed to determine SpO2 using a video camera [14]. The numerical results of non-contact determination of the oxygenation level showed, on average, an underestimation of the values for the arterial blood by 3% and an overestimation of the values for the venous one by 3–10% compared with those in the contact pulse oximeter readings. In [133], the applicability of the wavelengths of 880 and 760 nm for non-contact determination of the oxygenation level was investigated: the influence of the pulse rate and the oxygenation level on the measurement results was determined. Physical Principle of Noninvasive Oximetry, Determination of SpO2 The physical basis of the method is the presence of differences in the dependence of the light absorption coefficient of oxygenated (HbO2) and deoxygenated (Hb) forms of hemoglobin of erythrocytes, which make up the bulk of the blood corpuscles. In accordance with the Bouguer–Lambert–Beer law, the absorption of light by a substance in a solution is proportional to its concentration, therefore, when the blood oxygen saturation level changes, the amplitude of the PPGI signal at the selected wavelength also changes. The presence of other blood and skin chromophores can lead to a decrease in the accuracy of determining the oxygenation level by optical methods. The absorption spectra of the oxygenated (HbO2) and deoxygenated (Hb) blood in the visible and near infrared wavelength ranges are shown in Fig. 6. When choosing a pair of wavelengths for implementing noninvasive oximetry, it should be considered that the camera registering reflected and backscattered radiation has its own nonlinear spectral dependence of sensitivity usually significantly decreasing at the more than 800 nm. In the simplest case, two wavelengths λ1 and λ2 are chosen to implement an oximeter in the absorption spectrum (Fig. 6) so that the Hb absorption coefficient exceeds the HbO2 absorption coefficient at the wavelength λ1 and the inverse ratio is true at the wavelength λ2. With a decrease in the SpO2 oxygenation, the HbO2 concentration decreases and Hb increases, which is accompanied by a decrease in the reflection coefficient of the radiation at the wavelength λ1 and its increase at the wavelength λ2. When SpO2 increases, opposite changes in the reflection coefficient are expected. Fig. 6. Absorption spectra of oxygenated (HbO2) and deoxygenated (Hb) blood in the visible and near infrared ranges. λ1 = 660 nm, λ2 = 940 nm. To determine the percentage of oxygen in the blood using a video camera, as well as for a pulse oximeter, the classical method of the ratio of the pulse signals of the photoplethysmograms recorded simultaneously at two wavelengths is used:1 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RR = \frac{{A{{C}_{{\lambda 1}}}{\text{/}}D{{C}_{{\lambda 1}}}}}{{A{{C}_{{\lambda 2}}}{\text{/}}D{{C}_{{\lambda 2}}}}},$$\end{document} 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Sp}}{{{\text{O}}}_{2}} = A \cdot RR + B,$$\end{document} where RR is the parameter of the spectral pulsations, ACλ1 and DCλ1 are the variable and constant components of the PPGI signal at the wavelength λ1, ACλ2 and DCλ2 are similar components at the wavelength λ2, and A and B are the coefficients of Eq. (2). During the production and adjustment of the oximeter, the parameter RR is calibrated (coefficients A and B) as a percentage of the oxyhemoglobin saturation, determined by direct readings of gas analysis of blood samples, in the arterial blood (SaO2) [36]. Thus, the value of the pulse saturation SpO2 can be obtained by measuring RR and substituting the measured value into the calibration equation (Eq. (2)). The noninvasive oximetry method is considered in more detail in the lecture article by D.A. Rogatkin [36]. Non-Contact Imaging of the Oxygenation Level Considering the change in the epidemiological situation caused by the emergence of a new coronavirus infection SARS-CoV-2 (COVID-19), characterized by a decrease in blood oxygenation in the pulmonary form of the disease, non-contact determination of the SpO2 level becomes particularly relevant. The appropriate systems can be used, e.g., at the checkpoints with high traffic of people to identify potential carriers of infection. For the first time, the non-contact determination of the oxygenation level in the facial area using a visible range camera and using room lighting was demonstrated in a 2013 paper [134]. For this purpose, two monochrome CCD cameras, on the lens of each of which narrow bandpass filters were fixed to capture the PPGI signals at a wavelength of 520 and 660 nm, were used. Fourier filtering was used to isolate the pulse oscillations, and these signals were averaged over the entire area of interest to determine the saturation level. After having calibrated the camera on the basis of the results of the simultaneous measurements with a pulse oximeter and a camera during breath retention of one of the subjects, it was possible to obtain fairly accurate data on the blood oxygen saturation level using the camera on a group of subjects. 4.3.1. Variants of hardware implementation of imaging of the oxygenation level. To measure oxygenation, it is mandatory to record the pulse at least at 2 wavelengths of the optical range. When using monochrome cameras, the authors apply discrete switching on of the camera with each alternating switching of lighting [14, 47, 125, 133, 135]. To do this, a hardware trigger that switches the camera and lighting sources, as which semiconductor LEDs are used, is used. The disadvantage of this method is the low frame rate for each lighting channel, which at the moment is 20 frames per second. Recording speed limit is due to the delay in switching the trigger to turn on the camera and, first of all, to the launch of an array of LEDs. However, it should be noted that the attempts to increase the frame recording frequency for this recording method have not been purposefully carried out, although it is discussed that the recording frequency affects the SpO2 evaluation result [133]. In addition, it is important to assess the effect of the switching time of the LEDs on their brightness, which is associated with the warming up of the LEDs. To exclude changes in the LED brightness, some authors turn on the LEDs before the experiment and let them warm up at the selected switching frequency [125]. An alternative option can be the use of mechanical shutters of various types, providing luminous flux amplitude modulation. Another approach is to use several monochrome cameras with narrow-band light filters. In this method, a narrow-band filter is attached to each camera to isolate the necessary wavelength in the spectrum of the reflected light flux. At least 2 monochrome cameras identical in technical characteristics are used to measure oxygenation. White light [38], indoor or natural lighting [134, 136] can be used for illumination. There is also a method for processing data from 3 monochrome cameras in order to increase the accuracy of determining SpO2 [25, 26, 137, 138] or 4 cameras [139]. Multiple camera application allows you to simultaneously record a signal with a frame rate that is limited only by the parameters of the camera itself, which is an advantage over a single camera with switching lighting. At the same time, the processing of the results becomes more complicated because of the difference in the spatial location of the cameras and the need to use the algorithms to combine two images into one. The cost of a measuring system based on the use of several monochrome cameras also increases. The influence of the location of cameras on the accuracy of the SpO2 determination is investigated [138]. In addition to monochrome cameras, RGB cameras with indoor or natural lighting are used [140–145]. The increasing popularity of RGB cameras is associated with their high prevalence and low cost. To implement this method, it is enough to have an inexpensive, compared to monochrome one, RGB camera or even a webcam. External lighting can be used as probing radiation. To determine the oxygenation level, the original video data is divided into separate R, G, and B components, by which the amplitude variation of the pulse oscillations is determined. Further, using the ratio of the amplitudes mainly between the R and G components, the RR (Eq. (1)) is calculated by which the oxygen saturation of the blood is determined (Eq. (2)). For the sake of the method simplicity, one has to face up to the relatively low camera sensitivity and the limitation of the choice of wavelengths of incident radiation, since only broadband components of red, green, or blue illumination can be analyzed. 4.3.2. Selection of probing radiation wavelengths. When determining the oxygenation level, the choice of wavelength has the character of a compromise between the camera technical characteristics and the optimal absorption of oxygenated (HbO2) and deoxygenated (Hb) forms of hemoglobin to achieve the maximum amplitude of the blood flow optical signal. Most monochrome cameras have a high spectral sensitivity in the 500–700 nm band, which decreases sharply outside this area. This type of the spectral characteristics of monochrome cameras imposes a restriction on the use of a traditional pair of wavelengths of 660 and 940 nm because of the low signal-to-noise ratio in the region of 940 nm [13]. Because of this limitation, the specified pair of wavelengths is practically not used for non-contact determination of the oxygenation level. The use of green light makes it possible to record the pulse oscillations of the highest amplitude and with a sufficiently pronounced shape, which provides an acceptable level of accuracy in determining SpO2, since it is in the wavelength range of 530–550 nm that the blood hemoglobin has maximum absorption [11]. Thus, the authors of [136] reported an experimentally detected difference in the pulse amplitudes for a pair of blue (460 nm) and green (520 nm) illumination, and the authors of [134], using a pair of the wavelengths of green (530 nm) and red (660 nm) radiation, determined the variation of oxygen in the blood in the range of 92–98% with a deviation of several percent compared to that of the contact finger pulse oximeter. When using a color camera, the green and red channels were also used in most works to determine SpO2. The theoretical models considered in [26, 139] allowed us to plot the change in the PPGI signal relative amplitude both on the incident radiation wavelength and on the percentage of oxygen in the blood. Theoretical calculations confirm that in the range of 530–550 nm (green), the pulse signal amplitude has a maximum value compared to those at other wavelengths. At the same time, a change in the percentage of oxygen in the blood leads to a slight change in the pulse amplitude at green light, which is somewhat inconsistent with the interpretation of the results in [11]. In this case, it is recommended to use the radiation in the range from 600 to 1000 nm for non-contact oximetry. At the same time, the pulse amplitude recorded by the camera is several times lower, but the percentage change in the oxygen level in the blood leads to a more significant change in it compared to that in the green area. Thus, to increase the accuracy of determining SpO2, it is necessary to use such pairs of wavelengths at which changes in the oxygen content cause significant changes in the reflected signal amplitude and at the same time, fall into the region of the greatest spectral sensitivity of the camera. Also earlier in [135], the authors, using modeling based on the Bouguer–Lambert–Beer law, calculated the dependence of the RR change on saturation for several combinations of wavelengths and demonstrated that the pairs: red (660 nm) and infrared (IR) (880 nm), orange (610 nm) and IR (880 nm) demonstrate a change in RR by several times in the saturation range of 70–100%, while the pairs: green (528 nm) and IR (880 nm), blue (470 nm) and IR (880 nm) demonstrate 10 times smaller changes in RR. The authors themselves [135] chose a pair of orange (λ = 610 nm) and IR (λ = 880 nm) for their research, because in orange light, the amplitude of the pulsations is greater than that in red one. For practical purposes, e.g., in clinical settings, it is necessary to be able to measure SpO2 in a wider range (at least 80–100%). In the study [38], it was experimentally demonstrated that using a pair of the radiation of wavelengths of 675 and 842 nm at low temperature or with low oxygen content in the blood, it is possible to determine the oxygenation level with high accuracy. CONCLUSIONS 1. Advantages and limitations of the PPGI method compared to the contact photoplethysmography method. 1.1. Advantages lies in the following features: —recoding and color imaging of two-dimensional spatial distribution of blood pulsation amplitudes in surface vessels, spatial distribution of blood oxygenation SpO2, and pulse wave propagation time, which is relevant, e.g., in the study of hemodynamics in cerebral cortex vessels or hemodynamics in the area of the face and extremities; —non-contact determination of heart rate and heart rate variability averaged over a large surface area, which is relevant, e.g., when monitoring vital parameters of newborns and patients with allergic, trophic, and thermal skin lesions for whom contact of the sensor with the skin is not desirable. 1.2. The limitations of the PPGI method are: —in a lower signal-to-noise ratio when recording a reflected signal by a camera compared to recording using a photodiode; —in the possible influence of the reflected background radiation detected by the camera, but not related to the hemodynamics of the object of study on the results; —there is less versatility in choosing a camera detector (compared to that in a photodiode case), which as a rule has uneven and lower spectral sensitivity in the near infrared wavelength range compared to that in the visible one. 2. Physical and technical aspects of the PPGI: 2.1. The main reasons for the PPGI signal modulation can be: a change in the volume of circulating arterial and/or venous blood; movement of the wall of blood vessels, which creates a different level of compression of the skin and changes its dispersing properties; a change in the orientation of red blood cells in the blood flow depending on the speed of its movement into the systole and diastole; and a change in the ratio between the concentrations of oxygenated and deoxygenated blood hemoglobin. The mechanical movements of the body as a result of heartbeats and respiration can be considered either as an informative component for the ballistography based on the photoplethysmography or as a hindrance. 2.2. To visualize the pulsations of arterial blood flow, it is recommended to use the spectrum green region, because in this case, a more intense absorption of green light by the blood (compared with that when using the illumination in the red and blue regions of the spectrum) leads to a greater decrease in the signal amplitude with the arrival of each successive volume pulsation. A detailed consideration of the features of the propagation of light of various wavelengths in biological tissue is carried out in the monograph [146]. 2.3. One of the disadvantages of the PPGI method is the lack of the binding of the signal amplitude to the absolute units of measurement. The solution to this problem can be the development of an optimal PPGI signal calibration method used by all researchers and allowing us to compare the results obtained in various scientific groups. In this case, one of the calibration options can be a comparison of the temperature and PPGI data. 3. Medical application of the PPGI method. Among the areas of medical application of the PPGI method, it is possible to note studies devoted to the diagnosis of systemic scleroderma, changes in cerebral blood flow, migraines, and allergies on the skin surface. Considering the change in the epidemiological situation associated with the spread of the new coronavirus infection SARS-CoV-2 (COVID-19) and characterized by a decrease in the blood oxygenation level in the pulmonary form of the disease, the non-contact determination of the level of the blood pulse saturation with oxygen SpO2 by the PPGI methods becomes particularly relevant. Most of the work on the PPGI is aimed at developing methods for analyzing the frequency, heart rate variability, and amplitude of blood pulsations in the frequency range of 0.5–2 Hz. The use of the low-frequency range (0.005–0.5 Hz) in the field of the biomedical diagnostics remains relatively little studied today, therefore, a further increase in the number of works in this direction is predicted. 4. Ways of further development of the PPGI technology. 4.1. The main ways to improve the PPGI method include increasing the sensitivity and signal-to-noise ratio for the cameras in the visible and near infrared ranges; increasing the incident radiation monochromaticity; reducing the mirror-reflected component and the influence of optical interference unrelated to the object of study; improving the algorithms for tracking the object of study compensating for motion artifacts; and development of the algorithms for the mathematical analysis, methods for calculating statistical and spectral parameters of the PPGI images in the time, spatial, and spatial-time domains with color mapping of these parameters. 4.2. One of the promising directions is the combination of the PPGI method with other methods of the imaging of the hemodynamic phenomena such as: infrared thermography, laser Doppler imaging, or speckle contrast imaging, or the contact methods (pulse oximetry, electrocardiography, rheography, etc.). Moreover, the joint synchronized use of the methods can lead to the appearance of new features that are not available to each of the methods separately. For example, the determination of the biological tissue thermophysical properties by the delay of the temperature signal spectral components relative to the PPGI signal or the method of coherent demodulation, described in Section 3.2. 4.3. The development of the PPGI and oximetry methods using webcams is likely to lead to their widespread distribution and implementation on the basis of the cameras of smartphones and other mobile devices. FUNDING A review of the possibilities of determining oxygenation based on the photoplethysmographic imaging technology was carried out with the support of a grant from the President of the Russian Federation for state support of young Russian scientists, Candidates of Sciences of the Russian Federation MK-140.2021.4; a review of the possibilities of thermal imaging data verification by the photoplethysmographic imaging method was supported by the Russian Science Foundation (grant no. 21-75-00035). Translated by N. Petrov ==== Refs REFERENCES 1 Strokanev K. S. Intellekt Sist. Proizv. 2021 19 129 10.22213/2410-9304-2021-2-129-138 2 Hertzman A. B. Am. J. Physiol. 1938 124 328 10.1152/ajplegacy.1938.124.2.328 3 de Trafford J. Lafferty K. Med. Biol. Eng. Comput. 1984 22 479 10.1007/BF02447713 6482540 4 Sun Y. IEEE Trans. Biomed. Eng. 2015 63 463 10.1109/TBME.2015.2476337 26390439 5 Aoyagi T. Kishi M. Yamaguchi K. Watanabe S. Jpn. Soc. Med. Electron. Biol. Eng. 1974 974 90 6 Tremper K. K. Barker S. J. Anesthesiology 1989 70 98 10.1097/00000542-198901000-00019 2643368 7 V. Blazek, W. Rutten, and O. Such, German Patent No. P196 38 873.2 (1996). 8 Wu T. Blazek V. Schmitt H. J. Proc. SPIE 2000 4163 70 10.1117/12.407646 9 Takano C. Ohta Y. Med. Eng. Phys. 2007 29 853 10.1016/j.medengphy.2006.09.006 17074525 10 Verkruysse W. Svaasand L. Nelson J. Opt. E-xpress 2008 16 21434 10.1364/oe.16.021434 11 Kamshilin A. A. Miridonov S. Teplov V. Saarenheimo R. Nippolainen E. Biomed. Opt. Express 2011 2 996 10.1364/BOE.2.000996 21483621 12 Zaproudina N. Teplov V. Nippolainen E. Lipponen J. A. Kamshilin A. A. Närhi M. Giniatullin R. PloS One 2013 8 e80189 10.1371/journal.pone.0080189 24324592 13 Wieringa F. Mastik F. van der Steen A. Ann. Biomed. Eng. 2005 33 1034 10.1007/s10439-005-5763-2 16133912 14 Wieringa F. Mastik F. Comput. Cardiol. 2007 34 751 15 Simonyan M. A. Posnenkova O. M. Kise-lev A. R. Cardio-IT 2020 7 102 10.15275/cardioit.2020.0102 16 Semchuk I. P. Zmievskoi G. N. Muravskaia N. P. Samorodov A. V. Instrumenty 2018 217 1 17 Akishin A. D. Semchuk I. P. Nikolaev A. P. Sist. Anal. Upravl. Biomed. Sist. 2020 19 100 10.36622/VSTU.2020.19.4.012 18 Avanesov A. A. Kopeliovich M. V. Kalinin K. B. Shcherban I. V. Sev.- Kavk. Fil. Mosk. Tekh. Univ. Svyazi Inform. 2020 1 40 19 Semchuk I. P. Zmievskoy G. N. Muravskaya N. P. Volkov A. K. Murashko M. A. Samorodov A. V. Biomed. Eng. 2019 1 4 20 Kamshilin A. A. Sidorov I. S. Babayan L. Vo-lynsky M. A. Giniatullin R. Mamontov O. V. Biomed. Opt. Express 2016 7 5138 10.1364/BOE.7.005138 28018731 21 Kamshilin A. A. Krasnikova T. V. Volynsky M. A. Miridonov S. V. Mamontov O. V. Sci. Rep. 2018 8 1 10.1038/s41598-018-32036-7 29311619 22 Chatterjee S. Phillips J. P. Kyriacou P. A. Biomed. Phys. Eng. Express 2016 2 065018 10.1088/2057-1976/2/6/065018 23 K. Budidha, P. A. Kyriacou, and T. Y. Abay, Encycl. Biomed. Eng., 461 (2019). 10.1016/B978-0-12-801238-3.10886-4 24 Allen J. Physiol. Meas. 2007 28 R1 10.1088/0967-3334/28/3/R01 17322588 25 Gastel M. Stuijk S. Haan G. Biomed. Opt. Express 2018 9 102 10.1364/BOE.9.000102 29359090 26 Gastel M. Stuijk S. Haan G. Sci. Rep. 2016 6 38609 10.1038/srep38609 27924930 27 Nitzan M. Adar Y. Sensors 2013 13 14797 10.3390/s131114797 24184918 28 Aoyagi T. J. Anesthesia 2003 17 259 10.1007/s00540-003-0192-6 29 D. G. Lapitan and A. P. Tarasov, in Proceedings of the 2019 IEEE 8th International Conference on Advanced Optoelectronics and Lasers CAOL (2019), p. 103. 10.1109/CAOL46282.2019.9019552 30 Moço A. V. Stuijk S. de Haan G. Biomed. Opt. Express 2016 7 4718 10.1364/BOE.7.004718 27896011 31 Fine I. Proc. SPIE 2014 9031 903103 10.1117/12.2051228 32 Kamshilin A. Sci. Rep. 2015 5 10494 10.1038/srep10494 25994481 33 Moço A. V. Stuijk S. de Haan G. Biomed. Opt. Express 2016 7 1737 10.1364/BOE.7.001737 27231618 34 Sidorov I. S. Romashko R. V. Koval V. T. Giniatullin R. Kamshilin A. A. PLoS One 2016 11 e0165413 10.1371/journal.pone.0165413 27768753 35 Farrell T. J. Patterson M. S. Wilson B. Med. Phys. 1992 19 879 10.1118/1.596777 1518476 36 Rogatkin D. A. Med. Fiz. 2012 2 97 37 Marcinkevics Z. Rubins U. J. Biomed. Opt. 2016 21 35005 10.1117/1.JBO.21.3.035005 27027825 38 Verkruysse W. Bartula M. Anesthes. Analges. 2017 124 136 10.1213/ANE.0000000000001381 39 Rubins U. Erts R. Nikiforovs V. IFMBE Proc. 2010 29 304 10.1007/978-3-642-13039-7_76 40 Sun Y. Hu S. Azorin-Peris V. J. Biomed. Opt. 2011 16 077010 10.1117/1.3602852 21806290 41 Zheng J. Hu S. Azorin-Peris V. Proc. of SPIE 2008 6850 68500S 10.1117/12.761705 42 Trumpp A. Bauer P. L. Biomed. Opt. Express 2017 8 2822 10.1364/BOE.8.002822 28663909 43 Bousefsaf F. Maaoui C. Pruski A. Biomed. Signal Proces. Control 2013 8 568 10.1016/j.bspc.2013.05.010 44 A. A. Taranov and I. N. Spiridonov, in Proceedings of the Russian–German Conference on Biomedical Engineering (2014), Vol. 3, No. 33, p. 43. 45 Sun Y. Papin C. Azorin-Peris V. Kalawsky R. Greenwald S. Hu S. J. Biomed. Opt. 2012 17 037005 10.1117/1.JBO.17.3.037005 22502577 46 Y. Mironenko, K. Kalinin, M. Kopeliovich, and M. Petrushan, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2020, p. 296. 47 K. Humphreys, T. Ward, and C. Markham, in Proceedings of the Engineering in Medicine and Biology 27th Annual Conference (2005), p. 3494. 10.1109/IEMBS.2005.1617232 48 Y. Hsu, Y.-L. Lin, and W. Hsu, in Proceedings of the 2014 IEEE International Conference on Acoustical Speech Signal Process, 2014, p. 4433. 10.1109/ICASSP.2014.6854440 49 Poh M.-Z. McDuff D. J. Picard R. W. IEEE Trans. Biomed. Eng. 2011 58 7 10.1109/TBME.2010.2086456 20952328 50 S. Fallet and V. Moser, Comput. Cardiol. 43 (2016). 10.22489/CinC.2016.098-236 51 M. Kumar and A. Veeraraghavan, SPIE, 1 (2015). 10.1117/2.1201511.006184 52 Shao D. Liu C. Tsow F. Yang Y. Du Z. Iriya R. IEEE Trans. Biomed Eng. 2016 63 1091 10.1109/TBME.2015.2481896 26415199 53 Feng L. Po L. M. Xu X. Li Y. Ma R. IEEE Trans. Circuits Syst. Video Technol. 2015 25 879 10.1109/TCSVT.2014.2364415 54 Zou J. Chen T. Yang X. Comput. Mater. Contin. 2019 60 1029 10.32604/cmc.2019.05793 55 Trumpp A. Schell J. Curr. Direct Biomed. Eng. 2016 2 199 10.1515/cdbme-2016-0045 56 Poh M.-Z. McDuff D. J. Picard R. W. Opt. E-xpress 2010 18 10762 10.1364/OE.18.010762 57 S. Tulyakov, X. Alameda-Pineda, E. Ricci, L. Yin, J. F. Cohn, and N. Sebe, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR (2016), p. 2396. 58 Bousefsaf F. Maaoui C. Pruski A. J. Med. Biol. Eng. 2017 37 374 10.1007/s40846-017-0229-1 59 Bousefsaf F. Maaoui C. Pruski A. Biomed. Signal Process. Control 2013 8 568 10.1016/j.bspc.2013.05.010 60 Bobbia S. Macwan R. Benezeth Y. Mansouri A. Dubois J. Pattern Recogn. Lett. 2017 124 1 10.1016/j.patrec.2017.10.017 61 S. Bobbia, D. Luguern, Y. Benezeth, K. Nakamura, R. Gomez, and J. Dubois, in Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops CVPRW (2018), p. 1341. 10.1109/CVPRW.2018.00182 62 S. Bobbia, Y. Benezeth, and J. Dubois, in Proceedings of the 23rd International Conference on Pattern Recognition ICPR (2016), p. 361. 63 Wang W. Stuijk S. de Haan G. IEEE Trans. Biomed. Eng. 2017 64 2781 10.1109/TBME.2017.2676160 28278453 64 Tarassenko L. Villarroel M. Guazzi A. Jorge J. Clifton D. Pugh C. Physiol. Meas. 2014 35 807 10.1088/0967-3334/35/5/807 24681430 65 B. Chwyl, A. G. Chung, R. Amelard, J. Deglint, D. A. Clausi, and A. Wong, in Proceedings of the IEEE International Conference on Image Processing (ICIP) 2016 (2016), p. 1230. 10.1109/ICIP.2016.7532554 66 S. Chaichulee, M. Villarroel, J. Jorge, C. Arteta, G. Green, K. McCormick, et al., in Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition FG 2017 (2017), p. 266. 10.1109/FG.2017.41 67 S. Hu, V. Peris, A. Echiadis, J. Zheng, and P. Shi, in Proceedings of the 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2009), p. 6550. 10.1109/IEMBS.2009.5334505 68 Villarroel M. Guazzi A. Healthcare Technol. Lett. 2014 1 87 10.1049/htl.2014.0077 69 Wang W. Brinker A. C. D. Stuijk S. Haan G. D. IEEE Trans. Biomed. Eng. 2017 64 1479 10.1109/TBME.2016.2609282 28113245 70 X. Li, J. Chen, G. Zhao, and M. Pietikainen, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR (2014), p. 4264. 10.1109/CVPR.2014.543 71 Strokanev K. S. Korobeynikov A. V. Intellekt Sist. Proizv. 2016 3 56 10.22213/2410-9304-2016-3-56-59 72 Wu H.-Y. Rubinstein M. Shih E. Guttag J. V. Durand F. Freeman W. T. ACM Trans. Graph 2012 31 65 10.1145/2185520.2185561 73 M. Lewandowska, J. Ruminski, T. Kocejko, and J. No-wak, in Proceedings of the 2011 Federated Conference on Computer Science and Information Systems FedCSIS (2011), p. 405. 74 de Haan G. Jeanne V. IEEE Trans. Biomed. Eng. 2013 60 2878 10.1109/TBME.2013.2266196 23744659 75 de Haan G. van Leest A. Physiol. Meas. 2014 35 1913 10.1088/0967-3334/35/9/1913 25159049 76 Wang W. Stuijk S. de Haan G. IEEE Trans. Biomed. Eng. 2016 63 1974 10.1109/TBME.2015.2508602 26685222 77 G. Hsu, A. Ambikapathi, and M. Chen, in Proceedings of the IEEE International Joint Conference on Biometrics IJCB (2017), p. 383. 10.1109/BTAS.2017.8272721 78 W. Chen and D. McDuff, in Proceedings of the European Conference on Computer Vision ECCV (2018), p. 356. 79 Niu X. Shan S. Han H. Chen X. IEEE Trans. Image Process. 2020 29 2409 10.1109/TIP.2019.2947204 80 Z. Yu, X. Li, and G. Zhao, in Proceedings of the British Machine Vision Conference BMVC (2019), p. 1. 81 G. Heusch and S. Marcel, in Proceedings of the IEEE International Conference on Biometrics Theory, Applications and Systems BTAS (2018). 10.1109/BTAS.2018.8698579 82 S.-Q. Liu, X. Lan, and P. Yuen, in Proceedings of the European Conference on Computer Vision ECCV (2018), p. 577. 10.1007/978-3-030-01270-0_34 83 J. Speth, N. Vance, P. Flynn, K. Bowyer, and A. Czajka, arXiv: 2101.04096 (2021). 84 Mannapperuma K. Holton B. D. Physiol. Meas. 2015 36 67 10.1088/0967-3334/36/1/67 25501390 85 Forrester K. R. Tulip J. Leonard C. Stewart C. Bray R. C. IEEE Trans. Bio-Med. Eng. 2004 51 2074 10.1109/TBME.2004.834259 86 Serov A. Steinacher B. Lasser T. Opt. Express 2005 13 3681 10.1364/opex.13.003681 19495275 87 Kamshilin A. A. Teplov V. Nippolainen E. Mi-ridonov S. Giniatullin R. PloS One 2013 8 e57117 10.1371/journal.pone.0057117 23431399 88 Iakovlev D. Dwyer V. Hu S. Silberschmidt V. Proc. SPIE 2016 9887 988712 10.1117/12.2225216 89 Aarts L. A. Jeanne V. Cleary J. P. Lieber C. Nelson J. S. Oetomo S. B. Verkruysse W. Early Hum. Dev. 2013 89 943 10.1016/j.earlhumdev.2013.09.016 24135159 90 M. Kumar and J. Suliburk, in Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC (2016), p. 3904. 10.1109/EMBC.2016.7591581 91 M. Kumar and J. Suliburk, Sci. Rep. 10 (1) (2020). 10.1038/s41598-020-61576-0 92 Mamontov O. V. Krasnikova T. V. Volynsky M. A. Anokhina N. A. Shlyakhto E. V. Kamshilin A. A. Physiol. Meas. 2020 41 044004 10.1088/1361-6579/ab807c 32182594 93 M. A. Volynsky, N. B. Margaryants, and A. A. Kamshilin, Imaging Appl. Opt. (2019). 10.1364/ISA.2019.ITh3B.4 94 Kamshilin A. A. Lyubashina O. A. Lect. Notes Comput. Sci. 2019 11466 479 10.1007/978-3-030-17935-9_43 95 Mamontov O. V. Shcherbinin A. V. Appl. Sci. 2020 10 6192 10.3390/app10186192 96 Kamshilin A. A. Volynsky M. A. J. Headache Pain. 2018 19 43 10.1186/s10194-018-0872-0 29915934 97 Volynsky M. A. Mamontov O. V. J. Phys.: Conf. Ser. 2016 737 012053 10.1088/1742-6596/737/1/012053 98 Nirala N. Periyasamy R. Kumar A. Int. J. Adv. Intell. Paradigms 2020 16 241 10.1504/IJAIP.2020.10018682 99 Blanik N. Blazek C. Pereira C. Blazek V. Leonhardt S. Proc. SPIE 2014 9034 903429 10.1117/12.2043567 100 Allen J. Chen F. Photoplethysmography 2022 New York Academic 101 Nishidate I. Hoshi A. Aoki Y. Nakano K. Niizeki K. Aizu Y. Proc. SPIE 2016 9707 97070L 10.1117/12.2212558 102 I. Nishidate, C. Tanabe, D. J. McDuff, K. Nakano, K. Niizeki, Y. Aizu, and H. Haneishi, Proc. SPIE 10885 (2019). 10.1117/12.2509752 103 McDuff D. Nishidate I. Nakano K. Haneishi H. Aoki Y. Tanabe C. Aizu Y. Sci. Rep. 2020 10 1 10.1038/s41598-020-67647-6 31913322 104 Khanoka B. Slovik Y. Landau D. Nitzan M. Med. Biol. Eng. Comput. 2004 42 80 10.1007/BF02351014 14977226 105 Kublanov V. S. Purtov K. S. Biomed. Radioelectron. 2015 8 9 106 V. S. Kublanov and K. S. Purtov, in Proceedings of the 2015 International Conference on Biomedical Engineering and Computational Technologies SIBIRCON (2015), p. 87. 10.1109/SIBIRCON.2015.7361857 107 Kulminskiy D. D. Kurbako A. V. Skazkina V. V. Prokhorov M. D. Ponomarenko V. I. Kiselev A. R. Bezruchko B. P. Karavaev A. S. Izv. Sarat. Univ., New Ser Ser.: Fiz. 2021 21 58 10.18500/1817-3020-2021-21-1-58-68 108 Simonyan M. A. Skazkina V. V. Posnenkova O. M. Ishbulatov Yu. M. Shvartz V. A. Borovkova E. I. Gorshkov A. Yu. Fedorovich A. A. Dzhioeva O. N. Karavaev A. S. Gridnev V. I. Drapkina O. M. Kiselev A. R. Profil. Med. 2021 24 73 10.17116/profmed20212408173 109 Karavaev A. S. Borovik A. S. Borovkova E. I. Orlova E. A. Simonyan M. A. Ponomarenko V. I. Kiselev A. R. Biophys. J. 2021 120 2657 10.1016/j.bpj.2021.05.020 34087217 110 Kiselev A. R. Borovkova E. I. Shvartz V. A. Skaz-kina V. V. Karavaev A. S. Prokhorov M. D. Bo-ckeria O. L. Sci. Rep. 2020 10 1 10.1038/s41598-020-58196-z 31913322 111 Tankanag A. V. Grinevich A. A. Tikhonova I. V. Chemeris N. K. Biophysics 2020 65 159 10.1134/s0006350920010194 112 Tikhonova I. V. Grinevich A. A. Tankanag A. V. Biomed. Signal Proces. Control. 2022 71 103091 10.1016/j.bspc.2021.103091 113 Tankanag A. Krasnikov G. Mizeva I. Microvasc. Res. 2020 130 103993 10.1016/j.mvr.2020.103993 32194083 114 Tankanag A. V. Krasnikov G. V. Chemeris N. K. Physics of Biological Oscillators 2021 115 Sagaidachnyi A. A. Skripal A. V. Fomin A. V. Usanov D. A. Physiol. Meas. 2014 35 153 10.1088/0967-3334/35/2/153 24399251 116 Sagaidachnyi A. A. Fomin A. V. Usanov D. A. Skripal A. V. Physiol. Meas. 2017 38 272 10.1088/1361-6579/aa4eaf 28099162 117 Sagaidachnyi A. A. Fomin A. V. Usanov D. A. Skripal A. V. Comput. Methods Biomech. Biomed. Eng. 2019 22 1009 10.1080/10255842.2019.1615058 118 Allan D. Chockalingam N. Naemi R. J. Med. Eng. Technol. 2021 45 170 10.1080/03091902.2021.1891309 33750251 119 I. Iu. Volkov, A. V. Fomin, D. I. Mayskov, I. S. Zaletov, A. V. Skripal, and A. A. Sagaidachnyi, in Methods of Computer Diagnostics in Biology and Medicine-2021, Proceedings of the All-Russian School-Seminar, Ed. by An. V. Skripal (Sarat. Istochnik, Saratov, 2021), p. 107. 120 I. Yu. Volkov, A. V. Fomin, D. I. Mayskov, A. V. Skripal, and A. A. Sagaidachnyi, in Optical Methods of Flow Investigation 2021, Proceedings of the 16th International Scientific and Technical Conference, Moscow, June 28–July 2, 2021, p. 107. 121 Sun Y. Hu S. Azorin-Peris V. Kalawsky R. Greenwald S. J. Biomed. Opt. 2013 18 061205 10.1117/1.JBO.18.6.061205 23111602 122 Taranov A. A. Spiridonov I. N. Biomed. Radioelectron. 2014 10 80 123 Kublanov V. Purtov K. Belkov D. Sci. Technol. Publ. 2017 4 307 10.5220/0006176003070314 124 Kopeliovich M. V. Petrushan M. V. Pattern Recogn. Image Anal. 2016 26 150 10.1134/S1054661816010120 125 R. Imms and S. Hu, Proc. SPIE 8947 (2014). 10.1117/12.2044318 126 Blazek C. R. Merk H. F. Schmid-Schoenbein H. Huelsbusch M. Blazek V. J. Allergy Clin. Immun. 2006 117 S226 10.1016/j.jaci.2005.12.894 127 Hulsbusch M. Blazek V. Proc. SPIE 2002 4683 110 10.1117/12.463573 128 Wieringa F. P. Mastik F. Ann. Biomed. Eng. 2006 34 1870 10.1007/s10439-006-9198-1 17048103 129 L. Kobayashi et al., in Proceedings of the 2019 IEEE 10th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON) (IEEE, 2019), p. 0023. 10.1109/UEMCON47517.2019.8993087 130 Cho Y. Julier S. J. Bianchi-Berthouze N. JMIR Mental Health 2019 6 e10140 10.2196/10140 30964440 131 Blanik N. Abbas A. K. Venema B. Blazek V. Leonhardt S. J. Biomed. Opt. 2014 19 016012 10.1117/1.JBO.19.1.016012 132 Paul M. Behr S. C. Weiss C. Heimann K. Orlikowsky T. Leonhardt S. BioMed. Eng. OnLine 2021 20 1 10.1186/s12938-020-00841-9 33390158 133 Humphreys K. Ward T. Markham C. Rev. Sci. Instrum. 2007 78 044304 10.1063/1.2724789 17477684 134 Kong L. Zhao Y. Opt. Express 2013 21 17464 10.1364/OE.21.017464 23938616 135 Shao D. Liu C. IEEE Trans. Biomed. Eng. 2016 63 1091 10.1109/TBME.2015.2481896 26415199 136 F. Foroughian and C. J. Bauder, in Proceedings of the 2018 United States National Committee of URSI National Radio Science Meeting USNC-URSI NRSM (2018), p. 1. 137 Moco A. Verkruysse W. J. Clin. Monit. Comput. 2021 35 123 10.1007/s10877-019-00449-y 31893325 138 Gastel M. Wang W. Verkruysse W. Biomed. Opt. Express 2021 12 2813 10.1364/BOE.419199 34168904 139 Gastel M. Verkruysse W. Haan G. Appl. Sci. 2019 9 3857 10.3390/app9183857 140 Bal U. Biomed. Opt. Express 2015 6 86 10.1364/BOE.6.000086 25657877 141 U. S. Freitas, in Proceedings of the 6th International Conference on eHealth, Telemedicine, and Social Medicine (2014), p. 59. 142 Guazzi A. R. Villarroel M. Biomed. Opt. Express 2015 6 3320 10.1364/BOE.6.003320 26417504 143 Addison P. S. Healthcare Technol. Lett. 2016 3 1 10.1049/htl.2015.0052 144 J. Mathew, X. Tian, and M. Wu, arXiv: 2107.05087 (2021). 145 Ali A.-N. Khalid G. A. Appl. Sci. 2021 11 4255 10.3390/app11094255 146 Tuchin V. V. Tissue Optics, Light Scattering Methods and Instruments for Medical Diagnosis 2015	36466081	PMC9708136	NO-CC CODE	2022-12-01 23:20:28	no	Opt Spectrosc. 2022 Nov 29;:1-18	utf-8	Opt Spectrosc	2,022	10.1134/S0030400X22080057	oa_other
==== Front Can J Anaesth Can J Anaesth Canadian Journal of Anaesthesia 0832-610X 1496-8975 Springer International Publishing Cham 36447089 2366 10.1007/s12630-022-02366-2 Correspondence Impact of the COVID-19 pandemic on anesthesiology residents in Canada: a nationwide survey http://orcid.org/0000-0002-8003-0204 Zasso Fabricio Batistella MD, MBA, MHSc [email protected] 1 Liu Laura HBSc, BS 1 Siddiqui Naveed MD, MSc 1 Wild Evan MBBS, FRCPC 1 Massouh Faraj MD, FRCPC 2 You-Ten Kong Eric MD, PhD, FRCPC 1 1 grid.17063.33 0000 0001 2157 2938 Department of Anesthesia and Pain Management, Mount Sinai Hospital, University of Toronto, Toronto, ON Canada 2 grid.17063.33 0000 0001 2157 2938 Department of Anesthesia and Pain Management, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON Canada 29 11 2022 14 13 6 2022 9 10 2022 11 10 2022 © Canadian Anesthesiologists' Society 2022 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. ==== Body pmc To the Editor, Medical education in Canada underwent rapid and unforeseen changes in March 2020, due to the COVID-19 pandemic.1 A transition to online alternatives for medical education was observed. Disciplines that heavily use procedural skills, including anesthesiology, have been the most affected.2 The literature has shown that the COVID-19 pandemic has impacted the mental health of frontline healthcare workers.3 Anesthesiologists and anesthesia residents have been deeply involved in airway management and perioperative and intensive care unit care for COVID-19 patients. Nevertheless, the impact on anesthesia residents during the pandemic have not yet been studied in Canada. We conducted this survey to determine the educational, clinical, and psychological impacts of the COVID-19 pandemic on Canadian anesthesia residents. Approval was obtained from the Mount Sinai Hospital Research Ethics Board (REB 21-0190-E - September 2021). An 18-question survey was developed based on a literature review and the input of the investigators (Electronic Supplementary Material eAppendix). The questionnaire was pretested to guarantee understandability and usability. The target population was third-, fourth-, and fifth-year trainees from the 17 Canadian anesthesiology residency programs; first- and second-year trainees were not included because these individuals were not in residency before the pandemic. Based on the CAPER (Canadian Post-MD Education Registry) Census of Post-MD Trainees in Canada, our estimated target population was 360 participants. The residents were contacted through their program directors (PDs). An e-mail was sent to the PDs explaining the survey and asking them to disseminate it to their residents. Two subsequent reminder e-mails within a three-week interval were sent. The survey was hosted on the online survey instrument SimpleSurvey (Montreal, QC, Canada [a division of OutSideSoft Solutions Inc.]; available at URL: https://simplesurvey.com [accessed November 2022]) from October to December 2021. Respondents signed an online consent form. Responses were anonymized and confidential. Statistical analysis was descriptive (Microsoft Excel, Microsoft Corporation, Redmond, WA, USA). One hundred and four residents participated, and 79 completed the questionnaire (22%, 79/360) from 13 different residency programs. Of the residents, 58% (46/79) estimated a reduction in their procedure volumes, 95% (75/79) reported a decrease of in-person virtual educational activities, and 99% (78/79) reported an increase in virtual educational activities. Sixty-one percent (48/79) described a decrease in the overall number of educational activities. Despite 71% (56/79) of respondents alleging that their programs initially showed poor ability to support virtual activities, 99% (78/79) reported improvements later. Of the respondents, 76% (60/79) described a significant level of anxiety at the workplace, and 34% (27/79) related increased consumption of psychoactive substances, such as anxiolytics, alcohol, tobacco, coffee, and marijuana. Nevertheless, only 10% (8/79) sought professional assistance. Regarding how programs monitored wellbeing, 15% (12/79) of the residents alleged they were actively monitored, 68% (54/79) indirectly, and 17% (13/79) not at all. Sixty-seven percent (53/79) of the residents believed the COVID-19 pandemic negatively affected their training. Nevertheless, 64% (50/79) felt the quality of supervision and teaching was similar to before, and 70% (55/79) felt their future career would not be affected by the pandemic (Table). Our results showed that the COVID-19 pandemic had a negative educational, clinical, and psychological impact on Canadian anesthesia residents.Table Responses to select survey items Survey item Response, n/total N (%) A. COVID-19 impact on clinical activities Compared with your pre-COVID-19 pandemic status, how would you estimate your procedure volumes changed during the pandemic? More than before the pandemic 8/79 (9%) Same as before the pandemic 26/79 (33%) Reduction of less than 25% in procedure volumes 31/79 (39%) Reduction between 25% and 50% in procedure volumes 14/79 (18%) Reduction of more than 50% in procedure volumes 1/79 (1%) Compared with your pre-COVID-19 pandemic status, how did the pandemic affect the number of hours worked per week? Increased 13/79 (16%) Unchanged 59/79 (75%) Decreased 7/79 (9%) Compared with your pre-COVID-19 pandemic status, how did the pandemic affect the number of night shifts you worked per month? Increased 14/79 (18%) Unchanged 61/79 (77%) Decreased 4/79 (5%) Have you been redeployed (to work in other departments other than yours) during the COVID-19 pandemic? Yes 40/79 (51%) No 39/79 (49%) B. COVID-19 impact on educational activities How have the number of “in-person” (nonvirtual) educational activities changed during the COVID-19 pandemic? Increased 4/79 (5%) Unchanged 0/79 (0%) Decreased 75/79 (95%) How have the number of remote (virtual) educational activities changed during the COVID-19 pandemic? Increased 78/79 (99%) Unchanged 0/79 (0%) Decreased 1/79 (1%) Overall, how have the number of educational activities (virtual and nonvirtual) changed during the COVID-19 pandemic? Increased 6/79 (7%) Unchanged 25/79 (32%) Decreased 48/79 (61%) Have the types of educational activities used in your program changed during the COVID-19 pandemic? Yes, we are doing more journal clubs/seminars 0/79 (0%) Yes, we are doing more lectures/presentations 20/79 (25%) No, it was unchanged 59/79 (75%) During the COVID-19 pandemic, how would you assess your program’s ability to support virtual educational activities? Poorly prepared throughout (i.e., no to minimal support for virtual educational activities) 1/79 (1%) Poor at first, but significantly improved later 56/79 (71%) Adequately prepared before the COVID-19 pandemic (routinely performed virtual educational activities), but increased further during the pandemic 22/79 (28%) Virtual educational activities were fully implemented before COVID-19 0/79 (0%) C. COVID-19 psychological impact on the residents Please try to describe your level of anxiety in the workplace during the COVID-19 pandemic Very high 5/79 (6%) High 19/79 (24%) Moderate 36/79 (46%) Low 14/79 (18%) Unchanged 5/79 (6%) Did you need to seek professional assistance for your anxiety? Yes 8/79 (10%) No 71/79 (90%) Did you increase the consumption of any substance such as anxiolytics, alcohol, tobacco, coffee, marijuana, or other psychoactive substances during the COVID-19 pandemic? Yes 27/79 (34%) No 52/79 (66%) How has your program prepared residents to deal with the psychological stress of the COVID-19 pandemic? Residents are engaged in in-person or virtual meetings to monitor their psychological wellbeing and ensure access to resources if needed 12/79 (15%) There are no specific meetings for residents, but the residents have been actively involved in in-person or virtual meetings regarding COVID-19 20/79 (25%) Residents are not actively involved in COVID-19 preparation, but their specific concerns are addressed as they arise 34/79 (43%) There is no preparedness or support from the program for the residents whatsoever 13/79 (17%) D. COVID-19 overall impact on residents’ training and career Overall, how do you believe the COVID-19 pandemic affected your residency training? Positively 5/79 (6%) Unchanged 21/79 (27%) Negatively 53/79 (67%) How do you rate the quality of supervision and teaching that you receive during the COVID-19 pandemic? Better than before 1/79 (1%) Same as before 50/79 (64%) Worse than before. Nevertheless, I acknowledge that the program is doing the best it could 23/79 (29%) Worse than before, and I believe there was a lack of intention from the program to address the issue 5/79 (6%) How do you believe the changes due to the COVID-19 pandemic impacted your future career? Positively 9/79 (11%) Unchanged 55/79 (70%) Negatively 15/79 (19%) This study has limitations. It is a survey, which inherently contains the risk of respondent bias. In addition, the response rate was low at 22%. This low response rate was possibly caused by the lack of incentive to respond and emotional fatigue due to pandemic stress, which may have led to “survey fatigue.” An expansion of virtual learning was observed, which might be considered a positive “collateral effect” of the pandemic. Even though the superiority of conventional learning cannot be denied in some situations (i.e., clinical training, hands-on practice), we suggest a hybrid learning system should be used moving forward. Our data suggest that Canadian anesthesia programs only partially addressed the issue of the pandemic’s psychological impact on residents. Options to improve the response on this matter would include frequent proactive communication with residents regarding accessible resources to support mental health and creating safe spaces where concerns can be shared and validated.4,5 Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (PDF 190 KB) Disclosures None. Funding statement None. Editorial responsibility This submission was handled by Dr. Stephan K. W. Schwarz, Editor-in-Chief, Canadian Journal of Anesthesia/Journal canadien d’anesthésie. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Ross DA National Neuroscience Curriculum Initiative “Quarantine Curriculum” Committee. Creating a “quarantine curriculum” to enhance teaching and learning during the COVID-19 pandemic Acad Med 2020 95 1125 1126 10.1097/acm.0000000000003424 32744816 2. Anwar A Seger C Tollefson A Diachun CA Tanaka P Umar S Medical education in the COVID-19 era: impact on anesthesiology trainees J Clin Anesth 2020 66 109949 10.1016/j.jclinane.2020.109949 32504968 3. Aughterson H McKinlay AR Fancourt D Burton A Psychosocial impact on frontline health and social care professionals in the UK during the COVID-19 pandemic: a qualitative interview study BMJ Open 2021 11 e047353 10.1136/bmjopen-2020-047353 33558364 4. Gallagher TH Schleyer AM “We signed up for this!” - student and trainee responses to the Covid-19 pandemic N Engl J Med 2020 382 e96 10.1056/nejmp2005234 32268020 5. Alhaj AK Al-Saadi T Mohammad F Alabri S Neurosurgery residents’ perspective on COVID-19: knowledge, readiness, and impact of this pandemic World Neurosurg 2020 139 e848 e858 10.1016/j.wneu.2020.05.087 32426064	36447089	PMC9708137	NO-CC CODE	2022-12-01 23:20:30	no	Can J Anaesth. 2022 Nov 29;:1-4	utf-8	Can J Anaesth	2,022	10.1007/s12630-022-02366-2	oa_other
==== Front J Gen Intern Med J Gen Intern Med Journal of General Internal Medicine 0884-8734 1525-1497 Springer International Publishing Cham 36447066 7873 10.1007/s11606-022-07873-y Original Research Cluster Analysis of the Highest Users of Medical, Behavioral Health, and Social Services in San Francisco http://orcid.org/0000-0002-3654-2130 Hewlett Meghan M. MD, MPH [email protected] 1 Raven Maria C. MD, MPH, MSc 12 Graham-Squire Dave PhD 2 Evans Jennifer L. MS 2 Cawley Caroline MPH 12 Kushel Margot MD 23 Kanzaria Hemal K. MD, MSc 123 1 grid.266102.1 0000 0001 2297 6811 Department of Emergency Medicine, University of California, San Francisco, San Francisco, CA USA 2 grid.266102.1 0000 0001 2297 6811 Benioff Homelessness and Housing Initiative, University of California, San Francisco, San Francisco, USA 3 grid.266102.1 0000 0001 2297 6811 Center for Vulnerable Populations, University of California, San Francisco, USA 29 11 2022 19 28 3 2022 24 10 2022 © The Author(s), under exclusive licence to Society of General Internal Medicine 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Background In the City and County of San Francisco, frequent users of emergent and urgent services across different settings (i.e., medical, mental health (MH), substance use disorder (SUD) services) are referred to as high users of multiple systems (HUMS). While often grouped together, frequent users of the health care system are likely a heterogenous population composed of subgroups with differential management needs. Objective To identify subgroups within this HUMS population using a cluster analysis. Design Cross-sectional study of HUMS patients for the 2019–2020 fiscal year using the Coordinated Care Management System (CCMS), San Francisco Department of Public Health’s integrated data system. Participants We calculated use scores based on nine types of urgent and emergent medical, MH, and SUD services and identified the top 5% of HUMS patients. Through k-medoids cluster analysis, we identified subgroups of HUMS patients. Main Measures Subgroup-specific demographic, comorbidity, and service use profiles. Key Results The top 5% of HUMS patients in the study period included 2657 individuals; 69.7% identified as men and 66.5% identified as non-White. We detected 5 subgroups: subgroup 1 (N = 298, 11.2%) who were relatively younger with prevalent MH and SUD comorbidities, and MH services use; subgroup 2 (N = 478, 18.0%), who were experiencing homelessness, with multiple comorbidities, and frequent use of medical services; subgroup 3 (N = 449, 16.9%), who disproportionately self-identified as Black, with prolonged homelessness, multiple comorbidities, and persistent HUMS status; subgroup 4 (N = 690, 26.0%), who were relatively older, disproportionately self-identified as Black, with prior homelessness, multiple comorbidities, and frequent use of medical services; and subgroup 5 (N=742, 27.9%), who disproportionately self-identified as Latinx, were housed, with medical comorbidities and frequent medical service use. Conclusions Our study highlights the heterogeneity of HUMS patients. Interventions must be tailored to meet the needs of these diverse patient subgroups. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-022-07873-y. KEY WORDS cluster analysis health systems services use ==== Body pmcINTRODUCTION Five percent of the US population accounts for 50% of annual health care spending and 1% accounts for almost 25% of expenditures.1 Frequent users of the health care system are defined as patients with ≥ 4 emergency department (ED) visits or ≥ 3 hospitalizations annually.2, 3 This patient population commonly experiences comorbid mental health (MH) and substance use disorders (SUD), homelessness, incarceration, and unemployment.4–6 To decrease costs and address patient needs, policymakers have focused on reducing ED use and hospitalizations, although most efforts have been unsuccessful.7–9 Frequent users of medical services have high use of MH and SUD crisis services (e.g., inpatient psychiatric centers, alcohol sobering centers etc.), as well as homelessness services.4, 6, 10–15 Given the lack of care coordination between services, individuals engaging with multiple systems often experience fragmented care. The City and County of San Francisco developed the High Users of Multiple Systems (HUMS) score to identify individuals experiencing fragmented care who would benefit from improved coordination.14, 15 Analysis of frequent health care systems users, including HUMS patients, suggests a range of medical, behavioral health and social needs that require tailored interventions.14–16 Interventions for such patients, including case management and permanent supportive housing (PSH), vary by care model (e.g., medical, behavioral health, or social needs focus), intensity (e.g., staff/client ratio, staff training), and services offered (e.g., direct service delivery vs. coordination). Interventions may be applied in a uniform manner without accounting for varied needs across heterogeneous frequent user subgroups.16, 17 Prior frequent user studies focus on patterns of medical health comorbidities and medical service use to characterize subgroups.18, 19 No study has accounted for MH, SUD, or social service use. Integrated data that includes such information may facilitate understanding and addressing the needs of frequent users. 20 In 2007, the San Francisco Department of Public Health (SFDPH) implemented the Coordinated Care Management System (CCMS) which integrates patient-level medical, MH, SUD, and social data from multiple county-level services.14, 15 Leveraging this data, we sought to identify distinct subgroups within the HUMS population to inform tailored intervention strategies. METHODS Data Source and Patient Population We used the CCMS, which compiles information about complex, high-needs patients across multiple service domains by integrating data from several county agencies and the San Francisco Health Plan (SFHP), San Francisco County’s primary Medicaid managed care plan. The CCMS includes medical and behavioral electronic health care records, homelessness services, and jail encounters. The CCMS creates a record for any patient (a) reported as unhoused by a San Francisco County agency, or (b) with county jail contact, or (c) who uses urgent or emergent county medical, MH, or SUD services. The database integrates and matches data at the patient level. We previously detailed the CCMS dataset and the HUMS methodology and explain them succinctly below.14, 15 We obtained patients’ use of county urgent and emergent medical, MH, SUD, and social services from the CCMS for fiscal years 2017 through 2020. Our primary analysis year was the 2019–2020 fiscal year (July 1, 2019–June 30, 2020). Notably, San Francisco County issued a stay-at-home order on March 17, 2020, for the COVID-19 pandemic. The University of California San Francisco Institutional Review Board provided research approval on partially deidentified human subjects, and we conducted the analysis according to protected health information and Code of Federal Regulations (Confidentiality of Substance Use Disorder Patient Records, 42 C.F.R. Part 2 [2017]) protocols. We identified the top 5% of HUMS patients for the 2019–2020 fiscal year by calculating a use score for each patient, hereafter known as a HUMS score, by summing all specified encounters from nine urgent and emergent medical, MH, and SUD services during the fiscal year (Table 1). We restricted the study population to patients within the top 5% of HUMS scores for the fiscal year. For the cluster analysis, we obtained variables from the CCMS that characterized patient demographics, social risk factors, comorbidities, and service use. Table 1 Catalog of Services Used to Calculate High Users of Multiple Systems (HUMS) Score in San Francisco County System Urgent/emergent service Unit Medical health system Emergency department Visit Hospital medical inpatient Stay Urgent care clinic Visit Mental health system Psychiatric emergency services Visit Hospital psychiatric inpatient Stay Psychiatric urgent care clinic Visit Substance use disorder system Medical detoxification Stay Social detoxification Stay Emergency department Visit Demographics and Social Risk Factors We examined sociodemographic variables, including patient insurance and housing status. Among frequent health care users, prior studies report distinct patterns of service use and inequities related to age, gender, race, ethnicity, and disability status.15, 17, 21, 22 We included such variables as markers of differential experience of the health care system and to identify structural inequities for future interventions targeting ageist, sexist, racist, and ableist policies. For example, we chose to include race in our analysis, not to suggest any causal relation to frequent user subgroups, but rather to serve as a proxy for differential experiences of interpersonal and structural racism. Patient gender, race, and ethnicity were self-reported. We ascertained past and current homelessness through observed use of homelessness services and self-reported homelessness during service encounters.14 We defined prolonged homelessness as having a history of homelessness for ≥5 years. We stratified insurance status into four groups: receipt of Medicaid alone; Medicaid with Supplemental Security Income and/or Social Security Disability Insurance (SSI/SSDI) with or without Medicare; Medicare alone; or Other. We included SSI/SSDI as a separate category to identify individuals who were either ≥65, blind, or disabled. As all individuals entering county jail have a jail health screening, we included this as a proxy for a jail stay. Medical, Mental Health, and Substance Use Disorder Comorbidities We obtained International Classification of Diseases, Ninth and Tenth Revision, Clinical Modification (ICD-9-CM, ICD-10-CM), codes for principal diagnoses associated with service use and defined the presence of an Elixhauser medical, MH, or SUD comorbidity as having ≥2 diagnosis codes during service encounters for the respective comorbidity in the 2019–2020 fiscal year and the prior two fiscal years.23 Appendix 1 lists these Elixhauser comorbidities. We separately included reports of an involuntary psychiatric hold during the 2019–2020 fiscal year. Service Use We assessed use of urgent and emergent services across three domains (i.e., medical, MH, and SUD) during the 2019–2020 fiscal year for all patients — using the same services to calculate HUMS score (Table 1). This included out-of-network medical services use for SFHP beneficiaries. Persistent HUMS To assess prior service use among the study population, we calculated HUMS scores for patients with available data for the prior two fiscal years. From these scores, we created a dichotomous variable that defined a patient as a “persistent HUMS” if they also ranked within the top 5% of HUMS scores in any of the two prior fiscal years. Clustering and Statistical Analysis To identify subgroups within the study population, we employed a cluster analysis. We considered initial candidate variables for clustering based on clinical insight, identifying variables most informative for potential intervention efforts. We removed variables with a high degree of association to minimize redundancy and maximize parsimony. We selected 17 variables for inclusion and chose the k-medoids approach given the mixed composition of continuous, categorical, and ordinal variables (Table 2). As the algorithm requires a predetermined number of clusters (k), we ran multiple analyses with various values of k (k = 2 to k = 15) to identify distinct clusters with adequate group sample size to detect between-group differences.24 We calculated an optimal number of clusters using a silhouette width measure which is described in detail in Appendix 2. However, we based our final number of clusters on clinical judgment and utility to inform intervention strategies.25. We employed the k-medoids algorithm to identify subgroups based on correlations around a central point for each cluster, known as a medoid, represented by an individual HUMS patient. HUMS patients are assigned to the cluster with the closest medoid. More specifically, the algorithm deems data points as “similar” or “dissimilar” according to a well-defined distance metric between the points using the Partitioning Around Medoids (PAM) algorithm and Gower distance which accommodates continuous, categorical, and ordinal variables.24 To further examine subgroup robustness, we repeated our analysis using two other methods: k-means and latent class analysis (LCA). As k-means requires all variables to be numerical, we transformed non-numerical variables to a series of indicator variables with numerical values. We used the R Statistical Package to employ the k-means and k-medoids algorithms, and the Proc LCA package in SAS, version 9.4, to perform the LCA.26, 27 Table 2 Demographic, Comorbidity, and Service Use Variables Included for Cluster Analysis of the Top 5% of High Users of Multiple Systems (HUMS) Patients for the 2019–2020 Fiscal Year Variable description Variable category Age Numerical Race and ethnicity Categorical — 7 groups Gender Categorical — 4 groups Years of homelessness Ordinal — 5 levels Last known housing status Categorical — 4 groups Insurance status Categorical — 4 groups Jail stay Binary Shelter stay Binary Persistent HUMS patient Binary Elixhauser medical comorbidity Binary Elixhauser mental health comorbidity Binary Elixhauser substance use disorder comorbidity Binary Medical services use ranking* Ordinal — 4 levels Mental health services use Binary Substance use disorder services use Binary Number of service domains used† Ordinal — 3 levels Involuntary psychiatric hold Binary We defined medical services use ranking as the relative ranking of a patient’s urgent and emergent medical service use compared to all users of urgent and emergency medical services captured by the Coordinated Care Management System during the 2019–2020 fiscal year. †Service domains are defined as medical, mental health, and substance use disorder RESULTS We identified 2657 patients in the top 5% of HUMS patients for the 2019–2020 fiscal year (Table 3). The mean age (SD) was 48.2 (14.1) years, 69.7% self-identified as men, and 66.5% self-identified as non-White. Compared to the general population of San Francisco County, the study population had a higher proportion of patients who were unhoused; self-identifying as men, Black, Latinx, and Native American; and a lower proportion self-identifying as Asian/Pacific Islander.28–30 Overall, 82.4% reported a history of homelessness, 47.5% were housed, 22.2% had a jail stay, and 42.0% received SSI/SSDI. Additionally, 64.5% and 74.5% had a MH and SUD comorbidity, respectively; 39.7% and 16.3% used MH and SUD services, respectively; and 47.2% used multiple service domains. We identified five subgroups (Table 4). Most clustering occurred along housing characteristics, presence of a MH comorbidity, medical and MH service use, and receipt of SSI/SSDI. Table 3 Characteristics of the Top 5% of High Users of Multiple Systems (HUMS) Patients for the 2019–2020 Fiscal Year Characteristic No. (%) (N = 2657) Age, mean (SD), years 48.2 (14.1) Race and ethnicity Black 943 (35.5%) Asian/Pacific Islander 212 (8.0%) Latinx 464 (17.5%) Multiracial 85 (3.2%) Native American 41 (1.5%) White 889 (33.5%) Not reported 23 (0.9%) Gender Women 772 (29.1%) Men 1852 (69.7%) Transgender 27 (1.0%) Not reported 6 (0.2%) Years of homelessness Never 467 (17.6%) < 1 year 291 (11.0%) 1–4 years 548 (20.6%) 5–9 years 384 (14.5%) ≥ 10 years 967 (36.4%) Last known housing status Outdoors 431 (16.2%) Shelter 713 (26.8%) Housed 1262 (47.5%) Other 251 (9.4%) Insurance status† Medicaid only 1373 (51.7%) Medicaid and SSI/SSDI with or without Medicare 1116 (42.0%) Medicare only 81 (3.0%) Other/uninsured 87 (3.3%) Jail stay 589 (22.2%) Shelter stay 742 (27.9%) Persistent HUMS patient 1102 (41.5%) Elixhauser medical comorbidity 2025 (76.2%) Elixhauser mental health comorbidity 1715 (64.5%) Elixhauser substance use disorder comorbidity 1980 (74.5%) Medical services use ranking‡ Top 1% 535 (20.1%) 2–5% 1790 (67.4%) 6–10% 173 (6.5%) 11–100% 159 (6.0%) MH services use 1054 (39.7%) SUD services use 432 (16.3%) Involuntary psychiatric hold 660 (24.8%) Number of service domains used‡ 1 1404 (52.8%) 2 1027 (38.7%) 3 226 (8.5%) Abbreviations: SSI, Supplemental Security Income; SSDI, Social Security Disability Insurance; percentages may not sum to 100% due to rounding. Last known housing status is stratified into four categories: Outdoors status includes individuals living outdoors or another unhoused status not otherwise specified by other categories; shelter status includes those residing in a shelter, shelter-in-place hotel, isolation and quarantine hotel, or receiving housing and/or shelter services from the San Francisco Department of Homelessness and Supportive Housing; housed status includes those who are housed or living in permanent supportive housing; other status includes those residing in the following: temporary housing, treatment facility, institution, skilled nursing facility, Veterans Affairs hospital, inpatient psychiatric hospital, jail, prison, or have no reported housing status. †California residents receiving SSI and/or SSDI are automatically enrolled to receive Medicaid benefits. Only patients who have received 24 months of payments via SSDI qualify for Medicare outside of the standard Medicare eligibility requirements. Other/uninsured status includes those who are self-pay, receive private insurance benefits, or are uninsured. ‡Table 2 footnotes explain medical services use ranking and number of service domains used Table 4 k-Medoids Analysis of Subgroup Characteristics of the Top 5% of High Users of Medical Systems (HUMS) Patients for the 2019–2020 Fiscal Year Characteristic Subgroup 1 High MH, SUD, and Incarceration No. (%) (N = 298, 11.2%) Subgroup 2 Trimorbidity, High Shelter Use No. (%) (N = 478, 18.0%) Subgroup 3 Unhoused, High Multiple Services Use No. (%) (N = 449, 16.9%) Subgroup 4 Trimorbidity, High Medical Services Use No. (%) (N = 690, 26.0%) Subgroup 5 Housed, New High Medical Services Use No. (%) (N = 742, 27.9%) Age, mean (SD), years 37.7 (10.7) 47.2 (12.2) 46.9 (12.7) 52.7 (12.0) 49.8 (16.4) Race and ethnicity Black 77 (25.8%) 102 (21.3%) 216 (48.1%) 378 (54.8%) 170 (22.9%) Asian/Pacific Islander 24 (8.1%) 20 (4.2%) 27 (6.0%) 24 (3.5%) 117 (15.8%) Latinx 28 (9.4%) 66 (13.8%) 54 (12.0%) 70 (10.1%) 246 (33.2%) Multiracial 14 (4.7%) 16 (3.3%) 13 (2.9%) 18 (2.6%) 24 (3.2%) Native American 1 (0.3%) 11 (2.3%) 11 (2.4%) 10 (1.4%) 8 (1.1%) White 152 (51.0%) 258 (54.0%) 126 (28.1%) 190 (27.5%) 163 (22.0%) Not reported 2 (0.7%) 5 (1.0%) 2 (0.4%) 0 (0.0%) 14 (1.9%) Gender Women 56 (18.8%) 119 (24.9%) 132 (29.4%) 213 (30.9%) 252 (34.0%) Men 239 (80.2%) 354 (74.1%) 305 (67.9%) 471 (68.3%) 483 (65.1%) Transgender 3 (1.0%) 5 (1.0%) 12 (2.7%) 6 (0.9%) 1 (0.1%) Not reported 0 (0.0%) 0 (0.0%) 0 (0.0%) 0 (0.0%) 6 (0.8%) Years of homelessness Never 20 (6.7%) 1 (0.2%) 30 (6.7%) 83 (12.0%) 333 (44.9%) < 1 year 60 (20.1%) 40 (8.4%) 19 (4.2%) 58 (8.4%) 114 (15.4%) 1–4 years 106 (35.6%) 161 (33.7%) 47 (10.5%) 97 (14.1%) 137 (18.5%) 5–9 years 49 (16.4%) 99 (20.7%) 84 (18.7%) 99 (14.3%) 53 (7.1%) ≥ 10 years 63 (21.1%) 177 (37.0%) 269 (59.9%) 353 (51.2%) 105 (14.2%) Last known housing status Outdoors 149 (50.0%) 82 (17.2%) 74 (16.5%) 64 (9.3%) 62 (8.4%) Shelter 52 (17.4%) 295 (61.7%) 98 (21.8%) 161 (23.3%) 107 (14.4%) Housed 67 (22.5%) 64 (13.4%) 217 (48.3%) 393 (57.0%) 521 (70.2%) Other 30 (10.1%) 37 (7.7%) 60 (13.4%) 72 (10.4%) 52 (7.0%) Insurance status* Medicaid Only 234 (78.5%) 329 (68.8%) 107 (23.8%) 173 (25.1%) 530 (71.4%) Medicaid and SSI/SSDI with or without Medicare 37 (12.4%) 119 (24.9%) 321 (71.5%) 490 (71.0%) 149 (20.1%) Medicare only 15 (5.0%) 21 (4.4%) 13 (2.9%) 10 (1.4%) 22 (3.0%) Other/uninsured 12 (4.0%) 9 (1.9%) 8 (1.8%) 17 (2.5%) 41 (5.5%) Jail stay 188 (63.1%) 106 (22.2%) 115 (25.6%) 105 (15.2%) 75 (10.1%) Shelter stay 53 (17.8%) 377 (78.9%) 117 (26.1%) 124 (18.0%) 71 (9.6%) Persistent HUMS patient 65 (21.8%) 174 (36.4%) 330 (73.5%) 445 (64.5%) 88 (11.9%) Elixhauser medical comorbidity 83(27.9%) 372 (77.8%) 383 (85.3%) 627 (90.9%) 560 (75.5%) Elixhauser mental health comorbidity 280 (94.0%) 397 (83.1%) 441 (98.2%) 463 (67.1%) 134 (18.1%) Elixhauser substance use disorder comorbidity 271 (90.9%) 440 (92.1%) 409 (91.1%) 601 (87.1%) 259 (34.9%) Medical services use ranking† Top 1% 36 (12.1%) 124 (25.9%) 139 (31.0%) 163 (23.6%) 73 (9.8%) 2–5% 142 (47.7%) 266 (55.6%) 204 (45.4%) 520 (75.4%) 658 (88.7%) 6–10% 53 (17.8%) 58 (12.1%) 53 (11.8%) 4 (0.6%) 5 (0.7%) 11–100% 67 (22.5%) 30 (6.3%) 53 (11.8%) 3 (0.4%) 6 (0.8%) Mental health services use 273 (91.6%) 319 (66.7%) 449 (100.0%) 0 (0.0%) 13 (1.8%) Substance use disorder services use 58 (19.5%) 163 (34.1%) 96 (21.4%) 79 (11.4%) 36 (4.9%) Involuntary psychiatric hold 217 (72.8%) 115 (24.1%) 325 (72.4%) 0 (0.0%) 3 (0.4%) Number of service domains used† 1 16 (5.4%) 80 (16.7%) 1 (0.2%) 611 (88.6%) 696 (93.8%) 2 236 (79.2%) 314 (65.7%) 352 (78.4%) 79 (11.4%) 46 (6.2%) 3 46 (15.4%) 84 (17.6%) 96 (21.4%) 0 (0.0%) 0 (0.0%) Abbreviations: MH, mental health; SSI, Supplemental Security Income; SSDI, Social Security Disability Insurance; SUD, substance use disorder; percentages may not sum to 100% due to rounding. *Table 3 footnotes explain last known housing and insurance status stratifications. †Table 2 footnotes explain medical services use ranking and number of service domains used Subgroup 1 — High MH, SUD, and Incarceration Subgroup 1 (N = 298, 11.2%) was the youngest group (mean age (SD) 37.7 (10.7) years), with the highest proportion self-identifying as men. Most patients self-identified as White. This subgroup had prevalent prior and current homelessness; MH and SUD comorbidities; MH service use; and the least medical services use. The subgroup had the highest percentage of patients with jail stays (63.1%) and involuntary psychiatric holds (72.8%). Almost all patients used ≥ 2 service domains. Subgroup 2 — Trimorbidity, High Shelter Use Subgroup 2 (N = 478, 18.0%) had racial, ethnic, and gender demographics similar to subgroup 1. The subgroup had the lowest percentage of patients who were housed (13.4%) and the highest use of shelter services (78.9%); all but one patient had a history of homelessness. Most patients had a medical, MH, and SUD comorbidity; and 81.6% of patients were in the top 5% of medical services users. Subgroup 3 — Unhoused, High Multiple Services Use Subgroup 3 (N=449, 16.9%) patients largely self-identified as men and Black. The majority of patients were unhoused as of their last service encounter. Most patients had a medical, MH, and SUD comorbidity; all patients used MH services; and there was a higher prevalence of jail stays and involuntary psychiatric holds relative to most subgroups. The subgroup had the largest proportion of patients with prolonged homelessness (78.6%), receiving SSI/SSDI (71.5%), meeting criteria for persistent HUMS (73.5%), comprising the top 1% of medical services use (31.0%), and using services across all three service domains (21.4%). Subgroup 4 — Trimorbidity, High Medical Services Use Subgroup 4 (N= 690, 26.0%) patients were older (mean age (SD) 52.7 (12.0) years), and disproportionately self-identified as men and Black. Most patients had a history of prolonged homelessness; however, most were housed as of their last service encounter. The majority of patients received SSI/SSDI. The subgroup had the highest proportion of patients with a medical comorbidity and who were in the top 5% of medical services users (90.9% and 99%, respectively), with most meeting criteria for persistent HUMS. While most patients had a MH comorbidity, none used MH services. Subgroup 5 — Housed, New High Medical Services Use Subgroup 5 (N = 742, 27.9%) patients disproportionately self-identified as men and Latinx; however, the subgroup had the highest percentage of patients self-identifying as women (34%) and Asian/Pacific Islander (15.8%). The subgroup also had the highest percentage of patients who were housed (70.2%). Many patients had a medical comorbidity; and while almost all patients were in the top 5% of medical services users, only 11.9% met criteria for persistent HUMS. The subgroup had the lowest prevalence of MH and SUD comorbidities and minimal MH and SUD service use. Repeating our analysis using a k-means cluster algorithm and LCA, we found subgroup characteristics retained similarity between all three methodologic approaches (Appendix 3 & 4). DISCUSSION This study contributes to the growing literature acknowledging the vulnerability and heterogeneity of frequent health care users and provides guidance for targeted interventions. Expanding prior work, we found that HUMS patients commonly self-identified as Black, experienced homelessness, disability, and significant comorbidity.15 Our study is the first to incorporate cross-sector medical and social data in a cluster analysis to identify distinct subgroups, highlighting the heterogeneity of the HUMS population. Despite high medical services use overall, the subgroup-specific profiles suggest the need for tailored interventions to address differing medical, behavioral health, and social needs (Table 5). Table 5 Summary of Subgroup Characteristics and Proposed Interventions Subgroup 1 High MH, SUD, and Incarceration No. (%) (N = 298, 11.2%) Subgroup 2 Trimorbidity, High Shelter Use No. (%) (N = 478, 18.0%) Subgroup 3 Unhoused, High Multiple Services Use No. (%) (N = 449, 16.9%) Subgroup 4 Trimorbidity, High Medical Services Use No. (%) (N = 690, 26.0%) Subgroup 5 Housed, New High Medical Services Use No. (%) (N = 742, 27.9%) Demographics Younger age, predominantly White Predominantly White Predominantly Black Older age, predominantly Black Predominantly Latinx, more women Largely unhoused, prevalent jail stays, and involuntary psychiatric holds Largely unhoused and high shelter use Largely unhoused, historical prolonged homelessness, receiving SSI/SSDI, frequent psychiatric holds, persistent HUMS Largely housed, historical prolonged homelessness, receiving SSI/SSDI Largely housed, new HUMS Comorbidities MH and SUD comorbidities Medical, MH, and SUD comorbidities Medical, MH, and SUD comorbidities Medical, MH, and SUD comorbidities Medical comorbidities Service use High MH services use High medical services use High medical, MH, and SUD services use High medical services use High medical services use Proposed Interventions PSH with ACT PSH, addiction treatment with medical services, CM with a clinical/rehabilitation model PSH with ACT Medical and behavioral health-focused supplemental CM Identify and address racial and ethnic inequities in primary care Abbreviations: ACT, assertive community treatment; HUMS, high users of multiple services; MH, mental health; PSH, permanent supportive housing; SSI, Supplemental Security Income; SSDI, Social Security Disability Insurance; SUD, substance use disorder Such interventions vary in focus and have differing potential to serve subgroups. For example, PSH offers housing alongside customizable services ranging in intensity and scope (e.g., MH and SUD care, physical rehabilitation, employment services, and connection to legal services).31, 32 Case management programs also vary in focus, staff composition, and service intensity.33 A brokerage model provides service referral and coordination whereas a clinical model offers medically, behaviorally, or socially focused therapeutic services.34, 35 Intensive models include assertive community treatment (ACT) for clients with MH needs in which a multidisciplinary team with a small client-to-staff ratio delivers personalized 24-h, daily services to clients in their environment (e.g., MH treatment, integrated dual-disorder treatment, vocational rehabilitation, medication support, counseling). Intensive Case Management is less intensive than ACT, without shared caseloads.36, 37 Effective program tailoring for patients with diverse needs requires understanding the specific capabilities of such programs and their differences. Homelessness characterized subgroups 1–4, though each demonstrated differential needs. We observed co-existing MH and SUD comorbidities as well as a higher prevalence of jail stays in subgroups 1 and 3. Co-existing MH and SUD are associated with increased psychiatric hospitalization, and individuals with MH system contact prior to or after incarceration have higher shelter use and odds of re-incarceration.38, 39 The criminalization of homelessness and mental illness may contribute to the “institutional circuit” between incarceration, hospitals, psychiatric institutions, and shelters.40–42 Integrating PSH (shown to reduce the average number of shelter, psychiatric hospitalization, and incarceration days) with ACT (shown to reduce hospitalizations, improve housing stability and symptom management, and increase quality of life) may address housing needs while providing high-intensity supportive services.36, 43, 44 Our results reflect the well-known need for more MH and SUD services in San Francisco, resulting in recent reform efforts.45, 46 Subgroup 2 had low SUD service use compared to the prevalence of SUD comorbidities; however, most patients exclusively used medical services. In addition to PSH, these patients could benefit from integration of addiction treatment into medical care delivery and a clinical/rehabilitation model of case management for clients with SUD.47, 48 Despite a high prevalence of prior prolonged homelessness in subgroup 4, many patients were housed as of their last service encounter, often through PSH. However, we also observed no MH services use relative to the prevalence of MH comorbidities and high medical services use. PSH programs may therefore need supplemental case management services with a medical and behavioral health focus (e.g., a Masters-trained behavioral health specialist with physician oversight). Our results highlight inequities related to structural ableism and racism in the health care system.49 Individuals in subgroups characterized by SSI/SSDI receipt (a proxy we used for disability) had prevalent medical comorbidities and medical service use. Our results may be the result of downstream effects of interpersonal discrimination from health care providers, access limitations to preventative care and medications, and care dissatisfaction experienced by individuals with disabilities.50–54 With respect to race and ethnicity, the majority of patients in subgroups 3 and 4 self-identified as Black; and both subgroups had high burdens of patients with all three comorbidity domains, significant medical service use, and minimal SUD service use. Socioeconomic disinvestment in predominantly Black and Latinx neighborhoods contributes to the paucity of primary and MH care, as well as the poor health outcomes experienced by Black and Latinx individuals.55–57 Structural racism also exists in policies that limit the accessibility of SUD treatment and perpetuate the criminalization of SUD.58 Our findings may reflect the downstream effects of such social determinants of health. Additionally, subgroup 5 comprised mostly of members of racial and ethnic minority groups and almost all patients used medical services exclusively. The high percentage of patients with a medical comorbidity coupled with the lowest percentage of persistent HUMS patients may indicate temporary frequent use; however, this also may reflect racial and ethnic inequities in primary care which include lower quality care, poorer patient-physician communication, and lower likelihood of receiving indicated interventions.59–65 The strengths of our study included using an integrated, cross-sector dataset to identify frequent users across multiple systems. The HUMS score is a proxy for fragmented care, helping identify individuals that could benefit from improved care coordination. Our study had several limitations. The index year of study included the first 3.5 months of the COVID-19 pandemic in San Francisco County; therefore, our results may not reflect typical service use previously given changes in service availability during the pandemic. However, the County quickly implemented alternative services with non-congregate shelters to limit COVID-19 exposure among unhoused individuals and to offset service closures.66, 67 Also, while we obtained data across multiple non-medical service domains, we primarily accounted for service use within San Francisco County. However, we included Medicaid encounters (in- and out-of-network), which allowed for comprehensive capture of acute medical services use for SFHP beneficiaries. Our results may not be generalizable to non-safety net systems or those with marked differences in public health infrastructure. Additionally, we included more variables in our k-medoids cluster algorithm with the intent of producing clinically and practically informative clusters at the expense of a parsimonious model. Clusters may be less distinct from one another using silhouette width measures; however, we found consistency in subgroup characteristics across the three cluster algorithms, demonstrating the robustness of our findings. Cross-sector, integrated data informed our understanding of HUMS patients, and underscores the heterogeneity of this patient population both in characteristics and interventional needs. Our study emphasizes the benefit of subgroup identification and the need to match service provision to the underlying needs of patients. Supplementary Information ESM 1 (DOCX 123 kb) Acknowledgements The authors thank Dr. Hali Hammer and the members of the Whole Person Care team at the San Francisco Department of Public Health for their partnership. Funding The analysis of the work described was supported by the Benioff Homeless and Housing Initiative at the University of California, San Francisco. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Prior Presentation: Results from this manuscript have not been previously presented to the public or at any conference. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Cohen SB. The Concentration of Health Care Expenditures and Related Expenses for Costly Medical Conditions, 2012. In: Statistical Brief (Medical Expenditure Panel Survey (US)). Agency for Healthcare Research and Quality (US); 2012. http://www.ncbi.nlm.nih.gov/books/NBK470837/ 2. Jiang HJ, Weiss AJ, Barrett ML, Sheng M. Characteristics of Hospital Stays for Super-Utilizers by Payer, 2012: Statistical Brief #190. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Agency for Healthcare Research and Quality (US); 2015. http://www.ncbi.nlm.nih.gov/books/NBK310271/ 3. Jiang HJ, Weiss AJ, Barrett ML. Characteristics of Emergency Department Visits for Super-Utilizers by Payer, 2014: Statistical Brief #221. In: Healthcare Cost and Utilization Project (HCUP) Statistical Briefs. Agency for Healthcare Research and Quality (US); 2017. http://www.ncbi.nlm.nih.gov/books/NBK442038/ 4. Hunt KA Weber EJ Showstack JA Colby DC Callaham ML Characteristics of frequent users of emergency departments Ann Emerg Med. 2006 48 1 1 8 10.1016/j.annemergmed.2005.12.030 16781914 5. Sandoval E Smith S Walter J A comparison of frequent and infrequent visitors to an urban emergency department J Emerg Med. 2010 38 2 115 121 10.1016/j.jemermed.2007.09.042 18462906 6. Raven MC What we don’t know may hurt us: interventions for frequent emergency department users Ann Emerg Med. 2011 58 1 53 55 10.1016/j.annemergmed.2011.04.009 21689566 7. Williams BC Limited effects of care management for high utilizers on total healthcare costs Am J Manag Care. 2015 21 4 e244 246 26244786 8. Peikes D Chen A Schore J Brown R Effects of care coordination on hospitalization, quality of care, and health care expenditures among Medicare beneficiaries: 15 randomized trials JAMA. 2009 301 6 603 618 10.1001/jama.2009.126 19211468 9. Finkelstein A Zhou A Taubman S Doyle J Health care hotspotting - a randomized, controlled trial N Engl J Med. 2020 382 2 152 162 10.1056/NEJMsa1906848 31914242 10. Gawande A. Finding Medicine’s Hot Spots \| The New Yorker. https://www.newyorker.com/magazine/2011/01/24/the-hot-spotters 11. Hasselman D. Super-Utilizer Summit Common Themes from Innovative Complex Care Management Programs. Published online 2013:32. 12. Emeche U Is a strategy focused on super-utilizers equal to the task of health care system transformation? Yes Ann Fam Med. 2015 13 1 6 7 10.1370/afm.1746 25583884 13. Kushel MB Perry S Bangsberg D Clark R Moss AR Emergency department use among the homeless and marginally housed: results from a community-based study Am J Public Health. 2002 92 5 778 784 10.2105/ajph.92.5.778 11988447 14. Kanzaria HK Niedzwiecki M Cawley CL Frequent emergency department users: focusing solely on medical utilization misses the whole person Health Aff (Millwood). 2019 38 11 1866 1875 10.1377/hlthaff.2019.00082 31682499 15. Cawley C, Raven MC, Martinez MX, Niedzwiecki M, Kushel MB, Kanzaria HK. Understanding the 100 highest users of health and social services in San Francisco. Acad Emerg Med. n/a(n/a). 10.1111/acem.14299 16. Hong CS Siegel AL Ferris TG Caring for high-need, high-cost patients: what makes for a successful care management program? Issue Brief Commonw Fund. 2014 19 1 19 25115035 17. Johnson TL Rinehart DJ Durfee J For many patients who use large amounts of health care services, the need is intense yet temporary Health Aff Proj Hope. 2015 34 8 1312 1319 10.1377/hlthaff.2014.1186 18. Lee NS Whitman N Vakharia N PhD GBT Rothberg MB High-cost patients: hot-spotters don’t explain the half of it J Gen Intern Med. 2017 32 1 28 34 10.1007/s11606-016-3790-3 27480529 19. Grant RW McCloskey J Hatfield M Use of latent class analysis and k-means clustering to identify complex patient profiles JAMA Netw Open. 2020 3 12 e2029068 10.1001/jamanetworkopen.2020.29068 33306116 20. Kanzaria HK Leveraging cross-sector data to address social needs in the emergency department Ann Emerg Med. 2020 76 4 468 469 10.1016/j.annemergmed.2020.04.016 32471648 21. Billings J Raven MC Dispelling an urban legend: frequent emergency department users have substantial burden of disease Health Aff Proj Hope. 2013 32 12 2099 2108 10.1377/hlthaff.2012.1276 22. Kanzaria HK Niedzwiecki MJ Montoy JC Raven MC Hsia RY Persistent frequent emergency department use: core group exhibits extreme levels of use for more than a decade Health Aff (Millwood). 2017 36 10 1720 1728 10.1377/hlthaff.2017.0658 28971916 23. Elixhauser A Steiner C Harris DR Coffey RM Comorbidity measures for use with administrative data Med Care. 1998 36 1 8 27 10.1097/00005650-199801000-00004 9431328 24. Kaufman L, Rousseeuw PJ. Finding Groups in Data: An Introduction to Cluster Analysis. Vol 344. John Wiley & Sons; 2009. 25. Rousseeuw PJ Silhouettes: a graphical aid to the interpretation and validation of cluster analysis J Comput Appl Math. 1987 20 53 65 10.1016/0377-0427(87)90125-7 26. R Core Team. R: a language and environment for statistical computing. Published online 2014. http://www.R-project.org/ 27. SAS Institute Inc. SAS Software. Published online Copyright 2013. sas.com 28. U.S. Census Bureau QuickFacts: San Francisco County, California. https://www.census.gov/quickfacts/sanfranciscocountycalifornia 29. Census 2020 California Hard-to-Count Fact Sheet - San Francisco County. https://census.ca.gov/wp-content/uploads/sites/4/2019/06/San-Francisco.pdf 30. Applied Survey Research, Housing Instability Research Department. San Francisco Homeless Count and Survey Comprehensive Report. San Francisco Department of Homelessness and Supportive Housing; 2019:77. 31. Raven MC Niedzwiecki MJ Kushel M A randomized trial of permanent supportive housing for chronically homeless persons with high use of publicly funded services Health Serv Res. 2020 55 Suppl 2 797 806 10.1111/1475-6773.13553 32976633 32. Resnikoff N. Housing First is Not Housing Only. Benioff Homelessness and Housing Initiative. Published October 21, 2021. https://homelessness.ucsf.edu/blog/housing-first-not-housing-only 33. Raven MC Kushel M Ko MJ Penko J Bindman AB The effectiveness of emergency department visit reduction programs: a systematic review Ann Emerg Med 2016 68 4 467 483.e15 10.1016/j.annemergmed.2016.04.015 27287549 34. Guarino K. Step-by Step: A Comprehensive Approach to Case Management. https://www.homelesshub.ca/resource/step-step-comprehensive-approach-case-management 35. Raven M. Emergency Department Visit Reduction Programs. Presented at: Medicaid and CHIP Payment and Access Commission; February 2014; California Medicaid Research Institute. https://www.macpac.gov/wp-content/uploads/2014/02/Emergency-Department-Visit-Reduction-Programs.pdf 36. Bond GR Drake RE The critical ingredients of assertive community treatment World Psychiatry. 2015 14 2 240 242 10.1002/wps.20234 26043344 37. Stein LI Test MA Alternative to mental hospital treatment. I. Conceptual model, treatment program, and clinical evaluation Arch Gen Psychiatry. 1980 37 4 392 397 10.1001/archpsyc.1980.01780170034003 7362425 38. Antai-Otong D Theis K Patrick DD Dual diagnosis: coexisting substance use disorders and psychiatric disorders Nurs Clin North Am. 2016 51 2 237 247 10.1016/j.cnur.2016.01.007 27229278 39. Metraux S Culhane DP Homeless shelter use and reincarceration following prison release Criminol Public Policy. 2004 3 2 139 160 10.1111/j.1745-9133.2004.tb00031.x 40. Greenberg GA Rosenheck RA Jail incarceration, homelessness, and mental health: a national study Psychiatr Serv Wash DC. 2008 59 2 170 177 10.1176/ps.2008.59.2.170 41. Hopper K Jost J Hay T Welber S Haugland G Homelessness, severe mental illness, and the institutional circuit Psychiatr Serv Wash DC. 1997 48 5 659 665 10.1176/ps.48.5.659 42. Reid KW Vittinghoff E Kushel MB Association between the level of housing instability, economic standing and health care access: a meta-regression J Health Care Poor Underserved. 2008 19 4 1212 1228 10.1353/hpu.0.0068 19029747 43. Culhane DP Metraux S Hadley T Public service reductions associated with placement of homeless persons with severe mental illness in supportive housing Hous Policy Debate. 2002 13 1 107 163 10.1080/10511482.2002.9521437 44. Latimer EA Rabouin D Cao Z Cost-effectiveness of housing first with assertive community treatment: results from the Canadian at home/Chez Soi trial Psychiatr Serv. 2020 71 10 1020 1030 10.1176/appi.ps.202000029 32838679 45. Mental Health SF Legislation Approved Unanimously by Board of Supervisors \| Office of the Mayor. Published December 10, 2019. Accessed September 5, 2022. https://sfmayor.org/article/mental-health-sf-legislation-approved-unanimously-board-supervisors 46. Mental Health SF \| San Francisco. Published May 19, 2022. Accessed September 5, 2022. https://sf.gov/information/mental-health-sf 47. Center for Substance Abuse Treatment. Comprehensive Case Management for Substance Abuse Treatment. Substance Abuse and Mental Health Services Administration (US); 1998. http://www.ncbi.nlm.nih.gov/books/NBK64863/ 48. Martin M Snyder HR Coffa D Time to ACT: launching an Addiction Care Team (ACT) in an urban safety-net health system BMJ Open Qual. 2021 10 1 e001111 10.1136/bmjoq-2020-001111 33500326 49. Feagin J Bennefield Z Systemic racism and U.S. health care Soc Sci Med. 2014 103 7 14 10.1016/j.socscimed.2013.09.006 24507906 50. Rasch EK Gulley SP Chan L Use of emergency departments among working age adults with disabilities: a problem of access and service needs Health Serv Res. 2013 48 4 1334 1358 10.1111/1475-6773.12025 23278461 51. Coughlin TA Long SK Kendall S Health care access, use, and satisfaction among disabled Medicaid beneficiaries Health Care Financ Rev. 2002 24 2 115 136 12690698 52. Long SK Coughlin TA Kendall SJ Access to care among disabled adults on Medicaid Health Care Financ Rev. 2002 23 4 159 173 12500477 53. Lagu T, Iezzoni LI, Lindenauer PK. The Axes of Access — Improving Care for Patients with Disabilities. 10.1056/NEJMsb1315940. 54. Kirschner KL Breslin ML Iezzoni LI Structural impairments that limit access to health care for patients with disabilities JAMA. 2007 297 10 1121 1125 10.1001/jama.297.10.1121 17356035 55. Institute of Medicine (US) Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care. Unequal Treatment: Confronting Racial and Ethnic Disparities in Health Care. (Smedley BD, Stith AY, Nelson AR, eds.). National Academies Press (US); 2003. Accessed September 23, 2020. http://www.ncbi.nlm.nih.gov/books/NBK220358/ 56. Bailey ZD Krieger N Agénor M Graves J Linos N Bassett MT Structural racism and health inequities in the USA: evidence and interventions Lancet Lond Engl. 2017 389 10077 1453 1463 10.1016/S0140-6736(17)30569-X 57. Cummings JR Allen L Clennon J Ji X Druss BG Geographic access to specialty mental health care across high- and low-income US communities JAMA Psychiatry. 2017 74 5 476 484 10.1001/jamapsychiatry.2017.0303 28384733 58. Shim RS Dismantling structural racism in psychiatry: a path to mental health equity Am J Psychiatry. 2021 178 7 592 598 10.1176/appi.ajp.2021.21060558 34270343 59. Mauvais-Jarvis F Bairey Merz N Barnes PJ Sex and gender: modifiers of health, disease, and medicine Lancet Lond Engl. 2020 396 10250 565 582 10.1016/S0140-6736(20)31561-0 60. Brar A Markell M Impact of gender and gender disparities in patients with kidney disease Curr Opin Nephrol Hypertens. 2019 28 2 178 182 10.1097/MNH.0000000000000482 30652978 61. Shanshan L Fonarow Gregg C Mukamal Kenneth J Sex and race/ethnicity–related disparities in care and outcomes after hospitalization for coronary artery disease among older adults Circ Cardiovasc Qual Outcomes 2016 9 2_suppl_1 S36 S44 10.1161/CIRCOUTCOMES.115.002621 26908858 62. Cook NL Ayanian JZ Orav E John, Hicks LeRoi S. Differences in specialist consultations for cardiovascular disease by race, ethnicity, gender, insurance status, and site of primary care Circulation. 2009 119 18 2463 2470 10.1161/CIRCULATIONAHA.108.825133 19398667 63. Epstein AM Weissman JS Schneider EC Gatsonis C Leape LL Piana RN Race and gender disparities in rates of cardiac revascularization: do they reflect appropriate use of procedures or problems in quality of care? Med Care. 2003 41 11 1240 1255 10.1097/01.MLR.0000093423.38746.8C 14583687 64. Blendon RJ Buhr T Cassidy EF Disparities in physician care: experiences and perceptions of a multi-ethnic America Health Aff (Millwood). 2008 27 2 507 517 10.1377/hlthaff.27.2.507 18332509 65. Blendon RJ Buhr T Cassidy EF Disparities in health: perspectives of a multi-ethnic, multi-racial America Health Aff (Millwood). 2007 26 5 1437 1447 10.1377/hlthaff.26.5.1437 17848456 66. Fuchs JD Carter HC Evans J Assessment of a hotel-based COVID-19 isolation and quarantine strategy for persons experiencing homelessness JAMA Netw Open. 2021 4 3 e210490 10.1001/jamanetworkopen.2021.0490 33651111 67. Fleming MD Evans JL Graham-Squire D Association of shelter-in-place hotels with health services use among people experiencing homelessness during the COVID-19 pandemic JAMA Netw Open. 2022 5 7 e2223891 10.1001/jamanetworkopen.2022.23891 35895061	36447066	PMC9708142	NO-CC CODE	2022-12-01 23:20:30	no	J Gen Intern Med. 2022 Nov 29;:1-9	utf-8	J Gen Intern Med	2,022	10.1007/s11606-022-07873-y	oa_other
==== Front Multimed Tools Appl Multimed Tools Appl Multimedia Tools and Applications 1380-7501 1573-7721 Springer US New York 14165 10.1007/s11042-022-14165-4 Track 2: Medical Applications of Multimedia Detection of Diabetic Retinopathy using Convolutional Neural Networks for Feature Extraction and Classification (DRFEC) http://orcid.org/0000-0003-1518-3332 Das Dolly [email protected] Biswas Saroj Kumar [email protected] Bandyopadhyay Sivaji [email protected] grid.444720.1 0000 0004 0497 4101 Department of Computer Science and Engineering, National Institute of Technology Silchar, Cachar, Silchar, Assam 788010 India 29 11 2022 159 17 3 2022 14 6 2022 27 10 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Diabetic Retinopathy (DR) is caused as a result of Diabetes Mellitus which causes development of various retinal abrasions in the human retina. These lesions cause hindrance in vision and in severe cases, DR can lead to blindness. DR is observed amongst 80% of patients who have been diagnosed from prolonged diabetes for a period of 10–15 years. The manual process of periodic DR diagnosis and detection for necessary treatment, is time consuming and unreliable due to unavailability of resources and expert opinion. Therefore, computerized diagnostic systems which use Deep Learning (DL) Convolutional Neural Network (CNN) architectures, are proposed to learn DR patterns from fundus images and identify the severity of the disease. This paper proposes a comprehensive model using 26 state-of-the-art DL networks to assess and evaluate their performance, and which contribute for deep feature extraction and image classification of DR fundus images. In the proposed model, ResNet50 has shown highest overfitting in comparison to Inception V3, which has shown lowest overfitting when trained using the Kaggle’s EyePACS fundus image dataset. EfficientNetB4 is the most optimal, efficient and reliable DL algorithm in detection of DR, followed by InceptionResNetV2, NasNetLarge and DenseNet169. EfficientNetB4 has achieved a training accuracy of 99.37% and the highest validation accuracy of 79.11%. DenseNet201 has achieved the highest training accuracy of 99.58% and a validation accuracy of 76.80% which is less than the top-4 best performing models. Keywords Diabetic Retinopathy Fundus image Convolutional Neural Network Deep Learning Image classification ==== Body pmcIntroduction Diabetic Retinopathy (DR) is an eye disorder which is a consequence of Diabetes Mellitus (DM). DR causes inflammation and breach of retinal blood vessels for the formation of various irregular retinal lesions. It is frequently observed in patients having diabetes from a longer duration of time such as 10–15 years [47]. Statistics have stated that 80% of diabetic patients, suffering from protracted diabetes suffer from diverse phases of DR [28]. India has approximately 73 million individuals suffering from diabetes [77]. The early consultation of the patient with diabetes, with experts for DR assessment and evaluation has become necessary. The process of early diagnosis can reduce the development of DR and susceptibility to severe blindness [22]. If the disease remains undiagnosed, DR progresses in forms of various stages through formation of lesions such as Microaneurysms (MAs), Exudates (EXs), Hemorrhages (HEs), Cotton Wool Spots (CWSs), abnormal structure of the Optic Disc (OD), abnormal Foveal Avascular Zone (FAZ), Neovascularizations, Intra Retinal Microvascular Abnormalities (IRMAs), and many more [24–26, 52, 73, 76, 84]. The presence of these lesions can lead to vision problem and as the disease progresses to severe stages, it can lead to complete blindness [22, 73]. The experts of the domain, called as Ophthalmologist examines the human retina using a high-resolution digitized fundus camera for capturing fundus image. The fundus perceives various DR retinal lesions for image annotation and follow-up necessary treatments. The process of physical diagnosis is painstaking, and the dearth of unavailability of proper means for treatment makes early detection of DR, a challenging task. Thus, such a medical disorder necessitates advanced means for proper diagnosis and treatment. To overcome the challenges of labor-intensive DR detection, researchers have proposed intelligent expert systems, using Deep Learning (DL) for analysis and in-depth study of DR features from fundus images. Intelligent systems [4] are effective with respect to time, feature extraction, error recognition, and early diagnosis and treatment compared to traditional methods. These smart systems take fundus images as input, which are enhanced and analyzed for extraction of significant features, for classification of DR as moderate DR, moderate NPDR, mild DR, severe NPDR, mild Non-Proliferative DR (NPDR), early Proliferative DR (PDR), PDR, etc. [29]. Thus, intelligent systems using DL are proposed as an early and potentially scalable alternative for DR detection. Conventional ML models and data analysis approaches are shallow in nature, and have shown poor performance in learning and training complex non-linear features from larger datasets and hence, are unable to exhibit better analysis and interpretation [41]. Besides, DL based CNN models [7] have overshadowed ML models in performance, through inbuilt preprocessing, convolutional operations, better learning and generalization with deeper networks [88], less overfitting [88], data imbalance mitigation [88], optimization etc. Therefore, various state-of-the-art ML-based models [12, 49, 50] called DL models are proposed for deep feature extraction and image [6, 7] classification tasks. The inherent caliber of such models due to their hierarchical structures, enhances the learning of the model, to achieve state-of-the-art performance. In diagnosis of DR, different DL architectures proposed earlier, have exhibited different feature extraction and classification performances. However, in a single experimental set up, the effectiveness of assessing a comprehensive DL model is not exhaustive, and is unreliable. Therefore, this paper proposes a DL model entitled Diabetic Retinopathy Feature Extraction and Classification (DRFEC), an intelligent expert system using DL architectures, for an exhaustive comprehensive evaluation for detection of DR. The proposed approach is an intensive meta-analysis of 26 pretrained DL networks for identification of the optimal architecture suitable for a larger dataset such as Kaggle EyePACS DR detection with significant skewness, and thereby identify architectural patterns and corresponding loopholes in performance, to ease the process of traditional methods of detection. The model identifies suitable architecture-optimized goals to mitigate overfitting and poor generalization. The main contributions of the manuscript have been enlisted below: The proposed model performs a comprehensive comparative assessment and evaluation to determine the behaviour of 26 DL models on the DR dataset To conduct meta-analysis of traditional as well as state-of-the-art DL architectures on the highly skewed Kaggle DR detection dataset, with minimal resources and identify the best amongst them for future application and analysis. The proposed model identifies high bias and high variance during DR image classification to identify the best DL classifier This manuscript is sectionalized into various sections. Section 2 is an analysis of various related works and models proposed earlier, for identification of loopholes in early DR detection. Section 3 gives a detailed illustration of the proposed methodology, and establishes the significance of DL models for better feature extraction and classification. Section 4 illustrates the significance of the 26 DL models, based on the analysis and comparison of their performances, for determination of an optimal DL architecture(s) for DR detection. Section 5 concludes on a note identifying the suitable DL architecture(s) for DR detection. Literature review DR is a chronic health disease which requires early detection and treatment [48]. It is important to identify DR using an intelligent system for faster prediction since manual examination and detection of the disease are unreliable and highly prone to error. Therefore, various researchers and medical experts have adopted and approached for advanced feature extraction and image classification, for early DR detection. Thus, various works have been proposed in this respect using ML and DL techniques, and in which DL techniques have completely outperformed ML based models on grounds of processing large dataset, efficient computation, overfitting, generalization and better prediction. This section introduces some of the recognized works on image domain using DL techniques especially upon fundus images for early DR detection [32]. Wang et al. [85] have proposed a boosted CNN architecture using EfficientNet B3 for extraction of images features using integrated attention and feature fusion-based mechanisms, random center cropping upon Rectified Patch Camelyon (RPCam) datasets to predict and classify lymph node metastasis in breast cancer images. The model is compared with baseline models such as EfficientNet B3, ResNet50 and DenseNet121. Gurcan et al. [31] have proposed an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep CNN and ML methods. The model has extracted features from a pre-trained InceptionV3 model using transfer learning. The model has compared various ML methods namely Bagged Decision Trees, XGBoost, Random Forest, Extra Trees, Support Vector Machines, Logistic Regression, and multilayer perceptron in which XGBoost performs better. The model has used Grid search and calibration for analysis in addition to comprehensive preprocessing and fine-tuning. The model has extracted generic descriptors from one of the initial layers of InceptionV3, without layer-wise tuning and has achieved competitive classification accuracy with ML methods. It is observed that the descriptors are obtained and extracted from the initial layers of CNN which have comparatively low-level information than the higher layers of a CNN. This may be inefficient in the process of DR detection and hinder the detection of actual features thereby affecting the robustness of the model. Sarki et al. [69] have proposed a methodical study which identifies and signifies various preprocessing operations such as Contrast-Limited Adaptive Histogram Equalization (CLAHE), morphological operations, image segmentation for blood vessel segmentation etc. for Diabetic Eye Disease (DED) detection. The model has used a mini-batch size of 32, cross-entropy loss function, Adam and RMSprop upon the datasets namely DRISHTI-GS, Messidor, Retinal Dataset and Messidor-2. The proposed automated classification framework has used image enhancement, image augmentation and segmentation, and classification using very small datasets having poor category DR samples. Mayyaa et al. [56] have proposed a methodical review to examine the diagnostic use of automated microaneurysm detection for DR, and has identified various strengths and weaknesses. The methodologies appraised in this article confronts challenges that needs to be addressed in designing an effective algorithm for early diagnosis. Hattiya et al. [33] have appraised AlexNet DL mechanism as an ideal CNN architecture for DR detection and compared diverse CNN architectures namely MobileNet, DenseNet201, InceptionV3, ResNet50, NASNetMobile and MNASNet, using 23,513 retina images. Kamal et al. [44] have anticipated a transfer learning model for DR detection which mitigates imbalanced dataset. The model has fine-tuned Inception V3, VGG19, ResNet50, MobileNet, ResNet50V2, DenseNet121, MobileNetV2 and NASNetMobile using COVID-19 [5, 8] and Pneumonia datasets. Islama et al. [40] have reported a comprehensive systematic review of the performance of DL algorithms gauged precisely for computerized DR detection in fundus images. Lee et al. [53] have proposed a transfer learning NASNet-A (large) to extract bottleneck features from spectral-domain optical coherence tomography images and an ensemble training for prediction. Bodapati et al. [10] have anticipated a DR model using transfer learning, and feature extraction using VGG-16, Inception ResNetV2, Xception and NASNet to boost feature exemplification which are compared with handcrafted features, for DR detection. The model has compared feature fusion and pooling approaches and have identified averaging pooling simple fusion approach upon Deep Neural Networks (DNN) as effective in performance. It has extracted features of DR images collected from Kaggle APTOS 2019 contest dataset, using VGG16 and Xception. The authors blended these features to get the final feature representations, which are used to train DNN. Shah et al. [71] have proposed a DCNN model to distinguish referable DR using 1533 macula centered fundus images and MESSIDOR dataset. The model compares the evaluation process of ground truth data and machine learned data. The algorithm is a composition of three-version based training modules using 80,000, 96,500, and 112,489 unidentified fundus images, respectively. Pour et al. [62] have proposed EfficientNet based feature extraction and classification model using EfficientNet B5 for DR detection using MESSIDOR, MESSIDOR-2 and IDRiD datasets. The images are preprocessed using CLAHE, and the model has achieved an AUC of 0.945 on MESSIDOR, and AUC 0.932 on IDRiD. Chetoui et al. [14] have proposed an EfficientNet based feature extraction and classification model using EfficientNet B7 and Global Average Pooling, for DR detection. The model has used Kaggle EyePACs and APTOS 2019 datasets, and have extracted features such as EXs, HEs and MAs using Gradient-weighted Class Activation Mapping (Grad-CAM). Tymchenko et al. [83] have proposed a DCNN encoder-based feature extraction for DR detection using pre-trained EfficientNet-B5, EfficientNet-B4, SE-ResNeXt50 and ensemble of 20 models. Chaturvedi et al. [13] have proposed a modified DenseNet121 network on APTOS 2019 dataset and uses 3662 fundus images, for DR detection. Samanta et al. [66] have anticipated a fine-tuned DenseNet121 model which uses 3050 images for training, and is tested using VGG16, InceptionV1, InceptionV3, InceptionV2, Xception, AlexNet, ResNet-50 and DenseNet for DR detection. Sarki et al. [68] have proposed an inclusive assessment of 13 pretrained CNNs, using MESSIDOR and Kaggle dataset, for detection of DR. Ji et al. [42] have proposed an augmented DNN model using Inception V3, DenseNet121 and ResNet50 for transfer learning and improve computational proficiency and classification, for DR detection. The model has proposed various subnetworks for each of the DNN and analyzed their performance on large OCT image datasets of 83,484 images, for detection of DR lesions. The proposed model achieves accuracy and stability for InceptionV3 and ResNet50, however in case of DenseNet121, no significant improvement could be seen due to its dense connection architecture. Michele et al. [57] have proposed a fine-tuned pretrained feature extraction and classification model using MobileNetV2, dropout and a linear SVM classifier, for palmprint recognition. The model has used a PolyU palmprint dataset of 6000 images. Hui et al. [38] have anticipated a modified extreme inception-based U-Net segmentation module, to extract effective features using multitask learning and distance representation, from remote sensing images. The model has accomplished better performance with multitasking, on the datasets. Jiang et al. [43] have proposed an interpretable ensemble ResNets based feature mining and classification for DR detection using a DL model which constitutes Inception V3, ResNet152 and InceptionResNetV2, and Adaboost algorithm. The model has employed 28,244 training images, and achieved better performance using the integrated DL model than the individual models. Orlando et al. [59] have proposed an ensemble LeNet-CNN approach for the detection of MAs, HEs, red lesions for DR detection, using hand-crafted features, CNN features and a combination of both. The proposed model has achieved better results with the combination of CNN and handcrafted features. Suriyal et al. [78] have proposed a real-time DR model using MobileNet, on internet-deprived portable devices, for DR detection. The model has used 16,798 images. Huang et al. [37] have proposed a compact novel network architecture called CondenseNet which combines dense connectivity with novel learned group convolutions, using CIFAR-10, ImageNet and CIFAR-100 datasets, for image classification. The novel CNN architecture has achieved cost-efficient performance in comparison to MobileNets, DenseNet-190 and ShuffleNets. Pogorelov et al. [61] have compared global features extracted from gastrointestinal tract images using transfer learning models such as ResNet50 and Inception V3, and proposed a modified CNN. The model has compared the predictions of ML and DL classifiers, and has achieved better performance using ResNet50 than Inception V3 for feature extraction. Gulshan et al. [30] have proposed a DL InceptionV3 for detection of DR and diabetic macular edema in fundus images. The proposed methodology has deployed the EyePACS-1 dataset and MESSIDOR-2 dataset. The model has used an ensemble of ten networks and their linear average is used for prediction, upon 128,175 images. Nneji et al. [58] have proposed a two-channel preprocessing weighted fusion deep learning network on fundus images which uses CLAHE fundus images and the contrast-enhanced canny edge detection (CECED) fundus images, from 2000 Kaggle images and MESSIDOR dataset for the detection of DR. The model uses VGG-16 and a modified Inception-V3 for feature extraction and merges the channel output using a weighted fusion approach. The model performs a comprehensive analysis on six different baseline models, reduces the kernel of the Inception module B to (4 × 4), and improperly manipulates the size of the layers and the symmetricity of the image. Dong et al. [23] have proposed a DL model using InceptionV3 and VGG-16 for the detection of DR which is compared with mainstream models such as GoogLeNet, AlexNet and ResNet50 using 2693 wide-field optical coherence tomography augmented images. The real-time dataset employed is expensive and time-consuming. The authors have claimed that wide-field optical coherence tomography images are better than optical coherence tomography images due to incorporation of a better field-of-view (FOV). The model is built upon conventional DL models with a very limited dataset, which is unreliable. Padmanayana and Anoop [60] have proposed a CNN model for the detection of DR through comparison of the performance of various optimizers such as Adagrad, RMSProp with momentum and Adam, using the APTOS 2019 Kaggle dataset for training and 1000 images collected from a private institute for testing. It uses weighted CLAHE, Gaussian blur and Ben Grahams fraction maxpooling for preprocessing. Sivapriya et al. [75] have proposed a Recurrent Neural Network (RNN) to identify hard EXs for the detection of DR. The model uses limited data of 400 images from the MESSIDOR dataset and performs various preprocessing operations on them without any significant changes in the architecture of the RNN. It has compared the performance of the approach with some of the earlier works and reflects a conventional and unreliable behaviour. Bora et al. [11] have proposed an automated risk analysis system for the detection of development of DR using fundus images, in patients having diabetes with no DR. It reports a risk stratification tool and learns the various associated risk factors causing the development of DR from the stage of no DR. It uses a large retrospective longitudinal dataset for the analysis- 575,431 eyes/single images of the development set has 28,899 known outcomes, 546,532 to augment the training process via multitask learning, 3678 images in the internal validation set and 2345 images in the external validation set. Deepa et al. [18] have proposed a multistage ensemble DCNN using InceptionV3 and Xception which concatenates multi-stage patch based and image-based probability vectors for deep feature extraction and SVM based ensemble classification, for the detection of DR. It uses voting and stacking for probability vector concatenation which is classified using an Artificial Neural Network (ANN). An ensemble of SVM classifier is used for further prediction on the ensembled classification output. It is observed that the proposed architecture incorporates a single-tier ensemble for feature extraction and a double-tier ensemble using different classifiers for classification which makes the overall architecture computationally expensive and time consuming. Saeed et al. [65] have developed a two-stage Principal Component Analysis based transfer learning algorithm and initializes the pretrained model with extracted (64 × 64) ROIs of MAs, EXs and normal features of the fundus images of Kaggle’s EyePACs and MESSIDOR datasets, for detection of DR. It introduces a fixed-dimension based adaptive maxpooling to predict the label of the ROI, and compares the performance of DL models namely VGG-19, ResNet152 and Dual Path Network 107(DPN107) in which the fully connected layers are replaced with PCA for unsupervised feature discrimination. It uses Decision Tree (DT), RF and Gradient Boosting (GB) for classification and compares the overall performance of ResNet152 with all the other models. Tsai et al. [82] have proposed a DL model using Inception V3, ResNet101 and DenseNet121 on global Kaggle’s EyePACs dataset and local dataset from Taipei City Hospital (TCH), for DR detection and sets a higher overestimation rate on local dataset than global dataset due to differences in regional and ethnic factors. The proposed model uses a significant dataset for the DL models employed. However, it is biased and takes ethnic factors into account for the detection of DR and fails to learn region specific features. On comparison, DenseNet121 has performed better than InceptionV3 and ResNet101.Atwany et al. [3] have performed a literature review and analyses the significance of supervised, semi-supervised and vision transformer methodologies and learning paradigms and their significance in application to DL models for DR detection. It uses Kaggle’s EyePACs dataset of 88,702 images, DDR dataset of 13 K images and 30,244 fundus images from Beijing Tongren Eye Centre for the study and have concluded that supervised learning is not a good paradigm for noisy data. Instead, the study identifies Semi-Supervised Learning (SSL) effective but unexplainable, and less prone to inductive bias to be able to handle variance due to cross domain shift. However, it identifies SSL as non-robust for small-scale datasets. The study also reviews various amalgamation techniques for synthesis of dataset such as Generative Adversarial Network (GAN) and Variational Autoencoders (VAE), and identifies less complex DL attention models such as Vision Transformers (ViT). Das, S. et al. [16] have proposed a two-way CNN classifier based on Squeeze-and-Excitation memory module and CNN, using DIARETDB1 and a local dataset, for DR detection. The model introduces the significance of data augmentation in better model performance. Lim et al. [55] have performed a literature review on gradient-based interpretability methods in DL models such as saliency map, integrated gradient, layer-wise relevance propagation, occlusion testing, sensitivity analysis, class activation map, gradient-weighted class activation map and layer-wise relevance propagation, in the detection of DR. It identifies the drawbacks of these interpretability methods in detection of correct lesions for a given class and the lack of reliable ground truth. AbdelMaksoud et al. [1] have proposed an integrated CNN model called E-DenseNetBC-121 which is a combination of EyeNet [63] and DenseNet [39] and uses datasets such as EyePACS, IDRiD, MESSIDOR and APTOS 2019 for the detection of DR. Li et al. [54] have use a DL algorithm-based software for grading 1674 images for the detection of DR. However, it fails to detect DME which is responsible for DR, for early DR detection. It uses 1, 40,000 fundus images from publicly available EyePACs dataset and 1200 fundus images from Shanghai General Hospital. Deepa et al. [19] have proposed a comprehensive two-phase feature extraction algorithm which uses Xception and textural and transform based techniques to detect MAs for DR detection. It uses Siamese Network based CNN to perform hierarchical clustering along with Xception-based cluster selection, and a fine-tuned Xception model for extraction of patch-wise local and global features, respectively. The features are extracted from 2290 images and are classified using a Radial Basis Function (RBF) kernel based SVM which is compared with other classifiers such as RF, Adaboost and Multilayer Perceptron (MLP). Sau and Bansal [70] have proposed a Fitness based Newly Updated Grasshopper Optimization Algorithm (FNU-GOA) for the optimization of a DL model and to optimize the threshold value in active contour method for the segmentation of blood vessels, MAs, EXs and HEs for DR detection. It is compared with several other optimization algorithms such as Particle Swarm Optimization (PSO), Grey wolf optimization algorithm (GWO), Whale optimization algorithm (WOA) and Grasshopper Optimization Algorithm (GOA), and ML classifiers such as Neural Network (NN), RNN, Long Short Term Memory (LSTM) and Deep NN. The meta-heuristic optimized algorithm achieves a poor specificity and fails to classify negative samples correctly. Shaik and Cherukuri [72] have proposed a multi-stage end-to-end DNN pipeline called Hinge Attention Network (HA-Net) using gated attention VGG-16 discriminator and reconstruction autoencoder to produce attention maps, for learning latent representations in 3662 images of Kaggle’s APTOS 2019 dataset, and ISBI-2018 IDRiD2 dataset for the detection of DR using various optimizers. It achieves a poor accuracy on limited-graded dataset. The model employs various attention descriptors with minimum samples and suffers from the curse of dimensionality. It fails to generate a decision boundary and learn inter-spatial and inter-channel correlation present in latent features. It also fails to reduce latent spatial representations learned from baseline models such as VGG-16, VGG-19, ResNet50, ResNet50V2, Xception, MobileNet, Inception V3 and InceptionResNetV2, and from overlapping data. On the basis of the literature review, it is clear that conventional models are outperformed by state-of-the-art DL models. Many conventional models are shallow in nature, in contrast to DL models where DL models have shown convincing results. Based on this inspiration, the proposed DRFEC aims to perform an exhaustive analysis on DR image classification to define the characteristics, behaviour and pattern of DR data, and to identify, interpret and implement a convincing model for the problem. The use of conventional modes with a very small and limited dataset is the main drawback in DR detection. In addition to this, the use of the training set of Kaggle’s EyePACS dataset is very limited due to data imbalance and lack of features. Besides, processing a larger dataset through increase in the number of samples and data augmentation causes data explosion which is a huge challenge with constraints in adequate resources. Various research works have considered only a few images which ranges between a few thousand images. The existing models have shown resemblance in their performances through the use of a similar number of images with no significant improvement in the pattern of the collected data. Moreover, these models also cannot generalize real-time datasets because of biasness towards regional and ethnic factors, which remains an unsolved problem. They have also shown high variance thereby reflecting the poor learning process of the methodologies for the detection. The proposed model thus aims to train a DL model to gain insights on the problem, and identify and differentiate parameters responsible for various performance with respect to (w.r.t.) the optimal model. It aims to solve critical problems through identification and analysis of DR data from the inception using an imbalanced dataset and gradually optimizing the proposed baseline framework through hyperparameter tuning, model training and evaluation [64]. The proposed DRFEC The manual examination of DR is a feasible but lethargic process, and hence not recommendable for early DR detection. Therefore, it is essential to examine DR using a proficient system which employs ML techniques such as DL for better detection. Various explorative works have been performed earlier for DR detection, using a variety of datasets and especially using the Kaggle’s EyePACS dataset. The proposed DRFEC accomplishes inbuilt preprocessing, deep feature extraction and image classification using a comprehensive DL model which constitutes VGG-19, VGG-16 [74], Xception [15], InceptionV3 [79], MobileNet [35], MobileNetV2 [67], EfficientNet B0-B7 [81], DenseNet121, DenseNet169, DenseNet201 [36], ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2 [34], NASNetLarge [89], NASNetMobile [89] and InceptionResNetV2 [80]. The model uses popularly known ImageNet [51] pre-trained DL models for training and evaluation of Kaggle DR Detection dataset obtained from EyePACS [17, 21]. DL models have the proficiency to extract new and deep features, from previously learned features using representation learning. Thus, the proposed model emphases on finding scope for various improvements upon the data and the model, and thereafter determines the most suitable network amongst them for DR image classification. Figure 1 depicts a pictorial demonstration of DRFEC deploying DL models. It is implemented using a 64-bit operating system, ×64-based processor, Windows 10 Pro, DL package Keras from TensorFlow, Python 3.8, TensorFlow version 2.4, 64GB RAM and Intel(R) Xeon(R) W-2155 CPU @ 3.30GHz 3.31 GHz. The outline of the different steps of DRFEC is given in Fig. 1 below. Fig.1 a) Layout of the proposed DRFEC for DR detection at an early stage b) Architectural Illustration of DRFEC The proposed DRFEC processes the input data acquired from Kaggle repository and downsamples it accordingly based on different architectures employed in the model. Then the employed CNN is used to perform feature extraction and multi-class classification of DR fundus images. Flowchart 1 is an illustration of the working principle of DRFEC where each time a CNN is chosen to train the model. Every CNN employed is individually used to compute the training and validation/test accuracy values to check for model overfitting and generalization. Based on the performances and inferences drawn from every single DL CNN model, the best DL architecture for deep DR feature extraction and image classification is extracted. Flowchart 1 Working principle of DRFEC. Abbreviations: Training Accuracy (TA), Validation/Test Accuracy (VA) Experimental environment The proposed framework is implemented using a 64-bit operating system, x64-based processor, Windows 10 Pro, DL package Keras from TensorFlow, Python 3.8, TensorFlow version 2.4, 64GB RAM and Intel(R) Xeon(R) W-2155 CPU @ 3.30GHz 3.31 GHz. Image dataset acquisition In DRFEC, the model is assessed using a large dataset of fundus images, to analyze the performances of 26 DL architectures. The model uses Kaggle’s EyePACS dataset and is exceedingly disproportionate [20]. The model uses 35,126 images, and is trained using 27,446 images and validated using 7680 images. Figure 2 defines long tail distribution of the dataset. Figure 3 labels the fundus images (a) – (e) as left eye and (f) – (j) as right eye, with no DR, mild DR, moderate DR, severe DR and PDR, respectively. EyePACS [59] has 35,126 taken from different cameras with various FOV, with a resolution of 1440 × 960. It is available in jpeg format and is graded by many experts. Fig. 2 Long tail distribution of the skewed DR dataset Fig. 3 Fundus of left eye (a) – (e) and right eye (f) – (j) depicting Grade 0, Grade 1, Grade 2, Grade 3 and Grade 4 DR, respectively The noteworthy features are extracted and classified using 26 DL networks individually, in the DRFEC. The model is assessed and evaluated for classification, based on DR classes as 0, 1, 2, 3 and 4. The proposed model uses Adam optimizer, 0.0001 learning rate, categorical cross entropy loss function and softmax activation, for 50 epochs with a batch size of 10. The annotations of the images in the dataset are depicted in Table 1. Table 2 demonstrates the DR clinical manifestations corresponding to the category of DR in fundus images. Table 1 Number of labelled images Stage of DR No. of fundus images No DR 25,810 Mild DR 2443 Moderate DR 5292 Severe DR 873 PDR 708 Table 2 DR clinical manifestations corresponding to the category Category Level Clinical Manifestation No DR 0 No DR lesions Mild DR 1 MAs or HEs Moderate DR 2 MAs, soft EXs, HEs, Venous Beading Severe DR 3 Severe HEs, Venous Beading, mild IRMA Proliferative DR (PDR) 4 Neovascularization Image pre-processing The image preprocessing module is the transparent module in CNN after the process of image dataset acquisition. The fundus images are raw high-resolution images and requires downsampling with respect to the compatibility standards of different CNN architectures such as for VGG-16 the image is down-sampled to (224 × 224 × 3). This helps the model to better interpret the image. In addition to these, the detection of bright intensity structures such as EXs and Optic Disc (OD) is ambiguous as EXs refer to a lesion whereas OD refers to normal anatomy of the retina. An abnormal OD can lead to subtle lesions which remains undetected due to its bright intensity. Therefore, image preprocessing techniques are important for identification and differentiation of artefacts from lesions for better detection of the disease. Additionally, identification of lesions leading to intermediate stages of DR, is also important for early DR detection. In the proposed model, the CNN’s inbuilt pre-processing is used to extract image specifics which performs convolution and pooling, image striding and padding, using different filters and kernels. However, these DL CNN models are black box in nature and enhancement on images are implemented in real-time. Therefore, the enhanced image output cannot be visualized and only gradient details and edges can be extracted during feature extraction for classification. NN-DL for feature extraction and classification CNNs are convolutions with non-linear activation functions. CNN-DL feature extraction and classification is the subsequent component of the proposed DRFEC. The DL models perform feature extraction using numerous layers of convolution and pooling, and normalization and activation operations. In the meantime, such models also have the competence to learn and cause novel features from pre-existing features using representation learning, such as lines, boundary, points, edges, corners, vascular structure, etc., for simplification in detection. For instance, CNNs can perform edge detection using Prewitt Gradient Operator for the image [[3,0,1,2,7,4],[1,5,8,9,3,1],[2,7,2,5,1,3],[0,1,3,1,7,8],[4,2,1,6,2,8],[2,4,5,2,3,9]] as shown in Figs. 4 and 5(a) depicts Horizontal Prewitt Gradient kernel and Fig. 5(b) depicts Vertical Prewitt Gradient kernel. Fig. 4 Image Array Fig. 5 Prewitt Gradient Kernel (a). Horizontal (b). Vertical The image array is convolved with the given Vertical Prewitt Gradient kernel of size of 3 × 3, which divides the 6 × 6 image into 16 regions of size 3 × 3, for detection of vertical edges. The formula for the convolution operation is shown in Fig. 6. Fig. 6 Convolution of image array with Prewitt kernel for detection of vertical edges Thus, for the purpose of deep feature extraction from DR fundus images, state-of-the-art DL models such as VGG-19, VGG-16, Xception, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, EfficientNet B0-B7, DenseNet121, DenseNet169, DenseNet201, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, NASNetLarge and NASNetMobile are used, to extract better and enhanced features necessary for the purpose of better learning and image classification. Figure 7 demonstrates the working of CNN which takes a grayscale input image (28x28x1) and performs convolution and pooling for feature extraction, and fully connected neural network-based classification using ReLU activation, dropout and softmax activation [64]. Figure 8 demonstrates the working of the CNN as a feature extractor which takes an RGB image (224x224x3) as an input and performs 5 convolution and 5 pooling operations to enhance the representation of the images for better feature extraction and FCNN based classification. Fig. 7 Convolutional Neural Network [64] Fig. 8 CNN feature extractor Convolutional Networks are successful to a great extent in large-scale image classification using high performance computing and Graphical Processing Unit (GPU), thus making transitions from high-dimensional low feature encoding to deeper CNNs. These networks have undergone huge transformations, explored different connectivity patterns, and extracted multi-level features using skip-connections, inception modules, and dense blocks. Besides, cross-layer connections and architectural innovations contains small multi-layer perceptron in the kernels of convolutional layers to mine complex features. Additionally, the inner layers are supervised using auxiliary classifiers, to reinforce gradients expected from previous layers of intensely supervised networks, to expand the course of information by joining transitional stratums of dissimilar base networks, or growth of nets with paths that minimalize reconstruction losses. Results The proposed model is a composition of DL CNN models such as VGG-16, VGG-19, Xception, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, EfficientNet B0-B7, DenseNet121, DenseNet169, DenseNet201, ResNet50,, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, NASNetLarge and NASNetMobile which are trained on a training dataset of 27,446 fundus images and tested on a test dataset of 7680 fundus images, where both the training and test dataset contains images belonging to 5 classes of DR. The DL models are individually trained and tested upon the dataset of interest to evaluate their performance. The proposed model illustrates and demonstrates the behaviour of these models in a chronological fashion, and has achieved different training and validation values. The DL models are trained for a target size of (224 × 224), using inbuilt preprocessing of the CNNs, a batch size of 32, and using the fully connected classification layers for classification accompanied with softmax activation function. Besides, the DL models uses the state-of-the-art Adam optimizer, a learning rate of 0.0001 and categorical cross entropy loss function, for training for 50 epochs. VGG-16 The VGG-16 model has a total trainable parameter of 138,357,544 and 0 non-trainable parameters. On regularization, through minimization of two layers of the neural network, the model has a total trainable parameter of 134,281,029 and 0 non-trainable parameters, for a target size of (224 × 224), using the intermediate layers for output. It has achieved a training accuracy of 90.91% and validation accuracy of 62.07%, in the 50th epoch. It has also achieved a training loss of 0.26 and a validation loss of 2.32. It can be inferred that the training accuracy and validation loss are higher than validation accuracy and training loss, respectively. This implies that the model is complex and is overfitting. To regularize the model and prevent it from overfitting, the layers of the network are reduced and the number of neurons are minimized or dropped to reduce model parameters. Besides, other forms of regularization such as preprocessing, data augmentation and batch normalization can also be applied to regularize the model. Figure 9 depicts training and validation loss of VGG-16. Figure 10 depicts training and validation accuracy of VGG-16. Fig. 9 Training and validation loss of VGG-16 Fig. 10 Training and validation accuracy of VGG-16 VGG-19 The VGG-19 model has a total trainable parameter of 143,667,240 and 0 non-trainable parameter. On regularization, through minimization of two layers of the neural network, the model has a total trainable parameter of 139,590,725 and 0 non-trainable parameter, for a target size of (224 × 224). It has achieved a training accuracy of 97.98% and validation accuracy of 73.37%, in the 50th epoch. It has also achieved a training loss of 0.062 and a validation loss of 2.07. It can be inferred that the training accuracy and validation loss are higher than validation accuracy and training loss, respectively. This implies that the model is complex and is overfitting. It has many parameters that are capable of memorizing the training data, and hence are only capable of extracting information and not create it. Figure 11 depicts training and validation loss of VGG-19. Figure 12 depicts training and validation accuracy of VGG-19. Fig. 11 Training and validation loss of VGG-19 Fig. 12 Training and validation accuracy of VGG-19 ResNet101 The ResNet101 model has a total of 44,707,176 of which 44,601,832 parameters are trainable and 105,344 parameters are non-trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 42,668,421 of which 42,563,077 parameters are trainable and 105,344 parameters are non-trainable, for a target size of (224 × 224), upon the intermediate layers for output. It has achieved a training accuracy of 99.32% and a validation accuracy of 77.32%. It has also achieved a training loss of 0.0197 and a validation loss of 1.8714. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss. Figure 13 depicts training and validation loss of ResNet101. Figure 14 depicts training and validation accuracy of ResNet101. Fig. 13 Training and Validation loss of ResNet101 Fig. 14 Training and Validation accuracy of ResNet101 ResNet101V2 The ResNet101V2 model has a total of 44,675,560 of which 44,577,896 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 42,636,805 of which 42,539,141 parameters are trainable. It has achieved a training accuracy of 99.34% and a validation accuracy of 75.40%. It has also achieved a training loss of 0.0197 and a validation loss of 1.9905. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss. Figure 15 depicts training and validation loss of ResNet101V2. Figure 16 depicts training and validation accuracy of ResNet101V2. Fig. 15 Training and Validation loss of ResNet101V2 Fig. 16 Training and Validation accuracy of ResNet101V2 ResNet50 The ResNet50 model has a total of 25,636,712 of which 25,583,592 parameters are trainable and 53,120 parameters are non-trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 23,597,957 of which 23,544,837 parameters are trainable and 53,120 parameters are non-trainable, for a target size of (224 × 224), using the intermediate layers for output. It has achieved a training accuracy of 99.37% and a validation accuracy of 71.64%. It has also achieved a training loss of 0.0178 and a validation loss of 2.51. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 17 depicts training and validation loss of ResNet50. Figure 18 depicts training and validation accuracy of ResNet50. Fig. 18 Training and Validation accuracy of ResNet50 Fig. 17 Training and Validation loss of ResNet50 ResNet50V2 ResNet50 model has a total of 25,613,800 of which 25,568,360 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 23,575,045 of which 23,529,605 parameters are trainable. It has achieved a training accuracy of 99.19% and a validation accuracy of 72.97%. It has also achieved a training loss of 0.0238 and a validation loss of 1.8485. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 19 depicts training and validation loss of ResNet50V2. Figure 20 depicts training and validation accuracy of ResNet50V2. Fig. 19 Training and Validation loss of ResNet50V2 Fig. 20 Training and Validation accuracy of ResNet50V2 ResNet152 The ResNet152 model has a total of 60,419,944 of which 60,268,520 parameters are trainable and 151,424 parameters are non-trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 58,381,189 parameters of which 58,229,765 parameters are trainable and 151,424 parameters are non-trainable, for a target size of (224 × 224), using the intermediate layers for output. It has achieved a training accuracy of 99.44% and a validation accuracy of 76.65%. It has also achieved a training loss of 0.0163 and a validation loss of 1.81. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by two, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 21 depicts training and validation loss of ResNet152. Figure 22 depicts training and validation accuracy of ResNet152. Fig. 21 Training and Validation loss of ResNet152 Fig. 22 Training and Validation accuracy of ResNet152 ResNet152V2 The ResNet152V2 model has a total of 60,380,648 of which 60,236,904 parameters are trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 58,341,893 parameters of which 58,198,149 parameters are trainable. It has achieved a training accuracy of 99.26% and a validation accuracy of 76.03%. It has also achieved a training loss of 0.0212 and a validation loss of 1.9876. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by two, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 23 depicts training and validation loss of ResNet152V2. Figure 24 depicts training and validation accuracy of ResNet152V2. Fig. 23 Training and Validation loss of ResNet152V2 Fig. 24 Training and Validation accuracy of ResNet152V2 DenseNet121 The DenseNet121 model has a total of 8,062,504 parameters of which 7,978,856 parameters are trainable and 83,648 parameters are non-trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 7,042,629 parameters of which 6,958,981 parameters are trainable and 83,648 parameters are non-trainable, for a target size of (224 × 224), using the intermediate layers for output. It has achieved a training accuracy of 98.80% and a validation accuracy of 73.58%. It has also achieved a training loss of 0.0344 and a validation loss of 1.52. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 25 depicts training and validation loss of DenseNet121. Figure 26 depicts training and validation accuracy of DenseNet121. Fig. 25 Training and validation loss of DenseNet121 Fig. 26 Training and validation accuracy of DenseNet121 DenseNet169 The DenseNet169 model has a total of 14,307,880 parameters of which 14,149,480 parameters are trainable and 158,400 parameters are non-trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 12,651,205 parameters of which 12,492,805 parameters are trainable and 158,400 parameters are non-trainable, for a target size of (224 × 224), using the intermediate layers for output. It has achieved a training accuracy of 98.95% and a validation accuracy of 79.02%. It has also achieved a training loss of 0.0314 and a validation loss of 1.94. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 27 depicts training and validation loss of DenseNet169. Figure 28 depicts training and validation accuracy of DenseNet169. Fig. 27 Training and validation loss of DenseNet169 Fig. 28 Training and Validation accuracy of DenseNet169 DenseNet201 The DenseNet201 model has a total of 20,242,984 parameters of which 20,013,928 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 18,331,589 parameters of which 18,102,533 parameters are trainable. It has achieved a training accuracy of 99.58% and a validation accuracy of 76.80%. It has also achieved a training loss of 0.0132 and a validation loss of 1.9230. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 29 depicts training and validation loss of DenseNet201. Figure 30 depicts training and validation accuracy of DenseNet201. Fig. 29 Training and validation loss of DenseNet201 Fig. 30 Training and Validation accuracy of DenseNet201 NASNetLarge The NASNetLarge model has a total of 88,949,818 of which 88,753,150 parameters are trainable and 196,668 parameters are non-trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 84,936,983 of which 84,740,315 parameters are trainable and 196,668 parameters are non-trainable, for a target size of (224 × 224), upon the intermediate layers for output. It has achieved a training accuracy of 99.24% and a validation accuracy of 79.09% It has also achieved a training loss of 0.0211 and a validation loss of 2.089. Figure 31 depicts training and validation loss of NASNetLarge. Figure 32 depicts training and validation accuracy of NASNetLarge. Fig. 31 Training and validation loss of NASNetLarge Fig. 32 Training and validation accuracy of NASNetLarge NASNetMobile The NASNetMobile model has a total of 5,326,716 of which 5,289,978 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 4,275,001 parameters of which 4,238,263 parameters are trainable. It has achieved a training accuracy of 99.05% and a validation accuracy of 67.49% It has also achieved a training loss of 0.0288 and a validation loss of 1.7893. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 33 depicts training and validation loss of NASNetMobile. Figure 34 depicts training and validation accuracy of NASNetMobile. Fig. 33 Training and validation loss of NASNetMobile Fig. 34 Training and Validation accuracy of NASNetMobile Xception The Xception model has a total of 22,910,480 parameters of which 22,855,952 parameters are trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 20,871,725 parameters of which 20,817,197 parameters are trainable. It has achieved a training accuracy of 99.46% and a validation accuracy of 78.05%. It has also achieved a training loss of 0.0175 and a validation loss of 1.88. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 35 depicts training and validation loss of Xception. Figure 36 depicts training and validation accuracy of Xception. Fig. 35 Training and validation loss of Xception Fig. 36 Training and validation accuracy of Xception InceptionV3 The InceptionV3 model has a total of 23,851,784 parameters of which 23,817,352 parameters are trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 21,813,029 parameters of which 21,778,597 parameters are trainable. It has achieved a training accuracy of 99.03% and a validation accuracy of 73.72%. It has also achieved a training loss of 0.0265 and a validation loss of 1.51. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 37 depicts training and validation loss of InceptionV3. Figure 38 depicts training and validation accuracy of InceptionV3. Fig. 37 Training and validation loss of InceptionV3 Fig. 38 Training and validation accuracy of InceptionV3 MobileNet The MobileNet model has a total of 4,253,864 parameters of which 4,231,976 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 4,258,869 parameters of which 4,236,981 parameters are trainable. It has achieved a training accuracy of 98.92% and a validation accuracy of 77.20% It has also achieved a training loss of 0.0311 and a validation loss of 1.8153. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 39 depicts training and validation loss of MobileNet. Figure 40 depicts training and validation accuracy of MobileNet. Fig. 39 Training and validation loss of MobileNet Fig. 40 Training and validation accuracy of MobileNet MobileNetV2 The MobileNetV2 model has a total of 3,538,984 parameters of which 3,504,872 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 2,264,389 parameters of which 2,230,277 parameters are trainable. It has achieved a training accuracy of 98.66% and a validation accuracy of 77.42%. It has also achieved a training loss of 0.0392 and a validation loss of 1.7161. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 41 depicts training and validation loss of MobileNetV2. Figure 42 depicts training and validation accuracy of MobileNetV2. Fig. 41 Training and validation loss of MobileNetV2 Fig. 42 Training and validation accuracy of MobileNetV2 InceptionResNetV2 The InceptionResNetV2 model has a total of 55,873,736 of which 55,813,192 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 54,344,421 parameters of which 54,283,877 parameters are trainable. It has achieved a training accuracy of 99.36% and a validation accuracy of 79.05%. It has also achieved a training loss of 0.0185 and a validation loss of 2.2305. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by two, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 43 depicts training and validation loss of InceptionResNetV2. Figure 44 depicts training and validation accuracy of InceptionResNetV2. Fig. 43 Training and validation loss of InceptionResNetV2 Fig. 44 Training and validation accuracy of InceptionResNetV2 EfficientNet B0 The EfficientNetB0 model has a total of 5,330,571 of which 5,288,548 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 4,055,976 of which 4,013,953 parameters are trainable. It has achieved a training accuracy of 99.03% and a validation accuracy of 75.63% It has also achieved a training loss of 0.03 and a validation loss of 1.88. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 45 depicts training and validation loss of EfficientNetB0. Figure 46 depicts training and validation accuracy of EfficientNetB0. Fig. 45 Training and validation loss of EfficientNetB0 Fig. 46 Training and validation accuracy of EfficientNet B0 EfficientNet B1 The EfficientNetB1 model has a total of 7,856,239 of which 7,794,184 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 6,581,644 of which 6,519,589 parameters are trainable. It has achieved a training accuracy of 99.11% and a validation accuracy of 76.63% It has also achieved a training loss of 0.0268 and a validation loss of 1.7922. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 47 depicts training and validation loss of EfficientNetB1. Figure 48 depicts training and validation accuracy of EfficientNetB1. Fig. 47 Training and validation loss of EfficientNetB1 Fig. 48 Training and Validation accuracy of EfficientNetB1 EfficientNetB2 The EfficientNetB2 model has a total of 9,177,569 of which 9,109,994 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 7,775,614 of which 7,708,039 parameters are trainable. It has achieved a training accuracy of 99.20% and a validation accuracy of 76.35% It has also achieved a training loss of 0.0246 and a validation loss of 1.7861. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 49 depicts training and validation loss of EfficientNetB2. Figure 50 depicts training and validation accuracy of EfficientNetB2. Fig. 49 Training and validation loss of EfficientNetB2 Fig. 50 Training and Validation accuracy of EfficientNetB2 EfficientNet B3 The EfficientNetB3 model has a total of 12,320,535 of which 12,233,232 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 10,791,220 of which 10,703,917 parameters are trainable. It has achieved a training accuracy of 99.20% and a validation accuracy of 78.14% It has also achieved a training loss of 0.0249 and a validation loss of 1.7087. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 51 depicts training and validation loss of EfficientNetB3. Figure 52 depicts training and validation accuracy of EfficientNetB3. Fig. 51 Training and validation loss of EfficientNetB3 Fig. 52 Training and validation accuracy of EfficientNetB3 EfficientNet B4 The EfficientNetB4 model has a total of 19,466,823 of which 19,341,616 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 17,682,788 of which 17,557,581 parameters are trainable. It has achieved a training accuracy of 99.37% and a validation accuracy of 79.11% It has also achieved a training loss of 0.0192 and a validation loss of 1.5368. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 53 depicts training and validation loss of EfficientNetB4. Figure 54 depicts training and validation accuracy of EfficientNetB4. Fig. 53 Training and validation loss of EfficientNetB4 Fig. 54 Training and validation accuracy of EfficientNetB4 EfficientNet B5 The EfficientNetB5 model has a total of 30,562,527 of which 30,389,784 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 28,523,772 of which 28,351,029 parameters are trainable. It has achieved a training accuracy of 99.31% and a validation accuracy of 77.16% It has also achieved a training loss of 0.0196 and a validation loss of 1.5469. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 55 depicts training and validation loss of EfficientNetB5. Figure 56 depicts training and validation accuracy of EfficientNetB5. Fig. 55 Training and validation loss of EfficientNetB5 Fig. 56 Training and validation accuracy of EfficientNetB5 EfficientNet B6 The EfficientNetB6 model has a total of 43,265,143 of which 43,040,704 parameters are trainable for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 40,971,668 of which 40,747,229 parameters are trainable. It has achieved a training accuracy of 99.31% and a validation accuracy of 77.86% It has also achieved a training loss of 0.0197 and a validation loss of 1.5932. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 57 depicts training and validation loss of EfficientNetB6. Figure 58 depicts training and validation accuracy of EfficientNetB6. Fig. 57 Training and validation loss of EfficientNetB6 Fig. 58 Training and validation accuracy of EfficientNetB6 EfficientNet B7 The EfficientNetB7 model has a total of 66,658,687 of which 66,347,960 parameters are trainable and 310,727 parameters are non-trainable, for a target size of (224 × 224). The model is regularized by reducing the number of layers of the neural network by 2. This has led the model to have a total of 64,110,492 of which 63,799,765 parameters are trainable and 310,727 parameters are non-trainable, for a target size of (224 × 224), upon the intermediate layers for output. It has achieved a training accuracy of 99.35% and a validation accuracy of 72.97% It has also achieved a training loss of 0.0181 and a validation loss of 1.96. It is observed that the training accuracy and validation loss of the model are higher than validation accuracy and training loss, respectively which has led to the overfitting of the model. On reducing and minimizing the number of layers in the neural network by 2, for the purpose of regularization to reduce the overfitting caused, has however brought minimal changes in the gap of training and validation loss, except for minimization in the number of parameters. Figure 59 depicts training and validation loss of EfficientNetB7. Figure 60 depicts training and validation accuracy of EfficientNetB7. Fig. 59 Training and validation loss of EfficientNetB7 Fig. 60 Training and validation accuracy of EfficientNetB7 On the basis of the performance of the 26 DL models in training and testing accuracy, the proposed model is significant and uses Kolmogorov-Smirnov (KS) [27, 45] testing principle to determine the critical value Dcrit and maximum absolute difference. On comparison, the proposed model has achieved a Dcrit of 1 which is greater than 0.05, and a maximum absolute difference of 37.72. On this basis we reject our null hypothesis which claims on choosing and finding a best DL model for image classification, and accept our alternative hypothesis of finding and engineering a better DL model for DR image classification. Figures 61 and 62 depicts the plot of KSPA statistical test for 26 DL models, and comparison of them with contemporary and state-of-the-art works [1, 16, 18, 19, 23, 70, 72, 82], respectively. Fig. 61 KSPA statistical test for 26 DL models Fig. 62 KSPA statistical test for 26 DL models vs existing models Time complexity of DRFEC The proposed model is a comprehensive evaluation of 26 CNN Deep Learning networks. The ML models especially Neural Networks (NNs) when applied on image data requires processing of thousands of features from thousands of pixels in a given image. Moreover, NNs are fully connected since inception and hence are highly parameterized. When they perform image learning, the number of parameters increases exponentially, as we will see in this section while analysing the working of a fully connected network in one of the most popular DL models. These networks cause overfitting and requires a large number of samples for proper training, in addition to larger memory and greater computational power for prediction. In image data, there are two major characteristics namely – feature localization or feature correlation and feature independence of location. CNNs thus, have the ability to solve the problem associated with too-many-parameters, with shared parameters (feature independence of location) of locally connected networks (feature localization) called the Convolution Net (ConvNet), which reduces the total number of parameters. These ConvNets have undergone evolution in its depth, architecture and search space. They are incorporated factors such as activation function, dropout, augmentation, filter size for parameter reduction and non-linear transformation, doubling of channels to recover lost information, to excel in performance. However, issues such as vanishing gradient deaccelerates their performance and makes them hard to optimize. They have introduced the concepts of variable filter sizes, residual learning, skip connections, identity mappings, depthwise separable convolutions and reinforcement learning to overcome problems associated with the depth of the network, and in the search space to identify the best combination of parameters. In a CNN, the number of features is at most a constant time the number of input pixels say p, where the constant is <1. Therefore, the convolution of a fixed size filter across an image with p number of pixels takes O(p) time because each output is a sum of the product between k pixels in the image and k weights in the filter, where k does not vary with p. Similar is the case with maxpooling and average pooling operations of a CNN which does not take more than liner time in the given input size. Therefore, the overall runtime is linear [86]. With increasing depth, these CNNs transform to a Deep Neural Network (DNN). With the increase in the number of fully connected layers DNNs processes a feed-forward pass algorithm and a backpropagation algorithm [46]. Theoretically and mathematically, the training of a neural network-based model is implemented using matrices, and the time complexity of matrix multiplication (Mij∗Mjk) is O (i∗j∗k). For e.g., to compute the time complexity of the forward pass algorithm for VGG-16 with 16 layers and 5 convolutional blocks, let i denote the number of nodes of the input convolutional layer, j denotes the number of nodes in the second convolutional layer, k denotes the number of nodes in the third convolutional layer, l denotes the number of nodes in the MaxPooling layer, m denotes the number of nodes in the dense layer 1, n denotes the number of nodes in dense layer 2 and o denotes the number of nodes in the output layer. Since there are 7 layers, 6 matrices are required to represent weights between the layers. Let Wji, Wkj, Wlk, Wml, Wnm, Won be the matrices where Wji is a matrix with j rows and i columns and contains the weights from layer i to layer j, Wkj is a matrix with k rows and j columns and contains the weights from layer j to layer k. Similarly, Wlk is a matrix with l rows and k columns and contains the weights from layer k to layer l, Wml is a matrix with m rows and l columns and contains the weights from layer l to layer m. Again, Wnm is a matrix with n rows and m columns and contains the weights from layer m to layer n, and Won is a matrix with o rows and n columns and contains the weights from layer n to layer o. For t training examples (here t = 27,446), to propagate from layer i to j, the following matrix multiplication is performed Sjt = Wji∗Zit and has O (j∗i∗t) time complexity. The activation function Zjt = f (Sjt) has O (j∗t) time complexity because it is an element-wise linear operation. In total, from i → j there is a time complexity of i Oj∗i∗t+j∗t=Oj∗t∗i+1≈Oj∗i∗t Similarly, from j → k there is a time complexity of O (k∗j∗t), from k → l there is a time complexity of O (l∗k∗t), from l → m there is a time complexity of O (m∗l∗t), from m → n there is a time complexity of O (n∗m∗t), and from n → o there is a time complexity of O (o∗n∗t). In total, the time complexity for feedforward propagation will be ii Thus, the time complexity of the DNN during forward propagation is. iii Op+Ot∗ij+jk+kl+lm+mn+no This is repeated for all the convolution operations in a CNN- here VGG-16 with 5 convolutional blocks, and thus the total time-complexity for forward propagation in VGG-16 is. iv 5∗Op+Ot∗ij+jk+kl+lm+mn+no=Op+Ot∗ij+jk+kl+lm+mn+no which is linear. The similar concept is followed across all the other DL models depending upon the number of layers i.e., the depth of the network. The structures such as inception modules, skip connections, dense blocks in InceptionV3, ResNets and DenseNets, respectively helps in minimization of parameters and optimizes the depth of the network thereby optimizing the computational time. Thus, all the DL models have a time complexity which is linear and is only affected by the number of parameters in the network. In case, where the input size and the size of the kernel is same, the time complexity is O (p) [87]. The backpropagation algorithm proceeds as follows: Initially, the error EO, from the output layer o to n is computed and the matrix containing the error for the nodes at layer o is represented as -. v Eot=f`Sot⨂Zot−Pot Eot has o rows and t columns meaning each column is an error for the training example t, and ⨂ is the element-wise operation. The small change ‘delta, D’ in the weights called ‘delta weights’ between the layer o and n is computed as -. vi Don=Eot∗Ztn,whereZtnis the transpose ofZnt The weights are adjusted as Won = Won – Don, and for o to n the time complexity is. vii Oot+otn+on=Oo∗t∗n From layer n to m, Ent = f ` (Snt) ⨂ (Wno ∗ Eot), Dnm = Ent * Ztm and Wnm = Wnm – Dnm, where Wno is the transpose of Won. The time complexity for n to m is O (nt + not + ntm + nm) = O (nt (o + m)). Similarly, for m to l, l to k, k to j and j to i, the time complexities are O(mt(n + l)), O(lt(m + k)), O(kt(l + j)) and O(jt(k + i)), respectively. Therefore, the total complexity is. viii Ootn+tno+m+tmn+l+tlm+k+ktl+j+tjk+i=Ot∗on+mn+ml+lk+kj+ji which is the same as the feed-forward pass algorithm. Therefore, the time complexity for one epoch will be. ix Ot∗ij+jk+kl+lm+mn+no In case for e = 50 number of epochs, the DNN will be showing a time complexity of -. x Op+Oe∗t∗ij+jk+kl+lm+mn+no=Op+O50∗t∗ij+jk+kl+lm+mn+no which is again linear. Here, the time complexity of the proposed model is based on the number of parameters in the DL networks. It is directly proportional to the number of parameters i.e. it increases and decreases with the increase and decrease in the total number of parameters in the network, respectively. However, it is observed that models with higher complexity have performed comparatively better than models with lower complexity such as NasNetLarge [2]. A similar thing is observed in the proposed model where larger architectures such as DensNet169, NasNetLarge, and EfficientNetB4 have shown significant performances despite huge architectural, space and computational complexity. Each of these models have taken a maximum of 72 hrs of execution on 50 epochs using 35,126 images in the available experimental environment. The complexity of DL models is computed using multiply-accumulates (MACC) operation and Floating-Point Operations Per Second (FLOPs) [9] with a batch size of 32. The input and output to convolutional layers are not vectors but three-dimensional feature maps of size H × W × C where H is the height of the feature map, W the width, and C the number of channels at each location. The number of MACCs for a convolution layer with kernel size K is: xi K×K×Cin×Hout×Wout×Cout Each pixel in the output feature map is of size Hout × Wout and performs a dot product of the weights, and a K × K window of input values across all input channels, Cin. The layer has Cout different convolution kernels, therefore it is repeated Cout times to create all the output channels. For an e.g., a 3 × 3 convolution with 512 filters, on a 14 × 14 input feature map with 7 channels, the MACC is 3 × 3 × 7 × 14 × 14 × 512 = 6,322,176. The MACC-FLOPs obtained individually for each of the DL model is depicted in Table 3. Figures 63 and 64 illustrates the graphical presentation of the time complexity of 26 DL models in terms of MACCs and FLOPs, respectively. Table 3 MACCs and FLOPs of 26 DL models Models MACC Models FLOPs DenseNet121 9,834,496 MobileNetV2 19,251,752,448 NASNetMobile – EfficientNetB0 25,250,817,155 DenseNet169 9,834,496 NASNetMobile 36,386,375,168 DenseNet201 9,834,496 MobileNet 36,401,180,160 InceptionV3 301,989,888 EfficientNetB1 44,863,292,419 VGG-16 462,422,016 EfficientNetB2 64,744,811,203 MobileNetV2 983,449,600 EfficientNetB3 118,910,253,571 EfficientNetB1 1,677,721,600 DenseNet121 181,468,425,728 ResNet50 2,517,630,976 DenseNet169 215,113,858,560 ResNet50V2 2,517,630,976 ResNet50V2 223,153,548,800 ResNet101 2,517,630,976 ResNet50 247,310,282,240 ResNet101V2 2,517,630,976 DenseNet201 274,733,309,440 ResNet152 2,517,630,976 EfficientNetB4 285,362,507,779 ResNet152V2 2,517,630,976 InceptionV3 366,430,995,968 MobileNet 2,517,630,976 ResNet101V2 460,844,887,552 EfficientNetB2 3,251,736,576 ResNet101 485,056,212,480 EfficientNetB0 3,933,798,400 Xception 535,288,550,144 EfficientNetB3 5,898,240,000 EfficientNetB5 665,398,641,155 VGG-19 10,070,523,904 ResNet152V2 698,561,916,416 InceptionResNetV2 13,086,228,480 ResNet152 722,840,677,888 EfficientNetB4 16,647,192,576 InceptionResNetV2 842,993,092,096 Xception 37,853,593,600 VGG-16 990,726,778,368 EfficientNetB5 53,084,160,000 EfficientNetB6 1,234,453,191,939 EfficientNetB6 110,841,053,184 VGG-19 1,257,123,606,016 NasNetLarge – NASNetLarge 1,525,754,037,120 EfficientNetB7 213,517,926,400 EfficientNetB7 2,440,337,381,379 Fig. 63 Time complexity of 26 DL models in terms of MACCs Fig. 64 Time complexity of 26 DL models in terms of FLOPs Observations The proposed work aims to show a comparative analysis of state-of-the-art DL models for early detection of DR using a fundus image dataset. On comparison, it is observed that the DL architectures belonging to different ensembles/family have shown different behaviours using the image data. Tables 4 and 5 highlights the comparative performance of the DL models in terms of training and validation accuracy, training and validation loss, and loss difference. It is observed that the validation loss of the architectures implemented in the proposed approach is higher than the corresponding training loss. However, the training accuracy is significantly higher than the validation accuracy. This implies that the architectures inclines towards majority classification due to data imbalance which causes overfitting and poor generalization. Additionally, significant observations are analysed to identify the best DL architecture for early detection of DR which are discussed below: i. In Table 4, it is shown that VGG-16 has the lowest training accuracy amongst all the DL architectures, due to its simple-deep architecture. However, VGG-19 has comparatively a better training and testing accuracy than VGG-16, due to increase in the number of layers. This implies that VGG can perform better with increasing depth. This difference in the behaviour of both VGG-16 and VGG-19 infers that the depth of the network has a crucial role to play in achieving better accuracy and minimum loss. However, with the increasing depth and number of parameters in the networks, performance issues such as vanishing gradient problem arises, which minimizes the effect of the loss function on the activation function. This causes VGG to perform poorly than the family of ResNets. The ResNet family has a better performance than VGG due to the initialization of skip connections in the network which not only retains the depth of the network but also eliminates the vanishing gradient problem. ii. VGG-16 has achieved the lowest validation accuracy in comparison to all the other DL models. It imposes implicit regularization through greater depths and pre-initialization of network weights to avoid instability of gradients. It uses small convolutional filters for additional non-linearity in structure and captures spatial context based on non-trivial receptive fields. Thus, it is inferred that deeper models are beneficial for larger datasets, however VGG-16 has many weight parameters which makes the model heavy and causes larger inference time. This leads to vanishing gradient problem with higher loss and thus higher error with lesser ability to generalize, which ultimately affects validation performance. In addition to this, the model greatly suffers from overfitting as it is a simple yet overly deep network stacking 3 × 3 convolutional layers, and higher training error caused due to loss. VGG-19 is 11.3% better than VGG-16, 5.88% better than NasNetMobile, 1.73% better than ResNet50, and 0.40% better than ResNet50V2 and EfficientNetB7 respectively. Thus, VGGs are simple yet a better learning DL model than various state-of-the-art DL models. iii. ResNet101 is 15.25% better than VGG-16, 9.83% better than NasNetMobile, 5.68% better than ResNet50, 4.35% better than ResNet50V2, 4.35% better than EfficientNetB7, 3.95% better than VGG-19, 3.74% better than ResNet121, 3.6% better than Inception V3, 1.92% better than ResNet101V2 and 1.69% better than EfficientNetB0. Moreover, it is 1.29% better than ResNet152V2, 0.97% better than EfficientNetB2, 0.69% better than EfficientNetB1, 0.67% better than ResNet152, 0.52% better than ResNet201, 0.16% better than EfficientNetB5 and 0.12% better than MobileNet. ResNets take lesser memory due to skip connections, faster inference time, and identity mapping leading to optimized training error. iv. Amongst the inception-based modules which have better and optimized receptive fields of (1 × 1) and (3 × 3) instead of (5 × 5) and (7 × 7), to optimize the total number of parameters in the DL models of greater depth. This extreme inception module simplifies the learning task through explicit factoring at a smaller input space and maps all correlations using 1 × 1, 3 × 3 or 5 × 5 convolutions. It permits entire decoupling of spatial correlations and cross-channel correlations in the feature maps forming rich representations with fewer parameters. It has performed better with a training accuracy of 99.46% which is less than DenseNet201 having a training accuracy of 99.58%, by 0.12%. However, it is 15.98% better than VGG-16, 10.56% better than NasNetMobile, 6.41% better than ResNet50, and 5.08% better than ResNet50V2 and EfficientNetB7, respectively. Moreover, it is 4.68% better than VGG-19, 4.47% better than ResNet121, 4.33% better than Inception V3, 2.65% better than ResNet101V2 and 2.42% better than EfficientNetB0. Additionally, it is 2.02% better than ResNet152V2, 1.7% better than EfficientNetB2, 1.42% better than EfficientNetB1, 1.4% better than ResNet152, 1.25% better than ResNet201, 0.89% better than EfficientNetB5, 0.85% better than MobileNet, 0.73% better than ResNet101, 0.63% better than MobileNetV2, and 0.19% better than EfficientNetB6. Xception is 0.10% better in training accuracy but 1% lower in testing accuracy, than InceptionResNetV2. It achieves performance gains due to the increase in the capacity of the network. v. The family of DenseNets have especially a better performance than VGGs and ResNets because it reuses the features for learning, and eliminates the generation and learning of redundant features. It also has a better performance than various other DL models – it is 16.95% better than VGG-16, 11.53% better than NasNetMobile, 7.38% better than ResNet50, and 6.05% better than ResNet50V2 and EfficientNetB7, respectively. Moreover, it is 5.65% better than VGG-19, 5.44% better than ResNet121, 5.3% better than Inception V3, 3.62% better than ResNet101V2 and 3.39% better than EfficientNetB0. Additionally, it is 2.99% better than ResNet152V2, 2.67% better than EfficientNetB2, 2.39% better than EfficientNetB1, 2.37% better than ResNet152, 2.22% better than ResNet201, 1.86% better than EfficientNetB5, 1.82% better than MobileNet, 1.7% better than ResNet101, 1.6% better than MobileNetV2, 1.16% better than EfficientNetB6, 0.97% better than Xception and 0.88% better than EfficientNetB3. vi. The family of NasNets is equipped with enriched computational power and automated engineering and solves the problem of finding a better CNN for image classification problem using Reinforcement Learning. It introduces a search space to find the best suited parameters, filter sizes, output channels, strides and total number of layers. On the basis of reinforcement learning it is able to determine the best searched architectures for the dataset. Despite the huge computational power required, NasNetLarge is 17.02% better than VGG-16, 11.60% better than NasNetMobile, 7.45% better than ResNet50, and 6.12% better than ResNet50V2 and EfficientNetB7, respectively. Moreover, it is 5.72% better than VGG-19, 5.51% better than ResNet121, 5.37% better than InceptionV3, 3.69% better than ResNet101V2 and 3.46% better than EfficientNetB0. Additionally, it is 3.06% better than ResNet152V2, 2.74% better than EfficientNetB2, 2.46% better than EfficientNetB1, 2.44% better than ResNet152, 2.29% better than ResNet201, 1.93% better than EfficientNetB5, 1.89% better than MobileNet, 1.77% better than ResNet101, 1.67% better than MobileNetV2, 1.23% better than EfficientNetB6, 1.04% better than Xception, 0.95% better than EfficientNetB3, 0.07% better than DenseNet169 and 0.04% better than InceptionResNetV2. vii. DenseNet201 has achieved the highest training accuracy of 99.58% amongst all the DL models as well its corresponding performance-resembling architectures. It is observed that DenseNets have shown best results in representation of images, with strong gradient flow, better model complexity, diversified features, flexibility in feature complexity, smooth decision boundaries, and computational and parameter efficiency. However, the excessive connections of DenseNets can decrease its computational and parameter efficiency, and make it more prone to overfitting with highly skewed data. In brief, the depth of DenseNets have a crucial role to play in performance. viii. EfficientNetB4 is 17.04% better than VGG-16, 11.62% better than NasNetMobile, 7.47% better than ResNet50, and 6.14% better than ResNet50V2 and EfficientNetB7, respectively. Moreover, it is 5.74% better than VGG-19, 5.53% better than ResNet121, 5.39% better than InceptionV3, 3.71% better than ResNet101V2 and 3.48% better than EfficientNetB0. Additionally, it is 3.08% better than ResNet152V2, 2.76% better than EfficientNetB2, 2.48% better than EfficientNetB1, 2.46% better than ResNet152, 2.31% better than ResNet201, 1.95% better than EfficientNetB5, 1.91% better than MobileNet, 1.79% better than ResNet101, 1.69% better than MobileNetV2, 1.25% better than EfficientNetB6, 1.06% better than Xception, 0.97% better than EfficientNetB3, 0.09% better than DenseNet169, 0.06% better than InceptionResNetV2 and 0.02% better than NasNetLarge. ix. EfficientNetB4 and ResNet50 achieved an optimal and equivalent training accuracy of 99.37%, but a testing accuracy 79.09% and 77.32%, respectively. Besides, ResNet50V2 and EfficientNetB7, and EfficientNetB1 and ResNet152 have the same testing accuracy of 72.97% and 76.6%, respectively. From this behaviour, it can be implied that the ResNets outperforms the EfficientNets in terms of training accuracy but the EfficientNets outperforms the ResNets in terms of validation accuracy. However, comparatively ResNets do not explicitly address generalization and suffers greatly from optimization difficulty. On the other hand, the fastidious nature of EfficientNets and the engineering of hyperparameters w.r.t the choice of ML frameworks for training affects their top-line accuracy. Additionally, they induce greater cost for data movement between layers due to larger number of channels, and have lesser applications because current hardware accelerators are engineered on traditional learning frameworks. From, Fig. 65 it is observed that the training curve increases in ResNet50 but decreases in EfficientNetB7 and further decreases in ResNet101V2. However, it increases in ResNet152 and then in DenseNet201 -the highest, and then decreases in Xception and then in InceptionResNetV2. x. NASNetLarge and EfficientNetB4 have shown a near equivalent performance in training and validation accuracy. However, they are of different platforms. NASNet is based on reinforcement learning search method to optimize and find the best convolutional layer instead of searching for the entire network, and reduces computational complexity and improves generalization. It generates a search space that decouples complexity and depth of the network. However, EfficientNets such as EfficientNetB4 is a portable structure with depth and resembles MobileNets and NASNetMobile. From Tables 4 and 5, it is observed that these portable, less memory-based structures have shown better performance due to computational and parameter efficiency than all other large DL models such as DenseNets, ResNets, VGGs, etc. xi. EfficientNetB4 has achieved the highest validation accuracy amongst all the DL networks. Moreover, it has comparatively achieved a better training accuracy, training loss, validation loss and loss difference to that of DenseNet201 which has achieved the highest training accuracy. It can be inferred that EfficientNetB4 has shown remarkable performance compared to all the other DL models, despite overfitting and application of an imbalanced image dataset. ResNet50 and ResNet50V2 have shown highest overfitting while training the model and have achieved a highest loss difference of 2.49 each, whereas DenseNet121 and InceptionV3 have achieved a lowest loss difference of 1.48 each, thereby representing lowest overfitting. Table 4 Performance comparison of CNNs in the DL model in terms of accuracy Model Training Accuracy (%) Validation/Test Accuracy (%) Performance Difference of DL n/w(s) w.r.t EfficientNetB7 (%) VGG-16 90.91 62.07 17.04 NASNetMobile 99.05 67.49 11.62 ResNet50 99.37 71.64 7.47 ResNet50V2 99.19 72.97 6.14 EfficientNetB7 99.35 72.97 6.14 VGG-19 97.98 73.37 5.74 DenseNet121 98.80 73.58 5.53 InceptionV3 99.03 73.72 5.39 ResNet101V2 99.34 75.40 3.71 EfficientNetB0 99.03 75.63 3.48 ResNet152V2 99.26 76.03 3.08 EfficientNetB2 99.20 76.35 2.76 EfficientNetB1 99.11 76.63 2.48 ResNet152 99.44 76.65 2.46 DenseNet201 99.58 76.80 2.31 EfficientNetB5 99.31 77.16 1.95 MobileNet 98.92 77.20 1.91 ResNet101 99.32 77.32 1.79 MobileNetV2 98.66 77.42 1.69 EfficientNetB6 99.31 77.86 1.25 Xception 99.46 78.05 1.06 EfficientNetB3 99.20 78.14 0.97 DenseNet169 98.95 79.02 0.09 InceptionResNetV2 99.36 79.05 0.06 NASNetLarge 99.24 79.09 0.02 EfficientNetB4 99.37 79.11 0.00 Table 5 Performance comparison of CNNs in the DL model in terms of loss Model Training Loss (x) Validation/Test Loss (y) Loss Difference (z) z = y-x ResNet50 0.017 2.51 2.493 VGG-16 0.26 2.32 2.06 InceptionResNetV2 0.018 2.23 2.212 NASNetLarge 0.021 2.09 2.069 VGG-19 0.062 2.07 2.008 ResNet101V2 0.019 1.99 1.971 ResNet152V2 0.021 1.98 1.959 EfficientNetB7 0.018 1.96 1.942 DenseNet169 0.031 1.94 1.909 DenseNet201 0.013 1.92 1.907 EfficientNetB0 0.03 1.88 1.85 Xception 0.017 1.88 1.863 ResNet101 0.019 1.87 1.851 ResNet50V2 0.024 1.84 1.816 MobileNet 0.031 1.81 1.779 ResNet152 0.016 1.8 1.784 NASNetMobile 0.029 1.79 1.761 EfficientNetB1 0.026 1.79 1.764 EfficientNetB2 0.024 1.78 1.756 MobileNetV2 0.039 1.72 1.681 EfficientNetB3 0.025 1.71 1.685 EfficientNetB6 0.019 1.59 1.571 EfficientNetB5 0.019 1.54 1.521 EfficientNetB4 0.019 1.53 1.511 DenseNet121 0.034 1.52 1.486 InceptionV3 0.026 1.51 1.484 Figure 65 is a graphical illustration of the accuracy of 26 DL models and their performance difference w.r.t* EfficientNetB7 in terms of accuracy. Fig. 65 Performance accuracy of 26 DL models w.r.t EfficientNetB7 Figure 66 is a graphical illustration of the accuracy of 26 DL models and their performance difference w.r.t* EfficientNetB7 in terms of loss. Fig. 66 Performance of 26 DL models w.r.t EfficientNetB7 in terms of loss Discussion DRFEC is a competitive model and is able to achieve significant results. Dong et al. [23] have implemented an InceptionV3-VGG-16 based hybrid CNN through feature concatenation and have achieved 96.11% accuracy on 2693 images only whereas the proposed model has individually achieved an accuracy of 99.03% and 73.72% in InceptionV3 and 90.91% and 62.07% in VGG-16 using 35,126 images, which is significantly and comparatively larger to fit in a data hungry CNN. Deepa et al. [18] and Deepa et al. [19] have implemented a fine-tuned InceptionV3 and Xception based multi-stage patch-based and image-based CNN and have achieved an accuracy of 96.2% and 96%, respectively using a computationally powerful processing platform on various multi-sized image datasets, which are however overall, less than the Kaggle training dataset employed in proposed DRFEC. It has achieved comparatively an accuracy of 99.03% and 73.72% in InceptionV3 and 99.46% and 78.05% in Xception than Deepa et al. [18]. Tsai et al. [82] and AbdelMaksoud et al. [1] have implemented a DenseNet121 based CNN using Kaggle’s EyePACs train-test dataset and TCH dataset, and using EyePACS Kaggle training dataset, IDRiD, MESSIDOR and APTOS 2019 datasets, and have achieved an accuracy of 84.05% and 84.67%, and 91.2% respectively. Whereas DRFEC has achieved an accuracy of 98.80% and 73.58% using DenseNet121 on 35,126 images. Das, S. et al. [16] have implemented Squeeze-and-Excitation CNN using limited DIARETDB1 and local dataset and achieved an accuracy of 96.92% whereas DRFEC has consistently achieved an accuracy of more than 98% and 70% on 35,126 images. Sau and Bansal [70] have implemented a FNU-GOA-MDNN for optimization using a comparatively limited ISBI 2018 IDRiD dataset and have achieved an accuracy of 95.27%, whereas DRFEC has consistently achieved an accuracy of more than 98% and 70% on 35,126 images. Shaik and Cherukuri [72] have implemented a VGG-16 based HA-Net using 3662 Kaggle’s APTOS 2019 images and ISBI 2018 IDRiD images, and achieved an accuracy 85.54% and 66.41% respectively, whereas DRFEC has achieved an accuracy of 90.91% and 62.07% using VGG-16 and 97.98% and 73.37% using VGG-19 respectively on 35,126 images. It is observed the performance of the models, degrades with decreasing and limited data. The more is the data made available to a DL model the better it fits to be a better learning model which maintains its integrity and consistency, which is represented in DRFEC. Table 6 is an illustration which compares the performance of various existing and state-of-the-art DL models with the proposed DRFEC. Table 6 Performance comparison of DRFEC with state-of-the-art models Model Dataset images Models Accuracy (%) Environment Dong et al. [23] 2693 InceptionV3 -VGG-16 based CNN 96.11 Intel (R) Xeon (R) W-2245 CPU and NVIDIA GeForce RTX 2080 SUPER Deepa et al. [18] LFH, BH, DIARETDB, STARE, e-ophtha, ROC and Kaggle Fine-tuned InceptionV3 and Xception based multi-stage patch-based and image-based CNN 96.2 GPU 25GB RAM Tsai et al. [82] Kaggle’s EyePACs train and test dataset and TCH dataset DenseNet121 84.05 and 84.67 – Das, S. et al. [16] DIARETDB1 and local dataset Squeeze-and-Excitation CNN 96.92 – AbdelMaksoud et al. [1] EyePACS Kaggle training dataset, IDRiD, MESSIDOR and APTOS 2019 E-DenseNetBC-121 91.2 core i5/2.4 GHz, 8GB RAM, NVIDIA VGA card with 1GB VRAM. Deepa et al. [19] 2290 (LFH, BH) Fine-tuned Xception 96 GPU 25GB RAM Sau and Bansal [70] ISBI 2018 IDRiD Optimized CNN FNU-GOA-MDNN 95.27 – Shaik and Cherukuri [72] 3662 and ISBI 2018 IDRiD VGG-16 based HA-Net 85.54 and 66.41 32GB RAM, 2 TB HDD enabled with 16GB NVIDIA GPU Proposed DRFEC Kaggle training dataset 35,126 splitted into train and test dataset VGG-16 90.91 and 62.07 CPU 64GB RAM NASNetMobile 99.05 and 67.49 ResNet50 99.37 and 71.64 ResNet50V2 99.19 and 72.97 EfficientNetB7 99.35 and 72.97 VGG-19 97.98 and 73.37 DenseNet121 98.80 and 73.58 InceptionV3 99.03 and 73.72 ResNet101V2 99.34 and 75.40 EfficientNetB0 99.03 and 75.63 ResNet152V2 99.26 and 76.03 EfficientNetB2 99.20 and 76.35 EfficientNetB1 99.11 and 76.63 ResNet152 99.44 and 76.65 DenseNet201 99.58 and 76.80 EfficientNetB5 99.31 and 77.16 MobileNet 98.92 and 77.20 ResNet101 99.32 and 77.32 MobileNetV2 98.66 and 77.42 EfficientNetB6 99.31 and 77.86 Xception 99.46 and 78.05 EfficientNetB3 99.20 and 78.14 DenseNet169 98.95 and 79.02 InceptionResNetV2 99.36 and 79.05 NASNetLarge 99.24 and 79.09 EfficientNetB4 99.37 and 79.11 Conclusion This paper proposes an automated system for early detection of DR called Diabetic Retinopathy Feature Extraction and Classification (DRFEC) which employs DL CNN models such as VGG-16, VGG-19, Xception, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, EfficientNets B0-B7, DenseNet121, DenseNet169, DenseNet201, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152, ResNet152V2, NASNetLarge and NASNetMobile, for DR feature extraction and image classification. The proposed model performs an exhaustive analysis of these architectures upon fundus images, and derives the best performing DL architecture for DR feature extraction and fundus image classification. Amongst all the models, DenseNet201 has achieved the highest training accuracy whereas VGG-16 has achieved the lowest training accuracy. Again, VGG-16 has achieved lowest validation accuracy whereas EfficientNetB4 has achieved highest validation accuracy. The mobile-based structures such as EfficientNets and MobileNets have achieved better performance than residual networks, densely connected networks and inception modules. In the designed approach, the imbalanced dataset has caused overfitting of the models, but in addition to that the complexity of the DL models have also significantly contributed to overfitting, poor generalization, poor training time, poor gradient flow, and optimization and framework constraints. Moreover, ResNet50 has shown highest overfitting whereas InceptionV3 has shown the lowest overfitting. The proposed model has identified EfficientNetB4 and Xception as ideal DL models for DR deep feature extraction and image classification. EfficientNetB4 is the most optimal, efficient and reliable DL algorithm in detection of DR, followed by InceptionResNetV2, NasNetLarge and DenseNet169, and are the top-4 best performing models on Kaggle’s EyePACS DR detection dataset. The experimental results show that DR detection using DL CNN architectures can achieve significant performances. In future, the best DL architecture determined through the proposed work can be used to incorporate state-of-the-art techniques such as attention mechanism for extraction of relevant features, for detection of subtle lesions, for early detection of DR. The proposed model will further be extended through architectural engineering, and using a balanced fundus image benchmark dataset for comparison with benchmark models and to overcome the limited resource constraint, to achieve more convincing results. The model also aims to mitigate overfitting and poor generalization in future works, through fine-tuning of DL models for computation of significant evaluation metrics such as AUROC. The proposed baseline model shall be used for comparison with newly proposed future model for better research direction. Conflict of interest The authors have no relevant financial or non-financial interests to disclose. Data availability There is no additional data associated with this manuscript. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. AbdelMaksoud, E, Barakat, S, Elmogy, M (2022) A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique, Med Biol Eng Comput, pp.1–24 10.1007/s11517-022-02564-6 2. Agneeswaran, VS (n.d.) Computational Complexity of Deep Learning: Solution Approaches, Walmart Global Tech Blog, Available at:https://medium.com/walmartglobaltech/computational-complexity-of-deep-learning-a-birds-eye-view-2250b7c098a1, Accessed on: 20-05-2022 3. Atwany MZ Sahyoun AH Yaqub M Deep learning techniques for diabetic Retinopathy classification: a survey IEEE Access 2022 10 28642 28655 10.1109/ACCESS.2022.3157632 4. Bhatti UA Huang M Wu D Zhang Y Mehmood A Han H Recommendation system using feature extraction and pattern recognition in clinical care systems Enterprise Inf Syst 2018 13 3 329 351 10.1080/17517575.2018.1557256 5. Bhatti UA Huang M Wang H Zhang Y Mehmood A Di W Recommendation system for immunization coverage and monitoring Human Vaccines Immunotherapeutics 2018 14 1 165 171 10.1080/21645515.2017.1379639 29068748 6. Bhatti UA Yu Z Li J Nawaz SA Mehmood A Zhang K Yuan L Hybrid watermarking algorithm using Clifford algebra with Arnold scrambling and chaotic encryption IEEE Access 2020 8 76386 76398 10.1109/ACCESS.2020.2988298 7. Bhatti UA Yu Z Chanussot J Zeeshan Z Yuan L Luo W Nawaz SA Bhatti MA Ain Q Mehmood A Local similarity-based spatial–spectral fusion hyperspectral image classification with deep CNN and Gabor filtering IEEE Trans Geosci Remote Sens 2021 60 5514215 1 15 10.1109/TGRS.2021.3090410 8. Bhatti UA Zeeshan Z Nizamani MM Bazai S Yu Z Yuan L Assessing the change of ambient air quality patterns in Jiangsu Province of China pre-to post-COVID-19 Chemosphere 2022 288 1 10 10.1016/j.chemosphere.2021.132569 9. Bhilare, A (2022) MACC-FLOPS, Available at: https://github.com/AbhijeetBhilare777/MACC-FLOPS, Accessed on : 06-06-2022 10. Bodapati JD Veeranjaneyulu N Shareef SN Hakak S Bilal M Maddikunta PKR Jo O Blended multi-modal deep convnet features for diabetic retinopathy severity prediction Electronics 2020 9 6 914 10.3390/electronics9060914 11. Bora A Balasubramanian S Babenko B Virmani S Venugopalan S Mitani A Marinho GO Cuadros J Ruamviboonsuk P Corrado GS Peng L Webster DR Varadarajan AV Hammel N Liu Y Bavishi P Predicting the risk of developing diabetic retinopathy using deep learning Lancet Digit Health 2021 2020 3 e10 e19 10.1016/S2589-7500(20)30250-8 12. Chakraborty C Kishor A Rodrigues JJPC Novel enhanced-Grey wolf optimization hybrid machine learning technique for biomedical data computation Comput Electr Eng 2022 99 1 15 10.1016/j.compeleceng.2022.107778 13. Chaturvedi SS, Gupta K, Ninawe V, Prasad PS (2020) Automated diabetic retinopathy grading using deep convolutional neural network, arXiv:2004.06334v1 [eess.IV], pp 1–12. 10.48550/arXiv.2004.06334 14. Chetoui, M, Akhloufi, MA (2020) Explainable Diabetic Retinopathy using EfficientNET, In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp.1966–1969, 10.1109/EMBC44109.2020.9175664 15. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , arXiv:1610.02357v3 [cs.CV], pp 1251–1258. 10.48550/arXiv.1610.02357 16. Das S Kharbanda K Suchetha M Raman R Edwin DD Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy Biomed Signal Process Control 2021 68 1 10 10.1016/j.bspc.2021.102600 17. Das D Biswas SK Bandyopadhyay S A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning Multimed Tools Appl 2022 23 1 43 10.1007/s11042-022-12642-4 18. Deepa V, Kumar CS, Cherian T (2021) Ensemble of multi-stage deep convolutional neural networks for automated grading of diabetic retinopathy using image patches, Journal of King Saud University –Computer and Information Sciences, pp 1–11 10.1016/j.jksuci.2021.05.009 19. Deepa, V, Kumar, SC, Cherian, T (2022) Automated grading of diabetic retinopathy using CNN with hierarchical clustering of image patches by siamese network, Physical and Engineering Sciences in Medicine, pp.1–13 10.1007/s13246-022-01129-z 20. Diabetic Retinopathy Detection, Kaggle (n.d.) Available at: https://www.kaggle.com/c/diabetic-retinopathy-detection/data. Accessed 01-07-2021 21. Diabetic Retinopathy Detection, Kaggle repository (n.d.) Available at: https://www.kaggle.com/c/diabetic-retinopathy-detection/data, Accessed on 19-06-2022 22. Diabetic Retinopathy, Updated (2022), Available at: https://www.nei.nih.gov/learn-about-eye-health/eye-conditions-and-diseases/diabetic-retinopathy, Accessed on : 16-5-2022 23. Dong, B, Wang, X, Qiang, X, Du, F, Gao, L, Wu, Q, Cao, G, Dai, C (2022) A Multi-Branch Convolutional Neural Network for Screening and Staging of Diabetic Retinopathy Based on Wide-Field Optical Coherence Tomography Angiography, IRBM, pp.1–7 10.1016/j.irbm.2022.04.004 24. Ege BM Hejlesen OK Larsen OV Møller K Jennings B Kerr D Cavan DA Screening for diabetic retinopathy using computer based image analysis and statistical classification, Comput Methods Programs Biomed 2000 62 3 165 175 10.1016/S0169-2607(00)00065-1 25. Fadzil, MHA, Ngah, NF, George, TM, Izhar, LI, Nugroho, H, Nugroho, HA (2010) Analysis of foveal avascular zone in colour fundus images forgrading of diabetic retinopathy severity, 32nd Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 5632–5635, 10.1109/IEMBS.2010.5628041 26. Fadzil MHA Izhar LI Nugroho H Nugroho HA Analysis of retinal fundus images for grading of diabetic retinopathy severity Med Biol Eng Compu 2011 49 6 693 700 10.1007/s11517-011-0734-2 27. Fan GF Zhang LZ Yu M Hong WC Dong SO Applications of random forest in multivariable response surface for short-term load forecasting Int J Electr Power Energy Syst 2022 139 1 30 10.1016/j.ijepes.2022.108073 28. Fong DS Aiello L Gardner TW King GL Blankenship G Cavallerano JD Ferris FL Klein R Retinopathy in diabetes Diabetes Care 2004 27 1 84 87 10.2337/diacare.27.2007.S84 29. Goh JKH Cheung CY Sim SS Tan PC Tan GSW Wong TY Retinal imaging techniques for diabetic retinopathy screening J Diabetes Sci Technol 2016 10 2 282 294 10.1177/1932296816629491 26830491 30. Gulshan V Peng L Coram M Stumpe MC Wu D Narayanaswamy A Venugopalan S Widner K Madams T Cuadros J Kim R Raman R Nelson PC Mega JL Webster DR Development and validation of a deep learning algorithm for detection of diabetic Retinopathy in retinal fundus photographs JAMA 2016 316 22 2402 2410 10.1001/jama.2016.17216 27898976 31. Gurcan OF Beyca OF Dogan O A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification Int J Comput Intell Syst 2021 14 2 1132 1141 10.2991/ijcis.d.210316.001 32. Hagos MT (2020) Point-of-care diabetic retinopathy diagnosis: a standalone mobile application approach, arXiv:2002.04066v1 [eess.IV]. Vol. abs/2002.04066, pp 1–84. Available at: https://www.semanticscholar.org/paper/Point-of-Care-Diabetic-Retinopathy-Diagnosis%3A-AHagos/3b9a121e53cea1b30cd29eea13854eeb4faadff9 33. Hattiya T Dittakan K Musikasuwan S Diabetic Retinopathy Detection Using Convolutional Neural Network: A Comparative Study on Different Architectures Mahasarakham Int J Eng Technol 2021 7 1 50 60 10.14456/mijet.2021.8 34. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770–778. 10.1109/CVPR.2016.90 35. Howard, AG, Zhu, M, Chen, B, Kalenichenko, D, Wang, W, Weyand, T, Andreetto, M, Adam, H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications, arXiv:1704.04861v1 [cs.CV], pp 1–9. 10.48550/arXiv.1704.04861 36. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4700–4708, arXiv:1608.06993v5 [cs.CV]. 10.48550/arXiv.1608.06993 37. Huang G, Liu S, Maaten L, Weinberger KQ (2018) CondenseNet: an efficient densenet using learned group convolutionss. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2752–2761. 10.1109/CVPR.2018.00291 38. Hui J Du M Ye X Qin Q Sui J Effective building extraction from high-resolution remote sensing images with multitask driven deep neural network IEEE Geosci Remote Sens Lett 2019 16 5 786 790 10.1109/LGRS.2018.2880986 39. Iandola, F, Mskewicz, M, Karayev, S, Girshick, R, Darrell, T, Kuetzer, K (2014) DenseNet: Implementing Efficient ConvNet Descriptor Pyramids Technical Report, pp.1–11 10.1080/08839514.2013.848751 40. Islama MM Yanga H Poly TN Jiane WS Li YJ Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs: a systematic review and meta-analysis Comput Methods Prog Biomed 2020 191 105320 1 16 10.1016/j.cmpb.2020.105320 41. Janiesch C Zschech P Heinrich K Machine learning and deep learning Electron Mark 2021 31 685 695 10.1007/s12525-021-00475-2 42. Ji Q Huang J He W Sun Y Optimized deep convolutional neural networks for identification of macular diseases from optical coherence tomography images Algorithms 2019 12 3 1 12 10.3390/a12030051 43. Jiang, H, Yang, K, Gao, M, Zhang, D, Ma, H, Qian, W (2019) An interpretable ensemble deep learning model for diabetic retinopathy disease classification, In 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2045–2048, 10.1109/EMBC.2019.8857160 44. Kamal KC Yin Z Wu M Wu Z Evaluation of deep learning-based approaches for COVID-19 classification based on chest X-ray images, signal Image Vid Process 2021 15 959 966 10.1007/s11760-020-01820-2 45. Kawwa, N (2020) When to Use the Kolmogorov-Smirnov Test Theory, Application, and Interpretation, Towards Data Science, Available at: https://towardsdatascience.com/when-to-use-the-kolmogorov-smirnov-test-dd0b2c8a8f61, Accessed on: 12-06-2022 46. Kazem AM (2018) What is the time complexity for training a neural network using back-propagation?, ARTIFICIAL INTELLIGENCE, Stack Exchange, Available at: https://ai.stackexchange.com/questions/5728/what-is-the-time-complexity-for-training-a-neural-network-using-back-propagation, Updated on November 2021, Accessed on 20-05-2022 47. Kishor, A, Chakraborty, C (2021) Early and accurate prediction of diabetics based on FCBF feature selection and SMOTE, Int J Syst Assur Eng Manag, pp. 1–9, 10.1007/s13198-021-01174-z 48. Kishor, A, Chakraborty, C (2021) Artifcial Intelligence and Internet of Things Based Healthcare 4.0 Monitoring System, Wireless Personal Communications, pp.1–17 10.1007/s11277-021-08708-5 49. Kishor, A, Jeberson, W (2021) Diagnosis of Heart Disease Using Internet of Things and Machine Learning Algorithms, In: Singh, P.K., Wierzchoń, S.T., Tanwar, S., Ganzha, M., Rodrigues, J.J.P.C. (eds) Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, Lecture Notes in Networks and Systems, Vol 203, 10.1007/978-981-16-0733-2_49 50. Kishor A Chakraborty C Jeberson W A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning , Special Issue on Current Trends in Intelligent Multimedia Processing Systems Int J Interact Multimed Artif Intell 2020 6 7 7 17 10.9781/ijimai.2020.12.004 51. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (Eds.) Adv Neural Inf Process Syst., Curran Associates Inc, pp 1097–1105. 10.1145/3065386 52. Kumar PNS Deepak RU Sathar A Sahasranamam V Kumar RR Automated detection system for diabetic retinopathy using two field fundus photography Proc Comput Sci 2016 93 486 494 10.1016/j.procs.2016.07.237 53. Lee J Kim YK Park KH Jeoung JW Diagnosing Glaucoma with spectral-domain optical coherence tomography using deep learning classifier J Glaucoma 2020 29 4 287 294 10.1097/IJG.0000000000001458 32053552 54. Li N Ma M Lai M Gu L Kang M Wang Z Jiao S Dang K Deng J Ding X Zhen Q Zhang A Shen T Zheng Z Wang Y Peng Y A stratified analysis of a deep learning algorithm in the diagnosis of diabetic retinopathy in a real-world study J Diab 2022 14 2 111 120 10.1111/1753-0407.13241 55. Lim WX Chen ZY Ahmed A The adoption of deep learning interpretability techniques on diabetic retinopathy analysis: a review Med Biol Eng Comput 2022 60 633 642 10.1007/s11517-021-02487-8 35083634 56. Mayyaa, V, Kamath, SS, Kulkarni, U (2021) Automated microaneurysms detection for early diagnosis of diabetic retinopathy: A Comprehensive review, Computer Methods and Programs in Biomedicine Update, 1, pp-1-15 10.1016/j.cmpbup.2021.100013 57. Michele A Colin V Santika DD MobileNet convolutional neural networks and support vector Machines for Palmprint Recognition Procedia Comput Sci 2019 157 110 117 10.1016/j.procs.2019.08.147 58. Nneji GU Cai J Deng J Monday HK Hossin MA Nahar S Identification of diabetic Retinopathy using weighted fusion deep learning based on Dual-Channel fundus scans Diagnostics 2022 12 540 1 19 10.3390/diagnostics12020540 59. Orlando JI Prokofyeva E del Fresno M Blaschko MB An ensemble deep learning based approach for red lesion detection in fundus images Comput Methods Prog Biomed 2018 153 115 127 10.1016/j.cmpb.2017.10.017 60. Padmanayana, Anoop BK (2022) Binary Classification of DR-Diabetic Retinopathy using CNN with Fundus Colour Images, Materials Today: Proceedings, pp.1–5 10.1016/j.matpr.2022.01.466 61. Pogorelov, K, Riegler, M, Halvorsen, P , Griwodz, C, Lange, T, Randel, KR, Eskeland, SL, Dang-Nguyen, D , Ostroukhova, O, Lux, M, Spampinato, C (2017) A Comparison of Deep Learning with Global Features for Gastrointestinal Disease Detection, MediaEval’17, pp. 1–3 10.1007/s11042-017-4989-y 62. Pour AM Seyedarabi H Jahromi SHA Javadzadeh A Automatic detection and monitoring of diabetic Retinopathy using efficient convolutional neural networks and contrast limited adaptive histogram equalization IEEE Access 2020 8 136668 136673 10.1109/ACCESS.2020.3005044 63. Priya R Aruna P A new eyenet model for diagnosis of diabetic retinopathy Appl Artif Intell 2013 27 10 924 940 10.1080/08839514.2013.848751 64. Ratan, P (2020) What is the Convolutional Neural Network Architecture?, Analytics Vidhya, Available at: https://www.analyticsvidhya.com/blog/2020/10/what-is-the-convolutional-neural-network-architecture/, Data Science Blogathon, October 28, 2020, Accessed on 19-05-2022 65. Saeed F Hussain M Aboalsamh HA Automatic diabetic Retinopathy diagnosis using adaptive fine-tuned convolutional neural network IEEE Access 2021 9 41344 41359 10.1109/ACCESS.2021.3065273 66. Samanta A Saha A Satapathy SC Fernandes SL Zhang Y Automated detection of diabetic retinopathy using convolutional neural networks on a small dataset Pattern Recogn Lett 2020 135 293 298 10.1016/j.patrec.2020.04.026 67. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L (2018) MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 4510–4520, arXiv:1801.04381v4 [cs.CV]. 10.48550/arXiv.1801.04381 68. Sarki, R, Michalska, S, Ahmed, K, Wang, H, Zhang, Y (2019) Convolutional neural networks for mild diabetic retinopathy detection: an experimental study, bioRxiv, pp.1–18 10.1101/763136 69. Sarki R Ahmed K Wang H Zhang Y Ma J Wang K Image preprocessing in classification and identification of diabetic eye diseases Data Sci Eng 2021 6 455 471 10.1007/s41019-021-00167-z 34423109 70. Sau, PC, Bansal, A (2022) A novel diabetic retinopathy grading using modified deep neural network with segmentation of blood vessels and retinal abnormalities, Multimedia Tools and Applications, pp.1–29 10.1007/s11042-022-13056-y 71. Shah P Mishra DK Shanmugam MP Doshi B Jayaraj H Ramanjulu R Validation of Deep Convolutional Neural Network based algorithm for detection of diabetic retinopathy – Artificial intelligence versus clinician for screening Indian J Ophthalmol 2020 68 2 398 405 10.1007/s11760-020-01820-2 31957737 72. Shaik, NS, Cherukuri, TK (2022) Hinge attention network: A joint model for diabetic retinopathy severity grading, Applied Intelligence, pp. 1–17 10.1007/s10489-021-03043-5 73. Shukla, UV, Tripathy, K (2022) Diabetic Retinopathy, in: StatPearls [internet], Treasure Island (FL): StatPearls Publishing 74. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556v6 [cs.CV], pp 1–14. 10.48550/arXiv.1409.1556 75. Sivapriya, G, Praveen, V, Gowri, P, Saranya, S, Sweetha, S, Shekar, K (2022) Segmentation of Hard exudates for the detection of Diabetic Retinopathy with RNN based sematic features using fundus images, Materials Today: Proceedings, pp.1–9 10.1016/j.matpr.2022.05.189 76. Sopharak A Uyyanonvara B Barman S Automatic exudate detection from non-dilated diabetic retinopathy retinal images using fuzzy c-means clustering Sensors 2009 9 3 2148 2161 10.3390/s90302148 22574005 77. Sosale B Aravind SR Murthy H Narayana S Sharma U Gowda SGV Naveenam M Simple, Mobile-based Artificial intelligence algorithm in the detection of diabetic Retinopathy (SMART) study BMJ Open Diabetes Res Care 2020 8 1 1 6 10.1136/bmjdrc-2019-000892 78. Suriyal, S, Druzgalski, C, Gautam, K (2018) Mobile assisted diabetic retinopathy detection using deep neural network, 2018 Global Medical Engineering Physics Exchanges/Pan American Health Care Exchanges (GMEPE/PAHCE), pp. 1–4, 10.1109/GMEPE-PAHCE.2018.8400760 79. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 2818–2826. 10.1109/CVPR.2016.308 80. Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), pp 1–7. 10.1609/aaai.v31i1.11231 81. Tan M, Le QV (2019) EfficientNet: rethinking model scaling for convolutional neural networksm. In: Proceedings of the 36th International Conference on Machine Learning, vol 97, pp 6105–6114, arXiv:1905.11946v5 [cs.LG]. 10.48550/arXiv.1905.11946 82. Tsai CY Chen CT Chen GA Yeh CF Kuo CT Hsiao YC Hu HY Tsai IL Wang CH Chen JR Huang SC Lu TC Woung LC Necessity of local modification for deep learning algorithms to predict diabetic Retinopathy Int J Environ Res Public Health 2022 19 3 1 12 10.3390/ijerph19031204 83. Tymchenko B, Marchenko P, Spodarets D (2020) Deep learning approach to diabetic retinopathy detection, arXiv:2003.02261v1 [cs.LG], pp 1–9. 10.48550/arXiv.2003.02261 84. Walter T Klein JC Massin P Erginay A A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates incolor fundus images of the human retina IEEE Trans Med Imaging 2002 21 10 1236 1243 10.1109/TMI.2002.806290 12585705 85. Wang J Liu Q Xie H Yang Z Zhou H Boosted EfficientNet: Detection of Lymph Node Metastases in Breast Cancer Using Convolutional Neural Networks Cancers 2021 13 4 1 14 10.3390/cancers13040661 86. What is Big-O complexity of classifying an image using CNN?, Cross Validated (n.d.) Available at: https://stats.stackexchange.com/questions/527142/what-is-big-o-complexity-of-classifying-an-image-using-cnn, Accessed on: 20-05-2022 87. What is the computational complexity of the forward pass of a convolutional neural network?, Artificial Intelligence (n.d.) Available at: https://ai.stackexchange.com/questions/22913/what-is-the-computational-complexity-of-the-forward-pass-of-a-convolutional-neur/22929#22929, Accessed on: 20-05-2022 88. Zhang, C, Bengio, S, Hardt, M, Recht, B, Vinyals, O (2017) Understanding Deep Learning Requires Rethinking Generalization, ICLR, pp.1–15, 2017 10.48550/arXiv.1611.03530 89. Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 8697–8710 arXiv:1707.07012v4 [cs.CV]. 10.48550/arXiv.1707.07012	36467440	PMC9708148	NO-CC CODE	2022-12-01 23:20:28	no	Multimed Tools Appl. 2022 Nov 29;:1-59	utf-8	Multimed Tools Appl	2,022	10.1007/s11042-022-14165-4	oa_other
==== Front Eur Phys J B Eur Phys J B The European Physical Journal. B 1434-6028 1434-6036 Springer Berlin Heidelberg Berlin/Heidelberg 36467616 457 10.1140/epjb/s10051-022-00457-z Regular Article - Statistical and Nonlinear Physics Strain-stream model of epidemic spread in application to COVID-19 http://orcid.org/0000-0001-9291-320X Trigger S. A. [email protected] 12 Ignatov A. M. 3 1 grid.435259.c 0000 0000 9428 1536 Joint Institute for High Temperatures, Russian Academy of Sciences, 13/19, Izhorskaia Str., Moscow, 125412 Russia 2 grid.7468.d 0000 0001 2248 7639 Institut für Physik, Humboldt-Universität zu Berlin, 12489 Berlin, Germany 3 grid.424964.9 0000 0004 0637 9699 Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilova St., Moscow, 119991 Russia 29 11 2022 2022 95 11 1944 7 2022 16 11 2022 © The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Abstract The recently developed model of the epidemic spread of two virus strains in a closed population is generalized to the situation typical for the couple of strains delta and omicron, when there is a high probability of omicron infection soon enough after recovering from delta infection. This model can be considered as a kind of combination of SIR and SIS models for the case of competition of two strains of the same virus with different contagiousness in a population. The obtained equations and results can be directly implemented for practical calculations of the replacement of strains of the SARS-CoV-2 virus. A comparison between the estimated replacement time and the corresponding statistics shows reasonable agreement. Graphic abstract issue-copyright-statement© EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature 2022 ==== Body pmcIntroduction Existing models for the spread of infection describe the free-running development of an epidemic and all its stages. There are two basic models for such description: the susceptible–infected–susceptible (SIS) and susceptible–infectious–removed, susceptible–exposed–infectious–removed (SIR, SEIR). The SIS model goes back to the pioneering investigations of malaria by Ronald Ross [1] and uses the assumption that the recovered people can immediately get infection again. Existing SIR models assume that the recovered people save immunity during epidemic (see, e.g., [2, 3]). There are many versions of those models [4–10] (see also references therein). The balance between the susceptible and infected members of population under the various conditions of infection transfer is the subject of research in [8, 11, 12]. Recently, the delayed time-discrete epidemic model (DTDEM) considering the typical long duration of the COVID-19 disease has been developed [13]. In [14, 15], this specific delay has been presented in differential form. The delay discussed in [13–15] assumes that the recovered patient is immune, and in this respect fits the SIR model, rather than SIS. The considered delay models do not imply the allocation of a separate category of hidden virus carriers (see, e.g., the SEIR models in [16, 17]). Latent carriers of the virus can infect others without delay and are similar to the infected ones. Currently, the simplest SIS, SIR and SEIR baseline models are being developed taking into account the vaccination process [18–20]. Nowadays, the actual problem is the strain appearance and circulation in application to COVID-19 [21–23]. In the recent preprint [21], the SIR-type model was considered for the case of the coexistence of two strains of the COVID-19 virus spreading in the same population. In [21], as in the pioneer paper [22] devoted to the circulation of several strains in a population, it was assumed that after being ill with any strain, those who recovered were completely immune. We called this assumption about the properties of strains “strain orthogonality”, bearing in mind a certain analogy with the mathematical orthogonality of functions. An important difference between [21] and [22] is the complete disappearance of strains at long times. This is due to the fact that [21] considers times much shorter than the average time of a human life. It should be noted that in reality, the long-term circulation of strains (see [22]) is mainly not associated with the finite average lifetime and assumptions about the nature of the birth and death of healthy people in the population. The prolonged circulation of strains is due to the emergence of a new, more contagious strain in the patient’s body during a long-term illness with another strain that is easily susceptible to mutations (like SARS-CoV-2, for example) in the absence of a universal vaccine. For infections such as influenza, the main sources of mutations are carriers external to humans (birds, pigs, and other representatives of the animal world). In this paper, we do not include these factors, which differ significantly from those considered in [21, 22] and require separate study. Due to different contagiousness, a replacement of a less contagious strain to a more contagious one takes place; it was quantitatively described in [21] and, in concrete application to COVID-19, in [22] under the “strain orthogonality” condition. In [22] the characteristic details of such a process were revealed, such as the necessary conditions for the emergence of a maximum in the curves describing the current number of virus carriers, a decrease in the peak incidence of a less contagious strain when a more contagious strain appears, a faster depletion of the part of population that has not affected by any strain. This means a more rapid course of the epidemic when a more contagious strain appears (if a third, even more contagious strain, does not arise) and increase in the required level of recovered patients to achieve collective immunity (if it turns out to be possible) when a less contagious strain of the virus is replaced by a more contagious one, etc. In the present paper, the basic equations [21, 22] are generalized to the case of “strain non-orthogonality”. The respective generalized equation we named “strain-stream” ones. These equations are the development of a general mathematical approach of the “principle of competitive exclusion” (see, e.g., Murray [23]) in epidemiology. In papers [24, 25], the first applications of this principle were considered for the strain transmission. In [26] the model of malaria transmission, which considers the seasonal fluctuations in mosquito population density or spatial heterogeneity with periodic migration, was proposed. It was shown that the strain heterogeneity can generate periodic behavior as a consequence of the interaction between parasite strains and host immunological defences. The model of two strain of dengue circulation in host population due to the transmission from the infected mosquitoes has been developed in detail [27]. In both papers, a vector transmitted disease (caused by different sources of infection: parasites or viruses) is considered on the basis of various differential equations. The extended SIR-type model for COVID-19 waves has been presented in [28] (see also recent the paper [29]) to fit this model calculations to the data in Tonghua City in China (see also references therein). The qualitative consideration of “strain non-orthogonality” has been considered on the basis of simplified model in [30]. It should be stressed that the mathematical structure of various models allows some comparisons in spite of the different processes and conditions which lead to the specific results. The model under consideration makes it possible, in the presence of a minimum number of parameters, to quantitatively describe various specific situations of coexistence and struggle of two strains for dominance in a population of living organisms. The specific examples considered in this work were based on the choice of initial conditions that correspond to the emergence of a second strain of high contagiousness (for example, omicron) against the background of an already developed epidemic with the dominance of the delta strain. To describe such a situation, it suffices to take into account the initial conditions, bringing them into line with the actual level of delta disease in a certain population, to the time the strain appeared in South Africa, which was subsequently named omicron by WHO. At the same time, if we are interested in the rivalry of two strains (for specificity, below we designate delta-1 and omicron-2) in any country, region, city or locality, we naturally must use the available statistical data on the incidence, caused by the 1 strain when cases of the disease caused by the 2 strain appear. The detail data concerning COVID-19 disease for different countries, without differentiation on strains are quite fully reflected in [31]. City data are presented on the websites of the respective countries (e.g., the Robert Koch Institute in Germany, Stopcoronavirus in Russia, the Johns Hopkins Institute in the USA, etc.). There are also recent publications on the strain replacement cases and the respective statistics (see, e.g.,[32–34] and references therein). Equations for the case of “strain non-orthogonality” This generalization reflects the observable property to be infected with a high probability by the strain 2 of the COVID-19 disease for those who have already been ill and recovered from infection caused by the strain 1. This means that immunity to strain 2 is not developed (or is only partially developed) after disease caused by strain 1. Obviously, for strains that cause COVID-19 (as well as for influenza viruses), there is only a limited period of immunity but much longer than the average disease duration. In fact, the property of “strain non-orthogonality” means that infection caused by strain 2 (omicron) can appear with some probability even immediately after recovering from the disease caused by strain 1 (e.g., delta). According to our knowledge, the disease COVID-19 caused by two strains which simultaneously coexist in one sick person was not observed (in contrast with the rare cases of COVID-19 and flu). At the same time, according to the existing statistical data infection 1 was not observed after infection by strain 2. The additional reason for this is a fast disappearance of the less contagious strain, as we demonstrate below. As in [28] we denote S the number of never infected people in a closed population consisting of N people, I1 and I2 are the number of strain carriers of type 1 and 2. The simplified SIR-type hooking model equations describing the epidemic spread for the case of two “ non-orthogonal” strains in the close population N can be written in the form using the variables I1(t)/N=y1(t), I2(t)/N=y2(t), S(t)/N=u(t)1 du(t)dt=-p1y1u-p2y2u, 2 dy1(t)dt=p1y1u-y1T1, 3 dy2(t)dt=p2y2u-y2T2+p2γ2y2(t)y1(t)T1. The values T1 and T2 are the average durations of the diseases caused by strains 1 an 2. Parameters p1 and p2 are the characteristics of the contagiousness for two strains, which are determined as the product of the quantity of dangerous contacts nc of the infected people per day and the average susceptibility k of the healthy person on dangerous distance [13, 14]. The new term in Eq. (3) describes the infection process by strain 2 of the people recovered after the disease caused by strain 1. The coefficient 0≤γ2<1, hereinafter referred to as the Viral Link Attenuation Factor (VLAF), describes a certain decrease in the probability of getting 2 after being infected with 1 (partial increase in immunity) compared to the probability of getting 2 without having been ill before 1 (i.e., directly from the group u). This is due to the production of antibodies after the disease caused by the 1 strain (or after vaccination), which perform some protective function against strain 2. However, the last term in Eq. (3) only qualitative describes the infection process by strain 2 of the people recovered after the disease caused by strain 1. People after infection 1 can be infected by the strain 2 not immediately, but after some time. This circumstance should be accounted for the explicit description of the “strain non-orthogonality”. To find the respective equations we divide all virus carriers of strain 2 into two groups—y2←S and y2←1 . The proportion of those infected with strain 2 who were not ill with strain 1 (i.e., infected from the set S who were not ill with any of the strains) is designated y2←S and those infected with strain 2 from the set of those who had previously been sick by strain 1 are designated y2←1. The entire set of patients with strain 2 is equal to y2 = y2←S + y2←1 . Then, the system of equations reads4 du(t)dt=-p1y1u-p2y2u, 5 dy1(t)dt=p1y1u-y1T1, 6 dy2←S(t)dt=p2y2u-y2←ST2, 7 dy2←1dt=γ2p2f(t)y2-y2←1T2. Here, the function f(t) is the proportion of those who have or had at moment t strain 1, but did not have strain 2 . To find function f(t) necessary to take into account that only a part from all people who have been ill x1(t)8 x1(t)=∫0tdτp1y1(τ)u(τ) can contribute in f(t). The function x1(t) contains all people who have been ill with strain 1 by time t (they are extracted only from S). Among them are those identified as φ(τ) who obtain strain 2 after being ill with strain 1 at time t (they are taken from the set x1(τ)-φ(τ)). According to the model, they cannot get sick again with strain 2 and therefore must be subtracted from x1(t) to find f(t)9 f(t)=x1(t)-φ(t). Therefore, this subtracted function φ(t) is determined by the integral equation10 φ(t)=γ2∫0tp2f(τ)y2(τ)dτ=γ2∫0tp2[x1(τ)-φ(τ)]y2(τ)dτ. From (9) and (10), taking into account (8), it follows11 ∂f(t)∂t=p1y1(t)u(t)-γ2p2f(t)y2(t). Summarizing Eqs. (6), (7), we arrive at equations of the model of “strain non-orthogonality”, which considers that after disease caused by strain 1 one can be infected by strain 2 (but not vice versa)12 du(t)dt=-p1y1u-p2y2u, 13 dy1(t)dt=p1y1u-y1T1, 14 dy2(t)dt=p2y2u+γ2p2f(t)y2-y2T2, 15 ∂f(t)∂t=p1y1(t)u(t)-γ2p2f(t)y2(t). It is easy to see that all people x2(t) diseased by strain 2 at time t can be calculated by integral16 x2(t)=∫0tdt′p2y2(t′)u(t′)+∫0tdt′p2γ2y2(t′)f(t′). Fig. 1 Function y1(t) of strain 1 carriers, when the second strain is absent (p2=0) for u(0)=0.8 (solid) and for u(0)=0.7 (dashed). The parameters are p1=0.15, y1(0)=0.01, the average duration of the virus carrier T1=15 days All coefficients in Eqs. (12, 13, 14, 15) are positive, p1,2>0, T1,2>0, γ2>0, and values of all variable functions lie in the interval (0, 1). There is only one stationary solution to Eqs. (12, 13, 14, 15), y1,2=0, u=u0, where u0 is an arbitrary constant. The stationary state is unstable, that is, in the vicinity of zero one of the functions y1,2(t) grows in time, if u0>min[1/(p1T1),1/(p2T2)]. Suppose that initially u(0)=u0 and y1,2(0)≈0. Throughout the present study we are interested in the case when both y1,2(t) grow in time, that is, u0 should be large enough, u0>max[1/(p1T1),1/(p2T2)]. According to Eq. (12), u(t) is always a decreasing function of time and sooner or later infected fractions of population start decreasing. Evidently, y1,2(t→∞)→0, however an asymptotic value, u(t→∞)→u∞, depend on initial conditions. It may be only deduced from the qualitative analysis that u∞ should be small enough, u∞<min[1/(p1T1),1/(p2T2)]. The two-strain propagation model developed in [21] is the limiting case of the considered general model (12, 13, 14, 15) for γ2=0. In this case Eq.(15) is split off and Eqs. (12, 13, 14) are closed. Numerical solution for various immunity parameter VLAF The analysis of the stability of the stationary solution carried out above is similar to one in [21]. It shows that the necessary condition for the development of an epidemic process at γ2=0 is the condition piTiu0>1. This condition remains valid for Eqs. (12, 13, 14, 15). As was revealed in [21] for γ2=0, using the example of specific initial conditions and parameters pi and Ti, the coexistence of two viruses of different contagiousness leads over time to the replacement of the less contagious strain by a more contagious one, even if the share of the latter at the beginning of the process was significantly smaller than less contagious. The results of calculations for specific parameters that correspond to the simultaneous emergence of an epidemic with two strains of different contagiousness are shown in Figs. 1 (epidemic process in the presence of strain 1 only, when p2=0) and 2 (comparison of the dynamics of the epidemic in the presence of both strains for the model under consideration with γ2=0.3). Thus, Figs. 1 and 2 serve to demonstrate the process of mutual influence of strains during the development of an epidemic for the case of “strain non-orthogonality” based on Eqs. (12, 13, 14, 15).Fig. 2 Functions y1(t) (solid) and y2(t) (dashed) of the virus carriers for the case when both strains exist. The parameters are p1=0.15, p2=0.4, y1(0)=0.01, y2(0)=10-7, u(0)=0.8, the average duration of the virus carrier T1=T2=15 days, γ2=0.3 As is easy to see, strain 1 is slightly suppressed by strain 2 since the value of maximum for the solid curve in Fig. 2 is lower than in Fig. 1 for u(0)=0.8. A comparison of these figures shows that the duration of strain 1 circulation is suppressed (≃2 times shorter for the used parameters) due to the appearance of strain 2. A comparison of Figs. 1 and 2 shows that circulation of strain 2 is shorter than circulation of strain 1 in the case of strain 2 absence. It is easy to see that for arbitrary parameters the maximum for strain 1 in Fig. 2 is shifted to earlier time in comparison with Fig. 1. This peculiarity, mentioned in [21], is valid also for the strain-stream model under consideration. In this paper, we are interested in the impact of a possible infection with strain 2 after recovery from an infection caused by strain 1. This situation corresponds to the epidemic process observed with the appearance of the omicron strain. An important difference from the specific examples considered in [21] is the appearance of strain 2 under conditions of a developed epidemic of strain 1, which is characterized by rather large initial values of u(0) and y1(0). The results of the numerical solution of Eqs. (12, 13, 14, 15) for the initial conditions simulating the situation of the appearance of omicron in already developed epidemic of the delta strain are shown in Figs. 3, 4, 5.Fig. 3 Comparison of the function y1(t) (left) and y2(t) (right) for different values γ2=0 (solid), γ2=0.2 (dashed) and γ2=0.8 (dash-dotted) of virus carriers for the case when both virus strains exists. The parameters are p1=0.15, p2=0.4, y1(0)=0.01, y2(0)=10-7, u(0)=0.8, the average duration of the virus carrier T1=T2=15 days Fig. 4 Function u(t) for the various cases: the second strain is absent (p2=0), the parameters are p1=0.15, y1(0)=0.01, the average duration of the virus carrier T1=15 days for u(0)=0.8 (solid); both strains coexist, the recovered are immune (the case considered in [21]), the parameters are p1=0.15, p2=0.4, y1(0)=0.01, y2(0)=10-7, T1=T2=15 days for u(0)=0.8 (dash-dotted), γ2=0; both strains coexist, the parameters are p1=0.15, p2=0.4, y1(0)=0.01, y2(0)=10-7, T1=T2=15 days for u(0)=0.8, γ2=0.2 (dashed) Fig. 5 Function x1(t) (left) of the people affected by the strain 1 and x2(t) (right) of the people affected by the strain 2 for different values γ2=0 (solid), γ2=0.2 (dashed) and γ2=0.8 (dash-dotted) of virus carriers for the case when both virus strains exists. The parameters are p1=0.15, p2=0.4, y1(0)=0.01, y2(0)=10-7, u(0)=0.8, the average duration of the virus carrier T1=T2=15 days The proportions of y1(t) and y2(t) infected with strains 1 and 2 are shown in Fig. 3 left and right, respectively, for different parameters γ2. As in Figs. 1 and 2, the initial condition for the proportion of the population that did not encounter either of the two considered strains was chosen at the level of u(0)=0.8, which significantly exceeds the official statistics for, e.g., Germany at the time the omicron strain appeared in the country. By such an overestimation, we take into account a significant number of unreported cases of diseases with the delta strain at the time of the appearance of the omicron strain. The same qualitative picture is observable also in other countries. The initial proportion of those infected with strain 1 is chosen to be rather high y1(0)=0.01, which also corresponds to the presence of a significant number of hidden virus carriers that can actively infect others. Note, that the purpose of this work is to identify the general patterns of the development of the epidemic in the presence of two strains, and not a calculation based on a detailed analysis of the changing situation from day to day and incomplete statistical data. Nevertheless, the specific time for the essential replacement of strain 1 by strain 2 is in reasonable agreement with statistical data obtained in England [32]. We have stress that the statistical data on strain replacement can be different for different countries and regions due to various conditions. As follows from Fig. 3, the impact of the appearance of strain 2 capable of infecting those who have been ill with strain 1 depends significantly on the value of VLAF γ2. The larger 0≤γ2≤1, the faster the process of infection with strain 1 is suppressed, i.e., it is forced out faster than in the case with γ2=0 [21] (see also Fig. 1 ). At the same time, as γ2 grows, the current proportion of strain 2 carriers grows, exceeding by a factor of ≃3.5 at the maximum proportion of strain 1 carriers under the chosen parameters. The effect of a non-zero value γ2 on the fraction u(t) of non-affected by strains at all is shown on Fig. 4. The possibility to become infected with strain 2 soon after disease caused by strain 1 is high. There is much faster and complete depletion of the share of non-affected. This means that with a certain parameter γ2, herd immunity becomes practically unattainable and almost everyone must get sick due to strain 2. It is of interest to determine values γ2 for which the stationary value of the proportion of the population not affected by any of the viruses is reached. It can be considered as a numerical characteristic of herd immunity. The calculation carried out up to 1000 days (not presented in 4) showed that with the selected parameters, the solid curve corresponding to the absence of strain 2 tends to u(t=1000)=0.205, the dash-dotted curve (corresponding to the case γ2=0 [21] of full lengthy in time immunity after each of the diseases caused by the strain 1 or 2) tends to 0.052 and the dotted curve corresponding to the case under consideration Eqs. (12, 13, 14, 15) for γ2=0.2 tends to u(1000)=0.007. In the latter case the level of collective immunity is only 0.7 percent of the population. Figure 5 shows the curves for the total part of people (sick plus recovered, or affected) x1(t) with strain 1 (left) and strain 2 (right), calculated according Eqs. (8) and (16). All three curves for the function x1(t) (left) and for the function x2(t) (right) in Fig. 5 correspond to the circulation of two strains, but for different values of γ2. The parameters in Fig. 5 correspond to those selected in Fig. 3. As it follows from Fig. 5 the function x1(t) decreases as γ2 increases, while the function x2(t) grows. This behavior corresponds to the general pattern of replacement of a less contagious virus by a more contagious one, with the greater efficiency, the greater the VLAF value. The formulated equations and the model under consideration can be easily extended to the consideration of vaccination and different quarantine measures, accounting the government and personal restrictions, vaccination process, etc. In general we accounted, after recovering from strain 1, a person may not immediately become infected with strain 2. However, and taking into account the more elaborated equations which takes into account the delay factor associated with time shifts is beyond the scope of this work. Also the cases of death, re-infection with the same strain long time after recovery, limited time for vaccination efficiency and other known factors can be included in a more elaborated models. Above, we restricted our consideration to the case of the free-running epidemic under two “non-orthogonal” strains of a same virus. This assumption can be considered as realistic for fast developing epidemic caused by, e.g., the omicron strain (or another highly contagious virus strain) appeared in a population affected earlier by a less contagious virus strain. The considered model clarifies the main specific features of competition of two “non-orthogonal” viruses in population. Conclusions The principal picture of the replacement of one strain by another has already been revealed in the recently considered mathematical model [21], where the basic equations were proposed that describe the replacement of a less contagious strain by a more contagious one. Further development of the theory is connected with taking into account the incomplete “orthogonality” of the strains under consideration. This is manifested in the fact that with a significant mutation of the virus, leading to a different molecular structure, a different virulence, and a different clinical picture of the disease, both strains, spreading in the population, are more interdependent. Immunity to one of them (for example, due to a previous disease), generally speaking, does not means the presence of immunity in relation to another. So, for example, omicron can infect those who have recovered from the delta strain, but not vice versa. Thus, the situation cannot be described in the framework of SIR and similar models, where all recovered patients have a long immunity, nor within the SIS model, where immunity disappears immediately after recovery. This important property is taken into account by transferring to the “strain-stream” equations by formulation of Eqs. (12, 13, 14, 15). The additional term includes the new VLAF parameter γ2≤1, due to the development of partial immunity to strain 2 as a result of the disease caused by strain 1, or to the effective vaccination against strain 1, giving partial protection also against strain 2. Acknowledgements S.T. is thankful to infectious disease physician Dr. M. Karavaeva, Prof. Dr. F. Onufrieva and colleagues from the clinic Charité (Berlin) for many useful discussions. Author Contributions Both authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. Data Availability This manuscript has no associated data or the data will not be deposited. [Authors’ comment: This is a theoretical study with the reference on the open statistical data [31].] Declarations Conflict of interest There are no any competing interests. ==== Refs References 1. Ross R The Prevention of Malaria 1910 New York Dutton 2. Kermack WO McKendrick AG A contribution to the mathematical theory of epidemics Proc. R. Soc. A 1927 115 700 721 10.1098/rspa.1927.0118 3. Brauer F Castillo-Chavez C Mathematical Models in Population Biology and Epidemiology 2000 Cham Springer-Verlag 4. Ball F Stochastic and deterministic models for SIS epidemics among a population partitioned into households Math. Biosci. 1999 156 41 10.1016/S0025-5564(98)10060-3 10204387 5. Keeling M Eames K Networks and epidemic models J. R. Soc. Interface 2005 2 295 307 10.1098/rsif.2005.0051 16849187 6. Chowell G Sattenspiel L Bansald S Viboud C Mathematical models to characterize early epidemic growth: a review Phys. Life Rev. 2016 18 66 97 10.1016/j.plrev.2016.07.005 27451336 7. Bedford J Farrar J Ihekweazu C Kang G Koopmans M Nkengasong J A new twenty-first century science for effective epidemic response Nature 2019 575 130 136 10.1038/s41586-019-1717-y 31695207 8. G. Ghoshal, L.M. Sander, I.M. Sokolov, SIS epidemics with household structure: the self-consistent field method (2003) arXiv:cond-mat/0304301 v1 [cond-mat.stat-mech]. Accessed 12 Apr 2003 9. Postnikov EB Estimation of COVID-19 dynamics on a back-of-envelope: does the simplest SIR model provide quantitative parameters and predictions? Chaos Solitons Fractals 2020 135 109841 10.1016/j.chaos.2020.109841 32501369 10. Cooper I Argha Mondal A Antonopoulos CG A SIR model assumption for the spread of COVID-19 in different communities Chaos Solitons Fractals 2020 139 110057 10.1016/j.chaos.2020.110057 32834610 11. Frey F Ziebert F Schwarz U Stochastic dynamics of nanoparticle and virus uptake Phys. Rev. Lett. 2019 122 088102 10.1103/PhysRevLett.122.088102 30932591 12. Nakamura GM Martinez AS Hamiltonian dynamics of the SIS epidemic model with stochastic fluctuations Sci. Rep. 2019 9 15841 10.1038/s41598-019-52351-x 31676857 13. Trigger SA Czerniawski EB Equation for epidemic spread with the quarantine measures: application to COVID-19 Phys. Scr. 2020 95 105001 10.1088/1402-4896/abb2e2 14. Trigger SA Czerniawski EB Ignatov AM Epidemic transmission with quarantine measures: application to COVID-19 MedRxiv 2021 10.1101/2021.02.09.21251288 15. Ignatov AM Trigger SA Czerniawski EB Delay influence on epidemic evolution High Temp. 2021 59 6 960 16. Carcione Jose M Santos Juan E Bagaini Claudio Ba Jing A simulation of a COVID-19 epidemic based on a deterministic SEIR model Front. Public Health 2020 8 230 10.3389/fpubh.2020.00230 32574303 17. Keeling MJ Rohani P Modeling Infectious Diseases in Humans and Animals 2008 Princeton Princeton University Press 18. Li Xiao-Jie Li Xiang Vaccinating SIS epidemics under evolving perception in heterogeneous networks Eur. Phys. J. B 2020 93 185 10.1140/epjb/e2020-10355-3 33024413 19. Shnip A Epidemic Dynamics Kinetic Model and Its Testing on the COVID-19 Epidemic Spread Data J. Eng. Phys. Thermophys. 2021 94 1 6 10.1007/s10891-021-02268-y 20. Rella SA Kulikova YA Dermitzakis ET Rates of SARS-CoV-2 transmission and vaccination impact the fate vaccine-resistant strains Sci. Rep. 2021 11 15729 10.1038/s41598-021-95025-3 34330988 21. Ignatov AM Trigger SA Two viruses competition in the SIR model of epidemic spread: application to COVID-19 MedRxiv 2022 10.1101/2022.01.11.22269046 22. Bremermann HJ Thieme HR A competitive exclusion principle for pathogen virulence J. Math. Biol. 1989 27 179 10.1007/BF00276102 2723551 23. Murray JD Mathematical Biology I, An Introduction 2002 Berlin Springer-Verlag 24. Fudolig M Howard R The local stability of a modified multi-strain SIR model for emerging viral strains PLoS One 2020 15 12 e0243408 10.1371/journal.pone.0243408 33296417 25. Arruda EF Das SS Dias SM Pastore DH Modelling and optimal control of multi strain epidemics, with application to COVID-19 PLoS One 2020 16 9 e0257512 10.1371/journal.pone.0257512 26. Gupta S Swinton J Anderson RM Theoretical studies of the effects of heterogeneity in the parasite population on the transmission dynamics of malaria Proc. R. Soc. Lond. 1994 B256 231 27. Feng Z Velasco-Hernandez JX Competitive exclusion in a vector-host model for the dengue fever J. Math. Biol. 1977 35 523 10.1007/s002850050064 28. Guo Youming Tingting L Modeling the transmission of second-wave COVID-19 caused by imported cases: a case study Math. Methods Appl. Sci. 2021 10.1002/mma.8041 29. Schwarzendahl FJ Grauer J Liebchen B Löwen H Mutation induced infection waves in diseases like COVID -19 Sci. Rep. 2022 12 9641 10.1038/s41598-022-13137-w 35688998 30. S.A. Trigger, A.M. Ignatov, Strain-stream model of epidemic spread in application to COVID-19. MedRxiv (2022). 10.1101/2022.03.26.22272973 31. Worldometer counter. https://www.worldometers.info/coronavirus/ (2022) 32. Samoilov AE Case report: change of dominant strain during dual SARS-CoV-2 infection BMC Infect Dis. 2021 21 959 10.1186/s12879-021-06664-w 34530778 33. Khan K Omicron infection enhances Delta antibody immunity in vaccinated persons Nature 2022 607 356 10.1038/s41586-022-04830-x 35523247 34. Paton RS Christopher EO Ward T The rapid replacement of the SARS-CoV-2 Delta variant by Omicron (B.1.1.529) Sci. Transl. Med. 2022 14 652 55 10.1126/scitranslmed.abo5395	36467616	PMC9708149	NO-CC CODE	2022-12-15 23:15:19	no	Eur Phys J B. 2022 Nov 29; 95(11):194	utf-8	Eur Phys J B	2,022	10.1140/epjb/s10051-022-00457-z	oa_other
==== Front Travel Med Infect Dis Travel Med Infect Dis Travel Medicine and Infectious Disease 1477-8939 1873-0442 The Author(s). Published by Elsevier Ltd. S1477-8939(22)00257-5 10.1016/j.tmaid.2022.102511 102511 Article Comments on “Diagnosis of monkeypox virus – An overview” Zandi Milad ∗ Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Hosseinzadeh Adli Ahmad Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran Department of Bacteriology and Virology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran Shafaati Maryam Department of Microbiology, Faculty Science, Jahrom Branch, Islamic Azad University, Jahrom, Iran Occupational Sleep Research, Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran ∗ Corresponding author. Department of Virology, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. 30 11 2022 30 11 2022 1025119 10 2022 22 11 2022 © 2022 The Author(s) 2022 Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. Keywords Monkeypox Cross-activity Diagnosis ==== Body pmcLetter to the Editor We read with interest the article by Altindis et al. of Diagnosis of monkeypox virus – An overview [1]. We appreciate their article, however, the authors stated that serological testing may be useful for epidemiological purposes to further investigate previous infection [1]. However, according to scientific evidence cross-reactivity between different Orthopoxvirus species limits serological and protein-based tests [2,3]. Monkeypox is a new zoonotic disease caused by the monkeypox virus [4]. In the past, electron microscopy was used to distinguish herpesviruses from Orthopoxviruses when varicella was the most relevant differential diagnosis [5]. For the diagnosis of monkeypox virus, real-time PCR is used on suspected skin lesions. Because of the limited duration of monkeypox viremia, swabs, scabs, and aspirated lesion fluid should be preferably used for PCR over blood [6]. Serologic cross-reactivity between Orthopoxviruses is a significant barrier to laboratory diagnosis of specific Orthopoxvirus infections as well as epidemiologic characterization of disease outbreaks. Researchers have attempted to identify highly specific antibodies that could be used for screening and diagnostic testing in order to overcome this limitation [2]. For example it's reported 69–126–3–7 antibody is bound to the A27 protein of the human monkeypox [7]. In conclusion, methods for detecting antigens and antibodies are not recommended for monkeypox diagnosis because of the serological cross-reactivity among Orthopoxviruses and the possibility of false positive results in people who have recently or previously been immunized against smallpox. ==== Refs References 1 Altindis M. Puca E. Shapo L. Diagnosis of monkeypox virus–An overview Trav Med Infect Dis 2022 Sep 13 102459 2 Lansiaux E. Jain N. Laivacuma S. Reinis A. The virology of human monkeypox virus (hMPXV): a brief overview Virus Res 2022 Sep 19 198932 3 Dubois M.E. Slifka M.K. Retrospective analysis of monkeypox infection Emerg Infect Dis 14 4 2008 Apr 592 18394277 4 Shafaati M. Zandi M. Monkeypox virus neurological manifestations in comparison to other orthopoxviruses Trav Med Infect Dis 49 2022 Sep 1 102414 5 Focosi D. Novazzi F. Baj A. Maggi F. Monkeypox: an international epidemic Rev Med Virol 2022 Aug 27 e2392 6 Risk assessment: monkeypox multi-country outbreak 2022 European Centre for Disease Prevention and Control Available at:https://www.ecdc.europa.eu/sites/default/files/documents/Monkeypox-multi-country outbreak.pdf 7 Hughes L.J. Goldstein J. Pohl J. Hooper J.W. Pitts R.L. Townsend M.B. Bagarozzi D. Damon I.K. Karem K.L. A highly specific monoclonal antibody against monkeypox virus detects the heparin binding domain of A27 Virology 464 2014 Sep 1 264 273 25108113	36460576	PMC9708209	NO-CC CODE	2022-12-07 23:16:39	no	Travel Med Infect Dis. 2023 Nov 30 January-February; 51:102511	utf-8	Travel Med Infect Dis	2,022	10.1016/j.tmaid.2022.102511	oa_other
==== Front Travel Med Infect Dis Travel Med Infect Dis Travel Medicine and Infectious Disease 1477-8939 1873-0442 Published by Elsevier Ltd. S1477-8939(22)00258-7 10.1016/j.tmaid.2022.102512 102512 Article Authors' response comments on “Diagnosis of monkeypox virus – An overview” Altindis Mustafa Sakarya University School of Medicine, Dept of Clinical Virology and Microbiology, Sakarya, Turkey Puca Edmond ∗ Service of Infection Diseases, University Hospital Center, Tirane, Albania Shapo Laidon Head of Programme for Health and Social Care, London Borough of Southwark, United Kingdom ∗ Corresponding author. 30 11 2022 30 11 2022 10251213 11 2022 22 11 2022 Crown Copyright © 2022 Published by Elsevier Ltd. 2022 Elsevier has created a Monkeypox Information Center (https://www.elsevier.com/connect/monkeypox-information-center) in response to the declared public health emergency of international concern, with free information in English on the monkeypox virus. The Monkeypox Information Center is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its monkeypox related research that is available on the Monkeypox Information Center - including this research content - immediately available in publicly funded repositories, with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the Monkeypox Information Center remains active. ==== Body pmcWe appreciate Dr. Zandi et al.‘s thoughtful letter in response to our article “Diagnosis of monkeypox virus – An overview”. Monkeypox is an emerging zoonotic disease caused by monkeypox virus. It may be difficult to distinguish monkeypox on the basis of clinical presentation alone, because of the various conditions that cause skin rashes. Testing should be offered to suspected case definition for monkeypox infection. Suitable samples are surface lesion and/or skin materials. Laboratory confirmation of cases is done using nucleic acid amplification testing, such as Real-Time Polymerase Chain Reaction (RT PCR). Confirmation of MPXV infection should consider clinical and epidemiological information. Positive detection using an OPXV PCR assay followed by confirmation of MPXV via PCR and/or sequencing [1]. Other diagnostic tests besides PCR include virus isolation (in mammalian cell cultures), electron microscopy, ELISA, and immunofluorescent antibody testing (CDC developed the ELISA IgM and IgG test). Because orthopoxviruses are serologically cross-reactive, antigen and antibody detection methods do not provide monkeypox-specific confirmation. Therefore, serology and antigen detection methods are not recommended for diagnosis or case study where resources are limited. In addition, serology tests are not recommended in those vaccinated before the eradication of smallpox because of the vaccine response [2,3]. In their letter, Zandi et al. drew attention to the following sentence in our original article. "serological testing may be useful for epidemiological purposes to further investigate previous infection [1]. They add that “However, according to scientific evidence cross-reactivity between different Orthopoxvirus species limits serological and protein-based tests [4,5]." However, our statement is based on theinterim guidance from WHO published on 22/05/22. They state the following: "Confirmation of MPXV infection should consider clinical and epidemiological information. Positive detection using an OPXV PCR assay followed by confirmation of MPXV via PCR and/or sequencing, or positive detection using MPXV PCR assay in suspected cases indicates confirmation of MPXV infection. While it is preferable to perform MPXV specific confirmatory testing, positive detection using OPXV PCR assay is considered sufficient for laboratory confirmation of suspected cases. Member States are requested to immediately notify WHO of laboratory confirmed cases. When the clinical presentation and epidemiology suggest an infection with MPXV despite negative PCR results, serological testing may be useful to further investigate prior infection for epidemiological purposes. A number of factors could contribute to false-negative results, such as poor quality of specimen, wrong handling or shipping, or technical reasons inherent to the test, e.g. DNA extraction failure [6].” So what we are stating in our paper (in bold above) stands and we believe to be still correct. However, we acknowledge Zandi's et al. comment with regards to the limitation of serological and protein-based testing [4]. Sources of funding NO Declaration of competing interest No conflict of interest. ==== Refs References 1 Altindis M. Puca E. Shapo L. Diagnosis of monkeypox virus–An overview Trav Med Infect Dis 2022 Sep 13 102459 2 Tiecco G. Degli Antoni M. Storti S. Tomasoni L.R. Castelli F. Quiros-Roldan E. Monkeypox, a literature review: what is new and where does this concerning virus come from? Viruses 14 2022 1894 36146705 3 Monkeypox https://www.who.int/news-room/fact-sheets/detail/monkeypox(19 May 2022). 4 Lansiaux E. Jain N. Laivacuma S. Reinis A. The virology of human monkeypox virüs (hMPXV): a brief overview Virus Res 2022 Sep 19 198932 5 Dubois M.E. Slifka M.K. Retrospective analysis of monkeypox infection Emerg Infect Dis 14 4 2008 Apr 592 18394277 6 Laboratory testing for the monkeypox virus: interim guidance n.d https://www.who.int/publications-detail-redirect/WHO-MPX-laboratory-2022.1	36462748	PMC9708381	NO-CC CODE	2022-12-07 23:16:38	no	Travel Med Infect Dis. 2023 Nov 30 January-February; 51:102512	utf-8	Travel Med Infect Dis	2,022	10.1016/j.tmaid.2022.102512	oa_other
==== Front Curr Treat Options Gastroenterol Curr Treat Options Gastroenterol Current Treatment Options in Gastroenterology 1092-8472 1534-309X Springer US New York 404 10.1007/s11938-022-00404-y Geriatrics (A Faye and S Katz, Section Editor) The Use of Telemedicine in Older Patients with Gastrointestinal Diseases Dong Michelle D. MMSc 12 Steuwe Shelley 3 Barry Lauren A. 1 http://orcid.org/0000-0002-2240-6605 Siegel Corey A. MD, MS [email protected] 12 1 grid.413480.a 0000 0004 0440 749X Inflammatory Bowel Disease Center, Section of Gastroenterology and Hepatology, Dartmouth-Hitchcock Medical Center, Lebanon, NH 03756 USA 2 grid.254880.3 0000 0001 2179 2404 Geisel School of Medicine at Dartmouth, Hanover, NH USA 3 grid.413480.a 0000 0004 0440 749X Connected Care, Center for Telehealth, Dartmouth-Hitchcock Medical Center, Lebanon, NH USA 30 11 2022 111 14 9 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Purpose of Review The COVID-19 pandemic helped us understand that telemedicine provides an alternative way to manage patients remotely, with an added benefit of saving time and travel costs. However, barriers may exist in an older population of patients such as inadequate technology availability and knowledge, and lack of internet connectivity. This systematic review and analysis of data at an academic medical center examines the current literature and investigates the efficacy of telemedicine for older adults requiring gastrointestinal care. Recent Findings In the systematic review, we included 22 manuscripts from an initial 120 that were identified based on inclusion and exclusion criteria. In this existing literature, telemedicine visits were equivalent or better than in-person visits based on many metrics, including patient satisfaction, time and money saved, and standard patient outcomes. At a rural academic medical center, there was a steady decrease in the failure rate of telemedicine visits from April 2020 to May 2022 in all age groups, including the 65 + age group, from 17% in April 2020 to 3% in May 2022. Summary Telemedicine offers a comparable alternative to in-person visits that is convenient, low-cost, and does not compromise quality of care for older patients obtaining GI care. The COVID-19 pandemic has accelerated progress and uptake of telemedicine, and the successful use by all ages including older patients opens the broader conversation about the continued use of telemedicine for care in various areas of medicine. Keywords Telemedicine Gastroenterology Elderly Systematic review http://dx.doi.org/10.13039/100001063 Crohn's and Colitis Foundation of America 984541 Dong Michelle D. ==== Body pmcIntroduction While the concept of telemedicine is not new, it was not until the coronavirus disease of 2019 (COVID-19) pandemic that the use of telemedicine for routine clinical care became necessary across the world. As a result, virtual visits dramatically increased, including virtual visits to gastroenterology (GI) clinics. Patients with a broad range of GI illnesses including liver and pancreatic diseases, inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and motility disorders had to quickly adapt if they wanted to successfully continue care with their gastroenterologists. While medical centers had entire information technology (IT) departments immediately dedicated to facilitating virtual care, patients were isolated at home with whatever technology they had available at that time. There was not much concern that technologically savvy people would be able to engage in telemedicine visits, but certain populations were certainly at risk of not being able to adequately stay connected to the healthcare system. One at risk group is the older patient population. They form a subset of patients with unique needs and limitations regarding the successful use of telemedicine. This includes limited transportation, geographic isolation, lack of adequate electronic devices and internet connectivity and lower technological confidence [1]. In addition, this patient group was among the most at risk from COVID-19, so keeping them at home and away from health care facilities was a priority. Therefore, the patients that could benefit most from the use of telemedicine was also the patient population with the highest chance of disengaging from health care due to lack of capability to attend telemedicine visits. To examine how older patients seeking GI care engage with telemedicine services, we took two approaches. We have conducted a systematic review of older patients’ use of telemedicine in GI clinics, and also examined the real-world trends in telemedicine visits by age in a rural, academic health care setting. Our goals are to describe how telemedicine services impact outcomes of GI care, to understand the ability of an older patient population to use telemedicine services for GI care, and to examine how this changed throughout the COVID-19 pandemic. Methods To better understand the effectiveness of using telemedicine for GI services for an older patient population, we set out to perform both a high-level overview and a more specific analysis at a single medical center during the COVID-19 pandemic. The overview was accomplished through a systematic review of the published literature and the real-world analysis was done using data from the Dartmouth-Hitchcock Medical Center (DHMC). DHMC is a rural academic medical center in New Hampshire, USA performing a mix of primary, secondary and tertiary care for Northern New England. The Section of Gastroenterology and Hepatology performs over 15,000 patient visits annually and sees patients from New Hampshire, Vermont, and Maine. For both the systematic review and analysis of DHMC data, older age was defined as over the age of 65 years. Systematic Review of Literature Search criteria for the systematic literature review using the PICO (patient, intervention, comparison, outcome) method included Patients (adults over 65 requiring GI care), Intervention (telemedicine for GI clinics), Comparison (in-person healthcare in GI clinics and Outcome (equal or improved outcomes in care in GI). The research terms are summarized in the Table 1. The final advanced search combined the domains of telemedicine, GI and older patient population and restricted it to the years 2016–2022. Databases searched included PubMed, Medline, and Scopus. The resulting manuscripts were then sorted using the following inclusion/exclusion criteria: GI-related telemedicine must be mentioned for at least a couple of paragraphs; Older patients should make up a portion of their cohort studied, and should be included in the discussion; and Telemedicine should be compared to standard in-person care or should be a main form of healthcare provided. After a full text screening, the necessary data were extracted from the resulting manuscripts. Quality of evidence was analyzed, and pertinent information was synthesized.Table 1 Summary of research terms #1: Telemedicine #2: GI/IBD #3: Older patients Terms/subheadings “Telemedicine"[Mesh] OR "Digestive System Diseases"[Mesh] OR "Inflammatory Bowel Diseases"[Mesh] OR "Aged"[Mesh] OR "Health Services for the Aged"[Mesh] OR "Geriatrics"[Mesh] OR Textwords Telemedicine[tiab] OR Virtual provider[tiab] OR Virtual appointment[tiab] OR Virtual consult[tiab] OR Virtual visit[tiab] Inflammatory Bowel Disease[tiab] OR Ulcerative colitis[tiab] OR Crohn disease[tiab] Older population[tiab] OR Older adult[tiab] OR Older person[tiab] OR Advanced age [tiab] Dartmouth-Hitchcock Medical Center Data Data on all telemedicine visits in the DHMC Section of Gastroenterology and Hepatology from March 2020 to May 2022 were collected and analyzed. All patients were at least 18 years of age and had to have had at least one telemedicine visit scheduled within the electronic medical record (EMR) system. The EMR database was queried to identify the number of patients scheduled for telemedicine visits, age of patients, proportion of visits that were video or telephone only, and failure rate of the telemedicine visit. Failure rate is defined as the proportion of telemedicine visits that were intended to be delivered via a two-way audio-visual connection but were delivered via an audio-only connection due to a technical barrier that prevented the use of video. Results Systematic Review One hundred-twenty manuscripts were identified from the literature search with 22 meeting inclusion criteria. The mean age of patients studied in the 22 manuscripts meeting inclusion criteria was 52 years. A total of 3/22 publications had a median patient age over 65. In 21/22 manuscripts included in this analysis, patients aged 65 and over were within one standard deviation of the mean or median age, making up at least 15% of the population studied. A summary of the ages of patients studied can be found in Table 2.Table 2 Age of patients in studies GI subspecialty Median or mean age of patients Reference IBD 52 Rodriguez et al., 2021 [1] 42 Li et al., 2017 [2] 44 de Jong et al., 2020 [3] 46 Ruf et al., 2020 [4•] 41 Shah et al., 2022 [5] 40.5 Srinivasan et al., 2020 [6•] 51 Appel et al., 2022 [7] 43 Lahat et al., 2021 [8] Hepatology 32 Efe et al., 2020 [9] 51 Schulz et al., 2020 [10] 57.5 Talal et al., 2019 [11] 52 Serper et al., 2020 [12] 59 Louissaint et al., 2021 [13•] 60 Wegermann et al., 2022 [14•] GI-related cancer 61 Barsom et al., 2021 [15] 64.5 Edwards et al., 2020 [16] 46 Furniss et al., 2021 [17] 72.1 Haase et al., 2021 [18] Motility 57 Collins et al., 2021 [19] 81.65 Burns et al., 2019 [20] 67.5 Morrell et al., 2017 [21] 50.9 Bednarski et al., 2018 [22] Use of Telemedicine for Specific GI Disorders IBD Care Review of the literature shows that telemedicine for IBD care has been used for many years with success to provide high-quality care and measure disease progression. Telemedicine provides an alternative to in-person visits for many aspects of IBD care that do not require a physical exam such as medication adjustments (biological therapy, corticosteroids, and anti-inflammatory agents). Additionally, it offers more flexible timing and saves costs for patients who may be prohibited from traveling to outpatient appointments due to money or time restraints. Li et al. found that telemedicine IBD clinics allowed patients to save on average $62 of out-of-pocket costs, while de Jong et al. found that telemedicine lowered the annual cost of IBD care for patients by $612 without lowering quality life adjusted years [2, 3]. Furthermore, Ruf et al. found that before telemedicine for IBD care was available to their cohort of patients, the mean travelling distance (for both ways) was 310.1 km with a mean overall travel time of 318.2 min; telemedicine thus saved an average of US$36.61 in potential travelling cost per appointment [4•]. The preferred modality of telemedicine slightly differs among patients; older patients are more likely to complete telephone visits rather than videoconferencing visits despite availability of both [1]. Among IBD patients, both telephone visits and videoconferencing visits are appropriate for routine follow-up care during remission, although patients prefer in-person visits during flares [1]. Telemedicine was also positively viewed among all age groups with IBD; in one study 96% of patients reported a meaningful discussion during the visit, 98% reported the time allotted was adequate, 91% reported they perceived that their physician understood their disease state, 77% understood the follow-up plan after the visit, and 77% of patients would use telemedicine as their preferred method of follow-up [2]. Additionally, the video component of an IBD telemedicine visit significantly contributes to quality and satisfaction of the visit [5]. In patients with Crohn’s disease, one study found that those who were treated using telemedicine had higher success rates compared to in-person care as measured through biomarker remission (fecal calprotectin), disease monitoring, and treatment de-escalation [6•]. Patient-reported outcome-based telemedicine follow-up for patients with IBD has also shown a reduction in outpatient visits for patients with mild or no disease activity. [7] Overall, telemedicine shortened the time to treatment success relative to standard outpatient care [6•]. Across all gastroenterology departments, Lahat et al. found that IBD patients were the most likely to accept the virtual meeting and 86% of patients assessed their telemedicine experience as ‘good’ or ‘excellent.’ [8] Age once again played a difference; patients who supported long-term telemedicine were 10 years younger than those who did not (42 years versus 52 years) [8]. Hepatology Care Telemedicine has also been used successfully in hepatitis C (HCV) and autoimmune hepatitis treatment for many years. In a study that compared biochemical remission and relapse after remission in patients with autoimmune hepatitis, rates were similar in the telemedicine and standard care groups (89.5% vs. 89.1% and 15.8% vs. 25.9%) [9]. The telemedicine group also maintained remission significantly better than the standard care group (100% vs. 77.3%) [9]. Furthermore, one study looking at telemedicine for HCV care found that the median travel avoided for each telehealth consultation was 634 km and sustained virological response was achieved in 88% of those who had a planned telehealth consultation as part of their management [10]. In a subset of patients with opioid use disorders, HCV management via telemedicine integrated into an opioid substitution therapy program was a feasible model with excellent virologic effectiveness [11]. Serper et al. found that in patients with liver cirrhosis and advanced liver disease, telemedicine was rated as uniformly positive in both patient and provider-rated acceptability [12]. Patients overall were very receptive to telemedicine in hepatology clinics; a study looked at video telemedicine patients and found that more than 90% of patients would complete a video visit again in the future [13•]. However, 10% of patients had a failed video visit encounter, and one-fifth agreed to but did not complete a video visit, likely due to low digital health literacy exacerbated by cognitive dysfunction (hepatic encephalopathy, cognitive frailty) that are more prevalent in persons with chronic liver disease [13•]. Similar to IBD care, age plays a role in the modality of telemedicine used in hepatology; one study found that older adults tend to use phone appointments over video (median age of phone appointments was 63 years old, while the median age of video appointments was 58 years old) [14•]. GI-related Cancer Care In patients with colorectal cancer, the level of satisfaction with the individual performance and professional competence of the healthcare provider was consistently high in both the video telemedicine and face-to-face groups, and 42% of patients chose video telemedicine as their preferred follow-up modality [15]. In patients who underwent resection for stage I to III colorectal cancer, a virtual surveillance clinic increased guideline-concordant endoscopic surveillance after colorectal cancer resection from 30.6 to 50% [16]. One study also expanded the scope of telemedicine into genetic testing for pancreatic cancer. Remote genetic testing with telemedicine-based genetic education for those with a family history of pancreatic ductal adenocarcinoma and a relative with a germline pathogenic variant was performed and demonstrated genetic testing rates over 90% [17]. The high rate of testing may reflect increasing receptiveness to online genetic education and the success of providing genetic testing through physician-mediated testing. In older survivors of colorectal cancer (mean age of 72.1 years), requested improvements in their survivorship care included increased resources and information from healthcare professionals and the ability for caregivers to be on the call with them [18]. Overall, older patients were receptive to using technology if it would minimize delays to their cancer screening and follow-up schedule [18]. Motility-related Care In addition to standard patient visits, the COVID-19 pandemic has necessitated the need for innovation and expansion of the scope of practice of telemedicine in GI clinics. Included in these innovations are virtual speech-language pathologist consultations for dysphagia, and multi-disciplinary team consultation appointments completed in one telemedicine visit. In patients who needed speech pathology for swallowing, and nutrition and dietetics counseling post head and neck chemotherapy, the home-based telemedicine model of care was more efficient, with a reduced number and duration of appointments required until discharge as well as significant patient cost savings due to decreased travel requirements [19]. Moreover, in another dysphagia study (mean age of participants = 81.65), cost analysis revealed that the mean total cost of a telemedicine session was $70, compared with $288 for a standard care session [20]. Following a stroke, patients (mean age 67.5) evaluated by bedside and telehealth speech language pathologists found that for both liquid and solid textures, dysphagia evaluation via telemedicine was safe and effective following stroke [21]. However, one limitation of telemedicine dysphagia is the inability to perform laryngeal palpation for clinical swallow evaluations [21]. Another study found a 95% match in treatment in determining the need for anti-motility agents in patients with ileostomies [22]. Additionally, no difference was found when comparing telemedicine assessment and in-person assessment in the assessment of stool consistency [22]. Key Findings at the Dartmouth Hitchcock Medical Center (DHMC) Primary data from the GI clinic at DHMC was examined. Prior to 2020, telemedicine was being used in the GI clinic, but ranging between 25 and 166 visits annually. As displayed in Fig. 1, since the start of the COVID pandemic in the USA in 2020, the GI clinic at DHMC has seen an appreciable rise in annual telemedicine visits, from 166 virtual visits in 2019 to 7901 virtual visits in 2020, and 8164 virtual visits in 2021.Fig. 1 Annual volume of virtual visits at DHMC GI. Failure rates for telemedicine visits have declined among all groups between April 2020 and May 2022. The overall failure rate has declined from 17 to 3% from April 2020 to May 2022. While the 65 + age group had a higher failure rate than other age groups throughout most of this time period, the failure rate has declined from 16 to 0% from April 2020 to May 2022. Across all age groups, video calls were more frequent than telephone calls during GI visits. As age increases, telephone visits made up a higher proportion of the telemedicine visits. The 65 + age group had 32% of the telemedicine visits conducted via telephone call as compared to 9% of the 18–34 year olds. This is different from failure rate, which is defined by visits that were intended to be video that were subsequently conducted via phone (versus these visits that were intended to be phone from the start) (Figs. 2 and 3).Fig. 2 Telemedicine failure rates among all age groups. Fig. 3 Phone vs video use during telemedicine visits by age group from April 2020 to May 2022. Conclusions There has been a tremendous increase in the use of telemedicine since the beginning of the COVID-19 pandemic, and overall in GI, it has shown to give at least equivalent care by improving patient outcomes and saving costs while providing a high level of patient satisfaction. The older population had been among the most vulnerable during the pandemic, so it has been critical to keep them engaged in the health care system while also avoiding unnecessary exposure at in person visits. Our review of the literature and analysis of data from a rural academic medical center shows that older patient populations can indeed be successful using telemedicine as a care modality. As found in our systematic review, across sub-specialties in gastroenterology (IBD, hepatology, motility), telemedicine had comparable quality of care and equal or better outcomes to in-person visits when comparing remissions rates in IBD [6•], autoimmune hepatitis [9], and HCV [10]. Additionally, it saved patients money ranging from $36.61 per visit [4•] to $612 per year [2] and distance travelled, ranging from 310 [4•] to 634 km. [10] At DHMC, the use of telemedicine has been successful since the start of the COVID-19 pandemic and continues to be highly effective. The 65 + age group has successfully navigated the use of video telemedicine after a little bit longer learning curve as compared to younger populations. While this age group is still the largest users of telephone calls overall, it is not clear if this higher use of telephone calls in older patients is due to patient request, scheduler assumption that they may not be able to complete the visit using video, or a failure of the two-way connection for a variety of reasons. This is an important avenue of future research. While telemedicine has made many advancements in a short period of time, there are still steps to be taken to optimize care for every patient’s specific needs. In our literature review, some of the most common requests from patients were enhanced education before and after the appointment (either through written information, summaries of their appointment, or additional educations from advanced practice providers), the ability of caregivers such as family members to be present at their telemedicine appointments, and additional support in learning how to use telemedicine platforms [18]. Potential barriers to telemedicine may contribute to the diminished uptake among various patient groups, including infrastructural factors and patient-related considerations, access to electronic devices, and availability of reliable internet connection. Additional barriers for telemedicine adoption in older patients include small font size, confusing internet navigation and lack of internet experience, poor contrast in text and color, poor coordination, and culture change in adopting technology. [4•] While there is a wealth of evidence that telemedicine is safe and effective across a broad spectrum of GI disorders including IBD, chronic liver disease, esophageal disorders, and IBS, there are still many steps to be taken before equitable access to video-based telemedicine can be reached. It will be imperative to help educate our older patient population so that they can continue to take advantage of these technologic advances in healthcare delivery, and also be prepared to assist those who do not have access to appropriate devices or internet access. Providers’ offices can take on the responsibility of education and communicating with their patients, but it will also be necessary to establish community partnerships to help broaden the radius of access to all patients. Acknowledgements The authors would like to thank Pamela Bagley at Dartmouth Biomedical Libraries for her help in developing the search criteria for the literature review. Author contribution MDD (study design, analysis & interpretation, manuscript development and writing). SS (analysis and interpretation, manuscript review). LAB (analysis and interpretation, manuscript review). CAS (study design, analysis & interpretation, manuscript development and writing). Funding MDD received support from the Crohn’s & Colitis Foundation, award number 984541. Declarations Conflict of interest Michelle D. Dong declares that she has no conflict of interest. Shelley Steuwe declares that she has no conflict of interest. Lauren A. Barry declares that she has no conflict of interest. Corey A. Siegel declares that he has no conflict of interest. This article is part of the Topical Collection on Geriatrics Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Papers of particular interest, published recently, have been highlighted as: • Of importance 1. Rodriguez NJ Okwara NC Shen L Jajoo K Chan WW Impact of telemedicine modalities on equitable access to ambulatory gastroenterology care Gastroenterology 2021 161 3 742 747.e3 10.1053/j.gastro.2021.05.042 34051240 2. Li SX Thompson KD Peterson T Delivering high value inflammatory bowel disease care through telemedicine visits Inflamm Bowel Dis 2017 23 10 1678 1681 10.1097/MIB.0000000000001210 28817463 3. de Jong MJ Boonen A van der Meulen-de Jong AE Cost-effectiveness of telemedicine-directed specialized vs standard care for patients with inflammatory bowel diseases in a randomized trial Clin Gastroenterol Hepatol 2020 18 8 1744 1752 10.1016/j.cgh.2020.04.038 32335133 4.• Ruf B Jenkinson P Armour D Fraser M Watson AJ Videoconference clinics improve efficiency of inflammatory bowel disease care in a remote and rural setting J Telemed Telecare 2020 26 9 545 551 10.1177/1357633X19849280 31167590 5. Shah KP Triana AJ Gusdorf RE Demographic factors associated with successful telehealth visits in inflammatory bowel disease patients Inflamm Bowel Dis 2022 28 3 358 363 10.1093/ibd/izab068 33769496 6.• Srinivasan A van Langenberg DR Little RD Sparrow MP De Cruz P Ward MG A virtual clinic increases anti-TNF dose intensification success via a treat-to-target approach compared with standard outpatient care in Crohn’s disease Aliment Pharmacol Ther 2020 51 12 1342 1352 10.1111/apt.15742 32379358 7. Appel CW Pedersen SC Nielsen AS Larsen BF Telemedicine based on patient-reported outcomes in management of patients with inflammatory bowel disease in a real-life setting – a before and after cohort study Scand J Gastroenterol 2022 57 7 825 831 10.1080/00365521.2022.2041083 35195491 8. Lahat A Shatz Z Telemedicine in clinical gastroenterology practice: what do patients prefer? Therap Adv Gastroenterol 2021 14 1756284821989178 10.1177/1756284821989178 33633797 9. Efe C Simşek C Batıbay E Calışkan AR Wahlin S Feasibility of telehealth in the management of autoimmune hepatitis before and during the COVID-19 pandemic Expert Rev Gastroenterol Hepatol 2020 14 12 1215 1219 10.1080/17474124.2020.1822734 32909852 10. Schulz TR Kanhutu K Sasadeusz J Watkinson S Biggs BA Using telehealth to improve access to hepatitis C treatment in the direct-acting antiviral therapy era J Telemed Telecare 2020 26 3 180 185 10.1177/1357633X18806651 30336724 11. Talal AH Andrews P Mcleod A Integrated, co-located, telemedicine-based treatment approaches for hepatitis C virus management in opioid use disorder patients on methadone Clin Infect Dis 2019 69 2 323 331 10.1093/cid/ciy899 30329042 12. Serper M Cubell AW Deleener ME Telemedicine in liver disease and beyond: can the COVID-19 crisis lead to action? Hepatology 2020 72 2 723 728 10.1002/hep.31276 32275784 13.• Louissaint J Gibbs JT Lok AS Tapper EB Strategies to improve video visit use in persons with liver disease Gastroenterology 2021 161 4 1080 1084.e2 10.1053/j.gastro.2021.06.070 34197830 14.• Wegermann K Wilder JM Parish A Racial and socioeconomic disparities in utilization of telehealth in patients with liver disease during COVID-19 Dig Dis Sci 2022 67 1 93 99 10.1007/s10620-021-06842-5 33507442 15. Barsom EZ Jansen M Tanis PJ Video consultation during follow up care: effect on quality of care and patient- and provider attitude in patients with colorectal cancer Surg Endosc 2021 35 3 1278 1287 10.1007/s00464-020-07499-3 32198552 16. Edwards GC Broman KK Martin RL Virtual colorectal cancer surveillance: bringing scope rate to target J Am Coll Surg 2020 231 2 257 266 10.1016/j.jamcollsurg.2020.05.011 32454089 17. Furniss CS Yurgelun MB Ukaegbu C Novel models of genetic education and testing for pancreatic cancer interception: preliminary results from the GENERATE study Cancer Prev Res (Phila) 2021 14 11 1021 1032 10.1158/1940-6207.CAPR-20-0642 34625409 18. Haase KR Kain D Merchant S Older survivors of cancer in the COVID-19 pandemic: reflections and recommendations for future care J Geriatr Oncol 2021 12 3 461 466 10.1016/j.jgo.2020.11.009 33303410 19. Collins A Burns CL Ward EC Home-based telehealth service for swallowing and nutrition management following head and neck cancer treatment J Telemed Telecare 2017 23 10 866 872 10.1177/1357633X17733020 29081270 20. Burns CL Ward EC Gray A Implementation of speech pathology telepractice services for clinical swallowing assessment: an evaluation of service outcomes, costs and consumer satisfaction J Telemed Telecare 2019 25 9 545 551 10.1177/1357633X19873248 31631757 21. Morrell K Hyers M Stuchiner T Telehealth stroke dysphagia evaluation is safe and effective Cerebrovasc Dis 2017 44 3–4 225 231 10.1159/000478107 28848110 22. Bednarski BK Slack RS Katz M Assessment of ileostomy output using telemedicine: a feasibility trial Dis Colon Rectum 2018 61 1 77 83 10.1097/DCR.0000000000000945 29215474	36465489	PMC9708499	NO-CC CODE	2022-12-10 23:15:15	no	Curr Treat Options Gastroenterol. 2022 Nov 30; 20(4):594-604	utf-8	Curr Treat Options Gastroenterol	2,022	10.1007/s11938-022-00404-y	oa_other
==== Front Employ Respons Rights J Employee Responsibilities and Rights Journal 0892-7545 1573-3378 Springer US New York 9431 10.1007/s10672-022-09431-4 Article Industrial Killing in Bangladesh: State Policies, Common-law Nexus, and International Obligations http://orcid.org/0000-0003-3225-1448 Syed Robayet Ferdous [email protected] 1 Ikra Md. [email protected] 2 1 grid.440425.3 0000 0004 1798 0746 Department of Business Law and Taxation, MONASH University, Sunway, Malaysia 2 grid.443058.e 0000 0004 0487 3327 Department of Law, University of Information Technology & Sciences (UITS), Dhaka, Bangladesh 30 11 2022 118 17 11 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The legal system of Bangladesh does not directly acknowledge industrial killing in the form of penal offenses and thus is not equipped with a solid legal avenue to ensure justice for the victims. This article attempts to discuss industrial killing in Bangladesh through four particular incidents, the current domestic framework for preventing industrial killings, and common law countries’ legislations as best practices. Also, it reminds the state obligation under the provisions of international law. This qualitative study uses applied techniques from the professional constituency that deal with law reform research (socio-legal research/ “law in context”) from a labor rights lens. This study finds that recurring events of industrial killings come with a manifold threat to labor rights. The most immediate risk it poses is that it clashes with the workers’ right to life. Therefore, the recommendation is highly focused on the obligation under international law. It also focuses on legislative changes in punishing the offenders involved in industrial killings as part of its progressive realization of the duty to respect, protect and fulfill human rights obligations. Keywords Industrial killing State policies Common-law nexus State obligation Bangladesh ==== Body pmcIntroduction “Industrial killing” or workplace death due to employers’ gross negligence generally calls for retributive punishment (Gikonyo, 2020). The theory of retributive punishment is when an individual commits a wrongful act that justifies punishment; in that case, punishment would be proportional to the wrong committed (Spencer, 2022). But in many circumstances, industrial killing is termed an “industrial accident,” for which compensatory justice usually pitches in (Taqbir Huda, Jul 14, 2021). Arguably, compensatory justice for that type of heinous killing could not suffice or be proportionate to the wrong committed. Therefore, sometimes, criminal punishments and compensation are awarded to mitigate the irreplaceable loss suffered by the deceased’s family (Abdul Razak, 2018). The debate can continue as to which mode of criminal liability tends to offer better ‘justice’ to the victims and their families. But the central point is in the absence of strict liability; perpetrators may repeat the same offense with impunity. In the industrial sector, Bangladesh has witnessed some of the most brutal deaths in its history last decade. These deaths claimed thousands of lives, and one should simply know that these deaths are the result of a system that is coupled with weak legislation, dominant industrial positions, and a lack of effective enforcement systems. The Safety and Rights Society (2020) shows that the approximate number of accidents that occurred in 2020 was 373, which caused 433 deaths, while most of the time workplaces were closed due to the COVID-19 pandemic. The same report demonstrates that the scenario worsened in 2019 and 2018; workplace deaths were 572 and 593, respectively. Breadwinners come to work and die due to the employers’ gross breach of duty of care, but no one cares, deceased’s family blames their fate, calling industrial killing an incident, and thus the cycle continues. The blame definitely goes to the country’s existing strict and archaic framework of criminal legislation that becomes less victim-friendly when it comes to the point of proving criminal elements of the person so liable. Besides, political unwillingness and lack of a strict implementation mechanism under the labor law cannot ignore its liability (Syed, 2020). The question of liability must take place since these are preventable deaths. What raises the most vital question in this regard is that if the so-called accidents keep happening one after another, where do we hold the most fundamental right (right to life) of all rights of human beings (workers)? Taking no precautions, blindly accommodating hazardous and life-threatening work environments, and leaving workers to die alone in their workplaces while exploiting their labor to make fortunes by the privileged owners in absence of a proper legal framework must be condemned legally by shifting the term from accident to killing (or murder). As of now, the prevailing context offers impunity for such inclusive negligence through loopholes and inadequacy existing in the legal system, which resulted in the following four catastrophic industrial killings in Bangladesh. It is noted that many industrial accidents occurred during the timeline of 2012–2022. But these four unforgettable industrial catastrophes are in this decade. These events, however, are not given any superior consideration that dislodges the other cases that occurred at the same time in any aspect. Name of the Industrial Killing Events Brief Facts Tazreen Fashion Garments (Tazreen) Date of occurrence: 24 November 2012 Tazreen Fashion was a nine-story garment factory that was destroyed by a fire that killed 112 garment workers and injured about 200 more (Roy, 2022; Vanpeperstraete, 2021). The initial reason as noted by the fire service officials was short circuit began on the ground floor (Ullah, 2022). Though the tragedy was initially labeled as an accident, there was ample evidence that suggested the existence of ‘gross negligence’ from the employer’s end (Rahim et al., 2022; Solaiman, 2018; Solaiman, 2013). Because the workers were initially locked and trapped inside the building when the fire broke out and windows on the lower floors were barred which left the windows on the upper floor the only escape routes for the workers (Ullah, 2022; Shahab, 2021; Solaiman, 2013). Secondly, the short circuit could have been easily prevented if the electricity was cut off timely (Ullah, 2022). As such place was prone to fire, the installation of a sprinkler system could have prevented the greater damage palpably. Knowing that large mounds of fabric and yarn stored illegally on the ground floor are easily prone to fire and still trapping the workers to die proves negligence (Fakhoury, 2019). Rana Plaza Disaster Date of occurrence: 24 April 2013 Five months after the Tazreen tragedy took place, the collapse of the Rana Plaza factory building shocked the entire nation. The worst-ever industrial incident is still remembered due to its horrific experience and loss. The eight-story factory accommodated five garment factories. On 23 April 2013, workers discovered structural cracks in the building and informed the owner (Wagner, 2021). Shops and banks located on the lower floors of the building were immediately closed (Ullah, 2022) but garment factory owners ignored the possibility of great danger. Putting the risk aside, workers were ordered to continue their work the following day (Syed, 2020a). On the date of occurrence, the building collapsed. This killed 11,37 people and injured more than 2500 (Mansoor et al., 2021; Syed, 2020a). The quest for justice is still on. Hashem Foods Factory Fire Date of occurrence: 09 July 2021 The six-story food processing factory situated outside Dhaka produced food and drink which belonged to Sajeeb Corporation (Chatterjee, July 15, 2021). On the date of the incident, a fire broke out at the factory. No smoke detectors, fire alarms, or emergency exits were found in the building. As a result, at least 52 workers died (BBC, July 9, 2021) and another 20 were injured (The Daily Star, July 9, 2021). The owners were arrested later on and charged with murder (bdnews24.com, July 10, 2021). BM Container Depot Explosion Date of occurrence: 4 June 2022 The depot is situated at Sitakunda in Chittagong. At the midnight, the fire broke out triggering a huge blast and inviting several container explosions one after another (ALJAZEERA, June 5, 2022). This took 49 lives and over 450 injured which once again revealed the country’s poor track on safety compliance. It was believed that the chemical compound name hydrogen peroxide caused the deadly explosion (Prothom Alo, June 6, 2022). Shockingly, the depot authority did not inform the fire officials about the storage of chemicals that are prone to fire or explosion (DAWN, June 6, 2022). Research Statement As a research statement, this study argues that Bangladesh has been an active member of the United Nations (UN) since its inception. She has ratified different numbers of international instruments of the UN to protect labor rights. As a result, she must enact and practices national labor policies in line with the international labor standard to protect workers from industrial killing. In accordance with the research statement, this study attempts to answer the following questions: I. Do the present State policies and their practices suffice to tackle the industrial killings? II. What policies have been adopted in selected common-law countries to tackle such types of killings? III. What are the State’s obligations to stop industrial killing under international law and how can we prevent these industrial killings in Bangladesh? State Policies Against Industrial Killing There is no separate legislation to deal with industrial killing in Bangladesh. But Bangladesh Labor Act 2006 (BLA2006) is the primary law for the labor industry that enunciates several provisions for ensuring workers’ safety and security in the workplace. For example, chapter six deals with safety provisions for workers. Having a fire in buildings or unwanted collapsing is a common mode of industrial accidents in Bangladesh. Keeping this issue in mind, specific provisions have been enacted that when an inspector thinks that a building, part of a building, road, machinery, plant, or internal electrical system of a building is dangerous to human life or safety, then he may order to the employer to take the necessary measures within a specified time [s. 61 (1), BLA2006]. When an inspector sees an impending danger to human life or safety, he may compel the employer to stop using it until it’s repaired or corrected [s. 61 (2), BLA2006]. The amendment of BLA has enlisted several provisions in 2013 as precautions for fire safety. For example, at least one alternative staircase for a safe exit connecting with every floor, the requisite number of firefighting equipment on every floor as prescribed by Bangladesh Labor Rules, 2015 [s. 61 (1), BLA2006], doors shall not be locked or fastened while work is going on [s. 62 (3) (3a), BLA2006], an audible whistle shall be provided to alarm every worker employed therein in case of fire or danger [s. 62 (5), BLA2006], a free passage-way for easy exit during a fire [s. 62 (6), BLA2006] among others. Besides, workers should be familiar with the means of escape in case of fire and are adequately trained in the routine work [s. 62 (7), BLA2006]. And for doing this at least once every six months, mock firefighting shall be arranged by the employer where fifty or more workers are employed [s. 62 (8), BLA2006]. Further, when a worker finds that a structure or piece of machinery utilized by workers is in a dangerous condition, he must notify his employer in writing [s. 86 (1), BLA2006]. If, after receiving such information, the employer fails to take suitable steps within three days and a worker is harmed due to the usage of such building or machinery, he shall pay double the compensation payable for such injury [s. 86 (2), BLA2006]. The BLA2006 allows for criminal prosecution for contravention of law with dangerous consequences like serious bodily injury or loss of life. But BLA2006 does not have homicide penalty provisions to deal with serious bodily injury or loss of life. Instead, it imposes a much lower penalty than culpable homicide. For example, if a person violates any provision of this Act or any rules or regulations that causes death, he may be penalized with. imprisonment for up to four years or a fine of up to Tk.1 100,000 equivalents to $ 1053 or both [s. 309 (1)(a), BLA2006]; if the violation causes serious bodily injury, up to two years in prison or a fine of Tk. 10,000 equivalents to $ 105, or both [s. 309 (1)(b), BLA2006]; imprisonment for up to six months or a fine of up to Tk 2,000 equivalents to $ 21 or both, if the violation causes injury or danger to a worker or other person in an establishment [s. 309 (1)(c), BLA2006]. It is to be noted that clauses (a) and (b) will apply only to the workers, not to other persons. But clause (c) will also be included for the other persons. In the absence of severe penalty under BLA2006 [s. 309 (3), BLA2006], it permits the application of the Penal Code 1860 (PC1860) for any contravention that causes serious injury or death, which is the foremost criminal legislation in Bangladesh. But the shattering truth is that the present form of general criminal law is unable to punish such serious injuries and deaths. Instead, the penalty for workplace death remains elusive under the PC1860 of Bangladesh because of the following reasons: PC1860 defines ‘person’ “as any Company or Association, or body of persons, whether incorporated or not (s. 11, PC1860). This definition is confusing as it does not disclose whether the such definition is equally applicable in case of incidents in determining what we call industrial killing or corporate manslaughter as it is popularly understood in common law countries. Further, the PC1860 reads as-Whoever causes death by doing an act with the intention of causing death, or with the intention of causing such bodily injury as is likely to cause death, or with the knowledge that he is likely by such act to cause death, commits the offense of culpable homicide (s. 299, PC1860). . It is unclear whether the expression ‘whoever’ includes corporate bodies or not. Even if it does, the point of intentions of a corporate body would be next to impossible to prove since it does not have any solid human figure or intellect to participate in a witness examination. As a result, the recent cases of industrial killings in Bangladesh are either hovering around the court proceedings on different charges or put to a halt with no trace. It is also to be noted that the BLA2006 has no corporate penalty provision. Instead, it allows penalties for individuals for violation of any provision mentioned in BLA2006. The BLA2006 said that, “Where an offense punishable under this Act or any rules, regulations, or schemes is committed by a company or any other body corporate or a firm, every director, partner, manager, secretary, or any other officer or agent thereof, who is actively involved in the conduct of the business thereof shall be deemed to have committed that offense unless he proves that the offense was committed without his knowledge or consent or that he exercised all due diligence to prevent the commission of the offense (s. 312, BLA2006).” But, in fact, modern corporate decision-making is frequently the result of corporate culture, rules, and processes rather than individual choices (Ricketts & Heidi, 2009). In this context, punishing the individual concerned is becoming tough, let alone punishing the corporate bodies. Thus, justice is a far cry.Besides, a mere directory named ‘Occupational Safety and Health policy Bangladesh 2013’ has been enacted by the Bangladeshi government that lacks the core commitment to ensuring occupational health and safety. Few powerless bodies like safety committees, participation committees, and health centers were introduced but the periphery of their work was made subject to the directions of the worker’s union. Further, the Fire Safety Act 2003 (FSA2003) requires a person or group to obtain a license from the Director-General of the Fire Service and Civil Defense Department prior to using a building or establishment as a warehouse of the workshop (s. 30, FSA2003). Such building’s elevator shafts and vent shafts must be of at least 4-hour fire resistance as per the Bangladesh National Building Code 2006 (rule 2.11.5, BNBC2006). In fact, obtaining a license is mandatory before building any multi-storied building or commercial space. The such license gives a certificate stating the establishment’s inclusion of fire prevention safety measures (s. 7, FSA2003). The Bangladesh National Building Code 2006 necessitates separate boilers, heating plants, and electrical rooms of hot water supply boilers from the rest of the occupancy in order to prevent any hazardous event. Such sections must be constructed with noncombustible materials (rule 2.11.7, BNBC2006). This code has set the minimum ceiling heights to be 3.5 m for non-air-conditioned and 3.0 m for air-conditioned buildings (rule 1.12.2, BNBC2006), where a minimum height of 2.0 m is set as the width of staircases and 0.9 m in case of handrails (rule 1.12.5, BNBC2006). The exterior walls of the establishments must be of fire resistance for at least 2–3 hours (rule 2.4.1, BNBC2006). Furthermore, the Ship Breaking and Ship Recycling Rules, 2011 (SBSRR2011) require minimum safety distance in case of storing petroleum or combustible materials in the ship dismantling area or in the yard (rule 17.7, SBSRR2011). Also, the PC1860 imposes criminal liability on the person who makes an atmosphere obnoxious to health (s. 278, PC1860) or acted negligently with respect to substances that are poisonous, combustible, or explosive (ss. 284–288, PC1860). Moreover, the Factory Rule 1979 (FR1979) sets another safety measure that requires every factory to ensure its electric supply lines and apparatus are of proper size, measure, and sufficient strength (rule 41, FR1979). Any process or work that possesses any chance of causing risk of bodily injury must not be carried out (rule 42, FR1979). Though there are several legislative provisions in regard to building and fire safety in the workplace, there is no political will to enforce the law. Industrial killing is daily news in the setting of Bangladesh, but the victims are unwilling to file lawsuits. Mainly because of judicial biases, judicial backlog due to excessive workload, and judicial bureaucracy generate distrust of the litigation process of industrial killing in Bangladesh (Syed, 2020). Sometimes judges utilize employers’ political influence for their financial benefit and professional advancement, which erodes public confidence in the judiciary (Syed, 2020). Therefore, employees are defenseless and reluctant to prosecute their bosses. The absence of harsh punitive provisions in current labor and penal legislation against body corporate as well as companies is another aspect of unwillingness to file lawsuits. In addition, the fire and building safety provisions under several laws are not comprehensive due to a lack of inspection, implementation mechanisms, and legislative technical or mechanical deficiencies (Syed, 2020). So, in response to the first research question, it can arguably be demonstrated that the current labor policies do not suffice to tackle industrial killing. Though these policies have some deficiencies, they could have tackled industrial killing to some extent. But these policies have yet to be practiced to the fullest. Because, due to deficient policies, perpetrators can come out of their liabilities. Thus, they are not obedient to the law. As Bangladesh belongs to a common-law family, this finding creates room to discuss how selected common-law countries tackle industrial killing. Nexus Between Industrial Killing and Selected Common-law Countries’ Initiatives A common perception is that company is an artificial person, it has no hand, brain, or mind to carry out its functions. All the actions are carried out by a natural person in charge of the company. As a result, the company cannot be held criminally liable for the actions of others committed by a natural person while working for the company. Alternatively, the ‘doctrine of ultra vires’ inhibited the expansion of corporate criminal liability on the ground the corporation is run by human labor and does not warrant anything beyond its capacity (Turpin, 1970). In this context, it cannot be held liable for an act that is done solely by the persons running the body. But this perception is wrong while industrial killing is a common concern around the world. Therefore, company’s liability for manslaughter resulting from workplace accidents has garnered great public concern for many years (Clarkson, 1996). Perhaps Salomon v Salomon & Co (1897) is the first legal decision that a company is a legal person separate from shareholders. Consequently, prosecutions are rational against companies for industrial killing. Further, the doctrine of ultra vires contention was rejected in Citizens Life Assurance Company vs. Brown (1904) where it was held that corporations are capable of being liable for acts committed by their employees in the course of employment. This was later on expounded to be a corporate criminal liability [Harker vs. Britannic Assurance Co. Ltd (1928)]. Initially, the company’s criminal liability was limited to crimes of nonfeasance (the failure to satisfy a duty required by law) [Case of Langhforth Bridge (KB 1635)]. Later on, the liability was extended to misfeasance (the improper performance of a legal act) (Nanda, 2010). But the main difficulty faced by the court was finding ‘intent’ since it is one of the prerequisites of proving an act to be a crime [State vs. First National Bank (1872)]. While rejecting corporate bodies as fiction in the case of determining liability, the court extended the civil law doctrine of vicarious liability to criminal cases (Pieth, & Radha, 2011). British court further developed the identification doctrine to link the corporation to a human body as the state of mind of the managers is the state of the corporation [HL Boulton Co. Ltd vs. J Graham and Sons Ltd (1956)]. But it makes it hard for the law to find a real person who can be seen as the company’s controlling mind and who can be seen as “artificial legal personality.” The main problem with putting a company on the hook for a crime is figuring out and proving the intent of the person who is said to be speaking for the company. Though in Meridian Global Funds Management Asia Ltd vs. The Securities Commission (1995), Lord Hoffmann held that the doctrine of identification is based on a general rule and a specific rule of attribution, which can be found by looking at the memorandum and articles of association; it is tough to determine by looking into the specific memorandum or articles under which the company was charged. Because, modern company decision-making is frequently the result of company culture, rules, and processes rather than individual choices, memorandum, or articles of a company (Ricketts, & Heidi, 2009). Thus, the identification doctrine under the common law theory is ambiguous and fails to provide justice to the victims. For example, in March 1987, P&O was prosecuted for corporate manslaughter following the sinking of the Herald of Free Enterprise in Zeebrugge Harbour [R vs. P&O Ferries (1991)]. Despite extensive evidence of substandard health and safety management, the prosecution was unable to identify a person within P&O who was guilty of gross negligence manslaughter and was senior enough to be considered the company’s “directing mind.“ Therefore, the prosecution failed because the necessary mens rea could not be imputed to the corporation. In contrast, all successful cases for corporate manslaughter have been made against small corporations [R vs. Kite (1996)]. This kind of substantial failure may be the reason for enacting the Corporate Manslaughter and Corporate Homicide Act, (CMCHA2007) to fill the gap. In reaction to a number of large-scale disasters in the 1990s, CMCHA went into force in 2008 in the UK (CMCHA2007). The corporate offense under CMCHA2007 is explicit legislation designed with the corporate person in mind, which “allows an organization’s liability to be assessed on a wider basis, providing a more effective means of accountability for very serious management failings across the organization [emphasis added] [Ministry of Justice, (nd)].’ This Act stipulates two specific requirements for corporate homicide. First, the offense must have resulted in the death of a person; second, the organization’s applicable duty of care to the deceased must have been grossly breached. A ‘gross’ breach of duty occurs when the conduct alleged to constitute a breach of duty goes considerably below what can be reasonably expected of the organization under the circumstances [s.1(4)(b), CMCHA2007]. This Act provides a detailed discussion of the “relevant duty of care” (s. 2, CMCHA2007). In the event that a breach of duty of care ends in death, the penalty is a fine with no provision for the jail [s.1(6), CMCHA2007]. In addition to fines, the court has the right to issue a “remedial order” directing the convicted organization to do certain activities, such as addressing the relevant breach, any concerns that emerged as a result of the breach, and any health and safety deficiencies inside the organization (s. 9, CMCHA2007). And a violation of such an order is a separate offense that may result in extra sanctions [s. 9(5), CMCHA2007]. The CMCHA2007 also authorizes the court to issue a “publicity order” requiring the convict to make public the fact of conviction, the facts of the offense committed, the amount of the fine, etc. in a prescribed manner (s. 10, CMCHA2007). Noncompliance with such directions will result in a new offense punishable by a fine (s. 10, CMCHA2007). The CMCHA2007 eliminates the common law offense of homicide by gross negligence for companies and other organizations to which the CMCHA2007 applies (s. 20, CMCHA2007). It simply indicates that no people, regardless of their position within the offending company, can be sued under the CMCHA2007 (s. 18, CMCHA2007). However, the UK law commission has lately proposed increasing criminal liability to individuals when corporate criminality is attributable to their express participation, connivance, or negligence [Law Commission, (2022, June 10]. In Australia, the Crimes (Industrial Manslaughter) Amendment Act [C(IM)AA2003], amended the federal legislation (the Crimes Act 1900) that made corporate manslaughter liability more visible and practical. In addition to that, all provinces of Australia have separate industrial manslaughter legislation except Tasmania and Western Australia (Table 1). However, Western Australia is going to introduce new industrial manslaughter legislation expected to come into effect in 2022. A person conducting a business or undertaking (PCBU) or an officer of a PCBU commits industrial manslaughter if they participate in behavior that breaches a health and safety responsibility and results in the death of another person. The PCBU or officer that caused the death must have been reckless or negligent. Table 1 Province/Area Forced Maximum Jail for Individuals Maximum Compensation for Company Australian Capital Territory (ACT)a 1 March 2004 20 years $16.5 million Queensland 23 October 2017 20 years $10 million Northern Territory 1 February 2020 Life imprisonment $10.2 million New South Wales November 2021 25 years $10,295,000 Victoria 1 July 2020. 25 years $18.17 million Western Australia Expected to come into effect in 2022 20 years and fines of $5 million $10 million South Australia Bill passed the Legislative Council in November 2021b - - Tasmania - - - Source: Authors (collected from different sources) aAustralian Capital Territory (ACT) legislative amendments were introduced in August 2021, aligning the crime with other work safety offenses. bBut Bill’s second reading was put on hold. No new information has come out about whether or not the bill will be brought up again for debate. Canada introduced Bill C-45, in reaction to the Westray Mine disaster that occurred in May 1992, causing the death of 26 miners in Plymouth, N.S. The Westray bill, also known as Bill C-45, was a legislative amendment to the Canadian Criminal Code that became law on March 31, 2004 (Macpherson, 2004). The 2003-introduced bill established new legal obligations for occupational health and safety and set severe penalties for infractions that result in injury or death [Library of Parliament, (nd)]. Bill C-45 only deals with the criminal responsibility of the organization. It doesn’t change the current law about the directors, officers, and employees’ personal liability. Directors and officers are responsible for any crimes they commit on their own, just like everyone else. Thus, the rules impose additional criminal obligations and responsibilities on both individuals and entities (which are defined to include corporations). Individuals and organizations can now be found guilty of criminal negligence for failing to perform a duty in a willful or reckless disregard for the lives or safety of others. Two steps are required to convict an organization of criminal negligence in the context of workplace safety (Macpherson, 2004). First, the Crown must establish beyond a reasonable doubt that the actions of a single representative (employee, partner, contractor, or agent of the organization) violated the Criminal Code duty in a willful or reckless manner, resulting in serious injury or death. Second, after establishing a breach of duty, the Crown must demonstrate that a senior officer with operational or executive authority, or, as the drafters put it, a person with “real clout” who is responsible for the portion of the organization involved in the breach either failed to act or prevented themselves from acquiring the knowledge to act. The Crown must demonstrate a significant divergence from the reasonable expectations of a senior officer with responsibilities to protect workers and the public. The new duty found in Sect. 217.1 of the Criminal Code requires that: “Everyone who undertakes, or has the authority to direct how another person does work or performs a task, is under a legal duty to take reasonable steps to prevent bodily harm to that person, or any other person, arising from that work or task (c C-46, Criminal Code1985)”. “Everyone” encompasses individuals, organizations broadly construed, and companies. The Criminal Code mandates that reasonable efforts be taken to prevent physical harm to any person, including members of the public or volunteers who may attend the workplace or be affected by workplace activities. The Criminal Code further states that “Everyone is criminally negligent who (a) in doing anything, or (b) in omitting to do anything that it is his duty to do, shows wanton or reckless disregard for the lives or safety of other persons (s. 219, Criminal Code1985).” In addition, a breach of duty must constitute a “marked” and substantial divergence from the standard of a reasonably prudent person under the circumstances. There must be more than an accidental failure to comply with occupational health and safety or the Criminal Code norm. There must be evidence of blatant disdain or apathy towards the obligation. Under Bill C-45, the Criminal Code provisions have possible liabilities. The highest penalty for criminal carelessness resulting in death is life in prison, whereas the maximum penalty for criminal negligence resulting in bodily harm is ten years in jail. When the Crown pursues by summary conviction (the least serious method of proceeding), the maximum fine for organizations, including corporations, is $100,000. There is no maximum fine for a corporation or organization when the Crown proceeds by indictment (the most serious method of proceeding). In addition to monetary penalties, organizations may be placed on probation, and the terms of a probationary order may include the following: requiring the organization to make restitution, financial or otherwise, relating to the offense; requiring the organization to report to the court or the public on the implementation of remedial steps; requiring the appointment of a senior officer to be responsible for implementing remedial procedures and requiring the organization to comply with any other terms of the probationary order. This part of the discussion shows that the selected common-law countries have taken various initiatives to tackle industrial killing. While large industrial accidents have worsened in developing countries, the situation has improved in some wealthy common-law countries (Mihailidou, Antoniadis, & Assael, 2012). The improvements in these countries could be attributable to the changes in the law, which now effectively hold firms criminally liable for severe workplace accidents, which may be a lesson for developing countries like Bangladesh. State Obligation Under the International Policy to Tackle Industrial Killing In a plain sense, state obligation can be defined as the fulfillment of commitment (either positive or negative) by the states with respect to their consent to a particular international instrument (Lukashuk, 1989). While the debate is still going on whether international law construes as soft law or not, ‘state obligation’ particularly places a barrier on the ‘soft law’ debate (Guzman, & Timothy, 2010). The Vienna Convention on the Law of the Treaties (VCLT,1969) says “[e]very treaty in force is binding upon the parties to it and must be performed by them in good faith (art. 26, VCLT1969).” And when a state becomes a party to an instrument that is of international nature, its government expressly takes the obligation on itself to perform the said obligation. The term ‘obligation’ contains three key elements as expounded by the Office of the UN High Commissioner for Human Rights, namely, (i) the obligation to respect; (ii) the obligation to protect; and (iii) the obligation to fulfill [Human Rights Advocacy and the History of International Human Rights Standards, (nd)]. Obligation to respect denotes that the state must refrain from interfering with or curtailing the enjoyment of human rights while the obligation to protect imposes a duty on the state to protect individuals and groups against human rights abuses. The third element imposes a duty to take positive action to facilitate the enjoyment of basic human rights. Keeping this in mind, it would be pertinent to look into the case of Bangladesh with regard to its stance on international law. Therefore, this section explores Bangladesh’s commitments under different major international instruments and draws an analogy that to what extent it is under a legal obligation to make a safe workplace in order to prevent industrial killings. The expression ‘major international instruments’ is used within the strict sense of this paper that matches the subject of interest. A human being is definitely entitled to human rights simply because of his being one. For instance, a person fleeing to another country with fear of persecution may enjoy his rights under the Refugee Convention of 1951. In the same way, industrial workers shall have the right to life that goes without debate. It is that specific rights for a specific group of people that reserve the concentration in this paper. For the said purpose, this part explores Bangladesh’s ratification status with regard to the international instruments interconnected with workplace safety. Not just a mere milestone paper, the Universal Declaration of Human Rights (UDHR,1948) is regarded to be the foundation of international human rights law. In fact, the development of modern human rights is highly in debt to this instrument for setting a “foundation for our common future.” The declaration itself motivated the later journey of stealth expansion of human rights into more specific branches. Such bifurcation widened the scope of UDHR at domestic levels. It is in no doubt that UDHR is also reflected in the Constitution of Bangladesh like the Constitution of many other countries. UDHR possesses the right to life (art. 3, UDHR1948), protection from torture or cruel, inhuman, or degrading treatment or punishment (art. 5, UDHR1948), right to recognition of law (art. 6, UDHR1948), equality of law (art. 7, UDHR1948), right to social security (art. 22, UDHR1948), and right to favorable conditions of work [23(1), UDHR1948]. These rights are also enshrined in the country’s Constitution. Further, Bangladesh acceded to International Covenant on Civil and Political Rights (ICCPR1966) on 6th September 2000 where it undertook to give effect to the inherent right to life (art. 6, ICCPR1966), prohibition on forced labor [art. 8(3) (a) (b), ICCPR1966], equality before law (art. 16, ICCPR1966) amongst other obligations. Thus, it is obligatory for the country to perform its international obligation. Upon acceding to International Covenant on Economic, Social and Cultural Rights (ICESCR1966) on 05th October 1998 Bangladesh is said to have undertaken the responsibility to implement the provision in domestic systems. The ICESCR simply puts a clear obligation to ensure “safe and healthy working conditions” for everyone including the workers [art. 7(b), ICESCR1966]. Furthermore, the Human Rights Council of the United Nations endorsed the Guiding Principles (UNGP, 2011) of Business and Human Rights in its resolution 17/4 of 16 June 2011. The general principles as set out in the GP are grounded in states’ existing obligations to respect, protect, and fulfillment of human rights. This tells us why the principles are not something entirely new but a reformulation of states’ already existing human rights obligations in the context of business operations. The GP at the very outset also adds “the need for rights and obligations to be matched to appropriate and effective remedies when breached (UNGP, 2011).” The guiding principles brought three aspects under the same roof to make a divergence between human rights and corporate responsibility. First, it sets out the state’s duty to protect human rights that solely describes what state’s responsibility is from the foundational aspects. Secondly, it narrates the corporate responsibility to respect human rights and lastly, the remedial measures to be taken in case of violation of such responsibility. Further, corporate responsibility should be understood not as a responsibility to society only. Since the concern is human rights, it should be extended equally to the workers working in the corporation as well. Furthermore, the responsibility does not end with administrative requirements only, it also requires corporations to involve in remedial measures when they contribute to an adverse impact. Such remedial measures must maintain domestic laws and international standards. An extension of the state’s responsibility is seen in ensuring access to remedy. It simply calls for judicial, administrative, legislative, and other appropriate means for ensuring access to remedies that are effective in nature (UNGP 2011). It might be a state-based judicial mechanism, or state-based non-judicial grievance mechanism, or a non-state-based grievance mechanism. The effectiveness criteria for non-judicial grievance mechanisms must be legitimate, accessible, predictable, equitable, transparent, and right-compatible (UNGP 2011). Moreover, the “ILO Declaration on Fundamental Principles and Rights at Work”, (ILO DFPRW, 1998) which was made in 1998 and revised in 2022, is a promise by governments, employers’ organizations, and workers’ organizations to uphold basic human values, which are important to social and economic lives. It affirms the five obligations and commitments inherent in ILO membership, one of which is a “safe and healthy working environment.” Bangladesh is one of the forefront countries to ratify ILO Conventions. For example, she has ratified all [8 (eight)] fundamental Conventions. The last ratification was on 22 March 2022 and will enter into force for Bangladesh on 22 March 2023. She has also ratified the protocol of 2014 to convention no. 29 on 20 January 2022 (ILO, Ratification for Bangladesh). However, she has not ratified the Occupational Safety and Health Convention, 1981; Protocol of 2002 to the Occupational Safety and Health Convention, 1981; Promotional Framework for Occupational Health Convention, 2006; Occupational Safety and Health (Dock Work) Convention, 1979; Occupational Health Services Convention, 1985; Safety and Health in Construction Convention, 1988; Chemicals Convention, 1990; and Prevention of Major Industrial Accidents Convention, 1993, which are important to protect workers from industrial killing. Apart from those conventions, some technical instruments like the Social Security (Minimum Standards) Convention, 1952; Maintenance of Social Security Rights Convention, 1982; Employment Injury Benefits Convention, 1964 are also on the same list. This shows the country’s saddening position on occupational safety-related conventions that are not ratified, let alone considering bringing them into the domestic system. Thus, the escape route of liability is visible in the form of non-ratification. Even if the state has not yet to be ratified many conventions drafted by ILO, it can be said that she has obligations under the covenant and conventions she has ratified. Apart from that, though the current corporate labor policy is deficient, the state has liability under the constitution of Bangladesh, which is the supreme law of the land, to stop industrial killings. Conclusion and Policy Recommendation Though Bangladesh belongs to the common law family, in a real sense, common law does not represent the law of Bangladesh when it’s come to questioning about industrial killing (Solaiman, 2021, a; Solaiman, & Begum, 2014). The country’s legal system generally does not broadly incorporate common law ideas beyond the enunciation of statutory provisions. Therefore, in actual fact, the idea of common law responsibility or common law negligent manslaughter is largely absent (Solaiman, 2021, a; Solaiman, & Begum, 2014). As a result, corporate liability must be established in accordance with the PC1860 by mentioning a separate section about ‘corporate homicide.’ A separate section under the PC1860 may remove the current ambiguity to place arrant corporations under homicide liability, which has also been practiced in Australia and Canada. It also would be pertinent to shed light on the process of the corporate manslaughter justice system practice in the UK, from which common law has been inaugurated. In addition to corporate criminal liability, individuals whose negligent conduct directly relates to industrial killing may also be liable under criminal negligence. A legislative policy may be enacted to hold the corporate boss accountable by default because a corporate boss is the most important person responsible for correcting corporate activities in line with corporate policies. His failure indicates his failure to perform his duty. Besides criminal culpability upon errant corporations and individuals, reparation and compensation policy should be adequate for the victims and their families. For example, housing facilities may be provided to victims and their families. Competent family members of victims may get jobs based on their competency. Disabled workers may get job offers from companies to do alternatively from home. As of state obligation under international law, Bangladesh has been showing a keen interest in upholding international law since its independence in 1971. The very first document of this country from which the country formally stepped into the world as a free and independent nation is the ‘Proclamation of Independence’ (POI) which expressly undertook “all duties and obligations…under the charter of United Nations” [Bangladesh Awami League, (nd)]. However, the current Constitution of the country has taken a slight shift afterward from the POI since it merely talks about the “promotion of international peace, security and solidarity” (art. 25, Constitution of Bangladesh, 1972) that is ‘not judicially enforceable’ (art. 8, Constitution of Bangladesh, 1972) and thus solely depends upon the Government in power. Now, whether the country accepts international law as obligatory within its domestic framework and practices accordingly has become a separate stream of investigation. Further, the Constitution defines ‘law’ as “any Act, ordinance, order, rule, regulation, by-law, notification or other legal instruments, and any custom or usage, having the force of law in Bangladesh (art. 152 Constitution of Bangladesh, 1972).” Applying the literal rule (or strict rule) of interpretation, one would easily find the non-existence of any reference to any particular word that suffices to bring international instruments within the periphery of what is legally recognized as ‘law’ in Bangladesh. Such absence leads to confusion about whether international law has any effect on the domestic court of Bangladesh or not. Therefore, the position of international law in the domestic legal system of Bangladesh has gone and is still going through scholarly debates. Such confusion has traveled court corridors, intellectual debates, and even judicial proceedings. The constitution of Bangladesh does not accommodate international legal instruments as binding and these instruments do not have obligatory force in the country’s courts. This leads to absolutely no forum for judicial redressal for the violation of international law in its domestic court. To remove these gaps, current labor law should insert a legal avenue to bring the corporate bodies who evidently violate the country’s international commitments. In addition to that, the country’s apex court can play a significant role to articulate international law. For example, the Supreme Court of Bangladesh noted observations in two separate cases in this regard. In Hussain Muhammad Ershad vs. Bangladesh (2001) the court opined that though the court is not bound to comply with international law, it may take these laws into consideration when these laws are not in conflict with domestic laws. Also applying different international principles relating to human rights in domestic cases is a growing tendency of the Supreme Court of Bangladesh [Bangladesh National Women Lawyers Association v Government of Bangladesh (2009)].2 In addition to the court’s position about the application of international law, the state has ratified several international instruments; thus, she is responsible for following international policies to stop industrial killings. For example, Bangladesh has ratified ICCPR, so she is responsible for ensuring the inherent right to life. The state has ratified ICESCR, so she is responsible for providing workers with safe and healthy working conditions. She has ratified UDHR, so the state is accountable for ensuring favorable conditions of work, recognition of law, social security, and the right to life. Though Bangladesh is an active member of numerous ILO conventions, she is yet to ratify many ILO conventions pertinent to workplace safety and security. Therefore, this study recommended to ratified and practicing relevant conventions dealing with occupational safety and health. Further, ILO’s strict supervisory rule may play a significant role in following ILO policies while member states send reports on the progress of the implementation of the conventions they have ratified. Complain procedures by other member states, especially by trade unions, should be rewarded and protected to encourage complaints against errant companies and states to ensure workers’ rights. In addition to that, ILO may approve new policies to deal with industrial killing against disobedient companies and states. ILO also may provide technical help to states like Bangladesh for implementing and expanding workers’ rights with concrete safety regulations and dialogues among government, employers, buyers, and trade unions to stop industrial killings. The Guiding Principle for Business and Human Rights is a wonderful instrument under the UN. Under this principle, the state can enforce laws requiring business entities to respect human rights and ensure that other laws relating to business corporations are at par with such positions. For better implementation, the state should build a nexus with business entities. The principles should be kept in mind while legislating business-related laws or formulating policies so that no chance of systematic violation arises due to conflicting provisions of law that obstruct the free enjoyment of human rights. Though UNGP is not naturally binding on states (Öberg, 2006), unless the nature of the resolution warrants it to be binding, she can follow UNGP to draw the attention of the world business community for branding made in Bangladesh. It is to be noted that Bangladesh is one of the involved parties in meeting the 2030 agenda for Sustainable Development Goals (SDGs2030). Among the 17, at least four SDGs deal with workplace safety. For example, ‘ensuring healthy lives and promoting well-being for all (SDGs 3), productive employment, and decent work for all (SDGs 8), ‘building resilient infrastructure, promoting inclusive and sustainable industrialization (SDGs 9), providing access to justice for all, and building effective, accountable, and inclusive institutions at all levels (SDGs 16).’ Therefore, it is very pertinent to think that the state must be stopped industrial killing and be aware of the prediction of SGDs goals to meet by 2030. Limitations of the Study and Future Recommendation The discussion of this study is limited to industrial killing in the formal sector (global garment manufacturing supply chain, food supply chain, and container industry) in Bangladesh. There are other large numbers of formal and informal sectors in Bangladesh where several thousand workers lose their lives each year. Therefore, this manuscript recommends further study to include those sectors. Fund There is no grant or fund for this manuscript. Statements and Declarations Conflict of Interests The author declares that there is no conflict of interest with the content of the manuscript. 1 Tk (Taka) is the currency of Bangladesh. 2 It has been observed that in the absence of domestic laws and principles, the international covenants and treaties signed by the state are to be read with the fundamental rights of the constitution. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abdul Razak, A. (2018). Corporate manslaughter and the attempt to reduce work-related deaths: a comparative study of the United Kingdom, Australia and Malaysia’s legislative framework (Doctoral dissertation). Adelaide University, Australia. ALJAZEERA, (June 5 (2022). ). Fire sweeps through Bangladesh container depot killing dozens. Retrieved August 22, 2022, from https://www.aljazeera.com/gallery/2022/6/5/photos-fire-sweeps-through-bangladesh-container-depot-killing-dozens Bangladesh Awami League, (nd). 10th day of April 1971: The Proclamation Of Independence. Retrieved September 30 (2022). from https://albd.org/articles/news/31163/10th-day-of-April,-1971:The-Proclamation-Of-Independence BBC NEWS (July 9, 2021). Bangladesh factory fire: At least 52 people killed in overnight blaze Retrieved August 20, 2022, from https://www.bbc.com/news/world-asia-57763446 bdnews24.com (July 10, 2021). Factory fire: Sajeeb Group boss Hashem, 7 others placed on 4-day remand. Retrieved August 22, 2022, from https://bdnews24.com/bangladesh/2021/07/10/police-nab-sajeeb-group-ceo-over-factory-fire Chatterjee, P. (July 15, 2021). Fire at Hashem Foods factory in Bangladesh Kills 52. CORPWATCH. Retrieved from https://www.corpwatch.org/article/fire-hashem-foods-factory-bangladesh-kills-52 Clarkson CM Kicking corporate bodies and damning their souls The Modern Law Review 1996 59 4 557 572 10.1111/j.1468-2230.1966.tb02098.x DAWN (June 6, 2022). Bangladesh officials accuse depot operator over blast that killed at least 49. Retrieved August 23, 2022, from https://www.dawn.com/news/1693432/bangladesh-officials-accuse-depot-operator-over-blast-that-killed-at-least-49 Fakhoury, Y. (2019). An Ecofeminist Analysis of the Ready-made Garment Industry in Bangladesh.Honors Undergraduate Theses. 536. https://stars.library.ucf.edu/honorstheses/536. Gikonyo, S. W. (2020). Moving Towards a Paradigm of Corporate Criminal Responsibility for Corporate Manslaughter (Bachelor of Laws Dissertation, Strathmore University). Guzman AT Timothy LM International Soft Law Journal of Legal Analysis 2010 2 1 171 10.1093/jla/2.1.171 Human Rights Advocacy and the History of International Human Rights Standards, (nd). Government Obligations. Retrieved September 30 (2022). from http://humanrightshistory.umich.edu/accountability/obligationr-of-governments/ Commission, L. (June 10, 2022). Corporate Criminal Liability: an options paper. Retrieved October 7, 2022 Library of Parliament, (nd). ‘Bill C-45: An Act to Amend the Criminal Code (Criminal Liability of Organizoffenseations). Retrieved September 28 (2022). from https://lop.parl.ca/staticfiles/PublicWebsite/Home/ResearchPublications/LegislativeSummaries/PDF/37-2/c45-e.pdf Lukashuk II The Principle Pacta Sunt Servanda and the Nature of Obligation Under International Law American Journal of International Law 1989 82 3 513 517 10.2307/2203309 Macpherson DL Extending corporate criminal liability: some thoughts on Bill C-45 Manitoba Law Journal 2004 30 3 253 Mansoor, N., Rudhof-Seibert, T., & Saage-Maaß, M. (2021). Pakistan’s “Industrial 9/11”: Transnational Rights-Based Activism in the Garment Industry and Creating Space for Future Global Struggles. Transnational Legal Activism in Global Value Chains, pp. 107–120, p. 108. Mihailidou EK Antoniadis KD Assael MJ The 319 major industrial accidents since 1917 International review of chemical engineering 2012 4 6 529 540 Ministry of Justice, (nd). A guide to the Corporate Manslaughter and Corporate Homicide Act 2007. Retrieved August 25 (2022). from https://www.justice.gov.uk/downloads/legislation/bills-acts/manslaughter-homicide/guidetomanslaughterhomicide07.pdf Nanda VP Corporate criminal liability in the United States: is a New Approach Warranted? The American Journal of Comparative Law 2010 58 605 630 10.5131/ajcl.2009.0034 Öberg MD The Legal Effects of Resolutions of the UN Security Council and General Assembly in the jurisprudence of the ICJ The European Journal of International Law 2006 16 5 879 891 10.1093/ejil/chi151 Pieth M Radha I Emergence and convergence: corporate criminal liability principles in overview Corporate criminal liability 2011 Dordrecht Springer 3 60 Alo, P. (June 6, 2022). What happens in BM Container Depot, who are the owners. Retrieved August 23, 2022, from https://en.prothomalo.com/bangladesh/what-happens-in-bm-container-depot-who-are-the-owners Rahim, M. M., Kuruppu, S. C., & Islam, M. T. (2022). “Social auditing in the supply chain: business legitimisation strategy rather than a change agent”, Meditari Accountancy Research, Vol. ahead-of-print No. ahead-of-print. Ricketts A Heidi A Corporate liability for manslaughter: the need for further reform Southern Cross University Law Review 2009 13 57 86 Roy, S. K. (2022). Implementation of Safety Measures under Labor Law in a Factory Established in Bangladesh. Shahab, P. (2021). Confined Employment: Exploring Labor Marginalization in Workplace Safety.Transnational Legal Activism in Global Value Chains,237–278. Safety and Rights Society (2020). Bangladesh Workplace Death Report 2020. Retrieved August 25, 2022, from https://safetyandrights.org/wp-content/uploads/2021/08/Death-Report-2020-1.pdf Solaiman, S. M. (2021a). Laws governing manslaughter by Food Safety crimes in the United Kingdom, Australia, Bangladesh, and India: a critical review (47 vol., p. 75). NCJ Int’l L. Solaiman, S. M. (2021). Corporate vehicular manslaughter provisions in the Bangladesh Road Transport Act 2018: a textual comparison with their equivalents in Australia. Criminal Law Forum (32 vol., pp. 125–173). Springer Netherlands. 1. Solaiman, S. M. (2018). ‘Corporate social responsibility and workplace casualties in Bangladesh: an appraisal of Islamic principles as a potential solution’ in N. Hosen(ed), Research Handbook on Islamic Law and Society, 141–158. Solaiman SM Begum A Impunity of frequent corporate homicides by recurrent fires at garment factories in Bangladesh: bangladeshi culpable homicide compared with its equivalents in the United Kingdom and Australia Company Lawyer 2014 35 10 289 309 Solaiman SM Unprecedented factory fire of Tazreen fashions in Bangladesh: revisiting Bangladesh labor laws in light of their equivalents in Australia Hofstra Labor & Employment Law Journal 2013 31 1 125 157 Spencer, V. A. (2022). Retributive Justice: The Restoration of Balance. K. Standish et al. (eds.), The Palgrave Handbook of Positive Peace, 417. Syed R Ethical business strategy between east and west: an analysis of minimum wage policy in the garment global supply chain industry of Bangladesh Asian Journal of Business Ethics 2020 9 2 241 255 10.1007/s13520-020-00108-5 Syed R Mechanisms implementing minimum wage policies and compliance with the ILO’s provisions: the case of Bangladesh’s Garment Global Supply Chain E-Journal of International and Comparative Labor Studies 2020 9 2 56 81 Taqbir Huda. (Jul 14, 2021). We should stop calling acts of corporate manslaughter ‘accidents.’. The Daily Star, ‘JUSTICE’ IN PRACTICE. The Daily Star (July 9, 2021). 49 bodies recovered from Narayanganj factory; death toll now 52 Retrieved August 22, 2022, from https://www.thedailystar.net/news/bangladesh/accidents-fires/news/narayanganj-factory-fire-still-burning-38-remain-missing-2125761 Turpin CC The criminal liability of corporations in English Law The Cambridge Law Journal 1970 28 2 329 331 10.1017/S0008197300101023 Ullah, A. A. (2022). Tazreen Fashions, Rana Plaza, FR Tower, and then Hashem Food, what next? The ineffective OHS regulatory processes of the Bangladesh Government. Middle East Journal of Business. 2022; 17(1): 9–25. Vanpeperstraete B The Rana Plaza Collapse and the case for enforceable agreements with Apparel Brands Transnational legal activism in global value chains 2021 Cham p. 161. Springer 137 169 Wagner G Tort law and human rights Transnational legal activism in global value chains 2021 Cham Springer 209 233 International Instruments ILO Declaration on Fundamental Principles and Rights at Work (ILO DFPRW, 1998). Retrieved September 28, 2022, from https://www.ilo.org/declaration/lang--en/index.htm ILO Ratifications for Bangladesh. Retrieved September 29 (2022). from https://www.ilo.org/dyn/normlex/en/f?p=NORMLEXPUB:11200:0::NO::p11200_country_id:103500 International Covenant on Civil and Political, & Rights (1966). (ICCPR art. 6; 8(3) (a) (b); 16. International Covenant on Economic, Social and Cultural Rights (ICESCR1966), art. 7(b). Sustainable Development Goals (SDG 2030).SDGs3; 8; 9;16. United Nations Guiding Principles (UNGP (2011). of Business and Human Rights. Retrieved September 30, 2022, from https://www.ohchr.org/sites/default/files/documents/publications/guidingprinciplesbusinesshr_en.pdf Universal Declaration of Human Rights (UDHR (1948). art. 3; 5; 6; 7; 22; 23(1). Vienna Convention on the Law of Treaties (1969). (VCLT art. 26. Retrieved September 20, 2022, from https://legal.un.org/ilc/texts/instruments/english/conventions/1_1_1969.pdf. Statutes Bangladesh Labor Act (BLA (2006). ss. 61 (1); 61 (2); 62 (1); 62 (3) (3a); 62 (5); 62 (6); 62 (7); 62 (8); 86 (1); 86 (2); 309 (1)(a); 309 (1)(b); 309 (1)(c); 309 (3); 312. Retrieved from http://bdlaws.minlaw.gov.bd/act-952.html Bangladesh National Building Code (BNBC (2006). rules 2.11.5; 2.11.7; 1.12.2; 1.12.5; 2.4.1 Constitution of the People’s Republic of Bangladesh (1972). art. 8; 25; 152. Retrieved September 30, 2022, from http://bdlaws.minlaw.gov.bd/act-367.html Corporate Manslaughter and Corporate Homicide Act (CMCHA (2007). ss. 1(4)(b); 2; 1(6); 9; 9(5); 10; 18; 20. Retrieved from https://www.legislation.gov.uk/ukpga/2007/19/contents Crimes (2022). (Industrial Manslaughter) Amendment Act [C(IM)AA2003]. Retrieved September 28, from https://www.legislation.act.gov.au/View/a/2003-55/20040301-10529/PDF/2003-55.PDF. Criminal Code, R. S. C. (1985). c C-46. ss. 217.1; 219. Retrieved September 28, 2022, from https://canlii.ca/t/55h62 Factory Rule (Ed.). (1979). (FR rule 41; 42 Fire Safety Act (FSA (2003). s. 7; 30. Retrieved from http://bdlaws.minlaw.gov.bd/act-900.html Penal Code (PC1860), ss. 11; 299; 278; 284–288. Retrieved from http://bdlaws.minlaw.gov.bd/act-11.html Building, S. and Ship Recycling Rule (SBSRR2011), rule 17.7 Legal Decisions Bangladesh National Women Lawyers Association vs Government of Bangladesh (2009) 29 BLD (HCD) 415 Case of Langhforth Bridge 79 ER 9199 (KB 1635) Citizens Life Assurance Company vs Brown (1904) [1904] UKPC 20 Harker vs Britannic Assurance Co. Ltd [1928] 1 KB 766 HL Boulton Co. Ltd vs J Graham and Sons Ltd [1956] 3 All ER 624 Hussain Muhammad Ershad vs Bangladesh [21 BLD (2001) 69] Meridian Global Funds Management Asia Ltd vs The Securities Commission [1995] 3 W.L.R. 413 PC (NZ) R vs Kite [1996] 2 Cr. App. R. (S.) 295 Salomon vs Salomon and Co Ltd. [1897] A.C. 22 HL State vs First National Bank (1872) 2 SD 568	0	PMC9708500	NO-CC CODE	2022-12-01 23:20:30	no	Employ Respons Rights J. 2022 Nov 30;:1-18	utf-8	null	null	null	oa_other
==== Front Appl Res Qual Life Appl Res Qual Life Applied Research in Quality of Life 1871-2584 1871-2576 Springer Netherlands Dordrecht 10129 10.1007/s11482-022-10129-w Article Radicalism and Life Meaningfulness Among Hong Kong Youth http://orcid.org/0000-0003-3278-1633 Cheung Chau-kiu [email protected] grid.35030.35 0000 0004 1792 6846 Department of Social and Behavioural Sciences, City University of Hong Kong, Kowloon Tong, Hong Kong, China 30 11 2022 117 15 9 2022 18 11 2022 © The International Society for Quality-of-Life Studies (ISQOLS) and Springer Nature B.V. 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. According to significance quest theory, radicalism arises from a deficit in life meaningfulness. However, radicalism springs from life meaningfulness, according to meaning maintenance and other principles in existentialist How life meaningfulness predicts radicalism is thus a research question. This study addresses the question with a survey of 4,385 youths in Hong Kong, China. Results indicate that life meaningfulness positively predicted radicalism, slightly more positively when radicalism in the previous year had been higher. Meanwhile, education, employment, and native status positively predicted radicalism and life meaningfulness, showing their homology in meaning sources. These results imply that radicalism prevention needs to reform the meaning basis for life meaningfulness to be socially desirable. Keywords radicalism life meaningfulness existentialist theory significance quest theory meaning maintenance Public Policy Research Funding Scheme of Hong KongSR2020.A1.028 ==== Body pmcRadicalism, an orientation toward social change with antisocial, destructive, or violent means, is a concern for prediction and, thus, mitigation (Elshimi, 2017; Kruglanski et al., 2017). A prediction has suggested that meaningfulness in life prevents radicalism (Koehler & Fiebig, 2019). This suggestion stems from the quest for meaning or significance (Kruglanski & Bertelsen, 2020). Accordingly, significance quest theory posits that meaninglessness in conventional life prompts the quest for meaning in radicalism, which is alternatively meaningful (Jasko et al. 2019). This theory reflects the existentialist theory, a broader theory emphasizing the maintenance of meaning as the impetus of radicalism and other actions (Proulx, 2013; Seidler, 2013). Both theories thus hold that maintaining meaning to achieve meaningfulness positively predicts radicalism. Nevertheless, the positive and negative predictions between meaningfulness and radicalism are confusing and obscure, thus requiring empirical clarification to substantiate and enrich the theories. Such clarification is the objective of this study of Hong Kong Chinese youth, considering the concern about radicalism, life meaningfulness, and their relationship. This objective rests on the rationale for showing that existentialist theory explains the relationship better than significance quest theory. The rationale rests on the fuller view of meaning actualization and maintenance beyond the meaning quest in existentialist theory (Lyng, 2012; Proulx, 2013). Radicalism is a concern because of its risks imposed on society (Elshimi, 2017). The risks include the fomentation of separatism, racism, and terrorism, which is severely and widely perilous and deadly (McGlynn & McDaid, 2019; Rousseau et al., 2021). Such risks stem from conflict, crisis, deviant, hostile, intransigent, intolerant, misadjusted, outrageous, threatening, and willful tendencies inherent in radicalism (J. Berger, 2018; Elshimi, 2017; Kruglanski et al., 2017). Radicalism thus counts as a social disease or plague, which is awful because it is socially transmittable (J. Berger, 2018). Hence, radicalism is rampant due to mediatization, rapidly expanding with technological advancement and networking (McGlynn & McDaid, 2019). These features sustain the prevalence of radicalism among youth (Botticher, 2017; Ghosh et al., 2017). Meanwhile, countering radicalism or deradicalization is a policy priority, as in the rise of profiling, surveillance, risk management, and securitization (Clubb & O’connor, 2019; Frounfelker et al., 2021). Notably, life meaningfulness is a developmental focus for deradicalization (Sukabdi, 2019). Life meaningfulness, which represents the understanding of relations in life, is a quality of life underlying other life qualities, including happiness, health, pleasantness, satisfaction, and non-depression (Proulx 2013; Steger et al., 2009). In the youth, life meaningfulness has consolidated hope, identity, self-efficacy, self-esteem, and development generally and has alleviated anxiety and loneliness (Aviad-Wilchek et al., 2017; To et al., 2014). Life meaningfulness has also been socially beneficial by boosting civic engagement and volunteering in the youth (Duffy & Raque-Bogdan, 2010; Summers & Falco, 2020). The contributions of life meaningfulness rest on its personal coherence and social belongingness, covering purposefulness and significance (Scott & Cohen, 2020; Womick et al., 2022). For instance, life meaningfulness relates life to beauty, goodness, love, and quality (Cooney, 2000; Leontiev, 2013). Life meaningfulness is also the core value in existentialism as the cornerstone for existence (Moynihan et al., 2017). Existentialist meaningfulness treasures action, authenticity, experiencing, freedom, individuality, and responsibility to demonstrate existence (Cooney, 2000). Relating Radicalism and Life Meaningfulness According to significance quest theory and related research, life meaningfulness reduces the quest for meaning through radicalism (Koehler & Fiebig, 2019; Kruglanski & Bertelsen, 2020). The theory maintains the need and value of meaning to necessitate its quest (Kruglanski & Bertelsen, 2020; Renstrom et al., 2020). This quest arises from the deficit or loss of meaningfulness in life through dishonoring, humiliation, injustice, rejection, or shame (Jasko et al., 2019; Webber et al., 2018). Meanwhile, according to the theory, radicalism is a way to gain or restore life meaningfulness, such as by becoming a hero (Jasko et al., 2019; Lobato et al., 2018). The loss of meaning or significance has provoked radicalism in the adult (Koehler & Fiebig, 2019). Thus, the theory endorses the following hypotheses about the youth. Hypothesis 1 Life meaningfulness negatively predicts radicalism. Hypothesis 2 Radicalism positively predicts life meaningfulness. However, life meaningfulness is also likely to champion radicalism, premised on meaning maintenance in existentialist theory, which encompasses significance quest theory (Proulx, 2013; Seidler, 2013). Existentialist theory presumes the availability and value of authenticity, freedom, meaning, responsibility, and their actualization and maintenance of consistency (Koole et al., 2010; Proulx, 2013). Such valued factors, characterizing or demonstrating existence or living, are necessary for adjustment and behaving generally (Temple & Gall, 2018; van Deurzen, 2021). Like significance quest theory, existentialist theory emphasizes life meaningfulness as the impetus. Such emphasis concerns meaning maintenance, expression or manifestation, acting out or actualization, and the quest for meaning (Dilts, 2017; Proulx, 2013). In addition, existentialist theory suggests that the quest for meaning or significance because of the existential vacuum is only part of the story, as the quest can acquire or construct meaning for its actualization (G. Duncan, 2014; Eliason et al., 2010). Hence, existentialist theory posits that meaning maintenance or actualization operates to affirm, enable, or justify action, thus realizing authenticity (Proulx, 2013; van Tilburg & Igou, 2011). Authenticity or actualization to follow or realize the meaning of activity in life has activated youth behavior, including delinquency and violence (Gottschalk, 2020; Ribeaud & Eisner, 2015; Shen et al., 2012; Walters, 2021). According to existentialist theory, such realization is a cause of crime, as associated with radicalism (B. Hunter, 2009; Koehler, 2017). Moreover, radicalism has involved or stemmed from criminal or violent behavior or orientation (Amjad, 2009; J. Berger, 2018; Frounfelker et al., 2021; Koehler & Fiebig, 2019). Crime, radicalism, and violence can thus be the sources of meaning, which is not antisocial according to those exhibiting crime, radicalism, or violence (Barr and Simons, 2015; Sinko et al., 2021; Stern, 2016). Hence, the theory expects that radicalism results from actualizing meanings compatible with or underlying radicalism (Dilts, 2017; Seidler, 2013). Similarly, the theory regards meaningfulness as a cause of violence, which features radicalism (Bogg, 1999; Rousseau et al., 2021). In the youth, such meanings cover activism., agency, idealism, localism, protest, rebelliousness, romanticism, self-determination, and voicing (Cote 2014; Malin et al., 2014; Mukhitov et al., 2022). More generally, radicalism is meaningful to life as it is holy, just, moral, and righteous to address grievances or injustice, according to radicals (Jakubowska et al., 2021; Johnston & Bose, 2020). These meanings underlie radicalism (Elshimi, 2017; G. Tang et al., 2020). Overall, existentialist theory maintains that the existential vacuum of life meaning prompts the quest for meaning from various sources to construct meaning and its actualization and maintenance of authenticity or faith through radicalism (Proulx, 2013; Strenger, 2011). Existentialist theory thus regards radicalism as the acting out of lire meaningfulness in the youth. Hypothesis 1 ’: Life meaningfulness positively predicts radicalism. Existentialist theory also expects that some background characteristics, including education, employment and its achievement, and native status, are the sources of life meaning to facilitate radicalism. Education has cultivated meanings in aspiration, autonomy, disobedience, freedom, incompliance, independence, and ingratitude because of personal performance and merit (Giambra, 2018; Gibson, 2013). These meanings constitute radicalism (Botticher, 2017). Employment has brought meaning to politics and protest to influence and achieve fairness in reward (Brynner, 2000; Kerrissey & Schofer, 2013). These meanings have championed radicalism (G. Tang et al., 2020). Employment achievement, as indicated by earnings or income over education, has instilled meanings into aspiration and self-expression (Hallerod, 2006; Welzel & Inglehart, 2010). These meanings generate radicalism (Jasko et al., 2019; Rousseau et al., 2020). Locally born or native status consolidates meanings in localism and nativism, which privilege the local or natives and reject the alien (Portes & Rumbaut, 2001). Such meanings are the causes of radicalism (M. Chan, 2020; F. Lee 2018). Life meaningfulness has furthermore derived from education, employment, and native status (Kulik et al., 2015; Vogel & Human-Vogel, 2018). In predicting radicalism and life meaningfulness, other background and response characteristics are necessary control factors to minimize confounding. These characteristics include age, gender, marital status, family income per capita, and social desirability, which underlies socially desirable responses. Notably, radicalism has diminished with age, female gender, marriage, and family income (Jakubowska et al., 2021; Wong, Khiatani, & Chui, 2019). Hong Kong Chinese Context The Hong Kong Chinese context is apt for examining radicalism and life meaningfulness according to existentialist theory because of the context’s comparability with the West, where the theory and research originated, and its distinctiveness for scrutinizing the generalizability of the theory and research. Such comparability builds on Westernization and globalization expedited by the context’s entrepôt location and former British rule (K. Cheung, 2015; T. Hui et al., 2018). Hence, Hong Kong is comparable to the Western developed world in economic and sociopolitical systems. Meanwhile, Hong Kong is distinctive in its compact urbanization and cultural mix, combining Chinese and Western cultures (K. Cheung, 2015; T. Hui et al., 2018; Kuang & Kennedy, 2018). Compact urbanization, including transportation and dense housing, can facilitate social mobilization and thus radicalism and their social contagion (Ebers & Stephan, 2022). Meanwhile, the cultural mix can evolve into a clash between Chinese and Western cultures, particularly in sociopolitical arenas, to foment radicalism (Kuang & Kennedy, 2018). Radicalism in Hong Kong, particularly its Westernized youth, typically springs from the clash between Mainland Chinese culture upholding order and Westernized Hong Kong culture prioritizing freedom (K. Cheung, 2015; Zamecki, 2018). The clash consolidates with education, inculcating freedom, localism, or separation from Mainland China’s atheistic dominance (Wong & Au-Yeung, 2018; Zamecki, 2018). Such consolidation heightened radicalism in 2019 concerning the motion to surrender fugitives from Hong Kong to Mainland China when Hong Kong radicals asserted that the surrender was unfree and unjust (G. Tang et al., 2020). Accordingly, they enjoyed the freedom to demonize and offend Mainland China and thus rallied to disrupt support, agencies, and establishments for Mainland China, including the government of Hong Kong. Such radicalism went further to initiate Hong Kong’s separation from China. In response, China’s government inaugurated a national security law for Hong Kong in mid-2020 to restore order and prevent riotous radicalism afterward. Although Hong Kong’s prominent radicalism sprang from the cultural mix, such separatist radicalism is ubiquitous worldwide (Bukit, 2022; Zubok & Chuprov, 2010). Examining radicalism in Hong Kong is thus informative internationally, considering the international position for sharing knowledge (Kuah-Pearce & Fong, 2010). Method The study collected data from a survey of 4,385 Hong Kong Chinese youths aged 18 to 29 from April to August 2020. The survey applied a random sampling procedure to randomly select residential telephone numbers to contact households and select their youth members. This survey, conducted by trained interviewers, ran on weekday evenings and weekend daytime and evenings, achieved a response rate of 66.3%, based on the youth members approached. The sample was adequate for testing a tiny effect size of 0.0575 with 99.999% statistical confidence (i.e., p < .001) and 70% statistical power. Such statistical confidence was appropriate for a rigorous significance test with a large sample (Roberts & Robertson, 1992). Participants The youths had an average of 23.0 years in age and 14.9 years in education (i.e., tertiary level, starting from 13 years) (see Table 1). Among them, 51.9% were female, 80.1% were locally born or natives, 30.1% were students or not working, and 4.8% were married. Table 1 Means/percentages and standard deviations (N = 4,385) Variable Scoring M/% SD Age years 23.0 3.4 age Education years 14.9 2.5 eduy Female 0, 100 51.9 50.0 female Native 0, 100 80.1 40.0 bornhk Not working 0, 100 30.1 45.9 surplus Married 0, 100 4.8 21.3 marry Personal monthly income log(HK$) 8.4 3.0 linc Family monthly income per capita log(HK$) 9.1 0.9 lfincp Social desirability, 2019 0-100 61.7 12.2 desire Life meaningfulness, 2020 0-100 57.9 16.3 Radicalism, 2020 0-100 58.2 17.9 radical Radicalism, 2019 0-100 57.9 17.3 radical0 Note. HK$7.8 = US$1 Measurement The survey measured radicalism, life meaningfulness, and social desirability in 2020 and 2019, given the usefulness of retrospective measurement (Lobato et al., 2018; Tang et al., 2020). These measures, adapted from validated ones, employed multiple rating items to score intensity on a 0-100 scale. Some items employed negative phrasing and required reverse scoring to help reduce the acquiescent bias, which meant rating everything indiscriminately highly (Tourangeau et al., 2000). Radicalism at the time of the survey in 2020 and in 2019 combined seven items, such as “supporting the use of resistance to fight for rights” and “supporting shocking during the demonstration” (Moskalenko & McCauley, 2009). The measure exhibited validity in relationships with protesting, violence, and distrust of the establishment (Koehler & Fiebig, 2019; F. Lee, 2018). A concrete and impressive practice such as radicalism in 2019 should be recallable (Lawson et al., 2020). Social turmoil in 2019 and COVID-19 in 2020 would also be distinguishing temporal landmarks (Drasch & Matthes, 2011; Gaskell et al., 2000). The composite reliability was 0.965 and 0.957 for radicalism in 2020 and 2019, respectively. Life meaningfulness in the month before the survey in 2020 combined four items such as “living a full life” and “living meaningfully” (Schnell, 2009). The measure had demonstrated validity through its relationships with life perception, action, goal setting, meaning sourcing, satisfaction, and positive mood as opposed to anxiety and depression. The composite reliability was 0.824. Social desirability in 2019 combined four items such as “being ready to help others” and “willing to admit mistakes” (Paulhus, 1991). Its reliability, as usual, was relatively low (0.560) but adequate for a control variable (Mundia, 2011). Analysis The analysis started with confirmatory factor analysis to validate and create trait factors for subsequent structural relation analysis for hypothesis testing. The confirmatory factor analysis confirmed four trait factors, radicalism in 2020 and 2019, life meaningfulness, and social desirability, given the method factor representing acquiescent rating (Podsakoff et al., 2003). The analysis could generate robust estimates with the robust maximum likelihood estimation via Mplus (Muthen & Muthen, 2006). The analysis could demonstrate factorial or structural validity with the substantial convergence of items to their respective trait factors, which were discriminable from the method factor. As such, the analysis generated the trait factors free of the bias of the method factor for structural relation analysis. The latter analysis predicted radicalism in 2020, life meaningfulness, radicalism in 2019, social desirability, and background characteristics. In predicting radicalism in 2020, life meaningfulness and its interactions with radicalism, social desirability, and background characteristics were additional predictors. The inclusion of the interactions revealed conditioning or variation in the effects of life meaningfulness on radicalism in 2020. All interactions were products based on the standard scores of interacting variables to lessen unstable estimation due to multicollinearity (Dunlap & Kemery, 1987). Results On average, radicalism, life meaningfulness, and social desirability were at a moderate level (M = 57.9–61.7, see Table 1, based on raw scores rather than factor scores). Hence, radicalism was substantial enough for concern. Confirmatory Factor Analysis Confirmatory factor analysis verified the trait factors of radicalism in 2020 and 2019, life meaningfulness, and social desirability, given the method factor. Specifically, the trait factors exhibited convergent validity with high loadings (λ = 0.478-0.872 on radicalism in 2020, 0.481-0.824 on radicalism in 2019, 0.649-0.807 on life meaningfulness, and 0.372-0.585 on social desirability, see Table 2). The trait factors indicated discriminant validity from the method factor, particularly with lower and mostly negligible loadings on the method factor. Overall, the traits manifested factorial validity, combining convergent and discriminant validity, from the good fit of the analysis (L2(235) = 1338, SRMR = 0.039, RMSEA = 0.033, CFI = 0.955). The analysis was thus adequate to create trait factor scores that were free of the acquiescent bias for structural equation analysis. Table 2 Standardized factor loadings Factor/Indicator Trait Method Radicalism, 2020 0.925 Supporting the use of resistance to fight for rights 0.718 0.130 RADICAL1 Supporting the use of aggressive political actions 0.799 0.147 RADICAL2 Supporting the violence against others during demonstrations 0.801 0.133 RADICAL4 Supporting shocking during the demonstration 0.871 0.103 RADICAL5 (not) Hating to use violence to demonstrate 0.478 − 0.491 RADICAL6 Fighting the police during the demonstration 0.872 0.134 RADICAL7 Supporting other radical actions 0.863 0.098 RADICAL9 Radicalism, 2019 0.887 Supporting the use of resistance to fight for rights 0.734 0.165 RADICA01 Supporting the use of aggressive political actions 0.698 0.170 RADICA02 Supporting the violence against others during demonstrations 0.686 0.115 RADICA04 Supporting shocking during the demonstration 0.791 0.169 RADICA05 (not) Hating to use violence to demonstrate 0.481 − 0.536 RADICA06 Fighting the police during the demonstration 0.692 0.195 RADICA07 Supporting other radical actions 0.824 0.106 RADICA09 Life meaningfulness, 2020 0.824 Living a full life 0.649 − 0.042 MEAN1 Living meaningfully 0.677 − 0.020 MEAN2 Planning your life 0.807 0.035 MEAN3 Having a life direction 0.797 − 0.027 MEAN4 Social desirability, 2019 0.560 Being ready to help others 0.585 0.228 DESIRE1 Willing to admit mistakes 0.537 0.265 DESIRE6 Treating people with different opinions with courtesy 0.372 0.249 DESIRE7 Having confidence in your own judgment 0.403 0.232 DESIRE8 Structural Relation Analysis Life meaningfulness in the month before the survey did not significantly predict radicalism in 2020 after controlling for radicalism in 2019 and background and response characteristics (β = .019, see Table 3). By contrast, life meaningfulness significantly predicted radicalism in 2020, without controlling for radicalism in 2019 (β = .199, p < .001). This prediction suggested that life meaningfulness maintained but not raised radicalism. Furthermore, the square of life meaningfulness significantly predicted radicalism in 2020, even after controlling for radicalism in 2019 (β = .051, see Table 4). The significant effects supported Hypothesis 1’ based on existentialist theory. Particularly, the quadratic effect of the square life meaningfulness indicated that radicalism was high when life meaningfulness was extremely high. That is, extreme life meaningfulness positively predicted radicalism. Hypothesis 1’ rather than Hypothesis 1 thus attained support. Table 3 Standardized main effects Radicalism Meaning 2019 2020 2020 Predictor (1) (2) (1) (2) Native 0.089* 0.103* 0.016 − 0.038 − 0.060* bornhk Not working − 0.184* − 0.192* − 0.020 − 0.128* − 0.060 surplus Married − 0.183* − 0.158* 0.013 − 0.131* − 0.064* marry Female − 0.006 − 0.005 0.000 0.011 0.014 female Age − 0.239* − 0.239* 0.002 − 0.065 − 0.008 age Education 0.253* 0.226* 0.008 0.118* − 0.001 eduy Personal income − 0.027 − 0.033 − 0.015 0.048 0.069 linc Personal income over education 0.116* 0.117* 0.021 − 0.024 − 0.082 return Family income per capita − 0.062* − 0.010 0.040* 0.029 0.061* lfincp Social desirability − 0.220* 0.343* DES Radicalism, 2019 1.017* 0.216* RAD0 Life meaningfulness 0.019 MEA R 2 0.173 0.152 0.842 0.068 0.291 Note. Tolerance > 0.446 * p < .001 Table 4 Alternately additional standardized interaction effects on radicalism, 2020 Predictor Radicalism Meaningfulness × Social desirability 0.023 MEA_DES Meaningfulness × Radicalism, 2019 0.029* MEA_RAD0 Meaningfulness squared 0.051* MEA_MEA Meaningfulness × Native 0.006 MEA_bornhk Meaningfulness × Not working − 0.019 MEA_surplus Meaningfulness × Married − 0.021 MEA_marry Meaningfulness × Female 0.000 MEA_female Meaningfulness × Age − 0.016 MEA_age Meaningfulness × Education − 0.007 MEA_eduy Meaningfulness × Personal income 0.006 MEA_linc Meaningfulness × Personal income over education 0.024 Mea_return Meaningfulness × Family income per capita 0.000 MEA_lfincp Note. Tolerance > 0.547 * p < .001 Radicalism in 2019 significantly predicted life meaningfulness in 2020 after controlling for background and response characteristics (β = 0.216, see Table 3). The positive effect supported Hypothesis 2. The interaction or conjunction of radicalism in 2019 and life meaningfulness additionally showed a significant but slight positive effect on radicalism in 2020 (β = 0.029, see Table 4). This effect suggested that life meaningfulness associated with or based on prior radicalism perpetuated radicalism. The effect thus implied the positive reinforcement of radicalism. Meanwhile, interactions between life meaningfulness and other background or response characteristics did not reveal significant effects on radicalism in 2020 additionally. Some background characteristics significantly predicted radicalism and life meaningfulness. Accordingly, radicalism and life meaningfulness were lower when married or not working and were higher when more highly educated (see Table 3). These effects indicated that radicalism and life meaningfulness were homologous, based on the same meaning sources. By contrast, native status (β = 0.089 & 0.103, see Table 3) and personal income over education, representing employment achievement (β = 0.116 & 0.117), displayed significant positive effects on radicalism in 2019 and 2020, but not on life meaningfulness. Meanwhile, age and family income per capita exhibited significant negative effects on radicalism in 2019 (β = − 0.239 & − 0.062). Notably, age displayed the same negative effect on radicalism in 2019 and 2020 (β = − 0.239). Overall, the positive effects of education, employment, employment achievement, and native status on radicalism registered the contribution of meanings. Discussion Existentialist theory demonstrates its value from the analysis of radicalism and life meaningfulness. The analysis supports hypotheses 1 and 2 that radicalism in 2019 contributed to life meaningfulness, which predicted radicalism in 2020. Notably, life meaningfulness was more predictive when squared or extreme or derived from radicalism in 2019. In addition, education, employment and its achievement, and native status predicted radicalism. Education, and employment also predicted life meaningfulness. These predictions testify the central role of life meaningfulness in relating radicalism and meaning sources, which sustained radicalism and life meaningfulness. Hence, radicalism and life meaningfulness are homologous, commonly arising from some meaning sources. According to existentialist theory, the display of meaning and radicalism rests on the youth’s individualistic interpretation and meaning creation and maintenance (Seidler, 2013; Proulx, 2013). The youth thus derives life meanings from education, employment, and even radicalism to perpetuate radicalism. Additionally, the youth finds meanings in native status and employment achievement to actualize the meanings through radicalism. The central role of meanings champions existentialist theory, which explains the contribution of life meaningfulness to radicalism with some refinement. Besides predicting radicalism, life meaningfulness was more predictive by its squared or extreme form. Such enhanced prediction reflects that extreme life meaningfulness is meaningful and conducive to rationalism, which is similarly extreme (Elshimi, 2017; Ghosh et al., 2017). The reflection testifies consistent meaning maintenance in existentialist theory (Proulx, 2013). The coupling of life meaningfulness and radicalism in 2019 added a slight positive effect on radicalism in 2020. This coupling, reflecting the contribution of radicalism in 2019 to life meaningfulness, signifies the meaningfulness of radicalism. The latter meaningfulness thus boosts radicalism, according to meaning maintenance or actualization in existentialist theory (B. Hunter, 2009; Lester, 1995; Proulx, 2013). Such meaningfulness also represents a reward to radicalism to reinforce radicalism, as postulated in existentialist theory (Bogg, 1999). Native status and employment achievement indicated by personal income over education enhanced radicalism but not life meaningfulness, whereas education, and employment championed radicalism and life meaningfulness. Native or locally born status particularly incubates localism and nativism, which are the causes of radicalism against Mainland China (Ng & Kennedy, 2019; Y. Wang, 2019). Herein, localism or nativism regards Mainland China as alien and Mainlandizing or imposing Mainland China’s form on Hong Kong to erode its uniqueness (Adorjan et al., 2021). Similarly, employment achievement or earning power consolidates meaning in capitalism, which is antithetical to the socialist bedrock in Mainland China (Bernstein, 2012). According to existentialist theory, such antagonism in meaning fuels radicalism against the Mainland (Seidler, 2013). Age, marriage, and family income per capita diminished radicalism, and marriage additionally lessened life meaningfulness. The diminishment stems from the reduced meaning of crime with age, considering the criminal feature of radicalism (Frounfelker et al., 2021; Lyons, 2008). Similarly, marriage tends to dampen meanings in collective action, crime, and violence (Bjerk, 2009; Wright et al., 2015). The features characterize radicalism (Frounfelker et al., 2021; Rousseau et al., 2020). By contrast, youth or teen marriage reflects and incurs life problems because of inadequate preparation (Cotterell, 2007; Kuhl et al., 2012). Such problems and inadequate preparation have eroded life meaningfulness (Baumeister et al., 2013; Shin et al., 2018). Meanwhile, family income tends to dilute meanings in antisocial behavior, delinquency, and violence (Chen & Raine, 2018; Vander Ven et al., 2001). Such meanings transpire in radicalism (Frounfelker et al., 2021; Koirikivi et al., 2021). The effects of age, marriage, and family income are hence explicable with existentialist theory. Existentialist theory presents an understanding of the impetus of meaningfulness more adequately and thoroughly than significance quest theory. Notably, life meaningfulness positively rather than negatively predicts radicalism, which thus results from meaning actualization or maintenance instead of the meaning quest. This prediction arises because the meaning quest through education, employment, and residency already achieves life meaningfulness to justify radicalism. Accordingly, Westernization through education, native status, and other socialization processes in Hong Kong tends to foster youth’s life meaningfulness to oppose Mainlandization radically. Such socialized meaningfulness further makes radicalism as meaning actualization meaningful to reinforce life meaningfulness. This reinforcement builds on authenticity or meaning consistency (Crewe & Lippens, 2009; Proulx, 2013). Hence, existentialist theory incorporates significance quest theory and specifies the latter’s application in the early phase of life meaningfulness development. According to existentialist theory, such phasing proceeds with the meaning quest, creation, actualization, and maintenance (Proulx, 2013; van Tilburg & Igou, 2011). As such, significance quest theory applies when radicalism reinforces life meaningfulness. Limitations and Future Research The study’s findings are inconclusive because of limitations in cross-sectional survey design, self-report retrospective measurement, and the single Chinese society of Hong Kong. The survey design relying on retrospective measurement cannot ascertain the temporal order required for prediction and causality. Moreover, self-report measurement cannot ensure validity interpersonally and objectively. The single society, meanwhile, cannot bolster generalizability over the world. These limitations necessitate future research to corroborate the present findings. Notably, future research needs to enhance its designs to warrant causality and internal validity, measurements to achieve their validity objectively, and sampling to sustain generalizability. Such designs need to employ repeated measurement through a survey and/or experiment to strengthen ecological and internal validity, respectively. Notably, an experiment manipulating life meaningfulness can ascertain the short-term impact of the contrived meaningfulness. Meanwhile, the survey can integrate measurements from multiple sources, including informants additional to the youth, to raise their validity objectively. Future research also needs to expand its sampling frames to cover diverse societies representatively. Such coverage can enable a contextual analysis to tap conditions for generalizing effects across societies. Future research can also substantiate existentialist theory with a detailed analysis of its meaning actualization, creation, and maintenance processes (Proulx, 2013; van Tilburg & Igou, 2011). Such analysis can demonstrate mediation between meaning sources, radicalism, and meaningfulness to indicate meaning creation, mediation between meaningfulness and radicalism and its defense or restoration to unfold meaning actualization and maintenance, respectively. Moreover, the analysis needs to elaborate meanings conducive to life meaningfulness and radicalism, including freedom and resistance to unfreedom imposed by Mainland China. At this junction, existentialist factors, including authenticity, freedom, responsibility, and willfulness, will reveal their moderating roles in future research (Bogg 1999; Crewe & Lippen, 2009). Implications To the youth, radicalism is socially undesirable and thus worthy of prevention (see Table 3). Such prevention needs to disconnect radicalism from life meaningfulness and its meaning sources, including education, employment, and native status. The disconnection can build on prioritizing socially desirable or prosocial meanings and downplaying radical or antisocial meanings. Crucial for the prioritizing are the meanings of guilt, hope, humility, law, open-mindedness, safety, science or rationality and truth, security, shame, social cohesion, harmony, and integration, and trust (Cosic et al., 2018; Elshimi, 2017; Sukabdi, 2019). Simultaneously, the downplaying applies to anger, hatred, and humiliation (Cosic et al., 2018). The prioritizing and downplaying rely on education, abd employment reformed to cultivate and realize meanings about collaboration, inclusiveness, moderation, and respect, as opposed to absolutism (Ghosh et al., 2017; Richter et al., 2020; Susilo & Dalimunthe, 2019). More specifically, meanings about aging or maturation, marriage, and family income, which discouraged rationalism, are worthwhile to advance personal, interpersonal, and family development concerning reasoning, mutuality, and sharing in youth (Cotterell, 2007; M. Wen et al., 2020). Overall, the prevention renders radicalism and its meaning sources meaningless in life. Such prevention particularly needs to target youths with higher education, employment, earnings, native status, younger age, or lower family income, considering the targeting approach to deradicalization (Ebers & Stephan, 2022). Deradicalization education is thus imperative to remove the educational basis for radicalism and its ground for life meaningfulness (Susilo & Dalimunthe, 2019). Declarations Conflict of interest Authors declare no conflict of interest. Human research Authors declare that the study complied with human research ethics. Informed consent Authors declare that respondents showed their informed consent to the survey. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Adorjan, M., Khiatani, P. V., & Chui, W. H. (2021). The rise and ongoing legacy of localism as collective identity in Hong Kong: Resinicisation anxieties and punishment of political dissent in the post-colonial era. Punishment & Society, 146247452110403, doi:10.1177/14624745211040308. Amjad N Wood AM Identifying and changing the normative beliefs about aggression which lead young muslim adults to join extremist anti-semitic groups in Pakistan Aggressive Behavior 2009 35 514 519 10.1002/ab.20325 19790255 Aviad-Wilchek Y Ne’eman-Haviv V Malka M Connection between suicidal ideation, life meaning, and Leisure Time Activities Deviant Behavior 2017 38 621 632 10.1080/01639625.2016.1197590 Barr AB Simons RL Different dimensions, different mechanisms? Distinguishing relationship status and quality effects on desistance Journal of Family Psychology 2015 29 360 370 10.1037/fam0000079 25915091 Berger JM Extremism 2018 Cambridge, MA MIT Bernstein A Capitalist solutions: a philosophy of american moral dilemmas 2012 New Brunswick, NJ Transaction Bogg RA Dostoevsky’s enigmas Aggression & Violent Behavior 1999 4 371 386 10.1016/s1359-1789(98)00010-x Botticher A Towards academic consensus definitions of radicalism and extremism Perspectives on Terrorism 2017 11 4 73 77 Brynner J Crockett LJ Silbereisen RK Social change and the sequence of developmental transitions Negotiating adolescence in times of social change 2000 Cambridge, UK Cambridge University Press 89 103 Bukit ML Bangsamoro separatism and classical counterinsurgency: reconsidering revolutionary war in the Southern Philippines Studies in Conflict & Terrorism 2022 10.1080/1057610x.2021.2016541 Chan M Partisan selective exposure and the perceived effectiveness of contentious political actions in Hong Kong Asian Journal of Communication 2020 30 279 296 10.1080/01292986.2020.1781219 Chen FR Raine A Effects of harsh parenting and positive parenting practices on youth aggressive behavior: the moderating role of early pubertal timing Aggressive Behavior 2018 44 18 28 10.1002/ab.21720 28699265 Cheung KCK Child poverty in Hong Kong single-parent families Child Indicators Research 2015 8 517 536 10.1007/s12187-014-9256-4 Clubb G O’connor R Understanding the effectiveness and desirability of de-radicalisation: how de-radicalisation is framed in the Daily Mail British Journal of Politics & International Relations 2019 21 349 366 10.1177/1369148118819067 Cooney W The quest for meaning: a journey through philosophy, the arts, and creative genius 2000 Lanham, MD University Press of America Cosic K Srbljinovi A Popovi S Emotionally based strategic communications in the process of societal deradicalization International Journal of Strategic Communication 2018 12 196 214 10.1080/1553118X.2018.1426587 Cote J Youth studies: fundamental issues and debates 2014 Houndmills, UK Palgrave Cotterell J Social networks in youth and adolescence 2007 2 London Routledge Crewe D Lippens R Lippens R Crewe D Existentialism: Freedom, being and crime Existentialist criminology 2009 Abingdon, UK Routledge-Cavendish 1 11 Dilts A Justice as failure Law Culture & the Humanities 2017 13 184 192 10.1177/1743872115623518 Drasch K Matthes B Improving retrospective life course data by combining modularized self-reports and event history calendars: experiences from a large scale survey Quality & Quantity 2011 47 817 838 10.1007/s11135-011-9568-0 Duffy RD Raque-Bogdan TL The motivation to serve others: exploring relations to career development Journal of Career Assessment 2010 18 250 265 10.1177/1069072710364791 Duncan G Politics, paradoxes and pragmatics of happiness Culture Theory & Critique 2014 55 79 95 10.1080/14735784.2013.782676 Dunlap WP Kemery ER Failure to detect moderating effects: is multicollinearity the problem? Psychological Bulletin 1987 102 418 420 10.1037/0033-2909.102.3.418 Ebers A Stephan LT Evaluating an interactive film on the prevention of political radicalization Journal for Deradicalization 2022 30 169 222 Eliason GT Samide JL Williams G Lepore MF Existential theory and our search for spirituality Journal of Spirituality in Mental Health 2010 12 86 111 10.1080/19349631003730068 Elshimi MS De-radicalisation in the UK prevent strategy: security, identity, and Religion 2017 Abington, UK Routledge Frounfelker RL Frissen T Miconi D Lawson J Brennan RT d’Haenens L Rousseau C Transnational evaluation of the Sympathy for violent radicalization scale: measuring population attitudes toward violent radicalization in two countries Transcultural Psychiatry 2021 58 669 682 10.1177/13634615211000550 33990162 Gaskell GD Wright DB O’Muircheartaigh CA Telescoping of landmark events Public Opinion Quarterly 2000 64 77 89 10.1086/316761 10810077 Ghosh R Chan WYA Manuel A Dilimulati M Can education counter violent religious extremism? Canadian Foreign Policy Journal 2017 23 117 133 10.1080/11926422.2016.1165713 Giambra LM Character strengths: Stability and change from a military college education Military Psychology 2018 30 598 608 10.1080/08995605.2018.1522925 Gibson JL Measuring political tolerance and general support for pro-civil liberties policies Public Opinion Quarterly 2013 77 S1 45 68 10.1093/poq/nfs073 Gottschalk P Theory of convenience: determinants of white-collar crime intention Deviant Behavior 2020 41 1431 1439 10.1080/01639625.2019.1624101 Hallerod B Sour grapes: relative deprivation adaptive preferences and the measurement of poverty Journal of Social Policy 2006 35 371 390 10.1017/S0047279406009834 Hui T Yuen M Chen G Career-related filial piety and career adaptability in Hong Kong university students Career Development Quarterly 2018 66 358 370 10.1002/cdq.12156 Hunter B Lippens R Crewe D White-collar offenders after the fall from grace: Stigma, blocked paths and resettlement Existentialist criminology 2009 Abingdon, UK Routledge-Cavendish 145 168 Jakubowska U Kozeniowski K Radkiewicz P Social worldviews and personal beliefs as risk factors for radicalization: a comparison between Muslims and non-Muslims living in Poland International Journal of Conflict & Violence 2021 15 1 14 Jasko K Szastok M Grzymala-Moszczynska J Maj M Kruglanski AW Rebel with a cause: personal significance from political activism predicts willingness to self-sacrifice Journal of Social Issues 2019 75 314 349 10.1111/josi.12307 Johnston N Bose S Violence, power and meaning: the moral logic of terrorism Global Policy 2020 11 315 325 10.1111/1758-5899.12784 Kerrissey J Schofer E Civic and political participation: Union membership and political participation in the United States Social Forces 2013 91 895 928 10.1093/sf/sos187 Koehler D Fiebig V Knowing what to do: academic and practitioner understanding of how to counter violent radicalization Perspectives on Terrorism 2019 13 44 62 Koirikivi P Benjamin S Hietajärvi L Kuusisto A Gearon L Resourcing resilience: educational considerations for supporting well-being and preventing violent extremism amongst finnish youth International Journal of Adolescence & Youth 2021 26 553 569 10.1080/02673843.2021.2010578 Koole SL McCullough ME Kuhl J Roelofsma PHMP Why religion’s burdens are light: from religiosity to implicit self-regulation Personality & Social Psychology Review 2010 14 95 107 10.1177/1088868309351109 19949046 Kruglanski AW Bertelsen P Life psychology and significance quest: a complementary approach to violent extremism and counter-radicalisation Journal of Policing Intelligence & Counter Terrorism 2020 15 1 22 10.1080/18335330.2020.1725098 Kruglanski AW Jasko K Chernikova M Dugas M Webber D To the fringe and back: violent extremism and the psychology of deviance American Psychologist 2017 72 217 230 10.1037/amp0000091 28383975 Kuah-Pearce KE Fong YC Identity and sense of belonging in post-colonial education in Hong Kong Asia Pacific Journal of Education 2010 30 433 448 10.1080/02188791.2010.519691 Kuang X Kennedy K Hong Kong adolescents’ future civic engagement: do protest activities count? Compare 2018 50 428 446 10.1080/03057925.2018.1535268 Kuhl DC Warner D Wilczak A Adolescent violent victimization and precocious union formation Criminology 2012 50 1089 1127 10.1111/j.1745-9125.2012.00288.x 24431471 Lawson A Tan AC Naylor J Harris IA Is retrospective assessment of health-related quality of life valid? BMC Musculoskeletal Disorders 2020 21 415 10.1186/s12891-020-03434-8 32605559 Lee FLF Internet alternative media, movement experience, and radicalism: the case of post-Umbrella Movement Hong Kong Social Movement Studies 2018 17 219 233 10.1080/14742837.2017.1404448 Leontiev DA Personal meaning: a challenge for psychology Journal of Positive Psychology 2013 8 459 470 10.1080/17439760.2013.830767 Lester D Theories of personality: a systems approach 1995 Washington, DC Taylor Lobato RM Moya M Moyano M Trujillo HM From oppression to violence: the role of oppression, radicalism, identity, and cultural intelligence in violent disinhibition Frontiers in Psychology 2018 9 1505 10.3389/fpsyg.2018.01505 30177903 Lyng S Existential transcendence in late modernity: Edgework and hermeneutic reflexivity Human Studies 2012 35 401 414 10.1007/s10746-012-9242-0 Lyons CJ Individual perceptions and the social construction of hate crimes: a factoiral survey Social Science Journal 2008 45 107 131 10.1016/j.soscij.2007.12.013 Malin H Reilly TS Quinn B Moran S Adolescent purpose development: exploring empathy, discovering roles, shifting priorities, and creating pathways Journal of Research on Adolescence 2014 24 186 199 10.1111/jora.12051 McGlynn C McDaid S Radicalisation and higher education: students’ understanding and experiences Terrorism & Political Violence 2019 31 559 576 10.1080/09546553.2016.1258637 Moskolenko S McCauley C Measuring political mobilization: the distinction between activism and radicalism Terrorism & Political Violence 2009 21 239 260 10.1080/09546550902765508 Moynihan AB Igou ER van Tilburg WAP Free, connected, and meaningful: Free will beliefs promote meaningfulness through belongingness Personality & Individual Differences 2017 107 54 65 10.1016/j.paid.2016.11.006 Mundia L Social desirability, non-response bias and reliability in a long self‐report measure: illustrations from the MMPI‐2 administered to Brunei student teachers Educational Psychology 2011 31 207 224 10.1080/01443410.2010.545049 Muthen LK Muthen BO Mplus user’s guide 2006 Los Angeles, CA Muthen & Muthen Ng HY Kennedy KJ Localist groups and populist radical regionalism in Hong Kong China 2019 17 111 134 10.1353/chn.2019.0045 Paulhus DL Robinson JP Shaver PR Wrightsman LS Measurement and control of response bias Measures of personality and social psychological attitudes, Vol.1: measures of social psychological attitudes 1991 San Diego, CA Academic Press 17 60 Portes A Rumbaut RG Legacies: the story of the immigrant second generation 2001 Berkeley, CA University of California Press Proulx T Hicks JA Routledge C Meaning maintenance model: introducing Soren to existential social psychology The experience of meaning in life: classical perspectives, emerging themes, and controversies 2013 Dordrecht, Netherlands Springer 47 59 Renstrom EA Bäck H Knapton HM Exploring a pathway to radicalization: the effects of social exclusion and rejection sensitivity Group Processes & Intergroup Relations 2020 23 1204 1229 10.1177/1368430220917215 Ribeaud D Eisner M The nature of the association between moral neutralization and aggression: a systematic test of causality in early adolescence Merrill Palmer Quarterly 2015 61 68 84 10.13110/merrpalmquar1982.61.1.0068 Richter J Faragó F Swadener BB Roca-Servat D Eversman KA Tempered radicalism and intersectionality: Scholar‐activism in the neoliberal university Journal of Social Issues 2020 76 1014 1035 10.1111/josi.12401 Roberts D Robertson PJ Positive-findings bias and measuring methodological rigor in evaluations of organization development Journal of Applied Psychology 1992 77 918 925 10.1037/0021-9010.77.6.918 Rousseau C Oulhote Y Lecompte V Mekki-Berrada A Hassan G Hage E Collective identity, social adversity and college student sympathy for violent radicalization Transcultural Psychiatry 2021 58 654 668 10.1177/1363461519853653 31180826 Schnell T The sources of meaning and meaning in Life Questionnaire (SoMe): relations to demographics and well-being Journal of Positive Psychology 2009 4 483 499 10.1080/17439760903271074 Scott MJ Cohen AB Surviving and thriving: fundamental social motives provide purpose in life Personality & Social Psychology Bulletin 2020 46 944 960 10.1177/0146167219883604 31722617 Seidler M The beauty and the beast: Jean-Paul Sartre and the Baader-Meinhof Gang Terrorism & Political Violence 2013 25 597 605 10.1080/09546553.2013.814501 Shen CT Chiu MYL Gao J Predictors of dating violence among chinese adolescents: the role of gender-role beliefs and justification of violence Journal of Interpersonal Violence 2012 27 1066 1089 10.1177/0886260511424497 22328647 Sinko L Munro-Kramer M Conley T Saint Arnault D Internalized messages: the role of sexual violence normalization on meaning-making after campus sexual violence Journal of Aggression Maltreatment & Trauma 2021 30 565 585 10.1080/10926771.2020.1796872 Steger MF Oishi S Kashdan TB Meaning in life across the life span: levels and correlates of meaning in life from emerging adulthood to older adulthood Journal of Positive Psychology 2009 4 43 52 10.1080/17439760802303127 Strenger C The fear of insignificance: searching for meaning in the twenty-first century 2011 Houndmills, UK Palgrave Sukabdi ZA Gunaratina R Hussin S Psychological rehabilitation for ideology-based terrorism offenders Deradicalisation and terrorist rehabilitation: a framework for police making and implementation 2019 Abingdon, UK Routledge 95 116 Summers JJ Falco LD The development and validation of a new measure of adolescent purpose Journal of Experimental Education 2020 88 47 71 10.1080/00220973.2019.1575178 Susilo S Dalimunthe R Moderate southeast asian islamic education as a parent culture in deradicalization: urgencies, strategies, and challenges Religions 2019 10 45 10.3390/rel10010045 Tang G Hung E Au-Yeung HK Yuen S Politically motivated internet addiction: relationships among online information exposure, internet addiction, FOMO, psychological well-being, and radicalism in massive political turbulence International Journal of Environmental Research & Public Health 2020 17 633 10.3390/ijerph17020633 31963755 Temple M Gall TL Working through existential anxiety toward authenticity: a spiritual journey of meaning making Journal of Humanistic Psychology 2018 58 168 193 10.1177/0022167816629968 To SM Tam HL Ngai SSY Sung WL Sense of meaningfulness, sources of meaning, and self-evaluation of economically disadvantaged youth in Hong Kong: implications for youth development programs Children & Youth Services Review 2014 47 352 361 10.1016/j.childyouth.2014.10.010 Tourangeau R Rips LJ Rasinski K The psychology of survey response 2000 Cambridge Cambridge University Press van Deurzen E Rising from existential crisis: life beyond calamity 2021 Monmouth, UK PCCS van Tilburg WAP Igou ER On boredom and social identity Personality & Social Psychology Bulletin 2011 37 1679 1691 10.1177/0146167211418530 21844095 Vander Ven TM Cullen FT Carrozza MA Wright JP Home alone: the impact of maternal employment on delinquency Social Problems 2001 48 236 257 10.1525/sp.2001.48.2.236 Vogel FR Human-Vogel S The relevance of identity style and professional identity to academic commitment and academic achievement in a higher education setting Higher Education Research & Development 2018 37 620 634 10.1080/07294360.2018.1436526 Walters GD Explaining the drug-crime connection with peers, proactive criminal thinking and victimization: systemic cognition, social learning, and person proximity mechanisms Psychology of Addictive Behaviors 2021 35 366 376 10.1037/adb0000606 32378906 Wang Y Local identity in a global city: Hong Kong localist movement on social media Critical Studies in Media Communication 2019 36 419 433 10.1080/15295036.2019.1652837 Webber D Babush M Schori-Eyal N Vazeou-Nieuwenhuis A Hettiarachchi M Bélanger JJ Gelfand MJ The road to extremism: field and experimental evidence that significance loss-induced need for closure fosters radicalization Journal of Personality & Social Psychology 2018 114 270 285 10.1037/pspi0000111 28872332 Welzel C Inglehart R Agency, values, and well-being: a human development model Social Indicators Research 2010 97 43 63 10.1007/s11205-009-9557-z 20390031 Wen M Wang W Wan N Su D Family income and student educational and cognitive outcomes in China: exploring the material and psychosocial mechanisms Social Sciences 2020 9 225 223 10.3390/socsci9120225 Moskolenko, S., & McCauley, C. (2009). Measuring political mobilization: The distinction between activism and radicalism. Terrorism & Political Violence 21, 239-260. Womick J Woody B King LA Religious fundamentalism, right-wing authoritarianism, and meaning in life Journal of Personality 2022 90 277 293 10.1111/jopy.12665 34328224 Wong MYH Khiatani PV Chui WH Understanding youth activism and radicalism: chinese values and socialization Social Science Journal 2019 56 255 267 10.1016/j.soscij.2018.08.006 Wong V Au-Yeung TC How do ideas and discourses construct youth policies? The case of Hong Kong International Journal of Sociology & Social Policy 2018 38 280 294 10.1108/IJSSP-08-2017-0104 Wright EM Pinchevsky GM Benson ML Radatz DL Intimate Partner violence and subsequent depression: examining the Roles of Neighborhood supportive mechanisms American Journal of Community Psychology 2015 56 342 356 10.1007/s10464-015-9753-8 26391793 Zamecki L Hong Kong youth radicalization from the perspective of relative deprivation Contemporary Chinese Political Economy & Strategic Relations 2018 4 451 485 Zubok IA Chuprov VI The nature and characteristics of youthful extremism Russian Education & Society 2010 52 45 68 10.2753/RES1060-9393520104	36466126	PMC9708503	NO-CC CODE	2022-12-01 23:20:30	no	Appl Res Qual Life. 2022 Nov 30;:1-17	utf-8	Appl Res Qual Life	2,022	10.1007/s11482-022-10129-w	oa_other
==== Front Eur J Orthop Surg Traumatol Eur J Orthop Surg Traumatol European Journal of Orthopaedic Surgery & Traumatology 1633-8065 1432-1068 Springer Paris Paris 36446957 3447 10.1007/s00590-022-03447-0 Original Article Pes anserine detachment in posteromedial approach to the tibial plateau fracture provides better intraoperative exposure without compromising the flexor muscle strength Jabalameli Mahmoud 1 Yahyazadeh Hooman 12 Bagherifard Abolfazl 1 Askari Alireza 1 Imani Pahlavanloo Zahra 3 Ostovar Mohsen [email protected] 1 1 grid.411746.1 0000 0004 4911 7066 Bone and Joint Reconstruction Research Center, Department of Orthopedics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran 2 grid.411463.5 0000 0001 0706 2472 Department of Orthopedic Surgery, Farhikhtegan Hospital, Faculty of Medicine, Tehran Medical Sciences, Islamic Azad University, Tehran, Iran 3 grid.411746.1 0000 0004 4911 7066 Department of Physiotherapy, Iranian Center of Excellence in Physiotherapy, School of Rehabilitation Sciences, Iran University of Medical Sciences, Tehran, Iran 30 11 2022 16 2 9 2022 22 11 2022 © The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Purpose During the posteromedial approach to the tibial plateau fracture (TPF), pes anserine is generally retracted. However, pes anserine detachment could provide a better fracture site exposure. Even so, the general conception is that the latter could negatively affect flexor muscle strength. We aimed to evaluate the effect of pes anserine detachment on the flexion force and functional outcomes of TPF with posteromedial involvement. Methods In this retrospective-prospective cohort study, 22 TPF patients with Schatzker type IV who were managed with posteromedial approach and pes anserine detachment were included. The knee flexion force was measured 12 months after the surgery at several angles of flexion (30°, 60°, and 90°) and rotations (internal and external). The International Knee Documentation Committee (IKDC) and the Knee Injury and Osteoarthritis Outcome Score (KOOS) were used to assess knee function. A visual analog scale (VAS) was used to measure knee pain. Results The mean strength of the knee flexor muscle was not statistically different between the involved and non-involved sides at 30°, 60°, and 90° knee flexion, and also at the internal and external rotation. The mean IKDC score of the patients was 81.6 ± 7.8. The mean KOOS score of the patients was 82.2 ± 9.1. The mean VAS for pain was 2.4 ± 1.8. The mean knee range of motion was 124 ± 10.5°. Conclusion Pes anserine release and re-attachment in the posteromedial approach to the TPF has no detrimental effect on the flexion muscle strength and knee function. Level of Evidence Therapeutic Level IV. Keywords Tibial plateau fracture Posteromedial approach Pes anserine Knee flexor strength ==== Body pmcIntroduction Tibial plateau fracture (TPF) is associated with a complication rate as high as 54% [1, 2]. Therefore, optimization of the TPF surgery is of considerable importance. For TPF involving the posteromedial portion of the tibial plateau, a posteromedial approach is generally implemented to minimize intraoperative soft-tissue injury [3, 4]. In this approach, the pes is usually retracted to expose the fracture site [5]. This retraction does not generally provide a complete exposure. Pes release may allow for improved fracture site exposure and fixation, therefore enhancing TPF treatment outcomes [5]. Many surgeons, however, do not undertake this procedure due to worries regarding the potential negative effects of this release on flexor muscle strength [6, 7]. In this study, we compared the flexor muscle strength between the involved and non-involved knee following the treatment of TPF with a posteromedial approach and pes release. We hypothesized that if pes release in the posteromedial approach of TPF surgery does not cause a significant reduction in flexor muscle strength of the involved knee, it could be implemented instead of pes retraction to ensure better fracture site exposure, thereby leading to a superior fixation and more favorable surgical outcomes. Patients and methods This retrospective-prospective study was approved by the review board of our institute under the code IR.IUMS.REC.1400.1252. Medical profiles of 62 TPF patients who were treated with a posteromedial approach and pes release between 2016 and 2020 were retrospectively reviewed. The inclusion criteria were Schatzker type IV [8] and a minimum follow-up of 1 year. Exclusion criteria for the study included patients with an associated knee injury requiring extra-intervention, lateral plateau involvement, involvement of lateral leg muscles, a history of surgical treatment in the ipsilateral limb, pre-injury malunion, or non-union in the ipsilateral limb, severely limited range of motion in the involved knee, and history of the neurologic or rheumatologic disorder. Patients whose medical records were insufficient or who were lost to follow-up were also excluded from the research. Finally, 34 patients were identified as eligible for this study. Twenty-two patients were available for final follow-up and were included in the final analysis. Surgical technique The patient was placed in the supine position while the patient's knee was rotated to the outside (figure of four), and a tourniquet was inflated. Then, a longitudinal incision was performed, beginning roughly 3 cm from the proximal joint portion and finishing at the desired distal joint portion. The nerve of the Saphenous was retracted. During the incision, the fascia was also cut. Pes anserine and the medial head of gastrocnemius were approached by a posteromedial approach (Fig. 1). Pes anserine was released from its distal part (tibial attachment) with a one-centimeter stump. To avoid retraction of the detached pes anserine, it was kept with a suture Vicryl; polyglactin; Ethicon, Johnson & Johnson) during the procedure (Fig. 2). Then, the fracture was exposed through the space between the medial collateral ligament and medial head of the gastrocnemius. The medial collateral ligament was preserved. Then, the fracture was fixed by a plate and screw system (3.5-mm T-plate; Mork-Med, Germany, Fig. 3). The tendon and muscles are re-attached to their previous insertion site using Vicryl-coated absorbable suture (2 Vicryl; polyglactin; Ethicon, Johnson & Johnson).Fig. 1 Intraoperative photographs showing a the preoperative incision planning and b approaching pes Anserine and the medial head of gastrocnemius Fig. 2 Intraoperative photographs of tibial plateau fracture surgery with posteromedial approach: a detachment of pes anserine with 1 cm stump and fracture exposure through medial collateral ligament and medial head of the gastrocnemius; b preserving the pes anserine using a suture to avoid its retraction; c Pes anserine repair after fracture reduction and fixation Fig. 3 Fixation of tibial plateau fracture by a plate and screw system Postoperative protocol Postoperative protocols were included active rehabilitation with isometric knee exercises that were started 1 day after the operation and range of motion as tolerated, aiming to achieve full active extension and 90° of knee flexion by the end of the 4 weeks. The patient was allowed partial weight bearing with crutch. Crutches were weaned off progressively from the 8 to 10 weeks after the operation, when sign of union was observed in the radiographic evaluation. Physiotherapy was implemented to strengthen the limb and improve muscles strength, proprioception, and range of motion in patient with poor compliance during follow up. Outcome measures All the evaluations were done by one orthopedic surgeon who was not involved in the patients’ care. The outcome of interest was postoperative complication, knee range of motion, knee pain, knee function, and flexor muscle strength. Knee range of motion (ROM) was evaluated with a goniometer. Knee pain was measured using a visual analog scale (VAS) for pain. Accordingly, a score between 0 and 10 was given to each patient, with zero signifying no pain and 10 indicating extreme suffering. The knee function was assessed using the Persian translation of the International Knee Documentation Committee (IKDC) and the Knee injury and Osteoarthritis Outcome Score (KOOS). Both IKDC and KOOS were rated between 0 and 100, whereas a higher score was indicative of greater function. The validity and reliability of the questionnaires in the Persian language were approved in earlier studies [9, 10]. A manual dynamometer (Manual Masucle Tester, M-202) evaluated the flexor muscle strength of the injured and non-injured knees. First, the patients were asked to ride a stationary bike for 5 min and perform hamstrings and quadriceps stretching exercises. Then, the flexor muscle strength was assessed at three angles of 30°, 60°, and 90° of knee flexion in both internal and external knee rotation, making six numbers for each limb and 12 numbers for each patient. Each evaluation was performed three times, and the average of three evaluations was regarded as the patient’s flexor muscle strength (Fig. 4).Fig. 4 Evaluation of flexor muscle strength using a dynamometer Flexor muscle strength was evaluated two times for each limb and the largest value was considered in the analysis. The inter- and intra-observer reliability of the muscle strength evaluation was checked in a pilot study using intraclass correlation coefficient test and showed to be 0.89 and 0.91, respectively. Statistical analysis Statistical analysis was done with SPSS for Windows, version 16 (SPSS Inc., Chicago, Ill., USA). Descriptive data were presented by mean ± standard deviation for quantitative variables and with number and percentage for qualitative variables. Using the Kolmogorov–Smirnov test, the distributions’ normality was examined. Wilcoxon signed-rank test was used to compare the mean values between the two limbs. A P value less than 0.05 was determined to be statistically significant. Results Twenty-two TPF patients were included in the analysis. The study population consisted of 20 males and two females. The mean age of the patients was 41.1 ± 12.5 years (range 23–61). The ‏mean follow-up of the patients was ‏16.5 ± 7.2‏ months (range 6–22) (Table 1).‏Table 1 Characteristic features of TPF patients Variable Mean ± SD or number (%) Age (years) 41.1 ± 12.5 Sex Male 20 (91) Female 2 (9) Involved side Dominant 11 (50) Non-dominant 11 (50) Mean tourniquet time (min) 79.5 ± 12.8 Mean operation time (min) 105 ± 15.6 Mean surgical bleeding (ml) 389.5 ± 76.3 Follow-up (months) 16.5 ± 7.2 The mean strength of knee flexor muscle at 30°, 60°, and 90° knee flexion was not significantly different between the involved and non-involved sides, both at internal rotation and external rotation status. The detailed flexor muscle strengths of the involved and non-involved knees are demonstrated in Table 2.Table 2 Comparison of knee flexor strength between the involved and non-involved knee Knee position Side Mean ± SD (Ib) P value 30° flexion-internal rotation Involved knee 7.5 ± 1.8 0.44 Non-involved knee 7.3 ± 1.3 60° flexion-internal rotation Involved knee 7.1 ± 1.9 0.6 Non-involved knee 7.2 ± 1.3 90° flexion-internal rotation Involved knee 6.7 ± 2.3 0.39 Non-involved knee 7 ± 1.6 30° flexion-external rotation Involved knee 7.4 ± 2.1 0.11 Non-involved knee 7 ± 1.7 60° flexion-external rotation Involved knee 7.5 ± 2 0.89 Non-involved knee 7.4 ± 2.1 90° flexion-external rotation Involved knee 7 ± 1.8 0.79 Non-involved knee 7.1 ± 2 The mean IKDC score of the patients was 81.6 ± 7.8 (range 71–90). The mean KOOS score of the patients was 82.2 ± 9.1 (range 69–89). The mean VAS for pain was 2.4 ± 1.8 (range 1–4). The mean knee ROM was 124 ± 10.5° (range 100–135). All patients achieved bone union (bridging callus in 3 of 4 cortices) within an average of 11.2 ± 2.6 weeks (range 9–12). One varus deformity of 5° was the only postoperative complication in this series. No intervention was done for varus deformity because the patient had no complaints. No patient had pain in the incision site of over the pes anserine. Discussion We evaluated the effect of pes anserine release instead of retraction in the posteromedial approach of TPF surgery to provide better intraoperative exposure. The knee function, as measured by the IKDC score and KOOS, was satisfactory after this change. The average VAS for postoperative pain was 2.4. Knee range of motion averaged 124°. The flexor muscle strength was not significantly different between the involved and non-involved knees at different flexion degrees and knee rotations. No major postoperative complication was observed in our series. Pes anserine is a critical factor in knee flexion force. In a biomechanical analysis, Di Stefano et al. [7] evaluated the impact of pes anserine transfer on the knee flexion force in 15 patients. The mean torque value of the quadriceps and hamstring in the involved knee was significantly reduced compared to the contralateral knee (192.2 ft-Ib vs. 221.3 ft-Ib and 116.1 ft-Ib vs. 129.1 ft-Ib, respectively). The mean internal rotation torque values at 90° were also significantly reduced in the involved knee (19.3 ft-Ib vs. 22.8 ft-Ib). Murakami et al. observed the same decrease in flexion force [6]. Although the influence of pes anserine muscles on knee flexion force is well-established, no previous research has examined the effect of pes anserine detachment and re-attachment on knee flexion force. The same reduction in flexion force was reported by Murakami et al. While the effect of pes anserine muscles on the knee flexion force is accepted, no earlier study is available regarding the effect of pes anserine detachment and re-attachment on the knee flexion force. We hypothesized that temporary detachment of pes anserine from its tibial attachment for better exposure of TPF during the posteromedial approach and its re-attachment after fracture reduction and fixation does not affect the knee flexion force. This was confirmed by the comparable knee flexion force of the involved and non-involved knees at different flexion degrees and rotations. The pes is often retracted during the posteromedial approach for surgical treatment of TPF with posteromedial involvement. Several past studies have shown the results of this method. Weil et al. [11] evaluated the outcomes of the posteromedial approach for the reduction and fixation of the medial (n = 10) and bicondylar (n = 17) TPF. After a mean follow-up of 3.5 years, 75% of patients had a good reduction. The average Oxford knee score was 19.9/48. The articular malreduction rate was 4%. No wound complications occurred. Manikandan and Saravanakumar [12] prospectively investigated the functional and radiological results of the posteromedial surgical approach to the knee in the treatment of twenty patients with complicated TPF and a posteromedial column fracture. Fractures were Schatzker type IV or more. All fractures were healed without complication. The postoperative anatomic reduction was achieved in 16 patients. The median knee range of flexion was 135°. The mean Oxford knee score was 29.3 (range 25–33). In comparison with earlier studies with pes retraction, we obtained a similar Average knee ROM. Likewise, we had no serious postoperative complications. While different functional measures were used (Oxford knee score vs. IKDC and KOOS), the knee function seems to be superior. This superiority could be attributed to the better fracture exposure achieved by the pes anserine detachment instead of its retraction. However, the patient’s and fracture’s characteristics are determining factors in the surgical outcomes and potentially could be the underlying cause of these inconsistent functional outcomes. To the best of our knowledge, the present cohort was the first study reporting the outcomes of pes anserine release instead of retraction in the posteromedial approach of TPF surgery. Similar to any other study, this study was not without limitations. The main weakness of the research was the lack of a control group treated using a conventional posteromedial technique (pes anserine retraction). Manual evaluation of muscle strength is prone to bias. The small number of patients could be regarded as the other limitation of this study, which was posed by the concurrent COVID-19 pandemic and lack of patients’ cooperation to attend the final follow-up evaluation. Conclusion Pes anserine detachment in the posteromedial surgical approach to the knee in the treatment complex TPF with a posteromedial involvement provides a better fracture exposure. This modification seems to have no adverse effect on the functional outcomes of the patients. It does not affect the knee flexion strength. Besides, it is not associated with a particular postoperative complication. Future controlled studies with larger patients are required for better evaluation of the effect of the pes anserine detachment in TPF treatment. Funding None. Declarations Conflict of interest Mahmud Jabalameli, Hooman Yahyazadeh, Abolfazl Bagherifard, Alireza Askari, Zahra Imani Pahlavanloo, and Mohsen Ostovar have no conflict of interest to declare. Ethical statement This study was approved by the review board of our institute under the code IR.IUMS.REC.1400.1252. Patients provided written consent before participation in the study. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Sament R Mayanger JC Tripathy SK Sen RK Closed reduction and percutaneous screw fixation for tibial plateau fractures J Orthop Surg (Hong Kong) 2012 20 1 37 41 10.1177/230949901202000108 22535809 2. Mankar SH Golhar AV Shukla M Badwaik PS Faizan M Kalkotwar S Outcome of complex tibial plateau fractures treated with external fixator Indian J Orthop 2012 46 5 570 574 10.4103/0019-5413.101041 23162152 3. Barei DP Nork SE Mills WJ Coles CP Henley MB Benirschke SK Functional outcomes of severe bicondylar tibial plateau fractures treated with dual incisions and medial and lateral plates J Bone Joint Surg Am 2006 88 8 1713 1721 10.2106/jbjs.e.00907 16882892 4. Kandemir U Maclean J Surgical approaches for tibial plateau fractures J Knee Surg 2014 27 1 21 29 10.1055/s-0033-1363519 24357044 5. Rollick N Wellman D Posterior approaches to the tibial plateau Oper Tech Orthop 2018 28 3 152 156 10.1053/j.oto.2018.07.005 6. Murakami H Soejima T Inoue T Kanazawa T Noguchi K Katouda M Tabuchi K Noyama M Yasunaga H Nagata K Inducement of semitendinosus tendon regeneration to the pes anserinus after its harvest for anterior cruciate ligament reconstruction-A new inducer grafting technique Sports Med Arthrosc Rehabil Ther Technol 2012 4 1 17 10.1186/1758-2555-4-17 22607724 7. DiStefano V Nixon J O'Neil R Davis O Pes anserinus transfer: an in vivo biomechanical analysis Am J Sports Med 1977 5 5 204 208 10.1177/036354657700500507 907034 8. Kfuri M Schatzker J Revisiting the Schatzker classification of tibial plateau fractures Injury 2018 49 12 2252 2263 10.1016/j.injury.2018.11.010 30526924 9. Rahimi A Nowrouzi A Sohani SM The validity and reliability of the persian version of the international knee documentation committee (IKDC) questionnaire in Iranian patients after ACL and meniscal surgeries Arch Rahabil 2013 14 2 116 124 10. Saraei-Pour S Salavati M Akhbari B Kazem-Nezhad A Translation and adaptation of knee injury and osteoarthritis outcome score (KOOS) in to persian and testing persian version reliability among Iranians with osteoarthritis Arch Rahabil 2007 8 1 42 46 11. Weil YA Gardner MJ Boraiah S Helfet DL Lorich DG Posteromedial supine approach for reduction and fixation of medial and bicondylar tibial plateau fractures J Orthop Trauma 2008 22 5 357 362 10.1097/BOT.0b013e318168c72e 18448992 12. Manikandan N Saravanakumar K A study on functional and radiological outcome of complex tibial plateau fractures by posteromedial plating Int J Res Orthop 2019 5 2 223 226 10.18203/issn.2455-4510.IntJResOrthop20190055	36446957	PMC9708505	NO-CC CODE	2022-12-01 23:20:30	no	Eur J Orthop Surg Traumatol. 2022 Nov 30;:1-6	utf-8	Eur J Orthop Surg Traumatol	2,022	10.1007/s00590-022-03447-0	oa_other
==== Front Environ Monit Assess Environ Monit Assess Environmental Monitoring and Assessment 0167-6369 1573-2959 Springer International Publishing Cham 36446906 10670 10.1007/s10661-022-10670-z Article Impact of COVID-19 lockdown and health risk modeling of polycyclic aromatic hydrocarbons in Onne, Nigeria Lele Charity Kelechi 12 Oluba Olarewaju Michael [email protected] 12 Adeyemi Oluyomi Stephen [email protected] 12 1 grid.448923.0 0000 0004 1767 6410 Landmark University SDG 3 (Good Health & Well-Being Research Group), Landmark University, Omu-Aran, 251101 Kwara State Nigeria 2 grid.448923.0 0000 0004 1767 6410 Department of Biochemistry, Landmark University, Omu-Aran, 251101 Nigeria 30 11 2022 2023 195 1 16611 6 2022 19 10 2022 © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The people living in Onne are highly vulnerable to PAH exposure due to constant exposure to black soot through oral, dermal, and inhalation routes. This work aims to determine the PAHs profile of selected soils in Onne, to determine the health risks associated with PAHs exposure through the soil, and to determine the impact of reduced industrial and other activities on the PAHs profile and associated public health risks. This study evaluated 16 priority polycyclic aromatic hydrocarbon (PAHs) pollutants in soil samples from the four (4) major clans in Onne using a gas chromatography flame ionization detector (GC-FID) during and after the COVID-19 lockdown. The results showed a differential presence of PAHs during and after the lockdown. Of the 16 priority PAHs, 10 and 8 PAHs were respectively detected during and after the COVID-19 lockdown. High molecular weight PAHs such as benzo(k)fluoranthene and benzo(a)anthracene were major contributors during the lockdown, while low molecular weight PAHs such as naphthalene, acenaphthylene, and fluorene were present at higher levels after the lockdown. An assessment of health risk by incremental lifetime cancer risks revealed that the entire population of Onne might be at risk of cancer development across periods, though a higher risk was presented during the lockdown. In addition, children under the age of 18 may be at greater risk. To the best of our knowledge, there is no previous report on the impact of the COVID-19 lockdown on soil PAH profile and health risks, with particular attention to the Onne industrial host community. Earlier work considered the ecological risks of heavy metals on dumpsites in Onne. Taken together, the PAH-contaminated soil in Onne poses an immediate health concern. Therefore, reduced anthropological activities, as evident during the COVID-19 lockdown, may play a role in exposure and cancer risk reduction. While there may not be another lockdown due to the challenging impacts associated with a physical lockdown, firmly controlled economic activity can be a solution if embraced by stakeholders. The COVID-19-lockdown was encumbered with restricted movements and security checks, which limited the number of samples collected. However, the Local Government Council (Department of the Environment) granted permission for the researchers to work with a minimal threat to their lives. Supplementary Information The online version contains supplementary material available at 10.1007/s10661-022-10670-z. Keywords PAHs Toxic equivalent factor Health risk modeling Soil contamination issue-copyright-statement© Springer Nature Switzerland AG 2023 ==== Body pmcIntroduction PAHs are ubiquitous and persistent organic compounds (Achten & Hofmann, 2009; Ni et al., 2019). PAHs are produced frequently due to partial incineration of plant and animal remains (Sun et al., 2020; Zhang et al., 2019), are poorly soluble in water, and do not disappear quickly from the environment (Vane et al., 2014; Zhang et al., 2019). They can accumulate in biological and ecological food chains and are, therefore, easily accessible to humans (Singh & Agarwal, 2018). About 16 PAHs are classified by the United States Environmental Protection Agency (USEPA) as priority pollutants owing to their importance naphthalene, acenaphthylene, fluorene, acenaphthene phenanthrene, anthracene, pyrene, benzo(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, indenol(1,2,3 cd) pyrene, fluoranthene, dibenzo(ah)anthracene, and benzo (g, h, and i) perylene (Zhang et al., 2019; Zhu et al., 2019). Seven of the 16 priority PAHs are tagged as possible or probable human carcinogens (Santonicola et al., 2017). PAHs arise from a variety of sources, including biomass, volcanic eruptions, and fires (Keith, 2015; Mihankhah et al., 2020). However, many of these chemicals are after-effects of human activities, particularly in urbanized cities. Coal and wood burning, gasoline and diesel oil burning, and industrial plants are sources of PAHs. In addition, spilled liquid fuels can contribute to most of the PAHs in the environment (Farrington, 2020). The presence of PAHs in soils and sediments is frequently linked to pyrogenic non-point sources such as the incomplete burning of (fossil) organic matter (Hindersmann & Achten, 2018). Oil spills are one possible source of point sources (Zhang et al., 2019). Several studies have confirmed the presence of PAHs in soil (Abdel-Shafy & Mansour, 2016; Achten & Hofmann, 2009; Emoyan et al., 2020), water (Adetunde et al., 2018; Nwaichi & Ntorgbo, 2016), air (Akinrinade et al., 2020; Munyeza et al., 2019), and sediments (Edokpayi et al., 2016). Oral intake, inhalation, and skin interaction are the three main routes of human exposure to PAHs (Ferguson et al., 2020). Exposure to PAHs is also associated with various diseases and bodily disorders (Adekunle et al., 2017; Santonicola et al., 2017), especially cancer (Falcó et al., 2003; Santonicola et al., 2017). Urban soil represents a significant part of the environment contaminated by PAH chemicals, which are hazardous to ecological and human health (Tarafdar & Sinha, 2018). The soil framework seems to be the primary sink for PAHs and, therefore, a prominent indicator of PAH contamination (Chandra et al., 2018; Emoyan et al., 2020; Tarafdar & Sinha, 2018). There is confirmation that anthropogenic activities related to urbanization and industrialization significantly affect pollution levels in cities (Kumar et al., 2014; Oliveira et al., 2019). Moreso, the terrain and nearby wind and tide speeds and the plant canopy can affect the transmission and deposition of chemicals, including heavy metals and PAHs (Abderrahmane et al., 2021; Pal & Hogland, 2022). Other physical and chemical characteristics such as pH, temperature, total organic matter, and moisture content of the soil can affect the accumulation of PAHs in the soil. Environmental PAHs are generally carcinogenic to man and other animals, though some PAHs, such as dibenzo [a, h] anthracene, benzo[a]pyrene, and benzo [g, h, i] perylene, are categorized as mutagenic (Anyanwu et al., 2020; Yost et al., 2021). Exposure evaluation is a critical step in determining health risks to circumvent the harmful impact of PAHs on the environment. The USEPA multi-pathway exposure model is the principal strategy for assessing health risks globally (Tarafdar & Sinha, 2018). Onne, Eleme, Rivers State is situated in the Niger Delta part of Nigeria, where industrialization is commonplace. Several studies have detected PAHs in the soil of an oil-rich region (Orisakwe, 2021; Sojinu et al., 2010; Ugochukwu et al., 2018). The quality of soil affects vegetables and other food crops grown in an industrial region, and thus PAHs can disseminate across the food chain. (Kumar et al., 2014). In addition, there is a general concern about the impact of environmental pollutants from black soot deposits in the Niger Delta in Nigeria, mainly due to illegal petroleum exploration. This problem creates dissatisfaction among local people and other stakeholders (Orisakwe, 2021; Zabbey et al., 2017). Although a large body of work on PAHs’ contamination in the Nigerian Niger Delta is available, the COVID-19 lockdown provided a departure from the usual human lifestyle. After the outbreak of the COVID-19 virus in 2020, a total lockdown became necessary in many countries (Mboera et al., 2020). The COVID-19 lockdown was accompanied by a decline in physical business, academic, and tourist activities around the world (Oyewola et al., 2022; Pahrudin et al., 2021). Consequently, it is important to explore whether the reduced human activities evident during the COVID-19 lockdown are affecting the concentration, distribution, sources, and potential health risks of PAHs across all areas in Onne, Nigeria. Therefore, this study sought to:Determine the concentration of PAHs in soil samples in Onne Assess the major sources and distribution of PAHs in Onne using already established diagnostic ratios. Use the toxic equivalency factor and incremental lifetime cancer risk to assess the potential health risks associated with PAH-contaminated soils from Onne. To evaluate the impact of the COVID-19 lockdown on concentrations, distribution, source attribution, and health risks of PAHs in Onne, Nigeria, by assessing changes in concentrations, sources, and distribution of PAHs during and after the lockdown. At the time of writing this manuscript, the authors are not aware of any written works specifically about Onne, with particular emphasis on the ongoing decades of exposure of the population to black soot due to illegal bunkering activities and multidimensional industrial and human activities in Onne, Nigeria. This research will improve our understanding of the general properties of PAHs in the soil samples in Onne and the attendant health risks. This will assist stakeholders to adopt efficient pollution mitigation approaches in Onne, Nigeria. Materials and methods Study area Onne is among the ten communities in Eleme Local Government of Rivers State. It is located at longitudes and latitudes of 4.723816 and 7.151618° east. Alejor, Ekara, Agbeta, and Ogoloma are the four main clans that makeup Onne. It is situated between Okrika and Ogu in Rivers State (Fig. 1). The Nigerian Ports Authority (NPA), the Oil and Gas Free Zone (OGFZA), the Nigerian Navy Basic Training School, the Nigerian Naval College (officers), Integrated Logistics (intels), and Notore Chemicals (formerly the National Fertilizer Company of Nigeria) are located in Onne, thereby positioning the community as a key industrial hub of Rivers State. It is a semi-urban dwelling place for both indigenes and foreigners. The NPA is a center of attraction in Onne because it is one of the largest oil and gas-free zones supporting exploration and production in Nigeria. This port is responsible for 65% of all exported cargo via the Nigerian seaports. In addition to the oil and gas business, the port also operates several other businesses. Consequently, the port serves a variety of cargo needs. Onne settlement is about 1–2 km from the port. In the midst of heavy industries, natives use their land for agriculture, mechanical workshops, and residential areas, among other things.Fig. 1 Map depicting the different locations of Onne Collection and pretreatment of soil samples Multiple evenly spaced sampling sites utilized for mechanical workshops, farmland, NPA schools, churches, mosques, boundaries, and fertilizer company vicinities were used to collect soil samples. Four (4) different areas of Onne, namely Alejor, Ekara, Agbeta, and Ogoloma, were chosen, and the position of each location was recorded using a handheld Garmin GPS device. Using a soil auger at 0–25 cm depth, 5 sub-samples were collected 5 m apart in a triangular shape and mixed appropriately to prepare a composite sample for each site based on a previously established method (Tarafdar & Sinha, 2018; Wu et al., 2019). After the manual removal of non-soil particles, the samples were stored in an ice-filled cooler and then shipped to the International Energy Services Laboratory in Port Harcourt, Nigeria, for analysis. The soil samples were air dried in the lab for 3 days to maintain a constant weight throughout the test. A 2 mm stainless steel screen was used to sift the soil into uniform sizes and articles and to remove unwanted particles. Two composites, which consisted of five sub-soil samples, were collected from each of the communities, Alejor, Ekara, Agbeta, and Ogoloma, in July 2020 during the COVID-19 lockdown and in March 2021 after the lockdown. Analytical procedures All chemicals, including anhydrous sodium sulfate, dichloromethane, and activated silica gel, were analytical grade. Physicochemical properties of soil such as pH, temperature, electrical conductivity, moisture content, total organic matter content, and total organic matter were determined by established methods as reported (Emoyan et al., 2020). For PAH determination, 10 g of the sample was weighed into a clean 50-mL extraction bottle; 30 mL of dichloromethane (DCM, the extraction solvent) was added to the flask. The mixture was agitated by shaking for 2 min and allowed to settle. The extracted mixture was then passed through 42-size Whatman paper containing 5 g of activated silica gel and 5 g of the sodium sulfate into a vial and held ready for injection into the GC-FID. Sample dilution An aliquot (5 mL) of sample extract was diluted with 10 mL of dichloromethane. Instrumentation The carrier gas utilized was nitrogen (30 mL/min). The hydrogen and compressed air pressures were each 27.8 pounds per square inch (psi) at 35 mL per minute and 250 mL per minute, respectively. The results were scored using the sixteen (16) standard PAHs for the analysis. To rank the compounds, the retention times based on the standards were compared to those of a sample extract, while quantification incorporated individual PAH analysis. To ensure that all calculated PAHs are accurate, a blank analyte verification and an initial or trial demonstration were used to verify the method. The standards used for the analysis included naphthalene, 2-methyl naphthalene, acenaphthylene, fluorene, acenaphthene, phenanthrene, anthracene, pyrene, benzo(a)anthracene, chrysene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, indenol(1,2,3 cd) pyrene, fluoranthene, dibenzo(ah)anthracene, and benzo (g, h, and i) perylene. Source allocation studies Knowing the sources of PAHs makes it easier to determine how they are distributed in the environment. Commonly, the diagnostic ratio method is used to discriminate between different PAHs sources in the ecosystem (Edokpayi et al., 2016; Tobiszewski and Namieśnik (2012). Ratios include LPAHs/HPAHs (low molecular weight PAHs), Fla(fluoranthene)/(Fla + Pyr)(fluoranthene + pyrene), BaA(benzo(a)anthracene/(BaA + Chr(chrysene), and Ant(anthracene)/(Ant + Phe) (phenanthrene). For instance, a ratio of LPAHs/HPAHs < 1 indicates pyrogenic origin, whereas a ratio > 1 point to a petrogenic source. A ratio of Fla/(Fla + Pyr) < 0.4 specifies a petrogenic source, a ratio between 0.4 and 0.5 shows that the source of PAHs is from a fossil fuel combustion source, and a ratio > 0.5 indicates a coal, wood, or grass incineration source. For BaA/(BaA + CHR), a ratio < 0.2 signifies a petroleum source, a ratio between 0.2 and 0.35 indicates a mixed source, and a ratio > 0.35 shows a combustion source. Values of the Ant/(Ant + Phe) ratio are classified as either < 0.1 or > 0.1, representing petroleum and combustion sources, respectively (Brändli et al., 2007). Soil pollution scales To assess soil pollution levels, Maliszewska-Kordybach (1996) proposed that soil contaminated by PAHs could be classified into four levels: non-contaminated, weakly contaminated, contaminated, and heavily contaminated. Soil PAH concentrations less than 0.2 mg/kg are considered non-contaminated; concentrations between 0.2 and 0.6 mg/kg are considered weakly contaminated; concentrations between 0.6 and 1.0 mg/kg are considered contaminated; and concentrations greater than 1.0 mg/kg are considered heavily contaminated (Maliszewska-Kordybach, 1996). Health risk assessment Toxic equivalent concentration (BaPeq) and incremental lifetime cancer risk The health risk assessment model for carcinogenic risk was evaluated by the benzo(a)pyrene equivalent (BaPeq) concentration and the incremental lifetime cancer risk (ILCR) for carcinogenic risks. The BaPeq concentration with a toxic equivalent factor of one (1) is normally used as a basis for toxicity and carcinogenicity. This is because benzo(a)pyrene is the most extensively researched congener of PAHs. To appraise the hazardousness of soil samples, toxic equivalency factors (TEFs) were adopted to produce toxic equivalent concentrations, i.e., BaPeq, as reported earlier (Nisbet & LaGoy, 1992). To assess the potential toxicity of a PAH congener, its concentration was multiplied by the estimated TEF value (Appendix 1).ΣBaPeq=ΣC1×TEF1 where BaPeq is the equivalent concentration of benzo(a)pyrene, C1 is the concentration of PAH congener in soil, and TEFi is the toxic equivalency factor of PAH congener relative to benzo(a)pyrene (BaP). The carcinogenic potencies of PAHs were estimated by adding their BaPeq values and comparing them with a reference value (Canadian Council of Ministers of the Environment-CCME, 2010). Environmental PAHs pose a significant health risk. The USEPA (United States Environmental Protection Agency) model of the incremental lifetime cancer risks (ILCR) model, as reported by Qu et al. (2020), was used for the cancer risks in soil samples. Cancer risk modeling considered three pathways of exposure, viz., oral, dermal, and inhalation. The risk evaluation considered four exposure populations: children (0–18 years), young adults (20–44 years), middle-aged (45–59 years), and the elderly (> 60 years). Furthermore, the following equation was used:1 ILCRingestion=CS×IRsoil×EF×ED×CSFingestionBW×AT×106 2 ILCRdermalCS×SA×AF×ABS×EF×CSFdermalBW×AT×106 3 ILCRinhalationCS×IRair×EF×ED×CSFinhalationBW×AT×106 The equation terms are defined as follows: Cs = toxic equivalent PAHs compound concentration in soil (mg/kg). CSF = carcinogenic slope factors (in milligrams of BaP per kilogram or milligrams per liter) for the three major pathways (oral, dermal, and inhalation): CSF ingestion, CSF dermal, and CSF inhalation. They are, respectively, 1.0–25.0 and 3.85 mg.kg.day−1 (Tarafdar & Sinha, 2018). IRsoil = soil ingestion rate in milligrams per day. AFsoil = dermal adherence factor in milligrams per square centimeter. IRair = inhalation rate in cubic meters per day. ED = exposure duration in years. EF = exposure frequency in days per year (365 days per year). BW = body weight (kg). AT = average life span in days. PEF = particle emission factor in cubic meters per kilogram of soil. SA = exposed skin surface area. ABS = dermal absorption factor. Since children are most vulnerable to environmental pollutants, the main issue was to identify the risks associated with this population (Wirnkor et al., 2019)). According to USEPA cancer risk classifying standards, an ILCR of less than 10−6 is considered practically safe, a value between 10−6 and 10−4 is considered low risk, and above 10−4 indicates a potentially high risk for significant health concerns (Qu et al., 2020; USEPA, 1996). The parameters imputed and used for the calculation are explained in Appendix 2. Statistical analysis Statistical analysis was performed using GraphPad Prism Software 9.00 (San Diego, CA, USA). Values are expressed as the mean ± SEM of 2 replicates. P < 0.05 was considered to be statistically significant. The student’s t-test was used to determine the difference between PAH concentrations during and after confinement, while Pearson’s correlation coefficient was used to determine the relationship between other physicochemical parameters and PAHs. Results and discussions Physicochemical parameters and PAHs in soil samples The physicochemical parameters of the soil samples were assessed. The results of the mean values of the physicochemical parameters are summarized in Table 1. There was no consistency in the way the physicochemical properties changed, apart from temperature and conductivity, which showed a consistent increase in the four communities post-lockdown. The temperature ranged between 24.45 and 25.15 °C during the lockdown but increased to between 28 and 29 °C after the lockdown across the periods. Conductance range values were 17–19 during the lockdown and 20–36 after the lockdown. Tables 2 and 3 showed that Pearson correlation values were statistically not significant (p > 0.05).Table 1 Physicochemical properties of soil during (1) and after (2) COVID-19 lockdown Al1 Al2 Ek1 EK2 Ag1 Ag2 Og1 Og2 PAHs 2.02 ± 0.1 0.85 ± 0.25 1.14 ± 1.07 1.63 ± 0.32 2.03 ± 0.37 1.30 ± 0.32 1.82 ± 0.25 1.35 ± 0.10 pH 7.45 ± 1.35 6.1 ± 0.35 5.65 ± 0.45 6.1 ± 0.35 5.90 ± 0 6.0 ± 0.40 5.60 ± 0.60 6.1 ± 0.40 Temp °C 24.85 ± 0.35 29 ± 0.65 25.15 ± 0.05 28 ± 0.40 25.15 ± 0.05 28 ± 0.10 24.45 ± 1.05 28 ± 0.20 Cond 15.0 ± 1 20 ± 2.5 19.0 ± 5 23 ± 0.50 17.00 ± 1 20 ± 0.050 18.00 ± 10 36 ± 13 MC 12.50 ± 0.65 18 ± 1.9 13.80 ± 0.7 18 ± 1.6 12.37 ± 3.23 19 ± 1.5 13.80 ± 0.20 16 ± 2.5 TOC 2.27 ± 0.42 2.5 ± 0.63 2.71 ± 0.86 1.7 ± 0.090 4.07 ± 1.52 2.5 ± 0.035 1.61 ± 0.22 1.9 ± 0.86 TOM 6.86 ± 1.25 7.5 ± 1.9 8.21 ± 2.56 5.1 ± 0.29 12.31 ± 4.60 7.6 ± 0.095- 4.86 ± 0.64 5.8 ± 2.6- Al Alejor, Ek Ekara, Ag Agbeta, Og Ogoloma, Temp temperature, Cond conductivity, MC moisture content, TOM total organic matter Table 2 COVID-19 lockdown Pearson correlation PAHs pH Temp Cond MC TOC TOM PAHs 1 0.30 − 0.19 0.36 0.02 − 0.08 0.20 pH 1 − 0.77 0.04 − 0.33 − 0.06 − 0.05 Temp 1 0.24 0.21 −0.15 −0.14 Cond 1 −0.01 −0.10 0.13 Moisture C 1 0.47 0.47 TOC 1 0.96 TOM 1 PAHs polycyclic aromatic hydrocarbons, Cond conductivity, MC moisture content, TOC total organic carbon, TOM total organic matter, Temp temperature Table 3 Post COVID-19 lockdown Pearson correlation PAHs pH Temp Cond M C TOC TOM PAHs 1 − 0.28 − 0.83 0.28 0.18 − 0.31 − 0.45 pH 1 0.52 − 0.41 − 0.74 − 0.21 0.45 Temp 1 − 0.07 − 0.55 0.23 0.41 Cond 1 0.01 0.31 − 0.61 MC 1 0.30 − 0.42 TOC 1 0.10 TOM 1 PAHs polycyclic aromatic hydrocarbons, Cond conductivity, MC moisture content, TOC total organic carbon, TOM total organic matter, Temp temperature Correlation studies by Pearson show that pH, conductance, moisture content, and total organic matter were positively associated with PAHs during lockdown (Table 2), whereas conductance and moisture content were the only parameters in positive association with PAHs after the lockdown (Table 3). Table 4 illustrates the concentrations of individual PAHs in selected soil samples. Of the 16 priority PAHs assessed in this study, 10 PAHs were detected in Onne soils during the lockdown, while eight (8) PAHs were detected in post-lockdown soil samples. Student’s t-test analysis shows significant differences in mean total PAH concentrations in Alejor (p < 0.0001), Agbeta (1p < 0.0001), and Ogoloma (p < 0.005), but not in Ekara. In summary, 75% of the soil samples tested showed significant changes in concentrations after the lockdown. Both low molecular weight PAHs (LPAHs) and high molecular weight PAHs (HPAHs) were detected, but the LPAHs predominated over the HPAHs in both periods. However, HPAHs were in higher concentrations during the lockdown than after the lockdown. For example, BaA and BkF recorded mean values of 0.41 ± 0.02 and 0.95 ± 0.05, respectively, during the lockdown but were absent after the lockdown (Table 4). In general, naphthalene was present in all samples from all four communities: Alejor, Ekara, Agbeta, and Ogoloma. During the lockdown, seven (7) PAHs were detected in all samples: naphthalene, fluorene, fluoranthene, acenaphthene, anthracene, benzo(a)anthracene, and benzo(k)fluoranthene. Pyrene was detected in Alejor, Agbeta, and Ogoloma but not in Ekara. On the other hand, phenanthrene was not detected in Ogoloma but was present in Alejor, Ekara, and Agbeta. Acenaphthylene was only detected in Ogoloma. In all, eight (8) PAHs were detected in Ekara, whereas nine (9) PAHs were detected in Alejor, Agbeta, and Ogoloma. In contrast, naphthalene, acenaphthylene, acenaphthene, fluorene, phenanthrene, and anthracene were detected in the four communities, whereas fluorene was detected in Ogoloma, and pyrene was detected in Alejor and Ogoloma. Pyrene was the only HPAH detected in a low quantity after the lockdown.Table 4 Concentration of PAHs congeners (mg/kg) during (1) and after (2) COVID-19 lockdown PAHs (mg/kg)/ring no Al1 Al2 Ek1 EK2 Ag1 Ag2 Og1 Og2 Nap/2 0.32 ± 0.03 0.19 ± 0.19 0.20 ± 0.20 0.42 ± 0.08 0.46 ± 0.19 0.4111 ± 0.016 0.20 ± 0.04 0.49 ± 0.02 Acy/3 - 0.15 ± 0.08 - 0.28 ± 0.06 - 0.27 ± 00 0.02 ± 0.02 0.29 ± 0.02 Ace/3 0.02 ± 0.00 0.15 ± 0.02 0.02 ± 00 0.23 ± 0.08 0.01 ± 0.00 0.21 ± 0.03 0.01 ± 0.01 0.19 ± 0.02 Flu/3 0.18 ± 0.01 0.24 ± 0.03 0.08 ± 0.09 0.27 ± 0.06 0.07 ± 01 0.29 ± 0.02 0.08 ± 0.06 0.27 ± 0.02 Phe/3 0.01 ± 0.00 0.10 ± 0.02 0.01 ± 0.01 0.33 ± 0.30 0.01 ± 0.01 0.09 ± 0.01 - 0.01 ± 0.00 Ant/3 0.07 ± 0.00 0.01 ± 0.00 0.04 ± 0.00 0.33 ± 0.30 0.08 ± 0.02 0.002 ± 0.00 0.04 ± 0.02 0.01 ± 0.00 Fla/3 0.02 ± 0.00 - 0.01 ± 0.01 - 0.02 ± 0.00 - 0.01 ± 0.01 - Pyr/4 0.01 ± 0.01 0.002 ± 0.00 - 0.01 ± 0.01 - 0.02 ± 0.02 - BaA/4 0.41 ± 0.02 - 0.21 ± 0.17 - 0.17 ± 0.15 - 1.06 ± 0.75 - - - - - - - - - - BKF/5 0.95 ± 0.05 - 0.55 ± 0.5 - 1.035 ± 0.24 - 0.38 ± 0.38 - Total PAHs 2.02 ± 0.1 0.85 ± 0.25 1.14 ± 1.07 1.63 ± 0.32 2.03 ± 0.37 1.30 ± 0.32 1.82 ± 0.25 1.35 ± 0.10 Total LPAHs 1.06 ± 0.06 0.84 ± 0.33 0.58 ± 0.01 1.63 ± 0.02 0.99 ± 0.00 1.30 ± 0.12 1.44 ± 0.10 1.35 ± 0.10 Total HPAHs 0.95 ± 0.01 0.002 ± 0.00 0.56 ± 0.01 0 1.03 ± 0.00 0 0.38 ± 0.00 0.003 ± 0.00 LPAHs/HPAHs 0 351 0.07 1.63 0 1.30 1.04 450 WHO (mg/kg) 1 1 1 1 1 1 1 1 Comparison with soil pollution criteria The summation of PAH concentrations in the four communities in Onne revealed that the mean level of PAHs was 1.75 mg/kg and ranged between 1.14 and 2.04 mg/kg (Table 1) during the lockdown, while the mean value was 1.28 mg/kg and ranged between 0.85 and 1.35 mg/kg after the lockdown. Therefore, 100% of the soil’s concentrations in Alejor (2.02 ± 0.1; 0.85 ± 0.25 mg/kg), Ekara (1.14 ± 1.07; 1.63 ± 0.32 mg/kg), Agbeta (2.03 ± 0.37; 1.30 ± 0.32 mg/kg), and Ogoloma (1.82 ± 0.25; 1.35 ± 0.10 mg/kg) might be termed highly contaminated by PAHs based on the soil pollution scale (Maliszewska-Kordybach, 1996). Reduced industrial, commercial, and physical activity might have resulted in lower PAH after the COVID-19 lockdown. This work is in accordance with the report by Inam et al. (2016), which recorded 1.77 mg/kg PAHs in a mechanic shop at Uyo in the Niger Delta area of Nigeria. Other reports by Daniel et al. (2020) found a range of PAH concentrations between 214.83 and 537.22 mg/kg in urban soil samples used for mechanic work, and Onyedikachi et al. (2019) confirm the presence of PAHs above the WHO acceptable limit in the Niger Delta region of Nigeria. Separate studies on PAH concentrations in urban soils outside Nigeria showed that Greater London had values between 4 and 66 mg/kg (Vane et al., 2014), industrial areas of the Yangtze River Delta region in China had intermediate values of PAHs between 341.40 and 471.30 µg/kg and Moscow had < 1 to 1 mg/kg (Vane et al., 2014). However, all of these values are within the acceptable WHO standard of 1 mg/kg. The percentage composition of PAHs is illustrated (Figs. 2, 3, 4, and 5).Fig. 2 Percentage composition of PAHs in soil samples during and after the COVID-19 lockdown in Alejor. Legend: Nap = naphthalene, Acy = acenaphthylene, Ace = acenaphthene, Flu = fluorene, Phe = phenanthrene, Ant = anthracene, Fla = fluoranthene, Pyr = pyrene, BaA = benz(a)anthracene, and BKF = benzo(k)fluoranthene Fig. 3 Percentage composition of PAHs in soil samples during and after the COVID-19 lockdown in Ekara. Legend: Nap = naphthalene, Acy = acenaphthylene, Ace = acenaphthene, Flu = fluorene, Phe = phenanthrene, Ant = anthracene, Fla = fluoranthene, Pyr = pyrene; BaA = benz(a)anthracene, and BKF = benzo(k)fluoranthene Fig. 4 Percentage composition of PAHs in soil samples during and after the COVID-19 lockdown in Agbeta. Legend: Nap = naphthalene, Acy = acenaphthylene, Ace = acenaphthene, Flu = fluorene, Phe = phenanthrene, Ant = anthracene, Fla = fluoranthene, Pyr = pyrene; BaA = benz(a)anthracene, and BKF = benzo(k)fluoranthene Fig. 5 Percentage composition of PAHs in soil samples during and after the COVID-19 lockdown in Ogoloma. Legend: Nap = naphthalene, Acy = acenaphthylene, Ace = acenaphthene, Flu = fluorene, Phe = phenanthrene, Ant = anthracene, Fla = fluoranthene, Pyr = pyrene; BaA = benz(a)anthracene, and BKF = benzo(k)fluoranthene The fraction of low molecular weight PAHs significantly increased as NaP rose from 16.08% during lockdown to 22.65% after lockdown. Similarly, Acy and Ant increased from 9.05 and 3.52% to 17.88 and 28.61% in Alejor. In Ekara, NaP showed a positive percentage increase from 11.63 to 25.83% after the COVID-19 lockdown. Flu also increased from 4.65 to 16.61%. However, Ant increased (2.33–20.30%) during and after the COVID-19 lockdown. This trend holds for Agbeta and Ogoloma, except for Phe and Ant, which showed a decrease in percentage composition in Agbeta after the lockdown. Source allocation studies The possible sources of PAHs were predicted using PAH diagnostic ratio indices. Inferences show that soil PAHs were a combination of several sources during the COVID-19 lockdown but were majorly petrogenic after the lockdown (Tables 5, 6, 7, and 8). The predominance of low molecular weight PAHs against high molecular weight PAHs indicates more petrogenic sources rather than pyrogenic sources. LPAHs are common in petroleum mixtures. This work is in conformity with similar studies in the Niger Delta, Nigeria (Emoyan et al., 2020; Osu & Asuoha, 2010). The Niger Delta part of Nigeria is well known for its oil exploration, heavy traffic movement, and other industrial activities (Orisakwe, 2021; Ugochukwu et al., 2018). This undoubtedly leads to oil spillage and pollution by spent petroleum products. The continuous vehicular movements in and out of the seaport combined with ongoing bunkering activities and diesel combustion in the industrial plants could also explain the fact that pyrogenic activities were common prior to lockdown, leading to HPAHs accumulation, which was evident in the higher percentage contribution of BaA and BKF in Alejor (20.60 and 47.74%); Ekara (23.84 and 52.23%); Agbeta (9.12 and 55.5%); Ogoloma (58.24 and 20.88%) (Figs. 2, 3, 4, and 5).Table 5 Computed source allocation of PAHs in Alejor soil samples during and after COVID-19 lockdown Diagnostic ratios Alejor during COVID-19 lockdown Possible source Alejor after COVID-19 lockdown Possible source LPAHsHPAHs 0.001 Pyrogenic 351 Petrogenic AntAnt+Phe 0.885 Pyrogenic 0.09 Petrogenic BaABaA+Chr 1 Combustion Not applicable Not applicable FlaFla+Pyr 0.65 Grass coal combustion Not applicable Not applicable Ant anthracene, BaA benz(a)anthracene, Phe phenanthrene, Chr chrysene, Fla fluoreanthene, Pyr pyrene Table 6 Computed source allocation of PAHs in Ekara soil samples during and after COVID-19 lockdown Diagnostic ratios Ekara during COVID-19 lockdown Possible source Ekara after COVID-19 lockdown Possible source LPAHsHPAHs 0.069 Pyrogenic 1.63 Petrogenic AntAnt+Phe 0.84 Pyrogenic 0.7 Pyrogenic BaABaA+Chr 1 Combustion Not applicable Not applicable FlaFla+Pyr 1 Grass coal combustion Not applicable Not applicable Ant anthracene, BaA benz(a)anthracene, Phe phenanthrene, Chr chrysene, Fla fluoreanthene, Pyr pyrene Table 7 Computed source allocation of PAHs in Agbeta soil samples during and after COVID-19 lockdown Diagnostic ratios Agbeta during COVID-19 lockdown Possible source Agbeta after COVID-19 lockdown Possible source LPAHsHPAHs 0.009 Pyrogenic 1.3 Petrogenic AntAnt+Phe 0.89 Pyrogenic 0.08 Petrogenic BaABaA+Chr 1 Combustion Not available Not available FlaFla+Pyr 0.71 Grass coal combustion Not available Not available Ant anthracene, BaA benz(a)anthracene, Phe phenanthrene, Chr chrysene, Fla fluoreanthene, Pyr pyrene Table 8 Computed source allocation of PAHs in Ogoloma soil samples during and after COVID-19 lockdown Diagnostic ratios Ogoloma during COVID-19 lockdown Possible source Ogoloma after COVID-19 lockdown Possible source LPAHsHPAHs 1.03 Petrogenic 408 Petrogenic AntAnt+Phe 1 Pyrogenic 0.08 Petrogenic BaABaA+Chr 1 Combustion Not applicable Not applicable FlaFla+Pyr 1 Grass coal combustion Not applicable Not applicable Ant anthracene, BaA benz(a)anthracene, Phe phenanthrene, Chr chrysene, Fla fluoreanthene, Pyr pyrene Although USEPA identified 16 PAHs as priority pollutants, 7 compounds, namely benzo(a)anthracene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(k) fluoranthene, chrysene, dibenzo(ah)anthracene, and indeno(1,2,3-cd) pyrene are classified as probable human carcinogens (Abdel-Shafy & Mansour, 2016). The presence of benzo(a)anthracene and benzo(k) fluoranthene are pointers to higher health riska during the lockdown than after the lockdown. PAH diagnostic ratios are routinely used to analyze soil samples, but they give no information about the stability of soil PAHs. The primary fate of PAHs in soils is atmospheric desorption (Feng et al., 2019; Liu et al., 2016), and diagnostic ratios can vary depending on the altitude of the soil sampling site (Jiang et al., 2009). PAHs may desorb: fluoranthene and pyrene desorb at comparable rates, although Phe desorbs more rapidly than Ant (Enell et al., 2005). LPAHs can be metabolized by endogenous bacteria and fungi, resulting in a (potentially selective) drop in concentration over time based on soil type, organic carbon and nutrient content, humidity, and aeration (Gerhardt et al., 2009). Health risk assessment The health risks posed by PAH exposure are well researched (Abdel Shafy & Mansour, 2016; Canadian Council of the Ministers of the Environment-CCME, 2010; Adekunle et al., 2017; Santonicola et al., 2017). The health risk assessment model for carcinogenic risk was evaluated by the BaPeq concentration and the ILCR for carcinogenic risks. Toxic equivalent factors are used in the calculation (Appendix 1), while the details on how BaPeq concentration was calculated are provided in Supplementary information (1). Health risk assessment BaPeq concentration with a toxic equivalent factor of one (1) could be used as a basis for toxicity and carcinogenicity. To evaluate the hazardous potency of soil samples, toxic equivalency factors (TEFs) (Appendix 1) was used to produce toxic equivalent concentrations (BaPeq) for evaluation and quantification. BaPeq concentrations ranged from 0.01 to 0.02 mg/kg across Alejor, Ekara, Agbeta, and Ogoloma (Table 8). These values were below the 0.6 mg/standard allowed for PAH concentrations in soils (Canadian Council of the Ministers of the Environment-CCME, 2010). Based on these values, the soil likely does not pose any health risk due to BaPeq concentration. The computed values of the ILCR of soil PAHs within the specified period are presented in Table 9. The order of the risk is children > middle aged > elderly > young adults during the lockdown and children > middle aged = elderly > young adults (Table 10). Children may have a greater risk of developing cancer later in their lifetime. The inhalation route is the most effective route of exposure across all ages of life. The oral route was the weakest means of cancer risk exposure, whereas the dermal route showed a weak likelihood of cancer risk across all ages. The inhalation route is evident as inhaled black soot from colloids in the noses of individuals in Onne accumulates over time, hence gaining access to the systemic circulation of exposed people (Abdel-Shafy & Mansour, 2016). This present work contradicts the reports of Onyedikachi et al. (2019), where oral ingestion was the most effective exposure route in cancer risk assessment among other routes, such as inhalation and dermal routes of exposure. In addition, Parra et al. (2020) reported the dermal route as the most effective route of PAH exposure and that the elderly were more at risk, whereas our present study found that children are at higher risk. Some factors affect the mechanisms by which PAHs are absorbed in humans. For instance, the age and metabolism of the subject, routes of exposure, and environmental circumstances such as temperature, humidity, solar radiation, wind speed, and precipitation rates can influence PAH metabolism (Kim et al., 2013; Ma & Harrad, 2015).Table 9 Computed benzo(a)pyrene equivalent concentration of soil samples during and after COVID-19 lockdown Soil media Calculated BaPEq1(mg/kg) Calculated BaPEq2(mg/kg) Alejor 0.02 0.002 Ekara 0.01 0.005 Agbeta 0.01 0.002 Ogoloma 0.02 0.003 TBa(P)Eq 0.06 0.012 BaPeq1 benzo(a)pyrene equivalent concentration during COVID-19 lockdown, BaPeq2 benzo(a)pyrene equivalent concentration after COVID-19 lockdown Table 10 Comparison of ILCR of Onne population during (a) and immediately (b) after COVID-19 lockdown Population/route of exposure Elderly Middle aged Young adult Children Oral ingestion 6.26E + 02a 6.26 E − 07a 0.00000073a 5.84E − 06a 2.66E − 04b 1.04 E − 07b 1.22E − 07b 9.73 E − 04b Inhalation 3.1E + 08a 3.1E + 08a 3.62E + 08a 8.6E + 08a 5.17E + 07b 5.17E + 07b 6.03E + 07 1.43E + 8b Dermal contact 4.18E − 07a 3.88 E − 06a 4.53E − 06a 2.99E − 06a 6.97E − 08b 6.47E − 08b 7.55E − 07b 4.98E − 07b ILCRTotala 3.88 E 08* 5.88E 08* 3.62 E 08* 8.66E 08a* ILCRTotalb 5.17E + 07 5.17E + 07 6.04E + 06 1.43E + 08b* ILCR incremental lifetime cancer risk, a during lockdown, b after lockdown However, according to the National Academy of Science, children are predisposed to PAH-associated health risks. The reasons can be both behavioral and physiological. Due to their young age, there can be a significant time lag between exposure to PAHs and the point at which toxic manifestations appear (Oliveira et al., 2019). Children also spend more time playing on the field, both at school and at home. Similarly, the poor electricity supply in Onne prevents the closure of windows, which would otherwise provide ventilation, in most classrooms in Onne, exposing young pupils and school children to polluted air. Again, their reduced body weight can allow PAHs to accumulate and have dangerous effects (Wang et al., 2011) Taken together, the present study supports the findings of Miller et al. (2010), which indicated that exposure to PAHs is more likely in children. To further substantiate the at-risk population, Perera et al. (2009) discovered a similar adverse relationship between intellect and PAH exposure during prenatal assessments up to 5 years of age. Furthermore, the lack of adequately developed cytochrome P450 metabolizing enzymes in children could be a contributing factor (Björkman, 2006), while greater exposure to domestic and occupational PAHs is likely to increase the risk of toxicity in the working population. The senior population may be least in danger because the majority of them are no longer extremely active, whereas young adults’ higher level of exercise likely explains why they are at less risk than the other age groups. Conclusion Despite the extensive reports of pollution in the Niger Delta, the Nigerian government has yet to develop a plan to deal with PAH pollution. To our ultimate knowledge, this is the first study to examine the cancer risks of PAHs originating in the Onne community and the impact of the COVID-19 lockdown. Exposure to soil-borne PAHs via eating, ingestion, contact with the skin, and inhalation in the general population of Onne may present potential health risk concerns. This health risk warrants further investigation through biological monitoring to facilitate concerted efforts to mitigate the potential hazards. For this reason, anthropological activities such as uncontrolled bush burning and illegal oil refining should be prohibited in Onne and nearby communities. Since PAH exposure is literally unavoidable through the soil, concerted efforts should be made to engage researchers, nonprofit-making organizations, and other stakeholders to make policies that check PAH exposure in Onne. More research should be directed at the exposed population. People can be made aware of the public health risks associated with PAHs to enable them to embrace helpful lifestyle changes, such as the avoidance of cigarette smoking, which may increase exposure risks. Limitation of the study Although risk characterization is essential in health risk assessment studies, risk modeling is limited in predicting risk due to the possibility that risks may be overestimated or underestimated compared to the actual situation. In addition, soil sampling and laboratory experimentation are rigorous and require special expertise and skills. Electronic supplementary material Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 17 KB) Appendix Appendix 1 Toxic equivalency factors of 16 priority PAHs by Nisbet & LaGoy, 1992 PAHs/Code Toxic equivalency factor Naphthalene/Nap 0.001 Acenaphthylene/Acy 0.001 Acenaphthene/Ace 0.001 Fluorene/Flu 0.001 Phenantrene/Phe 0.001 Anthracene/Ant 0.01 Fluoreanthene/FluAn 0.001 Pyrene/Pyr 0.001 Benz(a)anthracene, BaA 0.1 Chrysene/Chr 0.01 Benzo(a)pyrene/BaP 1 Benzo (b)fluoranthene/BbF 0.1 Benzo(k)fluoranthene/BKF 0.1 Benzo (g,h,I)perylene/BPy 0.1 Indenol(1,2,3−cd) pyrene/InP 0.01 Di benz (a,h) anthracene(DahA) 1 Appendix 2 Values of parameter used for incremental cancer risk calculation Definition Units Children Young adults Middle age Elderly References Exposure frequency (EF) days/year 365 365 365 365 Peng et al. 2011 Exposure duration (ED) year 6 20 45 70 USEPA, 2014 Average body weight kg 15 60 70 70 Ohiozebau et al., 2016 Average time AT (days) 2190 7300 25,550 25,550 Soltani et al., 2015 Inhalation rate IRi M3/day 10 20 20 10 Soltani et al., 2015 Ingestion rate for soil Irs Mg/day 200 100 100 100 USEPA, 2011 Exposed skin surface area (SA) cm2 1150 2145 2145 2145 Qi et al., 2014 Inhalation rate (InhR M3/day 7.6 12.8 12.8 12.8 Qi et al., 2014 Particle emission factor (PEF) M3/kg 1.36 × 10–9 1.36 × 10–9 1.36 × 10–9 1.36 × 10–9 USEPA, 2011 Skin to skin adherence factor (AF) Mg/cm2-d 0.2 0.65 0.65 0.07 Wang et al., 2018; Qi et al., 2014 Dermal absorption factor ABS Unitless 0.13 0.13 0.13 0.13 USEPA, 2011 Absorption factor for GITwater Unitless 1 1 1 1 Qi et al., 2014 Carcinogenic slope factor (CSF) for ingestion, inhalation, and skin absorption Mg/l/day 7.3, 3.8, and 25 7.3, 38, and 25 7.3, 38, and 25 7.3, 3.8, and 25 Singh & Agarwal, 2018 Acknowledgements The authors are grateful to the management of E&S Integrated Services Ltd., Owerri, for supporting this work. We appreciate International Energy Services Ltd., Port Harcourt, for providing their facility for this study. Data availability Data will be made available upon request. Declarations Conflict of interest The authors declare no competing interests. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abdel-Shafy HI Mansour MS A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation Egyptian Journal of Petroleum 2016 25 1 107 123 10.1016/j.ejpe.2015.03.011 Abderrahmane, B., Naima, B., Tarek, M., & Abdelghani, M. (2021). Influence of highway traffic on contamination of roadside soil with heavy metals. Civil Engineering Journal, 7(8), 1459–1471. E-ISSN: 2476–3055; ISSN: 2676–6957 Achten C Hofmann T Native polycyclic aromatic hydrocarbons ( PAH ) in coals – A hardly recognized source of environmental contamination Science of the Total Environment, the 2009 407 8 2461 2473 10.1016/j.scitotenv.2008.12.008 Adekunle AS Oyekunle JAO Ojo OS Maxakato NW Olutona GO Obisesan OR Determination of polycyclic aromatic hydrocarbon levels of groundwater in Ife north local government area of Osun State, Nigeria Toxicology Reports 2017 4 39 48 10.1016/j.toxrep.2016.10.002 28959623 Adetunde, O. T., Mills, G. A., Oluseyi, T. O., Oyeyiola, A. O., Olayinka, K. O., & Alo, B. I. (2018). Polycyclic aromatic hydrocarbon in vegetables grown on contaminated soils in a Sub Saharan tropical environment–Lagos, Nigeria. Polycyclic Aromatic Compounds. 10.1080/10406638.2018.1517807 Akinrinade, O. E., Stubbings, W., Abou-Elwafa Abdallah, M., Ayejuyo, O., Alani, R., & Harrad, S. (2020). Status of brominated flame retardants, polychlorinated biphenyls, and polycyclic aromatic hydrocarbons in air and indoor dust in AFRICA: A review. Emerging Contaminants, 6(June 1998), 405–420. 10.1016/j.emcon.2020.11.005 Anyanwu IN Sikoki FD Semple KT Quantitative assessment data of PAHs and N PAHs in core sediments from the Niger Delta Nigeria. Data in Brief 2020 33 106484 10.1016/j.dib.2020.106484 33209968 Björkman S (2006) Prediction of cytochrome P450-mediated hepatic drug clearance in neonates, infants and children: How accurate are available scaling methods? Clinical Pharmacy 2006 45 1 1 11 10.2165/00003088-200645010-00001 Brändli, R. C., Bucheli, T. D., Kupper, T., Mayer, J., Stadelmann, F. X., & Tarradellas, J. (2007). Fate of PCBs, PAHs and their source characteristic ratios during composting and digestion of source-separated organic waste in full-scale plants. Environmental Pollution, 148(2), 520–528. 10.1016/j.envpol.2006.11.021 Canadian Council of Ministers of the Environment(CCME). (2010). Canadian soil quality guidelines(carcinogenic and other polycyclic aromatic hydrocarbons(PAHs) ((revised). In Environmental and Human Health Effects Scientific Criteria Document. Chandra I Linthoingambi N Li J Zhang G Science of the total environment polycyclic aromatic hydrocarbons in house dust and surface soil in major urban regions of Nepal : Implication on source apportionment and toxicological effect Science of the Total Environment 2018 616–617 223 235 10.1016/j.scitotenv.2017.10.313 Daniel UI Utuh IA Nwaichi EO Profile of polycyclic aromatic hydrocarbons in soil environment around selected auto-technicians’ workshop in ObioAkpor, Rivers State Nigeria Journal of Environmental Protection 2020 11 05 399 10.4236/jep.2020.115023 Edokpayi JN Odiyo JO Popoola OE Msagati TAM Determination and distribution of polycyclic aromatic hydrocarbons in rivers, sediments and wastewater effluents in Vhembe District, South Africa International Journal of Environmental Research and Public Health 2016 8 135 10.3390/ijerph13040387 Emoyan, O. O., Onocha, E. O., & Tesi, G. O. (2020). Concentration assessment and source evaluation of 16 priority polycyclic aromatic hydrocarbons in soils from selected vehicle-parks in southern Nigeria. Scientific African, 7, e00296.10.1016/j.sciaf.2020.e00296 Enell A Reichenberg F Ewald G Warfvinge P Desorption kinetics studies on PAHcontaminated soil under varying temperatures Chemosphere 2005 6 10 1529 1538 10.1016/j.chemosphere.2005.04.092 Falcó G Domingo JL Llobet JM Teixidó A Casas C Müller L Polycyclic aromatic hydrocarbons in foods: Human Exposure through the Diet in Catalonia Spain. Journal of Food Protection 2003 66 12 2325 2331 10.4315/0362-028X-66.12.2325 14672232 Farrington JW Need to update human health risk assessment protocols for polycyclic aromatic hydrocarbons in seafood after oil spills Marine Pollution Bulletin 2020 10.1016/j.marpolbul.2019.110744 Feng B Li L Xu H Wang T Wu R Chen J Zhang Y Liu S Ho SSH Cao J Huang W PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in Beijing: Seasonal variations, sources, and risk assessment Journal of Environmental Sciences 2019 77 11 19 10.1016/j.jes.2017.12.025 Ferguson A Solo-Gabriele H Mena K Assessment for oil spill chemicals: Current knowledge, data gaps, and uncertainties addressing human physical health risk Marine Pollution Bulletin 2020 150 110746 10.1016/J.MARPOLBUL.2019.110746 31910518 Gerhardt KE Huang XD Glick BR Greenberg BM Phytoremediation and rhizoremediation of organic soil contaminants: Potential and challenges Plant Science 2009 176 1 20 30 10.1016/j.plantsci.2008.09.014 Hindersmann B Achten C Urban soils impacted by tailings from coal mining: PAH source identification by 59 PAHs, BPCA and alkylated PAHs Environmental Pollution 2018 242 1217 1225 10.1016/j.envpol.2018.08.014 30114601 Inam E Ibanga F Essien J Bioaccumulation and cancer risk of polycyclic aromatic hydrocarbons in leafy vegetables grown in soils within automobile repair complex and environ in Uyo Nigeria. Environmental Monitoring and Assessment 2016 188 12 1 9 10.1007/S10661-016-5695-3/TABLES/6 Jiang YF Wang XT Wang F Jia Y Wu MH Sheng GY Fu JM Levels, composition profiles and sources of polycyclic aromatic hydrocarbons in urban soil of Shanghai China. Chemosphere 2009 75 8 1112 1118 10.1016/j.chemosphere.2009.01.027 19201443 Keith, L. H. (2015). The source of U.S. EPA’s sixteen PAH priority pollutants. Polycyclic Aromatic Compounds, 35(2–4). 10.1080/10406638.2014.892886 Kim KH Jahan SA Kabir E Brown RJC A review of airborne polycyclic aromatic hydrocarbons(PAHs) and their human health effects Environment International 2013 60 71 80 10.1016/j.envint.2013.07.019 24013021 Kumar SN Verma P Bastia B Jain AK Health risk assessment of polycyclic aromatic hydrocarbons: A review Jakraya Journal of Pathology and Toxicology 2014 1 1 16 30 10.1016/j.envint.2013.07.019 Liu M Feng J Hu P Tan L Zhang X Sun J Spatial-temporal distributions, sources of polycyclic aromatic hydrocarbons (PAHs) in surface water and suspended particular matter from the upper reach of Huaihe River, China Ecological Engineering 2016 95 143 151 10.1016/j.ecoleng.2016.06.045 Ma Y Harrad S Spatiotemporal analysis and human exposure assessment on polycyclic aromatic hydrocarbons in indoor air, settled house dust, and diet: A review Environment International 2015 84 7 16 10.1016/j.envint.2015.07.006 26197059 Maliszewska-Kordybach B Polycyclic aromatic hydrocarbons in agricultural soils in Poland: Preliminary proposals for criteria to evaluate the level of soil contamination Applied Geochemistry 1996 11 1–2 121 127 10.1016/0883-2927(95)00076-3 Mboera LEG Akipede GO Banerjee A Cuevas LE Czypionka T Khan M Urassa M Mitigating lockdown challenges in response to COVID-19 in Sub-Saharan Africa International Journal of Infectious Diseases 2020 96 308 310 10.1016/j.ijid.2020.05.018 32437938 Mihankhah, T., Saeedi, M., & Karbassi, A. (2020). Contamination and cancer risk assessment of polycyclic aromatic hydrocarbons (PAHs) in urban dust from different land-uses in the most populated city of Iran. Ecotoxicology and Environmental Safety, 187(August 2019), 109838. 10.1016/j.ecoenv.2019.109838 Miller, R. L., Garfinkel, R., Lendor, C., Hoepner, L., Li, Z., Romanoff, L., & Whyatt, R. M. (2010). Polycyclic aromatic hydrocarbon metabolite levels and pediatric allergy and asthma in an inner‐city cohort. Pediatric Allergy and Immunology, 21(2p1), 260–267. 10.1111/j.1399-3038.2009.00980.x.Polycyclic Munyeza CF Rohwer ER Forbes PBC A review of monitoring of airborne polycyclic aromatic hydrocarbons: An African perspective Trends in Environmental Analytical Chemistry 2019 24 e00070 10.1016/j.teac.2019.e00070 Ni W Yang W Jin L Liu J Li Z Wang B Ren A Levels of polycyclic aromatic hydrocarbons in umbilical cord and risk of orofacial clefts Science of the Total Environment 2019 678 123 132 10.1016/j.scitotenv.2019.04.404 31075579 Nisbet ICT LaGoy PK Toxic equivalency factors (TEFs) for polycyclic aromatic hydrocarbons (PAHs) Regulatory Toxicology and Pharmacology 1992 16 3 290 300 10.1016/0273-2300(92)90009-X 1293646 Nwaichi EO Ntorgbo SA Assessment of PAHs levels in some fish and seafood from different coastal waters in the Niger Delta Toxicology Reports 2016 3 167 172 10.1016/j.toxrep.2016.01.005 28959536 Ohiozebau, E., Tendler, B., Codling, G., Kelly, E., Giesy, J. P., Jones, P. D. (2016). Potential health risks posed by polycyclic aromatic hydrocarbons in muscle tissues of fishes from the Athabasca and Slave Rivers, Canada. Environmental Geochemistry and Health. 10.1007/s10653-016-9815-3 Oliveira M Slezakova K Delerue-Matos C Pereira MC Morais S Children environmental exposure to particulate matter and polycyclic aromatic hydrocarbons and biomonitoring in school environments: A review on indoor and outdoor exposure levels, major sources and health impacts Environment International 2019 10.1016/j.envint.2018.12.052 Onyedikachi, U. B., Belonwu, C. D., Wegwu, M. O., Ejiofor, E., & Awah, M. F. (2019). Sources and cancer risk exposure of polycyclic aromatic hydrocarbons in soils from industrial areas in Southeastern, Nigeria. Journal of Chemical Health Risks, 9(3), 203–216. Orisakwe OE Crude oil and public health issues in Niger Delta, Nigeria: Much ado about the inevitable Environmental Research 2021 194 January 110725 10.1016/j.envres.2021.110725 33428909 Osu, C., & Asuoha, A. N. (2010). Polycyclic aromatic hydrocarbons (PAHs) and benzene, toluene, ethylbenzene, and xylene (BTEX) contamination of soils in automobile mechanic workshops in port-harcourt Metropolis, Rivers State, Nigeria. Journal of American Science, 6(9), 242–246. http://www.americanscience.org Oyewola OM Ajide OO Osunbunmi IS Oyewola YV Examination of students’ academic performance in selected mechanical engineering courses prior-to-and-during COVID-19 era Emerging Science Journal 2022 6 247 261 10.28991/esj-2022-SPER-017 Pahrudin P Liu LW Li SY Supryadi DI Addressing the Impact of COVID-19 and non-pharmaceutical for perception tourism using frequentist PLS-SEMs Emerging Science Journal 2021 5 197 214 10.28991/esj-2021-SPER-15 Pal D Hogland W An overview and assessment of the existing technological options for management and resource recovery from beach wrack and dredged sediments: An environmental and economic perspective Journal of Environmental Management 2022 302 113971 10.1016/j.jenvman.2021.113971 34715612 Parra YJ Oloyede OO Pereira GM de Almeida Lima PHA da Silva Caumo SE Morenikeji OA de Castro Vasconcellos P Polycyclic aromatic hydrocarbons in soils and sediments in Southwest Nigeria Environmental Pollution 2020 259 113732 10.1016/j.envpol.2019.113732 31884216 Peng, C., Chen, W. P., Liao, X. L., Wang, M. E., Ouyang, Z. Y., Jiao, W. T., et al. (2011). Polycyclic aromatic hydrocarbons in urban soils of Beijing: Status, sources, distribution and potential risk. Environmental Pollution, 159(3), 802–808. 10.1016/j.envpol.2010.11.003 Perera FP Li Z Whyatt R Hoepner L Wang S Camann D Rauh V Prenatal airborne polycyclic aromatic hydrocarbon exposure and child IQ at age 5 years Pediatrics 2009 124 2 195 202 10.1542/peds.2008-3506 Qi, H., Li, W. L., Zhu, N. Z., Ma, W. L, Liu, L. Y., Zhang, F., & Li, Y. F. (2014). Concentrations and sources of polycyclic aromatic hydrocarbons in indoor dust in China. Science of Total Environment, 491-492, 100–107. 10.1016/j.scitotenv.2014.01.119 Qu Y Gong Y Ma J Wei H Liu Q Liu L Chen Y Potential sources, influencing factors, and health risks of polycyclic aromatic hydrocarbons ( PAHs ) in the surface soil of urban parks in Environmental Pollution 2020 260 114016 10.1016/j.envpol.2020.114016 32041009 Santonicola S De Felice A Cobellis L Passariello N Peluso A Murru N Mercogliano R Comparative study on the occurrence of polycyclic aromatic hydrocarbons in breast milk and infant formula and risk assessment Chemosphere 2017 175 383 390 10.1016/j.chemosphere.2017.02.084 28236708 Singh, L., & Agarwal, T. (2018). Polycyclic aromatic hydrocarbons in diet: Concern for public health. Trends in Food Science and Technology, 79, 160–170. 10.1016/j.tifs.2018.07.017 Sojinu OSS Wang JZ Sonibare OO Zeng EY Polycyclic aromatic hydrocarbons in sediments and soils from oil exploration areas of the Niger Delta Nigeria. Journal of Hazardous Materials 2010 174 1–3 641 647 10.1016/j.jhazmat.2009.09.099 19833432 Soltani N Keshavarzi B Moore F Tavakol T Lahijanzadeh AR Jaafarzadeh N Kermani M Ecological and human health hazards of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in road dust of Isfahan metropolis Iran. Science of the Total Environment 2015 505 712 723 10.1016/j.scitotenv.2014.09.097 25461074 Sun, C., Qu, L., Wu, L., Wu, X., Sun, R., & Li, Y. (2020). Advances in analysis of nitrated polycyclic aromatic hydrocarbons in various matrices. Trends in Analytical Chemistry, 115878. 1016/j.trac.2020.115878org/10. Tarafdar A Sinha A Public health risk assessment with bioaccessibility considerations for soil PAHs at oil refinery vicinity areas in India Science of the Total Environment 2018 10.1016/j.scitotenv.2017.10 Tobiszewski, M., & Namieśnik, J. (2012). PAH diagnostic ratios for the identification of pollution emissionsources. EnvironmentalPollution, 162,110–119. 10.1016/j.envpol.2011.10.025 Ugochukwu UC Ochonogor A Jidere CM Agu C Nkoloagu F Ewoh J Okwu Delunzu VU Exposure risks to polycyclic aromatic hydrocarbons by humans and livestock (cattle) due to hydrocarbon spill from petroleum products in Niger-delta wetland Environment International 2018 115 12 38 47 10.1016/j.envint.2018.03.010 29547867 United States. Hazardous Site Control Division, United States. Environmental Protection Agency. Office of Solid Waste, & Emergency Response Soil screening guidance: User's guide United States, Environmental Protection Agency, Office of Solid Waste and Emergency Response. 1996 10.1016/j.apgeochem.2014.09.013 United States Environmental Protection Agency. (2011). Toxics release inventory. Available from: http://www.epa.gov/tri/index/htm USEPA. (2014). Priority pollutant list.gov. Retrieved December 22, 2018, from https://www.epa/sites/production/files/2015-09/documents/priority-pollutant-list-epa.pdf Vane, C. H., Kim, A. W., Beriro, D. J., Cave, M. R., Knights, K., Moss-hayes, V., & Nathanail, P. C. (2014). Applied geochemistry polycyclic aromatic hydrocarbons ( PAH ) and polychlorinated biphenyls ( PCB ) in urban soils of Greater London , UK. 51, 303–314. Wang C Yang Z Zhang Y Zhang Z Cai Z PAHs and heavy metals in the surrounding soil of a cement plant Co-Processing hazardous waste Chemosphere 2018 210 247 256 10.1016/j.chemosphere.2018.06.177 30005346 Wang W Huang MJ Kang Y Wang HS Leung AO Cheung KC Wong MH Polycyclic aromatic hydrocarbons (PAHs) in urban surface dust of Guangzhou, China: Status, sources and human health risk assessment Science of the Total Environment. 2011 409 21 4519 4527 10.1016/j.scitotenv.2011.07.030 21855957 Wirnkor, V. A., Ngozi, V. E., Ajero, C. M., Charity, L. K., Ngozi, O. S., Ebere, E. C., & Emeka, A. C. (2019). Biomonitoring of concentrations of polycyclic aromatic hydrocarbons in blood and urine of children at playgrounds within Owerri, Imo State, Nigeria. Environmental Analysis, Health and Toxicology, 34(4). Wu H Sun B Li J Polycyclic aromatic hydrocarbons in sediments / soils of the rapidly urbanized lower reaches of the River International Journal of Environmental Research and Public Health Article 2019 16 2302 10.3390/ijerph16132302 Yost EE Galizia A Kapraun DF Persad AS Vulimiri SV Angrish M Druwe IL Health effects of naphthalene exposure: A systematic evidence map and analysis of potential considerations for dose–response evaluation Environmental Health Perspectives 2021 129 7 076002 10.1289/EHP7381 34251878 Zabbey N Sam K Onyebuchi AT Remediation of contaminated lands in the Niger Delta, Nigeria: Prospects and challenges Science of the Total Environment 2017 586 952 965 10.1016/j.scitotenv.2017.02.075 28214111 Zhang Y Peng C Guo Z Xiao X Xiao R Polycyclic aromatic hydrocarbons in urban soils of China : Distribution, influencing factors, health risk and regression prediction Environmental Pollution 2019 254 112930 10.1016/j.envpol.2019.07.098 31374490 Zhu Y Duan X Qin N Lv J Wu G Wei F Science of the total environment health risk from dietary exposure to polycyclic aromatic hydrocarbons ( PAHs ) in a typical high cancer incidence area in southwest China Science of the Total Environment 2019 649 731 738 10.1016/j.scitotenv.2018.08.157 30176483	36446906	PMC9708509	NO-CC CODE	2022-12-01 23:20:30	no	Environ Monit Assess. 2023 Nov 30; 195(1):166	utf-8	Environ Monit Assess	2,022	10.1007/s10661-022-10670-z	oa_other
==== Front Eur J Pediatr Eur J Pediatr European Journal of Pediatrics 0340-6199 1432-1076 Springer Berlin Heidelberg Berlin/Heidelberg 36446889 4720 10.1007/s00431-022-04720-4 Research Use of music therapy in pediatric oncology: an Italian AIEOP multicentric survey study in the era of COVID-19 Giordano Filippo [email protected] 1 Muggeo Paola 2 Rutigliano Chiara 2 Barzaghi Federica 3 Battisti Laura 4 Coccia Paola 5 Colombini Antonella 6 D’Amico Maria Rosaria 7 De Santis Raffaella 8 Mascarin Maurizio 9 Mura Rossella 10 Onofrillo Daniela 11 Perruccio Katia 12 Rinieri Simona 13 Trevisan Francesca 14 Zama Daniele 15 Ziino Ottavio 16 De Lucia Marica 17 Santoro Nicola 2 Cesaro Simone 18 1 grid.7644.1 0000 0001 0120 3326 Department of Emergency and Organ Transplants, University of Bari Aldo Moro, Piazza G. Cesare 11, 70124 Bari, Italy 2 Department of Pediatric Oncology and Hematology, University Hospital of Policlinico, Bari, Italy 3 grid.18887.3e 0000000417581884 Pediatric Immunohematology and Bone Marrow Transplantation Unit, IRCCS San Raffaele Hospital, Via Olgettina 60, 20132 Milan, Italy 4 grid.415844.8 0000 0004 1759 7181 Division of Pediatric Hematology Oncology, Pediatric Central Hospital of Bolzano, Bolzano, Italy 5 grid.415845.9 Pediatric Hemato-Oncology Unit, Hospital Salesi, Azienda Ospedali Riuniti Ancona, Ancona, Italy 6 grid.7563.7 0000 0001 2174 1754 Pediatric Hematology-Oncology Unit, Department of Pediatrics, University of Milano-Bicocca, MBBM Foundation/ASST Monza, Monza, Italy 7 Pediatric Hematology-Oncology Unit, AORN Santobono-Pausilipon, Naples, Italy 8 grid.413503.0 0000 0004 1757 9135 Hemato-Oncology Unit, Department of Pediatrics, ‘Casa Sollievo Della Sofferenza’ Hospital, San Giovanni Rotondo, Italy 9 grid.414603.4 Adolescent and Young Adult Oncology and Pediatric Radiotherapy Unit, CRO-Centro Di Riferimento Oncologico Di Aviano, IRCCS, Aviano, Italy 10 Pediatric Oncology Unit, Azienda Ospedaliera Brotzu, Cagliari, Italy 11 grid.415245.3 0000 0001 2231 2265 Hematology-Oncology Department, Pediatric Hematology and Oncology Unit, Santo Spirito Hospital, Pescara, Italy 12 Pediatric Hematology Oncology, Azienda Ospedaliera Universitaria, Ospedale Santa Maria Della Misericordia, Perugia, Italy 13 grid.416315.4 Pediatric Onco-Hematology Unit, Sant’Anna University Hospital, Ferrara, Italy 14 grid.413181.e 0000 0004 1757 8562 Pediatric Onco-Hematology Unit - AOU Meyer Firenze, Florence, Italy 15 grid.6292.f 0000 0004 1757 1758 Department of Pediatrics, Pediatric Oncology and Haematology Unit “Lalla Seràgnoli”, Sant’ Orsola Malpighi Hospital, University of Bologna, Bologna, Italy 16 Department of Pediatric Hemato-Oncology, ARNAS Ospedali Civico, G. Di Cristina, Palermo, Italy 17 grid.4691.a 0000 0001 0790 385X Department of Biology, University Federico II of Naples, Naples, Italy 18 grid.411475.2 0000 0004 1756 948X Pediatric Hematology Oncology, Department of Mother and Child, Azienda Ospedaliera Universitaria Integrata Verona, Verona, Italy Communicated by Peter de Winter 30 11 2022 18 15 10 2022 1 11 2022 16 11 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Music therapy (MT) is a complementary therapy offered to children, young adults, and their families in pediatric oncology and palliative care. We performed a survey to collect information about MT in pediatric oncology in Italy. The outbreak of COVID-19 unavoidably changed the scenario of MT, suggesting some considerations presented in this survey. 27/32 (84.4%) centers belonging to the Infections and Supportive Therapy Working Group of Association of Pediatric Hematology and Oncology (AEIOP) completed in 2 different time points (T1 and T2) an online survey on MT, before and after COVID-19 pandemia. Different kinds of music approach were used taking care of patients in 21/27 centers, while in 14/21 (66%), a specific project of MT conducted by a music therapist was present. In 6/14 centers, MT activities were delivered for < 3 h/week, in 3 centers for > 3 and < 10 h/week, and in the remaining 5 for > 3 h/week. MT sessions were in different areas, day hospital, or ward (patient rooms, operating rooms, waiting rooms), on an individual basis or by groups. Patients were invited to MT by psychologists, caring physician, or nurse, or on equipé decision. MT was evaluated with tools self-made by music therapist in 11/14 centers. After COVID-19, MT has been withdrawn in 3 centers, sessions in the waiting rooms were reduced, individual sessions were preferred, and enrollment by multidisciplinary teams increased. Conclusion: This survey represents the starting platform to compare and discuss different experience of MT in AIEOP centers, to implement MT in pediatric oncology for a more qualified assistance to patients, and to improve quality of care.What is Known: • Music therapy in pediatric oncology and palliative care can be used for the management and prevention of various somatic and psychological symptoms of patients and often is provided to children together with their families. • In Italy the application of Music therapy in the AIEOP pediatric oncology centers is constantly increasing, but due to the outbreak of Covid-19 Pandemic, Italian pediatric oncology departments were obliged to adopt restrictive measures. What is New: • Although the majority of Centres did not abrogate MT interventions, judgment about limitation should be carefully taken since MT helps children and even more adolescents in their fight against cancer. • The best practice of Music therapy in pediatric oncology requires communication and collaboration among qualified music therapists and multidisciplinary care team, using a model of family-centered care that actively involves parents/ caregivers in assessment, treatment planning, and care delivery. Keywords Music therapy Children Adolescents Cancer Complementary therapy ==== Body pmcIntroduction Worldwide every year, it is estimated that there will be more than 400,000 cases of childhood cancer [1]. In Italy, considering the reported incidence of about 170 cases/1 million children, there will be about 7000 children and 4000 adolescents suffering from cancer on a 5-year basis. During the various stages of oncological disease, due to frequent invasive medical treatments and the need for possible prolonged hospitalizations, children and their families experience severe stress and a compromise in their quality of life [2]. Standard medical care is increasingly supplemented by the use of different psychological and psychosocial complementary interventions, with the aim of alleviating the negative effect of cancer and its treatment process [3–5]. Music therapy (MT), differently by other music-based approaches used in health (Fig. 1), has received increasing attention in the last few years [3].Fig. 1 Types of music-based interventions in health care and medical setting. The figure is in Stegemann et al. [4] Music Therapy and Other Music-Based Interventions in Pediatric Health Care: An Overview MT in pediatric oncology and palliative care can be used for the management and prevention of various somatic and psychological symptoms of patients [6–10] and often is provided to children together with their families [11]. Although in Italy the application of MT in the field of pediatric oncology is constantly increasing, a survey regarding the presence and application of MT in the Italian Pediatric Hematology-Oncology Association (AIEOP) cancer centers has never been conducted. Furthermore, due to the outbreak of COVID-19 pandemic, Italian pediatric oncology departments were obliged to adopt restrictive measures to minimize the risk of in-hospital infections in frail patients and staff members [12, 13], reducing accesses to the wards and psychological and psycho-social support activities as MT. The purpose of this survey study was to collect and summarize information about MT in pediatric oncology in order to advance our understanding about current clinical practice before pandemic across Italian AIEOP hospitals. Moreover, the outbreak of COVID-19 unavoidably changed the scenario of MT in AIEOP centers, suggesting some considerations presented in this survey. Design and methods Survey The survey was developed using SurveyMonkey®. Nine questions were prepared and sent out by e-mail to 32 hematology oncology centers belonging to the Infections and Supportive Therapy Working Group of the Italian Pediatric Hematology-Oncology Association (AIEOP). The question format used included check boxes for lists and was made up of 2 parts according to the aim of the survey:Questions 1 and 2 explored if the music was being used and by whom (music therapist, musicians, volunteers, nurses, medical doctor, or others). Questions 3–9 regarded the modality of delivering MT according to frequency, setting and characteristics (individualized or by groups), modality of patients’ enrollment in MT, and evaluation instruments. Questions 3 to 9 were addressed only for the centers where there was a certified music therapist (Table 1).Table 1 Survey questions Topic Questions Music-based intervention 1. Is music used for the well-being of patients in your center? Music therapy focus 2. Who uses a music-based intervention for the well-being of patients? Frequency 3. How many hours a week is music therapist present? Setting 1 4. Where music therapy session is provided? Setting 2 5. Which environments are used for the session? Individual vs group 6. How are the sessions? Enrollment 7. Who enrolled patients to music therapy? Evaluation 8. How is the evaluation carried out? The answers were required to represent the local policies, therefore, faithfully reflecting current practice and not personal opinion. Data collection Data were collected at the beginning of 2020, from February to March 2020 (T1). Likewise, the survey was repeated from October 2021 to January 2022 (T2) to find out the effect of COVID-19 restrictions on the practice of MT in the AIEOP centers. Data analysis The responses within Survey Monkey were downloaded into a Microsoft Excel (2010) spreadsheet and organized by data type and content. The data were analyzed descriptively with the assistance of a statistical consultant. Results A total of 27/32 centers (84.4%) filled in the survey questionnaire. The participating centers showed a national wide distribution (Fig. 2).Fig. 2 The AIEOP centers participating to the survey. Twenty-seven out of 32 contacted centers, belonging to the Infection and Supportive Therapy Working Group of the Italian Paediatric Hematology-Oncology Association (AIEOP) In T1, 21 out of 27 (77.7%) centers declared to use music with patients. In T2 centers decreased from 21 to 18 (66.67%) (Fig. 3a).Fig. 3 a Music-based intervention in AIEOP centers. b Who uses a music-based intervention? c Reported frequency of music therapy intervention provided by a certified music therapist. d Setting of music therapy interventions. e Use of music therapy interventions in practice settings. f Type of music therapy sessions. g Team members who enrolled patients to music therapy. h Tools used to determine effectiveness of music therapy In T1, 14 out of 21 (66.6%) declared to have a music therapist, while a musician or a volunteer was present in 5 centers each. In T2 the presence of music therapists decreased to 61.11% (11 out of 21). Professionals and volunteers also decreased from 23.81 to 11.11% (Fig. 3b). Frequency In T1, 6 centers provided MT for < 3 h/week (42.85%), 3 centers from 3 to 10 h per week (21.43%), and 5 centers more than 10 h per week (35.71%). In T2, 6 centers provided MT for < 3 h/week (54.54%), 1 center for 3 to 10 h per week (9.09%), and 4 centers (36.36%) for more than 10 h per week (Fig. 3c). Setting MT was delivered in different areas of the ward. In T1, MT was provided in day hospital (10 centers (71.43%)), in the ward (13 centers (92.86%)), or other (5 centers (35.71%)). In T2 all alternatives were lower than T1, but with the same trend (Fig. 3d). In both periods, the most used environments for MT sessions were hospital rooms, with 11 centers (78.57%) in T1 and 8 centers (72.73%) in T2. The use of waiting rooms decreased between T1 and T2 by 5.2%, while all other options increased (Fig. 3e). Individual vs group session In T1, 64.29% of MT sessions were carried out with both modalities (individual and group). This option fell by 37.02% in T2 (27.27%). In T2, individual sessions increased by 63.64% compared to T1 (28.57%) (Fig. 3f). Enrollment In T1 and T2, the psychologists enrolled patients to MT (92.86% vs 90.91%). In T2, the enrollment by both medical staff (64.29 to 54.55%) and nurses (42.86 to 18.18%) decreased. Enrollment by multidisciplinary teams increased in T2 compared to T1 (72.73% vs 57.14%) (Fig. 3g). Evaluation In T1 MT was evaluated with tools self-made by music therapist (11 centers (78.57%)) and multidisciplinary equipe (2 centers (14.29%)), validate scale/test (4 centers (28.57%)), and none (2 centers (14.29%)). In T2, evaluation of MT decreased in all centers. Despite this, tools self-made by music therapist increased (9 centers (81.82%)) (Fig. 3h). Discussion In this survey, we collected and analyzed information about practice of MT in pediatric oncology in 32 AIEOP centers in Italy. MT is defined as the systematic use of musical experiences aimed at achieving a therapeutic goal by a trained music therapist and implies the establishment of a relationship between patient, music, and music therapist [14, 15]. MT requires always a process of initial assessment, treatment, and evaluation. According to this definition, all other music-based interventions were excluded from this analysis. Based on our knowledge, this is the first survey collecting information about MT application in pediatric oncology in Italy. This survey shows that, before the outbreak of pandemic, MT was provided in about 50% of centers responding to the survey (14 out 27). After pandemic, MT has been suspended in 4 centers and provided in 1. This is likely related to the limitation of accesses to the ward adopted in the hospitals to contain the COVID-19 spreading. Nevertheless, most centers had been able to maintain MT interventions. MT was delivered with variable frequency, up to 3 h per week or more than 10 h, and sessions took place in different environments, mainly in the hospital rooms, and were both individual and by groups. As expected, in the post-COVID period, there was an increase of individual sessions. In the majority of cases, patients have been enrolled to MT based on the psychologist’s assessment (13/14 centers); however, in some cases, the caring medical doctor or the nurse selected patients; in 8 cases, patients have been sent to MT based on equipe assessment. This is a positive result, and it would be desirable for music therapist to be always included in the multidisciplinary team, in order to share aims from the beginning of the cure and systematically evaluate progress taking care of patients. Indeed, the survey reveals that evaluation seems to be done primarily using self-made tools by music therapist. The evaluation of MT interventions must be carried out with the highest possible qualitative and quantitative standards, but evidence-based research and real-world evidence should also be used. For this reason, unlike other music-based interventions, music therapist must work in synergy in a multidisciplinary team, which requires a clear, well-organized interdisciplinary and interprofessional approach. Published literature suggests that music therapy is considered helpful taking care of children and adults in many contexts, particularly in young patients with cancer and their families. MT is one of the non-pharmacological interventions that has been increasingly indicated in psycho-oncological support for its benefits in addressing symptoms such as anxiety, low mood, and pain [16]. Studies with children undergoing autologous stem-cell transplantation have showed that patients who received MT experienced significantly lower disturbances in mood, decrease of pain perception, and an overall positive effect on depression and anxiety [7]. MT can be considered an excellent support for anesthesia as a complementary/non-pharmacological approach to reduce pre-operative anxiety in children undergoing invasive procedure [6, 17]. Studies have explored how MT can contribute to psycho-physiological changes [18] and improve quality of life in children receiving palliative care [19]. In a recent worldwide survey among professional members of organizations affiliated with the World Federation of Music Therapy (n = 2495), about half of the respondents reported working with children/preteens (50.6%) and teens (45.7%), whereas 38.2% indicated working with infant/children [20]. In particular, in a US survey of music therapists working in pediatric medical settings, most respondents (76%) declared providing services to hematology/oncology patients and their families [21]. Our survey demonstrates that MT is active in daily practice also in some Italian Pediatric Oncology and Hematology Centres and is likely considered a helpful complementary and alternative therapy. In several countries, music therapy services are well-established in the field of pediatric oncology, and some treatment guidelines include creative art therapies for this specific client population [5, 20]. In a recent review, authors found that music therapy interventions delivered by a trained music therapist led to consistent results across studies for QoL and fatigue, and this was not the case for other music-based interventions [22]. We believe that promoting MT in pediatric oncology should be a gold standard of holistic approach to pediatric patients and their family. Among the 15 psychosocial standards (PPS) of the Psychosocial Standards of Care project, which address a wide range of needs for patients and families across the cancer continuum [23, 24], nine standards have been identified that they are implemented through music therapy intervention (PSS 1, 4, 6, 7, 8, 9, 10, 13, 15). The presence of a certified music therapists can help to improve the standards through research and evidence-based interventions, thanks to an innovative and tailored care and ongoing assessment to provide the best care when and where it is mostly needed [25]. The best practice of MT in pediatric oncology requires communication and collaboration among multidisciplinary care team using a model of family-centered care that actively involves parents/caregivers in assessment, treatment planning, and care delivery. However, it is important to note that to exploit the potential of music therapy in an optimal way, specialized academic and clinical training and careful selection of intervention techniques to fit the needs of the client are essential [25]. Conclusions MT should be implemented involving more AIEOP centers, and this survey could be the starting platform to compare and discuss different experience and to widen the knowledge and networking for a more qualified assistance to pediatric oncology patients and to improve quality of care. Particular attention should be paid to rearrangements of accesses due to COVID pandemia: although the majority of centers did not abrogate MT interventions, judgment about limitation should be carefully taken since MT helps children and even more adolescents in their fight against cancer. Limitations The applications of MT are different in AIEOP centers. However, this could be just guessed based on this survey, since our objective was to explore the status of MT in Italy. A more detailed questionnaire should explore different applications of MT in pediatric oncology in Italy. Acknowledgements The authors would like to thank Tim Trevor-Briscoe (music therapist) and Chiara Acler (music therapist) for their contribution in reviewing draft of survey questions. Collaborators Angelica Barone, MD, Pediatric Onco-Hematology Unit, Pediatric Clinic, Pietro Barilla Children’s Hospital, University of Parma, Parma, Italy Letizia Brescia, MD, Hemato-Oncology Unit, SS. Annunziata Hospital, 74100, Taranto, Italy Francesca Carraro, MD, Pediatric Onco-Hematology, Stem Cell Transplantation and Cellular Therapy Division, AOU Città della Salute e della Scienza, Regina Margherita Children Hospital, Turin, Italy Monica Cellini, MD, Pediatric Oncology-Hematology Unit, Department of Mother and Child, Azienda Ospedaliero Universitaria Modena, Modena, Italy Maura Faraci, MD, Dipartimento di Scienze Pediatriche Generali e Specialistiche - Polo di Emato-Oncologia-Trapianto IRCCS Istituto Giannina Gaslini - Genova Nagua Giurici, MD, Institute for Maternal and Child Health IRCCS Burlo Garofolo Trieste Milena La Spina, MD, Pediatric Hematology and Oncology Unit-AOU Policlinico “Rodolico-San Marco”, University of Catania, Catania, Italy Laura Lauti, MD, U.O. Oncoematologia Pediatrica AOUP S. Chiara Pisa Cristina Meazza, MD, Pediatric Oncology Unit, IRCCS INT Milan, Italy Maria Grazia Petris, MD, Clinic of Pediatric Hemato-Oncology, Department of Women's and Children's Health, University Hospital of Padova, Italy Elena Soncini, MD, Pediatric Oncohematology and Bone Marrow Transplant Unit, Children's Hospital, Spedali Civili, 25100, Brescia, Italy Authors’ contributions F.G.: Conceptualization, investigation, methodology, writing–original draft. P.M.: Conceptualization, supervision. C.R.: Methodology, supervision. F.B.: Collecting data and final approval of the version to be published. L.B.: Collecting data and final approval of the version to be published. P.C.: Collecting data and final approval of the version to be published. A.C.: Collecting data and final approval of the version to be published. M.R.D.: Collecting data and final approval of the version to be published. R.D.: Collecting data and final approval of the version to be published. M.M.: Collecting data and final approval of the version to be published. R.M.: Collecting data and final approval of the version to be published. D.O.: Collecting data and final approval of the version to be published. K.P.: Collecting data and final approval of the version to be published. S.R.: Collecting data and final approval of the version to be published. F.T.: Collecting data and final approval of the version to be published. D.Z.: Collecting data and final approval of the version to be published. O.Z.: Collecting data and final approval of the version to be published. M.DL.: Statistical analysis. N.S.: Writing review and editing. C.C.: Writing review and editing. Data availability It was not applicable deposit data in a public repository. Declarations Ethics approval Not applicable. Consent for publication Not applicable. Conflict of interest The authors declare no competing interests. Nicola Santoro and Simone Cesaro are co-last authors. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Atun R Bhakta N Denburg A Sustainable care for children with cancer: a Lancet Oncology Commission Lancet Oncol 2020 21 e185 e224 10.1016/S1470-2045(20)30022-X 32240612 2. Stubbs RM A review of attachment theory and internal working models as relevant to music therapy with children hospitalized for life threatening illness Arts Psychother 2018 57 72 79 10.1016/j.aip.2017.10.001 3. Facchini M Ruini C The role of music therapy in the treatment of children with cancer: a systematic review of literature Complement Ther Clin Pract 2021 42 101289 10.1016/j.ctcp.2020.101289 33316592 4. Stegemann T Geretsegger M Phan Quoc E Riedl H Smetana M Music therapy and other music-based interventions in pediatric health care: an overview Medicines (Basel) 2019 10.3390/medicines6010025 30769834 5. Clerici CA Veneroni L Giacon B Mariani L Fossati-Bellani F Complementary and alternative medical therapies used by children with cancer treated at an Italian pediatric oncology unit Pediatr Blood Cancer 2009 53 599 604 10.1002/pbc.22093 19492321 6. Ghetti CM (2012) Music therapy as procedural support for invasive medical procedures: toward the development of music therapy theory. Nord J Music Ther 21(1):3–35. 10.1080/08098131.2011.571278 7. Uggla L Bonde L-O Hammar U Wrangsjö B Gustafsson B Music therapy supported the health-related quality of life for children undergoing haematopoietic stem cell transplants Acta Paediatr 2018 107 1986 1994 10.1111/apa.14515 30043415 8. Giordano F Zanchi B Leonardis FD Rutigliano C Esposito F Brienza N Santoro N The influence of music therapy on preoperative anxiety in pediatric oncology patients undergoing invasive procedures Arts Psychother 2020 68 101649 10.1016/j.aip.2020.101649 9. Giordano F Rutigliano C Muggeo P Fusaro T Santoro N Ettore’s song: the hero who had the courage to fight Achilles J Health Psychol 2021 26 5 11 10.1177/1359105320968144 33106044 10. Nguyen TN Nilsson S Hellström A-L Bengtson A Music therapy to reduce pain and anxiety in children with cancer undergoing lumbar puncture: a randomized clinical trial J Pediatr Oncol Nurs 2010 27 146 155 10.1177/1043454209355983 20386063 11. Uggla L Mårtenson Blom K Bonde LO Gustafsson B Wrangsjö B An explorative study of qualities in interactive processes with children and their parents in music therapy during and after pediatric hematopoietic stem cell transplantation Medicines (Basel) 2019 10.3390/medicines6010028 30781617 12. Clerici CA Massimino M Ferrari A On the clinical psychologist’s role in the time of COVID-19, with particular reference to experience gained in pediatric oncology Psychooncology 2020 29 1374 1376 10.1002/pon.5418 32501640 13. Amicucci M Biagioli V Rostagno E Canesi M Bergadano A Botta D Crotti Partel M Organizational challenges in the pediatric onco-hematology units during the first and second wave of the COVID-19 pandemic: a national survey in Italy Clin Hematol Int 2022 4 99 106 10.1007/s44228-022-00010-w 36131127 14. Giordano F Losurdo A Quaranta VN Campobasso N Daleno A Carpagnano E Gesualdo L Moschetta A Brienza N Effect of single session receptive music therapy on anxiety and vital parameters in hospitalized Covid-19 patients: a randomized controlled trial Sci Rep 2022 12 3154 10.1038/s41598-022-07085-8 35210504 15. Bradt J, Dileo C, Shim M (2013) Music interventions for preoperative anxiety. Cochrane Database Syst Rev CD006908 16. Wong KC Tan BWZ Tong JWK Chan MY The role of music therapy for children undergoing cancer treatment in Singapore Healthcare 2021 9 1761 10.3390/healthcare9121761 34946487 17. Giordano F Rutigliano C De Leonardis F Rana R Neri D Brienza N Santoro N Covid-19 and absence of music therapy: impact on mother-child dyad during invasive procedures in pediatric oncology Arts Psychother 2021 75 101839 10.1016/j.aip.2021.101839 34334856 18. Delaney AM, Herbert AR, Bradford N, Bernard A (2022) Associations between music therapy, pain and heart rate for children receiving palliative care. Music Ther Perspect miac003 19. Giordano F Rutigliano C Baroni M Grassi M Muggeo P Santoro N Music therapy and pediatric palliative care: songwriting with children in the end-of-life World J Pediatr 2022 10.1007/s12519-022-00578-6 35773435 20. Kern P Tague DB Music therapy practice status and trends worldwide: an international survey study J Music Ther 2017 54 255 286 10.1093/jmt/thx011 28992203 21. Knott D Biard M Nelson KE Epstein S Robb SL Ghetti CM A survey of music therapists working in pediatric medical settings in the United States J Music Ther 2020 57 34 65 10.1093/jmt/thz019 31901199 22. Bradt J Dileo C Myers-Coffman K Biondo J Music interventions for improving psychological and physical outcomes in people with cancer Cochrane Database Syst Rev 2021 10.1002/14651858.CD006911.pub4 34637527 23. Wiener L Kazak AE Noll RB Patenaude AF Kupst MJ Standards for the psychosocial care of children with cancer and their families: an introduction to the special issue: pediatric psychosocial standards of care Pediatr Blood Cancer 2015 62 S419 S424 10.1002/pbc.25675 26397836 24. Quality of service, music therapy standards \| American Music Therapy Association (AMTA). https://www.musictherapy.org/. Accessed 30 Aug 2022 25. Knott D Krater C MacLean J Robertson K Stegenga K Robb SL Music therapy for children with oncology & hematological conditions and their families: advancing the standards of psychosocial care J Pediatr Hematol Oncol Nursing 2022 39 49 59 10.1177/27527530211059726	36446889	PMC9708510	NO-CC CODE	2022-12-01 23:20:30	no	Eur J Pediatr. 2022 Nov 30;:1-8	utf-8	Eur J Pediatr	2,022	10.1007/s00431-022-04720-4	oa_other
==== Front Curr Psychol Curr Psychol Current Psychology (New Brunswick, N.j.) 1046-1310 1936-4733 Springer US New York 3902 10.1007/s12144-022-03902-5 Article Effectiveness of an online short-term audio-based mindfulness program on negative emotions during the COVID-19 pandemic: Latent growth curve analyses of anxiety and moderated mediation effects of anxiety between mindfulness and negative affect Kang Man Ying 1 Nan Joshua K. M. [email protected] 2 Yuan Yue 3 1 grid.221309.b 0000 0004 1764 5980 Department of Social Work, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong 2 grid.194645.b 0000000121742757 Present Address: Department of Social Work and Social Administration, Hong Kong University, Hong Kong Island, Hong Kong 3 grid.412260.3 0000 0004 1760 1427 School of Psychology, Northwest Normal University, Lanzhou City, Gansu Province China 30 11 2022 113 18 10 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. This pilot study aims to explore the effects and mechanisms of a mindfulness-based intervention on negative emotions in community settings during the COVID-19 pandemic. Participants (N = 100) were randomized into an intervention group (n = 50) and a waitlist control group (n = 50). Participants in the mindfulness group underwent 3 weeks (21 sessions) of an online audio-based mindfulness-based intervention program and completed the online measures four times whereas those in the waitlist control group needed to complete the measures twice. Participants completed measures of the Hospital Anxiety and Depression Scale and Positive and Negative Affect Schedule. The results of the measures of the two groups were compared. Moderated mediation analysis was used to analyze intervention outcomes on negative affect through anxiety. Unconditional quadratic latent growth analysis was used to test the growth trajectories of anxiety. The results showed that this intervention program was effective at improving positive affect and at reducing depression, anxiety, and negative affect. The baseline anxiety moderator was found to be significant, and indirect effects of anxiety post-intervention were found between the mindfulness-based intervention and negative affect. Anxiety levels of participants were not at the same starting point and had similar but non-quadratic growth trajectories. The mindfulness-based intervention program was effective at promoting mental wellbeing and reducing mental problems in community settings in China. Mindfulness practices were beneficial to people with different anxiety levels but had more obvious benefits on anxiety and a negative affect for participants with low anxiety levels. Clinical trial registration: ISRCTN16205138 on 26/02/2021. Keywords Mindfulness Audio-based Community settings COVID-19 Anxiety Negative affect ==== Body pmcIntroduction Benefits of online mindfulness for negative emotions According to the World Health Organization report, from March 31 to May 2, 2021, the total number of confirmed cases of COVID-19 increased from 102,333 to 103,649 and the total number of deaths increased from 4,849 to 4,858 in China (World Health Organization, 2021), where the prevalence of depression, anxiety, and stress-related symptoms during the outbreak of COVID-19 has been reported to be 26.9%, 21.8%, and 48.1% respectively (Bareeqa et al., 2021). During the COVID-19 pandemic, depressive, stress, and anxiety symptoms increased (Leong Bin Abdullah et al., 2021). China implemented aggressive containment measures. Despite the small number of new cases, the Chinese government acted decisively to lock down the city to curb the spread of the contagious virus to other areas, including school closure, work suspension, and production stoppage, and community containment (Wang et al., 2021), so that face-to-face mental health treatment was largely halted in order to control virus transmission (Liu et al., 2020). Online psychological services and self-help mental health kits become essential in China. As one of the common mental health intervention approaches, mindfulness-based interventions (MBIs) help to cultivate consciousness in the current moment with non-judgement and intention (Kabat-Zinn, 2013). Individuals who perceive lower levels of depression, anxiety, and stress about COVID-19 have higher levels of mindfulness (Yalçın et al., 2022), indicating that mindfulness is a protective factor during the pandemic. Four to eight weeks of online home-based audio guided MBI is effective for adults’ anxiety, depression, and stress in different countries (Hall et al., 2018; Lahtinen et al., 2021; Si et al., 2021; Simonsson et al., 2021). A sample of communities trained in mindfulness practices has demonstrated a significant increase in positive affect (Garland et al., 2015). However, the effectiveness of 10–20-min online short-term (less than four weeks) audio-based mindfulness (SAM) programs on negative emotions and positive affect in community settings in China during the epidemic has not been tested. Therefore, the first contribution of this pilot study is to provide empirical evidence for the effects of a specific MBI. Potential mechanisms of anxiety between mindfulness and negative affect MBIs can reduce the negative emotions of adults, especially anxiety and negative affect (Sears & Kraus, 2009), and mindfulness can significantly predict lower levels of anxiety (de Abreu Costa et al., 2019) and negative affect (Raes et al., 2013). Mindfulness is negatively related to negative emotions as conscious actions or consideration promote self-regulation of thoughts and emotions (Gautam et al., 2019). Mindfulness is associated with less anxiety and depressive symptoms, as more mindful individuals have greater awareness and acceptance of negative emotions, less difficulty with impulse control, and more efficient use of emotion regulation strategies (Cheung & Ng, 2019). Negative affect is positively related to anxiety levels (Hughes & Kendall, 2009; Sauer-Zavala et al., 2012). The effects of anxiety symptoms can cause people to automatically pay attention to negative information that matches their negative emotions, and it is difficult not to pay attention to negative information, leading to a vicious cycle that exacerbates negative emotions (McKee et al., 2007). However, few empirical research studies have tested whether anxiety can positively predict negative affect. Therefore, the second contribution of this pilot study is to provide empirical evidence of the relationship between anxiety and negative affect. This pilot study proposes the hypothesis that mindfulness will mitigate negative affect via changes of anxiety based on previous research and will fill the research gap. Additionally, one study found no significant linear relationship between anxiety levels at baseline and the effect of MBI, but the curve estimates displayed an inverted U-shaped approximation. The study proposed that individuals with moderate anxiety received the most benefits from MBIs compared with those having low or high levels of anxiety. For individuals with high levels of anxiety, medical intervention is superior to psychotherapy (Chen et al., 2013). Thus, the baseline anxiety may influence the effectiveness of MBIs on negative affect and this study will further examine this. Thus the third contribution of this pilot study is to provide some empirical evidence support for the mechanisms and theories of MBIs’ impact on negative affect. Differences in the effect of MBIs on anxiety of individuals The wide use of MBIs needs careful and rigorous consideration. Most researchers suggest the application of MBIs to different samples, but they assume that the MBIs’ effectiveness for each individual is homogeneous based on the group average outcomes (Tang & Braver, 2020). However, empirical research demonstrates the existence of individual differences when receiving the same psychotherapies (Caspi & Bell, 2004; Gully et al., 2002). Without attention to individual differences, MBIs may not achieve the desired effects, and individual differences may make the results of empirical studies irreproducible (MacCoon et al., 2014; Rosenkranz et al., 2013) because samples have characteristics that may make them more responsive to treatment (Tang & Braver, 2020). The differences in the efficacy of MBI may be due to various reasons, including personality traits (Uher, 2011), participants’ age (Halladay et al., 2019), or education levels (Akase et al., 2020) and so on. These individual differences, which can lead to receiving the same MBI but having different outcomes, are particularly relevant for anxiety (Fumero et al., 2020), because compared with other psychiatric disorders, MBIs have the greatest impact on anxiety disorders (Khoury et al., 2013). From a practical standpoint, consideration of individual differences in treatment response is important for the popularity of MBIs in different settings, as this will allow for a better understanding of specific subgroups that are most or least likely to effectively achieve desired outcomes (Tang & Braver, 2020). Therefore, this pilot study will explore the individual differences in anxiety of participants after MBIs to observe whether they can achieve valuable results. So, the fourth contribution is to determine which anxiety subgroups MBIs are more effective for, and to provide a reference for future mindfulness training. Objectives To test the effects of the SAM on negative emotions (e.g., negative affect, anxiety, and depression) and positive affect between the mindfulness group and the waitlist control group. To test the mediation and moderation effects of anxiety between SAM and negative affect after a three-week intervention. To explore individual differences and weekly change patterns of mindfulness treatment effects on anxiety. Hypotheses SAM would influence negative affect, anxiety, depression, and positive affect. Anxiety at post-intervention would mediate the relationship between SAM and negative affect at post-intervention. Baseline anxiety would moderate the mediation relationship between SAM and negative affect at post-intervention. There would be individual differences in the effects of weekly SAM on anxiety. Methods Participants Sample size estimation using GPower for repeated-measures analysis of variance (ANOVA) with two groups (mindfulness group and waitlist control group) and four phases (baseline and at 1, 2, and 3 weeks) implied that 74 participants are required to test a small to medium effect size (f = 0.25), with alpha = 0.05, and power = 0.95. The small to medium effect size is in line with previous studies (Mak et al., 2018). Due to withdrawal, dropout, technical failure, and data loss, the attrition rate is estimated as 30% (Torous et al., 2020). The pilot study therefore needs to recruit at least 97 participants. The researchers disseminated recruitment information to online community groups and recruited participants from various community settings in China via the Internet or by phone. Inclusion criteria consist of those who were over 18 years old, lived in community settings, could understand and read Mandarin, had consistent Internet access, could receive audio every day, and had spare time to listen to the training audio for 10–20 min every day for 21 consecutive days. Exclusion criteria include those who had practiced mindfulness meditation before, were currently receiving any medication or psychotherapies, or had been diagnosed with depression, anxiety, or other mental illness. Participants were assigned to two groups in an expected 1:1 allocation ratio (intervention group, n = 50; waitlist control group, n = 50) using a computer-generated random number. Each participant was assigned a number code matched with their online assessment forms to ensure that personal identities cannot be ascertained from the questionnaires. In terms of the blinding setting, the trial is open label, so both the researchers and participants know which intervention groups the participants are assigned to. Intervention In the SAM group, participants received one 10–20-min audio consisting of mindfulness practices every day via social media, such as WeChat or QQ, for a period of 21 days. Participants confirmed whether they had completed the practice each day by answering a question about the audio content and how they felt about the audio sent via a group sending function of chat tools on the following day before noon. If a participant missed an audio session, he/she could make up for it in the next training session and catch up by listening to the audios he/she had missed. If participants did not reply to the message, researchers reminded them again before sending another new audio. Participants were deemed to automatically drop out if they did not respond to messages for more than 3 days or did not complete the scales during the intervention. Researchers kept a record of attendance or missed sessions for the participants every day. The 21-session mindfulness program is adapted from an online mindfulness-based model developed in a previous study (Harnett et al., 2010), including typical and traditional mindfulness practices. The order of mindfulness practice in the audio in the first week included 10-min mindful sitting, 20-min body scan, 15-min mindful breathing, 20-min mindful eating, 15-min mindful walking, 3-min breathing space plus 15-min mindful yoga, as well as 15-min flashlight mindfulness. The 7 audio files were recorded by the third author who spoke fluent Mandarin and has practiced mindfulness meditation for three years and were reviewed by a qualified mindfulness instructor. The content of mindfulness practice in the first week was repeated in the second and third weeks. In the waitlist control group, participants only needed to complete the scales and follow their usual routine. Participants of both groups started and ended the practices on the same dates. Participants who completed the intervention and all measures received an exquisite gift that was selected by them, not limited to dolls, small fans, water cups, notebooks, umbrellas, clothes, or coupons and they could also receive free counseling sessions from qualified counselors. Measures All participants provided demographics and background information, such as phone number, age, gender, education level, and marital status. Participants in the SAM group filled in online measures at baseline, 7-days, 14-days and 21-days of mindfulness practice while those in the control group filled in scales at baseline and 21-days. Depression & anxiety The Chinese Hospital Anxiety and Depression Scale is a 4-point Likert scale from 0 to 3 and has 7-item depression (DEP) and 7-item anxiety (ANX) subscales (Leung et al., 1993). The reliability for the anxiety subscale was 0.82 and that for the depression subscale was 0.71 (Spinhoven et al., 1997). In this study, the alpha coefficient of reliability for the anxiety subscale was 0.76 at baseline, 0.73 at 1-week, 0.78 at 2-weeks, and 0.75 at 3-weeks respectively while that for the depression subscale was 0.72 at baseline, 0.74 at 1-week, 0.76 at 2-weeks, and 0.78 at 3-weeks respectively. Positive affect & negative affect The Five-point Likert Positive and Negative Affect Schedule measures mood status, involving a 10-item positive affect (PA) subscale and a 10-item negative affect (NA) subscale (Watson et al., 1988). This scale was validated in for the Chinese population including teenagers and young adults (Huang et al., 2003). In this study, the alpha coefficient of internal consistency for the PA subscale was 0.89 at baseline, 0.88 at 1-week, 0.90 at 2-weeks, and 0.91 at 3-weeks respectively, while that for the NA subscale was 0.92 at baseline, 0.91 at 1-weeks, 0.94 at 2-weeks, and 0.96 at 3-weeks respectively. Statistical analysis The study used the IBM SPSS version 25.0. Baseline characteristics of the two groups were analyzed via independent t-tests and chi-square tests. In the two groups, within-group effects were analyzed via paired sample t-test and between-group effects via independent sample t-test. The effect sizes of 0.2, 0.5, and 0.8 were denoted by Cohen’s d (d) as small, medium, and large effect sizes (Cohen, 2013). A repeated-measure ANOVA was used to test data changes over time and partial eta-squared (ηp2) was used as the effect size in the SAM group. The moderated mediation model was tested by using PROCESS SPSS computational tool (Hayes, 2013). Bootstrapping procedures were set to 5,000 samples to test the estimated indirect effect, that was, a 95% confidence interval of indirect effect did not include zero. Simple slope test by the pick-a-point approach and the Johnson-Neyman approach was used to further explore moderation effects. The processes of the pick-a-point approach included selecting the values of the moderator, calculating conditional effects of independent variables on dependent variables on those values, and generating confidence intervals (Hayes & Matthes, 2009). In the moderated mediation model (Fig. 1), the binary independent variable was Group (1 = SAM, 0 = Control) and Time 3 negative affect (T3NA) was the dependent variable. Anxiety at baseline (T0ANX) was set as the moderator while anxiety at Time 3 (T3ANX) was set as the mediator. T0ANX was not related to the intervention because of randomization, so it fulfilled the requirement of moderator according to Kraemer et al. (Chmura Kraemer et al., 2008). GroupT0ANX was the product of Group and mean-centered T0ANX to explore the interaction effects. Age, gender, education, and marital status were regarded as control variables in the model. Conditional effects through plots and tests of simple effects to interaction effects (Mean-1SD, Mean, Mean + 1SD) across levels of T0ANX were explored and observed (Preacher et al., 2007).Fig. 1 Moderated mediation model The unconditional quadratic latent growth model (LGM), which allowed non-linear growth on variable, explored the trajectories of anxiety and interpersonal differences in anxiety changes in the intervention group (Duncan & Duncan, 2004) in Mplus 8.0 under the robust maximum likelihood estimator (Muthen & Muthen, 2017). In LGM, latent intercept represented the initial level of anxiety trajectory while the slope was the speed of change of anxiety trajectory. Quadratic growth factors were added into the model to examine whether it had a better model fitting the trajectory shape. The Chi-square (χ2), degree of freedom (df), comparative fit index (CFI), root mean squared error of approximation (RMSEA) and standardized root mean square residual (SRMR) were used to measure model fits (Hu & Bentler, 1999). Results Demographic information A total of 120 participants were recruited. However, 8 participants were ineligible, 4 participants declined to participate, and 8 participants did not sign the consent form, producing a total of 100 samples. Then 50 samples were randomized to the intervention group and 50 samples were randomized to the control group. All participants finished the trial without dropout during the study (Fig. 2). Table 1 displays the demographic information of the samples in the two groups. In the intervention group, the mean age of samples was 37.9 years (SD = 8.16, Min = 23, Max = 55). 48% of the people were males and 52% females. Most samples had high school or college education (60%) and were married (84%). The demographic variables of the two groups did not show a significant difference at T0 (p > 0.05).Fig. 2 Flow diagram of randomized controlled trial Table 1 Demographic and profiles of participants in two groups Demographic SAM (n = 50) Control (n = 50) p Mean (Min) SD (Max) Mean (Min) SD (Max) Age (Years) 37.90 (23) 8.16 (55) 36.02 (23) 7.83 (55) .24 Frequency Percent Frequency Percent Gender Male 24 48 23 46 .84 Female 26 52 27 54 Education Junior secondary and below 1 2 6 12 .19 High School or college degree 30 60 28 56 Bachelor degree 18 36 16 32 Master degree and above 1 2 0 0 Marital Status Unmarried 7 14 13 26 .21 Married 42 84 37 74 Divorced 1 2 0 0 Widowed 0 0 0 0 SD = Standard deviation, Min = Minimum, Max = Maximum, p = Significant level Intervention outcomes Table 2 presents the results of within-group and between-group effects. Compared with those in the control group, participants in the intervention group showed significantly improved positive affect (F = 10.75, p < 0.001, d = 0.18), reduced anxiety (F = 41.41, p < 0.001, d = 0.46), depression (F = 66.93, p < 0.001, d = 0.58), and NA (F = 7.66, p < 0.001, d = 0.14) after 3-weeks SAM. As for the control group, there was no significant difference in participants’ mental health data during the intervention period. There was no significant difference between the two groups at T0, but there was a significant difference in all scale scores at T3. The scores of PA (t = 2.46, p < 0.05, d = 0.49) in the SAM group were significantly higher than those in the control group at T3. Moreover, the scores of NA (t = -2.14, p < 0.05, d = 0.43), ANX (t = -7.12, p < 0.001, d = 0.81), and DEP (t = -8.07, p < 0.001, d = 0.62) in the SAM group were significantly lower than those in the control group at T3.Table 2 Results of within-group and between-group outcomes Within-group effects Group Variable Mean SD Lower Bound Upper Bound F/t-test ηp2/d SAM PA 10.75* .18 T0 29.30 5.34 27.78 30.82 T1 30.88 5.55 29.30 32.46 T2 32.14 5.92 30.46 33.82 T3 32.80 6.41 30.98 34.62 NA 7.66* .14 T0 20.98 6.52 19.13 22.83 T1 19.28 5.44 17.73 20.83 T2 18.60 4.62 17.29 19.91 T3 18.06 5.08 16.62 19.50 ANX 41.41* .46 T0 15.10 2.25 14.46 15.74 T1 12.68 1.99 12.11 13.25 T2 12.78 2.05 11.20 13.36 T3 12.36 2.36 10.69 13.03 DEP 66.93* .58 T0 12.60 1.77 12.10 13.10 T1 9.56 1.88 9.03 10.09 T2 9.38 1.82 8.86 9.90 T3 8.96 1.97 8.40 9.52 Control PA .03 .00 T0 29.48 6.13 27.87 31.09 T3 29.46 7.14 26.56 31.37 NA .38 .05 T0 20.74 6.22 18.95 22.53 T3 20.42 5.94 18.87 21.97 ANX .18 .03 T0 15.84 2.88 15.11 16.57 T3 15.76 2.41 15.09 16.43 DEP -1.43 .13 T0 12.28 2.15 11.73 12.83 T3 12.32 2.19 11.74 12.90 Between-group effects Time Scale Group Mean SD Lower Bound Upper Bound t d T0 PA SAM 29.30 5.34 -2.46 2.10 -.16 .03 Control 29.48 5.34 NA SAM 20.98 6.52 -2.29 2.77 .19 .03 Control 20.74 6.22 ANX SAM 15.10 2.25 -1.77 .29 -1.43 .09 Control 15.84 2.88 DEP SAM 12.60 1.77 -.46 1.10 .81 .16 Control 12.28 2.15 T3 PA SAM 32.80 6.41 .65 6.03 2.46* .49 Control 29.46 7.14 NA SAM 18.06 5.08 -4.55 -.17 -2.14* .43 Control 20.42 5.94 ANX SAM 12.36 2.36 -4.35 -2.45 -7.12* .81 Control 15.76 2.41 DEP SAM 8.96 1.97 -4.19 -2.53 -8.07* .62 Control 12.32 2.19 p < .05, p < .01, p < .001. SD = Standard deviation Conditional mediation model Table 3 shows regression estimates in the mediation model, moderated mediation model, and conditional effects. As for the mediation model, there was a negative and significant total effect between Group and T3NA (β = -2.43, p < 0.05). T3ANX partially mediated the relationship between Group and T3NA (Indirect effect: β = 4.09, CI = 2.20 to 6.23; Direct effect: β = -6.52, p < 0.001). Controlling for the auto-regressive effect of demographic variables, mediation analysis revealed that Group was significantly negatively related to T3ANX (β = -3.48, p < 0.001) and T3NA (β = -6.52, p < 0.001). Higher T3ANX predicted flatter T3NA (β = -1.18, p < 0.001). The mediation model explained around 39% of the total T3ANX (F = 11.90, p < 0.001) variance and 32% of the total T3NA (F = 7.15, p < 0.001). Moderated mediation analysis implied that indirect effects of SAM on T3NA through T3ANX were significantly moderated by T0ANX, and there was a positive and significant effect of the interaction term on T3ANX (β = 0.38, p < 0.05). The moderated mediation model explained around 54% of the total T3ANX variance (F = 15.25, p < 0.001) and 32% of the total T3NA (F = 7.39, p < 0.001). These results indicate that people’s NA can be effectively reduced through lower anxiety via three-week SAM and different levels of baseline anxiety of participants can influence the mediation relationship.Table 3 Regression estimates in mediation model, moderated mediation model and conditional effects Results of mediation analysis DV IV β SE t LLCI ULCI F R2 T3ANX Constant 14.72* 1.86 7.90 11.02 18.42 11.90* .39 Group -3.48* .49 -7.16 -4.44 -2.51 Age .08* .03 2.25 .01 .14 Gender -.56 .48 -1.16 -1.52 .40 Education .02 .40 .05 -.77 .81 Marital Status -.49 .60 -.82 -1.68 .70 T3NA Constant 40.05* 4.91 8.15 30.29 49.80 7.15* .32 Group -6.52* 1.23 -5.29 -8.97 -4.07 T3ANX -1.18* .21 -5.58 -1.59 -.76 Age -.06 .07 -.92 -.20 .07 Gender -1.07 .99 -1.08 -3.04 .90 Education .22 .82 .26 -1.40 1.84 Marital Status 1.39 1.23 1.14 -1.04 3.83 Total effect -2.43* 1.14 -2.13 -4.69 -.17 Direct effect -6.52* 1.23 -5.29 -8.97 -4.07 Indirect effect 4.09 1.03 2.20 6.23 Results of moderated mediation analysis T3ANX Constant 16.14* 1.69 9.58 12.79 19.49 15.25* .54 Group -3.06* .43 -7.05 -3.92 -2.20 T0ANX .27* .11 2.45 .05 .48 GroupT0ANX .38 .18 2.13 .03 .73 Age .06 .03 1.92 .00 .12 Gender -.73 .43 -1.70 -1.58 .12 Education -.07 .37 -.19 -.81 .66 Marital Status -.73 .53 -1.38 -1.79 .32 T3NA Constant 40.05* 4.91 8.15 30.29 49.80 7.39* .32 Group -6.52* 1.23 -5.29 -8.97 -4.07 T3ANX -1.18* .21 -5.58 -1.60 -.76 Age -.07 .07 -.92 -.20 .08 Gender -1.07 .99 -1.08 -3.04 .90 Education .22 .82 .26 -1.41 1.84 Marital Status 1.39 1.23 1.14 -1.04 3.83 Results of conditional effects Label T0ANX Effect t SE LLCI ULCI Direct effect - -.52* -5.29 1.23 -8.97 -4.07 Conditional effects of Group on T3ANX -2.60 -4.04* -6.70 .60 -5.24 -2.85 .00 -3.06* -7.05 .43 -3.92 -2.20 2.60 -2.07* -3.11 .67 -3.39 -.75 Conditional indirect effects of Group on T3NA -2.60 4.76 - 1.18 2.53 7.14 .00 3.60 - .93 1.92 5.57 2.60 2.44 - .97 .80 4.60 p < .05, p < .01, p < .001. DV = Dependent variable, IV = Independent variable, β = Unstandardized estimates, SE = Standard error, LLCI = Lower limit confidence interval, ULCI = Upper limit confidence interval Figure 3a shows the interaction effect of T0ANX on the relationship between Group and T3ANX. Participants with high levels of T0ANX had less reduction in T3ANX in the SAM group than participants with moderate and low baseline anxiety levels. Apart from the moderation effect mentioned above, the Johnson-Neyman approach was used to examine when the effects took place. Figure 3b describes the conditional indirect effects of T0ANX with confidence bands. The results of the Johnson-Neyman approach showed when the effects of SAM on T3ANX were significant across the levels of T0ANX. The conditional indirect effect was statistically significant with the 95% CI when T0ANX was less than 3.72. The results indicate that participants whose baseline anxiety level was lower than 3.72 can effectively reduce their NA through the lower anxiety via three-week SAM.Fig. 3 Interaction effect and conditional indirect effect with confidence bands Unconditional quadratic latent growth model Table 4 presents the coefficients information of quadratic unconditional LGM. Model fit of quadratic LGM (χ2 = 13.85, df = 5, p < 0.05, CFI = 0.90, RMSEA = 0.19, and SRMR = 0.05) was acceptable and better than linear LGM (χ2 = 38.03, df = 13, p < 0.001, CFI = 0.7, RMSEA = 0.20, and SRMR = 0.12). Therefore, this study just showed the results of quadratic LGM. The intercepts of age (B = 0.32, p < 0.05) and of education (B = 0.26, p < 0.05) were significant, indicating that participants had significant differences in age and education regarding their initial levels of anxiety. The means of intercept, slope, and quadratic were 6.92 (p < 0.01), -1.19 (p > 0.05), and -0.36 (p > 0.05), demonstrating that participants’ anxiety levels were not at the same starting point and that they had similar decreased but insignificant and nonquadratic growth trajectories. The variances of intercept, slope, and quadratic were not significant (p > 0.05), which indicates the homogeneity of the individuals’ starting levels, and the different rates of anxiety levels did not show obvious inter-individual differences. The correlation between slope and intercept was not significant (β = -2.21, p > 0.05), indicating that higher baseline anxiety levels did not predict more obvious reduction in anxiety after participants receiving MBI. The relationship coefficients between quadratic factor and intercept and slope were 0.60 (p > 0.05) and -0.02 (p > 0.05), implying that both intercept and slope were not related to quadratic factor. Figure 4a displays the unconditional quadratic LGM of anxiety with standardized coefficient and Fig. 4b shows developmental trajectories of the mean of anxiety over time. The levels of anxiety decreased obviously in the first week, then rebounded slightly, and finally dropped slightly. The results indicate that there were individual differences in baseline anxiety, which may be affected by their education or age. After three weeks SAM, the individuals’ anxiety development trends were similar. All of them decreased in the first week, rebounded in the second, and continued to reduce in the third week.Table 4 Coefficients information of unconditional quadratic LGM Items Estimate SE Est/SE Mean Intercept 6.92 2.41 2.87 Slope -1.19 2.61 .46 Quadratic -.36 .88 -.41 Variance Intercept 4.03 2.60 1.55 Slope .89 2.92 .31 Quadratic -.11 .25 -.43 Covariance Slope with Intercept -2.21 3.00 -.74 Quadratic with Intercept .60 .77 .78 Quadratic with Slope -.02 .72 -.03 Intercept on Age .32* .13 2.85 Gender .23 .14 1.70 Education .26* .11 2.60 Marital Status .09 .10 .93 Slope on Age -.19 .35 -.65 Gender -.05 .23 -.22 Education -.57 .63 -2.40 Marital Status .06 .18 .32 p < .05, p < .01, ** p < .001. SE = Standard error, Est/SE = Estimate divided by SE Fig. 4 The unconditional quadratic LGM and developmental trajectories of anxiety Discussion Intervention effects for negative emotions SAM can improve PA and reduce NA, anxiety, and depression in 3 weeks. The results were consistent with previous studies that SAM can be beneficial for individuals in the community (Mak et al., 2018). Apart from the effectiveness of the program, an expected finding of this pilot study is the different timing of changes of variables during the intervention period. Participants’ anxiety and depression reduced remarkably in the first week of MBI. A possible mechanism for these quick and effective outcomes is that the benefit of mindfulness meditation could be the state relaxation effect mediated by the activation of parasympathetic functions when participants try to develop their awareness of breathing sensations and accept pressure sources (e.g. worried thoughts, negative moods, and suffering) (Harrison et al., 2017; Jerath et al., 2015). The 10-min audio-instructed mindfulness meditation condition can elevate heart rate variability (Azam et al., 2019), which is an index of continuous and real-time changes in parasympathetic function at rest and under specific conditions (Allen et al., 2007). The role of anxiety in the intervention effects and the trajectories of anxiety A new contribution of this pilot study is the discovery of how anxiety directly mediated the relationship between SAM and NA. Previous studies using community samples (Chen et al., 2013; Schumer et al., 2018) found that brief MBI directly predicted not only reduced NA, but also decreased anxiety, and identified a correlation between anxiety and NA. In this study, anxiety was proven to be the mediator between SAM and NA. Moreover, baseline anxiety moderated the relationship between SAM and post-intervention NA through post-intervention anxiety. More specifically, the indirect effects of the SAM on post-intervention NA through post-intervention anxiety were weakened in participants with higher levels of anxiety. These findings are consistent with a previous study that reported that the sensitivity of higher levels of anxiety was significantly related to lower levels of awareness and acceptance of mindfulness skills (McKee et al., 2007). People who readily experience negative moods, such as sadness, anxiety, and anger, may not focus on current activities or the present moment in the short term; similarly, those with restricted abilities to focus on their current states without judging themselves (or others) are more likely to experience NA (McKee et al., 2007). Studies showed that standardized MBIs have large effect sizes on reducing anxiety and depression symptoms (Snippe et al., 2017). One mindfulness session or ultra-short mindfulness practice, on the other hand, such as a 5-min lesson or 10 min of practice every day for 2 weeks, has a small effect size on decreasing anxious symptoms and NA (Schumer et al., 2018). Therefore, people with higher levels of anxiety may need more frequent and longer MBIs. SAM may be more suitable to a low-anxiety level or non-clinical population looking for stress-reduction interventions, and the potential of online and self-help interventions for promoting community mindfulness practices is proved (Cavanagh et al., 2018). This pilot study found that SAM has similar effects on people of different backgrounds, but at different levels. The initial levels of anxiety of the participants were different, but their anxiety reduction trends were similar during and after intervention, which means SAM is suitable for people with different anxiety levels. This differs from the study by Kim et al. (2020), in which the trend of anxiety levels showed individual differences. This may be because they did not include the covariates in the model, which may influence the model fit and final results. The reasons for the non-significant slope of anxiety in the present study may be that the intervention and measurement time were too short, so the anxiety of most participants in the short-term (3 weeks) intervention had similar trajectories. They might show different trajectories if the practice time became longer. Therefore, the anxiety trajectories for longer practice time need to be further explored in future studies. Limitations and future directions The first limitation of this pilot study is that it did not provide any information on the enduring effects of SAM. Further research with a follow-up study is required to confirm whether there is maintenance of observed effects or increasing effects for individuals who begin to practice mindfulness regularly. The second limitation is that there was no active control group in this study. Although only pre- and post-tests are sufficient to draw conclusions (Zhang & Zhang, 2021), in the absence of an active control intervention, any observed group differences may simply be due to nonspecific treatment effects (i.e., placebo effects). In the future, when resources and funds are sufficient, the number of measurements in the control group could be made the same as that in the intervention group. The third limitation is that the current recruitment procedure may potentially lead to a selection bias. The use of online platforms as a primary source of recruitment may result in the exclusion of older populations, as there may be an age divide in the use of new information and communication technologies by the elderly (Nimrod, 2017). If targeting community-dwelling elderly, future efforts in the field of online therapy should broadly address this issue (Fischer et al., 2014). Conclusion This pilot study may offer a promising line of future research, indicating that SAM may be a meaningful route into mindfulness practice for some people to reduce negative emotions, especially low-anxiety levels of a non-clinical population. Author contribution Man Ying KANG: Conceptualization, methodology, formal analysis and investigation, writing-original draft preparation. Dr. Joshua NAN: Writing-review and editing, supervision. Yue YUAN: Resources preparation and production. Data availability The datasets generated during and/or analysed during the current study are available in the [Open Science Framework] repository, [https://osf.io/49kmf/?view_only=99251a147dda481a9a36b7f1cadf560e]. Declarations Ethics approval The study was approved by the Research Ethics Committee of the Hong Kong Baptist University (REC/20–21/0270). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Informed consent Informed consent was obtained from all individual participants included in the study. Conflict of interest The authors have no competing interests to declare that are relevant to the content of this article. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Akase, M., Terao, T., Kawano, N., Sakai, A., Hatano, K., Shirahama, M., Hirakawa, H., Kohno, K., & Ishii, N. (2020). More purpose in life and less novelty seeking predict improvements in self-compassion during a mindfulness-based intervention: The EXMIND study [Original Research]. Frontiers in Psychiatry, 11. 10.3389/fpsyt.2020.00252 Allen JJB Chambers AS Towers DN The many metrics of cardiac chronotropy: A pragmatic primer and a brief comparison of metrics Biological Psychology 2007 74 2 243 262 10.1016/j.biopsycho.2006.08.005 17070982 Azam, M. A., Latman, V. V., & Katz, J. (2019). Effects of a 12-Minute Smartphone-Based mindful breathing task on heart rate variability for students with clinically relevant chronic pain, depression, and anxiety: Protocol for a randomized controlled trial. Jmir Research Protocols, 8(12). 10.2196/14119 Bareeqa SB Ahmed SI Samar SS Yasin W Zehra S Monese GM Gouthro RV Prevalence of depression, anxiety and stress in china during COVID-19 pandemic: A systematic review with meta-analysis International Journal of Psychiatry in Medicine 2021 56 4 210 227 10.1177/0091217420978005 33243029 Caspi O Bell IR One size does not fit all: aptitude x treatment interaction (ATI) as a conceptual framework for complementary and alternative medicine outcome research. Part 1—What Is ATI research? The Journal of Alternative and Complementary Medicine 2004 10 3 580 586 10.1089/1075553041323812 15253866 Cavanagh K Churchard A O’Hanlon P Mundy T Votolato P Jones F Gu J Strauss C A randomised controlled trial of a brief online Mindfulness-Based intervention in a non-clinical population: Replication and extension Mindfulness 2018 9 4 1191 1205 10.1007/s12671-017-0856-1 30100934 Chen Y Yang X Wang L Zhang X A randomized controlled trial of the effects of brief mindfulness meditation on anxiety symptoms and systolic blood pressure in Chinese nursing students Nurse Education Today 2013 33 10 1166 1172 10.1016/j.nedt.2012.11.014 23260618 Cheung RYM Ng MCY Mindfulness and symptoms of depression and anxiety: The underlying roles of awareness, acceptance, impulse control, and emotion regulation Mindfulness 2019 10 6 1124 1135 10.1007/s12671-018-1069-y Chmura Kraemer H Kiernan M Essex M Kupfer DJ How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches Health Psychology 2008 27 2S S101 S108 10.1037/0278-6133.27.2(Suppl.).S101 18377151 Cohen J Statistical power analysis for the behavioral sciences 2013 Academic press de Abreu Costa M D’Alò de Oliveira GS Tatton-Ramos T Manfro GG Salum GA Anxiety and Stress-Related Disorders and Mindfulness-Based Interventions: A Systematic Review and Multilevel Meta-analysis and Meta-Regression of Multiple Outcomes Mindfulness 2019 10 6 996 1005 10.1007/s12671-018-1058-1 Duncan TE Duncan SC An introduction to latent growth curve modeling Behavior Therapy 2004 35 2 333 363 10.1016/S0005-7894(04)80042-X Fischer SH David D Crotty BH Dierks M Safran C Acceptance and use of health information technology by community-dwelling elders International Journal of Medical Informatics 2014 83 9 624 635 10.1016/j.ijmedinf.2014.06.005 24996581 Fumero, A., Peñate, W., Oyanadel, C., & Porter, B. (2020). The effectiveness of mindfulness-based interventions on anxiety disorders. A systematic meta-review. European Journal of Investigation in Health, Psychology and Education, 10(3), 704–719. https://www.mdpi.com/2254-9625/10/3/52. Accessed 22 Jan 2022 Garland, E. L., Geschwind, N., Peeters, F., & Wichers, M. (2015). Mindfulness training promotes upward spirals of positive affect and cognition: Multilevel and autoregressive latent trajectory modeling analyses [Original Research]. Frontiers in Psychology, 6. 10.3389/fpsyg.2015.00015 Gautam, A., Polizzi, C. P., & Mattson, R. E. (2019). Mindfulness, procrastination, and anxiety: Assessing their interrelationships. Psychology of Consciousness: Theory, Research, and Practice. 10.1037/cns0000209 Gully SM Payne SC Koles K Whiteman J-AK The impact of error training and individual differences on training outcomes: An attribute-treatment interaction perspective Journal of Applied Psychology 2002 87 1 143 155 10.1037/0021-9010.87.1.143 11924540 Hall BJ Xiong P Guo X Sou EKL Chou UI Shen Z An evaluation of a low intensity mHealth enhanced mindfulness intervention for Chinese university students: A randomized controlled trial Psychiatry Research 2018 270 394 403 10.1016/j.psychres.2018.09.060 30300870 Halladay JE Dawdy JL McNamara IF Chen AJ Vitoroulis I McInnes N Munn C Mindfulness for the mental health and well-being of post-secondary students: A systematic review and meta-analysis Mindfulness 2019 10 3 397 414 10.1007/s12671-018-0979-z Harnett PH Whittingham K Puhakka E Hodges J Spry C Dob R The short-term impact of a brief group-based mindfulness therapy program on depression and life satisfaction Mindfulness 2010 1 3 183 188 10.1007/s12671-010-0024-3 Harrison AM Scott W Johns LC Morris EMJ McCracken LM Are we speaking the same language? Finding theoretical coherence and precision in "Mindfulness-Based Mechanisms" in chronic pain Pain Medicine 2017 18 11 2138 2151 10.1093/pm/pnw310 28082525 Hayes AF Introduction to mediation, moderation, and conditional process analysis: A regression-based approach 2013 Guilford Press Hayes AF Matthes J Computational procedures for probing interactions in OLS and logistic regression: SPSS and SAS implementations Behavior Research Methods 2009 41 3 924 936 10.3758/BRM.41.3.924 19587209 Hu LT Bentler PM Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives Structural Equation Modeling: A Multidisciplinary Journal 1999 6 1 1 55 10.1080/10705519909540118 Huang L Yang T Li Z Applicability of the positive and negative affect scale in Chinese Chinese Mental Health Journal 2003 17 1 54 56 Hughes AA Kendall PC Psychometric properties of the positive and negative affect scale for children (PANAS-C) in children with anxiety disorders Child Psychiatry and Human Development 2009 40 3 343 352 10.1007/s10578-009-0130-4 19142724 Jerath R Crawford MW Barnes VA Harden K Self-regulation of breathing as a primary treatment for anxiety Applied Psychophysiology and Biofeedback 2015 40 2 107 115 10.1007/s10484-015-9279-8 25869930 Kabat-Zinn, J. (2013). Full catastrophe living: Using the wisdom of your body and mind to face stress, pain, and illness. Bantam Books. https://books.google.com.hk/books?id=QACPDQAAQBAJ. Accessed 15 Jan 2022 Khoury B Lecomte T Fortin G Masse M Therien P Bouchard V Chapleau M-A Paquin K Hofmann SG Mindfulness-based therapy: A comprehensive meta-analysis Clinical Psychology Review 2013 33 6 763 771 10.1016/j.cpr.2013.05.005 23796855 Kim SY Suh H Oh W Daheim J Daily change patterns in mindfulness and psychological health: A pilot intervention Journal of Clinical Psychology 2020 77 3 496 451 10.1002/jclp.23043 32860443 Lahtinen O Aaltonen J Kaakinen J Franklin L Hyönä J The effects of app-based mindfulness practice on the well-being of university students and staff Current Psychology 2021 10.1007/s12144-021-01762-z 33967565 Leong Bin Abdullah MFI Ahmad Yusof H MohdShariff N Hami R Nisman NF Law KS Depression and anxiety in the Malaysian urban population and their association with demographic characteristics, quality of life, and the emergence of the COVID-19 pandemic Current Psychology 2021 40 12 6259 6270 10.1007/s12144-021-01492-2 33623353 Leung CM Ho S Kan CS Hung CH Chen CN Evaluation of the Chinese version of the Hospital Anxiety and Depression Scale. A cross-cultural perspective International Journal of Psychosomatics 1993 40 1–4 29 34 8070982 Liu S Yang L Zhang C Xiang Y-T Liu Z Hu S Zhang B Online mental health services in China during the COVID-19 outbreak The Lancet Psychiatry 2020 7 4 e17 e18 10.1016/S2215-0366(20)30077-8 32085841 MacCoon DG MacLean KA Davidson RJ Saron CD Lutz A No sustained attention differences in a longitudinal randomized trial comparing mindfulness based stress reduction versus active control PLoS ONE 2014 9 6 e97551 10.1371/journal.pone.0097551 24955584 Mak WW Tong AC Yip SY Lui WW Chio FH Chan AT Wong CC Efficacy and moderation of mobile app-based programs for mindfulness-based training, self-compassion training, and cognitive behavioral psychoeducation on mental health: Randomized controlled noninferiority trial JMIR Ment Health 2018 5 4 e60 10.2196/mental.8597 30309837 McKee L Zvolensky MJ Solomon SE Bernstein A Leen-Feldner E Emotional-vulnerability and mindfulness: A preliminary test of associations among negative affectivity, anxiety sensitivity, and mindfulness skills Cognitive Behaviour Therapy 2007 36 2 91 101 10.1080/16506070601119314 17530495 Muthen, L. K., & Muthen, B. (2017). Mplus version 8 user's guide. Muthen & Muthen. https://books.google.com.hk/books?id=dgDlAQAACAAJ. Accessed 11 Jan 2022 Nimrod G Older audiences in the digital media environment Information, Communication & Society 2017 20 2 233 249 10.1080/1369118X.2016.1164740 Preacher KJ Rucker DD Hayes AF Addressing moderated mediation hypotheses: Theory, methods, and prescriptions Multivariate Behavioral Research 2007 42 1 185 227 10.1080/00273170701341316 26821081 Raes AK Bruyneel L Loeys T Moerkerke B De Raedt R Mindful attention and awareness mediate the association between age and negative affect The Journals of Gerontology: Series B 2013 70 2 179 188 10.1093/geronb/gbt074 Rosenkranz MA Davidson RJ Maccoon DG Sheridan JF Kalin NH Lutz A A comparison of mindfulness-based stress reduction and an active control in modulation of neurogenic inflammation Brain, Behavior, and Immunity 2013 27 1 174 184 10.1016/j.bbi.2012.10.013 23092711 Sauer-Zavala S Boswell JF Gallagher MW Bentley KH Ametaj A Barlow DH The role of negative affectivity and negative reactivity to emotions in predicting outcomes in the unified protocol for the transdiagnostic treatment of emotional disorders Behaviour Research and Therapy 2012 50 9 551 557 10.1016/j.brat.2012.05.005 22738907 Schumer MC Lindsay EK Creswell JD Brief mindfulness training for negative affectivity: A systematic review and meta-analysis Journal of Consulting and Clinical Psychology 2018 86 7 569 583 10.1037/ccp0000324 29939051 Sears S Kraus S I think therefore I om: Cognitive distortions and coping style as mediators for the effects of mindfulness meditation on anxiety, positive and negative affect, and hope Journal of Clinical Psychology 2009 65 6 561 573 10.1002/jclp.20543 19241400 Si M Xiao W Pan C Mindfulness-based online intervention on mental health and quality of life among COVID-19 patients in China: an intervention design Infectious Diseases of Poverty 2021 10 1 1 10 10.1186/s40249-021-00836-1 33397494 Simonsson O Bazin O Fisher SD Goldberg SB Effects of an eight-week, online mindfulness program on anxiety and depression in university students during COVID-19: A randomized controlled trial Psychiatry Research 2021 305 114222 10.1016/j.psychres.2021.114222 34601450 Snippe E Dziak JJ Lanza ST Nykliek I Wichers M The shape of change in perceived stress, negative affect, and stress sensitivity during mindfulness-based stress reduction Mindfulness 2017 8 3 728 736 10.1007/s12671-016-0650-5 29057017 Spinhoven P Ormel J Sloekers P Kempen G Speckens A Van Hemert A A validation study of the Hospital Anxiety and Depression Scale (HADS) in different groups of Dutch subjects Psychological Medicine 1997 27 2 363 370 10.1017/S0033291796004382 9089829 Tang, R., & Braver, T. S. (2020). Towards an individual differences perspective in mindfulness training research: Theoretical and empirical considerations [Review]. Frontiers in Psychology, 11. 10.3389/fpsyg.2020.00818 Torous J Lipschitz J Ng M Firth J Dropout rates in clinical trials of smartphone apps for depressive symptoms: A systematic review and meta-analysis Journal of Affective Disorders 2020 263 413 419 10.1016/j.jad.2019.11.167 31969272 Uher R Genes, environment, and individual differences in responding to treatment for depression Harvard Review of Psychiatry 2011 19 3 109 124 10.3109/10673229.2011.586551 21631158 Wang X Shi L Zhang Y Chen H Sun G Policy disparities in fighting COVID-19 among Japan, Italy, Singapore and China International Journal for Equity in Health 2021 20 1 33 10.1186/s12939-020-01374-2 33441144 Watson D Clark LA Carey G Positive and negative affectivity and their relation to anxiety and depressive disorders Journal of Abnormal Psychology 1988 97 3 346 10.1037/0021-843X.97.3.346 3192830 World Health Organization. (2021). COVID-19 weekly epidemiological update. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports#. Accessed 27 Oct 2021 Yalçın İ Can N Mançe Çalışır Ö Yalçın S Çolak B Latent profile analysis of COVID-19 fear, depression, anxiety, stress, mindfulness, and resilience Current Psychology 2022 41 1 459 469 10.1007/s12144-021-01667-x 33821112 Zhang A Zhang Q How could mindfulness-based intervention reduce aggression in adolescent? Mindfulness, emotion dysregulation and self-control as mediators Current Psychology 2021 10.1007/s12144-021-01778-5 34511861	36468167	PMC9708511	NO-CC CODE	2022-12-01 23:20:30	no	Curr Psychol. 2022 Nov 30;:1-13	utf-8	Curr Psychol	2,022	10.1007/s12144-022-03902-5	oa_other
==== Front Environ Sci Pollut Res Int Environ Sci Pollut Res Int Environmental Science and Pollution Research International 0944-1344 1614-7499 Springer Berlin Heidelberg Berlin/Heidelberg 36447104 24435 10.1007/s11356-022-24435-1 Research Article Socioeconomic determinants of environmental efficiency: the case of the European Union http://orcid.org/0000-0002-5801-1998 Lacko Roman [email protected] 1 Hajduová Zuzana [email protected] 2 Markovič Peter [email protected] 2 1 grid.127098.5 0000 0001 2336 9159 Department of Tourism, Faculty of Commerce, University of Economics in Bratislava, Dolnozemská Cesta 1, 852 35 Bratislava, Slovakia 2 grid.127098.5 0000 0001 2336 9159 Department of Business Finance, Faculty of Business Management, University of Economics in Bratislava, Dolnozemská Cesta 1, 852 35 Bratislava, Slovakia Responsible Editor: Ilhan Ozturk 30 11 2022 112 19 7 2022 23 11 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The study’s main objective is to assess and evaluate the models of socioeconomic determinants of environmental efficiency in the European Union countries from 2010 to 2018. The two-step data envelopment analysis is implemented, using both constant and variable returns to scale assumption. Moreover, the results of the model of environmental efficiency determinants from four areas—tourism, circular economy, energy and resources use and quality of life—are presented. Based on our findings, it can be concluded that it is necessary to develop the concept of sustainable tourism because the enormous increase in foreign tourists harms environmental efficiency. It is also necessary to gradually transform economies into less energy-intensive towards knowledge-based economies. The positive impact of measures related to the pain of the circular economy was also demonstrated. In conclusion, we present several recommendations for EU policies concerning the current economic and energy situation. Keywords Tourism Quality of life Efficiency Data envelopment analysis European Union Bootstrap http://dx.doi.org/10.13039/501100006109 Vedecká Grantová Agentúra MŠVVaŠ SR a SAV Project No.1/0240/20 Markovič Peter ==== Body pmcIntroduction Environmental efficiency is currently one of the most considered topics. Therefore, governments are directing their recovery policies towards improving the economy and quality of life, the circular economy, energy intensity and efficiency. According to Reinhard et al. (2000), the ratio of minimum feasible to observed use of environmentally detrimental inputs, which are conditional on levels of the desirable outputs and conventional inputs, is the most appropriate definition of environmental efficiency. The current development of knowledge and the political situation indicates serious shortcomings in slowing climate change and reducing greenhouse gas emissions. Therefore, improving environmental efficiency is a critical aspect of evaluating the effectiveness of environmental policies. However, this is not enough if the determinants of this efficiency are not evaluated. There are many reasons for the deterioration of the environment and the constant increase in greenhouse gases. Some are probably natural processes, but most are anthropogenic, so it is appropriate to investigate how and why humans worsen the state of the environment (Ji et al. 2018). In addition, it can be noted that the differences in the environmental significance between high-income and low-income countries are deepening (Li and Wang 2014; Woo et al. 2015). These differences can also be found within the countries of critical political groupings, such as the European Union (EU) (Duman and Kasman 2018; Lacko and Hajduová 2018; Halkos and Petrou 2019; Tenente et al. 2020). More developed regions are also more efficient to a greater extent (Borozan 2018). In doing so, we must emphasise that EU policies are based on convergence goals (Arbolino et al. 2018), and the EU Green Deal is proof of this. One of the solutions is the increase taxes related to behaviour, but this affects the competitiveness of subjects to the extent of the environment (Moutinho et al. 2017). One of the possibilities for increasing the environment’s efficiency is eco-innovation and improving the awareness of citizens (Cai and Li 2018; Vaninsky 2018; Liu et al. 2018b; Halkos and Petrou 2019). Research also contributes to human capital and innovation’s positive potential in improving performance (Borozan 2018). That is why research should be oriented not only on economic determinants but also on the state of the society in the given countries. A significant source of greenhouse gas emissions is travel and tourism transport, as noted by several studies (Sun 2016; Peng et al. 2017; Liu et al. 2018a; Zha et al. 2020). In addition, increasing greenhouse gas emissions may lead to climate change, impacting tourism performance and unique destinations (Day et al. 2013). According to the current literature, there is still a lot of space for increasing eco-efficiency in tourism (Zha et al. 2020). In addition, the environment is damaged by tourism, mainly in developed countries, where there are a large number of tourist arrivals (Usman et al. 2021). On the other hand, tourism economic development can in the short term lead to the improvement of ecological efficiency, while the relationship of inverted U shape has been proven (Balsalobre-Lorente et al. 2020; Haibo et al. 2020). One of the possibilities for improving the state of the environment affected by tourism is the adoption of measures and tools for sustainable tourism (Jiang et al. 2022). Sustainable growth can ultimately contribute to increasing the number of tourists and improving the state of the environment at the same time (Azam et al. 2018; Sellers-Rubio and Casado-Díaz 2018). One of the possibilities is also increasing the energy efficiency of buildings—tourism facilities (Hossain and Ng 2018). Investments in tourism, the structure of industry, urbanisation and also environmental regulation help to improve the eco-efficiency of tourism (Song 2019; Guo et al. 2022). Unsustainable economic growth is one of the reasons why pollution occurs (Neves et al. 2020). Another option for solving the problems of environmental efficiency is the orientation towards the circular economy (De Pascale et al. 2020; Mhatre et al. 2021). In recent years, environmental awareness and tools of the circular economy have also been widespread in the scientific field (Hossain and Ng 2018; Aguilar-Hernandez et al. 2021). There are still large differences between the countries of the world in the efficiency of the use of resources and tools of the circular economy (Mavi and Mavi 2019). Countries that use intensive tools of the circular economy are more efficient when dealing with municipal solid waste, which is still a big problem in many countries of the world but also in the EU (Halkos and Petrou 2019). In addition, reducing waste also helps to reduce energy consumption (Wu et al. 2019). It is also important to examine the share of renewable resources that are inputs for economic growth (Liu et al. 2019). The rate of use of renewable resources has a positive effect on improving environmental efficiency (Neves et al. 2020). Increasing the output of economies following the idea of a circular economy is one of the possibilities for improving the state of the living environment, but it turns out that reducing the generation of waste and reducing emissions are not the only way (Robaina et al. 2020). In addition, closed-loop principles help to increase efficiency (Camilleri 2018). However, other indicators express the level of quality of life in the countries of the world. Such indicators can include indicators related to education, the level of health concern, the degree of urbanisation and many others (Ma et al. 2021). Furthermore, there are many indicators of the use of energy resources, while the relationship between the efficient use of resources and the reduction of emissions is proven (Iram et al. 2020). Only a few studies comprehensively connect selected industries as the main causes of increasing emissions and growth in the level of pollution in the countries of the world, and there are even fewer of these studies in the area of EU countries, while precisely, such a comprehensive perception of the problem can also help to improve the state of the environment (Abbasi et al. 2021). The inclusion of some indicators, such as the use of renewable resources and energy use (Li and Wang 2014), directly in the models for measuring efficiency may not provide an answer to what effect these indicators have when using different technologies in different countries (Lacko and Hajduová 2018). All of the above-mentioned areas have a demonstrable impact on environmental efficiency, but they are comprehensively used relatively little extensively in the evaluation of efficiency. Therefore, it is necessary to research how the selected indicators affect the change in environmental efficiency in a complex way. However, a study that includes the factors mentioned above is absent; therefore, we identified the need for such a study to expand scientific research. Furthermore, we identified some research gaps in environmental efficiency. Thus, the primary goal of the study is to assess and evaluate socioeconomic determinants from the fields of tourism, quality of life, circular economy and energy consumption in the environmental efficiency in the European Union countries. Material and methods Efficiency measurement Based on the literature review, we found that the most used method for evaluating environmental efficiency is the data envelopment analysis (DEA) (Mardani et al. 2017). Farrell (1957) laid its theoretical foundations and developed them in many other studies. Charnes et al. (1984) and Cooper et al. (2007) have contributed to significant theoretical development. We will use input-oriented models assuming constant returns to scale (CRS) and variable returns to scale (VRS). For the decision-making unit (DMU) to be efficient, it must achieve efficiency equal to 1 (Charnes et al. 1984, 1994; Cooper et al. 2007).1 minθB,λθBs.t.θBxo-Xλ≥0Yλ≥yoλ≥0 2 minθB,λθBs.t.θBxo-Xλ≥0Yλ≥yoeλ=1λ≥0 where the θB values of efficiency, X=xj∈Rm×n and Y =yj∈Rs×n, are the matrix of inputs and outputs; e is a vector whose elements are equal to 1, λ∈Rn—non-negative vector; and xo and yo are the vectors of the inputs and outputs. For completeness, m is the number of inputs, s is the number of outputs, and n is the number of DMUs. Input efficiency values are within the range < 0;1 > , and an entity that has reached 1 is efficient. We will also use the two-step DEA method in this work to help us achieve the study’s goal. This method is often used in other studies (Afonso and Aubyn 2011; Liu et al. 2013; Tajudeen et al. 2018; Lacko and Hajduová 2018). Research object In this study, we will research the environmental efficiency in the countries of the European Union. We will therefore examine 27 countries (without Great Britain). Based on the availability of data and also the fact that we want to measure efficiency in the so-called ‘inter-crisis’ period (between the economic crisis of 2008 and 2009 and the crisis caused by the global pandemic), we decided to monitor the years 2010 to 2018, i.e. 9 years. Therefore, we created a data panel with 243 observations (DMU). Data for the needs of our work were collected from Eurostat (Eurostat 2022) and World Bank databases (The World Bank 2021). Data The variables have been chosen based on previous studies that have addressed the measurement of efficiency in these areas. The rationale for selecting variables will be given in Table 1. Table 1 presents the input and output variables of the environmental efficiency model.Table 1 Variables used in the environmental model of efficiency DEA model of environmental efficiency The name Units Inputs Number of employed persons Thousand people Gross fixed capital formation m. EUR Arable land Thousand hectares Outputs Greenhouse gas emissions Thousand tons Gross domestic product (current prices) m. EUR As inputs, DEA models measure the environmental efficiency use variables related to primary production factors—labour, land and capital. The number of people employed in the country is a variable which is linked to a labour factor and shows how the country’s labour capacity is relatively often used in various researches (Woo et al. 2015; Madaleno et al. 2016; Toma et al. 2017; Zeng et al. 2018; Busu and Trica 2019). Another input representing the capital area is the gross fixed capital formation. Capital is the primary driver of progress and development in many areas; this variable is used in many studies (Dinda 2005; Moutinho et al. 2015; Alsaleh et al. 2017; Halkos and Petrou 2019). The last input variable is the arable land area, which indicates the soil’s production factor. Authors use different categories of land, and we decided not to use the area of the whole country, as it also includes areas that cannot be used industrially or agriculturally, such as forests (which in turn improve the status of the environment) or protected areas (Vlontzos et al. 2014; Toma et al. 2017). We included two variables in the outputs. The first variable is gross domestic product, which measures the performance and output of a given economy. The second variable is CO2 emissions, but it is an undesirable output. In the calculation process, it will be used as a negative input. These output variables are the most commonly used in environmental efficiency assessment (Vaninsky 2009; Kwon et al. 2017; Iftikhar et al. 2018). Subsequently, regressions can be performed where dependent variables are efficiency values after a double bootstrap (Simar and Wilson 2007). Based on the study of literature as well as current trends in policies aimed at improving the state of the environment, quality of life and socioeconomic factors, we decided to build model (3), which has the following form:3 δEE^^=β0+β1X1+β2X2+β3X3+β4X4+β5X5+β6X6+β7X7+β8X8+β9X9+β10X10+β11X11+β12X12+β13X13εi where δEE^^ are CRS and VRS environmental efficiency values calculated using a second algorithm developed by Simar and Wilson (2007). The individual variables of the model are described in Table 2.Table 2 Description of explanatory variables Area Variable Symbol Description Units Tourism Tourism openness X1 Expenditures of inbound and outbound tourists % Arrivals of foreign tourists X2 Arrivals of non-residents at tourist accommodation establishments m. persons Arrivals of domestic tourists X3 Arrivals of residents at tourist accommodation establishments m. persons Energy measures Renewable energy share X4 Share of renewable energy to total energy consumption % Source productivity X5 Share of GDP and material consumption of households PPS/kg Energy consumption in industry X6 Energy consumption of companies according to NACE—industry Ton/capita Energy consumption in services X7 Energy consumption of companies according to NACE—services Ton/capita Use of an organic fertilisers X8 Consumption of nitrogen fertilisers Ton/capita Quality of life Life expectancy X9 The expected average length of life Years number Risk of poverty and social exclusion X10 Share of citizens at risk of poverty and exclusion % Tertiary educated citizens X11 Share of citizens with tertiary education (ISCED 5–8) % Circular economy Municipal solid waste recyclation X12 The volume of recycled MSW Thousand tons Use of circular materials X13 Share of materials returned to the economy for further use % In Table 2, we describe the selected characteristics of the explanatory variables, which will be modelled using truncated regression. As seen in Table 2, we have chosen these variables from 4 categories which have impact on environmental efficiency, which we would like to verify in the next chapter. Based on the “Introduction” section and literature review, we have found that the tourism sector, circular economy management, energy use and quality of life indicators are commonly used in the present literature as determinants of environmental and eco-efficiency. These four categories are currently the objectives of many of the policies of the European Union and the world. Moreover, many of these areas are in the future recovery plans following the devastating consequences of the coronavirus pandemic.Tourism—we have chosen the country’s openness to tourism as an explanatory variable in this area. This is expressed as a proportion of the expenditure of all tourists leaving but also coming to GDP. The higher this ratio, the greater the country’s openness to tourism. The numbers of domestic and foreign tourist arrivals were selected as explanatory variables two and three. We expect that the higher the number of tourists, the higher the inefficiency. This will happen in countries with low levels of sustainable tourism. Energy and agriculture—there are five indicators in this category, which point to using energy and other resources and using fertilisers in the country. These indicators are essential in terms of the environment, as countries with higher productivity, lower energy consumption, a higher share of renewable energy and less fertiliser use could tend to be more efficient. Quality of life—this is a very up-to-date area that directly impacts the population, and based on a verification of the impact of the selected variables, the effects of selected attributes of the population on the environment can be verified. So, for example, in countries where life expectancy is higher, at-risk-of-poverty rates are lower and the share of the population with tertiary education is higher, they could be higher in terms of efficiency. These are the areas of healthcare, education and economic levels. Circular economy—this area includes variables related to waste management and its further use in economic processes. Recycling and use rates vary considerably across the EU; therefore, we want to verify the impact these differences can have on individual efficiency values. We have chosen these variables, which descriptive statistics are presented in Table 3, primarily based on data availability and current trends in EU policies. Since this is a unique research, their validation will be more experimental, and these models may or may not have high explanatory power. Some of these variables are also used directly in DEA models, but (Martín et al. 2017; Nurmatov et al. 2021) when used in DEA models, quantification of their impact is difficult.Table 3 Descriptive statistics of explanatory variables Variable Mean Median Standard deviation Sample variance Kurtosis Skewness Range Minimum Maximum X1 7.89 6.00 4.52 20.40 0.78 1.29 19.40 2.10 21.50 X2 11.79 4.39 15.87 251.96 2.34 1.85 64.98 0.79 65.77 X3 18.47 6.98 31.50 992.37 4.81 2.39 140.45 0.05 140.49 X4 19.28 15.90 11.49 132.04 0.48 0.89 53.67 0.98 54.65 X5 1.77 1.59 0.77 0.59 0.21 0.82 3.57 0.62 4.19 X6 0.57 0.43 0.38 0.14 4.21 1.95 1.92 0.10 2.02 X7 0.30 0.28 0.13 0.02 3.06 1.42 0.74 0.09 0.83 X8 0.02 0.02 0.02 0.00 2.24 1.34 0.08 0.00 0.08 X9 79.59 80.90 2.88 8.28 − 0.93 − 0.69 10.40 73.10 83.50 X10 24.36 23.00 7.52 56.53 0.74 1.00 37.10 12.20 49.30 X11 26.14 26.90 7.20 51.82 − 1.07 − 0.07 28.60 11.90 40.50 X12 33.38 32.50 15.73 247.37 − 0.94 0.15 63.20 4.00 67.20 X13 8.42 6.90 6.27 39.26 1.29 1.25 28.50 1.20 29.70 Results Environmental efficiency in EU countries In this section, we will interpret environmental efficiency modelling results using the DEA method. First, we compute the individual efficiencies using the DEA window approach. In the second step, we bias-correct efficiency values computed in the first step and use them as dependent variables, as proposed in the methodology section. Table 4 presents the results of the CRS and VRS environmental efficiency models. As the scoreboard for each country in each year of examination would be extensive, only the essential descriptive characteristics of the resulting efficiencies are given.Table 4 Summary statistics of environmental efficiency measurement RTS CRS VRS Country Average Mr Stdev MIN MAX Average Mr Stdev MIN MAX Belgium 0.7143 0.0114 0.6967 0.7352 0.9005 0.0402 0.8611 1.0000 Bulgaria 0.7642 0.1297 0.6132 1.0000 0.8295 0.1040 0.7080 1.0000 Czechia 0.5023 0.0171 0.4758 0.5318 0.5273 0.0233 0.4970 0.5656 Denmark 0.7981 0.0385 0.7320 0.8437 0.9922 0.0058 0.9814 1.0000 Germany 0.7546 0.0093 0.7384 0.7645 0.9965 0.0084 0.9729 1.0000 Estonia 0.5896 0.1606 0.4671 1.0000 0.7720 0.1547 0.5887 1.0000 Ireland 0.7819 0.1047 0.6188 0.9524 0.9652 0.0442 0.8689 1.0000 Greece 0.9578 0.0667 0.7926 1.0000 0.9640 0.0551 0.8282 1.0000 Spain 0.7346 0.0378 0.6505 0.7766 0.8761 0.0534 0.7663 0.9489 France 0.6918 0.0110 0.6799 0.7118 0.9611 0.0292 0.9153 1.0000 Croatia 0.5604 0.0149 0.5271 0.5797 0.5795 0.0150 0.5425 0.5977 Italy 0.8357 0.0438 0.7539 0.8813 0.9826 0.0289 0.9194 1.0000 Cyprus 0.8454 0.1264 0.6725 1.0000 0.8527 0.1198 0.6843 1.0000 Latvia 0.5224 0.0366 0.4614 0.5678 0.5768 0.0493 0.5002 0.6735 Lithuania 0.5855 0.0264 0.5566 0.6398 0.6293 0.0418 0.5856 0.7138 Luxembourg 0.9864 0.0148 0.9549 1.0000 0.9940 0.0084 0.9732 1.0000 Hungary 0.5268 0.0235 0.4948 0.5618 0.5328 0.0253 0.5015 0.5712 Malta 0.9457 0.0614 0.8092 1.0000 0.9977 0.0052 0.9837 1.0000 Netherlands 0.8439 0.0478 0.7546 0.9305 0.9825 0.0247 0.9238 1.0000 Austria 0.6845 0.0117 0.6653 0.7083 0.8412 0.0206 0.8138 0.8729 Poland 0.5629 0.0326 0.5210 0.6155 0.9544 0.0651 0.8308 1.0000 Portugal 0.8097 0.0620 0.6721 0.8648 0.8844 0.0690 0.7307 0.9515 Romania 0.4422 0.0377 0.3918 0.5124 0.4500 0.0475 0.3946 0.5530 Slovenia 0.7575 0.0371 0.6918 0.8226 0.7862 0.0368 0.7179 0.8527 Slovakia 0.5554 0.0223 0.5157 0.5821 0.5611 0.0215 0.5228 0.5831 Finland 0.6904 0.0208 0.6609 0.7286 0.8257 0.0247 0.7767 0.8572 Sweden 0.6747 0.0221 0.6378 0.7045 0.9092 0.0215 0.8608 0.9393 The best average results were for the CRS models of Greece, Luxembourg and Malta. The results for VRS models are slightly different; Denmark, Germany and the Netherlands achieve the highest efficiency, but countries that have been relatively highly efficient for CRS models are performing well for VRS models. For CRS models, Romania, Czechia and Latvia are the least effective. Romania, Hungary and Latvia are the least efficient VRS models. In general, the CRS and VRS models do not show too much variation. It should also be noted that countries that are less industry-oriented and more service-oriented perform better, helping them to produce lower emissions at comparable levels of GDP. Individual values were bias-corrected and used as dependent variables in the next step. Determinants of the environmental efficiency In this section, we will discuss the results of environmental efficiency modelling. Table 5 presents the results of correlations for the explanatory variables of the models.Table 5 Correlation matrix of explanatory variables Based on the correlation results, some correlations can be considered high. For example, there is a high correlation between the arrival of foreign tourists and the arrival of domestic tourists. However, this is expected, and using a variable that captures the summary value of arrivals would not give us a detailed view of the issue. It is also the case with variables relating to energy consumption in industry and services. Table 6 presents the results of the environmental efficiency modelling.Table 6 Results of truncated regression model Explanatory variables Dependent variable CRS DB EE VRS DB EE Estimate Significance Estimate Significance Intercept − 3.22009427 * − 5.49636282 * X1 0.00054986 − 0.00371363 * X2 − 0.00263558 * − 0.00745529 * X3 − 0.00022504 0.00354060 * X4 − 0.00216031 * − 0.00029968 X5 0.02518974 0.02110514 X6 − 0.07834813 * − 0.15586555 * X7 0.43359585 * 0.55397655 *** X8 − 0.83007752 * 1.68750903 * X9 0.04566588 * 0.07361613 * X10 0.00893056 * 0.01255099 * X11 − 0.00191737 * − 0.00234422 X12 0.00231901 * 0.00274507 * X13 − 0.00275734 ** 0.00449347 * Sigma 0.06941549 * 0.09204174 * Log-likelihood 309.78 329.79 R-squared 0.7150 0.6497 significance levels: 0.1; 0.05; 0.01. Double bootstrap. EE, environmental efficiency From tourism-related variables, in both models, arrivals of foreign tourists harm the environmental efficiency. This may be caused by tourism transport, as foreign tourists use more air transport and other pollution-extensive types of transport. On the contrary, domestic tourists even increase environmental efficiency in the case of the VRS model. For the CRS model, this variable is not statistically significant. In the case of the CRS model, tourism openness is not statistically significant, and in the VRS model, it has a negative impact. It is interesting from industrial indicators that there is lower environmental efficiency in countries with a higher share of renewable sources. It may be linked to the fact that even the use of these sources of energy generates various by-products. On the contrary, resource productivity has a positive impact on environmental efficiency. Research has confirmed the expected and that industrial energy consumption has a negative impact on environmental efficiency and vice versa. On the other hand, energy consumption in services is affected positively by environmental efficiency. Concerning the quality of life indicators, there is also higher efficiency in countries with higher population life expectancy. An interesting fact, however, is that countries with higher poverty rates also have higher environmental efficiency. This may, of course, be due to a lower degree of industrialisation in these countries. Similarly, the population’s education may be due to poor qualifications, the population and consequently fewer investors, which increases production and, inevitably, greenhouse gas emissions. In terms of circular economy indicators, we have found interesting facts. Recycling rates and the use of circulating materials have a positive impact on improving environmental efficiency for the VRS model. The results of the CRS and VRS models are slightly different. Their further testing, or a slight modification of the variables used can produce more consistent results. This analysis confirmed that the presented socioeconomic factors have a largely expected and, moreover, statistically significant impact. Robustness testing Although Simar and Wilson’s (2007) procedure of double bootstrap procedure brings consistent and robust estimates, we decided to conduct a robustness check for our key result which is the regression model. In this way, we have used several procedures to check for robustness as proposed by Wolszczak-Derlacz and Parteka (2011). At first, we raised the number of replications in the second loop to 2000; the next step was raising the number to 5000 (originally 200 replications were used). The next step was changing the truncation point to 0.99 (originally 1) which caused omitting efficient DMUs. The last test was performed by using only 2 input variables instead of 3. In this way, we have omitted the variable arable land—one can argue arable land could be autocorrelated with some explanatory variables so this check would be an advantage. Individual results of the computations are presented in Table 7, constant returns to scale efficiency model, and Table 8, variable returns to scale efficiency model.Table 7 Robustness check of the CRS model Explanatory variables Number of replications Truncation point 2 input model 2000 5000 0.99 Est Sig Est Sig Est Sig Est Sig Intercept − 3.2541 * − 3.2243 * − 3.2052 * − 2.4882 * X1 0.0007 0.0007 0.0007 − 0.0014 X2 − 0.0028 * − 0.0027 * − 0.0026 * 0.0002 X3 − 0.0002 − 0.0002 − 0.0002 − 0.0007 X4 − 0.0021 * − 0.0022 * − 0.0022 * − 0.0019 * X5 0.0265 0.0256 0.0264 0.0044 X6 − 0.0770 * − 0.0778 * − 0.0789 * − 0.0574 X7 0.4273 * 0.4295 * 0.4517 * 0.5511 * X8 − 0.8404 * − 0.8436 * − 0.8496 * − 0.1552 X9 0.0461 * 0.0457 * 0.0454 * 0.0352 * X10 0.0089 * 0.0089 * 0.0090 * 0.0086 * X11 − 0.0020 * − 0.0019 * − 0.0019 * − 0.0006 X12 0.0023 * 0.0023 * 0.0023 * 0.0025 * X13 − 0.0027 − 0.0028 − 0.0030 − 0.0031 Sigma 0.0697 * 0.0699 * 0.0696 * 0.0657 * Log-likelihood 308.6400 308.2800 310.5400 319.8500 R-squared 0.7150 0.7130 0.7124 0.7046 Table 8 Robustness check of the VRS model Explanatory variables Number of replications Truncation point 2 input model 2000 5000 0.99 Est Sig Est Sig Est Sig Est Sig Intercept − 5.5105 * − 5.5157 * − 5.7652 * − 4.6326 * X1 − 0.0038 * − 0.0040 * − 0.0033 − 0.0042 * X2 − 0.0074 * − 0.0074 * − 0.0087 * − 0.0047 * X3 0.0035 * 0.0034 * 0.0046 * 0.0032 * X4 − 0.0004 − 0.0003 − 0.0003 − 0.0011 X5 0.0197 0.0207 0.0319 0.0037 X6 − 0.1549 * − 0.1582 * − 0.1664 * − 0.1401 * X7 0.5545 * 0.5484 * 0.6259 * 0.6830 * X8 1.7270 * 1.7015 * 2.0446 1.9664 X9 0.0738 * 0.0740 * 0.0764 * 0.0617 * X10 0.0127 * 0.0124 * 0.0133 * 0.0124 * X11 − 0.0024 0.0740 − 0.0028 − 0.0006 X12 0.0028 * 0.0027 * 0.0029 * 0.0023 * X13 0.0046 0.0049 0.0043 * 0.0059 Sigma 0.0924 * 0.0921 * 0.0926 * 0.0917 *** Log-likelihood 329.3700 329.7900 347.0000 319.8300 R-squared 0.6487 0.6527 0.6235 0.6400 Compared to the originally computed model, there are no significant changes when changing the number of replications, or truncation points. Only differences arise when removing one input in the first step of the analysis. It has changed the signs and significance of the tourism-oriented explanatory variables. Changing the number of inputs in our output could always lead to even more significant changes. The first step of DEA model and its variables have already been proven relevant and useful in many studies before. The differences between original estimates and estimates computed using different considerations are even smaller when it comes to the VRS model. All the signs (impacts) remained unchanged, and only slight changes in values and statistical significance were encountered. Therefore, we can conduct that using VRS assumption models are more appropriate. Discussion Discussion on efficiency measurement During the research period, high levels of environmental efficiency were mainly achieved by countries such as Germany, Ireland, the Netherlands, Cyprus, Luxembourg and Italy. It should be noted that countries such as Cyprus, Luxembourg or Malta are omitted in many research to measure environmental efficiency. It would, in our view, be unprofitable for this work, as these countries are essential precisely in the field of tourism. Furthermore, efficiency gains (CRS and VRS) were increasing in most countries. The results of modelling environmental efficiency have demonstrated this. This fact is a very good signal for further progress during the next period. Based on the current trends in research and authorities’ measures in implementing policies, we decided to use variables oriented to tourism, resource productivity, industry and services energy consumption, the quality of life and indicators focused on the circular economy. Based on the results, it can be concluded that the presented models have good explanatory power, and the VRS model is especially robust. Indeed, some variables of the model are not statistically significant. This is also an opportunity for future research, whereby these variables can be replaced and the statistical significance of similar variables from the given research area tested. In the overall evaluation, it can be concluded that the growth of tourism volumes and tourism openness towards foreign tourists in the period before the pandemic could have a negative impact precisely on environmental efficiency. It is also necessary to note that energy measures may not have a direct impact on environmental efficiency, but the indirect effects of cost reduction can, together with other measures, help to improve the environment. The transformation of economies into knowledge-based ones encourages the development of services, and thus, we can more effectively reduce energy consumption and thus also the state of the living environment. The development of the quality of life in the EU countries can also help to improve the state of the environment; in our case, the improvement of the state of health has proved to be significant, which of course also has an impact as a prevention of diseases caused by the deterioration of the state of the environment. The risk of poverty indicator pointed out that as well-being increases, non-environmental efficiency may not always improve. A strong positive impact was also demonstrated in the case of circular economy indicators. Comparing the results of this study with other research could be biased since no research has examined the same period. It should be noted that the regression models developed can be adapted to the needs and trends of setting up EU policies. Instead, they are model concepts which indicate which areas should be affected by explanatory variables and can be continuously examined and tested depending on the availability of new indicators. Limitations This research has been affected by some limitations. One of the main limitations is the unavailability of some data. In many cases, only one or several countries are missing. In addition, during data collection, we encountered relevant variables that could be used in research, but such situations made using these variables impossible. In many cases, for example, there were variables related to the circular economy and tourism of the EU countries. Another limitation is that the scientific community has disagreed on which DEA models are more suitable for the types of efficiency. Therefore, the results are presented for models with constant and variable returns to scale. Indeed, models do not show considerable differences; even in regression models, there are more minor differences. In the literature survey, we have often met that the authors claimed that their model was the most appropriate, but they differed in their opinions. It is also worth mentioning that, because of the size of some data, we have been made more effective clarity about the presentation of data and results and, therefore, for example, the detailed development of explanatory variables of DEA models is not mentioned in this work. Conclusions and policy implications The theme of this study was environmental efficiency. We measured these efficiencies at the EU-27 level. The period we studied was from 2010 to 2018. The efficiencies we measured were then modelled using various economic, travel and tourism, energy, quality of life and circular economy aspects. The European Union is an interesting research subject because the diversity of performance and attributes of its countries is considerable. It leads to the possibility of exploring the causes and consequences of this diversity. Moreover, the subject of this work is very topical because the policy of the EU’s authorities is currently being targeted at several areas, including tourism, which is probably the most affected by a post-health pandemic. The current energy crisis pushes governments and people, even more, to make energy use more efficient. This is especially evident in energy-intensive industries, which are currently extremely affected by the increase in energy prices. Ultimately, this may lead to their demise and the transformation of some countries from industrial and knowledge-based economies, in which the service sector is the focus of attention. Another area is the improvement of the state of the environment by reducing greenhouse gas emissions and related areas such as the circular economy and energy efficiency. The quality of life of citizens is no less critical. All these areas are part of recovery plans following the profound economic and health crisis the world is still going through. The main benefit lies in broadening the investigation and outlining new scientific challenges in the search for interdisciplinary efficiency. Research that has been carried out in this work can be explored on other objects of investigation. The benefits of science can also be reflected in the benefits of the learning process in the various economic fields, as this work has a strong interdisciplinary character. It explores the environment; tourism; the circular economy, i.e. production processes and materials processing; quality of life, i.e. social aspects; economic growth; and many others. It may be an appropriate methodological complement to practical application in modelling the efficiency of production units. It should also be pointed out that recovery plans need to be tailor-made, as our research has also shown that there are still clusters of countries with different attributes within the EU. The EU aims to converge countries to the same standard of living, but these objectives will not be met long because economic and political crises negatively influence convergence. Therefore, we will summarise several recommendations for EU policies, which are changing rapidly mainly due to the current economic and security situation in Europe: (1) it is necessary to support the attractiveness of countries for foreign tourists mainly due to economic growth, but this is conditioned by the very intensive application of sustainable tourism tools; (2) it is necessary to increase the use of renewable resources, but it is necessary not only to make the efficiency of these devices more efficient, but also to reduce the burden on the environment caused by their production in other parts of the world; (3) to emphasise the shift of countries with extensive industry towards knowledge-based forms of economy service-oriented; (4) increasing the level of health systems towards prevention, and also environmental education and awareness with an emphasis on the quality of the education provided, as it seems that the number of educated people does not necessarily indicate the quality; and (5) continue to support the principles of the functioning of the circular economy and municipal waste recycling, in order to save resources, the prices of which are increasing significantly. Of course, all these recommendations are synergistically applicable and, in the long term, help to significantly improve the efficiency of countries in using inputs and converting them into economic outputs with the lowest possible production of harmful emissions. Therefore, future research must undoubtedly be directed into this area. Future research can also be directed towards smaller groupings, individual countries or regions of countries. Examining the efficiency of these clusters could make detailed recommendations to authorities who decide on measures and policies to improve the state of the environment and related factors. Research possibilities also lie in research into new methods that may focus on artificial intelligence, networks and other ever-increasing modifications of the DEA method. Author contribution Conceptualisation: Roman Lacko, Peter Markovič; methodology: Zuzana Hajduová, Roman Lacko; formal analysis and investigation: Peter Markovič, Zuzana Hajduová; writing—original draft preparation: Roman Lacko; writing—review and editing: Zuzana Hajduová; funding acquisition: Zuzana Hajduová, Peter Markovič; resources: Roman Lacko, Peter Markovič, Zuzana Hajduová; supervision: Zuzana Hajduová. Funding The research was supported by the Scientific Grant Agency of the Ministry of Education of Slovak Republic and the Slovak Academy of Sciences VEGA (project no. 1/0240/20). Data availability All the data used in this study are available online at the Eurostat and The World Bank databases. Please contact the corresponding author for a data request. Declarations Ethical approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Publisher's note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abbasi KR Lv K Radulescu M Shaikh PA Economic complexity, tourism, energy prices, and environmental degradation in the top economic complexity countries: fresh panel evidence Environ Sci Pollut Res 2021 28 68717 68731 10.1007/s11356-021-15312-4 Afonso A Aubyn MST Assessing health efficiency across countries with a two-step and bootstrap analysis Appl Econ Lett 2011 18 1427 1430 10.1080/13504851.2010.541149 Aguilar-Hernandez GA Dias Rodrigues JF Tukker A Macroeconomic, social and environmental impacts of a circular economy up to 2050: a meta-analysis of prospective studies J Clean Prod 2021 278 123421 10.1016/j.jclepro.2020.123421 Alsaleh M Abdul-Rahim AS Mohd-Shahwahid HO Determinants of technical efficiency in the bioenergy industry in the EU28 region Renew Sustain Energy Rev 2017 78 1331 1349 10.1016/j.rser.2017.04.049 Arbolino R Carlucci F De Simone L The policy diffusion of environmental performance in the European countries Ecol Ind 2018 89 130 138 10.1016/j.ecolind.2018.01.062 Azam M Mahmudul Alam M Haroon Hafeez M Effect of tourism on environmental pollution: further evidence from Malaysia, Singapore and Thailand J Clean Prod 2018 190 330 338 10.1016/j.jclepro.2018.04.168 Balsalobre-Lorente D Driha OM Shahbaz M Sinha A The effects of tourism and globalization over environmental degradation in developed countries Environ Sci Pollut Res 2020 27 7130 7144 10.1007/s11356-019-07372-4 Borozan D Technical and total factor energy efficiency of European regions: a two-stage approach Energy 2018 152 521 532 10.1016/j.energy.2018.03.159 Busu M Trica CL Sustainability of circular economy indicators and their impact on economic growth of the European Union Sustainability 2019 11 5481 10.3390/su11195481 Cai W Li G The drivers of eco-innovation and its impact on performance: evidence from China J Clean Prod 2018 176 110 118 10.1016/j.jclepro.2017.12.109 Camilleri M Closing the loop for resource efficiency, sustainable consumption and production: a critical review of the circular economy IJSD 2018 1 1 10.1504/IJSD.2018.10012310 Charnes A Clark CT Cooper WW Golany B A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. air forces Ann Oper Res 1984 2 95 112 10.1007/BF01874734 Charnes A, Cooper WW, Lewin AY, Seiford LM (1994) Data envelopment analysis: theory, methodology, and applications. Springer Science & Business Media, US, New York Cooper WW Seiford LM Tone K Data envelopment analysis: a comprehensive text with models, applications, references and DEA-Solver Software 2007 2 US, New York, US Springer Day J Chin N Sydnor S Cherkauer K Weather, climate, and tourism performance: a quantitative analysis Tour Manag Perspect 2013 5 51 56 10.1016/j.tmp.2012.11.001 De Pascale A, Arbolino R, Szopik-Depczyńska K, et al (2020) A systematic review for measuring circular economy: the 61 indicators. J Clean Prod 124942. 10.1016/j.jclepro.2020.124942 Dinda S A theoretical basis for the environmental Kuznets curve Ecol Econ 2005 53 403 413 10.1016/j.ecolecon.2004.10.007 Duman YS Kasman A Environmental technical efficiency in EU member and candidate countries: a parametric hyperbolic distance function approach Energy 2018 147 297 307 10.1016/j.energy.2018.01.037 Eurostat (2022) Database - Eurostat. https://ec.europa.eu/eurostat/data/database. Accessed 15 Jun 2022 Guo L Li P Zhang J Do socio-economic factors matter? A comprehensive evaluation of tourism eco-efficiency determinants in China based on the Geographical Detector Model J Environ Manage 2022 320 115812 10.1016/j.jenvman.2022.115812 35947907 Haibo C Ke D Fangfang W Ayamba EC The spatial effect of tourism economic development on regional ecological efficiency Environ Sci Pollut Res 2020 27 38241 38258 10.1007/s11356-020-09004-8 Halkos G Petrou KN Analysing the energy efficiency of EU member states: the potential of energy recovery from waste in the circular economy Energies 2019 12 3718 10.3390/en12193718 Hossain MdU Ng ST Critical consideration of buildings’ environmental impact assessment towards adoption of circular economy: an analytical review J Clean Prod 2018 205 763 780 10.1016/j.jclepro.2018.09.120 Iftikhar Y Wang Z Zhang B Wang B Energy and CO2 emissions efficiency of major economies: a network DEA approach Energy 2018 147 197 207 10.1016/j.energy.2018.01.012 Iram R Zhang J Erdogan S Economics of energy and environmental efficiency: evidence from OECD countries Environ Sci Pollut Res 2020 27 3858 3870 10.1007/s11356-019-07020-x Ji X Yao Y Long X What causes PM2.5 pollution? Cross-economy empirical analysis from socioeconomic perspective Energy Policy 2018 119 458 472 10.1016/j.enpol.2018.04.040 Jiang G Zhu A Li J Measurement and impactors of tourism carbon dioxide emission efficiency in China J Environ Public Health 2022 2022 1 10 10.1155/2022/9161845 Kwon DS Cho JH Sohn SY Comparison of technology efficiency for CO 2 emissions reduction among European countries based on DEA with decomposed factors J Clean Prod 2017 151 109 120 10.1016/j.jclepro.2017.03.065 Lacko R Hajduová Z Determinants of environmental efficiency of the EU countries using two-step DEA approach Sustainability 2018 10 3525 10.3390/su10103525 Li M Wang Q International environmental efficiency differences and their determinants Energy 2014 78 411 420 10.1016/j.energy.2014.10.026 Liu H Wu J Chu J Environmental efficiency and technological progress of transportation industry-based on large scale data Technol Forecast Soc Chang 2018 10.1016/j.techfore.2018.02.005 Liu JS Lu LYY Lu W-M Lin BJY A survey of DEA applications Omega 2013 41 893 902 10.1016/j.omega.2012.11.004 Liu L Chen Y Wu T Li H The drivers of air pollution in the development of western China: the case of Sichuan province J Clean Prod 2018 197 1169 1176 10.1016/j.jclepro.2018.06.260 Liu X Guo P Guo S Assessing the eco-efficiency of a circular economy system in China’s coal mining areas: emergy and data envelopment analysis J Clean Prod 2019 206 1101 1109 10.1016/j.jclepro.2018.09.218 Ma D He F Li G Deng G Does haze pollution affect public health in China from the perspective of environmental efficiency? Environ Dev Sustain 2021 23 16343 16357 10.1007/s10668-021-01352-w Madaleno M Moutinho V Robaina M Economic and environmental assessment: EU cross-country efficiency ranking analysis Energy Procedia 2016 106 134 154 10.1016/j.egypro.2016.12.111 Mardani A Zavadskas EK Streimikiene D A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency Renew Sustain Energy Rev 2017 70 1298 1322 10.1016/j.rser.2016.12.030 Martín JC Mendoza C Román C A DEA travel–tourism competitiveness index Soc Indic Res 2017 130 937 957 10.1007/s11205-015-1211-3 Mavi NK Mavi RK Energy and environmental efficiency of OECD countries in the context of the circular economy: common weight analysis for malmquist productivity index J Environ Manage 2019 247 651 661 10.1016/j.jenvman.2019.06.069 31279142 Mhatre P Panchal R Singh A Bibyan S A systematic literature review on the circular economy initiatives in the European Union Sustain Prod Consump 2021 26 S2352550920302232 10.1016/j.spc.2020.09.008 Moutinho V Costa C Bento JPC The impact of energy efficiency and economic productivity on CO2 emission intensity in Portuguese tourism industries Tour Manag Perspect 2015 16 217 227 10.1016/j.tmp.2015.07.009 Moutinho V Madaleno M Robaina M The economic and environmental efficiency assessment in EU cross-country: evidence from DEA and quantile regression approach Ecol Ind 2017 78 85 97 10.1016/j.ecolind.2017.02.042 Neves SA Marques AC Patrício M Determinants of CO2 emissions in European Union countries: does environmental regulation reduce environmental pollution? Econ Anal Policy 2020 68 114 125 10.1016/j.eap.2020.09.005 Nurmatov R Fernandez Lopez XL Coto Millan PP Tourism, hospitality, and DEA: where do we come from and where do we go? Int J Hosp Manag 2021 95 102883 10.1016/j.ijhm.2021.102883 Peng H Zhang J Lu L Eco-efficiency and its determinants at a tourism destination: a case study of Huangshan National Park, China Tour Manage 2017 60 201 211 10.1016/j.tourman.2016.12.005 Reinhard S, Knox Lovell CA, Thijssen GJ (2000) Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA. Eur J Oper Res 121:287–303. 10.1016/S0377-2217(99)00218-0 Robaina M Murillo K Rocha E Villar J Circular economy in plastic waste - efficiency analysis of European countries Sci Total Environ 2020 730 139038 10.1016/j.scitotenv.2020.139038 32438212 Sellers-Rubio R Casado-Díaz AB Analyzing hotel efficiency from a regional perspective: the role of environmental determinants Int J Hosp Manag 2018 75 75 85 10.1016/j.ijhm.2018.03.015 Simar L Wilson PW Estimation and inference in two-stage, semi-parametric models of production processes Journal of Econometrics 2007 136 31 64 10.1016/j.jeconom.2005.07.009 Song M Estimating the efficiency of a sustainable Chinese tourism industry using bootstrap technology rectification Technol Forecast Soc Chang 2019 143 10 10.1016/j.techfore.2019.03.008 Sun Y-Y Decomposition of tourism greenhouse gas emissions: revealing the dynamics between tourism economic growth, technological efficiency, and carbon emissions Tour Manage 2016 55 326 336 10.1016/j.tourman.2016.02.014 Tajudeen IA Wossink A Banerjee P How significant is energy efficiency to mitigate CO 2 emissions? Evidence from OECD countries Energy Econ 2018 72 200 221 10.1016/j.eneco.2018.04.010 Tenente M Henriques C da Silva PP Eco-efficiency assessment of the electricity sector: evidence from 28 European Union countries Econ Anal Policy 2020 66 293 314 10.1016/j.eap.2020.05.003 The World Bank (2021) World Bank Open Data. https://data.worldbank.org/. Accessed 10 Feb 2021 Toma P Miglietta PP Zurlini G A non-parametric bootstrap-data envelopment analysis approach for environmental policy planning and management of agricultural efficiency in EU countries Ecol Ind 2017 83 132 143 10.1016/j.ecolind.2017.07.049 Usman M Yaseen MR Kousar R Makhdum MSA Modeling financial development, tourism, energy consumption, and environmental quality: is there any discrepancy between developing and developed countries? Environ Sci Pollut Res 2021 28 58480 58501 10.1007/s11356-021-14837-y Vaninsky AY Energy-environmental efficiency and optimal restructuring of the global economy Energy 2018 153 338 348 10.1016/j.energy.2018.03.063 Vaninsky AY Environmental performance of the United States energy sector: a DEA model with non-discretionary factors and perfect object Int J Energy Environ Eng 2009 3 6 Vlontzos G Niavis S Manos B A DEA approach for estimating the agricultural energy and environmental efficiency of EU countries Renew Sustain Energy Rev 2014 40 91 96 10.1016/j.rser.2014.07.153 Wolszczak-Derlacz J Parteka A Efficiency of European public higher education institutions: a two-stage multicountry approach Scientometrics 2011 89 887 10.1007/s11192-011-0484-9 22081733 Woo C Chung Y Chun D The static and dynamic environmental efficiency of renewable energy: a Malmquist index analysis of OECD countries Renew Sustain Energy Rev 2015 47 367 376 10.1016/j.rser.2015.03.070 Wu J Li M Zhu Q Energy and environmental efficiency measurement of China’s industrial sectors: a DEA model with non-homogeneous inputs and outputs Energy Econ 2019 78 468 480 10.1016/j.eneco.2018.11.036 Zeng S Jiang X Su B Nan X China’s SO 2 shadow prices and environmental technical efficiency at the province level Int Rev Econ Financ 2018 10.1016/j.iref.2018.02.014 Zha J Yuan W Dai J Eco-efficiency, eco-productivity and tourism growth in China: a non-convex metafrontier DEA-based decomposition model J Sustain Tour 2020 28 663 685 10.1080/09669582.2019.1699102	36447104	PMC9708512	NO-CC CODE	2022-12-01 23:20:30	no	Environ Sci Pollut Res Int. 2022 Nov 30;:1-12	utf-8	Environ Sci Pollut Res Int	2,022	10.1007/s11356-022-24435-1	oa_other
==== Front J Intell Inf Syst J Intell Inf Syst Journal of Intelligent Information Systems 0925-9902 1573-7675 Springer US New York 764 10.1007/s10844-022-00764-y Article An image and text-based multimodal model for detecting fake news in OSN’s Uppada Santosh Kumar [email protected] Patel Parth [email protected] B. Sivaselvan [email protected] grid.504246.1 0000 0004 1808 3086 Department of Computer Science and Engineering, IIITDM Kancheepuram, Melakottiyur, Chennai, 600127 Tamil Nadu India 30 11 2022 127 22 7 2022 18 10 2022 3 11 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Digital Mass Media has become the new paradigm of communication that revolves around online social networks. The increase in the utilization of online social networks (OSNs) as the primary source of information and the increase of online social platforms providing such news has increased the scope of spreading fake news. People spread fake news in multimedia formats like images, audio, and video. Visual-based news is prone to have a psychological impact on the users and is often misleading. Therefore, Multimodal frameworks for detecting fake posts have gained demand in recent times. This paper proposes a framework that flags fake posts with Visual data embedded with text. The proposed framework works on data derived from the Fakeddit dataset, with over 1 million samples containing text, image, metadata, and comments data gathered from a wide range of sources, and tries to exploit the unique features of fake and legitimate images. The proposed framework has different architectures to learn visual and linguistic models from the post individually. Image polarity datasets, derived from Flickr, are also considered for analysis, and the features extracted from these visual and text-based data helped in flagging news. The proposed fusion model has achieved an overall accuracy of 91.94%, Precision of 93.43%, Recall of 93.07%, and F1-score of 93%. The experimental results show that the proposed Multimodality model with Image and Text achieves better results than other state-of-art models working on a similar dataset. Keywords Fake news detection Xception Bert + Dense Fusion models Click-baits Fakeddit Visual sentiment analysis Error level analysis ==== Body pmcIntroduction The widespread use of the internet and online platforms has changed the perspective of information sharing, making it quick and straightforward to reach the masses. The rapid growth of various platforms resulted in news from various sources that lacked credibility. As news comes from different sources, it is always challenging to determine the credibility of news or posts. From the Hadson river plane crash incident to the prevailing pandemic situations, online media has become a prominent tool to get diversified news across the globe within no time. Online social media has become a buzz, and popularity in social networks has opened doors to the news patterns that are informative and target the users’ emotions interacting with such news. In order to attract people, users started creating news that creates a sensation, which might be far enough from reality. It is estimated that a massive amount of unverified news is being generated by WhatsApp Univeristy. Fake news or Yellow journalism is a process of creating, spreading, and propagating news that could be biased or unreal (Campbell, 2001). Fake news is generally an intentional or unintentional spread of news that could be unreal. In general, Fake News can be biased, unreal, satirical, propaganda, clickbaits, satirical news, or disinformation to degrade the reputation or create hoaxes in the society. The intentional spread of fake news creates chaos and panic in society (Shu et al., 2017). Even though there are many fact-checking sites, it takes time to check the credibility of every news that populates in multiple platforms (Robertson et al., 2020). There is also the’ power law’ observed in such social media that posts can spread more quickly and reach wider audiences if the posts target a few influential people in the online social network (Adamic & Huberman, 2000). Stance detection and reliability study of the Fake posts in online social networks is another challenging task. It deals with evaluation of reliability of statements and news articles. It relies on the Statement conflict data, with a prior assumption that the news article and statement relationships are already known (Zhang et al., 2022). Users tend to believe in the news when it comes from trusted groups or sites, which is generally termed as Homophily (McPherson et al., 2001). Confirmation bias is the psychological aspect that makes users interpret and recall the news they hear and support one’s beliefs and faith. Frequency Heuristic is another factor that makes users believe in the news they interact with, as the same news is spread from different sources or spread multiple times. Therefore unintentional spread will also have some psychological aspects related to the spread of news (Pennycook & Rand, 2021). If a user is biased to a specific domain, channel, person, or site, they blindly tend to believe news without even thinking about its credibility, hence will be termed as biased users. The echo chamber effect has a significant impact on users believing in the news coming from specific sources (Cinelli et al., 2021). When it comes to intentional spread, users tend to publish news targeting the users who have more followers. Targeting a more influenced person makes the news spread deeper into the network. Users even tend to create click-baits, tempting headings to draw the attention of the users, or by creating automated programs or bots, to make news reach with more intensity (Bazaco, 2019). As social posts with images have higher reachability than other posts, users tend to spread the news with more images to attract the masses. In spreading fake news, images spread can be either tampered with (edited) or used out of context (Uppada et al., 2022). Tracking the tampered images is easy, but dealing with images used out-of-context is always tedious and time-consuming. Users even tend to spread the news as a combination of images and text. Here image-related or context-related captions are added to the images and published online, as it is always challenging to classify such posts. Fact-checking website Boom reports that the number of false/misleading claims and misinformation has a positive correlation with the number of COVID-19 cases in the country (Chowdhury, 2020). Around 65% of COVID-19 related misinformation is shared using multimedia, mainly images and videos. For example, during the COVID-19 pandemic, a piece of news that got attention and circulated more was about Cocaine killing the coronavirus. Social networking sites like Facebook, Twitter, and Whatsapp are significant sources for spreading such news. Figure 1 depicts the fake news circulated the most in China about cocaine killing coronavirus. Users aim to spread the news in terms of image or video, as it draws more attention and has more reachability and retweet frequency than regular posts. Tweets with videos, for example, posts with images, receive 18 percent more clicks, 89% more likes, and 150 percent more retweets than those without videos (Cao et al., 2020). As fake news is coming in multimedia format, there is a need for multimodal detection systems for fake news. As Fake images can be either tampered with or used in a different context, traditional forensic techniques are inadequate to handle such diverse nature of fake images on social media. There is a need to develop a framework that can effectively learn useful features from the varied nature of images in fake news to distinguish them from those in actual posts. Such a framework could hugely benefit online social networks in their efforts to curb the proliferation of fake news on their platforms (Jin et al., 2016). Fig. 1 Cocaine kills Coronavirus (Clever et al., 2020) Fake images are often eye-catching and have a substantial emotional impact. Fake images are generally misleading and thus provokes the user’s attention. Thus, it becomes necessary to map psychological triggers to the characteristics of the image. These psychological patterns are limited to visual appearance and beyond the standard object-level features. Hence traditional image sets are not suitable for this task of fake image classification (Jin et al., 2017). Gathering large labeled datasets containing posts with real and fake images is difficult because human verification and labeling of posts is time-taking and not fast enough to deal with the big data online (Jin et al., 2016). Images often get auto-compressed when shared using specific social networking sites. Most forensic techniques that aim to detect fake images rely on features retrieved from these compression factors; hence fail to work with these compressed images when uploaded and downloaded multiple times. Metadata and sentiment of the images also play a vital role in detecting fake images (Luo et al., 2007). Users generally depend on specific third-party tools like Mechanical Turk for analyzing sentiment. The polarity of images will help in understanding the impact that image creates on users (Ragusa et al., 2022). Similar to the text polarity, images will also have positive, negative, and neutral (Ragusa et al., 2019) polarity. Therefore, observing text-related and image-related features makes the system detect fake news more accurately. Image-based social posts often get combined with text related to the image (Image Caption) or context. Users get misled when textual data is added to the images, as clickbait to attract attention. It is often tricky to work with posts with more than one form (image combined with text data). Thus, multimodal models are getting attention from the research community to combine features of text and image for detecting fake posts very quickly with more accuracy (Shah & Kobti, 2020; Giachanou et al., 2020). As social media posts contain multimedia data, it is heterogeneous data that should be handled, and hence, multimedia-related frameworks are to be proposed that work on multiple modals. This process became interesting with the intended use of Machine Learning and Deep Learning models. There is a need to propose different methods that work on different feature combinations to detect Fake posts, mostly with misleading images (Galli et al., 2022). Therefore, multimodal analysis has become a prominent research objective in detecting Fake posts in Online Social Networks. The significant outcomes of the study can be summarized as follows. The proposed framework, given the input post containing Images/Images with Caption, extracts features such as the probability of image being manipulated, polarity of the image, and the probability of image caption being manipulated using different learners, treating each feature independently. Fusion models such as Maximum and Concatenate are used to learn the classifier model. A dense layer is added to the BERT model to enhance the learning capability of the model. Various models (algorithms) have been tested on the data, including ELA (Error Level Analysis). The learners with high accuracy and less loss have been chosen for the proposed framework. The proposed model helps detect fake posts on Social Networks, especially Images with embedded captions. The proposed ensemble framework has shown better results compared to the state-of-art models working on similar data. The proposed work outperforms existing models in terms of sample size and accuracy. The remainder of the paper is as follows. Section 2 describes the proposed models similar to the proposed models, finally comparison is derived with the proposed models. Section 3 has the methodology section that describes the overall framework. Further Section 4 is given with an introduction to the proposed Image Manipulation and Polarity based Fake News detection model. Sections 4.1, 4.2, and 4.3 has Image manipulation and polarity based fake posts detection models, including Error Level Analysis. For every modality different pre-trained models like VGG-16, Vgg-19, Xception, Inception-Resnet50 are tried. Even ELA based analysis is also performed on the images. In Section 4.4, Image caption data is analyzed. Section 5 has the proposed framework for Fake post detection. Section 5.1 has the summary of Fusion Models, Section 5.2 has Result Analysis, Section 5.3 has Error Analysis for the proposed model. Further Section 6 has conclusion and future scope discussion. Related work Anastasia et al. have proposed a multimodal Multi-image Fake News detection that works on the posts’ textual, Visual, and Semantic Features. The BERT model is used for the textual part, and VGG16 is used for the visual aspect of the posts. Tokens derived from the textual data and image tags are given to a similarity metric (cosine similarity of title and image tags), a semantic branch. These branches are fused using Concatenate, and finally, the attention layer is added. VGG16+BERT+fusion (attention) recorded an accuracy of 76.83%, VGG16+BERT+fusion (Concatenation) recorded 78.30% accuracy, and VGG16+BERT+similarity+fusion (attention) recorded an accuracy of 76.83% (Giachanou et al., 2020). Kai Nakamura et al. proposed a multimodal model for Fake News Detection. For analysis, Reddit and Fakeddit datasets are considered, where the samples are classified into six classes. For combining the class labels from various models, fusion methods like Maximum, Concatenate, Add, and Average are considered. It is observed that BERT for Text and ResNet50 for image classification, combined with the fusion method as Maximum, has shown better results. BERT+ResNet50 achieved an accuracy of 89.29% for 2-way, 89.05% for 3-way, and 86% for 6-way classification with Maximum as fusion method (Nakamura et al., 2019). Kirchknopf Armin et al. proposed a multimodal detection for Information Disorder in social media. The model proposed works on various combinations of Text data, Visual content, Comments for the visual content, and metadata. Classification results are fused using Sum, Concatenate, and maximum methods, and Fakeddit data is used for analysis. The combination of Visual content and Comments related to the visual content recorded a Validation and Testing accuracy of 88% and 88.1%, respectively. The model achieved better results when text, image content, comments, and metadata related to social posts were considered (Kirchknopf et al., 2021). Priyanka Meel and Dinesh Kumar V have proposed an ensemble multimodal for Fake News detection that utilizes a Hierarchal Attention Network (HAN), Image Captioning, and Error Level Analysis. Max-voting is the fusion method employed to combine the models’ results. The Text part of the dataset is analyzed using HAN, ELA, and Noise Variant Inconsistency for the images and Max fusion for attaining the Max vote class label for the Image with Text (caption and comments) content embedded. It is observed that the combined model outperformed individuals and other state-of-art models when working on the Fake News Samples dataset. The proposed ensemble model on the Fake News Sample dataset achieved an accuracy of 94.7% (Meel & Vishwakarma, 2021). Yan Wu et al. have proposed multimodal Co-Attention networks based fusion network for Fake News detection. The model used BERT for working on the Textual aspects and VGG19 for working on the visual features of the data. Textual and Image related features from Twitter and Weibo datasets are used for analysis. Textual, spatial, and frequency domain aspects are fused to detect Fake posts. It is observed that the proposed MCAN model achieved an accuracy of 80.9% on the Twitter dataset and 89.9% on Weibo dataset (Wu et al., 2021). Dhruv Khattar et al. proposed a multimodal variational autoencoder for detecting fake news. A bimodal variational autoencoder and a binary classifier were used for fake news classification. This model contains three components: an encoder that transforms data from text and images into latent vectors, a decoder that uses these latent vectors to re-construct the text and images, and latent vectors to detect fake news and images. Each encoder and decoder contains individual blocks for text and images. Twitter and Weibo datasets are used for processing. Proposed MVAE gave an accuracy of 74.5% on Twitter and 82.4% on Weibo datasets (Khattar et al., 2019). Rina Kumari and Asif Ekbal proposed an Attention-based Multimodal factorized Bilinear Pooling model to detect Fake posts with Image and Textual data. The proposed framework has four modules for textual features representation (Attention-based Stacked BiLSTM), image feature representation (Attention-based Multilevel CNN-RNN), a Multimodal Factorized Bilinear pooling for the fusion of textual and image features, and Muli-Layer Perceptron (MLP) for final classification. Data from Twitter and Weibo is used for analysis, and it is observed that the proposed model gave an accuracy of 88.3% on Twitter and 89.23% on the Weibo dataset (Kumari & Ekbal, 2021). Peng Qi et al. proposed exploiting multi-domain visual information for fake news detection. The CNN-based network captured complex patterns and multi-branch CNN-RNN for extracting visual features at semantic levels. The MVNN model has three sub-models: Frequency domain sub-network, Discrete Cosine Transform, and Pixel domain sub-network. The fusion domain sub-network utilizes pixel and frequency domain sub-networks features to detect whether the images are fake or real. Verified data from Twitter and Weibo are considered for processing. When Attention-RNN (attRNN) was used, the proposed MVNN model gave an accuracy of 90.1%, Event Adversarial Neural Networks (EANN) with 89.7%, and MVAE around 89.1% (Qi et al., 2019). Mathieu and Brahim proposed a multimodal sentiment analysis model for text and image-based posts and a fusion network to combine both modalities. Flickr Emotion VSO (Visual Sentiment Ontology) datasets are used in this model. The model proposed has achieved an accuracy of 91.17% on the Flickr Emotion dataset and 86.35% VSO dataset. As a part of the second experiment, multitask framework was enabled to be trained only with monomodal data. When trained with more images, Multitask model achieved an accuracy of 91.59% on the Flickr dataset. In addition, two auxiliary image and text-based classifiers are introduced to the traditional multimodal framework to handle missing modalities (Fortin & Chaib-Draa, 2019). Jiangfeng et al. proposed a multimodal correlation model for detecting Fake News for epidemic emergencies using the deep correlations between text and images. The model has three phases; wherein the first phase, the image representatives are learned using a pre-trained VGG model and used for learning the textual representations using a hierarchical attention mechanism. In the second phase, multimodal representations are modeled to learn the fused text and image representations. In the third phase, image–enhanced text representations and the fused eigenvectors are combined to detect Fake News. It is observed that the proposed model achieved an accuracy of 83.4% (Zeng et al., 2021). Motivation for the study Often Image analysis is carried out using Forensic techniques, which alone are insufficient to handle the problem of fake image detection. There is a need for a universal approach that can handle the scale and varied nature of fake news images. Often Image Caption data is neglected for analysis. Image Captions are being used as Clickbaits to enhance the reachability of the posts. Therefore, it is important to work on Image captions to detect clickbaits or misleading captions. There is also significant amount of work in using multiple modalities for sentiment analysis. However, different research works use varied datasets and hence comparison of performances of various frameworks developed is required. There is a need to develop a framework which focuses on identifying features unique to fake news images and its corresponding captions to aid in their identification. There is need to design a framework that works on Manipulated as well as sentiment cues from the images. Methodology Social posts often contain a combination of images and text. Text embedded might be caption related to the image published or event that took place. Hence, it is always tedious to work with such social posts that combine image and text. Sophisticated methods are to be employed to work on such posts. Figure 2 depicts a model that works with image and text data separately and then combines the insights from both models. Fig. 2 Overview of the proposed approach The proposed framework has an image model that works on visual features, Φip and image polarity, Φim. The textual network handles the textual portion or caption of the social post, Φt. A multimodal classifier combines the features from these networks using a fusion method, Cm. When a social post is published, the framework works on both models independently and gives the combined classification result. Fakeddit, a publicly available dataset, is used for analysis. It has 1 million large-scale multimodal fake news data containing text, image, metadata, and comments data gathered from a wide range of sources. The fake news articles in this dataset are scraped from Reddit, social news and discussion platform, where users can submit submissions on various subreddits. Data scrapped is between March 2008 to October 2019. Data samples have multiple labels- namely 2-way, 3-way, and 6-way. Here, 2-way classification states whether the news is authentic or fake; 3-way classification states if it is entirely true, completely fake, or either with fake text with correct sample and vice versa; 6-way classification that states if the samples come into categories like Satire, True, Fake, Misleading content, Manipulated content, False content or Imposter content (Nakamura et al., 2019). For analysis, samples with both text and images are only considered, and other samples are ignored. It is observed that around 64% of the samples have both image and text embedded with the image. The statistics of the dataset are as given in Table 1. Table 1 Statistics of fakeedit dataset Samples Training Validation Testing Real 222081 23320 23507 Fake 341519 35979 35763 Total 563600 59299 59270 Image manipulation and image polarity based fake news detection It is observed that tweets or social posts with images spread faster and have a high level of retweets and shares. Images generally target the emotions of the people, and hence users are targeted with image-based fake news as it not only catches the attention but also has high spread and interaction patterns. Fake images can be either tampered images or images used out of context. Visual information from the social posts is used to determine if they are manipulated. Figure 3 depicts the famous manipulated images that have created a buzz on online social media. The left image is the famous image manipulation example, the composite photo of Senator Millard Tydings and American Communist Party leader Earl Browder (Thakur & Rohilla, 2020). The right image is the most circulated image during Hurricane sandy that took place in 2012 (Boididou et al., 2015). Fig. 3 Image manipulation examples Fake news detection based on image manipulation Pre-trained deep neural networks are used to detect images that are manipulated. Initially, images are trained on different neural networks and the model that gives best accuracy is chosen for construction of our model. Figure 4 depicts the design of image modality models. Fig. 4 Design of image modality models Inception-ResNet-V2 Inception-ResNet-V2 is a 164 layer deep network capable of identifying images into 1000 categories. This model is trained on an ImageNet dataset with more than 1 million images, and this model analyzes the images and returns a list of class probabilities (Szegedy et al., 2017). For analysis, images from the Fakeddit dataset are used to fine-tune Inception-ResNet-V2. The convolutional part of the model is instantiated, and pre-trained weights from ImageNet are loaded. Images are scaled to 150 x 150, ReLu is the activation function used for every connected layer, and Softmax activation function is added on the top of the convolutional part. The model is run for a batch size of 256, with Adam optimizer and at a learning rate of 0.0005. The model is trained for 15 epochs, and it is observed that the model has the best validation loss at the 5th epoch. The model obtained validation accuracy of 80.49% and test accuracy of 80.6%. Xception For analysis, images from the Fakeddit dataset are used to fine-tune Xception model. The convolutional part of the model is instantiated, and pre-trained weights from ImageNet are loaded (Chollet, 2017). Images are scaled to 150 x 150, ReLu is the activation function used for every connected layer, and Softmax activation function is added on the top of the convolutional part. The model is run for a batch size of 256, with Adam optimizer and at a learning rate of 0.0005. The model is trained for 15 epochs, and it is observed that the model has the best validation loss at the 2nd epoch. The model obtained validation accuracy of 82.07% and test accuracy of 82.32%. Fake news based on image manipulation using error level analysis The use of image editing tools has now made the manipulation of images very convenient. Visual content is an essential promoter for fake news propaganda as images offer a perception of reality, and hence users are often easily misled. Forensic techniques like Error Level Analysis (ELA) helps in identifying the digital alterations in the images, which analyses compression artifacts and helps identify regions in the image with different compression levels (Sudiatmika et al., 2019). ELA intentionally re-saves images at a compressed level and then computes the difference between these images (Abd Warif et al., 2015). Figure 5 depicts the original image with its ELA and Fig. 6 depicts ELA for the modified image. Images clearly show that edited images have higher errors at the tampered regions. ELA images help identify digitally altered images since the error levels in such images are not uniform. Therefore, ELA for all the images is computed and these images are used to fine-tune CNN architectures to identify the altered images. Different CNN architectures are used to work on the ELA images. Fig. 5 Original Image and its ELA output Fig. 6 Edited Image and its ELA output Inception-ResNet-V2 with ELA images Inception-ResNet-V2 is a convolutional network trained on 1 million images from the ImageNet database. The network is 164 layers deep and can classify images into 1000 categories (Szegedy et al., 2017). Every fully connected layer in the model has a ReLu activation function, and a fully connected classifier with Softmax activation function is added on the top of the convolutional part. The entire model is trained on the Fakeddit dataset. Images from Fakeddit dataset is resized to 150X150, into a batch size of 256. The model is trained with Adam optimizer at a learning rate of 0.0005. The model is trained for 15 epochs. The best validation loss is obtained at the fifth epoch. The model obtained validation accuracy of 80.49% and test accuracy of 80.6%. ResNet50 with ELA images ResNet50 has 48 deep convolutional layers. ELA computed images from the Fakeddit dataset are used to fine-tune the ResNet50 model for analysis (He et al., 2016a; Rezende et al., 2017). The convolutional part of the model is instantiated, and pre-trained weights from ImageNet are loaded. Images are scaled to 150 x 150, ReLu is the activation function used for every connected layer, and Softmax activation function is added on the top of the convolutional part. The model is run for a batch size of 256, with Adam optimizer and at a learning rate of 0.0005. The model is trained for 15 epochs, and it is observed that the model has the best validation loss at the fourth epoch. The model obtained validation accuracy of 79.01% and test accuracy of 79.58%. Xception with ELA images ELA computed images from the Fakeddit dataset are used to fine-tune the Xception model for analysis. The convolutional part of the model is instantiated, and pre-trained weights from ImageNet are loaded (Chollet, 2017). Images are scaled to 150 x 150, ReLu is the activation function used for every connected layer, and Softmax activation function is added on the top of the convolutional part. The model is run for a batch size of 256, with Adam optimizer and at a learning rate of 0.0005. The model is trained for 15 epochs, and it is observed that the model has the best validation loss at the 2nd epoch. The model obtained validation accuracy of 79.58% and test accuracy of 80.01%. Table 2 depicts the Performance measures of different Neural networks on Images from Fakeddit as well as for the ELA images. It is observed that the Xception model has better Validation and Testing accuracy. Therefore, the Xception model is used to construct the proposed model on the Fakeddit dataset. Table 2 Validation and testing accuracy of image manipulation data Text model Validation accuracy Test accuracy VGG16 (Baseline) 73.55% 73.76% EfficientNet (Baseline) 61.15% 60.87% ResNet50 (Baseline) 80.43% 80.70% Inception-ResNet-V2 80.49% 80.66% Xception 82.07% 82.32% Inception-ResNet-V2 with ELA 79.52% 79.70% ResNet50 with ELA 79.01% 79.58% Xception with ELA 79.58% 80.01% Bold indicates models with better performance measures (here validation and Test accuracy) Fake news based on visual sentiment of images Online social posts with images often target the users’ sentiment and induce strong visual impact. Analyzing the polarity of the images could help in detecting fake posts quickly. The proposed model is added with an additional branch that works on images with sentiment-related data. Transfer learning on CNN architecture is chosen for learning the features to analyze the sentiment of the images. A subset of the CrowdFlower dataset with positive and negative sentiment is chosen for analysis. Table 3 depicts the statistics of the CrowdFlower dataset. Table 3 Statistics of CrowdFlower dataset with Positive and Negative sentiment Samples Training Validation Testing Positive sentiment 1000 112 250 Negative sentiment 1000 113 250 Transfer learning with VGG19 The pre-trained VGG19 model with transfer learning detects the images with sentiment data. Pretrained weights from ImageNet data are used for analysis (Simonyan & Zisserman, 2014; Rajinikanth et al., 2020). The model is run in two phases with varying learning rates and epochs. In the first phase, all layers are frozen with preloaded ImageNet weights. Adam optimizer, with a learning rate of 0.00001 and binary cross-entropy loss function, is used. The model is run with a batch size of 100 and 40 epochs. The model is run in two phases. In the first phase, it is observed that the model gave the best accuracy in the 37th epoch with a validation accuracy of 67.56%. In the second phase, 18 layers are frozen for the base model with pre-trained ImageNet weights with Adam optimizer, a learning rate of 0.00001, and a binary cross-entropy loss function. The model is run with a batch size of 100 and 40 epochs. It is observed that the model gave the best accuracy in the 31st epoch with a validation accuracy of 74.22%. Transfer learning with ResNet50 ResNet50 has 48 convolutional layers with one max pool and an average pool layer. For fine-tuning ResNet50, the base model is instantiated with pre-trained weights from ImageNet (Rezende et al., 2017). The model is run in two phases with varying learning rates and epochs. In the first phase, 170 layers are frozen for base-model with preloaded ImageNet weights. Adam optimizer, with a learning rate of 0.00001 and binary cross-entropy loss function, is used. The model is run with a batch size of 100 and 40 epochs. It is observed that the model gave the best accuracy in the 15th epoch with a validation accuracy of 68.44%. In the second phase, 165 layers are frozen for the base model with pre-trained ImageNet weights with Adam optimizer, learning rate of 0.00001, and binary cross-entropy loss function. The model is run with a batch size of 100 and 30 epochs. It is observed that the model gave the best accuracy in the fifth epoch with a validation accuracy of 71.11%. Transfer learning with ResNet50V2 In order to fine-tune ResNet50V2, the base model is instantiated with input size (150,150,3), and pre-trained weights from ImageNet are loaded and run in two phases (He et al., 2016b; Siegfried, 2020). In phase-1, the first 170 layers are frozen for the base model with preloaded ImageNet weights. Adam optimizer, with a learning rate of 0.00001 and binary cross-entropy loss function, is used. The model is run with a batch size of 100 and 40 epochs. It is observed that the model gave the best accuracy in the 15th epoch with a validation accuracy of 68.44%. In the second phase, 180 layers are frozen for the base model with pre-trained ImageNet weights with Adam optimizer, a learning rate of 0.00001, and a binary cross-entropy loss function. The model is run with a batch size of 100 and 30 epochs. It is observed that the model gave the best accuracy in the fifth epoch with a validation accuracy of 72.89%. Transfer learning with InceptionV3 InceptionV3 is a 48 layers deep convolutional network. In order to fine-tune InceptionV3, the base model is instantiated with input size (150,150,3), and pre-trained weights from ImageNet are loaded and run in three phases (Szegedy et al., 2016). In the first phase, 290 layers are frozen for the base model with preloaded ImageNet weights. Adam optimizer, with a learning rate of 0.00001 and binary cross-entropy loss function, is used. The model is run with a batch size of 100 and 30 epochs. It is observed that the model gave the best accuracy in the 16th epoch with a validation accuracy of 70.67%. In the second phase, 250 layers are frozen for the base model with preloaded ImageNet weights. Adam optimizer, with a learning rate of 0.00001 and binary cross-entropy loss function, is used. The model is run with a batch size of 100 and 40 epochs. It is observed that the model gave the best accuracy in the 16th epoch with a validation accuracy of 72.44%. In phase-3, all layers are frozen with Adam optimizer, learning rate of 0.00001, and binary cross-entropy loss function. The model is run with a batch size of 100 and 30 epochs. It is observed that the model gave the best accuracy in the third epoch with a validation accuracy of 73.33%. Transfer learning with Xception The Xception model is fine-tuned by instantiating the base model with input size (150,150,3). Pre-trained weights from ImageNet are loaded and run in three phases (Chollet, 2017). In phase-1, all layers are frozen for the base model with preloaded ImageNet weights. With a learning rate of 0.0001 and binary cross-entropy loss function, Adam optimizer is used. The model is run with a batch size of 100 and 30 epochs. It is observed that the model gave the best accuracy in the seventh epoch with a validation accuracy of 75.11%. In the second phase, 115 layers are frozen for the base model with preloaded ImageNet weights. With a learning rate of 0.0001 and binary cross-entropy loss function, Adam optimizer is used. The model is run with a batch size of 100 and 40 epochs. It is observed that the model gave the best accuracy in the 16th epoch with a validation accuracy of 72%. In phase-3, all layers are frozen with Adam optimizer, learning rate of 0.00001, and binary cross-entropy loss function. The model is run with a batch size of 100 and 30 epochs. It is observed that the model gave the best accuracy in the third epoch with a validation accuracy of 74.67%. Table 4 depicts the validation and Testing accuracy of different models on the visual sentiment data. It is observed that the testing accuracy is higher for the Xception model. Therefore, the Xception model is used to construct the proposed model on the Fakeddit dataset. Table 4 Validation and testing accuracy for visual sentiment data Text model Validation accuracy Test accuracy VGG19 74.22% 68% ResNet50 71.11% 66.44% ResNet50V2 72.89% 68.89% InceptionV3 73.33% 68.89% Xception 75.11% 70% Bold indicates models with better performance measures (here validation and Test accuracy) Fake news detection based on image caption Fake news potentially differs from the truth in writing style and quality, word count, and sentiment expressed. As a result, it is fair to identify fake news using linguistic features that capture various writing styles and sensational headlines. Various text modality models were implemented and evaluated. The models were fine-tuned on the Fakeddit dataset on the textual information (image caption) in posts to determine whether they were fake or not. Various pre-trained neural networks are employed to classify the image captions into fake and real categories. LSTM + CNN LSTM, added with a layer of one-dimensional CNN with max pool layer, is used to learn the spatial features of the image caption. A dense layer with softmax as an activation function is finally used to classify the captions (Xia et al., 2020). Adam optimizer with binary cross-entropy loss and three callback functions (CSV logger, Tensorboard, and Model check) is used, and the model is run for 20 epochs with a batch size of 1024. The best validation loss was obtained at the fourth epoch with validation accuracy of 85.51% and test accuracy of 85.25%. BiGRU + CapsuleNet This model uses word embeddings from pre-trained Glove and Paragram, combined with meta-embedding. A bidirectional GRU layer added with the Capsule network is used to classify the data (Deng et al., 2020). Adam optimizer with binary cross-entropy loss and three callback functions (CSV logger, Tensorboard, and Model check) is used, and the model is run for 20 epochs with a batch size of 1024. The best validation loss was obtained at the third epoch with validation accuracy of 85.98% and test accuracy of 86.18%. BiLSTM+BiGRU+attention BiLSTM helps understand the context of the sentences by using the words before and after the current word, thereby offering better predictions (Zhou & Bian, 2019). Word embeddings are used from Glove and fasttext. The attention layer is added after BiGRU, and the output is passed through the Global max Pooling layer. Adam optimizer with binary cross-entropy loss and three callback functions (CSV logger, Tensorboard, and Model check) is used, and the model is run for 15 epochs with a batch size of 512. The best validation loss was obtained at the fourth epoch with validation accuracy of 87.89% and test accuracy of 87.90%. 2D CNN In the 2D CNN model, the word is embedded from pre-trained Glove ad Fasttext. The concatenated word embeddings are reshaped, and 2D CNN is applied with different filter sizes (Zhao et al., 2019). Adam optimizer with binary cross-entropy loss and three callback functions (CSV logger, Tensorboard, and Model check) is used, and the model is run for 15 epochs with a batch size of 512. The best validation loss was obtained at the second epoch with validation accuracy of 86.52% and test accuracy of 86.77%. BERT+Dense The BERT model helps understand and process ambiguous text by learning the context of the sentence (Wang et al., 2021). A dense output layer with softmax as an activation function is added to the BERT model. Adam optimizer with binary cross-entropy loss and one callback function (Model check) is used, and the model is run for ten epochs with a batch size of 144. The best validation loss was obtained at the second epoch with validation accuracy of 89.34% and test accuracy of 89.46%. RoBERTa+Dense RoBERTa is the model built based on the BERT model that helps predict the unintentionally hidden sections of the text. RoBERTa is pre-trained on Books Corpus and English Wikipedia; in addition to this dataset, RoBERTa is trained on CommonCrawl, Web text corpus, and stories from Common Crawl datasets. A dense output layer with softmax as an activation function is added to the RoBERTa model (Kalyan & Sangeetha, 2020). Adam optimizer with binary cross-entropy loss and one callback function (Model checkpoint) is used, and the model is run for ten epochs with a batch size of 144. The best validation loss was obtained at the sixth epoch with validation accuracy of 88.52% and test accuracy of 87.90%. Table 5 depicts the performance measures of various pre-trained models on the image caption data. As Validation and Testing accuracy is high for BERT+Dense model, this model is used to construct the proposed model. Table 5 Performance measures of various text models Text model Validation accuracy Test accuracy BERT (Baseline) 86.54% 86.44% InferSent (Baseline) 86.34% 86.31% LSTM+CNN 85.51% 85.25% BiGRU+Capsule 85.98% 86.18% BiLSTM+BiGRU+attention 87.89% 87.90% 2D CNN 86.52% 86.77% BERT+Dense 89.34% 89.46% RoBERTa+Dense 88.52% 88.62% Bold indicates models with better performance measures (here validation and Test accuracy) Inferences from image manipulation, visual sentiment and image caption After analyzing the data using different Deep Learning models on the manipulated and visual sentiment of the data, the models with better accuracy are considered further. Different models like VGG16, EfficientNet, ResNet50, Inception-ResNet-V2, Xception; Inception-ResNet-V2, ResNet50, and Xception with ELA images are used on manipulated data. Further VGG19, ResNet50, ResNetV2, ResNetV3, and Xception on the visual sentiment, and finally, the models with better accuracies are considered for developing the proposed framework. The following are inferences drawn Xception (Extreme Inception), with 71 layers, has shown better accuracy when compared to other models. Instead of partitioning input data into several compressed chunks, the Xception model tries to map the spatial correlations for each output channel separately and then perform a 1x1 depthwise convolution to capture cross-channel correlations. Xception combines the advantage of the Inception module with the Residual feature, making Xception give good results combined with other models (A Dense layer, as in the proposed model). Xception also reduces the effect of the vanishing gradient problem, which makes this model better in classifying Fake and Real images. Xception, contrary to the models like Inception, has no non-linearity module (No intermediate ReLU non-linearity). ELA (Error Level Analysis), a forensic technique, is not observed to give better results than other models. Therefore, ELA models are not considered for further analysis. Visual sentiment analysis from the CrowdFlower dataset is chosen for analysis. Transfer learning (Retraining the Pre-trained model) is used to work on the image polarity data. As the dataset is around 2000 samples, it is observed that the accuracy level is less when compared to the manipulated data. As BERT and RoBERTa has contextual embedding and are trained on a larger dataset, BERT and RoBERTa outperformed other models like BiGRU, LSTM, and InferSent. As a deep layer is added to the BERT/RoBERTa model, it has shown a significant improvisation in terms of accuracy. For the dataset crawled for the current analysis, BERT has shown to be a bit better than RoBERTa (which might not be the case all the time, as RoBERTa, is said to be an Optimized version of BERT and worked on mode data points, and BERT model without NSP (Next Sentence Prediction)). Upon analyzing the data, BERT+Dense is considered for Textual data, and Xception is used for Image Manipulation and Visual Polarity data while creating the multi-modal framework. Proposed ensemble model for fake news detection An ensemble model was designed to improve the identification of fake news with Xception model to help in identifying images with high digital alterations (Chollet, 2017). BERT to learn contectual knowledge. Visual sentiment analysis to learn features that distinguish an image with negative sentiment from that which induces positive emotions, thereby identifying misleading and tampered fake images with high confidence. Algorithm 1 Proposed multimodality model. The design of the ensemble construction is depicted in Fig. 7. The leftmost vertical branch consists of layers from the BERT model fine-tuned on image captions, the middle branch is composed of layers from the Xception model fine-tuned on Fakeddit dataset images, and the rightmost vertical branch is comprised of layers from the Xception model fine-tuned on Sentiment dataset of images. This proposed ensemble model includes all layers except the last classification layers. Finally, the last module is a multimodal fusion module that combines representations from various modalities (such as text and image) to form a new feature vector. This news representation is fed into a completely connected neural network with softmax activation for fake news classification. Fig. 7 Proposed model design Fusion models Social Media consists of multimedia posts which can normally be Text, Images, Audio, or Video. Data from various modals should be analyzed, and the prediction probabilities from different modals should be grouped to predict a final class label for the post. Multimodal Fusion, therefore, acts as a process of combining features from various modalities to perform a prediction. Fusion can be either Early, Late or Intermediate (Kiela et al., 2018). Early fusion Early Fusion tends to concatenate features from various modals into a single feature vector and is fed into the model to obtain the prediction. It becomes tedious to work with features with higher granularity and can be very highly dimensional (due to the Fusion of pre-processed features from different modals). Figure 8 denotes Early Fusion. Late fusion Late Fusion or decision-level Fusion aims at aggregating decisions from multiple modalities, each trained separately. The method is feature independent, and any errors from multiple modals tend to be uncorrelated. Figure 9 denotes Late Fusion. Intermediate fusion Intermediate Fusion aims at creating representation layers (typically a single shared layer) that merge the units from multiple modality-specific paths. The representation layer created can be either a single layer that maps multiple channels or can be a combination of layers fusing different sets of modals at different levels. Fig. 8 Early fusion Fig. 9 Late fusion Available fusion techniques The fusion Model aims at merging independent features into unique features. If fnt is the normalized text feature representation and fnv is the visual feature representation; and U, V, and W are weight matrices or kernel that helps in combining different features, The following are some of the major fusion models in practice Element-wise Sum: It is also termed Component-wise Sum, which combines the features from multiple modalities. The features from the combining modalities should be similar in terms of their shape. The Additive/ Element-wise sum is given by W(Ufnt+Vfnt) The Element-wise sum is generally disordered. This method does not account for being best when working with relatively large data or where the ordering is prominent. Attention/ Gated: This Fusion method is used if one modality needs to be given attention or importance over the other. Attention is a hyper-parameter. Sigmoid non-linearity is used to gate one modality over the other. The Attention/Gate is given by W(σ(Ufnt)∗Vfnt) or W(Ufnt∗σ(Vfnt)) Maximum: It is also called Max-Pooling, generally used when combining features from multiple modalities. This function computes component-wise maximum, given by W(max(Ufnt,Vfnt)) This helps attain the features with maximum weight when comparing different modalities like Text and Image. Concatenate: In general, when combining features computed from the same algorithm, either Element-wise addition or attention can be used. If the features are generated from different algorithms (each modality learned by a different algorithm), then Concatenate can be used to improve the performance of the combined model. Combining features from different modalities computed by different algorithms using attention or gated mechanisms is given by. W(UfntVfnt) Other fusion models used include Average, Element-wise Product, and Polling. The fusion model to be chosen depends on what modals are being combined and the weightage to be given to each modal. In case no attention is granted to a particular modal, fusion models like Sum, Average, and Concatenate can be used. One might also come across fusion techniques which combine features from different layers and then combine all these features, which comes under intermediate fusion models (Boulahia et al., 2021; Baltrušaitis et al., 2018). Proposed fusion model for image and text modality data As the proposed framework works with Textual and Visual content (Visual Manipulated and Visual Polarity related data), independently learning features using different models (algorithms), hence Maximum and Concatenate are the fusion models considered for analysis. In the proposed framework, the textual data is analyzed using pre-trained BERT, Visual data, which has two characteristics related to Image Polarity and Image manipulation is analyzed using pre-trained Xception models. Finally, the features obtained from these branches are combined using fusion models like Concatenate and Maximum to finally classify the social posts as Fake/Real. Maximum fusion model While working with Maximum Fusion model, all the layers from visual and Textual branches are freezed (i.e, the layer weights of the trained model are not changed). Freezing helps in retaining weights from its pretrained phase (ImageNet for Xception and Wikipedia/Brown Corpus for BERT). All the layers in the Xception branch are freezed until the Flatten layer, and all layers in the BERT branch are freezed until the Text embeddings. The Image Polarity-related data was freezed till the Merge layer. Both modalities’ 32-dimensional vectors are combined using the maximum fusion method and fed into a fully connected neural network classifier with a 32-layer hidden and a 2-layer classification layers with softmax activation with batch size 256 and Adam optimizer with 0.0005 learning rate. The model is run for 20 epochs. The Maximum fusion model proposed is shown in Fig. 10. The best validation loss was obtained at the 12th epoch. The classification scores are shown in Table 6Fig. 10 Proposed framework with maximum as fusion method Table 6 Classification scores of max fusion Epoch Accuracy Loss Val_Accuracy Val_Loss Test_Accuracy 12 94.43% 13.76% 91.68% 21.95% 91.94% Concatenate fusion model While working with Concatenate Fusion model, all the layers from visual and Textual branches are freezed (i.e, the layer weights of the trained model are not changed). Freezing helps in retaining weights from its pretrained phase (ImageNet for Xception and Wikipedia/Brown Corpus for BERT). All the layers in the Xception branch are freezed until the Flatten layer, and all layers in the BERT branch are freezed until the Text embeddings. The Image Polarity-related data was freezed till the Merge layer. Both modalities’ 32-dimensional vectors are combined using the concatenation fusion method and fed into a fully connected neural network classifier with a 32-layer hidden, and a 2-layer classification layers with softmax activation with batch size 256, and an Adam optimizer with 0.0005 learning rate. The model is run for 20 epochs. The Maximum fusion model proposed is shown in Fig. 11. The best validation loss was obtained at the 13th epoch. The classification scores are shown in Table 7Fig. 11 Proposed framework with concatenate as fusion method Table 7 Classification scores of concatenate fusion Epoch Accuracy Loss Val_Accuracy Val_Loss Test_Accuracy 13 94.48% 13.66% 91.70% 22.31% 91.87% Coupling Coupling generally aids the fusion models in integrated feature representation and feature fusion mechanisms. Coupling Layers may exist at different layers or the fusion step, depending on the type of fusion model considered for analysis. When a vast dataset is analyzed as batches using the same algorithm, or when Intermediate coupling is employed where features at various levels are to be combined, a strong coupling is generally used. Loose coupling is generally employed when different modalities are being coupled using various algorithms. In the proposed algorithm, as the Textual features, Image features (Image Manipulation and Image Polarity related features) are independently analyzed using BERT and Xception models and are coupled to classify the posts; loose coupling is employed (Song et al., 2021). Result analysis The proposed ensemble model is loaded with the best weights obtained from 3 models trained independently - fine-tuning Xception on Fakeddit dataset images, fine-tuning BERT on image captions(i.e., text), and fine-tuning Xception network for sentiment analysis. All layers in the Xception branch are made untrainable until the Flatten layer, and all layers in the BERT branch are made untrainable until the Text embeddings. The whole sentiment branch was made untrainable till the Merge/combine layer. The ensemble model was fine-tuned again on Fakeddit dataset samples with both images and text. The padded and tokenized text was passed into the BERT model to receive word vectors of dimension 768. The images were rescaled to 150x150 pixels before being passed into the models. As depicted in Fig. 7, both modalities’ 32-dimensional vectors are combined using Maximum and Concatenate fusion method (Atrey et al., 2010) and fed into a fully connected neural network classifier with a 32-layer hidden layer and a 2-layer classification layer with softmax activation. The model was trained for 20 epochs, a batch size of 256, and an Adam optimizer with a learning rate of 0.0005. Better validation accuracy for Maximum as fusion method is observed at 12th epoch (91.68%) and for Concatenate, it is observed at 13th epoch (91.94%). Table 8 shows the result of multi-modality models trained on the Fakeddit dataset. Table 9 displays both the baseline results and the proposed method result on Fakeddit dataset. Table 8 Performance measures of the text + image models on fakeddit dataset Text+Image model Fusion method Validation Test Precision Recall F1-score accuracy accuracy BERT+Xception Maximum 91.61% 91.87% 93.43% 93.07% 93.25% BERT+Xception Concatenate 91.67% 91.88% 93.31% 93.22% 93.26% (BERT+Dense)+Xception Maximum 91.68% 91.94% 93.76% 92.83% 93.29% (BERT+Dense)+Xception Concatenate 91.70% 91.87% 93.39% 93.29% 93.25% Bold indicates models with better performance measures (here validation and Test accuracy) Table 9 Performance of the proposed model in comparison to the baseline models Multimodality Models Fusion method Validation accuracy Test accuracy InferSent+VGG16 (Baseline) Maximum 86.55% 86.58% InferSent+EfficientNet (Baseline) Maximum 83.28% 83.39% InferSent+ResNet50 (Baseline) Maximum 88.88% 88.91% BERT+VGG16 (Baseline) Maximum 86.94% 86.99% BERT+EfficientNet (Baseline) Maximum 83.34% 83.18% BERT+ResNet50 (Baseline) Maximum 89.29% 89.09% BERT+ResNet50 (Baseline) Concatenate 85.64% 85.68% BERT+Xception (Proposed) Maximum 91.61% 91.87% BERT+Xception (proposed) Concatenate 91.67% 91.88% (BERT+Dense)+Xception (proposed) Maximum 91.68% 91.94% (BERT+Dense)+Xception (proposed) Concatenate 91.94% 91.87% Bold indicates models with better performance measures (here validation and Test accuracy) In terms of accuracy, precision, recall, and F1 score, the proposed method outperforms the current methods overall. It is evident from the tables that multimodal models outperform unimodal models. These results further validate that multi-modality helps learn better distinguishing features between fake and real news. When it comes to assessing the accuracy of the news, data from various sources complement each other. A good recall score is crucial in this context of fake news identification since we would not want to miss flagging a fake news post. At the same time, we also need to be reasonably precise with predictions. The proposed method has a high recall, precision, and an F1-score of ∼ 93% as depicted in Table 8. As the proposed framework works on Manipulated data and Visual sentiment data, there is more scope for analyzing the social posts with high polarity images. Often Fake News is spread with higher sentiment to grab the users’ attention by targeting the psychological aspects of the users interacting with such posts. As text analysis is added to work with the embedded image captions, the proposed framework helps detect the clickbaits, which is one of the major aspects that helps in the easy propagation of the posts. Further, the error analysis is discussed in the next Section 5.3. Error analysis The proposed model incorporates various concepts relating to the visual and linguistic components of the post (Image caption). Several combinations are attempted for the visual and textual features of the social posts, and models with higher accuracy are fused for analysis. Fake images online tend to have a substantial visual impact and induce high sentiment. The visual sentiment branch suffers from a higher error rate when compared to others, as the sentiment of the data can often be misleading. Hence, Image polarity detection is often challenging (identifying whether an image reflects positive or negative emotion). It is also difficult to gather a large sample set for visual sentiment data. In most cases, there is either human power to explicitly tag image sentiment after crawling the data or third-party tools like Amazon Mechanical Turk (crowd-sourcing marketplace) to the tag sentiment of the images. For image captions, it is observed that most of the captions are intentionally written to grab attention, and Click-baits are common while spreading Fake News. The error rate is determined for each modality and the combination of modalities. The error rate for several models is shown in Table 10. Table 10 Error rate of different modalities Model Error rate Image modality (Xception) 0.18 Visual sentiment (Xception) 0.30 Text modality (BERT+Dense) 0.11 Fusion (Concatenate) 0.08 It is observed that using either text or an image alone might not be sufficient for detecting falsification. However, in the multimodality framework, Image caption is observed to have a high impact on correctly classifying Fake News. Table 11 depicts examples of the error analysis for Image, Text, and Multimodality. Table 11 Samples of error alalysis- true (class related fake news), and false (class related to real news) Actual label Predicted label Percentage Image Caption Multi Text Image Multi Text Image Real Real Real Fake Fake Fake 10.51 Penguin battle Fake Fake Fake Fake Real Real 3.41 Exotic plant Real Real Real Fake Real Fake 10.45 The rottness monster Fake Fake Fake Fake Fake Real 21.92 Hamsters care about dental hygiene too Real Real Real Real Fake Fake 0.41 Outlet with usbc port Real Real Real Real Real Fake 6.99 There is a chicken sitting on my car Fake Fake Fake Real Fake Real 17.15 A yawning seal Actual label Predicted label Percentage Image Caption Multi Text Image Multi Text Image Fake Fake Fake Real Real Real 29.13 A tortoise near a grove of mushrooms Conclusion and future scope This paper proposed a framework that combines visual and textual features to detect fake news. Posts are crawled from the Fakeddit dataset, with an image and its caption, and Fakeddit has around 1 million images crawled from Reddit. Fine-tuned BERT is implemented on textual information in posts to determine whether they are fake or real. Fine-tuned BERT achieved an accuracy of 89.31%. On the other hand, fine-tuned Xception network is used on the visual content of the posts, and it showed an accuracy of 82.32%. The fusion of models is considered, and unlike the traditional image forensic methods, a framework is proposed to identify both tampered and images that are not altered. The proposal model achieved an accuracy of 91.94% and an F1-score of 93%. It is evident that the textual (caption of the image) part of the social post, followed by the visual component of the image, plays a vital role in detecting fake posts. As part of the future scope, the plan is to use metadata and comments of the posts and combine these with the user-related data to track the user’s credibility in the interactions. More samples is planned to be collected with visual sentiment (polarity) to enhance the capability of the visual sentiment branch. A cross-domain generalization model is planned to be implemented on social posts across domains, topics, websites, and languages. The user engagement patterns can also be combined to the models that helps in attaining generalization across domains. Author Contributions Santosh Kumar Uppada: Conceptualization, Mathematical Modeling, Methodology, Writing the draft, Validation. Parth Patel: Formal Analysis, Visualization, Data Curation. Dr. Sivaselvan B: Draft Review and Editing, Supervision, Investigation. Funding This researchreceived no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Availability The paper does not include any supporting data. Declarations Consent for Publication There is no content that requires permission of any third-party organizations or persons to publish the above manuscript. Competing interests The Authors does not have any competing interests. Parth Patel and B. Sivaselvan contributed equally to this work. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Abd Warif, N.B., Idris, M.Y.I, Wahab, A.W.A, & et al. (2015). An evaluation of error level analysis in image forensics. In 2015 5th IEEE international conference on system engineering and technology (ICSET) (pp. 23–28). IEEE. 10.1109/ICSEngT.2015.7412439. Adamic LA Huberman BA Power-law distribution of the world wide web Science 2000 287 5461 2115 2115 10.1126/science.287.5461.2115a Atrey PK Hossain MA El Saddik A Multimodal fusion for multimedia analysis: A survey Multimedia Systems 2010 16 6 345 379 10.1007/s00530-010-0182-0 Baltrušaitis T Ahuja C Morency LP Multimodal machine learning: A survey and taxonomy IEEE Transactions on Pattern Analysis and Machine Intelligence 2018 41 2 423 443 10.1109/TPAMI.2018.2798607 29994351 Bazaco, A. (2019). Clickbait as a strategy of viral journalism: Conceptualisation and methods. 10.4185/RLCS-2018-1323en. Boididou C Andreadou K Papadopoulos S Verifying multimedia use at mediaeval 2015 MediaEval 2015 3 3 7 10.1145/1235 Boulahia SY Amamra A Madi MR Early, intermediate and late fusion strategies for robust deep learning-based multimodal action recognition Machine Vision and Applications 2021 32 6 1 18 10.1080/24725854.2021.1987593 Campbell WJ Yellow journalism: Puncturing the myths, defining the legacies 2001 United States Greenwood Publishing Group Cao, J., Qi, P., Sheng, Q., & et al. (2020). Exploring the role of visual content in fake news detection. In Disinformation, misinformation, and fake news in social media (pp. 141–161). Cham: Springer. 10.1007/978-3-030-42699-6_8. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251–1258). 10.1109/CVPR.2017.195. Chowdhury, A. (2020). Fake news in the time of coronavirus: A BOOM study. BOOM. Cinelli M De Francisci Morales G Galeazzi A The echo chamber effect on social media Proceedings of the National Academy of Sciences 2021 118 9 e2023301118 10.1073/pnas.202330111 Clever, L., Assenmacher, D., Müller, K., & et al. (2020). FakeYou!- A gamified approach for building and evaluating resilience against fake news. In Multidisciplinary international symposium on disinformation in open online media (pp. 218–232). Cham: Springer. 10.1007/978-3-030-61841-4_15. Deng, J., Cheng, L., & Wang, Z. (2020). Self-attention-based BiGRU and capsule network for named entity recognition. arXiv:2002.00735. 10.48550/arXiv.2002.00735. Fortin, M.P., & Chaib-Draa, B. (2019). Multimodal sentiment analysis: A multitask learning approach. In ICPRAM (pp. 368–376). 10.5220/0007313503680376. Galli A Masciari E Moscato V A comprehensive Benchmark for fake news detection Journal of Intelligent Information Systems 2022 59 237 261 10.1007/s10844-021-00646-9 35342227 Giachanou, A., Zhang, G., & Rosso, P. (2020). Multimodal multi-image fake news detection. In 2020 IEEE 7th international conference on data science and advanced analytics (DSAA) (pp. 647–654). IEEE. 10.1109/DSAA49011.2020.00091. He, K., Zhang, X., Ren, S., & et al. (2016a). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778). 10.1109/CVPR.2016.90. He, K., Zhang, X., Ren, S., & et al. (2016b). Identity mappings in deep residual networks. In European conference on computer vision (pp. 630–645). Cham: Springer. 10.48550/arXiv.1603.05027. Jin, Z., Cao, J., Luo, J., & et al. (2016). Image credibility analysis with effective domain transferred deep networks. arXiv:1611.05328. Jin, Z., Cao, J., Guo, H., & et al. (2017). Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In Proceedings of the 25th ACM international conference on multimedia (pp. 795–816). 10.1145/3123266.3123454. Kalyan, K.S., & Sangeetha, S. (2020). Social media medical concept normalization using roberta in ontology enriched text similarity framework. In Proceedings of knowledgeable NLP: The first workshop on integrating structured knowledge and neural networks for NLP (pp. 21–26). Khattar, D., Goud, J.S., Gupta, M., & et al. (2019). Mvae: Multimodal variational autoencoder for fake news detection. In The world wide web conference (pp. 2915–2921). 10.1145/3308558.3313552. Kiela, D., Grave, E., Joulin, A., & et al. (2018). Efficient large-scale multi-modal classification. In Proceedings of the AAAI conference on artificial intelligence, (Vol. 32, no. 1, pp. 5198–5204). 10.48550/arXiv.1802.02892 . Kirchknopf, A., Slijepçević, D., & Zeppelzauer, M. (2021). Multimodal detection of information disorder from social media. In 2021 International conference on content-based multimedia indexing (CBMI) (pp. 1–4). IEEE. 10.1109/CBMI50038.2021.9461898. Kumari R Ekbal A Amfb: Attention based multimodal factorized bilinear pooling for multimodal fake news detection Expert Systems with Applications 2021 184 115412 10.1016/j.eswa.2021.115412 Luo W Qu Z Pan F A survey of passive technology for digital image forensics Frontiers of Computer Science in China 2007 1 2 166 179 10.1007/s11704-007-0017-0 McPherson M Smith-Lovin L Cook JM Birds of a feather: Homophily in social networks Annual Review of Sociology 2001 27 415 444 10.1146/annurev.soc.27.1.415 Meel P Vishwakarma DK HAN, image captioning, and forensics ensemble multimodal fake news detection Information Sciences 2021 567 23 41 10.1016/j.ins.2021.03.037 Nakamura, K., Levy, S., & Wang, W.Y. (2019). r/fakeddit: A new multimodal benchmark dataset for fine-grained fake news detection. arXiv:1911.03854. Pennycook G Rand DG The psychology of fake news Trends in Cognitive Sciences 2021 25 5 388 402 10.1016/j.tics.2021.02.007 33736957 Qi, P., Cao, J., Yang, T., & et al. (2019). Exploiting multi-domain visual information for fake news detection. In 2019 IEEE international conference on data mining (ICDM) (pp. 518–527). IEEE. 10.1109/ICDM.2019.00062. Ragusa E Cambria E Zunino R A survey on deep learning in image polarity detection: Balancing generalization performances and computational costs Electronics 2019 8 7 783 811 10.3390/electronics8070783 Ragusa E Apicella T Gianoglio C Design and deployment of an image polarity detector with visual attention Cognitive Computation 2022 14 1 261 273 10.1007/s12559-021-09829-6 Rajinikanth V Joseph Raj AN Thanaraj KP A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection Applied Sciences 2020 10 10 3429 10.3390/app10103429 Rezende, E., Ruppert, G., Carvalho, T., & et al. (2017). Malicious software classification using transfer learning of resnet-50 deep neural network. In 2017 16th IEEE international conference on machine learning and applications (ICMLA) (pp. 1011–1014). 10.1109/ICMLA.2017.00-19. Robertson CT Mourão RR Thorson E Who uses fact-checking sites? The impact of demographics, political antecedents, and media use on fact-checking site awareness, attitudes, and behavior The International Journal of Press/Politics 2020 25 2 217 237 10.1177/1940161219898055 Shah, P., & Kobti, Z. (2020). Multimodal fake news detection using a cultural algorithm with situational and normative knowledge. In 2020 IEEE congress on evolutionary computation (CEC) (pp. 1–7). IEEE. 10.1109/CEC48606.2020.9185643. Shu K Sliva A Wang S Fake news detection on social media: A data mining perspective ACM SIGKDD Explorations Newsletter 2017 19 1 22 36 10.1145/3137597.3137600 Siegfried, I.M. (2020). Comparative study of deep learning methods in detection face mask utilization. OSF Preprints. 10.31219/osf.io/3gph4. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. Song K Zhou L Wang H Deep coupling recurrent auto-encoder with multi-modal EEG and EOG for vigilance estimation Entropy 2021 23 10 1316 10.3390/e23101316 34682040 Sudiatmika IBK Rahman F Trisno T Image forgery detection using error level analysis and deep learning TELKOMNIKA (Telecommunication Computing Electronics and Control) 2019 17 2 653 659 10.12928/telkomnika.v17i2.8976 Szegedy, C., Vanhoucke, V., Ioffe, S., & et al. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818–2826). 10.1109/CVPR.2016.308. Szegedy, C., Ioffe, S., Vanhoucke, V., & et al. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-first AAAI conference on artificial intelligence (pp. 4278–4284). 10.5555/3298023.3298188. Thakur R Rohilla R Recent advances in digital image manipulation detection techniques: A brief review Forensic Science International 2020 312 110311 10.1016/j.forsciint.2020.110311 32473526 Uppada SK Manasa K Vidhathri B Novel approaches to fake news and fake account detection in OSNs: User social engagement and visual content centric model Social Network Analysis and Mining 2022 12 1 1 19 10.1007/s13278-022-00878-9 35845751 Wang, S., Zhuang, S., & Zuccon, G. (2021). Bert-based dense retrievers require interpolation with bm25 for effective passage retrieval. In Proceedings of the 2021 ACM SIGIR international conference on theory of information retrieval (pp. 317–324). 10.1145/3471158.3472233. Wu, Y., Zhan, P., Zhang, Y., & et al. (2021). Multimodal fusion with co-attention networks for fake news detection. In Findings of the association for computational linguistics: ACL-IJCNLP 2021 (pp. 2560–2569). 10.18653/v1/2021.findings-acl.226. Xia K Huang J Wang H LSTM-CNN architecture for human activity recognition IEEE Access 2020 8 56855 56866 10.1109/ACCESS.2020.2982225 Zeng J Zhang Y Ma X Fake news detection for epidemic emergencies via deep correlations between text and images Sustainable Cities and Society 2021 66 102652 10.1016/j.scs.2020.102652 Zhang D Xu J Zadorozhny V Fake news detection based on statement conflict Journal of Intelligent Information Systems 2022 59 173 192 10.1007/s10844-021-00678-1 Zhao J Mao X Chen L Speech emotion recognition using deep 1D & 2D CNN LSTM networks Biomedical Signal Processing and Control 2019 47 312 323 10.1016/j.bspc.2018.08.035 Zhou, L., & Bian, X. (2019). Improved text sentiment classification method based on BiGRU-attention. In Journal of physics: Conference series, (Vol. 1345, No. 3, pp. 032097). IOP Publishing. 10.1088/1742-6596/1345/3/032097.	36465146	PMC9708513	NO-CC CODE	2022-12-01 23:20:30	no	J Intell Inf Syst. 2022 Nov 30;:1-27	utf-8	J Intell Inf Syst	2,022	10.1007/s10844-022-00764-y	oa_other
==== Front Eur J Pediatr Eur J Pediatr European Journal of Pediatrics 0340-6199 1432-1076 Springer Berlin Heidelberg Berlin/Heidelberg 36446888 4711 10.1007/s00431-022-04711-5 Research Supplementation of mother’s own milk with term versus preterm donor human milk: a randomized controlled trial Soni Vimlesh 1 Jain Suksham [email protected] 1 Chawla Deepak 1 Khurana Supreet 1 Rani Shikha 2 1 grid.413220.6 0000 0004 1767 2831 Department of Neonatology, Government Medical College Hospital, Chandigarh, India 2 grid.413220.6 0000 0004 1767 2831 Department of Obstetrics and Gynecology, Government Medical College Hospital, Chandigarh, India Communicated by Gregorio Milani 30 11 2022 110 1 7 2022 2 11 2022 9 11 2022 © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The purpose of this is to evaluate the effect of supplementation of enteral feed volume with preterm versus term donor human milk (DHM) on short-term physical growth in very low birth weight (VLBW) neonates. In this open-label, variable block-sized, superiority, randomized controlled trial with allocation concealment, VLBW neonates with insufficient volume of mother’s own milk (MOM) were assigned to receive either preterm (n = 48) or term (n = 54) DHM till discharge. Preterm DHM was defined as the breast milk expressed within 28 days of delivery at ≤ 34 weeks of gestation. The primary outcome was days to regain birth weight. Maternal and neonatal demographic variables were comparable in the two study groups. Days to regain birth weight were significantly more in the preterm DHM group, 17.4 (7.7) vs 13.6 (7.2) days, mean difference (95% CI) being 3.74 (0.48–7.0) days, P = 0.02). The proportion of MOM use was 82% in preterm vs 91.1%, P = 0.03 in the term milk group. Duration of skin-to-skin contact was also significantly lower in the preterm vs term milk group, the median (IQR) was 4 (0, 6) vs 4 (2, 6) hours/day, P < 0.01. However, bronchopulmonary dysplasia was higher in the preterm milk group (13% vs. 4%, P = 0.17). The velocity of gain in weight was similar in the two groups from week 1–3 but higher in the term DHM supplementation group during the 4th week. Conclusion: Supplementing MOM with preterm DHM did not result in a faster regaining of birth weight. Trial registration: CTRI/2020/02/023569; Date: 17.02.2020. What is Known: • Human milk content is influenced by various parameters including maternal, neonatal, and methodological. • Preterm DHM should cause rapid short-term weight gain in VLBW neonates as it has higher protein content. What is New: • In the setting of MOM contributing a major fraction of feeding volume, selective supplementation of feeding volume with preterm DHM does not result in rapid short-term weight gain in VLBW neonates. Supplementary Information The online version contains supplementary material available at 10.1007/s00431-022-04711-5. Keywords Mother’s own milk Donor human milk Preterm neonate ==== Body pmcIntroduction Extrauterine growth retardation (EUGR) develops in approximately 75–97% of the very low birth weight (VLBW) babies during postnatal life [1]. Auxological extrauterine growth retardation is associated with adverse long-term outcomes [2]. The nutritional regimen for VLBW infants should be able to achieve a postnatal growth rate that mimics intrauterine growth. The protein needed of preterm neonates is particularly high and plays a very important role during the postnatal period. Mother’s own milk (MOM) is considered the best nutrition during the first six months of birth [3]. Pasteurized donor human milk (DHM) is the preferred alternative for vulnerable neonates in cases of unavailability or insufficient quantity of MOM [4, 5]. The energy, protein, and fat content of milk are dependent upon: the type of milk as colostrum, mature or transition milk, term, and preterm milk, or human milk fortifiers. Although fortification of human milk in preterm neonates is standard global practice, the World Health Organization guidelines on the feeding of VLBW infants do not support routine multicomponent fortification except in infants who fail to gain weight despite adequate breast milk feeding [6]. Different feeding strategies aim to match growth velocity to the intrauterine foetal growth with an average weight gain of 24–26 g/d in the late third trimester [7]. Milk volume for neonates up to 200 ml/kg/d has been used in different studies and found to be associated with improved weight gain [8]. The use of any exogenous protein is akin to additional cost and gut dysbiosis [9]. Preterm milk is a naturally available human source of protein. Preterm milk is usually defined as milk donated by women within 28 days of giving birth at less than 34 weeks of gestation, and term milk is donated by women delivered after 34 weeks of gestation or after 28 days of birth if delivered at less than 34 weeks of gestation [10]. Since preterm milk has higher protein content with a maximum mean difference of up to 0.7 g/dL, it should provide extra calories and should have a short-term effect on growth with potential long-term implications [11]. Protein supplementation increases the fat-free mass (FFM) accretion in infants. Weight gain resulting from FFM gain increases brain size and reduces the risk of adverse neurodevelopmental outcomes among preterm infants [12]. Human milk content is influenced by many parameters which are broadly classified as maternal, neonatal, and methodological [13]. Because of differences in the macronutrient composition of preterm and term milk, pooled pasteurized preterm DHM should ideally cause early short-term weight gain in VLBW neonates. Although the protein content will be variable and will differ with the proportion of milk used [13], the evidence on the same is yet to be generated. More so, donor milk is generally collected from mothers of term infants and at a mature stage of lactation when milk production is more than the own baby’s requirement [14]. To the best of our knowledge, this is the first study comparing the effect of preterm and term DHM on the growth of VLBW babies where MOM is insufficient. Methods The randomized controlled trial was conducted from July 2020 to March 2021 in a level III neonatal intensive care unit (NICU) at a tertiary care hospital in India. Study subjects Potentially eligible neonates were identified from a clean labour room nursery immediately following birth. All consecutive inborn VLBW (birth weight ≤ 1500 g) neonates on enteral feeds having an insufficient volume of MOM were enrolled in the study after obtaining written informed consent from the mother/guardian. Insufficient volume of MOM was defined as MOM volume less than 80% on any day after the beginning of enteral nutrition and later any volume lesser than the requirement of that particular day. Neonates with major congenital malformation, necrotizing enterocolitis stage IIa or higher before enrolment, HIV-positive mothers, birth at gestation age < 27 weeks or birth weight < 800 g, galactosemia or other contraindications to human milk feeding, and maternal infection by severe acute respiratory syndrome by coronavirus 2 (SARS-CoV-2) were excluded from the study. Randomization and allocation concealment A researcher not involved in the study recruitment or outcome assessment generated the random number sequence of block sizes 4 or 6 using a web-based random number generator. The random number sequence was stratified by birth weight (≤ 1000 g and > 1000 g). The sequence was kept in serially numbered opaque sealed envelopes. Multiple births were assigned to the same study group. Physicians, researchers, and nurses were aware, whereas parents were unaware of the group allocation. Physicians were not blinded as they were involved in providing intervention. Independent investigator cross-verified outcome of random samples. Milk disbursal bottles were labelled and stored by milk bank staff who were not aware of group allocation and were not involved in any clinical care. Interventions Participants were randomly assigned to receive enteral feed volume supplementation with preterm (preterm milk group) or term pasteurized DHM (term milk group) whenever MOM volume was insufficient. A dedicated milk bank staff is used to screen and enrol mothers, maintain both online and offline records, and collect milk according to the term and preterm groups. Colour-coded labels were applied to the collection and disbursal bottles of both the milk groups. Milk collection and disbursal according to groups were ensured daily by the primary investigator. DHM was frozen at − 200 °C after the Holder method of pasteurization (30 min at 62.5 °C) and thawed in lukewarm water at the time of disbursal for use. A daily log of the time of initiation of the first feed, total feed volume per day, and the proportion of MOM and DHM was maintained. Patients were followed up till 40 weeks of postmenstrual age or discharge whichever was earlier. Criteria for discharge included resolution of medical illness, weight ≥ 1450 g with a gain of weight for consecutive 3 days, postmenstrual age ≥ 34 weeks, acceptance of feed by spoon or direct breastfeeding, and ability to maintain normal body temperature outside a radiant warmer or incubator. Anthropometric data collection The weight of the baby was taken daily at approximately the same time of the day using the electronic weighing scale with an accuracy of 10 g. Once a week, the accuracy of the weighing scale was checked using a standard weight. Time of initiation and quantity of human milk fortifier, nutritional supplements, feed intolerance episodes, and daily duration of skin-to-skin (STS) contact hours were recorded. The length of the neonate was measured using an infantometer to the nearest 1 mm correction while the occipitofrontal circumference (OFC) was measured using a non-stretchable plastic measuring tape with 0.1 cm accuracy. Anthropometric measurements were done using the methodology prescribed by the Intergrowth-21 study [15]. Feeding protocol and other interventions common to both groups The eligible neonates were assessed daily for feed initiation. Once neonates were hemodynamically stable with the soft abdomen and audible bowel sounds, feeds were initiated as intermittent boluses at 2-h intervals. The standard protocol of feeding was uniformly followed in both groups. For neonates born at 260 to 286 weeks of gestation, minimal enteral nutrition was started at 10–15 ml/kg/day on day 1, and the enteral feed volume was increased by 20 to 30 ml/kg/day. For neonates born at 290 to 306 weeks of gestation, the feed was started at 20 to 30 ml/kg/day and increased by 30 to 40 ml/kg/day. Neonates born at ≥ 310 weeks of gestation were given all milk feed (80 ml/kg/day total fluid volume) from day 1 of life. Later daily total fluid and milk requirement of every neonate was decided according to day of life, loss or gain of weight every day, and associated comorbidities like patent ductus arteriosus and urine output. Advancement of feeds was done till infants reached a feed volume of 180 to 200 mL/kg/day. Once infants were transitioned from gavage feeds to direct feeds, volume was not controlled but was offered ad libitum. MOM feeding was strongly recommended for all neonates. Every possible effort was made to procure MOM for each neonate. As per the unit policy, all the mothers were counselled to express breast milk within 6 h of delivery and then two to three times a day, by the lactation counsellor, resident doctors, and nursing staff. Human milk fortifier (macronutrient composition in grams in 1 g powder sachet: protein 0.27; total fat, 0.04; carbohydrates, 0.49) was added (1 sachet per 25 ml of expressed breast milk) once the baby reached 100 ml/kg/day of enteral feeds. Neonates who were not on total enteral feeds were initiated on parenteral nutrition to achieve a total protein intake of 3 to 3.5 g/kg/day and calorie intake of 120 to 130 kcal/kg/day. Probiotics, caffeine, and other nutrition supplements were added to both groups as per the standard policy of the unit. Outcome measures The primary outcome of interest was days to regain birth weight. The number of days taken to regain birth weight after initial physiological weight loss was noted. It was taken as the first of three successive days when the weight was greater than or equal to the birth weight. Secondary outcomes included rate of gain in weight, length, and OFC till discharge or 40 weeks postmenstrual age whichever came earlier, time to achieve full feed (day 1 of 120 ml/kg/day if tolerated for 3 consecutive days), the incidence of NEC stage IIa or above, and episodes of feeding intolerance. Feed intolerance was defined as the presence of any of the following signs: significant gastric residuals of > 33% on 2 consecutive occasions or > 50% on a single occasion when the total feed volume was > 8 ml or aspirates of > 4 ml on two occasions if the total feed volume was < 8 ml, increase in abdominal girth by > 2 cm from the previous value, and the presence of brownish/bilious/bloodstained gastric aspirates or vomiting for which feeds had to be withheld for ≥ 12 h [16]. NEC was defined by modified Bell’s staging criteria [17]. The primary outcome, time to regain birth weight, was calculated as mean days in achieving birth weight beginning from day one to the day of birth. The secondary outcome, weight gain velocity, was calculated by the 2-point average weight model. This was calculated by dividing the total difference in weight at 2 points by the number of days and average weight using the following formula [18]. 1000×Wn-W1Dn-D1×Wn-W12 (Wn = Weight at endpoint of primary outcome, W1 = weight on day 1 of regaining of birth weight, Dn = last day of primary outcome, D1 = first day of regaining of birth weight) Sample size calculation The sample size was calculated based on a previous study where DHM was used in neonates (birth weight < 1600 g and gestation age 27 to 33 weeks) as a sole diet and reported 7 days lesser time in regaining birth weight with preterm as compared with term milk [19]. Based on the unit’s experience, neonates in the current study would need 20 to 80% DHM as a supplement to MOM on any given day. A difference in duration of regaining birth weight of 3 days was predicted in the index study due to the lesser need for DHM. Taking a difference of 3 (5) days between two groups (18 days preterm and 21 days in term milk), the minimum required sample size with 80% power and a 2-sided significance of 5% was 44 subjects in each group. Considering an attrition rate of 20%, 50 VLBW neonates were enrolled in each group. The trial was approved by the institutional ethics committee and was prospectively registered in the clinical trials registry of India, (CTRI/2020/02/023569). Statistical analysis Continuous variables were expressed as mean and standard deviation if normally distributed and as median and interquartile range when skewed. Categorical variables were expressed as numbers and proportions. Quantitative data with normal distribution were compared using the Student’s t test and those with skewed distribution were analysed using the Mann–Whitney U test. Categorical data were compared using chi-square or Fisher exact test as applicable. A p value of < 0.05 was considered significant. Time to regain birth weight was also analysed using the Kaplan–Meier survival analysis and log-rank test. A linear regression analysis was done to adjust for the variables that could influence the primary outcome and were found to be significantly different in the two study groups in univariate analysis. The regression model included days to achieve birth weight (primary outcome) as the dependent variable and study group (binary variable), birth weight less than 1000 g (binary variable), the proportion of DHM in the total milk intake during the hospital stay (continuous variable), and total STS hours during the hospital stay (continuous variable) as the independent (predictor) variables. The collinearity of the included variables was checked by calculating the variance inflation factors (not found to be significant) and robust estimates were calculated. Analysis was done using the intention to treat principle. Statistical analysis was conducted using SPSS version 23.0. Results Out of a total of 173 VLBW neonates born over the study period of nine months (July 2020 to March 2021), 102 were enrolled: 54 in preterm and 48 in the term milk group and 71 neonates were excluded as per predefined criteria (Fig. 1). Maternal and neonatal baseline characteristics were comparable in the two groups (Table 1).Fig. 1 Trial flow Table 1 Baseline characteristics in preterm and term milk group Preterm milk group (n = 54) Term milk group (n = 48) p value Maternal characteristics Age#, years 26.6 (5.2) 26.9 (4.9) 0.80 Education less than high school 30 (56%) 31 (65%) 0.42 Maternal moderate or severe undernutrition 12 (22.2%) 5 (10.4%) 0.18 Lower socioeconomic status 29 (53.7%) 33 (68.7%) 0.45 Receipt of any dose antenatal steroids 43 (80%) 38 (79%) 1.00 Pre-eclampsia or eclampsia 15 (27.7%) 18 (37.5%) 0.39 Clinical chorioamnionitis 1 (2%) 3 (6.3%) 0.25 AREDF in umbilical artery 2 (4%) 7 (14%) 0.07 Caesarean section 23 (43%) 20 (42%) 1.00 Neonatal characteristics Birth weight#, g 1262 (184) 1253 (181) 0.80 Birth weight < 1000 g 7 (13%) 7 (15%) 1.00 Gestational age#, weeks 31.3 (2.8) 31.8 (2.4) 0.40 Small-for-gestational age (< 3rd centile) 17 (31%) 21 (44%) 0.22 Birth length#, cm 39.0 (2.4) 38.9 (2.3) 0.85 Birth length (< 3rd centile) 14 (26%) 19 (40%) 0.20 OFC, cm # 27.3 (1.7) 27.6 (1.6) 0.49 OFC (< 3rd centile) 15 (28%) 17 (35%) 0.52 Multiple birth 5 (9.2%) 4 (8.3%) 1.00 Male 31 (57%) 30 (62%) 0.69 SNAPPE-II score* 6 (0–12) 12 (0–18) 0.08 Apgar score at 5 min < 7 1 (1.8%) 3 (6.2%) 0.34 Data are expressed as number (%), #mean ± SD, or median (interquartile range) Time to regain birth weight, mean (SD) in preterm group 17.4 (7.7) as compared to term 13.6 (7.2) milk group, was longer, 3.74 (0.48 to 7.0); P = 0.03 (Fig. 2). The number of days taken to regain birthweight remained significantly lower in the term DHM group with an adjusted mean difference (95% CI) being 3.6 (− 5 to − 2.2) days even after adjusting for birth weight being less than 1000 g, the proportion of DHM in cumulative feed volume during the hospital stay and the cumulative duration of STS during the hospital stay. The average duration of weight loss was 7.8 (4.4) and 6.7 (5) days (P = 0.28) in the preterm and term milk groups, respectively. The volume of MOM was similar but the proportion of its usage was significantly less in the preterm milk group (82% vs 91.1%, P = 0.03) (Supplementary Fig. 1). Daily DHM requirement was significantly lesser in the term milk group (Supplementary Fig. 2). No difference was found in calculated energy 123.35 (7.32) vs 121 (9.97), P = 0.23, and protein 2.84 (0.48) vs 2.75 (0.39), P = 0.37, intake in both the study groups (Table 2).Fig. 2 Kaplan–Meier survival curve for time to regain birth weight of neonates in both preterm [days; mean (SD) 17.4 (7.7)] and term [13.6 (7.2)] milk group, log rank P = 0.02 Table 2 Details of intervention Intervention Preterm milk group (n = 54) Term milk group (n = 48) p value Age at first feed, hrs 7.5 (2, 16) 6 (2, 15.5) 0.84 Total volume of feed (ml/kg/d)* 136 (109,150.4) 121 (78.3,141.4) 0.01 Mother’s own milk volume (ml/kg/d)* 116.8 (68.7, 136.3) 107.1 (24.2, 132.8) 0.35 Donor human milk volume (ml/kg/d)* 11.2 (4.2, 25.5) 7 (3.6, 16.1) 0.02 Proportion of Mother’s own milk usage (%) 82 91.1 0.03 Duration of intervention (d) * 6.5 (4.2, 14.7) 7 (4, 9) 0.17 Total parenteral nutrition, n (%) 8 (14.8%) 8 (16.7%) 0.79 STS contact duration (hrs/d)* 4 (0, 6) 4 (2, 6) < 0.01 Total energy (Kcal/kg/d) 123.35 (7.32) 121 (9.97) 0.23 Total proteins (g/kg/d) 2.84 (0.48) 2.75 (0.39) 0.37 Median (interquartile range) Weight gain velocity was significantly less during the fourth week of hospital stay in the preterm milk group (Table 3 and Fig. 3). However, among 7 neonates out of a total of 14, who weighed < 1000 g at birth and survived till discharge, days to regain birth weight in the preterm and term milk group were 16.7 (7.8) vs 28.3 (12.4) days, respectively, mean difference (95% CI) being − 11.5 (− 36 to 13) days, P = 0.2). Time to achieve full feed, the incidence of NEC stage IIa or above, and episodes of feeding intolerance were similar in both the groups. However, bronchopulmonary dysplasia and neonates who died or were discharged against medical advice were more in the preterm milk group (Table 4).Table 3 Study outcomes in preterm and term milk group Preterm milk group (n = 48) Term milk group (n = 37) p value MD (95% CI) Primary outcome Time to regain birth weight# 17.4 (7.7) 13.6 (7.2) 0.02 3.7 (0.4 to 7) Secondary outcomes Weight gain#, g/kg/day 8.2 (4.6) 9.3 (5.1) 0.33 − 1.1 (− 3.2 to 1.1) Weight at discharge#, g 1573 (243) 1547 (207) 0.60 26.1 (− 74 to 125) Occipitofrontal circumference gain#, cm/wk 0.6 (0.3) 0.7 (0.3) 0.47 0.04 (− 0.07 to 0.1) Occipitofrontal circumference at discharge#, cm 30.2 (1.2) 30.2 (1.2) 0.88 0.04 (− 0.4 to 0.5) Length gain#, cm/wk 0.5 (0.2) 0.5 (0.2) 0.81 − 0.02 (− 0.1 to 0.12) Length at discharge#, cm 42.4 (2.1) 41.9 (2.4) 0.29 0.5 (− 0.4 to 1.5) Weight gain in 1st wk#, g/kg/d − 9.4 (2.5) (n = 48) − 8.7 (4.2) (n = 37) 0.35 − 0.6 (− 2.1 to 0.7) Weight gain in 2nd wk#, g/kg/d 6 (3.1) (n = 46) 6.6 (1.9) (n = 33) 0.33 − 0.5 (− 1.7 to 0.6) Weight gain in 3rd wk#, g/kg/d 7.7 (3.4) (n = 30) 8.9 (2.4) (n = 23) 0.13 − 1.2 (− 2.8 to 0.4) Weight gain in 4th wk#, g/kg/d 10.6 (4) (n = 20) 14.3 (3.9) (n = 20) < 0.01 − 3.7 (− 6.2 to − 1.1) Growth velocities in the stratified group of neonates in birth weight ≤ 1000 g Time to regain birth weight # 16.7 (7.8) 28.3 (12.4) 0.2 − 11.5 (− 36 to 13) Weight gain#, g/kg/d 10.3 (2.4) 8.8 (1.9) 0.4 1.4 (− 2.7 to 5.6) Occipitofrontal circumference gain#, cm/wk 0.7 (0.2) 0.6 (0.1) 0.5 0.08 (− 0.3 to 0.4) Length gain#, cm/wk 0.7 (0.1) 0.6 (0.2) 0.3 0.1 (− 0.2 to 0.5) Other secondary outcomes Time to reach full feeds#, d 6 (3.5) (n = 51) 6.5 (4.6) (n = 41) 0.6 − 0.6 (− 2.3 to 1) Necrotizing enterocolitis ≥ Stage IIa 0 1 (2%) 0.47 – Transient feed intolerance 19 (35%) 21 (44%) 0.42 – #Mean ± SD or median (interquartile range) Fig. 3 Growth velocity per week in two study groups following birth Table 4 Other clinical outcomes in preterm and term milk group Preterm milk group (n = 54) Term milk group (n = 48) p value Proven or clinical sepsis 17 (31.4%) 15 (31.2%) 0.97 Invasive ventilation 10 (19%) 13 (27%) 0.35 Bronchopulmonary dysplasia 7 (13%) 2 (4%) 0.17 Shock€ 10 (18.5%) 15 (31.3%) 0.14 Anaemia€ 5 (9%) 3 (6%) 0.72 Polycythemia€ 1 (2%) 4 (8%) 0.18 Hypoglycemia€ 1 (2%) 11 (23%) < 0.01 Hemodynamically significant patent ductus arteriosus € 10 (19%) 11 (23%) 0.63 Retinopathy of prematurity€ 1 (2%) 1(2%) 1.00 Intraventricular haemorrhage (> grade 2) 5 (9%) 7 (15%) 0.54 Neonatal intensive care unit, stay, d 9.5 (1, 20.2) 8 (1.2, 15) 0.57 Length of hospital stay#, d 31.0 (18.9) 24.8 (15.5) 0.07 Mortality before discharge 4 (7%) 8 (17%) 0.14 €Requiring intervention. Data are expressed as number (%), #mean ± SD, or median (interquartile range) Discussion In low- and middle-income countries where the higher cost and limited availability of human milk-based fortifiers exists, preterm milk with higher protein content can provide a better form of nutrition for at-risk neonates. This study randomized VLBW neonates to receive either preterm or term DHM when their MOM was not sufficiently available. Time to regain birth weight in the study was significantly more in preterm as compared to the term milk group which was non-consistent with the hypothesis. Firstly, this might be a chance finding. The second cause of faster regaining of birth weight in the term milk group could be the effect of co-interventions like overall lesser DHM supplementation and significantly higher use STS contact duration. The overall need for DHM was less than 20% in the current study. A dedicated lactation counsellor in the unit helped all the mothers in expressing milk soon after delivery. All the mothers were also advised daily to 3–4 L of water intake, a balanced diet, and sleep by a lactation counsellor. In most neonates, the available MOM volume was less than 20% on the day of initiation of feeds but it soon increased after daily counselling for milk expression, diet, and liquid intake. On subsequent days, the available MOM volume was more than 80–90%. Neonates once enrolled remained in the study irrespective of DHM volume requirement. This brought large variation in DHM and MOM volume in the two groups. In a previous study where DHM was given as the sole diet, birth weight was regained earlier in the preterm milk supplementation group (11.4 vs 18.8 days). Each aliquot of DHM was also analysed for nutrient content both before and after pasteurization.15 More protein, fat, and energy content of mother’s milk improves weight gain in comparison with DHM where these macronutrients are affected by pasteurization [20]. A reduction of 3.5% in fat, 3.9% in protein, and 2.8% in energy has been reported post pasteurization [21, 22]. Duration of STS (hours/d) in the current study was also significantly higher 4 (0, 6) vs 4 (2, 6), P < 0.01 in the term milk group, and it is a well-described intervention associated with better weight gain. A recent Cochrane review showed that STS was associated with increased weight gain of 4.1 (2.3 to 5.9) g/d [23]. The third cause could be the difference in sickness level of the two groups during the progression of the study. Higher mortality of the sick neonates in the term milk group who at enrolment had a difference in the score of neonatal acute physiology and hence the survival of relatively more stable babies in term milk group in the current study overestimated the result in rapid and better weight gain, although this difference was not statistically significant. Few parameters causing a negative effect on growth such as bronchopulmonary dysplasia were higher in the preterm vs term milk group (13% vs. 4%, P = 0.17). More than one-third of the study population was with severe intrauterine growth retardation which had a poor genetic potential for postnatal growth [24]. These small for gestation age neonates had relatively advanced gestation age and started taking direct breastfeeds with measured feeds after the acute illness was over. This led to a total quantified feed volume of 120–130 ml/kg/d and protein intake of less than 3 g/kg/d. The study population characteristics (30% infants with shock and sepsis, 12% infants with severe IVH) could also be a barrier to optimal weight gain. Among the stratified group of neonates weighing less than 1000 g showed a faster regain of the birth weight and better weight, length, and OFC gain velocities till discharge. However, the number of enrolled neonates weighing less than 1000 g was less due to the limitation of the number of deliveries due to the SARS-CoV-2 pandemic, and only seven neonates survived till discharge. Time to regain birth weight remained significantly lesser in the term milk group even after adjusting for birth weight being less than 1000 g, the proportion of DHM in cumulative feed volume during the hospital stay, and the cumulative duration of STS during the hospital stay. The preterm DHM has higher protein content as compared to the term milk. However, this difference becomes narrower after the first week (from 0.7 to 0.2 g/dL). Hence, the clinical significance of more proteins is likely to be high during the initial week [11]. Gross et al. have also shown a similar trend of protein difference in preterm and term DHM with a difference in protein content up to 0.9 to 1.2 g/dL during the initial 2 weeks. This difference reduces to 0.2 to 0.4 g/dL over the next 2 weeks [19]. There is emerging data that higher protein intake is associated with better head growth and neurodevelopment [3]. Low intakes of proteins during the first week of life have found to be associated with impaired neurocognitive development at 18–22 months of age [25]. Isolated weight gain could be a manifestation of fat mass accumulation which may not reflect brain growth [26]. The study has tried to see the effect of preterm DHM on weight gain; however, head growth could be a better parameter. However, there was no difference in head growth velocity in this study during the first week and throughout the hospital stay. A larger sample size might be required to see the effect of preterm and term DHM on head growth and neurodevelopmental outcome. Growth is a multidimensional and continuous process, and it depends upon several other factors such as body composition, gender, and genetic makeup [27]. Hence, valid estimation requires consideration of these parameters with long-term follow-up too. Variability in the composition of MOM or DHM obtained from a single donor and multiple donor pool has been observed. High variability in the composition of fat, protein, and energy was found for all types of samples. This variability remained persistent even after standard fortification. Feeding with pooled DHM may result in inadequate or nonuniform growth hence individualized fortification was recommended [20]. Short of biochemical analysis for nutrients in each pooled sample of milk in our study, the actual higher protein content of preterm milk cannot be assured. Strengths of the study Every step was meticulously followed from preterm and term milk collection to disbursal to prevent cross-contamination between the groups. Limitations Limitations of the study are the non-availability of biochemical analysis of pooled DHM, the absence of long-term neurodevelopmental follow-up, fewer neonates in < 1000 g stratified group, and an open-label design. The overall requirement of DHM was lesser than the assumption while calculating sample size and limits the validity of the results. Further, the findings of this study could be related to important confounders such as MOM intake per day and STS duration, even though such confounding could creep in when a pragmatic approach to study conduct is undertaken. Conclusion In a setting where MOM constitutes a large fraction of enteral feeding volume, selective supplementation with preterm donor milk to meet the feed volume requirement did not result in improved physical growth during the hospital stay. Future research with preterm milk as supplemental DHM is warranted in extremely low birth weight neonates who are at higher risk for EUGR. Further, the same may be supplemented with biochemical analysis of macronutrients in pooled or single donor human milk, used as supplemental feed. Differences in human milk composition at different gestation and different time points can also be assessed to correlate the effect on growth. Supplementary Information Below is the link to the electronic supplementary material.Supplementary file1 (TIFF 242 KB) Supplementary file2 (TIFF 343 KB) Authors’ contributions Dr. Vimlesh Soni collected data, carried out the initial analyses, and drafted the initial manuscript. Dr. Suksham Jain conceptualized and designed the study, supervised data collection, and reviewed and revised the manuscript. Dr. Deepak Chawla carried out the analyses and reviewed the manuscript for intellectual content. Dr. Supreet Khurana carried out the analyses and interpretation of data and reviewed the manuscript for intellectual content. Dr. Shikha Rani carried out the analyses and interpretation of data and reviewed the manuscript for intellectual content. All authors approved the final manuscript as submitted and agreed to be accountable for all aspects of the work. Declarations Ethics approval The study was approved by ethics committee of GMCH no. GMCH/IEC/2019/238 Dated 30.12.2019. Consent to participate Written informed consent was obtained from parents of all participants. Consent for publication Not applicable. Conflict of interest The authors declare no competing interests. Summary Different macronutrient compositions of preterm donor human milk than of term should cause early short-term weight gain in VLBW neonates. The current study was done to assess the same. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Dusick AM Poindexter BB Ehrenkranz RA Lemons JA Growth failure in the preterm infant: can we catch up? Semin Perinatol 2003 27 302 310 10.1016/s0146-0005(03)00044-2 14510321 2. De Curtis M Rigo J Extrauterine growth restriction in very-low-birthweight infants Acta Paediatr 2004 93 1563 1568 10.1080/08035250410022198 15841762 3. Agostoni C Buonocore G Carnielli VP Enteral nutrient supply for preterm infants: commentary from the European Society of Paediatric Gastroenterology, Hepatology and Nutrition Committee on Nutrition J Pediatr Gastroenterol Nutr 2010 50 85 91 10.1097/MPG.0b013e3181adaee0 19881390 4. Committee ESPGHAN on Nutrition, Arslanoglu S, Corpeleijn W, Donor human milk for preterm infants: current evidence and research directions J Pediatr Gastroenterol Nutr 2013 57 535 542 10.1097/MPG.0b013e3182a3af0a 24084373 5. Committee on Nutrition; Section on Breastfeeding; Committee on Fetus and Newborn, Donor human milk for the high-risk infant: preparation, safety, and usage options in the United States Pediatrics 2017 139 e20163440 10.1542/peds.2016-3440 27994111 6. World Health Organization (WHO) (2011) Guidelines on optimal feeding of low birth-weight infants in low- and middle-income countries [Internet]. Geneva, Switzerland: World Health Organization. http://www.ncbi.nlm.nih.gov/books/NBK298973/PubMed. Accessed 6 Dec 2014 7. Owen P Donnet ML Ogston SA Christie AD Howie PW Patel NB Standards for ultrasound fetal growth velocity Br J Obstet Gynaecol 1996 103 60 69 10.1111/j.1471-0528.1996.tb09516.x 8608100 8. Abiramalatha T, Thomas N, Thanigainathan S (2021) High versus standard volume enteral feeds to promote growth in preterm or low birth weight infants. Cochrane Database Syst Rev 9(3):CD012413. 10.1002/14651858.CD012413.pub3 9. Chan GM Lee ML Rechtman DJ Effects of a human milk-derived human milk fortifier on the antibacterial actions of human milk Breastfeed Med 2007 2 205 208 10.1089/bfm.2007.0015 18081457 10. Faerk J Skafte L Petersen S Peitersen B Michaelsen KF Macronutrients in milk from mothers delivering preterm Adv Exp Med Biol 2001 501 409 413 10.1007/978-1-4615-1371-1_51 11787710 11. Gidrewicz DA, Fenton TR (2014) A systematic review and meta-analysis of the nutrient content of preterm and term breast milk. BMC Pediatr 14:216. Published 2014 Aug 30. 10.1186/1471-2431-14-216 12. Ramel SE Gray HE Christiansen E Boys C Georgieff MK Demerath EW Greater early gains in fat-free mass, but not fat mass, are associated with improved neurodevelopment at 1 year corrected age for prematurity in very low birth weight preterm infants J Pediatr 2016 173 108 115 10.1016/j.jpeds.2016.03.003 27056450 13. Hsu YC Chen CH Lin MC Tsai CR Liang JT Wang TM Changes in preterm breast milk nutrient content in the first month Pediatr Neonatol 2014 55 449 454 10.1016/j.pedneo.2014.03.002 24861533 14. Fomon SJ Ziegler EE Vázquez HD Human milk and the small premature infant Am J Dis Child 1977 131 463 467 10.1001/archpedi.1977.02120170089018 848468 15. Villar J Cheikh Ismail L Victora CG International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the Intergrowth-21st Project Lancet 2014 384 857 868 10.1016/S0140-6736(14)60932-6 25209487 16. Dorling J Abbott J Berrington J (2019) Controlled trial of two incremental milk-feeding rates in preterm infants N Engl J Med 2019 381 1434 1443 10.1056/NEJMoa1816654 31597020 17. Neu J Necrotizing enterocolitis: the search for a unifying pathogenic theory leading to prevention Pediatr Clin North Am 1996 43 409 432 10.1016/s0031-3955(05)70413-2 8614608 18. Patel AL Engstrom JL Meier PP Jegier BJ Kimura RE Calculating postnatal growth velocity in very low birth weight (VLBW) premature infants J Perinatol 2009 29 618 622 10.1038/jp.2009.55 19461590 19. Gross SJ Growth and biochemical response of preterm infants fed human milk or modified infant formula N Engl J Med 1983 308 237 241 10.1056/NEJM198302033080501 6848932 20. de Halleux V Rigo J Variability in human milk composition: benefit of individualized fortification in very-low-birth-weight infants Am J Clin Nutr 2013 98 529S S535 10.3945/ajcn.112.042689 23824725 21. García-Lara NR Vieco DE De la Cruz-Bértolo J Lora-Pablos D Velasco NU Pallás-Alonso CR Effect of Holder pasteurization and frozen storage on macronutrients and energy content of breast milk J Pediatr Gastroenterol Nutr 2013 57 377 382 10.1097/MPG.0b013e31829d4f82 23752081 22. Vieira AA Soares FV Pimenta HP Abranches AD Moreira ME Analysis of the influence of pasteurization, freezing/thawing, and offer processes on human milk's macronutrient concentrations Early Hum Dev 2011 87 577 580 10.1016/j.earlhumdev.2011.04.016 21592688 23. Conde-Agudelo A, Diaz-Rossello JL, Belizan JM (2003) Kangaroo mother care to reduce morbidity and mortality in low birth weight infants. Cochrane Database Syst Rev (2):CD002771. 10.1002/14651858.CD002771 24. Sharma D Shastri S Sharma P Intrauterine growth restriction: antenatal and postnatal aspects Clinical Medicine Insights Pediatr 2016 10 67 83 10.4137/CMPed.S40070 25. Stephens BE Walden RV Gargus RA Tucker R McKinley L Mance M Nye J Vohr BR First-week protein and energy intakes are associated with 18-month developmental outcomes in extremely low birth weight infants Pediatrics 2009 123 1337 1343 10.1542/peds.2008-0211 19403500 26. Bell KA Matthews LG Cherkerzian S Palmer C Drouin K Pepin HL Ellard D Inder TE Ramel SE Belfort MB Associations of growth and body composition with brain size in preterm infants J Pediatr 2019 214 20 26.e2 10.1016/j.jpeds.2019.06.062 31377040 27. Merrick J Child health and human development over the lifespan Front Public Health 2013 19 1 1 10.3389/fpubh.2013.00001	36446888	PMC9708515	NO-CC CODE	2022-12-01 23:20:30	no	Eur J Pediatr. 2022 Nov 30;:1-10	utf-8	Eur J Pediatr	2,022	10.1007/s00431-022-04711-5	oa_other
==== Front Chem Heterocycl Compd (N Y) Chem Heterocycl Compd (N Y) Chemistry of Heterocyclic Compounds 0009-3122 1573-8353 Springer US New York 3140 10.1007/s10593-022-03140-4 Article Synthesis, structure, and biological activity of 4-hetaryl-2-pyrrolidones containing a pyrazole ring Gorodnicheva Natal’ya V. 1 Vasil’eva Olga S. 1 Ostroglyadov Evgeny S. 1 Baichurin Ruslan I. 1 Litvinov Igor A. [email protected] 2 Tyurenkov Ivan N. [email protected] 3 Kovalev Nikolay S. 3 Bakulin Dmitry A. 3 Kurkin Denis V. 3 Baichurina Larisa V. 4 Makarenko Sergey V. [email protected] 1 1 grid.440630.5 Herzen State Pedagogical University of Russia, 48 Moyka River Embankment, Saint Petersburg, 191186 Russia 2 grid.465285.8 0000 0004 0637 9007 A. E. Arbuzov Institute of Organic and Physical Chemistry, 8 Akademika Arbuzova St., Kazan, 420088 Russia 3 grid.445050.0 0000 0000 8790 3085 Volgograd State Medical University, 1 Pavshikh Bortsov Sq., Volgograd, 400131 Russia 4 grid.415628.c 0000 0004 0562 6029 Kirov Military Medical Academy, 6 Akademika Lebedeva St., Saint Petersburg, 194044 Russia 30 11 2022 110 6 6 2022 5 9 2022 © Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Single diastereomers of 4-hetaryl-2-pyrrolidone-3(5)-carbo- and 2-[4-hetaryl-2-pyrrolidon-1-yl]acetohydrazides were used in reactions with 2,4-pentanedione, providing (3R,4S)-3-, (4R,5R)-5-(3,5-dimethyl-1H-pyrazole-1-carbonyl)- and 1-[2-(3,5-dimethyl-1H-pyrazol-1-yl)-2-oxoethyl]-4-hetaryl-2-pyrrolidones. The structures of the synthesized compounds were confirmed by spectral methods and X-ray structural analysis. Some of the obtained compounds were shown to possess nootropic and anxiolytic activity. Supplementary Information The online version contains supplementary material available at 10.1007/s10593-022-03140-4. Keywords carbohydrazides 3,5-dimethylpyrazole hydrazides 2-pyrrolidone biological activity racetams X-ray structural analysis ==== Body pmcγ-Lactams and pyrazoles represent nitrogen-containing heterocyclic compounds that form the key pharmacophoric motifs in a significant number of natural and synthetic compounds characterized by a broad spectrum of biological activity.1–6 For example, the active pharmaceutical ingredients of many synthetic nootropic drugs known as racetams (piracetam, Phenotropil, levetiracetam, rolipram, and others) and detoxifiers or enterosorbents (polyvinyl-pyrrolidone, Hemodez, Enterodez) contain the structural units of 2-pyrrolidone1,7–10 (Fig. 1). Pyrrolidone ring forms the structural basis of several effective investigational drug molecules (PF-07321332 (nirmatrelvir), GC376, AG-7088 (rupinavir, Rupintrivir)) applied for the treatment and prevention of COVID-19 coronavirus infection11–13 (Fig. 1). The examples of currently approved synthetic drugs containing pyrazole rings include the anti-inflammatory drug celecoxib, the weight loss medication rimonabant, as well as fomepizole (an antidote against toxic alcohols) and others4,6,7 (Fig. 1).Figure 1 The structures of some active pharmaceutical ingredients containing lactam and pyrazole rings. Thus, functionalized 2-pyrrolidones containing in their molecules hetaryl substituents along with two pharmacophoric moieties – lactam and pyrazole heterocycles can be considered as key structures for targeted synthesis of pharmacological agents with various types of activity. The synthesis of such substituted 2-pyrrolidones has been described only in a very limited number of literature sources: 1-hetaryl-4-(pyrazol-1-ylcarbonyl)-2-pyrrolidones were obtained in good yields by cyclocondensation of 1-hetaryl-2-oxopyrrolidine-4-carbohydrazides with 2,4-pentanedione (acac) in i-PrOH medium in the presence of a catalytic amount of hydrochloric acid14–19 (Scheme 1).Scheme 1 For these reasons, we were interested in studying the reactions of diastereomerically pure (3R,4S)-4-hetaryl-2-oxopyrrolidine-3-carbohydrazides 1a–e, (4R,5R)-4-hetaryl-2-oxopyrrolidine-5-carbohydrazides 2a–g, and 2-(4-aryl-2-oxopyrrolidin-1-yl)acetohydrazides 3a–d with 2,4-pentanedione. The starting materials 1a–e, 2a–g, and 3a–d were synthesized according to our previously developed procedures.20–22 The successful completion of these reactions was found to substantially depend on the catalyst that was used. For example, the interaction of hydrazides 1a–с with 2,4-pentanedione under the conditions described in earlier publications14–19 (catalyst hydrochloric acid, solvent i-PrOH) was accompanied by rapid hydrolysis of compounds 1а–с, followed by esterification of the intermediate pyrrolidone carboxylic acids and resulting in the isolation of the respective isopropyl esters 4a–c (Scheme 2).Scheme 2 The use of p-toluenesulfonic acid (TsOH) as catalyst allowed to successfully accomplish the reactions of hydrazides 1a–e, 2a–g, 3a–d with 2,4-pentanedione under identical conditions: catalyst TsOH and heating at reflux in MeOH for 30 min. The target 2-pyrrolidone pyrazole-carbonyl derivatives 5a–e, 6a–g, 7a–d were obtained in good yields (61–84%) (Schemes 3–5).Scheme 3 Scheme 4 Scheme 5 Compounds 5a–e, 6a–g, and 7a–d were isolated as stable colorless crystals with clearly identifiable melting points. Their structures were confirmed by physicochemical methods of analysis (IR spectroscopy, one-dimensional 1H, 13С NMR spectroscopy, two-dimensional 1H–13C HMQC, 1H–13C HMBC experiments, as well as by X-ray structural analysis. IR spectra of all compounds 5a–e, 6a–g, and 7a–d had similar features and were in good agreement between themselves, containing strong broadened absorption bands of carbonyl groups (at the ranges of 1727–1719 and 1700–1685 cm–1), while IR spectra of compounds 5a–e, 6a–g showed the absorption bands of amide NH groups (at the ranges of 3219–3193 and 3114–3080 cm–1). 1Н and 13C NMR spectra of compounds 5a–e, 6a–g featured one set of proton and 13C signals for all structural units, pointing to the presence of only one diastereomer. For example, in 1Н NMR spectra of compounds 5a, 6a there were the methine proton signals of chiral centers: a 3-СН proton doublet at 5.17 ppm (3J3-4 = 11.1 Hz), a 4-СН proton multiplet at 4.01–4.11 ppm (compound 5a), split doublets of 5-СН proton at 5.71 ppm (3J4-5 = 8.4 Hz) and 4-СН proton at 4.07 ppm (J = 8.7 Hz, compound 6a). The downfield region in 1Н NMR spectra of compounds 5a–e, 6a–g showed characteristic singlet signals of pyrrolidine ring 1-NH protons at 8.17–8.23 ppm (compounds 5a–e) and 8.00–8.14 ppm (compounds 6a–g). Taking into account the fact that the chiral centers in the diastereomerically pure carbohydrazides 1a–e and 2a–g were not affected during their reactions with 2,4-pentanedione, the obtained compounds 5a–e had (3R,4S) configuration, while compounds 6a–g had (4R,5R) configuration. Indeed, the spin-spin coupling constants of 3,4-CH methine protons (3J3-4 = 10.7–11.2 Hz) in 1Н NMR spectra of compounds 5a–e and 4,5-CH methine protons (3J4-5 = 8.2–8.7 Hz) in the spectra of compounds 6a–g indicated their trans and cis orientation relative to the lactam ring plane. This orientation corresponded to (3R,4S) and (4R,5R) configurations of the chiral centers and was in good agreement with the spin-spin coupling соnstants established by us for the starting carbohydrazides 1a–e, 2a–g,20–22 as well as the literature data available for trans- and cis-isomers of similar molecules.23–25 The formation of pyrazole ring was confirmed by the presence of characteristic =CH proton quartet signals in 1Н NMR spectra of compounds 5a–e, 6a–g, and 7a–d in the region of 5.88–6.20 ppm. These quartets showed a long range spin-spin coupling (4J = 0.5–0.7 Hz) to the pyrazole ring methyl group protons manifested as a doublet in the region of 1.98–2.44 ppm. The methylene and methine proton signals were assigned to the pyrrolidone and pyrazole rings of compounds 5a–e, 6a–g, and 7a–d according to the results of 1H–13C HMQC experiment. The assignment of C-2,12,13 carbon signals belonging to C=O groups, as well as the 3,4,5-СH proton signals of pyrrolidone ring and =CH proton signals of pyrazole ring in the molecules of compounds 5a–e, 6a–g, and 7a–d was performed on the basis of 1Н–13С HMBC data. For example, in the spectrum of compound 5a, the signal assignment relied on the correlations between 1-NH proton and С-2 carbon atom (8.19/172.1 ppm), 3-CH proton and C-12 carbon atom (5.17/170.8 ppm), as well as other spectral features (Fig. 2). Analogously, 1H–13С HMВC spectrum of compound 7a was interpreted on the basis of cross peaks between the 3-СH2 protons and С-2 carbon atom (2.42 ppm, 2.73/174.3 ppm), as well as the 12-СH2 protons and С-13 carbon atom (4.74 ppm, 4.79/168.7 ppm) (Fig. 2).Figure 2 The main (reference) correlations in 1Н–13С HMBC spectra of compounds 5a and 7a. The most downfield signals in 13C NMR spectra of compounds 5a–e, 6a–g, and 7a–d were assigned to the carbonyl carbon atoms of lactam groups (172–177 ppm) and the side chain (169–171 ppm). The spatial structure of the obtained compounds 5a–e, 6a–g, and 7a–d was examined by X-ray sructural analysis in the case of compounds 5–7 a (Figs. 3–7). According to the results of X-ray structural analysis, compounds 5–7 a crystallized in the form of racemates in centrosymmetric triclinic crystals.Figure 3 The molecular geometry of compound 5a in crystal (the predominant positions of disordered atoms are denoted with letter A). The anisotropic displacement ellipsoids are shown at the 50% probability level. Figure 4 The geometry of independent molecule А of compound 6a in the crystal structure. The anisotropic displacement ellipsoids are shown at the 50% probability level. Figure 5 The geometry of independent molecule А of compound 7a in the crystal structure. The anisotropic displacement ellipsoids are shown at the 50% probability level. Figure 6 The hydrogen bond network in the crystals of compounds а) 5a and b) 6a. The formation of centrosymmetric dimers via N–H···O hydrogen bonds was demonstrated in these crystals. Figure 7 A fragment of crystal packing observed for compound 7a. Projection along the а axis. The crystal structure of compound 5a contained a single molecule in the independent part of the unit cell, in which the pyrrolidone ring together with substituents at the С(3) and С(4) atoms were significantly disordered between two positions. The disordered atoms have been denoted with letters А and В. The relative configuration of the two chiral centers C(3) and С(4) in compound 5a was R* and S* (molecule А) or S* and R* (molecule В), respectively. Thus, the crystal of compound 5a can hold both enantiomers (RS/SR) or pair of diastereomers at the same position. In the crystal structures of compounds 6a and 7a, the independent part of the unit cell contained two independent molecules of enantiomers А and В. For compound 6a, the chiral atoms С(4А) and С(5А) had S configuration, while C(4B) and C(5B) had R configuration. In the case of compound 7a, the chiral С(4А) atoms had R configuration and С(4В) had S configuration. The lactam rings in molecules 5–7 a had the same С(4)-envelope conformation with the С(4) atom deviating from the common plane defined by the С(3)–С(2)–N(1)–C(5) atom chain. At the same time, in the disordered molecules А and В of compound 5а, the С(4) atom deviated from the С(3)–С(2)–N(1)–C(5) plane in different directions by approximately equal distances: by 0.21 Å in molecule A and by 0.28 Å in molecule B. The phenyl and carbonylpyrazole substituents at the C(3) and С(4) chiral atoms of compound 5a assumed equatorial positions and had an anticlinal conformation relative to each other (torsion angle С(6)–С(4)–С(3)–С(12) –99(1)°), while in the molecules of compound 6a the substituents at the C(4) and C(5) atoms were in axial and equatorial positions, respectively, and assumed an eclipsed conformation (the torsion angle С(6)–С(4)–С(5)–С(12) was equal to –10.0(4)° and –24.1(4)° in the molecules of compounds А and В, respectively). In the molecules of compound 7a, the substituents at the С(4) and N(1) atoms occupied equatorial positions. The molecules of compounds 5а, 6а in crystalline state formed centrosymmetric dimers linked by N–H···O hydrogen bonds. There were ordinary van der Waals contacts between the dimers (Fig. 6). In the crystal of compound 7а, С–Н···О type bonds were observed, mediated by dispersion interactions (Fig. 7). Compounds 5a,c, 6a,b,d, 7a,c representing each group of the obtained products were tested as potentially biologically active compounds, anticipated to have psychotropic properties. Standard pharmacological testing methods were used, which were appropriate for the given type of activity: “open field” (OF), “elevated plus maze” (EPM), “conditioned passive avoidance reflex” (CPAr), “extrapolation avoidance” (EA), and Vogel conflict test (VCT).26 The experiments were performed with male Wistar rats in accordance with the animal testing regulations set by the legislation of the Russian Federation and the EASC technical standards for Good Laboratory Practice (GOST R 53434-2009 and GOST R 51000.4-2011). Compounds 5a,c, 6a,b,d, 7a,c were injected into the animals intraperitoneally (IP, physiological saline) in equimolar amounts as a single dose equal to 1/10 of the molecular mass, 30 min prior to the test. The acute toxicity level (LD50, mg/kg) of compounds 5a,c, 6a,b,d, and 7a,c was evaluated from the survival rate of white mice and exceeded 2000 mg/kg, allowing to recognize them as relatively harmless materials, belonging to the IV class of toxicity. The selected reference drug was phenibut (20 mg/kg, IP), which possesses anxiolytic and nootropic activity and is prescribed for the treatment of anxiety, cognitive, and asthenic disorders.7 Statistical processing of the test results was performed using the Prism 6 program according to Shapiro–Wilk, Kruskal–Wallis, and Dunn’s criteria. The OF test characterizes the locomotor activity (by the number of traversed squares) and orienting-exploratory activity of the animals (by the sum of rearing and head-dipping acts). As shown in Table 1, compounds 5a,c, 6a,b,d, 7a,c did not produce a significant effect on the motor activity of animals in this test, pointing to the absence of psychostimulant or sedative activity associated with these compounds. It should be noted that, after the injection of compound 7c (32 mg/kg), the animals of experiment group showed a statistically significant increase in rearing and head-dipping behaviors, as well as the number of visits to the brightly lit central area (aversive for nocturnal rodents), pointing to the prevalence of orienting-exploratory activity by the animals over the aversion toward their environment (Table 1). Thus, compound 7c in the OF test showed anxiolytic activity, comparable to that of phenibut.Table 1 The effect of compounds 5a,c, 6a,b,d, 7a,c on animal behavior in OF, EPM, VCT, CPAR, and EA tests (M ± m) Compound OF EPM VCT CPAR EA MA* EB OA* – frequency of entry OA – residence time, s Number of punished approaches Residence time in closed arm, s. Problem solving time, s. Control 44.0 ± 3.2 13.3 ± 0.9 1.2 ± 0.4 12.4 ± 1.8 3.5 ± 0.7 126.4 ± 13.4 6.8 ± 1.7 5a 46.0 ± 3.5 14.0 ± 2.1 2.0 ± 0.5 24.0 ± 5.2 180.0 ± 0.04 2.5 ± 0.54 5c 35.0 ± 10.0 17.0 ± 10.0 1.5 ± 0.5 15.2 ± 9.4 180.0 ± 0.04 2.0 ± 1.04 6a 41.0 ± 0.9 13.3 ± 1.0 1.6 ± 0.5 8.9 ± 1.2 137.5 ± 24.0 4.5 ± 1.0 6b 35.6 ± 2.6 11.0 ± 0.6 2.6 ± 0.44 31.9 ± 3.34 7.3 ± 0.64 145.8 ± 22.6 4.3 ± 0.7 6d 38.1 ± 2.3 13.0 ± 0.9 2.1 ± 0.6 22.0 ± 3.0 140.9 ± 25.8 4.3 ± 0.7 7a 42.5 ± 9.5 14.5 ± 2.5 1.5 ± 0.5 10.0 ± 6.7 103.8 ± 24.7 3.5 ± 1.3 7c 48.5 ± 12.5 19.0 ± 3.54 3.0 ± 0.54 49.0 ± 9.04 5.5 ± 1.5 180.0 ± 0.04 3.8 ± 1.8 Phenibut 41.6 ± 4.0 19.0 ± 2.84 3.0 ± 0.94 45.4 ± 14.54 9.3 ± 1.24 180.0 ± 0.04 2.6 ± 0.44 MA – motor activity (number of squares traversed in the experimental apparatus). EB – exploratory behavior (total number rearing and head-dipping acts). * OA – the open arm of the EPM apparatus. 4 The differences were statistically significant compared to the control group, at p < 0.05. The EPM test was used to evaluate the anxiety level in animals by recording the total time spent, time spent in the open arm, and the number of excursions into the open arm. After a single injection of compounds 5a,c, 6a,b,d, 7a,c, only compounds 6b and 7c statistically significantly increased the number and duration of excursions into the open arms of the apparatus, amount of rearing and head-dipping behaviors in the open arms among the experiment group compared to the control group of animals. This indicated a reduction in the level of anxiety (Table 1). The anxiolytic effect of compound 7c surpassed the activity of compound 6b and was comparable to that of phenibut. The activity of the most promising compounds 6b, 7c was also studied according to the Vogel conflict test, which is a highly specific test for evaluating the anxiolytic action of new compounds. It was established from this test that, after the injection of compounds 6b, 7c, the number of animals in the experimental group punished by withdrawal of water was statistically significantly higher compared to the control group (Table 1). Remarkably, the anxiolytic activity of compound 6b exceeded that of compound 7c, the activity of which in the EPM test was higher than in the case of compound 6b and comparable to the activity of phenibut. Within the framework of studying compounds 5a,c, 6a,b,d, and 7a,c with regard to their psychotropic activity, their effects on the learning and memory (formation and retrieval of memory trace) were also characterized using animals in CPAR and EA tests. During the CPAR test, the animals treated with compounds 5a,c, 7c prior to the training retained the memory trace and did not enter the closed arm with electrode floor at 7 days after the training (Table 1). Along with that, the animals treated with compounds 5a,c solved the extrapolation task significantly faster during the EA test compared to the control group (Table 1). Thus, according to the results of both tests, from the series of compounds 5a,c, 6a,b,d, and 7a,c we selected compounds 5a,c that improved the formation and retrieval of memory trace in experimental animals and showed nootropic effects. In conclusion, the results of this study allowed us to develop an effective method for the preparation of previously unknown diastereomerically pure 3(5)-(3,5-dimethyl-1H-pyrazole-1-carbonyl)-4-hetaryl-2-pyrrolidones and 4-aryl-1-[2-(3,5-dimethyl-1H-pyrazol-1-yl)-2-oxoethyl]-2-pyrrolidones – new members of the racetam family containing the pharmacophoric lactam and pyrazole rings in their molecules, along with hetaryl substituents. Some of these compounds were shown to possess nootropic and anxiolytic activity. The obtained data open further possibilities for more detailed study of these compounds as promising lead compounds in the drug development targeted toward the prevention and treatment of anxiety disorders. Experimental IR spectra were recorded on a Shimadzu IR Prestige-21 FT-IR spectrometer for samples in KBr pellets. One-dimensional 1Н, 13С, 15N NMR spectra and two-dimensional 1H–13C HMQC and 1H–13C HMBC spectra were acquired on a Jeol ECX400A spectrometer at the working frequencies of 400 MHz (for 1H nuclei), 100 MHz (for 13С nuclei), and 40 MHz (for 15N nuclei) for samples in DMSO-d6 solutions. The residual non-deuterated solvent signals (for 1Н nuclei) or deuterated solvent signals (for 13С nuclei) were used as standards, while 15N NMR chemical shifts were recorded relative to MeNO2. Elemental analysis was performed on a EuroVector EA3000 (CHN Dual) analyzer. The reaction progress and purity of the obtained compounds were controlled by TLC on Silufol UV-254 plates using 9:1:2 i-PrOH–NH4OH–H2O mobile phase and visualization under UV light (λ = 254 nm). The synthesis of (3R,4S)-4-hetaryl-2-pyrrolidone-3-carbohydrazides 1a–e, (4R,5R)-4-hetaryl-2-pyrrolidone-5-carbohydrazides 2a–g, and 2-[4-aryl-2-pyrrolidon-1-yl]acetohydrazides 3a–d was accomplished by following published procedures.20–22 Synthesis of isopropyl (3 R ,4 S* )-4-aryl-2-oxopyrrolidine-3-carboxylates 4a–c (General method). A mixture of the appropriate 4-aryl-2-oxopyrrolidine-3-carbohydrazide 1а–с (5 mmol), 2,4-pentanedione (15 mmol), and 6 N HCl (0.25 ml) in i-PrOH (20 ml) was heated at reflux for 2 h. The solvent was evaporated at reduced pressure (15–20 mmHg) by 2/3 of the initial volume, and the residue was treated with H2O (4 ml). The crystalline product was filtered off, air-dried, and recrystallized from MeOH. Isopropyl (3 R* ,4 S* )-2-oxo-4-phenylpyrrolidine-3-carboxylate (4a) was obtained from carbohydrazide 1a (1095 mg, 5 mmol). Yield 1000 mg (81%), colorless crystals, mp 124– 125°С (MeOH). IR spectrum, ν, cm–1: 1729, 1689 (С=О), 3209, 3100 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 1.08 (3H, d, 3J = 6.2, CH3); 1.14 (3H, d, 3J = 6.2, CH3); 3.22 (1H, t, J = 9.4, 5-CH2); 3.54 (1H, d, 3J = 11.1, 3-CH); 3.59 (1H, t, J = 8.9, 5-CH2); 3.80–3.90 (1H, m, 4-CH); 4.88 (1H, sept, 3J = 6.2, ОCH); 7.18–7.25 (1H, m, H-4 Ph); 7.26–7.33 (4H, m, H-2,3,5,6 Ph); 8.11 (1H, s, NH). 13C NMR spectrum, δ, ppm: 22.0 (CH3); 22.1 (CH3); 45.5 (C-4); 47.1 (C-5); 55.7 (C-3); 68.7 (ОCH); 127.7 (C-4 Ph); 127.8 (C-2,6 Ph); 129.2 (C-3,5 Ph); 140.3 (C-1 Ph); 169.8 (C-12); 171.9 (C-2). Found, %: С 68.07; Н 7.08; N 5.64. C14H17NO3. Calculated, %: C 68.00; H 6.93; N 5.66. Isopropyl (3 R* ,4 S* ) - 4-(4-methylphenyl)-2-oxopyrrolidine-3-carboxylate (4b) was obtained from carbohydrazide 1b (1165 mg, 5 mmol). Yield 980 mg (75%), colorless crystals, mp 114–116°С (MeOH). IR spectrum, ν, cm–1: 1734, 1700 (С=О), 3215, 3112 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 1.08 (3H, d, 3J = 6.2, CH3); 1.14 (3H, d, 3J = 6.2, CH3); 2.23 (3H, s, CH3 Ph); 3.17 (1H, t, J = 9.5, 5-CH2); 3.48 (1H, d, 3J = 11.1, 3-CH); 3.51–3.58 (1H, m, 5-CH2); 3.74–3.84 (1H, m, 4-CH); 4.86 (1H, sept, 3J = 6.2, ОCH); 7.10 (2H, d, J = 8.0, H Ph); 7.17 (2H, d, J = 8.0, H Ph); 8.07 (1H, s, NH). 13C NMR spectrum, δ, ppm: 21.1 (CH3 Ph); 22.0 (CH3); 22.1 (CH3); 45.1 (C-4); 47.1 (C-5); 55.8 (C-3); 68.7 (CH); 127.6 (CН Ph); 129.7 (CН Ph); 136.8 (C Ph); 137.2 (C Ph); 169.9 (C-12); 171.9 (C-2). Found, %: C 68.72; H 7.22; N 5.38. C15H19NO3. Calculated, %: C 68.94; H 7.33; N 5.36. Isopropyl (3 R* ,4 S* ) - 4-(4-methoxyphenyl)-2-oxopyrrolidine-3-carboxylate (4c) was obtained from carbohydrazide 1с (1245 mg, 5 mmol). Yield 1165 mg (84%), colorless crystals, mp 110–112°С (MeOH). IR spectrum, ν, cm–1: 1733, 1699 (С=О), 3215, 3110 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 1.08 (3H, d, 3J = 6.2, CH3); 1.14 (3H, d, 3J = 6.2, CH3); 3.17 (1H, t, J = 9.5, 5-CH2); 3.47 (1H, d, 3J = 11.1, 3-CH); 3.50–3.56 (1H, m, 5-CH2); 3.69 (3H, s, CH3O); 3.72–3.82 (1H, m, 4-CH); 4.86 (1H, sept, 3J = 6.2, ОCH); 6.85 (2H, d, J = 8.7, H-3,5 Ph); 7.21 (2H, d, J = 8.7, H-2,6 Ph); 8.06 (1H, s, NH). 13C NMR spectrum, δ, ppm: 22.0 (CH3); 22.1 (CH3); 44.9 (C-4); 47.3 (C-5); 55.6 (CH3O); 55.9 (C-3); 68.7 (CH); 114.5 (C-3,5 Ph); 128.9 (C-2,6 Ph); 132.1 (C-1 Ph); 158.9 (C-4 Ph); 169.9 (C-12); 171.9 (C-2). Found, %: C 64.81; H 6.79; N 5.38. C15H19NO4. Calculated, %: C 64.97; H 6.91; N 5.05. Synthesis of 3(5)-(3,5-dimethyl-1 H -pyrazole-1-carbonyl)-4-hetarylpyrrolidin-2-ones 5a–e, 6a–g and aryl-1-[2-(3,5-dimethyl-1 H -pyrazol-1-yl)-2-oxoethyl]-4-pyrrolidin-2-ones 7a–d (General method). A mixture of the appropriate compound 2a–g, 3a–d (5 mmol), 2,4-pentanedione (15 mmol), and p-toluenesulfonic acid (1 mmol) in MeOH (15 ml) was heated at reflux for 30 min. The solvent was evaporated at reduced pressure (15–20 mmHg) by 2/3 of the initial volume, and the residue was treated with H2O (4 ml). The crystalline product was filtered off and air-dried. (3 R* ,4 S* ) - 3-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)- 4-phenylpyrrolidin-2-one (5a) was obtained from carbohydrazide 1a (1095 mg, 5 mmol). Yield 1090 mg (77%), colorless crystals, mp 143–145°С (i-PrOH). IR spectrum, ν, cm–1: 1722, 1700 (С=О), 3202, 3107 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.11 (3H, s, 15-CH3); 2.42 (3H, d, 4J = 0.8, 17-CH3); 3.33 (1H, t, J = 9.4, 5-CH2); 3.66 (1H, ddd, J = 9.4, J = 8.2, J = 1.2, 5-CH2); 4.01–4.11 (1H, m, 4-CH); 5.17 (1H, d, 3J = 11.1, 3-CH); 6.19 (1H, q, 4J = 0.8, 16-CH); 7.17–7.23 (1H, m, H-4 Ph); 7.24–7.29 (2H, m, H-3,5 Ph); 7.30–7.34 (2H, m, H-2,6 Ph); 8.19 (1H, s, NH). 13C NMR spectrum, δ, ppm: 14.0 (15-CH3); 14.7 (17-CH3); 45.3 (C-4); 47.2 (C-5); 54.1 (C-3); 112.8 (C-16); 127.7 (C-4 Ph); 127.8 (C-2,6 Ph); 129.2 (C-3,5 Ph); 140.3 (C-1 Ph); 144.0 (C-17); 152.6 (C-15); 170.8 (C-12); 172.1 (C-2). Found, %: С 67.76; Н 5.97; N 14.81. C16H17N3O2. Calculated, %: C 67.83; H 6.05; N 14.83. (3 R* ,4 S* ) - 3-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)-4-(4-methylphenyl)pyrrolidin-2-one (5b) was obtained from hydrazide 1b (1165 mg, 5 mmol). Yield 1025 mg (69%), colorless crystals, mp 141–143°С (i-PrOH). IR spectrum, ν, cm–1: 1724, 1699 (С=О), 3206, 3109 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.11 (3H, s, 15-CH3); 2.19 (3H, s, CH3 Ph); 2.41 (3H, d, 4J = 0.5, 17-CH3); 3.30 (1H, t, J = 9.4, 5-CH2); 3.62 (1H, ddd, J = 9.4, J = 8.4, J = 0.8, 5-CH2); 3.97–4.07 (1H, m, 4-CH); 5.15 (1H, d, 3J = 11.1, 3-CH); 6.17 (1H, q, 4J = 0.5, 16-CH); 7.07 (2H, d, 3J = 8.0, H Ph); 7.20 (2H, d, 3J = 8.0, H Ph); 8.19 (1H, s, NH). 13C NMR spectrum, δ, ppm: 14.0 (CH3); 14.7 (CH3); 21.1 (CH3 Ph); 45.0 (C-4); 47.3 (C-5); 54.2 (C-3); 112.8 (C-16); 127.7 (CН Ph); 129.8 (CН Ph); 136.9 (C Ph); 137.2 (C Ph); 143.9 (C-17); 152.5 (C-15); 170.8 (C-12); 172.2 (C-2). Found, %: С 68.26; Н 6.46; N 13.65. C17H19N3O2. Calculated, %: C 68.67; H 6.44; N 14.13. (3 R ,4 S ) - 3-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)-4-(4-methoxyphenyl)pyrrolidin-2-one (5c) was obtained from hydrazide 1c (3735 mg, 15 mmol). Yield 3286 mg (70%), colorless crystals, mp 147–149°С (i-PrOH). IR spectrum, ν, cm–1: 1727, 1691 (С=О), 3209, 3108 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.11 (3H, s, 15-CH3); 2.41 (3H, d, 4J = 0.7, 17-CH3); 3.29 (1H, t, J = 9.4, 5-CH2); 3.61 (1H, ddd, J = 9.4, J = 8.2, J = 1.1, 5-CH2); 3.66 (3H, s, CH3O); 3.95–4.05 (1H, m, 4-CH); 5.13 (1H, d, 3J = 11.2, 3-CH); 6.18 (1H, q, 4J = 0.7, 16-CH); 6.83 (2H, d, 3J = 8.7 H-3,5 Ph); 7.25 (2H, d, 3J = 8.7, H-2,6 Ph); 8.17 (1H, s, NH). 13C NMR spectrum, δ, ppm: 14.0 (15-CH3); 14.7 (17-CH3); 44.7 (C-4); 47.4 (C-5); 54.4 (C-3); 55.6 (CH3O Ph); 112.8 (C-16); 114.6 (C-3,5 Ph); 128.9 (C-2,6 Ph); 132.1 (C-1 Ph); 144.0 (C-17); 152.5 (C-15); 158.9 (C-4 Ph); 170.9 (C-12); 172.2 (C-2). Found, %: C 65.07; H 6.22; N 13.33. C17H19N3O3. Calculated, %: C 65.16; H 6.11; N 13.41. (3 R ,4 S )- 4-(4-Chlorophenyl)-3-(3,5-dimethyl-1 H -pyrazole-1-carbonyl)pyrrolidin-2-one (5d) was obtained from hydrazide 1d (1775 mg, 7 mmol). Yield 1622 mg (73%), colorless crystals, mp 139–141°С (MeOH). IR spectrum, ν, cm–1: 1719, 1689 (С=О), 3198, 3097 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.10 (3H, s, 15-CH3); 2.41 (3H, br. s, 17-CH3); 3.31 (1H, t, J = 9.0, 5-CH2); 3.57–3.68 (1H, m, 5-CH2); 3.98–4.12 (1H, m, 4-CH); 5.14 (1H, d, 3J = 10.7, 3-CH); 6.17 (1H, br. s, 16-CH); 7.32 (2H, d, 3J = 8.2, H Ph); 7.36 (2H, d, 3J = 8.2, H Ph); 8.19 (1H, s, NH). 13C NMR spectrum, δ, ppm: 14.0 (CH3); 14.6 (CH3); 44.7 (C-4); 47.1 (C-5); 54.1 (C-3); 112.8 (C-16); 129.1 (CН Ph); 129.8 (CН Ph); 132.4 (C Ph); 139.3 (C Ph); 144.0 (C-17); 152.6 (C-15); 170.6 (C-12); 171.9 (C-2). Found, %: C 60.33; H 5.01; N 13.20. C16H16N3O2Cl. Calculated, %: C 60.47; H 5.08; N 13.22. (3 R* ,4 S* ) - 3-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)-4-(pyridin-3-yl)pyrrolidin-2-one (5e) was obtained from hydrazide 1e (1100 mg, 5 mmol). Yield 1008 mg (71%), colorless crystals, mp 148–150°С (i-PrOH). IR spectrum, ν, cm–1: 1725, 1686 (С=О), 3219, 3091 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.10 (3H, s, 15-CH3); 2.42 (3H, d, 4J = 0.6, 17-CH3); 3.38 (1H, t, J = 9.3, 5-CH2); 3.67 (1H, ddd, J = 9.3, J = 8.3, J = 1.0, 5-CH2); 4.03–4.14 (1H, m, 4-CH); 5.21 (1H, d, 3J = 11.0, 3-CH); 6.18 (1H, q, 4J = 0.6, 16-CH); 7.33 (1H, dd, J = 7.8, J = 4.7, H-5' pyridine); 7.84 (1H, dt, J = 7.8, J = 1.9, H-4' pyridine); 8.23 (1H, s, NH); 8.42 (1H, dd, J = 4.7, J = 1.5, H-6' pyridine); 8.52 (1H, d, J = 1.9, H-2' pyridine). 13C NMR spectrum, δ, ppm: 14.0 (CH3); 14.6 (CH3); 43.0 (C-4); 46.9 (C-5); 53.9 (C-3); 112.8 (C-16); 124.3 (CН pyridine); 135.7 (CН pyridine); 135.9 (C-3' pyridine); 144.0 (C-17); 148.9 (CН pyridine); 149.3 (CН pyridine); 152.7 (C-15); 170.6 (C-12); 171.9 (C-2). Found, %: C 63.12; H 5.59; N 19.54. C15H16N4O2. Calculated, %: C 63.37; H 5.67; N 19.71. (4 R* ,5 R* ) - 5-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)- 4-phenylpyrrolidin-2-one (6a) was obtained from carbohydrazide 2a (1095 mg, 5 mmol). Yield 1075 mg (76%), colorless crystals, mp 186–187°С (i-PrOH). IR spectrum, ν, cm–1: 1737, 1699 (С=О), 3211, 3111 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 1.98 (3H, d, 4J = 0.7, 17-CH3); 2.09 (3H, s, 15-CH3); 2.43 (1H, dd, 2J = 16.6, 3J = 6.9, 3-CH2); 2.66 (1H, dd, 2J = 16.6, 3J = 9.0, 3-CH2); 4.07 (1H, dt, J = 8.7, J = 6.9, 4-CH); 5.71 (1H, dd, J = 8.4, J = 0.5, 5-CH2); 5.92 (1H, q, 4J = 0.7, 16-CH); 6.88–6.93 (2H, m, H Ph); 7.03–7.09 (3H, m, H Ph); 8.06 (1H, s, NH). 13C NMR spectrum, δ, ppm: 13.8 (17-CH3); 13.9 (15-CH3); 36.7 (С-3); 43.5 (C-4); 60.2 (C-5); 111.6 (C-16); 127.5 (C-4 Ph); 127.9 (CН Ph); 128.2 (CН Ph); 139.2 (C-1 Ph); 143.5 (C-17); 152.4 (C-15); 171.3 (C-12); 177.0 (C-2). 15N NMR spectrum, δ, ppm: –267.9 (N-1); –147.4 (N-13); –79.6 (N-14). Found, %: С 67.82; Н 6.10; N 14.81. C16H17N3O2. Calculated, %: C 67.83; H 6.05; N 14.83. (4 R* ,5 R* ) - 5-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)-4-(4-methylphenyl)pyrrolidin-2-one (6b) was obtained from hydrazide 2b (1165 mg, 5 mmol). Yield 906 mg (61%), colorless crystals, mp 172–174°С (i-PrOH). IR spectrum, ν, cm–1: 1725, 1699 (С=О), 3205, 3114 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 1.99 (3H, d, 4J = 0.5, 17-CH3); 2.11 (3H, s, 15-CH3); 2.12 (3H, s, CH3 Ph); 2.38 (1H, dd, 2J = 16.6, 3J = 6.5, 3-CH2); 2.67 (1H, dd, 2J = 16.6, 3J = 9.0, 3-CH2); 4.02 (1H, dt, J = 8.5, J = 6.5, 4-CH); 5.67 (1H, br. d, 3J = 8.2, 5-CH); 5.96 (1H, q, 4J = 0.5, 16-CH); 6.76 (2H, d, 3J = 8.0, H-2,6 Ph); 6.86 (2H, d, 3J = 8.0, H-3,5 Ph); 8.04 (1H, s, NH). 13C NMR spectrum, δ, ppm: 13.8 (17-CH3); 13.9 (15-CH3); 21.0 (CH3 Ph); 36.9 (С-3); 43.2 (C-4); 60.4 (C-5); 111.6 (C-16); 127.7 (C-2,6 Ph); 128.7 (C3,5 Ph); 136.3 (C-1 Ph); 136.5 (C-4 Ph); 143.6 (C-17); 152.4 (C-15); 171.3 (C-12); 177.0 (C-2). Found, %: С 68.52; Н 6.33; N 14.12. C17H19N3O2. Calculated, %: C 68.67; H 6.44; N 14.13. (4 R* ,5 R* ) - 5-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)-4-(4-methoxyphenyl)pyrrolidin-2-one (6c) was obtained from hydrazide 2c (249 mg, 1 mmol). Yield 222 mg (71%), colorless crystals, mp 172–174°С (i-PrOH). IR spectrum, ν, cm–1: 1723, 1696 (С=О), 3208, 3106 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.02 (3H, d, 4J = 0.7, 17-CH3); 2.10 (3H, s, 15-CH3); 2.38 (1H, dd, 2J = 16.6, 3J = 6.9, 3-CH2); 2.63 (1H, dd, 2J = 16.6, 3J = 9.0, 3-CH2); 3.59 (3H, s, CH3O); 4.02 (1H, dt, J = 8.5, J = 6.9, 4-CH); 5.66 (1H, dd, J = 8.2, J = 0.5, 5-CH); 5.95 (1H, q, 4J = 0.7, 16-CH); 6.62 (2H, d, 3J = 8.7, H-3,5 Ph); 6.81 (2H, d, 3J = 8.7, H-2,6 Ph); 8.00 (1H, s, NH). 13C NMR spectrum, δ, ppm: 13.86 (17-CH3); 13.91 (15-CH3); 36.8 (С-3); 42.8 (C-4); 55.6 (CH3O Ph); 60.3 (C-5); 111.6 (C-16); 113.6 (C-3,5 Ph); 128.9 (C-2,6 Ph); 131.0 (C-1 Ph); 143.6 (C-17); 152.4 (C-15); 158.7 (C-4 Ph); 171.4 (C-12); 177.0 (C-2). Found, %: С 64.85; Н 6.06; N 13.36. C17H19N3O3. Calculated, %: C 65.16; H 6.11; N 13.41. (4 R* ,5 R* ) - 4-(4-Chlorophenyl)-5-(3,5-dimethyl-1 H -pyrazole-1-carbonyl)pyrrolidin-2-one (6d) was obtained from hydrazide 2d (1268 mg, 5 mmol). Yield 1143 mg (72%), colorless crystals, mp 210–212°С (i-PrOH). IR spectrum, ν, cm–1: 1720, 1699 (С=О), 3201, 3094 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.04 (3H, d, 4J = 0.7, 17-CH3); 2.09 (3H, s, 15-CH3); 2.41 (1H, dd, 2J = 16.6, 3J = 7.1, 3-CH2); 2.66 (1H, dd, 2J = 16.6, 3J = 9.0, 3-CH2); 4.10 (1H, dt, J = 8.5, J = 7.1, 4-CH); 5.69 (1H, dd, J = 8.4, J = 0.5, 5-CH); 5.97 (1H, q, 4J = 0.7, 16-CH); 6.94 (2H, d, 3J = 8.5, H Ph); 7.13 (2H, d, 3J = 8.5, H Ph); 8.07 (1H, s, NH). 13C NMR spectrum, δ, ppm: 13.9 (15,17-CH3); 36.5 (С-3); 42.9 (C-4); 60.1 (C-5); 111.8 (C-16); 128.1 (CН Ph); 129.8 (CН Ph); 132.1 (C Ph); 138.2 (C Ph); 143.6 (C-17); 152.6 (C-15); 171.2 (C-12); 176.8 (C-2). Found, %: С 60.10; Н 4.93; N 13.28. C16H16N3O2Cl. Calculated, %: C 60.47; H 5.08; N 13.22. (4 R* ,5 R* ) - 5-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)-4-(4-nitrophenyl)pyrrolidin-2-one (6e) was obtained from hydrazide 2e (1056 mg, 4 mmol). Yield 918 mg (70%), colorless crystals, mp 197–199°С (MeOH). IR spect- rum, ν, cm–1: 1722, 1696 (С=О), 1519, 1347 (NO2), 3193, 3087 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.03 (3H, d, 4J = 0.6, 17-CH3); 2.06 (3H, s, 15-CH3); 2.49 (1H, dd, 2J = 16.7, 3J = 7.5, 3-CH2); 2.70 (1H, dd, 2J = 16.7, 3J = 9.0, 3-CH2); 4.24–4.33 (1H, m, 4-CH); 5.73 (1H, dd, J = 8.5, J = 0.5, 5-CH); 5.93 (1H, q, 4J = 0.6, 16-CH); 7.23 (2H, d, 3J = 8.8, H-2,6 Ph); 7.95 (2H, d, 3J = 8.8, H-3,5 Ph); 8.14 (1H, s, NH). 13C NMR spectrum, δ, ppm: 13.8 (17-CH3); 13.9 (15-CH3); 36.2 (С-3); 43.4 (C-4); 60.0 (C-5); 111.9 (C-16); 123.2 (CН Ph); 129.5 (CН Ph); 143.7 (C-17); 146.9 (C Ph); 147.3 (C Ph); 152.8 (C-15); 171.1 (C-12); 176.5 (C-2). Found, %: C 58.36; H 5.06; N 17.04. C16H16N4O4. Calculated, %: C 58.53; H 4.91; N 17.06. (4 R* ,5 R* ) - 5-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)-4-(3-nitrophenyl)pyrrolidin-2-one (6f) was obtained from hydrazide 2f (1320 mg, 5 mmol). Yield 1378 mg (84%), colorless crystals, mp 178–180°С (i-PrOH). IR spectrum, ν, cm–1: 1732, 1700 (С=О), 1529, 1348 (NO2), 3193, 3080 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.01 (3H, d, 4J = 0.7, 17-CH3); 2.03 (3H, s, 15-CH3); 2.57 (1H, dd, 2J = 16.7, 3J = 8.3, 3-CH2); 2.66 (1H, dd, 2J = 16.7, 3J = 9.0, 3-CH2); 4.29–4.38 (1H, m, 4-CH); 5.75 (1H, dd, J = 8.7, J = 0.5, 5-CH); 5.89 (1H, q, 4J = 0.7, 16-CH); 7.40 (2H, t, J = 7.9, H-5' Ph); 7.47 (1H, dt, J = 7.9, J = 1.2, H-6' Ph); 7.83 (1H, t, J = 1.9, H-2' Ph); 7.92 (1H, ddd, J = 7.9, J = 1.9, J = 1.2, H-4 Ph); 8.11 (1H, s, NH). 13C NMR spectrum, δ, ppm: 13.7 (17-CH3); 13.8 (15-CH3); 35.6 (С-3); 43.1 (C-4); 59.8 (C-5); 111.8 (C-16); 122.5 (CН Ph); 123.3 (CН Ph); 129.8 (CН Ph); 134.5 (CН Ph); 141.1 (C Ph); 143.7 (C-17); 147.4 (C Ph); 152.9 (C-15); 171.3 (C-12); 176.7 (C-2). Found, %: С 58.47; Н 4.93; N 17.01. C16H16N4O4. Calculated, %: C 58.53; H 4.91; N 17.06. (4 R* ,5 R* ) - 5-(3,5-Dimethyl-1 H -pyrazole-1-carbonyl)-4-(pyridin-3-yl)pyrrolidin-2-one (6g) was obtained from hydrazide 2g (1100 mg, 5 mmol). Yield 1008 mg (71%), colorless crystals, mp 205–207°С (i-PrOH). IR spectrum, ν, cm–1: 1730, 1699 (С=О), 3195, 3094 (NH). 1H NMR spectrum, δ, ppm (J, Hz): 2.03 (3H, d, 4J = 0.7, 17-CH3); 2.07 (3H, s, 15-CH3); 2.48 (1H, dd, 2J = 16.7, 3J = 7.4, 3-CH2); 2.67 (1H, dd, 2J = 16.7, 3J = 9.0, 3-CH2); 4.11–4.19 (1H, m, 4-CH); 5.71 (1H, dd, J = 8.4, J = 0.5, 5-CH); 5.94 (1H, q, 4J = 0.7, 16-CH); 7.10 (1H, dd, J = 7.9, J = 4.7, H-5' pyridine); 7.36 (1H, dt, J = 7.9, J = 1.9, H-4' pyridine); 8.10 (1H, s, NH); 8.12 (1H, d, J = 1.9, H-2' pyridine); 8.23 (1H, dd, J = 4.7, J = 1.5, H-6' pyridine). 13C NMR spectrum, δ, ppm: 13.9 (2CH3); 36.0 (С-3); 41.2 (C-4); 60.0 (C-5); 111.9 (C-16); 123.3 (C-5' pyridine); 134.6 (C-3' pyridine); 135.0 (C-4' pyridine); 148.8 (C-6' pyridine); 149.6 (C-2' pyridine); 143.6 (C-17); 152.8 (C-15); 171.2 (C-12); 176.7 (C-2). Found, %: C 58.31; H 4.78; N 19.26. C15H16N4O2. Calculated, %: C 58.53; H 4.91; N 19.71. 1-[2-(3,5-Dimethyl-1 H -pyrazol-1-yl)-2-oxoethyl]-4-phenylpyrrolidin-2-one (7a) was obtained from acetohydrazide 3a (699 mg, 3 mmol). Yield 579 mg (65%), colorless crystals, mp 120–122°С (i-PrOH). IR spectrum, ν, cm–1: 1741, 1684 (С=О). 1H NMR spectrum, δ, ppm (J, Hz): 2.16 (3H, s, 16-CH3); 2.42 (1H, dd, 2J = 16.7, 3J = 8.7, 3-CH2); 2.44 (3H, br. s, 18-CH3); 2.73 (1H, dd, 2J = 16.7, 3J = 9.0, 3-CH2); 3.44 (1H, dd, J = 8.9, J = 7.8, CH2); 3.57–3.68 (1H, m, 4-CH); 3.80 (1H, t, J = 8.6, 5-CH2); 4.74 (1H, d, 2J = 18.3, 12-CH2); 4.79 (1H, d, 2J = 18.3, 12-CH2); 6.20 (1H, br. s, 17-CH); 7.18–7.26 (1H, m, H-4 Ph); 7.28–7.36 (4H, m, H-2,6,3,5 Ph). 13C NMR spectrum, δ, ppm: 14.0 (16-CH3); 14.3 (18-CH3); 37.4 (С-4); 38.5 (C-3); 46.0 (C-12); 54.9 (C-5); 111.9 (C-17); 127.3 (C-4 Ph); 127.5 (C-2,6 Ph); 129.2 (C-3,5 Ph); 143.3 (C-1 Ph); 144.2 (C-18); 153.0 (C-16); 168.7 (C-13); 174.3 (C-2). Found, %: C 68.52; H 6.57; N 14.17. C17H19N3O2. Calculated, %: C 68.67; H 6.44; N 14.13. 1-[2-(3,5-Dimethyl-1 H -pyrazol-1-yl)-2-oxoethyl]-4-(4-methylphenyl)pyrrolidin-2-one (7b) was obtained from hydrazide 3b (247 mg, 1 mmol). Yield 236 mg (76%), colorless crystals, mp 144–146°С (i-PrOH). IR spectrum, ν, cm–1: 1743, 1687 (С=О). 1H NMR spectrum, δ, ppm (J, Hz): 2.16 (3H, s, 16-CH3); 2.24 (3H, s, CH3 Ph); 2.38 (1H, dd, 2J = 16.7, 3J = 8.7, 3-CH2); 2.44 (3H, d, 4J = 0.7, 18-CH3); 2.70 (1H, dd, 2J = 16.7, 3J = 9.0, 3-CH2); 3.41 (1H, dd, J = 9.1, J = 7.5, 5-CH2); 3.52–3.64 (1H, m, 4-CH); 3.77 (1H, dd, J = 9.1, J = 8.4, 5-CH2); 4.72 (1H, d, 2J = 18.3, 12-CH2); 4.78 (1H, d, 2J = 18.3, 12-CH2); 6.20 (1H, q, 4J = 0.7, 17-CH); 7.11 (2H, d, J = 8.0, H Ph); 7.19 (2H, d, J = 8.0, H Ph). 13C NMR spectrum, δ, ppm: 14.0 (16-CH3); 14.3 (18-CH3); 21.1 (CH3 Ph); 37.0 (С-4); 38.6 (C-3); 46.0 (C-12); 54.9 (C-5); 111.9 (C-17); 127.3 (CН Ph); 129.7 (CН Ph); 136.3 (C Ph); 140.2 (C Ph); 144.2 (C-18); 152.9 (C-16); 168.7 (C-13); 174.4 (C-2). Found, %: С 69.14; Н 6.85; N 13.59. C18H21N3O2. Calculated, %: C 69.43; H 6.80; N 13.49. 1-[2-(3,5-Dimethyl-1 H -pyrazol-1-yl)-2-oxoethyl]-4-(4-methoxyphenyl)pyrrolidin-2-one (7c) was obtained from hydrazide 3c (789 mg, 3 mmol). Yield 647 mg (66%), colorless crystals, mp 125–127°С (i-PrOH). IR spectrum, ν, cm–1: 1740, 1684 (С=О). 1H NMR spectrum, δ, ppm (J, Hz): 2.16 (3H, s, 16-CH3); 2.38 (1H, dd, 2J = 16.6, 3J = 8.8, 3-CH2); 2.44 (3H, d, 4J = 0.5, 18-CH3); 2.68 (1H, dd, 2J = 16.6, 3J = 9.0, 3-CH2); 3.39 (1H, dd, 5-CH2); 3.50–3.62 (1H, m, 4-CH); 3.70 (3H, s, CH3O Ph); 3.76 (1H, dd, J = 9.1, J = 8.5, 5-CH2); 4.72 (1H, d, 2J = 18.3, 12-CH2); 4.78 (1H, d, 2J = 18.3, 12-CH2); 6.20 (1H, q, 4J = 0.5, 17-CH); 6.87 (2H, d, J = 8.7, H-3,5 Ph); 7.23 (2H, d, J = 8.7, H-2,6 Ph). 13C NMR spectrum, δ, ppm: 14.0 (16-CH3); 14.3 (18-CH3); 36.7 (С-4); 38.7 (C-3); 45.9 (C-12); 55.1 (C-5); 55.6 (CH3O Ph); 111.9 (C-17); 114.5 (C-3,5 Ph); 128.5 (C-2,6 Ph); 135.1 (C-1 Ph); 144.2 (C-18); 152.9 (C-16); 158.6 (C-4 Ph); 168.7 (C-13); 174.4 (C-2). Found, %: С 65.68; Н 6.47; N 12.81. C18H21N3O3. Calculated, %: C 66.04; H 6.47; N 12.84. 4-(4-Chlorophenyl)-1-[2-(3,5-dimethyl-1 H -pyrazol-1-yl)-2-oxoethyl]pyrrolidin-2-one (7d) was obtained from hydrazide 3d (267 mg, 1 mmol). Yield 252 mg (76%), colorless crystals, mp 127–129°С (i-PrOH). IR spectrum, ν, cm–1: 1740, 1676 (С=О). 1H NMR spectrum, δ, ppm (J, Hz): 2.16 (3H, s, 16-CH3); 2.40 (1H, dd, 2J = 16.7, 3J = 8.5, 3-CH2); 2.43 (3H, d, 4J = 0.5, 18-CH3); 2.73 (1H, dd, 2J = 16.7, 3J = 9.0, 3-CH2); 3.41 (1H, dd, J = 9.3, J = 7.3, 5-CH2); 3.59–3.69 (1H, m, 4-CH); 3.79 (1H, dd, J = 9.3, J = 8.5, 5-CH2); 4.73 (1H, d, 2J = 18.3, 12-CH2); 4.78 (1H, d, 2J = 18.3, 12-CH2); 6.20 (1H, q, 4J = 0.5, 17-CH); 7.35 (2H, d, J = 9.0, H Ph); 7.37 (2H, d, J = 9.0, H Ph). 13C NMR spectrum, δ, ppm: 14.0 (16-CH3); 14.3 (18-CH3); 36.7 (С-4); 38.4 (C-3); 46.0 (C-12); 54.7 (C-5); 111.9 (C-17); 129.1 (CН Ph); 129.5 (CН Ph); 131.8 (C Ph); 142.4 (C Ph); 144.2 (C-18); 153.0 (C-16); 168.7 (C-13); 174.2 (C-2). Found, %: C 61.33; H 5.91; N 12.69. C17H18N3O2Cl. Calculated, %: C 61.54; H 5.47; N 12.66. Single crystal X-ray structural analysis of compounds 5–7 a was performed on a Bruker Kappa APEX II CCD automatic diffractometer (graphite monochromator, λ(MoKα) 0.71073 Å, temperature 293(2)K, ω-scanning). Crystals of compounds 5–7 a suitable for X-ray structural analysis were obtained by crystallization from i-PrOH. Data collection and indexation, the determination and refinement of unit cell parameters were performed by using the APEX2 software suite.27 Empirical correction for the absorption derived from crystal shape, additional spherical correction, and accounting for systematic errors was accomplished with the SADABS program.28 The structures were solved by direct method using the SHELXT program29 and refined by full matrix method of least squares by F2 with the SHELXL program.30 A disordered pyrrolidone ring position along with the substituents at С(3) and С(4) atoms were observed in the crystal structure of compound 5а. The disordered atoms were denoted with letters А and В. The population of atomic positions А was 61%, while the population of atomic positions В was 39%. The moderate precision of structure 5а resulted from the small number of reflections observed due to the poor quality of the disordered crystal. For compounds 6а and 7а, the independent part of the crystals contained two molecules of these compounds (independent molecules А and В). The non-hydrogen atom positions were refined in anisotropic approximation. The hydrogen atom positions at the carbon atoms were calculated from stereochemical criteria and refined according to the riding model. The analysis of intra- and intermolecular interactions, as well as graphic presentation were accomplished using the PLATON31 and Mercury 2020.3 programs.32 The complete X-ray structural analysis dataset has been deposited at the Cambridge Crystallographic Data Center (deposits CCDC 2157868 (compound 5a), CCDC 2157855 (compound 6a), and CCDC 2157869 (compound 7a)). Supplementary information file containing IR spectra, 1H and 13C NMR spectra of all synthesized compounds 5a–e, 6a–g, 7a–d, as well as 1H–13С HMQC and 1H–13С HMBC spectra of compounds 5а,c, 6a,b, 7a and crystallographic data of structures 5–7 a is available at the journal website http://link.springer.com/journal/10593. This study received funding from the Ministry of Education of the Russian Federation (project No. FSZN-2020-0026) and Russian Science Foundation (project No. 21-15-00192). The spectral characterization and elemental analysis of the synthesized compounds were performed using the equipment at the Center for Collective Use “Physicochemical methods for the study of nitro compounds, coordination compounds, biologically active substances, and nanostructured materials” of the Interdisciplinary Resource Center for Collective Use “Contemporary physicochemical methods of formation and research of materials for the needs of industry, science, and education” of the Herzen Russian State Pedagogical University. The X-ray structural analysis was performed at the Collective Use Center for Spectral Analysis of Structure, Properties, and Composition of Compounds and Materials at the Federal Research Center “Kazan Scientific Center of the Russian Academy of Sciences”. This study was performed within the framework of State contract No. 122011800131-8 with the Federal Research Center “Kazan Scientific Center of the Russian Academy of Sciences”. Supplementary Information ESM 1 (PDF 2338 kb) Translated from Khimiya Geterotsiklicheskikh Soedinenii, 2022, 58(11), 598–607 ==== Refs References 1. Berestovitskaya, V. M.; Tyurenkov, I. N.; Vasilyeva, O. S.; Perfilova, V. N.; Ostroglyadov, E. S.; Bagmetova, V. V. Ratsetamy: metody sinteza i biologicheskaya aktivnost'. (Racetams: methods of synthesis and biological activity [in Russian]); Asterion: St. Petersburg, 2016. 2. Küçükgüzel ŞG Şenkardeş S Eur. J. Med. Chem. 2015 97 786 10.1016/j.ejmech.2014.11.059 25555743 3. Caruano J Muccioli GG Robiette R Org. Biomol. Chem. 2016 14 10134 10.1039/C6OB01349J 27748489 4. Mert S Kasımoğulları R İça T Çolak F Altun A Ok S Eur. J. Med. Chem. 2014 78 86 10.1016/j.ejmech.2014.03.033 24681068 5. Faria JV Vegi PF Miguita AGC dos Santos MS Boechat N Bernardino AMR Bioorg. Med. Chem. 2017 25 5891 10.1016/j.bmc.2017.09.035 28988624 6. Ansari, A.; Ali, A.; Asif, M.; Shamsuzzaman New J. Chem. 2017, 1, 16. 7. Mashkovskiy, M. D. Lekarstvennye sredstva (Drugs [in Russian]); Novaya Volna: Moscow, 2012, 16th Ed. 8. (a) Granik, V. G. Lekarstva (farmakologicheskii, biokhimi- cheskii i khimicheskii aspekty) (Drugs (pharmacological, biochemical and chemical aspects) [in Russian]); Vuzovskaya Kniga: Moscow, 2006, 2nd Ed., p. 149. (b) Granik, V. G. Lekarstva (farmakologicheskii, biokhimicheskii i khimicheskii aspekty) (Drugs (pharmacological, biochemical and chemical aspects) [in Russian]); Vuzovskaya Kniga: Moscow, 2006, 2nd Ed., p. 153. (c) Granik, V. G. Lekarstva (farmakolo- gicheskii, biokhimicheskii i khimicheskii aspekty) (Drugs (pharmacological, biochemical and chemical aspects) [in Russian]); Vuzovskaya Kniga: Moscow, 2006, 2nd Ed., p. 360. 9. Gouliaev AH Monster JB Vedso M Senning A Org. Prep. Proced. Int. 1995 27 273 10.1080/00304949509458465 10. Malykh AG Sadaie MR Drugs 2010 70 287 10.2165/11319230-000000000-00000 20166767 11. Hoffman, R. L.; Kania, R. S.; Brothers, M. A.; Davies, J. F.; Ferre, R. A.; Gajiwala, K. S.; He, M.; Hogan, R. J.; Kozminski, K.; Li, L. Y.; Lockner, J. W.; Lou, J.; Marra, M. T.; Mitchell, L. J., Jr.; Murray, B. W.; Nieman, J. A.; Noell, S.; Planken, S. P.; Rowe, T.; Ryan, K.; Smith III, G. J.; Solowiej, J. E.; Steppan, C. M.; Taggart, B. J. Med. Chem. 2020, 63, 12725. 12. Vandyck K Deval J Curr. Opin. Virol. 2021 49 36 10.1016/j.coviro.2021.04.006 34029993 13. Zhang L Lin D Kusov Y Nian Y Ma Q Wang J von Brunn A Leyssen P Lanko K Neyts J de Wilde A Snijder EJ Liu H Hilgenfeld R J. Med. Chem. 2020 63 4562 10.1021/acs.jmedchem.9b01828 32045235 14. Vaickelioniene R Mickevicius V Chem. Heterocycl. Compd. 2006 42 753 10.1007/s10593-006-0157-4 15. Mickevičius V Vaickelioniené R Chem. Heterocycl. Compd. 2008 44 170 10.1007/s10593-008-0031-7 16. Brokaite K Mickevicius V Mikulskiene G Chem. Heterocycl. Compd. 2006 42 1158 10.1007/s10593-006-0220-1 17. Sapijanskaite B Mickevičius V Chem. Heterocycl. Compd. 2008 44 807 10.1007/s10593-008-0113-6 18. Mickevičius V Vaickelioniené R Jonuškienė I Mikulskienė G Kantminienė K Monat. Chem. 2009 140 1513 10.1007/s00706-009-0201-z 19. Anusevičius K Jonuškienė I Sapijanskaitė B Kantminienė K Mickevičius V Res. Chem. Intermed. 2016 42 6975 10.1007/s11164-016-2510-2 20. Gorodnicheva NV Vasil'eva OS Ostroglyadov ES Baichurin RI Makarenko SV Karamov FA Lodochnikova OA Litvinov IA Russ. Chem. Bull. 2020 69 470 10.1007/s11172-020-2786-7 21. Gorodnicheva NV Ostroglyadov ES Vasil'eva OS Pelipko VV Gurzhii VV Berestovitskaya VM Lipina ES Rus. J. Org. Chem. 2016 52 1616 10.1134/S1070428016110129 22. Vasil'eva OS Berestovitskaya BM Tyurenkov IN Ostroglyadov ES Perfilova VN Gorodnicheva NV Yaremchuk AI Russ. Chem. Bull. 2017 66 1491 10.1007/s11172-017-1913-6 23. Montoya-Balbás IJ Valentín-Guevara B López-Mendoza E Linzaga-Elizalde I Ordoñez M Román-Bravo P Molecules 2015 20 22028 10.3390/molecules201219830 26690390 24. Pachaly P Chem. Ber. 1971 104 412 10.1002/cber.19711040209 25. Pachaly P Chem. Ber. 1971 104 429 10.1002/cber.19711040211 26. Klimochkin YN Tkachenko IM Reznikov AN Shiryaev VA Kazachkova MS Kovalev NS Bakulin DA Abrosimova EE Kurkin DV Tyurenkov IN Russ. J. Bioorg. Chem. 2021 47 1276 10.1134/S1068162021060108 27. APEX2 (Version 2.1); Bruker AXS, Inc.: Madison, 2006. 28. SADABS; Bruker AXS, Inc.: Madison, 1997. 29. Sheldrick, G. Acta Crystallogr., Sect. A: Found. Adv. 2015, A71, 3. 30. Sheldrick, G. Acta Crystallogr., Sect. C: Struct. Chem. 2015, C71, 3. 31. Spek, A. Acta Crystallogr., Sect. A: Found. Crystallogr. 1990, A46, 34. 32. Farrugia L J. Appl. Crystallogr. 2012 45 849 10.1107/S0021889812029111	36467774	PMC9708518	NO-CC CODE	2022-12-08 23:16:02	no	Chem Heterocycl Compd (N Y). 2022 Nov 30; 58(11):598-607	utf-8	Chem Heterocycl Compd (N Y)	2,022	10.1007/s10593-022-03140-4	oa_other
==== Front J Control Release J Control Release Journal of Controlled Release 0168-3659 1873-4995 The Authors. Published by Elsevier B.V. S0168-3659(22)00767-2 10.1016/j.jconrel.2022.11.022 Article Optimization of storage conditions for lipid nanoparticle-formulated self-replicating RNA vaccines Kim Byungji a Hosn Ryan R. a Remba Tanaka a Yun Dongsoo b Li Na a Abraham Wuhbet a Melo Mariane B. a Cortes Manuel ac Li Bridget de Zhang Yuebao f Dong Yizhou fg Irvine Darrell J. ahij⁎ a Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA b Nanotechnology Materials Core, Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA c J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA d Department of Biology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA e Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA f Division of Pharmaceutics & Pharmacology, College of Pharmacy, The Ohio State University, Columbus, OH 43210, United States g Department of Biomedical Engineering, The Center for Clinical and Translational Science, The Comprehensive Cancer Center, Dorothy M. Davis Heart & Lung Research Institute, Department of Radiation Oncology, Center for Cancer Engineering, Center for Cancer Metabolism, Pelotonia Institute for Immune-Oncology, The Ohio State University, Columbus, OH 43210, United States h Departments of Biological Engineering and Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA i Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02139, USA j Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA ⁎ Corresponding author at: Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA 30 11 2022 1 2023 30 11 2022 353 241253 7 8 2022 3 11 2022 13 11 2022 © 2022 The Authors 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The recent clinical success of multiple mRNA-based SARS-CoV-2 vaccines has proven the potential of RNA formulated in lipid nanoparticles (LNPs) in humans, and products based on base-modified RNA, sequence-optimized RNA, and self-replicating RNAs formulated in LNPs are all in various stages of clinical development. However, much remains to be learned about critical parameters governing the manufacturing and use of LNP-RNA formulations. One important issue that has received limited attention in the literature to date is the identification of optimal storage conditions for LNP-RNA that preserve long-term activity of the formulations. Here, we analyzed the physical structure, in vivo expression characteristics, and functional activity of alphavirus-derived self-replicating RNA (repRNA)-loaded LNPs encoding HIV vaccine antigens following storage in varying temperatures, buffers, and in the presence or absence of cryoprotectants. We found that for lipid nanoparticles with compositions similar to clinically-used LNPs, storage in RNAse-free PBS containing 10% (w/v) sucrose at −20 °C was able to maintain vaccine stability and in vivo potency at a level equivalent to freshly prepared vaccines following 30 days of storage. LNPs loaded with repRNA could also be lyophilized with retention of bioactivity. Graphical abstract Unlabelled Image Keywords Lipid nanoparticle Vaccine storage RNA delivery Freeze-storage Lyophilization ==== Body pmc1 Introduction One of the first experiments on the delivery of RNA molecules dates back to 1978, when mouse lymphocytes were transfected in vitro with mRNA encoding rabbit globin using liposomes [1]. Since then, advances in ionizable lipids and RNA loading techniques led to the approval of the first siRNA-based lipid nanoparticle (LNP) therapeutic, Onpattro (Patisiran) by the US Food and Drug Administration (FDA) in 2018 [[2], [3], [4], [5]]. By early 2020 when the COVID-19 pandemic occurred, several mRNA-loaded LNP formulations had reached clinical trials [6]. In December 2020, the FDA issued Emergency Use Authorization (EUA) for COVID-19 vaccines from Pfizer-BioNTech (BNT162b2) and Moderna (mRNA-1273), both of which utilize LNPs loaded with mRNA encoding the viral spike protein as the immunogen [7,8]. Currently, LNP formulations of diverse RNA products including oligonucleotides (e.g., siRNA, miRNA, anti-sense oligonucleotides, etc.) [[9], [10], [11], [12], [13]], base-modified RNA [[14], [15], [16]], sequence-optimized RNA [[17], [18], [19], [20]], and self-replicating RNAs [[21], [22], [23], [24]] derived from alphaviruses, flaviviruses, measles viruses, and rhabdoviruses are in various stages of clinical development for applications ranging from vaccines to cancer therapy. Most recently, a COVID-19 vaccine based on LNP-formulated self-replicating RNA (ARCT-154) achieved promising phase 3 efficacy data in Vietnam [25,26]. While both of the approved mRNA vaccines for COVID-19 are similar in structure, BNT162b2 uses phosphate-buffered saline (PBS) as the solvent (with 20% w/v sucrose), whereas mRNA-1273 uses tris-HCl-buffered saline (TBS; with 8% w/v sucrose) [[27], [28], [29], [30], [31], [32]]. Both formulations have reported reliable maintenance of vaccine stability and efficacy in these sucrose buffers, but the range of storage temperature and lifetime is broad; mRNA-1273 is stored at −20 °C for up to 6 months, whereas BNT162b2 is concentrated and stored at −70 °C for up to 6 months, with more recent data indicating storage at −25 °C to −15 °C for two weeks is also stable [27,33,34]. There is limited public knowledge regarding the impact of key storage parameters (e.g., cryoprotectant, dispersant, temperature, etc.) on LNP-RNA vaccines, as noted by many authors in light of the recent pandemic [28,33,[35], [36], [37], [38]]. In the limited number of published studies, several common parameters are found to affect storage outcomes. Sugar-based cryoprotectants, such as sucrose, trehalose, and mannitol, improve the stability of LNPs during freeze-thawing and lyophilization [35,39,40]. Constituting the LNPs in different aqueous solvents (e.g., water, saline) or in a mix with organic solvents (e.g., ethanol) displayed minor effects in improving the stability of LNP-RNA [35,39,40]. However, the most dominant variable seems to be the storage temperature, ranging from flash freezing in liquid nitrogen to refrigeration at 4 °C, where different formulations seem to favor different temperatures. We previously reported a formulation comprised of TT3 (a lipid-like ionizable molecule), DOPE, cholesterol, and DMG-PEG2k loaded with mRNA encoding luciferase that was successfully flash frozen in liquid nitrogen, and stored for up to 3 months with 5% w/v sucrose or trehalose [39]. On the other hand, a formulation composed of an unidentified ionizable lipid, DSPC, cholesterol, and DMG-PEG2k loaded with mRNA encoding the receptor binding domain (RBD) of SARS-CoV-2 mouse-adapted strain was maintained at room temperature for at least 7 days, and potentially longer at 4 °C [41]. Another work presented imidazole-modified lipids that seem to form highly stable RNA-loaded structures by addition of ether bonds and amine head groups that allow for pi-stacking [42]; impressively, this LNP was able to retain RNA integrity and function for out to 25 weeks at 4 °C, 18 weeks at 25 °C, and 3 weeks at 37 °C in PBS without the need for cryoprotectants. The exact reason for the wide range of shelf lives reported for LNP-RNA vaccines remains unknown. In addition, many studies of LNP-RNA stability have focused on functional measures using mRNA encoding for a reporter gene (e.g., luciferase, GFP) rather than direct assessments of vaccine immunogenicity, and analyses linking structural integrity of LNPs to vaccine activity are lacking. To help fill this knowledge gap, here we investigated the effects of cryoprotectants, buffer type (phosphate- and tris-buffered saline), and storage temperature on both the structural and functional maintenance of self-replicating RNA-loaded LNP (LNP-RNA) vaccines. 2 Material and methods 2.1 Materials N1,N3,N5-tris(3-(didodecylamino)propyl)benzene-1,3,5-tricarboxamide (TT3) was synthesized as previously described [43]; (6Z,9Z,28Z,31Z)-Heptatriaconta-6,9,28,31-tetraen-19-yl 4-(dimethylamino) butanoate (DLin-MC3-DMA) was purchased from MedChemExpress (CAT#HY-112251); 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine (DOPE; CAT#850725), Cholesterol (CAT#700100), 1,2-dimyristoyl-rac-glycero-3-methoxypolyethylene glycol-2000 (DMG-PEG2k; CAT#88015) were purchased from Avanti Polar Lipids. HIV Env trimer N332-GT2 was prepared as previously described [44]. Citrate buffer (pH 3; CAT#J61391-AK) was purchased from Alfa Aesar. For dialysis, 20 K MWCO Slide-A-Lyzer™ MINI Dialysis Device (ThermoFisher Scientific), RNAse-free PBS (AM9625; ThermoFisher Scientific), Tris Buffer (ThermoFisher Scientific), and D-(+)-Sucrose, Ultrapure DNAse-, RNAse-free (97061–432; VWR) were used. mRNA encoding for GFP was purchased from APExBIO (CAT# R1007). For bioluminescence studies, luciferin was purchased from GoldBio (CAT# LUCK-1G). 2.2 RNA synthesis Venezuelan equine encephalitis virus (VEE) replicon plasmid DNA was prepared based on mutant constructs previously described [[45], [46], [47]]. Firefly luciferase or HIV immunogen N332-GT2 (a stabilized SOSIP trimer of the HIV envelope glycoprotein spike [44]) were cloned after the subgenomic promoter as previously described [45,46]. Replicon RNAs were in vitro transcribed (IVT) from templates of linearized VEE DNA constructs using the HiScribe T7 Quick High Yield RNA Synthesis Kit (New England Biolabs) following the manufacturer's instructions. The resulting replicon RNAs were capped and methylated using the ScriptCap Cap 1 Capping System (Cellscript) according to the manufacturer's instructions. RNA was purified in water with 300,000 PES columns (Sartorius), and purity was assessed by gel electrophoresis. 2.3 Lipid nanoparticle synthesis Lipid nanoparticles (LNPs) were synthesized using a microfluidic organic-aqueous precipitation method. The organic phase was prepared by solubilizing the lipids TT3, Dlin-MC3-DMA, DOPE, Cholesterol, and DMG-PEG2k in ethanol at a molar ratio of 10:25:20:40:5. The aqueous phase of RNA was prepared by diluting the RNA (stored in RNAse-free water) with 10 mM citrate buffer at pH 3.0 (CAT#J61391-AK; Alfa Aesar). Lipids were stored in ethanol at −20 °C, and RNA constructs were stored in RNAse-free water at −80 °C and were thawed on ice before use. The two phases were prepared at an ethanol:aqueous volume ratio of 1:2, and RNA and lipids combined at an N:P ratio of 2:1. Each phase was loaded into a syringe (BD), and locked onto the NxGen microfluidic cartridge for mixing using a NanoAssemblr Ignite instrument (Precision Nanosystems). The Ignite was set to operate with the following settings: volume ratio- 2:1; flow rate- 12 mL/min; waste volume- 0 mL. The resulting LNPs were dialyzed against predetermined buffers (0–30% w/v sucrose in pH 7.4 PBS or TBS) using 20 K MWCO Slide-A-Lyzer™ MINI Dialysis casettes (ThermoFisher Scientific) at 25 °C for 90 min, with an exchange of the buffer reservoir after 45 min. The dialyzed LNPs were then stored under predetermined test conditions. 2.4 LNP-RNA lyophilization LNP-RNAs were synthesized and diluted (50, 20, 10, or 3.3 ng/μL) in different buffers (PBS, 10%, or 30% w/v sucrose) and frozen (−20 °C, −80 °C or − 200 °C) for 24 h according to the test conditions. The frozen samples were then lyophilized for 24 h in a LabConco Freezone 4.5 Liter Benchtop Freeze Dry System. Eppendorf tubes containing the frozen samples were opened, covered with perforated parafilm, and placed in a pre-chilled 50 mL conical tube during lyophilization. Lyophilization was conducted at the default fixed setting of −60 °C. Lyophilized samples were stored at 4 °C unless hydrated immediately, and were reconstituted in deionized water before use. 2.5 Particle characterization Dynamic light scattering (DLS; Malvern Panalytical) was performed to determine the hydrodynamic size, polydispersity index, and zeta-potential of the LNPs. For hydrodynamic size measurements, 10 μL of particles in different buffers were diluted in 800 μL of deionized water and placed into the 1.5 mL cuvette (Fisher Scientific) for measurement. The same sample was transferred to the folded capillary zeta cell (Malvern Panalytical) to measure the zeta-potential. Electron microscopy was conducted to qualitatively assess the LNP size and polydispersity. In sample preparation for cryogenic electron microscopy (cryo-EM), 3 μL of the particles sample in buffer containing solution was dropped on a lacey copper grid coated with a continuous carbon film and blotted to remove excess sample without damaging the carbon layer by Gatan Cryo Plunge III. Grid was then mounted on a Gatan 626 single tilt cryo-holder equipped in the TEM column. The specimen and holder tip were cooled down by liquid‑nitrogen, and the temperature was maintained during transfer into the microscope and subsequent imaging. For negative stained-transmission electron microscopy (TEM), 10 μL of the particle sample in buffer containing solution was dropped on a 200 mesh copper grid coated with a continuous carbon film and excess solution was removed after 60 s of waiting by blotting with a wipe. Then 10 μL of negative staining solution, phosphotungstic acid as a 1% aqueous solution was dropped on the TEM grid and immediately removed by blotting. Another 10 μL of the stain is applied to the grid with blotted removal after 30 s, and the grid is dried at room temperature. The dried grid was mounted on a JEOL single tilt holder equipped in the TEM column. The specimen was cooled down by liquid‑nitrogen. Imaging on a JEOL 2100 FEG microscope was conducted using a minimum dose method that is essential to avoid sample damage under the electron beam. The microscope was operated at 200 kV and with a magnification in the ranges of 10,000–60,000 for assessing particle size and distribution. All images were recorded on a Gatan 2kx2k UltraScan CCD camera. 2.6 Mice Female Balb/C (JAX Stock No. 000651) mice 6–8 weeks of age were maintained in the animal facility at the Massachusetts Institute of Technology (MIT). All animal studies and procedures were carried out following federal, state, and local guidelines under an IACUC-approved animal protocol. 2.7 In vivo mouse imaging To qualitatively assess the efficacy of thawed LNPs compared with freshly prepared samples using repRNA encoding for luciferase (repLuc) as a reporter, we simulated vaccination in cohorts of Balb/C mice by intramuscularly (i.m.) injecting 1 μg RNA doses of the LNPs loaded with repLuc in each of the left and right gastrocnemius muscle. At days 1, 3, 7, 10, and 15 post-i.m. injection, the mice were intraperitoneally (i.p.) administered 200 μL of luciferin (50 mg/mL in PBS), and imaged using the In Vivo Imaging System (Xenogen IVIS 200; PerkinElmer) 10 min post-i.p. injection. 2.8 Serum antibody titer quantification To quantitatively assess the efficacy of thawed LNPs compared with freshly prepared samples using repRNA encoding for the HIV env trimer, we vaccinated healthy Balb/C mice by injecting 1 μg RNA doses of the LNP-RNA i.m. in each of the left and right gastrocnemius muscle. At weeks 2 and 4 post-i.m. injection, the mice underwent retro-orbital bleeding; blood was collected in Z-gel PP tubes for blood serum collection (CAT#41.1500.005; Sarstedt). Serum was collected by centrifuging blood at 10,000 ×g for 4 min, and stored at −80 °C prior to use. To conduct ELISAs, NUNC MaxiSorp plates were coated overnight with 1 μg/mL purified HIV antigen in PBS, then blocked for 2 h with 10% BSA in PBS. Mouse sera were initially diluted 50× in blocking buffer, followed by 3× serial dilutions. Diluted sera were transferred to blocked plates and incubated for 2 h. HRP-conjugated immunoglobulins (e.g., IgG, IgG1, IgG2a, IgG2b, IgG3, IgM; Bio-Rad) were used as detection antibodies at 1:5000 for endpoint titer assessments, with gp120-specific monoclonal antibody VRC01 used as a positive control. 3,3′,5,5’-Tetramethylbenzidine (TMB) signal was read using a microplate reader by subtracting the absorbance at 450 nm by that at 550 nm. 2.9 Germinal center (GC) assay Balb/C mice were immunized as described above, and at 2 weeks post-vaccination, popliteal lymph nodes were collected and mechanically dissociated to obtain single cell suspensions. Zombie Aqua (BioLegend) in PBS was used to stain for cell viability, and antibody staining was performed in fluorescence-activated cell sorting (FACS) buffer (PBS, 1% bovine serum albumin (BSA), 0.02% NaN3, and 2 mM EDTA). Fc-mediated binding was blocked using purified anti-CD16/32 (2.5 μg/mL; 93; BioLegend) at 4 °C for 15 min, on top of which we added primary antibodies for cell surface staining at 4 °C for 30 min. GC B cells were stained using anti-GL7 PerCPCy5.5 (GL7; BioLegend), anti-CD38 AF488 (90; BioLegend), anti-B220 PECy7 (RA3-6B2; BioLegend), and anti-CD4 BV711(GK1.5; BioLegend). Follicular helper T cells were assessed using anti-CD4-BV711 (GK1.5; BioLegend), anti-B220-PECy7 (RA3-6B2; BioLegend), anti-CXCR5-PE (phycoerythrin) (L138D7; BioLegend), and anti-PD1-BV421 (29F.1A12; BioLegend). The HIV env trimer used as the antigen was conjugated to either BV605 (streptavidin-conjugated; BioLegend) or APC-Cy7 (streptavidin-conjugated; BioLegend), and both probes were used to detect antigen-specific B cells. Stained cells were fixed with 4% paraformaldehyde (ThermoFisher Scientific) for 10 min at 25 °C, washed and resuspended in FACS buffer for flow cytometric analysis on a BD FACSymphony™ A3 Cell Analyzer (BD Biosciences). 2.10 Enzyme-linked immunosorbent spot (ELISpot) assay Spleens were harvested 2 weeks after mice had been intramuscularly vaccinated with LNPs loaded with repRNA encoding for the HIV immunogen in the left and right gastrocnemius muscles of mice. Splenocytes were isolated by mechanical dissociation of the spleen and erythrocytes were removed using the Gibco Ammonium-Chloride-Potassium lysing buffer (Thermo Fisher Scientific). ELISpot was conducted using the mouse IFN-γ ELISPOT Kit (BD Biosciences). Cells were seeded on IFN-γ-coated wells at 106 cells/well in triplicate for three pools of the HIV env trimer peptides, which were added to the cells at 2 μg/mL. Cells were stimulated by wrapping the plate in foil and incubating overnight at 37 °C. Plates were developed and detected according to manufacturer's instructions. Plates were scanned using the CTL-ImmunoSpot Plate Reader, and data were analyzed using CTL ImmunoSpot Software. 2.11 Bead-based ELISA cytokine quantification Mouse muscles were analyzed using the Legendplex mouse antivirus response panel (Biolegend) following the manufacturer's suggested protocol and analyzed using the LEGENDplex Data Analysis Software Suite. At 24 h post-vaccination, gastrocnemius muscles were collected from mice. Tissues were homogenized in GentleMACS M Tubes (Miltenyi Biotec) filled with 1.5 mL of lysis buffer (20 mM Tris (pH 7.8), 137 mM NaCl, 2.7 mM KCl, 1 mM MgCl2, 1% Triton X-100, 10% (w/v) glycerol, 1 mM EDTA, 1 mM dithiothreitol, HALT Protease Cocktail (ThermoFisher)) using the GentleMACS Octo Dissociator (Miltenyi Biotec). The tissues were homogenized and then further incubated in the lysis buffer overnight at room temperature on a rotor shaker before being analyzed or flash frozen and stored at −80 °C until later analysis. 3 Results 3.1 Effect of sucrose on maintenance of structural and functional integrity of LNP-RNA Lipid nanoparticles (LNPs) were synthesized using a microfluidic system mixing an organic phase of lipids in ethanol with aqueous phase repRNA in water to induce self-assembly of LNPs encapsulating the replicon (LNP-RNA). LNPs were prepared using a 2:1 ratio of ionizable lipid amine groups to RNA phosphates (equivalent to a 2.9:1 ionizable lipid:RNA mass ratio). The resulting particles were dialyzed into pH 7.4 phosphate-buffered saline (PBS) or tris-buffered saline (TBS) containing 0%, 5%, 10%, or 30% w/v sucrose to test the effects of buffer and cryoprotectant. The particles were then stored at 4 °C, −20 °C, −80 °C, or − 200 °C (flash freezing in liquid nitrogen). After 7 days, the LNP-RNA were thawed at 25 °C, and their hydrodynamic size and polydispersity were measured using dynamic light scattering (DLS). Freshly prepared LNPs in their respective buffer and sucrose concentrations served as controls. As shown in Table 1 , regardless of buffer and sucrose concentration, storage of LNPs at −80 °C resulted in particle aggregation and high polydispersity, while storage at other temperatures (4 °C, −20 °C, and − 200 °C) maintained LNP size distributions comparable to fresh particles. Notably, the absence of sugar in the buffer for both PBS- and TBS-stored LNPs resulted in a 20–50 nm increase in the particle size distribution, hinting at the cryopreservative effects of sucrose. Interestingly, structural integrity was well maintained at all tested temperatures when mRNA was loaded into the same LNPs instead of repRNA (Supplementary Fig. S1).Table 1 Dynamic light scattering (DLS) assessment of lipid nanoparticles. (n = 3). Table 1PBS 0% sucrose 5% sucrose 10% sucrose 30% sucrose Temp Fresh 4 °C −20 °C −80 °C −200 °C Fresh 4 °C −20 °C −80 °C −200 °C Fresh 4 °C −20 °C −80 °C −200 °C Fresh 4 °C −20 °C −80 °C −200 °C PDI ± st. dev 0.233 ± 0.030 0.401 ± 0.016 0.273 ± 0.012 0.912 ± 0.153 0.282 ± 0.041 0.294 ± 0.060 0.357 ± 0.050 0.293 ± 0.072 0.818 ± 0.261 0.212 ± 0.018 0.292 ± 0.061 0.280 ± 0.027 0.273 ± 0.056 0.980 ± 0.035 0.171 ± 0.062 0.283 ± 0.049 0.336 ± 0.027 0.286 ± 0.063 0.688 ± 0.209 0.280 ± 0.092 Z-avg diam. ± st. dev (nm) 114 ± 3 107 ± 40 125 ± 1 4037 ± 2267 141 ± 5 92 ± 18 95 ± 7 96 ± 6 1448 ± 781 124 ± 51 90 ± 7 88 ± 10 88 ± 13 3927 ± 4238 93 ± 21 96 ± 8 97 ± 24 86 ± 14 741 ± 498 83 ± 7 TBS PDI ± st. dev 0.293 ± 0.016 0.225 ± 0.008 0.246 ± 0.01 0.784 ± 0.201 0.255 ± 0.016 0.251 ± 0.021 0.257 ± 0.060 0.239 ± 0.071 0.787 ± 0.188 0.228 ± 0.047 0.231 ± 0.012 0.247 ± 0.036 0.225 ± 0.054 0.866 ± 0.155 0.235 ± 0.043 0.230 ± 0.036 0.281 ± 0.103 0.239 ± 0.088 0.907 ± 0.161 0.255 ± 0.042 Z-avg diam. ± st. dev (nm) 113 ± 11 159 ± 7 162 ± 5 1959 ± 786 155 ± 8 123 ± 17 113 ± 7 120 ± 17 1140 ± 590 120 ± 14 118 ± 12 114 ± 10 120 ± 17 1558 ± 1302 121 ± 24 119 ± 13 120 ± 15 122 ± 19 4856 ± 3625 118 ± 13 A similar overall trend was seen in the polydispersity index of the LNPs (Table 1 and Supplementary Fig. S2), where the average PDI of LNPs increased significantly to ∼0.9 when LNPs were thawed from storage at −80 °C, versus a baseline of ∼0.3 when freshly prepared. The zeta-potential of the LNPs also increased significantly from +3 mV to ∼24 mV after storage at −80 °C, suggesting reorganization of the particle surface composition (Supplementary Fig. S3). On the other hand, retention of encapsulated RNA by the LNPs showed a different trend (Supplementary Fig. S4); while LNP-RNA that were freshly prepared, stored at −20 °C, −80 °C, and − 200 °C maintained an average encapsulation efficiency of approximately 90%, particles that were stored at 4 °C displayed a significant drop to below 70% (p < 0.001), with an increase in RNA detectable outside of the particles (p < 0.001). Overall, temperature seemed to play a more dominant role in determining structural retention of replicon-loaded LNPs over sucrose concentrations in the 0–30% w/v range. As storage in 0% w/v sucrose seemed to maintain the overall physical integrity of the particles despite a slight shift in the hydrodynamic size, we aimed to determine whether the presence of sugar was necessary for preservation of vaccine potency in vivo. To this end, we loaded LNPs with repRNA encoding for a stabilized HIV Env SOSIP trimer immunogen (termed N332-GT2) designed for priming B cells targeting the N332 supersite of the HIV Env spike [44]. The vaccine particles were stored in PBS containing 0%, 5%, or 10% w/v sucrose for a week at −20 °C, then thawed and injected i.m. into mice, and serum antibody titers were evaluated 4 weeks later by ELISA (Fig. 1 ). Overall, there was a trend for increased antibody production with increasing concentrations of sucrose: While LNPs stored in PBS with 0% w/v sucrose (Fig. 1a) were able to induce antibody production against the immunogen, the level was significantly lower than that of freshly prepared vaccines (p = 0.0118) and the vaccine stored with 10% w/v sucrose (p = 0.0185, Fig. 1d). Storage in 5% w/v sucrose elicited antibody titers that were not statistically significantly different from that of the fresh vaccines (p = 0.1069) but showed a trend toward a weaker response in some animals (Fig. 1b). However, animals vaccinated with LNP-RNAs stored in 10% w/v sucrose generated serum antibody dilution curves that overlapped well with those of mice vaccinated with freshly prepared LNP-RNAs, with no statistically significant difference (p = 0.9959, Fig. 1c, d).Fig. 1 Effects of buffer and sucrose concentration on storage of lipid nanoparticle vaccines. (a-d) Groups of balb/C mice (n = 5 animals/group) were immunized i.m. with 1 μg RNA in each leg using LNPs that had been stored under the indicated conditions at −20 °C for 7 days prior to vaccination. Shown are raw ELISA absorbances vs. serum dilution curves for each animal for LNP-RNA in PBS containing: (a) 0% w/v sucrose; (b) 5% w/v sucrose; and (c) 10% w/v sucrose. The area under the curve (AUC) of the ELISA absorbance vs. dilution curves are shown in (d) as means ± s.e.m. Statistics represent one-way ANOVA and Tukey's HSD Test (ns = not significant; , p < 0.05). Fig. 1 As we observed the lowest PDI measurements in both PBS and TBS, and the most reliable antibody responses storing in 10% w/v sucrose, we decided to move forward using this concentration of sucrose for subsequent studies. 3.2 Effect of buffer on the in vivo performance of LNP-RNAs Next, we carried out measurements of in vivo RNA expression for LNPs stored under different conditions. LNPs were prepared carrying repRNA encoding for firefly luciferase, and the LNP-RNA were dialyzed in 10% w/v sucrose in PBS or TBS. The LNPs were then stored at 4 °C, −20 °C, −80 °C, or − 200 °C (liquid nitrogen) for 7 days prior to thawing for injection. On the day of injection, fresh samples were synthesized and dialyzed against 10% w/v sucrose in PBS or TBS. LNPs were administered i.m. into the left and right gastrocnemius muscles of mice, followed by longitudinal bioluminescence imaging of luciferase expression using an IVIS Spectrum whole-animal imaging system (Fig. 2a-c). As expected from our prior studies of this replicon system [48], luciferase expression rapidly climbed for ∼7–10 days, then slowly decayed toward baseline after ∼30 days. However, in alignment with the particle distribution data shown in Fig. 1, LNPs stored at −80 °C in either PBS or TBS exhibited 10 to 100-fold lower peak luciferase signals, indicating significantly diminished RNA delivery (p peak = 0.0003). LNPs stored at other temperatures in PBS and TBS generally produced approximately 2 to 4-fold lower signal compared to the freshly prepared particles at the peak timepoint of day 7 post-injection, despite having demonstrated particle distributions similar to the fresh samples. In addition, samples stored at −200 °C showed an early decay in expression between days 15 and 20. The preparation that performed most closely to the fresh sample was LNPs prepared in 10% w/v sucrose in PBS and stored at −20 °C (p peak = 0.8777), suggesting this condition may provide optimal retention of LNP structure and function.Fig. 2 In vivo transfection and antibody titer response following administration of LNPs loaded with RNA stored under different conditions. (a-c) Groups of balb/c mice (n = 3 animals/group averaged across a total of 6 legs/group) were injected i.m. in both the left and right gastrocnemius muscles with 1 μg replicon RNA in LNPs stored in 10% sucrose under the indicated conditions. (a) shows photographs of mice at day 6 post-injection with luciferin channel overlay and a log-scale gradient in radiance (p/s/cm2/sr). Luciferase reporter signals over time of vaccines in (b) PBS; or (c) TBS are plotted, where dotted lines indicate background signal of untreated mice; shown are means ± standard deviation (n = 12). Statistics represent two-way ANOVA and Tukey's HSD Test (, p < 0.05; , p < 0.01; , p < 0.001). (d-m) Groups of balb/C mice (n = 4 animals/group) were immunized i.m. in each leg with 1 μg RNA loaded in LNPs that had been stored under the indicated conditions for 7 days prior to vaccination, and serum antibody responses were quantified by ELISA assay conducted on mouse sera collected at 4 weeks post-vaccination. Shown are raw ELISA signal vs. serum dilution curves for each animal for PBS- (d-g) and TBS- (i-l) stored samples. Area-under-the-curve (AUC) values for each absorbance vs. dilution data set are shown for PBS- (h) and TBS- (m) stored samples with means ± s.e.m. Statistics represent one-way ANOVA and Tukey's HSD Test (ns = not significant; **, p < 0.0001). Fig. 2 We next assessed the immunogenicity of LNP-replicon vaccines that were loaded with repRNA encoding the N332-GT2 HIV immunogen, and stored in 10% sucrose in PBS or TBS for 7 days at 4 °C, −20 °C, −80 °C, or − 200 °C (liquid nitrogen). Mice were immunized i.m. with recovered LNPs, and serum antibody responses were evaluated 4 weeks later by ELISA (Fig. 2d-m). Interestingly, vaccines stored either at 4 °C or − 80 °C showed a dramatic decrease in antibody responses compared to the other storage conditions or fresh LNPs (Fig. 2d, f, i and k, p < 0.0001). By contrast, PBS- and TBS-prepared vaccines stored at −20 °C (Fig. 2e and j, p -20 °C /PBS = 0.9113; p -20 °C /TBS = 0.5545) and − 200 °C (Fig. 2g and l, p -200 °C /PBS = 0.8791; p -200 °C /TBS = 0.5431) induced antibody titers that were not statistically different from freshly prepared vaccines. While very similar in outcome, we narrowed our focus to analysis of LNPs prepared in PBS for downstream analyses, rather than TBS based on the luminescence reporter and antibody response statistics (Fig. 2h and m). 3.3 Qualitative assessment of LNP stability While dynamic light scattering (DLS) offers quantitative information about particle size distribution and hydrodynamic size, the information tends to be skewed toward larger particles or aggregates in polydisperse samples [[49], [50], [51]]. To gain further insights into particle structure, we next carried out transmission electron microscopy (TEM) and cryogenic electron microscopy (cryo-EM) imaging of samples recovered from different storage temperatures. Both the TEM and cryo-EM images show relatively monodispersed LNPs of approximately 45 nm in diameter in the freshly prepared samples and those stored at 4 °C or − 20 °C for a week (Fig. 3a-c and f-h). However, samples thawed after storing at −80 °C or − 200 °C (flash frozen in liquid nitrogen) showed significant aggregation and/or fusion of LNPs into large structures (Fig. 3d, e and i, j).Fig. 3 Cryogenic electron microscope (Cryo-EM; top row) and transmission electron microscope (TEM; bottom row) images of LNPs stored for 7 days. LNPs freshly prepared or stored at indicated temperatures in PBS containing 10% w/v sucrose for 7 days followed by thawing at 25 °C were imaged: (a,f) fresh synthesis; (b,g) 4 °C; (c,h) -20 °C; (d.i) -80 °C; and (e,j) -200 °C. TEM images were obtained with negative stain using 1% phosphotungstic acid (PTA). Fig. 3 3.4 Immunogenicity of LNP-replicon vaccines stored at different temperatures To gain further insight regarding storage effects on LNP-RNA vaccine efficacy, we evaluated additional readouts of the immune response following immunization with freshly prepared particles vs. LNPs stored at different temperatures. To this end, LNPs were loaded with repRNA encoding the N332-GT2 HIV immunogen, stored in 10% w/v sucrose in PBS at either 4 °C, −20 °C, −80 °C, −200 °C for 7 days, and then thawed and administered to balb/C mice alongside freshly prepared samples. At week 2 post-injection, mice were sacrificed and popliteal lymph nodes (draining nodes from the gastrocnemius injection site) were harvested for evaluating germinal center (GC) responses (Fig. 4a-e) and splenocytes were collected for assessing T cell responses using enzyme-linked immune absorbent spot (ELISpot) (Fig. 4f). Flow cytometric analyses of cells recovered from the draining popliteal lymph nodes revealed that while vaccines stored at 4 °C, −20 °C, and − 200 °C induced prominent GC B cell responses, −80 °C stored vaccines did not elicit a response statistically different from naïve control animals (p = 0.9619, Fig. 4a and c). Interestingly, despite inducing GC B cell differentiation, vaccines stored at 4 °C or − 200 °C failed to prime a meaningful population of antigen-specific GC B cells that could bind to recombinant HIV Env trimer probes, and only vaccines stored at −20 °C elicited a strong antigen-specific GC B cell response matching that elicited by freshly-prepared vaccines (Fig. 4b and d). Follicular helper T cell (Tfh) responses were modest and not statistically different among any of the groups (Fig. 4e). In contrast to the GC data, IFN-γ-producing antigen-specific T cell responses in the spleen were induced by all of the vaccines, with modest differences between groups (Fig. 4f and Supplementary Fig. S5). Based on these results, we selected the −20 °C freezing as the optimum storage condition for our LNP-RNA vaccine formulation.Fig. 4 Evaluation of storage temperatures on vaccine immunogenicity. Mice were immunized for GC assay of popliteal lymph nodes and ELISpot of splenocytes at 2-weeks post-vaccination. Gating for GC B cells are shown in (a) and antigen-specific B cells are shown in (b). Shown are frequency of total GC B cells (c), antigen-specific B cells (d), follicular helper T cells (e), and number of spots per 106 cells (f). Bar graphs are geometric means ± s.e.m. Statistics represent one-way ANOVA and Tukey's HSD Test (ns = not significant; , p < 0.05). Fig. 4 We also assessed endpoint titers of diverse mouse antibody isotypes (IgG1, IgG2a, IgG2b, IgG3, and IgM) to detect any differences in antibody class switching elicited by this optimal storage condition. While the low overall antibody titer induced by the 4 °C stored vaccines rendered near background levels of other Ig isotypes, the −20 °C stored vaccines produced IgG1, IgG2a, IgG2b, IgG3, and IgM levels that were not statistically different from those of the freshly prepared vaccine (Supplementary Fig. S6). Lastly, we investigated whether LNPs thawed from different storage conditions elicit different inflammatory responses at the injection site, by profiling cytokine production in the muscle at 24 h post-vaccination (Supplementary Fig. S7). Overall, we found similar increases in IFN-γ, KC, MCP-1, RANTES, IP-10, IL-10, IFN-β, IFN-α, and IL-6 levels in the muscle following immunization with vaccines that were freshly prepared, stored at −20 °C, and stored at −200 °C. In contrast, vaccines that were stored at 4 °C or − 80 °C show more muted responses. 3.5 Shelf-life of frozen LNPs at −20 °C and thawed LNPs at 4 °C We next evaluated the stability of LNPs over a more extended period of storage time. LNPs were loaded with repRNA encoding firefly luciferase, and stored for 7 or 30 days in 10% w/v sucrose in PBS at −20 °C. Then, we administered the thawed particles or a freshly synthesized batch i.m. in groups of mice and evaluated bioluminescence signals in the muscles over 30 days by IVIS imaging. As shown in Fig. 5a and Supplementary Fig. S8, there was no statistical significance between freshly prepared batch and stored batches (p > 0.2). Next, we prepared LNPs loaded with replicons encoding the N332-GT2 immunogen, and stored them for 7, 14, or 30 days before thawing for intramuscular injection in mice. Mice were then retro-orbitally bled six-weeks post-vaccination for ELISA quantification of antibody titer in the sera. Vaccines stored out to 30 days elicited equivalent antibody responses against the HIV immunogen as the freshly prepared vaccines (Fig. 5b-e).Fig. 5 Shelf-life of LNP-RNA vaccines stored long-term in frozen or thawed states. (a) Groups of balb/c mice (n = 3 animals/group averaged across a total of 6 legs/group) were injected i.m. in both the left and right gastrocnemius muscles with 1 μg replicon RNA in LNPs stored in 10% w/v sucrose at −20 °C for indicated durations. Shown are luciferase reporter signals over time; dotted lines indicate background signal of untreated mice, shown are means ± standard deviation. (b-e) balb/c mice were i.m. vaccinated in both the left and right gastrocnemius muscles with 1 μg replicon RNA in LNPs that were recovered from indicated duration of storage at −20 °C: (b) 7 days; (c) 14 days; (d) 30 days. (f-j) balb/C mice were vaccinated i.m. (1 μg LNP-RNA per animal, 5 animals/group) with vaccines that had been thawed from −20 °C and refrigerated at 4 °C for: (f) 1 day; (g) 7 days; (h) 14 days; (i) 30 days. Shown are serum IgG dilution curves of individual mouse at 4-weeks post-vaccination. Error bar represents standard error mean in n = 5. Statistics represent one-way ANOVA and Tukey's HSD Test (ns = not significant; , p < 0.01; , p < 0.0001). Fig. 5 Once we validated that LNP-RNA vaccines in 10% w/v sucrose in PBS can be stored for at least 30 days at −20 °C without losing potency, we then investigated the shelf-life of the LNPs when stored under refrigeration (4 °C) post-thawing. This experimental setup simulates a clinical setting, in which frozen vaccines are thawed for patient dosing, and remaining doses are stored at 4 °C for dosing at another time. To this end, LNPs loaded with repRNA encoding the N332-GT2 immunogen were prepared in 10% w/v sucrose in PBS were stored at −20 °C for 7 days, then thawed at room temperature briefly before being placed in the refrigerator at 4 °C for 7, 14, or 30 days. The thawed LNPs at 4 °C and a freshly prepared set of LNP vaccines were intramuscularly administered to mice. Mouse sera were collected at 4-weeks post-vaccination for ELISA quantification of antibody titer. As shown in Fig. 5f-j, there is a strong negative correlation between vaccine-induced antibody titer and increasing storage time at 4 °C post-thawing from −20 °C. A single day of storage at 4 °C post-thawing generates strong antibody titers that are statistically not different from that of the fresh sample, although already trending downward (Fig. 5f; p 1d = 0.4615). LNPs kept for 7 days post-thaw failed to seroconvert 2 out of 5 animals (Fig. 5g; p 7d = 0.0014), and this decay in antibody responses continued with further 4 °C storage for 14 or 30 days (Fig. 5h-i). 3.6 Lyophilization of LNP-RNA vaccines While we have established a reliable freezing protocol for long-term storage of LNP-formulated replicon vaccines, lyophilization to keep vaccines in a dry powder form is another clinically-relevant storage strategy. Dry solids simplify aseptic handling procedures, decrease sample weight and volume for easier shipping, improve stability, and allow easy dose adjustments by dissolution at desired concentrations [[52], [53], [54]]. However, freeze drying has proven to be challenging for LNP-RNA formulations [29,35,39]. To determine key factors that affect stability of lyophilized LNP-RNA particles, we selected three parameters to test in lyophilization: freezing temperature, cryoprotectant concentration, and sample concentration. To this end, the formulations were frozen, lyophilized, and re-hydrated for DLS assessment of particle distribution and hydrodynamic diameter. We tested initial freezing at −20 °C along with −80 °C and flash freezing at −200 °C using liquid nitrogen, which are the more commonly used freezing temperatures for lyophilization. Particles that were lyophilized and resuspended after being frozen at −20 °C were able to retain a polydispersity index (PDI) of 0.272, whereas those of the −80 °C and − 200 °C groups showed aggregation with PDI reaching 0.710 (−80 °C) and 0.517 (−200 °C) (Fig. 6a). The size of the particles also increased from an average hydrodynamic diameter (by intensity distribution) of 421 nm (−20 °C) to 877 nm (−80 °C) and 1219 nm (−200 °C). While none of the freezing temperatures were able to retain the correct Z-average size of approximately 90 nm by intensity distribution on DLS (equivalent to approximately 45 nm by number distribution and cryo-EM), we observed a trend toward smaller particles with increasing temperature. The −20 °C stored vaccines displayed an average hydrodynamic diameter of 421 nm, but was statistically not different from the fresh batch (p = 0.1858). We next tested the effects of cryoprotectant concentration on lyophilization of LNP-RNA (Fig. 6b). The vaccine particles were prepared in PBS, 10% w/v sucrose, or 30% w/v sucrose, and frozen at −20 °C prior to undergoing lyophilization. The dry particles were then rehydrated in deionized water for DLS assessment. Here, we found a striking difference between samples that were lyophilized in PBS versus samples that were cryoprotected with sucrose. Compared with freshly prepared LNPs that had an average PDI of 0.233, the PBS samples presented an average PDI of 0.638, the 10% w/v sucrose samples 0.165, and the 30% w/v sucrose samples 0.235, indicating a more monodisperse population of particles in the presence of sucrose. Moreover, the average hydrodynamic size of the particles in PBS significantly increased to an average of 613 nm compared to the fresh samples, which average at 96 nm (p < 0.0001). Lyophilization in 10% w/v sucrose led to a slight increase in the average particle size to 144 nm, but this difference was not statistically significant (p = 0.7957). Lastly, the 30% w/v sucrose samples averaged at 94.0 nm, which was statistically the closest to the fresh samples (p > 0.9999). As a final parameter, we assessed the effect of LNP-RNA concentration on lyophilization. Here, we froze and lyophilized the same mass of LNP-RNA in increasing volumes of 10% w/v sucrose in PBS at −20 °C, and resuspended the lyophilized particles in equal volumes for DLS assessment. The 10% w/v sucrose concentration was selected over the 30% w/v sucrose to accommodate for the final concentration of sucrose appropriate for in vivo administration after resuspension; in fact, the presence of excessive amounts of sucrose in low resuspension volumes resulted in viscous consistency in the hydrated vaccine that was ill-suited for downstream analyses and in vivo administration. Fig. 6c shows that while there were no statistically significant differences in the formulations (p 50 μg /uL = 0.2223; p 20 μg /uL = 0.0688; p 10ug/uL = 0.9319; p 3.3 μg /uL = 0.4302), there was a trend toward lower PDI and hydrodynamic size with decreasing LNP concentration.Fig. 6 Evaluation of lyophilized LNP-RNA formulations. (a-c) LNP-RNA was synthesized (n = 3 samples/condition), lyophilized under indicated conditions, then rehydrated and analyzed by DLS. (a) LNPs were frozen at the indicated temperatures in PBS without sucrose. (b) LNPs were frozen at −20 °C in indicated buffers prior to lyophilization. (c) LNPs at the indicated concentrations (in terms of RNA amount) were frozen at −20 °C in PBS with 10% sucrose prior to lyophilization. Error bars represent standard deviation, and statistics indicate Two-way ANOVA and Tukey's HSD Test (ns = not significant; , p < 0.001; **p < 0.0001). Black columns show PDI on the left y-axis, while the colored bars show the average hydrodynamic diameter from intensity distribution on the right y-axis. (d-e) IVIS bioluminescence imaging in mice that were administered i.m. freshly prepared or lyophilized LNPs loaded with RNA encoding luciferase. Lyophilized LNPs were placed in 10% w/v sucrose and frozen at a concentration of 3.3 ng/uL in −20 °C prior to undergoing lyophilization. Shown are representative photograph/false-color overlays (d) and total bioluminescence signal 1 day post-LNP administration (e). Statistics represent unpaired two-tailed t-test with , p < 0.05. Fig. 6 Finally, we intramuscularly administered freshly prepared and lyophilized LNPs loaded with repRNA encoding for firefly luciferase to evaluate the in vivo transfection efficiency using the IVIS bioluminescence imaging system (Fig. 6d-e). For this study, we selected the lyophilization protocol that was found to best maintain the particle structure – LNPs were placed in 10% w/v sucrose and frozen at a concentration of 3.3 ng/μL in −20 °C prior to undergoing lyophilization. At day 1 post administration, both freshly prepared and lyophilized LNP-replicons showed strong luciferase expression, but the signal of lyophilized particles was ∼50% that of the freshly prepared samples (p = 0.0191). In summary, we investigated three parameters that contribute to maintenance of size and distribution of lyophilized particles. By optimizing and combining these factors, we were able to improve the retention of LNP-RNA structure and dispersity post-lyophilization and resuspension. 4 Discussion The present study was conducted with the intention of shedding light on physical changes occurring with self-replicating RNA-loaded lipid nanoparticles (LNP-RNA) in commonly employed storage conditions (by cryoprotectant concentration, buffer type, and maintenance temperature), and how these differences relate to in vivo transfection and vaccine potency. The particular LNP formulation that we employed in this study includes two ionizable lipids (TT3 and DLin-MC3-DMA), which we found gave effective transfection in the muscle and humoral immune responses in combination, and 5 mol% of DMG-PEG to supplement the stability of the near-neutrally charged LNPs. Using this formulation, we found that both phosphate and tris buffers were suitable for storing LNPs, and that sucrose was an important cryoprotectant for the maintenance of the structural integrity, physical stability, or activity of the repRNA payload. The dominating factor was the storage temperature. The optimal sucrose concentration was found to be 10% w/v (Fig. 1), similar to other published work on cryoprotection of biological materials [[55], [56], [57], [58], [59]]. It is believed that for a set material concentration, there is a minimum number of sugar molecules required to sufficiently disrupt interactions between the polar water molecules (by formation of water-sucrose hydrogen bonds [60]) to slow down the freezing rate, and form larger ice crystal with minimal ice-water interface wherein the materials tend to localize [61,62]. In fact, rather than being homogenously dispersed throughout the sample, sucrose is reported to form a thin sheet in a nonfrozen state along the surface of lipid bilayers, and keeps materials from making direct contact with ice [63,64]. Empirically, 10% w/v sucrose enabled these processes to effectively promote retention of the particles' structure without aggregation. Dynamic light scattering (DLS) is a technique that is commonly used to assess particle stability and hydrodynamic size distribution. However, relying solely on scattering data provided by DLS can be misleading in terms of accuracy, particularly with polydisperse sub-100 nm particles where neither the intensity nor number distributions offer accurate information [[49], [50], [51]]. Thus, we also carried out transmission electron microscopy (TEM) and cryogenic electron microscopy (cryo-EM) to fully capture the physical state of the LNPs upon thawing from storage. Interestingly, storage at −20 °C was able to maintain LNP structural integrity, while irreversible aggregation was observed following thawing of particles from −80 °C. But this seems to be the case only for repRNA; when the same LNPs were loaded with mRNA and stored, the particles retained their structural integrity at all tested temperatures (Supplementary Fig. S1). Other studies have also reported LNP-mRNA formulations that are stable at these lower temperatures (e.g., BNT162b2's − 70 °C storage of COVID-19 vaccine [7,28,29]). We hypothesize these differences between the behavior of LNPs carrying repRNA vs. mRNA may reflect potential differences in the packing structure of the RNA molecules with the lipids in the nanoparticle at a molecular level and/or differences in particle stability associated with differences in the N:P ratios used for mRNA vs. repRNA, leading to different sensitivities to the freezing/thawing process. Alphavirus-based self-replicating RNA is ∼10-fold larger than typical mRNA used in vaccines, and may organize with the LNP core in a distinct manner from shorter mRNAs. Further, we employed a 2:1 N:P ratio for effectively packaging repRNA in LNPs with ∼90% RNA encapsulation efficiency and effective in vivo delivery, whereas literature reports the same N:P ratio can reduce the encapsulation efficiency of mRNA molecules down to only 40% [65]). Additionally, −80 °C offers an intermediate between −20 °C slow freezing and − 200 °C flash freezing. While cooling from room temperature to −20 °C is expected to induce slow but short cooling that yields large ice crystals dispersed in unfrozen liquid and sheets of sucrose (liquid + ice phase), −200 °C flash freezing is expected to instantaneously form solid ice with little to no ice nucleation (solid phase) [66,67]. In contrast, freezing down to −80 °C from room temperature is expected to have an accelerated cooling rate compared to −20 °C, as water is able to freeze without nucleation below −40 °C [67]. Thus, the resulting state will have a mix of smaller ice crystals intermixed with glass phase in which the particles and sucrose molecules are concentrated. Smaller ice crystals have greater interfacial area compared to large crystals, which is not favorable for cryopreservation [62]. Moreover, concentration of particles in the glass phase may further facilitate aggregation of unstable particles [68]. Thus, we hypothesize that repRNA-loaded LNPs are more vulnerable to aggregation during freezing to or thawing from −80 °C due to a mix of the particles' unique structural properties when loaded with repRNA over mRNA and the type of ice phase that is formed at this temperature and cooling rate. We found that maintenance of LNP structure (as determined by light scattering and morphological analysis by TEM) is not necessarily a faithful indicator of in vivo functionality post storage. For example, storage of LNP-RNA at 4 °C was able to retain the physical state of the particles based on DLS and electron microscopy results compared with freshly prepared samples (Table 1 and Figs. 3b and g), but failed to function as reliably when it came to activity studies using either the reporter repRNA or the antigen-encoding repRNA (Fig. 2, Fig. 4). This outcome indicates that while 4 °C storage is sufficient to keep the LNP delivery vehicles intact, the encapsulated repRNA molecules are negatively affected [29]. This idea is further supported by results from a test of post-thaw shelf life in Fig. 5f-j, where vaccines that were stored effectively and thawed for 4 °C storage showed decreased vaccine efficacy beyond one day at 4 °C. A study carried out to calculate the theoretical cleavage rate of the RNA molecules predicted that an mRNA molecule of 4000 nucleotides would have a half-life of 941 days when stored at 5 °C under RNAse-free conditions, but that longer repRNAs would be more susceptible to hydrolytic cleavage [69]. While such reactions are not a concern at freezing temperatures, prevention of RNA degradation is a must in refrigeration despite the protective coating the LNPs may offer RNA molecules; thus, it may be possible that the decreased and variable efficacies seen in our LNP-RNA samples kept at 4 °C may have been due to such degradative reactions [31]. Indeed, we found a significant amount of RNA leakage only from the LNPs that were stored at 4 °C for a week (Supplementary Fig. S4). Another observation we noted is that while 4 °C storage is able to retain a relatively high transfection (Fig. 2b and c), its downstream efficacy in generating humoral responses is significantly reduced (Fig. 2i). It may be possible that the RNA sequence has an influence on transfection, especially in the long repRNAs where differences in nucleotide sequence may affect its packing within LNPs, and in turn its stability during freezing and thawing. Moreover, Supplementary Fig. S7 shows that vaccines stored at 4 °C generate decreased cytokine production in the muscle compared to vaccines that are freshly prepared or stored at −20 °C or − 200 °C; thus, we believe that LNPs thawed from 4 °C may have reduced adjuvant activity despite maintaining sufficient ability to transfect cells. We hypothesize RNA leakage out of the particles following 4 °C storage may lead to changes to the internal packing structure of the LNPs to a state that is less inflammatory, and/or decreased repRNA delivered into cells at the injection site may have led to a diminished innate immune response. In contrast to 4 °C storage, LNPs flash frozen to −200 °C performed moderately well in terms of in vivo gene expression (Fig. 2), although this storage regimen showed severe aggregation by electron microscopy (Fig. 3e and j). This aggregation may potentially be the result of sample preparation steps in electron microscopy – such as drying on the grid for TEM or undergoing another round of flash freezing for cryo-EM. This fragility or sensitivity of flash frozen LNP-RNA to post-thaw handling may suggest unknown material properties that we do not observe for freshly prepared, 4 °C stored, or − 20 °C stored samples. Coupled with the fact that liquid nitrogen maintenance for cold-chain transport is impractical, flash freezing does not appear to be a suitable storage option. Overall, LNP-RNA vaccine in 10% w/v sucrose in PBS was maintained well both physically (Fig. 1, Fig. 3c and h) and functionally (Fig. 2, Fig. 4, Fig. 5) in −20 °C storage for at least 30 days. This temperature offers a feasible solution for cold-chain transport of vaccines, as demonstrated by Moderna for their COVID-19 vaccine (mRNA-1273) [27,33,34]. For our LNP-RNA system, −20 °C may be a sufficiently high temperature to offer an ideal ice nucleation temperature and a slow cooling rate to help retain particle structure, while also being a sufficiently low temperature to inhibit RNA degradation by hydrolytic cleavage events. A more ideal solution would be the lyophilization of LNP-RNA vaccines to a dry powder form that is well maintained at room temperature. However, this endeavor has proven to be challenging, with little to no successful lyophilization of LNP-repRNA formulations yet reported in the literature. Our results in Fig. 6 show that while optimization of several parameters is able to improve the structural maintenance of the formulations, in vivo transfection fully equivalent to fresh LNPs remains difficult to achieve, which echoes findings from another prior study of LNPs carrying mRNA [39]. It may be possible that freeze-drying the vaccine leaves RNA molecules more vulnerable to hydrolytic degradation during resuspension or to interactions with serum proteins in vivo. On the other hand, Ball et al. flash froze LNPs loaded with siRNA for 30 min in liquid nitrogen prior to lyophilization, and found that reconstitution of the lyophilized LNPs in deionized water substantially reduced their in vitro gene silencing capability to 35%, whereas the addition of 22% ethanol during reconstitution successfully maintained gene silencing at a similar level to that of freshly prepared LNPs (80% vs 90%) [35]. Though recovery was successful, administration of formulations in ethanol or an added process of removing the ethanol prior to administration would be difficult to translate to clinical settings [29]. Nonetheless, successful lyophilization of LNP-RNA formulations may require greater control over freezing and drying temperatures than is generally available in the basic benchtop freeze-dryers that are used in academic labs. In industry, Moderna has reported that their cytomegalovirus vaccine (mRNA-1647) currently in phase 2 clinical trial can be successfully lyophilized and stored for over 18 months at 5 °C [29,70,71]. In addition, Arcturus Therapeutics in collaboration with Duke-NUS Medical School in Singapore have also reported successful lyophilization of their repRNA-encapsulating LNP vaccine (ARCT-021) [29,72]. Unfortunately, details of the processes are not publicly available. More recently, an effective lyophilization method for an mRNA vaccine was reported to stably store them at 4 °C in dry powder form for out to 24-weeks using an optimized freezing step with two separate sublimation and desorption dry cycles [73]. 5 Conclusions In light of the COVID-19 pandemic, long-term storage and cold-chain transport of vaccines have become a critical step in the successful translation and use of RNA-loaded lipid nanoparticles (LNP-RNA). At the same time, we came to realize that there is very scarce information on the relationship between LNP-RNA's physical properties, storage conditions, and vaccine efficacy. Here, we investigated what physical changes the vaccines undergo in different storage conditions (by concentration of cryoprotectant, type of buffer, and storage temperature), and how those changes relate to the vaccine efficacy in vivo using both RNA encoding for reporter proteins (e.g. luciferase) and for an actual HIV immunogen. Ultimately, we found that LNP-RNA vaccines are stably stored in 10% w/v sucrose in PBS at −20 °C for at least 30 days. Further, we found that replicon-carrying LNPs could also be lyophilized and retain substantial in vivo bioactivity. Funding This work was supported by the NIH (awards AI161297, AI144462, and AI048240 to DJI, and CA265706 to DJI and YD), the Marble Center for Nanomedicine, and the 10.13039/100012802 Ragon Institute of MGH, MIT, and Harvard. DJI is an investigator of the Howard Hughes Medical Institute. Author contributions BK and DJI conceptualized the study and wrote the manuscript. BK also designed experiments, obtained experimental data, and analyzed results. RRH obtained experimental data, and contributed to data analyses and manuscript writing. TR, DY, NL, WA, MBM, MC, BL, YZ, and YZ contributed to obtaining experimental data and editing of the manuscript. CRediT authorship contribution statement Byungji Kim: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Ryan R. Hosn: Validation, Investigation, Writing – original draft, Writing – review & editing. Tanaka Remba: Investigation, Writing – review & editing. Dongsoo Yun: Investigation, Writing – review & editing. Na Li: Investigation, Writing – review & editing. Wuhbet Abraham: Investigation, Writing – review & editing. Mariane B. Melo: Investigation, Writing – review & editing. Manuel Cortes: Investigation, Writing – review & editing. Bridget Li: Investigation, Writing – review & editing. Yuebao Zhang: Resources, Writing – review & editing. Yizhou Dong: Resources, Writing – review & editing. Darrell J. Irvine: Conceptualization, Resources, Supervision, Writing – review & editing, Funding acquisition. Declaration of Competing Interest The authors declare no conflicts of interest. Appendix A Supplementary data Supplementary figures Image 1 Data availability Data will be made available on request. Acknowledgements We thank the Koch Institute's Robert A. Swanson (1969) Biotechnology Center for technical support. We also thank Dr. William R. Schief from The Scripps Research Institute (TSRI) for providing the sequence for the HIV immunogen N332-GT2 used in the study. Biorender was used for generation of graphical abstract. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jconrel.2022.11.022. ==== Refs References 1 Dimitriadis G.J. Translation of rabbit globin mRNA introduced by liposomes into mouse lymphocytes Nature 274 1978 923 924 10.1038/274923a0 683336 2 Fyfe I. Treatment success in hereditary transthyretin amyloidosis Nat. Rev. Neurol. 14 2018 509 10.1038/s41582-018-0048-1 30030478 3 Semple S.C. Akinc A. Chen J. Sandhu A.P. Mui B.L. Cho C.K. Sah D.W. Stebbing D. Crosley E.J. Yaworski E. Hafez I.M. Dorkin J.R. Qin J. Lam K. Rajeev K.G. Wong K.F. Jeffs L.B. Nechev L. Eisenhardt M.L. Jayaraman M. Kazem M. Maier M.A. Srinivasulu M. Weinstein M.J. Chen Q. Alvarez R. Barros S.A. De S. Klimuk S.K. Borland T. Kosovrasti V. Cantley W.L. Tam Y.K. Manoharan M. Ciufolini M.A. Tracy M.A. de Fougerolles A. MacLachlan I. Cullis P.R. Madden T.D. Hope M.J. Rational design of cationic lipids for siRNA delivery Nat. Biotechnol. 28 2010 172 176 10.1038/nbt.1602 20081866 4 Akinc A. Zumbuehl A. Goldberg M. Leshchiner E.S. Busini V. Hossain N. Bacallado S.A. Nguyen D.N. Fuller J. Alvarez R. Borodovsky A. Borland T. Constien R. de Fougerolles A. Dorkin J.R. Jayaprakash K.N. Jayaraman M. John M. Koteliansky V. Manoharan M. Nechev L. Qin J. Racie T. Raitcheva D. Rajeev K.G. Sah D.W. Soutschek J. Toudjarska I. Vornlocher H.P. Zimmermann T.S. Langer R. Anderson D.G. A combinatorial library of lipid-like materials for delivery of RNAi therapeutics Nat. Biotechnol. 26 2008 561 569 10.1038/nbt1402 18438401 5 Akinc A. Maier M.A. Manoharan M. Fitzgerald K. Jayaraman M. Barros S. Ansell S. Du X. Hope M.J. Madden T.D. Mui B.L. Semple S.C. Tam Y.K. Ciufolini M. Witzigmann D. Kulkarni J.A. van der Meel R. Cullis P.R. The Onpattro story and the clinical translation of nanomedicines containing nucleic acid-based drugs Nat. Nanotechnol. 14 2019 1084 1087 10.1038/s41565-019-0591-y 31802031 6 Crommelin D.J.A. Anchordoquy T.J. Volkin D.B. Jiskoot W. Mastrobattista E. Addressing the cold reality of mRNA vaccine stability J. Pharm. Sci. 110 2021 997 1001 10.1016/j.xphs.2020.12.006 33321139 7 Oliver S.E. Gargano J.W. Marin M. Wallace M. Curran K.G. Chamberland M. McClung N. Campos-Outcalt D. Morgan R.L. Mbaeyi S. Romero J.R. Talbot H.K. Lee G.M. Bell B.P. Dooling K. The advisory committee on immunization practices’ interim recommendation for use of Pfizer-BioNTech COVID-19 vaccine - United States, December 2020 Morb. Mortal. Wkly Rep. 69 2020 1922 1924 10.15585/mmwr.mm6950e2 8 Oliver S.E. Gargano J.W. Marin M. Wallace M. Curran K.G. Chamberland M. McClung N. Campos-Outcalt D. Morgan R.L. Mbaeyi S. Romero J.R. Talbot H.K. Lee G.M. Bell B.P. Dooling K. The advisory committee on immunization practices’ interim recommendation for use of Moderna COVID-19 vaccine - United States, December 2020 MMWR Morb. Mortal. Wkly Rep. 69 2021 1653 1656 10.15585/mmwr.mm695152e1 33382675 9 Kim B. Park J. Sailor M.J. Rekindling RNAi therapy: materials design requirements for in vivo siRNA delivery Adv. Mater. 31 2019 1903637 10.1002/adma.201903637 10 Hu B. Zhong L. Weng Y. Peng L. Huang Y. Zhao Y. Liang X.-J. Therapeutic siRNA: state of the art Signal Transduct. Target. Ther. 5 2020 101 10.1038/s41392-020-0207-x 32561705 11 Rupaimoole R. Slack F.J. MicroRNA therapeutics: towards a new era for the management of cancer and other diseases Nat. Rev. Drug Discov. 16 2017 203 222 10.1038/nrd.2016.246 28209991 12 Roberts T.C. Langer R. Wood M.J.A. Advances in oligonucleotide drug delivery Nat. Rev. Drug Discov. 19 2020 673 694 10.1038/s41573-020-0075-7 32782413 13 Rinaldi C. Wood M.J.A. Antisense oligonucleotides: the next frontier for treatment of neurological disorders Nat. Rev. Neurol. 14 2017 9 21 10.1038/nrneurol.2017.148 29192260 14 Minnaert A.-K. Vanluchene H. Verbeke R. Lentacker I. Smedt S.C.D. Raemdonck K. Sanders N.N. Remaut K. Strategies for controlling the innate immune activity of conventional and self-amplifying mRNA therapeutics: getting the message across Adv. Drug Deliv. Rev. 176 2021 113900 10.1016/j.addr.2021.113900 34324884 15 Chien K.R. Zangi L. Lui K.O. Synthetic chemically modified mRNA (modRNA): toward a new technology platform for cardiovascular biology and medicine Cold Spring Harb. Perspect. Med. 5 2014 a014035 10.1101/cshperspect.a014035 16 Kim S.C. Sekhon S.S. Shin W.-R. Ahn G. Cho B.-K. Ahn J.-Y. Kim Y.-H. Modifications of mRNA vaccine structural elements for improving mRNA stability and translation efficiency Mol. Cell. Toxicol. 18 2021 1 8 10.1007/s13273-021-00171-4 34567201 17 Sahin U. Oehm P. Derhovanessian E. Jabulowsky R.A. Vormehr M. Gold M. Maurus D. Schwarck-Kokarakis D. Kuhn A.N. Omokoko T. Kranz L.M. Diken M. Kreiter S. Haas H. Attig S. Rae R. Cuk K. Kemmer-Brück A. Breitkreuz A. Tolliver C. Caspar J. Quinkhardt J. Hebich L. Stein M. Hohberger A. Vogler I. Liebig I. Renken S. Sikorski J. Leierer M. Müller V. Mitzel-Rink H. Miederer M. Huber C. Grabbe S. Utikal J. Pinter A. Kaufmann R. Hassel J.C. Loquai C. Türeci Ö. An RNA vaccine drives immunity in checkpoint-inhibitor-treated melanoma Nature 585 2020 107 112 10.1038/s41586-020-2537-9 32728218 18 Gebre M.S. Rauch S. Roth N. Yu J. Chandrashekar A. Mercado N.B. He X. Liu J. McMahan K. Martinot A. Martinez D.R. Giffin V. Hope D. Patel S. Sellers D. Sanborn O. Barrett J. Liu X. Cole A.C. Pessaint L. Valentin D. Flinchbaugh Z. Yalley-Ogunro J. Muench J. Brown R. Cook A. Teow E. Andersen H. Lewis M.G. Boon A.C.M. Baric R.S. Mueller S.O. Petsch B. Barouch D.H. Optimization of non-coding regions for a non-modified mRNA COVID-19 vaccine Nature 601 2021 410 414 10.1038/s41586-021-04231-6 34794169 19 Leppek K. Byeon G.W. Kladwang W. Wayment-Steele H.K. Kerr C.H. Xu A.F. Kim D.S. Topkar V.V. Choe C. Rothschild D. Tiu G.C. Wellington-Oguri R. Fujii K. Sharma E. Watkins A.M. Nicol J.J. Romano J. Tunguz B. Participants E. Barna M. Das R. Combinatorial Optimization of mRNA Structure, Stability, and Translation for RNA-Based Therapeutics 2021 Biorxiv Prepr. Serv. Biology 10.1101/2021.03.29.437587 2021.03.29.437587 20 Foster H. Sharp P.S. Athanasopoulos T. Trollet C. Graham I.R. Foster K. Wells D.J. Dickson G. Codon and mRNA sequence optimization of microdystrophin transgenes improves expression and physiological outcome in dystrophic mdx mice following AAV2/8 gene transfer Mol. Ther.: J. Am. Soc. Gene Ther. 16 2008 1825 1832 10.1038/mt.2008.186 21 L. K Replicon RNA viral vectors as vaccines Nato Adv. Sci. Inst. Se. 4 2016 39 10.3390/vaccines4040039 22 Lundstrom K. Self-replicating RNA viruses for RNA therapeutics Molecules 23 2018 3310 10.3390/molecules23123310 30551668 23 Blakney A.K. Ip S. Geall A.J. An update on self-amplifying mRNA vaccine development Nato Adv. Sci. Inst. Se. 9 2021 97 10.3390/vaccines9020097 24 Pollock K.M. Cheeseman H.M. Szubert A.J. Libri V. Boffito M. Owen D. Bern H. McFarlane L.R. O’Hara J. Lemm N.-M. McKay P. Rampling T. Yim Y.T.N. Milinkovic A. Kingsley C. Cole T. Fagerbrink S. Aban M. Tanaka M. Mehdipour S. Robbins A. Budd W. Faust S. Hassanin H. Cosgrove C.A. Winston A. Fidler S. Dunn D. McCormack S. Shattock R.J. Adams K. Amini F. Atako N.B. Bakri A. Barclay W. Brodnicki E. Brown J.C. Byrne R. Chilvers R. Coelho S. Day S. Desai M. Dorman E. Elliott T. Flight K.E. Fletcher J. Galang J. Gohil J. Gupta A. Harlow C. Hu K. Kalyan M. Lagrue D. Liscano E. Njenga C. Polra K. Powlette D.A. Randell P. Rauchenberger M. Redknap I. Ricamara M. Rogers P. Sallah H. Samnuan K. Schumacher M. Shah Z. Shaw R. Shaw T. Sivapatham S. Slater S. Sorley K. Storch R. Tan E. Tan T. Thielemans L. Whitely S. Valentine C. Varghese J. Vikraman A. Wilkins M. Safety and immunogenicity of a self-amplifying RNA vaccine against COVID-19: COVAC1, a phase I, dose-ranging trial Eclinicalmedicine 44 2022 101262 10.1016/j.eclinm.2021.101262 25 A Trial Evaluating the Safety and Immunogenicity of 3 COVID-19 SARS-CoV-2 RNA Vaccines in Healthy Adults https://clinicaltrials.gov/ct2/show/NCT05037097 2021 26 The ARCT-154 Self-Amplifying RNA Vaccine Efficacy Study (ARCT-154-01) https://clinicaltrials.gov/ct2/show/NCT05012943 2022 27 Hatziantoniou S. Maltezou H.C. Tsakris A. Poland G.A. Anastassopoulou C. Anaphylactic reactions to mRNA COVID-19 vaccines: a call for further study Vaccine 39 2021 2605 2607 10.1016/j.vaccine.2021.03.073 33846043 28 Uddin M.N. Roni M.A. Challenges of storage and stability of mRNA-based COVID-19 vaccines Nato Adv. Sci. Inst. Se. 9 2021 1033 10.3390/vaccines9091033 29 Schoenmaker L. Witzigmann D. Kulkarni J.A. Verbeke R. Kersten G. Jiskoot W. Crommelin D.J.A. mRNA-lipid nanoparticle COVID-19 vaccines: structure and stability Int. J. Pharm. 601 2021 120586 10.1016/j.ijpharm.2021.120586 30 Hassett K.J. Benenato K.E. Jacquinet E. Lee A. Woods A. Yuzhakov O. Himansu S. Deterling J. Geilich B.M. Ketova T. Mihai C. Lynn A. McFadyen I. Moore M. Senn J. Stanton M.G. Almarsson Ö. Ciaramella G. Brito L.A. Optimization of lipid nanoparticles for intramuscular administration of mRNA vaccines Mol. Ther. Nucleic Acids 15 2019 1 11 10.1016/j.omtn.2019.01.013 30785039 31 Brader M.L. Williams S.J. Banks J.M. Hui W.H. Zhou Z.H. Jin L. Encapsulation state of messenger RNA inside lipid nanoparticles Biophys. J. 120 2021 2766 2770 10.1016/j.bpj.2021.03.012 33773963 32 COVID-19 Vaccine Moderna 2021 European Medicines Agency 33 Hou X. Zaks T. Langer R. Dong Y. Lipid nanoparticles for mRNA delivery Nat. Rev. Mater. 6 2021 1 17 10.1038/s41578-021-00358-0 34 EMA Approves New Storage Option for Pfizer-BioNTech Vaccine, Easing Distribution and Storage of Doses across European Union 2021 35 Ball R.L. Bajaj P. Whitehead K.A. Achieving long-term stability of lipid nanoparticles: examining the effect of pH, temperature, and lyophilization Int. J. Nanomedicine 12 2017 305 315 10.2147/ijn.s123062 28115848 36 Muralidhara B.K. Baid R. Bishop S.M. Huang M. Wang W. Nema S. Critical considerations for developing nucleic acid macromolecule based drug products Drug Discov. Today 21 2016 430 444 10.1016/j.drudis.2015.11.012 26674130 37 Muralidhara B.K. Wong M. Critical considerations in the formulation development of parenteral biologic drugs Drug Discov. Today 25 2020 574 581 10.1016/j.drudis.2019.12.011 31926137 38 Gerhardt A. Voigt E. Archer M. Reed S. Larson E. Hoeven N.V. Kramer R. Fox C. Casper C. A Thermostable, Flexible RNA Vaccine Delivery Platform for Pandemic Response 2021 BioRxiv 39 Zhao P. Hou X. Yan J. Du S. Xue Y. Li W. Xiang G. Dong Y. Long-term storage of lipid-like nanoparticles for mRNA delivery Bioact. Mater. 5 2020 358 363 10.1016/j.bioactmat.2020.03.001 32206737 40 Shirane D. Tanaka H. Nakai Y. Yoshioka H. Akita H. Development of an alcohol dilution-lyophilization method for preparing lipid nanoparticles containing encapsulated siRNA Biol. Pharm. Bull. 41 2018 1291 1294 10.1248/bpb.b18-00208 30068880 41 Zhang N.N. Li X.F. Deng Y.Q. Zhao H. Huang Y.J. Yang G. Huang W.J. Gao P. Zhou C. Zhang R.R. Guo Y. Sun S.H. Fan H. Zu S.L. Chen Q. He Q. Cao T.S. Huang X.Y. Qiu H.Y. Nie J.H. Jiang Y. Yan H.Y. Ye Q. Zhong X. Xue X.L. Zha Z.Y. Zhou D. Yang X. Wang Y.C. Ying B. Qin C.F. A thermostable mRNA vaccine against COVID-19 Cell 182 2020 1271 1283 e16 10.1016/j.cell.2020.07.024 32795413 42 Ripoll M. Bernard M.-C. Vaure C. Bazin E. Commandeur S. Perkov V. Lemdani K. Nicolaï M.-C. Bonifassi P. Kichler A. Frisch B. Haensler J. An imidazole modified lipid confers enhanced mRNA-LNP stability and strong immunization properties in mice and non-human primates Biomaterials 286 2022 121570 10.1016/j.biomaterials.2022.121570 43 L. B., L. X., D. B., W. J., M. W. D., S. Y., G. M. K., T. X., D. L. A., K. A. B., D. Y An orthogonal array optimization of lipid-like nanoparticles for mRNA delivery in vivo Nano Lett. 15 2015 8099 8107 10.1021/acs.nanolett.5b03528 26529392 44 Steichen J.M. Lin Y.-C. Havenar-Daughton C. Pecetta S. Ozorowski G. Willis J.R. Toy L. Sok D. Liguori A. Kratochvil S. Torres J.L. Kalyuzhniy O. Melzi E. Kulp D.W. Raemisch S. Hu X. Bernard S.M. Georgeson E. Phelps N. Adachi Y. Kubitz M. Landais E. Umotoy J. Robinson A. Briney B. Wilson I.A. Burton D.R. Ward A.B. Crotty S. Batista F.D. Schief W.R. A generalized HIV vaccine design strategy for priming of broadly neutralizing antibody responses Science 366 2019 10.1126/science.aax4380 45 Li Y. Su Z. Zhao W. Zhang X. Momin N. Zhang C. Wittrup K.D. Dong Y. Irvine D.J. Weiss R. Multifunctional oncolytic nanoparticles deliver self-replicating IL-12 RNA to eliminate established tumors and prime systemic immunity Nat. Can. 1 2020 882 893 10.1038/s43018-020-0095-6 46 Li Y. Teague B. Zhang Y. Su Z. Porter E. Dobosh B. Wagner T. Irvine D.J. Weiss R. In vitro evolution of enhanced RNA replicons for immunotherapy Sci. Rep. UK 9 2019 6932 10.1038/s41598-019-43422-0 47 Wroblewska L. Kitada T. Endo K. Siciliano V. Stillo B. Saito H. Weiss R. Mammalian synthetic circuits with RNA binding proteins for RNA-only delivery Nat. Biotechnol. 33 2015 839 841 10.1038/nbt.3301 26237515 48 Melo M. Porter E. Zhang Y. Silva M. Li N. Dobosh B. Liguori A. Skog P. Landais E. Menis S. Sok D. Nemazee D. Schief W.R. Weiss R. Irvine D.J. Immunogenicity of RNA replicons encoding HIV Env immunogens designed for self-assembly into nanoparticles Mol. Ther. 27 2019 2080 2090 10.1016/j.ymthe.2019.08.007 31515132 49 Malm A.V. Corbett J.C.W. Improved dynamic light scattering using an adaptive and statistically driven time resolved treatment of correlation data Sci. Rep. UK 9 2019 13519 10.1038/s41598-019-50077-4 50 Wishard A. Gibb B.C. Dynamic light scattering - an all-purpose guide for the supramolecular chemist Supramol. Chem. 31 2019 608 615 10.1080/10610278.2019.1629438 34675465 51 Farkas N. Kramar J.A. Dynamic light scattering distributions by any means J. Nanopart. Res. 23 2021 120 10.1007/s11051-021-05220-6 52 Lale S.V. Goyal M. Bansal A.K. Development of lyophilization cycle and effect of excipients on the stability of catalase during lyophilization Int. J. Pharm. Investig. 1 2011 214 221 10.4103/2230-973x.93007 53 Molnar A. Lakat T. Hosszu A. Szebeni B. Balogh A. Orfi L. Szabo A.J. Fekete A. Hodrea J. Lyophilization and homogenization of biological samples improves reproducibility and reduces standard deviation in molecular biology techniques Amino Acids 53 2021 917 928 10.1007/s00726-021-02994-w 34002278 54 Patel S.M. Pikal M.J. Emerging freeze-drying process development and scale-up issues AAPS PharmSciTech 12 2011 372 378 10.1208/s12249-011-9599-9 21347620 55 Rumsey S. Galeano N. Arad Y. Deckelbaum R. Cryopreservation with sucrose maintains normal physical and biological properties of human plasma low density lipoproteins J. Lipid Res. 33 1992 1551 1561 10.1016/s0022-2275(20)41409-9 1431579 56 Wang L. Ma Y. Gu Y. Liu Y. Zhao J. Yan B. Wang Y. Cryoprotectant choice and analyses of freeze-drying drug suspension of nanoparticles with functional stabilisers J. Microencapsul. 35 2018 1 26 10.1080/02652048.2018.1462416 29168930 57 Voci S. Gagliardi A. Salvatici M.C. Fresta M. Cosco D. Influence of the dispersion medium and cryoprotectants on the physico-chemical features of gliadin- and zein-based nanoparticles Pharm 14 2022 332 10.3390/pharmaceutics14020332 58 Alkilany A.M. Abulateefeh S.R. Mills K.K. Yaseen A.I.B. Hamaly M.A. Alkhatib H.S. Aiedeh K.M. Stone J.W. Colloidal stability of citrate and mercaptoacetic acid capped gold nanoparticles upon lyophilization: effect of capping ligand attachment and type of cryoprotectants Langmuir 30 2014 13799 13808 10.1021/la504000v 25356538 59 Doktorovova S. Shegokar R. Fernandes L. Martins-Lopes P. Silva A.M. Müller R.H. Souto E.B. Trehalose is not a universal solution for solid lipid nanoparticles freeze-drying Pharm. Dev. Technol. 19 2013 922 929 10.3109/10837450.2013.840846 24099511 60 Roozen M.J.G.W. Hemminga M.A. Molecular motion in sucrose-water mixtures in the liquid and glassy state as studied by spin probe ESR J. Phys. Chem. 94 1990 7326 7329 10.1021/j100381a068 61 Passot S. Trelea I.C. Marin M. Galan M. Morris G.J. Fonseca F. Effect of controlled ice nucleation on primary drying stage and protein recovery in vials cooled in a modified freeze-dryer J. Biomech. Eng. 131 2009 74511 10.1115/1.3143034 62 Arsiccio A. Pisano R. The ice-water interface and protein stability: a review J. Pharm. Sci. 109 2020 2116 2130 10.1016/j.xphs.2020.03.022 32240686 63 Yu G. Li R. Hubel A. Interfacial interactions of sucrose during cryopreservation detected by Raman spectroscopy Langmuir 35 2019 7388 7395 10.1021/acs.langmuir.8b01616 30398347 64 Mathlouthi M. Genotelle J. Role of water in sucrose crystallization1 presented at the second international meeting of the Portuguese carbohydrate chemistry group.1 Carbohydr. Polym. 37 1998 335 342 10.1016/s0144-8617(98)00079-4 65 Carrasco M.J. Alishetty S. Alameh M.-G. Said H. Wright L. Paige M. Soliman O. Weissman D. Cleveland T.E. Grishaev A. Buschmann M.D. Ionization and structural properties of mRNA lipid nanoparticles influence expression in intramuscular and intravascular administration Commun. Biol. 4 2021 956 10.1038/s42003-021-02441-2 34381159 66 Reiser P. Birch G.G. Mathlouthi M. Sucrose, Properties and Applications 1995 186 222 10.1007/978-1-4615-2676-6_8 67 Roos Y. Karel M. Amorphous state and delayed ice formation in sucrose solutions Int. J. Food Sci. Technol. 26 1991 553 566 10.1111/j.1365-2621.1991.tb02001.x 68 Chung N.-O. Lee M.K. Lee J. Mechanism of freeze-drying drug nanosuspensions Int. J. Pharm. 437 2012 42 50 10.1016/j.ijpharm.2012.07.068 22877696 69 Wayment-Steele H.K. Kim D.S. Choe C.A. Nicol J.J. Wellington-Oguri R. Watkins A.M. Sperberg R.A.P. Huang P.S. Participants E. Das R. Theoretical Basis for Stabilizing Messenger RNA through Secondary Structure Design 2021 Biorxiv 10.1101/2020.08.22.262931 2020.08.22.262931 70 A Study to Evaluate the Efficacy, Safety, and Immunogenicity of mRNA-1647 Cytomegalovirus (CMV) Vaccine in Healthy Participants 16 to 40 Years of Age https://clinicaltrials.gov/ct2/show/nct05085366 2021 71 Kon E. Elia U. Peer D. Principles for designing an optimal mRNA lipid nanoparticle vaccine Curr. Opin. Biotechnol. 73 2021 329 336 10.1016/j.copbio.2021.09.016 34715546 72 Machado B.A.S. Hodel K.V.S. Fonseca L. Mascarenhas L.A.B. Andrade L. Rocha V.P.C. Soares M.B.P. Berglund P. Duthie M.S. Reed S.G. Badaro R. The importance of RNA-based vaccines in the fight against COVID-19: an overview Nato Adv. Sci. Inst. Se. 9 2021 1345 10.3390/vaccines9111345 73 Muramatsu H. Lam K. Bajusz C. Laczkó D. Karikó K. Schreiner P. Martin A. Lutwyche P. Heyes J. Pardi N. Lyophilization provides long-term stability for a lipid nanoparticle-formulated, nucleoside-modified mRNA vaccine Mol. Ther. 30 2022 1941 1951 10.1016/j.ymthe.2022.02.001 35131437	36414195	PMC9708520	NO-CC CODE	2022-12-01 23:20:30	no	J Control Release. 2023 Jan 30; 353:241-253	utf-8	J Control Release	2,022	10.1016/j.jconrel.2022.11.022	oa_other
==== Front Comput Econ Comput Econ Computational Economics 0927-7099 1572-9974 Springer US New York 10337 10.1007/s10614-022-10337-4 Article Bayesian Inference for Mixed Gaussian GARCH-Type Model by Hamiltonian Monte Carlo Algorithm Liang Rubing [email protected] Qin Binbin [email protected] http://orcid.org/0000-0003-2477-1742 Xia Qiang [email protected] grid.20561.30 0000 0000 9546 5767 College of Mathematics and Informatics, South China Agricultural University, Guangzhou, 510642 People’s Republic of China 30 11 2022 128 30 9 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. MCMC algorithm is widely used in parameters’ estimation of GARCH-type models. However, the existing algorithms are either not easy to implement or not fast to run. In this paper, Hamiltonian Monte Carlo (HMC) algorithm, which is easy to perform and also efficient to draw samples from posterior distributions, is firstly proposed to estimate for the Gaussian mixed GARCH-type models. And then, based on the estimation of HMC algorithm, the forecasting of volatility prediction is investigated. Through the simulation experiments, the HMC algorithm is more efficient and flexible than the Griddy-Gibbs sampler, and the credibility interval of forecasting for volatility prediction is also more accurate. A real application is given to support the usefulness of the proposed HMC algorithm well. Keywords Mixed Gaussian GARCH-type models HMC algorithm Forecasting Baysesian inference http://dx.doi.org/10.13039/100014718 innovative research group project of the national natural science foundation of china No.91746102 Liang Rubing ==== Body pmcIntroduction Financial time series, such as exchange rates and stock returns, often have exhibited time-varying volatility, excess kurtosis and volatility clustering reported by Mandelbrot (1963) and Fama (1965). The family of autoregressive conditional heteroskedastic (ARCH) model of Engle (1982) and the generalized ARCH (GARCH) model of Bollerslev (1986) provide effective techniques to fit the volatility of the financial time series. Since then, huge research works for the GARCH model and its extensions can be found in the literature for the past three decades, such as the GJR-GARCH model of Glosten et al. (1993), the threshold GARCH (TGARCH) model of Zakoian (1994), the exponential GARCH (EGARCH) model of Nelson (1991), the integrated GARCH (IGARCH) model of Engle and Bollerslev (1986), the power-transformed and threshold GARCH model (PTTGARCH) of Pan et al. (2008). For the GARCH type models, Westerfield (1977) and McFarland et al. (1982) have shown that the assumption of the GARCH model with normal errors can not provide an appropriate framework for some return series with excessive kurtosis and volatility clustering. Bollerslev (1987) suggested that the GARCH models with t-student innovations should be considered to describe the conditional distributions of the stock returns. However, these models can not capture volatility clustering, high kurtosis, heavy-tailed distributions and the phenomenon of extreme events. A mixture normal GARCH-type model with zero mean and different variances is generated by a normal density with a small variance, while a small number of innovations are generated by a normal density with a large variance. Therefore it becomes very popular to model the financial return data, and provide a better fitting and forecasting than the GARCH-type models with normal or t-student innovations. See Bauwens et al. (1999), McLachlan and Peel (2000), Bai et al. (2003), Wong and Li (2001), Haas et al. (2004), Zhang et al. (2006) and Alexander and Lazar (2006) among others. In the literature, maximum likelihood, quasi-maximum likelihood, the generalized method of moments and the least absolute deviations approach are traditionally carried out to infer the GARCH-type models, see Bollerslev and Wooldridge (1992) and Pan et al. (2008) for details. It is well known that the Bayesian inference offers a natural way to overcome computing problems and to avoid analytical difficulties in the estimation of volatilities. Some authors have applied Markov chain Monte Carlo (MCMC) algorithm to approximate the posterior distributions of the parameters for the GARCH-type models. For example, Geweke (1994) suggested the importance sampling to provide an efficient and generic method for updating posterior distributions. The Griddy-Gibbs sampler suggested by Ritter and Tanner (1992) has been used by Bauwens and Lubrano (1998) and Xia et al. (2017) for a GARCH-type model with normal or t-distributed errors, and Ausín and Galeano (2007) for a GARCH model with Gaussian mixture errors. In fact, finding an appropriate proposal distribution in the Metropolis-Hastings algorithm suggested by Metropolis et al. (1953) and Hastings (1970) or an importance function is not easy. Although the Griddy-Gibbs sampler is easier to implement than other methods, it takes much computation time. Therefore, designing an algorithm that is easier to implement and less heavy to run, is very important for inferring the mixed normal distribution GARCH-type models in practise. In recent years, attention has been paid for Hamiltonian Monte Carlo (HMC) algorithm proposed by Neal (2011) in the literature, because people realized that it can search the typical set of parameters effectively by using the gradient information of the target distribution. Different from the Metropolis-Hastings algorithm, the HMC sampler may not lead to random walk. Hence, the HMC algorithm has been introduced for inferring the time series models. For instance, Paixão and Ehlers (2017) used the HMC algorithm to estimate the GJR-GARCH model proposed by Glosten et al. (1993) with normal and t-Student errors. Burda and Bélisle (2019) applied HMC algorithm to overcome the difficulty of inferring the Copula-GARCH model, of which the distribution of parameters is skewness, asymmetry and truncation. Kreuzer and Czado (2021) proposed Bayesian inference for a single factor copula stochastic volatility model. Their related results show that the HMC algorithm can be implemented more easily in practice. Stan suggested by Carpenter et al. (2016) can provide HMC and No-U-Turn [NUTS, Hoffman and Gelman (2014)] methods to carry out Bayesian inference, but it aim at the models with continuous-variable. In the GARCH-type models with mixed normal errors, the latent variable is regarded as discrete parameter to define the likelihood function, thus we can not implement HMC procedure directly by Stan. Therefore, the main objective of this article is to propose a procedure for Bayesian inference and prediction of the general GARCH-type models with the Gaussian mixture innovations based on the HMC algorithm. The arrangements of this paper are as follows. Section 2 presents the general GARCH-type models with mixed normal innovations and describes a Bayesian inference of the model. A Hamiltonian Monte Carlo algorithm for sampling the posterior density of volatilities and VaR forecasts is also addressed. Section 3 provides some simulation experiments and a real data application, which illustrate the accuracy in the estimation of the parameters and the prediction of volatilities and VaR. Section 4 is our conclusions. Gaussian Mixture GARCH-Type Models and Bayesian Inference GARCH-type Models with Mixed Gaussian Innovations A series {yt} is said to follow the normal mixture GARCH-type models given by2.1 yt=htϵt,ht=var(yt\|Ft-1)=f(Θ,Ft-1), where f(Θ,Ft-1) is a differentiable function of Θ and given the previous information Ft-1={yt-1,yt-2,…}. The error term ϵt is taken from independent mixed normal distributions as follows,2.2 φ(ϵt)=φ1(ϵt),withprobabilityρ,φ2(ϵt),withprobability1-ρ, where φ1(ϵt)=12πσ2exp(-ϵt22σ2), φ2(ϵt)=λ2πσ2exp(-λϵt22σ2) and σ2=λ1+(λ-1)ρ with 0<λ<1. Then E(ϵt)=0, var(ϵt)=1. As we can see, model (2.1) includes the following conditional heteroscedasticity models: If f(Θ,Ft-1)=α0+∑i=1pαiyt-i2+∑j=1qβjht-j, then (2.1) becomes the standard GARCH model [Bollerslev (1986)]; If f(Θ,Ft-1)=α0+∑i=1pαiyt-i2, then (2.1) is the ARCH model [Engle (1982)], which is a special case of GARCH; If f(Θ,Ft-1)=α0+∑i=1p(αi+γiNt-i)yt-i2+∑j=1qβjht-j, where γi>0,i=1,..,p and Nt-i=1 if yt-i<0 or Nt-i=0 if yt-i⩾0, then (2.1) is the GJR-GARCH model [Glosten et al. (1993)]; If f(Θ,Ft-1)=α0+β1ht-1+(1-β1)yt-12, then (2.1) is the IGARCH(1,1) model [Engle and Bollerslev (1986)]; If lnf(Θ,Ft-1)=α0+∑i=1pαig(ϵt-i)+∑j=1qβjlnht-j, then (2.1) becomes the EGARCH model [Nelson (1991)], where α1=1, g(ϵt)=θϵt+γ[\|ϵt\|-E(\|ϵt\|)]. If f(Θ,Ft-1)=α0+∑i=1pα1i(yt-i+)2δ+∑i=1pα2i(yt-i-)2δ+∑j=1qβjht-jδ1/δ, (2.1) represents the PTTGARCH model [Pan et al. (2008)], where δ is a known positive number. Remark 2.1 Diebolt and Robert (1994) defined the latent variables z={zt,1≤t≤n}, where zt is a Bernoulli random variable with probability ρ, and I· is the indicator function. Then (2.2) can be interpreted as2.3 ϵt=ϵt(1)I{zt=1}+ϵt(2)I{zt=0}, 2.4 ϵt(1)∼N(0,σ2),ϵt(2)∼N(0,σ2/λ). Thus, if zt can be identified, then the distribution of ϵt can be determined. The Posterior Distribution Let Θ denote the parameter vector of the Gaussian mixed GARCH-type models (2.1), and y={yt,t=1,2,…,n} is observed series with n sample size. Then the posterior density can be written as2.5 p(Θ\|y)∝π(Θ)l(Θ;y,z), where π(Θ) is the prior and l(Θ;y,z) is the likelihood function, which can be derived in terms of latent variables z={zt,t=1,2,…,n}, i.e.,2.6 l(Θ;y,z)∝∏t:zt=1ρφ1ytf(Θ,Ft-1)∏t:zt=0(1-ρ)φ2ytf(Θ,Ft-1). The parameter ρ controls the proportion of the two normal distributions. When ρ goes to 1, the innovations ϵt will almost come from the first normal distribution; when ρ tends to 0, ϵt will almost come from the second normal distribution. It would be assumed that 0.5<ρ<1, so that most of the innovation ϵt comes from the first part. To implement the Bayesian inference about the parameters Θ in model (2.1), we need the joint posterior distribution P(Θ\|y), which can be obtained by using the conditional posterior distribution in a HMC process. Therefore, we need to choose priors to derive the conditional posterior distribution for the unknown parameters. For any priors π(Θ), as ht is a function of Θ in (2.1), the conditional posterior densities of most parameters in Θ will contain ht in (2.5). Consequently, they cannot be a normal or any other well known density, from which random numbers could be easily generated. Also, they are less likely to have the property of conjugacy. Therefore, the uninformative prior distributions will be preferred, and the relevant posterior distribution will be obtained. If model (2.1) is the standard GARCH model, the uniform prior of Θ=(α0,α1,…,αp,β1,…,βq,ρ,λ)′ can be chosen as follows:2.7 α0∼U(0,+∞);αi∼U(0,1),i=1,…,p;βj∼U(0,1),j=1,…,q;ρ∼U(0.5,1);λ∼U(0,1). Therefore, according to the uninformative prior distribution, the joint posterior distribution function of Θ is,2.8 p(Θ\|y,z)∝∏t:zt=1ρ1σ2htexp-yt22σ2ht∏t:zt=0(1-ρ)λσ2htexp-λyt22σ2ht. Sampling Scheme Using HMC Since Neal (2011) introduced the HMC algorithm, which originated from the algorithm when Duane et al. (1987) studied molecular dynamics simulation, the statistical inference method based on Hamiltonian dynamics became popular. According to Betancourt (2017), Girolami and Calderhead (2011) and Neal (2011), the HMC algorithm has an excellent performance in solving some difficult high-dimensional inference problems and pathological behaviors of distribution functions. In Hamiltonian dynamics, there are two parameters with the same dimension, the position vector Θ and the momentum vector Φ, to describe the motion process. The system is described by a function H(Θ,Φ) defined on the phase space (Θ,Φ). At the point (Θ,Φ), H(Θ,Φ) is known as the Hamiltonian, and can be decomposed into two parts, i.e.,2.9 H(Θ,Φ)=U(Θ)+K(Φ), where U(Θ) and K(Φ) are called the potential energy and the kinetic energy, respectively. In particular, the canonical distribution of H(Θ,Φ) has the form π(Θ,Φ)=exp(-H(Θ,Φ)). From Hamilton’s equations (2.10), we can know how Θ and Φ change over time t,2.10 dΘidt=+∂H∂Φi,dΦidt=-∂H∂Θi. In a non-physical context, the position parameter corresponds to the parameter of interest with the density π(Θ), and the parameter momentum is assumed to be a normal distribution random vector with the density π(Φ) independent of the parameter position. The joint probability distribution is written as the product of two densities,2.11 π(Θ,Φ)=π(Θ)π(Φ). Since the joint distribution is regarded as a canonical distribution, then the Hamiltonian function can be written as follows,2.12 H(Θ,Φ)=-lnπ(Θ)π(Φ)=-lnπ(Θ)-lnπ(Φ)=U(Θ)+K(Φ). In the mixed normal GARCH-type models, the parameter Θ corresponds to the position in Hamiltonian function H(Θ,Φ), and the momentum variable is denoted as Φ with the same dimension as Θ. In order to calculate the Hamiltonian H(Θ,Φ) of the Gaussian mixed GARCH type models, we adopt Φ follows a normal distribution with mean zero and covariance Σ, then the Hamiltonian function can be further written as,H(Θ,Φ)=U(Θ)+K(Φ)=-lnp(Θ\|y,z)+12Φ′Σ-1Φ. The normalising constant omitted in the HMC iteration will not affect the program. Using the above Hamiltonian function, we perform the HMC algorithm to update the parameter Θ. Each iteration of the HMC algorithm has two steps. The first changes only the momentum, while the second can change both position and momentum. Both steps leave the canonical joint distribution of (Θ,Φ) invariant, and hence their combination also maintains the distribution invariant. In the first step, new value of Φ is randomly drawn from its Gaussian distribution, independently of the current value of Θ. Because Θ is not changed, and Φ is drawn from its correct conditional distribution. Thus, this step obviously keeps the canonical joint distribution invariant. In the second step, a Metropolis update is performed to propose a new state by using Hamiltonian dynamics. Starting with the current state (Θ,Φ), Hamiltonian dynamics is simulated for L steps using the leapfrog method with a stepsize of Δs. Here, L and Δs are parameters of the algorithm, which need to be tuned to obtain good performance. The momentum variables at the end of this L-step trajectory are then negated, giving a proposed state (ΘL,ΦL). It is easy to obtain P(Θ,Φ)/P(ΘL,ΦL)=exp{-H(Θ,Φ)+H(ΘL,ΦL)}. Then this proposed state is accepted as the next state of the Markov chain with probability2.13 min1,π(ΘL,ΦL)/π(Θ,Φ)=min1,exp(-H(ΘL,ΦL)+H(Θ,Φ)). If the proposed state is not accepted, the next state is the same as the current state. The negation of the momentum variables at the end of the trajectory makes the Metropolis proposal symmetrical, as needed for the acceptance probability (2.13) to be valid. For the mixed normal GARCH-type models, the gradient of the kinetic function ∇K(Φ) is Σ-1Φ, and the potential function U(Θ)=U(Θ\|y,z) is first-order differentiable. Then the gradient ∇U(Θ) can be calculated. Note that ρ,λ is different from the other parameters in U(Θ). The partial derivatives of U with respect to ρ and λ can be calculated directly. However, the other parameters in U(Θ) can not be expressed as the explicit formula, and can be calculated according to the chain rule as follows:2.14 ∂U∂Θi=∑t(U(Θ\|y,z))f(Θ,Ft-1)′(f(Θ,Ft-1))Θi′=∑t(U(Θ\|y,z))ht′(ht)Θi′. Example 2.1 Notice that the partial derivative (ht)Θi′ can be obtained indirectly. For example, for the standard GARCH model, the parameters in f(Θ,Ft-1) are α0,αi,βj,i=1,2,…,p,j=1,2,…,q. The derivative of α0 can be written as follows,2.15 (ht)α0′=1+β1(ht-1)α0′+…+βq(ht-q)α0′, 2.16 ∂U/∂α0=∑t=ln(ht)α0′ut, where l=max(p+1,q+1), ut=12(1ht-yt2σ2ht2)I{t:zt=1}+12(1ht-λyt2σ2ht2)I{t:zt=0}. (ht-k)αi′, (ht-k)βj′ and hk, for l-q≤k≤l-1, i=0,1,2,⋯,p, j=1,2,⋯,q are assumed to be known. The derivatives of other parameters αi,βj have similar expressions, for i=1,2,⋯,p,j=1,2,⋯,q . Thus using the above formula, the gradient of U(Θ) can be calculated. If f(Θ,Ft-1) is the case 5 in (2.1), one can treat lnht as ht to calculate the partial derivatives. In the mixed normal GARCH-type models, generating the latent data z is very important for inferring the interested parameter. There are several methods can be found in the literature. For example, Tanner and Wong (1987) led to the posterior of the interested parameter by combining the observed data with latent data. Diebolt and Robert (1994) presented a Bayesian method to evaluate the interested mixture distribution in terms of the missing data scheme. Denote y=(y1,y2,⋯,yn)′ to be the observed data. Referring to Tanner and Wong (1987) and Diebolt and Robert (1994), we first generate the missing values z=(z1,z2,⋯,zn)′, and then sample the parameters from the posterior function p(Θ\|y,z) based on the complete data (yt,zt),t=1,2,…,n. The algorithm works at the step m for m=1,…,N as follows: Use Θ(m) and y to generate zt(m) from posterior p(zt\|Θ(m),yt), where 2.17 p(zt\|Θ(m),yt)=B1,pr1pr1+pr2, B(1,pr1pr1+pr2) is the Bernoulli distribution with probability pr1pr1+pr2 and pr1=ρσ2htexp(-yt22σ2ht), pr2=(1-ρ)σ2ht/λexp(-λyt22σ2ht). Draw momentum variable Φ from a zero-mean Gaussian distribution with covariance matrix Σ=diag(1,1,⋯,1), Φ∼N(0,Σ). Use the Algorithm 1 to propose a new state (Θ∗,Φ∗). Accept the proposal (Θ∗,Φ∗) with probability Pr and reject the proposal with probability 1-Pr, where 2.18 Pr=min1,exp(-H(Θ∗,Φ∗)+H(Θ,Φ)). Remark 2.2 In fact, Algorithm 1 is the leapfrog method. Here, UBi and LBi represent the upper and lower bounds of each parameter respectively. The simplest method of discretization equations (2.10) is leapfrog method with order (Δs)3 local error, while Euler’s method and its modified version have order (Δs)2 local error [See Leimkuhler and Reich (2004) for details]. Because a big step size will lead to a poor acceptance rate and a small step size will waste computation time. As Neal’s comments, a relatively suitable step size Δs should be considered based on computational efficiency. The adjustment of parameter Δs is necessary as well as misleading. Hence, it is essential to run multiple Markov chains with different initial values to ensure that the parameter Δs is sufficient to keep the algorithm stable. According to Beskos et al. Beskos et al. (2013) and Hoffman and Gelman Hoffman and Gelman (2014), tuning of parameters Δs and L determines the performance of the HMC algorithm. An inappropriate step size Δs will destroy the stability of simulated trajectory, and then affects the acceptance rate. The steps L means the total run in the leapfrog trajectory. Care must be taken when we choose the step size of HMC algorithm, because too small step size can maintain the stability of the trajectory. After a leapfrog iteration we can only get proposal point, which is close to the previous one. Moreover, an overlong step size will destroy the stability. A selected critical step size can keep the algorithm stable and efficient. The initial L is assumed to be 1. Given the initial Θ0,Φ0 and s, then one use the leapfrog method to get ΘL,ΦL. If exp{-H(ΘL,ΦL)+H(Θ0,Φ0)}>0.5, then s=1/2s; otherwise, s=2s. In simulation experiments and empirical analysis, taking into account the acceptance rate of these two stages, we set a threshold 0.5 and run the Metropolis update multiple times. According to Algorithm 1, repeating this process until the algorithm remains stable. The final acceptance rate for the proposed points is about 0.85. After getting the stable step size, the number of steps L can be increased according to the correlation of the sampling results. One can find an appropriate step size through sufficient preliminary runs of several chains. Furthermore, it’s necessary to choose an appropriate L that avoids producing a dependent point. In the previous works, the HMC algorithm performs well in sampling unconstrained parameters. As for handling distributions with the constraints on the variables, we can reparameterize the variables. Taking the GARCH model as an example, because the coefficients of autoregressive term αi is on interval (0,1), we can take logarithm of αi. Under the transformation αi∗=ln(αi/(1-αi)), the HMC algorithm also performs well. However, the reparameterization will take more arithmetic operations. Another way for the posterior with restricted variables can produce similar results. In order to deal with the constraints, once the variable violates any constraints, we set the value of the potential energy and obtain an acceptance rate of almost zero immediately. Thus the parameters will fall in the limited interval, see e.g. Algorithm 1. In each leapfrog iteration, the boundaries of variables can be seen as “walls”. If Θi is beyond the boundaries, the state (Θi,Φi) bounces off the “walls" perpendicularly, see Betancourt (2011) for details. Bayesian Forecasting Volatility and VaR forecasting are important in analyzing derivative pricing, yielding a good portfolio for investments and managing market risks in financial markets. In this section, the estimation of in-sample volatilities and the prediction of future volatilities are also investigated. In order to predict the volatility and VaR, based on the HMC iterations, a simulation-based approach is used to obtain the posterior samples and distributions of volatilities ht,t=1,2,⋯,n, where ht=f(Θ,Ft-1) is a function of parameter Θ. For each HMC iteration of Θ(m),m=1,2,⋯,N, we estimate the in-sample volatilities ht(1),⋯,ht(N),2.19 h^t≃E(ht\|yn)≃1N∑m=1Nht(m),t=1,2,⋯,n, where yn=(y1,y2,⋯,yn)′. For one-step ahead prediction of yn+1 and hn+1, one can obtain samples from the predictive distributions f(yn+1\|yn) and f(hn+1\|yn), respectively. Note that hn+1(m) is the function of parameter Θ(m) and yn. Then hn+1(1),hn+1(2),⋯,hn+1(N) can be obtained and form a sample of the predictive distribution f(hn+1\|yn). The posterior distribution f(yn+1\|yn,Θ(m)) has a simple form, so yn+1(m) can be drawn from a zero mean mixed normal distribution with variance hn+1(m). Hence, yn+1(1),yn+1(2),⋯,yn+1(N) is a sample of the predictive distribution f(yn+1\|yn). From this perspective, the distribution of yn+1 can be calculated by means of the posterior sample mean, that is2.20 f(yk+1\|yn)≃1N∑m=1Nf(yk+1\|yn,Θ(m)). For the prediction of two-step ahead volatility, we can obtain the predictive distribution of hn+2 through the calculated yn+1 and hn+1. According to the Θ(m) and hn+1(m), the predictive yn+1(m) and hn+2(m) can be obtained in the same way. Similarly, yn+2(m) also follows the mixed normal distribution given hn+2(m). Then it can be generated from the mixed normal density with known Θ(m) and hn+1(m). For t=n+j,j≥2, the prediction of the volatilities ht and yt can be carried out through the above process. If we need to obtain the predicted distributions f(ht\|yn+j-1) and f(yt\|yn+j-1), we can repeat the prediction process above. The final prediction distribution can help us compute the prediction interval and the prediction mean at time t. Due to the common presence of extreme events in financial time series, VaR has become a widely used measure of market risk. It is usually defined as the loss of a financial asset or securities portfolio, which is exceeded with a predetermined probability α over a time horizon of d periods,2.21 Pr(y[d]≤-VaR)=α, where y[d]=yn+1+yn+2+⋯+yn+d. In fact, VaR is the α-quantile of distribution of y[d] at a given confidence level α. To forecast VaR, we need to estimate the extreme αth percentiles. In this paper, the probability α of interest is 0.01 and 0.05. Given yn+1(m),yn+2(m),⋯,yn+d(m),m=1,2,⋯,N, the d-period α% VaR can be evaluated from distribution of f(y[d]\|y), see Jorion (2000) for details. Simulated Example and Real Data Example In this section, we first use the simulated experiment to illustrate the performance of the HMC method for the standard GARCH model with mixed normal errors, and then apply the proposed method to a real data analysis. The program is written based on R. Simulation Experiment Here, the mixed Gaussian GARCH(1,1) model is adopted as the simulated example, that is4.1 yt=htϵt,ht=α0+α1yt-12+β1ht-1, where ϵt follows a mixture Gaussian distribution (2.3) and the true values are set as follows,4.2 α0=0.1,α1=0.2,β1=0.5,ρ=0.8,λ=0.15. We first generated three series with sample size 800, 1000, 2000, which are denoted as (s1), (s2) and (s3) respectively. Then 10,000 HMC iterations are carried out for all series with the same initial value Θ(0)=(0.5,0.4,0.4,0.65,0.5). The first 5000 iterations considered as burn-in, the trace plots and histograms of all series are shown in Figs 9–14 in Appendix. The trace plots indicate that the HMC algorithm is successful in exploring the posterior density of the parameters. The CUMSUM plots for simulated s2 are displayed in Fig. 1, which shows that the estimates of parameters have good convergence. Meanwhile, convergence diagnosis was investigated by the Geweke test [Geweke (1992)], and the results are more convinced that the chains have converged from Table 8. Remark 3.1 In Algorithm 1, as Neal (2011) discussed, the stability and periodicity of the trajectory needs to be considered when the HMC algorithm is executed. The leapfrog step L=59 and multiple step sizes Δs are 0.003, 0.002, 0.002, 0.012 and 0.003 are reasonable. They ensure the stability as well as take into account the excellent performance of the program. The multiple step sizes used here can reduce the correlation of the samples. As discussed above, when discretizing the Hamilton equations, the discretization error can be kept equal to 0 theoretically. However, if the discretization error exists, then the acceptance rate is less than 1, that is round 85%. Remark 3.2 Here two tests are used to test the convergence of the Markov chain. One is the visual inspection of CUMSUM statistics proposed by Yu and Mykland (1994), and defined by4.3 CSt=1t∑i=1tθi-μθ/σθ, where μθ and σθ are the empirical mean and standard deviation of the N draws. Another is the Geweke test, which can be obtained by the R package “coda" [see Plummer et al. (2008) for details]. Fig. 1 The CUMSUM plots of posterior mean estimates for s2 of the model 4.1 Table 1 Comparison results for the mixture normal GARCH(1,1) model using HMC algorithm and GG sampler based on 100 replicates samples Parameter α0(0.1) α1(0.2) β1(0.5) ρ(0.8) λ(0.15) Time HMC-mean 0.1167 0.2155 0.4478 0.7922 0.1486 HMC-median 0.1187 0.2139 0.4356 0.7970 0.1486 12.23 mins (0.0269) (0.0621) (0.0866) (0.0449) (0.0210) GG-mean 0.1163 0.2339 0.4611 0.7929 0.1406 GG-median 0.1128 0.2244 0.4636 0.7961 0.1416 56.22 mins (0.0247) (0.0604) (0.0917) (0.0467) (0.0200) To verify the usefulness of our method a bit further, we conduct an comparison between the HMC algorithm and the Griddy-Gibbs (GG) sampler through 100 simulations. The summary of the results for the two methods is recorded in Table 1, which includes the posterior means, medians and standard deviations (SD) in parentheses. From Table 1, we can see that the estimated results of two methods are very well. The average of 100 samples of all estimated parameters is very close to the true parameters, excluding a little bias for β1. In particular, the posterior estimate of ρ is good and equal to 0.7922, its SD is small. Meanwhile, the median and mean of each parameter are very close, which indicate that the distributions are approximately symmetrical.Table 2 Comparisons of HMC algorithm and GG sampler in terms of MSE, ESS/min and MAD for each parameter α0 α1 β1 ρ λ HMC MSE 0.0015 0.0304 0.0193 0.0018 0.0006 ESS/min 411 496 381 28 159 MAD 0.0318 0.0498 0.1212 0.0413 0.0230 GG MSE 0.0036 0.0223 0.0203 0.0022 0.0030 MAD 0.04360 0.0917 0.1288 0.0388 0.0335 ESS/min 8 27 5 12 43 The posterior estimates of the HMC algorithm are close to the GG sampler. However, the HMC algorithm runs more quickly than the Griddy-Gibbs sampler. As shown in the last column of Table 1, the computation time of the HMC algorithm for the mixed Gaussian GARCH(1,1) model is 12.23 minutes (mins), while the GG sampler is 56.22 mins. In other words, with the same sample size, the time consumption of the GG sampler is almost 5 times that of the HMC algorithm. In addition, Table 2 shows the detailed comparisons of the two methods, including mean square error (MSE), mean absolute deviation (MAD) for each parameter and effective sample size per minute (ESS/min). We can see that compared with GG sampler, HMC algorithm has a lower MAD and a lower MSE for most parameters. However, ESS/min of HMC algorithm is much higher than GG sampler. Furthermore, we consider the forecasting by the proposed HMC algorithm. 1005 time series data by model (4.1) is used for predictions, while the first 1000 data are used for the posterior estimation of the model. The HMC algorithm is also performed 10000 iterations with the initial value Θ(0)=(0.5,0.4,0.4,0.65,0.5)′, and the first 5000 iterations are as burn-in values. We repeat the process to obtain the posterior means, SD’s, 95% credibility intervals and predictive distributions of volatilities and VaR. Moreover, in order to ensure the validity of the prediction, we calculate the results of 100 replicates and obtain the absolute error (AE) of volatilities, which are \|h^1000+m-h1000+m\|,m=1,2,…,5 plotted in Fig. 2.Fig. 2 The boxplot of AE of predictive volatilities for future times T=1001,..,1005 Fig. 3 The histograms of predictive dsitribution of volatilities for simulated series at times T=1001,..,1005 with sample size 1000 Remark 3.3 Ausín and Galeano (2007) and Xia et al. (2017) suggested choosing fixed grids with 40 points to compute the value of GG sampler. For the GG sampler, we choose 40 griddy points and the same initial value Θ(0), which can explore the parameter space sufficiently. In addition, linear interpolation is used to implement the approximation of the cumulative distribution function. Fig. 4 The predicitve distributions of VaR1001 ∼ VaR1005 at 5% (top) and 1% (bottom) level Table 3 The predictive means, SD’s and 95% credibility intervals for volatilities at times T=1001,..,1005 based on 100 replicates h1000+1 h1000+2 h1000+3 h1000+4 h1000+5 True 0.2304 0.1984 0.1898 0.1872 0.1823 Mean 0.2402 0.2051 0.1948 0.1915 0.1905 (0.2254,0.2550) (0.1953,0.2149) (0.1851,0.2046) (0.1817,0.2014) (0.1805,0.2005) SD 0.0813 0.0534 0.0530 0.0538 0.0542 Using the steps described above, one can obtain the estimation of predictive distribution. The histograms of the predictive distributions of volatilities and VaR are shown in Figs. 3 and 4. The estimated means, SD’s and 95% credibility intervals are summarized in Tables 3 and 4. From the histograms of predictive volatilities in Fig. 3, we believe that the distributions of future volatilities are nearly symmetric. For the forecasting of volatility, the predictive mean is very close to the true value, and the SD’s are very small. For the boxplot of predictive volatilities in Fig. 2, we can see that AE plots are pretty small, which indicates that our suggested method provides an accurate estimation. Figure 4 shows that most of the predictive distributions of VaR are symmetric, while a small part of them are skewed.Table 4 The predictive means, SD’s and 95% credibility intervals of VaR at times T=1001,..,1005 with probabilities α=0.05 and 0.01 based on 100 replicates VaR1000+1 VaR1000+2 VaR1000+3 VaR1000+4 VaR1000+5 Mean1 0.5165 0.7085 0.8519 0.9718 1.0783 (0.5029,0.5302) (0.6911,0.7260) (0.8315,0.8723) (0.9486,0.9950) (1.0524,1.1043) SD1 0.0729 0.0933 0.1090 0.1241 0.1385 Mean2 0.7458 1.0332 1.2477 1.4287 1.5866 (0.7256,0.7660) (1.0073,1.0591) (1.2176,1.2778) (1.3946,1.4628) (1.5486,1.6246) SD2 0.1089 0.1397 0.1621 0.1836 0.2047 1 VaR with probability 0.05 2 VaR with probability 0.01 Compared with GG sampler, the simulation results show that HMC algorithm not only provides more accurate estimations and predictions, but also performs higher efficiency for the mixture normal GARCH model. Real Data Example Fig. 5 The the log return plot of SP 500 In order to illustrate the good performance of the mixed GARCH type models in practice, we demonstrate the feasibility of the model with the real data analysis. The daily closing prices of SP500 index from Sep./3/2015 to Apr./7/2021 are selected with T = 1407 observed data. The sample mean, variance, skewness and kurtosis of log return series are 0.0525, 1.196, −1.0865 and 24.1190 respectively. The return of the observation series is defined by yt=100(logPt-logPt-1) shown in Fig. 5, where Pt is the closing price at time t. The Dickey-Fuller test is used to verify the stationarity of this time series, and the results show that the log return series is stationary. Pt is used to study the estimation problem of the volatility model. The volatility means the movement of the stock price. For example, the bigger volatility indicates that the stock will increase in price at a future point in time. Then, the prediction of volatilities in the future time can help investors to judge whether to hold the stock or not. Meanwhile, the predicted distribution of VaR is also carried out, which is concerned with a forecast of the possible losses of the investment portfolio over a given time interval. Thus, using the forecast results, financial institutions or individuals can respond in time to reduce losses when dealing with market risks. The analysis of the real data is based on model (4.1). We use the HMC algorithm to estimate parameters of the model, and GG sampler is also used for the comparison. 10,000 iterations are carried out for the HMC algorithm and the GG sampler with discarding the first 5000 iterations. The posterior means, medians and SD’s in parentheses are shown in Table 5.Table 5 The posterior estimations for SP500 by using HMC algorithm and GG sampler Parameter α0 α1 β1 ρ λ Time HMC-mean 0.0239 0.1936 0.8032 0.8873 0.1631 14.52 mins HMC-median 0.0231 0.1915 0.8046 0.8964 0.1618 (0.0068) (0.0307) (0.0273) (0.0426) (0.0323) GG-mean 0.0453 0.2351 0.7649 0.8593 0.1560 55.65 mins GG-median 0.0431 0.2370 0.7630 0.8644 0.1609 (0.0191) (0.0626) (0.0489) (0.0732) (0.0504) Table 5 shows that the estimation for posterior mean of the two methods are very close, indicating that the HMC algorithm is as much reliable as the GG sampler. Although the posterior results of the parameters are generally close, the SD’s of the estimated parameters of the HMC method is smaller, and HMC algorithm consumes less time in terms of running time. Moreover, the posterior mean of parameter ρ equals to 0.8873 and SD is 0.0426. That is to say, about 89% of the data may come from a normal distribution with a small variance 0.64, and the other 11% of the data may come from a normal distribution with a big variance 3.96. In reality, this finding result is very close to the features of the log return series. For example, there were many trade disputes around the world in 2018, which caused the daily closing price of stocks to fall. Another notable example is the global epidemic of COVID-19 in 2020. The epidemic has caused unprecedented trauma to the economies of countries around the world, and the stock market has also experienced severe shocks. Many stock prices, including the SP500 index, have been strongly affected by the epidemic. The observed data in Fig. 5 has a little volatility for most of the period before 2020, and large fluctuations in the short term after 2020. The excess kurtosis of the observed series is 24.12, which implies that capturing the extreme events using t or normal distribution is not a good choice. Under the framework of the mixture normal GARCH model, the heavy-tailed distributions of the returns can be well fitted. In addition, the diagrams and histograms of the posterior estimation for each parameter are shown in Figs. 15, 16 of Appendix. It can be seen from trace plots of each parameter that the HMC algorithm explores the parameter space well and does not fall into the local region in Fig. 15. According to the histograms in Fig. 16, except that the histogram of ρ is slightly right skew, the marginal posterior distribution of the other parameter is almost symmetrical. We use the visual inspection of CUMSUM statistics to check convergence of the HMC algorithm, and the plots based on 5000 draws with discarding the first 5000 draws are reported in Fig. 6. In the CUMSUM plots, we can see that the convergence of parameter ρ is slower than other parameters, and the convergence is relatively good for the parameters α0, α1, β1 and λ. The Geweke statistics of α0, α1, β1, ρ and λ are 0.6575, −0.3713, −0.1276, −0.2118 and 0.1332, respectively, indicating that all estimated results are convergent. For the parameters of the HMC algorithm, we run several Markov chains to find a suitable step size. In fact, we can refer to Beskos et al. (2013), which provided theoretical analysis of optimal step sizes for HMC algorithm. It’s worth noting that the multiple step size Δs selected to keep a good performance are {0.0025,0.0017,0.0025,0.0067,0.0042}, and the leapfrog step is 55. Considering the discretization error of the leapfrog, the selection of Δs and L results in an acceptance rate of 77%.Fig. 6 The CUMSUM plots of posterior mean estimates for mixed normal GARCH model Table 6 The predictive means, SD’s and 95% credibility intervals for volatilities hT at times T=1408,…,1412 of SP 500 using HMC method h1407+1 h1407+2 h1407+3 h1407+4 h1407+5 Mean 1.1798 1.1027 1.0324 0.9683 0.9101 (1.1795,1.1801) (1.1019,1.1035) (1.0313,1.0335) (0.9669,0.9697) (0.9086,0.9117) SD 0.0014 0.0041 0.0058 0.0069 0.0078 For the future prediction, we use the same approach described in Sect. 2.4, which is to perform 10000 iterations in each HMC algorithm. We consider 5-step predictions of 1407 observations. Table 6 displays the estimations and confidence intervals of volatilities. As can be seen from Table 6, the length of confidence intervals and the SD’s of volatilities are quite small, although the SD’s of volatilities increases over time. The results of credibility intervals show that HMC algorithm provides an accurate forecasting intervals. The predictive histogram of yt and forecasts of ht are shown in Fig. 7, which can be found that the predictive distributions of yt and ht are nearly symmetric. The mean of yt in the next 5 steps is about zero, which indicates that the future log returns are unlikely to occur large volatilities. The predictive distributions of VaR at 1% and 5% level can be seen from Fig. 8 that they are almost symmetric. The posterior estimations of VaR are shown to be well in Table 7. From the results shown in Table 7 and Fig. 8, the predictive distributions and credibility intervals for VaR are fairly symmetric. From the Table 7, we can see that the estimated values of VaR increase over time, which imply that the maximum possible loss increases as the future period [d] increases. VaR is related to the market risk, investors should choose a reasonable trading time to prevent from loss. The log return of the SP500 index exhibits heavy-tailed distributions and volatility cluster. The GARCH model with mixed normal distribution can capture the extreme events of market risk as well as the heavy-tailed distribution of the real data well. Meanwhile, the final estimated results illustrate that HMC algorithm performs more accurate and faster than GG sampler. Therefore, we can conclude that the HMC algorithm provides a good estimate and forecast for the log return of the SP500 index using GARCH model with mixed normal errors.Fig. 7 The histograms of predictive h1408∼h1412 and y1408∼y1412 for SP 500 Fig. 8 The predicitve distributions of VaR1408 ∼ VaR1412 for SP 500 at 5%(top) and 1%(bottom) level Table 7 The predictive means, SD’s and 95% credibility intervals of VaR at times T=1408..,1412 with probabilities α=0.05 and 0.01 for SP 500 VaR1407+1 VaR1407+2 VaR1407+3 VaR1407+4 VaR1407+5 Mean1 1.3472 1.8703 2.2513 2.555 2.8108 (1.3417,1.3527) (1.8627,1.8778) (2.2416,2.2611) (2.5442, 2.5662) (2.7989,2.8228) SD1 0.0279 0.0377 0.0491 0.0556 0.0603 Mean2 1.9090 2.7373 3.3527 3.8446 4.2381 (1.8982,1.9189) (2.7223,2.7523) (3.3356,3.3718) (3.8236,3.8657) (4.2086,4.2677) SD2 0.0545 0.0756 0.0963 0.1061 0.1489 1 VaR with probability 0.05 2 VaR with probability 0.01 Conclusion In this article, we carry out Bayesian inference and prediction for the GARCH-type models with mixed normal errors by the HMC algorithm. This new approach can be simply constructed to capture the GARCH effect in the heavy-tailed behavior of the distributions. Comparing with the GG sampler, the HMC algorithm performs higher sampling efficiency and accuracy for the mixture normal GARCH model in the simulation experiments as well as the empirical analysis. Because the step size and the number of steps will affect the performance of the HMC algorithm, attention should be paid to design the scheme for adjusting the parameters to obtain best performance in the HMC algorithm. Meanwhile, the extension of the multiple mixture distribution is a challenging problem, it is not easy to identify mixture components. These issues are still worthy of further study and explore. Appendix See Figures 9, 10, 11, 12, 13, 14, 15 and 16, Tables 8, 9 and 10. Fig. 9 The trace plots of parameters for s1 Fig. 10 The histograms of parameters for s1 Fig. 11 The trace plots of parameters for s2 Fig. 12 The histograms of parameters for s2 Fig. 13 The trace plots of parameters for s3 Fig. 14 The histograms of parameters for s Fig. 15 The trace plots of parameters for SP 500 Fig. 16 The histograms of parameters for SP 500 Table 8 Convergence diagnosis by Geweke test for each parameter Parameter α0 α1 β1 ρ λ s1 0.95 -0.16 0.07 -0.45 0.58 s2 2.42 0.86 -2.12 -0.59 0.17 s3 1.03 1.22 -1.77 -0.95 1.25 Table 9 Statistics summay of three simulation series Series Mean Variance Skewness Kurtosis s1 -0.0184 0.3556 0.4520 6.2986 s2 0.0161 0.3007 0.2480 7.2510 s3 -0.001 0.3614 -0.698 7.6601 Table 10 Posterior means for the simulated s1, s2 and s3 using HMC sampler Parameter α0 α1 β1 ρ λ Time 0.1 0.2 0.5 0.8 0.15 s1 0.1265 0.2528 0.4396 0.8276 0.1197 10.61 mins (0.0335) (0.0720) (0.0891) (0.0386) (0.0228) s2 0.1217 0.1820 0.4238 0.7896 0.1584 12.94 mins (0.0318) (0.0513) (0.1161) (0.0414) (0.0235) s3 0.1034 0.2032 0.5158 0.7615 0.1507 20.07 mins (0.0228) (0.0390) (0.0716) (0.0320) (0.0222) Acknowledgements The research of Qiang Xia was supported by the National Natural Science Foundation of China (No.12171161, 91746102), the Natural Science Foundation of Guangdong Province of China (No. 2022A1515011754), and Ministry of Education in China Project of Humanities and Social Sciences (No.17YJA910002). Funding The authors have not disclosed any funding. Declarations Conflict of interest The authors have not disclosed any competing interests. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Alexander C Lazar E Normal mixture garch(1,1): Applications to exchange rate modelling Journal of Applied Econometrics 2006 21 3 307 336 10.1002/jae.849 Ausín MC Galeano P Bayesian estimation of the gaussian mixture garch model Computational Statistics & Data Analysis 2007 51 5 2636 2652 10.1016/j.csda.2006.01.006 Bai X Russell JR Tiao GC Kurtosis of garch and stochastic volatility models with non-normal innovations Journal of Econometrics 2003 114 349 360 10.1016/S0304-4076(03)00088-5 Bauwens, L., Bos, C. S. & Van Dijk, H. K. (1999) . Adaptive polar sampling with an application to a bayes measure of value-at-risk, Working paper, CORE, Universite Catholique de Louvain . Bauwens L Lubrano M Bayesian inference on garch models using gibbs sampler Econometrics Journal 1998 1 1 23 46 10.1111/1368-423X.11003 Beskos A Pillai N Roberts G Sanz-Serna JM Stuart A Optimal tuning of the hybrid monte carlo algorithm Bernoulli 2013 19 5A 1501 1534 10.3150/12-BEJ414 Betancourt, M. (2011) . Nested sampling with constrained hamiltonian monte carlo, AIP Conference Proceedings, Vol. 1305, American Institute of Physics, pp. 165–172. Betancourt, M. (2017) . A conceptual introduction to hamiltonian monte carlo, arXiv preprint arXiv:1701.02434 . Bollerslev T Generalised autoregressive conditional heteroscedasticity Journal of Econometrics 1986 31 3 307 327 10.1016/0304-4076(86)90063-1 Bollerslev T A conditionally heteroscedastic time series model for speculative prices and rates of return The Review of Economics and Statistics 1987 69 3 524 547 10.2307/1925546 Bollerslev T Wooldridge JM Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances Econometric reviews 1992 11 2 143 172 10.1080/07474939208800229 Burda M Bélisle L Copula multivariate garch model with constrained hamiltonian monte carlo Dependence Modeling 2019 7 1 133 149 10.1515/demo-2019-0006 Carpenter B Gelman A Hoffman M Lee D Goodrich B Betancourt M Brubaker MA Guo J Li P Riddell A Stan: A probabilistic programming language Journal of Statistical Software 2016 76 1 1 32 Diebolt J Robert CP Estimation of finite mixture distributions through bayesian sampling Journal of the Royal Statistical Society: Series B 1994 56 2 363 375 Duane S Kennedy AD Pendleton BJ Roweth D Hybrid monte carlo Physics Letters B 1987 195 216 222 10.1016/0370-2693(87)91197-X Engle RF Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation Econometrica 1982 50 987 1008 10.2307/1912773 Engle RF Bollerslev T Modelling the persistence of conditional variances Econometric Reviews 1986 5 1 1 50 10.1080/07474938608800095 Fama EF The behaviour of stock market prices Journal of Business 1965 38 1 34 105 10.1086/294743 Geweke J Evaluating the accuracy of sampling-based approaches to the calculations of posterior moments Bayesian Statistics 1992 4 641 649 Geweke, J. (1994) . Bayesian comparison of econometric models, Working Paper, Federal Reserve Bank of Minneapolis (35). Girolami M Calderhead B Riemann manifold langevin and hamiltonian monte carlo methods Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2011 73 2 123 214 10.1111/j.1467-9868.2010.00765.x Glosten LR Jagannathan R Runkle DE On the relation between the expected value and the volatility of the nominal excess return on stocks The Journal of Finance 1993 48 5 1779 1801 10.1111/j.1540-6261.1993.tb05128.x Haas M Mittnik S Paolella MS Mixed normal conditional heteroskedasticity Journal of Financial Econometrics 2004 2 211 250 10.1093/jjfinec/nbh009 Hastings WK Monte carlo sampling methods using markov chains and their applications Biometrika 1970 57 1 97 109 10.1093/biomet/57.1.97 Hoffman M Gelman A The no-u-turn sampler: Adaptively setting path lengths in hamiltonian monte carlo Journal of Machine Learning Research 2014 15 1593 1623 Jorion, P. (2000) . Value at Risk: The New Benchmark for Managing Financial Risk(second ed.). Kreuzer A Czado C Bayesian inference for a single factor copula stochastic volatility model using hamiltonian monte carlo Econometrics and Statistics 2021 19 130 150 10.1016/j.ecosta.2020.12.001 Leimkuhler, B. & Reich, S. (2004). Simulating hamiltonian Dynamics, Cambridge University Press. Mandelbrot B New method in statistical economics Journal of Political Economy 1963 71 5 421 440 10.1086/258792 McFarland JW Pettit RR Sung SK The distribution of foreign exchange price changes: Trading day effects and risk measurement Journal of Finance 1982 37 3 693 715 10.1111/j.1540-6261.1982.tb02218.x McLachlan G Peel D Finite mixture models 2000 New York Wiley Metropolis N Rosenbluth AW Rosenbluth MN Teller AH Teller E Equation of state calculations by fast computing machines Journal of Chemical Physics 1953 21 6 1087 1092 10.1063/1.1699114 Neal, R. M. (2011) . MCMC Using Hamiltonian Dynamics, Handbook of Markov Chain Monte Carlo(S. Brooks, A. Gelman, G. L. Jones and X.-L. Meng, eds.) CRC Press, New York. Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach, Econometrica: Journal of the Econometric Society pp. 347–370. Paixão, R. S. & Ehlers, R. S. (2017) . Zero variance and hamiltonian monte carlo methods in garch models, arXiv preprint arXiv: 1710.07693 . Pan J Wang H Tong H Estimation and tests for power-pransformed and threshold garch models Journal of Econometrics 2008 142 352 378 10.1016/j.jeconom.2007.06.004 32287880 Plummer, M., Best, N., Cowles, K. & Vines, K. (2008). Coda: Output analysis and convergence diagnosis for mcmc. https://CRAN.R-project.org/package=coda Ritter C Tanner MA Facilitating the gibbs sampler: The gibbs stopper and the griddy-gibbs sampler Journal of the American Statistical Association 1992 87 861 868 10.1080/01621459.1992.10475289 Tanner MA Wong WH The calculation of posterior distributions by data augmentation Journal of the American Statistical Association 1987 82 398 528 540 10.1080/01621459.1987.10478458 Westerfield, R. (1977). The distribution of common stock price changes: An application of transactions time and subordinated stochastic models, Journal of Financial and Quantitative Analysis pp. 743–765. Wong CS Li WK On a mixture autoregressive conditional heteroscedastic model Journal of the American Statistical Association 2001 96 455 982 995 10.1198/016214501753208645 Xia Q Wong H Liu J Liang R Bayesian analysis of power-transformed and threshold garch models: A griddy-gibbs sampler approach Computational Economics 2017 50 3 353 372 10.1007/s10614-016-9588-x Yu B Mykland P Looking at markov samplers through cumsum path plots: A simple diagnostic idea, Technical Report 413 1994 Department of Statistics University of California at Berkeley Zakoian J-M Threshold heteroskedastic models Journal of Economic Dynamics and control 1994 18 5 931 955 10.1016/0165-1889(94)90039-6 Zhang Z Li WK Yuen KC On a mixture garch time-series model Journal of Time Series Analysis 2006 27 4 577 597 10.1111/j.1467-9892.2006.00467.x	36467873	PMC9708521	NO-CC CODE	2022-12-01 23:20:30	no	Comput Econ. 2022 Nov 30;:1-28	utf-8	Comput Econ	2,022	10.1007/s10614-022-10337-4	oa_other
==== Front Alexandria Engineering Journal 1110-0168 1110-0168 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. S1110-0168(22)00301-5 10.1016/j.aej.2022.04.039 Article Study of fractional order dynamics of nonlinear mathematical model Shah Kamal ab Ali Amjad b Zeb Salman b Khan Aziz a Alqudah Manar A. c Abdeljawad Thabet ad⁎ a Department of Mathematics and Sciences, Prince Sultan University, P.O. Box 66833, 11586 Riyadh, Saudi Arabia b Department of Mathematics, University of Malakand, Dir(L), Khyber Pakhtunkhwa, Pakistan c Department of Mathematical Sciences, College of Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia d Department of Medical Research, China Medical University, Taichung 40402, Taiwan ⁎ Corresponding author at: Department of Mathematics and Sciences Prince Sultan University P.O.Box.66833,11586 Riyadh Saudi Arabia/ Department of Medical Research, China Medical University, Taichung 40402,Taiwan. 11 5 2022 12 2022 11 5 2022 61 12 1121111224 14 2 2022 31 3 2022 21 4 2022 © 2022 THE AUTHORS 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. This manuscript is devoted to establishing some theoretical and numerical results for a nonlinear dynamical system under Caputo fractional order derivative. Further, the said system addresses an infectious disease like COVID-19. The proposed system involves natural death rates of susceptible, infected and recovered classes respectively. By using nonlinear analysis feasible region and boundedness have been established first in this study. Global and Local stability analysis along with basic reproduction number have also addressed by using the next generation matrix method. Upon using the fixed point approach, existence and uniqueness of the approximate solution for the mentioned problem has also investigated. Some stability results of Hyers-Ulam (H-U) type have also discussed. Further for numerical treatment, we have exercised two numerical schemes including modified Euler method (MEM) and nonstandard finite difference (NSFD) method. Further the two numerical schemes have also compared with respect to CPU time. Graphical presentations have been displayed corresponding to different fractional order by using some real data. Keywords COVID-19 Problem Feasible region Numerical solution MEM and NSFD methods Global and local stability CPU time ==== Body pmc1 Introduction Fractional calculus has got much attention in recent times. The foundation of the said area had been provided by Newton and Lebnitz during seventeenth century. Later on Reimann, Liouville, Fourier, Abel and Euler developed this branch very well [1]. The first notable definition had been given by Reimann-Liouville in 1832. Also the mentioned definition has been modified by Caputo in 1967 to elaborate the concept more clearly in geometry [2], [3]. Since fractional derivative infact is a definite integrals which can be defined in numbers of ways. Geometrically the said operator gives accumulation of a function which includes its integer counter part as a special case. Therefore, various researchers have given different definitions for fractional differential operators [4]. The said operators have numerous application in modeling of various real world problems. As in most of the situations where memory terms or index laws are involved cannot be well explained via classical operators. For comprehensive descriptions and well explanations fractional calculus is powerful tool to use (see [5], [6]). Researchers have used the concept of fractional calculus for detailed explanations of many real world problems including physical, biological, dynamical and chemical phenomenons (we refer few article as [7], [8], [9], [10], [11]). Recently various authors have used other kids of fractional differential operators like Hilfer, nonsingular kernel type, Caputo-Fabrizio operator to investigate various models. Here we state that each operator has own merits and de-merits. Here we remark that using various fractional differential operators numerous results relating controllability, existence theory, optimization and numerical analysis have been published in last few years. The concept of Hilfer derivative of fractional order has been applied to investigate some controllability results for delay type problems in [12], [13]. In same line authors [14] have established existence and controllability results for nonlocal mixed Volterra–Fredholm type fractional delay integro-differential equations. Some advanced results in controllability using fractional order derivatives have been established in [15], [16]. In same line authors have used neutral fractional order derivative to investigate a class of partial differential equations in [17]. Also authors have developed very applicable results for controllability and existence theory for neutral fractional integro-differential equations. For detail work, we refer [18], [19], [20]. Various results regarding analysis of various problems by using generalized proportional fractional integral operators, Atangana-Baleanue-Caputo operators have been established in last few years. Some recent contribution can be read as [21], [22], [23], [24], [25]. The analysis in aforesaid work has also applicable and can be further extended to COVID-19 or other infectious disease model. Here we remark that nonsingular kernel type and Caputo-Fabrizio operators suffer from initialization effect and extra assumptions need to be imposed on right hand side of a differential equations involving such like operators. This is not good effect and therefore now reducing the use of the said operators in applications. Although significant work has been done by using the aforesaid operators in epidemiology. But to the best of our knowledge in epidemiological models, the most powerful operators are those introduced by Reimann-Liouville and Caputo. Because they have clear geometrical interpretation like integer order differential equations and upon using integer value, the concerned equations reduce to their corresponding classical form. The research area devoted to mathematical models for analysis of various physical phenomenons in real life is very interesting and has received wide attention among researchers in last several years. The idea of mathematical model had been originated by Bernoulli in 1776 (see [26]). Later on the concept adopted by Mekendric and Kermark in 1927 to describe an infectious disease model which is known as SIR. The said model then further modified and extended to form new models for various infectious disease like TB, Cholerea, Typhoid, Dengue, HIV and AIDs, etc (see [27], [28]). Recently the Coronavirous disease (COVID-19) which has been originated from China and transmitted all over the world. The aforesaid disease which reported in an outbreak in 2019 in Wuhan, Hubei province, China, is caused by the SARS-CoV-2 virus (for further etiological information see [29]). According to researchers of virology, the said virus belongs to the class of beta-Coronavirus and like Middle East Respiratory Syndrome Coronavirus and Severe Acute Respiratory Syndrome Coronavirus. The novel virus began to cause pneumonia, and was named as Coronavirus disease (2019) on 11 February 2020 by WHO (see detail in [30]). Also many researchers have formulated various mathematical models for COVID-19. This disease has greatly affected the whole globe. Due to COVID-19, more than fifty millions people have been died. Nearly five hundred million people have got infectious throughout the world (see some detail in [31], [32]). The said disease has greatly destroyed the life style and economical situation of many countries around the globe. Recently some countries have worked on to prepare vaccine for permanent cure of the disease in which they have got some success like UK, USA, China and Germany, etc (see [33], [34]). Researchers from bioengineering side, virology, epidemiology and those working on mathematical biology have worked significantly in last two years to investigate various procedure how to eradicate or control the disease from further spreading in community. Those who are working on computational biology have developed several mathematical models to understand the transmission dynamics of the said disease. Therefore large numbers of mathematical models have been formulated for COVID-19. Authors [42] have formulated the following SIRD type model to demonstrate the diffusion of the infection in various communities(1) S˙(t)=γSII˙(t)=γSI-(α+d)IR˙(t)=αID˙(t)=dI,S(0)=S0>0,I(0)=I0⩾0,R(0)=R0⩾0,D(0)=D0⩾0, where S stand for susceptible, I for infected, R for recovered and D for death class. Further, d denote death rate, γ is used for infection rates and α represents rate of conversion to susceptible. Recently various analysis, investigations and procedure have been conducted to deal the disease from various aspects. The contribution done in this regards, we refer few article as [35], [36], [37], [38], [39], [40], [41]. Since the concept of fractional calculus has got significant attention during last few decades. Therefore mathematician as well as researchers in biomathematics have given much attention to study said models for the mentioned infection under various type derivatives of arbitrary orders (see [43], [44], [45], [46], [47], [48], [49]). The investigation of biological model of infection disease under fractional derivatives is an interesting study as compared to classical order derivative. Because fractional calculus provides dynamical interpretations of real world phenomenon with more degree of freedom (see few as [50], [51], [52], [53]). Therefore inspired from the mentioned applications and importance of fractional calculus and biological models, we update model (1) by incorporating natural death rates of various compartments as well as recruitment rate to form the following model(2) Dtβ0C[S(t)]=λ-γSI-δ1S+αR,Dtβ0C[I(t)]=γSI-(δ2+μ+d)I,Dtβ0C[R(t)]=μI-(α+δ3)R,Dtβ0C[D(t)]=dI,S(0)=S0>0,I(0)=I0⩾0,R(0)=R0⩾0,D(0)=D0⩾0, where λ is recruitment rate, δi,i=1,2,3 denote natural death of susceptible, infected and recovered class respectively. Also the notation Dtβ0C stands for standard Caputo derivative of fractional order β∈(0,1]. Also μ is recovery rate from disease. Here we present the flow chart of the model (2) in Fig. 1 asFig. 1 Flow chart of the proposed model (2). Here we will establish feasible region and verify boundedness of the solution for the corresponding model (2). Further using the next generation matrix method, we will develop global and local stability results also. Keeping the importance and real approach analysis of fractional calculus, we will investigate model(2) under formal arbitrary order derivative of Caputo type numerically. We establish some sufficient results for existence and uniqueness of solution to the model(2) by using some fixed point theorem due to Banach and Schauder. Some stability results about H-U type are also established for our problem. Further we will establish numerical algorithm based on NSFD scheme to simulate the results for various fractional order against the available real initial data. The concerned NSFD scheme has already used to handle many problems (see [54], [55], [56], [57], [58]). Also we will use modified Euler method (MEM) to describe the numerical results. The said method has also used in many papers for instance [50], [51], [52]. A comparison between both classical and fractional order model to conclude which one is more better for numerical interpretation of the proposed problem is given. We will use Matlab for our numerical analysis in this research work. Here we organized our work as: In first part we give introduction. Then we give some elementary results in Section second. Third section is devoted to some fundamental results about the model under consideration. Fourth section is devoted to qualitative analysis. Fifth section is related to numerical analysis which is further divided into four subsections. Sixth section is devoted to comparison of both schemes. Further last section seven is related to a brief conclusion. 2 Fundamental result We recall some basic definition of fractional calculus [1], [2], [3].Definition 2.1 Arbitrary order integration of F∈L[0,T] for β>0 is define asI+0β[F(t)]=1Γ(β)∫0t(t-ρ)β-1F(ρ)dρ provided that integral on right exists pointwise on (0,∞). Definition 2.2 If F∈C[0,T], then Caputo derivative of arbitrary order with 0<α≤1 is recalledDtβ0CF(t)=1Γ(1-β)∫0t(t-ρ)-βF′(ρ)dρ,0<β<1,dFdt,β=1, provided that integral on right exists pointwise on (0,∞). Lemma 2.3 The solution of FDEDtβ0CF(t)=G(t),0<β≤1, is given byF(t)=C0+I+0βG(t),C0∈R. 3 Feasible region and stability theory Here we derive some conditions for feasible region as well as for stability theory using the next generation matrix method.Lemma 3.1 The solution(S,I,R,D)∈R+4is bounded and attracted towards the feasible region defined byT=(S,I,R,D)∈R+4:0<N(t)⩽λ(δ1+δ2+δ3). Proof Summing all the equations of the Model (2) yields(3) dNdt=λ-δ1S-δ2I-δ3R-αRdNdt⩽λdNdt⩽λ+(δ1+δ2+δ3)NdNdt-(δ1+δ2+δ3)N⩽λ. By solving(3), we get(4) N(t)⩽λ(δ1+δ2+δ3)+Cexp(-(δ1+δ2+δ3)t). when t→∞, then one has N(t)⩽λ(δ1+δ2+δ3). Hence the required result. □ Theorem 3.2 The disease free equilibrium and pandemic equilibrium points of the model(2)are given byE0=(λδ1,0,0,0)and(5) E∗=(S∗,I∗,R∗,D∗), such that(6) S∗=δ2+μ+dγ,I∗=(α+δ3)(δ1γ-λγ(δ2+μ+d))γ(α+δ3)+α,R∗=μδ1γ-μλγ(δ3+μ+d)γ(α+δ3)+α,D∗=d(α+δ3)[δ1γ-λγ(δ2+μ+d)]. Proof For local and global stability analysis, the equilibrium points are important and can be computed from model (2) as(7) Dtβ0CS(t)=0Dtβ0CI(t)=0Dtβ0CR(t)=0Dtβ0CD(t)=0. Hence using (7) in model (2), one can easily computed the equilibrium points which are described in (5), (6) respectively. □ Theorem 3.3 The basic Reproduction number corresponding to the proposed model(2)is computed asR0=γλδ1(δ2+μ+d). Proof Let X=I and considering second equation of (2) asDtβ0C(X)=Dtβ0C(I)=γSI-(δ2+μ+d)IDtβ0C(X)=F-V, whereF=γSI,V=(δ2+μ+d)IF=[δ1(γSI)δ1I=[γS]V=δ1(δ2+μ+d)Iδ1I=[(δ2+μ+d)]V-1=[1(δ2+μ+d)]FV-1=[γλδ1(δ2+μ+d)]. Since R0 is equivalent to the maximum eigenvalue of FV-1 at disease free equilibrium point E0=(λδ1,0,0,0). Therefore(8) ρ(FV-1)=γλδ1(δ2+μ+d). Hence the required quantity is given by(9) R0=γλδ1(δ2+μ+d). Thus we arrived at our intended result. Theorem 3.4 The proposed model(2)is locally asymptotically stable atE0ifR0<1, while the the model(2)is locally asymptotically stable atE∗ifR0>1. Proof Since the jacobian matrix is computed from all four equations of the model (2), but here we take the first three equations of the model as these are independent of D. Therefore the Jacobian matrix for the model (2) can be computed asJ=∂f1∂S∂f1∂I∂f1∂R∂f2∂S∂f2∂I∂f2∂R(f2)∂f3∂S∂f3∂I∂f3∂R. On setting the corresponding values, one has(10) J=-δ1-γI-SγαγIγS-δ2-μ-d00μ-α-δ3. After putting the value of E0 in (10), we haveJ=-δ1-γλδ1-α0γλδ1-(∂+μ+d)00μ-(α+δ3). Now the characteristics equation can be computed asdet(J-ηI)=-δ1-η-γλδ1-α0γλδ1-(δ2+μ+d)-η00μ-(α+δ3)-η=0. Thus the eigen values are given byη1=-δ1η2=γλδ1-(δ2+μ+d)η3=-(α+δ3). Further η2 can be written asη2=γλ-δ1(δ2+μ+d)δ1η2=γλδ11-1R0. Since we see that η2<0, if R0<1. Hence the needful results recived. □ 4 Theoretical analysis Here in this part of our work, fixed point theory is used to establish sufficient conditions for existence and puniness of solution to the considered model. Therefore, the existence of approximate solution and its uniqueness are investigated by using Schauder and Banach fixed point results [59], [60]. We write our proposed model (2) with 0<β≤1 as(11) Dtβ0C[S(t)]=f1(t,S,I,R,D),Dtβ0C[I(t)]=f2(t,S,I,R,D),Dtβ0C[R(t)]=f3(t,S,I,R,D),Dtβ0C[D(t)]=f4(t,S,I,R,D),S(0)=S0,I(0)=I0,R(0)=I0,D(0)=I0. Thank to Lemma 2.3, the system (11) is equivalent to the given system of nonlinear integral equations as(12) S(t)=S0+1Γ(β)∫0t(t-ρ)β-1f1(ρ,S(ρ),I(ρ),R(ρ),D(ρ))dρ,I(t)=I0+1Γ(β)∫0t(t-ρ)β-1f2(ρ,S(ρ),I(ρ),R(ρ),D(ρ))dρ,R(t)=R0+1Γ(β)∫0t(t-ρ)β-1f3(ρ,S(ρ),I(ρ),R(ρ),D(ρ))dρ,D(t)=D0+1Γ(β)∫0t(t-ρ)β-1f1(ρ,S(ρ),I(ρ),R(ρ),D(ρ))dρ. Also t∈[0,T] with T<∞, then E1=C([0,T]×R4+,R+) is the Banach space. Further H=E1×E2×E3×E4× is also complete norm space endowed with norm‖U‖=supt∈[0,T]\|U(t)\|=supt∈[0,T][\|S(t)\|+\|I(t)\|+\|R(t)\|+\|D(t)\|]. Writing (12) as(13) U(t)=U0(t)+1Γ(β)∫0t(t-ρ)β-1G(ρ,U(ρ))dρ, where(14) U(t)=S(t)I(t)R(t)D(t),U0(t)=S0(t)I0(t)R0(t)D0(t),G(t,U(t))=f1(t,S(t),I(t),R(t),D(t))f1(t,S(t),I(t),R(t),D(t))f1(t,S(t),I(t),R(t),D(t))f2(t,S(t),I(t),R(t),D(t)). For establishing theoretical results, we need the given hypothesis to be hold:(E1) There exists constant LG>0, for each U(t),U(t)¯∈R×R, such that\|G(t,U(t))-G(t,U(t)¯)\|⩽LG\|U(t)-U(t)¯\|, (E2) There exist constants CG>0 and MG>0, with\|G(t,U(t))\|⩽CG\|U\|+MG. Theorem 4.1 Under the hypothesis(E2)and continuity ofG, at least one solution will be exists corresponding to the model(2). Proof Thank to Schauder fixed point theorem, considering a closed set A⊂H withB={U∈H:‖U‖⩽r,r>0}. If B:A→A be the operator, then inview of (13), one has(15) B(U(t))=U0(t)+1Γ(β)∫0t(t-ρ)β-1G(ρ,U(ρ))dρ, For any U∈A, one has\|B(U)(t)\|⩽\|U0\|+1Γ(β)∫0t(t-ρ)β-1\|G(ρ,U(ρ))\|dρ,⩽\|U0\|+1Γ(β)∫0t(t-ρ)β-1[CG\|U\|+MG]dρ,⩽\|U0\|+TρΓ(β+1)[CGr+MG], which implies that(16) ‖B(U)‖⩽r. Thus U∈A which yields B(A)⊂A and hence B is bounded. Let t1<t2∈[0,T], taking(17) \|B(U)(t2)-B(U)(t1)\|=1Γ(β)∫0t2(t2-ρ)β-1G(ρ,U(ρ))dρ-1Γ(β)∫0t1(t1-ρ)β-1G(ρ,U(ρ))dρ⩽1Γ(β)∫0t1[(t1-ρ)β-1-(t2-ρ)β-1]\|G(ρ,U(ρ))\|dρ+∫t1t2(t2-ρ)β-1\|G(ρ,U(ρ))\|dρ⩽(CGr+MG)Γ(β+1)[t2β-t1β+2(t2-t1)β]→0ast1→t2. Therefore‖B(U)(t2)-B(U)(t1)‖→0,ast1→t2. Hence B is equi- continuous operator. Hence model (2) has at least one solution. For existence of unique solution., we have the following result.Theorem 4.2 Under the hypothesis(E1)and ifTβΓ(β+1)LG<1, then the model(2)has a unique solution. Proof Let B:H→H be the operator and taking U,U¯∈H, then one has(18) ‖B(U)-B(U¯)‖=supt∈[0,T]1Γ(β)∫0t(t-ρ)β-1G(ρ,U(ρ))dρ-1Γ(β)∫0t(t-ρ)β-1G(ρ,U¯(ρ))dρ,⩽TβΓ(β+1)LG‖U-U¯‖. (18) yields(19) ‖B(U)-B(U¯)‖⩽TβΓ(β+1)LG‖U-U¯‖. Hence B is a contraction mapping, so by the use of Banach theorem, the considered system has a unique solution. □ Let f∈C[0,T] with f(0)=0 independent of U as.• \|f(t)\|⩽ϱ,forϱ>0; • Dtβ0CU(t)=G(t,U(t))+f(t). Lemma 4.3 The solution of nonlinear FDE(20) Dtβ0CU(t)=G(t,U(t))+f(t),U(0)=U0, satisfies the given relation(21) U(t)-U0(t)+1Γ(β)∫0t(t-ρ)β-1G(ρ,U(ρ))dρ⩽TβΓ(β+1)ϱ=ΩT,βϱ. Proof The proof is similar done in [44], [48], [50]. □ Theorem 4.4 Due to hypothesis(E2)andLemma 4.3, the solution of model(2)is H-U stable ifΔ=TβΓ(β+1)LG<1. Proof If U¯∈E is the unique solution of (13), then for any other solution U∈E, we have(22) \|U(t)-U¯(t)\|=U(t)-U0(t)+1Γ(β)∫0t(t-ρ)β-1G(ρ,U¯(ρ))dρ,⩽U(t)-U0(t)+1Γ(β)∫0t(t-ρ)β-1G(ρ,U¯(ρ))dρ+1Γ(β)∫0t(t-ρ)β-1G(ρ,U¯(ρ))dρ-1Γ(β)∫0t(t-ρ)β-1G(ρ,U¯(ρ))dρ,⩽ΩT,ρϱ+Δ‖U-U¯‖. From (22), we have(23) ‖U-U¯‖⩽ΩT,ρ1-Δϱ. Thus (23) yields that solution of (2) is H-U stable. □ 5 Numerical solution This part is devoted to establish two numerical schemes. We apply these schemes to our model one by one and compared the results. 5.1 Numerical simulation by MEM For the model (2), the numerical approximations are performed in this section by using MEM. Let [0,T] be the set of points, on which we must have to evaluate the series solution of the model (2). Upon further subdivision of [0,T] into m sub-intervals [tp,tp+1] of equal difference j=pm between consequent points using tP=pj with p=0,1,⋯,m, then obviouslyS(t),I(t),R(t),D(t),Dtβ0CS(t),Dtβ0C[I(t)],Dtβ0C[R(t)],Dtβ0C[D(t)],Dt2β0C[S(t)],Dt2β0C[I(t)],Dt2β0C[R(t)],Dt2β0C[D(t)] are continuous on [0,T]. Applying the modified Euler’s or Taylor’s method about t=t0 to the considered model expressed in (??) and for each value of t taking a∈(0,T), the expressions for t1 is given as(24) S(t1)=S(t0)+f1(t0,S(t0),I(t0),R(t0),D(t0))tβΓ(β+1)+Dt2β0CS(t)\|t=at2βΓ(2β+1)⋯,I(t1)=I(t0)+f2(t0,S(t0),I(t0),R(t0),D(t0))tβΓ(β+1)+Dt2β0CI(t)\|t=at2βΓ(2β+1)⋯,R(t1)=R(t0)+f1(t0,S(t0),I(t0),R(t0),D(t0))tβΓ(β+1)+Dt2β0CR(t)\|t=at2βΓ(2β+1)⋯,D(t1)=D(t0)+f1(t0,S(t0),I(t0),R(t0),D(t0))tβΓ(β+1)+Dt2β0CD(t)\|t=at2βΓ(2β+1)⋯,. By taking the difference between consecutive points as j very very small, such that we may ignore terms containing higher-order derivatives to get(25) S(t1)=S(t0)+f1(t0,S(t0),I(t0),R(t0),D(t0))tβΓ(β+1),I(t1)=I(t0)+f1(t0,S(t0),I(t0),R(t0),D(t0))tβΓ(β+1),R(t1)=R(t0)+f1(t0,S(t0),I(t0),R(t0),D(t0))tβΓ(β+1),D(t1)=D(t0)+f2(t0,S(t0),I(t0)R(t0),D(t0))tβΓ(β+1). On repeating the procedure, we get sequence of point to approximate (S(t),I(t),(R(t),(D(t)) is formed. A generalized formula in this regard at tp+1=tp+h is given by(26) S(tr+1)=S(tr)+f1(tr,S(tr),I(tr),R(tr),D(tr))hβΓ(β+1),I(tr+1)=I(tr)+f1(tr,S(tr),I(tr),R(tr),D(tr))hβΓ(β+1),R(tr+1)=R(tr)+f1(tr,S(tr),I(tr),R(tr),D(tr))hβΓ(β+1),D(tr+1)=D(tr)+f2(tr,S(tr),I(tr),R(tr),D(tr))hβΓ(β+1), where r=0,1,2,⋯,n-1. 5.2 Numerical results and Discussion Here we take real data of Pakistan as the total pollution of the country N=220 millions [61] and the other values are given in Table 1 : Here we now present numerical interpretation of the proposed model (2) using MEM in Fig. 2, Fig. 3, Fig. 4, Fig. 5 . From Fig. 2, we see that susceptibility is decreasing at various rate due to different fractional orders. As fractional order tends to integer order the solution converges to the integer order solution. In same line the infected class is increasing in initial 100 days with various rate of transmission due to various fractional order then it turns to decrease with same behaviors as in Fig. 3. Consequently the increase in death class cause increase in recovered class. The recovery class dynamics raises at various fractional order as shown in Fig. 4. In Fig. 5, the dynamics of death class is also increasing until become stable due to faster infection rate.Table 1 Interpretation and approximate values of parameters involve in the model (2). Compartment/Parameters Description of parameter Approximate value S0 Initial density of susceptible class 217.388901 millions I0 Initial density of infected class 1.29 million R0 Initial density of recovered class 1.256337 millions D0 Initial density of death class 0.028921 million λ Recruitment rate 0.00009 assumed α transmission rate from infection 0.009978 μ Recovery rate 0.0025 assumed d Death rate due to infection 0.019 γ rate of infection 0.0028 assumed δ1 Natural death rate of Susceptible class 0.0009 assumed δ2 Natural death rate of infected class 0.00056 assumed δ3 Natural death rate of recovered class 0.000013 assumed Fig. 2 Transmission dynamics of susceptible class at various fractional order of the proposed model (2). Fig. 3 Transmission dynamics of infected class at various fractional order of the proposed model (2). Fig. 4 Transmission dynamics of recovered class at various fractional order of the proposed model (2). Fig. 5 Transmission dynamics of death class at various fractional order of the proposed model (2). 5.3 Numerical interpretation of the Model (2) by NSFD scheme Using NSFD scheme under the concept of fractional order derivative, we approximate the proposed model (2). We use Grünwald-Letnikov approximation for Caputo derivative. About some detail for this scheme, we refer [54], [55], [56], [57], [58]. Since for nonlinear systems the investigation of exact solution in most cases is impossible, so we focuss on best approximation of the solution. In this regards, some NSFD schemes have been introduced. The said scheme has the ability to avoid the full implicit scheme and preserve positivity, monotonicity and convergency which make this method popular. For detail applications of the method see [62]. To construct the scheme, consider Grünwald-Letnikov approximation for the fractional order derivative given by(27) Dtβ0C[V(t)]=limp→0p-β∑i=0ℵ(-1)qωqV(t-qp), with t=ℵh, such that h is the step size. Consider the following FDE as(28) Dtβ0C[V(t)]=F(t,V(t)),t∈[0,T],T<∞,V(t0)=V0. Using (27), from (12) one has(29) ∑q=0ℵKβV(tn-i)=F(t,V(t)),n=1,2,3,⋯, where tn=nh and Kiβ are the Grünwald-Letnikov coefficients calculated as(30) Kiβ=1-1+βiKi-1β,i=1,2,3,⋯ and(31) K0β=h-β. Based on the above definition, our considered model (2) can be discritized as(32) S(tn+1)=1K0β-∑j=1n+1KjβS(tn+1-j)+λ-γS(tn)I(tn)-δ1S(tn)+αR(tn)I(tn+1)=1K0β-∑j=1n+1KjβI(tn+1-j)+γS(tn+1)I-(δ2+μ+d)I(tn+1)R(tn+1)=1K0β-∑j=1n+1KjβI(tn+1-j)+μI(tn+1)-(α+δ3)R(tn+1)D(tn+1)=1K0β-d∑j=1n+1KjβI(tn+1-j). 5.4 Numerical interpretation and explanation Here, we now present the transmission dynamics of the proposed model (2) using NSFD scheme in Fig. 6, Fig. 7, Fig. 8, Fig. 9 . The dynamical behaviors presented in Fig. 6, Fig. 7, Fig. 8, Fig. 9 is also same as given in Fig. 2, Fig. 5 respectively.Fig. 6 Transmission dynamics of susceptible class at various fractional order of the proposed model (2). Fig. 7 Transmission dynamics of infected class at various fractional order of the proposed model (2). Fig. 8 Transmission dynamics of recovered class at various fractional order of the proposed model (2). Fig. 9 Transmission dynamics of death class at various fractional order of the proposed model (2). 6 Comparison of both methods Here we compare graphs of both methods for fixed time t=300 in Fig. 10, Fig. 11, Fig. 12, Fig. 13 as Here in Fig. 10, Fig. 11, Fig. 12, Fig. 13, we compare the numerical solutions of various compartments of the proposed model by using MEM and NSFD schemes respectively. Both have similar results for same range of time. As MEM slightly simpler than NSFD scheme. Here we compare the numerical simulations of both methods with some real data of Pakistan for 200 days [63] in Fig. 14 . We see that our simulated results have close agreement with real data in both cases for the given fractional orders. This shows that both schemes can be use as a powerful tool to investigate fractional order dynamics. Further we compare CPU time of both method by using Matlab 13 and Machine Cori-7 of HP with 8th generation in Table 2 .Fig. 10 Transmission dynamics of susceptible class at various fractional order using: (a). Modified Euler method. (b). Nonstandard Finite Difference Method. Fig. 11 Transmission dynamics of infected class at various fractional order using: (a). Modified Euler method. (b). Nonstandard Finite Difference Method.. Fig. 12 Transmission dynamics of recovered class at various fractional order using: (a). Modified Euler method. (b). Nonstandard Finite Difference Method. Fig. 13 Transmission dynamics of death class at various fractional order using: (a). Modified Euler method. (b). Nonstandard Finite Difference Method. Fig. 14 Comparison between real and simulated results by using NSFD schemes and MEM. Table 2 Comparison of both methods. Range of t CPU time of MEM in seconds CPU time of NSFD method in seconds 50 4.99 4.66 100 4.75 4.71 150 4.83 4.73 200 4.85 4.76 250 4.90 4.80 300 4.95 4.83 7 Conclusion In this research work, we have established a modified type COVID-19 model by incorporating natural death rates of susceptible, infected and recovered classes respectively. The model under investigation has been studied under fractional order derivative of Caputo type. Further by using fixed point theory, we have established existence theory for numerical solutions. Also we have developed various results for global and local stability by using the next generation matrix method. The basic reproduction number has been computed. Moreover, the feasible region has also established for the proposed model along with its boundedness. The numerical interpretations have been performed by using two different numerical approaches based on MEM and NSFD methods. Both methods provide nearly same results for our model. Therefore we have compared both procedures in CPU time to see which one is most expensive with respect to time. In this regards NSFD method which is slightly general than MEM. Further MEM is slightly expensive in time than MEM for same number of iteration corresponding to different range of time. Graphical presentations have been provided for taking some real data about COVID −19 transmission in Pakistan. Both scheme have been compared with real data. The concerned graphs have been plotted against different fractional order. Hence we concluded that fractional calculus provides more best explanations to real world problems for understanding their dynamics. In future, we will investigate some mathematical models under fractional order stochastic differential equations. Also piecewise concept will be applied for investigating epidemiological models. Funding There does not exist any funding source. Availability of data All data used in this paper is included within the article. Authors contributions All authors played their role equally. First and second authors designed the models and did theoretical analysis. Third author did numerical. Last two authors edited and drafted the article. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments Authors Kamal Shah, Aziz Khan and Thabet Abdeljawad would like to thank Prince Sultan University for the support through the TAS research lab. Manar A. Alqudah: Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R14), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Peer review under responsibility of Faculty of Engineering, Alexandria University. ==== Refs References 1 Diethelm K. Ford N.J. Analysis of fractional differential equations J. Math. Anal. Appl. 265 2 2002 229 248 2 I. Podlubny, Fractional Differential Equations, Elsevier, Amester Dam, North Holland, 1998. 3 Kilbas A.A. Srivastava H.M. Trujillo J.J. Theory and Applications of Fractional Differential Equations 2006 Elsevier Gordon and Breach, Holland 4 Lakshmikantham V. Vatsala A.S. Basic theory of fractional differential equations Nonl. Anal.: Theory, Methods Appl. 69 8 2008 2677 2682 5 Abbas S. Benchohra M. Guerekata G.M.N. Topics in Fractional Differential Equations, Science & Business Media 2012 Springer Berlin 6 Rahimy M. Applications of fractional differential equations Appl. Math. Sci. 4 50 2010 2453 2461 7 Almeida R. Bastos N.R. Monteiro M.T.T. Modeling some real phenomena by fractional differential equations Math. Method. Appl. Sci. 39 16 2016 4846 4855 8 Zhou Y. Wang J. Zhang L. Basic Theory of Fractional Differential Equations 2016 World Scientific Singapore 9 Almeida R. Bastos N.R. Monteiro M.T.T. Modeling some real phenomena by fractional differential equations Math. Method. Appl. Sci. 39 16 2016 4846 4855 10 Diethelm K. Ford N.J. Freed A.D. A predictor-corrector approach for the numerical solution of fractional differential equations Nonlinear Dyn. 29 1 2002 3 22 11 Oldham K.B. Fractional differential equations in electrochemistry Adv. Eng. Soft. 41 1 2010 9 12 12 Kavitha K. Vijayakumar V. Udhayakumar R. Ravichandran C. Results on controllability of Hilfer fractional differential equations with infinite delay via measures of noncompactness Asian J. Control 2021 10.1002/asjc.2549 13 Nisar K.S. Vijayakumar V. Results concerning to approximate controllability of non-densely defined Sobolev-type Hilfer fractional neutral delay differential system Mathe. Methods Appl. Sci. 44 17 2021 13615 13632 14 Kavitha Williams W. Vijayakumar V. Udhayakumar R. Panda S.K. Nisar K.S. Existence and controllability of nonlocal mixed Volterra-Fredholm type fractional delay integro-differential equations of order 1<r<2 Num. Methods Partial Diff. Eqs. 2020 10.1002/num.22697 15 Vijayakumar V. Ravichandran C. Nisar K.S. Kucche K.D. New discussion on approximate controllability results for fractional Sobolev type Volterra-Fredholm integro-differential systems of order 1<r<2 Num. Methods Partial Diff. Eqs. 2021 10.1002/num.22772 16 Ma Y.K. Raja M.M. Nisar K.S. Shukla A. Vijayakumar V. Results on controllability for Sobolev type fractional differential equations of order 1<r<2 with finite delay AIMS Mathe. 7 6 2022 10215 10233 17 Ravichandran C. Munusamy K. Nisar K.S. Valliammal N. Results on neutral partial integro-differential equations using Monch-Krasnoselskii fixed point theorem with nonlocal conditions Fractal Fract. 6 2 2022 75 18 Jothimani K. Valliammal N. Ravichandran C. Existence result for a neutral fractional integro-differential equation with state dependent delay J. Appl. Nonlinear Dyn. 7 4 2018 371 381 19 Goufo E.F.D. Ravichandran C. Birajdar G.A. Self-similarity techniques for chaotic attractors with many scrolls using step series switching Mathe. Modell. Anal. 26 4 2021 591 611 20 Valliammal V. Ravichandran C. Results on fractional neutral integro-differential systems with state-dependent delay in Banach spaces Nonlinear Stud. 25 1 2018 159 171 21 Ravichandran C. Logeswari K. Jarad F. New results on existence in the framework of Atangana-Baleanu derivative for fractional integro-differential equations Chaos, Solitons Fractals 125 2019 194 200 22 Rashid S. Jarad F. Noor M.A. Kalsoom H. Chu Y.M. Inequalities by means of generalized proportional fractional integral operators with respect to another function Mathematics 7 12 2019 1225 23 Gambo Y.Y. Jarad F. Baleanu D. Abdeljawad T. On Caputo modification of the Hadamard fractional derivatives Adv. Diff. Eqs. 2014 1 2014 1 12 24 Khan A. Khan H. Gómez-Aguilar J.F. Abdeljawad T. Existence and Hyers-Ulam stability for a nonlinear singular fractional differential equations with Mittag-Leffler kernel Chaos, Solitons Fractals 127 2019 422 427 25 Khan H. Jarad F. Abdeljawad T. Khan A. A singular ABC-fractional differential equation with p-Laplacian operator Chaos, Solitons Fractals 129 2019 56 61 26 Fowler A.C. Mathematical Models in the Applied Sciences 1997 Cambridge University Press Cambridge 27 Tedeschi L.O. Assessment of the adequacy of mathematical models Agri. Sys. 89 2–3 2006 225 247 28 Castillo-Chavez F.B. Feng Z. Mathematical Models in Epidemiology 2019 Springer New York 29 Rauf A. COVID-19 pandemic: epidemiology, etiology, conventional and non-conventional therapies Int. J. Environ. Res. Public Health 17 21 2020 8155 33158234 30 Shchelkanov M.Y. Kolobukhina L.V. Burgasova O.A. Kruzhkova I.S. Maleev V.V. COVID-19: etiology, clinical picture, treatment Russian J. Infect. Immunity 10 3 2020 421 445 31 Wang J. Mathematical models for COVID-19: applications, limitations, and potentials J. Pub. Health Emer. 4 2020 1 12 32 Ceylan Z. Estimation of COVID-19 prevalence in Italy, Spain, and France Sci. Total Environ. 729 2020 138817 32360907 33 Thomas D.M. Sturdivant R. Dhurandhar N.V. Debroy S. Clark N. A Primer on COVID-19 Mathematical Models Obesity 2020 34 Mustafa S.K. Ahmad M.A. Sotnik S. Zeleniy O. Lyashenko V. Alzahrani O. Brief review of the mathematical models for analyzing and forecasting transmission of COVID-19 J. Crit. Rev. 7 2020 4206 4210 35 Wagner C.E. Saad-Roy C.M. Grenfell B.T. Modelling vaccination strategies for COVID-19 Nat. Rev. Immunol. 2022 2022 1 3 36 Zhang M. Human mobility and COVID-19 transmission: A systematic review and future directions Annals GIS 2022 2022 1 14 37 Jasper F.W.C. Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from patients with acute respiratory disease in Wuhan, Hubei, China Emerging Microbes Infect. 9 1 2020 221 236 38 Mourad A. Mroue F. Taha Z. Stochastic mathematical models for the spread of COVID-19: a novel epidemiological approach Mathe. Med. Biol.: J. IMA 39 1 2022 49 76 39 Riou J. Althaus C.L. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV) Eurosurveillance 25 4 2020 2000058 32019669 40 Pais R.J. Taveira N. Predicting the evolution and control of the COVID-19 pandemic in Portugal F1000Research 9 283 2020 283 32983416 41 Lin Q. A conceptual model for the coronavirus disease (COVID-19) outbreak in Wuhan, China with individual reaction and governmental action Int. J. Infec. Dis. 93 2021 211 216 42 Fanelli D. Piazza F. Analysis and forecast of COVID-19 spreading in China, Italy and France Cha. Solit. Fract. 134 2020 109761 43 Atangana E. Atangana A. Facemasks simple but powerful weapons to protect against COVID-19 spread: Can they have sides effects,? Results Phys. 19 2020 103425 33014697 44 Shah K. Abdeljawad T. Mahariq I. Jarad F. Qualitative analysis of a mathematical model in the time of COVID-19 Bio. Res. Int. 2020 2020 16 45 Shah K. Khan Z.A. Ali A. Amin R. Khan H. Khan A. Haar wavelet collocation approach for the solution of fractional order COVID-19 model using Caputo derivative Alex. Eng. J. 59 5 2020 3221 3231 46 Atangana A. A novel Covid-19 model with fractional differential operators with singular and non-singular kernels: Analysis and numerical scheme based on Newton polynomial Alex. Eng. J. 60 4 2021 3781 3806 47 Atangana A. Modelling the spread of COVID-19 with new fractal-fractional operators Can the lockdown save mankind before vaccination Chaos Solitons Fractals 136 2020 109860 32501371 48 Atangana A. Araz S.I. Mathematical model of COVID-19 spread in Turkey and South Africa: theory, methods, and applications Adv. Diff. Equs. 2020 1 2020 1 89 49 Boudaoui A. El hadj M.Y. Hammouch Z. Ullah S. A fractional-order model describing the dynamics of the novel coronavirus (COVID-19) with nonsingular kernel Chaos, Solitons Fractals 146 2021 110859 33776249 50 Sher M. Shah K. Khan Z.A. Khan H. Khan A. Computational and theoretical modeling of the transmission dynamics of novel COVID-19 under Mittag-Leffler power law Alex. Eng. J. 59 5 2020 3133 3147 51 Mehran M. Kamyad A.V. Modified fractional Euler method for solving fuzzy fractional initial value problem Commun. Nonl. Sci. Numer. Simul. 18 1 2013 12 21 52 Ahmed H. Fractional Euler method; an effective tool for solving fractional differential equations J. Egyptian Mathe. Soc. 26 1 2018 38 43 53 Treibert S. Brunner H. Ehrhardt M. A nonstandard finite difference scheme for the SVICDR model to predict COVID-19 dynamics Mathe. Biosci. Eng. 19 2 2022 1213 1238 54 Khalsaraei M. Rashidi M.M. Shokri A. Ramos H. Khakzad P. A nonstandard finite difference method for a generalized Black-Scholes equation Symmetry 14 1 2022 141 55 Treibert S. Brunner H. Ehrhardt M. A nonstandard finite difference scheme for the SVICDR model to predict COVID-19 dynamics Math. Bio. Eng. 19 2 2022 1213 1238 56 Mickens R.E. Applications of nonstandard finite difference schemes 2000 World Scientific Singapore 57 Mickens R.E. Nonstandard finite difference models of differential equations 1994 World Scientific Singapore 58 Treibert S. Brunner H. Ehrhardt M. A nonstandard finite difference scheme for the SVICDR model to predict COVID-19 dynamics Math. Bio. Eng. 19 2 2022 1213 1238 59 Granas A. Dugundji J. Elementary fixed point theorems 2003 Springer New York 60 Amar A.B. Jeribi A. Mnif M. Some fixed point theorems and application to biological model Numer. Func. Anal. Opt. 29 1–2 2008 1 23 61 https://www.worldometers.info, Pakistan COVID - Coronavirus Statistics, 23 December 2021. 62 Ongun M.Y. Arslan D. Garrappa R. Nonstandard finite difference schemes for a fractional-order Brusselator system Adv. Diff. Equ. 2013 1 2013 1 13 63 https://www.worldometers.info/coronavirus/country/pakistan/2021.	0	PMC9708522	NO-CC CODE	2022-12-01 23:20:30	no	2022 Dec 11; 61(12):11211-11224	utf-8	null	null	null	oa_other
==== Front Ann Phys Rehabil Med Ann Phys Rehabil Med Annals of Physical and Rehabilitation Medicine 1877-0657 1877-0665 Elsevier Masson SAS. S1877-0657(22)00081-1 10.1016/j.rehab.2022.101709 101709 Original Article Home-based respiratory muscle training on quality of life and exercise tolerance in long-term post-COVID-19: Randomized controlled trial del Corral Tamara a Fabero-Garrido Raúl b Plaza-Manzano Gustavo c⁎ Fernández-de-las-Peñas César de Navarro-Santana Marcos f López-de-Uralde-Villanueva Ibai a a Department of Radiology, Rehabilitation and Physiotherapy, Faculty of Nursing, Physiotherapy and Podiatry, Universidad Complutense de Madrid (UCM); IdISSC, Madrid, Spain b Department of Radiology, Rehabilitation and Physiotherapy, Faculty of Nursing, Physiotherapy and Podiatry, Universidad Complutense de Madrid (UCM); Madrid, Spain c Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid (UCM); IdISSC, Madrid, Spain d Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Universidad Rey Juan Carlos, Alcorcón, Spain e Cátedra Institucional en Docencia, Clínica e Investigación en Fisioterapia: Terapia Manual, Punción Seca y Ejercicio Terapéutico, Universidad Rey Juan Carlos, Alcorcón, Spain f Faculty of Health Sciences. Universidad Católica de Ávila, Ávila, Spain ⁎ Corresponding author: Dr. Gustavo Plaza-Manzano, Universidad Complutense de Madrid (UCM), Department of Radiology, Rehabilitation and Physiotherapy, Plaza Ramón y Cajal n° 3, Ciudad Universitaria, 28040 Madrid, Spain, Phone: + 34 91 394 15 17. 30 9 2022 2 2023 30 9 2022 66 1 101709101709 9 2 2022 10 9 2022 © 2022 Elsevier Masson SAS. All rights reserved. 2022 Elsevier Masson SAS Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objective To evaluate the effects of a home-based respiratory muscle training programme (inspiratory [IMT] or inspiratory/expiratory muscles [RMT]) supervised by telerehabilitation on quality of life and exercise tolerance in individuals with long-term post-COVID-19 symptoms. The secondary objective was to evaluate the effects of these programmes on respiratory muscle function, physical and lung function, and psychological state. Methods 88 individuals with long-term symptoms of fatigue and dyspnoea after COVID-19 diagnosis were randomly (1:1 ratio) assigned to IMT, IMTsham, RMT or RMTsham groups for an 8-week intervention (40min/day, 6 times/week). Primary outcomes were quality of life (EuroQol-5D questionnaire) and exercise tolerance (Ruffier test). Secondary outcomes were respiratory muscle function (inspiratory/expiratory muscle strength; inspiratory muscle endurance), physical function (lower and upper limb strength [1-min Sit-to-Stand and handgrip force]), lung function (forced spirometry), and psychological status (anxiety/depression levels and post-traumatic stress disorder). All outcomes were measured pre-, intermediate- (4th week), and post-intervention. Results At post-intervention, there was a statistically significant and large (d>0.90) improvement in quality of life, but not in exercise tolerance, in the RMT group compared with the RMTsham group. Both of the real training groups produced a statistically significant and large increase in inspiratory muscle strength and endurance (d≥0.80) and in lower limb muscle strength (d≥0.77) compared with the 2 sham groups. Expiratory muscle strength and peak expiratory flow showed a statistically significant and large (d≥0.87) increase in the RMT group compared with the other 3 groups. Conclusion Only an 8-week supervised home-based RMT programme was effective in improving quality of life, but not exercise tolerance, in individuals with long-term post-COVID-19 symptoms. In addition, IMT and RMT programmes were effective in improving respiratory muscle function and lower limb muscle strength, but had no impact on lung function and psychological status. Keywords SARS-CoV-2 Respiratory muscle training Quality of life Maximal respiratory pressures Telerehabilitation Abbreviations HRQoL, health-related quality of life IMT, inspiratory muscle training RMT, inspiratory/expiratory muscle training VAS, visual analogue scale MIP, maximum inspiratory pressure MEP, maximum expiratory pressure IME, inspiratory muscle endurance 1-min STS, 1-min sit-to-stand PTSD, post-traumatic stress disorder PCL-C, post-traumatic stress disorder Check List-Civilian PEF, peak expiratory flow ==== Body pmcIntroduction After COVID-19 infection, up to 60% of survivors experience symptoms not explained by an alternative diagnosis [1], and these individuals are considered to have long-term post-COVID-19 symptoms when their duration persists for more than 3 months [1,2]. These symptoms include, but are not limited to, fatigue, dyspnoea, pain, muscle weakness, limited exercise capacity, depression, confusion, memory problems, difficulty concentrating (“brain fog”), neurological symptoms, smell/taste disorders, impaired lung function, and poor health-related quality of life (HRQoL) [3,4]. The pathogenesis of these persistent symptoms is largely unknown, although various hypotheses have been proposed, including the presence of hypoxia and hypoxic tissue injury induced by COVID-19-associated pneumonia and the subsequent low pulmonary diffusing capacity, ventilation-perfusion mismatching, and fibrosis [5]. As a result of increased ventilatory requirements, it is possible that symptoms such as fatigue, dyspnoea, and limited exercise tolerance are associated with diaphragm fatigue and an increase in the concentration of metabolites that activate the so-called “metaboreflex”, with subsequent reduced exercise tolerance by causing a systemic vasoconstrictor response in the limb skeletal muscle [6]. Thus, exaggerated metaboreflex activation could explain the reduced quality of life and functional capacity observed in people with inspiratory muscle weakness [7]. Respiratory muscle training has been shown to attenuate the metaboreflex in healthy individuals [8] and in individuals with chronic heart failure [9]. The beneficial effects of respiratory muscle training also include increased HRQoL, exercise tolerance, respiratory muscle strength and endurance, and lung function, and also reduced fatigue and dyspnoea levels [10,11]. Accordingly, exercise interventions designed to improve HRQoL, exercise tolerance, respiratory muscle strength and pulmonary function could be beneficial for individuals with long-term post-COVID-19 symptoms. In addition, respiratory muscle training can be implemented through telerehabilitation, thus reducing healthcare and respecting socially implemented measures due to the COVID-19 pandemic [12]. This study evaluated the effects of a home-based respiratory muscle training programme (inspiratory [IMT] or inspiratory/expiratory muscles [RMT]) supervised by telerehabilitation on HRQoL and exercise tolerance in individuals with long-term post-COVID-19 symptoms. The secondary objective was to evaluate the effects of these programmes on respiratory muscle function, physical and lung function, as well as on the psychological state of these individuals. Methods Study design A parallel 4-arm, double-blinded, randomised controlled trial was conducted according to the Consolidated Standards of Reporting Trials (CONSORT) 2017 Statement for Randomized Trials of Nonpharmacologic Treatments. All of the study procedures were conducted in accordance with the Declaration of Helsinki and were approved by the Ethics Committee of Hospital Clínico San Carlos (20/715-E_BS). The study was also registered in the United States Clinical Trials Registry (NCT04734561). Participants Participants were recruited from two non-profit organisations for the support of individuals with long-term post-COVID-19 symptoms (Long Covid ACTS [Autonomous Communities Together Spain] and Covid persistente España [Persistent COVID Spain]) via flyers, social networks, and internet platforms. Individuals interested in participating contacted an external researcher by email, who corroborated their potential eligibility and arranged a face-to-face visit for them to undergo the baseline assessment. At the baseline visit, the remaining face-to-face visits were scheduled for follow-up assessments. The study included COVID-19 survivors aged over 18 years who presented long-term post-COVID-19 symptoms of fatigue and dyspnoea for at least 3 months after the COVID-19 diagnosis confirmed by positive reverse-transcription-polymerase chain reaction (RT-PCR) SARS-CoV-2 test from a nasopharyngeal or oropharyngeal swab or serological tests positive for SARS-CoV-2 antibodies. Candidates were excluded if they presented 1) a diagnosis of progressive respiratory, neuromuscular or neurological disorders and/or psychiatric or cognitive conditions that hindered their ability to cooperate; 2) any contraindication to respiratory muscle training treatment; 3) lack of internet access; and 4) previous inclusion in a rehabilitation programme for their long-term post-COVID-19 symptoms. Participants who missed more than 15% of the treatment sessions were also excluded. Randomization and blinding Using a computer-generated randomization list (GraphPad Software©), an external researcher who was blinded to the participants’ identities independently performed a block randomization (block size of 8). Allocation concealment was conducted using opaque sealed envelopes, which were prepared by this external researcher. Participants were randomly assigned to one of 4 parallel groups, in a 1:1 ratio: 1) IMT group; 2) IMTsham group; 3) RMT group; and 4) RMTsham group. A second external researcher provided the participants with the training device (sham or real) assigned to their group in a face-to-face visit and indicated when they should start the home-based respiratory muscle training (always 3–7 days after randomization). In addition, this second external researcher sent e-mails to the physiotherapist in charge of administering the training indicating whether the participants should perform IMT or RMT. Thus, although the participants and therapist knew the training modality (IMT or RMT), they were all blinded to whether the training was real or sham, given that the external appearance of the training devices was identical. The assessor was also blinded to the treatment allocation, and participants were specifically asked not to discuss their intervention with the assessor. At the end of the last evaluation, all participants were encouraged to state which treatment group they believed they had been allocated to, to assess whether the participant blinding was effective. Outcome measures Outcome measures were assessed at baseline, at the end of the 4th week of intervention, and at postintervention (8 weeks), except for cognitive status (baseline and postintervention). At baseline, individuals were weighed and measured and their age, sex, smoking habits, COVID-19-related medical history and clinical characteristics (dichotomous response for the presence of dyspnoea, fatigue, chest pain, etc.) were recorded by clinical interview and medical history. Data on self-perceived dyspnoea and fatigue when performing activities of daily living were also collected at each follow-up period. The primary and secondary outcome measures of the study are briefly presented below. For a detailed description of these outcomes and the measurement process, see Appendix A. Primary outcomes 1 Health-related quality of life. The EuroQol-5D questionnaire (EQ-5D-5L) [13] was employed to measure HRQoL. Additionally, the participants had to rate their current overall health on a visual analogue scale (VAS). 2 Exercise tolerance. Cardiorespiratory fitness was assessed by the Ruffier test according to a standardized protocol [14]. Secondary outcomes 1 Respiratory muscle function. Maximum static inspiratory and expiratory pressures (MIP and MEP) at the mouth were recorded using a digital mouth pressure meter (MicroRPMTM; Carefusion, San Diego, CA, USA), according to the American Thoracic Society/European Respiratory Society (ATS/ERS) guidelines [15]. Inspiratory muscle endurance (IME) was measured during a constant load breathing test using the POWERbreathe KH1© device (POWERbreathe International Ltd., Southam, UK) according to a published protocol [16]. 2 Peripheral muscle strength. Lower limb muscle strength was determined using the 1-min sit-to-stand (1-min STS) test according to a standardized protocol [17]. Upper limb muscle strength (handgrip force) was assessed using a hand-held dynamometer (Jamar®, Patterson Medical, IL, USA) according to a standardized protocol [18]. 3 Lung function. Forced spirometry measurements were assessed using a portable spirometer (Spirobank II USB®, MIR, Rome, Italy) according to ATS/ERS guidelines [19]. 4 Cognitive and psychological status. The Montreal Cognitive Assessment was used as a screening test to estimate the severity of the participants’ global cognitive impairment [20]. Anxiety/depression levels were evaluated with the Hospital Anxiety and Depression Scale [21]. Post-traumatic stress disorder (PTSD) was evaluated using the PTSD CheckList-Civilian Version (PCL-C) self-rating questionnaire [22]. Interventions Participants undertook a home-based respiratory muscle training programme using a threshold pressure device. The regimen was 40 min/day, split into two 20-min sessions (morning and afternoon), 6 times per week, over 8 weeks. The training load, regardless of whether participants performed IMT or RMT (real or sham), was individually tailored and increased according to the same distribution schedule for both inspiratory and expiratory muscle training. The only difference between the real and sham groups was the device. Specifically, the sham groups received a device without resistance (0 cm H2O) because it lacked a threshold valve. A complete description of the respiratory muscle training programme based on the Template for Intervention Description and Replication (TIDieR checklist) can be found in Appendix B. All evening weekly sessions of the study were supervised by a physiotherapist through a virtual platform, who was blinded to the group allocation. The participants allocated to the sham group shared the training sessions with the participants allocated to the experimental group, receiving the same training programme to avoid bias related to the amount of attention. Sample size calculation To detect between-group differences for primary outcomes (HRQoL and exercise tolerance), we chose a repeated measures analysis of variance (ANOVA) with an interaction within-between factors, given that the main factor of interest was the group × time interaction (4 groups and 3 measurement times). According to the criteria established by Cohen [23], an effect size of moderate magnitude is required to detect clinically relevant differences. A small-moderate effect size (f=0.20) was chosen because the differences for primary outcomes between the two actual training groups were likely to be small [24] and to increase the study's power. This theoretical model was chosen because of the lack of previous information on the effects of interventions on individuals with long-term post-COVID-19 symptoms. Thus, applying a significance level of 5% (α error) and a power of 90% (β error = 10%), a sample size of 76 participants was established. Assuming a 15% dropout rate, the study sample size was set at least at 88 participants (22 per group). Data analysis The Statistical Package for Social Sciences 21 (SPSS Inc., Chicago, IL USA) software was employed to analyse the data. A p-value <0.05 was considered statistically significant, and an intention-to-treat analysis was performed. The Kolmogorov-Smirnov test and normal probability Q-Q plots showed that the underlying residuals of the variables had a reasonably normal distribution. Missing data were imputed by the missForest (sequential random forest) multiple imputation method using the “missForest” package of R software (version 4.1.2). This method has been shown to produce the lowest imputation error for continuous and categorical variables [25]. The effects of the interventions on the quantitative variables were assessed by a separate 4 × 3 mixed-model ANOVA. The group × time interaction was considered the main hypothesis of interest. Partial eta-squared (η2 p) was calculated as a measure of effect size (strength of association) for the interaction in the ANOVAs. Multiple comparisons with Bonferroni adjustment were performed in the case of significant ANOVA findings. Following Vicker's recommendations [26], between-group differences after the interventions were compared using an analysis of covariance adjusting for the values of the respective outcomes at baseline, given that this analysis has the highest statistical power [26]. Between-group effect sizes (Cohen's d) were classified as small (<0.5), medium (0.5–0.7) or large (≥0.8) [23]. Lastly, the effects of the interventions on the qualitative variables (dyspnoea and fatigue) at the end of treatment were assessed with binary logistic regression analysis. Results A total of 154 individuals were screened between February 2021 and July 2021 for study participation. Fig. 1 shows the flow diagram of the participants through the study. The baseline characteristics of the included individuals did not differ significantly between the 4 groups (Table 1 ). Five individuals dropped out of the study for various reasons (Fig. 1), and only 1 person presented adverse effects during the study (symptom exacerbation); however, this person belonged to the IMTsham group, so the worsening was not considered due to the intervention. Analyses of the training session compliance diaries revealed that the participants in all groups completed more than 95% of the training. Most of the participants felt that they had undergone real training, with only 8 (20%) of them believing that they had undergone sham training (3 from the IMTsham group and 5 from the RMTsham group).Fig. 1 Flowchart. Fig 1 Table 1 Descriptive data on anthropometry, smoking habits, COVID-19 related medical history and clinical characteristics of the sample. Values are mean (SD) and n (%). Table 1 Muscle Training Groups Inspiratory Inspiratorysham Respiratory Respiratorysham (n = 22) (n = 22) (n = 22) (n = 22) Anthropometric characteristics Age (years) 48.9 (8.3) 45.3 (12.8) 46.5 (9.6) 45 (10.2) Sex (female) 17 (77 %) 16 (73 %) 14 (64 %) 16 (73 %) Height (m) 1.6 (0.1) 1.7 (0.1) 1.7 (0.1) 1.7 (0.1) Weight (Kg) 71.7 (15.5) 77.2 (17) 76 (17.6) 78.1 (19.1) Body mass index (Kg/m2) 26.6 (6.6) 27.1 (5.1) 27 (5.2) 27.1 (6) Smoking habits Non-smoker 10 (45 %) 11 (50 %) 11 (50 %) 13 (59 %) Ex-smoker 6 (27 %) 8 (36 %) 6 (27 %) 7 (32 %) Smoker 6 (27 %) 3 (14 %) 5 (23 %) 2 (9 %) Cumulative amount of smoking (in pack-years) 8.3 (15.5) 6.8 (12.7) 4.6 (7.4) 5.8 (12.9) COVID-19 related medical history Time from COVID-19 diagnosis (days) 349 (86) 334 (107) 364 (67) 356 (95) Pneumonia 8 (36 %) 9 (41 %) 9 (41 %) 9 (41 %) Hospital admission 8 (36 %) 7 (32 %) 6 (27 %) 7 (32 %) Intensive care unit admission 1 (5 %) 0 (0 %) 2 (9 %) 2 (9 %) Clinical characteristics Dyspnoea 22 (100 %) 22 (100 %) 22 (100 %) 22 (100 %) Fatigue 22 (100 %) 22 (100 %) 22 (100 %) 22 (100 %) Chest pain 13 (59 %) 12 (55 %) 12 (55 %) 15 (68 %) Joint pain 8 (36 %) 8 (36 %) 9 (41 %) 7 (32 %) Muscle pain 7 (32 %) 8 (36 %) 8 (36 %) 8 (36 %) Headache 10 (45 %) 6 (27 %) 8 (36 %) 9 (41 %) Brain fog 13 (59 %) 10 (45 %) 13 (59 %) 13 (59 %) Insomnia 11 (50 %) 8 (36 %) 8 (36 %) 8 (36 %) Tachycardia 6 (27 %) 7 (32 %) 6 (27 %) 5 (23 %) Tingling 7 (32 %) 7 (32 %) 6 (27 %) 4 (18 %) Anosmia 3 (14 %) 3 (14 %) 1 (5 %) 3 (14 %) Dysphonia 2 (9 %) 1 (5 %) 1 (5 %) 4 (18 %) Medications Corticosteroids 1 (5%) 2 (9%) 1 (5%) 1 (5%) Muscle relaxant 1 (5%) 2 (9%) 1 (5%) 3 (14%) Sedatives 4 (18%) 2 (9%) 2 (9%) 4 (18%) Analgesics 5 (23%) 4 (18%) 3 (14%) 3 (14%) Antidepressants 1 (5%) 2 (9%) 2 (9%) 2 (9%) Antiepileptics - - 2 (9%) 1 (5%) Health-related quality of life and exercise tolerance The results of the HRQoL and exercise tolerance outcomes are presented in Table 2 . There were statistically significant interactions between the time and group factors for HRQoL outcomes [EQ-5D-5L, index (F=2.459; p=0.031; η2=0.081) and VAS (F=3.373; p=0.004; η2=0.108)]. There were no statistically significant between-group differences in terms of quality of life at the 4-week follow-up. At post-intervention, there was statistically significant improvement in HRQoL with a large magnitude (d>0.9) in the RMT group compared with the RMTsham group. The VAS-reported quality of life showed moderate-high improvements (d≈0.7) but did not reach statistical significance in the IMT and RMT groups compared with the RMTsham and IMTsham groups, respectively. In addition, all groups showed significantly improved quality of life after the intervention with respect to baseline values (IMT, p<0.001; IMTsham, p=0.003; RMT, p<0.001), except for the RMTsham group.Table 2 Change in health-related quality of life and physical function outcomes. Table 2Outcome Muscle Training Groups Mean (SD) Baseline 4-weeks Post-intervention HRQoL, EQ-5D-5L (index) Inspiratory 0.583 (0.204) 0.686 (0.215)a 0.746 (0.199)a Inspiratorysham 0.622 (0.201) 0.739 (0.148)a 0.742 (0.193)a Respiratory 0.615 (0.253) 0.725 (0.237)a 0.827 (0.203)a,b Respiratorysham 0.628 (0.180) 0.701 (0.194) 0.684 (0.212) EQ-5D-5L (VAS) Inspiratory 52.5 (15.3) 63.6 (16.8)a 68.4 (14.7)a Inspiratorysham 58.9 (16.3) 65.0 (17.2) 68.1 (17.6)a Respiratory 60.2 (15.5) 65.7 (17.3) 79.9 (14.3)a,b Respiratorysham 55.3 (15.6) 60.9 (17.9) 60.1 (21.7) CRF (Ruffier index) Inspiratory 8.3 (2.8) 9.4 (3.6) 7.7 (3.3)b Inspiratorysham 8.5 (3.7) 9.6 (2.8) 8.8 (3.6) Respiratory 9.2 (2.6) 9.7 (2.8) 7.6 (2.4)a,b Respiratorysham 8.9 (2.5) 9.1 (2.6) 8.7 (2.7) 1-min STS (n of squats) Inspiratory 33.9 (7.8) 39.4 (7.4)a 46.0 (8.2)a,b Inspiratorysham 35.3 (8.1) 37.2 (7.7)a 38.6 (8.6) Respiratory 34.5 (7.3) 39.3 (7.3)a 45.5 (7.8)a,b Respiratorysham 35.9 (7.3) 38.1 (8.4) 39.4 (8.8) Handgrip (Kg) Inspiratory 26.3 (8.6) 28.0 (7.6) 29.2 (9.5)a Inspiratorysham 26.0 (8.2) 28.1 (8.0) 29.8 (9.0)a Respiratory 31.4 (10.5) 32.6 (11.1) 34.0 (10.0)a Respiratorysham 30.0 (7.4) 30.7 (9.0) 32.3 (8.7)a Outcome Comparisons between Muscle Training Groups Adjusted difference (95%CI); Cohen's d At 4-weeks At Post-intervention HRQoL, EQ-5D-5L (index) Inspiratory vs Inspiratorysham -0.027 (-0.144; 0.090); d=0.1 0.029 (-0.094; 0.151); d=0.3 Inspiratory vs Respiratory -0.017 (-0.134; 0.100); d=0.1 -0.060 (-0.182; 0.063); d=0.1 Inspiratory vs Respiratorysham 0.015 (-0.102; 0.132); d=0.4 0.091 (-0.032; 0.214); d=0.7 Inspiratorysham vs Respiratory 0.010 (-0.107; 0.127); d=0.1 -0.089 (-0.211; 0.034); d=0.5 Inspiratorysham vs Respiratorysham 0.042 (-0.075; 0.159); d=0.3 0.062 (-0.060; 0.184); d=0.4 Respiratory vs Respiratorysham 0.032 (-0.085; 0.149); d=0.3 0.151 (0.029; 0.273); d=1⁎⁎ EQ-5D-5L (VAS) Inspiratory vs Inspiratorysham 3.8 (-5.9; 13.5); d=0.5 4.4 (-7.1; 15.9); d=0.5 Inspiratory vs Respiratory 4.2 (-5.6; 14.0); d=0.5 -6.2 (-17.8; 5.4); d=0.3 Inspiratory vs Respiratorysham 5.1 (-4.6; 14.7); d=0.5 10.1 (-1.4; 21.5); d=0.7 Inspiratorysham vs Respiratory 0.4 (-9.2; 10.1); d=0.1 -10.6 (-22.1; 0.8); d=0.7 Inspiratorysham vs Respiratorysham 1.3 (-8.4; 10.9); d<0.1 5.7 (-5.8; 17.1); d=0.3 Respiratory vs Respiratorysham 0.8 (-8.9; 10.5); d<0.1 16.3 (4.8; 27.8); d=0.9⁎⁎ CRF (Ruffier index) Inspiratory vs Inspiratorysham Grouptime interaction not statistically significant Inspiratory vs Respiratory Inspiratory vs Respiratorysham Inspiratorysham vs Respiratory Inspiratorysham vs Respiratorysham Respiratory vs Respiratorysham 1-min STS (n of squats) Inspiratory vs Inspiratorysham 2.8 (-2.6; 8.3); d=0.4 8.0 (1.6; 14.4); d=1.1⁎⁎ Inspiratory vs Respiratory 0.3 (-5.1; 5.8); d=0.1 0.7 (-5.6; 7.1); d=0.1 Inspiratory vs Respiratorysham 2.3 (-3.2; 7.8); d=0.4 7.4 (1.0; 13.8); d=1.0 Inspiratorysham vs Respiratory -2.5 (-8.0; 3.0); d=0.4 -7.2 (-13.6; -0.9); d=0.8* Inspiratorysham vs Respiratorysham -0.5 (-6.0; 4.9); d<0.1 -0.6 (-6.9; 5.8); d<0.1 Respiratory vs Respiratorysham 2.0 (-3.5; 7.5); d=0.4 6.7 (0.3; 13.1); d=0.8* Handgrip (Kg) Inspiratory vs Inspiratorysham Grouptime interaction not statistically significant Inspiratory vs Respiratory Inspiratory vs Respiratorysham Inspiratorysham vs Respiratory Inspiratorysham vs Respiratorysham Respiratory vs Respiratorysham a Statistically significant differences with respect to baseline values p<0.05. b Statistically significant differences with respect to the 4-weeks values p<0.05. ⁎ Statistically significant differences p<0.05. ⁎⁎ Statistically significant differences p<0.01. CRF, cardiorespiratory fitness; HRQoL, health-related quality of life; STS, sit-to-stand; VAS, visual analogue scale. There were no statistically significant interactions between the time and group factors for exercise tolerance. There were no statistically significant between-group differences for exercise tolerance. With respect to baseline values, however, exercise tolerance improved only in the RMT group (p=0.047). Long-term post-COVID-19 symptoms and respiratory muscle function The binary logistic regression analysis revealed statistically significant between-group differences only for long-term post-COVID-19 dyspnoea after the intervention (Wald test=10.6; p=0.014) and not for long-term post-COVID-19 fatigue (Wald test=5.4; p=0.142). Specifically, the RMT group had an approximately 5–6-fold greater reduction in dyspnoea than the 2 sham groups (IMTsham, p=0.031; RMTsham, p=0.008), whereas the IMT group had an approximately 5-fold greater reduction in dyspnoea than the RMTsham group (p=0.017) (Fig. 2 ). There were no statistically significant differences among the other groups.Fig. 2 Change in dyspnoea and fatigue outcomes. Statistically significant associations are presented as Odds Ratios (95%CI); p-value. Fig 2 The results of the respiratory muscle function outcomes are presented in Table 3 . There were statistically significant interactions between the time and group factors for respiratory muscle function outcomes (MIP: F=5.797; p<0.001; η2=0.172; MEP: F=6.840; p<0.001; η2=0.196; and IME: F=5.231; p<0.001; η2=0.157). Both real training groups showed a statistically significant and large (d>0.8) increase in inspiratory muscle strength and endurance compared with the 2 sham groups at the postintervention. Expiratory muscle strength showed a statistically significant and large (d=0.9–1.4) increase in the RMT group compared with the other 3 groups, both at 4-week follow-up and at the postintervention. In addition, all groups significantly improved MIP (IMT and RMT, p<0.001; IMTsham, p=0.004; RMTsham, p=0.006), MEP (IMT, RMT and RMTsham, p<0.001; IMTsham, p=0.032), and IME (IMT, IMTsham and RMT, p<0.001; RMTsham, p=0.003) after the intervention with respect to baseline.Table 3 Change in respiratory muscle function outcomes. Table 3Outcome Muscle Training Groups Mean (SD) Baseline 4-weeks Post-intervention MIP (cmH2O) (% pred) Inspiratory 77.8 (21.6) 79 (19) 99.1 (13.6)a 101 (14) 109.5 (17.6)a,b 111 (18) Inspiratorysham 85.2 (22.8) 79 (20) 93.5 (23.6) 87 (21) 98.6 (21.9)a 91 (18) Respiratory 90.0 (22.1) 81 (17) 110.7 (25.3)a 99 (18) 123.5 (28.1)a,b 111 (21) Respiratorysham 91.4 (27.2) 86 (22) 102.4 (30.3)a 96 (24) 104.1 (27.5)a 98 (24) MEP (cmH2O) (% pred) Inspiratory 102.8 (28.6) 73 (18) 113.1 (18.2) 81 (13) 123.9 (24.9)a,b 88 (16) Inspiratorysham 104.9 (35.6) 69 (20) 108.2 (30.6) 71 (20) 116.0 (35.2)a,b 75 (22) Respiratory 113.1 (32.0) 71 (20) 143.5 (37.2)a 90 (17) 154.9 (38.7)a,b 97 (17) Respiratorysham 109.1 (39.9) 70 (23) 122.1 (36.1)a 78 (19) 127.1 (34.0)a 81 (19) IME (sec) Inspiratory 198.0 (101.1) 400.4 (184.2)a 456.7 (143.8)a Inspiratorysham 191.7 (99.3) 233.9 (115.4) 322.6 (174.8)a,b Respiratory 180.8 (96.9) 352.4 (129.9)a 459.3 (175.4)a,b Respiratorysham 194.8 (108.5) 288.6 (145.1)a 308.0 (130.6)a Outcome Comparisons between Muscle Training Groups Adjusted difference (95%CI); Cohen's d At 4-weeks At Post-intervention MIP (cmH2O) Inspiratory vs Inspiratorysham 11.5 (-1.1; 24.0); d=0.9 16.2 (1.8; 30.7); d=1.0 Inspiratory vs Respiratory -1.9 (-14.6; 10.8); d<0.1 -5.3 (-19.8; 9.3); d=0.2 Inspiratory vs Respiratorysham 7.5 (-5.3; 20.3); d=0.6 15.1 (0.5; 29.7); d=1.0* Inspiratorysham vs Respiratory -13.3 (-25.9; -0.8); d=0.8* -21.5 (-35.9; -7.2); d=1.0⁎⁎ Inspiratorysham vs Respiratorysham -3.9 (-16.5; 8.6); d=0.2 -1.1 (-15.5; 13.2); d<0.1 Respiratory vs Respiratorysham 9.4 (-3.1; 21.9); d=0.5 20.4 (6.0; 34.7); d=1.0⁎⁎ MEP (cmH2O) Inspiratory vs Inspiratorysham 6.5 (-8.4; 21.4); d=0.3 9.7 (-5.8; 25.1); d=0.5 Inspiratory vs Respiratory -22.6 (-37.6; -7.6); d=1.0⁎⁎ -22.6 (-38.1; -7.0); d=0.9⁎⁎ Inspiratory vs Respiratorysham -4.2 (-19.2; 10.7); d=0.1 2.0 (-13.5; 17.5); d=0.1 Inspiratorysham vs Respiratory -29.1 (-44.1; -14.2); d=1.4⁎⁎ -32.3 (-47.8; -16.8); d=1.4⁎⁎ Inspiratorysham vs Respiratorysham -10.7 (-25.7; 4.2); d=0.5 -7.7 (-23.2; 7.8); d=0.3 Respiratory vs Respiratorysham 18.4 (3.5; 33.3); d=0.9⁎⁎ 24.6 (9.1; 40.0); d=1.0⁎⁎ IME (sec) Inspiratory vs Inspiratorysham 161.9 (58.4; 265.5); d=1.2⁎⁎ 130.9 (9.2; 252.5); d=0.8* Inspiratory vs Respiratory 35.6 (-68.2; 139.3); d=0.2 -11.5 (-133.3; 110.3); d=0.1 Inspiratory vs Respiratorysham 109.4 (5.9; 213.0); d=0.7* 147.0 (25.4; 268.6); d=1.0⁎⁎ Inspiratorysham vs Respiratory -126.4 (-230.0; -22.8); d=1.2⁎⁎ -142.4 (-264.1; -20.6); d=0.9* Inspiratorysham vs Respiratorysham -52.5 (-156.0; 51.0); d=0.5 16.1 (-105.5; 137.8); d=0.1 Respiratory vs Respiratorysham 73.9 (-29.8; 177.5); d=0.6 158.5 (36.7; 280.3); d=1.1⁎⁎ a Statistically significant differences with respect to baseline values p<0.05. b Statistically significant differences with respect to the 4-weeks values p<0.05. ⁎ Statistically significant differences p<0.05. ⁎⁎ Statistically significant differences p<0.01. IME, inspiratory muscle endurance; MIP, maximal inspiratory pressure; MEP, maximal expiratory pressure; % pred, percentage of predicted value. Peripheral muscle strength The results of the peripheral muscle strength outcomes are presented in Table 2. The only physical function variable that showed a statistically significant group × time interaction was lower limb muscle strength (1-min STS: F=3.833; p=0.001; η2=0.120). There were no statistically significant between-group differences in lower limb muscle strength at the 4-week follow-up. At the postintervention, both real training groups showed a statistically significant and large (d≥0.8) increase in lower limb muscle strength compared with the 2 sham groups, especially the RMT group (d≥1). Only the real training groups showed a significant increase in lower limb muscle strength after the intervention over baseline (IMT and RMT, p<0.001). There were no statistically significant between-group differences for upper limb muscle strength. With respect to baseline values, however, upper limb muscle strength increased significantly in all groups (IMT, p=0.002; IMTsham, p<0.001; RMT, p=0.008; RMTsham, p=0.023). Lung function The results of the lung function outcomes are presented in Table 4 . The only lung function variable that showed a statistically significant group × time interaction was peak expiratory flow (PEF; F=3.612; p=0.003; η2=0.114). The RMT group showed a statistically significant and large (d≥0.9) increase in PEF compared with the other 3 groups at the postintervention. In fact, only the RMT group significantly increased PEF after the intervention with respect to baseline (p<0.001).Table 4 Change in pulmonary function outcomes. Table 4Outcome Muscle Training Groups Mean (SD) Baseline 4-weeks Post-intervention FVC (liters) (% pred) Inspiratory 3.8 (0.7) 118 (16) 3.9 (0.8) 121 (17) 3.9 (0.8) 120 (18) Inspiratorysham 4.0 (1.0) 108 (16) 4.0 (0.9) 109 (14) 4.0 (1.0) 110 (14) Respiratory 4.1 (0.9) 113 (17) 4.2 (0.9) 117 (17) 4.2 (1.0) 118 (20) Respiratorysham 4.0 (0.7) 110 (20) 4.1 (0.8) 112 (19) 4.1 (0.8) 112 (16) FEV1 (liters) (% pred) Inspiratory 2.9 (0.5) 107 (15) 3.0 (0.6) 109 (17) 3.0 (0.6) 109 (17) Inspiratorysham 3.1 (0.9) 101 (17) 3.1 (0.8) 101 (15) 3.0 (0.9) 100 (16) Respiratory 3.1 (0.8) 100 (19) 3.1 (0.7) 103 (16) 3.1 (0.8) 104 (19) Respiratorysham 3.1 (0.6) 99 (19) 3.1 (0.6) 102 (18) 3.1 (0.6) 102 (15) FEV1/FVC (%) Inspiratory 77 (5) 76 (6) 76 (4) Inspiratorysham 78 (6) 78 (6) 76 (6) Respiratory 74 (8) 74 (4) 74 (6) Respiratorysham 76 (6) 76 (7) 76 (8) PEF (liters) (% pred) Inspiratory 6.5 (1.3) 98 (19) 6.5 (1.2) 97 (16) 6.5 (1.1) 98 (18) Inspiratorysham 7.1 (2.1) 96 (25) 7.0 (2.0) 97 (23) 6.8 (2.2) 94 (24) Respiratory 6.6 (1.8) 89 (20) 7.3 (2.2)a 100 (25) 7.6 (1.8)a 104 (20) Respiratorysham 7.2 (1.7) 98 (24) 7.4 (1.6) 102 (20) 7.3 (1.6) 101 (20) Outcome Comparisons between Muscle Training Groups Adjusted difference (95%CI); Cohen's d At 4-weeks At Post-intervention FVC (liters) Inspiratory vs Inspiratorysham Grouptime interaction not statistically significant Inspiratory vs Respiratory Inspiratory vs Respiratorysham Inspiratorysham vs Respiratory Inspiratorysham vs Respiratorysham Respiratory vs Respiratorysham FEV1 (liters) Inspiratory vs Inspiratorysham Grouptime interaction not statistically significant Inspiratory vs Respiratory Inspiratory vs Respiratorysham Inspiratorysham vs Respiratory Inspiratorysham vs Respiratorysham Respiratory vs Respiratorysham FEV1/FVC (%) Inspiratory vs Inspiratorysham Grouptime interaction not statistically significant Inspiratory vs Respiratory Inspiratory vs Respiratorysham Inspiratorysham vs Respiratory Inspiratorysham vs Respiratorysham Respiratory vs Respiratorysham PEF (liters) Inspiratory vs Inspiratorysham -0.1 (-1.0; 0.8); d<0.1 0.1 (-0.7; 0.8); d=0.1 Inspiratory vs Respiratory -0.7 (-1.6; 0.1); d=0.6 -1.1 (-1.9; -0.3); d=1.2⁎⁎ Inspiratory vs Respiratorysham -0.4 (-1.3; 0.5); d=0.3 -0.3 (-1.1; 0.5); d=0.3 Inspiratorysham vs Respiratory -0.6 (-1.5; 0.2); d=0.7 -1.2 (-1.9; -0.4); d=1.2⁎⁎ Inspiratorysham vs Respiratorysham -0.3 (-1.2; 0.6); d=0.3 -0.4 (-1.2; 0.4); d=0.4 Respiratory vs Respiratorysham 0.3 (-0.5; 1.2); d=0.4 0.8 (0.01; 1.6); d=0.9 a Statistically significant differences with respect to baseline values p<0.05. ⁎ Statistically significant differences p<0.05. ⁎⁎ Statistically significant differences p<0.01. FEV1, force expiratory volume 1st second; FVC, force vital capacity; PEF, peak expiratory flow; % pred, percentage of predicted value. Cognitive and psychological status The results of the cognitive and psychological status outcomes are presented in Appendix C. There were no statistically significant interactions between the time and group factors for the cognitive and psychological status outcomes. There were no statistically significant differences between the groups for the cognitive and psychological status. However, all groups significantly improved their cognitive function (IMT and RMTsham, p<0.001; IMTsham, p=0.022; RMT, p=0.011) and reduced their distress (anxiety and depression; IMT and IMTsham, p<0.001; RMT, p=0.005) and post-traumatic stress (IMT and RMT, p<0.001; IMTsham, p=0.004; RMTsham, p=0.017) after the intervention with respect to baseline, except for the distress of the RMTsham (p=0.083). Discussion This study assessed the effects of a home-based respiratory muscle training programme monitored by videoconferencing on the HRQoL and exercise tolerance in individuals with long-term post-COVID-19 symptoms. The respiratory muscle training programme was effective in improving HRQoL, but not exercise tolerance, only when combined inspiratory and expiratory muscle training was performed. The participants complied with all interventions and achieved large and clinically important improvements in lower limb muscle strength, inspiratory muscle strength and endurance regardless of the muscle group trained. The RMT group (inspiratory/expiratory muscles) achieved higher improvements in expiratory muscle function (i.e., strength and PEF) compared with the 2 sham training groups. With regard to the comparison between the 2 real training groups (combined training vs. IMT in isolation), the RMT group had superior results for expiratory muscle function (i.e., strength and PEF) to those of the IMT group. To our knowledge, no previous studies have investigated the clinical effects of a home-based respiratory muscle training programme on individuals with long-term post-COVID-19 symptoms. Therefore, the results are discussed considering other respiratory conditions with similar features, while recognizing the many differences between them. Only one study has investigated the effects of a 6-week respiratory rehabilitation programme including respiratory muscle training on elderly people with COVID-19 [27]. However, the findings should be interpreted with caution due to the very low methodological quality of the study, which might have biased its results and conclusions. For this reason, those results should not be compared with ours. Another pilot study [28] involved individuals who had recovered from COVID-19 after weaning from mechanical ventilation. The participants performed a 2-week IMT programme and showed similar improvements in HRQoL, dyspnoea and functional performance, except for lung function, although the two samples of individuals (theirs and ours) are not in fact comparable. Specifically, the study by Abodonya et al. [28] consisted mainly of men who had required intensive care unit admission and with reduced lung function. However, our sample consisted mostly of young women (∼50 years mean age) who had not required mechanical ventilation and with preserved lung function, but with long-term post-COVID-19 symptoms. Hence, our sample reinforces findings that middle-aged women have a higher risk of long-term post-COVID-19 symptoms [29]. The relevance of respiratory muscle training in severe COVID-19 has already been proven; however, our findings provide new evidence for the effects of this intervention on individuals with long-term post-COVID-19 symptoms. Health-related quality of life and exercise tolerance Only the RMT group showed a large and clinically important increase in HRQoL compared with the RMTsham group, with no differences in the other between-group comparisons. This improvement could be associated with an increase in the metaboreflex activation threshold, improving functional capacity, which could have an impact on constructs such as mobility and daily life activities, improving individual-reported outcomes related to the physical component. In addition, the RMT group was the only one that could be considered to have had a clinical improvement in quality of life compared with both sham training groups, given that the effect size with respect to the IMTsham group was large and exceeded the minimum clinically important difference established for the VAS of the EQ-5D-5L [30]. The coordinated action of the diaphragm and the abdominal muscles is essential for trunk stabilisation to perform functional activities [31]. Therefore, when individuals improve the strength of their expiratory muscles, they will also increase their ability to perform activities that require spinal stability, such as numerous activities of daily living. This aspect could explain the superiority observed in the RMT group (and not in the IMT group) compared with the sham training groups. However, it should be emphasised that the RMT group did not show statistically significant differences in quality of life compared to the IMTsham, so our hypothesis and results should be interpreted with caution. In the temporary situation of uncertainty and social distancing in which the study took place, it is possible that the fact of receiving care, as well as an individualised training programme by health professionals, could generate a hormonal and immunological response in our participants that was responsible for their therapeutic improvement [32]. However, although the placebo effect would also affect the real training groups, our results seem to show that respiratory muscle training provides a greater improvement in quality of life than is attributable to the placebo effect alone. Further research involving a larger training duration is needed to further substantiate these results. We hypothesized that, as has been observed in other diseases [33,34], respiratory muscle training could increase exercise tolerance by attenuating the inspiratory metaboreflex [8]. However, this hypothesis was not confirmed, as the actual training of the respiratory muscles was not superior to the placebo training. The lack of effect on cardiorespiratory fitness could be because the respiratory muscle training intervention is not a dynamic exercise and therefore does not produce an increased workload on the cardiovascular system. Larger studies are needed to examine the effectiveness of the combined intervention (RMT group) proposed in this study as an adjunct to another exercise programme (i.e., aerobic exercises) on the functional exercise tolerance of individuals with long-term post-COVID-19 symptoms. Long-term post-COVID-19 symptoms and respiratory muscle function With respect to the long-term post-COVID-19 symptoms, the RMT group had an approximately 5–6-fold greater reduction in their dyspnoea than both sham groups after the intervention, whereas the IMT group had an approximately 5-fold grater reduction in their dyspnoea than the RMTsham group. Changes in fatigue failed to reach statistical significance but resulted in improvement. These results might be explained by the fact that respiratory muscle training can decrease the neural respiratory drive by improving respiratory mechanics [35] and increasing MIP, thereby reducing the ratio of inspiratory effort (oesophageal pressure expressed as a fraction of maximal oesophageal pressure at isovolume; Pes/MIP) [36], which, in turn, reduces the sensation of dyspnoea. The lack of effect on fatigue would suggest that other types of training should be applied for this long-term post-COVID-19 symptom. Both real training groups produced a large increase (d>0.8) in inspiratory muscle strength and endurance compared with both sham groups at the postintervention assessment. The improvement in MIP could be considered clinically relevant because the effect size was large, especially because it exceeded the recently redefined minimum clinically important difference of 17 cmH2Ocm 2O in other populations [37,38]. Our results are in line with a case series that demonstrated that moderate intensity respiratory muscle training could lead to greater improvements and are perfectly tolerated by individuals with COVID-19, even by those who had been admitted to intensive care units [39]. Our findings would reinforce evidence supporting that improvements in inspiratory muscle function depend on the magnitude of the inspiratory load, with more substantial gains achieved by individuals training at higher loads [33]. Furthermore, the minimum clinically important difference for inspiratory endurance time (261 s) [34] was exceeded by the RMT group and was approached by the IMT group. This result is supported by a study of individuals with chronic obstructive pulmonary disease that showed significant increases in the proportion of type I fibres and the size of type II fibres in the external intercostal muscles after IMT [40]. Expiratory muscle strength showed a large increase (d=0.88–1.42) in the RMT group compared with the other 3 groups, which was to be expected. Peripheral muscle strength The respiratory muscle training, regardless of the modality, increased the muscle strength in the lower body. In fact, both real training groups showed a large effect size compared with the sham training groups and exceeded the minimum clinically important difference established for the 1-min STS test [41], so this increase could be considered clinically relevant. This finding is supported by a previous study [28] that showed similar improvements in functional performance after an IMT programme for individuals who had recovered from COVID-19. A potential explanation for this improvement in limb strength following the respiratory muscle training programme could be the attenuation of the inspiratory metaboreflex and, consequently, the larger proportion of cardiac output that can be redirected to peripheral muscles [8], as has been observed in other diseases [33,34]. Thus, the improvement in lower limb strength would be related to an indirect effect of the training programme, although the influence of other factors such as dyspnoea and/or fatigue during the test cannot be ruled out. In fact, the reduction in dyspnoea could be responsible for the increase in strength, as the training of the respiratory muscles reduced dyspnoea. However, upper limb grip strength did not increase. Grip strength requires less recruitment of muscle groups and therefore a lower energy cost than performing squats for 1 min [42]. In addition, a few seconds of isometric grip strength will result in less blood flow restriction than that caused by the eccentric/concentric contraction required for the 1-min STS. These reasons could explain why improvements would be obtained only in lower limb muscle strength, given that the metaboreflex would indirectly affect grip strength to a lesser extent. Lung function Changes in lung function failed to reach statistically significant between-group differences in line with meta-analyses in individuals with other respiratory diseases [43,44]. The intrinsic factors of airway limitation might be more determinant for spirometric values than the increase in respiratory muscle strength provided by the respiratory muscle training. Only the PEF value improved in the RMT group, given that expiratory muscle contraction is necessary to build up high positive intrapleural and intra-airway pressures for developing the PEF rate [45]. Cognitive and psychological status The cognitive and psychological status of the individuals with long-term post-COVID-19 symptoms improved over the 8-week period, irrespective of group. One possible explanation for these slight improvements might be that the supervision and reinforcement provided by the physiotherapist during the training sessions facilitated the participants’ progression [46]. Although our intervention was not focused on “group therapy”, the participants addressed personal issues during the sessions that were able to influence these outcomes [46], especially when dealing with new, destabilizing conditions. In addition, according to the cut-off scores [47,48], our cohort reported low levels of anxiety, depression and post-traumatic stress, which implies that individuals with long-term post-COVID-19 symptoms were still experiencing a deficit in their emotional aspects months after the acute infection. These results reinforce the literature reporting that infection-related psychiatric symptoms persist long after recovery [49]. Based on these findings, we hypothesise that emotional state might be one of the most important determinants for the development of long-term post-COVID-19 symptoms, as our sample had no other risk factors for their development (i.e. hypertension, obesity, psychiatric or immunosuppressive condition) [29]. In addition, it makes sense that individuals who had a more severe acute phase requiring hospital admission, another risk factor that may predispose a person to develop long-term post-COVID-19 symptoms [29], may also present more psychological impairment. Further studies focused on psychological interventions to treat these aspects in individuals with long-term post-COVID-19 symptoms are needed. Limitations The present study has certain limitations. First, conventional cardiopulmonary exercise testing was not used in this study to assess exercise tolerance. However, the field test employed has been demonstrated as reliable and valid for assessing cardiorespiratory fitness [14]. Second, the presence of dyspnoea and fatigue symptoms were collected as dichotomous variables; the effects of the intervention for varying levels of dyspnoea and fatigue could therefore not be calculated. Third, this study exclusively assessed the effects of respiratory muscle training, with greater improvements likely to be obtained when that training is combined with other exercise modalities such as cardiorespiratory and/or endurance exercise. In addition, although a sample size calculation was performed, the number of individuals per group was small, so extrapolation of the results to clinical practice should be done with caution. Lastly, the medium and long-term effects of the interventions were not evaluated because we considered it relevant to provide timely evidence-based data on the effects of respiratory muscle training in individuals with long-term post-COVID-19 symptoms, to show the advantages and challenges of rehabilitation, and to address the effects of this new condition that has radically changed our existence. Further studies with a more pragmatic approach should explore the full potential of various programmes in these individuals’ recovery and should include a longer follow-up and longer training durations. Clinical implications Therapies focused on respiratory muscle training could be included in treatments to improve inspiratory muscle strength and endurance in individuals with long-term post-COVID-19 symptoms, especially individuals with high levels of dyspnoea. After COVID-19 infection, individuals appear to have impaired respiratory muscle strength [50]; these improvements might therefore be of clinical relevance to these individuals. To obtain the greatest improvements in HRQoL and expiratory muscle function, the use of combined training (inspiratory/expiratory muscles) is recommended over their application in isolation. Remote supervision has the potential to improve access to rehabilitation programmes and help improve individuals’ adherence to training, which could increase the motivation of healthcare providers to prescribe and provide the intervention. Conclusions An 8-week home-based respiratory muscle training programme, supervised by videoconferencing, was effective in improving HRQoL, but not exercise tolerance, only when combined inspiratory and expiratory muscle training in individuals with long-term post-COVID-19 symptoms. The home-based respiratory muscle training programme was effective in improving inspiratory muscle strength/endurance and lower limb muscle strength regardless of the muscle group trained. The combined training was also more effective in improving expiratory muscle function (i.e., strength and PEF) than IMT and the 2 sham training modalities. However, respiratory muscle training had no impact on exercise tolerance, lung function, and cognitive and psychological status. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix Supplementary materials Image, application 1 Image, application 2 Image, application 3 Image, application 4 Funding The study was supported by grant from “Premio ayudas a la investigación en fisioterapia y covid-19” by the Illustrious Professional Association of Physiotherapists of the Community of Madrid, Spain. This funding source had no role in the design of this study and had no role during its execution, statistical analysis, interpretation of the data, or decision to submit results. Acknowledgements The authors thank the participants who took part in this study, the Long Covid ACTS and Covid persistente España (Persistent COVID Spain) for their help and especially Elizabeth Semper. Clinical Trials Registry. (ClinicalTrials.gov: NCT04734561) Supplementary material associated with this article can be found in the online version at doi:10.1016/j.rehab.2022.101709. ==== Refs References 1 Fernández-de-las-Peñas C Palacios-Ceña D Gómez-Mayordomo V Florencio LL Cuadrado ML Plaza-Manzano G Prevalence of post-COVID-19 symptoms in hospitalized and non-hospitalized COVID-19 survivors: A systematic review and meta-analysis Eur J Intern Med 92 2021 55 70 10.1016/J.EJIM.2021.06.009 34167876 2 Venkatesan P. NICE guideline on long COVID Lancet Respir Med 9 2021 129 10.1016/S2213-2600(21)00031-X 33453162 3 Raman B Cassar MP Tunnicliffe EM Filippini N Griffanti L Alfaro-Almagro F Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge EClinicalMedicine 31 2021 100683 10.1016/J.ECLINM.2020.100683 4 Miskowiak KW Johnsen S Sattler SM Nielsen S Kunalan K Rungby J Cognitive impairments four months after COVID-19 hospital discharge: Pattern, severity and association with illness variables Eur Neuropsychopharmacol 46 2021 39 48 10.1016/J.EURONEURO.2021.03.019 33823427 5 Mittal J Ghosh A Bhatt SP Anoop S Ansari IA Misra A. High prevalence of post COVID-19 fatigue in patients with type 2 diabetes: A case-control study Diabetes Metab Syndr 15 2021 102302 10.1016/J.DSX.2021.102302 6 Sheel AW Derchak PA Morgan BJ Pegelow DF Jacques AJ Dempsey JA. Fatiguing inspiratory muscle work causes reflex reduction in resting leg blood flow in humans J Physiol 537 2001 277 289 10.1111/J.1469-7793.2001.0277K.X 11711580 7 Ribeiro JP Chiappa GR Callegaro CC. The contribution of inspiratory muscles function to exercise limitation in heart failure: pathophysiological mechanisms Rev Bras Fisioter 16 2012 261 267 10.1590/S1413-35552012005000034 22801449 8 Shei RJ. Recent Advancements in Our Understanding of the Ergogenic Effect of Respiratory Muscle Training in Healthy Humans: A Systematic Review J Strength Cond Res 32 2018 2665 2676 10.1519/JSC.0000000000002730 29985221 9 Moreno AM Toledo-Arruda AC Lima JS Duarte CS Villacorta H Nóbrega ACL. Inspiratory Muscle Training Improves Intercostal and Forearm Muscle Oxygenation in Patients With Chronic Heart Failure: Evidence of the Origin of the Respiratory Metaboreflex J Card Fail 23 2017 672 679 10.1016/J.CARDFAIL.2017.05.003 28499979 10 Ribeiro JP Chiappa GR Neder AJ Frankenstein L. Respiratory muscle function and exercise intolerance in heart failure Curr Heart Fail Rep 6 2009 95 101 10.1007/S11897-009-0015-7 19486593 11 Beaumont M Forget P Couturaud F Reychler G. Effects of inspiratory muscle training in COPD patients: A systematic review and meta-analysis Clin Respir J 12 2018 2178 2188 10.1111/CRJ.12905 29665262 12 Turolla A Rossettini G Viceconti A Palese A Geri T. Musculoskeletal Physical Therapy During the COVID-19 Pandemic: Is Telerehabilitation the Answer? Phys Ther 100 2020 1260 1264 10.1093/PTJ/PZAA093 32386218 13 Herdman M Gudex C Lloyd A Janssen M Kind P Parkin D Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L) Qual Life Res 20 2011 1727 1736 10.1007/S11136-011-9903-X 21479777 14 Sartor F Bonato M Papini G Bosio A Mohammed RA Bonomi AG A 45-Second Self-Test for Cardiorespiratory Fitness: Heart Rate-Based Estimation in Healthy Individuals PLoS One 11 2016 e0168154 10.1371/JOURNAL.PONE.0168154 15 ATS/ERS Statement on respiratory muscle testing Am J Respir Crit Care Med 166 2002 518 624 10.1164/RCCM.166.4.518 12186831 16 Langer D Charususin N Jácome C Hoffman M McConnell A Decramer M Efficacy of a Novel Method for Inspiratory Muscle Training in People With Chronic Obstructive Pulmonary Disease Phys Ther 95 2015 1264 1273 10.2522/PTJ.20140245 25858974 17 Núñez-Cortés R Rivera-Lillo G Arias-Campoverde M Soto-García D García-Palomera R Torres-Castro R. Use of sit-to-stand test to assess the physical capacity and exertional desaturation in patients post COVID-19 Chron Respir Dis 18 2021 1479973121999205 10.1177/1479973121999205 18 Peolsson A Hedlund R Oberg B. Intra- and inter-tester reliability and reference values for hand strength J Rehabil Med 33 2001 36 41 10.1080/165019701300006524 11480468 19 Miller MR Crapo R Hankinson J Brusasco V Burgos F Casaburi R General considerations for lung function testing Eur Respir J 26 2005 153 161 10.1183/09031936.05.00034505 15994402 20 Delgado C Araneda A Behrens MI. Validation of the Spanish-language version of the Montreal Cognitive Assessment test in adults older than 60 years Neurologia 34 2019 376 385 10.1016/J.NRL.2017.01.013 28364958 21 Herrero MJ Blanch J Peri JM de Pablo J Pintor L Bulbena A. A validation study of the hospital anxiety and depression scale (HADS) in a Spanish population Gen Hosp Psychiatry 25 2003 277 283 10.1016/S0163-8343(03)00043-4 12850660 22 Miles JNV Marshall GN Schell TL. Spanish and English versions of the PTSD Checklist-Civilian version (PCL-C): testing for differential item functioning J Trauma Stress 21 2008 369 376 10.1002/JTS.20349 18720394 23 Cohen J. Statistical power analysis for the Behavioral Sciences 2nd Edition 1988 Routledge New York 24 Fabero-Garrido R del Corral T Angulo-Díaz-Parreño S Plaza-Manzano G Martín-Casas P Cleland JA Respiratory muscle training improves exercise tolerance and respiratory muscle function/structure post-stroke at short term: A systematic review and meta-analysis Ann Phys Rehabil Med 65 2021 101596 10.1016/J.REHAB.2021.101596 25 Alsaber A Al-Herz A Pan J AL-Sultan AT Mishra D. Handling missing data in a rheumatoid arthritis registry using random forest approach Int J Rheum Dis 24 2021 1282 1293 10.1111/1756-185X.14203 34382756 26 Vickers AJ. The use of percentage change from baseline as an outcome in a controlled trial is statistically inefficient: a simulation study BMC Med Res Methodol 1 2001 6 10.1186/1471-2288-1-6 11459516 27 Liu K Zhang W Yang Y Zhang J Li Y Chen Y. Respiratory rehabilitation in elderly patients with COVID-19: A randomized controlled study Complement Ther Clin Pract 39 2020 101166 10.1016/J.CTCP.2020.101166 28 Abodonya AM Abdelbasset WK Awad EA Elalfy IE Salem HA Elsayed SH. Inspiratory muscle training for recovered COVID-19 patients after weaning from mechanical ventilation: A pilot control clinical study Medicine 100 2021 e25339 10.1097/MD.0000000000025339 33787632 29 Crook H Raza S Nowell J Young M Edison P Long covid-mechanisms, risk factors, and management BMJ 374 2021 10.1136/BMJ.N1648 30 Nolan CM Longworth L Lord J Canavan JL Jones SE Kon SSC The EQ-5D-5L health status questionnaire in COPD: validity, responsiveness and minimum important difference Thorax 71 2016 493 500 10.1136/THORAXJNL-2015-207782 27030578 31 Saunders SW Rath D Hodges PW. Postural and respiratory activation of the trunk muscles changes with mode and speed of locomotion Gait Posture 20 2004 280 290 10.1016/J.GAITPOST.2003.10.003 15531175 32 Czerniak E Davidson M. Placebo, a historical perspective European Neuropsychopharmacology 22 2012 770 774 10.1016/j.euroneuro.2012.04.003 22608298 33 Gomes Neto M Ferrari F Helal L Lopes AA Carvalho VO Stein R The impact of high-intensity inspiratory muscle training on exercise capacity and inspiratory muscle strength in heart failure with reduced ejection fraction: a systematic review and meta-analysis Clin Rehabil 32 2018 1482 1492 10.1177/0269215518784345 29984598 34 Gosselink R de Vos J van den Heuvel SP Segers J Decramer M Kwakkel G. Impact of inspiratory muscle training in patients with COPD: what is the evidence? Eur Respir J 37 2011 416 425 10.1183/09031936.00031810 21282809 35 Jensen D Schaeffer MR Guenette JA. Pathophysiological mechanisms of exertional breathlessness in chronic obstructive pulmonary disease and interstitial lung disease Curr Opin Support Palliat Care 12 2018 237 245 10.1097/SPC.0000000000000377 30074922 36 O'Donnell DE Bertley JC Chau LKL Webb KA. Qualitative aspects of exertional breathlessness in chronic airflow limitation: pathophysiologic mechanisms Am J Respir Crit Care Med 155 1997 109 115 10.1164/AJRCCM.155.1.9001298 9001298 37 Balbás-Álvarez L Candelas-Fernández P del Corral T la Touche R López-de-Uralde-Villanueva I. Effect of Manual Therapy, Motor Control Exercise, and Inspiratory Muscle Training on Maximum Inspiratory Pressure and Postural Measures in Moderate Smokers: A Randomized Controlled Trial J Manipulative Physiol Ther 41 2018 372 382 10.1016/J.JMPT.2017.11.004 30001903 38 Iwakura M Okura K Kubota M Sugawara K Kawagoshi A Takahashi H Estimation of minimal clinically important difference for quadriceps and inspiratory muscle strength in older outpatients with chronic obstructive pulmonary disease: a prospective cohort study Phys Ther Res 24 2020 35 42 10.1298/PTR.E10049 33981526 39 Li L Yu P Yang M Xie W Huang L He C Physical Therapist Management of COVID-19 in the Intensive Care Unit: The West China Hospital Experience Phys Ther 101 2021 pzaa198 10.1093/PTJ/PZAA198 33152093 40 Ramírez-Sarmiento A Orozco-Levi M Güell R Barreiro E Hernandez N Mota S Inspiratory muscle training in patients with chronic obstructive pulmonary disease: structural adaptation and physiologic outcomes Am J Respir Crit Care Med 166 2002 1491 1497 10.1164/RCCM.200202-075OC 12406842 41 Vaidya T de Bisschop C Beaumont M Ouksel H Jean V Dessables F Is the 1-minute sit-to-stand test a good tool for the evaluation of the impact of pulmonary rehabilitation? Determination of the minimal important difference in COPD Int J Chron Obstruct Pulmon Dis 11 2016 2609 2616 10.2147/COPD.S115439 27799759 42 White C Dixon K Samuel D Stokes M. Handgrip and quadriceps muscle endurance testing in young adults Springerplus 2 2013 451 10.1186/2193-1801-2-451 24052933 43 Silva IS Fregonezi GAF Dias FAL Ribeiro CTD Guerra RO Ferreira GMH. Inspiratory muscle training for asthma Cochrane Database Syst Rev 2013 2013 CD003792 10.1002/14651858.CD003792.PUB2 44 Lee EN Kim MJ. Meta-analysis of the Effect of a Pulmonary Rehabilitation Program on Respiratory Muscle Strength in Patients with Chronic Obstructive Pulmonary Disease Asian Nurs Res (Korean Soc Nurs Sci) 13 2019 1 10 10.1016/J.ANR.2018.11.005 30481604 45 McCool FD. Global physiology and pathophysiology of cough: ACCP evidence-based clinical practice guidelines Chest 129 2006 48S 53S 10.1378/CHEST.129.1_SUPPL.48S 16428691 46 Boutevillain L Dupeyron A Rouch C Richard E Coudeyre E. Facilitators and barriers to physical activity in people with chronic low back pain: A qualitative study PLoS One 12 2017 e0179826 10.1371/JOURNAL.PONE.0179826 47 Yeager DE Magruder KM Knapp RG Nicholas JS Frueh BC. Performance characteristics of the posttraumatic stress disorder checklist and SPAN in Veterans Affairs primary care settings Gen Hosp Psychiatry 29 2007 294 301 10.1016/J.GENHOSPPSYCH.2007.03.004 17591505 48 Singer S Kuhnt S Götze H Hauss J Hinz A Liebmann A Hospital anxiety and depression scale cutoff scores for cancer patients in acute care Br J Cancer 100 2009 908 912 10.1038/sj.bjc.6604952 19240713 49 Mak IWC Chu CM Pan PC Yiu MGC Chan VL. Long-term psychiatric morbidities among SARS survivors Gen Hosp Psychiatry 31 2009 318 326 10.1016/J.GENHOSPPSYCH.2009.03.001 19555791 50 Boutou AK Georgopoulou A Pitsiou G Stanopoulos I Kontakiotis T Kioumis I. Changes in the respiratory function of COVID-19 survivors during follow-up: A novel respiratory disorder on the rise? Int J Clin Pract 75 2021 e14301 10.1111/IJCP.14301 33932073	36191860	PMC9708524	NO-CC CODE	2022-12-02 23:17:37	no	Ann Phys Rehabil Med. 2023 Feb 30; 66(1):101709	utf-8	Ann Phys Rehabil Med	2,022	10.1016/j.rehab.2022.101709	oa_other
==== Front J Commun Inq J Commun Inq JCI spjci The Journal of Communication Inquiry 0196-8599 1552-4612 SAGE Publications Sage CA: Los Angeles, CA 10.1177/01968599221141082 10.1177_01968599221141082 Original Article “I Don’t Want to Die in Here”: Absence and Vulnerability in COVID-19 News Coverage of Prisons https://orcid.org/0000-0003-0969-4785 Schneeweis Adina Foss Katherine A. 2 1 Department of Communication, Journalism, and Public Relations, Oakland University, Rochester, Michigan, USA 2 School of Journalism and Strategic Media, Middle Tennessee State University, Murfreesboro, Tennessee, USA Adina Schneeweis, Department of Communication, Journalism, and Public Relations, Oakland University, 317 Wilson Hall, Rochester, MI 48309, USA. Email: [email protected] 27 11 2022 27 11 2022 01968599221141082© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. This research examines how news constructed vulnerability in the coverage of COVID-19 and populations in prisons and jails. Focused on key moments during the moral panic around the pandemic, the analysis of publications from across the U.S. found substantial reporting earlier in 2020, and a striking absence and ignorance of key developments later into 2021. Six news discourses - journalistic objectivity, blaming and abandonment, vulnerability, compassion, vilification, and absence - were complicated by the climate of demonstrations for racial justice. COVID-19 news prisons vulnerability absence edited-statecorrected-proof typesetterts19 ==== Body pmc There's one side that says we’re all going to die… The other isn’t going to let it bother them. - Jim Quinn, The Atlanta Journal-Constitution, March 28, 2020 By spring 2020, the coronavirus COVID-19 had become a global health crisis spreading throughout the United States especially in congregate settings. At that time, approximately 2.3 million people were involuntarily confined in jails, prisons, and detention facilities and institutions (Sawyer & Wagner, 2020). March 26 marked the first COVID-19 death among inmates – Anthony Cheek in New York's Lee State Prison (Park & Meagher, 2020). Over the next two years, at least 593,611 prisoners tested positive and more than 2,898 died of the disease (“National COVID-19 Statistics”, 2022). The health status of incarcerated people has been of varying concern throughout the pandemic. Close-living quarters, changing inmate populations, inconsistent hygiene measures, and unreliable access to personal protective equipment (PPE) amplify disease in jail and prison environments, increasing the crisis inside and the spread to outside communities. And yet, prisoners denote a group that is typically stigmatized, marginalized, or ignored, a belief system bolstered by a focus in the news media on deviance or absence (Dunn, 2004). Particularly during trying times like a moral panic – that is, during an overwhelming state of hyper-awareness, stress, stigma, blame, anxiety, paranoia, conspiracy theories, and generalized fear, typical during outbreaks, epidemics, and pandemics (Awaludin, 2020; Gilman, 2010) – fear of others is enhanced and generalized to encompass all perceived as a threat or outside the norm (Awaludin, 2020). This research examines coverage of COVID-19 related to prison issues, from early in the pandemic through vaccine expansion to the general population. It contributes to the literature on the role of news in establishing meaning, and contributing to a public forum, about the incarcerated. It provides insight into the discourses that characterize the historic pandemic, and also into the larger collective, cultural, and institutional Discourse of the country's relationship with one of its marginalized communities. Background Even in non-pandemic times, prisoners face health issues at much higher rates than the general population, especially obesity, diabetes, high blood pressure, tuberculosis, hepatitis, HIV, other chronic and infectious diseases, and shorter life expectancy (Maruschak et al., 2015; Wildeman, 2016). Contagious diseases can spread quickly through confined spaces, infecting and sometimes killing large numbers. Prisons have been of concern during outbreaks such as the 1793 yellow fever epidemic in Philadelphia or nineteenth century cholera waves, as well as influenza and other respiratory diseases in the last century (Bick, 2007; Foss, 2020; Marland et al., 2020). Jail and prison populations are composed of already-vulnerable individuals, clustered together in environments that increase disease transmission. Despite this heightened risk, inmates have historically received the least attention in times of crisis; newspapers and other media outlets tend to focus on outbreaks’ threat to privileged classes, ignoring those who are most vulnerable to disease (Foss, 2020). Marginalized populations around the world are typically the most affected by the intersection of inequity and health. As people of color are disproportionately represented in prison populations – according to the Federal Bureau of Prisons, 38.1% of inmates are Black, compared to 14% of the public (“Inmate Race,” 2021; Tamir, 2021) – health inequalities for the incarcerated contribute to overall health disparities across race and ethnicity (Wildeman & Wang, 2017). COVID-19 has only exacerbated and shed light on vulnerability and disadvantaged communities (Hebert et al., 2008; Rendon et al., 2021). Since marginalization is defined as an imbalance of power and unequal relationships between groups, as well as exclusion from mainstream life in a society (Sevelius et al., 2020), it is important to investigate populations in jails and prisons and the severe effects of the COVID-19 pandemic on the vulnerable community. Theoretical Framework: COVID-19 Pandemic as a Moral Panic During health-related moral panics, medical information often becomes hyperbolic, personalized, or vilified in the social imaginary. People's reactions to the disease and understanding of the real threat to one's health increasingly have less to do with scientific data and nearly everything to do with the emotions experienced around the moral panic (Gilman, 2010). At their most basic level, moral panics are overblown reactions. They occur when a condition “emerges to become defined as a threat to societal values and interests,” generally generating “stylized and stereotypical” representations by “moral barricades” and moral figures – be they the media, politicians, opinion leaders, experts, or the clergy (Cohen, 1972, p. 9). Syphilis in the nineteenth century, the HIV/AIDS epidemic in the 1980s, the SARS outbreak in 2003 – COVID-19 since 2020 – such instances led to moral panics, with real political and socio-cultural implications and stigma. At the same time as all were significant health events, response to the diseases were determined equally by the development of medical knowledge as by societal meanings (Gilman, 2010). Exploratory research on the current pandemic has found specific behaviors and themes in people's reactions to the spread of COVID-19 as a moral panic – severe hypochondria, indifference, annihilation, nihilism, paranoia, sadness, fear, transmission of virus, shock, government blaming, anxiety, relating to past pandemics, worry for self, family, or others, information dissemination, composure, compliance, protection, cautiousness, optimism, and health consciousness (Nicomedes & Avila, 2020). Prison Coverage in the News Media Moral panics have been shown to also enhance a fear of immigrants, criminals, and in general of others, preliminarily confirmed in the contemporary context in trends towards overcriminalization (Awaludin, 2020). No other site illustrates this more clearly than media constructions of prison populations, as the platform where beliefs are enacted into representation and public discourse. Research on news constructions of crime and prison has long documented distorted representations and messages. Contemporary news articles perpetuate “traditional definitions of newsworthiness,” by centering around the “dangers of prison,” reinforcing stereotypes about prison, which paints incarceration as the primary solution to reducing crime (Cecil, 2019, p. 230). Reporting tends to oversimplify other salient topics (such as privatization of prisons), suggesting a disconnect between real life debates and their mediated coverage (Cecil, 2017; Montes et al., 2020). Overall, media typically constructs prison populations as other – marginalized either through deviance or through absence. Arguably, the latter is the more dangerous or harmful of the two strategies, as it practically eliminates the plight, needs, and voice of a group of people, dehumanizing them. As such, the approach has been used to justify policy and systemic discrimination (Dunn, 2004). News outlets also perpetuate racialized stereotypes in crime coverage. In their analysis of television crime news, Dixon and Williams (2015) found that network news underrepresented Black people for both crime victims and offenders, rendering them nearly invisible. News unequally racialize and stigmatize particular communities, without adequately covering the complex issues that contribute to the overcriminalization of Black and brown people (Dixon, 2007). Film and television portrayals also contribute to distorted perceptions of prison and criminal justice, exaggerating the prevalence of violent offenders and depicting prisons as places of violence (Britton, 2003; Cecil, 2017). Crime narratives heighten public interest in understanding the realism of depictions of the criminal justice system (Huey, 2010). Much of the research in this area has centered around cultivation, demonstrating the extent to which higher consumption contributes to a mediated view of reality, overestimated crime rates and risk of one's own victimization, and justified views of imprisonment over rehabilitation (Custers & Van den Bulck, 2011; Gerbner & Gross, 1976; Goidel et al., 2006; Romer et al., 2003). As for news constructions of the prison population in the context of the pandemic, studies have thus far focused on the reduction of community transmission, mitigation protocol, case studies of early outbreaks, and vaccine uptake in specific prison populations (Cingolani et al., 2021; Leibowitz et al., 2021; Lessard et al., 2022; Montoya-Barthelemy et al., 2020; Vest et al., 2021). The literature has also explored perspectives of the incarcerated during the pandemic. Pyrooz et al. (2020) interviewed inmates in April and May 2020, finding that most assumed that they would contract COVID-19, yet were not overly concerned about the risk, while Pettus-Davis et al. (2021) found that prisoners relied on television, and not correctional facility announcements, as the primary source of pandemic-related information for most inmates. What emerges thus far is that the existing literature has identified the role of news media during the pandemic, the exceptional health crisis and moral panic of COVID-19 in the prison populations, and the importance of inmates’ experiences. This study contributes to this literature by adding attention to how news media establish and convey messaging about the incarcerated to the general public. In our analysis, we therefore asked how news outlets covered the COVID-19 pandemic related to prisons and incarcerated populations. Does the coverage suggest a moral panic? If so, what aspects of a moral panic are evident in the news coverage? Do discourses shift over time? What is missing or absent in the news discourses? Methodology This study used critical discourse analysis (Fairclough, 2004) to analyze the news coverage of COVID-19 in jails and prisons. News stories were treated as texts rich with meaning. News subjects (the people and voices included) as well as object positions or cognitive aspects (practices talked about, the type of knowledge conveyed) were examined and categorized thematically. Emerging themes and patterns were then interpreted contextually, positioned in conversation with one another and within a wider cultural framework (Fairclough, 2004; Hall, 1997; Saukko, 2003; Schneeweis, 2015; Stewart, 2005). In frequent discussions, the authors refined the common stories emerging from the news coverage. Guiding questions in the analysis included, how is the prison and jail population spoken about in the news at key moments during the COVID-19 pandemic? How do news talk about the spread of the virus, the management and mitigation of the crisis, and vaccine distribution? What is included and what is omitted? What aspects of a moral panic are evident – and how are they constructed – in the news coverage? Starting from attention to the minute – wording, tone, emphasis, repetitions, metaphors, descriptions, and voices/sources in a story – the analysis contextualized the emerging little (d) discourses to make sense of the broader big (D) Discourse of the incarcerated in contemporary U.S. It is this larger Discourse that shapes beliefs and behavior (Fairclough, 1993; Gee, 1996; Rogers, 2002). As others have suggested, critical discourse analysis of news practices must also engage with the question of “why” and not just of “what” in news. The latter tends to overemphasize a journalistic preoccupation with, and overplay of, objectivity, a detached style of reporting that ignores ideological content in the name of fairness and routines of the craft (Xie, 2018) and is often reductive of the issues covered. To that effect, our analysis engages with the question of “why” such coverage of the pandemic in the context of prison populations? What is constructed as an explanation or justification for pandemic mitigation or vaccine-related decisions? Sample Three time-frames with key developments in the pandemic were selected for analysis: (1) March 11–May 8, 2020 to capture the pandemic beginning, including the rise in COVID-19 cases and deaths in prisons and jails; (2) December 1, 2020–January 31, 2021, which constituted the peak of the pandemic for prison populations (Park et al., 2021); and (3) May 1–July 1, 2021 when COVID-19 vaccines were approved for the broadest population in the U.S. News stories were collected through digital searches on ProQuest's U.S. Southeast Newsstream database, using the terms (1) “prison”/“incarcerated”/“jail” in conjunction with “COVID”/“coronavirus,” (2) “prison”/“incarcerated” and “COVID”/“vaccine,” and (3) “prison”/“incarcerated” and “COVID” for each time period respectively. The change in keyword selection mirrored a dramatic fall in the number of news stories as the pandemic evolved. Narrowing the dataset to stories featuring the keywords in the headlines and to U.S. newspapers in English only, and removing unrelated stories (for example, on people who served time in prison at some point in their past, etc.), news, features, human interest articles, and op-eds were included in the sample to gauge a comprehensive picture of the coverage. After removing duplicates, the search yielded 136 unique articles, published in 33 publications. Among the papers were The Baltimore Sun, The Christian Science Monitor, The Journal Record, The Washington Informer, and The Missoulian, yet nearly half of the reporting appeared in USA Today (17 articles or 12.5% of the sample), The Wall Street Journal (20 stories or 14.7% of the sample), and The Washington Examiner (23 news or 16.9% of the total articles). Worth mentioning is that, even though the reporting featured in regional and local publications across the country, about ten different publications (nearly a third of the sample) are based in Florida, a state that ranks third for prison population in the country, and tenth highest for imprisonment rate (World Population Review, 2022). From Factual Reporting to Vulnerability to Absence The news coverage across the U.S. at three key moments between March 2020 and June 2021 is characterized by distinct yet interconnected discourses – (1) journalistic objectivity that provides expected yet detached information about the challenges presented by the pandemic in jails and prisons; (2) a discourse of scapegoating and abandonment that constructs a villain to assign fault for the moral panic; (3) vulnerability that emphasizes the victim status of populations in prisons; (4) a related discourse of human rights; (5) a discourse that vilifies the prison population; and (6) a discourse of absence that is striking later in the pandemic in its ignorance of key developments around vaccination. Journalistic Objectivity This predominant discourse is characterized by reporting that “separates facts from values” with little to no interpretation (in linguistic choices or types of quotes), “balanced” voices that typically represent the proverbial two sides of a coin, short and to-the-point news stories, and no emotion or human-interest appeal (Schudson, 2001, p. 150). As such, the discourse used news briefs and announcements to cite official voices as subject positions (the Federal Bureau of Prisons [BOP], departments of corrections, governors, and mayors) who spoke to an efficient implementation of necessary measures – at the same time as the reporting acknowledged the unprecedented challenges of the time. The tone in hard news represented a fairly positive representation of the government's handling of the pandemic, and was especially prevalent in the reporting of the second and third data sets. At the peak of the winter surge when prison COVID-19 cases were at an all-time high, news focused on the number of positive cases and on mitigation measures with leads like, “The Tennessee Department of Correction reported Tuesday that it was suspending visits at four state prisons until further notice because of the coronavirus” (Hineman, 2020). Such news stories omitted description of the prison conditions, images of COVID-19 wards in prisons, and other humanizing elements. Coverage of vaccine prioritization debates used a similar framework, emphasizing the number of inmates and staff at risk for disease transmission, as opposed to how individuals felt or were experiencing the pandemic. The corresponding “other side” to officials’ handling of the pandemic appeared in news through the voice of advocacy groups like the American Civil Liberties Union (ACLU), the Southern Poverty Law Center, or prisoner watchdog groups such as the Justice Roundtable or Families Against Mandatory Minimums (FAMM). Their role has typically been to deplore hygiene conditions in prisons, capacity for social distancing, access to PPE, soap, and hand sanitizer for inmates, as well as quarantine, transfer, and release procedures, and campaigns and priorities for vaccination. The news offered the differing viewpoints at face value; for example, in The Tennessean, “Knoxville attorney Jonathan Cooper, president of the Tennessee Association of Criminal Defense Lawyers, said officials should act now to release some inmates and blunt the potential for an outbreak” (Tamburin, 2020). Similarly, Florida State Policy Director for FAMM was quoted to explain the role of advocacy in the pandemic: “From the beginning of the COVID-19 pandemic, advocacy organizations across the ideological spectrum have encouraged state leaders to take actions to slow the spread of the coronavirus in Florida's prisons and mitigate the damages from inevitable prison outbreaks” (Newburn, 2020, p. A13). The news approached reporting rather simplistically – yet assessing the quality of sources, the degree of realism of a claim, or the usefulness of a news development were avoided; so was adding emotion. As such, the voice of those hurting of COVID-19 – incarcerated people, their families, and affected staff – were generally absent in the discourse. As much as either journalists or other industry practitioners may assert the allegiance of hard news to objectivity, it is well-documented that so-called objective news is still rife with ideology, meaning, and, frequently, adherence to opinion (Gee, 1996; Hall, 1997; Xie, 2018). Even seemingly straightforward reporting constructs a viewpoint – accomplished through framing, wording, information order, source selection, viewpoint repetition, photos and captions, or linking. Many of the articles that included an inmate perspective (or that of their families) also made note of the person's crime or reason to be in jail. Such addition is not common practice in journalism (H. Gilbert, personal communication, 2021), yet the practice contributes to a familiar approach to constructing the deviance of the prison populations, questioning the credibility and legitimacy of sources and removing empathy.Her face was beet red. She was wheezing, struggling to breathe. The 38-year-old woman -- an inmate at Metro Transitional Center -- had been experiencing stomach discomfort since early last week, one of her cellmates said. … The sick woman is serving out a sentence for drug and assault charges. (Boone, 2020a) “Our sentences have turned into death sentences,” Sterling Rivers, a 32-year-old from Tennessee serving time at Oakdale for a drug conspiracy conviction said in an interview. (Gurman, 2020b) Another Minnesota prison inmate has died from COVID-19. The man is the third inmate from the Faribault state prison to die from the disease and the sixth system wide. Larry Joseph Roberts, 64, died on Dec. 4 at St. Mary's Hospital in Rochester, according to the Department of Corrections. … Roberts, of Bemidji, was serving a 39-month sentence following his 2019 felony conviction for possessing pornography involving minors. (Featherly, 2020) Other stories made more subtle inferences. In this example from the Atlanta Journal-Constitution, a former mayor and newspaper editor seemingly represented the two sides of an argument at the same time: “Two wildly divergent extremes have prevailed, said Jim Quinn, former mayor of Leesburg… and editor of the local newspaper. ‘There's one side that says we’re all going to die,’ Quinn said. ‘The other isn’t going to let it bother them’” (Boone, 2020b). The other side is not that inmates are well protected and safe, but that their deaths are negligible, expendable. While at first glance the news reporting appeared balanced, the inference is problematic. This language was rarer in the later samples, likely in part due to the heightened awareness of issues of racial justice across the country in the context of the 2020 demonstrations. Yet still only two articles in each of the second and third samples documented racial disparity in prisons – the Journal Record noted the increased risk of contracting and dying from the virus given that “Black Oklahomans were disproportionately booked into prison at even higher levels than usual” (“Rising Racial Disparity,” 2020), while an USA Today editorial noted that those incarcerated “are among those most at risk for contracting COVID-19, becoming gravely ill, or worse,” especially for “Black, indigenous and communities of color that are disproportionately harmed by over-criminalization” (Chan & Hooks, 2020). Blaming and Abandonment Establishing the “fact” of a crisis in the jail and prison system in the context of the pandemic must include an actor to blame. This is a common aspect of moral panics – the need for constructing a scapegoat and someone to blame. State officials generally blamed the pandemic for unusual circumstances and unprecedented times; advocacy groups blamed state officials for mitigation strategies, and for lapses and delays in providing proper hygiene, social distancing, and quarantining. The news contributed to this discourse through clickbait headlines and inflammatory language, at the same time that an article may include factual reporting. For example, Tampa Bay Times published a well-sourced article rich with statistics and sources that included inmates and family members, but headlined it with “Virus races in prison” (Sullivan, 2020). From the Washington Examiner,Florida inmates test positive for COVID-19, enflaming fears of a prison pandemic wildfire The spread of COVID-19 inside Florida's prison system, with 95,000 inmates and 23,000 employees in 143 sites, has been a dry tinder incendiary in many pandemic wildfire scenarios. (Haughey, 2020; emphases added) Such news presentation is grounded in a conflictual reporting approach. Promoters of solutions journalism have pointed out that identifying issues without following through with how these are managed and solved does not provide a complete picture of the challenges at hand, nor does it accomplish the mission and purpose of journalism as an institution (McIntyre, 2019; Wenzel et al., 2016). Occasional news stories in the sample speak to this issue, yet opinion pieces were the most vocal to articulate the need for nuanced solutions, policies, and reactions to the pandemic, as illustrated in these op-eds:We could debate all day whether the majority of these people should be locked up in the first place. But no matter where you land in that discussion, we can all agree that these individuals do not deserve death sentences or to be exposed to serious illness. The reality remains that if serious steps are not taken, this is exactly the fate many prisoners face. The jails and prisons in our country are filthy and prisoners lack access to supplies that could help them prevent the spread of the virus. Basic items such as soap, tissues, and cleaning supplies are often only available for purchase. And other items, such as alcohol-based hand sanitizer, are flat-out banned. (Cox, 2020) Reducing the jail population, which is typically those awaiting trial, is key so when the virus strikes the jail it will be manageable. Additionally, not arresting nonviolent offenders such as those accused of shoplifting and simple drug possession, while giving them a court notice, isn’t a threat to the public. (Zimmerman, 2020) It is also in these sorts of stories – not news but opinion pieces – that the subject of the reporting were not always addressed as inmates, but rather as prison population, citizens, “those stuck in jail,” or just people. Towards the end of spring 2020, investigating real blame began to surface, documented by the Wall Street Journal, for instance, when the ACLU requested access to public records to assess why BOP has moved “far too slowly” to manage the crisis and to “stave off some of the worst impacts of Covid” (Kusisto & Gurman, 2020). In the later dataset in the winter 2020–2021 time-frame, evidence emerged that prison staff underreported or started withholding virus data from the public – evidence of “moral barricades” in the management of the crisis (Cohen, 1972). Vulnerability as Victimization In sharp contrast to objective reporting, a discourse of vulnerability depicted populations in jails and prisons as victims of a harsh and unprepared system. Explicit vulnerability was phrased, for instance, in USA Today: “‘As some prison officials have already warned, prisons are like petri dishes, leaving inmates vulnerable to COVID-19,’ a bipartisan coalition of senators said…”; later in the same article, “Waiting is not an option. Let's send these vulnerable nonviolent inmates home for the sake of our nation's health” (Johnson, 2020). News articulated risk occasionally through the voice of inmates or family members, and predominantly through advocacy groups that built on arguments of human rights violations in the prison system that such organizations have long deplored. The argument was unequivocal: Inmates are at risk of dying in prison; vulnerability can equate death sentences. For instance, a letter to the editor in the Atlanta Journal-Constitution noted, “Those incarcerated cannot choose where they want to be, but their living circumstances have reached the point where they clearly constitute cruel and unusual punishment” (2021). Another op-ed by a law professor and a public defender stated,[O]ur jails and prisons are filled with poor people, mostly of color. Many of us also know that our prisons are being used to house people experiencing mental illness. We have not yet come up with a humane response to this reality. COVID-19 is now becoming a threat that takes our system's inhumanity to a new and even more horrific level: We know that the virus spreads in confined groups with frightening speed and efficiency. In a prison environment, in a prison environment, huge numbers will die. Essentially, we are transforming their sentence of incarceration into a sentence to death. (Mills & Galvin-Almanza, 2020) The two stories connected vulnerability to marginalization and racial markers, whereas others articulated the risk for vulnerable groups in the context of politicized mitigation efforts – “When partisan politics take over the policymaking process, it is ultimately the most vulnerable in our society who lose” (Desmond & Volz, 2020). What is significant is not that the issue of vulnerability is included in the news –journalistic objectivity relies on including opposing sources in the same news story; and it was a fairly common theme to refer to people with vulnerable health or risk factors from the beginning of the pandemic. Rather, representing vulnerability emphasizes that those in prisons need protection as a collective group that is marginalized or at a disadvantage. The call for attention to vulnerability remained throughout the three samples; for instance, news reported that prisoners were not prioritized for vaccination in Georgia, “despite the vulnerability of any population living in a congregate setting. About 8% of Georgia's incarcerated population is over age 60 and 60% are Black, two additional risk factors for poor outcomes with COVID-19” (Landers, 2021). Human Rights in Compassion and Litigation A discourse emphasizing human rights emerged early in the pandemic, flourishing into calls for clemency and justice. This reporting approach presented interest on its own, as the news emphasized the humanity of prisoners unlike any other discourse. Two developments shaped this discourse – compassionate release early in the pandemic, and legal action and investigations into prison conditions later into 2021. As for the former, news introduced this position almost exclusively through advocacy voices and op-eds, often written by former correction officers and judges, and by political commentators. In 2020, more than 44,000 prisoners had been recommended for early release – nonviolent offenders with little time left to serve and/or at a higher risk of COVID-19 complications (Chan & Hooks, 2020). At least 24,000 inmates were released to home confinement (Phillips, 2021). As prisons and jails began implementing this process of release to home confinement, several news stories broke of mainly famous individuals asking to be considered for release – well-known politicians, actors, musicians, and business figures, most of them men. Most requests from celebrity figures were denied, though overall the number of releases increased into the summer of 2020 and continued into 2021. A typical example was, for instance, a pharmaceutical executive's (denied) request, claiming “mental health issues weakened his immune system and made him more susceptible to contracting the coronavirus” (“Pharmaceutical exec loses second bid,” 2021). Some reporting narrowed in on the unusual circumstances of a particular inmate – a certain Mr. Rochell, who was released to home confinement, was described as a “decent, kindly, honest and immensely likable man, beloved in the residential unit” (Gurman, 2020a). Others articulated a nuanced approach to compassionate release, seeking to improve the criminal justice system and uniquely engaging with the “why” of the current circumstances:[S]omehow, top officials across the country haven’t recognized the urgency of this issue. Why the hesitation? Do our leaders believe that anyone accused or convicted of a crime, no matter the nature, must still be dangerous? Do they consider the lives of people behind bars so devoid of value that they’d prefer to see them dead than risk any possibility of future crime? Do they not understand that an outbreak in one of these facilities will ripple outward? If our governors, sheriffs, prosecutors, and president want to save as many lives as possible, they must be willing to reassess the view that sees confinement as the default. (Mills & Galvin-Almanza, 2020) Many inmates requested release during this time and faced denial, yet only the cases of well-known, formerly powerful, mostly white, mostly male inmates received news coverage. By the second data set, late in 2020 and into January 2021, news coverage on compassionate release focused more on reducing transmission than on individual cases or impact, whereas by June 2021, given the challenges of the prolonged pandemic, reporting focused on success stories, the low rates of recidivism, and implications around return to prison. Those released were presented as model citizens that should not be incarcerated, suggesting that the end of COVID-19 spread could be detrimental to inmates released. The other aspect to the discourse of human rights was to cover concern over the hidden numbers, the underreporting of cases, and the alleged general withholding of information that threaten prisoners’ human and citizen rights. A range of issues came to light and made the news, including low testing rates that skewed data for some states and that led to underreporting of cases (Griesbach & Turcotte, 2021), mishandling of mitigation protocols – such as prohibiting correctional staff from wearing masks, not allowing for social distancing or being able to decrease overcrowding, lacking adequate soap, isolating high-risk individuals, prioritizing vaccination, etc. (Cordeiro, 2021; McCoy, 2021). Vilification and Threat to Community As advocacy groups called for release to home confinement, for better mitigation efforts, and for transparency during the pandemic, news coverage also emphasized the prison population as deviant, especially reporting on the notion that prisoner rates of infection constitute a threat to the wider community. For instance:“You’re talking about the inside of an institution, that's like a little petri dish,” [Joe Rojas, Southeast regional vice president of the Council of Prison Locals] said. “That's a life and death situation because we take that home to our families.” (Gurman, 2020c) Absent a shift in the state's approach, this will mean more infections and deaths among incarcerated people and corrections professionals. These outbreaks will affect the wider community, as well; the person who serves lunch at a prison eats dinner near your family at a restaurant. (Newburn, 2020) Similarly, an editorial pointed out the threat to communities surrounding prisons in an argument for vaccine prioritization among the incarcerated: “In areas that are rural or have small cities that don’t belong to a larger metropolitan area a substantial inmate population could be contributing to the severity of a county's outbreak” (Velazquez, 2021). The threat of prison outbreaks spreading to external communities is a possibility. At the same time, such messages can further stigmatize incarcerated populations, painting them as “diseased,” and in general build on the fear and vilification of people that have passed through the criminal justice system. It also further enhances the victim status of inmates already vulnerable to actors in power positions - prison staff, correctional officers, the wider public even. Although advocacy is a genre of strategic communication and it should not surprise that news stories attribute this argument of public threat to activists and lawyers – who likely need to emphasize the urgency of the requests – the discourse also solidifies a moral barricade between the public and the challenging circumstances of those in prison. Absence Overwhelmingly newspapers left out or downplayed the risks and impact for incarcerated populations. Early in 2020 concerns about congregate settings dominated coverage, as cruise ships were quarantined and nursing homes began to restrict visitors. Stories and videos of isolated senior citizens, notices of deaths in nursing homes, and narratives of the families impacted by visitor restrictions circulated online and became feature news – while prisons were primarily overlooked or included few humanizing moments or inmate experiences. Put in a broader socio-cultural context, the discourse of absence is vastly different than the narrative chronicled in The Marshall Project series on coronavirus, which offered stories on health in prison, compassionate release, vaccine hesitancy, along with articles by current inmates documenting the impact of mitigation restrictions (“The Marshall Project,” 2021). Nearly absent in the coverage is the disproportionate impact of the pandemic on prisoners of color. In the general population, COVID-19 has impacted people of color more significantly, with hospitalizations and deaths for American Indian, Hispanic, and Black persons more than double the rate of those for white individuals (“Risk for COVID Infections,” 2021). Since Black and Hispanic men are overrepresented in prisons (“Inmate Race,” 2021; Tamir, 2021), one can assume that, like the general population, Black individuals were significantly more affected by COVID-19 than other racial groups, even if demographic data was not consistently reported for inmate deaths (Saloner et al., 2020). Race and ethnicity should have been an integral part of the news coverage on protecting vulnerable incarcerated populations. The downplay and outright absence of news that distinctly highlighted the increased risk for prisoners of color is a notable invisibility that further marginalizes these communities, continuing to contribute to racial stereotypes and distorted public perception (Dixon, 2007). Conclusion The news coverage of the COVID-19 pandemic in relation to populations in jails and prisons relied and contributed to six discourses that collectively represent a comprehensive picture of vulnerability. Journalistic objective reporting depicted an exceptional health crisis, blamed by officials for their challenges. News articulated a critical position towards governmental agencies and their mitigation efforts, yet overall supported a complex and challenging moral panic. Vulnerability and human rights were also emphasized clearly in the reporting. Yet seen in a broader socio-political context, these four discursive approaches – objectivity, blame, vulnerability, and human rights – have dehumanized prisoners and stripped the storytelling of emotion and the capacity for readers and the wider public to empathize and understand the real challenges facing those within jail and prison walls. A disconnect occurs between real-life experiences and salient issues from news coverage. Based on the literature, emphasis on deviance was also anticipated, yet the news shied away from overt representation. It was less common in hard news reporting to lean discriminatory in the language – with the notable exception of articles that did include the crime of an inmate who also served as witness or inside voice in a news story, a reporting practice that merits further investigation given its seeming uniqueness. Instead, most of the othering came through the discourse of vilification and chiefly through that of absence, arguably a more harmful approach. News emphasized the protection of vulnerable populations in relation to nursing homes and those with medical complexities, yet the incarcerated groups have been absent from this focus; the prison has been and will continue to pose high risks of disease, with periodic outbreaks that target the most vulnerable. Related, an area that presents an opportunity to further research relates to the ongoing victimization of the marginalized through advocacy voices that must emphasize vulnerability, yet they do so by constructing the prison populations as deviant – and therefore replicating problematic stereotypes. There was also an observable deracialization of the news coverage in the context of the pandemic – which does not echo existing literature on prior concerns related to criminalization and stereotyping of prison populations. In the context of 2020 and the protests for racial justice, this absence may speak to a cultural sensitivity or to the spread of the moral panic as a general, global challenge; yet it discursively covers up, and thus perpetuates and amplifies, substantive. Such complications, while embedded in the context of the news coverage, may have offered confounding complications to understanding societal stance on disease and incarcerated populations. Related, also absent in the news coverage – and in the present study – are gender and cross-cultural comparisons. Examining how media outlets across the world covered incarceration during the COVID-19 pandemic could prove useful, as would more engagement with inmates directly and their experiences during this global health crisis. Examining how the moral panic was internalized, either as fear of the pandemic or in further victimization, could shed light on the disconnect between lived experience and its mediated/news construction. Such questions could constitute the starting point for further research, as would the noted heightened coverage of the pandemic in prisons in Floridian publications. Finally, this study started from attention to the minute storytelling in news to identify the little (d) discourses that helped the country make sense of the COVID-19 pandemic in the context of jails and prisons. Taken collectively, the broader big (D) Discourse of the incarcerated in contemporary U.S. that emerges was one of ongoing vulnerability and absence. To the point of the former, the news media (and other power players institutionally relevant for this group) has become complacent to real, substantive challenges inside prisons as the pandemic has unfolded; as regards the latter, absence and emotional distance from the populations increasingly shaped the news. Since Discourse shapes beliefs and behavior (Fairclough, 1993; Gee, 1996; Rogers, 2002), such news approach continues to be problematic - and this study calls attention to the need for ongoing surveillance of this mediated genre of communication. Author Biographies Adina Schneeweis (Ph.D., Mass Communication, University of Minnesota) is a professor of journalism in the Department of Communication, Journalism, and Public Relations at Oakland University. Her research focuses on transnational advocacy communication, ethnicity and race, and the analysis of institutional discourse. Katherine A. Foss (Ph.D., Mass Communication, University of Minnesota), is professor of Media Studies in the School of Journalism & Strategic Media at Middle Tennessee University, where she teaches courses in health communication, children and media, and digital literacy. Her books include Constructing the Outbreak: Epidemics in Media and Collective Memory and Breastfeeding and Media: Exploring Conflicting Discourses That Threaten Public Health. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ORCID iD: Adina Schneeweis https://orcid.org/0000-0003-0969-4785 ==== Refs References Awaludin A. (2020, December). Overcriminalization due to moral panic COVID-19 pandemic. In The 2nd international conference of law, government and social justice (ICOLGAS 2020) (pp. 613–624). Atlantis Press. Bick J. A. (2007). Infection control in jails and prisons. Clinical Infectious Diseases, 45 (8 ), 1047–1055. 10.1086/521910 17879924 Boone J. S. C. (2020a, April 6). Coronavirus risk within prisons: Inmates raise alarm on possible COVID-19 case. The Atlanta Journal-Constitution. B2. Boone J. S. C. (2020b, March 28). Georgia prisons and jails: COVID-19 prison outbreak kills Ga. inmate. The Atlanta Journal-Constitution. A6. Britton D. M. (2003). At work in the iron cage. New York University Press. Cecil D. K. (2017). Prisons in popular culture. Criminology and Criminal Justice, 1–25. 10.1093/acrefore/9780190264079.013.194 Cecil D. K. (2019). Newsworthiness of reform: Prison news stories in an era of change. Journal of Crime and Justice, 42 (2 ), 221–235. 10.1080/0735648X.2018.1507834 Chan P. Hooks B. (2020, December 22). COVID-19 compels America to rethink who we lock up in prison. USA Today. https://www.usatoday.com/story/opinion/2020/12/22/covid-19-should-drive-prison-reform-priscilla-chan-brian-hooks-column/3989099001/ Cingolani M. Caraceni L. Cannovo N. Fedeli P. (2021). The COVID-19 epidemic and the prison system in Italy. Journal of Correctional Health Care, 27 (1 ), 3–7. 10.1089/jchc.20.04.0026 34232761 Cohen S. (1972). Folk Devils and Moral Panics: The Creation of the Mods and Rockers. MacGibbon and Kee. Cordeiro M. (2021, January 14). Federal prison banned masks for staff as pandemic began. Orlando Sentinel. B1. Cox H. (2020, March 17). Coronavirus will turn our prisons into death zones without reform. Washington Examiner. https://www.washingtonexaminer.com/opinion/coronavirus-will-turn-our-prisons-into-death-zones-without-reform Custers K. Van den Bulck J. (2011). The relationship of dispositional and situational fear of crime with television viewing and direct experience with crime. Mass Communication and Society, 14 (5 ), 600–619. 10.1080/15205436.2010.530382 Desmond M. Volz N. (2020, April 15). “People first” policies needed to confront COVID-19 in prisons, jails. South Florida Sun Sentinel. A10. Dixon T. L. (2007). Black criminals and White officers: The effects of racially misrepresenting law breakers and law defenders on television news. Media Psychology, 10 (2 ), 270–291. 10.1080/15213260701375660 Dixon T. L. Williams C. L. (2015). The changing misrepresentation of race and crime on network and cable news. Journal of Communication, 65 (1 ), 24–39. 10.1111/jcom.12133 Dunn K. (2004). Islam in Sydney: Contesting the discourse of absence. Australian Geographer, 35 (3 ), 333–353. 10.1080/0004918042000311359 Fairclough N. (1993). Critical discourse analysis and the marketization of public discourse: The universities. Discourse & Society, 4 (2 ), 133–168. 10.1177/0957926593004002002 Fairclough N. (2004). Discourse and social change. Polity Press. Featherly K. (2020, Dec. 9). COVID-19 kills sixth prison inmate in Minnesota. Minnesota Lawyer. https://minnlawyer.com/welcome-ad/?retUrl=/2020/12/09/covid-19-kills-sixth-prison-inmate-in-minnesota/ Federal Bureau of Prisons. BOP statistics: Inmate race. ( 2021 , October 23). https://www.bop.gov/about/statistics/statistics_inmate_race.jsp Foss K. A. (2020). Constructing the outbreak: Epidemics in Media & Collective Memory. University of Massachusetts Press. Gee J. (1996). Social linguistics and literacies: Ideology in discourses. Falmer Press. Gerbner G. Gross L. (1976). Living with television: The violence profile. Journal of Communication, 26 (2 ), 172–199. 10.1111/j.1460-2466.1976.tb01397.x Gilman S. L. (2010). Moral panic and pandemics. The Lancet, 375 (9729 ), 1866–1867. 10.1016/S0140-6736(10)60862-8 Goidel R. K. Freeman C. M. Procopio S. T. (2006). The impact of television viewing on perceptions of juvenile crime. Journal of Broadcasting & Electronic Media, 50 (1 ), 119–139. 10.1207/s15506878jobem5001_7 Griesbach R. Turcotte M. (2021, January 29). Mississippi prisons boast of low virus rate, but few have been tested. New York Times. A9. Gurman S. (2020a, April 24). Coronavirus provides way out for Florida inmate. Wall Street Journal. https://www.wsj.com/articles/coronavirus-provides-way-out-for-florida-inmate-11587768598 Gurman S. (2020b, April 6). Coronavirus puts a prison under siege. Wall Street Journal. https://www.wsj.com/articles/inside-oakdale-prison-our-sentences-have-turned-into-death-sentences-11586191030 Gurman S. (2020c, April 30). More than 70% of inmates tested in federal prisons have coronavirus. Wall Street Journal. https://www.wsj.com/articles/more-than-70-of-inmates-tested-in-federal-prisons-have-coronavirus-11588252023 Hall S. (1997). Representation & the media. Media Education Foundation Transcript. Produced and directed by S. Jhally. Haughey J. (2020, April 7). Florida inmates test positive for COVID-19, enflaming fears of a prison pandemic wildfire. Washington Examiner. https://www.washingtonexaminer.com/politics/florida-inmates-test-positive-for-covid-19-enflaming-fears-of-a-prison-pandemic-wildfire Hebert P. L. Sisk J. E. Howell E. A. (2008). When does a difference become a disparity? Conceptualizing racial and ethnic disparities in health. Health Aff (Millwood), 27 (2 ), 374–382. 10.1377/hlthaff.27.2.374 18332492 Hineman B. (2020, December 3). TDOC suspends prison visits at 4 sites due to coronavirus. The Tennessean. A4. Huey L. (2010). ‘I’ve seen this on CSI’: Criminal investigators’ perceptions about the management of public expectations in the field. Crime, Media, Culture, 6 (1 ), 49–68. 10.1177/1741659010363045 Johnson K. (2020, March 24). Feds to quarantine all new inmates for 14 days to try to guard against COVID-19 in prisons. USA Today. https://www.usatoday.com/story/news/politics/2020/03/24/coronavirus-bureau-prisons-quarantine-new-inmates-14-days/2908560001/ Journal Record staff (2020, December 15). Report shows rising racial disparity in prison bookings. Journal Record. https://journalrecord.com/2020/12/15/report-shows-rising-racial-disparity-in-prison-bookings/ Kusisto L. Gurman S. (2020, April 29). ACLU presses prisons for coronavirus infection data. Wall Street Journal. https://www.wsj.com/articles/aclu-presses-prisons-for-coronavirus-infection-data-11588165202 Landers M. (2021, June 21). Full-court press: Prison’s vaccine rate tops Chatham’s. Savannah Morning News. A1. Leibowitz A. I. Siedner M. J. Tsai A. C. Mohareb A. M. (2021). Association between prison crowding and COVID-19 incidence rates in Massachusetts prisons, April 2020–January 2021. JAMA Internal Medicine, 181 (10 ), 1315–1321. 10.1001/jamainternmed.2021.4392 34369964 Lessard D. Ortiz-Paredes D. Park H. Varsaneux O. Worthington J. Basta N. E. Kronfli N. (2022). Barriers and facilitators to COVID-19 vaccine acceptability among people incarcerated in Canadian federal prisons: A qualitative study. Vaccine: X, 10 , 100150. 10.1016/j.jvacx.2022.100150 35243324 Marland H. Anderson C. Murphy W. (2020, April 22). Coronavirus: A history of pandemics in prison. The Conversation. https://theconversation.com/coronavirus-a-history-of-pandemics-in-prison-136776 Maruschak L. M. Berzofsky M. Unangst J. (2015). Medical problems of state and federal prisoners and jail inmates, 2011–12. US Department of Justice, Office of Justice Programs, Bureau of Justice Statistics. McCoy K. (2021, May 8). ‘Afraid of dying in here’: Inmates fought for COVID-19 vaccinations, protections after jails and prisons failed to protect them. USA Today. https://www.usatoday.com/story/news/nation/2021/05/08/covid-prison-how-us-inmates-fought-vaccines-protection/6879647002/ McIntyre K. (2019). Solutions journalism: The effects of including solution information in news stories about social problems. Journalism Practice, 13 (1 ), 16–34. 10.1080/17512786.2017.1409647 Mills D. Galvin-Almanza E. (2020, April 2). As many as 100,000 incarcerated people in our jails and prisons will die from the coronavirus, unless the US acts now. Business Insider. https://www.businessinsider.in/politics/news/as-many-as-100000-incarcerated-people-in-our-jails-and-prisons-will-die-from-the-coronavirus-unless-the-us-acts-now/articleshow/74955833.cms Montes A. N. Mears D. P. Gricius M. Sanchez J. (2020). Simplifying reality: The media’s portrayal of the private prison debate. Crime & Delinquency, 66 (9 ), 1242–1267. 10.1177/0011128719901109 Montoya-Barthelemy A. G. Lee C. D. Cundiff D. R. Smith E. B. (2020). COVID-19 and the correctional environment: The American prison as a focal point for public health. American Journal of Preventive Medicine, 58 (6 ), 888–891. 10.1016/j.amepre.2020.04.001 32387174 “National COVID-19 Statistics.” (2022). The COVID Prison Project. https://covidprisonproject.com/data/national-overview/ Newburn G. (2020, December 5). Facing COVID surge, prisons need to act. Orlando Sentinel, A13. Nicomedes C. J. C. Avila R. M. A. (2020). An analysis on the panic during COVID-19 pandemic through an online form. Journal of Affective Disorders, 276 , 14–22. 10.1016/j.jad.2020.06.046 32697692 Park K. Blakinger K. Lauer C. (2021, June 30). A Half-Million People Got COVID. The Marshall Project. https://www.themarshallproject.org/2021/06/30/a-half-million-people-got-covid-19-in-prison-are-officials-ready-for-the-next-pandemic Park K. Meagher T. (2021). A state-by-state look at 15 months of coronavirus in prisons. The Marshall Project. https://www.themarshallproject.org/2020/05/01/a-state-by-state-look-at-coronavirus-in-prisons Pettus-Davis C. Kennedy S. C. Veeh C. A. (2021). Incarcerated individuals’ experiences of COVID-19 in the United States. International Journal of Prisoner Health, 17 (3 ), 335–350. https://www.emerald.com/insight/content/doi/10.1108/IJPH-11-2020-0094/full/html. 10.1108/IJPH-11-2020-0094 Pharmaceutical exec loses second bid for early prison release (2021, January 20). The Atlanta Journal-Constitution. A4. Phillips K. (2021, May 5). Inmates sent home during COVID-19 got jobs, started school. Now, they face possible return to prison,” USA Today. https://www.usatoday.com/in-depth/news/politics/2021/05/05/trump-memo-says-inmates-sent-home-due-covid-must-return-prison/7194735002/ Pyrooz D. C. Labrecque R. M. Tostlebe J. J. Useem B. (2020). Views on COVID-19 from inside prison: Perspectives of high-security prisoners. Justice Evaluation Journal, 3 (2 ), 294–306. 10.1080/24751979.2020.1777578 Rendon A. F. V. Volschan I. M. Pereira M. D. N. Pimentel A. D. F. Monteiro W. L. Oliveira G. M. M. D. (2021). Marginalization, vulnerability and economic dynamics in COVID-19. International Journal of Cardiovascular Sciences, 34 (3), 319–323. 10.36660/ijcs.20210029 Rogers R. (2002). Between contexts: A critical discourse analysis of family literacy, discursive practices, and literate subjectivities. Reading Research Quarterly, 37 (3 ), 248–277. 10.1598/RRQ.37.3.1 Romer D. Jamieson K. H. Aday S. (2003). Television news and the cultivation of fear of crime. Journal of Communication, 53 (1 ), 88–104. 10.1111/j.1460-2466.2003.tb03007.x Saloner B. Parish K. Ward J. A. DiLaura G. Dolovich S. (2020). COVID-19 cases and deaths in federal and state prisons. Jama, 324 (6 ), 602–603. 10.1001/jama.2020.12528 32639537 Saukko P. (2003). Doing research in cultural studies: An introduction to classical and new methodological approaches. Sage Publications Ltd. Sawyer W. Wagner P. (2020, March 24). Mass incarceration : The whole pie 2020. Prison Policy Initiative. https://www.prisonpolicy.org/reports/pie2020.html Schneeweis A. (2015). Communicating the victim: Nongovernmental organizations advocacy discourses for Roma rights. Communication, Culture & Critique, 8 (2 ), 235–253. 10.1111/cccr.12077 Schudson M. (2001). The objectivity norm in American journalism. Journalism, 2 (2 ), 149–170. 10.1177/146488490100200201 Sevelius J. M. Gutierrez-Mock L. Zamudio-Haas S. McCree B. Ngo A. Jackson A. Clynes C. Venegas L. Salinas A. Herrera C. Stein E. Operario D. Gamarel K. (2020). Research with marginalized communities: Challenges to continuity during the COVID-19 pandemic. AIDS and Behavior, 24 (7 ), 2009–2012. 10.1007/s10461-020-02920-3 32415617 Stewart C. O. (2005). A rhetorical approach to news discourse: Media representations of a controversial study on “Reparative Therapy.”. Western Journal of Communication, 69 (2 ), 147–166. 10.1080/10570310500076858 Sullivan D. (2020, Dec. 18). Virus races inside prison. Tampa Bay Times. 1. Tamburin A. (2020, April 5). Tennessee prison inmate tests positive for COVID-19: Officials: He will be held in isolation in infirmary. The Tennessean. A1. Tamir C. (2021, March 25). The growing diversity of Black America. Pew Research Center. https://www.pewresearch.org/social-trends/2021/03/25/the-growing-diversity-of-black-america/ The Marshall Project (n.d.). Our reporting on coronavirus. https://www.themarshallproject.org/coronavirus Velazquez R. (2021, January 29). Where do prisons fall in Md. Vaccine priority? Daily Times. A2. Vest N. Johnson O. Nowotny K. Brinkley-Rubinstein L. (2021). Prison population reductions and COVID-19: A latent profile analysis synthesizing recent evidence from the Texas state prison system. Journal of Urban Health, 98 (1 ), 53–58. 10.1007/s11524-020-00504-z 33337529 Wenzel A. Gerson D. Moreno E. (2016). Engaging communities through solutions journalism. Columbia Journalism School. Wildeman C. (2016). Incarceration and population health in wealthy democracies. Criminology; An Interdisciplinary Journal, 54 (2 ), 360–382. Wildeman C. Wang E. A. (2017). Mass incarceration, public health, and widening inequality in the USA. The Lancet, 389 (10077 ), 1464–1474. 10.1016/S0140-6736(17)30259-3 World Population Review (2022). Prison population by state 2022. https://worldpopulationreview.com/state-rankings/prison-population-by-state Xie Q. (2018). Critical discourse analysis of news discourse. Theory and Practice in Language Studies, 8 (4 ), 399–403. 10.17507/tpls.0804.06 Zimmerman L. (2020, April 2). Public order, coronavirus and prison policy. Wall Street Journal. https://www.wsj.com/articles/public-order-coronavirus-and-prison-policy-11585857637	0	PMC9708528	NO-CC CODE	2022-12-01 23:20:30	no	J Commun Inq. 2022 Nov 27;:01968599221141082	utf-8	J Commun Inq	2,022	10.1177/01968599221141082	oa_other
==== Front spgem GEM Journal of General Management 0306-3070 1759-6106 SAGE Publications Sage UK: London, England 10.1177_03063070221116510 10.1177/03063070221116510 Original Empirical Article Determinants of teleworkers' job performance in the pre-COVID-19 period: Testing the mediation effect of the organizational impact of telework https://orcid.org/0000-0002-2436-2562 Park Seejeen 34978 Department of Public Administration, KwangWoon University , Seoul, Republic of Korea Jae Moon M 26721 Yonsei University , Seoul, Republic of Korea Moon M Jae, Department of Public Policy and Management, Yonsei University, Seoul, Republic of Korea. Email: [email protected] 28 11 2022 28 11 2022 03063070221116510© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. Although pre-COVID-19 research on telework is well established, most research has compared teleworkers against nonteleworkers to assess the adoption of telework. Thus, the investigation of the determinants of teleworker performance is limited. The current study seeks to identify the determinants of teleworker job performance in the pre-COVID-19 period. Using U.S. federal government employee telework data, the current study tests the effects of work, technology, and management factors on teleworker performance mediated by the organizational impact of telework. The findings revealed that work similarity, telework frequency, accessibility of technologies, and quality of performance management positively affected the organizational impact of telework. Telework frequency, quality of performance management, and quality of supervision showed a positive association with job performance. However, in contrast to expectations, evidence of a mediation effect was not found as the organizational impact of telework was negatively associated with teleworker performance. Implications for telework implementation and research in the post-COVID-19 period are offered. telework teleworker performance telework implementation telework management Ministry of Education of the Republic of Korea National Research Foundation of Korea NRF-2021S1A5A2A03065493 edited-statecorrected-proof typesetterts10 ==== Body pmcIntroduction Telework initially emerged as a technological innovation but quickly became an important tool in contemporary information communication technology (ICT)-based human resource management (HRM) practice (Bentley et al., 2016; Pérez et al., 2002, 2004). Today, business organizations often use telework to transcend geographic limitations, have instant access to employees outside traditional office settings, attract a high-quality workforce, increase flexibility in HRM, and improve organizational performance (Baruch, 2000; Greer and Payne, 2014). More recently, the SARS-CoV-2 (COVID-19) outbreak has triggered the need for the rapid expansion of telework because telework is considered a necessary alternative to in-person work to prevent the spread of COVID-19 (Centers for Disease Control and Prevention (CDC), 2020). A recent OECD (2021a) report states that in all countries where comparable data exist, the rates of telework usage increased during 2020. For instance, Eurofound (2020) estimated that nearly 40% of employees in the European Union (EU) worked full time via telework in April 2020 (Fana et al., 2020). This rapid expansion of telework on a global scale is a result of the response to COVID-19 rather than an adoption based on a thorough review of the pros and cons of telework. Despite the recent expansion, the determinants of teleworker performance in both the pre- and post-COVID-19 periods have been studied on a limited scale. Therefore, the current study analyzes the determinants of telework performance in the pre-COVID-19 period and seeks to provide implications for the continuance of telework use in the post-COVID-19 era. The current study focused on the U.S. federal government to investigate the effects of factors related to telework and organization management on teleworker performance. Although pre-COVID-19 research on telework is well established, most of this research has compared teleworkers against nonteleworkers to assess the adoption of telework technologies and programs by examining whether using telework is better than not using it (Vega et al., 2015). Such an approach creates a gap in the literature by limiting the opportunity to evaluate the factors that affect teleworker performance. Thus, this study begins by briefly reviewing the pre-COVID-19 adoption of telework by the U.S. federal government. Then, this study conducts a review of the extant determinants of teleworker performance and investigates the work, technology, and management factors that are hypothesized to significantly influence teleworkers’ performance. During this process, the mediation effect of the organizational impact of telework between the determinants and teleworker performance is also tested. Last, this study offers implications for telework research, as well as for the management of teleworkers in organizations, in the post-COVID-19 period. Pre-COVID-19 adoption of telework in the U.S. Federal government Telework was first introduced in the federal government in 1990 by the President’s Council on Management Improvement, which commissioned the first government-wide telework pilot (Weisberg and Porell, 2011). Since then, the recently adopted Telework Enhancement Act of 2010 (TEA) has had the most extensive impact in expanding telework in the federal government by establishing a framework of requirements for federal agencies and assigning the Office of Personnel Management (OPM) the leadership of telework duties (Cook et al., 2013). Amid these changes in telework policies, both positive and negative perspectives on teleworking have appeared in the literature. The organizational advantages of telework are mostly related to improving efficiency and saving costs (Caillier, 2013; Day and Burbach, 2015; Office of Personnel Management (OPM), 2012; Overmyer, 2011). For employees, advantages include improved work–life balance, lower absentee rates, and higher employee satisfaction (Caillier, 2012, 2013; Denison et al., 2014; Major et al., 2008; Maruyama and Tietze, 2012; Snyder, 2012). In contrast, problems also exist in implementing telework. These problems include dissatisfaction among nonteleworkers, increased turnover intention among employees who are not allowed to telework, and undermined teamwork due to decreased physical presence at the office (Caillier, 2013; Mahler, 2012; Pérez et al., 2004). Moreover, the managerial challenges of controlling, coordinating, training, and evaluating employees who work remotely from traditional offices and determining telework eligibility are daunting (Cook et al., 2013; Denison et al., 2014; Lee and Kim, 2018; Maruyama and Tietze, 2012; Weisberg and Porell, 2011). Despite the mixed views on its benefits and problems, telework has continued to be adopted by agencies of various governments, including the United States. Nonetheless, telework has received little attention in the field of public management. Only a handful of researchers (e.g., Caillier, 2012; Mahler, 2012) have sought to validate the claim that the implementation of telework increases the productivity and performance of both organizations and employees by comparing teleworking against nonteleworking situations. Moreover, human factors (Guest, 1997: 269), such as teleworkers’ characteristics (individual employee factors), have not been extensively examined simply because many previous studies have largely focused on the adoption of telework (Anderson et al., 2015). Thus, the current study aims to extend the literature by examining the factors that affect teleworker performance. Determinants of teleworker performance in the pre-COVID-19 period How does telework affect individual and organizational outcomes? It has been well documented how telework affects individual-level outcomes such as job satisfaction (Morgan, 2004; O'Neill et al., 2009), job commitment (Martin and MacDonnell, 2012), and job performance (Gajendran and Harrison, 2007). While the adoption and outcome of telework has been a popular research topic, comprehensive models or frameworks of telework have received less attention, and only a limited number of studies have sought to propose them. For example, Campbell and McDonald (2009: 818) presented a “systems-based framework for understanding telework adoption and use” composed of three factors: (1) telework drivers, (2) telework processes, and (3) telework impact. To Campbell and McDonald, telework drivers include organizational factors (e.g., availability of skilled staff and need for strategic intervention), industry work practices related to telework, and employee preferences for telework, and these drivers are in a reciprocal relationship with the telework process (e.g., management support and ICT environment for telework). Furthermore, the authors contend that the telework process and telework impacts (organizational, societal, and employee impact) also affect each other. Bélanger et al. (2013: 1259) applied sociotechnical system (STS) theory to illustrate how telework-related factors affect teleworker work outcomes and proposed a multilevel STS telecommuting framework. Bélanger and colleagues categorized work systems into three subsystems: (1) the telecommunicating personnel subsystem, (2) the telecommuting technical subsystem, and (3) the organizational structure subsystem. The descriptions of each subsystem are as follows. First, the personnel subsystem depicts demographic characteristics, psychosocial aspects (e.g., attitude toward work environment and motivation), and the degree of professionalism in performing the work. Second, the technical subsystem refers to factors including technologies and practices such as ICT support for work, the types of ICT used for telework, individual tasks and work design when teleworking, and the reward or compensation systems of the organization. Third, the organizational structure subsystem relates to the centralization and formalization of structures. To Bélanger and colleagues, all three subsystems of the framework result in telework work outcomes such as individual performance. Nakrošiene et al. (2019: 93) used job demand-resources theory to identify the factors of telework and possible outcomes. The authors explain that telework factors such as supervisor support, communication time with coworkers, work–life balance-related issues, and the telework environment (e.g., accessing organization documents from home) affect telework outcomes, including satisfaction with telework, the perceived advantages of telework, and self-reported productivity. Despite these attempts to construct a comprehensive framework for telework research, there are some limitations. First, while various positive outcomes of telework have been discussed, individual performance levels (ratings) have rarely been used as a dependent variable. Assuming that performance appraisal ratings or scores can be a proxy for directly measuring individual performance, the current study uses the performance ratings of employees as the outcome of telework-related variables. Second, research that proposes a framework for telework analysis often focuses on individual factors that affect telework outcomes rather than offering an integrated model of the telework environment in organizations. More specifically, the possibility exists that telework-related organizational-level variables such as the perceived impact of telework may affect individual performance. The current study tests the mediation effect of the organizational impact of telework to address this limitation. Last, previous conceptual frameworks have not been empirically tested. The current study integrates an extant conceptual framework for telework factors and outcomes and attempts to provide preliminary results for the development of an integrated model. Thus, based on the previous literature, the current study proposes that work factors, technological factors, and management factors affect teleworker performance. Therefore, the current study posits a theoretical framework in which work factors, technology factors, and management factors jointly affect the organizational impact of telework (as a mediating variable), which eventually affects teleworkers’ individual performance. Figure 1 presents an exploratory theoretical framework that outlines how work factors (expected work similarity and telework frequency), technology factors (accessibility of technologies and device availability), and management factors (quality of performance management and supervision) affect the organizational impact of telework (mediating variable) and individual performance (dependent variable). Each factor will be discussed, along with related hypotheses.Figure 1. Theoretical framework for the determinants of teleworkers' job performance. Work factors The technical and work subsystem described in STS theory stresses that individual satisfaction with task design is an antecedent to individual task performance (Bélanger et al., 2013: 1272). In regard to the aspect of performance management, it is essential that in implementing telework, performance standards should be the same for both teleworkers and nonteleworkers (Office of Personnel Management (OPM), 2011, 2021). Moreover, it is well documented that a fair workload between teleworkers and nonteleworkers is one of the key antecedents of successful telework implementation (Park and Cho, 2022). Past research has indicated that the intensity of telework, which is often represented by the proportion of teleworkers among the workforce, is positively associated with organizational impact and job performance (Baruch, 2000; Martínez-Sánchez et al., 2008). A review by Allen et al. (2015) focused on the relationship between the extent of telework and work outcomes. The authors proposed that employees who telework more often tend to demonstrate a lower-level job performance because of professional isolation compared to those who spend less time teleworking. More recent studies have proposed that while increasing telework intensity can improve the efficiency, satisfaction, and productivity of workers, too much telework can result in negative results, thereby forming an inverted U-shaped relationship between the amount of telework and worker efficiency (Lodovici, 2021; OECD, 2020). Despite these interests in the work assignments of teleworkers and their telework amount, few studies have sought to directly test the effect of these variables on employee job performance measured by performance ratings. Therefore, the current study tests the following research hypotheses: Hypothesis 1a The perceived similarity of work between teleworkers and nonteleworkers is positively associated with the organizational impact of telework and the job performance of teleworkers. Hypothesis 1b The frequency of telework is positively associated with the organizational impact of telework and the job performance of teleworkers. Technology factors Technological determinism (Smith and Marx, 1994) emphasizes technologies, particularly information and communication technologies (ICTs), as a main driver of organizational and social changes, which eventually affect not only individual behaviors but also social interactions and organizational outcomes. Office of Personnel Management (OPM) (2011) reports that telework programs are functional when teleworkers are allowed to resolve any technology, equipment, and workflow issues during their telework. Kowalski and Swanson (2005) contend that organizations need to provide technology and sufficient equipment to improve teleworking management. Technological factors such as technological accessibility or device availability are also important to the nature and outcomes of telework simply because telework is essentially conducted via ICTs in remote locations from regular offices. Unless teleworkers are fully equipped with appropriate technologies, the intended outcomes are difficult to achieve. This situation leads to the following hypotheses regarding the effects of accessibility of technologies and availability of work devices on the organizational impact of telework and job performance of teleworkers: Hypothesis 2a The accessibility of technologies is positively associated with the perception of the organizational impact of telework and teleworkers’ job performance. Hypothesis 2b The availability of technological devices is positively associated with the perception of the organizational impact of telework and teleworkers’ job performance. Management factors Performance management is “a broad set of activities aimed at improving employee performance” (DeNisi and Pritchard, 2006: 255). Therefore, an organization with a good performance management system is likely to exhibit high employee performance. The success of telework requires organizations to develop a sound performance measurement, designs tasks fit for an independent working environment, and establishes reward systems that can motivate employees (Wicks, 2002). Organizations that aim to implement telework should pursue effective performance management practices such as maintaining fairness in performance evaluation, work assignment, and setting performance standards to make a telework program work well (OPM, 2011). To monitor and enhance teleworker performance, supervisors must adjust job requirements, performance measures, and approaches to providing feedback (Lautsch et al., 2009). Moreover, positive effects of perceived fairness on employee performance appraisal, which is a task conducted mostly by supervisors, have been found in past research (e.g., DeNisi and Pritchard, 2006). In the telework context, supervisors often contend that monitoring, mentoring, and performance monitoring with fewer face-to-face interactions are difficulties faced while managing teleworkers and that the failure to address these challenges can negatively impact the effectiveness of telework practices (Greer and Payne, 2014; Kurkland and Bailey, 2000). In summary, an organization needs a sound performance management system and high-quality supervision for the successful management of teleworking. Thus, the current study assumes that if the quality of performance management and supervision is high, employees will perceive that telework is working well and positively affecting their organization. Therefore, the following hypotheses are proposed: Hypothesis 3a The quality of performance management is positively associated with the organizational impact of telework and the job performance of teleworkers. Hypothesis 3b The quality of supervision is positively associated with the organizational impact of telework and the job performance of teleworkers. Mediating effect of Telework’s organizational impact Several meta-analyses have sought to comprehensively examine the effects of teleworking on individual and organizational outcomes. Gajendran and Harrison (2007) performed a meta-analysis of 46 telework studies and confirmed the existence of psychological mediators (e.g., perceived autonomy, work–family conflict, and relationship quality) that basically mediate teleworking and individual outcomes (e.g., job satisfaction, performance, turnover intention, role stress, and perceived career prospects). Moreover, Martin and MacDonnell (2012) used data from 22 studies to conduct a meta-analysis and validated the positive relationship between teleworking and organizational outcomes (e.g., productivity, retention, organizational commitment, and performance). Nevertheless, these comprehensive studies overlooked the potential linkage between the impact of telework on organization and individual performance. Although few such studies exist, previous studies have explored the relationship between organizational-level variables as measured by individual perception and organizational performance in the ICT research context. For instance, Bayo-Moriones et al. (2013) collected data via face-to-face interviews with 267 Spanish manufacturing SME executive managers (small- and medium-sized enterprises) and examined the direct and indirect effects of ICT resources on firm performance. The authors revealed that the use of ICT leads to improved internal communication and coordination, which positively affects firm performance. Other studies have investigated the relationship between individuals’ perception of the telework environment and their performance. For instance, Wicks (2002) analyzed survey data from a Canadian financial services company’s employees and suggested that employees may view increased individual performance from telework as a result of an improved work environment, such as less distraction and more control over the environment compared to the traditional office work environment. Following Bayo-Moriones et al. (2013), Gajendran and Harrison (2007), and Wicks (2002), the current study assumes the existence of direct and indirect effects of telework-related variables on individual performance for several reasons. First, Gajendran and Harrison (2007) performed a meta-analysis and found that psychological mediators exist between teleworking and individual performance. Thus, the current study assumes that mediating variables exist between telework-related variables and teleworker performance. Second, following Bayo-Moriones et al. (2013), who used individual perceptions to measure organizational-level variables to depict the overall effect of telework on organizations, the current study integrates the individual perceptions of teleworkers. Third, extant studies have shown that the benefits of telework at the individual level can be aggregated into organizational-level benefits (Martin and MacDonnell, 2012; Verbeke et al., 2008). Therefore, teleworkers’ individual perception of the organizational impact of telework can be aggregated to represent the organizational-level benefits. In addition, via the same logic, the dependent variable investigated in Bayo-Moriones et al.’s (2013) study was firm performance, which can be disaggregated into individual-level performance to test the effect of organizational-level benefits on individual-level performance. Finally, as the successful implementation of telework is commonly assumed to increase employee productivity (Caillier, 2013; Day and Burbach, 2015; Office of Personnel Management (OPM), 2012; Overmyer, 2011), it is hypothesized that teleworkers’ positive perceptions of the organizational impact of telework, which can be attained by successful implementation, are positively associated with individual-level performance. These conditions lead to the following hypothesis: Hypothesis 4 The positive effects of work factors, technology factors, and management factors on job performance are mediated by the organizational impact of telework. Research design The empirical analysis in this study involves three steps. First, data were collected from the U.S. Merit Systems Protection Board (MSPB) telework survey. Next, measures were defined, and principal factor analysis (PFA) was conducted to develop measures for variables that included multiple items. Finally, as the current study includes a mediating variable (organizational impact of telework), a path analysis was used to test the direct and indirect effects of the independent variables (expected work similarity, telework frequency, accessibility of technologies, device availability, quality of performance management, and quality of supervision) on the dependent variable (job performance of teleworkers). Data collection According to both the literature and federal government archives, the MSPB telework survey is the only study conducted for the sole purpose of studying telework in the federal government and includes the performance rating of employees. For that reason, the present study uses data from the 2011 U.S. MSPB Telework Study, which contains the most recent available federal-level data concerning telework. The data were collected from federal employees and released by the MSPB (see MSPB, 2011 for reference). The data were obtained by contacting the MSPB and relating the current research purpose. Despite the limitation that the period of data collection was restricted to 2009, the MSPB data is the only publicly available, large-scale, telework-related data with individual performance ratings. Using these data allows for the inclusion of the telework-related perceptions of more than 9000 employees to identify the determinants of teleworker performance in the pre-COVID-19 period. In 2009, a web-based survey was administered to 18,406 federal employees, and 9773 employees responded, which resulted in a 53.1% response rate (Merit Systems Protection Board (MSPB), 2011). A total of 9686 participants responded to the question “Was your request for routine telework granted?” Excluding the skipped responses (67.5%, 6541) and responses still in progress (1.2%, 120), 27.5% (2664) participants responded that their request was granted, and 3.7% (361) stated that their request was denied. As teleworkers are the main interest of this study, only those who responded that their request to telework was granted were included in the current study. After observations with missing data or nonresponses were excluded, data from a total of 2125 teleworkers were used for analysis. Although federal telework reports existed before the MSPB study was conducted, reports issued before the Telework Enhancement Act of 2010 use different measures and methodology in analysis, thereby making their data less relevant to the purposes of the current study (Office of Personnel Management (OPM), 2015). Factors related to organization management and telework implementation aspects in the MSPB study are relevant to the hypotheses of the current study and were therefore used. As the current study uses MSPB telework survey data for both independent and dependent variables, the data are drawn from a single source, which possibly creates a common-source bias (CSB). All survey items were extracted from the MSPB telework survey (see MSPB, 2011 for items). Although using distinct sources of data for measuring teleworker behavior and teleworker performance is ideal, in practice, it is difficult to attain actual employee performance ratings and even more difficult to find the data that match the survey responses related to the independent variables. Thus, the MSPB telework survey data are the only viable data that include both telework-related variables and federal employee performance ratings. Furthermore, as CSB is often a problem when both independent and dependent variables are drawn from survey data based on subjective self-reports (George and Pandey, 2017), the dependent variable of the current study is an objective measure, namely, employee performance rating. Thus, the issue of CSB is mitigated, and the reliability of the data depends on the honesty of the survey respondents in providing their actual performance rating. As the survey was administered by the federal government under the promise of anonymity, the current study assumes that the self-reported actual performance rating is reasonably accurate. Following the suggestion of numerous scholars who have emphasized transparency in data disclosure (e.g., George and Pandey, 2017; Lee et al., 2012), the current study offers a detailed explanation of the measurement procedures. Measures The current section discusses the operationalization of the proposed dependent, independent, and mediating variables. Tables 1 and 2 list the specific dimensions and items that compose the independent, mediating, and dependent variables. The dependent variable for this study is the job performance of teleworkers, which is measured by the self-reported actual performance rating received in the latest evaluation period (1 = unsuccessful, 2 = less than fully successful, 3 = fully successful, 4 = exceeds fully successful, and 5 = outstanding).Table 1. Independent variables. Variable Question Factor loading Factor eigenvalue Alpha Management factors Quality of performance management (5 = strongly agree to 1 = strongly disagree) 1. I know what is expected of me on the job 0.768 4.453 0.897 2. I have individual performance goals that clearly define the results I am expected to achieve 0.849 3. My performance goals are clearly linked to organizational or work unit goals 0.841 4. Appropriate, objective measures or metrics are used to evaluate my achievement of my performance goals 0.84 5. I am held accountable for achieving the results expected of me 0.743 6. Recognition and rewards are based on performance in my work unit 0.800 7. I am satisfied with the recognition and rewards I receive for my work 0.733 Quality of supervision (5 = strongly agree to 1 = strongly disagree) 1. My supervisor has a good understanding of my job performance and accomplishments 0.837 3.702 0.909 2. My supervisor supports my need to balance work and family issues 0.809 3. My supervisor gives me autonomy to accomplish my work 0.809 4. Overall, I have a positive relationship with my supervisor 0.926 5. Overall, I am satisfied with my supervisor 0.914 Work factors Expected work similarity To what extent do you agree or disagree with the following statements about telework in your agency: Employees that telework have similar work assignments and work expectations as employees that do not telework (5 = strongly agree to 1 = strongly disagree) Telework frequency 0 = never; 1 = on an ad hoc basis; 2 = 1 day per week; 3 = 2–3 days per week; 4 = 4–5 days per week Technology factors Accessibility of technologies To what extent do you agree or disagree with the following statements about telework in your agency: I have access to (use my own or my agency provides) the hardware, software, internet connections, etc., I need for telework (5 = strongly agree to 1 = strongly disagree) Device availability My organization provides me with: Laptop, cell phone, personal data assistant PDA, all other mobile devices (number of devices 0–4) Table 2. Mediating variables and dependent variable. Variable Question Factor loading Factor eigenvalue Alpha Mediator Organizational impact of telework (1 = very negative to 5 = very positive) Overall, how has teleworking impacted your organization’s 4.042 0.902 1. Productivity and performance 0.822 2. Ability to ensure effective communication 0.851 3. Ability to support effective teamwork 0.879 4. Ability to support effective work relationships 0.865 5. Ability to recruit high-quality employees 0.744 6. Ability to retain high-performing employees 0.754 Dependent Job performance of teleworker Performance rating received in the latest evaluation period (1 = unsuccessful, 2 = less than fully successful, 3 = fully successful, 4 = exceeds fully successful, and 5 = outstanding) Work factors include expected work similarity and telework frequency. Expected work similarity is based on a single measure that asks the respondents to what extent they think that teleworkers have similar work assignments and work expectations compared to nonteleworkers. Telework frequency is measured by the frequency of teleworking. Technology factors are composed of two separate measures. The first is the accessibility of technologies, which is measured by asking the respondents whether their agency provides hardware, software, and internet connections for teleworking. The second is device availability, which represents the number of devices provided among the presented equipment list. Respondents were asked whether their organization provides them with a laptop, cell phone, personal data assistant (PDA), and other mobile devices. Management factors include the quality of performance management and supervision. The quality of performance management is measured based on seven items representing individual-level perception of performance management. The measure illustrates the degree to which the employee believes the organization provides clear job expectations, clearly defined individual performance goals, clear links between individual and organizational goals, appropriate measures for individual performance goals, accountability for achieving individual expectations, recognition and reward based on the employee’s performance in the work unit, and satisfaction with received recognition and rewards. Principal factor analysis showed that seven items were loaded on a single factor (eigenvalue = 4.453), with a Cronbach’s alpha value of 0.897. Factor loadings ranged from 0.733 to 0.849. Quality of supervision is based on five items that measure the employee’s satisfaction with the supervisor in his or her understanding of the employee’s performance and accomplishments, supporting the balance of work and family issues, providing autonomy in accomplishing work, having a positive relationship with the employee, and the employee’s overall satisfaction with the supervisor. According to the results of principal factor analysis, a total of five items were loaded on a single factor (eigenvalue = 3.70), with Cronbach’s alpha value of 0.909. Factor loadings ranged from 0.809 to 0.926. The organizational impact of telework, which is the mediating variable between the independent variables and the job performance of teleworkers, is composed of six items that measure the teleworker’s overall perception on the effects of telework on their organization. According to the results of principal factor analysis, all items were loaded on a single factor (eigenvalue = 4.042), with a Cronbach’s alpha value of 0.902. Factor loadings ranged from 0.744 to 0.879. Table 3 provides the intercorrelation among the variables. The results indicate that among the independent, mediating, and dependent variables, the correlation coefficients were not high enough to cause serious multicollinearity issues.Table 3. Inter-correlation table of variables. Variable N Mean SD 1 2 3 4 5 6 7 1. Job performance of teleworker 2125 4.03 0.791 1 2. Expected work similarity 2125 4.36 0.813 0.089 1 3. Telework frequency 2125 2.30 0.939 0.094 0.145 1 4. Accessibility of technologies 2125 4.48 0.750 0.077 0.361 0.123 1 5. Device availability 2125 1.36 0.942 0.038 0.001 −0.036 0.097 1 6. Quality of performance management 2125 28.03 4.84 0.239 0.268 0.098 0.283 0.02 1 7. Quality of supervision 2125 21.19 3.66 0.226 0.220 0.075 0.250 0.014 0.630 1 8. Organizational impact of telework 2125 23.80 3.84 0.038 0.234 0.213 0.229 0.027 0.294 0.209 Note: p < .01. Results In the current study, the analysis of the hypothesized model, which is presented in Figure 2, revealed that the model was just-identified with zero degrees of freedom. In such just-identified models, to calculate the chi-square goodness-of-fit, revising the model by excluding the nonsignificant paths is recommended (Scott and Bruce, 1994). Thus, the current study obtained additional degrees of freedom by excluding the nonsignificant paths. More specifically, work similarity, accessibility to technologies, and device availability had no impact on the job performance of teleworkers. Additionally, device availability and quality of supervision did not affect the organizational impact of telework. As the device availability hypothesis was not supported within the scope of this study, the amount of equipment provided to teleworkers had no effect on either the organizational impact of telework or teleworker job performance. Therefore, Hypothesis 2b was not supported. The results of the final model are presented in Figure 3.Figure 2. Initial path analysis model. Figure 3. Final path analysis model. The fit statistics for the final model suggest that the data fit the model well. All the fit indices used in the current study showed good fit according to a chi-square test (chi-square = 1.105, df = 3, p = 0.776), the relative fit index (RFI) >0.95, the comparative fit index (CFI) >0.95, the normed-fit index (NFI) >0.95, the goodness-of-fit index (GFI) >0.95, and the root mean square error of approximation (RMSEA) <0.001). According to the squared multiple correlation values in the final model, the model accounted for 15% of the variance in the organizational impact of telework and 7.4% of the variance in the job performance of teleworkers. To confirm the mediation effect of the organizational impact of telework between the independent variables and teleworker job performance, bootstrapping with 500 samples and a 95% bias-corrected confidence level was applied. The results of the hypothesis testing in the final model are as follows. The final model involved two sets of results: 1) the direct relationship between the independent and dependent variables and 2) the inconsistent mediation effects of telework impact. First, regarding the direct relationship between the independent and dependent variables, expected work similarity (b = 0.114, p < 0.001), telework frequency (b = 0.162, p < 0.001), accessibility of technologies (b = 0.106, p < 0.001), and quality of performance management (b = 0.218, p < 0.001) directly affected the organizational impact of telework. These findings suggest that teleworkers who expect that they have similar work assignments and expectations compared to those of nonteleworkers, telework more frequently, have more access to technologies used for teleworking, and perceive that their organization’s performance management is high quality tend to have positive perceptions about the organizational impact of telework. Regarding the job performance of teleworkers, telework frequency (b = 0.079, p < 0.001), quality of performance management (b = 0.168, p < 0.001), quality of supervision (b = 0.125, p < 0.001), and organizational impact of telework (b = −0.055, p < 0.05) showed statistical significance. This result illustrates that teleworkers who carry out jobs more often through telework tend to perform better than employees who telework less often. The findings of this study also indicate that management matters and greatly influences teleworkers’ performance. Teleworkers who perceived that their organization was doing well in performance management practices and that their supervisors were doing well in supervising them showed higher performance outcomes compared to their counterparts. However, in contrast to the expectations of the current study, the relationship between the organizational impact of telework and the job performance of teleworkers was negative, which suggests that teleworkers who believe that telework has positive impacts on their organization demonstrate a lower performance level than employees who do not. In other words, high-performing teleworkers evaluate the organizational impact of telework more negatively than do low-performing teleworkers. In sum, Hypotheses 1b and 3a were supported, while Hypotheses 1a, 2a, and 3b were partially supported. Among the two supported hypotheses, by comparing the standardized path estimates, as shown in Table 4, the current study revealed that the quality of performance management has a stronger effect on both the organizational impact of telework and the job performance of teleworkers than telework frequency.Table 4. Results of path analysis. Dependent variable Path Standardized path estimates p Organizational impact of telework Expected work similarity->Organizational impact of telework 0.114* .000 Telework frequency-> organizational impact of telework 0.162* .000 Accessibility of technologies-> organizational impact of telework 0.106* .000 Quality of performance management-> organizational impact of telework 0.218* .000 Job performance of teleworkers Organizational impact of telework-> job performance of teleworkers −0.055* .013 Telework frequency-> job performance of teleworkers 0.079* .000 Quality of performance management-> 0.168* .000 Quality of supervision-> job performance of teleworkers 0.125*** .000 Note: p < .05, p < .001. Subsequently, regarding the expected work similarity and accessibility of technologies, there was no statistical significance in the direct relationship with teleworker job performance, whereas the indirect relationship mediated by the organizational impact of telework showed statistical significance. This result may suggest perfect mediation; however, as shown in Table 5, because the relationship between the organizational impact of telework and job performance of teleworkers was negative, the sign of the indirect relationship was negative, which is in contrast with the expectations of the current study. Telework frequency and quality of performance management both directly and indirectly affected the job performance of teleworkers. Thus, a partial mediation effect seems to exist. However, because the signs of the direct (+) and indirect effects (−) were different, the existence of suppression effects was found, which resulted in an inconsistent mediation (Mackinnon and Fairchild, 2009; Shrout and Bolger, 2002). In such a case, Zhao et al. (2010) suggest the possibility that an omitted variable exists between the independent variables and dependent variables and recommend that the researchers look for alternative mediators. Therefore, in the current study, the mediation effect of telework impact was not found, and Hypothesis 4 was not supported.Table 5. Standardized effects on performance rating with bias-corrected percentile p values. Effect Variable Expected work similarity Telework frequency Accessibility of technologies Quality of performance management Quality of supervision Organizational impact of telework Direct (pvalue) — 0.079 (.005) — 0.168 (.004) 0.125 (.003) −0.055* (.046) Indirect −0.006* (0.022) −0.009* (.033) −0.006* (.026) −0.012* (.041) — — (pvalue) Note: p < .05, *p < .01. Discussion and conclusions This study simply aims to identify what factors determine teleworkers’ performance in public organizations in the pre-COVID-19 period. An exploratory analytical framework was posited to identify which factor is more critical to teleworkers’ performance among work factors, technology factors, and management factors. During this process, the mediating effect of the organizational impact of telework was also tested. The implications for future research and practice are as follows. First, whether teleworkers perceive that they perform similar work assignments and experience the same work expectations as nonteleworkers did not affect teleworkers’ job performance. This result implies that in contrast to past research that argues the importance of improving teleworker performance by adjusting job requirements and performance measures (Bogdanski and Setliff, 2000; Lautsch et al., 2009), maintaining fairness in work assignments and expectations is not a determinant of the performance of teleworkers. However, a positive association was found between work similarity and organizational impact. Therefore, providing fair work expectations for telework may be important for employees to think positively of the impact of telework on their organization. Future studies may consider that providing similar work to teleworkers can lead to positive perceptions regarding telework, but it does not improve individual performance. To validate this finding, it may be possible to analyze the relationship between work similarity and job performance of nonteleworkers and examine whether similar results are found. Second, the present findings also reveal that telework frequency positively affects teleworker performance. This result contradicts previous studies (e.g., Bailey and Kurland, 2002; OECD, 2020) that argue that frequent teleworking may lead to social isolation, missing out on information at the workplace, poor performance appraisals, and decreased work efficiency. Thus, this finding may imply that high-performing teleworkers may simply be high-level performers who happen to telework more frequently. Future studies may consider investigating how factors such as work efficiency and social isolation mediate the relationship between teleworking amount and teleworker performance. In addition, subsequent research can attempt to first identify the high-level performers and examine the level of telework-related variables to reveal if employee performance can act as an antecedent of telework. Third, the accessibility of technologies and device availability were not associated with the job performance of teleworkers. One possible reason is that the accessibility of technologies and provided devices are simply not determinants of teleworker performance. Another possible explanation may be that when the quality of the accessibility and number of devices reaches a certain point, the positive effect of technological factors may be subject to the law of diminishing marginal utility. Subsequent research may focus on validating the reasons underlying the insignificant relationship between technological factors and job performance. Thus, to save unnecessary investments in telework accessibility and devices, practitioners may consider identifying the proper level of investments in technological environments. Fourth, all variables related to management factors were positively related to teleworker performance; thus, within the scope of the current study, management factors are more critical to teleworkers’ performance than technology factors. This result suggests caution among those who believe in technological determinism with an optimistic view of the prospects of a technology-mediated work environment. Future studies should consider that teleworkers are likely to feel detached from their supervisors and easily lost in an autonomous but unchecked working environment. Managers may worry about the “out of sight, out of mind” phenomenon being present among teleworkers (Weisberg and Porell, 2011). Thus, practitioners need to consider that the quality of performance management practices might be more important to organizational performance in the telework environment than in the traditional work environment simply because teleworkers tend to frequently be physically distant from their managers. Fifth, regarding the mediating effect of telework impact, in contrast to the expectation of the current study, the organizational impact of telework was negatively associated with teleworker performance. This finding implies that high-performing teleworkers do not view telework as having a positive influence on their organization. High-performing teleworkers may even view telework as a deterrent to organizational management because the more teleworkers view the organizational impact of telework as negative, the higher their performance ratings are. Thus, determining the reasons why teleworkers with negative perceptions of the organizational impact of telework show high performance warrants more research. These findings offer implications for past research that argues that teleworkers are likely to exhibit more positive work attitudes than nonteleworkers (Caillier, 2012; Lee and Kim, 2018; Martin and MacDonnell, 2012). Future research and practitioners interested in telework implementation might consider re-examining whether the attitude of teleworkers is positive before merely comparing teleworkers against nonteleworkers. In summary, the analysis of the pre-COVID-19 period data shows that the perceived impact of telework negatively affected teleworker performance, while more frequent telework and management factors showed a positive association. Within the scope of the study, although telework frequency positively affected job performance, it had a weaker effect than that of management factors. Therefore, telework itself and positive perceptions regarding telework may not be major determinants of employee performance. Future research and practice can consider analyzing the perceptual differences in telework between high- and low-level performers independently rather than merely assuming that telework improves overall organizational performance. The results of this study have implications for post-COVID-19 telework research. Since telework has been expanded as a response to the pandemic, assessing teleworker performance before the COVID-19 pandemic is necessary to identify the possible determinants of performance and apply the findings to post-COVID-19 teleworking. Furthermore, during the crisis, to prevent infection, organizations have been encouraged to let employees telework (Centers for Disease Control and Prevention (CDC), 2020). The prolonged COVID-19 pandemic period has required organizations to develop the means by which to maintain performance using telework. Based on the findings of this study, researchers and practitioners can investigate how those determinants have affected performance during the crisis. Recent studies have discussed telework in the post-COVID-19 period, during which organizations may or may not have offered telework to their employees (Belzunegui-Eraso and Erro-Garcés, 2020). Belzunegui-Eraso and Erro-Garcés proposed that future studies should analyze whether the COVID-19 virus was a driver for change or if it was simply a temporary alternative. Organizations’ decision about whether to continue using telework would depend on the effect of telework during the COVID-19 period, which remains unclear (OECD, 2021b). Some studies have claimed that business organizations need to adjust their work practices to solve challenges related to teleworking and that governments should implement relevant policies to sustain the benefits of telework (OECD, 2021a). Therefore, the determinants identified in the current study can be used to assess telework-related practices for the better implementation of telework in the post-COVID-19 era. Despite the contributions of the current study, there are several limitations. First, the empirical analysis was conducted by using a data set produced by the U.S. federal government. As items listed on the survey were used in the analysis, it cannot be claimed that all the constructs were based on theoretical development. This limitation could not be solved within the scope of the current study because the Telework Study data represent the first and only federal government survey that exclusively investigated the perceptions of federal employees regarding telework in the pre-COVID-19 period. Future researchers can attempt to develop their own measurement model that can incorporate telework practices during the COVID-19 period and conduct a survey independent of federal government-led telework studies. Second, the empirical findings are strictly based on the U.S. federal government teleworking environment before the COVID-19 pandemic. Therefore, the generalization of the results of the current study to a broader population and time frame may be limited. Subsequent research can attempt to test the results of the current study in other regions or settings over multiple periods of time that include telework during the COVID-19 period. ORCID iD Seejeen Park https://orcid.org/0000-0002-2436-2562 Declaration of conflicting interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2021S1A5A2A03065493). ==== Refs References Allen TD Golden TD Shockley KM , et al. (2015) How effective is telecommuting? Assessing the status of our scientific findings. Psychological Science in the Public Interest 16 (2 ): 40–68.26403188 Anderson AJ Kaplan SA Vega RP , et al. (2015) The impact of telework on emotional experience: when, and for whom, does telework improve daily affective well-being? European Journal of Work and Organizational Psychology 24 (6 ): 882–897. Bailey DE Kurland NB (2002) A review of telework research: findings, new directions, and lessons for the study of modern work. Journal of Organizational Behavior 23 (4 ): 383–400. Baruch Y (2000) Teleworking: benefits and pitfalls as perceived by professionals and managers. New Technology, Work and Employment 15 (1 ): 34–49. Bayo‐Moriones A Billón M Lera‐López F , et al. (2013) Perceived performance effects of ICT in manufacturing SMEs. Industrial Management & Data Systems 113 (1 ): 117–135. Bélanger F Watson-Manheim MB Swan BR , et al. (2013) A multi-level socio-technical systems telecommuting framework. Behaviour & Information Technology 32 (12 ): 1257–1279. Belzunegui-Eraso A Erro-Garcés A (2020) Teleworking in the context of the COVID-19 crisis. Sustainability 12 (9 ): 3662. Bentley TA Teo STT McLeod L , et al. (2016) The role of organisational support in teleworker wellbeing: a socio-technical systems approach. Applied Ergonomics 52 : 207–215.26360212 Bogdanski C Setliff RJ (2000) Leaderless supervision: a response to Thomas. Human Resource Development Quarterly 11 (2 ): 197–201. Caillier JG (2012) The impact of teleworking on work motivation in a US federal government agency. The American Review of Public Administration 42 (4 ): 461–480. Caillier JG (2013) Are teleworkers less likely to report leave intentions in the United States federal government than non-teleworkers are? The American Review of Public Administration 43 (1 ): 72–88. Campbell J McDonald C (2009) Defining a conceptual framework for telework and an agenda for research in accounting and finance. International Journal of Business Information Systems 4 (4 ): 387–402. Centers for Disease Control and Prevention (CDC) (2020) Interim Guidance for Businesses and Employers to Plan and Respond to Coronavirus Disease 2019 (COVID-19). Atlanta, GA: CDC. Available at: https://www.cdc.gov/coronavirus/2019-ncov/specific-groups/guidance-business-response.html (accessed 3 March 2021). Cook J DeMarco J Doherty W , et al. (2013) OPM’s 2012 telework report shows vast opportunities for improvement. Public Manager 42 (4 ): 13. Day FC Burbach ME (2015) Does organization sector matter in leading teleworker teams? A comparative case study. International Journal of Business Research and Development 3 (4 ): 29–42. DeNisi AS Pritchard RD (2006) Performance appraisal, performance management and improving individual performance: a motivational framework. Management and Organization Review 2 (2 ): 253–277. Denison MB Banks D Bosley E , et al. (2014) Effective engagement strategies for an increasingly dispersed workforce. Public Manager 43 (3 ): 62–65. Eurofound (2020) Living, working and COVID-19 dataset, Dublin. Available at: http://eurofound.link/covid19data (accessed 25 November 2021). Fana M Milasi S Napierala J , et al. (2020). Telework, work organisation and job quality during the COVID-19 crisis: a qualitative study (No. 2020/11). JRC Working Papers Series on Labour, Education and Technology. Seville, Spain: European Commission, Joint Research Centre (JRC). Gajendran RS Harrison DA (2007) The good, the bad, and the unknown about telecommuting: meta-analysis of psychological mediators and individual consequences. Journal of Applied Psychology 92 (6 ): 1524–1541.18020794 George B Pandey SK (2017) We know the yin-but where is the yang? Towards a balanced approach on common source bias in public administration scholarship. Review of Public Personnel Administration 37 (2 ): 245–270.29046599 Greer TW Payne SC (2014) Overcoming telework challenges: outcomes of successful telework strategies. The Psychologist-Manager Journal 17 (2 ): 87–111. Guest DE (1997) Human resource management and performance: a review and research agenda. The International Journal of Human Resource Management 8 (3 ): 263–276. Kowalski KB Swanson JA (2005) Critical success factors in developing teleworking programs. Benchmarking: An International Journal 12 (3 ): 236–249. Kurkland NB Bailey DE (2000) Telework: the advantages and challenges of working here, there, anywhere, and anytime. IEEE Engineering Management Review 28 (2 ): 49–60. Lautsch BA Kossek EE Eaton SC , et al. (2009) Supervisory approaches and paradoxes in managing telecommuting implementation. Human Relations 62 (6 ): 795–827. Lee D Kim SY (2018) A quasi-experimental examination of telework eligibility and participation in the US federal government. Review of Public Personnel Administration 38 (4 ): 451–471. Lee G Benoit-Bryan J Johnson TP , et al. (2012) Survey research in public administration: assessing mainstream journals with a total survey error framework. Public Administration Review 72 (1 ): 87–97. Lodovici MS (2021) The Impact of Teleworking and Digital Work on Workers and Society. Luxembourg, Europe: European Parliament. Mackinnon DP Fairchild AJ (2009) Current directions in mediation analysis. Current Directions in Psychological Science 18 (1 ): 16–20.20157637 Mahler J (2012) The telework divide. Review of Public Personnel Administration 32 (4 ): 407–418. Major DA Verive JM Joice W , et al. (2008) Telework as a dependent care solution: Examining current practice to improve telework management strategies. The Psychologist-Manager Journal 11 (1 ): 65–91. Martin BH MacDonnell R (2012) Is telework effective for organizations? Management Research Review 35 (7 ): 602–616. Martínez-Sánchez A Pérez‐Pérez M José Vela‐Jiménez M , et al. (2008) Telework adoption, change management, and firm performance. Journal of Organizational Change Management 21 (1 ): 7–31. Maruyama T Tietze S (2012) From anxiety to assurance: concerns and outcomes of telework. Personnel Review 41 (4 ): 450–469. Merit Systems Protection Board (MSPB) (2011) Telework: Weighing the Information, Determining an Appropriate Approach. Washington, DC: Merit Systems Protection Board. Morgan RE (2004) Teleworking: an assessment of the benefits and challenges. European Business Review 16 (4 ): 344–357. Nakrošiene A Buciuniene I Goštautaite B , et al. (2019) Working from home: characteristics and outcomes of telework. International Journal of Manpower 40 (1 ): 87–101. O'Neill TA Hambley LA Greidanus NS , et al. (2009) Predicting teleworker success: An exploration of personality, motivational, situational, and job characteristics. New Technology, Work and Employment 24 (2 ): 144–162. OECD (2020) Productivity Gains from Teleworking in the Post COVID-19 Era: How Can Public Policies Make it Happen? Paris, France: OECD Publishing. OECD (2021a) Teleworking in the COVID-19 Pandemic: Trends and Prospects. Paris, France: OECD Publishing. OECD (2021b) Telework before the COVID-19 Pandemic: Trends and Drivers of Differences across the EU. Paris, France: OECD Publishing. Office of Personnel Management (OPM) (2011) Guide to Telework in the Federal Government. Washington, DC: Office of Personnel Management. Office of Personnel Management (OPM) (2012) Status of Telework in the Federal Government: Report to the Congress. Washington, DC: Office of Personnel Management. Office of Personnel Management (OPM) (2015) Annual Reports. Washington, DC: Office of Personnel Management. Office of Personnel Management (OPM) (2021) 2021 Guide to Telework and Remote Work in the Federal Government. Washington, DC: Office of Personnel Management. Overmyer SP (2011) Implementing Telework: Lessons Learned from Four Federal Agencies. Arlington, TX: IBM Center for the Business of Government. Park S Cho YJ (2022) Does telework status affect the behavior and perception of supervisors? Examining task behavior and perception in the telework context. The International Journal of Human Resource Management 33 (7 ): 1326–1351. Pérez MP Sánchez AM De Luis Carnicer MP , et al. (2002) Benefits and barriers of telework: perception differences of human resources managers according to company’s operations strategy. Technovation 22 (12 ): 775–783. Pérez MP Sánchez AM De Luis Carnicer P , et al. (2004) A technology acceptance model of innovation adoption: the case of teleworking. European Journal of Innovation Management 7 (4 ): 280–291. Scott SG Bruce RA (1994) Determinants of innovative behavior: a path model of individual innovation in the workplace. Academy of Management Journal 37 (3 ): 580–607. Shrout PE Bolger N (2002) Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychological Methods 7 (4 ): 422–445.12530702 Smith MR Marx L (1994) Does Technology Drive History? the Dilemma of Technological Determinism. Cambridge, MA: MIT Press. Snyder K (2012) Enhancing telework: a guide to virtual leadership. Public Manager 41 (1 ): 11–14. Vega RP Anderson AJ Kaplan SA , et al. (2015) A within-person examination of the effects of telework. Journal of Business and Psychology 30 (2 ): 313–323. Verbeke A Schulz R Greidanus N , et al. (2008) Growing the Virtual Workplace: The Integrative Value Proposition for Telework. Massachusetts, MA: Edward Elgar Publishing. Weisberg A Porell M (2011) Moving telework from compliance to competitiveness. Public Manager 40 (1 ): 12–14. Wicks D (2002) Successfully increasing technological control through minimizing workplace resistance: understanding the willingness to telework. Management Decision 40 (7 ): 672–681. Zhao X Lynch JG Chen Q , et al. (2010) Reconsidering Baron and Kenny: myths and truths about mediation analysis. Journal of Consumer Research 37 (2 ): 197–206.	0	PMC9708530	NO-CC CODE	2022-12-01 23:20:30	no	2022 Nov 28;:03063070221116510	utf-8	null	null	null	oa_other
==== Front spgem GEM Journal of General Management 0306-3070 1759-6106 SAGE Publications Sage UK: London, England 10.1177_03063070221116512 10.1177/03063070221116512 Original Empirical Article Relationship between stress due to COVID-19 pandemic, telecommuting, work orientation and work engagement: Evidence from India https://orcid.org/0000-0002-0125-4534 Chidambaram Vijayabanu Ramachandran Gayathri https://orcid.org/0000-0001-7052-9805 Chandrasekar Therasa School of Management, SASTRA Deemed to be University, Thanjavur, India https://orcid.org/0000-0001-5565-4413 Parayitam Satyanarayana Department of Management and Marketing, Charlton College of Business, 14709 University of Massachusetts Dartmouth , North Dartmouth, MA, USA Satyanarayana Parayitam, Department of Management and Marketing, Charlton College of Business, University of Massachusetts Dartmouth, 285Old Westport Road, North Dartmouth, MA 02747, USA. Email: [email protected] 28 11 2022 28 11 2022 03063070221116512© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. The purpose of the present study is to examine the effect of stress induced by coronavirus (COVID-19), telecommuting, and work orientation on work engagement among employees in the Information Technology (IT) sector. Using a structured survey instrument, data were collected from 285 respondents from four cosmopolitan cities in the southern part of India—Chennai, Coimbatore, Tiruchirappalli, and Madurai using a structured instrument. The hierarchical regression results reveal that (i) stress induced by COVID-19 was negatively related to work engagement, (ii) work orientation is positively associated with work engagement, and (iii) telecommuting is positively associated with work engagement. The results also reveal that (i) telecommuting weakened the relationship between stress induced by COVID-19 and work engagement and (ii) strengthened the positive relationship between work orientation and work engagement. These results are consistent with Job Demands and Resources (JDR), Conservation of Resources Theory (COR), and Career Construction Theory (CCT). The study highlights the importance of telecommuting as a strategic move on the part of the companies to reduce stress and enhance work engagement. Considering the global pandemic situation, employees in the IT sector would find it comfortable to work from their homes and contribute their best for the success of organizations. The present study also suggests ways for the organizations to promote work engagement and remain committed to performing during stressful situations like a global pandemic. The theoretical and practical implications are discussed. Perceived stress due to COVID-19 telecommuting work orientation work engagement edited-statecorrected-proof typesetterts10 ==== Body pmcIntroduction The sudden and unprecedented outbreak of coronavirus (COVID-19) brought paradigmatic changes in the socio-economic and business environment worldwide. The initial alarm bell that rang sometime in December 2019 went unnoticed until it adversely affected over 220 countries resulting in a total death toll of over 3.8 million (Worldometers, 2021). In addition to creating a panic situation regarding health, COVID-19 has evoked substantial psychological, physical, and professional challenges in all the industries at the global level (Achterberg et al., 2021; De et al., 2020; Nath et al., 2021). The pandemic has increased the demand for front-line workers, employees in essential services (safety and security forces), and the information technology (IT) sector. The physical and psychological pressure and uncertainty in the labor market has been so unexpected that many developing countries, such as India were unprepared to face the pandemic. On the other hand, developed countries (USA, UK, Japan, Canada, Australia, and other European countries) had the in-built infrastructure to adjust to the new work environment created by the pandemic and successfully adapted to the challenging situation. The present study aims to unravel the impact of stress caused by the COVID-19 global pandemic on employee work engagement, especially in the Indian context. When the global pandemic spread, several countries imposed mandatory lockdowns for a relatively long period, thus connecting individuals and organizations through online platforms. In India, too, because of lockdowns and social distancing, organizations require the employees to work from their homes (WFH). The academic institutions (schools, colleges, and universities) shifted their teaching from face-to-face to online, organizations conducting operations in virtual platforms, and gradually employees are getting adjusted to this “new way of work-life.” Following the COVID-19 pandemic, it is imperative to examine how changing work style has affected employee work engagement, especially in the IT sector. Most importantly, how the stress caused by COVID-19 and the strategies employed by organizations to bring normality to the workplace influence an individual’s career path, career flexibility, and advancement. As an adage says: “blessing in disguise,” individuals interested in a career advancement tends to take opportunities stemming from crises. During the last 18 months, several research papers have been published all over the world about various aspects—health-related, environment-related, business-related, consumer-related, and COVID-19 has been “buzzword” of the present century (Davidescu et al., 2020; Nath et al., 2021; Pedrozo-Pupo et al., 2020; Yan et al., 2021). Several researchers have examined the effect of COVID-19 on the online shopping motives of consumers (Koch et al., 2020; Loxton et al., 2020), impulse buying (Ahmed et al., 2020; Naeem, 2021), job crafting (Svicher and Fabio 2021), work-life balance, and job stress (Irawanto et al., 2021), tourism (Jaipuria et al., 2021),and performance of students in educational institutions (Gopal et al., 2021). However, the research focusing on the impact of COVID-19 among the employees in the information technology (IT) sector, particularly in the Indian context, was understudied. Research gap and the rationale for the present study in the Indian context The present study examines the relationship between global pandemic-induced stress, telecommuting, work orientation, and work engagement of remote employees. During the pandemic and lockdowns, many business organizations worldwide have laid off thousands of workers, causing severe unemployment problems, and India is not an exception. For example, over nine million people lost their jobs in India (Jaipuria et al., 2021). The outbreak of COVID-19 has resulted in an increase in the number of infected cases throughout the world, including India; governments at the state and center have implemented various measures to prevent the further spread of the virus. These measures include mandatory lockdowns, social distancing, working from home, staying at home, and maintaining self-quarantine. These have resulted in colossal loss of profits to businesses, and to survive, the only strategy they had was heavy downsizing. For example, several migration laborers lost their jobs in the construction industry as the real estate property builders could not afford to pay them because the work had come to a standstill. While some people lost their jobs, others had no alternative than to work from home. At the same time, the demand for first-line workers and employees in the IT industry has increased manifold. Unfortunately, the technological infrastructure needed to work from home is lacking, thus causing additional stress to employees. As the organizations and employees did not have enough time to prepare for new setups and arrangements, they did not have any alternative other than to adjust to whatever resources they had. In this scenario, the present study focuses on how individuals shape their career paths in organizations by aligning their soft skills to meet the demand from supervisors and by remaining flexible and adaptive (Jung and Takeuchi, 2018). Some forms of workplace flexibility include telecommuting, working from home, which existed earlier during flu season, bad weather, and crises. Now, workplace flexibility is expected to continue for a long time because of COVID -19 pandemic. Before the global pandemic, telecommuting was available to individual employees as an option, whereas employees were forced into telework without any preparation time during the pandemic. Unsurprisingly, employees were not given enough time to make home-based telework nearly for a long time. However, to our knowledge, there is a shortage of studies concerning the effect of COVID-19 on work engagement, especially in the context of changing work climate. To bridge the gap, the present study has the following research questions: RQ 1: What is the effect of stress caused due to COVID-19 global pandemic, telecommuting, and work orientation on employee work engagement? RQ 2: Does telecommuting reduce the negative impact of COVID-19 on work engagement and increase the positive effects of work orientation on work engagement? This study makes five significant contributions to stress and work engagement literature. First, in the present conditions of the global pandemic, the work culture in many organizations worldwide has changed. The present study examined the stress caused by the COVID-19 global pandemic on employees' work engagement. The adverse impact of the worldwide pandemic on work engagement, though expected, adds to the literature on stress and work. When work engagement, which is a positive psychological state, is adversely affected, productivity in organizations suffers. As evidenced in this study, aligning with findings from similar studies on the impact of the global pandemic, COVID-19 has harmful consequences. Second, this study found that if employees focus on their work while managing the health concerns of the global pandemic, it contributes to work engagement. Given the nature of the global pandemic crisis, the employees need to adopt a positive attitude towards work and see that productivity does not fall short of expectations. Third, to counter the negative impact of the crisis on work, organizations’ employees’ telecommuting a resilient strategy. This study reported that the positive association between telecommuting and work engagement contributes to the growing literature on stress and work. Fourth, in addition to the direct positive effect of telecommuting, the moderating effect of telecommuting in mitigating the ill-effects of COVID-19 on work engagement, as found in this study, is a pivotal contribution to the literature. Further, telecommuting strengthens the relationship between work orientation and work engagement, thus providing a solution to combat crises. In developed countries, telecommuting existed during the pre-pandemic, and moving to work from home (WFH) became very easy. In developing countries like India, infrastructure does not have the capability for the organizations to move to WFH. The pandemic created a window of opportunity for implementing telecommuting so that work is not interrupted. Fifth, the simple conceptual model we developed in this research contributes to the literature on stress and work, underscoring the importance of telecommuting. Literature review and the variables in the present study Perceived stress due to COVID-19 Organizational scholars have exhaustively researched job stress (Sauter and Murphy, 1995; Shinn et al., 1984). Previous researchers documented that job stress is an occupational hazard that has deleterious consequences on individuals’ physical health, mental and psychological health, work performance, and job satisfaction (Maslach & Leiter, 2008). Clinical psychologists identified several physical, psychological, behavioral, and biological determinants of stress (Schneiderman et al., 2005). Stress causes include heavy workload, long working hours, working under dangerous conditions, and emotional problems such as anxiety, anger, and low self-esteem. While these are job-related work stressors, one new source of stress has been added to the list of stressors, that is, pressure due to COVID-19. As mentioned earlier, the outbreak of a global pandemic has become a threat to the existence of humankind, and it has adversely affected the physical and mental health of the world population. Clinical scientists argue that the impact of social distancing and quarantine caused by the pandemic on the mental health of individuals is significant; and a higher incidence of depression, posttraumatic stress disorder and anxiety disorders in the patients admitted to intensive care units, front-line healthcare workers, and families of infected patients (Polšek, 2020). There is unanimity among researchers that the COVID-19 outbreak is considered as a potential stressor resulting in emotional disturbances (Pedrozo-Pupo et al., 2020; Yan et al., 2021), anxiety (Licaocoet et al., 2020), and adverse health and well-being of individuals (Achterberg et al., 2021; Liu et al., 2021). Initially originating in Wuhan, China, the COVID-19 has officially declared a global pandemic on 11 March 2020, and subsequently, several countries in the world have imposed lockdowns and social distancing (Cucinotta and Vanelli, 2020). The global pandemic caused significant stress to populations worldwide (Bao et al., 2020). For example, in China, around 110 million people reported stress (Wang et al., 2020), and in Italy, 16 million people said that they experienced acute stress (Mazza et al., 2020). Similarly, several countries reported that the people experienced moderate to severe anxiety caused by the global pandemic. The COVID-19 induced stress has essential ramifications in the lives of people. It is essential to study how individuals and organizations adapt to the challenges during the pandemic. At the organizational level, the managers introduced changes in the work climate and required the employees to ‘stay home and stay safe and at the same time work from home. There was a sudden change in the demands from employers, and individuals were almost forced to adapt to the conditions. Telecommuting Also known as telework or teleworking, telecommuting represents remote working (away from offices). Telecommuting is one of the flexible working policies of organizations and one of the new ways of working that is being practiced for over a decade. Employees used to work remotely on some days, whereas they performed their duties in office locations on some other days. Remote working was purely optional, and the managers and employees collectively decided when to work remotely and when to work face-to-face in offices. But during the global pandemic, there was no other choice for managers and employees but work remotely. Thus, telecommuting has been a necessity rather than a choice, and organizations worldwide switched to telecommuting; dual-career parents were forced to work from home (Feng and Savani, 2020). As World Health Organization (2020) reported, the global pandemic has resulted in unprecedented changes in people’s lives across the globe. Telecommuting refers to employees communicating with their offices, customers, and others by telephones and emails and conducting business only online (Hornby et al., 2011). Telecommuting has become a standard mode of work across all sectors in the global pandemic. In telework, employees are primarily self-reliant in their efforts to overcome challenges and work productively to the satisfaction of their supervisors. The impact of the global pandemic on various workforce levels are visible, and the variety, volume, velocity, and value of the work has increased in alarming proportions (Chang et al., 2021). More notably, as work from homes has become a norm rather than an exception, the organizations have shifted to digital mode and provided digital infrastructure to handle the heavy workload (Akala, 2020; Khetarpal, 2020). The digital transformation requires adopting a digital workforce mindset, especially for the employees who are not habituated to telecommuting. It is predicted that the post-COVID world would have contactless interfaces, strengthened digital infrastructure, telemedicine, online shopping, digital events, and increased reliance on robots (Marr, 2021). Work orientation Work orientation is concerned with how individuals perceive their work in life and find the meaning of work (Fossen & Vredenburgh, 2014). Work orientation is tri-dimensional, and individuals differ in their work orientation (Bellah et al., 1985). The three dimensions of work orientation are job orientation, career orientation, and calling orientation (Pitacho et al., 2019). Individuals who view work from a “job orientation” angle focus on the material benefits of performing work. These individuals are driven by extrinsic motivations and focus on financial rewards. Individuals who view work from “career orientation” tend to see work as career advancement. These individuals aspire to increase the organizational hierarchy and enjoy increased power, status, and prestige (Wrzesniewski, 2003). The third dimension is that some individuals who see work as “calling orientation” have a life mission and are more oriented towards self and emphasize the intrinsic motivation to derive happiness and a sense of self-fulfillment (Duffy and Dik, 2013). For example, most of the novel writers see work as a life mission with which they were destined and be identified, and in this process, they make personal and financial sacrifices (Bunderson and Thompson, 2009). Thus, work orientation is three-dimensional, viz., job dimension, career dimension, and calling dimension. Depending on their goals, individuals work orientation (i) to satisfy their financial needs and motivated by extrinsic factors, (ii) to satisfy their career goals and motivated by intrinsic factors, and (iii) to satisfy their life goals of happiness and self-fulfillment. It is exciting to note the linkages between these dimensions. For instance, job orientation leads to career orientation, which leads to calling intention of life satisfaction. In organizations, career advancement is accompanied by an increase in financial reward, and hence individuals who see their work as a job can also eventually enjoy progress in their careers. The direct positive relationship between the three dimensions of work orientation has been documented by researchers (Pitacho et al., 2019). The effect of the work orientation of employees on work engagement during the global pandemic is studied in this research. Work engagement Employee engagement at work is “a positive, fulfilling, work-related state of mind characterized by vigor, dedication, and absorption” (Schaufeli et al., 2002: 74). Engaged workers exhibit high dedication to complete given tasks on scheduled time. Previous researchers found that work engagement is positively associated with creativity, performance, citizenship behavior, and client satisfaction (Bakker, 2017; Schaufeli and Bakker, 2004). This is because individuals who are high on work engagement show enthusiasm and get immersed in work until they complete given tasks. Though individuals differ in their level of work engagement, depending on their personality characteristics and behavioral strategies, studies have shown that work engagement has positive outcomes (Bakker et al., 2014). Some of the antecedents of work engagement include job redesign, job resources, personal resources of individuals in terms of cognitive abilities, leadership style, and organizational climate (Alfes et al., 2013; Breevaart et al., 2014; Holman and Axtell, 2016). Work engagement is different from organizational engagement in the sense that the former focuses on the psychological state of an individual while fulfilling a task (Wefald and Downey, 2008), whereas later reflects an individual’s focus on full work performance by investing physical, cognitive, and emotional energies (Rich et al., 2010). Early scholars on organizational behavior found that engaged employees are affectively connected to their tasks and experience a stable condition of positive energy, whereas the disengaged employees feel emotionally detached from their tasks and experience tiredness, cynicism, and burnout (Baker et al., 2014; Hobfoll, 1989). Theoretical background and development of hypotheses The theoretical framework for the present research comes from three sources viz., Job Demands and Resources (JDR) model (Schaufeli and Bakker, 2004), Conservation of Resources Theory (COT) (Hobfall, 1989), and Career Construction Theory (CCT) (Savickas, 2005). According to JDR, two sets of variables: job resources and job demands, influence the work of individuals. Job resources include physical, social, psychological, and organizational resources available to employees to achieve their work goals. Job resources help the employee in personal learning and development and help them in career growth. On the other hand, job demands refer to physical, social, and psychological efforts the employees are required to complete tasks. The presence of needs and absence of available resources result in adverse consequences such as burnout, absenteeism, low commitment, job dissatisfaction (Maslach and Leiter, 2008). JDR is relevant to the present study because it was documented in the literature, those job resources are positively related to work engagement and job demands were negatively related to job engagement (Bakker and Demerouti, 2007). Job demands include high work pressure (for example, due to global pandemic) and emotional needs (due to adjustment of change in the work style), leading to disengagement and low job satisfaction. On the other hand, job resources such as performance feedback, social support, support from peers and citizenship behaviors lead to work engagement and employee commitment (Demerouti and Bakker, 2011). Another theory relevant to the present study is the Conservation of Resources (COR) (Hobfall, 1989). The basic tenet of COR is that individuals acquire and conserve resources for survival. When individuals feel the threat of loss of resources, they try to gain resources to survive, and if they cannot gain resources, they face stressful situations. Thus, individuals attempt to obtain and retain personal, social, and material resources to meet stressful challenges (Hobfall et al., 2018). COR emphasizes the objectively stressful nature of events and posits that resource loss is more potent than resource gain. Further, when the resources are exhausted, individuals tend to go to the defensive mode or become aggressive. The COR theory is relevant to the present study in a sense; the global pandemic created a situation of severe resource losses to the individuals, and to survive, they need to invest in acquiring resources. For example, faculty who were habituated to classroom teaching may find it difficult to switch to online education because of a lack of training. To survive, they need to learn how to deliver lectures online and discover the technological tools required. Getting the necessary training needed to learn technical skills on virtual platforms are not an overnight task. Another theory relevant to employees in today’s global pandemic context is the Career Construction Theory (CCT) (Savickas, 2005). The basic tenet of the CCT is that individuals use their vocational personalities to adapt to changes in jobs or job requirements. Individuals focus on their careers while performing jobs and invest their time learning new ways of doing things. Employees build their careers by imposing meaning on what they do in organizations, that is, work orientation. Individuals inherently develop career-related abilities, needs, values, and interests and behave in ways that help their career development. As imposed by the global pandemic, flexible working conditions would create opportunities for vocational personalities to engage in behavior that promotes their career. As mentioned before, the objective of the present study is to unravel the interrelationships between perceived stress caused by a global pandemic, telecommuting, and work orientation among IT employees in India. Hypotheses development a Perceived stress due to COVID-19 and work engagement Extant research on the negative relationship between occupational stress and work engagement, worker productivity, and worker satisfaction has been well documented in the literature (Amin et al., 2018; Hobubi et al., 2017). Work-related stress, to some degree, is vital to work engagement as long as the stress is considered ‘positive’ (called ‘eustress). On the contrary, when stress is considered “negative” (called “distress”), employees would not engage in the work productively. Abundant research documented that excessive stress has several adverse consequences on organizations in addition to serious health problems at the individual level (Morrissette and Kisamore, 2020; Rees, 1995). Previous researchers documented that work-related stress results in physical and psychological illness, and employees may eventually leave the organizations unless organizations introduce intervention mechanisms to reduce stress (Bhui et al., 2017). In addition, some studies reported that economic conditions, such as recession, may increase the stress of employees (Evans-Lacko et al., 2013; Stranks, 2015). Earlier researchers found that stress may also be related to adverse life events and environments where the transactional interaction between an individual and environment may trigger stressors (Cox, 1993; Florio et al., 1998). In the present study, the perceived stress due to the COVID-19 global pandemic, unprecedented and expected, has a deleterious effect on work engagement. This observation has intuitive appeal (logos) and is also consistent with the existing literature on stress. Based on the above, the following hypothesis was proposed: H1 Perceived stress due to COVID-19 is significantly and negatively related to work engagement. b Work orientation and work engagement In the present competitive work environment, the employees must exhibit work orientation to survive (Ehnert et al., 2014). As the work conditions have undergone significant changes and flexibility in the workplace has become a norm, employers expect that workplace flexibility may increase job performance, higher level of comfort, increase work motivation of employees, and contribute to improved relationships between employees and management. On the other hand, employees focus on work orientation to balance the demands coming from the employers. The career theory and research scholars argue that organizational dynamics shape individual career decisions in terms of flexible work policies and administrative practices (Tomlinson et al., 2018). The changes in the work-life demands stemming from crises, such as the global pandemic, affect employees' career management. Career-oriented employees have strong work orientations and perform better than those who do not care about their career growth. From a sociological viewpoint, work orientation consists of interpretation patterns, subjective interests, behavioral strategies to perform work, and react to employment situations (Pongratz. and Voß, 2003). From a psychological point of view, work orientation is concerned with personal needs and preferences regarding work and whether the individual preference of career orientation, performance orientation, and relationship between work and non-work are aligned perfectly or not. Work orientation is concerned with how individuals can optimize their working methods and get the best out of themselves. Work orientation also includes the willingness to work overtime when needed, willing to help others, exhibit extra-role behaviors, and not allow the family to hinder the work (Hoge, 2011). Intuitively, work orientation makes an individual engage in work and increases productivity. Based on the above, the following hypothesis was offered: H2 Work orientation is significantly and positively related to work engagement c Telecommuting and work engagement One of the hallmarks of the present decade is workplace flexibility as a management strategy to achieve business results (Kurland and Bailey, 1999). Work flexibility includes part-time work schedules, flexitime, telework on an ad hoc basis, telework regularly, shifts flexibility, and telework full-time. Telecommuting is one of the widely used workplace flexibility methods in organizations. The empirical evidence suggests that telecommuting results in better health, increased job satisfaction, improved objective performance, lower stress, reduced absenteeism and turnover (Gajendran and Harrison, 2007; Kossek and Michel, 2011). Though telecommuting has been in vogue for a long time, during the COVID-19 global pandemic, organizations considered home office as a strategy to continue their operations, and it is also expected that shortly (until things come to normal), the procedures will be conducted remotely (Gomez et al., 2020). Previous researchers documented a positive association between telecommuting and work engagement and suggested that leadership can promote telecommuting to increase productivity (Tate et al.., 2019). For instance, Gerards et al. (2018) found that “time and location-independent work” (one of the five new work ways related to work engagement) is positively associated with work engagement, mediated by transformational leadership. Based on the above, the following hypothesis was proposed: H3 Telecommuting is significantly and positively related to work engagement d Moderation hypotheses Sometime back, several authors have proposed new ways of work consisting of various facets—viz., flexibility, working at home, working together at a distance, time-and location-sindependent work—suggesting the organizations refurbish offices from traditional face-to-face offices to open-office layouts (De Leede and Kraijenbrink, 2014; Peters et al., 2014). A recently held study found that various facets of new ways of working are positively related to work engagement (Gerards et al., 2018). The authors argue that one of the facets of these new ways of working, that is, telecommuting, has, in addition to a direct effect on work engagement, a potential to moderate the relationship between other independent variables and work engagement. Individuals will be motivated to invest their time and energy into work when they have the discretion to perform duties according to their convenience, and telecommuting offers such inconvenience. Employee engagement is a psychological state characterized by the passion for working and commitment and thus resulting in positive organizational outcomes (Bakker and Albrecht, 2018). During the global pandemic, telecommuting acts as a positive and motivating force for the employees to alleviate the stress caused by the pandemic and severely affects the work engagement. Though stress is negatively related to work engagement, the authors argue that telecommuting reduces the strength of the negative relationship. Though, to our knowledge, no previous studies were available to vouch for the moderating effect of telecommuting; the authors are making a priori hypothesis in an exploratory way. It is also contended that telecommuting acts to strengthen the positive relationship between employee work orientation and work engagement. Tomlinson et al. (2018) argue that individuals on a career path would invest their resources in grabbing opportunities stemming from crises and react positively. Some individuals prefer flexibility in their careers, which is most often influenced by the institutional and organizational environment. Sometimes, problems such as a global pandemic takes the opportunities for their benefit. Telecommuting helps these individuals to realize their career goals by self-motivation and interacting with the changing environment positively. Based on the above arguments and existing literature, the moderation hypotheses were offered: H4a: Telecommuting moderates the relationship between perceived stress and work engagement such that at higher levels of telecommuting, the relationship between stress and work engagement becomes weaker than at lower levels of telecommuting H4b: Telecommuting moderates the relationship between work orientation and work engagement such that at higher levels of telecommuting, the relationship between work orientation and work engagement becomes more substantial than at lower levels of telecommuting. The conceptual model is presented in Figure 1.Figure 1. Conceptual model. Method Sample A survey instrument was sent to the employees working in the information and technology (IT) sector in four major cities of the southern part of India (Tamil Nadu). Because of the global pandemic, the authors could not directly contact the respondents. The surveys were sent using the Google forms, and asked the respondents to answer only if they are in the IT sector and working remotely. The authors got data from 285 respondents from four cosmopolitan cities (Chennai, Coimbatore, Tiruchirappalli, and Madurai). To collect data, the authors used convenience sampling because of several reasons:1. It is impossible to have a fixed list of IT employees in various companies to have probability sampling. 2. IT employees are dispersed over several locations due to social distancing and global pandemic and all the employees were working from homes. 3. Some IT employees may not show any interest in participation. To have representativeness of data and elicit unbiased responses, the focus was only on interested participants who are willing to provide accurate information about what they feel. Non-serious respondents may skew the results, so the authors preferred to avoid them. It took around 4 months for us to collect data from the respondents. This is consistent with past researchers’ work in social science research (Badgaiyan and Verma, 2014). The authors also tested the data for non-response bias by comparing the mean differences between the first fifty respondents and the last fifty respondents and noted no significant differences. Demographic profile The demographic profile of the respondents was presented in Table 1.Table 1. Demographic profile of respondents. Category Profile Total number Percentage Gender Male 154 54.4 Female 129 45.6 Age Below 30 207 73.1 30–40 49 17.3 40–50 21 7.4 50 and over 6 2.1 Educational qualification Undergraduate (Bachelors’ degree) 207 73.1 Post-graduate (Masters’ degree) 76 26.9 Annual income Below 240,000 ($3300) 63 22.3 Rs 240,000–Rs. 360,000 ($3300–$5000) 103 36.4 Rs.360,000–Rs. 480,000 ($5000–$6700) 30 10.6 Rs. 480,000–Rs. 600,000 ($ 6700–$8000) 66 23.3 Over Rs. 600,000 ($8000) 21 7.4 Occupation Lower level employees 60 9.6 Middle-level employees 81 13 Work from home Yes 219 77.4 No 64 22.6 Experience Less than 1 year 67 23.7 1 year–5 years 132 46.6 6 years–10 years 50 17.7 More than 10 years 34 12.0 Measures In this research, all the indicators were measured on a Likert-type five-point scale (“1” = strongly disagree; “5” strongly agree). Perceived stress due to COVID-19 was measured using seven items adapted from Gomez et al. (2020). The sample items read as: “I feel tense or worried about the effects that the coronavirus might have”; and “I feel anxious or nervous about the coronavirus.” The reliability coefficient Cronbach’s alpha for stress was 0.68. Work orientation was measured using six items adapted from Hoge (2011), and the sample items read as: “I constantly optimize my working methods”; “I get the best out of myself.” The reliability coefficient of work orientation was 0.641. The telecommuting was measured using nine items adapted from Gomez et al. (2020), and the sample items read as: “I can cover my work responsibilities from home (work from home)”; “I have the right conditions to do my work from home.” The reliability coefficient for telecommuting was 0.72. Finally, work engagement was measured using ten items adapted from Schaufeli and Bakker (2004). The sample items read as: “At work, I feel full of energy”; “In my job, I feel strong and vigorous.” The reliability coefficient of work engagement was 0.813. Results Multicollinearity The means, standard deviations, and zero-order correlations are presented in Table 2.Table 2. Descriptive statistics: Means, standard deviations, and correlations. Variables Mean SD 1 2 3 4 1. Telecommuting 3.65 0.62 1 — — — 2. Work orientation 3.77 0.40 0.474a 1 — — 3. Perceived stress due to COVID-19 3.18 0.74 0.303a 0.209a 1 — 4. Work engagement 4.25 0.46 0.264a 0.396a –.275a 1 aCorrelation is significant at the 0.01 level (2-tailed). First, the authors checked for multicollinearity, which is very common in survey research. If the correlations between the variables are over 0.75, as Kennedy (1979) suggests, multicollinearity may be a problem with the data. The correlations ranged between 0.209 and 0.474, less than the general rule of thumb of 0.75 (Tsui et al., 1995). The authors also performed another statistical check, that is, variance inflation factor (VIF), found that the VIF values for all the variables were less than 5, which render support that multicollinearity should not be a problem with data (Hair et al., 2011). Hypotheses testing Hierarchical regression analysis was used to test the hypotheses that independent variables (perceived stress due to COVID-19, work orientation, and telecommuting) are related to the dependent variable (work engagement). The regression results are presented in Table 3.Table 3. Results of hierarchical regression analysis of the effect of Perceived Stress. Due to COVID–19, work orientation, and telecommuting on work engagement. DV = work engagement Step 1 Step 2 Variables Coeff se ‘t’ ‘p’ Coeff se ‘t’ ‘p’ Age −.017 0.035 −.493 0.623 −.012 0.035 –.348 0.728 Gender –.046 0.051 –.897 0.370 –.032 0.051 –.638 0.524 Perceived stress due to COVID–19 H1 –.080* 0.036 –2.245 0.026 –.204 0.062 –3.266 0.001 Work orientation H2 0.548*** 0.070 7.821 0.000 0.433 0.090 4.835 0.000 Telecommuting H3 0.091* 0.044 2.06 0.046 0.314 0.089 3.510 0.001 Perceived stress due to COVID – 19 x Telecommuting H1a — — — — 0.035* 0.016 2.229 0.027 Work orientation x telecommuting H2a — — — — 0.038* 0.018 2.111 0.048 F 13.56* — — — 11.16* — — — R2 0.197 — — — 0.221 — — — Adj R2 0.182 — — — 0.202 — — — ΔR2 — — — — 0.025 — — — ΔF — — — — 4.34* — — — df1 5 — — — 7 — — — df2 277 — — — 275 — — — **p < .001; p < .05. Step 1 (Table 3) shows the direct effects of the independent variables on the dependent variable. As can be seen in Table 3, the regression coefficients of control variables (age and income) were not significant. The regression coefficient of age (β = −.015, p = 0.623) was not significant, and the regression coefficient of income (β = −.046, p = .37) was also not significant. Hypothesis 1 suggests that perceived stress due to COVID-19 is negatively related to work engagement. The hierarchical regression results reveal that the regression coefficient of stress on work engagement was negative and significant (β = −.080, p < .05), thus supporting H1. The beta coefficient of work orientation was positive and significant (β = .548, p < 001), thus supporting hypothesis 2. Finally, the regression coefficient of telecommuting was positive and significant (β = .091, p <. 05), thus supporting hypothesis 3. Step 2 (Table 3) shows the interaction effect of telecommuting on work engagement. The authors followed the steps suggested by Aiken and West (1991) and entered the interaction terms into the regression equation. The regression coefficient of the interaction term (perceived stress due to COVID–19 x telecommuting) was significant (β = .035, p < 05) suggesting that telecommuting moderates the relationship between stress and work engagement. Thus, Hypothesis 1a is supported. The regression coefficient of the interaction term (work orientation x telecommuting) was significant (β = .038, p < 05), thus supporting the hypothesis 2a that telecommuting moderates the relationship between work orientation and work engagement. The interaction effect of telecommuting is presented in Figures 2 and 3. As can be seen in Figure 2, as expected, stress is negatively related to work engagement, at both lower and higher levels of telecommuting. In the beginning, lower telecommuting is associated with higher levels of work engagement under the conditions of lower levels of stress due to COVID-19. However, as the stress is increasing from “low” to “high,” low levels of telecommuting results in a significant reduction of work engagement when compared to “higher” levels of telecommuting. That means, at the higher levels of telecommuting, increased stress results in a decrease in work engagement at a slower pace and lower levels of telecommuting, the decrease in work engagement is alarmingly high. These results corroborate the moderating effect of telecommuting in the relationship between perceived level of stress due to COVID-19 and work engagement. Figure 3 shows the moderating effect of telecommuting in the relationship between work orientation and work engagement. When the work orientation is low, lower levels of telecommuting are associated with higher levels of work engagement, and higher levels of telecommuting is associated with lower levels of work engagement. However, the rate of growth in work engagement is more significant when telecommuting levels are ‘high’ than ‘low’. The difference in the slopes of the curves render strong support for the moderating effect of telecommuting on work engagement. The empirical model is presented in Figure 4. Figure 2. Telecommuting as a moderator in the relationship between Perceived stress due to COVID-19 and work engagement. Figure 3. Telecommuting as a moderator in the relationship between work orientation and work engagement. Figure 4. Empirical model. Discussion As the COVID-19 global pandemic brought significant change in the work culture in many countries, including India, the number of employees working from home has increased tremendously. The frequent lockdowns, mandatory social distancing, and risk of getting infected by the virus forced many organizations to introduce and encourage the work from home climate. In developed countries, work from home was a common phenomenon (as some employees work on virtual platforms), the experience of developing countries like India is different. This is because of lack of infrastructure, lack of adequate training to work from home, lack of training to deal with emergencies like global pandemics (or natural calamities such as cyclones), resulting in unpreparedness of employees to meet the changing circumstances. This paper aims to develop a conceptual model and test the hypotheses to see the effect of pandemic-generated stress, work orientation, and telecommuting on work engagement. First, as predicted, our results reveal that stress is significantly and negatively related to work engagement (hypothesis 1). This finding is aligning with the results from the previous studies in the literature on stress (Bhui et al., 2017; Evans-Lacko et al., 2013; Stranks, 2015). In organizations, managers realize the importance of work engagement in driving success and hence invest a considerable amount of time and resources in motivating the employees to work. When organizations suffer huge losses because of the global pandemic, one of the resilient strategies is to bring employees back to work to stage a recovery. The pandemic created a platform for the managers to rethink and reposition their organizations to deal with changing customer needs and see that the employees understand the dynamics of the changing scenario. Though in developing countries like India, work from home is not ingrained in employees’ blood, and employees did not have any other choice than to accept it as a requirement. For example, the local governments moved from face-to-face to online in schools and universities, which is a new concept for both faculty and administrators. This has a spillover effect on employees in the IT sector because the demand for employees in the IT sector has escalated during the pandemic. An increase in work pressure created by the stress caused by the global pandemic may harm work engagement. The results of a negative relationship between pandemic-generated stress and work engagement is consistent with the stress research (Motamedzade et al., 2018). These results are consistent with JDR and COR theories. Second, the results from the present study also support the positive relationship between work orientation and work engagement (hypothesis 2). Following the CCT theory, these results support the findings from the previous researchers that career adaptability is significantly related to work engagement, which, in turn, positively affects employee well-being (Yang et al., 2019). In addition, our results also suggest that telecommuting is positively related to work engagement (hypothesis 3), which is consistent with the previous findings from the literature (Gomez et al., 2020; Tate et al.., 2019). As work has undergone significant changes with the internet revolution, it has become a common phenomenon for organizations to have frequent virtual meetings and conferences, especially in developed nations. With the pandemic situation, organizations did not have an alternative other than to switch to telecommuting. The results from the present study are consistent with the findings from the previous studies about the effect of telecommuting on work engagement (Sardeskhmukh et al., 2012). Telecommuting involves working away from offices where employees use technology and work from home. Our study also indicated that telecommuting has weakened the negative relationship between stress due to COVID-19 and work engagement (hypothesis 1a). Higher levels of telecommuting have resulted in a decrease in work engagement due to stress, albeit at a lesser rate. In contrast, lower levels of telecommuting have resulted in a sharp decrease in work engagement. Though prior studies did not explore the moderating relationship of telecommuting, our results are intuitively convincing. Further, telecommuting has increased the strength of the positive relationship between work orientation and work engagement (hypothesis 2a). Although again, prior studies did not explore the moderating effect of telecommuting in the relationship between work orientation and work engagement, our results provide evidence of the positive impact of telecommuting. Theoretical and practical implications The present study contributes to both literature on human resource management and practicing managers. First, the conceptual model tested in this research provides evidence that COVID-19 induced stress has an adverse effect on work engagement, which is somewhat expected and self-explanatory. Undoubtedly, the global pandemic has created unprecedented stress among the employees and fear of getting infected by the virus if they make private visits (to friends or even the office). Pandemic also created uncertainty about the future, as far as careers are concerned, as several companies have engaged in downsizing because of decreased sales of the products and services. The global pandemic also resulted in mental health problems for many individuals (Bradbury-Jones and Isham, 2020). There is widespread agreement that COVID-19 brought substantial psychological, professional, and social changes in the work environment in organizations. So, in light of the present pandemic scenario, as this study suggests, companies implementing the telecommuting strategy may benefit from retaining employees as telecommuting reduces stress and enhances work engagement. When COVID-19 started spreading worldwide, the World Health Organization has suggested to all the government and organizations to implement telecommuting, teleworking, and work from home to prevent the spread of the virus and protect the health of the health employees (Irawanto et al., 2021). Though it existed before as a choice, work from home has now become inevitable. The results from this study provide strong evidence that telecommuting, a new work innovation model, represents an effective way of performing jobs and reducing the ill-effects of the pandemic on work engagement. This study indicated that employees working in the IT sector would not find it difficult to move to off-site working (i.e., working from home), and anecdotal evidence reveals that employees were comfortable working from home, especially during the pandemic. A second significant contribution of this study is that work orientation is a necessary precursor to work engagement. During the COVID-19 crisis, the relationship between workers and employees has taken a different shape. Instead of face-to-face meetings, organizations implemented virtual meetings and expanded communication networks to increase employee engagement and positively influence the work orientation. Further, as anecdotal evidence reveals, some organizations introduced digital learning programs for upgrading the employees’ technical skills, thus promoting work orientation. Third, the present study also contributes to the managers in the human resource department to suggest that telecommuting enhances the positive effect of work orientation on work engagement. As some researchers documented, teleworking is positively associated with job performance, reduces stress levels, and maintains work-family balance (increases job performance, lessens work–family imbalance, reduces stress levels, and lessens turnover intentions (Contreras et al., 2020). This study underscores the importance of telecommuting in strengthening the relationship between work orientation and work engagement, thereby increasing job performance. In sum, the significant contribution of this study is that telecommuting helps reduce the negative effect of COVID-19 induced stress on work engagement and strengthens the positive association with work engagement. Limitations of the study The results from the present study should be interpreted considering some limitations. First, as with any survey research, common method bias is a potential problem and the current research is not an exception. However, to address the common method bias problem, the authors followed the procedures recommended by Podsakoff et al. (2003) and did perform Harman’s one-factor analysis. The total variance explained by a single factor accounted for 28.43%, which is less than 50% and confirms the absence of common method bias. Further, the researchers also checked the correlation matrix as Bagozzi et al. (1991) suggest that correlations of over 0.9 indicate the presence of common method bias. The authors found that the correlations are less than 0.5, and common method bias is not a problem with the data. Another limitation of the study is social desirability bias which is very common in survey research. In general, individuals tend to present themselves positively so that their behavior is culturally acceptable, and hence they provide biased responses in surveys. Finally, to address the problem, the researchers assured the respondents about the confidentiality and anonymity of the answers, as suggested by researchers (Chung and Monroe, 2003). The third problem is about the generalizability of findings from the present study. Since our sample consists of employees in the information technology sector, the results may not be generalizable across other sectors. However, the COVID-19 had a multi-sectoral impact worldwide; it is more likely that the conceptual model developed and tested in this research may be generalizable. Suggestions for future research The conceptual model developed and tested in this research provides several avenues for future research. First, the present model is focused on only four variables. During the post-pandemic scenario, future research can examine the role of trust employees have in the organization. Interpersonal trust (cognitive and affective trust) plays a vital role in enhancing productivity and effectiveness (Parayitam and Dooley, 2007). Second, future studies can also investigate how employees’ organizational citizenship behavior helps train employees who are not conversant with technological and administrative changes and adapt to crises. Third, future studies may focus on the effect of knowledge sharing among the employees to foster work engagement. Past studies (Shea et al., 2021) documented the positive impact of knowledge sharing and helping employees deal with crises such as the global pandemic (D’Souza et al., 2021; Usman et al., 2021). Fourth, stress apart, how employees cope with emotional exhaustion is another exciting area for future researchers to address, especially during and post-COVID situations (Parayitam et al., 2021). Fifth, as organizations plan to engage in resilient strategies to bring back operations to regular, psychological contract changes are likely to occur. Finally, an increase in digital surge and more gig workers may increase the threat to the existing labor force during the post-pandemic. Hence, work orientation plays an essential role in retaining their employment. The changes in employment conditions during the post-COVID period may be on the agenda of future researchers. Conclusion The work pressure due to the COVID-19 global pandemic has escalated the stress levels of IT employees worldwide, as several sectors depend on the services of IT professionals. This research concludes that work orientation and telecommuting are possible solutions to combat stress and enhance work engagement. Therefore, leaders of organizations must pay attention to creating a climate of telecommuting and expanding the networks for communication channels. Further, implementing digital learning programs to upgrade technical skills and increasing the work orientation would be helpful resilient strategies. It is speculated that during the post-pandemic, telecommuting, videoconferencing, and virtual platform meetings would continue until complete normalcy and a virus-free world are reached. Acknowledgements The authors would like to thank Professor Teck Yong Eng, the Editor-in-Chief and the anonymous reviewers for the constructive suggestions in the earlier versions of the manuscript. ORCID iDs Vijayabanu Chidambaram https://orcid.org/0000-0002-0125-4534 Therasa Chandrasekar https://orcid.org/0000-0001-7052-9805 Satyanarayana Parayitam https://orcid.org/0000-0001-5565-4413 The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ==== Refs References Achterberg M Dobbelaar S Boer OD , et al. (2021) Perceived stress as mediator for longitudinal effects of the COVID-19 lockdown on wellbeing of parents and children. Scientific Reports 11 (1 ): 1–14.33414495 Ahmed RR Streimikiene D Rolle J-A , et al. (2020) The COVID-19 Pandemic and the Antecedants for the Impulse Buying Behavior of US Citizens. Journal of Competitiveness 12 (3 ): 5–27. DOI: 10.7441/joc.2020.03.01. Aiken LS West SG (1991) Multiple Regression: Testing and Interpreting Interactions. Sage Publications, Inc. Akala A (2020) More big employers are talking about permanent work-from-home positions. Retrieved https://www.cnbc.com/2020/05/01/major-companies-talking-about-permanent-work-from-home-positions.html 12 June 2021). Alfes K Shantz AD Truss C , et al. (2013) The link between perceived human resource management practices, engagement and employee behavior: a moderated mediation model. The International Journal of Human Resource Management 24 (2 ): 330–351. Amin M Khattak AZ Khan MZ (2018) Effects of job stress on employee engagement and organizational commitment: A study on employees of emergency rescue service. City University Research Journal 8 (2 ): 200–208. Badgaiyan AJ Verma A (2014) Intrinsic factors affecting impulsive buying behavior: evidence from India. Journal of Retailing and Consumer Services 21 (4 ): 537–549. Bagozzi RP Yi Y Phillips LW , et al. (1991) Assessing Construct Validity in Organizational Research. Administrative Science Quarterly 36 (3 ): 421–458. Bakker AB (2017) Strategic and proactive approaches to work engagement. Organizational Dynamics 46 (1 ): 67–75. Bakker AB Albrecht S (2018) Work engagement: current trends. Career Development International 23 (1 ): 4–11. Bakker AB Demerouti E (2007) The Job Demands-Resources model: State of the art. Journal of Managerial Psychology 22 (3 ): 309–328. DOI: 10.1108/02683940710733115. Bakker AB Demerouti E Sanz-Vergel AI , et al. (2014) Burnout and work engagement: the JD-R approach. Annual Review of Organizational Psychology and Organizational Behavior 1 (1 ): 389–411. Bao Y Sun Y Meng S , et al. (2020) 2019-nCoV epidemic: Address mental health care to empower society. Lancet 395 (10224 ): e37–e38.32043982 Bellah RN Madsen R Sullivan WM , et al. (1985) Habits of the Heart. New York: Harper & Row. Bhui K Dinos S Galant-Miecznikowska M , et al. (2017) Perceptions of work stress causes and effective interventions in employees working in public, private and non-governmental organisations: a qualitative study. British Journal of Psychological Bulletin 40 : 318–325. DOI: 10.1192/pb.bp.115.050823. Bradbury Jones C Isham L (2020) The pandemic paradox: the consequences of COVID‐19 on domestic violence. Journal of Clinical Nursing 29 (13–14 ): 2047–2049.32281158 Breevaart K Bakker AB Hetland J , et al. (2014) Daily transactional and transformational leadership and daily employee engagement. Journal of Occupational and Organizational Psychology 87 (1 ): 38–157. Bunderson J Thompson J (2009) The call of the wild: Zookeepers, callings, and the double-edged sword of deeply meaningful work. Administrative Science Quarterly 54 (1 ): 32–57. Chung J Monroe GS (2003) Exploring social desirability bias. Journal of Business Ethics 44 : 291–302. Contreras F Baykal E Abid G , et al. (2020) E-Leadership and Teleworking in Times of COVID-19 and Beyond: What We Know and Where Do We Go. Frontiers in Psychology 11 : 3484. Cox T (1993) Stress Research and Stress Management: Putting Theory to Work (HSE Contract Research Report No 61/1993). Health and Safety Executive. http://www.hse.gov.uk/research/crr_pdf/1993/crr93061.pdf. Cucinotta D Vanelli M (2020) WHO declares COVID-19 a pandemic. Acta Bio-Medica: Atenei Parmensis 91 (1 ): 157–160.32191675 De R Pandey N Pal A , et al. (2020) Impact of digital surge during Covid-19 pandemic: A viewpoint on research and practice. International Journal of Information Management 55 : 102171. DOI: 10.1016/j.ijinfomgt.2020.102171.32836633 De Leede J Kraijenbrink J (2014) The mediating role of trust and social cohesion in the effects of new ways of working: a Dutch case study. In: Bondarouk T Olivas-Luján MR (eds), , 14 , pp. 3–20.Human Resource Management, Social Innovation and Technology, Advanced Series in Management. Demerouti E Bakker AB (2011) The Job Demands–Resources model: Challenges for future research. SA Journal of Industrial Psychology 37 (2 ). D’Souza GS Irudayasamy FG Syed Aktharsha Usman SA , et al. (2021) The Effect of Emotional Intelligence and Psychological Capital on Knowledge, Service and Leadership Excellence: Knowledge Sharing and Trust as Moderators. FIIB Business Review 1 : 231971452110650. DOI: 10.1177/23197145211065087. Duffy R Dik B (2013) Research on calling: What have we learned, and where are we going? Journal of Vocational Behavior 83 : 428–436. Ehnert I Harry W Zink KJ (2014) Sustainability and HRM. An Introduction to the Field. In: Ehnert I Harry W Zink KJ (eds), Sustainability and Human Resource Management: Developing Sustainable Business Organizations. Germany: SpringerBerlin/Heidelberg, pp. 3–32. Evans-Lacko S Knapp M McCrone P , et al. (2013) The mental health consequences of the recession: economic hardship and employment of people with mental health problems in 27 European countries. Plos One 8 : e69792.23922801 Feng Z Savani K (2020) Covid-19 created a gender gap in perceived work productivity and job satisfaction: implications for dual-career parents working from home. Gender in Management: An International Journal 35 (7/8 ): 719–736. Florio GA. Donnelly JP Zevon MA , et al. (1998) The structure of work-related stress and coping among oncology nurses in high-stress medical settings: a transactional analysis. Journal of Occupational Health Psychology 3 : 227–242.9684214 Fossen RS Vredenburg D (2014) Exploring differences in work’s Meaning: An investigation of individual attributes associated with work orientations. Journal of Behavioral and Applied Management 15 (1 ): 101–120. Gajendran RS Harrison DA (2007) The good, the bad, and the unknown about telecommuting: Meta-analysis of psychological mediators and individual consequences. Journal of Applied Psychology 92 (6 ): 1524–1541.18020794 Gerards R de Grip A Baudewijns C , et al. (2018) Do new ways of working increase work engagement? Personnel Review 47 (2 ): 517–534. DOI: 10.1108/PR-02-2017-0050. Gomez SM Mendoza OEO Ramirez J , et al. (2020) Stress and myths related to the COVID-19pandemic’seects on remote work. Management Research: Journal of the Iberoamerican Academy of Management 18 : 401–420. DOI: 10.1108/MRJIAM-06-2020-1065. Gopal R Singh V Aggarwal A (2021) Impact of Online Classes on the Satisfaction and Performance of Students during the Pandemic Period of COVID 19, Education and Information Technologies. DOI: 10.1007/s10639-021-10523-1. Hair JF Ringle CM Sarsted M , et al. (2011) PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice 19 (2 ): 139–151. DOI: 10.2753/MTP1069-6679190202. Hobfoll SE (1989) Conservation of resources: A new attempt at conceptualizing stress. American Psychologist 44 (3 ): 513–524. DOI: 10.1037/0003-066X.44.3.513.2648906 Hobfoll SE Halbesleben J Neveu J-P. , et al. (2018) Conservation of Resources in the Organizational Context: The Reality of Resources and Their Consequences. Annual Review of Organizational Psychology and Organizational Behavior 5 : 103–128. Hoboubi N Choobineh A Ghanavati FK , et al. (2017) The Impact of Job Stress and Job Satisfaction on Workforce Productivity in an Iranian Petrochemical Industry. Safety and Health Work 8 (1 ): 67–71. Hoge T (2011) Perceived Flexibility Requirements at Work and the Entreployee-Work-Orientation: Concept and Measurement. Psychology of Everyday Activity 4 (1 ): 3–21. Holman D Axtell C (2016) Can job redesign interventions influence a broad range of employee outcomes by changing multiple job characteristics? A quasi-experimental study. Journal of Occupational Health Psychology 21 (3 ): 284–295.26641482 Irawanto DW Novianti KR Roz K , et al. (2021) Work from Home: Measuring Satisfaction between Work–Life Balance and Work Stress during the COVID-19 Pandemic in Indonesia. Economies 9 : 96. DOI: 10.3390/economies9030096. Jaipuria S Parida R Ray P , et al. (2021) The impact of COVID-19 on tourism sector in India. Tourism Recreation Research 46 (2 ): 245–260. DOI: 10.1080/02508281.2020.1846971. Jung Y Takeuchi N (2018) A lifespan perspective for understanding career self-management and satisfaction: The role of developmental human resource practices and organizational support. Human Relations 71 (1 ): 73–102. Kennedy P (1979) A Guide to Econometrics. 2nd ed.. Cambridge, MA: MIT Press. Khetarpal S (2020) Post-COVID, 75% of 4.5 Lakh TCS Employees to Permanently Work from Home by’ 25; from 20%. Business Today. https://www.businesstoday.in/current/corporate/post-coronavirus-75-percent-of-3-5-lakhtcs-employees-permanently-work-from-home-up-from-20-percent/story/401981.html. Koch J Frommeyer J Schewe G , et al. (2020) Online Shopping Motives during the COVID-19 Pandemic—Lessons from the Crisis. Sustainability 12 : 10247. DOI: 10.3390/su122410247. Kossek EE Michel J (2011) Flexible work scheduling. In: Zedeck S. (ed), Handbook of Industrial-Organizational Psychology. Washington, DC: American Psychological Association, 1 , pp. 535–572. Kurland NB.] Bailey DE (1999) Telework: The advantages and challenges of working here, there, anywhere, and anytime. Organizational Dynamics 28 (1 ): 53–68. Liu S Lithopoulos A Zhang CQ , et al. (2021) Personality and perceived stress during COVID-19 pandemic: Testing the mediating role of perceived threat and efficacy. Personality and Individual Differences 168 : 110351. DOI: 10.1016/j.paid.2020.110351.32863508 Loxton M Truskett R Scarf B , et al. (2020) Consumer Behaviour during Crises: Preliminary Research on How Coronavirus Has Manifested Consumer Panic Buying, Herd Mentality, Changing Discretionary Spending and the Role of the Media in Influencing Behaviour. Journal of Risk and Financial Management 13 (8 ): 166–175. Marr B (2021) 9 Future Predictions for A Post-Coronavirus World. Forbes. https://www.forbes.com/sites/bernardmarr/2020/04/03/9-future-predictions-for-a-post-coronavirus-world/?sh=25ba7efa5410. Mazza C Ricci E Biondi S , et al. (2020) A nationwide survey of psychological distress among Italian people during the COVID-19 pandemic: Immediate psychological responses and associated factors. International Journal of Environmental Research and Public Health 17 (9 ): 3165.32370116 Morrissette AM Kisamore JL (2020) A Meta-Analysis of the Relationship between Role Stress and Organizational Commitment: the Moderating Effects of Occupational Type and Culture. Occupational Health & Safety 4 (1 ): 23–42. DOI: 10.1007/s41542-020-00062-5. Motamedzade M Asghari M Basati M , et al. (2018) The relationship between Work Engagement and Job Stress among Emergency Technicians of Markazi Province, Iran. Iranian Journal of Health, Safety & Environment 6 (2 ): 1259–1266. Naeem M (2021) Understanding the customer psychology of impulse buying during COVID-19 pandemic: implications for retailers. International Journal of Retail & Distribution Management 49 (3 ): 377–393. DOI: 10.1108/IJRDM-08-2020-0317. Nath D Sasikumar K Nath R , et al. (2021) Factors Affecting COVID-19 Outbreaks across the Globe: Role of Extreme Climate Change. Sustainability 13 : 3029. DOI: 10.3390/su13063029. Parayitam S Dooley RS (2007) The Relationship Between Conflict and Decision Outcomes: The Moderating Role of Affect-and Cognition-Based Trust in Strategic Decision-Making Teams. International Journal of Conflict Management 18 (1 ): 42–73. Parayitam S Usman SA Namasivaayam RR , et al. (2021) Knowledge management and emotional exhaustion as moderators in the relationship between role conflict and organizational performance: evidence from India. Journal of Knowledge Management 25 (6 ): 1456–1485. DOI: 10.1108/JKM-03-2020-018. Pedrozo-Pupo JC Pedrozo-Cortés MJ Campo-Arias A , et al. (2020) Perceived stress associated with COVID-19 epidemic in Colombia: an online survey. Cadernos de saude publica 36 : e00090520.32490918 Peters P Poutsma E Van der Heijden BI , et al. (2014) Enjoying new ways to work: an HRM‐process approach to study flow. Human Resource Management 53 (2 ): 271–290. Pitacho LA Palma P Correia P , et al. (2019) Work orientation: Dimensionality and internal model. Análise Psicológica 4 (47 ): 479–491. Podsakoff PM Mackenzie SB Lee J-Y , et al. (2003) Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. Journal of Applied Psychology 88 (5 ): 879–903.14516251 Polšek D (2020) Psychiatry of Pandemics: A Mental Health Response to Infection Outbreak: Springer International Publishing. In: Huremović D (ed), Croatian Medical Journal, 61 , p. 306. DOI: 10.3325/cmj.2020.61.306. Pongratz HJ Voß GG (2003b) From employee to ‘entreployee“: Towards a ‘self-entrepreneurial“ work force? Concepts and Transformation 8 : 239–254. Rees DW (1995) Work‐related stress in health service employees. Journal of Managerial Psychology 10 (3 ): 4–11. DOI: 10.1108/02683949510081329. Rich BL Lepine JA Crawford ER , et al. (2010) Job Engagement: Antecedents and Effects on Job Performance. Academy of Management Journal 53 : 617–635. Sardeshmukh SR Sharma D Golden TD , et al. (2012) Impact of telework on exhaustion and job engagement: a job demands and job resources model. New Technology Work and Environment 27 (3 ): 193–207. Sauter SL Murphy LR (eds), (1995) Organizational risk factors for job stress. Washington, DC: American Psychological Association. Savickas ML (2005) The theory and practice of career construction. In: Brown S. D. Lent R.W. (eds), Career development and counseling: Putting theory and research to work. Hoboken, NJ: Wiley, pp. 42–70. Schneiderman N Ironson G Siegel SD , et al. (2005) Stress and Health: Psychological, Behavioral, and Biological Determinants. Annual Review of Clinical Psychology 1 : 607–628. Schaufeli WB Salanova M Gonzalez-Roma V , et al. (2002) The measurement of engagement and burnout: a two-sample confirmatory factor analytic approach. Journal of Happiness Studies 3 (1 ): 71–91. Schaufeli WB Bakker AB (2004) Job demands, job resources, and their relationship with burnout and engagement: a multi‐sample study. Journal of Organizational Behavior 25 (3 ): 293–315. Shea T Usman SA Sengottuvel A , et al. (2021) ‘Knowledge Management Practices’ as moderator in the relationship between Organizational Culture and Performance in information technology companies in India. VINE Journal of Information and Knowledge Management. DOI: 10.1108/VJIKMS-12-2020-0232. Shinn M Rosario M Morch H , et al. (1984) Coping with job stress and burnout in the human services. Journal of Personality and Social Psychology 46 : 864–876.6737197 Stranks J (2015) Stress at Work: Management and Prevention. Elsevier Butterworth-Heinemann. Svicher A Fabio AD (2021) Job Crafting: A Challenge to Promote Decent Work for Vulnerable Workers. Frontiers in Psychology 12 : 681022. DOI: 10.3389/fpsyg.2021.681022.34093375 Tate TD Lartey FM Randall PM , et al. (2019) Relationship between Computer-Mediated Communication and Employee Engagement among Telecommuting Knowledge Workers. Journal of Human Resource and Sustainability Studies 7 : 328–347. Tomlinson J Baird M Berg P , et al. (2018) Flexible careers across the life course: Advancing theory, research and practice. Human Relations 71 (1 ): 4–22. Tsui AS Ashford SJ Clair L , et al. (1995) Dealing with discrepant expectations: Response strategies and managerial effectiveness. Academy of Management Journal 38 (6 ): 1515–1543. Usman SA Kowalski KB Andiappan VS , et al. (2021) Effect of Knowledge Sharing and Interpersonal Trust on Psychological Capital and Emotional Intelligence in Higher-educational Institutions in India: Gender as a Moderator. FIIB Business Review. DOI: 10.1177/23197145211011571. Worldometers (2021) Countries where COVID-19 Has Spread. https://www.worldometers.info/coronavirus/countries-where-coronavirus-has-spread/(accessed on 11 June 2021). Wang C Pan R Wan X , et al. (2020) Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China. International Journal of Environmental Research and Public Health 17 (5 ): 1729.32155789 Wefald AJ Downey RG (2008) Job engagement in organizations: Fad, fashion, or folderol? Journal of Organizational Behavior 30 (1 ): 141–145. World Health Organization (2020) Coronavirus disease (COVID-19) pandemic. Retrieved on June, 12 2021). Wrzesniewski A (2003) Finding positive meaning at work. In: Cameron K Dutton JE Quinn RE (eds), Positive organizational scholarship. San Francisco, CA: Berrett-Koehler Publishers, pp. 296–308. Yan L Gan Y Ding X , et al. (2021) The relationship between perceived stress and emotional distress during the COVID-19 outbreak: Effects of boredom proneness and coping style. Journal of Anxiety Disorders 77 : 102328.33160275 Yang X Feng Y Meng Y , et al. (2019) Career Adaptability, Work Engagement, and Employee Well-Being Among Chinese Employees: The Role of Guanxi. Frontiers in Psychology 10 : 1029. DOI: 10.3389/fpsyg.2019.01029.31139112	0	PMC9708535	NO-CC CODE	2022-12-01 23:20:31	no	2022 Nov 28;:03063070221116512	utf-8	null	null	null	oa_other
==== Front spgem GEM Journal of General Management 0306-3070 1759-6106 SAGE Publications Sage UK: London, England 10.1177_03063070221088371 10.1177/03063070221088371 Original Empirical Article Staying afloat? Using a reflective cycle approach to examine the effects of crisis on the business resilience of SMEs during COVID-19 https://orcid.org/0000-0002-3970-1694 Zakaria Norhayati College of Business Administration, 120627 University of Sharjah, Sharjah , United Arab Emirates Sehgal Ritu Faculty of Business, 120627 University of Wollongong in Dubai , United Arab Emirates https://orcid.org/0000-0003-1005-1592 Watson Alastair School of Business, University of Dundee, Dundee, Scotland, United Kingdom https://orcid.org/0000-0003-3620-9466 Kamarudin Khairul Anuar Faculty of Business, 120627 University of Wollongong in Dubai , United Arab Emirates Norhayati Zakaria, Faculty of Business, University of Wollongong in Dubai, Room 4.19, UOWD Building, Dubai Knowledge Park, Dubai, United Arab Emirates, P.O. Box 20183. Email: [email protected] 27 11 2022 27 11 2022 03063070221088371© The Author(s) 2022 2022 SAGE Publications This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. This study seeks to explore the effects of the COVID-19 crisis on the business resilience of SMEs in the United Arab Emirates (UAE) as an emerging economy, and specifically in Dubai, a thriving global business hub. Our objective is to examine the challenges experienced by small and medium enterprises (SMEs) in this region and how business leaders respond to the unprecedented crisis to stay afloat. We obtained rich descriptions from 26 respondents comprising SME owners and managers, using semi-structured interviews and a reflective process model to discern different aspects of business volatility, leadership roles, and financial management. All respondents endured the crisis periods by implementing numerous changes and initiatives to explore new norms of working, uncharted business territories, fulfill current projects, and develop innovative solutions and diversification in their businesses. Many have turned challenges into opportunities, progressing successfully through the three challenging periods of crisis using diverse approaches to stay resilient. The paper concludes with a discussion of theoretical and practical implications and future research directions. business resilience COVID-19 responsible leadership crisis management small medium enterprises Dubai reflective cycle qualitative study crisis-attuned challenges edited-statecorrected-proof typesetterts10 ==== Body pmcIntroduction While there has been extensive analysis of business resilience for multinational corporations during crises in the developed world (Ballesteros and Kunreuther, 2018; Fainshmidt et al., 2017; Harries et al., 2018; Smallbone and Welter, 2012), research geared toward understanding the business conditions of small and medium enterprises (SMEs) in the context of crisis management has been limited (Herbane, 2010; Kraus et al., 2020; Smallbone and Welter, 2012; Yaari et al., 2020). In early 2020, the COVID-19 pandemic swept across the globe, affecting businesses in an unprecedented manner. In a survey encompassing 5800 small businesses in the USA, Bartik et al. (2020) found that they suffered from various aspects of economic shock linked to the unpredictable length of the crisis and loss of financial support, thus threatening their viability. The United Arab Emirates (UAE) saw similar repercussions. SMEs represent 98% of all companies in the country, and Dubai’s businesses have suffered greatly since the beginning of 2020 (Al-Hares, 2020). The SME sector in Dubai employs 1.41 million workers, equivalent to a net employment contribution of 56% of the economy. A crisis with the wide-reaching impact of the pandemic leads to inevitable business discontinuity and disruption, and people face challenges in managing their businesses (Barro et al., 2020; Bundy and Pfarrer, 2015; Cleeren et al., 2017). However, there have been no definitive measures and solutions for SMEs, particularly in Dubai as an emerging economy (Gerth et al., 2021; Kenny and Dutt, 2021). A crisis of this sort is historical because its impact on businesses is felt locally and globally. Businesses need to operate with new norms, and this requires leaders to make changes, adopt innovative measures, employ leadership and entrepreneurial agility, rectify financial constraints, and build tenacity and cultural fitness to adapt successfully (Applegate et al., 2016; Hu and Pang, 2018). The effects of viruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome coronavirus (MERS-COV), the unprecedented financial crises of 1997 and 2008, and environmental disasters such as tsunamis and earthquakes are not comparable to COVID-19’s widespread impact. Business entities, civil society, government bodies, information technology specialists, financiers, and economists around the globe have spoken out about how the COVID-19 crisis has led to multifaceted business challenges (Maliszewska et al., 2020; Liu, et al., 2020; Xing et al., 2020). This study aims to examine the effects of the crisis on the business resilience of SMEs in the UAE as an emerging economy, specifically in Dubai, a thriving global business hub. Our objectives are to examine the challenges experienced by SMEs in this region and how business leaders have responded to the crisis to stay afloat. The article is divided into several sections. The literature review identifies research gaps by exploring pertinent aspects of business resilience during crisis and the role of responsible leadership. The methodology describes the primary data collection from 26 SME owners or managers, using semi-structured interviews and a reflective process model to discern different aspects of challenges experienced. The subsequent section provides a rich description of the study findings and respondents’ narratives, thoughts, feelings, and actions taken in relation to managing their businesses during COVID-19. Finally, the findings are discussed based on theoretical and managerial implications, followed by the conclusion and future research directions. Literature review Business resilience and crisis Many businesses have achieved success in times of adversity and challenge, such as recession, and will undoubtedly witness further disruption due to the impact of COVID-19. During these times, positive emotions help protect business owners, shifting attitudes toward success instead of failure (Fredrickson et al., 2003). Business resilience has developed into a systems-based model aimed at developing growth, wealth, closure, and quick response to change (Beech et al., 2020; Salvia and Quaranta, 2015). It has grown from a meaning of “robustness” to “an ability to endure (or weather) disturbance” (Holling, 1996). It is clearly argued that it is challenging to develop a single definition, as it must negotiate the context in which it is being applied (Beech et al., 2020). As the world is a dynamic environment of disturbance and complex incidents that require responses to change (Beech et al., 2020; Scheffer et al., 2001), resilience involves the ability to learn and adapt to factors that impact “normal” operations. Based on past studies, we define business resilience as the ability of organizations to adapt in response to a changing environment (Beech et al., 2020). In this context, resilience describes an organization’s ability to ride out the storm by continuing operations and returning to a stable state after high-impact low-probability disturbances (Cumming et al., 2005; Gunderson, 2000; Hearnshaw and Wilson, 2013), including economic factors, pandemics, or even war (Beech et al., 2020). Resilience informs organizations’ responses to change or shock (such as COVID-19) that prompt innovative behavior (Dahles and Susilowati, 2015). It is the ability to survive and adapt, which is critical for economic development (Fiksel, 2006; Hamel and Valikangas, 2003; Williams and Vorley, 2014). Many global and localized crises have affected international business in the past few decades, including the 2008 Great Recession, the 2011 Christchurch earthquake, terrorist attacks, and political conflict, to highlight but a few (Doern et al., 2019). To an extent, it is possible to say that crises on different levels have become commonplace business disruptors, and unprecedented and unsettling events are the new normal for “business as usual.” The more rigid a business’s structure and culture, the less likely it is to secure positive outcomes during adverse events. It has been noted that SMEs are typically more responsive than larger enterprises during such times due to their flexibility (Williams and Vorley, 2014). Crisis management is by no means a new phenomenon. Historical business research suggests that a “crisis” refers to extreme and unexpected events that require organizations to make prompt decisions in response to challenges that can interfere with normal operations, impact the organization’s reputation, and ultimately affect their financial well-being (see, for example, Doern et al., 2019; Dutton, 1986; Hermann, 1963; Hills, 1998; Quarantelli, 1988). The outcomes of crises for small businesses are mixed, and in some instances, they are found to be positive as well as negative. For example, following the 2001 foot-and-mouth outbreak in the UK, many small rural tourism businesses experienced failure, decline, loss of resources, and significantly decreased profitability (Irvine and Anderson, 2004). Similar impacts were experienced in London following the 2011 riots, with vandalism and looting causing property damage and leaving businesses with a loss of stock and substantial repair costs (Doern, 2016). The USA had similar experiences after the natural disaster damage caused by Hurricane Katrina (Runyan, 2006). Doern et al. (2019) demonstrated the negative impact on entrepreneurs’ personal and psychological well-being after experiencing such crisis-induced loss, which, as previously stated, relates closely to emotional impact on entrepreneurs and a positive or negative effect on business resilience (Williams and Vorley, 2014). We already understand that a positive approach from entrepreneurs can motivate and inspire the development of opportunities (Brünjes and Revilla-Diez, 2013; Doern et al., 2019) during critical times when resources can be somewhat depleted. Disaster (or crisis) can prompt entrepreneurs to develop alternative products or services and even expand through means that are not purely financially driven. Recently, social entrepreneurship and aiding victims of crisis has created prospects that may have been previously overlooked (Grube and Storr, 2018; Williams and Shepherd, 2016). It is also important to consider how crisis impacts the way entrepreneurs stay afloat as well as undergo changes. Li et al. (2021), focusing on the Chinese restaurant industry, identified key elements that aid businesses response(s) to, and take future precautions for, disruptions such as COVID-19. The concept of change involves elements such as precaution, proactiveness, and the need for innovative approaches to maintain operations during adversity (Direction, 2021). In crisis management literature, there is inadequate understanding of business resilience and the multifaceted challenges, complex behaviors, and diverse leadership outcomes that result from crises such as COVID-19 (Cuyper et al., 2020; Schroeder, 2020). There is a critical need for this research gap to be filled in terms of innovative measures to ensure business continuity. Scholarly views are insufficient with regard to rich understanding and in-depth explanations of the experiences, trends, challenges, and events surrounding business resilience during widespread crises such as COVID-19 (Branicki et al., 2018; Herbane, 2019; Pal et al., 2014). We contextualized our research in the UAE, which gives rise to unique challenges that may require similar or divergent approaches than other contexts or locations around the world. While a number of studies have focused on resilience in the context of the UAE, they are usually on specific industries such as logistics (Sundakani and Onyi, 2021), finance (Gerth et al., 2021) or a specific service (Madi Odeh et al., 2021). There is still a lot of uninformed misunderstanding in the field of business disruptions and discontinuities faced by SMEs, which is not accounted for in the literature in regards to industries like manufacturing, accommodation and services, professional, scientific and technical services and others. Such challenges need to be managed and resolved through different adjustments, changes, or approaches. Therefore, this study aims to address this gap and understand the steps taken by UAE SME entrepreneurs in the abovementioned industries to ensure business continuity and whether these have driven positive change in their businesses. This information could then be used as a means to shape new working practices. We aim to identify the types of challenges faced by business operators and how they have responded to them, and how this has impacted the sustainability of organizations across different sectors, rather than focusing only on specific industries, which has been the case in previous studies (Gerth et al., 2021; Madi Odeh et al., 2021; Sundarakani and Onyi, 2021). The role of responsible leadership during crisis In prosperous times, the role of leadership is to provide vision and direction for the future. This need is amplified when an organization faces unprecedented crises (Brumfield, 2012; DuBrin, 2013; Reiche et al., 2020). Mitroff (2005) addressed “the rise of the abnormal” when emerging stronger and better from a crisis. When faced with a crisis, responses can range from pandemonium to a controlled, well-orchestrated resolution (Bowers et al., 2017). Preparation, however, is often not a matter of priority for organizations, and many operate in reactive mode (Girboveanu and Pavel, 2010). Not all leaders are equipped to handle change during crises, and they differ in their interpretations of problems, which leads to variation in responses (Crayne and Medeiros, 2020). The complexities of disruption give rise to new models of leadership (Kaiser, 2020), and transformational leaders have proven successful in crisis management (Van Knippenberg and Sitkin, 2013). Participatory decision making, big-picture thinking, and leveraging the expertise of those closest to the situation helps leaders formulate logical, flexible, and adaptive crisis response plans (Bowers et al., 2017; Wartzman and Tang, 2020; White and Shullman, 2010; Yukl and Mahsud, 2010). For a responsible leader, an integral part of crisis management is communicating and liaising with internal and external stakeholders to enhance success (Kempster and Jackson, 2021). Such versatility is empirically related to several measures of leadership effectiveness, especially team adaptability and productivity, in the circumstances of the COVID-19 pandemic (Kaiser, 2020). New opportunities often arise when difficulties appear on the horizon, but to create opportunity, leaders need to take their teams along. The more turbulent the environment, the greater the need to demonstrate that control is possible (and needed). Employees look to leaders for guidance, vision, direction, and proactive approaches to the situation (Rajah and Arvey, 2013; Van Vugt et al., 2008). Leaders’ influence on subordinates increases when their value systems align, and an enabling, empowerment-oriented culture can help create a team of proactive problem solvers and learners (Kempster and Jackson, 2021; Zhao, et al., 2021). Leadership creates and changes cultures while management and administration act within a culture (Schein, 2004). In a complex, fast-paced world, organizations and their leaders are thus required to become perpetual learners. The relationship between leadership and culture is interactive, and leadership style sets the tone for the beliefs and value systems that establish the organizational culture (Schein, 1996). Chong et al. (2018) described leadership and organizational culture as two sides of the same coin. Effective strategy execution requires an environment where people are empowered and enabled to challenge the status quo, leading to a change-ready culture. When disruption occurs, the change process can be derailed by a lack of acceptance from relevant stakeholders (Johnston, 2001). Leaders and their teams orchestrate the joint construction of change, which requires a receptive culture. Past research has also analyzed the effect of leadership on cultural formation in organizations (Borekci et al., 2014; Limaj and Bernroider, 2019). While some may argue that it is challenging to create a change-ready culture (LePine, 2005), others have established that an innovative culture characterized by flexibility and rapid response to market changes is likely to result from the leadership style and persuasive influence strategies adopted (Chong et al., 2018). Schein (2004) stated that a learning culture needed a learning “gene” in its DNA. Learning-oriented, adaptive, and flexible cultures enable responsible leaders and their teams to formulate optimal responses and recovery. Emergencies test the best of leaders, and support from the team can play a crucial role in navigating the worst of crises. At times, such crises may even be used to the advantage of the organizations involved. In light of the unprecedented characteristics of the COVID-19 pandemic, we aim to understand how UAE SME leaders reacted and demonstrated resilience in their responses and changes to operational practices, which could determine business success, and how they were able to facilitate this in line with staffing restrictions. We also seek to understand the nature of flexibility, innovation, and the concept of promptness in action taken by responsible leaders in the context of this study. The controls put in place by government undoubtedly impacted the way organizations and their people could carry out normal day-to-day functions, given that nations were locked down with staff mostly unable to leave their own homes to engage in normal workday routines. Past studies on crisis management suggest that responsible leaders need to take not only proactive actions, but also reactive actions to achieve entrepreneurial resilience and prevent venture failure (Corner et al., 2017). Unfortunately, few studies have examined the multifaceted role of responsible leaders in the face of challenges and business disruptions during the COVID-19 crisis (Haque, 2021; Mehta et al., 2020; Pounder, 2021). With business outcomes that are anticipated to be volatile, unpredictable, complex, and ambiguous, leaders have to be responsive and responsible not only to shareholders, but also to multi-stakeholders, in integrating triple bottomline: profit, social, and environmental goals (Maak, 2007; Kempster and Jackson, 2021). By identifying the actions taken by responsible leaders to support business continuity and the strategies employed to support staff’s ability to work, there is a need to explore how leadership decision making facilitates performance and its implications for future working practices to strengthen the success of these businesses sustainably. Understanding leadership responses and action taken during the pandemic will help us identify how UAE entrepreneurs and business leaders identified the key elements of their business and their responses for the all-important workforce. Methodology: Qualitative research Research setting and respondents Since 2010, Dubai has positioned itself globally as a fast-growing city and emerging business hub, attracting foreign investors and business people from across the world. Meanwhile, when the pandemic began in early 2020, it has swept into business scenarios in an atypical and pervasive manner, threatening small and medium-sized enterprises (SMEs) in particular. As a result, the Dubai economy has suffered greatly, with the critical sectors of real estate and hospitality having suffered the most (Turak, 2020). In this context, the aim of the present study was to obtain an in-depth understanding of the experiences that SME owners and managers underwent when facing the recent turbulent business conditions and associated challenges. Data elicitation: Approach and strategies We interviewed 26 small business owners and managers in Dubai over a 6-week period (from September 2 to 10 October 2020). The respondents varied in business nature, size of business, years of operation, and industry categories (see Table 1). The data collection was conducted in three phases. In the first phase, 50 emails asking for volunteers were sent out based on a referral list published by an international business franchise network of SMEs in Dubai. From this early pool of emails, just 10 people agreed to participate, and the first round of interviews was made. In the second phase, further contact data were elicited by sending reminders to the same pool of initial contacts, whereupon another 10 respondents agreed to participate. Finally, in the last phase, six further respondents were recruited from social media networks for a final round of interviews. Due to the restrictions of COVID-19, all the respondents were interviewed using online platforms, such as Webex and Zoom meetings. The interviews lasted for around 60–70 min. The conversations were recorded, and all respondents signed and attached the consent forms via email.Table 1. Respondents’ demographic information. Fictitious name Gender Nature of business Industry No. of employees Year of existence Resp1 M Printing press Accommodation and food services 50 13 years Resp2 F Marketing consultancy Accommodation and food services 10 8 years Resp3 F Construction Construction 15 22 years Resp4 M Legal firm Information technology 30 8 years Resp5 F Mindset coaching Manufacturing 1 1 year Resp6 M Automobile Manufacturing 70 30 years Resp7 M Service Other services (except public administration) 6 15 years Resp8 M Retail Other services (except public administration) 5 5 years Resp9 M Logistic Other services (except public administration) 30 4 years Resp10 F Travel Professional, scientific, and technical services 15 41 years Resp11 M Technology Professional, scientific, and technical services 50 20 years Resp12 M Retail Professional, scientific and technical services 80 42 years Resp13 M Service Professional, scientific, and technical services 50 3 years Resp14 M Food & beverage Professional, scientific, and technical services 104 7 years Resp15 F Marketing Professional, scientific, and technical services None 2 years Resp16 M Logistics Professional, scientific, and technical services 42 5 years Resp17 M Travel Professional, scientific ,and technical services 32 9 years Resp18 M Accounting Professional, scientific and technical services 55 15 years Resp19 M Insurance Professional, scientific, and technical services 15 10 years Resp20 F Food & beverage Professional, scientific, and technical services 5 13 years Resp21 F Logistics Retail 7 6 years Resp22 M Logistics Retail 7 6 years Resp23 F Accounting Transportation and warehousing 2 3 years Resp24 M Accounting Transportation and warehousing 70 30 years Resp25 M Packaging Transportation and warehousing 50 15 years Resp26 M E-commerce Transportation and warehousing 7 3 years *The industry classification is as per the North America Industry Classification System (NAICS) Canada 2017. https://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD&TVD=1,181,553&CVD=1,181,554&CLV=0&MLV=5&D=1. Semi-structured interviews were used to engage the respondents in a process called the “reflective cycle of practices.” This process was utilized to make sense of the conditions they had undergone during the COVID-19 pandemic (see Figure 1). As an iterative technique, it allowed the respondents to express their opinions, feelings, and actions related to leadership roles, organizational culture, company values, and financial and business strategies. Since the pandemic has been an unprecedented crisis, the questions were carefully extended and shaped towards what transpired over the first 6 months after it flared up. To best learn about the respondents’ experiences, multiple layers of questions were applied using dynamic and reflective methods of questioning designed to encourage them to think, feel, and express themselves through their stories. The probing questions asked were intended to deepen the responses gained.Figure 1. Reflective cycle model for data elicitation. According to Gibbs (1988), the purpose of engaging in reflective practices is to elicit information from respondents by questioning them about their experiences and explanations, seeking different ways of knowing through exploring ideas and strategies, and enhancing their self-awareness and self-development of the actions taken to combat challenges experienced. Harries et al. (2018, p. 715) notes that making sense of a situation “is a way of understanding the usually unconscious process of interpreting ambiguous or confusing events.” Hence, respondents needed to be helped to think about and reflect on the situations they had experienced and thus make sense of the situations, incidents, and journeys they had undergone during the tough times of the initial COVID-19 period. Zakaria (2019) further suggests that making sense of a situation will allow people to uncover the accounts and incidents that most deeply touched them, including experiences that may be difficult to discuss. Based on the model shown in Figure 1, the respondents were interviewed using the six iterative and multiple stages of reflective questioning. In the first and second stage of the questioning, the respondents were asked “What happened during the first period of the COVID-19 crisis and what did you think and feel about it?” The key issue at this stage concerns identification of the meaning of the crisis by making sense of it in retrospect. The reflective approach allowed the SME owners and/or managers to identify and extensively describe their experiences. It could also help them identify the root causes of events by unpacking elements and characteristics and assist them in finding meaning or making sense of their feelings. In the second phase of questioning, the respondents were asked to relate to the event from an emotional standpoint and disclose a deeper feeling or sensory experience regarding why an event or situation occurred. During the third, evaluative stage, they were asked to reflect on their understanding of the situation by answering a key question: “What was positive or negative about your COVID-19 experience?” When such an unprecedented situation occurs, an individual should look at it objectively, describe the it based on an evaluation, and acquire an in-depth understanding of their behaviors. In the fourth stage, to make sense of resilience, respondents needed to think analytically. At this stage of the model, respondents might fully diagnose and then create certain resilience-attuned conditional rules regarding their actions. Once people have evaluated a situation that triggers certain reactions, they need to analyze it from various perspectives. In the fifth stage, the respondents were guided to understand how the pandemic had affected them from an overall standpoint. They started to provide a holistic view of what the specific incidents offered them in terms of lessons learned and ways to address challenges. Finally, in the sixth stage, a key question arose “What else could have been done?” Here, the respondents were invited to take a step back, critically examine the what, why, when, who, where, and how of the situations they had experienced and contemplate whether things could have turned out differently. Data analysis The online interviews were transcribed between [October] and [December] 2020 before commencing the data analysis procedures. We applied the same reflective analysis approach throughout based on the large datasets to ensure that all the information was well organized and coded into themes and sub-themes. The stories presented were also coded for latent and manifest meanings to obtain the richness of the narrations (Neuendorf, 2002; Creswell and Poth, 2016). The broad categories of these codes were then refined by subgrouping them into a more parsimonious form to illustrate the researchers’ in-depth understanding of each dynamic process in analyzing the effects of crisis on business resilience. Finally, the formulation of themes and sub-themes based on the codes was made. Three themes emerged to understand business resilience during the Pandemic: crisis period, crisis-attuned challenges, and adjustments made during the crisis. The codebook was developed based on the hierarchical data analytic framework comprising of three thematic layers (see Figure 2). A deductive approach was used for the first theme coded as crisis periods, offering a broad understanding of the COVID-19 phenomenon. Using reflective questioning approach, we further developed sub-themes on three distinctive crisis periods based on the researchers’ practical understanding of the COVID-19 situation and confirmed by all respondents. After several reflective iterations (Stages 3–5, see Figure 1), an inductive approach was also applied, with further exploration based on how the respondents narrated their stories around the crisis-attuned challenges and trials of staying afloat (coded as theme 2), followed by different types of adjustments made due to emergent opportunities to achieve business resilience (coded as theme 3).Figure 2. Hierarchical data analytic framework. In the initial phase, all the transcribed data was divided according to how it will be analyzed: deductive or inductive. Rigorous procedures were applied, which are detailed thus. First, we aimed to understand the broad category of situational analysis. For this, we focused on demographic information (respondent’s name, nature of business, years of operation, and number of employees) as contextual information about the small businesses and their owners or managers. Second, we applied the deductive coding. For this, the data was partitioned and coded into the three distinctive crisis periods identified (see Figure 2). Once the transcriptions were categorized, we began the inductive process, in which we determined the core and sub-concepts to code. Since the data collection was performed in three different rounds, prior codes and categories were developed and refined after the responses were received from each batch of respondents. This was done to enable our immersion in the data with a full (general-to-in-depth levels) of understanding. Once a batch of respondents was completed, we divided the number transcripts among us equally and started to read the content carefully and thoroughly. An initial set of themes emerged and was coded based on the explicit and implicit meanings extracted from the responses. Third, we all read the assigned transcripts thoroughly and looked (again) for emerging themes. We began to look for a specific theme of challenges that emerged from the three crisis periods of the pandemic. In this stage of the analysis, we wanted to explore the different types of crisis-attuned challenges that all the respondents experienced and which addressed the questions of what, why, when and how the challenges were accounted for. For example, when we asked them to reflect of “What was positive or negative about your COVID-19 experience?” The respondents were able to delve on this question deeply by making sense of the conditions and context they were situated in by providing stimulating accounts. Fifteen initial codes were developed through one of our brainstorming sessions (refer to Table 2). This progressive generation (of codes and sub-themes) enabled us to obtain a complete picture of what transpired in each of the three crisis periods in a gradual manner and thus developed the sub-themes of challenges.Table 2. Codes and sub-themes developed for crisis-attuned challenges. Sub-themes: Business and operation Sub-themes: Personal and managerial 1. Diversification 1. Low morale 2. Business growth 2. Productivity 3. Business volatility 3. Trust 4. Customer’s demand 4. Empathy 5. Risk taking 5. Lack of communication 6. Unpredictable conditions CODES 6. New competency 7. Financial constraints 7. Stress and anxiety 8. Market downturn 8. Well-being 9. Suppliers 9. Compliance to SOPs 10. Cash flow 10. Work norms 11. Low capital 11. Organizational culture 12. Overhead cost 12. Training 13. Salary cut 13. Digital skills 14. Stiff competitors 14. Safety and security 15. Business shutdown 15. Socialization As suggested by Ren et al. (2021) and Zakaria (2017), we engaged in a systematic multilayering approach of data analysis whereby the four researchers together underwent the coding process through several iterative loops, from one layer to another (see Figure 2). To ensure reliability and validity, we also constantly compared data and codes by jotting down memos about the codes and its meaning and shared among researchers using google docs. After the first 10 interviews were completed, we coded independently and then discussed the emerging codes. At the initial phase of creating sub-themes based on the 15 codes developed (see Table 2), we established the following: (1) business continuity, (2) financial problems, (3) leadership roles and support, (4) working norms and culture, and (5) financial and cash flow. As we proceeded to the second round of interviews, we repeated the process of reading and coding inductively but with more intensity as we obtained more data to refine based on what we had found in the first batch. We contextualized the challenges based on the different crisis periods and different demographics information to determine whether the challenges were similar or not. We compared these with the prior sub-codes under the theme of challenges, aiming to build on what had newly emerged as well as confirming what we had initially found. At this stage of the analysis we continued to check against one another’s identified codes and discussed any discrepancy arising from our different understandings of the constructs and their meanings. Several measurements of validity were taken when designing the coding procedures. The first step was to develop a tentative set of a priori aspects. These were based on the literature reviews on resilience and crisis and the role of responsible leadership. We also identified some tentative aspects discussed among the research team and some professional colleagues, and the challenges were thus further modified (for further specification and clarity). Once the first set of coding had been derived from the first batch, we developed a further refinement of the themes. For the iteration of coding that continued until the last (third) batch of interviews, we referred to the existing list of codes to ensure that we reached category saturation as suggested by Guba and Lincoln (1994). Thus, the codes were finally refined and agreed based on the challenges the respondents had experienced during the first 6 weeks of the COVID-19 pandemic. Within the descriptions of each coded datum identifying the crisis-attuned challenges identified, we arrived at an agreement that these could be best divided into two types: (1) business and operations and (2) personal and managerial (see Table 2 and Figure 2). Next, we looked at theme 3: adjustments undertaken due to the crisis situation that signified resilience. By using the same iterative inductive coding process and procedures as abovementioned, we finally reached consensus amongst researchers and finalized the sub-themes as (1) financial management, (2) business diversification, and (3) new workplace norms. These codes are considered to apply at the bottom of the hierarchical structure as a funneling effect of the COVID-19 crisis situation on business resilience. Finally, in order to ensure reliability and validity, each of the four researchers considered the coding process separately before cross-checking for the level of agreement, and at the end, we discussed and deliberated about the emergent codes. During this abstraction process, the main themes and sub-themes were renamed as appropriate. The abstraction process was repeated until we were able to interpret and understand the phenomenon of COVID-19 and its effects on business resilience. For example, similar or overlapping codes were first grouped into generic, main themes and sub-themes (Elo and Kyngäs, 2008). We also checked the level of agreement among the researchers engaged in the coding process to ensure that intercoder reliability was achieved. In the first stage of this test, we sat down and checked the level of agreement on the codes; we arrived at 80%, which is acceptable according to Neuendorf (2002). Based on the developed codebook, we went ahead with the inductive and deductive coding processes, upon which we completed the final round of coding the data exhaustively and mutually exclusive. For this, we attained an agreement level of 85%. Research findings Using the reflective cycle approach, we obtained in-depth descriptions and found that all respondents endured the crisis by implementing numerous changes and initiatives to explore uncharted business territories, fulfill current projects, and develop innovative solutions and diversification in their businesses. The findings aim at addressing the objective of the study, which is to understand the multifaceted challenges people have experienced during the COVID-19 crisis based on three key periods, and the adjustments they underwent to keep business afloat and thus achieve resilience. The COVID-19 crisis periods: Challenges and changes initiated Period one: Business endures initial shock The global lockdowns witnessed in March and April 2020 were unprecedented. Our respondents shared feelings of hope and resilience in keeping their businesses afloat amid the upheaval of the pandemic. Respondents operating in several segments noted a steep decline in one or two areas, while others witnessed a marked upturn that bolstered their bottom line. Resp11, in the technology sector (e.g., radio-frequency identification, data-capture technologies, and internet of things), described exponential growth in healthcare and food services, despite the extensive closure of retail and some construction sectors. Different industries suffered different impacts, with responses ranging from sheer helplessness (e.g., travel and hotel managers, marketing consultants) to a rapid understanding of the need to take change and move quickly (e.g., transport, construction, logistics, technology solutions, and printing). Resp19 lamented the volatility of the insurance industry.[Our business was] definitely not doing well [...] [but] we have emerged with a balance. We did adapt quickly to the new norms, which mitigated our losses. However, [as a result], I am focusing less on other aspects of my life, and the effects of that have been very direct. That has also happened with my employees, who continue to work from home. (Resp19) At such a trying time, most respondents also described the difficulty of coping with their fears while simultaneously allaying those of their team members, many of whom were concerned about potential job loss. Resp9 expressed apprehension about dealing with blue-collar workers, whom the pandemic hit particularly hard.The main concern was handling our team of 70, most of whom were talking of death. People’s fear was—if you get COVID-19, you will die. To allay their fears as much as possible, the first thing we did was move people from shared accommodation into our own premises. Second, we arranged our transport. Among the team, I am the oldest, and I ensured I was the first person to reach work. That gave the team the confidence that nothing would happen to them. We got them all to the office, as sitting at home does not work for our industry; we had to find a way to sustain our business during this time. (Resp9) Many of the essential businesses allowed to operate during lockdown faced the challenging task of maintaining operations while ensuring safe travel and accommodation for their staff. Resp6 expressed that he was certain he understood what it took to undergo a crisis, yet claimed COVID-19 was unusually difficult.We have seen ups and downs in business before, but nothing like this! We moved from retail, which is inherently risky, to corporate [services], even before COVID-19. Looking at industry trends, since last year, we have been shifting away from our non-profit business model. (Resp6) For nonessential businesses, the rapid transition to working from home (WFH) in an online environment involved challenges. Many respondents moved away from WhatsApp groups and switched off their TVs and mobile phones to help focus and strategize. While a few already had WFH strategies pre-lockdown, most found themselves quickly arming their businesses with licenses for internet telephony and procuring laptops. After the shock of the first few days, many respondents saw opportunities to use the lockdown time to improve operations or make urgent decisions (e.g., canceling orders to meet reduced market demand). Hence, the lockdown created new opportunities for our respondents, as most reconnected with forgotten business channels. Resp17, a partner in the travel and tourism business, ventured into acrylic partitions and repatriation flights for stranded, jobless people. Resp3 and her brother used this phase of volatility to reflect on their business.The upside is that we got a chance to clear up our admin work, which has been pending for so long. We had time to learn about some new best practices: clean admin, clean-up backlog, launch a new website, and increase our online presence, which we didn’t have the mental bandwidth to do earlier. (Resp3) Respondents used different strategies for keeping their businesses afloat, either organizational or personal. Resp1, who experienced the initial shock during the lockdown period, looked for professional support. He described the role played by his coach and mentor in helping him combat the crisis.My coach (mastermind) asked me to talk to other people, even if they were not in the same business. I saw an acrylic partition in my friend’s office and got in touch with him, gave him a flyer, and mentioned I needed to get this online on my website on an urgent basis—and started getting calls the very next day. I feel all businesses can survive if they try to see what they can do best; there are still lots of opportunities out there. In fact, our months’ (July to August) billing has been the highest ever since the start of business! (Resp1) Technology was integral to connecting with employees and clients, empowering people to communicate and work in a flexible time and space, despite the early challenges of the transition. Most respondents (Resp24, Resp20, Resp21, Resp4, Resp15, Resp18, and Resp23) agreed that productivity was unaffected and that their people showed exemplary courage, willingness to learn, and support for teammates. Resp11 and his business partners reached out to their people for work and fun.We organized one or two activities each month, including cultural meetings on Zoom. We also got employees’ children to emcee events, too. Someone from India even came online to do laughter therapy. We still do not encourage people to come to the office, although we just did on Onam lunch. We are trying to keep people connected, even though we are not in the same physical space. I believe motivation is still high among the team. (Resp11) Fundamentally, the essential lessons learned from this crises periods allowed us to understand and appreciate the change process and strategies of the respondents at the initial period of the COVID-19 pandemic. Respondents felt numb, uneasy, distressed, nervous, and fearful of the situations that affected business globally. Many of the respondents stated that on several occasions, they thought they would never get out of the crisis in a short time frame, given the enormous effects on business, social activities, and the environment. In some key affected sectors, such as service, travel, food and beverages, and marketing, the respondents were feeling down (Resp10, Resp17, Resp13, Resp7, Resp1, Resp5, Resp2), but were agile in making adjustments by quickly shifting to different strategies and exploiting opportunities that were waiting to be seized. Period two: Business adopts crisis As the government began easing the lockdown on 3 June 2020, economic activities re-accelerated, and businesses faced new challenges. First, despite the easing of restrictions, they struggled with financial liquidity and cash flow as they attempted to generate enough revenue to cover overheads. Our respondents were unsure whether their business would “get back to normal.” As Resp19 noted, “Our clients are affected, so we lose business.” With regret, Resp2 expressed, “We have cut expenses, revised salaries.” Although such a strategy is never ideal, it was necessary to stay afloat, and they hoped it was a temporary measure. Many businesses diversified into new products, entered new markets, or added new value to existing products or services. Businesses had limited options for handling low revenues and high overheads. As an accountant, Resp23 reflected on financial management: “The major reason for closing down is nothing else…cash flow.” She moved beyond regular services (i.e., accounting and VAT) to offer personalized business and financial consulting.I always try to look on the bright side. I had a low month during the total lockdown, but I did try to help other business owners [clients] without charging them since I couldn’t meet them in person. (Resp23) Second, businesses struggled to manage human capital. Most businesses sought to stay afloat primarily for their people, including employees, customers, vendors, and partners. The interviews revealed that respondents were reluctant to close due to concerns about employees and their dependents. Our respondents felt strong connections with their employees (who were expatriates like them), to the extent that they were willing to make sacrifices to protect the financial security of their employees. For example, at the time of this interview, Resp12 had taken only 6 months’ pay for the year to avoid cutting pay for his 80 employees. A salary cut was unavoidable for some businesses, but laying off employees was the last resort. Further, to ensure the safety and well-being of employees, businesses reorganized operations, for example, by moving to WFH, reducing the number of workers on the business premises, and so on. Despite uncertainties, our respondents remained positive in motivating their employees and providing extra support. Resp2 stressed the importance of staying positive, noting that “whatever goes down has to come up.” Resp8 encouraged his employees to stay optimistic and take care of their expenses and well-being.Life has not been well, and [our employees] have had many expenses. I know they need food to survive [and] have to pay rent. I told them, “Don’t worry–I’ll take care of them [the expenses].” I have always been by their side and trying my best to be there for them. (Resp8) Resp20 took a leadership-responsive and compassionate approach in dealing with volatile business conditions, especially when businesses began to take off during this crisis adoption period.I made sure everyone knew what was happening and consistently kept them in the loop. I sent emails and WhatsApp messages on a 24/7 basis. There were no attitudes like “That’s not possible” or “That is not my job”–people became more sensitive because they all went through [the same hardships]. We became like a family and embraced an attitude of ‘I help you, you help me!’ (Resp20) The third challenge businesses faced when reopening was adapting quickly in the face of uncertainty and making wise decisions on “how to sail safely through a storm,” as mentioned by Resp14. The massive scale and sheer unpredictability of the outbreak necessitated quick thinking and decision making. Communication was a vital part of this process. Though decisions are never final, simply because change is never absolute, continuous communication among partners, managers, employees, and business networks helped the respondents to devise good strategies, adapting quickly to online tools (e.g., online meetings, marketing, and retail) or business-to-consumer or business-to-business internet operational models. Resp24 pursued a flexible way of staying afloat and engaged with technological support to keep strategies intact.We have to accept the situation. If we cannot, [we will go] out of business. There are many people we have responsibilities to. We have to devise plans and use technologies to adapt. This is very important, so clients can see that we can manage systems properly—if [we] cannot do that, how can we advise them? We are business advisers. I don’t push people to work. Instead, I provide them with more flexibility and trust that they will all be accountable. (Resp24) In most cases, however, businesses were more reactive than proactive in dealing with COVID-19. Only a few respondents could predict the problem and move to an online model before the pandemic. Resp25 took precautionary action by improving credit control—that is, by collecting cash earlier from debtors—before the pandemic started. At the same time, he was more selective when importing goods from suppliers and revising sales prices according to market demand. Other challenges included complying with new legislation regarding social distancing and movement restrictions. Resp19 tried to reduce risk by implementing work rotations and encouraging carpools (to reduce the use of public transport). Businesses conducted regular meetings and offered continuous support to alleviate employees’ fear, anxiety, and hopelessness. Some of them were opportune and content to continue the business because of their existing customers.We were okay until July because we had a project pipeline that we still had to deliver on. We still make enough to sustain [the company]. The biggest challenge is that there are too many uncertainties, but we don’t blame our clients [for that]. For the past three months, we have tried to make ourselves more visible on social media. (Resp15) To recapitulate what was reflected by the respondents, once the government opened the door for businesses to revitalize by adapting to crisis, we found that all respondents felt more relieved, reassured, and optimistic than during the lockdown period. However, our respondents also expressed their continued concerns, apprehensions, and unenthusiastic feelings about the volatility of COVID-19. Hence, they needed to stay agile, responsive, and strategic in handling unpredictable business situations and likely turbulence in the near future. As Resp17 asserted, “Nothing is for certain; clearly, we need to be alert and cautious at all times or at any time.” Period three: Business normalizes August and September 2020 saw a return to a “new normal” business experience. Tourism reopened in July, and businesses had to consider operating at close to 100% capacity, albeit with new precautions and restrictions regarding entry and exit. The respondents generally described a positive approach to changing their practices, but many also considered different ways of engaging in business activity and staffing.There is no guidebook or protocol to handle this situation, as economic activities were halted by order… We have to try many different things to survive for another year. I have the ability to proceed, it works, and we started to generate revenue. (Resp26) Our respondents understood the available opportunities, and they explored strategies carefully. Changes were made where the strategies employed showed different approaches to core business activities and secondary features like administration, staffing, and compliance with government mandates. Many respondents operated on either a regional or a global basis, prompting them to take early action to continue operating via adapted working methods or to prepare for summer and autumn. Many respondents also reexamined their business practices and incorporated more flexibility in terms of staff operations. For example, in a logistics SME, the partners stressed that as they were asset-light, they viewed their team members as their key assets and were open to continuing with flexible WFH arrangements, even once restrictions had eased.We took steps early in March and initiated a work-from-home policy. This has continued. We have access to Microsoft Teams, which we will continue to use, as it has brought both our local and global teams closer together. (Resp22) The re-evaluation of the value and purpose of staff was also demonstrated by others.We share information [about] what is happening with the company to demonstrate that we are moving forward and to be transparent. This helps to reduce fear, even when business is difficult. Our people help each other. (Resp21) [We] suggested putting staff on a 15-day leave during lockdown, which the staff agreed to. Any illness cover was to be adjusted back in favor of the staff. [We wanted to] show how important the staff are for the business. Staff were evacuated from their normal lodgings (dormitories) to the office/warehouse space and were restricted from mixing with others outside the organization. We provided staff with the basic supplies they needed to be safe in their temporary spaces. (Resp9) The owners, who are like family to the staff, are always there for the team. They keep their doors open and are responsive to staff requests, staying flexible about them coming to work or not. The owners are a support mechanism for the staff and help people feel actively involved in the company. (Resp25) Others re-evaluated staff members’ value and purpose. Resp25, whose organization provided packaging for fast-moving goods and consumption products, experienced an increase in demand due to the lack of cargo transit into the country, despite supply from competitors outside the UAE.Business was better when the borders were closed. Now we are seeing competition again with our original packaging product competitors from China and Africa. (Resp25) Their challenge was to manage cash flow and material stocks to respond more fully in the event of another lockdown. Overall, business owners needed to increase their workforces to meet demand and supply needs. As many participants stated, the UAE government provided clear guidelines for continuing business practices post-lockdown. Due to Dubai’s very diverse population, businesses had to interpret these regulations carefully. Resp16 noted that misunderstandings came not only from misinterpretations, but also from elements of social conspiracy. In order to be clear with their teams, they followed advice across the organization.“Be firm about listening to facts and not hearsay” was the message to employees. [We were] working within guidelines as advised. We informed staff and clients about how to operate safely in order to focus on the customers’ best interests. (Resp16) Further, an emphasis on “blue-collar” warehouse workers requiring continued reinforcement of Ministry guidelines down to personal hygiene helped to guide them for reinforcement. They repeated the information to all staff at twice-daily meetings, stressing the importance of regular communication, whether staff were onsite or working remotely.This has brought us down to earth…[it has made us place importance and] value on personal life and safety for all. We can only get things done if we work together. (Resp5) In a nutshell, the findings at this crisis periods seemed to indicate several important insights into COVID-19 challenges and changes. First, most of the respondents seemed to allude to the notion that “once the opportunity comes knocking, we need to seize it!” Second, all respondents mentioned that flexibility and agility were the two key ingredients to staying afloat because the turbulence and unpredictability of business conditions needed to be managed. Third, they all agreed that policy and rules needed to be adhered to, and compliance with the SOPs set for COVID-19 needed to be monitored for assurance of the health, safety, and well-being of the teams in the workplace. Discussions and implications We have explored the challenges experienced by SMEs as they underwent a wave of change brought on by COVID-19 and the approaches of responsible leaders to combat the crisis and stay afloat. We found that in order to remain in business and become resilient, business owners and managers have to continuously learn new skills to capture the potential of disrupted business with turbulent conditions, especially regarding the diverse roles of leadership, financial management, and adeptness. Our study aimed to fill the gaps and contributes to the literature on business resilience, crisis management, and SMEs by offering rich insights and an in-depth understanding of managerial responses to the unanticipated COVID-19 crisis in emerging economies like Dubai. In the past, crises have taken place that created disruption to SMEs, but these were nothing like the global crisis of COVID-19, which has impacted the world at large. In such situations, our study suggests that positive emotions protect the resilience of business owners, operators, and managers, encouraging success instead of failure, which is also asserted by past studies, such as Li et al. (2021), Corner et al. (2017), and Ayala and Manzano (2014). Theoretical contribution Our study provides three key theoretical contributions based on our research aim. First, the nature of the crisis and how it leads to changes in regulations, including lockdowns of individual movement and workplace closures, affect business continuity. Therefore, we considered how businesses approached the gradual move to arrive at the period of business normalizing. The primary driver for respondents to take actions in an agile way was employees’ values, indicating a clear relationship between respondents’ actions and stakeholder theory. While a study by Williams et al. (2020) found a negative relationship between managing relationships with stakeholders and resilience, we suggest that the opposite has been true for business owners or managers of SMEs in the UAE during COVID-19. Businesses that prioritized managing staff and customer relationships have continued to operate through temporary closures and many staff location restrictions, and are now willing to integrate more contemporary practices and policies, aligning their business with their organizational culture. Our respondents explained how they intended to continue allowing staff to work from home as required/desired to manage a healthier work-life balance, and they acknowledged the value of their human and social capital. Grube and Storr (2018) noted that business activities often reduce negative impacts during times of crisis or disaster, observing that owners or managers are often embedded in their communities and, thus, positioned to address societal needs. The narratives from respondents support this contention and illustrate their resilience-building strategies. The majority of our respondents mentioned that their ongoing business activities had contributed socially and economically by maintaining a supply of goods and services to employees or victims of disaster and donating time, materials, and money where possible. Such business continuity can reduce the impact of crises and maximize business and economic recovery (Doern et al., 2019). Williams and Shepherd (2016) and Linnenluecke and McKnight (2017) also posited that entrepreneurs could bridge gaps where recovery systems fail, thereby helping to rebuild and redesign socio-economic infrastructures. Second, the diverse role of responsible leadership played by the respondents, who were managers and owners of SMEs, seemed to promote a heightened level of individual resilience. As such, our study allowed us to fill the gap on how businesses can develop different strategies when encountering a global crisis like COVID-19. Research shows that self-efficacy contributes to self-belief and, therefore, resilience (Benight and Bandura, 2004), as does belief in one’s ability to cope with stressors. As such, our study shows that, combined with individual self-efficacy, this increases an individual’s ability to take the necessary steps to make business decisions during challenging times, increasing the chances of people “bouncing back” instead of giving in to negative emotions and actions. Our findings also illustrate that responsible leadership enables leaders and employees to endure hardship, as depicted over the 6 months of COVID-19. This study further elucidates the crucial need to develop responsive leaders who are effective when managing their people during a crisis (Miska et al., 2018). Respondents established that employees seemed to stay motivated, and feel secure and involved in changes when leaders were able to provide security, extend empathy, and stay optimistic during the first two crisis periods, when conditions were harsh and unpredictable. These factors extend the theoretical understanding of leadership responsiveness and responsibility in a crisis (Miska et al., 2018; Varma, 2020). Third, in terms of change and resilience, all interviewed respondents demonstrated positive emotional responses toward protecting both themselves and their businesses. In line with Fredrickson et al. (2003), we found that stopping, thinking, evaluating, and then planning what to do next (rather than simply crumbling in the face of adversity) enabled the respondents to continue operating their businesses and stay afloat. Our findings also show that the ability to demonstrate conviction and determination to focus on what was important for the future of the business enabled respondents to remodel their businesses and meet stakeholders’ needs through reflection or consultation with a supportive (business) network (Grube and Storr, 2018). This included diversification but rarely involved divestment. Benight and Bandura (2004) showed that building self-belief leads to intentions to act during adversity, which increases business resilience. The leaders we interviewed recalled having to instigate (sometimes mandatory) changes in how their businesses ran. This required analysis, reflection, decision making, and implementation of innovative practices to support staff and customers, ensuring safety and support during and after the three key periods of crises shown in our study. This supports research on participatory decision making (e.g., Bowers et al., 2017; Wartzman and Tang, 2020; White and Shullman, 2010; Yukl and Mahsud, 2010). This had a positive outcome for SMEs in the UAE, with our respondents demonstrating that they could weather the COVID-19 storm, sometimes pulling ahead of global rivals or engaging in further reviews to anticipate future disruptions and crises. Practical implications Our research findings have practical implications for businesses and policymakers. First, our study integrates both operational and managerial elements; hence, business owners should not limit their investment to tangible assets, but give importance to human capital and business leadership. Business leaders must be proactive, dynamic, and change-ready for managing any crisis (Jia et al., 2020; Vodonick, 2018). Second, since businesses are prone to bankruptcy in this pandemic, business leaders must quickly assess external threats and the dilution of business competitive advantages by enhancing communication and taking the lead for innovation to minimize risk and grab new opportunities. Businesses that pursue diversification initiatives and innovative work behavior have successfully secured unexpected opportunities and windfall gains. Third, explaining the three periods of crisis sheds light on the importance of business leaders’ resilience in the face of challenges for each stage in ensuring business sustainability and protecting employment. Fourth, the findings alert SME business owners to pay greater attention to establishing strong and effective financial risk management, particularly in maintaining liquidity and cash flow planning (Brown et al., 2020; Zjady, 2020). Several businesses were not equipped to counterbalance the immediate shortfall of cash and suffered liquidity tension. Businesses in the service sector are often more flexible, as they have relatively low fixed overheads. Finally, our study also raises the importance of financial support from third parties such as landlords, banks, and suppliers to stimulate business recovery and government determination in opening the economy. Conclusions and future research directions Numerous global and localized crises have affected international businesses in specific geographical location, yet the current COVID-19 situation impacts businesses at a global level in an unprecedented way. Our empirical study suggests that crises on different levels are common disruptors, such that unprecedented and unsettling events are the new “business as usual” for owners and managers. We have described and explained business owners’ various leadership roles and responsiveness, exemplifying their resilience through the three periods of the crisis. Crisis management is by no means a new phenomenon; historical business research suggests that a “crisis” is an extreme and unexpected event requiring organizations to make prompt decisions regarding challenges that can interfere with normal operations, reputation, and financial well-being. In conclusion, our study sheds light on the importance of business resilience in the face of challenges and waves of change, which we have depicted through success stories on ways to stay afloat during the COVID-19 pandemic. Additionally, we conceptualize business resilience as “not returning to the original condition,” but rather as acclimatizing to new norms and elevating existing business models using innovative approaches. Finally, we examined how responsible leaders developed inner strengths that were transferred to their teams, equipping them with strong financial management and agile leadership skills. Based on our study, several key research directions for the future could be considered.1. Examining the organizational culture, which is shaped by responsible leaders and seeking to explore the question: How do cultural factors influence the way SMEs change their structure, values, and norms when experiencing a crisis? 2. Exploring the different forms and meaning of resilience based on a comparative analysis: What are the change processes that SMEs undergo (pre- and post-crises) in the face of unpredictable business conditions? 3. Using mixed methodology to understand the relationship between crisis and organizational culture by looking at cultural factors influencing organizational resilience and developing a change process model through hypothesis testing and rich narratives. ORCID iDs Norhayati Zakaria https://orcid.org/0000-0002-3970-1694 Alastair Watson https://orcid.org/0000-0003-1005-1592 Khairul Anuar Kamarudin https://orcid.org/0000-0003-3620-9466 The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article. ==== Refs References Al-Hares O (2020) Is now a good time to be an entrepreneur in the UAE? Gulf Business . Available at: https://gulfbusiness.com/is-now-a-good-time-to-be-an-entrepreneur-in-the-uae/ (accessed on 2 September 2020). Applegate ML Kraus J Butler T (2016) Skills and behaviors that make entrepreneurs successful. Harvard Business Studies Working Knowledge. Available at: https://hbswk.hbs.edu/item/skills-and-behaviors-that-make-entrepreneurs-successful (accessed on 29 August 2020). Ayala JC Manzano G (2014) The resilience of the entrepreneur. Influence on the success of the business. A longitudinal analysis. Journal of Economic Psychology 42 : 126–135. Ballesteros L Kunreuther H (2018) Organizational decision making under uncertainty shocks. National Bureau of Economic Research Working Paper Series 24924. Available at: www.nber.org/papers/w24924 (accessed on 2 September 2020). Barro RJ Ursúa JF Weng J (2020) The coronavirus and the great influenza pandemic: lessons from the “Spanish flu” for the coronavirus’s potential effects on mortality and economic activity. National Bureau of Economic Research Working Paper Series 26866. Available at: https://www.nber.org/papers/w26866 (accessed on 2 September 2020). Bartik AW Bertrand M Cullen Z , et al (2020) The impact of COVID-19 on small business outcomes and expectations. Proceeding of National Academy of Sciences of the United States of America 30 : 17656–17666 Beech N Devins D Gold J , et al (2020) In the family way: an exploration of family business resilience. International Journal of Organizational Analysis 28 (1 ): 160–182. Benight CC Bandura A (2004) Social cognitive theory of posttraumatic recovery: the role of perceived self-efficacy. Behaviour Research and Therapy 42 (10 ): 1129–1148.15350854 Borekci D Rofcanin Y Sahin M (2014) Effects of organizational culture and organizational resilience over subcontractor riskiness. European Business Review 26 (1 ): 2–22. Bowers M Reggie Hall J Srinivasan M (2017) Organizational culture and leadership style: the missing combination for selecting the right leader for effective crisis management. Business Horizons 60 (4 ): 551–563. Branicki LJ Sullivan-Taylor B Livschitz SR (2018) How entrepreneurial resilience generates resilient SMEs. International Journal of Entrepreneurial Behavior & Research 24 (7 ): 1244–1263. Brown R Rocha A Cowling M (2020) Financing entrepreneurship in times of crisis: exploring the impact of COVID-19 on the market for entrepreneurial finance in the United Kingdom. International Small Business Journal: Researching Entrepreneurship 38 (5 ): 380–390. Brumfield K (2012) Succeeding in crisis leadership. Financial Executive 28 (8 ): 45–48. Brünjes J Revilla-Diez J (2013) ‘Recession push’ and ‘prosperity pull’ entrepreneurship in a rural developing context’. Entrepreneurship and Regional Development 25 (3–4 ): 251–271. Bundy J Pfarrer M (2015) A burden of responsibility: the role of social approval at the onset of a crisis. Academy of Management Review 40 (3 ): 345–369. Chong MPM Shang Y Richards M , et al (2018) Two sides of the same coin? Leadership and organizational culture. Leadership and Organization Development Journal 39 (8 ): 975–994. Cleeren K Dekimpe MG Van Heerde HJ (2017) Marketing research on product-harm crises: a review, managerial implications, and an agenda for future research. Journal of Academic Marketing Science 45 (5 ): 593–615. Crayne MP Medeiros KE (2020) Making sense of crisis: charismatic, ideological, and pragmatic leadership in response to COVID-19. American Psychologist , DOI: 10.1037/amp0000715. Corner PD Singh S Pavlovich K (2017) Entrepreneurial resilience and venture failure, International Small Business Journal: Researching Entrepreneurship, 35 (6 ): 687–708. Creswell JW Poth CN (2016) Qualitative Inquiry and Research Design: Choosing Among Five Approaches. Los Angeles, CA: SAGE Publications. Cumming GS Barnes G Perz S , et al. (2005) An exploratory framework for the empirical measurement of resilience. Ecosystems 8 (8 ): 975–987. Cuyper LD Kucukkeles B Reuben R (2020) Discovering the Real Impact of COVID-19 on Entrepreneurship. World Economic Forum, Available at: https://www.weforum.org/agenda/2020/06/how-covid-19-will-change-entrepreneurial-business/ (accessed on 19 August 2020). Dahles H Susilowati TP (2015) Business resilience in times of growth and crisis. Annals of Tourism Research 51 : 34–50. Direction S (2021) Firm capacity to manage new trends: business model innovation can increase resilience. Strategic Direction, 37 (4 ), 15–18. Doern R (2016) Entrepreneurship and crisis management: the experiences of small businesses during the London 2011 riots. International Small Business Journal 34 (3 ): 276–302. Doern R Williams N Vorley T (2019) Special issue on entrepreneurship and crises: business as usual? An introduction and review of the literature. Entrepreneurship and Regional Development 31 (5–6 ): 400–412. DuBrin AJ (2013) Handbook of Research on Crisis Leadership in Organizations. Cheltenham: Edward Elgar Publishing. Dutton J (1986) The processing of crisis and non-crisis strategic issues. Journal of Management Studies 23 (5 ): 501–517 Elo S Kyngäs H (2008) The qualitative content analysis process. Journal of Advanced Nursing 62 : 107–115 18352969 Fainshmidt S Nair A Mallon MR (2017) MNE performance during a crisis. An evolutionary perspective on the role of dynamic managerial capabilities and industry content. International Business Review 26 (6 ): 1088–1099. Fiksel J (2006) Sustainability and resilience: toward a systems approach. Sustainability: Science, Practice and Policy 2 (2 ): 14–21. Fredrickson BL Tugade MM Waugh CE , et al. (2003) What good are positive emotions in crises? A prospective study of resilience and emotions following the terrorist attacks on the United States on September 11th, 2001. Journal of Personality and Social Psychology 84 (2 ): 365–376.12585810 Gerth F Ramiah V Toufaily E , et al. (2021) Assessing the effectiveness of Covid-19 financial product innovations in supporting financially distressed firms and households in the UAE. Journal of Financial Services Marketing, DOI: 10.1057/s41264-021-00098-w. Gibbs G (1988) Learning by Doing: A Guides to Teaching and Learning Methods. Further Education Unit. Oxford: Oxford Polytechnic Girboveanu S Pavel S (2010) How to manage a public relations crisis. Annals of University of Craiova—Economic Sciences Series 3 (38 ): 320–331. Grube LE Storr VH (2018) Embedded entrepreneurs and post-disaster community recovery. Entrepreneurship and Regional Development 30 (7–8 ): 800–821 Guba EG Lincoln YS (1994) Competing paradigms in qualitative research. In: Denzin NK Lincoln YS (eds). Handbook of Qualitative Research. Sage Publications, Inc, 105–117. Gunderson LH (2000) Ecological resilience – in theory and application. Annual Review of Ecology and Systematics 31 (1 ): 425–439. Hamel G Välikangas L (2003) The quest for resilience. Harvard Business Review 81 (9 ): 52–63. Haque A (2021) The COVID-19 pandemic and the role of responsible leadership in health care: thinking beyond employee well-being and organisational sustainability. Leadership in Health Service Leadership, 34 (1 ), 52–68. Harries T McEwen L Wragg A (2018) Why it takes an ‘ontological shock’ to prompt increases in small firm resilience: sensemaking, emotions and flood risk. International Small Business Journal 36 (6 ): 712–733. Hearnshaw EJ Wilson MM (2013) A complex network approach to supply chain network theory. International Journal of Operations and Production Management 33 (4 ): 442–469. Herbane B (2010) Small business research: time for a crisis-based view. International Small Business Journal 28 (1 ): 43–64. Herbane B (2019) Rethinking organizational resilience and strategic renewal in SMEs. Entrepreneurship & Regional Development: An International Journal 31 (5–6 ): 476–495. Hermann CF (1963) Some consequences of crisis which limit the viability of organizations. Administrative Science Quarterly 8 (1 ): 61–82 Hills A (1998) Seduced by recovery: The consequences of misunderstanding disaster. Journal of Contingencies and Crisis Management 6 (3 ): 162–170 Holling CS (1996) Engineering resilience versus ecological resilience. In: Schulze P (ed) Engineering within ecological constraints. Washington: National Academy Press, pp. 31–44. Hu Y Pang A (2018) The indigenization of crisis response strategies in the context of China. Chinese Journal of Communication 11 (1 ): 105–128. Irvine W Anderson A (2004) Small tourist firms in rural areas: agility, vulnerability and survival in the face of crisis. International Journal of Entrepreneurial Behaviour and Research 10 (4 ): 229–246 Jia X Chowdhury M Prayag G , et al. (2020) The role of social capital on proactive and reactive resilience of organizations post-disaster. International Journal of Disaster Risk Reduction 48 : 1–12. Johnston M (2001) Breaking the code of change. Long Range Planning 34 (4 ): 516–522. Kaiser RB (2020) Leading in an unprecedented global crisis: the heightened importance of versatility. Consulting Psychology Journal: Practice and Research 72 (3 ): 135–154. Kempster S Jackson B (2021) Leadership for what, why, for whom and where? A responsibility perspective? Journal of Change Management 21 (1 ): 45–65. Kenny J Dutt CS (2021) The long-term impacts of hotel’s strategic responses to COVID-19: the case of Dubai. Tourism and Hospitality Research 21 (1 ): 71–85, DOI: 10.1177/14673584211034525. Kraus S Clauss T Breier M , et al. (2020) The economics of COVID-19: initial empirical evidence on how family firms in five European countries cope with the corona crisis. International Journal of Entrepreneurial Behavior and Research 28 (5 ): 1067–1092. LePine JA (2005) Adaptation of teams in response to unforeseen change: effects of goal difficulty and team composition in terms of cognitive ability and goal orientation. Journal of Applied Psychology 90 (6 ): 1153–1167.16316271 Li B Zhong Y Zhang T , et al. (2021) Transcending the COVID-19 crisis: Business resilience and innovation of the restaurant industry in China. Journal of Hospitality and Tourism Management 49 : 44–53. Liu Y Lee JM Lee C (2020) The challenges and opportunities of a global health crisis: the management and business implications of COVID-19 from an Asian perspective, Asian Business & Management.19 : 277–297. Limaj E Bernroider EW (2019) The roles of absorptive capacity and cultural balance for exploratory and exploitative innovation in SMEs. Journal of Business Research 94 : 137–153. Linnenluecke MK McKnight B (2017) Community resilience to natural disaster: the role of disaster entrepreneurship. Journal of Enterprising Communities: People and Places in the Global Economy 11 (1 ): 166–185. Maak T (2007), Responsible leadership, stakeholder engagement, and the emergence of social capital. Journal of Business Ethics 74 (4 ): 329–334. Madi Odeh RBS Obeidat BY Jaradat MO , et al. (2021) The transformational leadership role in achieving organizational resilience through adaptive cultures: the case of Dubai service sector. International Journal of Productivity and Performance Management, DOI: 10.1108/IJPPM-02-2021-0093. Maliszewska M Mattoo A Van Der Mensbrugghe D (2020) The potential impact of COVID-19 on GDP and trade: A preliminary assessment, Policy Research Working Paper 9211, The World Ban, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7216126/ Mehta M Sarvaiya H Chandani A (2020) Community engagement through responsible leadership in managing pandemic: insight from India using ethnography. International Journal of Sociology and Social Policy 4 (9 ): 1–14. Miska C Stahl G Mendenhall ME (2018) Responsible leadership: a mapping of extant research and future directions. Journal of Business Ethics 148 : 117–134. Mitroff II (2005) Why Some Companies Emerge Stronger and Better from A Crisis: Seven Essential Lessons for Surviving Disaster. New York: AMACOM/American Management Association. Neuendorf KA (2002) The Content Analysis Guidebook . Thousand Oaks, CA: Sage Publications. Pal R Torstensson H Mattila H (2014) Antecedents of organizational resilience in economic crises—an empirical study of Swedish textile and clothing SMEs. International Journal of Production Economics 147 : 410–428. Pounder P (2021) Responsible leadership and COVID-19: small island making big waves in cruise tourism. International Journal of Public Leadership 17 (1 ): 118–131. Quarantelli EL (1988) Disaster crisis management: a summary of research findings. Journal of Management Studies 25 (4 ): 373–385 Rajah R Arvey R (2013) Helping group members develop resilience. In: DuBrin AJ (ed), Handbook of research on crisis leadership in organizations. Northampton: Edward Elgar Publishing, 149–174. Reiche BS Mendenhall ME Szkudlarek B , et al. (2020) At the heart and beyond: what can global leadership researchers learn from perspectives on the COVID-19 Pandemic? In: Osland JS Szkudlarek B Mendenhall ME , et al. (eds) Advances in Global Leadership. Bingley: Emerald Publishing Limited, pp. 261–282. Ren S Collins N Rowley C (2021). Managerial leadership within Vietnam’s transition. Journal of General Management 46 (3 ): 197–209. Runyan RC (2006) Small business in the face of crisis: identifying barriers to recovery from a natural disaster. Journal of Contingencies and Crisis Management 14 (1 ): 12–26 Salvia R Quaranta G (2015) Adaptive cycle as a tool to select resilient patterns of rural development. Sustainability 7 (8 ): 11114–11138. Scheffer M Carpenter SR Foley JA , et al. (2001) Catastrophic shifts in ecosystems. Nature 413 : 591–596.11595939 Schein E (2004) Organizational Culture and Leadership 3rd ed. San Francisco: Jossey-Bass Publishers. Schein EH (1996) Culture: the missing concept in organization studies. Administrative Science Quarterly 41 (2 ): 229–240. Schroeder DG (2020) Turn your COVID-19 solution into a viable business. Harvard Business Review, 2 July, Available at: https://hbr.org/2020/07/turn-your-covid-19-solution-into-a-viable-business (accessed on 30 July 2020). Smallbone D Welter F (2012) Entrepreneurship and institutional change in transition economies: the commonwealth of independent states, central and eastern Europe and China compared. Entrepreneurship and Regional Development 24 (3–4 ): 215–233. Sundarakani B Onyi OP (2021) Fast, furious and focused approach to Covid‐19 response: An examination of the financial and business resilience of the UAE logistics industry, Journal of Financial Services Marketing 26 (4 ): 237–258. DOI: 10.1057/s41264-021-00118-9 Turak N (2020) Dubai, hit by lockdown and oil price crash, could be headed for another debt crisis, CNBC, Available at: https://www.cnbc.com/2020/04/24/coronavirus-lockdown-dubai-could-be-headed-for-another-debt-crisis.html (accessed on 31 July 2020). Van Knippenberg D Sitkin SB (2013) A critical assessment of charismatic—Transformational leadership research: Back to the drawing board? The Academy of Management Annals 7 (1 ): 1–60. Van Vugt M Hogan R Kaiser RB (2008) Leadership, followership, and evolution: Some lessons from the past. American Psychologist 63 (3 ): 182–196.18377108 Varma TM (2020) Responsible leadership and reputation management during a crisis: the cases of Delta and United Airlines. Journal Business Ethics 173 :29–45. Vodonick J (2018) The key to organizational sustainability: nurturing a culture of change. Systems Research and Behavioral System Research 35 (4 ): 458–468. Wartzman R Tang K (2020) The Key to Being a Successful Leader? It’s Adaptability. Wall Street Journal . Available at: https://www.wsj.com/articles/the-key-to-being-a-successful-leader-its-adaptability11585242768 (accessed on 1 August 2020). White RP Shullman SL (2010) Acceptance of uncertainty as an indicator of effective leadership. Consulting Psychology Journal: Practice and Research 62 (2 ): 94–104. Williams C You JJ Joshua K (2020) Small-business resilience in a remote tourist destination: exploring close relationship capabilities on the island of St Helena. Journal of Sustainable Tourism 28 (7 ): 937–955. Williams N Vorley T (2014) Economic resilience and entrepreneurship: lessons from the Sheffield City region. Entrepreneurship and Regional Development: An International Journal 26 (3–4 ): 257–281. Williams TA Shepherd DA (2016) Building resilience or providing sustenance: different paths of emergent ventures in the aftermath of the Haiti Earthquake. Academy of Management Journal 59 (6 ): 2069–2102. Xing Y Liu Y Boojihawon DK , et al (2020) Entrepreneurial team and strategic agility: A conceptual framework and research agenda. Human Resource Management Review. 30 (1 ):100696 Yaari M Blit-Cohen E Savaya R (2020) Management in social enterprises—management style, challenges, and strategies. Journal of General Management 46 (1 ): 36–46. Yukl G Mahsud R (2010) Why flexible and adaptive leadership is essential. Consulting Psychology Journal: Practice and Research 62 (2 ): 81–93. Zakaria N. (2017) Emergent Patterns of Switching Behaviors and Intercultural Communication Styles of Global Virtual Teams During Distributed Decision Making. Journal of International Management 23 (4 ): 350–366. Zakaria N (2019) Making Sense of Culture: Cross-Cultural Expeditions and Management Practice of Self-Initiated Expatriates Working in Foreign Workplace. New York: Routledge, Taylor & Francis Group. Zhao H Zhou Q He P , et al. (2021) How and when does socially responsible HRM affect employees’ organizational citizenship behaviors toward the environment? Journal Business Ethics, 169 , 371–385. Zjady H (2020) It’s too late for this pandemic. But everyone wants insurance against the next one, CNN Business, 19 March. Available at: https://edition.cnn.com/2020/03/19/business/pandemic-insurance-coronavirus/index.html (accessed on 20 March 2020).	0	PMC9708537	NO-CC CODE	2022-12-01 23:20:31	no	2022 Nov 27;:03063070221088371	utf-8	null	null	null	oa_other
==== Front Rev Neurol (Paris) Rev Neurol (Paris) Revue Neurologique 0035-3787 0035-3787 Published by Elsevier Masson SAS. S0035-3787(22)00821-9 10.1016/j.neurol.2022.11.002 Article Cerebrospinal fluid biomarkers in SARS-CoV-2 patients with acute neurological syndromes Chaumont H. abc⁎ Kaczorowski F. de San-Galli A. a Michel P.P. c Tressières B. g Roze E. cf Quadrio I. de Lannuzel A. abcg a Centre Hospitalier Universitaire de la Guadeloupe, Service de Neurologie, Pointe-à-Pitre/Abymes, French West Indies, France b Faculté de Médecine de l’université des Antilles, French West Indies, Pointe-à-Pitre, France c Faculté de Médecine de Sorbonne Université, Institut National de la Santé et de la Recherche Médicale, U 1127, CNRS, Unité Mixte de Recherche (UMR) 7225, Institut du Cerveau, ICM, Paris, France d Laboratory of Neurobiology and Neurogenetics, Department of Biochemistry and Molecular Biology, Lyon University Hospital, Bron, France e BIORAN Team, Lyon Neuroscience Research Center, CNRS UMR 5292, INSERM U1028, Lyon 1 University, Bron, France f AP-HP, Hôpital de la Pitié-Salpêtrière, Département de Neurologie, Paris, France g Centre d’investigation Clinique Antilles Guyane, Inserm CIC 1424, CHU de la Guadeloupe, Pointe-à-Pitre, France ⁎ Corresponding author: Department of Neurology, University Hospital of Guadeloupe, 97139, Pointe-à-Pitre/Abymes, French West Indies, France 30 11 2022 30 11 2022 19 9 2022 28 10 2022 3 11 2022 © 2022 Published by Elsevier Masson SAS. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background and purpose: Mechanisms underlying acute brain injury in SARS-CoV-2 patients remain poorly understood. A better characterization of such mechanisms remains essential to preventing long-term neurological sequelae. Our present aim was to study a panel of biomarkers of neuroinflammation and neurodegeneration in the cerebrospinal fluid (CSF) of NeuroCOVID patients. Methods: We retrospectively collected clinical and CSF biomarkers data from 24 NeuroCOVID adults seen at the University Hospital of Guadeloupe between March and June 2021. Results: Among 24 NeuroCOVID patients, 71% had encephalopathy and 29% meningoencephalitis. A number of these patients also experienced de novo movement disorder (33%) or stroke (21%). The CSF analysis revealed intrathecal immunoglobulin synthesis in 54% of NeuroCOVID patients (two with a type 2 pattern and 11 with a type 3) and elevated neopterin levels in 75% of them (median 9.1 nM, IQR 5.6-22.1). CSF neurofilament light chain (NfL) was also increased compared to a control group of non-COVID-19 patients with psychiatric illnesses (2905 ng/l, IQR 1428-7124 versus 1222 ng/l, IQR 1049-1566). Total-tau was elevated in the CSF of 24% of patients, whereas protein 14-3-3, generally undetectable, reached intermediate levels in two patients. Finally, CSF Aß1-42 was reduced in 52.4% of patients (median 536 ng/l, IQR 432-904) with no change in the Aß1-42/Aß1-40 ratio (0.082, IQR 0.060-0.096). Conclusions: We showed an elevation of CSF biomarkers of neuroinflammation in NeuroCOVID patients and a rise of CSF NfL, evocative of neuronal damage. However, longitudinal studies are needed to determine whether NeuroCOVID could evolve into a chronic neurodegenerative condition. Keywords Encephalitis Encephalopathy COVID-19 Neuroinflammation Neuronal injury CSF biomarker ==== Body pmc	0	PMC9708608	NO-CC CODE	2022-12-01 23:21:33	no	Rev Neurol (Paris). 2022 Nov 30; doi: 10.1016/j.neurol.2022.11.002	utf-8	Rev Neurol (Paris)	2,022	10.1016/j.neurol.2022.11.002	oa_other
==== Front Phys Chem Earth (2002) Phys Chem Earth (2002) Physics and Chemistry of the Earth (2002) 1474-7065 1873-5193 The Author. Published by Elsevier Ltd. S1474-7065(22)00226-1 10.1016/j.pce.2022.103333 103333 Article Combating COVID-19 crisis and exploring heat-based simple solutions Roy Indrani University College London, Gower Street, London, WC1E 6BS, UK 30 11 2022 2 2023 30 11 2022 129 103333103333 29 8 2022 2 11 2022 27 11 2022 © 2022 The Author 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Covid-19 pandemic affected whole of the world taking many lives and impacting the economy and mental health severely. Exit pathways via vaccination though ignited optimism initially but attenuated by the emergence of several new variants which are less sensitive to vaccines. Considering emergency situations, some urgent, simple heat-based solutions for the initial stages of the disease were also proposed at the beginning of pandemic and further elaborated here. Solutions were proposed based on science as follows: exploring results of statistical analyses on the global transmission of COVID-19; observed temperature-dependent behaviours of similar category viruses; temperature-based clinical trial experiments with similar category viruses; successful clinical trial experiments with heat-based intervention for COVID-19 patients; and finally, biological mechanism/response in human bodies to heat-based solution for COVID-19 from medical doctor's perspective. Solutions proposed are practically without side effects, can be even practised in own home and there is no vested interest involved. Keywords COVID-19 Seasonality Temperature Clinical trial Solutions Mass vaccination ==== Body pmc1 Background The COVID-19 pandemic first originated in the Wuhan Province of China in December 2019 and spread all over the globe at a very rapid rate (Worldometers and https, 2022). The disease is highly contagious and hence at the beginning of the outbreak, most countries worldwide imposed strict lockdown (Ourworldindata and https). Rapid regulatory approval of vaccines took place though alerts were triggered raising concerns (Doshi, 2020a, 2020b) and mass vaccination roll out started from December 2020 (Ourworldindata and https). Adverse effects on people after the vaccine and how those are monitored, scrutinized and attended by respective authorities are some of the most crucial areas before the launch of any mass vaccination programme. Some adverse effects of COVID-19 vaccination were reported by CDC, US and other government regulatory bodies around the globe e.g., anaphylaxis, thrombosis with thrombocytopenia syndrome (TTS), Guillain-Barré Syndrome (GBS), myocarditis and pericarditis among others (CDC1 (Centers for Disease Control and Prevention), 2019). A recent review sufficiently discussed the scientific basis of many adverse reactions to the COVID-19 vaccine (Seneff and Nigh, 2021; Seneff et al., 2022). Knowing that the effect of vaccines wanes in 3–10 weeks’ time (Shrotri et al., 2021) and percentage of COVID-19 deaths among children are very low [ WHO ], child vaccination was still initiated. Apart from direct side effects, indirect effects after mass vaccination which were often overlooked also need attention. In this regard, questions were raised on the indirect effect of mass vaccination (Roy, 2021a). Few points raised are as follows: i) After initiation of the vaccine programme, almost all countries experienced a sudden surge and most countries had to impose strict lockdown measures. ii) Even for UK/Israel, where massive vaccination started first, total deaths in three months after vaccination reached overall deaths of the past 10 months before vaccination. iii) A highly populated country India was having a steady decrease for five months. Vaccination started on 16 January 2021 and from around 16th February 2021, India started showing a rise in cases and thereafter deaths. iv) For Brazil, vaccination started in mid-January 2021 and a sharp rise in cases is observed since mid-February 2021. Such a steep rise in deaths in Brazil that happened after that never happened in the whole period of the pandemic. Those are also discussed in later works (Roy, 2021b). One proposition in this regard is made by Professor Bieniasz, Rockefeller University, USA who mentioned COVID-19 vaccines can add fuel to the evolution of mutation of virus as vaccines themselves can drive viral mutations (Website: npj, 2021). The time between the initial vaccine dose and the next shot to boost the immune response might act a kind of breeding ground to acquire new mutations of the virus. It may give one plausible explanation for why there is a surge in mutated variants after the start of mass vaccination programme, so as the increase in cases of transmission worldwide. To develop useful timely urgent insights, the resemblance between Influenza (Flu) and COVID-19 needs attention too. Every winter, tens of thousands of people die in the UK, Europe and northern America from Flu, a virus-borne respiratory disease. In spite of many differences, there are striking similarities between COVID-19 and Flu as discussed by the American Society for Microbiology (ASM (American Society for Microbiology), 2020) [ASM, 2021]. People mainly from old and vulnerable groups are vaccinated against Flu viruses at the beginning of every winter in affected countries, but still, it is not yet been possible to eradicate Flu. On the contrary, it became more destructive and powerful in later years. The main reason is that the virus is mutating over space and time, in spite of several new vaccines that were developed over time. We are noting similar situations for COVID-19 too. Between the COVID-19 vaccine and other known vaccines (polio, smallpox, etc.) there are dissimilarities too. Unlike other vaccines, if people are vaccinated for COVID-19, they can still be infected and transmit the disease and even can die (CDC 2 (Centers for Disease Control and Prevention), 2019). Cases and deaths among the vaccinated are observed to be a very high percentage in all countries (Keehner et al., 2021; PHS; UHSA). It is also worth mentioning that vaccines have not yet been invented for many virus-borne diseases like, AIDS, MERS and H1N1 etc. Hence attention to varied research and exploration of alternate solutions for COVID-19 other than the vaccine is equally necessary. If any simple home-based solution is possible that does not have any vested interest that could be worth a try. It would be more important especially when UK, Europe and northern America are approaching third winter of the pandemic. 2 Rationale of heat-based solution Considering emergency situations some urgent, simple heat-based solutions for the initial stages of the disease were also proposed as early as 17th March 2020 (Roy, 2020a) and a series of research afterwards has since been completed (Roy, 2020b; Roy, 2020c; Roy, 2020d; Roy and AGU (American Geophysical Union), 2020e; Roy, 2021b) purely based on science. Those solutions were proposed (and supported by) based on science as follows: i) exploring results of statistical analyses on the global transmission of COVID-19 as numerous studies showed seasonal temperature played a profound role in the transmission (Roy, 2020c; Roy, 2020d; Roy and AGU (American Geophysical Union), 2020e; Scafetta, 2020); ii) observed temperature-dependent behaviours of similar category viruses (Van Doremalen et al., 2013; Casanova et al., 2010; Chan et al., 2011; Lowen et al., 2007; Seung et al., 2007); iii) temperature-based clinical trial experiments with similar category viruses (Casanova et al., 2010; Lowen et al., 2007); iv) successful clinical trial experiments with heat-based intervention for COVID-19 patients (Marca et al., 2021) and v) biological mechanism/response in human bodies to heat-based solution for COVID-19 from a medical doctor's perspective (Cohen, 2020). Those solutions [Fig. 1 ] are practically without side effects, can be even practised in own home and there is no vested interest involved.Fig. 1 Heat-based simple solutions to combat COVID-19: a) general measures at the initial stages of the disease, b) an overview in the form of a schematic, depicting actions towards solutions at an individual level (right) and public level (left). Fig. 1(Source: (Roy, 2020c; Roy, 2020d; Roy and AGU (American Geophysical Union), 2020e; Roy, 2021b)) Here further elaborations on the rationales for proposing heat-based solutions are attempted. Studies explored the role of global temperature in the spread and vulnerability to COVID-19 (Roy, 2020a; Roy, 2020b; Roy, 2020c; Roy, 2020d; Roy and AGU (American Geophysical Union), 2020e; Scafetta, 2020) and indicated that global temperature played an important role. Correlation study before the period of vaccination suggested that the lethality due to COVID-19 significantly aggravates (on an average 4 times) when weather temperature lies between 4 °C and 12 °C (Scafetta, 2020). Possible co-factors e.g., air pollution and median population age were also explored but suggested the important contribution from the former than the latter. Based on temperature, it further made prediction for many countries (e.g., United Kingdom, East Europe, Germany, North America and Russia) in different seasons. The risk from the virus was reduced significantly in warm places and countries; whereas, a moderately cool environment was the most susceptible to the spread of the virus (Roy, 2020c; Roy, 2020d; Roy and AGU, 2020e). The dependency of temperature is true for similar Coronavirus MERS and SARS (Van Doremalen et al., 2013; Chan et al., 2011) and also correct for similar seasonal air-borne Flu viruses (Lowen et al., 2007). In low temperatures, the virus remains active for a long time (Van Doremalen et al., 2013) and low temperatures significantly contributes to their transmission and survival (Casanova et al., 2010; Chan et al., 2011; Seung et al., 2007). Study suggested MERS-CoV virus could still be recovered after 48 h at the temperature of 20 °C with 40% relative humidity condition. On the other hand, for two different conditions the virus remained viable for only 24 h (temperature 30 °C with relative humidity 30%) and 8 h (temperature 30 °C, with relative humidity 80%) respectively (Van Doremalen et al., 2013). Clinical trials were also conducted to study the effect of temperature using seasonally dependent endemic virus (Lowen et al., 2007). It noted that when the temperature was 5 °C and relative humidity between 35% and 50%, the infection rate was as high as 75–100%. If the temperature was increased to 30 °C, keeping relative humidity at 35%, surprisingly, the infection rate decreased to zero. A laboratory experiment, using a variable temperature, with the SARS-CoV (Casanova et al., 2010) indicated that inactivation of the virus was faster at all humidity levels if the temperature was simply raised to 20 °C from 4 °C. The inactivation was much faster if the temperature was further increased from 20 °C to 40 °C. COVID-19 is highly contagious and invaded most of the globe in less than two months (WHO, 2020). However, warm countries like SAARC (South Asian Association for Regional Cooperation), SEAC (South East Asian countries) and many countries of Africa though were also affected, the scale of severity was much less in comparison, during the whole of 2020 (Roy, 2020c; Roy, 2020d; Roy and AGU (American Geophysical Union), 2020e). Such observation indicated the importance of exploring the nature of contact transmission in addition to airborne transmission. Contact transmissions in variable temperatures were studied in clinical trial experiments with guinea pigs and showed contact transmission is still possible at high temperatures, even at 30 °C (Lowen et al., 2007). When guinea pigs were kept in separate cages at 30 °C for 1 week, no recipient guinea pigs were infected indicating an increase in temperature arrested airborne transmission. However, when guinea pigs were kept in the same cage to simulate contact transmission, between 75% and 100% became infected indicating contact transmission play role in all temperatures. No role of humidity was found in those experiments. As temperature played such an important role in transmitting COVID-19 and similar diseases, heat-based simple solutions are proposed. Fig. 1a shows general measures at the initial stages of the disease; whereas, Fig. 1b suggests an overview in a form of a schematic, depicting useful actions towards solutions at the individual level (right) and public level (left). These measures in Fig. 1a at the initial stages of the disease are proposed because the virus, which is very sensitive to temperature, mainly accumulates at high volume in the back of the mouth as well as in the nose. Testing is done with swabs from those places. It is very similar to another respiratory pathogen MERS-CoV, where high-level viral loads are detected in samples from the lower respiratory tract of infected patients (Drosten et al., 2013; Guery et al., 2013) and the virus are mainly shed during coughing and via exudates from the lower respiratory tract (Drosten et al., 2013; Guery et al., 2013). The important point here is that - the high temperature (of course keeping comfort level) at early stages of the disease can reduce viral loads in the areas where the virus largely accumulates initially, and hence the body can have time and strength to fight against the virus. However, if people already developed major symptoms, then these methods discussed will not be effective and proper medical advice need to be solicited (Roy, 2020c; Roy, 2020d; Roy and AGU (American Geophysical Union), 2020e; Roy, 2021b). After receiving approval from the ethical committee, successful clinical trial experiments following heat-based solutions to fight against COVID-19 were also conducted and published (Marca et al., 2021). Furthermore, a thorough literature review in support of heat-based solutions for COVID-19 was published in a peer-reviewed journal too (Cohen, 2020). The author being a medical doctor, discussed different mechanisms linked to the biological processes (Cohen, 2020). Scientific rationale for such relationship is clearly discussed. Author discussed that enveloped viruses, such as coronaviruses and rhinoviruses are most active in cool dry situations, and are linked with increased cases of respiratory tract infections (Mäkinen et al., 2009). That also include infections with SARS-CoV (Chan et al., 2011) and SAR-CoV-2 (Wang et al., 2020; Sajadi et al., 2020). The lipid envelopes of enveloped viruses can remain active for long times in cold conditions, but are ruined by temperatures tolerable to humans. In vaccine, deactivation of viruses is done routinely by applying the knowledge of heat sensitivity of viruses, and the range of temperatures between 55 and 65 °C for 15–30 min are reported to deactivate a range of enveloped viruses, that also include coronaviruses (Lelie et al., 1987; Kampf et al., 2020; Hu et al., 2011; Darnell et al., 2004; Duan et al., 2003; WHO Report, 2003). Knowing many limitations of COVID-19 vaccines, heat-based solutions those are simple, can be practiced at home, and had scientific basis, deserve attention. 3 Conclusion and outlook This work discusses the scientific rationale for heat-based simple solutions to combat COVID-19 crisis. Some simple practices are proposed at the initial stages of the disease which can also be followed in the home environment. Few measures are proposed for personal level as well as the public level (Fig. 1). Those solutions were proposed exploring results of statistical analyses on the global transmission of COVID-19; observed temperature-dependent behaviours of similar category viruses; temperature-based clinical trial experiments with similar category viruses; successful clinical trial experiments with heat-based intervention for COVID-19 patients and finally, biological mechanism/response in human bodies to heat-based solution for COVID-19 from a medical doctor's perspective. If at the initial stages of the disease these heat-based simple solutions become popular [Fig. 1] then long COVID, whilst further lockdown, mutated variants, losing immunity from vaccine after few months etc would be minimized. The empirical experiences which have been clinically proven are important to improve the response to this and other pandemics and provide a quick exit strategy. In this regard, a quote from a famous Physicist is worth mentioning “Education isn't about the ability to remember and repeat, in which people study to pass exams, and teach others to pass exams, but nobody knows anything. It is the ability to learn from experience, to think, solve problems, and use our knowledge to adapt to new situations” [Prof Richard Feynman]. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability No data was used for the research described in the article. Acknowledgement This study did not receive any funding and there is no conflict of interest. ==== Refs References ASM (American Society for Microbiology), https://asm.org/Articles/2020/July/COVID-19-and-the-Flu, accessed on 06/03/2021. Casanova L.M. Jeon S. Rutala W.A. Weber D.J. Sobsev M.D. Effects of air temperature and relative humidity on coronavirus survival on surfaces Appl. Environ. Microbiol. 76 9 2010 2712 2717 10.1128/AEM.02291-09 20228108 CDC1(Centers for Disease Control and Prevention): Website https://www.cdc.gov/coronavirus/2019-ncov/vaccines/safety/adverse-events.html accessed 8/05/2020. CDC2 (Centers for Disease Control and Prevention) Website: https://www.cdc.gov/coronavirus/2019-ncov/vaccines/effectiveness/why-measure-effectiveness/breakthrough-cases.html. Chan K.H. Peiris J.S. Lam S.Y. Poon L.L. Yuen K.Y. Seto W.H. The effects of temperature and relative humidity on the viability of the SARS coronavirus Adv Virol 2011 734690 10.1155/2011/734690 2011 Cohen M. Turning up the heat on COVID-19: heat as a therapeutic intervention F1000Research, pubmed.ncbi.nlm.nih.gov/32742639 2020 Darnell M.E. Subbarao K. Feinstone S.M. Inactivation of the coronavirus that induces severe acute respiratory syndrome, SARS-CoV J. Virol. Methods 121 1 2004 85 91 15350737 Doshi P. Covid-19 vaccine trial protocols released, A rare opportunity for public scrutiny of these key trials BMJ 371 2020 10.1136/bmj.m4058 m4058 Published Doshi P. Will covid-19 vaccines save lives? Current trials aren't designed to tell us BMJ 371 2020 10.1136/bmj.m4037 Published https://www.bmj.com/content/371/bmj.m4037 https://www.bmj.com/company/newsroom/covid-19-vaccine-trials-cannot-tell-us-if-they-will-save-lives/ BMJ 2020;371:m4037 Drosten C. Seilmaier M. Corman V.M. Hartmann W. Scheible G. Sack S. Clinical features and virological analysis of a case of Middle East respiratory syndrome coronavirus infection Lancet Infect. Dis. 13 9 2013 745 751 10.1016/S1473-3099(13)70154-3 23782859 Duan S.M. Zhao X.S. Wen R.F. Stability of SARS coronavirus in human specimens and environment and its sensitivity to heating and UV irradiation Biomed. Environ. Sci. 16 3 2003 246 255 14631830 Guery B. Poissy J. el Mansouf L. Séjourné C. Ettahar N. Lemaire X. Clinical features and viral diagnosis of two cases of infection with Middle East Respiratory Syndrome coronavirus: a report of nosocomial transmission Lancet 381 9885 2013 2265 2272 10.1016/S0140-6736(13)60982-4 23727167 Hu L. Trefethen J.M. Zeng Y. Biophysical characterization and conformational stability of Ebola and Marburg virus-like particles J. Pharmacol. Sci. 100 12 2011 5156 5173 Kampf G. Voss A. Scheithauer S. Inactivation of coronaviruses by heat J. Hosp. Infect. S0195–6701 20 2020 30124 30129 Keehner SARS-CoV-2 infection after vaccination in health care workers in California N. Engl. J. Med. 2021 10.1056/NEJMc2101927 Lelie P.N. Reesink H.W. Lucas C.J. Inactivation of 12 viruses by heating steps applied during manufacture of a hepatitis B vaccine J. Med. Virol. 23 3 1987 297 301 2828525 Lowen A.C. Mubareja S. Steel J. Palese P. Inﬂuenza virus transmission is dependent on relativehumidity and temperature Pathogens 3 2007 1470 1476 Mäkinen T.M. Juvonen R. Jokelainen J. Cold temperature and low humidity are associated with increased occurrence of respiratory tract infections Respir. Med. 103 3 2009 456 462 18977127 Marca G.L. Barp J. Frenos S. Mugelli A. Galli L. Calistri E. Biasucci G. Masi S.D. Guerrini R. Thermal inactivation of SARS COVID-2 virus: are steam inhalations a potential treatment? Life Sci. 265 2021 118801 10.1016/j.lfs.2020.118801 ISSN 0024-3205 Ourworldindata, https://ourworldindata.org/coronavirus-data-explorer . PHS (Public Health Scotland) : https://publichealthscotland.scot/media/11076/22-01-12-covid19-winter_publication_report.pdf. Roy I. Combating recent pandemic of COVID-19 - an urgent Solution Mar 2020 10.13140/RG.2.2.22632.83208 17th 2020 Roy I. Atmospheric variables and additional urgent solutions for combating COVID-19 10.20944/preprints202003.0366.v2 2020 Roy I. Influence of temperature on the global spread of COVID-19 Authorea. November 26 2020 2020 10.22541/au.159301639.90704061/v2 Roy I. The role temperature on the global spread of COVID-19 and urgent solutions https://link.springer.com/article/10.1007/s13762-020-02991-8 2020 (International Journal of Environmental Science and Technology, Publisher Springer Nature) Roy I. AGU (American Geophysical Union) iPoster, Roy,I, ‘ Influence of Temperature on the global spread of COVID-19’ 2020 https://agu.ipostersessions.com/default.aspx?s=4F-D9-45-B2-4C-B0-2A-F4-10-B5-71-05-DA-1D-45-4D 2020 accessed on Roy I. Bmj Rapid Response https://www.bmj.com/content/371/bmj.m4037/rr-20 2021 Roy I. Exit Strategy from COVID-19: Vaccination and Alternate Solution 2021 LNCS, Publisher, Springer Nature 1 16 12940 https://link.springer.com/chapter/10.1007/978-3-030-88163-4_38 Sajadi M.M. Habibzadeh P. Vintzeleos A. Temperature and Latitude Analysis to Predict Potential Spread and Seasonality for COVID-19 2020 Scafetta N. Distribution of the SARS-CoV-2 pandemic and its monthly forecast based on seasonal climate patterns Int. J. Environ. Res. Publ. Health 17 10 2020 3493 10.3390/ijerph17103493 Seneff S. Nigh G. Worse than the disease? Reviewing some possible unintended consequences of the mRNA vaccines against COVID-19 Int. J. Vacc. Theory Pract. Res. 2 1 2021 38 79 Seneff S. Nigh G. Kyriakopoulos A.M. Innate immune suppression by SARSCoV-2 mRNA vaccinations: the role of G-quadruplexes, exosomes, and MicroRNAs Food Chem. Toxicol. 164 2022 113008 10.1016/j.fct.2022.113008 0278-6915 Seung W.K. Ramakrishnan M.A. Raynor P.C. Goyal S.M. Effects of humidity and other factors on the generation and sampling of a coronavirus aerosol Aerobiologia 23 2007 239 248 10.1007/s10453-007-9068-9 32214623 Shrotri A.M. Navaratnam V. Nguyen T. Byrne C. Geismar E. Fragaszy S. Beale W.L.E. Fong P. Patel J. Kovar Spike-antibody waning after second dose of BNT162b2 or ChAdOx1 Lancet 398 10298 2021 385 387 34274038 UHSA (UK Health Security agency), Website : https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1036047/Vaccine_surveillance_report_-_week_47.pdf. Van Doremalen N. Bushmaker T. Munster V.J. Stability of Middle East respiratory syndrome coronavirus (MERS-CoV) under different environmental conditions Euro Surveill. 18 38 2013 pii=20590 10.2807/1560-7917.ES2013.18.38.20590 Wang J. Tang K. Feng K. High Temperature and High Humidity Reduce the Transmission of COVID-19 2020 Website: npj Vaccines could add fuel to evolution of coronavirus mutations https://www.npr.org/sections/health-shots/2021/02/10/965940914/covid-19-vaccines-could-add-fuel-to-evolution-of-more-coronavirus-mutation 2021 WHO Multi-center Collaborative Network on SARS Diagnosis WHO Report: First Data on Stability and Resistance of SARS Coronavirus Compiled by Members of WHO Laboratory Network 2003 Worldometers, https://www.worldometers.info/coronavirus/accessed on 8th July 2022.	36466955	PMC9708609	NO-CC CODE	2022-12-14 23:45:35	no	Phys Chem Earth (2002). 2023 Feb 30; 129:103333	utf-8	Phys Chem Earth (2002)	2,022	10.1016/j.pce.2022.103333	oa_other
==== Front Telecomm Policy Telecomm Policy Telecommunications Policy 0308-5961 0308-5961 Elsevier Ltd. S0308-5961(22)00182-3 10.1016/j.telpol.2022.102480 102480 Article Ultra-broadband investment and economic resilience: Evidence from the Covid-19 pandemic☆ Abrardi Laura a Sabatino Lorien b⁎ a Politecnico di Torino, Department of Management, C.so Duca degli Abruzzi 24, Torino, Italy b Politecnico di Torino, Department of Management, Corso Duca degli Abruzzi 24, Torino, Italy ⁎ Corresponding author. 30 11 2022 30 11 2022 1024807 10 2022 24 11 2022 27 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. We study the role of ultra-broadband infrastructures in reducing the economic recession caused by the 2020 pandemic. We exploit the variation in GDP and employment that happened between 2019 and 2020 as a result of the Covid-19 pandemic outbreak, and we investigate whether UBB investments had an impact on economic resilience. We use micro-level data on UBB exposure in 2019 matched with municipality-level information on local GDP and employment levels based on tax declarations for the period 2019–2020. We address the endogeneity between UBB and local income by exploiting the distance from the closest backbone node of the upstream telecommunication network. We find that exposure to UBB mitigates the negative effect of the pandemic on local employment. One additional year of UBB exposure increases local employment by 1.3 percentage points. The effect is stronger in areas hit more severely by the pandemic, thus confirming the role of advanced broadband infrastructures on the economic resilience from negative shocks. Keywords Ultra-broadband (UBB) Fiber-based networks Resilience Covid-19 ==== Body pmc1 Introduction The Covid-19 outbreak plummeted the global economy in unprecedented depths. The second quarter of 2020 has seen global GDP mark a contraction of −11.5% relative to 2019, resulting in an exceptional drop in activity and unprecedented job losses (OECD, 2022). The succession of local or national lockdowns and voluntary social distancing have led the economy to rely on e-commerce, remote working, and online learning more than ever. However, the subsequent increase in internet traffic has been accompanied by a degradation of its quality. Ultra-broadband (UBB) connections, by mitigating the increase in latency and drop in download speeds (Katz et al., 2020), have been fundamental in keeping economies and societies working and may have been a key factor in mitigating the economic slowdown during the pandemic (European Commission, 2021a). In this paper, we use micro-level information in Italy to study the role played by UBB infrastructures in reducing the economic recession caused by the 2020 pandemic. We exploit the variation in GDP and employment that happened between 2019 and 2020 as a result of the Covid-19 pandemic outbreak, and we investigate whether UBB investments had an impact on economic resilience. UBB involves connections based on fiber optical cables in the last mile, which enable significantly higher performance compared to traditional copper-wire connections. Since UBB deployment started in 2015, we can identify municipalities relatively more exposed to such technology in 2019 by measuring the number of years since UBB was first introduced. In this way, we can explore whether pre-existing UBB infrastructures played a role in attenuating the negative economic shock induced by the Covid-19 pandemic. In this context, Italy represents an ideal research case, being one of the European countries that faced the strongest economic slowdown in 2020. Italian GDP recorded an impressive decline in terms of volumes of −8.9% compared to 2019, against the European average of −6% (European Commission, 2021b). The recession in Italy was accompanied by an unparalleled decline in employment, corresponding to a reduction of hours worked by 11% (Banca d’Italia, 2021). We leverage on a unique dataset with municipality-level information on UBB exposure in 2019. We match this data with granular information on GDP and employment from the tax declarations for the years 2019–2020 published by the Italian Ministry of Economy and Finance (MEF). We then derive the percentage difference in GDP (and related variables) between 2019 and 2020 to identify the variation induced by the Covid-19 pandemic outbreak. The data allow us to analyze the impact on municipality employment – measured as the number of taxpayers, and the average per capita income. Moreover, we gather municipality demographics from the Italian Statistical Office (ISTAT). Finally, we also obtain information on the number of infections at a provincial level at the end of 2020 using data from the Italian Ministry of Health. Our empirical model identifies the impact of UBB exposure on the variation of GDP between 2019 and 2020 if, conditional on observable characteristics, UBB roll-out is exogenous to local GDP. We relax such an identifying assumption through an instrumental variable approach that exploits the upstream telecommunication infrastructure (Cambini and Sabatino, 2021, Campante et al., 2017, Falck et al., 2014). In particular, we construct the distance between each municipality and the closest backbone node of the national telecommunication network. Such a distance is negatively correlated with UBB exposure since distant municipalities are less likely to receive UBB connections. This happens because the main UBB input, called Optical Line Terminal (OLT), is first installed in locations relatively closer to the backbone nodes. The instrument is valid under the identifying assumption that any correlation with the dependent variable is fully driven by UBB exposure. We test such an assumption through a placebo test that shows no significant correlation between the instrument and GDP or employment variations in municipalities without UBB by 2019. Our results show that UBB has a positive significant effect on employment during the pandemic. However, we do not detect any significant effect on both aggregate and per capita local income. Our estimates suggest that one additional year of UBB exposure increases local employment by 1.3 percentage points. Such a positive effect is stronger in municipalities more severely hit by the pandemic in terms of the percentage of the population infected. All in all, our results suggest that advanced broadband technologies increase the resilience of local areas to negative economic shocks, although only in terms of employment. Our findings contribute to the literature on the economic impact of ultra-fast broadband technologies. Such a literature is scant, and focuses on the effect of high speed networks on GDP, employment and market entry dynamics (Briglauer et al., 2021, Briglauer and Gugler, 2019, Fabling and Grimes, 2016, Hasbi, 2020). To the best of our knowledge, this is the first study to exploit detailed micro-level data to study the impact of last-generation broadband infrastructures on local economic resilience during the Covid-19 pandemic, using information on the availability of ultra-broadband connections. The rest of the paper is organized as follows. Section 2 introduces the relevant literature. Section 3 describes the institutional background of UBB technology and roll-out in Italy, and the role of digitization during the pandemic period. Section 4 discusses the data in detail and presents preliminary descriptive evidence. Section 5 describes the empirical setting and the adopted identification strategy. Section 6 presents and discusses the results. Finally, Section 7 concludes. 2 Literature The literature focusing on the effects of broadband during the pandemic consistently finds that the presence of broadband infrastructure has a positive effect on the economy, although such literature only focuses on basic broadband at an aggregated level. Zhang (2021), focusing on China from April 2019 to April 2020, shows that an increase of 10% in broadband penetration results in a growth rate of 1.3% of GDP during the first four months of 2020. Comparing the results in the same periods of 2019, the contribution of broadband to GDP growth during the period of the Covid-19 pandemic is greater than that of the same period of the previous year. Along the same lines, a study by Katz et al. (2020), using a data panel from 170 countries, finds that countries with higher broadband penetration could mitigate 75% of economic losses resulting from measures taken to control Covid-19 spread (e.g., quarantine, social distancing, disruption of air traffic). The authors also analyze the impact of broadband infrastructure on economic growth during the SARS-CoV pandemic.1 Although the quarantine and social distancing measures, in that case, were more limited and less stringent than the ones adopted to deal with Covid-19, Katz et al. (2020) find that countries with the most developed broadband infrastructure were better able to offset the negative economic effects of the pandemic. Finally, a study by Isley and Low (2022) explores the relationship between broadband and employment rates in April and May 2020 in rural US counties. They find that, during the height of the pandemic-related outages, broadband availability and wired broadband adoption had a statistically significant causal relationship with the employment rate in low-population rural counties. In particular, a one percentage point increase in the broadband availability rate results in a 0.37 percentage point increase in the employment rate. A one percentage point increase in the adoption rate of wired broadband would have led to a 0.87 percentage point increase in the employment rate. Moving beyond the pandemic focus, the literature on the economic impact of UBB is extremely scant and mostly based on aggregated data at a country or regional level (Abrardi & Cambini, 2019). Nonetheless, it suggests that UBB has a positive, albeit small, effect on GDP at an aggregated level (Briglauer et al., 2021, Briglauer and Gugler, 2019). At a micro-level, Fabling and Grimes (2016) do not find a significant impact of ultra-broadband on average employment in New Zealand, except for companies that make complementary investments in organizational capital. Other studies provide indirect information on the effects of UBB in the labor market by focusing on firm entry or exit dynamics, with mixed results. Cambini and Sabatino (2021) exploit municipality-level data to study the effect of UBB on firm turnover in Italy, finding that UBB increases firm exit - particularly that of small enterprises. On the contrary, Hasbi (2020), using data from almost 5000 French municipalities for the period 2010–2015, finds that the presence of UBB increases the number of companies and enhances business creation, thus suggesting a positive impact on employment. The available literature on the effects of broadband on micro-level GDP focuses on basic (first-generation) broadband networks, and finds a positive relationship between broadband expansion and economic growth (see, e.g., Kolko, 2012 for a study at the ZIP code level in the US), employment and wages, but only for specific types of workers or areas (Akerman et al., 2015, Czernich et al., 2011, Forman et al., 2012). 3 Institutional background Ultra-broadband technology. The roll-out of fiber-based UBB in Italy started in 2015 as a result of the implementation of the Italian Strategy for High-Speed Broadband, which incorporates the main objectives of the 2020 Digital Agenda for Europe.2 Ultra-broadband networks are based on optical fiber cables in the last mile, which enable significantly higher performance compared to traditional copper-wire connections. Two configurations were developed in Italy based on the portion of optical fiber deployed in the last mile. The first one is called Fiber-to-the-Cabinet (FTTC), and leverages on a first portion of optical fiber, up to a cabinet located nearby the customers’ building from which copper line departs, allowing at least 30 Mbps speed. The second one is the Fiber-to-the-Home (FTTH), in which the last mile is full fiber-based, allowing the highest speed of 1 Gbps. In all these settings, the length of the fiber portion of the last mile does not affect much connection performance, as fiber optical lines have a very low dispersion rate. However, in FTTC settings the length of the last leg - that is the distance between the cabinet and the final consumer - dampens connection performance because it is made of copper wire. Notably, in Italy, the average length of the last mile is 1.5 km, with an average distance between the cabinet and consumers’ premises of 200 m. Hence, the short length of the final portion of the last mile ensures a significantly higher performance with respect to ADSL connections.3 Ultra-broadband roll-out. By the end of 2019, around 55% of Italian municipalities had access to UBB services with connection speeds higher than 30 Mbps, corresponding to about 90% of the Italian population. However, a large variation exists in terms of exposure to UBB. As shown in Fig. 1, because of limited funding, only a few municipalities (around 10%) received UBB in 2015, with an exposure of 5 years in 2019. However, this accounts for more than 50% of the Italian population, since UBB was introduced first in large metropolitan areas. On the opposite side, 45% of municipalities do not have access to UBB in 2019, which however accounts for a small proportion of the total population. We exploit the variability of UBB exposure in our empirical analysis, in which the main variable of interest counts the number of years since UBB introduction for each municipality. The UBB deployment plan was implemented through a combination of public and private investments. The Italian telecommunication incumbent Telecom Italia Mobile (TIM) owns the main network, leveraging on the pre-existing telecommunication facilities used for voice telephone and ADSL connections. For this reason, TIM invested in UBB infrastructures throughout Italy since 2015,4 covering the vast majority of municipalities. However, in 2017 another wholesale operator entered the market, providing an alternative fiber-based network. This new player, called OpenFiber, is owned by Cassa Depositi e Prestiti – the investment branch of the Ministry of Treasury – and the electricity incumbent operator Enel. Since its entry, OpenFiber invested in fiber connections mainly in large cities. By the end of 2019, OpenFiber covers only 392 municipalities, 261 of which are also (fully or partially) covered by TIM.5 Fig. 1 UBB exposure across Italian municipalities. The figure shows the share of municipalities (blue bar) and the share of population (red bar) for each year exposure since UBB introduction in 2019. When “Years since UBB” is equal to zero, municipalities do not have access to UBB connections in 2019.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) TIM and OpenFiber data. Covid-19 and digitization. The first cases of Covid-19 were reported in Italy at the end of January 2020. While initially the infection clusters were confined in a few municipalities in northern Italy, which were placed under quarantine, by March the lockdown measures were extended to the whole country. On 11 March 2020, the Italian government prohibited nearly all commercial activity except for supermarkets and pharmacies. By the end of the month, all non-essential businesses and industries were closed, and the movement of people was restricted. Such restrictions were gradually eased starting from May 2020, although they were brought again in place in October as Italy was hit by the second wave of the pandemic. Fig. 2 illustrates the spread of the coronavirus at the end of 2020, showing the uneven diffusion of the virus across Italian provinces. The availability of high-speed internet connections played a central role in Italy during the pandemic. In the second quarter of 2020, 33% of workers hired in the public administration carried out their homework at least once a week, while in the non-agricultural private sector remote working went from below 1.5% in 2019 to over 14% in the second quarter of 2020 (Depalo & Giorgi, 2021). The e-commerce sector also grew by 46% in 2020 (Statista, 2022). Daily internet traffic increased by 51% during March and April 2020, causing a deterioration in connection speed performance by 8.5% (Agcom, 2020a). Since access to the internet with a quality connection was essential to use many services, such as distance learning or smart working, the difficulties related to the connection speed have been one of the problems most felt by citizens. Indeed, in September 2020, accesses to ultra-broadband connections saw a ＋41.7% increase compared to September 2019 (Agcom, 2020b).Fig. 2 Covid-19 infections in the Italian provinces. The blue (yellow) figure shows the percentage (absolute number) of population infected by the coronavirus in the Italian provinces by the end of 2020.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) Italian Ministry of Health. 4 Data We construct a rich cross-sectional municipality-level dataset matching information on UBB access in 2019 with the percentage variation in GDP between 2019 and 2020. Broadband data. For UBB data, we rely on two main sources. First, we have access to TIM network data, collecting information on UBB roll-out for the Italian incumbent operator since the introduction of UBB networks in Italy, which happened in 2015. In particular, we observe which municipalities have access to TIM’s UBB by 2019. The second data source comes from OpenFiber, which collects additional information on the municipalities covered by the only alternative UBB operator that entered the market in 2017. The combination of the two datasets provides us with the full picture of UBB roll-out in the Italian municipalities. From this information, we derive our main variable of interest, yearsUBBm, which indicates the number of years since municipality m has access to UBB in 2019. This variable provides information on the intensity of exposure to last-generation broadband connections in each municipality, by the end of the year before the Covid-19 pandemic outbreak. Moreover, we have information on the location of TIM’s national backbone nodes, that is, large telecommunication infrastructures that reroute the traffic at the national level. From this information, we construct our instrumental variable, which defines the distance (in kilometers) between each municipality and the closest backbone node. We also observe the diffusion of the main UBB input, called “optical line terminal” (OLT), from which we the distance (in kilometers) between each municipality and the closest OLT. (see Fig. 3). Local economic data. From the Italian Ministry of Economy and Finance (MEF) we obtain data on municipal GDP. Data are based on tax declarations of Italian citizens and collect information on aggregate local income together with the number of taxpayers declaring that income. The number of taxpayers proxies the level of employment in each municipality, as it measures the number of workers gaining some income in a specific year. Moreover, from the aggregate income and the number of workers, we obtain the average per capita income in a municipality. We exploit the percentage difference between 2019 and 2020 to capture the variation in GDP (and related variables) induced by the Covid-19 pandemic. That is, our main outcomes of interest take the following form: (1) ym=Ym,2020−Ym,2019Ym,2019, where Y is either aggregate income, average per capita income, or employment as measured by the number of workers declaring some income. Infections data & demographics. From the Italian Ministry of Health we exploit publicly available information on the cumulated number of infections at the provincial level6 at the end of 2020. This allows us to control for potential heterogeneities in the variation of GDP include by the Covid-19 outbreak based on the number of infections. Moreover, from this information, we also compute the share of the provincial population infected, which gives us a measure of the penetration of the virus. We exploit this information in the empirical analysis to assess the potential heterogeneous effects of UBB exposure on GDP based on the penetration of Covid-19 infections. We also gather provincial-level information on the number of minimum wage recipients from the Italian National Institute for Social Security (INPS), which allows us to control for heterogeneities in the variation of income and employment based on the implementation of such a policy, which was introduced in 2019. Finally, we obtain demographic information from the Italian statistical office (ISTAT), including municipality population, population density, employment rate, the share of the population with a university degree, together with topological characteristics such as altitude (in meters), and distance from the closest province capital. Our final datasets include 7485 municipalities for which we observe UBB exposure and GDP variation between 2019 and 2020. Table 1 provides summary statistics. Fig. 3 A geographic representation. The figure shows the location of the backbone nodes used to derive the instrumental variable (Panel a), and the municipalities with access to UBB in 2019 (Panel b). TIM & OpenFiber data. Fig. 4 Instrument’s relevance. The figure plots the average years since UBB introduction over different distances from the closest backbone node. TIM & OpenFiber data. 5 Empirical strategy We employ two complementary approaches to estimate the causal impact of UBB on GDP growth, implementing a fixed-effects specification and an IV approach, respectively. In the following, we provide more details on the models adopted in our analysis. Main specification. Our baseline econometric model is as follows: (2) ym=β0+γyearsUBBm+β1Xm′+β2covidp(m)+β3minwagep(m)+αr(m)+ɛm where Xm is a vector of municipality-level characteristics, covidp(m) measures the number of infections in municipality’s province p(m), minwagep(m) is the number of recipients of the minimum wage policy in municipality’s province p(m), αr(m) are administrative regional fixed effects, and ϵm is the mean-zero error term. yearsUBBm is our main variable of interest, and it identifies the years since the municipality m has access to ultra-broadband connections in 2019. In all regressions, standard errors are robust to heteroskedasticity.Table 1 Summary statistics. Variables N mean sd p50 min max GDP 2019 7485 75951252 555294167 20456588 154820 34836475904 GDP 2020 7485 72852587 532493208 19590914 161426 33383446528 Δ% GDP 7485 −0.04 0.05 −0.04 −0.64 0.75 N workers 2019 7485 5776.40 32856 1807 21 2132788 N workers 2020 7485 5733.60 32494 1787 22 2109257 Δ% N workers 7485 −0.01 0.02 −0.01 −0.20 0.23 Per capita GDP 2019 7485 11427 2503.9 11366 4297.10 39427 Per capita GDP 2020 7485 11042 2355.7 10980 3970 29102 Δ% Per capita GDP 7485 −0.03 0.05 −0.03 −0.46 1.15 Years since UBB 7485 1.95 1.95 2 0 5 Distance from closest OLT 7485 43.46 25.48 38.33 0 297.73 Distance from closest OPB 7485 5.18 5.31 4.24 0 59.99 N Infections 7485 23389 29379 13322 1024 158717 Share of population infected 7485 0.03 0.01 0.03 0.01 0.06 Recipients minimum wage 7485 28091 47030 12199 889 361124 Distance from closest prov capital 7485 23.32 13.43 21.15 0 219.12 Population 7485 7806.40 42865 2544 29 2752020 Unemployment rate 7485 0.10 0.06 0.08 0 0.41 Population density 7485 310.22 651.49 112.48 0.92 12224 Share with higher education 7485 0.07 0.03 0.07 0 0.28 Altitude 7485 353.71 294.78 289 0 2035 This table collects summary statistics for the main variables used in the analysis. All variables are at the municipality level excluding infections’ and minimum wage recipients data, which are at the province level. Distances are measured in Kilometers, while altitude is measured in meters. Sources: TIM & OpenFiber, ISTAT, INPS, and Italian Ministry of Health. Our main outcome of interest is the percentage variation of income in municipality m between 2019 and 2020, both aggregate and disaggregated by the number of workers and their per capita income. This allows us to explore both the intensive and extensive margins of UBB impact on economic resilience. IV approach. Eq. (2) resembles a first-difference model that exploits the variation of the dependent variable between 2019 and 2020 induced by the Covid-19 pandemic. The model correctly predicts the impact of UBB exposure on such a difference if, conditional on controls, there is no correlation between yearsUBBm and the error term. However, broadband roll-out in Italy may have been driven by unobservable factors not included in (2), thereby confounding our results.7 We deal with the potential endogeneity of UBB roll-out by applying an instrumental variable approach that exploits plausibly exogenous variation in the physical and geographical peculiarities of the telecommunication infrastructure. Our instrument builds on the relative proximity of each municipality with the closest backbone node (Cambini and Sabatino, 2021, Campante et al., 2017, Falck et al., 2014). These nodes derive from pre-existing facilities of the old voice telecommunication network realized between 2001 and 2005. The actual location of the nodes was finalized in 2012. Distant municipalities are less likely to receive UBB because Optical Line Terminal nodes (OLTs) are first installed close to such nodes, so deployment costs are higher. In particular, the excluded instrument measures the distance (in kilometers) between each municipality and its closest backbone node. In the regression, we also control for the diffusion of the main UBB input. That is, we include as an additional control the distance (in kilometers) between each municipality and its closest OLT.8 Fig. 4 shows the relevance of our proposed instrument. Municipalities closer to the backbone nodes are more likely to receive UBB first than distant municipalities, implying a negative first-stage correlation between the instrument and the endogenous variable. We test the validity of the instrument through a placebo test that exploits the presence of municipalities without UBB by 2019. Recall that the instrument is valid if it does not correlate with confounders of GDP variation and UBB roll-out. Therefore, any correlation between the OPBm and ym should only be driven by the presence of UBB. That is, we should observe no significant correlation between OPBm and ym in municipalities without UBB in 2019. Table 2 collects reduced form estimates for the three outcomes of interest for subsamples of municipalities with (columns 2, 4, and 6) and without (columns 1, 3, 5) UBB by 2019. It is reassuring to observe that the estimated coefficients show no significant correlation in municipalities that do not receive UBB 2019 for all dependent variables. On the contrary, our instrument correlates with ym in municipalities with some UBB access, thus validating our identification strategy. Table 2 Placebo test. (1) (2) (3) (4) (5) (6) Variables Δ% GDP Δ% GDP Δ% N workers Δ% N workers Δ% per capita GDP Δ% per capita GDP Distance from closest OPB −0.0001 0.0000 −0.0000 −0.0001* −0.0001 0.0001* (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) UBB by 2019 NO YES NO YES NO YES Observations 3377 4108 3377 4108 3377 4108 Presented are estimated OLS coefficients from reduced form regressions on sub-samples of municipalities with (columns 2, 4, and 6) and without (columns 1, 3, 5) UBB access in 2019. The instrument OPB measures the distance (in kilometers) between a municipality and its closest backbone node. All regressions include region fixed effects and the following additional covariates: distance from the closest OLT, provincial infection levels, provincial number of minimum wage recipients, distance from the closest provincial capital, population, population density, altitude, share of population with university degree and unemployment rate. Robust standard errors in parentheses. * p<0.01, p<0.05, * p<0.1. 6 Results and discussion Main results. Table 3 collects the estimated coefficients of Eq. (2) when the dependent variable is the percentage variation of total municipal income between 2019 and 2020. Columns 1–3 estimate the model via OLS with different sets of controls. We find a positive and significant effect in column 1, which however does not survive the inclusion of additional controls. In columns 4 and 5, we estimate the model through 2SLS, exploiting the distance between each municipality and its closest backbone node as excluded instrument. We find a weak positive impact on local GDP (column 4), which however disappears when we include further controls (column 5), thus suggesting that ultra-broadband connection exposure had negligible effects in attenuating the economic downturn caused by the Covid-19 pandemic on local GDP.9 We iterate the same sort of analysis for employment and per capita GDP, to understand potential heterogeneous effects on the intensive and extensive margins of local GDP. Focusing on employment, as measured by the number of workers from tax declarations, OLS estimates in Table 4 columns 1–3 display a small but significant positive effect of UBB exposure on employment that survives to the inclusion of additional controls. When moving to the IV estimates (columns 4 and 5), the coefficient becomes even larger and economically meaningful. More specifically, the estimated coefficients suggest that one additional year of exposure to UBB increases employment by 1.3 percentage points. Given the general reduction in employment observed as a result of the Covid-19 pandemic, the IV estimate implies that municipalities more exposed to UBB are more resilient to the negative effect of the Covid-19 pandemic in terms of local employment.Table 3 Result — Δ% GDP. (1) (2) (3) (4) (5) Variables OLS OLS OLS IV IV Years since UBB 0.001*** 0.000 0.000 0.010* −0.000 (0.000) (0.000) (0.000) (0.005) (0.011) N infections 0.000 0.000 −0.000 0.000 (0.000) (0.000) (0.000) (0.000) Recipients minimum wage −0.000* −0.000* −0.000* −0.000* (0.000) (0.000) (0.000) (0.000) Distance from closest OLt −0.000 −0.000 0.001 −0.000 (0.000) (0.000) (0.001) (0.001) Population −0.000 −0.000 −0.000 −0.000 (0.000) (0.000) (0.000) (0.000) Distance from closest prov capital −0.000* −0.000* (0.000) (0.000) Unemployment rate 0.058 0.059 (0.027) (0.038) Population density −0.000* −0.000 (0.000) (0.000) Share with higher education 0.044 0.048 (0.029) (0.124) Altitude −0.000 −0.000 (0.000) (0.000) First stage F-test 37.29 8.417 First stage coeff. −0.006* −0.003* (0.001) (0.001) Observations 7485 7485 7485 7485 7485 Presented are estimated coefficients of Eq. (2). Columns 1–3 estimated the model via OLS, with different sets of controls. Columns 4–5 report 2SLS estimates, exploiting the distance between a municipality and its closest backbone node in the first stage. All regression include region fixed effects. Robust standard errors in parentheses. * p<0.01, p<0.05, * p<0.1. Table 4 Result — Δ% N workers. (1) (2) (3) (4) (5) Variables OLS OLS OLS IV IV Years since UBB 0.001* 0.000 0.000* 0.013* 0.013 (0.000) (0.000) (0.000) (0.003) (0.007) N infections 0.000 0.000 −0.000* −0.000 (0.000) (0.000) (0.000) (0.000) Recipients minimum wage 0.000 0.000 −0.000 0.000 (0.000) (0.000) (0.000) (0.000) Distance from closest OLT −0.001* −0.000* 0.002* 0.001 (0.000) (0.000) (0.001) (0.001) Population −0.000* −0.000*** −0.000* −0.000 (0.000) (0.000) (0.000) (0.000) Distance from closest prov capital −0.000* −0.000* (0.000) (0.000) Unemployment rate −0.009 −0.040* (0.012) (0.020) Population density −0.000 −0.000* (0.000) (0.000) Share with higher education 0.000 −0.136* (0.014) (0.073) Altitude −0.000 0.000 (0.000) (0.000) First stage F-test 37.29 8.417 First stage coeff. −0.006* −0.003* (0.001) (0.001) Observations 7485 7485 7485 7485 7485 Presented are estimated coefficients of Eq. (2). Columns 1–3 estimated the model via OLS, with different sets of controls. Columns 4–5 report 2SLS estimates, exploiting the distance between a municipality and its closest backbone node in the first stage. All regression include region fixed effects. Robust standard errors in parentheses. * p<0.01, p<0.05, * p<0.1. In terms of intensive margin, we estimate Eq. (2) on the percentage variation of local per capita GDP, and we collect the results in Table 5. For this case, we find no evidence of any significant effect of UBB exposure on local per capita GDP during the Covid-19 pandemic. If anything, coefficients turn out to be negative, especially in IV, but never statistically significant at standard confidence levels. UBB exposure and infection penetration. Does the positive impact on employment concentrate in areas hit harder by the Covid-19 pandemic? We answer this question by exploiting the distribution of Covid-19 infection penetration in the Italian provinces. Through an indicator function, we identify provinces with high (low) penetration levels as those above (below) the median of the share of the population infected within a province. We then interact these indicators with our yearsUBBm variable in order to capture potential heterogeneous effects based on infections’ penetration. When estimating the model in IV, we also interact with the instrument to have the same number of endogenous regressors and instrumental variables. Table 6 collects the results from such an experiment for all outcomes of interest, both in OLS (columns 1, 3, and 5) and IV (columns 2, 4, and 6). First, we observe that local aggregate GDP is never affected by UBB exposure, and the same applies to its per capita levels. Second, the positive impact on local employment is mostly driven by municipalities hit harder by the Covid-19 pandemic. In particular, IV estimates in column 4 suggest a rise in employment of 1.8% in provinces above the median of infections, while below the median the impact is lower (1.5%). Moreover, the two coefficients are statistically different from each other, thus reinforcing the heterogeneous effects across provinces with high and low infection penetration.Table 5 Result — Δ% per capita GDP. (1) (2) (3) (4) (5) Variables OLS OLS OLS IV IV Years since UBB −0.001 −0.000 −0.000 −0.003 −0.011 (0.000) (0.000) (0.000) (0.004) (0.011) N infections −0.000 0.000 0.000 0.000 (0.000) (0.000) (0.000) (0.000) Recipients minimum wage −0.000* −0.000* −0.000* −0.000*** (0.000) (0.000) (0.000) (0.000) Distance from closest OLT 0.000 0.000* −0.000 −0.001 (0.000) (0.000) (0.001) (0.001) Population 0.000 0.000 0.000 0.000 (0.000) (0.000) (0.000) (0.000) Distance from closest prov capital 0.000 −0.000 (0.000) (0.000) Unemployment rate 0.062* 0.086 (0.023) (0.034) Density −0.000* 0.000 (0.000) (0.000) Share with higher education 0.043 0.153 (0.027) (0.120) Altitude −0.000 −0.000* (0.000) (0.000) First stage F-test 37.29 8.417 First stage coeff. −0.006* −0.003* (0.001) (0.001) Observations 7485 7485 7485 7485 7485 Presented are estimated coefficients of Eq. (2). Columns 1–3 estimated the model via OLS, with different sets of controls. Columns 4–5 report 2SLS estimates, exploiting the distance between a municipality and its closest backbone node in the first stage. All regressions include region fixed effects. Robust standard errors in parentheses. * p<0.01, p<0.05, * p<0.1. Table 6 Result — UBB Exposure and Infection Penetration. (1) (2) (3) (4) (5) (6) Δ% GDP Δ% N workers Δ% per capita GDP Variables OLS IV OLS IV OLS IV Years since UBB × Infections<p50 0.000 0.000 0.000 0.015** −0.000 −0.012 (0.000) (0.011) (0.000) (0.008) (0.000) (0.011) Years since UBB × Infections>p50 0.001 0.000 0.000* 0.018 0.000 −0.014 (0.000) (0.012) (0.000) (0.008) (0.000) (0.012) H0:γH=γL, F-test 7.60 Observations 7485 7485 7485 7485 7485 7485 Presented are estimated coefficients of Eq. (2) when we interact yearsUBB with dummies identifying provinces above and below the median in terms of Covid-19 infections. Columns 1, 3, and 5 estimate the model via OLS, while columns 2, 4, and 6 account for the endogeneity of broadband roll-out through an IV approach that exploits the distance between a municipality and its closest backbone node. All regressions include region fixed effects and the following additional covariates: distance from the closest OLT, provincial infection levels, provincial number of minimum wage recipients, distance from the closest provincial capital, population, population density, altitude, share of population with university degree and unemployment rate. Robust standard errors in parentheses. * p<0.01, ** p<0.05, * p<0.1. All in all, our results suggest that last-generation high-speed connections had little impact on attenuating the negative impact of the Covid-19 pandemic on local aggregate and per capita GDP. However, when investigating the relationship between UBB exposure and employment, we find a positive and significant effect. Such a result is driven mostly by those provinces that were hit particularly hard by the Covid-19 outbreak, implying that the presence of UBB infrastructures significantly contributed to the resilience of local employment during the crisis generated by the pandemic. 7 Concluding remarks As home working, online learning, and remote services surged during the coronavirus pandemic, past investments in telecommunication network upgrades in terms of higher speed, lower latency, and more bandwidth allowed broadband networks to manage the unprecedented spikes in internet traffic. High-speed broadband has been essential to keep our economy working. In this paper, we study how the presence of high-speed broadband in Italian municipalities has contributed to the resilience of their economy during the crisis caused by the pandemic. We exploit a unique dataset with information on the roll-out of fiber-based networks in Italy from 2015 to 2019 and take into account the potential endogeneity between local income and high-speed broadband investment. Our results show that municipalities with access to high-speed broadband networks have coped better with the economic slowdown in 2020, exhibiting a lower reduction in the number of workers, the longer the UBB network has been present in the municipality. This effect is stronger in areas hit more severely by the pandemic, where social distancing measures were particularly strict and which had to rely more on remote working and services. Our findings have relevant policy implications. The creation of a “Gigabit society” through the development of adequate broadband infrastructure has long been at the center of the attention of European policymakers. In September 2016, the European Commission identified, as strategic objectives for 2025, that all European households will be able to access internet connections with at least 100 Mb/s speed, and that connectivity at 1 Gb/s will be guaranteed for key socio-economic development sites (such as schools, railways, subways, public service providers, etc.). Due to the generally very long repayment periods and the low financial returns, to achieve the goals of the Digital Agenda, a direct public intervention will be required to subsidize the large-scale deployment of the broadband infrastructure (Gruber et al., 2014). It is therefore important to fully understand the economic implications of such significant investments. In this paper, we provide further insights into the role that advanced digital technologies may play during negative economic shocks. Our work highlights that access to high-speed internet can constitute an important engine of recovery, playing a crucial role in addressing the economic challenges of the post-pandemic world. Data availability The authors do not have permission to share data. ☆ We thank the Editor and two anonymous reviewers for useful comments and suggestions to a previous version of the paper. We acknowledge financial support from Ministero dell’Istruzione, dell’Universita e della Ricerca, Award TESUN - 83486178370409, finanziamento dipartimenti di eccellenza, CAP. 1694 TIT. 232 ART. 6. 1 In 2003, the virus of SARS-CoV spread from China to 26 other countries, causing around 800 deaths (Wilder-Smith et al., 2020). 2 The Digital Agenda for Europe specifies the goals in terms of network coverage and service adoption for the whole European population. See here for more. 3 Source: here. 4 Before 2015 only the city of Milan enjoyed fiber-based connections realized by the local company Metroweb. 5 OpenFiber deployment plan can be found here. 6 From a geographical perspective, Italy is partitioned into 107 provinces, and 20 administrative regions. From the data matching process, we lose one province because of an administrative change that occurred in Sardinia in 2020. 7 For instance, Cambini and Sabatino (2021) provides evidence of endogeneity of UBB roll-out in Italy through an event study design of firm dynamics that display non-parallel pre-trends. 8 Because OLT acts as endpoint device in a passive optical network, and optical fibers need to be laid underground in the last mile, such distance proxies the deployment costs necessary to provide UBB services. Since OLT location choice is itself a decision of the telecommunication provider, this variable may correlate with both UBB deployment and local income. 9 Notice that the first-stage coefficient is negative as expected and strongly significant at the 1% level in both specifications of columns 4 and 5. ==== Refs References Abrardi L. Cambini C. Ultra-fast broadband investment and adoption: A survey Telecommunications Policy 43 3 2019 183 198 Agcom (2020a). Le comunicazioni nel 2020. L’impatto del coronavirus nei settori regolati. Allegato alla relazione annuale: Technical report. Agcom (2020b). Osservatorio sulle comunicazioni, n. 4/2020: Technical report. Akerman A. Gaarder I. Mogstad M. The skill complementarity of broadband internet Quarterly Journal of Economics 130 4 2015 1781 1824 Banca d’Italia (2021). Relazione annuale anno 2020, Roma, 31 Maggio 2021: Technical report. Briglauer W. Dürr N. Gugler K. A retrospective study on the regional benefits and spillover effects of high-speed broadband networks: Evidence from german counties International Journal of Industrial Organization 74 2021 102677 Briglauer W. Gugler K. Go for gigabit? First evidence on economic benefits of high-speed broadband technologies in Europe Journal of Common Market Studies 57 5 2019 1071 1090 Cambini C. Sabatino L. Digital highways and firm turnover: Technical report 2021 Mimeo Campante F. Durante R. Sobbrio F. Politics 2.0: The multifaceted effect of broadband internet on political participation Journal of the European Economic Association 16 4 2017 1094 1136 Czernich N. Falck O. Woessmann L. Broadband infrastructure and economic growth The Economic Journal 121 552 2011 505 532 Depalo, D., & Giorgi, F. (2021). Il lavoro da remoto in italia durante la pandemia: I lavoratori del settore privato: Technical report, Banca d’Italia, note Covid-19. European Commission (2021a). 2030 digital compass: The European way for the digital decade: Technical report, European commission 118 final report, Brussel. European Commission (2021b). European economic forecast, summer 2021, institutional paper 156: Technical report. Fabling, R., & Grimes, A. (2016). Picking up speed: Does ultrafast broadband increase firm productivity?: Technical report. Motu working paper 16–22. Falck O. Gold R. Heblich S. E-lections: Voting behavior and the internet American Economic Review 104 7 2014 2238 2265 Forman C. Goldfarb A. Greenstein S. The internet and local wages: A puzzle American Economic Review 102 1 2012 556 575 Gruber H. Hätönen J. Koutroumpis P. Broadband access in the EU: An assesment of future economic benefits Telecommunications Policy 38 2014 1046 1058 Hasbi M. Impact of very high-speed broadband on company creation and entrepreneurship: Empirical evidence Telecommunications Policy 44 3 2020 101873 Isley C. Low S.A. Broadband adoption and availability: Impacts on rural employment during COVID-19 Telecommunications Policy 46 7 2022 102310 Katz R. Jung J. Callorda F. Can digitization mitigate the economic damage of a pandemic? Evidence from SARS Telecommunications Policy 44 10 2020 102044 Kolko J. Broadband and local growth Journal of Urban Economics 71 1 2012 100 113 OECD (2022). Quarterly GDP (indicator): Technical report, (Accessed 23 September 2022). Statista (2022). eCommerce - Italy: Technical report, https://www.statista.com/outlook/dmo/ecommerce/italy. (Accessed 7 October 2022). Wilder-Smith A. Chiew C.J. Lee V.J. Can we contain the Covid-19 outbreak with the same measures as for sars? The Lancet Infectious Diseases 20 5 2020 e102 e107 32145768 Zhang X. Broadband and economic growth in China: An empirical study during the COVID-19 pandemic period Telematics and Informatics 58 2021 101533	36467971	PMC9708610	NO-CC CODE	2022-12-12 23:20:58	no	Telecomm Policy. 2022 Nov 30;:102480	utf-8	Telecomm Policy	2,022	10.1016/j.telpol.2022.102480	oa_other
==== Front Women Birth Women Birth Women and Birth 1871-5192 1878-1799 The Authors. Published by Elsevier Ltd on behalf of Australian College of Midwives. S1871-5192(22)00358-4 10.1016/j.wombi.2022.11.012 Article Emotional wellbeing of student midwives during COVID-19 Kuipers Yvonne ab⁎12 Mestdagh Eveline b3 a School of Health and Social Care, Edinburgh Napier University, Sighthill Campus, Edinburgh EH11 4BN, Scotland, UK b School of Health and Social Care, AP University of Applied Sciences, Noorderplaats 2, 2000 Antwerp, Belgium ⁎ Correspondence to: Edinburgh Napier University, School of Health and Social Care, Sighthill Campus, Edinburgh EH11 4BN, Scotland, UK. 1 ORCID 0000-0002-4200-0522. 2 @YvonneFontein. 3 ORCID 0000-0003-0116-0738. 30 11 2022 30 11 2022 2 8 2022 24 10 2022 28 11 2022 © 2022 The Authors 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Mental health of students in higher education was affected during the COVID-19 pandemic. Aim To examine the emotional wellbeing of midwifery students in the Netherlands and Flanders (Belgium) during COVID-19. Methods A cross-sectional online-based survey with 619 Dutch and Flemish midwifery students. Sociodemographic details were obtained. Anxiety and depression were measured twice (T1, T2) during the COVID-19 pandemic. Findings Flemish students had significantly higher mean depression and anxiety scores than Dutch students during the total period of study (p < .001; p < .001). Total group mean depression and anxiety scores were significantly higher at T2 compared to T1 (p < .001; p < .001). In the Dutch student group, there was a significant increase of depression from T1 to T2 (p < .001). In the Flemish student group, both depression and anxiety scores significantly increased from T1 to T2 (p < .001; p < .001). A history of psychological problems predicted both depression and anxiety, irrespective of COVID-19 period or country (p < .001; p < .001). Being single (p.015) and having a job (p.046) predicted depression, irrespective of period or country. A history of psychological problems predicted depression (p.004; p < .001) and anxiety (p.003; p.001) during the total period of study. Being single also predicted depression during T2 (p.024). Conclusion These findings inform how emotional wellbeing of midwifery students was affected during the COVID-19 pandemic and identify those students that might need extra attention after the pandemic, during another pandemic or similar situations with social restrictions. Keywords Anxiety COVID-19 Depression Education Mental health Midwifery students ==== Body pmc STATEMENT OF SIGNIFICANCE Problem or issue The COVID-19 pandemic presented unique emotional challenges for midwifery students. What is already known Irrespective of the impact of COVID-19, midwifery students seem to be at risk for emotional health problems, based on age, gender, the emotional demanding and clinically and academic challenging nature of the study programme, and balancing student- and personal life. What this paper adds This study, conducted in two different countries, provides information on midwifery students’ emotional wellbeing throughout the first 15 months of the COVID-19 pandemic with successive (complete and partial) lockdown restrictions. Introduction The severe acute respiratory syndrome coronavirus (COVID-19) disease was declared as a pandemic by the World Health Organization on 11 March 2020. The COVID-19 pandemic affected higher education students due to disruptions in school and in social behaviours [1]. The pandemic presented unique challenges for students, of which academic work, mental health and isolation were most often reported [2], [3], [4], [5]. Midwifery education was greatly affected by the lockdown measures imposed by governments [6], [7], causing anxiety, uncertainty, and emotional burden among students [8]. Theoretical education in the university context changed, adopted, and enhanced digital readiness in a rapid pace to manage student learning [9]. Mental health of midwifery students pre and peri-COVID-19 In Europe, midwifery students are predominantly women and nearly two-third are younger than 21 years of age [10]. Adolescent students are known to be more at risk for feeling distressed compared to other age groups [11], [12], and female students are more likely to experience depression and anxiety than male peers [3], [13], [14]. There were serious concerns about the pre-COVID mental health of student midwives because assessment of emotional health indicated that student midwives’ emotional wellbeing was not optimal [15]. Midwifery education is recognised as being stressful with the (emotional) demands of the theoretical and practical components of the study, including clinical, academic, and financial challenges [15], [16], [17]. Younger midwifery students [18] and students who juggle their study with other commitments, such as a job, social activities, and family life, struggle more to keep up with their academic and clinical demands and their personal commitments [19]. Irrespective of the impact of COVID‐19, midwifery students seem to be more at risk for emotional health problems, based on age, gender, the demanding and challenging nature of the study programme, and balancing student- and personal life [3], [14], [20], [21]. Mental health problems of students in higher education increased during the pandemic compared to pre-pandemic prevalence rates [2], [3], [12], [13], [14], [22], [23]. Students reported fear and worry about their own health and that of family and friends, difficulty in concentrating and disruptions to sleeping patterns. They reported worries about their course progression and assessment, increased concerns on academic performance, decreased motivation to study, and difficulties with adjusting to new online teaching methods, study strategies and the reduction of social interactions with their peers [1], [2], [3], [12], [21], [22]. Being younger and a higher number of years of study were positively associated with peri-pandemic depression and anxiety [13]. Midwifery research collaboration between the Netherlands and Flanders In a European cross-border mental health project (https://path-perinatal.eu), operational during the COVID-19 pandemic, a Dutch and a Flemish Higher Education Institution (HEI) collaborated in monitoring the peri-pandemic emotional wellbeing of Dutch and Flemish student midwives. Midwifery related research collaboration between Flanders (the northern and Dutch speaking part of Belgium) and the Netherlands, and midwifery research involving both populations is common [24], [25], [26], [27]. Approximately 48 % of the midwifery students in Flanders are Dutch, and after graduation they often start their career as a qualified midwife in a Dutch primary or secondary care setting [28], [29], [30]. Of the current Dutch practising midwives, 20 % have been educated in Flanders [29]. In many ways Flanders and the Netherlands are similar with comparable political systems and demographic characteristics, and both countries face the same societal challenges such as ethnical diversity and socio-economic inequalities [24]. Flanders and the Netherlands have some vocabulary differences but overall speak the same language. Midwifery education in the Netherlands and Flanders In the Netherlands, midwifery education is a four-year programme while in Flanders it is a three-year programme, both full-time undergraduate, direct entry programmes. In both countries, professional development of student midwives occurs through theoretical learning and exposure to relevant practice learning activities. Students spend over a third of their program in the clinical area, being community practices and/or hospital settings (for further details, see Box 1). A significant difference with the Netherlands is that Flanders has a five-year combined nursing-midwifery Bachelor programme and a top-up nursing-midwifery programme at master’s level [28], [30].Box 1 Midwifery education in Flanders and the Netherlands. Table Flanders Netherlands Number of HEIs* 9 3 Number of schools where the Bachelor midwifery programme is provided 12 4 Approximate number of students who enrol on the Bachelor midwifery program/year 700 220 Approximate number of students graduating/year 300 150 Approximate total number of midwifery students on the programme (all years) 2240 880 Total number of years pre-registration midwifery programme 3 4 Number of European Credit Transfer and Accumulation System (ECTS) at graduation 180 240 Restricted number of available programme places (Numerus Clausus) No Yes Entry/admission/selection tests No Yes Entry requirements: Biology Not required Required Entry requirements: Chemics Not required Required Entry requirements: Mathematics Not required Required Pre-entry required number of years of formal education 12 11 Clinical practice-theory ratio* * 33 %−67 % 35 %−65 % Study fees per year €1.120 €2.143 Compulsory attendance in-school education No No Hours of face-to-face/online education theoretical education year 1a 285 279 Hours of face-to-face/online education theoretical education year 2a 266 264 Hours of face-to-face/online education theoretical education year 3a 172 80–93b Hours of face-to-face/online education theoretical education year 4a N/A 0–58b Higher Education Institution; * Linear increase of clinical practice hours associated with year of study; a Estimated hours calculated based on various curricula of HEIs; b Minor 20 weeks (specialisation within or next to midwifery study Midwifery education in the Netherlands and Flanders during the COVID-19 pandemic In both countries the first COVID-19 pandemic wave with its concurrent lockdown, began in March 2020, easing in June 2020. A quick succession of a second wave and a third wave, both with lockdown regulations followed in October 2020 and March 2021, respectively. In each country, regulations for HEIs showed similarities in adapting the organisation of clinical placement and education up to June 2020, apart from examination and campus access [31], [32], [33]. Exams in the Netherlands were a combination of both on-campus and online exams, opposed to the Flemish students who did all their exams online. The first three months of the pandemic, clinical placements were discontinued. In June 2020, students returned to the clinical area. There was no one-size-fits-all approach about the presence and involvement of the students. This depended on hospital and practice regulations, the situation and the woman’s preferences [31], [32], [33], [34]. In both Flanders and the Netherlands, students who do not meet the minimal required European Credits (ECs) at the end of the first year of study, receive a binding recommendation that they cannot continue their study. During the pandemic, the binding recommendations were postponed to the end of the second year of study, allowing students more time to obtain the necessary ECs [32], [33], [34]. In both countries, theoretical education was provided online although from June 2020 and onwards, Dutch students were allowed to come to campus for individual studying, opposed to the Flemish students. Weekly in-school skills/practical training re-started in the Netherlands in June 2020 with an adjusted group size adhering to the hygiene and social distancing rules and the square meters per classroom, while in Flanders this re-started in September 2020 with one lesson per fortnight in small groups of 15 students [31], [33]. In April 2021, in-school education returned to one-day per week for the Dutch students, while there was no scale up of in-school activities for the Flemish students [32], [34]. Belgium had a more active approach to vaccination compared to the Netherlands and the overall uptake was higher in Belgium than in the Netherlands [35]. From April 2021 and onwards, tests were freely available for students in both countries. HEIs in both countries were not allowed to ask students whether they were vaccinated or whether they had (self-) tested because both were not mandatory [32], [34]. Earlier studies about emotional wellbeing of midwifery students during COVID-19 did not distinguish between the pandemic waves [7], [8], [12], [20]. Being able to follow students based on common features such as country, age group, education programme, and being exposed to similar pandemic (lockdown) regulations, is of merit to make a statement whether emotional wellbeing of midwifery students changed during the pandemic and what affected their emotional wellbeing. Including several pandemic waves allows the effect of the initial shock and uncertainty of the first wave, and the adaptation to the lockdown and social distancing measures during the subsequent waves [36], [37]. Although the COVID-19 pandemic is a universal stressor experienced across the globe, it is likely that the psychological health impact of this event will differ among students, based on (inter)personal and contextual factors [38]. By exploring these factors, this study can contribute to the recognition of students who have been vulnerable to the adverse psychological effects of pandemic, providing suggestions for post-pandemic care [38], [39]. Additionally, addressing this knowledge gap is important to understand the effects of a global pandemic and prepare for effective emotional support mechanisms for midwifery students during similar disruptions [40]. In this study we examined the emotional wellbeing of midwifery student in the Netherlands and Flanders during the first, second and third pandemic COVID-19 pandemic waves. We sought answers to the following questions:• What is the level of emotional wellbeing among midwifery students in the Netherlands and Flanders during the COVID-19 pandemic? • Are there differences in emotional wellbeing between Dutch and Flemish students during the COVID-19 pandemic? • Are there changes in emotional wellbeing over time within and between the groups of Dutch and Flemish students during the COVID-19 pandemic waves? • Are there student characteristics that predict changes in emotional wellbeing during the COVID-19 pandemic? Methods Design A cross-sectional online survey study was conducted with midwifery students in the Netherlands and Flanders. Eligible participants were 18 years of age or older, during any stage of their study. The data were collected between 17 March 2020–23 June 2021, using online self-completed questionnaires (Limesurvey©). Sampling The three Dutch and nine Flemish HEIs providing midwifery education were approached and informed about the study with all agreeing to recruit students. To obtain a representative sample, students were purposively recruited. To secure informed consent and confidentially requirements, we followed a procedure: A lecturer of each HEI (who was not involved in the study) distributed information about the study to the students, including the link and quick response (QR) code to the questionnaire through the HEIs’ intranet and, if available, HEI-moderated private social media platforms (Instagram©, Facebook©). The link and QR-code anonymously directed the participants to the questionnaire. Measures Sociodemographic and personal details (e.g., age, country of education, year of education, hours spent on individual study/week, clinical hours up to point of measurement, job, children, relational status and living circumstances) were collected. Two items measured the participant’s history of psychological problems (yes/no) by asking if the participant had ever experienced psychological problems pre-COVID or was still experiencing psychological problems, with or without treatment (e.g., medication, professional help). The following item included a list of several psychological issues (e.g., depression, burn out), of which one or more could be selected. Emotional wellbeing was measured with the Hospital Depression and Anxiety Scale (HADS). Hospital anxiety depression scale (HADS) The Hospital Anxiety Depression Scale (HADS) was designed to measure anxiety and depression in a general population [41] and has shown to have good psychometric properties and to perform well in assessing anxiety and depressive disorders [42]. The HADS is a 14-item self-administered measure including two 7-item subscales, one measuring anxiety and the other subscale measuring depression. Each item is rated on a scale from 0 to 3 and participants rate the response which comes closest to how they have been feeling in the past week. A subscale score between 8 and 10 identifies possible presence and a score of ≥11 the probable presence of a clinically meaningful anxiety or depressive condition [43]. The HADS has been translated into Dutch [43] and has been validated for use among different age groups, including a random sample of young adults [44] and undergraduate students [45]. The HADS showed an overall good sensitivity/specificity balance for the anxiety and depression subscales in adolescents and students (0.73/0.93; 0.82/0.88) [44], [45]. Statistical analysis The analyses were performed using the Statistical Package for the Social Sciences© (SPSS) version 28. We calculated descriptive statistics for the participants’ characteristics. The scores of the HADS depression and anxiety subscales were summed and the possible and probable presence of anxiety and depression were established using the HADS cut-off values [43]. Cronbach’s alpha was calculated for the HADS total, and the depression and anxiety subscales. Normality of distribution was checked with the Kolmogorov-Smirnov test. The Mann-Whitney U test was used for continuous data and Chi-square for dichotomous data. The strategy for model building was as follows: based on the pandemic waves, we divided the sample in two periods. T1 included participants who completed the questionnaire between 17 March-30 September 2020 (wave 1). T2 included participants who completed the questionnaire between 1 October 2020–23 June 2021 (wave 2 and 3). We hypothesized that there would be differences in emotional wellbeing based on the initial shock about the pandemic and complete lockdown and being accustomed to the pandemic regulations during the following waves with its alternating partial and complete lockdown regulations, affecting emotional distress (i.e. wearing off or intensifying) [8], [36], [37]. We calculated the differences in depression and anxiety between T1 and T2. We examined the within-group changes of the Dutch and Flemish students by comparing T1 and T2 means for HADS depression and anxiety and the proportions according to the cut-off levels. To investigate which characteristics (predictors) predict anxiety or depression (outcome measures), two dichotomous dependent outcomes variables were computed: possible depression/anxiety, scores 8–10 (yes/no) and probable depression/anxiety, scores ≥11 (yes/no). Possible and probable scores of depression and anxiety were collapsed into one dichotomous outcome variable (yes/no heightened scores). The predicting variables (students’ characteristics) were computed in dichotomous variables (yes/no) and compared with the heightened anxiety and depression scores, using two-tailed Pearson Chi-square coefficients. Binary logistic regression and Chi-square tests examined the variables predicting depression and anxiety. Predictors were chosen based on significant Pearson Chi-square coefficients. The p-value was set at <.05. Sample size A power analysis, with GPower (3.9.1.2) indicated that a sample size of 482 would be sufficient to detect a significant small effect (Odds Ratio [OR] = 1.68) [46], assuming a power of 0.80 and an alpha of 0.05, based on a 19 % increase of depression among student midwives pre and peri-COVID-19 [12], [13]. To draw true inferences about the population, a minimum sample of 329 Dutch students and 268 Flemish students was required (95 % Confidence Interval, p.<05). Ethical approval The study protocol was reviewed and approved by the Antwerp University Hospital Ethics Committee (Reference nr. EA_SHW_19_34). Participation was voluntary and anonymous and informed consent was obtained before the questionnaire could be completed (via box ticking). Results Of the 650 responders, 31 respondents discontinued the questionnaire after providing consent, leaving 619 completed questionnaires (95.2 % completion rate), showing no missing values. Of the 619 questionnaires (323 Dutch students and 296 Flemish students), 326 were completed between 17 March-30 September 2020 (T1), and 293 between 1 October 2020–23 June 2021 (T2). HADS total scores and the depression and anxiety subscale scores showed a non-normal distribution (D(619) = .7, p < .001; D(619) = .11, p < .001: D(619) = .07, p < .001). Participants The characteristics of the participants are shown in Table 1. Most of the respondents (64.8 %) were in their second or third year of study and most of the participants (73.3 %) indicated they spent more than 21 h per week on individual studying. The differences in study hours per week (p < .001) between Dutch and Flemish students were observed in all categories. Over a third of the participants (34.2 %) had spent more than 600 h in clinical practice. The differences in clinical hours (p.004) between Dutch and Flemish students were observed in the categories 200–400 and >600 clinical hours. There were significant more students with a study delay among Dutch midwifery students compared to Flemish students (p.014). We observed significant differences in partner status between Dutch and Flemish students (p.003). Dutch students were more often co-habiting with their partner compared to Flemish students. Also, the living circumstances of Dutch and Flemish students were significantly different (p < .001), showing variation in with whom students lived, either being with other students, parents/family, partner and/or children. More than a third of the midwifery students in both countries (69.9 %) had a job next to their study. A third of the sample reported a history of psychological problems before the COVID-19 period. Flemish students significantly more often reported a history of psychological problems (p < .001). Dutch students more often reported a history of depression, personality disorders, and Attention Deficit (Hyperactivity) Disorder compared to the Flemish students, while Flemish students more often reported a history of panic and anxiety problems.Table 1 Characteristics student midwives. Table 1 Total group N = 619 (100%) Dutch students N = 323 (52.2%%) Flemish students N = 296 (47.8%) P-value Age (in years) mean (SD±) range 22.56 (±4.26)18-49 22.72 (±4.59) 22.38 (±3.86) .812 Age categories n (%) .286 18-21 years 303 (48.9) 163 (50.5) 140 (47.3) 22-25 years 246 (39.7) 118 (36.5) 128 (43.2) 26-30 years 35 (5.7) 21 (6.5) 14 (4.7) >30 years 35 (5.7) 21 (6.5) 14 (4.7) Partnership status n (%) .003 in relationship and co-habiting 224 (36.2) 84 (26) 49 (16.5) in relationship but not co-habiting 133 (21.5) 109 (33.7) 115 (38.9) single 262 (42.3) 130 (40.2) 132 (44.6) Living circumstances n (%) <.001 living with parents 281 (45.5) 134 (41.5) 147 (49.7) (student) accommodation sharing with others 131 (21.2) 79 (24.5) 52 (17.6) combination of living with others/students & parental home 12 (1.9) - 12 (4.1) living with partner and/or children 128 (20.7) 82 (25.4) 46 (15.5) independent accommodation/ living alone 67 (10.8) 28 (8.7) 39 (13.2) Caring for (non)biological children n (%) .481 no 566 (91.4) 296 (91.6) 270 (91.2) yes 53 (8.6) 27 (8.4) 26 (8.8) Job next to study n (%) .375 no 186 (30.1) 92 (28.5) 94 (31.8) yes 433 (69.9) 231 (71.5) 202 (68.2) on an on-call basis 135 (31.2) 70 (30.3) 65 (32.2) <10 hours/week* 164 (37.9) 100 (43.3) 64 (31.7) 10-20 hours a week, 113 (26) 57 (24.7) 56 (27.7) >20 hours/week 21 (4.9) 4 (1.7) 17 (8.4) Year of education n (%) <.001 year 1 122 (19.7) 71 (22) 51 (17.2) year 2 185 (29.9) 80 (24.8) 105 (35.5) year 3 216 (34.9) 82 (25.4) 134 (45.3) year 4 77 (12.4) 71 (22) - Extended study n (%) year 4 or more Flemish students/year 5 or more Dutch students 25 (4) 19 (5.9) 6 (2) .014 Hours per week spent on study (school & personal study) n (%) <.001 <10 hours/week 64 (10.3) 20 (6.2) 44 (14.9) 10-20 hours/week 73 (11.8) 20 (6.2) 53 (17.9) 21-30 hours/week 129 (20.8) 55 (17) 74 (25) 31-40 hours/week 199 (32.1) 117 (36.2) 82 (27.7) 41-50 hours/week 126 (20.4) 94 (29.1) 32 (10.8) >50 hours/week 28 (4.5) 17 (5.3) 11 (3.7) Hours of clinical practice in total n (%) .004 <200 hours 144 (23.3) 75 (23.2) 69 (23.3) 200-400 hours 131 (21.2) 54 (16.7) 77 (26.0) 401-600 hours 132 (21.3) 65 (20.1) 67 (22.6) >600 hours 212 (34.2) 129 (39.9) 83 (28) History of psychological problems n (0%) <.001 none 436 (70.4) 256 (79.3) 180 (60.9) yes 183 (29.6) 67 (20.7) 116 (39.2) depression* 117 (63.9) 48 (71.6) 69 (59.5) burn out* 63 (34.4) 24 (35.8) 39 (33.6) personality disorder* 43 (23.5) 20 (29.6) 23 (19.8) panic* 52 (28.4) 13 (19.4) 39 (33.6) anxiety* 88 (48.1) 24 (35.8) 64 (55.2) AD(H)D* 4 (2.2) 3 (4.5) 1 (.9) eating disorder* 8 (4.4) 7 (10.4) 1 (.9) post-traumatic stress* 3 (1.6) 2 (3) 1 (.9) HADS total score mean (SD±) 14.7 (±8.23) 0-38 11.8 (±6.86) 0-35 17.8 (±8.48) 0-38 <.001 Depression scale mean (SD±) 5.5 (±3.99) 0-20 4.1 (±3.24) 0-17 7 (±4.18) 0-20 <.001a Anxiety scale mean (SD±) 9.2 (±4.77) 0-21 7.7 (±4.24) 0-19 10.7 (±4.84) 0-21 <.001b Depression cut-off scores n (0%) <.001 no symptoms 444 (71.7) 271 (83.9) 173 (58.4) possible depression 93 (15) 38 (11.8) 55 (18.6) probable presence clinically meaningful depression 82 (13.2) 14 (4.3) 68 (23) Anxiety cut-off scores n (0%) <.001 no symptoms 240 (38.8) 167 (51.7) 73 (24.7) possible anxiety 128 (20.7) 58 (18) 70 (23.6) probable presence clinically meaningful anxiety 251 (40.5) 98 (30.3) 153 (51.7) Age was not normally distributed (D(619) = .25, p < .001). * Percentages of positive answers (‘yes’ considered as the 100 % group). a U = 28385, z = −8.74, r = −.35. b Mann-Whitney U: U = 27860, z = −9, r = −.36. The T1 sample more often included first and second year students than third year and higher (T1 45 % vs T2 58 %, X 2 8.5, p.003) and the T1 respondents more often had a study delay (T1 6 % vs T2 1 %, X 2 8.7, p.001). The students spending of <21 h on study increased from T1 to T2 (T1 8 % vs T2 47 %, X 2 123.9, p < .001). The T1 and T2 respondents showed no further differences in characteristics. HADS scores HADS scores Dutch and Flemish student groups The HADS showed excellent internal consistency for the total scores (α.92), and good internal consistency for the depression subscale (α.85), and the anxiety subscale (α.89). The Mann-Whitney U test showed a significant difference in the HADS depression and in the anxiety scores between Dutch and Flemish students during the total period of study (p < .001; p < .001), with significantly higher mean scores for depression and anxiety among Flemish students compared to Dutch students. The scores below and above the HADS cut-off scores for possible and the probable presence of depression and anxiety showed significant differences between the two groups (p < .001). Flemish students more often reported elevated levels of depression and anxiety compared to the Dutch students (see Table 1). HADS scores at T1 and T2 within and between Dutch and Flemish student groups We observed a significant difference in the HADS depression and in the anxiety scores between T1 and T2 (p < .001; p < .001). Among the Dutch student group, the HADS depression scores showed a significant within-group change (U = 3608, z = −3.55, r = −.20, p < .001) but not the anxiety scores (U = 4646.5, z = −1.63, r = −.09, p.102). Among the Flemish student group, both the HADS depression and anxiety scores showed a significant within-group change (U = 6607.5, z = −5.27, r = −.31, p < .001; U = 7493, z = −4.03, r = −.23, p < .001). Table 2 shows the significant increase of the mean depression and anxiety scores from T1 to T2. For both depression and anxiety there was a significant total group decrease of scores below the cut-off level ≤ 7 from T1 to T2 in both groups. However, this decrease was more evident in the Flemish student group (p < .001) compared to the Dutch student group (p < .001 vs p.06) as well as for anxiety (p < .001 vs p.3).Table 2 Between-group and within-group HADS scores from T1 to T2. Table 2 Total students (N = 619) Students Netherlands (N = 323) Students Flanders (N = 296) T1 (N = 326) T2 (N = 293) T1 (N = 212) T2 (N = 111) T1 (N = 114) T2 (N = 182) Mean ( ± SD) Mean ( ± SD) p Mean ( ± SD) Mean ( ± SD) p Mean ( ± SD) Mean ( ± SD) p HADS depression 4.3 (3.40) 7.7 (4.07) <.001 3.9 (3.17) 5.8 (3.28) < .001 5.4 (3.74) 8.1 (4.11) <.001a HADS anxiety 8.1 (4.45) 11.2 (4.70) <.001 7.6 (4.26) 8.7 (4.03) .102 9.2 (4.73) 11.7 (4.67) <.001b N (%) N (%) X2 N (%) N (%) X2 N (%) N (%) X2 Depression 0–7 259 (82.4) 185 (64.5) 59.83 <.001 177 (83) 94 (85.6) 7.75 .06 82 (72) 91 (50) 13.88 <.001 Depression 8–10 48 (15.4) 45 (15.6) 7.67 .06 28 (13.2) 10 (9) 8.23 .004 20 (17.5) 35 (19.2) .13 .717 Depression 11–21 19 (5.7) 63 (19.9) 54.10 <.001 7 (3.8) 7 (6.3) 7.21 .02 12 (10.5) 56 (30.8) 16.23 <.001 Anxiety 0–7 154 (49.4) 86 (32.2) 37.57 <.001 113 (53.3) 54 (48.7) 1.08 .3 41 (36) 32 (17.6) 12.75 <.001 Anxiety 8–10 61 (19.6) 67 (21.5) 1.2 .27 35 (16.5) 23 (20.7) .15 .7 26 (22.8) 44 (24.2) .07 .787 Anxiety 11–21 111 (35.6) 140 (46.3) 26.29 <.001 64 (30.2) 34 (30.6) .66 .42 47 (41.2) 106 (58.2) 7.31 .007 a Mann-Whitney U: U = 23018, z = −9.86, r = −.40. b Mann-Whitney U: U = 28160, z = −7.43, r = −30. Correlations between predictors and outcome measures at T1 and T2 The predicting characteristics are presented in Table 3. During both T1 and T2, heightened levels of both depression and anxiety significantly correlated with being single (T1 p.048; p.036; T2 p.054; p < .039) and having a history of psychological problems (T1 p < .001; p < .003; T2 p < .001; p < .001). During T1, depression significantly correlated with spending more than 21 h on study (p < .001), having children (p.042), having a job next to study (p.009), while living alone/independent significantly correlated with depression during T2 (p.041).Table 3 Chi-square coefficients dichotomized students’ characteristics (both countries) correlating with heightened levels of depression and anxiety per period (T1, T2). Table 3T1 Characteristics Depression Anxiety <26 years of age or younger .084 .048 Single (yes) .16* 1.00* Living alone (yes) .06 .052 Having children .075* .014 Job next to study 1.30 .06 ≥3 years of education (yes) .09 .062 Study delay (yes) .061 .069 Spending >21 hours/week on study (yes) 1.6* .053 <401 hours clinical practice in total (yes) .083 .021 History of psychological problems (yes) 1.7* 1.4 T2 Characteristics Depression Anxiety <26 years of age or younger .006 .045 Single (yes) .12* .14* Living alone (yes) .33* .011 Having children .063 .019 Job next to study .039 .047 ≥3 years of education (yes) .04 .089 Study delay (yes) .008 .043 Spending >21 hours/week on study (yes) .062 .065 <401 hours clinical practice in total (yes) .03 .065 History of psychological problems (yes) 2.7* 1.8* Notep <.05 (2-tailed); p <.01 (2-tailed); *p <.001 (2-tailed) Predicting depression and anxiety during COVID-19 Table 4 shows the crude and adjusted odd ratios for the difference on the heightened anxiety and depression scores among the students per period, and per period and country. In the crude and adjusted models for anxiety, a history of psychological problems is the only predictor for anxiety, irrespective of period or country (p <.001; p <.001; p <.001). Being single (p.026; p.008; p.015), having a job (p.036; p.041; p.046), and a history of psychological problems (p <.001; p <.001; p <.001) remained as predictors for depression in the adjusted models (Table 4). An additional binary logistic regression analysis and Chi-square test for T1 and T2 ( Table 5) showed that a history of psychological problems predicted depression and anxiety (p.004; p <.001). Being single also predicted depression during T2 (p.024).Table 4 Crude and adjusted Odds Ratios (period, country) for depression and anxiety. Table 4DEPRESSION Crudea Adjusted b Adjusted *c Predictor OR 95%CI P OR 95%CI P OR 95%CI P (Constant) 1.57 - .31 3.86 - .005 3.94 - .005 Being single 1.65 1.44-1.95 .026 1.58 1.44-1.09 .008 1.61 1.37-1.19 .015 Living alone .68 .39–1.18 .17 .63 .34-1.09 .10 .67 .37-1.19 .17 Having children .76 .40–1.43 .40 .73 .38-1.39 .34 .71 .37-1.38 .32 Having a job next to study 1.52 1.03–2.26 .036 1.53 1.02-2.30 .041 1.52 1.01-2.29 .046 Spending >21 hours/week on study 1.69 1.1-2.58 .016 .95 .59-1.53 .83 .96 .60-1.54 .86 History of psychological problems 1.29 1.20-1.43 <.001 1.37 1.25-1.5 <.001 1.40 1.25-1.57 <.001 ANXIETY Crude d Adjusted e Adjusted *f Predictor OR 95%CI P OR 95%CI P OR - P (Constant) 2.55 - .031 1.41 - .002 6.36 - .002 Being single 1.26 .89–1.76 .19 1.36 .96-1.94 .084 1.32 .96-1.88 .13 Living alone .75 .43–1.31 .32 .85 .49-1.50 .57 .70 .45-1.41 .44 Having children 1.34 .73–2.48 .35 1.31 .70-2.47 .40 1.34 .71-2.54 .37 Having a job next to study 1.33 .92–1.92 .13 1.30 .89-1.91 .17 1.31 .89-1.93 .17 Spending >21 hours/week on study 1.05 .70-1.58 .82 .76 .49-1.18 .22 .55 .33-.90 1 History of psychological problems 1.37 1.25-.156 <.001 1.44 1.29-1.66 <.001 1.48 1.32-1.74 <.001 Adjusted for period (T1, T2) **Adjusted for period (T1, T2) & country a X2 69.96, p <.001, -2 Log likelihood 673.264, Cox & Snell R2.098, Nagelkerke R2.141 b X2 97.73, p <.001, -2 Log likelihood 639.493 Cox & Snell R2.146, Nagelkerke R2.210 c X2 107.82, p <.001, -2 Log likelihood 629.412 Cox & Snell R2.160, Nagelkerke R2.230 d X2 32.14, p <.001, -2 Log likelihood 794.49, Cox & Snell R2.051, Nagelkerke R2.069 e X2 71.74, p <.001, -2 Log likelihood 755.16, Cox & Snell R2.109, Nagelkerke R2.148 f X2 81.60, p <.001, -2 Log likelihood 745.04, Cox & Snell R2.124, Nagelkerke R2.168 Table 5 Differences Odds Ratios depression and anxiety per period (T1, T2). Table 5DEPRESSION T1 a T2 b Predictor OR 95%CI P OR 95%CI P (Constant) 1.60 - .68 1.79 - .023 Being single 1.20 .82-1.76 .035 1.58 1.36-1.93 .024 Having a job next to study 1.31 .86-1.2 .2 1.13 .69-1.83 .63 History of psychological problems 1.50 1.31-1.83 .004 1.29 1.29-1.83 <.001 ANXIETY T1 T2 X2 P X2 P History of psychological problems 8.82 .003 10.38 .001 a X2 11.49, p.009, -2 Log likelihood 293.31, Cox & Snell R2.096 Nagelkerke R2.035 b X2 29.13, p <.001, -2 Log likelihood 419.73, Cox & Snell R2.084, Nagelkerke R2.113 Discussion This study attempted to disentangle how midwifery students’ emotional wellbeing evolved during the COVID-19 pandemic waves with different forms of lockdown (i.e. complete or partial), and which student characteristics played a role in heightened levels of depression and anxiety. This study covered a 15-month period and included three waves of the pandemic, allowing the effect of the initial shock and disruption of the first wave and the effect of adaptation to the restrictions during the two subsequent waves [8], [36], [37]. Overall, depression and anxiety scores were significantly lower during the first COVID-19 wave compared to the following waves, suggesting the reoccurrence of the pandemic and lockdown to be an important factor contributing to emotional distress [36]. The results acknowledge that there is variation within emotional wellbeing between countries [2], [3], [12], [13], [14], [22], [23], [47]. This might be due to differences in organisational changes in education during the pandemic (e.g., returning to campus), as apparent in Dutch and Flemish midwifery education, but also to the extent individual students felt affected by the lockdown restrictions such as for example a curfew, closure of bars and restaurants, and number of people allowed to meet [7], [8], [28], [37]. Having a history of psychological problems explained the vulnerability of the Dutch and Flemish midwifery students for anxiety as well as for depression during the pandemic. Having a history of psychological problems has been recognised as a pivotal factor for the heightened depression and anxiety levels during the pandemic [48]. In Europe, approximately 16% of adolescents have a history of or pre-existing psychological problems, usually anxiety or depression [49] - a lower number than reported by the students in our sample. Pre-pandemic research showed that pre-existing mental health problems of students are associated with experiencing emotional problems during education [50], [51]. Based on the students’ self-report of pre-pandemic emotional health problems, it can be assumed these intensified or reoccurred during COVID-19 [12], highlighting the importance of offering services to support the emotional wellbeing of midwifery students [17], [51], especially those with a history of/ pre-existing problem. A history of psychological problems played a significant role during the whole period of the study, while being single was only of influence on depression during the second measurement (T2). On a psychosocial level peri-pandemic loneliness carried the risk for the onset of negative feelings and emotions [48], [55]. Being single was associated with students’ vulnerability for depression during the second period of measurement. Loneliness caused by pandemic-related contact restrictions, seemed to have affected the emotional wellbeing of student midwives while the pandemic and restrictions continued [12], [51]. Additionally, during the pandemic midwifery students experienced being neglected by staff and women, expendable and excluded from clinical practice [6], [8]. It has also been suggested that relationships with parents and peers deteriorated during the pandemic [21]. All these aspects might have added to already existing feelings of loneliness caused by having no partner(relationship) and exacerbated in the context of the continuing COVID-19 pandemic [8], contributing to emotional fatigue, leading to stress and depression [56]. Midwifery students tend to develop resilience, that is, emotionally adapting to sources of stress such as COVID-19 [1], [9], [52]. As resilience has a linear association with emotional wellbeing, our findings suggest that the midwifery students in our sample were not able to build or maintain resilience during the pandemic [53]. Resilience in midwifery students involves to working out how to act in and how to respond to a situation, adopting a proactive approach [54]. Associating the significant reduction of mental health during the pandemic with the assumption that resilience and thus proactive behaviour reduced, suggest that the students in our study were less able to deal with the continuing pandemic. Additionally, Dutch students are more likely to show proactive behaviour compared to Flemish students, which might explain the mental health differences between the Flemish and Dutch students in our sample, although it is unclear why this difference exists [26]. Reduced peri-COVID-19 resilience should not be ignored as this might result to post-traumatic stress, emphasizing to put post-pandemic emotional support mechanisms in place for students with affected peri-pandemic emotional wellbeing [9], [40]. Signs and symptoms of depression and anxiety were self-reported via a validated questionnaire. However, it should be taken into consideration that self-reporting questionnaires are not diagnostic instruments, and thus actual mental health problems might have been under- or overreported. A study among students showed that the high HADS anxiety scores overestimate the extent of clinical anxiety [45] as well as higher anxiety scores are associated with adolescence [11]. Therefore, the level of increased anxiety in this study might be overreported, but nevertheless present. Moreover, considering peri-pandemic anxiety to be higher than pre-pandemic anxiety [2], [3], [4], [12], [13], [14], [22], [23], the number of students with heightened levels is therefore quite worrying, particularly the Flemish students with a nearly 60% anxiety rate during the second period of measurement. Several study limitations warrant discussion. We did not perform a longitudinal study, allowing the follow up of the same sample of students during the different pandemic periods. Instead, our cross-sectional study, included different students during different pandemic periods, albeit there were only a few significant differences in student characteristics between T1 and T2. Additionally, due to the cross-sectional nature of this study, no causality can be established between reduced emotional wellbeing and its predictors. A further limitation of the study is that we did not measure emotional wellbeing before COVID-19. It is therefore not possible to assess possible within-group differences of pre versus peri-pandemic emotional wellbeing, although other studies suggest that emotional wellbeing decreased during COVID-19, among the general population, midwives, as well as among (midwifery) students [2], [3], [4], [12], [13], [14], [22], [23], [55], [57]. Acknowledging the worrying pre-COVID mental health of student midwives [15], the further peri-pandemic poor mental health is quite alarming. Regarding the already existing concerns about the mental health of student midwives, continuous monitoring can be recommended to offer adequate support which seems necessary as midwifery students more often have a history of psychological problems or more often develop psychological problems compared to students in other than midwifery studies [59]. Although we included enough respondents to allow reliable statistical inferences, we do not know which students completed the survey. Due to self-selection, we might have included students with a particular interest in the topic or maybe did not reach students who were not motivated or unable to focus or engage [1]. Moreover, Flemish students were better represented than Dutch midwifery students, although the number were sufficient to draw true statistical inferences. Including or excluding certain students is likely to induce confounding and a possible over- or underreport of emotional wellbeing. A large part of the sample consisted of second- and third-year students, who are known to report higher levels of emotional exhaustion than first year students [16]. Additionally, some of the characteristics of the student midwives in both countries showed to be significantly different in addition to programme differences (e.g., hours spent-on study) - also likely to cause confounding. Flemish students had significantly higher depression and anxiety scores than Dutch students but comparing the sample’s heightened depression and anxiety to that of Australian midwifery students, the prevalence of moderate and severe depression in our sample showed to be lower, while anxiety was higher [13]. Therefore, generalisation of the findings warrants some caution as our findings acknowledge that peri-pandemic emotional wellbeing of midwifery students varied between countries. Despite the differences in emotional wellbeing, the predictors for depression and anxiety during COVID-19 were similar for the Dutch and Flemish students. A history of psychological problems was also found to predict reduced peri-pandemic emotional wellbeing in a general student population [60], although it might be that other factors play a role in other countries. The models with added covariates, showed higher R2 values than the models without, but it is difficult to judge whether the difference is large enough to be important as the R2 values are low. Although the prediction models were significant, it is likely that other variables predicted possible and probable depression and anxiety among midwifery students during COVID-19. We did not ask if students had been infected and/or ill due to COVID-19, whether they had lost (significant) others, how they experienced the restrictions, nor did we ask details about their study progress - which all without a doubt must have affected their emotional wellbeing [1], [2], [3], [4], [12], [22]. Additionally, we did not have pre-pandemic prevalence rates of emotional wellbeing of the midwifery students in our sample, we would be able to examine post-COVID 19 emotional wellbeing of midwifery students to compare peri and post-pandemic figures, to better advice on post-pandemic emotional support as some scores are extremely worrying (e.g., anxiety scores Flemish students). We cannot assume that peri-pandemic emotional problems and changes just cease to exist after the pandemic and its restrictions [21], [58] and therefore further research and post-pandemic emotional support is necessary to prevent post-traumatic stress, depression, and substance use [58]. We have no information about the midwifery students’ course of emotional wellbeing throughout the course without the pandemic. Therefore, further research is needed to conclude if anxiety and depression worsened because of the pandemic of whether this a phenomenon that is typical for midwifery students due to challenges and demands of the course and balancing student- and personal life [3], [14], [20], [21]. Conclusion The emotional wellbeing of midwifery students in Flanders and the Netherlands during the first three waves of the COVID-19 pandemic was significantly reduced, likely to be worse than pre-pandemic prevalence rates and to have intensified while the pandemic and its restrictions continued. Certain students were more affected than others, specifically students with a history of psychological problems and students that were single. The study contributes to the knowledge of the negative effect of the pandemic on midwifery students’ emotional wellbeing, highlighting the emotional vulnerability of midwifery students - regardless of whether there is a pandemic or not. There is an urgent need for post-pandemic emotional support of student midwives. Because students’ emotional wellbeing continues to matter, we recommend that HEIs make a systematic plan for routinely regulated support of midwifery students’ emotional wellbeing, including potential other serious non-pandemic or future pandemic-related sources of stress. CRediT authorship contributions statement Yvonne Kuipers Conceptualization; Formal analysis; Funding acquisition; Investigation; Methodology; Roles/Writing – original draft; Supervision. Eveline Mestdagh Investigation; Formal analysis; Funding acquisition; Validation; Project administration; Writing – review & editing Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding Interreg 2 Seas Mers Zeeën https://www.interreg2seas.eu/nl [grant number 2S05-002, 2019], Province Antwerp Service for Europe, Department of Economy, Local Policies and Europe [grant number BBSPATH 1564468860780, 2019]. ==== Refs References 1 Rasmussen B. Hutchinson A. Lowe G. Wynter K. Redley B. Holton S. Manias E. Phillips N. McDonall J. McTier L. Kerr D. The impact of covid19 on psychosocial well-being and learning for Australian nursing and midwifery undergraduate students: a cross-sectional survey Nurse Educ. Pract. 58 2022 103275 2 Nieuwenhuijs W. Stringa K. Vancauwenbergh V. Bijker K. The mental wellbeing of students during the COVID-19 pandemic Prelim. Data Caring Univ. Cons. 2020 3 Son C. Hegde S. Smith A. Wang X. Sasangohar F. Effects of COVID-19 on College Students' Mental Health in the United States: interview survey study Published 2020 Sep 3 J. Med. Internet Res. 22 9 2020 e21279 10.2196/21279 4 Wathelet M. Duhem S. Vaiva G. Factors associated with mental health disorders among university students in France confined during the COVID-19 pandemic JAMA Netw. Open 3 10 2020 e2025591 10.1001/jamanetworkopen.2020.25591 5 Scott S.R. Rivera K.M. Rushing E. Manczak E.M. Rozek C.S. Doom J.R. "I Hate This": a qualitative analysis of adolescents' self-reported challenges during the COVID-19 pandemic J. Adolesc. Health 68 2 2021 262 269 10.1016/j.jadohealth.2020.11.010 33288454 6 Bogossian F. McKenna L. Levett-Jones T. Mobilising the nursing student workforce in COVID-19: the value proposition Collegian. 27 2 2020 147 149 33519279 7 Luyben A. Fleming V. Vermeulen J. Midwifery education in COVID-19- time: challenges and opportunities Midwifery 89 2020 102776 10.1016/j.midw.2020.102776 8 Kuliukas L. Hauck Y. Sweet L. Vasilevski V. Homer C. Wynter K. Wilson A. Szabo R. Bradfield R. A cross sectional study of midwifery students’ experiences of COVID-19: Uncertainty and expendability Nurse Educ. Pract. 51 2021 102988 9 Ang W.H.D. Shorey S. Lopez V. Chew H.S.J. Lau Y. Generation Z undergraduate students’ resilience during the COVID-19 pandemic: a qualitative study Curr. Psychol. 2021 10.1007/s12144-021-01830-4 10 Ross L. McSherry W. Giske T. Nursing and midwifery students’ perceptions of spirituality, spiritual care, and spiritual care competency: a prospective, longitudinal, correlational European study Nurse Educ. Today 67 2018 64 71 10.1016/j.nedt.2018.05.002 29763841 11 Lemyre A. Gauthier‐Légaré A. Bélanger R.E. Shyness, social anxiety, social anxiety disorder, and substance use among normative adolescent populations: a systematic review Am. J. Drug Alcohol Abuse 45 3 2019 1 18 10.1080/00952990.2018.1536882 30675818 12 Holm-Hadulla R.M. Klimov M. Juche T. Möltner A. Herpertz S.C. Well-being and mental health of students during the COVID-19 pandemic Psychopathology 54 2021 291 297 10.1159/000519366 34564075 13 Wynter K. Redley B. Holton S. Manias E. McDonall J. McTier L. Hutchinson A.M. Kerr D. Lowe G. Phillips N.N.M. Rasmussen B. Depression, anxiety and stress among Australian nursing and midwifery undergraduate students during the COVID-19 pandemic: a cross-sectional study 10 Int. J. Nurs. Educ. Scholarsh. 18 1 2021 10.1515/ijnes-2021-0060 14 Chen T. Lulock M. The mental health of university students during the COVID-19 pandemic: an online survey in the UK PLoS ONE 2022 10.1371/journal.pone.0262562 15 Oates J. Topping A. Aria T. Charles P. Hunter C. Watts K. The mental health and wellbeing of midwifery students: an integrative review Midwifery 72 2019 80 89 10.1016/j.midw.2019.02.007 30826662 16 Eaves J.L. Payne N. Resilience, stress and burnout in student midwives Nurse Educ. Today 79 2019 188 193 10.1016/j.nedt.2019.05.012 31153089 17 Oates J. Topping A. Watts K. Charles P. Hunter C. Arias T. ‘The rollercoaster’: a qualitative study of midwifery students’ experiences affecting their mental wellbeing Midwifery 88 2020 102735 10.1016/j.midw.2020.102735 18 Fenwick J. Cullen D. Gamble J. Sidebotham M. Being a young midwifery student: a qualitative exploration Midwifery 19 8 2016 33 35 19 Carolan-Olah M G. Kruger M.G. Final year students’ learning experiences of the Bachelor of Midwifery course Midwifery 30 8 2014 956 961 10.1016/j.midw.2013.07.010 23938164 20 Sögüt S. Dolu İ. Cangöl E. The relationship between COVID-19 knowledge levels and anxiety states of midwifery students during the outbreak: A cross-sectional web-based survey Perspect. Psychiatr. Care 57 1 2021 246 252 10.1111/ppc.12555 32537821 21 Von Soest T. Kozák M. Rodríguez-Cano R. Adolescents’ psychosocial well-being one year after the outbreak of the COVID-19 pandemic in Norway Nat. Human Behav. 6 2022 217 228 35058644 22 Hewitt R. Students’ views on the impact of Coronavirus on their higher education experience in 2020/2021. YouthSight. HEPI Policy Note 27 2020 Higher Education Policy Institute Oxford 23 Van de Velde S. Buffel V. Bracke P. The COVID-19 international student well-being study Scand. J. Public Health 49 1 2021 114 122 10.1177/1403494820981186 33406995 24 Christiaens W. Nieuwenhuijze M. de Vries R. Trends in the medicalisation of childbirth in Flanders and the Netherlands Midwifery 29 1 2013 e1 e8 10.1016/j.midw.2012.08.010 23266221 25 Fontein-Kuipers Y. Duivis H. Schamper V. Schmitz V. Stam A. Koster D. Reports of work-related traumatic events: a mixed-methods study Eur. J. Midw. 2 18 2018 10.18332/ejm/100611 26 Mestdagh E. Timmermans O. Colin P.J. Van Rompaey B. A cross-sectional pilot study of student's proactive behavior in midwifery education: Validation of a developed questionnaire Nurse Educ. Today 62 2018 22 29 10.1016/j.nedt.2017.12.006 Epub 2017 Dec 12. PMID: 29275018 29275018 27 Mestdagh E. Van Rompaey B. Timmermans O. Fontein-Kuipers Y. Proactive behaviour in midwifery practice: a qualitative overview based on midwives’ perspectives Sexual Reprod. Healthcare 20 2019 87 92 10.1016/j.srhc.2019.04.002 28 Bleijenbergh R. Mestdagh E. Kuipers Y. Midwifery practice and education in Antwerp: forecasting its future with scenario planning J. Contin. Educ. Nurs. 53 1 2022 21 29 10.3928/00220124-20211210-07 34978478 29 Kenens R. van der Velden L. Vis E. Batenburg R. Cijfers uit de registratie van verloskundigen. Peiling 2018 2020 Nivel Utrecht, Netherlands 30 Vlhora. Oktobertelling 2019. Brussel. 2019. 〈http://www.vlhora.be/partners/index.html〉. 31 Ministerie van Onderwijs, Cultuur en Wetenschap. Service document HO – 2021-2022. Versie 11. Rijksoverheid: The Hague, Netherlands. 2021. 32 Maenhout K. Nooddecreet Coronacrisis Hoger Onderwijs. Decr.Vl.Parl. van XXX over maatregelen in het hoger onderwijs voor het academiejaar 2019-2020 als gevolg van de coronacrisis, B.S., XX april 2020, hierna genoemd “Nooddecreet”. Gedr.st., Vl.Parl., 2019-2020, nr. 265/2. Parl.Hand., Vl.Parl., 15 april 2020. 33 Vhlora. Pandemiematrix academiejaar 2020-2021. Vlaammse Hogescholenraad: Brussels, Belgium. 2020. 34 Ministerie van Onderwijs, Cultuur en Wetenschap. Service document HO – 2021-2022. Versie 13.2. Rijksoverheid: The Hague, Netherlands. 2022. 35 Iftekhar E.N. Prieseman V. Baling R. Bauer S. Beutels P. Calero Valdez A. A look into the future of the COVID-19 pandemic in Europe: an expert consultation Lancet Reg. Health Eur. 8 2021 100185 10.1016/j.lanepe.2021.100185 36 Brooks S.K. Webster R.K. Smith L.E. Woodland L. Wessely S. Greenberg N. Rubin G.J. The psychological impact of quarantine and how to reduce it: rapid review of the evidence Lancet 395 10227 2020 912 920 10.1016/S0140-6736(20)30460-8) 32112714 37 Kuipers Y. Bleijenbergh R. Van den Branden L. Van Gils Y. Rimaux S. Claerbout A. Mestdagh E. Psychological health of pregnant and postpartum women before and during the COVID-19 pandemic PLoS ONE 17 4 2022 e0267042 10.1371/journal.pone.0267042 38 Brock R.L. O’Hara M.W. Hart K.J. Peritraumatic distress mediates the effect of severity of disaster exposure on perinatal depression: the Iowa flood study J. Trauma Stress. 28 6 2015 515 522 10.1002/jts.22056 26584403 39 Purdy N. Building back better in 2022 – with a renewed focus on pastoral care, emotional health and wellbeing for all Pastoral. Care Educ. 2022 10.1080/02643944.2022.2036419 40 Dodds W. Disease now and potential future pandemics World's Worst Prob. 2019 31 44 10.1007/978-3-030-30410-2_4 41 Zigmond A.S. Snaith R.P. The hospital anxiety and depression scale Acta Psychiatr. Scand. 67 1983 361 370 6880820 42 Bjelland I. Dahl A.A. Haug T.T. Neckelmann D. The validity of the Hospital Anxiety and Depression Scale. An updated literature review J. Psychosom. Res. 52 2002 69 77 11832252 43 Pouwer F., Snoek F.J., van der Ploeg H.M. HADS-NL. Dutch translation of the hospital anxiety and depression scale. 1997. Available: 〈https://meetinstrumentenzorg.nl/instrumenten/hospital-anxiety-and-depression-scale/〉. 44 Spinhoven P.H. Ormel J. Sloekers P.P.A. Kempen G.I.J.M. Speckens A.E.M. A validation study of the Hospital Anxiety and Depression Scale (HADS) in different groups of Dutch subjects Psychol. Med. 27 2 1997 363 370 10.1017/S0033291796004382 9089829 45 Andrews B. Heijdenberg J. Wilding J. Student anxiety and depression: comparison of questionnaire and interview assessments J. Affect. Disord. 95 1–3 2006 29 34 10.1016/j.jad.2006.05.003 16764939 46 Chen H., Cohen P., Chen S. How big is a big odds ration? Interpreting the magnitudes of odds ratios in epidemiological studies. Communications in Statistics - Simulation and Computation, 39:4, 860-864, DOI: 10.1080/03610911003650383. 47 Auerbach R.P. WHO World Mental Health Surveys International College Student Project: prevalence and distribution of mental disorders J. Abnorm. Psychol 127 2018 623 638 30211576 48 Andersen A.J. Mary-Krause M.M. Bustamante J.J. Héron M. El Aarbaoui T. Melchior M. Symptoms of anxiety/depression during the COVID-19 pandemic and associated lockdown in the community: longitudinal data from the TEMPO cohort in France BMC Psychiatry 21 381 2021 10.1186/s12888-021-03383-z 49 UNICEF. The state of the world’s children 2021. On my mind. Promoting, protecting and caring for children’s mental health. Regional brief: Europe. United Nations Children’s Fund. 2021. 50 Yates J. James D. Aston I. Pre-existing mental health problems in medical students: a retrospective survey Med. Teacher 30 2008 319 321 51 Sheldon E. Simmonds-Buckley M. Bone C. Prevalence and risk factors for mental health problems in university undergraduate students: a systematic review with meta-analysis J. Affect. Disord. 287 15 2021 282 292 33812241 52 Clohessy N. McKellar L. Fleet J. Bounce back – bounce forward: midwifery students experience of resilience Nurse Educ. Pract. 37 2019 22 28 10.1016/j.nepr.2019.04.011 31035076 53 Sood S. Sharma A. Resilience and psychological well-being of higher education students during COVID-19: The mediating role of perceived distress J. Health Manag. 22 4 2022 606 617 10.1177/0972063420983111 54 Williams J. Lathlean J. Norman K. An exploration of the development of resilience in student midwives Br. J. Midwif. 29 6 2022 55 Wang C. Pan R. Wan X. Tan Y. Xu L. Ho C.S. Ho R.C. Immediate psychological responses and associated factors during the initial stage of the 2019 coronavirus disease (COVID-19) epidemic among the general population in China Int. J. Environ. Res. Public Health 17 5 2020 1729 10.3390/ijerph17051729 32155789 56 Edwards M. Anderson J. What do you know about compassion fatigue in midwifery? Aust. Midw. News 16 4 2016 27 28 57 Båtsman A. Fahlbeck H. Hildingsson I. Depression, anxiety and stress in Swedish midwives: a cross-sectional survey July Eur. J. Midw. 4 2020 10.18332/ejm/124941 58 Furr J.M. Comer J.S. Edmunds J.M. Kendall P.C. Disasters and youth: a meta-analytical examination of posttraumatic stress J. Consult. Clin. Psychol. 78 2010 765 780 21114340 59 Charitou A. Fifi P. Vivlaki V. Is empathy an important attribute of midwives and other health professionals?: a review Eur. J. Midw. 3 4 2019 10.18332/ejm/100612 60 Meda N. Pardini S. Slongo I. Bodini L. Zordan MA,Rigobella P. Students’ mental health problems, before, during, and after COVID-19 lockdown in Italy J. Psychiatr. Res. 135 2021 69 77 10.1016/j.jpsychires.2020.12.045	36473798	PMC9708611	NO-CC CODE	2022-12-05 23:15:28	no	Women Birth. 2022 Nov 30; doi: 10.1016/j.wombi.2022.11.012	utf-8	Women Birth	2,022	10.1016/j.wombi.2022.11.012	oa_other
==== Front Am J Obstet Gynecol Am J Obstet Gynecol American Journal of Obstetrics and Gynecology 0002-9378 1097-6868 Elsevier Inc. S0002-9378(22)02207-4 10.1016/j.ajog.2022.11.1301 Research Letter Is the risk of still and preterm birth affected by the timing of symptomatic SARS-CoV-2 infection during pregnancy? – Data from the CRONOS Network, Germany Iannaccone Antonella MD 1∗ Mand Nadine MD 2 Schmidt Börge PhD 3 Rüdiger Mario MD 4 Reisch Beatrix 1 Pecks Ulrich MD 5 Schleußner Ekkehard MD 6 on behalf of the CRONOS Network 1 Department of Obstetrics and Gynecology, University Hospital, University of Duisburg-Essen, Essen, Germany 2 Department of Pediatrics, Philipps University of Marburg, Germany 3 Institute for Medical Informatics, Biometry and Epidemiology, University of Duisburg-Essen, Essen, Germany 4 Saxony Center for Feto-Neonatal Health, Department for Neonatology and Pediatric Intensive Care, Faculty of Medicine Carl Gustav Carus, Dresden, Germany 5 Department of Obstetrics, Jena University Hospital, Jena, Germany 6 Department of Obstetrics and Gynecology, University Hospital of Schleswig-Holstein, Kiel, Germany ∗ Corresponding Author:Antonella Iannaccone; Department of Obstetrics and Gynecology, University Hospital, University of Duisburg-Essen, Essen, Germany, Hufelandstr. 55; 45147 Essen; Germany Tel.: +497233574 30 11 2022 30 11 2022 19 11 2022 23 11 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Key Words Preterm Birth Stillbirth Sars-CoV-2 Infection in Pregnancy Obstetric Surveillance Vaccination in Pregnancy ==== Body pmcKlinik für Gynäkologie und Geburtshilfe Condensation:The risk for preterm birth and stillbirth after symptomatic Sars-CoV-2 in pregnancy is increased especially after early infection and within the first 4 weeks after infection. AJOG at glance Why was this study conducted? This study aimed to elucidate the associations between the timing of symptomatic SARS-CoV-2 infection during pregnancy and the risk of still and preterm birth. Key findings: In this prospective observational study including 1149 symptomatic SARS-CoV-2 infected pregnant women, infection within the first two trimesters of pregnancy was associated with an increased risk of early preterm birth (PTB) (≤32 weeks) and stillbirth compared to infection within the third trimester. The risk of PTB was higher within the first 4 weeks after infection. What does this add to what is known? Infection should be prevented during pregnancy. Pregnant women with a SARS-CoV-2 infection during the early pregnancy could benefit from extensive obstetric surveillance during the rest of gestation.	36460095	PMC9708612	NO-CC CODE	2022-12-01 23:21:33	no	Am J Obstet Gynecol. 2022 Nov 30; doi: 10.1016/j.ajog.2022.11.1301	utf-8	Am J Obstet Gynecol	2,022	10.1016/j.ajog.2022.11.1301	oa_other
==== Front Rev Neurol (Paris) Rev Neurol (Paris) Revue Neurologique 0035-3787 0035-3787 Elsevier Masson SAS. S0035-3787(22)00823-2 10.1016/j.neurol.2022.10.002 Original Article Can Google Trends analysis confirm the public's need for information about the rare association of facial nerve paralysis with COVID-19 or the COVID-19 vaccination? Schubert R. b Kaatz M. a Schubert R. a Springer S. a Zieger M. a* a SRH Wald-Klinikum Gera GmbH, Gera, Germany b University of Louisiana Monroe, Monroe, USA ⁎ Corresponding author. 30 11 2022 30 11 2022 23 7 2022 18 10 2022 20 10 2022 © 2022 Elsevier Masson SAS. All rights reserved. 2022 Elsevier Masson SAS Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Facial nerve paralysis or Bell's palsy have been suggested as possible consequences of SARS-CoV-2 infections, as well as possible side effects of COVID-19 vaccinations. Google Trends data have been used to evaluate worldwide levels of public awareness for these topics for pre- and post-pandemic years. The results demonstrate a relatively low public interest in facial nerve paralysis in comparison to other more common COVID-19 related topics. Some peaks of interest in Bell's palsy can most likely be explained as triggered by the media. Therefore, Google Trends has shown public's relatively low awareness of this rare neurological phenomenon during the pandemic. Keywords COVID-19 SARS-CoV-2 Search engine data Google Trends Bell's palsy Facial nerve paralysis Ramsay–Hunt syndrome type 2 ==== Body pmc1 Introduction Since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide during the coronavirus pandemic, millions of people have contracted or died from coronavirus disease 2019 (COVID-19) or are affected by possible long-term impairments, e.g., long COVID [1]. Accordingly, from early on, great efforts were made worldwide to counteract the pandemic with suitable measures such as hygiene, social distancing, lockdowns, and other restrictions. An important factor in the fight against the pandemic was also the development of effective vaccines [2]. COVID-19 vaccines have been developed using various, partially novel principles including mRNA, adenovirus vectors, protein subunits and inactivated SARS-CoV-2 [2] Various COVID-19 symptoms have been described during the course of the pandemic (e.g., [1]), including initial reports of possible reactivations of herpes viruses in COVID-19 patients [3] After the development of the first COVID-19 vaccines, these early reports were soon followed by reports of suspected reactivations of persistent viruses related to COVID-19 vaccinations, similar to those already described for other vaccines [4], [5]. Varicella zoster (VZV) and herpes simplex viruses (HSV) can persist after the primary infection, e.g., chickenpox, and reactivations can occur. Risk factors for this include age, stress, HIV infection, cancer, or other causes that lead to immunosuppression [4], [5]. VZV reactivation can be largely asymptomatic or cause shingles with possible attendant symptoms such as transient or persistent facial nerve paralysis. Serious complications of herpes virus reactivations can be meningitis and encephalitis. Facial nerve paralysis can have very heterogeneous causes, such as viral or bacterial infections, ischemia, trauma, or tumour diseases. However, the most common form (ca. 60–75%) of facial nerve palsy is Bell's palsy, the causes of which are not yet fully understood [6]. For Bell's palsy, the idiopathic facial paralysis whose exact aetiology is still unclear, reactivation of HSV, for example, is also discussed as a cause [6]. Moreover, Bell's palsy is also discussed in direct connection with a SARS-CoV-2 infection [7] or COVID-19 vaccinations [7]. These possible connections are also mentioned in the current guideline of the German Society for Neurology [Deutsche Gesellschaft für Neurologie (DGN) e. V.] [6]. In the context of millions of cases of COVID-19 infections and vaccine doses administered worldwide in connection with the pandemic, the question arises as to whether possible rare neurological complications of COVID-19 infections or vaccinations such as facial nerve paralysis are not only scientifically published, but also whether the public is aware of them. The present study is therefore intended to address the question of whether the search interest for this possible complication can be proven in the search interest data of a large search engine during the course of the pandemic. Investigations using Google Trends analyses are already established for such investigations [1]. We hypothesize that the risk of virus reactivation, e.g., expression as herpes zoster oticus (Ramsay–Hunt syndrome type 2), and resulting facial nerve paralysis is not focused on by the public and does not generate a sustained interest, leading to the phenomenon being neglected in public interest. Accordingly, the aim of this study is to evaluate the worldwide representation and awareness of the topics facial nerve paralysis, Bell's palsy, shingles, and Ramsay–Hunt syndrome (type 2) through the search interest, as shown through the analysis of available Google Trends search data about public interest or the need for information. This study considered the terms that are serious, specific, and obvious to the layperson, such as facial nerve paralysis and Bell's palsy. 2 Data & methodology Google Trends data have been widely used for tracking the COVID-19 pandemic or COVID-19 related symptoms. Google Trends data are anonymous and Google Trends provides comparisons of up to five search terms or topics. In this study, data were collected with the following settings in Google Trends (https://trends.google.com/trends): the period was set the “Past five years”, the region was selected as “Worldwide” and “All categories” and “Web search” were set. In this study, search topics were used instead of search terms for better coverage as described [1]. Google Trends data were accessed in April 2022 and the following search topics were used: “Coronavirus disease 2019”, “COVID-19 vaccine”, “Long COVID”, “Clouding of consciousness”, “Chickenpox”, “Shingles”, “Herpes simplex”, “Bell's palsy”, “Ramsay–Hunt syndrome type 2”, and “Facial nerve paralysis”. 3 Observations The relative search interest for the topic of COVID-19 clearly dominated compared to all other topics in this study (data not shown) as already shown for other common disease topics [1]. The relative search interest for the topic COVID-19 vaccine was far lower than for COVID-19 (data not shown), but still significantly higher than the interest for all the other topics in this study, as shown for some examples in Fig. 1 A. Interest in a COVID-19 vaccine emerged early in the pandemic. A further increase occurred with the advances in COVID-19 vaccine candidates and when vaccinations became available worldwide [2]. As shown in Fig. 1B with a different reference topic, more details were visible. Thus, the seasonality for the search topic of chickenpox in the pre-pandemic years could be shown. Due to the pandemic, there was a significant drop in relative search interest for chickenpox at the beginning of 2020. There was a similar decrease in topics herpes simplex and shingles. The interest in chickenpox increased slightly over the course of the pandemic. Likewise, the relative interest in searching for shingles also increased again after the drop, sometimes even exceeding the level of the last pre-pandemic years (Fig. 1B). In contrast, a search interest for long COVID only arose with the pandemic, as already described elsewhere [1]. The interest in Bell's palsy, on the other hand, showed little changes over the period of the study, apart from two significant peaks in July 2017 and in December 2020 (Fig. 1B).Fig. 1 Relative search interest in different search topics as indicated according to Google Trends data with topic COVID-19 vaccine (A) or topic herpes simplex (B) as reference. In Fig. 2 , the relative interests in Bell's palsy, facial nerve paralysis, and Ramsay–Hunt syndrome (type 2), hereinafter referred to as Ramsay–Hunt syndrome for short, were shown. The well-known “Long COVID” and an associated symptom with a known relative low search interest, “Clouding of consciousness” [1] were shown as a comparison (Fig. 2). While an interest in “Long COVID” and “Clouding of consciousness” had developed during the pandemic, Bell's palsy, facial nerve paralysis and Ramsay–Hunt syndrome showed no noticeable changes as a result of the pandemic, apart from slight peaks in facial nerve paralysis and Bell's palsy (Fig. 2).Fig. 2 Relative search interest in different search topics as indicated according to Google Trends data with topic Bell's palsy (A) or topic facial nerve paralysis (B) as reference. 4 Discussion The results of Google Trends analysis demonstrate the public interests, documented by corresponding search volumes [1]. The interest in COVID-19 associated topics like COVID-19 vaccine or long COVID was reconfirmed in this study. This study has some limitations, such as the different popularity of the Google search engine around the world. Since Google Trends operates with relative search volumes, it is not possible to make an exact statement about the absolute number of searches. Furthermore, only search terms, for which topics were available at Google Trends, were examined [1]. The infections with other communicable diseases, e.g., seasonal influenza and chickenpox, were reduced during the coronavirus pandemic. The reduced frequency of infection and illness is most likely to have benefited from the measures taken in the context of the pandemic [8]. Google Trends data also confirm a drop in interest in chickenpox during the pandemic. On the other hand, the relative search interest for shingles has largely recovered after a brief dip in early 2020, sometimes even exceeding the pre-pandemic level. The scientific discussion about the possible connection between COVID-19 or the associated vaccinations may have contributed to the interest here [3], [9]. However, the relative interest of Internet and especially Google users in the topics of other possible serious complications such as Ramsay–Hunt syndrome, facial nerve or Bell's palsy is low. Compared to the millions of COVID-19 cases and the millions of vaccine doses administered, the number of Ramsay–Hunt syndrome, facial nerve or Bell's palsy cases is comparatively very small. Due to the severity of the resulting limitations and dangers for the patients, a need for information and education is nevertheless necessary. Despite the relatively small number of cases, Bell's palsy, for example, is listed as a possible side effect of vaccination [10]. Facial paralysis as a result of vaccination is also discussed as a non-specific inflammatory reaction [5] However, its severity is hardly reflected in the search volume, which is probably due to the relative low number of cases. In addition to the neurological and functional aspects of a paresis, however, the psychological impact on the affected patients must also be considered. Nonetheless, there have been two interest peaks for Bell's palsy in the past five years. The first of the two significant peaks for Bell's palsy can probably be traced back to reports about the American actress Angelina Jolie in July 2017, in which she revealed her Bell's palsy diagnosis [11]. While the second peak in December 2020 was likely triggered by public media releases bringing public attention to possible relationship between Bell's palsy cases and mRNA vaccines (e.g., [12]). In terms of search volume, however, facial nerve paralysis or Bell's palsy remain of secondary importance. 5 Conclusions Despite some limitations of this study, our analysis of the available data shows a trend that, apart from a few brief spikes, facial nerve paralysis and Bell's palsy mostly show no appreciable increased public interest or information needs due to the pandemic compared to more common topics such as COVID-19 vaccine or long COVID. Although there are clear indications of the risk of facial nerve paralysis as part of a vaccination side effect, we agree with critical remarks by Volk et al. [5] on the outweighing of the benefits of vaccination against COVID-19 but also emphasize the need to inform those who have been vaccinated. Especially since episodes of facial nerve palsy can also be expected in connection with SARS-CoV-2 infections [5]. Google Trends data provide a useful tool to show the relative search interest and therefore the representation or awareness of topics within the population. Furthermore, analyses of search engine data may help to improve information materials for the public. Disclosure of interest The authors declare that they have no competing interest. Funding This research did not receive any specific funding. Acknowledgements We thank the Editor-in-Chief and the anonymous reviewers, who made it possible for us to significantly improve our research with their valuable suggestions. ==== Refs References 1 Kaatz M. Springer S. Schubert R. Zieger M. Representation of long COVID syndrome in the awareness of the population is revealed by Google Trends analysis Brain Behav Immun Health 2022 10.1016/j.bbih.2022.100455 [100455] 2 Springer S. Kaatz M. Zieger M. Evaluation of weekly COVID-19 vaccination and case data supports negative correlation between incidence and vaccination in German federal states and cities during 4th wave Vaccine 40 22 2022 2988 2992 35437192 3 Diez-Domingo J. Parikh R. Bhavsar A.B. Cisneros E. McCormick N. Lecrenier N. Can COVID-19 increase the risk of herpes zoster? A narrative review Dermatol Ther 11 4 2021 1119 1126 4 Walter R. Hartmann K. Fleisch F. Reinhart W.H. Kuhn M. Reactivation of herpesvirus infections after vaccinations? Lancet 353 9155 1999 810 5 Volk G.F. Kuttenreich A.M. Geitner M. Guntinas-Lichius O. Eine akute Fazialisparese als mögliche Impfkomplikation bei einer Impfung gegen SARS-CoV-2 Laryngo-Rhino-Otologie 100 07 2021 526 528 33975372 6 Heckmann JG, et al. Therapie der idiopathischen Fazialisparese (Bell's palsy), S2k-Leitlinie, 2022; in: Deutsche Gesellschaft für Neurologie (Hrsg.), Leitlinien für Diagnostik und Therapie in der Neurologie. [Online: http://www.dgn.org/leitlinien/ (accessed: 03.06.2022)]. 7 Garg R.K. Paliwal V.K. Spectrum of neurological complications following COVID-19 vaccination Neurol Sci 43 2022 3 40 10.1007/s10072-021-05662-9 34719776 8 Sanz-Muñoz I. Tamames-Gómez S. Castrodeza-Sanz J. Eiros-Bouza J.M. de Lejarazu-Leonardo R.O. Social distancing, lockdown and the wide use of mask; a magic solution or a double-edged sword for respiratory viruses epidemiology? Vaccines 9 6 2021 595 34205119 9 Iwanaga J. Fukuoka H. Fukuoka N. Yutori H. Ibaragi S. Tubbs R.S. A narrative review and clinical anatomy of herpes zoster infection following COVID-19 vaccination Clin Anat 35 1 2022 45 51 34554601 10 Shafiq A. Salameh M.A. Laswi I. Mohammed I. Mhaimeed O. Mhaimeed N. Neurological immune-related adverse events after COVID-19 vaccination: a systematic review J Clin Pharmacol 62 2022 291 303 34921562 11 Peretz E. Cover story: Angelina Jolie Solo 2017 Vanity Fair [retrieved July 17, 2022, from https://www.vanityfair.com/hollywood/2017/07/angelina-jolie-cover-story] 12 Higgins-Dunn N. FDA staff recommends watching for Bell's Palsy in Moderna and Pfizer vaccine recipients 2020 CNBC [retrieved July 17, 2022, from https://www.cnbc.com/2020/12/15/fda-staff-recommends-watching-for-bells-palsy-in-moderna-and-pfizer-vaccine-recipients.html]	36473747	PMC9708614	NO-CC CODE	2022-12-05 23:15:28	no	Rev Neurol (Paris). 2022 Nov 30; doi: 10.1016/j.neurol.2022.10.002	utf-8	Rev Neurol (Paris)	2,022	10.1016/j.neurol.2022.10.002	oa_other
==== Front Clin Oncol (R Coll Radiol) Clin Oncol (R Coll Radiol) Clinical Oncology (Royal College of Radiologists (Great Britain) 0936-6555 1433-2981 The Authors. Published by Elsevier Ltd on behalf of The Royal College of Radiologists. S0936-6555(22)00549-0 10.1016/j.clon.2022.11.018 Original Article The Impact of COVID-19 on Radiotherapy Services in Scotland, UK: A Population-based Study Grocutt L. ∗†∗ Rutherford A. † Caldwell D. ‡ Wilkinson C. ‡ Chalmers A.J. § Dempsey L. ¶ Kelly C. ¶ O'Cathail S.M. § ∗ CRUK RadNet Glasgow, University of Glasgow, Glasgow, UK † Department of Radiotherapy Physics, The Beatson West of Scotland Cancer Centre, Glasgow, UK ‡ NRS CRN-W, Radiotherapy Department, The Beatson West of Scotland Cancer Centre, Glasgow, UK § Institute of Cancer Sciences, University of Glasgow, Glasgow, UK ¶ Cancer Research UK Glasgow Clinical Trials Unit, The Beatson West of Scotland Cancer Centre, Institute of Cancer Sciences, University of Glasgow, Glasgow, UK ∗ Author for correspondence: CRUK RadNet Glasgow, University of Glasgow, Glasgow G61 1QH, UK. 30 11 2022 30 11 2022 © 2022 The Authors 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Aims The effect of the COVID-19 pandemic on cancer radiotherapy services is largely unknown. The aim of the present study was to investigate the impact of the resultant contingency plans on radiotherapy cancer services in Scotland. Materials and methods Detailed data of radiotherapy activity at our centre were collected from 1 April 2019 to 31 March 2021. Differences in mean weekly radiotherapy courses, dose and fractionation patterns and treatment intent were compared with corresponding pre-pandemic months for all treatment sites. Qualitative data were collected for a subgroup of radical radiotherapy patients. Results Total radiotherapy courses decreased from 6968 to 6240 (–10%) compared with the previous year, prior to the pandemic. Average weekly radiotherapy courses delivered were 134 (standard deviation ±13), decreasing by 10% to 120 (standard deviation 15) (Welch's t-test, P < 0.001). The greatest decrease in new start treatment courses was observed from May to August 2020 (–7.7%, –24.0%, –16.7% and –18.7%) compared with the corresponding months in 2019. A significant reduction was seen for female patients <70 years (–16%) compared with females >70 years (–8%) or their male counterparts (–7% and –6%, respectively). By diagnosis, the largest reductions between pre- and post-pandemic levels were for anal (–26%), breast (–18%) and prostate (–14%) cancer. Contrarily, a significant increase was found for bladder (28%) and oesophageal (11%) cancers. Conclusions Over the first 12 months of the COVID-19 pandemic, radiotherapy activity significantly decreased compared with the 12 months prior. Due to issued guidance, the use of hypofractionated regimens increased, contributing to the reduction in treatments for some tumour sites. An increase in other tumour sites can probably be attributed to the reduction or cancellation of surgical interventions. These results will inform our understanding of the indirect consequences of the pandemic on radiotherapy services. Key words COVID-19 pandemic radiotherapy SARS-CoV-2 Scotland UK ==== Body pmcIntroduction In March 2020, the World Health Organization declared a global pandemic of the coronavirus disease (COVID-19) [1]. As a result, the UK's National Health Service was redeployed and restructured to cope with the increased demand on healthcare services [2]. Cancer screenings were initially suspended, routine diagnostic investigations were deferred and elective surgeries were postponed. The impact of the efforts used to control the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on cancer services is of particular interest. From the outset of the pandemic, there was significant concern that cancer patients were at increased risk of serious COVID-19-related complications due to immunosuppression and co-morbidities [3]. Service providers, commissioners and professional bodies within the UK, and internationally, issued revised guidance for cancer care [2,4,5]. For this cohort of patients, a risk–benefit management strategy was used, balancing the need to reduce patients' risk from the SARS-CoV-2 infection with the need for continued access to diagnostic intervention and the delivery of optimal treatment [6]. Radiotherapy is a crucial treatment modality in cancer management, estimated to be responsible for 40% of all cures, with 60% of all patients receiving radiation at some stage in their disease [7]. To allow radiotherapy services to continue, site-specific guidance was issued [8]. With the aim of reducing the number of hospital attendances and exposure of high-risk patients to COVID-19, recommendations such as the omission or delay in radiotherapy treatments, the use of radiotherapy to replace surgery and changes to radiotherapy treatment doses and schedules (hypofractionation) were provided [9]. Understanding the impact that these adapted radiotherapy practices have had at a population level is challenging. A limited number of population-based studies have been carried out assessing the impact of COVID-19 on cancer services within the UK [10] during the first wave of the pandemic. No studies have been carried out to assess the effects of the pandemic on radiotherapy services beyond this short interval. The aim of the present study was to determine the changes in radiotherapy cancer care in Scotland and to quantify the impact on patients' treatments, by comparing data from 12 months before and after the pandemic. Our centre is one of the largest cancer centres in the UK and the largest in Scotland, serving around 60% of the Scottish population. Each year, our centre sees more than 8000 new patients, delivers over 25 000 courses of chemotherapy and over 6500 courses of radiotherapy [11]. Materials and Methods Study Design A detailed cross-sectional dataset of radiotherapy activity within our centre was collected and analysed. Following institutional approval, we extracted anonymised data from the electronic radiotherapy health record on International Classification of Diseases (ICD-10) code, age, gender, radiotherapy dose/fractionation and treatment start dates for the period 1 April 2019 to 31 March 2021. In May 2020, a national UK-wide initiative was launched by the National Cancer Research Institute (NCRI) Clinical and Translational Radiotherapy Research Working Group (CTRad) that aimed to study the impact of COVID-19 and the contingency plans on the radiotherapy cancer services within the UK. CTRad produced guidance and a minimum dataset (Table 1 ) for data collection to ensure that prospective data quality was consistent across all centres. All adult cancer patients for whom radiotherapy was considered or given in the curative definitive or adjuvant treatment setting from 1 March 2020 were eligible for inclusion. The data fields were attached to all external beam radiotherapy courses across all tumour sites registered from 1 April 2020 to 1 October 2020 within the ARIA CarePath workspace (Varian Medical Systems, Palo Alto, CA, USA). Data collection continued for a reduced number of sites (bladder, brain, head and neck, oesophagus, pancreas and colorectal cancers) until 28 February 2021. The referring clinician or delegate completed a questionnaire for each individual patient. These qualitative data were used to explore the reasons for deviations to the accepted standard of care across all tumour sites.Table 1 Clinical and Translational Radiotherapy Research Working Group minimum dataset utilised for data collection Table 1Question Response options Category of radiotherapy treatment Primary, neoadjuvant, radiotherapy as a replacement for surgery, adjuvant, radiotherapy as a bridge to surgery or other. Has treatment timing changed due to COVID-19? Radiotherapy proceeding as normal, radiotherapy omitted due to clinical decision or patient refusal, radiotherapy deferred/delayed due to clinical decision or patient refusal or other. Has radiotherapy intent changed? Radical to palliative, palliative to radical, unchanged, or other. Is the patient having concurrent chemotherapy? No, full chemotherapy, reduced chemotherapy, chemotherapy omitted, chemotherapy modified or other. Is this patient receiving standard of care pre-COVID-19? Patient receiving pre-COVID-19 standard of care, hypofractionated treatment, stereotactic (including steretotactic ablative body radiotherapy) treatment. Data Analysis Radiotherapy activity was summarised as the mean number of weekly radiotherapy courses per month, allocated to the week in which they began. Weeks were derived from course start date and defined as beginning on a Monday. Week 1 of the year was defined as the week that included both 4 January and the first Thursday of the year (using the SAS WEEK function and the ‘V Descriptor’), equivalent to the International Organization for Standardisation calendar. Weeks were then allocated to the months in which they began to account for weekly variability and seasonality. The radiotherapy activity for the period 1 April 2019–31 March 2020 (year 1) was compared with 1 April 2020–31 March 2021 (year 2) by intent of radiotherapy (palliative or radical), age (<70 years versus ≥70 years), sex and diagnosis (anal, bladder, brain, breast, cervix, head and neck, lung, lymphoma, oesophageal, prostate, rectal, skin cancer and other diagnoses). Change in treatment fractionation was determined for specified diagnoses (radical treatments) and palliative radiotherapy overall. For radical radiotherapy, the dose per fraction for each course was calculated and assigned a category (<2 Gy, 2–2.49 Gy, 2.5–4.99 Gy, ≥5 Gy). The mean number of weekly courses per month and the standard deviations were calculated for each diagnosis, stratified by fractionation category. Palliative courses were categorised separately based on the number of prescribed fractions only (single, 2–5, 6–10, ≥10 fraction(s)). The proportion of radiotherapy activity per fractionation category was calculated by diagnosis, and separately for palliative treatments. Means and standard deviations of the weekly radiotherapy courses were calculated with changes reported as percentages. Group comparisons used Welch's t-test at a 5% significance level. All statistical analyses were carried out using SAS version 9.4. All plots and figures were created in R version 3.6.1. Results The total number of radiotherapy courses analysed was 13208 over 24 months. Of these, 1491 had completed questionnaires available for qualitative analysis. Radiotherapy courses decreased from 6968 in year 1 to 6240 in year 2, a decrease of 10%. The average weekly radiotherapy courses delivered was 134 (standard deviation ±13), decreasing by 10% to 120 (standard deviation 15) (Welch's t-test, P < 0.001) in year 2. The first wave of the COVID-19 pandemic from March 2020 to June 2020 showed the greatest impact on average weekly radiotherapy courses: May (–7.7%), June (–24.0%), July (–16.7%) and August (–18.7%) compared with the corresponding months in 2019 (see Supplementary Table S1). In fact, the mean radiotherapy delivery remained depressed throughout year 2, only noting a slight increase by March 2021, compared with year 1. These trends are summarised in Figure 1 .Fig 1 Total average weekly radiotherapy courses between April 2019 and March 2020 (blue) and April 2020 and March 2021 (orange). Fig 1 The distribution of radiotherapy delivery between radical and palliative treatment courses showed a greater reduction in radical treatments (4472 versus 3900 courses) than palliative treatments (2496 versus 2340 courses) (see Supplementary Table S1). The average weekly radiotherapy courses of radical treatment significantly decreased from 86 (standard deviation 10) in year 1 to 75 (standard deviation 11) in year 2, a decrease of 13% (Welch's t-test, P < 0.001). The single largest monthly change in radical radiotherapy delivery was June 2020 (–33.3%). Overall, the mean monthly delivery of radical radiotherapy remained depressed in year 2 relative to year 1, as of March 2021. The average weekly radiotherapy courses of palliative radiotherapy courses showed more variation, with the annualised rates, at 48 (standard deviation 6) in year 1 and 45 (standard deviation 8) in year 2, a non-statistically significant 5% drop (Welch's t-test, P = 0.16). The single biggest decrease was in November 2020 (–23.6%) and the single biggest increase was in March 2021 (16.4%). The average weekly radiotherapy courses were also analysed by gender and age, with a threshold of <70 or ≥70 years given the early data that SARS-CoV-2 was particularly lethal in this age group [12] (see Supplementary Table S1). A significant reduction in the average weekly radiotherapy courses was seen in women compared with men over the 2-year period (means 136, 117, Welch's t-test P < 0.001). The reduction was greatest in women <70 years, with an average decrease of –16% compared with pre-pandemic (Welch's t-test, P < 0.005). By contrast, the male <70 years, female ≥70 years and male ≥70 years cohorts all saw decreases, but none were statistically significant (see Supplementary Table S1). Changes in mean weekly curative treatment courses and attendances by diagnoses are provided in Supplementary Table S2. The largest relative reduction in courses from April 2020 to March 2021 was observed in anal cancer, with an overall decrease of –26% from the previous year, shown in Figure 2 a. Decreases in mean weekly curative treatment doses were also seen in breast (–18%), brain (–10%), head and neck (–13%), lung (–9%), prostate (–14%) and colorectal (–7%) cancers (Figure 2b) when compared with pre-pandemic levels. Only breast (P < 0.001) and prostate (P = 0.03) were statistically significant. Bladder (+28%), oesophageal (+11%) and skin (+38%) had increases in average weekly radiotherapy courses; none were statistically significant.Fig 2 Total average weekly radiotherapy courses between April 2019 and March 2020 (blue) and April 2020 and March 2021 (orange) for (a) anal and (b) colorectal cancer. Fig 2 Mean weekly courses for breast cancer decreased 18%, as shown in Figure 3 . Prior to the pandemic, the historical standard dose and fractionation regimen of 40.05 Gy in 15 fractions accounted for 96.9% of breast treatments; average weekly radiotherapy courses 33.53. This decreased to 9.62 and accounted for 37.0% of all radical breast treatments between April 2020 and March 2021. The adoption of hypofractionated radiotherapy of 26 Gy in five fractions significantly increased from average weekly radiotherapy courses of 1.02 (2.65%) to average weekly radiotherapy courses of 11.98 (46.1%). Per the qualitative data for breast patients (n = 481), the timing, intent and indication for breast radiotherapy remained unchanged during the pandemic, with only 3.5% receiving a deferred or modified treatment schedule. The main reasons for altered treatment timing were the delay of chemotherapy or surgery prior to radiotherapy treatment. However, in all breast cases where 26 Gy/5 fractions was prescribed, the reason attributed was ‘hypofractionated for COVID’.Fig 3 (a) Total average weekly radiotherapy courses between April 2019 and March 2020 (blue) and April 2020 and March 2021 (orange) and (b) average weekly radiotherapy courses between age groups for breast cancer between April 2019 and March 2021 (orange threshold lines showing mean values for April 2019 to March 2020). Fig 3 The use of hypofractionated radiotherapy varied significantly in other tumour sites. 25 Gy/5 fractions in the neoadjuvant treatment for rectal cancer significantly increased (400%) from 0.35 mean weekly courses (10.47% of all colorectal treatments) to 1.75 (51.12% of all colorectal treatments). However, the rationale for selecting 25 Gy in 5 fractions was given as ‘hypofractionated for COVID’ in only 18% of cases, with one case receiving radiation as replacement for surgery. The timing and intent for rectal radiotherapy was unchanged. Prostate cancer saw a drop in average weekly radiotherapy courses, with April (–27.5%), May (–58.0%), June (–42.8%), July (–12.6%) and December (–20.5%) noting the largest decreases compared with similar months in 2019. Trends are summarised in Figure 4 . Of all prostate patients, for which radiotherapy was decided as their primary treatment, 14.5% of treatments were deferred. These deferrals mostly coincided with decisions made in May, June, July and September 2020, with most patients receiving their deferred treatments in July, August, September and October 2020. Of the 337 prostate patients for whom qualitative data were collected, only six of those underwent radiotherapy as a replacement for surgical intervention. Twelve patients (3.5%) had their treatment hypofractionated from 74 Gy in 37 fractions to 60 Gy in 20 fractions to reduce the number of hospital attendances.Fig 4 (a) Total average weekly radiotherapy courses between April 2019 and March 2020 (blue) and April 2020 and March 2021 (orange) and (b) average weekly radiotherapy courses between age groups for prostate cancer between April 2019 and March 2021 (orange threshold lines showing mean values for April 2019 to March 2020). Fig 4 All bladder patients were treated with the standard 55 Gy in 20 fractions regimen. Average weekly radiotherapy courses increased 28% post-COVID compared with the previous year. Significant increases were found in April (172.7%), May (214.3%) and June (48.6%) 2020 when compared with equivalent months in 2019 (Welch's t-test P = 0.006), as summarised in Figure 5 a. Interestingly, of those completed questionnaires, there was no increase in responses suggesting that radiotherapy indication had changed, with all indicating that radiotherapy was the primary treatment. Ten per cent (3/28) of patients received some modification of concurrent chemotherapy.Fig 5 Total average weekly radiotherapy courses between April 2019 and March 2020 (blue) and April 2020 and March 2021 (orange) for (a) bladder and (b) oesophageal cancer. Fig 5 Oesophageal patients were treated with the standard 50 Gy/25 fraction dose and fractionation schedule. The average weekly radiotherapy courses for oesophageal cancers significantly increased in April (125.0%), May (50%), July (150.0%) and August (40.0%) in 2020 compared with the equivalent months in 2019, as summarised in Figure 5b. The incidences of radiotherapy treatment courses for oesophageal cancers decreased in the months September 2020 to February 2021, increasing again in March 2021 by 100.0%. Within the completed questionnaires, 97% (35/36) were receiving radiotherapy as either primary treatment or replacement of surgery. There was no notable reason for the marked decreases for brain, head and neck and lung treatments. For each of these disease sites the radiotherapy intent remained unchanged, with a low deferral rate (between 0.8 and 2.4%) as per the patient request. Radiotherapy treatment waiting times for all treatments were evaluated as part of this study. Current standards for cancer waiting times are that 95% of all eligible patients should wait no longer than 62 days from referral from primary care clinician with suspected cancer to first cancer treatment, or 31 days from decision to treat to first cancer treatment [13]. The extent to which the COVID-19 pandemic impacted these results was analysed using these waiting time criteria. In year 1, it was found that 265 patients (3.8% of total patients) exceeded the 31 day waiting time and 216 patients (3.1% of total patients) exceeded the 62 day waiting time. In year 2, it was found that 196 patients (3.1% of total patients) exceeded the 31 day waiting time and 46 patients (0.7% of total patients) exceeded the 62 day waiting time. The proportion of patients whose treatment notes reflected that their radiotherapy treatments were interrupted due to a COVID-19 infection for year 2 were also assessed. The number of cancelled appointments was analysed and found that only 58 patients had alterations to their treatment due to COVID-19. Of those patients, 13 patients requested their treatment be deferred due to concerns about COVID-19 or waiting for vaccination. Twenty-two patients had their treatment start date delayed or had a change in their treatment schedule due to having COVID-19 or being a close contact (two palliative). Within our centre, the service-efficiency machine was used as the designated treatment machine for patients with COVID-19 or suspected COVID-19. Following hygiene and distancing measures, this allowed 23 patients to be treated with adjusted breaks in their treatment schedule. Discussion As far as we are aware, these are the first data to assess the effect of the COVID-19 pandemic on radiotherapy delivery in the UK over a full 12-month period. By examining the first 12 months since 1 April 2020 we can assess the effect of both the first and second waves, when most of the population were unvaccinated and health systems had to rely on social mitigations. A full year comparison with the 12 months leading up to the pandemic provides a more rigorous assessment (without short-term changes) and, thus, can better assess genuine shifts. A comprehensive study carried out by Spencer et al. [10] assessed the indirect consequences of the COVID-19 pandemic on radiotherapy services in England between February and June 2020 compared with corresponding months in 2019. Thus, although it covered a larger population, it captured only a brief snapshot of the pandemic's effects on radiotherapy activity. However, up-to-date follow-up data of radiotherapy activity changes due to COVID-19 are publicly accessible from the National Radiotherapy Dataset (RTDS) of providers of radiotherapy services in England [14]. We have shown that the number of patients receiving radiotherapy in the West of Scotland cancer network fell significantly (572 fewer radical treatments) between April 2020 and March 2021, compared with the previous year. It is not possible from the observational nature of these data to ascertain the exact cause. However, the curtailment of diagnostic services almost certainly resulted in a decrease in referrals for treatment. This fall is also masked, to an extent, by significant increases in the use of radiotherapy in bladder and oesophageal cancers. This trend is clearly seen between April and June 2020, but over the 12 months most tumours showed non-significant reductions. The nature and extent of the recovery is also important as this has not previously been shown. We show that although service recovery occurred, it remained depressed relative to 2019. Therefore, it is likely that smaller numbers of patients across all tumour types were diagnosed, referred and deemed suitable for radical treatment. From a population perspective, one could hypothesise that this may result in a future uptick in cancer-related mortality. The one significant outlier in the data is breast radiotherapy, partly due to a change in practice, accelerated by the pandemic, in the administration of hypofractionated radiotherapy [9]. Prior to the pandemic, hypofractionated radiotherapy accounted for about 1% of breast radiotherapy and almost half during. Although the pivotal phase III clinical trial FAST-Forward [15] was published at the outset of the pandemic, it is interesting that all clinicians still attributed the use of 26 Gy/five fractions to COVID-19. It could reflect the rapid adoption of a change that would normally take longer to incorporate. The change was most significant in women under the age of 70 years. Although early reports revealed that more men died as a result of COVID-19 [16], studies have shown that women were more indirectly affected by the COVID-19 pandemic [17]. This was partly a consequence of women bearing the brunt of the social and economic effects [18]. This burden, alongside reallocation of health screening resources for 50–70 year olds [19], may further explain the significant decrease in overall radiotherapy treatments in women <70 years. The use of hypofractionation also significantly increased in rectal cancer. In contrast to breast cancer, only 18% of the subset in the qualitative study were due to COVID-19, reflecting the longstanding use of 25 Gy/5 fractions in rectal radiotherapy [20] and guidance that proposed greater adoption during the pandemic [21]. Most diagnostic and treatment pathways in the detection and management of lower gastrointestinal cancer were severely affected [22]. The initial phases of the COVID-19 service reorganisation led to the National Health Service Bowel Cancer Screening Programme being paused in March 2020, resuming in October 2020, and the main diagnostic tests of colonoscopy and computed tomography colonography being limited to emergency settings. This would have resulted in many patients with suspected lower gastrointestinal cancer experiencing delay in both diagnosis and treatment. Prostate cancer is the most common cancer in men over the age of 50 years [23]. In Scotland, more than 3000 men are diagnosed with prostate cancer every year. Guidance on external beam radiotherapy prostate cancer treatment at the onset of the COVID-19 pandemic was to defer any patients who had not yet started radiotherapy until the disruption had eased [5]. Randomised evidence has shown that the delivery of external beam radiotherapy can be delayed up to 6 months between diagnosis and treatment if patients receive neoadjuvant hormonal therapy or undergo active surveillance. The decrease in radiotherapy treatments for men (–8%) and prostate treatments (–14%) might be a consequence of decisions to employ watchful waiting and active surveillance strategies for low-risk prostate cancer, and deferring treatments for those in the high-risk categories. Although guidance for most treatment sites adopted the Remote, Avoid, Defer, Shorten (RADS) principle [4] for radiotherapy treatments at the onset of the pandemic, bladder and oesophageal cancers were treated with radiotherapy as an alternative to surgery [5]. This change of practice was adopted due to the widespread cancellation of cystectomies and oesophagectomies for bladder and oesophageal cancers, respectively, together with the omission or reduction of chemotherapy. The marked increase (28%) of new-start courses of bladder radiotherapy treatments observed may be attributed to the introduction of radical radiotherapy with a radiosensitiser (gemcitabine) adopted within our centre. Similarly, an increase of 11% was found for oesophageal cancers, with 97% of cases receiving radiotherapy as their primary treatment or as a replacement for surgery. As the use of surgical interventions is the standard approach to the primary care treatment of bladder and oesophageal cancers, the effects on excess deaths due to limitations to surgical services and the replacement of such with radiotherapy are not yet known. Despite routine diagnostic services and screening programmes being reinitiated towards the end of 2020, average weekly radiotherapy courses remained lower than the year prior to the pandemic. In Scotland, the COVID-19 pandemic has impacted patient waiting times, with some Boards highlighting staffing and capacity issues as the main contributing factors [13]. However, the waiting time statistics showed that COVID-19 had no impact on our centres' waiting times and treatments remained well within the 5% tolerance level for both 31 and 62 days. This suggests that the reduction in treatments reflects a lower throughput of patients further up the treatment pathway, for example in diagnostic and screening services, many of which were suspended. Finally, the effect of routine referrals from general practitioners has not been considered as part of this study. However, with patients being urged to only present if they had major or urgent health concerns will have had unknown consequences on cancer services. Additionally, the introduction of remote appointments and consultations meant fewer patient examinations, which could have led to a higher proportion of missed diagnoses, leading to fewer radiotherapy treatments. Conclusion From the onset of the COVID-19 pandemic, and the first national lockdown, many healthcare services were suspended or operated at a substantially reduced capacity, leading to a reduced number of people seeking health care. The significant fall in new-start radiotherapy treatments can probably be attributed to the initial suspension of cancer diagnostic services and interventional surgeries, together with the rapid increase in the use of hypofractionated treatment regimens across several treatment sites. Due to frequent changes in local and national lockdown measures, and healthcare guidance over the course of the pandemic, it will be challenging to interpret further associations between COVID-19 and radiotherapy treatments. The qualitative data collected as part of this study provided a succinct and robust method for data collection, ensuring consistency among clinicians; it did not allow for interpretation on why specific changes were made. Therefore, assumptions were made as to the reasoning behind the results found in the population. As there is no centralised data collection in Scotland with this extended dataset, the impact of COVID-19 has to be assessed by each radiotherapy centre independently, making the process of data collection time-consuming. Data collection for a follow-up study looking at the subsequent effect 2 years post-COVID-19 is currently underway, with the additional aims of assessing the effect of stage migration due to COVID in treatment patterns, as well as cancer-specific outcomes. A recent publication [24] estimated the excess deaths worldwide due to the COVID-19 pandemic and reported that the full impact has been much greater than indicated by reported deaths due to COVID-19 alone. As no longer-term cancer-outcome data are currently available, the effects on patient outcomes from changes in radiotherapy activity are not yet known and require urgent review. Outcomes and post-treatment survival rates in the medium (1 year) and long (5 year) term will be of significant interest in the future. Funding LG funding is provided by CRUK RadNet Glasgow (C16583/A28803). SMO’C is a CRUK-funded clinical senior lecturer at the Institute of Cancer Sciences (grant number CAN-RES- UK (C7932/A25142). CW/ DC are funded by Radiotherapy Research Infrastructure Award, Beatson Cancer Charity(20-21-037). Author Contributions All authors wrote, read and approved the final manuscript. Conflicts of Interest The authors declare no conflicts of interest. Appendix A Supplementary data The following is the Supplementary data to this article:Multimedia component 1 Multimedia component 1 Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.clon.2022.11.018. ==== Refs References 1 World Health Organization https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19–-11-march-2020 2022 2 England N.H.S. Next steps on NHS response to COVID-19: Letter from Sir Simon Stevens and Amanda Pritchard. Available at 17 Mar https://www.england.nhs.uk/coronavirus/wp-content/uploads/sites/52/2020/03/20200317-NHS-COVID-letter-FINAL.pdf 2020 3 Liang W. Guan W. Chen R. Wang W. Li J. Xu K. Cancer patients in SARS-CoV-2 infection: a nationwide analysis in China Lancet Oncol 21 3 2020 335e337 10.1016/S1470-2045(20)30096-6 32066541 4 National Institute for Health and Care Excellence COVID-19 rapid guideline: delivery of radiotherapy Available at https://www.nice.org.uk/guidance/NG162 2020 5 Royal College of Radiologists Coronavirus (COVID-19): cancer treatment documents Available at https://www.rcr.ac.uk/college/coronavirus-covid-19-what-rcr-doing/clinical-information/coronavirus-covid-19-cancer 2021 6 Maringe C. Spicer J. Morris M. Purushotham A. Nolte E. Sullivan R. The impact of the COVID-19 pandemic on cancer deaths due to delays in diagnosis in England, UK: a national, population-based, modelling study Lancet Oncol 21 8 2020 1023 1034 10.1016/S1470-2045(20)30388-0 32702310 7 Sharma R. Plummer R. Stock J. Greenhalgh T.A. Ataman O. Kelly S. Clinical development of new drug – radiotherapy combinations Nat Rev Clin Oncol 13 2016 627 642 10.1038/nrclinonc.2016.79 27245279 8 Royal College of Radiologists Coronavirus (COVID-19): cancer treatment documents. Available at https://www.rcr.ac.uk/college/coronavirus-covid-19-what-rcr-doing/clinical-oncology-resources/coronavirus-covid-19-cancer 9 Brunt A.M. Haviland J.S. Wheatley D.A. Sydenham M.A. Alhasso A. Bloomfield D.J. Hypofractionated breast radiotherapy for 1 week versus 3 weeks (FAST-Forward): 5-year efficacy and late normal tissue effects results from a multicentre, non-inferiority, randomised, phase 3 trial Lancet 395 10237 2020 1613 1626 10.1016/S0140-6736(20)30932-6 32580883 10 Spencer K. Jones C.M. Girdler R. Roe C. Sharpe M. Lawton S. The impact of the COVID-19 pandemic on radiotherapy services in England, UK: a population-based study Lancet Oncol 22 3 2021 309 320 10.1016/S1470-2045(20)30743-9 33493433 11 The Beatson West of Scotland Cancer Centre Available at https://www.beatsoncancercharity.org/about-us/the-beatson-west-of-scotland-cancer-centre/ 12 Zhu X. Yuan W. Shao J. Juang K. Wang Q. Yao A. Risk factors for mortality in patients over 70 years old with COVID-19 in Wuhan at the early break: retrospective case series BMC Infect Dis 821 2021 21 10.1186/s12879-021-06450-8 13 Public Health Scotland Cancer Waiting Times. Available at https://publichealthscotland.scot/publications/cancer-waiting-times/cancer-waiting-times-1-january-to-31-march-2022/ 14 Data Cancer RTDS COVID-19 Dashboard. Available at https://www.cancerdata.nhs.uk/covid-19/rtds 15 Brunt A.M. Haviland J.S. Sydenham M. Agrawal R.K. Algurafi H. Alhasso A. Ten-year results of FAST: a randomized controlled trial of 5-fraction whole-breast radiotherapy for early breast cancer J Clin Oncol 38 28 2020 3261 3272 10.1200/JCO.19.02750 32663119 16 Capuano A. Rossi F. Paolisso G. Covid-19 kills more men than women: an overview of possible reasons Front Cardiovasc Med 131 2020 7 10.3389/fcvm.2020.00131 17 Connor J. Madhavan S. Mokashi M. Amanuel H. Johnson N.R. Pace L.E. Health risks and outcomes that disproportionately affect women during the Covid-19 pandemic: a review Soc Sci Med 113364 2020 266 10.1016/j.socscimed.2020.113364 18 Burki T. The indirect impact of COVID-19 on women Lancet Infect Dis 20 8 2020 904 905 10.1016/S1473-3099(20)30568-5 32738239 19 Duffy S.W. Seedat F. Kearins O. Press M. Walton J. Myles J. The projected impact of the COVID-19 lockdown on breast cancer deaths in England due to the cessation of population screening: a national estimation Br J Cancer 126 2022 1355 1361 10.1038/s41416-022-01714-9 35110696 20 Erlandsson J. Holm T. Pettersson D. Berglund A. Cedermark B. Radu C. Optimal fractionation of preoperative radiotherapy and timing to surgery for rectal cancer (Stockholm III): a multicentre, randomised, non-blinded, phase 3, non-inferiority trial Lancet Oncol 18 2017 336 346 10.1016/S1470-2045(17)30086-4 28190762 21 O'Cathail S.M. Gilbert D.C. Sebag-Montefiore D. Muirhead R. Challenges and consequences of COVID-19 in the management of anorectal cancer: coming together through social distancing Clin Oncol 32 2020 413 416 10.1016/j.clon.2020.04.009 22 Morris E.J.A. Goldacre R. Spata E. Mafham M. Finan P.J. Shelton J. Impact of the COVID-19 pandemic on the detection and management of colorectal cancer in England: a population-based study Lancet Gastroenterol Hepatol 6 3 2021 199 208 10.1016/S2468-1253(21)00005-4 33453763 23 Cancer Research UK Prostate cancer statistics. Available at https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/prostate-cancer#heading-Zero 24 Wang H. Paulson K.R. Pease S.A. Watson S. Comfort H. Zheng P. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21 Lancet 399 10334 2022 1513 1536 10.1016/S0140-6736(21)02796-3 35279232	0	PMC9708615	NO-CC CODE	2022-12-15 23:15:54	no	Clin Oncol (R Coll Radiol). 2022 Nov 30; doi: 10.1016/j.clon.2022.11.018	utf-8	Clin Oncol (R Coll Radiol)	2,022	10.1016/j.clon.2022.11.018	oa_other
==== Front Advances in Family Practice Nursing 2589-4722 2589-420X Published by Elsevier Inc. S2589-420X(22)00034-X 10.1016/j.yfpn.2022.11.011 Article Emerging Mental Health Issues in Children and Adolescents Secondary to the COVID-19 Pandemic Bishop Kellie DNP, APRN, CPNP-PC, PMHS* 30 11 2022 30 11 2022 © 2022 Published by Elsevier Inc. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Keywords COVID-19 Coronavirus Pandemic Adolescents Depression Anxiety Suicide Mental Health ==== Body pmcKey Points • Determine the prevalence of anxiety, depression, and suicide among adolescents prior to and during the COVID-19 pandemic. • Identify evidence-based screening tools to assess for mental illness in adolescents. • Identify strategies to foster resilience and promote well-being in adolescents. • Implication strategies for advanced practice nurses, including educating adolescents and families on mental health promotion, and advocating for mental health awareness and resources for adolescents. Synopsis This article will examine and compare the incidence and prevalence of mental health issues, including depression, anxiety, and suicide, among adolescents prior to and during the COVID-19 pandemic. It will discuss contributing factors, clinical presentation, screening tools, treatment options, and implications for advance practice nurses. This article will prepare the advance practice nurse to promote mental wellness and identify, screen for, and appropriately manage emerging mental health issues in this vulnerable population. Once a highly stigmatized and avoided topic, mental health, particularly among children and adolescents, has become widely recognized societally. Children and adolescents have long experienced depression, anxiety, and devastating rates of suicide. These issues are more widely recognized today due in large part to the drastic increase in mental health issues in children and adolescents during the COVID-19 pandemic. This article will highlight common mental health issues in children and adolescents and the impact of COVID-19 on their overall mental health. It will provide practitioners with the screening tools and opportunities to foster resilience in children and adolescents during this unprecedented time. Background In April 2022, the American Academy of Pediatrics (AAP), the American Academy of Child and Adolescent Psychiatry (AACAP), and the Children’s Hospital Association (CHA) declared a national emergency in children’s mental health and called on policymakers to join them in taking action1. Between March and October of 2020, emergency department visits for mental health issues in children aged 5-11 years rose 24% and 31% in adolescents aged 12-17 years1. Miller et al. explains that current evidence indicates that adolescent mental health symptoms of anxiety, depression, and posttraumatic stress disorder (PTSD) during the pandemic range from 21% to 65% incidence rates. Furthermore, preexisting mental health and behavioral concerns in children and adolescents are exacerbated by stressful events, such as the pandemic and its consequences2. Significance Understanding the mental and social development of children and adolescents is critical in attempting to understand the drastic increase in mental health issues during the pandemic. The U.S. Surgeon General’s Advisory explains that mental health is shaped by two primary factors: biological and environmental3. Biological factors include genes and brain chemistry which are inherent factors to the person. Environmental factors include life experiences, which can determine whether genetic pre-disposition to mental health disorders manifest. Environmental factors include prenatal considerations, such as drug and alcohol exposure, birth complications, discrimination, racism, and adverse childhood experiences (ACEs), such as abuse, neglect, exposure to violence, and socioeconomic disparities. These ACEs negatively affect the child’s sense of safety, stability, bonding, and well-being3. The biologic and environmental factors interact with each other so a child who is genetically predisposed to anxiety may be more affected by an environmental stressor than children without the biologic predisposition. Due to an increase in environmental stressors, social isolations and shutdowns, many children and adolescents are experiencing increased rates of mental health disorders. Discussion COVID-19 The COVID-19 pandemic has caused upheaval and stress in all aspects of life. Children and adolescents are particularly vulnerable to such stressors as they are still learning how to regulate themselves and overcome stressful situations. Daily life changed quickly resulting in a sudden and drastic change in how our youth attend school, interact with peers, receive healthcare, and participate in activities. Simultaneously, many parents became unemployed, families became financially unstable and many were left with concerns about food, healthcare, and housing. Many children and adolescents were affected by contracting COVID-19 themselves, watching loved ones become seriously ill, and experiencing death. The U.S. Surgeon General’s Advisory reports that as of June 2021, more than 140,000 American children had lost a parent or grandparent caregiver to COVID-193. Furthermore, vulnerable populations, such as those with lower socioeconomic status, have contracted and died from COVID-19 at higher rates than less vulnerable populations. Therefore, the children and adolescents in those vulnerable populations have the environmental risk factors of socioeconomic disparities, discrimination, racism, etc., with the added stresses of losing loved ones at higher rates and living in a pandemic. In understanding the concepts of executive function and resiliency, and how children develop those skills, it is logical that the pandemic has led to an increase in mental health issues among our youth. Depression The prevalence of depression among children and adolescents has been increasing for years. However, the rates have drastically increased since the beginning of the pandemic in early 2020. In a poll conducted by the CDC in 2019, pre-pandemic, 36.7% of American high school students reported experiencing persistent feelings of sadness and hopelessness4. In a similar poll conducted by the CDC in 2021, one year into the pandemic, 44% of American high school students reported persistent feelings of sadness and hopelessness4. Contributing factors to this increase include stress at home related to the pandemic and increased rates of child abuse. In the 2021 poll, 55% of students reported experiencing emotional abuse by a parent or other adult in their own home, including swearing at and insulting the child4. Furthermore, 11% reported experiencing physical abuse by a parent or other adult in their home, including hitting, kicking, and beating the child4. Twenty-nine percent reported that a parent or other adult in the home lost a job due to the pandemic, causing increased stress in the home4. Social connectedness with peers through school is of vital importance. Due to the lack of social interaction and alternate methods of teaching during the pandemic, children and adolescents have lacked the social interaction that promotes mental well-being, leading to increased prevalence of depression (see Table 1 for symptoms). Additionally, schools and healthcare providers are pivotal in detecting and reporting child maltreatment. Many of these incidences have gone undetected during the pandemic due to alternate methods of teaching and an increase in the use of telemedicine.Table 1 Common Symptoms of Depression, Anxiety, and Suicide in Children and Adolescents10,11,12 Depression at least 2 weeks of a depressed or irritable mood and/or loss of interest or pleasure in most activities Symptoms present most of the day, nearly every day Appetite increase or decrease Sleeping too much or too little Decreased energy Decreased activity level Impaired concentration Thoughts of worthlessness, hopelessness, and guilt Mood changes Irritability Suicidal thoughts or actions Anxiety Generalized and persistent fear and worry Weight loss Pallor Tachycardia Tremors Muscle cramps Paresthesias Hyperhidrosis Headaches Abdominal pain Specific fears and worries related to type of anxiety- separation, generalized, social, OCD, panic, phobias Suicide Threatening to hurt or kill oneself or talking about wanting to hurt or kill oneself Seeking methods to kill oneself (firearms, pills, etc.) Talking or writing about death, dying, or suicide (out of the ordinary for the person) Feeling hopeless and trapped Uncontrolled anger or rage Seeking revenge Reckless and risky behavior Increasing substance use Withdrawing from family, friends, activities Inability to sleep or sleeping too much, anxious, agitated Dramatic mood changes Seeing no reason for living Anxiety In addition to the increase in depression, there has also been a marked increase in anxiety among children and adolescents during the pandemic. Name et al. note that 11.6% of children and adolescents experienced symptoms of anxiety prior to the pandemic5. The pandemic has shown to increase the prevalence of anxiety symptoms in children and adolescents to 20.5%5. Anxiety and depression are often comorbid conditions and have many of the same contributing factors. Social isolation, the unknown of what will happen, and the fear of illness or death of oneself or loved ones all contribute to the rising anxiety in our youth. Hawes et al. examined the incidence of mental health symptoms during the pandemic and noted that there were high rates of clinically elevated symptoms (see Table 1) of anxiety during the pandemic in subjects aged 12-22 years who were examined6. The increased use of technology in place of social interaction has also contributed to the increase in anxiety among children and adolescents. As Gray et al. note, social distancing efforts have resulted in interaction primarily via virtual platforms7. Adolescents, particularly those with existing anxiety or anxious tendencies, are preoccupied with how others perceive them. The increase in social media and virtual platforms during the pandemic has led to adolescents making social comparisons based on online presentations to attain a desired image. This leads to a conflict between the virtual and real self, increasing anxiety. Additionally, online bullying can reach people much faster than face to face conflict, forcing those who are bullied to socially isolate even further7. Children and adolescents with anxiety require stability to feel secure and the unknown nature of the pandemic has led to more dysregulation in these children. Suicide The most devastating consequence of mental illness is suicide. Due to the increase in mental health disorders, there has also been an increase in the prevalence of suicide. Suicide attempt emergency department visits in adolescents aged 12-17 years increased by more than 50% in early 2021 in comparison to the same period in 2019, prior to the pandemic1. The contributing factors to the drastic increase in attempted suicide are similar to those of the increased prevalence of depression and anxiety: increased rates of loneliness and sadness, social isolation, losing loved ones to COVID-19, stress at home, online influences, more time at home with things like medications and guns, and an increase in child abuse and maltreatment. It is vital to recognize the risk factors and symptoms of suicidal children and adolescents (see Table 1). The increased prevalence of suicide among our youth is a staggering statistic that demands attention from healthcare professionals. Mental Health Interventions It is critical that all healthcare providers evaluating and treating children and adolescents are aware of the emerging mental health issues among this vulnerable population. Thorough wellness visits should include obtaining past medical history, family medical history, family assessment, medications and supplements used, developmental surveillance, physical examination, and anticipatory guidance. As the pandemic continues, it is especially important to fulfill all portions of the wellness exam. The family history and family assessment can alert the provider to a predisposition or environmental factors that could contribute to the child developing a mental health disorder. The developmental surveillance is crucial to identify any developmental issues in younger children, many of which can be worsened by social isolation and alternative methods of teaching. The private interviews with adolescent patients allow the patient to disclose symptoms of mental health issues, as well as any safety concerns they have, such as abuse in the home. Performing proper surveillance allows the provider to determine if further screening for mental health issues is indicated. Screening and Assessment of Mental Illness The drastically increasing prevalence rates of mental health issues in children and adolescents make it vitally important for healthcare providers to be aware of mental health surveillance and screening. Providers serving the pediatric population should approach children and families with an open, empathetic, and nonjudgmental demeanor. Particularly when discussing sensitive topics, such as mental health, it is important to gain trust. Every wellness visit, and episodic visits if indicated, should include a targeted history focused on the child’s behavior and any functional impairment13. The provider must be aware of developmentally appropriate behavior at various ages to discern if behavior exhibited is abnormal. It is important to listen and address behavioral, mental, and functional concerns from both the caregiver and child. If a patient or caregiver indicate a concern about the child’s behavior or mental wellness either directly or through developmental surveillance, an appropriate screening tool should be utilized to assess for mental illness. Several evidence-based screening tools (see Table 2 ) are available to screen for mental health disorders, including depression and anxiety. If the screening tool utilized indicates a concern for the disorder examined, i.e. depression or anxiety, the provider should further inquire about specific symptoms to determine if a diagnosis is indicated. Providers should utilize the DSM-V diagnostic criteria in assessing and diagnosing mental health disorders. Through more extensive discussion of symptoms, the provider will be able to discern if the child meets diagnostic criteria for a specific mental illness and what type of treatment or management is indicated. See Figure 1 for an assessment algorithm.Table 2 Evidence-Based Screening Tools13 Instrument Ages (years) Reporter Number of Items Time to Complete (minutes) General Mental Health Pediatric Symptom Checklist (PSC) 4-18 Parent Child 35, 17 (different versions for different ages) 5-10 Strengths and Difficulties Questionnaire (SDQ) 4-18 Parent Child Teacher 25 5 Anxiety Self-Report for Childhood Anxiety Related Emotional Disorders (SCARED) 8-18 Parent Child 41 5 Depression Patient Health Questionnaire (PHQ-9) 12+ Child 9 < 5 Center for Epidemiological Studies Depression Scale for Children (CES-DC) 6-18 Child 20 5 Beck Depression Inventory 7-14 13+ Child Child 21 20 5-10 5-10 Figure 1 Assessment and management algorithm14 Treatment of Pediatric Mental Health Concerns Initiating proper treatment of mental health disorders in children is imperative to prevent worsening symptoms. A child who verbalizes suicidal ideation with or without a plan should be referred immediately to a child behavioral health provider or hospitalized. If the patient denies current suicidal ideation and the screening tool utilized and patient interview do not indicate a diagnosable mental health condition, the patient and caregiver should be educated about signs and symptoms that would indicate the need for a follow-up appointment. Motivational interviewing can be beneficial in these scenarios for stress management and problem solving13. If the patient requests to see a mental health specialist in the absence of a diagnosed condition, a referral should be initiated to a pediatric mental health specialist. Zhang et al. demonstrate how research-based psychological counseling reduces the symptoms of depression and anxiety in adolescents8. A child who meets diagnostic criteria for a mental health disorder should be treated with medication, if indicated, and referred to a pediatric behavioral health provider for therapy and advanced management. There are no FDA approved anxiolytic medications for pediatric patients. Patients presenting with an anxiety disorder should be referred for behavioral therapy. The urgency and determination of inpatient or outpatient treatment will depend on the level of severity (see Figure 1). Play Therapy for preschool aged children and Cognitive Behavioral Therapy (CBT) for school aged and older are often effective alone in treating pediatric anxiety13. In older children and adolescents, or if CBT alone is ineffective, there are several selective serotonin reuptake inhibitors (SSRI) that are FDA approved for pediatric patients and have proven safe and effective in treating anxiety (see Table 3 )13.Table 3 FDA approved medications for depression and anxiety in pediatrics13 Drug Class & Examples Conditions Treated Primary Care Drug Interactions Common Side Effects Selective Serotonin Reuptake Inhibitors (SSRIs) Fluoxetine (Prozac, Sarafem) FDA approved 8+ Anxiety Major depressive disorder Obsessive compulsive disorder Selective mutism Multiple drug interactions Contraindicated drugs- MAOIs, tryptophan, St. John’s wort, thioridazine, TCAs Diet- avoid grapefruit juice and alcohol Headache, nervousness, insomnia or sedation, fatigue, nausea, diarrhea, dyspepsia, appetite loss Escitalopram (Lexapro) FDA approved 7+ Depression Anxiety Same as above but better drug interaction profile Same as above Fluvoxamine FDA approved 8+ Major depressive disorder Obsessive compulsive disorder Increased risk of bleeding- NSAIDs, aspirin, warfarin Same as above Sertraline (Zoloft) FDA approved 6+ Major depressive disorder Obsessive compulsive disorder Same as above Diet- may interact with grapefruit juice Same as above Serotonin Norepinephrine Reuptake Inhibitors (SNRIs) Duloxetine (Cymbalta, Irenka) FDA approved 7+ Major depressive disorder Generalized anxiety disorder Multiple drug interactions Risk for toxic levels- SSRIs, amphetamines, guanfacine (potentiates BP effects) Diet- avoid grapefruit juice and alcohol Nausea, headache, dizziness, diaphoresis, behavior activation Venlafaxine (Effexor XR) FDA approved 8+ Major depressive disorder Generalized anxiety disorder Same as above Same as above The priority intervention in regard to pediatric depression is to determine suicidality. Suicidal risk is greatest within 4 weeks of an initial depressive episode13. Patients with acute suicidal ideation and intent with a plan, unstable behavior, psychosis, or risk of abuse should be referred for immediate psychiatric evaluation and treatment at an inpatient pediatric behavioral health facility or a hospital. If the patient is at low risk for suicide, the provider can initiate treatment through outpatient modalities. There are several FDA approved medications for depression in pediatrics (see Table 3). SSRIs are typically first-line treatment. Sertraline and fluoxetine are commonly prescribed as they can be prescribed safely at younger ages (6 years and 8 years, respectively). The evidence indicates that the best treatment responses result from a combination of CBT and SSRIs13. Medication maximum response can take 4 to 6 weeks to attain, but doses can be titrated every 2 to 4 weeks if significant side effects are absent13. Upon initiating antidepressant therapy, it is important to inform caregivers and patients that activation and mania can occur with antidepressant use. If any concerning symptoms arise, such as decreased impulse control, increased risk taking behavior, and significantly elevated mood/mania, immediate follow-up is indicated. Providers should make contact with the patient or caregiver within 3 days of initiating pharmacotherapy and follow-up in clinic weekly through the first 4 weeks of treatment13. Once stable, follow-ups should occur every 3 months or as needed for side effects and changes in symptoms13. The specialty of pediatrics is unique in that the patient includes the child, but also the family/caregiver and environment in which the child lives. Therefore, it is crucial to involve the family in the treatment of pediatric mental health conditions. Individual CBT is indicated for children with mental health concerns, but family therapy is also beneficial. A study conducted by Inscoe et al. indicated that caregiver involvement in trauma-informed mental health services led to better outcomes for children with co-occurring traumatic stress and suicidal thoughts and behaviors9. The COVID-19 pandemic has caused trauma for many children and adolescents, leading to increased prevalence of mental health issues and suicidal thoughts and behaviors. Therefore, obtaining trauma-informed mental health services for those patients could prove beneficial. Healthcare providers may be the only mandated reporters that children and adolescents encounter regularly during the pandemic, so it is crucial to ask the important questions and perform thorough physical examinations. Anticipatory guidance of developmental milestones should continue, and providers should also include strategies for fostering resilience in children and adolescents. The concept of resiliency is one that helps many at-risk youth overcome the obstacles that put them at risk for developing mental health problems and is largely determined by executive function and regulation. As Miller et al. explains, schools incorporate executive function into their curriculum. Executive function involves the processes responsible for regulating emotion, coordinating brain function, and influencing emotional expression to promote healthy social-emotional development and resilience2. However, the alternate methods of teaching during the pandemic have limited the ability of the schools to teach those concepts well. Families can also foster resilience in their children with a variety of positive methods. To promote resilience, it is important to empower children and caregivers to recognize, manage, and learn from their emotions3. This includes caregivers addressing their own mental health and substance use problems, modeling positive relationships, and promoting healthy and positive relationships between their children and others, social media, and technology3. Caregivers should be educated about the connection between mental health and physical health2, and that toxic stress affects the long-term health of children and adolescents3. Providing thorough education to caregivers allows them to empower and instill resilience in their children. Implications for Advanced Practice Nurses Healthcare providers are patient advocates and trusted professionals. One of the responsibilities of this role is to advocate for policies that promote well-being for the patients served. The evidence indicates a mental health crisis among American children and adolescents, and primary care providers serving this population should be involved in advocating for and promoting access to quality healthcare and mental health services1 , 3. There are emerging mental health concerns in children and adolescents since the onset of the pandemic. In utilizing the appropriate interviewing strategies, screening tools, and diagnostic and management processes, advanced practice nurses can properly identify and treat the rapidly emerging mental health conditions among America’s youth. Additionally, the specialty knowledge that advanced practice nurses have regarding mental health and child development equip them to be advocates for enhanced coverage of pediatric mental health services. Many public medical insurances cover mental health services but there remain many private insurance policies that have limited coverage for mental health conditions. Access to care continues to be a barrier for children to obtain mental health services. Unfortunately, there are still many areas in America that lack mental health services for pediatric patients. Advocating for legislation that allows for advanced practice nurses to practice at the full scope of their certification and licensure can create opportunities for advanced practice nurses to serve this underserved population. The role of the advanced practice nurse in providing primary care for pediatric patients spans well beyond the physical health of the child. It is essential for advanced practice nurses to recognize the disparities in pediatric mental health and work diligently to diminish them. Conclusion In conclusion, the literature demonstrates an increase in the incidences of depression, anxiety, and suicidal behavior among children and adolescents5 , 6. This evidence supports the declaration of a national emergency in pediatric mental health. Primary care healthcare providers serving children and adolescents should know the risk factors, assessment strategies, and treatment modalities for pediatric mental health issues. Furthermore, pediatric healthcare providers should be aware of the increasing incidences of mental illness among American youth and advocate for the resources to combat this national emergency. Further research should be conducted to observe the efficacy of various mental health treatments in pediatric patients, advance the assessment tools available to screen for and diagnose mental health conditions in children and adolescents, and how the presentation and incidence of pediatric mental health issues change as the COVID-19 pandemic evolves. Clinical Instructor; College of Nursing; University of Arkansas for Medical Sciences; Little Rock, AR PO Box 118 Plumerville, AR 72127; [email protected] Teresa Whited, DNP, APRN, CPNP-PC Associate Dean for Academics; College of Nursing; University of Arkansas for Medical Sciences; Little Rock, AR 3080 Windcrest Drive Conway, AR, 72034; [email protected] Disclosure Statement The Authors have nothing to disclose. ==== Refs References 1 Ray G. Pediatricians, child and adolescent psychiatrists and children’s hospitals declare national emergency in children’s mental health. Indiana State Nurse’s Association. 2022. http://www.publications.nursingald.com/indiana-bulletin-february-2022/66246745/5. Accessed June 1, 2022. 2 Miller R. Moran M. Shomaker L.B. Seiter N. Sanchez N. Verros M. Rayburn S. Johnson S. Lucas-Thompson R. Health effects of covid-19 for vulnerable adolescents in a randomized controlled trial School Psychology 36 5 2021 293 302 10.1037/spq0000458 34591584 3 Murthy, VH. Protecting youth mental health. The U.S. Surgeon General’s Advisory. 2021. http://www.hhs.gov/sites/default/files/surgeon-general-youth-mental-health-advisory.pdf. Accessed June 1, 2022. 4 Centers for Disease Control and Prevention. New CDC data illuminate youth mental health threats during the covid-19 pandemic. CDC Newsroom Releases. 2022. https://www.cdc.gov/media/releases/2022/p0331-youth-mental-health-covid-19.html. Accessed June 21, 2022. 5 Racine N. McArthur B.A. Cooke J.E. Eirich R. Zhu J. Madigan S. Global prevalence of depressive and anxiety symptoms in children and adolescents during covid-19 JAMA Pediatrics 2021 10.1001/jamapediatrics.2021.2482 6 Hawes M.T. Szenczy A.K. Klein D.N. Hajcak G. Nelson B.D. Increases in depression and anxiety symptoms in adolescents and young adults during the covid-19 pandemic Psychological Medicine 2021 10.1017/S0033291720005358 7 Gray H, Makowski A, Parrish E. From social distancing to social isolation: adolescent anxiety and the impact of covid-19. Kentucky Nurse. 2022. https://www.s3.amazonaws.com/nursing-network/production/files/109005/original/Kentucky_Nurse_March_2022.pdf?1647622250. Accessed June 1, 2022. 8 Zhang J. Zixiang Z. Zhang W. Intervention of research-based psychological counseling on adolescents’ mental health during the covid-19 epidemic Psychiatria Danubina 33 2 2021 209 216 10.24869/psyd.2021.209 9 Inscoe A.B. Donisch K. Cheek S. Stokes C. Goldston D.B. Asarnow J.R. Trauma-informed care for youth suicide prevention: a qualitative analysis of caregivers’ perspectives Psychological Trauma: Theory, Research, Practice, and Policy 14 4 2022 653 660 10.1037/tra0001054 34166044 10 Walter HJ, DeMaso DR. Mood disorders. In: Kliegman RM, St Geme JW, Blum NJ, Shah SS, Tasker RC, Wilson KM, Behrman RE, eds. Nelson Textbook of Pediatrics. Elsevier; 2020:217-224. 11 Walter HJ, DeMaso DR. Suicide and attempted suicide. In: Kliegman RM, St Geme JW, Blum NJ, Shah SS, Tasker RC, Wilson KM, Behrman RE, eds. Nelson Textbook of Pediatrics. Elsevier; 2020:225-228. 12 Rosenberg DR, Chiriboga JA. Anxiety disorders. In: Kliegman RM, St Geme JW, Blum NJ, Shah SS, Tasker RC, Wilson KM, Behrman RE, eds. Nelson Textbook of Pediatrics. Elsevier; 2020:211-216. 13 Walter HJ, DeMaso DR. Psychosocial assessment and interviewing. In: Kliegman RM, St Geme JW, Blum NJ, Shah SS, Tasker RC, Wilson KM, Behrman RE, eds. Nelson Textbook of Pediatrics. Elsevier; 2020:184-188. 14 Garzon DL, Starr NB, Chauvin J. Neurodevelopmental, behavioral, and mental health disorders. In: Maaks, DLG, Starr NB, Brady MA, Gaylord NM, Driessnack M, Duderstadt KG, eds. Burns’ Pediatric Primary Care. Elsevier; 2020:421-455.	0	PMC9708616	NO-CC CODE	2022-12-02 23:17:31	no	2022 Nov 30; doi: 10.1016/j.yfpn.2022.11.011	utf-8	null	null	null	oa_other
==== Front Transplant Proc Transplant Proc Transplantation Proceedings 0041-1345 1873-2623 Elsevier Inc. S0041-1345(22)00831-4 10.1016/j.transproceed.2022.11.009 Article Two Cases of Possible Exacerbation of Chronic Rejection After Anti-SARS-CoV-2 mRNA Vaccination: Case Report. Okamoto Tatsuya 1⁎ Okajima Hideaki 12 Ogawa Eri 1 Uebayashi Elena Yukie 1 Yamamoto Miki 1 Kadohisa Masashi 1 Yamada Yosuke 3 Minamiguchi Sachiko 3 Haga Hironori 3 Hatano Etsurou 1 1 Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan. 2 Department of Pediatric Surgery, Kanazawa Medical University, Kanazawa 920-0265, Japan. 3 Department of Diagnostic Pathology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan. ⁎ Address all correspondence to: Tatsuya Okamoto, M.D., Department of Surgery, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan 30 11 2022 30 11 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. In post-liver transplant patients, SARS-CoV-2 infection is a health threat and novel mRNA vaccines such as Pfizer-BioNTech BNT162b2 and Moderna mRNA-1273 are aggressively recommended. However, there are few reports on its side effects, and some of them may have potentially fatal side effects. We have experienced two post-liver transplant patients with exacerbated chronic rejection after vaccination, one of whom had to be re-transplanted, and the other who is still in the process of liver function without improvement. These alarming cases will be presented as case reports. Key words Liver transplantation (LT) SARS-CoV-2 mRNA vaccination Acute cellular rejection (ACR). Chronic rejection (CR) Abbreviations ACR, Acute cellular rejection CR, Chronic rejection DDLT, Deceased donor liver transplantation LDLT, living donor liver transplantation LT, liver transplantation SARS-CoV-2, severe acute respiratory syndrome coronavirus 2 ==== Body pmcIntroduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global public health threat that shows no signs of abating. With the rapid development and widespread use of novel mRNA vaccines, such as the Pfizer-BioNTech (New York, NY, USA; and Mainz, Germany) BNT162b2 and Moderna (Cambridge, MA, USA) mRNA-1273 vaccines, it has become possible to prevent infectious diseases and to control the severity of infection when it occurs. Patients using immunosuppressants after organ transplantation are more prone than healthy individuals to exacerbation of clinical symptoms associated with SARS-CoV-2 infection, which is directly related to increased mortality, so it is recommended that these patients be proactively vaccinated against SARS-CoV-2 [1,2]. However, information on the safety and side effects of SARS-CoV-2 vaccination remains insufficient, such as the possibility that it may accelerate the immune-response leading to rejection in these patients. We herein report two liver transplantation (LT) recipients with exacerbation of chronic rejection (CR) after SARS-CoV-2 vaccination. Case presentation Case 1: A 16-year-old girl had undergone living donor liver transplantation (LDLT) at 9 months old due to biliary atresia from an ABO blood type identical donor. She had a history of acute cellular rejection (ACR) at 9 years old, and chronic rejection (CR) and liver fibrosis were revealed with a biopsy at 12 years old; she was therefore followed up with Tacrolimus, Everolimus, and Prednisolone as immunosuppressants. Her liver function at the time of the first vaccination was a Child-Pugh score of 8 points (AST 92 U/L, ALT 60 U/L, GGTP83 U/L, T-Bil 3.4 mg/dL, Alb 2.8 g/dL, PT-INR 1.12). Three weeks after receiving the first dose of the SARS-CoV-2 vaccine, the second dose was given (BNT162b2 for both vaccinations). Subsequently, a blood test 49 days after the second vaccination revealed a Child-Pugh score of 10 points (AST 230 U/L, ALT 182 U/L, GGTP 100 U/L, T-Bil 17.4 dL, Alb 2.5 g/d L, PT-INR 1.29) (Figure 1 A). A liver biopsy was performed on the same day, showing suppurative cholangitis and chronic cholestasis with bile duct loss, which was consistent with CR (Figure 1B, C). On the 70th day after the second vaccination, the patient was registered for deceased donor liver transplantation (DDLT), which was performed 34 days after the registration. The pathological findings of the explanted liver were bile duct paucity corresponding to CR and severe hepatic fibrosis (Figure 1D-F). Postoperatively, although she was complicated with small intestinal perforation that required surgical repair, her condition improved, and she was discharged on post-operative day 188. She has been doing well since re-LT with no abnormalities in her liver function.Figure 1 (A) The time course of hepatobiliary enzymes and bilirubin levels in the first case. (B) Liver biopsy findings on day 49 after the second vaccination. Suppurative cholangitis and suspicion of early chronic rejection were diagnosed. Rejection activity index: RAI=2 (P2 B0 V0), METAVIR=A1 F2 (C) Gross findings of the explanted liver graft at the time of re-LT (DDLT). (D) Bridging fibrosis and cirrhosis were revealed, METAVA=A1F4. (E) The bile duct structures in the portal area disappeared, which is consistent with chronic rejection. Figure 1 Case 2: A 19-year-old girl had undergone LDLT for end-stage liver disease from biliary atresia with the left lateral segment of the liver from her ABO blood type identical father as a graft at 1 year and 4 months old, and at 17 years old, she had undergone DDLT due to progressive graft liver dysfunction caused by CR. After DDLT, mild to moderate ACR and CR relapsed but were controlled with four immunosuppressive agents (Tacrolimus, Mycophenolate mofetil, Prednisolone, and Everolimus). There were no liver functional abnormalities according to a blood sample obtained at the first SARS-CoV-2 vaccination, showing a Child-Pugh score of 6 points (AST 20 U/L, ALT 29 U/L, GGTP1088 U/L, T-Bil 1.7 mg/dL, Alb 3.4 g/dL, PT-INR 0.87). After the first and second vaccinations (both mRNA-1273), no liver functional abnormalities were observed. However, 14 days after the third vaccination (using BNT162b2), hepatobiliary enzymes were elevated (AST 204 U/L, ALT 252 U/L, GGTP 1257 U/L, T-Bil 2.0 mg/dL, Alb 3.8 g/dL, PT-INR 0.94) (Figure 2 A), and 17 days after the third vaccination, a liver biopsy showed moderate ACR relapse and CR. The patient was treated with steroid bolus therapy and increased doses of Everolimus. However, ACR was uncontrolled, and a liver biopsy performed 44 days after the third vaccination indicated CR with 60% bile duct loss and moderate ACR (Figure 2B, C). Her hepatobiliary enzymes and jaundice worsened to a Child-Pugh score of 10 points (AST 54 U/L, ALT 60 U/L, GGTP 989 U/L, T-Bil 16.9 mg/dL, Alb 3.1 g/dL, PT-INR 1.47).Figure 2 The time course of hepatobiliary enzymes and bilirubin levels in the second case. (B) Liver biopsy findings 44 days after the third vaccination. Moderate ACR (RAI=P2 B2 V1) and fibrosis (METAVIA=A1F2) were prolonged. (C) Immunohistochemical staining of CK7 revealed extensive bile duct paucity in the portal region, which is consistent with chronic rejection in the late phase. Figure 2 Discussion BNT162b2 and mRNA-1273 are novel mRNA-based vaccine, and information on their safety and side effects in solid organ transplant recipients is still limited. Recent reports have shown that vaccination in healthy individuals cause liver function abnormalities and autoimmune hepatitis-like changes after vaccination, events attributed to the activation of CD8+ T cells by the mRNA vaccine [3]. A case series of post-liver transplant patients who developed acute cellular rejection following vaccination has also been reported [4,5], suggesting that the immunogenic potential of these mRNA vaccines may be higher than that of conventional inactivated vaccines or recombinant vaccines. In the author's group, transient elevation of hepatobiliary enzymes and ACR have been observed in post-liver transplant recipients vaccinated with the SARS-CoV-2 mRNA vaccine, and they generally responded to treatment without any problems. However, in the two cases reported here, CR, such as bile duct epithelial metaplasia and ductopenia, worsened after vaccination, leading to DDLT in the first case. Before vaccination, both patients required immunosuppressants with multiple regimens, but their graft liver function was preserved, and vaccination was recommended as usual. Whether or not vaccination was the direct cause of the hepatic dysfunction is unclear, but no other events inducing hepatic injury were specifically noted in either case. Regarding the cause of the CR, we speculate that the ACR triggered after vaccination may have been the cause of the CR in the second case, but in the first case, there was a time lag between the appearance of liver injury and the liver biopsy, so the image of ACR was not captured. In conclusion, we encountered two cases of exacerbation of CR after SARS-CoV-2 mRNA vaccination in LT recipients. The causal relationship between vaccination and CR and underlying mechanism in these two recipients are unclear at present, so we cannot recommend avoiding SARS-CoV-2 mRNA vaccination in LT recipients. However, we should closely monitor LT recipients who have undergone vaccination. We also believe that an evaluation protocol for pre-vaccination and a post-vaccination monitoring protocol for post-LT recipients may need to be promptly developed. Grant information No funding. References [1] American Society of Transplantation. COVID-19 vaccine FAQ sheet. https://www.myast.org/sites/default/files/2022.05.04%20AST%20Vaccine%20FAQ-CLEAN.pdf. Accessed September 2022. [2] American Association for the Study Liver Diseases. AASLD expert panel consensus statement: vaccines to prevent COVID-19 infection in patients with liver disease. https://www.aasld.org/sites/default/files/202207/AASLD%20COVID19%20Vaccine%20Document%20Update%203.28.2022%20FINAL.pdf. Accessed September 2022. [3] Boettler T, Csernalabics B, Salié H, Luxenburger H, Wischer L, Salimi Alizei E, et al. SARS-CoV-2 vaccination can elicit a CD8 T-cell dominant hepatitis. J Hepatol. 2022; 77: 653-659. [4] Vyhmeister R, Enestvedt CK, VanSandt M, Schlansky B. Steroid-Resistant Acute Cellular Rejection of the Liver After Severe Acute Respiratory Syndrome Coronavirus 2 mRNA Vaccination. Liver Transpl. 2021; 27:1339-1342. [5] Hume SJ, Jackett LA, Testro AG, Gow PJ, Sinclair MJ. A Case Series of Patients With Acute Liver Allograft Rejection After Anti-SARS-CoV-2 mRNA Vaccination. Transplantation. 2022;106: e348-e349. Conflict of Interest Disclosures (includes financial disclosures) The authors declare no conflicts of interest in association with the present study. The authors have no financial relationships relevant to this article to disclose.	0	PMC9708617	NO-CC CODE	2022-12-01 23:21:33	no	Transplant Proc. 2022 Nov 30; doi: 10.1016/j.transproceed.2022.11.009	utf-8	Transplant Proc	2,022	10.1016/j.transproceed.2022.11.009	oa_other
==== Front Journal of Accounting and Public Policy 0278-4254 0278-4254 Elsevier Inc. S0278-4254(22)00110-7 10.1016/j.jaccpubpol.2022.107047 107047 Full Length Article An exploratory study on the impact of COVID-19 on U.S. State boards of accountancy Gregory Jenkins J. a Popova Velina b⁎ Sheldon Mark D. c a Auburn University, Harbert College of Business, 301 Lowder Hall, Auburn, AL 36849, United States b Kennesaw State University, Coles College of Business, Burruss Building 205, Kennesaw, GA 30188, United States c John Carroll University, Boler College of Business, Kramer School of Accountancy and Information Sciences, 6 Bruening Hall, University Heights, OH 44118, United States ⁎ Corresponding author. 30 11 2022 30 11 2022 107047© 2022 Elsevier Inc. All rights reserved. 2022 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. In the United States (U.S.) individual state boards of accountancy govern the accounting profession within each state. When COVID-19 struck the U.S., state boards worked to maintain normal operations. This study examines how COVID-19 affected the regulatory and oversight activities of the state boards of accountancy and the ways in which boards adapted to the pandemic. We interview executive directors from 21 state boards to determine the pandemic’s impact on board operations and continuing professional education requirements. We also evaluate whether state boards implemented guidance from parties such as the National Association of State Boards of Accountancy (NASBA), and the resources boards had available to navigate the pandemic. Finally, we examine our analyses and findings through the lens of institutional theory. In doing so, we describe how state boards’ individual reactions to the pandemic resulted in a largely homogenous response, as affected by coercive, mimetic, and normative isomorphic mechanisms. Keywords COVID-19 Pandemic State boards of accountancy Executive directors Institutional theory ==== Body pmc1 Introduction The rapid spread of COVID-19 in early 2020 forced the world to adjust to a new normal with almost no time to prepare. Virtually overnight, many activities were either eliminated altogether or dramatically reimagined. While the impact of COVID-19 on business professionals and companies has been well documented (e.g., in trade journals, news stories, and social media), it is not well known how the pandemic impacted the regulation and oversight of the professions. The accounting profession in the United States (U.S.), the focus of this study, features a governance model in which state boards of accountancy control initial and ongoing licensing of individual Certified Public Accountants (CPAs), continuing professional education requirements (CPE), compliance with state-specific ethics requirements, and firm peer reviews for CPAs practicing/operating within their jurisdiction. In this study, we examine how COVID-19 affected CPAs in the U.S. in terms of regulatory and oversight activities of the state boards of accountancy and how boards responded to the pandemic.1 Our study has three primary first-order goals (Van Maanen, 1979) that contribute to an understanding of the changes in regulatory and oversight activities of state boards in response to the COVID-19 pandemic. Our first goal is to explore the effects of COVID-19 on board operations, while our second and third goals respectively are to understand the pandemic’s effects on state-specific CPE requirements and the implementation of recommendations from the National Association of State Boards of Accountancy (NASBA), the leading professional association for state boards of accountancy in the U.S. Our study also has an important second-order goal of organizing and explaining our findings through a theoretical lens. Like other studies (e.g., Jenkins et al., 2018b, Westermann et al., 2019; and Carlisle et al., 2022), we employ “downstream theorizing” to examine theoretical implications of our interview findings. We examine our findings through the theoretical lens of institutional theory (DiMaggio and Powell, 1983, Greenwood et al., 2002). To accomplish our first-order goals, we conducted semi-structured interviews with executive directors at state boards of accountancy. Our interview questions focused on the effects of COVID-19 on several key areas: (1) daily functions, operating budgets, and staffing levels, (2) state-specific CPE requirements, and (3) implementation of recommendations from NASBA on how to address pandemic-related challenges. We conducted our interviews from September to December 2020, after the initial shock of the pandemic had abated and executive directors and board staff had the time and opportunity to formulate plans on how to move forward. Interviews were conducted with 21 executive directors from states throughout the U.S. that comprise the majority of the nation’s population and more than half of the persons regulated by state boards of accountancy. Using interview data alongside data gathered from NASBA and other publicly available sources, we also analyze the varying resources that state boards had available during the pandemic. Our study reveals several ways in which state boards experienced and/or responded to the pandemic differently, including board staff capabilities to work remotely, limitations/benefits of available information technology, CPE requirements/extensions, and CPA exam deadlines/extensions. For example, state boards typically use one of five CPE reporting windows: annual, biennial rolling, biennial fixed, triennial rolling, or triennial fixed. Within these windows the reporting deadline varies, and might be set as December 31, June 30, September 30, or by the licensee’s birth month.2 As such, several states adjusted CPE requirements and deadlines based on their own unique environment. The study also reveals that state boards navigated the pandemic with a wide range of available resources. For example, California maintains the highest number of board staff (99) and persons regulated (99,167), for a ratio of approximately-one staff member per 1,000 persons regulated. In contrast, Nevada has nearly double this ratio (i.e., 6 staff for 3,424 persons regulated)3 and North Carolina (among others) maintains a ratio of about half as much (i.e., 12 staff for 22,243 persons regulated). Our interviewees also indicated some changes implemented in response to the pandemic are likely to become permanent, such as (the option for) virtual board meetings, expanded options for public access/participation in board meetings, and flexible work arrangements for board staff. To accomplish our second-order goal, we examine and organize our data to identify higher level second-order themes that we use to explain our findings through a theoretical lens. Specifically, we examine our findings through the overarching lens of institutional theory, the three isomorphic mechanisms (i.e., coercive, mimetic, and normative) (DiMaggio and Powell, 1983), and by considering the role of theorization and professional associations in state board responses to COVID-19 (Greenwood et al., 2002). A primary tenet of institutional theory is that “individual efforts to deal rationally with uncertainty and constraint often lead, in the aggregate, to homogeneity in structure, culture, and output” (DiMaggio and Powell, 1983, 147). Indeed, our interviews and analyses indicate that the various state board responses resulted in a largely homogenous response that helped them continue upholding their public interest responsibilities. Coercive isomorphism is driven by formal and informal political influence to conform to certain rules and practices (DiMaggio and Powell, 1983, Mizruchi and Fein, 1999). The coercive mechanisms we observe seem to have imposed necessary changes on state boards that, if not coercively imposed, might have taken longer to implement. Mimetic isomorphism is driven by responses to uncertainty, and typically manifests as an organization modelling certain rules and practices based on an exemplary peer (DiMaggio and Powell, 1983, Mizruchi and Fein, 1999). Although we do not believe state boards specifically modelled one another’s response to the pandemic, we believe many modelled responses (e.g., remote work) of the broader business community and accounting profession as those responses were quickly and easily observable. Under normative isomorphism, organizations adopt similar rules and practices as a result of employees’ common formal education as well as their participation in similar professional networks, trainings, conferences, and trade associations (DiMaggio and Powell, 1983). For instance, many of the interviewed executive directors share a common educational/professional background, and also mentioned the influence of their close personal and professional relationships with other executive directors developed over time at NASBA meetings. Finally, Greenwood et al. (2002) discuss the importance of professional associations, such as NASBA, in providing an arena to theorize (i.e., assess), legitimate (i.e., endorse), and diffuse (i.e., communicate) particular actions. NASBA occupies a unique position that allowed it to assess, endorse, and communicate particular actions, although we do not observe blanket adherence to its recommendations. This study is important for several reasons. First, it provides a timely examination of how state boards of accountancy changed their regulatory and oversight activities in response to COVID-19.4 Interviews with executive directors were conducted within months of the pandemic’s onset, which allowed time for these leaders and their staff to work through the initial shock of the virus and implement initial plans on how to move forward. As such, the qualitative and quantitative data for this study were captured in near real-time, and are less vulnerable to forgotten details as might be the case with a retrospective study of the pandemic. The study also provides insight into the varying levels of resources that state boards have available, and in doing so reveals a considerable degree of resilience and adaptation among state boards to leverage their resources to continue their oversight of the accounting profession during the pandemic’s onset. Finally, this study suggests that the collective state board responses to the pandemic produced a largely homogenous response, a phenomenon consistent with institutional theory. The remainder of the paper is organized as follows. First, we provide an overview of regulation in the U.S. accounting profession and discuss why this study focuses on state boards of accountancy. Next, we describe the methodology and present the study’s findings. The paper concludes with a discussion of these findings through the lens of institutional theory. 2 Background 2.1 Governance of the U.S. Accounting profession As the result of a brief but dynamic history, the U.S. accounting profession maintains a governance model with both centralized (i.e., the American Institute of Certified Public Accountants (AICPA) and NASBA) and distributed (i.e., state boards) components. States secured the legal authority to regulate the accounting profession based on the 10th Amendment to the U.S. Constitution (Flesher, 2007), and New York passed the first law to recognize the CPA accreditation in 1896 (Zeff, 2003).5 Soon after New York, the majority of states passed CPA laws, and in 1908 the National Association of CPA Examiners (today’s NASBA) was formed to help establish a uniform CPA exam. Ultimately, interstate commerce created a need for more national uniformity in education, licensing, and regulation of the profession. In response, the Institute of Accountants in the United States of America (the Institute) was created in 1916 and required members to pass a common exam (Carey, 1969). The Institute then merged with the American Society of Accountants in 1936 to form the AICPA, which also required members to pass a common exam and appointed state CPA society presidents as members of its council to strengthen relations with state-level entities. Today, the AICPA, NASBA, and state boards of accountancy are instrumental in governing the profession. The AICPA (the largest professional association for accountants in the U.S.) provides audit/attest standards for private companies, technical support for CPAs, maintains a national-level code of professional conduct, and develops/maintains the current CPA exam. NASBA works to enhance the effectiveness and advance the common interests of state boards by “creating innovative avenues for accounting regulators, educators and practitioners alike to address emerging issues relevant to the viability of the accounting profession” (NASBA, 2021a). Finally, state boards control licensing, which includes defining educational and professional experience requirements, establishing CPE requirements (i.e., hours, topics, and reporting frequency), administering the CPA exam (unless delegated to NASBA), and setting licensing fees. State boards can also implement a code of professional conduct and discipline licensees for any related violations. 2.2 Theoretical lens and research questions Institutional theory posits that individual organizations which exist within a larger organizational field respond to similar environmental conditions, and in doing so, end up homogenizing to resemble one another through a process referred to as isomorphism (Meyer and Rowan, 1977, DiMaggio and Powell, 1983). Organizational fields, as used here, are broadly defined as “organizations that, in the aggregate, constitute a recognized area of institutional life: key suppliers, resource and product consumers, regulatory agencies, and other organizations that produce similar services or products” (DiMaggio and Powell, 1983, 148). For example, state boards of accountancy in the U.S. are organizations that exist within an organizational field of accounting regulators. Changes implemented as part of isomorphism are intended to (further) legitimize the organization within its environment, and thus increase its chances of survival (Meyer and Rowan, 1977, DiMaggio and Powell, 1983, Zucker, 1987). Isomorphic changes occur through one of three mechanisms: coercive isomorphism, mimetic isomorphism, and normative isomorphism (DiMaggio and Powell, 1983). Coercive isomorphism is driven by formal and informal political influence (or other parties an organization depends upon) to conform to certain rules and practices (DiMaggio and Powell, 1983, Mizruchi and Fein, 1999). For example, a state board of accountancy might be coerced into certain actions requested by the state governor when the board depends on the state legislature for funding allocations. Mimetic isomorphism is driven by responses to uncertainty, and typically manifests as an organization modelling certain rules and practices based on an exemplary peer (DiMaggio and Powell, 1983, Mizruchi and Fein, 1999). DiMaggio and Powell (1983, 151) point out that model diffusion can occur “unintentionally, indirectly through employee transfer or turnover, or explicitly by organizations such as consulting firms or industry trade associations.” For example, the executive director of a struggling state board might decide to model specific practices after those of a thriving state board. Finally, normative isomorphism results from professionalization, meaning organizations adopt similar rules and practices as a result of employees’ (common) formal education as well as their participation in similar professional networks, trainings, conferences, and trade associations (DiMaggio and Powell, 1983). For instance, state boards might adopt a common point-of-view on a matter after executive directors attend a training hosted by NASBA. While the three institutional isomorphic mechanisms carry discrete definitions, it is also important to note an observation by Mizruchi and Fein (1999, 657), that “two or more [mechanisms] could operate simultaneously and their effects will not always be clearly identifiable.” As such, it will not always be obvious which mechanism (or whether a single mechanism) is responsible for organizational change. While the notion of isomorphism describes how an organization changes to establish or maintain legitimacy, institutional theory also seeks to explain why these changes occur (Greenwood et al., 2002, Griffith et al., 2015). Particularly relevant to the current study, changes might be implemented in response to (unanticipated) events that challenge the feasibility of existing practices (Greenwood et al., 2002). For a change to occur, it must be subject to a theorization process in which it is presented in an understandable format and accompanied by a compelling case as to why it is legitimate and “consistent with prevailing values” (Greenwood et al., 2002, 75). Professional associations can play a critical role in the theorization process. Specifically, professional associations provide an arena in which organizations can interact and develop understandings and expectations of reasonable behavior, and in doing so, these associations “ease change because they enable theorization” (Greenwood et al., 2002, 74). In our study, NASBA is a professional association that provides state boards of accountancy with an arena to gather and evaluate changes using a theorization process. As such, it is possible that NASBA, in its role as a professional association, affected the theorization process of changes implemented by state boards in their response to the pandemic. The COVID-19 pandemic challenged the status quo across all types of organizations and organizational fields. Such events have the potential to disrupt organizational practices and rules, and thus lead to changes in institutions (Greenwood and Suddaby, 2006). We focus our study on the state boards of accountancy as organizations that make up a larger professional organizational field (i.e., accounting regulators), and examine the operational changes, if any, they implemented in response to the pandemic. In doing so, we use institutional theory as a lens through which to examine the (lack of) changes implemented by state boards in response to the spread of COVID-19. To guide the study, we pose the following three research questions: RQ1: How did state boards of accountancy adapt their operations in response to the COVID-19 pandemic? RQ2: How did state boards of accountancy adapt their continuing professional education requirements in response to the COVID-19 pandemic? RQ3: To what extent did state boards of accountancy implement recommendations from NASBA in response to the COVID-19 pandemic? 3 Empirical design This study employs a positivist approach to examine the three research questions. Under a positivist approach, researchers seek to remain objective, and in doing so view quotations from participants “as being self-evident” (Malsch and Salterio, 2016, 9) and assume “readers are able to make sense of quotations un-problematically” (Power and Gendron, 2015, 154). Furthermore, positivist researchers seek generalizability and use theory to “suggest causal explanations for the patterns observed in the field” (Malsch and Salterio, 2016, 6). More specifically, we examine our findings using institutional theory given its focus on organizational change, and also highlight when our field observations depart from this long-standing theory. 3.1 Interview methodology To conduct this study, we employed a semi-structured interview approach (Wengraf, 2001, Gibbins and Qu, 2005) to understand how state boards adapted their policies and practices in response to the pandemic. We inquired about matters related to several key areas: (1) the immediate and anticipated impact of COVID-19 on state board daily functions, operating budgets, and staffing levels, (2) the effects of COVID-19 on state-specific CPE requirements, and (3) whether state boards implemented NASBA recommendations related to CPE (i.e., allowable course formats and reporting deadlines) and the CPA exam (i.e., expiration of Notices to Schedule and exam credits) matters.6 Because an important purpose of our research was to understand how COVID-19 affected the oversight of CPAs, we focused our interview questions on matters under the jurisdiction of state boards. In doing so, we also designed our questions to explore whether state boards coordinated their responses to the pandemic (or came to similar responses) given the influence of NASBA. We used our interview script as a guide to ensure that we asked about each area of interest, but we also allowed for flexibility so interviewees could discuss matters that came to them during our conversation (Power and Gendron, 2015). To allow conversations to flow freely, we interrupted to ask follow-up questions only when necessary. We interviewed executive directors, or their equivalent, because they are responsible for daily operations and have authority to speak on behalf of their state boards regarding matters of policies and practices. As part of developing the interview script, we pilot tested our questions with executive directors of state boards in Alabama, Georgia, and Ohio. In addition to obtaining their responses to our questions, we requested their feedback on the clarity and completeness of our interview questions and whether any of our questions were being asked prematurely (i.e., before the impact of COVID-19 could be known). Doing so allowed us to refine our interview script (e.g., removing questions on the pandemic’s effects on licensee ethical conduct and adding a question about a board’s use of centralized services like NASBA’s CPA Examination Services, or CPAES) and to determine the feasibility and usefulness of the overall content of our interview script (Creswell, 2009). The final interview script is included in the Appendix. To continue the data collection (i.e., interview) process, we requested interviews with executive directors from the most populous states from each of the NASBA regions. We then utilized a “snowball approach” (Malsch and Salterio, 2016, 7) in which we asked the initial interviewees to suggest other executive directors that might be interested and willing to participate in our study. The snowball approach was appropriate in our setting as executive directors represent a small pool of experts that likely know their peers at other state boards (Malsch and Salterio, 2016). We ceased requesting interviews when we stopped learning new information, which Guest, Bunce, and Johnson (2006) suggest may happen as early as 12 interviews. We obtained responses from executive directors of 21 state boards which provided coverage of 63.3 % of the U.S. population, 55.3 % of the regulated persons, and, except for the Northeast region, included at least two states from each of the eight NASBA regions. Of the executive directors who agreed to an interview, seventeen (81 percent) agreed to meet with us via video conference. Four (19 percent) interviewees were unable to participate via video conference but agreed to provide written responses to our interview questions and respond to any follow-up questions.7 In lieu of recording, two of the researchers wrote detailed notes during the interviews.8 This design choice was also employed by Griffith et al. (2015) in order to “obtain frank responses from interviewees” (p. 839). Each interview began with introductions and an exchange of pleasantries followed by a brief overview of our study. The first interview question was asked after obtaining an interviewee’s consent to participate in the study.,9 , 10 Interviews lasted an average of 49.3 min, with a range from 25 to 80 min. All interviews were conducted from September to December 2020. We included several tactics to ensure the trustworthiness of our data and findings. First, the same researcher conducted each interview using a consistent approach and interview script while the other two researchers took detailed notes of the conversation. Second, following the conclusion of each interview, each of the researchers reviewed the detailed interview notes for accuracy and completeness. At this point, any discrepancies were identified, discussed, and resolved among the research team. Although note-taking differences were found during the review process, none of these differences was material. Third, to ensure the completeness and accuracy of notes taken during interviews and an accurate telling of interviewees’ experiences, we took two actions. First, we employed a member checking technique in which we sent our detailed interview notes to the 17 interviewees for their review and with a request to provide any corrections or clarifications (Malsch and Salterio, 2016).11 Although no interviewee notified us of either material omissions or errors in the detailed interview notes, four interviewees provided minor wording changes which are included in the paper. In addition, we shared our paper with all 21 executive directors whom we invited to comment on our findings (Power and Gendron, 2015). Their feedback was complimentary and suggested that findings were interesting and informative. 3.2 Analysis of interviewees’ responses Each of the researchers independently reviewed the responses from all executive directors who participated in the study. After this process was completed, following Bauer and Estep (2019), we discussed what we had learned, combined our insights, and selected quotes for inclusion in the paper. We present and discuss “power quotes” that depict some of the most interesting observations from our interviewees and “proof quotes” that are representative of general observations from multiple interviewees (Pratt, 2009, Westermann et al., 2019). To faithfully tell the stories of the executive directors, we also performed a deviant analysis to identify outlier comments or minority views (Power and Gendron, 2015). This process is important because reported responses represent unique experiences of different individuals and may include perspectives and insights that are at odds with those obtained from others (Malsch and Salterio, 2016). In accordance with qualitative research methodology, we worked to strike a balance between “showing” (i.e., using quotes) and “telling” (i.e., interpreting and explaining our findings) in reporting our analysis (Golden-Biddle and Locke, 2007). We use a non-anonymous “state” identifier because our study examines matters of public policy and executive directors responded to our questions in their capacity as public officials such that attribution to a state is appropriate. To further summarize and analyze our data and identify first-order concepts, two of the authors independently coded responses to our interview questions using Microsoft Excel (as later presented in Table 1 and Fig. 1 ). One author first read and categorized interviewee responses into thematic categories. These categories were not identified ex ante, but were allowed to arise from the reading of responses and evolve during the coding process (Miles and Huberman, 1994). A second author independently read responses and categorized them based on the categories identified by the first author. This coder was free to identify new categories during the coding process, however, no new categories were identified. Because our questions inquired about non-technical matters, the identification of categories was not complex. Our coding approach is consistent with the process reported in Jenkins et al., 2018b, Griffith et al., 2015. The initial agreement on coding between these two authors came to 96.95 %, with an intercoder reliability (Cohen’s κ) of 0.9324. After the two authors completed their initial analysis and categorizations, the third author reviewed the assigned categories and resolved any differences. We expected this high level of agreement given the objective nature of the coded items (i.e., whether a state board took a particular action or maintained a specific policy) and our efforts to confirm the accuracy of our interview notes (which we used to code the items).Table 1 State Boards’ Responses to COVID-19. Alabama Arizona California Florida Georgia Idaho Illinois Iowa Kentucky Louisiana Mississippi Nevada North Carolina Ohio Oklahoma Pennsylvania South Dakota Tennessee Texas Virginia Washington In response to COVID-19, the state board… allowed (or continued to allow) CPE reporting until at least October 31, 2020 (or provided accommodations for extensions based on local deadlines and policies) ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ provided a blanket extension on Notice to Schedule until at least December 31, 2020 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ provided a blanket extension on CPA Exam credits until at least December 31, 2020 ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ decreased current fees and/or allowed for fee waivers (e.g., individual CPA license renewal, original application, firm fee, etc.) ✓ ✓ allowed (or continued to allow) self-study CPE ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ issued (some) waivers on the required number of CPE hours ✓ ✓ ✓ ✓ ✓ ✓ issued (some) waivers on required CPE topics ✓ modified/suspended the previously planned 2020 CPE audit schedule/coverage ✓ ✓ # ✓ ✓ ✓ Given the impact of COVID-19, the state board… avoided decreases to its current operating budget ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ expects future operating budgets to be unaffected (i.e., will not decrease due to COVID-19) ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ Legend: ✓Indicates a positive/affirmative response. #Executive director indicated that it is too early to know the response to this question. Indicates a response was not obtained for the respective state board/question combination. State boards responded similarly to several questions that are not included in this table. Specifically, all state boards interviewed: (1) allowed (or continued to allow) CPE program sponsors to convert registered live programs into virtual presentations, (2) allowed (or continued to allow) online options for courses, trainings, and/or conferences, (3) expect future fees (e.g., individual CPA license renewal, original application, firm fee, etc.) to be unaffected (i.e., will not increase due to COVID-19), and (4) expect board staffing levels to be unaffected. Furthermore, none of the state boards interviewed deferred planned changes to the rules regarding the ways a licensee can earn CPE (e.g., a new CPE requirement for an ethics course). Note that we did not obtain responses to these five questions from Arizona, and only obtained a response to the first question from Iowa. Fig. 1 Infographic of Responses to Select Interview Questions. This figure presents an infographic of responses to select interview questions, organized into the three categories of questions as defined in this study. * Indicates that one executive director did not respond to the question; ** Indicates that two executive directors did not respond to the question. After completing the initial data coding described above, we engaged in a robust discussion to identify higher level second-order themes in our data. This process required a careful reading and consideration of the data and involved discussions among the researchers that captured the relationships and connections among the first-order concepts in our data. Finally, we grouped similar themes into several aggregate dimensions which provide the basis for a theoretical understanding of our study’s data. Our analysis approach is similar to those used by Corley and Gioia, 2004, Stolowy et al., 2019. We present our final data structure along with a discussion of our findings in the Discussion and Conclusion section. 4 Results 4.1 Overview As previously mentioned, our interviews focused on three key areas: (1) the immediate and anticipated impact of COVID-19 on state board daily functions, operating budgets, and staffing levels, (2) the effects of COVID-19 on state-specific CPE requirements, and (3) whether state boards implemented NASBA recommendations related to CPE (i.e., allowable course formats and reporting deadlines) and the CPA exam (i.e., expiration of Notices to Schedule and exam credits) matters. In this section, we share our findings with regards to these three areas and present analyses of the varying resources state boards had available to navigate the pandemic. 4.2 RQ1 – Impact on state board operations To understand the impact of COVID-19 on state board operations, we began by asking whether the pandemic had any immediate effects in March 2020 on board functioning and the ways board staff worked. In general, the state boards we study shifted to remote work; however, boards that did not had to navigate the challenges of having limited staff onsite due to capacity constraints and social distancing requirements. We also note that in several instances changes implemented prior to COVID-19 laid the groundwork for a smoother transition for both board staff and licensees. For example, Tennessee was able to minimize the impact of COVID-19 on board operations given other changes it had recently made to work arrangements12 : AWS, alternative workplace solutions, was implemented a few years ago by the Governor to reduce the real estate footprint.13From that point, we were working from home three days a week and in the office two days a week. Having already had this in place, we just moved to working only from home, which was a smooth transition. A lot of the work we do is paperless, which dates back a few years to when we moved to a new licensing database. Our board meetings were normally in person, but are now remote and are set up so that the public can attend. – Tennessee14. Other states that made similar changes prior to the pandemic also experienced a more seamless transition. In addition, states such as Ohio that made investments in information technology (IT) prior to the pandemic fared better in shifting to remote work: We put a telework policy into place and board staff were sent home with computers that had just been upgraded. We also got every-one a cell phone to take calls at home. Our board meetings are now happening via Microsoft Teams. – Ohio. Changes in work style led to both unanticipated challenges and benefits. For example, a few boards had to request executive actions from their governor to be allowed to conduct virtual meetings and hearings while others had to determine whether votes in virtual meetings were legal and binding.15 At the same time, several executive directors, including those from large states such as California and Florida, noted that virtual meetings were better attended by the public than face-to-face meetings in the past, and that the better attendance allowed them to feel more connected to licensees. Florida is one of the states that had to determine whether virtual board meetings were legal and binding, yet found that these virtual meetings were better attended: The board used to meet every-six weeks and would travel around the state to meet. When COVID hit, they moved to virtual board meetings. We were trying to decide whether the board had to physically be in the same room to have a legal vote. Ultimately, the state decided that virtual board meetings were binding. Lots of people attended the virtual meetings since they are public. Previously, not many people would come and watch, but under the virtual format many people have been attending. We also have much more participation now from stakeholders. – Florida. Besides determining the legality of board meetings, other unexpected challenges included redesigning processes and safeguarding assets, like California experienced: We quickly shifted employees to telework when the offices were closed to the public in March. Transitioning work from the office to staff members’ remote locations has been a departure from typical state procedures, so safeguarding our assets (equipment, information, etc.) was a priority. As staff access to the physical applications and other materials was now limited, new workflow was designed and implemented to streamline the processing of the issuance of licenses. The board and committee meetings also pivoted to virtual platforms. – California. We also spoke to executive directors who faced challenges stemming from inadequate, or nonexistent, virtual private networks (VPNs), home computers, and even cell phones. Boards heavily dependent on paper-driven processes faced some of the greatest challenges, as such processes required employees to be in the office: Key employees were set up for teleworking. They don’t have the IT support for taking remote calls, and some didn’t have a VPN. So, all of these things had to be addressed. Every-one teleworked for about a week, even though this was not mandated by the state Governor. Some people continued to work from home and then some people came to the office. We have a very paper driven process, so people have needed to come in, and also to come in to take phone calls. – Virginia. There are also boards that experienced multiple challenges. Louisiana, for example, had a paper-driven process, lacked the technology needed to work remotely, and had to confront the legal implications of virtual meetings and remote work: We have a very paper-based and manual operation, and were not prepared (and remain ill prepared) to work from home. Our database is not in a cloud that can be accessed anywhere, so things are difficult working in a remote environment. COVID added a lot more layers of work on the board given all the proclamations being issued by the Governor. We probably spent the first month not knowing if we were doing work legally by having people in the office, and given our reliance on paper and manual procedures, we needed to have staff onsite. We also didn’t know if we were allowed to pay staff given the stay-at-home order, but we continued to and eventually had it approved that the work staff were doing could be compensated. There was lots of initial confusion, and we had to spend a lot of time figuring out what they were allowed to do and how to do it. – Louisiana. After discussing the impact to operations, we inquired about the financial effects. To begin, we sought to understand the level of autonomy the state boards have over their operating budgets. Of the 21 boards included in the study, only five (Florida, Georgia, Idaho, Iowa, and Mississippi) reported not having budget autonomy, while the remaining boards reported being fully or semi-autonomous. To further examine state board finances, we asked about COVID-19′s effects on state board operating budgets and fees, both immediate and longer-term effects. In general, boards did not make immediate operating budget cuts, and the few that did made only modest adjustments to their current budget. Some executive directors noted that much of the anticipated cost savings that they identified stemmed from the elimination of travel along with reduced office costs from not having staff onsite. Executive directors from Louisiana and Virginia also noted a decrease in fees stemming from the reduced pipeline of CPA candidates, but each executive director admitted to not knowing if this was attributable to COVID-19 or to prior trends already showing a decrease in the number of CPA candidates. In terms of current operating budgets, California implemented one of the more proactive approaches to cost cutting: The State has provided direction to departments to limit expenditures and hiring to those necessary to support core functions, emergency response activities, and the ability to maintain operations in a telework environment. In addition, in response to the State’s economic crisis caused by the COVID-19 pandemic, effective July 1, 2020, each full-time employee received a 9.23 percent reduction in pay in exchange for 16 h of leave credits. – California. In terms of future operating budgets and fees, most states did not anticipate ongoing cuts or fee increases. However, five states (California, Florida, Georgia, Louisiana, and Ohio) were making (or anticipating) cuts that will impact operations. California provided the following details on its efforts to reduce future operating budgets: Our board will assume a permanent five percent reduction in operating budget, which is in addition to the employee compensation savings that became effective July 1, 2020. The employee compensation savings is expected to be in place for two years. – California. Georgia highlighted the impact these cuts are expected to have on enforcement-related activities: The FY 21 budget (year end June 30, 2021) is taking a 14 % reduction, which was originally supposed to be a 6 % reduction before COVID. This will have an incredibly negative impact on the second half of the year for our board of accountancy. This is barely enough to keep going and will severely impact enforcement activities. Exams and licensing will hopefully continue as planned, but the decrease in funds will impact things like CPE audits and hearings on misconduct issues. Things will be very tight. – Georgia. Most executive directors noted that there were no changes to current fees (i.e., individual CPA license renewal fees, original application fees, or firm fees). Only Louisiana reduced firm fees for in-state businesses based on guidance from its governor, and Arizona was allowed to temporarily waive fees. With respect to future fees, almost all executive directors did not anticipate increased fees due to the pandemic. In fact, only one executive director (California) commented that fees might need to be increased to address anticipated cash flow shortages. Even with the financial impact of COVID-19, none of the executive directors expected cuts to board staff positions. In closing, our discussions on the effects of COVID-19 on state board operations, we asked what lessons had been learned during the pandemic that might influence future operations, and what changes had been made in response to COVID-19 that were likely to remain in place. Most executive directors indicated that policies regarding remote work and virtual meetings will continue, but some commented that there is value in maintaining a physical work environment. Further, investments in technology are critical, including the move away from paper-based systems. Finally, while the transition to remote work was and continues to be disruptive, this new environment has also led to gains in efficiency (e.g., meetings are more to the point), more flexibility (e.g., hybrid meetings where not every-one needs to be physically present), and cost savings (e.g., reduced travel and hosting of events). Some other examples of lessons learned include: technology used during the pandemic helped California to facilitate teleworking and virtual meetings that were ultimately less expensive and better attended; Idaho highlighted the benefits of virtual meetings, and is considering hybrid meetings in the future; for Pennsylvania, the underlying lesson focused on remaining flexible; and in Tennessee, the board remains open for new ways to use technology in future operations. Finally, some executive directors acknowledged the importance of technology but also mentioned the advantages of face-to-face interactions. For example, Kentucky’s executive director was one of several interviewees to note a specific reason to go back to in-person board meetings: The Board was meeting physically before the pandemic and then went virtual, but the plan is to get back to meeting in person when the pandemic is over. We miss the small talk and the relationships built between board members. You lose something when you don’t meet in person. – Kentucky. Similarly, the executive director from Texas expressed concern regarding the challenge associated with decisions related to work arrangements: I’m not convinced an alternative work scenario is in the best interest of the board or for the workers, as some people might lose their edge or become complacent by not coming into the office. It’s a balancing act with the work from home options, but we will revisit these options when COVID passes. – Texas. 4.3 RQ2 – Impact on state-specific CPE Our interviewees shared that some licensees expressed concerns about how to earn CPE during the pandemic. Given the importance of maintaining the CPA credential, we asked about changes implemented to help licensees earn CPE. In terms of allowing more online options for CPE, most states had policies in place for online learning that were flexible enough so that no additional changes needed to be made in response to COVID-19. Still, three states (Alabama, South Dakota, and Texas) allowed for more online options and/or allowed a grace period for live instruction to be transitioned to online delivery. Prior to COVID-19, most states already allowed unlimited self-study CPE, and thus did not need to modify this policy. However, two states (Iowa and Pennsylvania) with limits on the number of self-study hours temporarily relaxed these limits. Two other states (Illinois and Texas) that had allowed limited self-study prior to the pandemic did not adjust this policy, while Florida maintained its pre-existing ban on self-study CPE. When asked whether states were issuing waivers on the required number of CPE hours in response to COVID-19, most executive directors responded that they were not issuing such waivers. Of the remaining states, two (Kentucky and Nevada16 ) were considering waivers on a case-by-case basis, one (Ohio) was issuing “some” waivers, one (Alabama) was issuing waivers for medical reasons (which could include being sick or acting as a caregiver), one (Georgia) was issuing waivers for licensees who could not get to their office to provide evidence of CPE completion, and one (Mississippi) was continuing its existing practice of waiving penalty hours (i.e., for CPE not earned during the reporting cycle) if a licensee was sick. We also asked whether boards were issuing waivers on required CPE topics, and found that most states did not adjust these requirements in response to the pandemic. However, Ohio allowed adjustments to required tax and audit topics to help licensees re-enter the profession: There are lots of people coming back into the CPA profession from consulting, and they need 24 h of audit or tax depending on their focus. We’ve waived some of these hours to help get people back into practice. – Ohio. We also asked whether state boards modified their approach to ensure proper reporting of CPE, including whether additional CPE audits would be performed as a result of COVID-19. Thirteen executive directors reported no changes had been made in response to the pandemic. However, three state boards (California, Louisiana, and Nevada) suspended CPE audits, with Nevada’s executive director commenting: We’re not performing CPE audits this year since lots of people did not have access to their files/offices. We’ve also waived all penalties stemming from 2019. As for calendar year 2020, if individuals can express how they’ve had a hardship, our board might extend these people on a case-by-case basis. In waiving the CPE audit this year, we’ll likely increase the percentage of those audited next year. – Nevada. Two states, Georgia and Tennessee, provided extensions on CPE audits, with Georgia’s executive director commenting: There have been some issues if people had documentation in their office that they couldn’t access, so some of them were given extensions until they could get into their offices to gather their CPE evidence. – Georgia. Executive directors from Georgia and Louisiana predicted that they would perform fewer CPE audits in the future, in part due to lower operating budgets, while Kentucky’s executive director commented that it was too early to know the impact of COVID-19 on 2020 CPE audits. 4.4 RQ3 – Implementation of NASBA recommendations A priori, it would seem that some level of centralized governance or leadership would be helpful for distributed bodies to figure out how to navigate an event as significant as a pandemic. For state boards of accountancy distributed throughout the U.S., this centralized body is most likely NASBA. To begin, we did not know whether recommendations from NASBA had previously influenced the decisions of the state boards, and therefore asked about this influence. In general, the executive directors viewed NASBA as a partner rather than as an authoritative body. Seven executive directors indicated that NASBA recommendations were typically “more-so” influential, in that they followed NASBA closely and depended on their guidance. Nine executive directors indicated that NASBA recommendations typically carried “some” influence, in that they would choose whether the specific guidance fit with the state’s needs. Finally, three executive directors indicated that NASBA’s recommendations had typically been “less-so” influential, in that they remained aware of the recommendations but also felt free to act independently.17 The executive directors also largely downplayed the significance of regional influences from other state boards. Rather, they believed that any influence from other state boards came from executive directors with whom they had built close relationships. NASBA made several recommendations to state boards during the pandemic. Four of these recommendations include: (1) allow CPE program sponsors to convert registered live programs to virtual presentations, (2) extend CPE reporting deadlines due to cancelled conferences and shifts in busy season, (3) extend CPA candidates’ Notice to Schedule expiration dates, and (4) extend CPA candidates’ exam credit expiration dates (NASBA, 2020; NASBA, 2021b). We asked executive directors whether their state adopted this guidance, and if not, why not. For the first recommendation, to allow CPE program sponsors to convert registered live programs to virtual presentations, five states approved this change while another 15 states already had policies in place that were flexible enough to accommodate this shift without any change in policy.18 States that subscribed to the NASBA registry of CPE providers relied on the fact that if the CPE provider remained compliant with NASBA requirements, then the state did not need to perform additional gatekeeping on the provider for online courses. Other states allowed non-NASBA registry CPE providers, as long as the course met specific-state requirements for online delivery. Comments from South Dakota and Louisiana help illustrate the transition to an online delivery method: Group live was already allowed to be counted as online. Some providers on NASBA’s registry weren’t approved for group-online, so we allowed any of those providers who had only been approved for group-live to be allowed to provide group-online from March to June 2020. This then gave these providers time to be approved by NASBA for group-online courses. – South Dakota. We have always allowed CPE credits for webinars and online, without limits. So, these were already in place and accepted. Vendors just needed to convert the course to an appropriate online course format (for example, by meeting certain provisions such as polling questions and ways to make sure the attendee was engaged with the course), then these online courses could be implemented. – Louisiana. NASBA also recommended that state boards extend CPE reporting deadlines until October 31, 2020 due to cancelled conferences and shifts in busy season. Here, five states accepted the guidance, one rejected it, five rejected it but provided accommodations for extensions based on local deadlines and policies, and for the ten others it was irrelevant because the end of their reporting period was after October 31, 2020.19 In terms of meeting minimum hours in individual years, no executive director indicated that meeting these minimum annual requirements presented challenges to licensees, so no state waived these requirements. The last two recommendations from NASBA dealt with the CPA exam. First, NASBA recommended that state boards extend expiring Notice to Schedule for the CPA exam through December 31, 2020. Most states followed this guidance, some of which by default because they outsource CPA exam administration to NASBA’s CPAES. Boards that did not provide a blanket extension for Notice to Schedule (Florida, Idaho, and Ohio) were considering extensions on a case-by-case basis. It was also highlighted that Notice to Schedule extensions were particularly important to international candidates who could not take the CPA exam in their home country and were not able to travel. As one executive director commented: A lot of foreign candidates are having to travel to other countries to take the exam, so this is becoming problematic in keeping this policy consistent for all of our state’s candidates around the world. – Virginia. NASBA also recommended that state boards extend CPA exam credits through December 31, 2020, and most states followed this guidance. Of the remaining states, only one executive director (North Carolina) commented that credits would not be extended through December 31, 2020 while the remaining five (Idaho, Ohio, South Dakota, Virginia, and Washington) were handling extensions on a case-by-case basis. Similar to the issues noted with Notice to Schedule, international candidates remained a group with a heightened need for exam credit extensions due to not having testing centers in their home country. One state, South Dakota, not allowed to issue a blanket extension on exam credits worked around the issue using a brute-force tactic: We are only allowed to approve extensions on a case-by-case basis, so we reached out to those with expiring credits and had them submit letters to the board, which approved them all individually through the end of the year. We weren’t allowed to otherwise implement blanket extensions. – South Dakota. Based on the range of responses, it appears that each state board experienced the pandemic and/or addressed the associated challenges in a unique way (see Table 1 and Fig. 1 for a summary of state board responses). Even so, each board navigated COVID-19 in the sense that each maintained critical operations, did not reduce staff, and is working on future plans. However, there are state boards that seemingly managed the pandemic with less difficulty because of investments in technology or implementation of alternative work solutions, which motivates an examination of the resources boards had available to navigate COVID-19.20 In the next section, we examine these resources and how they varied among state boards. 4.5 Supplemental analyses 4.5.1 State board resources To analyze the varying resources that state boards had available during the pandemic, we gathered additional data from the executive directors, NASBA, and other publicly available sources. During interviews, we asked executive directors about the number of staff that work for their respective state board. Then, from NASBA, we obtained the number of persons regulated by state (as of May 2020), whether the state uses NASBA’s CPAES (as of November 2020), the state boards’ CPE reporting period type (i.e., annual, biennial rolling, biennial fixed, triennial rolling, or triennial fixed), and the current CPE reporting period end date. Finally, from publicly available sources, we gathered all available state board operating budgets from 2008 to 2016 along with state population data (as of 2019). Our analyses of board resources began by creating a visualization to examine the geographic distribution of states in our study, on top of which we layered the number of persons regulated by state and the number of staff working for these respective boards. The resulting visualization, as presented in Fig. 2 , suggests there are no obvious regional concentrations with higher numbers of board staff or persons regulated. Staff sizes range from 2 in Iowa to 99 in California (average size 13.3), while persons regulated ranges from 1,775 in South Dakota to 99,167 in California (average size 22,992). California and Texas appear to stand apart from the other states in terms of the number of board staff (99 and 40) and persons regulated. Based on untabulated analyses, we also note that Arizona, California, Idaho, Mississippi, Oklahoma, and South Dakota have one board staff member for approximately every 1,000 persons regulated. In other states such as Georgia, Illinois, Iowa, Kentucky, and Ohio, the ratio of board staff to persons regulated is noticeably lower.21 These data suggest a wide disparity in the number of board staff available to serve the persons regulated during the pandemic.Fig. 2 Heat Map of Persons Regulated (May 2020) Overlaid by Number of State Board of Accountancy Staff. This figure presents the 21 states for which we conducted interviews with the Executive Director (or equivalent) of the State Board of Accountancy (or equivalent). The shade of each state is determined by the number of persons regulated in that state (i.e., active and/or inactive CPA licensees currently paying a renewal fee), and the number presented within each state indicates the size of the board of accountancy staff. Furthermore, interviews were not conducted with Alaska or Hawaii. For boards with fewer staff per persons regulated (i.e., lower ratios), it would be reasonable to seek assistance in running operations by outsourcing resource-intensive functions, such as services related to the CPA exam. NASBA offers a centralized service, CPAES, to assist states with administering the CPA exam. It is reasonable to expect that boards with lower ratios might outsource such services, thus freeing up staff to work on non-outsourceable functions. Based on untabulated analyses, we find that boards with lower staff to persons regulated ratios are more likely to outsource CPA exam administration responsibilities to NASBA, which likely benefited them during the pandemic. As just discussed, state boards have options for how to accomplish required tasks with fewer board staff (e.g., by utilizing NASBA's CPAES). State boards might also lessen the burden of administrative duties by opting for less frequent CPE reporting / license renewals, which could free up resources. Five CPE reporting periods are used by state boards: annual, biennial rolling, biennial fixed, triennial rolling, and triennial fixed. Here, “rolling” indicates that licensees must remain in compliance with CPE requirements within any two (or three) year window, while “fixed” indicates that compliance is measured within set two (or three) year windows. Based on the five available options, we interpret the administrative burden of these reporting periods on state boards to be (from most to least burdensome): annual, biennial rolling, triennial rolling, biennial fixed, then triennial fixed. Upon examining (untabulated) states that use these different reporting windows and how many board staff they employ per 1,000 persons regulated, we find that states with more administratively burdensome reporting periods (i.e., annual) also have a higher (on average) number of board staff. We also examine (untabulated) the average operating budget for boards per person regulated, and find that (on average) states with more frequent reporting also have higher operating budgets per capita. The analyses in this section highlight the resources available to state boards, which likely impacted how states managed the pandemic. Specifically, states vary in the number of board staff available to serve persons regulated, and boards with lower ratios were more likely to have already outsourced administratively burdensome tasks related to the CPA exam. Further, boards choose from a variety of CPE reporting periods, and our analyses indicate that boards with fewer staff and lower operating budgets (per person regulated) tend to use less administratively burdensome reporting periods. Together, these state-level decisions likely affected the resources boards had available to address mission-critical functions during COVID-19. 4.5.2 Relative spread of COVID-19 Considering the variability in state board resources and responses to COVID-19, we performed a final analysis to determine whether any state(s) experienced a disproportionate virus case load that might have impacted their response to the pandemic. Using publicly available data from the U.S. Centers for Disease Control and Prevention (https://covid.cdc.gov), we developed Fig. 3 to examine the seven-day moving average number of positive case results per 100,000 people for the states included in this study from March to December 2020. This time period reflects the first month in which any meaningful number of positive tests occurred in the U.S. through the final month of our interviews. As shown in Fig. 3, several states appear to have experienced periods of noticeably higher virus caseloads during the timeframe studied (e.g., Louisiana, Arizona, Florida, South Dakota, Iowa, and Tennessee). However, our interviews and analyses offer no evidence that these states responded to the pandemic with any more urgency or aggressiveness than the other states studied.Fig. 3 Spread of COVID-19 from March to December 2020: Seven-Day Moving Average Case Rate per 100,000 People. This figure presents data on the spread of COVID-19 in terms of the seven-day moving average number of positive cases per 100,000 people from March to December 2020 in the states examined in this study (data is publicly available from the United States Centers for Disease Control and Prevention (CDC), at: https://covid.cdc.gov). Interviews for this study occurred during the following months: September: Alabama, Georgia, October: Ohio, November: Arizona, Florida, Idaho, Illinois, Iowa, Kentucky, Louisiana, Mississippi, Nevada, North Carolina, Oklahoma, Pennsylvania, South Dakota, Tennessee, December: California, Texas, Virginia, Washington. 5 Discussion and conclusion 5.1 Understanding our findings through the lens of institutional theory In this section, we address our second-order goal of organizing and explaining our findings through the lens of institutional theory, including coercive, mimetic, and normative isomorphism. As previously noted about isomorphism, Mizruchi and Fein (1999, 657) find that “two or more [mechanisms] could operate simultaneously and their effects will not always be clearly identifiable.” As such, the discussion of isomorphism that follows presents some of the clearer instances of how our findings can be organized and explained through these mechanisms. As a roadmap for the following discussion, Fig. 4 presents a summary of the connection between first-order concepts and second-order themes, as well as the connection between second-order themes and the aggregate dimensions of coercive, mimetic, and normative isomorphism. Furthermore, the theoretically-based insights articulated in this section are applicable to other accountancy bodies outside the U.S. to the extent that they operate in constrained environments such as the state boards in the U.S.Fig. 4 Data Structure. A primary tenet of institutional theory is that “individual efforts to deal rationally with uncertainty and constraint often lead, in the aggregate, to homogeneity in structure, culture, and output” (DiMaggio and Powell, 1983, 147). Indeed, in this study, state boards exerted individual efforts to deal with the uncertainty of COVID-19, and did so facing time constraints to abandon in-person work as well as political/resource constraints that dictated various aspects of remote work. State boards also entered the pandemic with differing CPE and CPA exam policies, including various reporting cycles and deadlines. Specifically, in responding to pandemic-induced constraints, state boards adapted key pillars of public protections, such as CPE requirements, CPA exam requirements, and regular board meetings/hearings. By adapting these activities, state boards also sustained their legitimacy, a key organizational goal according to institutional theory (Meyer and Rowan, 1977, DiMaggio and Powell, 1983, Zucker, 1987). 5.1.1 Coercive isomorphism As a result of our discussions about first-order concepts, we identified six second-order themes. The first two themes include: (1) state government regulatory actions and (2) state government financial control. These second-order themes emerged based on first-order concepts such as permissible types of meetings, legality of remote workflow, budget authority, and budget cuts (see Fig. 4). These second-order themes reflect the formal and informal political influences on state boards to conform to certain rules and practices established by their state’s government (DiMaggio and Powell, 1983, Mizruchi and Fein, 1999), and as such fall under the aggregate dimension of coercive isomorphism. To further substantiate the presence and/or role of coercive isomorphism on state board responses to COVID-19, several executive directors highlighted that specific board operations could not be changed without first being evaluated and approved by their state’s legislature (e.g., transition to remote board meetings, remote access to records). Furthermore, in some instances, state governors took direct action on matters that impacted state boards. For instance, governors in Idaho, Illinois, Tennessee, and Virginia took actions to explicitly allow the transition to virtual board meetings, while governors in Ohio, Oklahoma, and Texas set a deadline for board staff to return to the office. 5.1.2 Mimetic isomorphism The third and fourth second-order themes we identified were: (3) model exemplary peers and (4) response to uncertainty. These second-order themes emerged based on first-order concepts such as prior investment in technology, the conversion to digital processes, availability of online courses/trainings/conferences, safeguarding IT assets, and the lack of changes to future fees and staffing levels (see Fig. 4). These second-order themes are representative of responses to uncertainty and the modelling of rules and practices based on an exemplary peer (DiMaggio and Powell, 1983, Mizruchi and Fein, 1999), and as such fall under the aggregate dimension of mimetic isomorphism. As further substantiation of the role of mimetic isomorphism on state board responses to COVID-19, state boards appear to have observed how the broader business community and the accounting profession, in particular, pivoted to remote work in light of COVID-19′s impact in the U.S. in early 2020. This shift to remote work likely served as a signal to organizations like state boards of accountancy that it was acceptable to work remotely and conduct historically important face-to-face interactions (e.g., quarterly meetings of the board, enforcement hearings) using online video conferencing technologies such as Zoom or WebEx. In this way, state boards modelled themselves after the broader business community and transitioned their operations to a remote environment. This change is consistent with how Greenwood et al. (2002) discuss legitimized changes. Here, state boards had little time to theorize (i.e., assess) the change, and cues of legitimation came from the wider business world and accounting profession who quickly adopted such practices (e.g., Jenkins et al., 2022, Luo and Malsch, 2022; Deloitte, n.d.; EY Foundation, n.d.; PwC, n.d.). 5.1.3 Normative isomorphism The fifth and sixth second-order themes we identified were: (5) professionalization and (6) NASBA influence. These second-order themes emerged based on first-order concepts such as close relationships among the state boards executive directors, professional conferences, CPE and CPA exam accommodations, and (other) NASBA recommendations (see Fig. 4). We believe these second-order themes are representative of organizations adopting similar rules and practices as a result of employees’ common formal education as well as their participation in similar professional networks, trainings, conferences, and trade associations (DiMaggio and Powell, 1983), and as such fall under the aggregate dimension of normative isomorphism. To further substantiate the role of normative isomorphism on state board responses to COVID-19, many executive directors mentioned being influenced by close personal relationships with other executive directors developed over time at NASBA meetings, a professional network effect indicative of normative influences. Furthermore, in terms of formal education, many executive directors are CPAs and/or have a business financial background, meaning there is considerable overlap in their university and/or professional education, which also contributes to normative isomorphism. 5.1.4 Theorization and professional associations Greenwood et al. (2002) discuss the importance of professional associations in providing an arena to theorize (i.e., assess), legitimate (i.e., endorse), and diffuse (i.e., communicate) changes. As discussed throughout this paper, NASBA is the leading professional association for U.S. state boards of accountancy. In response to the pandemic, NASBA made recommendations to state boards related to CPE (i.e., allowable course formats and reporting deadlines) and the CPA exam (i.e., expiration of Notice to Schedule and exam credits). Given the various pre-existing allowable CPE course formats and reporting deadlines across state boards, we do not observe blanket adherence to NASBA’s recommendations. For instance, some states operate on a bi-annual or tri-annual CPE reporting cycle, such that adjusting these requirements for 2020 was moot. However, having a professional association like NASBA to theorize, legitimate, and diffuse these changes may have signaled to state boards that it was acceptable, or even responsible, to implement modified CPE and CPA exam policies to help licensees and CPA exam candidates navigate the challenges of the pandemic. 5.2 Conclusion Each state board of accountancy experienced challenges during the COVID-19 pandemic, yet each implemented responses to maintain operations and their charge to regulate the accounting profession. Our study reveals several ways in which state boards experienced and/or responded to the pandemic differently, including board staff capabilities to work remotely, limitations/benefits of available information technology, CPE requirements/extensions, and CPA exam deadlines/extensions. While some boards were well equipped to transition to remote work, it was surprising that others did not have the technology or processes in place to support such a transition. Multiple executive directors mentioned their eagerness to return to face-to-face interactions, but also acknowledged the increased attendance at virtual board meetings. We note with interest that an increasing number of businesses are embracing either remote work or hybrid work arrangements; such mimetic isomorphic forces may alter the plans of these executive directors. Some executive directors even highlighted that they were deemed essential personnel within their state which afforded them more freedoms to continue their work. These examples show the importance of CPAs to states’ economies. Finally, it should be noted that some changes implemented in response to the pandemic are likely to be permanently adopted, such as (the option for) virtual board meetings, expanded options for public access/participation in board meetings, and flexible work arrangements for board staff. COVID-19 exposed lingering weaknesses in state boards and forced many to go through a long-overdue modernization in processes and thought. While each state board’s response to the pandemic could be a case study in itself, the collective responses observed in this study suggest that boards achieved a largely homogenous response, a phenomenon consistent with institutional theory. Indeed, updated technologies and shifts to alternative work solutions (i.e., teleworking) have created a state board environment now positioned to withstand some of the greatest organizational challenges posed by a pandemic or similarly disruptive event. Data availability: Data used in this study are available upon request and by permission granted by individual U.S. state boards of accountancy. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix Interview script 1. Demographic questionsa. What is the number of board staff? b. Does the board have budgetary autonomy? c. What is the number of licensees in your state? 2. What are you hearing from licensees in your state and how COVID-19 is affecting their work (i.e., new challenges licensees are facing due of COVID-19). If interviewee introduces NASBA in their response, ask the following:3. NASBA recommended that state boards allow CPE program sponsors to convert registered live programs to virtual presentations. Did your state board adopt this guidance directly or in some modified format? If not, why not? 4. NASBA recommended that state boards extend CPE reporting deadlines due to cancelled conferences and shifts in busy season. Did your state board adopt this guidance directly or in some modified format? If not, why not? 5. NASBA recommended that state boards extend CPA candidates’ notice to schedule (NTS) expiration dates. Did your state board adopt this guidance directly or in some modified format? If not, why not? 6. Have recommendations from NASBA previously influenced the decisions of your state board? Do you think these influences will continue in the future? 7. Please briefly summarize any changes to policies / regulations / requirements that licensees are asking of your state board in response to COVID-19. [e.g., Peer Review] 8. Have you implemented any other changes that are intended to help licensees comply with state board requirements during COVID-19? If so, can you describe? COVID-19 impact on state board 9. Did COVID-19 have any immediate impacts (i.e., near the time of March 2020) on the operations of your state board? By operations, we mean the functioning of the board and the ways in which employees carried out their work. 10. What immediate financial impact has your state board experienced due to COVID-19? ▪ Your current operating budget has (please check one):o increased / decreased / not changed ▪ Fees (i.e., individual CPA license renewal, original application, firm fee, etc.) in your current operating budget have (please check one):o increased / decreased / not changed 11. What longer term financial impact does your state board anticipate due to COVID-19? ▪ In the coming year(s), the state board operating budget will (please check one):o increase / decrease / not change ▪ In the coming year(s), fees will (please check one):o increase / decrease / not change ▪ In the coming year(s), the number of board staff will (please check one):o increase / decrease / not change 12. What lessons have been learned during the pandemic that might impact the future operations of the state board? What changes have been made in response to COVID-19 that will likely remain in place? 13. Are there regional (i.e., neighboring states) influences on your state board’s deliberations and decisions related to COVID-19 responses? 14. Have regional influences previously impacted the decisions of your state board? Do you think these influences will continue in the future? CPE-related questions: 15. What changes has your state board made to rules regarding the ways a licensee can earn CPE as a result of COVID-19? Please indicate whether each of these changes is permanent or temporary. ▪ Extended timeframe for reporting of CPE?o Yes / No o Permanent / Temporary ▪ Allowed CPE for more online options (courses, trainings, conferences)?o Yes / No o Permanent / Temporary ▪ Allowed self-study?o Yes / No o Permanent / Temporary ▪ Issued waivers on CPE requirements related to required number of hours?o Yes / No o Permanent / Temporary ▪ Issued waivers on CPE requirements related to required topics?o Yes / No o Permanent / Temporary ▪ Other (please explain) ▪ No changes have occurred 16. How has your state board changed its approach to ensuring licensees properly report their CPE? Is the state board performing more “CPE audits” as a result of COVID-19? If so, can you estimate the % change relative to the prior year? How have these audits changed in response to COVID-19, if at all? 17. Have any planned changes to the rules regarding the ways a licensee can earn CPE been deferred as a result of COVID-19 (e.g., a new CPE requirement for an ethics course)? ▪ Yes (please explain) ▪ No Data availability Data will be made available on request. 1 Brennen et al. (2022, 1) succinctly describe COVID-19 as follows: “The acronym COVID-19 stands for COrona VIrus Disease 2019. Corona virus is so called because of its appearance under microscope as a halo or crown. Following an outbreak in Wuhan China in December 2019, COVID-19 led to the first worldwide pandemic in over one hundred years.” As COVID-19 became an official pandemic declared by the World Health Organization on March 11, 2020 (WHO, 2020), news media drew parallels to similar events from history. Anecdotally, the most common reference was to the Flu of 1918 (e.g., Phillips and Cole, 2020;Usero, 2020; Dudding, 2021). This means that a comparable event had not occurred for more than 100 years, at a time when nearly half of the 55 current state boards of accountancy did not yet exist (Jenkins et al., 2018a). 2 We use the term licensee to describe what NASBA refers to as a regulated person, which is an active CPA licensee or an inactive CPA licensee that pays a renewal fee. 3 Nevada operates with two full-time staff, one seasonal/part-time clerical worker, and three part-time/hourly CPA investigators. 4 We refer the interested reader to a special issue of the Accounting, Auditing, & Accountability Journal on the impact of COVID-19 on accounting, accountability, and management practices (Leoni et al., 2021). The majority of the studies included in the special issue examine international experiences. Our paper contributes to the COVID-related literature by examining the related regulatory response of U.S. state boards of accountancy to the pandemic. 5 The 10th Amendment to the U.S. Constitution reads: “The powers not delegated to the United States by the Constitution, nor prohibited by it to the States, are reserved to the States respectively, or to the people” (U.S. Congress, 1791). 6 CPA exam candidates must first apply to take the exam, which includes verification of eligibility based on education requirements and payment of testing fees. If approved, NASBA issues a Notice to Schedule, which lists the exam section(s) the candidate is allowed to schedule (Elkins, 2018). State boards of accountancy determine the period of time a Notice to Schedule is valid, but it is ty-pically-six months (Elkins, 2018). 7 We did not observe any systematic differences across responses obtained from the executive directors via email compared to responses obtained from executive directors with whom we conducted a video conference. In addition, we were able to obtain follow-up responses from the four executive directors who originally provided answers to our questions via email such that our data gathering approaches were generally parallel. 8 It is common for interview-based studies to report word counts; however, because we do not have verbatim transcripts to obtain such data and we do not report these counts. 9 The research reported in this paper and the use of human subjects was reviewed and approved by the Institutional Review Board (IRB) where the research occurred. The IRB is the university’s office that protects the rights and welfare of human research participants. 10 Pratt (2009, 859) recommends disclosing the relationship between the researcher and the researched because doing so “informs the reader about how you approached your study” and is important to a study’s credibility. None of the researchers has either a close personal or professional relationship with any of the interviewees. Moreover, none of the researchers served on a state board of accountancy or had other regulatory responsibilities that would create a conflict of interest or potential bias in analyzing the data or reporting findings from the study. 11 Of the 21 executive directors, we received written responses from four. It was therefore not necessary for them to verify our notes. 12 Here, and throughout the study, comments from executive directors have been sanitized to remove references to specific individuals such as staff members or other executive directors. 13 This statement refers to the Governor of Tennessee implementing alternative workplace solutions (AWS), which is a “cultural and physical transformation that uses non-traditional workspaces to promote efficiency and flexibility across state government” (Tennessee.gov, 2022). In doing so, “AWS reduces square footage needs across state-owned, managed and leased facilities by creating policies and workspaces that allow employees flexibility in their work style” (Tennessee.gov, 2022). 14 Discussions with executive directors revealed that laws in many states required board meetings and hearings to be in-person to be legally binding prior to the onset of COVID-19. State boards must make their meetings available to the general public given their responsibility to protect the public interest. Although not a specific interview question, we learned that during the pandemic, states announced their board meetings and hearings on their website, where the general public could obtain a link to the live meeting and/or recordings of previous meetings. State boards also allowed public interaction in these meetings, which oftentimes involved the board recognizing any requests to speak prior to the commencement of the meeting. 15 We use the following terms to describe proportions of interviewees throughout the remainder of the study: “few” represents 20 percent or less, “some” represents 21 to 40 percent, “about half” represents 41 to 59 percent, and “most” and “many” represent 60 percent or greater (Westermann et al., 2015). 16 Although Nevada did not approve extensions to CPE requirements, the Board did waive penalties if delays occurred in obtaining the CPE within the calendar year required based on hardships from the pandemic. 17 States that indicated “more-so” influence include: Florida, Georgia, Iowa, Nevada, Tennessee, Texas, and Washington. States that indicated “some” influence include: California, Idaho, Kentucky, Louisiana, Mississippi, North Carolina, Oklahoma, Pennsylvania, and Virginia. Finally, states that indicated “less-so” influence include: Alabama, Ohio, and South Dakota. A response to this question was not received from three states. 18 States that approved this change include: Idaho, North Carolina, Oklahoma, South Dakota, and Texas. States that already had policies in place flexible enough to accommodate this shift include: Alabama, California, Florida, Georgia, Illinois, Iowa, Kentucky, Louisiana, Mississippi, Nevada, Ohio, Pennsylvania, Tennessee, Virginia, and Washington. A response to this question was not received from one state. 19 States that accepted this guidance include: Arizona, California, Florida, Louisiana, and Mississippi. Alabama rejected this guidance due to the proximity of the proposed change to its existing deadline of September 30. States that rejected the guidance but provided accommodations include: Illinois, Iowa, South Dakota, Tennessee, and Texas. Finally, this guidance was irrelevant to the following states because the end of their reporting period was after October 31, 2020: Georgia, Idaho, Kentucky, Nevada, North Carolina, Ohio, Oklahoma, Pennsylvania, Virginia, and Washington. 20 Several examples from this section help demonstrate the range of state boards’ preparedness for the pandemic. In Tennessee, changes implemented before the pandemic put the state in a position to better navigate COVID-19. For instance, prior to the pandemic, the governor imposed modern technologies on state agencies to better enable remote work and thus reduce the state’s real-estate footprint (i.e., a cost-cutting measure). Tennessee also outsourced its CPA examination services to NASBA and implemented more robust CPE reporting procedures (i.e., beyond a simple “check-the-box” attestation of compliance), a move that helped reduce CPE non-compliance from approximately 29 percent down to 2 percent. The state also implemented a licensing database to replace its manual tracking system, and allowed all CPE to be earned online in a self-study format. These changes prepared Tennessee for a major disruptive event, especially when compared to states such a Louisiana, Georgia, and Virginia. Louisiana maintained historic institutional practices with manual paper-based processes and low reliance on technology. These manual-based processes required a person to be present in the state board’s office to deal with issues such as CPE reporting, which involved licensees physically submitting forms. Budget cuts during the pandemic (i.e., as opposed to pre-pandemic cuts) impacted board operations in states such as Georgia, which was left with fewer resources to carry out enforcement actions and CPE audits. Finally, states that lacked certain technologies had a harder time transitioning to telework, such as Virginia when it initially did not have enough virtual private network (VPN) connections for all staff nor the ability to answer office calls remotely. 21 Illinois maintains two oversight bodies relevant to CPAs. The Illinois Board of Examiners is specific to CPAs, while the Illinois Department of Financial and Professional Regulation has oversight over various professional licenses (e.g., doctors, nurses, veterinarians, barbers, engineers, detectives, and CPAs). We interviewed the executive director (or equivalent) from each board as part of this study and responded to items relevant to their specific role with Illinois CPAs. However, in presenting and discussing our analyses/results, we do not differentiate which party provided the response to specific items. ==== Refs References National Association of State Boards of Accountancy (NASBA), 2020. State Board Report, Summer 2020. Available at: <https://nasba.org/app/uploads/2020/07/03-NASBA-SBR-July-Q3-2020_Final_v2.pdf> (last accessed June 2021). National Association of State Boards of Accountancy (NASBA), 2021a. About us. Available at: <https://nasba.org/about/> (last accessed June 2021). National Association of State Boards of Accountancy (NASBA), 2021b. COVID-19 updates. Available at: <https://nasba.org/blog/2020/03/17/covid-19-update/> (last accessed July 2021). Bauer T.D. Estep C. One team or two? Investigating relationship quality between auditors and IT specialists: Implications for audit team identity and the audit process Contempor. Account. Res. 36 4 2019 2142 2177 Brennen N.M. Edgar V.C. Power S.B. COVID-19 profit warnings: Delivering bad news in a time of crisis British Account. Rev. 54 2 2022 1 24 Carey J.L. The rise of the accounting profession: From technician to professional 1896–1936 1969 American Institute of Certified Public Accountants New York, NY Carlisle M. Gimbar C. Jenkins J.G. Auditor-client interactions – An exploration of power dynamics Forthcoming in Audit.: J. Pract. & Theory. 2022 Corley K.G. Gioia D.A. Identity ambiguity and change in the wake of a corporate spin-off Admin. Sci. Qrtly. 49 2 2004 173 208 Creswell J.W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches 3rd ed. 2009 Sage Thousand Oaks, California Deloitte LLP., n.d. COOs and the pandemic: Shape a more resilient future. Available at: <https://www2.deloitte.com/us/en/pages/consulting/articles/coo-agenda-response-to-covid-19.html> (last accessed July 2022). DiMaggio P.J. Powell W.W. The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields Amer. Sociol. Rev. 48 2 1983 147 160 Dudding W. Echoes of another pandemic: How the Times covered the 1918 Flu. The N.Y. Times January 5, 2021. Available at: <https://www.nytimes.com> (last accessed December 2021 Elkins, J., 2018. What is an NTS? Available at: <https://nasba.org/blog/2018/03/14/what-is-an-nts/#:∼:text=Once%20your%20CPA%20Exam%20application,issue%20an%20NTS%20to%20you.> (last accessed April 2022). EY Foundation., n.d. EY Foundation’s response to COVID-19. Available at: <https://eyfoundation.com/uk/en/news/ey-foundation-s-response-to-covid-19.html> (last accessed July 2022). Flesher D. 100 Years of NASBA: Serving the public interest 2007 National Association of State Boards of Accountancy (NASBA) publication Nashville, Tennessee Gibbins M. Qu S.Q. Eliciting experts’ context knowledge with theory-based experiential questionnaires Behav. Res. Account. 17 1 2005 71 88 Golden-Biddle K. Locke K. Composing qualitative research 2nd ed. 2007 Sage Thousand Oaks, California Greenwood R. Suddaby R. Hinings C.R. Theorizing change: The role of professional associations in the transformation of institutionalized fields Acad. Manag. J. 45 1 2002 58 80 Greenwood R. Suddaby R. Institutional entrepreneurship in mature fields: The big five accounting firms Acad. Manag. J. 49 1 2006 27 48 Griffith E. Hammersley J. Kadous K. Audits of complex estimates as verification of management numbers: How institutional pressures shape practice Contempor. Account. Res. 32 3 2015 833 863 Guest G. Bunce A. Johnson L. How many interviews are enough? An experiment with data saturation and variability Field Methods 18 1 2006 59 82 Jenkins J.G. Negangard E.M. Oler M.J. Getting comfortable on audits: Understanding firms’ usage of forensic specialists Contempor. Account. Res. 35 4 2018 1766 1797 Jenkins J.G. Popova V. Sheldon M.D. In support of public or private interests? An examination of sanctions imposed under the AICPA Code of Professional Conduct J. Bus. Ethics 152 2 2018 523 549 Jenkins J.G. Loraas T.M. Stanley J.D. A memorialization of auditors’ remote work experiences during the early days of the COVID-19 pandemic 2022 Auburn University Working paper. Leoni G. Lai A. Stacchezzini R. Steccolini I. Brammer S. Linnenluecke M. Demirag I. Accounting, management and accountability in times of crisis: Lessons learned from the COVID-19 pandemic Acc, Aud., & Acc. J. 34 6 2021 1305 1319 Luo Y. Malsch B. Exploring improvisation in audit work through auditors’ response to COVID-19 2022 Toronto Metropolitan University and Queen’s University Working paper. Malsch B. Salterio S. “Doing good field research”: Assessing the quality of audit field research Audit.: J. Pract. & Theory 35 1 2016 1 22 Meyer J.W. Rowan B. Institutionalized organizations: Formal structure as myth and ceremony Am. J. Sociol. 83 2 1977 340 363 Miles M.B. Huberman A.M. Qualitative data analysis: An expanded sourcebook 1994 SAGE Publications Inc Thousand Oaks, CA Mizruchi M.S. Fein L.C. The social construction of organizational knowledge: A study of the uses of coercive, mimetic, and normative isomorphism Adm. Sci. Q. 44 4 1999 653 683 Phillips M. Cole D. Coronavirus advice is everywhere – It was the same with the Spanish Flu Wall Street J. June 12, 2020. Available at: <https://www.wsj.com> (last accessed July 2021 Power M.K. Gendron Y. Qualitative research in auditing: A methodological roadmap Audit.: J. Pract. & Theory 34 2 2015 147 165 Pratt M. From the editors: For the lack of a boilerplate – Tips on writing up (and reviewing) qualitative research Acad. Manag. J. 52 5 2009 856 862 PricewaterhouseCoopers LLP (PwC)., n.d. TMT companies beta test the office of the future. Available at: <https://www.pwc.com/us/en/industries/tmt/library/remote-work-transition-success.html> (last accessed July 2022). Stolowy H. Gendon Y. Moll J. Paugam L. Building the legitimacy of whistleblowers: A multi-case discourse analysis Contempor. Account. Res. 36 1 2019 7 49 Tennessee.gov, 2022. What is AWS? Available at: < https://www.tn.gov/cfg/alternative-workplace-solutions/aws.html> (last accessed May 2022). United States (U.S.) Congress U.S 1791. Bill of Rights Amendment X Usero A. Reopening too soon: Lessons from the deadly second wave of the 1918 flu pandemic. The Washington Post. May 24, 2020. Available at: <https://washingtonpost.com> (last accessed July 2021 Van Maanen J. The fact of fiction in organizational ethnography Admin. Sci. Qrtly. 24 4 1979 539 550 Wengraf T. Qualitative research interviewing: Biographic narrative and semi-structured methods 2001 Sage Westermann K.D. Bedard J.C. Earley C.E. Learning the “craft” of auditing: A dynamic view of auditors’ on-the-job learning Contempor. Account. Res. 32 3 2015 864 896 Westermann K.D. Cohen J. Trompeter G. PCAOB inspections: Public accounting firms on trial Contempor. Account. Res. 36 2 2019 694 731 World Health Organization (WHO), 2020. WHO Director-General’s opening remarks at the media briefing on COVID-19. Available at: <https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020> (last accessed February 2021). Zeff S.A. How the U.S. accounting profession got where it is today: Part I Account. Horizons 17 3 2003 189 205 Zucker L.G. Institutional theories of organizations Annu. Rev. Sociol. 13 1987 443 464	0	PMC9708618	NO-CC CODE	2022-12-05 23:15:28	no	2022 Nov 30;:107047	utf-8	null	null	null	oa_other
==== Front Wellbeing Space Soc Wellbeing Space Soc Wellbeing, Space and Society 2666-5581 The Authors. Published by Elsevier Ltd. S2666-5581(22)00046-X 10.1016/j.wss.2022.100117 100117 Article “Make sure I hear snoring”: Adolescent girls, trans, and non-binary youth using sound for sexual wellbeing boundary-making at home during COVID-19 Coppella Leah I. a⁎ Flicker Sarah b Goldstein Alanna b a Simon Fraser University, 205-2040 York Ave, Vancouver, BC V6J 1E7, Canada b York University, Faculty of Environmental and Urban Change, 4700 Keele St, Toronto ON M3J 1P3. Canada ⁎ Corresponding author. 30 11 2022 2023 30 11 2022 4 100117100117 30 12 2021 31 10 2022 17 11 2022 © 2022 The Authors. Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. To understand how COVID-19′s stay-at-home orders impacted youths’ sexual and social development, we conducted five virtual focus groups (n = 34) with adolescent girls’, trans’, and non-binary youths’ aged 16–19 between April-June 2021 in the GTA. We queried experiences of home, privacy, and sexual wellbeing during Canada's third wave. Auto-generated zoom transcripts were coded using an inductive framework with NVivo. Field notes and team discussions on the coded data informed the analysis. This paper explores how sexual wellbeing during the pandemic is practiced in relation to, dependent upon, and negotiated at home. Using intersectionality theory and embodiment theory, this research analyzes how youth's diverse identities shape their understandings and experiences of sexual wellbeing. We found youth needed spaces where they were not only unseen, but importantly, unheard. We argue sound as an important piece of boundary-work that reveals the way youth construct space during precarious times. Youth primarily negotiated sonic privacy through (a) sound-proofing, (b) sound warnings and (c) “silent reassurance”, a term we coined to describe the precursor of silence from other household members in order for youth to feel safe enough to practice sexual wellbeing. We found that white youth cited the bedroom as the best space for sexual wellbeing practices, but BIPOC youth felt the bedroom was only their best available option and still found they had to negotiate privacy. Attending to intersectionality theory, we expand on McRobbie and Garber's (1976) bedroom culture concept and widen Hernes’ (2004) concept of physical, social and mental boundary-work to include sound as a fourth type, which straddles among them. This research shows how privacy, gender and sexual identities were negotiated at home in times of extreme uncertainty, highlighting how implications of home as a ‘place’ during the pandemic, constructs sexual wellbeing. Mapping how and where youth practice embodied sexual wellbeing exposes the ways that private and public understandings of identity relate to sexuality and geographies of home. We understand the home as a complex space that can not only determine sexual wellbeing, but where health promoting boundaries can be negotiated. We conclude with suggestions for supporting adolescent sexual wellbeing, inside and outside the home, during and after COVID-19. Keywords Home Youth Sexual wellbeing Sound Boundary-work Intersectionality ==== Body pmcIntroduction The COVID-19 pandemic, and its concomitant repeated calls for social and physical distancing measures, have fundamentally reorganized social, romantic, and sexual relations. For young women, whose lives were already subject to high degrees of social and familial surveillance and control (Seedall and Anthony, 2015), this period of heightened confinement exacerbated pre-existing inequalities related to mobility (Mitra et al., 2014) and accessing the social determinants of health (Paremoer et al., 2021; Hankivsky and Christoffersen, 2008). During stay-at-home orders imposed in response to the COVID-19 pandemic, many “experienced the scarcity of space” (Risi et al., 2021, p. 471). This scarcity was inequitably patterned, experienced, and resisted. Young peoples’ sexual, social, and intimate lives were lived almost entirely within the confined environment of the home, and mediated almost entirely through communications technology (Goldstein and Flicker, 2020). Therefore, drawing on feminist embodiment theory to examine narratives, we delineate how youth navigated visual and sonic privacy conditions in their homes to make space for sexual wellbeing practices. Sexual wellbeing is a positive concept that encompasses sexual health, pleasure, and justice, as well as safety, respect and self-determination (Mitchell et al., 2021). As Lorimer et al. (2019) argue, sexual wellbeing is both socially influenced and individually experienced and is “likely to be a self-assessment by individuals” (Lorimer et al., 2019, p. 844). Considering that there is no objective determination of what constitutes sexual wellbeing, this paper understands sexual wellbeing as lying on a spectrum, encompassing both young people's stated desires for sexual health, exploration, pleasure, and justice, as well as their relative ability to realize these aspects of sexual wellbeing given the social and structural conditions they encounter in their daily lives. Race, gender, sexual orientation, and economic situations will all influence possibilities for negotiating sexual well-being and accessing determinants of sexual health. For adolescents, many sexual wellbeing practices are typically enacted outside the home - away from the eyes and ears of parents and guardians (e.g., date nights, sex in the family car, shopping for sex toys or accessing safer sex paraphernalia). Moreover, many young people find and connect with their partners in public spaces like schools, community programs, peer groups, malls and parks. In Ontario, Canada, where the study discussed in this article took place, access to all these spaces was severely restricted during pandemic waves and lockdowns in the Winter and Spring of 2021. These spatial restrictions may have disproportionately impacted the wellbeing of LGBTIQ young people (Grant et al., 2021). Additionally, COVID-19 restrictions limited access to sexual and reproductive health services, including contraception, abortion, screening and treatment of STBBIs (Mmeje et al., 2020). At home, the pandemic has also increased vulnerability to family violence and reduced the availability of support (Usher et al., 2020) while rates of gender-based violence have increased during the pandemic (Mittal and Singh, 2020). Opportunities to connect with old and new partners were severely restricted and largely mediated through online encounters (Goldstein and Flicker, 2020). In short, for most young people, sexual wellbeing, along with other social and intimate activities, moved into the confined and often limiting space of the home. Consequently, youth faced increased parental monitoring and reduced privacy (Lindberg et al., 2020, p. 75). Intimate geographies of the home The home is an intensely intimate space. Many youths first confront power relations and learn about safety, danger, and privacy at home (Blunt, 2005). It is a place of intersecting identities and feelings that are “bound up” within wider structures like labor, the state and the family (Valentine, 2003, p. 39). Blunt and Varley (2004) define home as “a space of belonging and alienation, intimacy and violence, desire and fear” where “meanings, emotions, experiences and relationships… lie at the heart of human life” (p. 3). They argue that geographies of home are “both material and symbolic” (Blunt and Varley, 2004, p. 3). Moreover, homes are highly gendered spaces: boys and girls tend to congregate and prefer different kinds of spaces (see for example: Abbott-Chapman and Robertson, 2009 p. 431). Following Ahmed and others (see: Antwi et al. 2013, p. 117), we consider the home as a complex, multifaceted space where both toxicity and love can exist. Boundary-making and negotiating privacy at home has been shown to promote youth wellbeing. Gale & Park (2010) found that “setting up boundaries can help individuals cope with aspects of infringement on areas they feel they have control over” (p. 35). There is also work on the physical manifestations of requests for privacy at home. As early as 1979, Parke & Sawin found that as children transition to adolescence, they make greater use of physical privacy markers, such as closing the door to their bedrooms. While physical boundaries have been given primacy in the literature on adolescent boundary-making in the home, some new geographers are also examining the importance of auditory boundaries. For instance, recent research around soundscapes of the home indicate that small moments of sound “can tell us much about the larger social forces operating within everyday life” (Duffy and Waitt, 2013 p. 47). Smith (2000) first described how sound is essential to space and our relations to it, while Mills (2017) outlined the emerging field of sonic geographies of childhood, which considers how sound and space intersect with age and social status. For example, in their examination of the impact of environmental factors on privacy at home, Gale and Park (2010) noted how some youth concerned with the acoustical properties of their home, chose to take private phone calls outside (p. 34). Some have found that perceived acoustic conditions of home during the pandemic have had a direct correlation to occupants’ well-being (Torresin et al., 2021). Others have noted the heightened salutary sensory aspects of gardens, such as birdsongs (Marsh et al., 2021). To the best of our knowledge, this is the first paper to explore the relationships between sonic soundscapes, geographies of the home and sexual well-being among adolescent girls. Embodiment theory and intersectionality theory as theoretical framework Embodiment is a lived process that examines how the social makes place and space in the body. Feminist understandings of embodiment (Conboy et al., 1997; Ahmed, 2000; Heinämaa, 2003; Crenshaw, 2017) have furthered our understandings on the relationships between subjectivity, corporeality and identity. Intersectionality, on the other hand, is a “complex intersection of different forces that make and unmake value as they materialize on bodies in different spaces and time” (Skeggs, 2019, p. 32). This points to processes of embodiment and the theory of intersectionality as inseparable. Embodiment also blends with intersectionality in that intersectionality has been described as lived experience in feminist geography (Valentine, 2007; Pratt, 1999; Saad and Carter, 2005; Rodó-de-Zárate, 2014; Sang, 2018). Through postcolonial embodiment theory, Ahmed considers “how some are made into the aliens in spaces they call home” (Antwi et al., 2013, p. 117). This connects to Crenshaw's theory of intersectionality (1989) that was created to challenge the discrimination space that alienated so many experiences of discrimination. Also drawing on Ahmed's (2000) embodiment theory, this study shows the very lived and everyday coping strategies that youth relied on during the pandemic through boundary-making. Here,we define boundary-making as the power relations and associated emotions that fuel decisions to create or sustain an identity that is meaningful to the person. The connection between intersectionality and embodiment is clear, as Hopkins (2019), a feminist geographer has pointed out. He argues employing intersectionality in work on embodiment is key to moving “beyond the simplistic assumption that intersectionality is only about multiple identities'' and towards an understanding of how (un)belonging is contested and (dis)embodied (Hopkins, 2019, p. 943–4). Okafor (2018, p. 379) also writes about both embodied processes and intersectionality when she illustrates Black feminism as “a theoretical home” and says it can be both “lived and embodied”, proving the importance of incorporating an understanding of intersectionality into a geographical project. We argue that intersectionality and embodiment as theoretical frameworks for researching the home also allow us to see how understandings of home are multiple, especially for queer communities, as Elwood's (2000) work on lesbian living spaces reveals its multiplicities. The youth we talked to explained how negotiating privacy was a project of brokering sight and sound; these senses collide, combine and conflict to create embodied experience. As youth tried their best to be both unseen and unheard to better perform and practice sexual wellness at home, their embodied success related to both their physical and social circumstances. This paper draws on participant narratives to consider the home as a physical, yet porous space, where privacy and boundary-making negotiations for sexual wellness take place. Intersectionality and embodiment frameworks are unique but also compatible. While intersectionality prioritizes the lived and embodiment prioritizes the body, sexual wellbeing is made up of the lived experience of the body's feelings towards sexual pleasure and identity. Adolescent girls’, trans’, and nonbinary youths’ sexual wellbeing is inherently bound up within space, place, and bodies, therefore intersectionality and embodiment as theoretical frameworks prioritize the lived experience at the center of individual and intersecting identities. Most importantly, intersectionality looks at systemic oppressions while embodiment considers how those systemic forces are felt and made up within the body. This study is part of a larger, ongoing project (Flicker & Goldstein, 2020; 2021) that considers the effects of lockdowns on youth's romantic and intimate relationships. Earlier work has examined the challenges and affordances for teen girls to navigate relationships exclusively online (Flicker & Goldstein, 2020) and how physical distancing measures and school closures have created the conditions for some youth to reflect on the emotional labor involved in the maintenance of their relationships (Flicker & Goldstein, 2021). This study received ethical clearance through the Office of Research Ethics at York University. Methods Sampling We co-facilitated five online focus groups with adolescent girls’, trans’, and nonbinary youths’ aged 16–19 years old residing in the Greater Toronto area during the Spring of 2021. Participants were recruited through ads posted on social media. Interested participants were emailed an online consent form and a link to complete an anonymous demographic survey. Those who returned signed consent forms, completed the survey, and met age and residence eligibility criteria were emailed a secure Zoom link for their focus group session. Procedure Researchers opened the focus group by introducing themselves, the study, and explaining the functions of Zoom. Participants were encouraged to label themselves with a chosen pseudonym and preferred gender pronouns (we respect these preferences herein). We let participants decide to join the Zoom focus group by audio, video, and/or chat. We reviewed our Confidentiality and Disclosure Policies and procedures. To elicit discussions, we followed a focus group procedure with questions divided into 3 themes: (1) Getting into a Relationship; (2) Being in Relationships; and (3) Looking Ahead. An ice-breaker activity prior to the focus group questions asked participants to draw a map of their home and situated themselves. The focus groups discussions centered on how relationships and sexual wellbeing have been impacted by COVID-19, and their experiences of being at home. Each focus group lasted approximately 90 min. Participants were sent a $20 gift certificate of their choice following the session. This focus group procedure was informed by a feminist methodology that prioritized lived experience and focused on the goal of validating youth's experience, especially paying “attention to lived experience exposes the role that space plays in the processes of identification and disidentification” (Valentine, 2007, p. 14). There have been urgent calls from Black scholars to incorporate both intersectionality and culturally safe approaches to women's health research in particular. But Black scholars have also argued that there are important theoretical challenges when integrating intersectionality in public health research, particularly when deciding which identities should be included and recognizing that intersectionality itself was not developed to predict health at all (Bowleg, 2012, p. 1270). Therefore, we informed our focus group with an understanding of the lived experience that Crenshaw (2017) described in her writings on intersectionality. Embodiment theory played a role in our method and our analysis, especially Ahmed's (2000, p. 41) critique of intersectionality and embodiment, where viewing the body as “already determined and as differentiated in terms of gender and sexuality, and also race and class, does not always involve in practice an analysis of the particularity of bodies or of subjectivity in general”. Therefore, in this paper, we view gender and sexuality as an embodied spectrum, where specific identities are named and others have no name. More simply put, this understanding respects how participants identified through the ways they expressed, defined, negotiated and created their identity. By framing identity in this way, as well as seeing sexual wellbeing on a spectrum, we can understand how power relations intersect with space and youth's sexual wellbeing at home, which is a site that has only been intensified since COVID-19 and stay-at-home orders. De Craene and Gorman-Murray (2017) argue that feminist geographers have led the literature particularly on how bodies and their intersectionalities shape and change space. More specifically, feminist geographers have placed the body as a site of social reproduction when looking at health and wellbeing (Dyck, 2003; Longhurst, 1997). Therefore, feminist approaches to research methodology in geography require an understanding of intersectionalities and their ability to change in place. It is this thinking that led us to use the online focus group method; a method that could allow participants to truly be in place, in the home, while also, hopefully relatively comfortable and safe(r). Taylor's (2009) too suggests that focus groups can better reflect the lived experiences of youth, while also providing them with an important social experience with other peers. Data analysis At least two members of the research team attended each session – a facilitator and a note taker. To capture the discussions, the research team utilized the Zoom recording and auto-transcription features. Chat transcripts were also downloaded. Transcripts were manually reviewed and checked for accuracy by an undergraduate research assistant. Copious field notes were taken following each focus group. All data was imported into NVivo for inductive thematic analysis. Drawing on the DEPICT model for participatory analysis (Flicker and Nixon, 2015), our team met repeatedly to collaboratively come up with a coding framework and then review, synthesize, and make sense of coded data. We examined individual stories and ideas and read them in conversation with markers of identity, and then compared them to the experiences of others across focus groups. In addition to noting moments of agreement and disagreement, we also marked obvious silences or absences. We decided upon 12 codes: Privacy; Sexual wellbeing; Risk navigation; Home as social space; Family and boundaries; School; Dating; Friendship; Harassment, violence and bullying; Mental health; Missed opportunities; Personal growth and development. Table 1 lists these codes with their corresponding discussion topics.Table 1 Focus group codes and discussion topics. Table 1Key Codes Discussion Topics Privacy • What does privacy mean to you? • What makes a space feel private to you? Sexual wellbeing • How would you define sexual wellbeing? • Do you think you have “good” sexual wellbeing? Why/why not? • Did your sexual wellbeing change during lockdown? Risk navigation • What kinds of risks do you feel okay taking with regards to COVID-19 lockdowns and dating? • What does it mean to do something risky? Home as social space • Do you feel that you can be yourself at home? • Is home a safe space for you? • How do you feel when you are at home? Family and boundaries • What does it mean to set up a boundary with family/a family member? • Did your boundaries change during lockdown? School • What did school look like for you during lockdown? Dating • What did dating look like for you during lockdown? • Is dating important to you? • What makes dating fun? Friendship • Did your friendships change during lockdown? If so, how? Harassment, violence and bullying • Did you experience a toxic home environment during lockdown? • Do you know anyone that experienced harassment, violence or bullying during lockdown? Mental health • Do you feel that you are mentally healthy? • How has your mental health changed during lockdown? • Were you able to access mental health supports during Covid-19 lockdowns? Missed opportunities • Do you feel that you've missed opportunities during • lockdown? Personal growth and development • Do you feel that you've grown during lockdown? If so, in what ways? Demographics Adolescent girls, trans and nonbinary youth were recruited because, historically, this population has been underserved by sexual health policy, curriculum, education and resources. In addition to this, binary understandings of relationships as either ‘healthy’ or ‘unhealthy’, as well as a biological emphasis on sexuality or gender alone, do not “reflect the nuance associated with most young people's romantic and platonic relationships” (Goldstein and Flicker, 2021, p. 7). All participants resided in the Greater Toronto area and were therefore subject to similar public health lockdown orders and school closures. In total, we had 67 people respond to our survey, of which, 34 youth participated in a focus group. (Many people simply did not “show up” on the day of the focus group). Table 2 summarizes the participant demographics. Based on the demographic survey (n = 34), Table 2 describes the reported age, race/ethnicity, living situation, place of birth, and relationship status of participants since COVID-19 started. It illustrates that the majority of participants were 17–18 years old, with 16 years old making up the smallest percentage. A large percentage were white, followed by Black, with Latinx/Hispanic and Middle Eastern representing the smallest percentages. The majority of participants lived with family and were born in Canada. Most participants reported they started or ‘sort of’ started a relationship since COVID-19 started. Table 3 summarizes participant gender and sexual identity. It illustrates that the majority of participants identified as women. Many identified as nonbinary. The largest sexual identity reported was Bisexuality with the minority identifying as Asexual or Queer.Table 2 Participant demographics based on participant survey. Table 2 DESCRIPTOR NUMBER PERCENT AGE 16 4 12 17 18 9 9 26 26 19+ 12 35 RACE/ETHNICITY Black 7 21 East Asian Latinx/Hispanic 3 1 9 3 Middle Eastern 1 3 Mixed 2 6 South/Southeast Asian White 4 14 12 41 Other 2 6 LIVING ARRANGEMENTS Living with a romantic partner 2 6 Living with family Living on own 27 1 79 3 Living with roommates 4 12 PLACE OF BIRTH Africa 2 6 Asia Canada 3 24 9 71 Latin America 1 3 United States 2 6 Europe Unreported 1 1 3 3 RELATIONSHIP STATUS SINCE COVID-19 Already in a relationship 2 6 No 6 18 Sort of Yes 14 12 41 35 Table 3 Participant gender and sexual identity based on participant survey. Table 3 DESCRIPTOR NUMBER PERCENT GENDER Man 2 6 Nonbinary Transman 7 3 21 9 Transwoman 1 3 Woman 21 62 SEXUALITY Asexual Bisexual 1 13 3 38 Gay 2 6 Lesbian 2 6 Pansexual Queer 7 1 21 3 Straight 8 24 It is important to note that these demographics rely on the survey answers of participants. Often, participants came to focus groups with a differing or expanded identity compared to their survey answers. For example, only one participant identified as a transwoman, a Hungarian immigrant, who later identified in the focus group as Two-Spirited as well. In order to ascertain meaning from intersectionality-informed research, scholars must understand how the inclusion of various social identity categories through quantitative data collection can be fraught with problems, as “such quantitative approaches do not problematize the static and unchanging nature often assumed in the use of such categorical data” (Hankivsky and Grace, 2015, p. 15). Surely, the data collected from these focus group surveys are important and necessary for understanding who was there, but they do not tell the full story of who was there. Sound proofing, sound warnings and silent reassurance: sonic privacy for sexual wellbeing To practice sexual well-being, youth shared that they needed private spaces where they were unseen, but most importantly, unheard. They primarily negotiated sonic privacy through (a) sound-proofing (finding ways to limit being heard); (b) sonic reassurances (e.g. waiting for snores or silence); (c) sound warnings (e.g. knocking). Additionally, white youth cited the bedroom as the best space for sexual wellbeing practices, but BIPOC youth felt the bedroom was only their best available option and still found they had to negotiate privacy. We also found BIPOC and sexual minority youth often had to resort to physical boundary negotiations, in addition to sound boundaries. Youth's boundary-making in the bedroom represents their ability to negotiate boundaries in a space rife with power relations (home), during a time where home is a more intense locale than ever (due to the COVID-19 pandemic). McRobbie and Garber's (1976) concept of bedroom culture argues that while boys tended to dominate the street cultures, girls created a culture of their own within the space of the bedroom. Participant's acoustic and physical experience of their bedroom shows that the bedroom is not just as a site of cultural production, but one of negotiating for sexual wellbeing and therefore, agency of self. Here, we expand this concept to include not only gender, but sexuality and ethnicity, two facets of individual identities that McRobbie and Garber (1976) left out in their analysis. Finally, we use Hernes’ (2004) concept of boundary work and expand it from physical, social and mental, to include sound as a fourth type of boundary that intersects and connects all other forms of boundary work.(a) Sound proofing: being unheard Almost all participants pointed to sonic privacy as a major factor in whether they could practice sexual wellbeing. Alkyl, a bisexual, East Asian youth said in the Zoom chat: “sorry I do not want to unmute because I'm not sure how my parents feel about focus groups, but if my brother could just bother me a little less, i'd have lots of privacy lol”. This quote embodies the fear that many participants had surrounding being “overheard” during stay-at-home orders. Alkyl's fear was a potent reminder that even during the focus group, participants were positioned at home, where surveillance mechanisms and constellations of power persist. Similarly, many participants made liberal use of the chat for “privacy” reasons. Being unheard (even during focus groups) was critical for youth to feel free to express themselves. Youth often found sonic privacy by moving farther away from other household members or closing doors in between rooms to soundproof their space. While issues with parents checking text messages or tracking their location were often discussed, the limiting factor to being able to practice sex virtually was often the ability to find a quiet space where they could be unheard. When asked about sexual wellbeing, Asha (she/her) a nonbinary, pansexual, black youth says she “love[s] going in [her] closet” for privacy, but also said she will go to her underground patio area for “personal conversations and stuff” because the area is farther away from her family's ability to hear. Asha's creative efforts to find places where she would not be heard was echoed by several others. Giselle (they/she), a nonbinary, lesbian, white youth said that she goes to the basement and turns the television on in order to have extra privacy “because it's definitely the place where no one can hear as much”. Giselle adds that she will sometimes turn the “TV on or something in the background and then being on the phone and having the floors and doors and stairs in between helps.” She adds that her room is “more of a physical safe space, rather than a sound one” and therefore, not as ideal if she needs to be loud. Giselle makes an important distinction here: that there are multiple understandings of privacy for youth during the pandemic. In her case, sonic privacy (the basement) and physical privacy (the bedroom) were different spaces. Giselle's boundary work is complex, coupling multiple modes of sound to account for the different moments when she needs privacy. Giselle represents how both sonic and physical privacy flow together to create her ideal private space at home, where she can engage in intimate conversations. Again, while auditory buffers as major factors in perceived privacy is not new (Lincoln, 2005), sound as related to privacy during a pandemic is, especially as our interconnected world turns more virtual with youth online for school, sex, and downtime. Giselle also described how lockdown measures facilitated an exploration of their queerness. Moving online to find new partners encouraged them to try out lesbian dating apps. Giselle shared that this was challenging terrain and she:“wish[ed] parents knew that they actually need to put in work to create a safe space for their child to be independent and discover themselves…creating this safe space [would] allows us to create relationships safely and more openly because we would not feel the need to be so private/hidden/secretive.” Giselle's identity as a nonbinary and lesbian, coupled with the power struggles at home with her parents, may account for the effort she takes at navigating sound boundaries for her sexual wellbeing. In response to another participant, Giselle continued: “boundaries are crucial. Setting boundaries can be so hard, so I wish that parents understood that too”. Giselle's experiences were reflected in other queer participants who were managing the labor of exploring their identities and practicing sexual wellbeing without the usual freedom and supports provided by access to public and community spaces. Other youth agreed with the importance of sonic privacy. Bri (she/they), a nonbinary, pansexual, white youth says that she and her parents use the basement for downtime and privacy, “but privacy is kind of an issue sometimes because [she is] scared that they're gonna over hear [her] since they're so close…” Bri explained that it's the only area available because her parents have given her brother permission to use the entire main floor to play video games. Bri's relegation to the basement brings up the question of a gendered division of space: their brother receives one floor for playing, but she is confined to a shared communal space. Bri cannot help but connect this to her family’ lack of acceptance of her sexual and gender identity. The division of space in this home represents a complex relationship between power and identity, where parents divide the space and the youth must comply. Bell and Valentine (1995) argue that queer bodies existing in certain spaces allows others to understand that the space has been produced as heterosexual, heterosexist and heteronormative (p.18). We can see here that Bri's taking up of space in the basement of a larger home may be an indicator of the heterosexual and heteronormative space that their home employs. Fortunately, Bri points to one result of the pandemic that has been positive for her sexual wellbeing. Bri says the pandemic has actually made it easier for her to have uninterrupted sex with her boyfriend at home. Her parents wanted to keep their distance (due to fears of COVID-19 transmission):“So, they just leave us alone. It's kind of annoying being home with my mom and my brother. The house I live in is not very soundproof, like I can hear my neighbor's through my walls like I can hear my mom and my brother, and it is not very good. The Covid gives us some privacy to be alone, but not be very loud.” Similar to Giselle, Bri underscores the value of sonic privacy. While Bri can be alone, she cannot be private. The two do not necessarily relate here. Therefore, sound itself is embodied in Bri's ability to engage in sexual wellbeing. These narratives reveal that sexual wellbeing, as an embodied practice, is stunted even when “alone” at home. Others also made the distinction that their most private places (spaces where they could get work done or have personal conversations with friends), where they could also have uninterrupted time, were often determined by their allowance for sonic privacy. Liz (she/her) a queer, white, youth made the distinction that her room allowed her to both focus and talk out loud to people over the phone because it was quiet and she was left uninterrupted. Similarly, Two, a bisexual, East Asian girl, said a lot of sound travels back and forth between her room and the rest of the house, but that other than the sound issue, her house respects the privacy she requests when she is in her room. Linking this back to our theoretical framing of embodiment, and how boundaries are embodied at home, we can see how sound is used in boundary-work in everyday life at home. Home is “imbued with meaning and is part of the process of identity-making and a matrix of social relations” (Forsberg and Strandell, 2007, p. 395), but is also filled with boundary-making and a matrix of sounds. The home remains a critical place for better understanding “the embodied, everyday socio-spatial relations through which subjectivities are forged” (Hörschelmann 2017, p. 236), and narratives of where youth find sonic privacy reveal how sexual wellbeing is embodied. Many participants also made the distinction that although they considered their bedroom to be their most private place, they would often leave it to find sonic privacy. For example, Aaliya, a bisexual girl who identified her race as “Other” in our demographic survey, shares:Usually I just stay in my room. Or, I have this part to the back of my house that used to be like an outdoor part but then we enclosed it. I'll just go in the back there and kind of just chat and usually I'll wear my AirPods too so nobody hears them obviously. We found that participants often wore AirPods to ‘find’ or rather make, privacy. Most of them said they would always have AirPods, earphones, or a headset on when engaging in intimate conversations. This kind of boundary work involves a lot of movement within space. Youth in this study mobilized their sexual wellbeing practices often, such as through the incorporation of earphones for better sound-privacy or the moving between rooms. Similarly, geographers have come to see boundary work as always in movement. Take Beasy et al. (2021) study on the boundaries of place and identity during schooling at home during COVID-19 which reveals how boundary making is “continuous” (p. 343). This fluidity represented at home is critical in understanding how lockdown orders were not simply stagnant or unchanging, but in constant flow and negotiation. In addition to this, the incorporation of earphones represents an embodied soundscape, where an object must be used to gain the soundscape the youth desires.(a) Sonic reassurances: listening for snores or silence A sexual wellbeing practice that youth engaged in often, especially during the restrictive pandemic lockdowns, was sexting, phone, and virtual sex. The following quotes are from the chat when participants were asked when they were able to engage in sexual wellbeing practices like sexting:Two(she/her, bisexual, Southeast Asian): absolutely no phone sex D: sasha(she/her, straight, Black): phone sex after hours lol… make sure I hear snoring Jade(she/her, bisexual, Latinx/Hispanic): gotta wait until we're both home alone to make sure Being unheard and having “silent reassurance” was a major factor in whether youth would even begin engaging in intimate conversations/sexual wellbeing practices. The performance of sexual identity, pleasure and wellbeing were contingent on not being surveilled. Similar to Johnson's (1995) understanding that “at-home’ sexual identities are both performed and come under surveillance” (p. 88), Sasha, and others, shared that they often waited until household members were out or asleep before having sex. Two, on the other hand, made it clear that they would never engage in phone sex due to the fear of being overheard. Youth participants each negotiated their own boundaries that were context dependent Sound warnings: knocking As studies during the pandemic have found that people “need… a private and controlled soundscape for home working” (Torresin et al., 2021 p. 10), we argue that youth in this research also needed a private and somewhat ‘controllable’ soundscape for their sexual wellbeing practices. In addition to this, we found that sound warnings were important boundaries that adolescent girls negotiated during the pandemic. Knocking, specifically, was discussed as one of the most important factors in whether youth felt that they had privacy for sexual wellbeing. The importance of ‘sound warning’ requests was visible when participants discussed interruptions, with many youth indicating that they desired a ‘knock’ on their door before a family member entered. Often, knocking was described as just as important for privacy, as a closed door. Unfortunately, this request for a knock was also one that household members often ignored. Avleen (she/her), a straight, South Asian youth, explains that her parents walk into her room without knocking, even when her door is closed. Avleen says she tells them that even if she is not doing anything, they “just need to knock before [they] come in”. She reflects:But I just assume, like their idea of privacy growing up is much different than my idea of privacy, because we're like, two completely different generations. They were raised in a different environment. …. But I, my privacy now is not what I want it to be ideally. Avleen describes a sentiment that many others felt. Not only was knocking a boundary for youth, but a closed door signaled this request for a knock. While Avleen noted differences in understandings of privacy between her generation and her parents, others did not feel so empathetic towards their parents. Olivia (she/her), a bisexual, East Asian youth, was frustrated that she was often having to “reinforc[e] with [her] mum who I live with, that you need to knock before you enter”. Hernes’ (2004) defined a framework for studying organizational boundaries, pointing to three parts: physical, social and mental. We argue that within the home, sonic boundaries represent an important addition to this typology. For example, Misty's experiences of negotiating for privacy blends the physical and the sonic. A straight, Black youth, Misty says her siblings often “barge in” and she has “told them countless times to knock”. She says: “I have little privacy, the most I got is my bed. It's not really a private area. Yeah that's the only place, just one little corner of privacy.” While Misty refers to her bed as a potential space of privacy, “it's not really a private area” due to the physical act of siblings often barging in. Misty's experience represents how the social need for privacy has real physical implications at home, especially embodied within the bedroom. These “little corners of privacy” that Misty speaks of are also important to consider. Kearney (2007, p. 130) says that describing all bedrooms as personal and autonomous is problematic because it “suggests that all girls’ homes contain private spaces where they can freely relax and socialize”. Based on previous research, and findings from this study, we know this is not true. While analyzing who deems their bedroom as their refuge, we found that white youth were far more likely than BIPOC to identify their bedroom as a private space where they had to do little to no negotiations for privacy. Take Liz (she/her), a queer, white youth, who's bedroom is entirely her own private space, with “[she] and [her] cat, just vibing alone”. BIPOC youth, on the other hand, said they still had to make constant negotiations for privacy within what they identified as their own private space of the bedroom. Meaning: BIPOC youth were more likely to deem the bedroom as their private space solely due to it being the best option they had in the home, and not because they were guaranteed privacy within it. Take Valerie, a bisexual, Southeast Asian and Latinx/Hispanic girl who says she does not “have much privacy elsewhere”. Similarly, Tye, a bisexual, middle-eastern girl explained that ther grandparents are “always, like, in and out of [her] room”, while Janice, a bisexual, East Asian girl shared that “privacy is definitely an issue, [my parents] always go into my room without asking and probably check my devices too.” Some BIPOC youth described their bedroom as spaces where they are “usually pretty alone”, but many still noted having to negotiate privacy through social boundary-making, such as having discussions with parents about where their privacy boundaries lie in the bedroom, particularly during the pandemic and in order to engage in sexual wellbeing practices. Based on our data, BIPOC youth also had to resort most often to physical boundary negotiations, such as the moving of furniture to block access to a bedroom door or the addition of a tapestry to muffle both sound and sight. This discrepancy in the experiences of privacy between white youth and BIPOC youth may point to differentiating cultural understandings of one's “right” to privacy. It may also point to intersections of race, ethnicity, and socioeconomic status, as BIPOC youth may be more likely to be living in mutli-generational homes, or in less spacious homes with fewer rooms, making privacy more difficult to come by (COP-COC, 2019). Understanding the home as being both a potential site of care and anxiety, illuminates the relationship of belonging and safety to sexual wellbeing. While youth did not report feeling particularly scared or anxious about this, they voiced their frustration and exhaustion at having to repeat and justify their request for sound warnings. There is significance between the bedroom as ‘refuge’ versus “best option”. McRobbie and Garber (1976) saw the bedroom as a significant site of privacy and personal space, but they neglected to describe the boundary-making processes that make that place private and personal. Therefore, we argue that sound in the bedroom as a boundary-maker and -marker played an important role in youths’ perception of their own privacy, which is vital for sexual wellbeing. It should be noted that some of the participants in our focus groups, often white youth, had positive experiences during the pandemic, using it as an opportunity to create communal boundaries for the entire household. Take Bri (they/them), a nonbinary, pansexual, white youth who had made a clear boundary that everyone must knock on closed bedroom doors. Similarly, Liz (she/her) felt that with her changed living situation due to the pandemic, knocking was the least that household members could do for her privacy. She explained that she could ask for more spatial privacy, but she did not “want to ask”. Instead, she requested a sound warning: “You know, I'm just like, please knock on my door. And I'll knock on yours too.” Her request is collective. Youth's ability to uphold their sexual wellbeing relied heavily on knocking as a sound warning. This warning was a way for youth to be alerted that others were near or about to enter, but it also acted as a boundary signal from youth who felt knocking was about respect for others. Expanding on Hernes’ (2004) concept, sound is a pivotal piece of boundary-work for youth at home, where physical, social, and mental boundaries blend with sound to create a space where youth feel safe enough to explore their sexuality, and engage in sexual wellbeing practices. Conclusion This analysis reveals how navigating sound (and silence) at home influences youth's sexual wellbeing. Furthermore, it underscores how challenging it has been for youth to navigate sonic boundaries during stay-at-home orders. Our analysis, coupled with a more intersectional understanding of McRobbie and Garber's (1976) concept of bedroom culture, reflects the call from feminist geographers for more diverse work on the various dimensions of home and the ways that gender, sexuality, and racialized identities are embodied. Hernes’ (2004) concept can be deepened to include sound, as the physical, social and mental boundaries inherently connected with the sonic in creating what youth understand to be a private space at home, where they can engage in intimate practices. Several limitations impacted this research. This was a small, qualitative, self-selected sample of young people who all had access to the technology, resources and negotiated privacy to participate. Results may not be generalizable. Also, the focus group format may have precluded discussions about masturbation- often a taboo topic that young women are shy to discuss (Kaestle and Allen, 2011). Therefore, masturbation was discussed in coded ways, as this was a sensitive topic, so we often heard instances where youth did not mention a sexual experience as an explicitly partnered one, but rather neglected to announce it solely as solo. In addition, queer youth were over-represented in our sample. This could be due to our inclusive targeted advertisements which may have been shared on social media among queer communities. Implications of this bias include the prevalence of narratives throughout the focus group that rely on creative ways to subvert the heteronormative home, alongside the more generalizable need for privacy. This over-representation may also influence our findings in that depending on the home, the perception of privacy is dependent on more than personal needs for privacy, but potentially even safety-based needs for privacy due to an unwelcoming home or a youth who is still discovering their identity. Nevertheless, the rich narratives gathered provided a nuanced and specific analysis of home in this historical moment. As more COVID-19 waves continue, it is critical to consider the role that sonic soundscapes play in youth sexual wellbeing. Parents may wish to dialog with their youth about how to negotiate privacy and what supports can be put into place to uphold boundaries. Headsets, music, doors, knocking, and alone time can all be intentionally negotiated. In addition to this, future work could look at parental sexual wellbeing and privacy, as the experience of the parent is often underlooked in home studies. While this research serves as a better way to understand the spatial and political implications of home in sexual wellbeing practices and understandings, it also had the radical goal of generating resources that can respond to the impact of the pandemic on youth's sexual wellbeing. This includes creating, adapting, and improving current programming and services. While others have looked at sexual wellbeing during the pandemic through a health risk lens, recommending risk reduction counseling, only having sex with quarantined partners, and virtual sex as safer sexual wellbeing practices during the pandemic (Banerjee and Rao, 2020), we argue that youth's sexual wellbeing needs can be better served through directly listening to their desires. Research is continuing to show how COVID-19 is particularly affecting adolescent lives “as teens are more likely than older people to be living at home, subject to parental or guardian scrutiny, and [have] restricted mobility” (Goldstein and Flicker, 2020, p. 67). Therefore, it is more important than ever to understand the adolescent sexual experience as contingent on place, but also as connected to the sheer time spent at home during lockdowns. The narratives shared in this paper also have implications for those seeking to conduct research or engage in conversations with young people around intimate and sensitive topics. Participants in our focus groups appreciated the chat feature available on Zoom for its sound-less engagement. Finding ways to confidentially reach youth at home may require further creativity from researchers and service providers to better utilize the privacy-enabling affordances of new technologies. We've learned that the home and the bedroom is not always a “pulling away” from public life space. Instead, many youths used private space at home to practice sexual wellbeing on their own terms. Instead of a “pulling away”, practices of sexual wellbeing connect youth to their peers, popular culture, and a sense of community. In fact, the focus groups we conducted themselves were situated in private in participants’ homes (virtually) and we had many participants reach out afterwards to let us know they found the opportunity to speak with other youth incredibly therapeutic during a time of high anxiety. Our research reveals that the conditions adolescents need to practice sexual wellbeing include privacy and space. While we cannot define sexual wellbeing, nor did we come close to it, we can say that all youth participants indicated their need to create boundaries in their homes to give themselves opportunities to at least explore their relationships, sexualities, and identities free from the scrutiny and interference of others. In saying this, we call for more literature on the importance of space for young people's burgeoning identities, and for specific considerations of what it might have meant to them to lose certain spaces for extended periods of time during the pandemic. In addition to this, more work on why youth depend on seemingly small boundary-making gestures, such as knocking, is needed. Feminist geographer Mona Domosh (1998) said “the home is rich territory indeed for understanding the social and the spatial. It's just that we've barely begun to open the door and look inside” (p. 281). We hope that emerging research on youth operates as a small crack in that door, one that shines light on young people's resilience during unprecedented times, their creativity and their autonomy in places that can do so much, so often, to restrict it. One of our participants, Olivia aptly described why we must work to provide these research spaces as feminist geographers: “I guess one of the things is just like knowing where… not feeling like you have to be yourself in every place, but knowing where you can be yourself and then using those spaces to show who you are”. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Leah I. Coppella reports financial support was provided by LaMarsh center for Child & Youth Research. Leah I. Coppella reports financial support was provided by Social Sciences and Humanities Research Council of Canada. Acknowledgments The authors appreciate all the young folks who shared their stories with us. We also want to thank Stephanie Giroud for her research assistance. We acknowledge and thank the LaMarsh center for Child & Youth Research, Planned Parenthood Ottawa, and SSHRC for their funding and support of this research. ==== Refs References Abbott-Chapman J. Robertson M. Adolescents’ favourite places: redefining the boundaries between private and public space Space Cult. 12 4 2009 419 434 10.1177/1206331209348091 Ahmed S. Strange Encounters: Embodied Others in Post-Coloniality 2000 Routledge London Antwi P. Brophy S. Strauss H. Troeung Y.D. Not without ambivalence’: an interview with Sara Ahmed on postcolonial intimacies Interventions 15 1 2013 110 126 10.1080/1369801X.2013.771011 Banerjee D. Rao T. Sexuality, sexual wellbeing, and intimacy during COVID-19 pandemic: an advocacy perspective Indian J. Psychiatry 62 4 2020 418 426 10.4103/psychiatry.IndianJPsychiatry_484_20 33165372 Beasy Murphy, C. Hicks D. Muir T Learning from home learning: crossing boundaries of place and identity Geogr. Res. 59 3 2021 341 348 10.1111/1745-5871.12482 Bell Valentine G Introduction: orientations Bell & Valentine, G. Mapping desire: Geographies of Sexualities 1995 Routledge Blunt A. Cultural geography: cultural geographies of home Prog. Hum. Geogr. 29 4 2005 505 515 Blunt A. Varley A. introduction: geographies of home Cult. Geogr. 11 1 2004 3 6 Bowleg L. The problem with the phrase women and minorities: intersectionality— an important theoretical framework for public health Am. J. Public Health 102 7 2012 1267 1273 10.2105/AJPH.2012.300750 22594719 Conboy K. Medina N. Stanbury S. Writing on the Body: Female Embodiment and Feminist Theory 1997 Columbia University Press New York, NY COP-COC COLOUR OF POVERTY – COLOUR OF CHANGE. (2019). (rep.). Racialized poverty in housing & homelessness. Retrieved from https://colourofpoverty.ca/wp-content/uploads/2019/03/cop-coc-fact-sheet-9-racialized-poverty-in-housing-homelessness-2.pdf. Crenshaw K. On Intersectionality: Essential Writings 2017 The New Press New York, NY De Craene Gorman-Murray A Embodied emotions in the geographies of sexualities Tijdschrift Voor Economische En Sociale Geografie 108 3 2017 356 360 10.1111/tesg.12260 Domosh M. Geography and gender: home, again? Prog. Hum. Geogr. 22 2 1998 276 282 10.1191/030913298676121192 Duffy Waitt G Home sounds: experiential practices and performativities of hearing and listening Soc. Cult. Geogr. 14 4 2013 466 481 10.1080/14649365.2013.790994 Dyck I. Feminism and health geography: twin tracks or divergent agendas? Gend. Place Cult. 10 4 2003 361 368 10.1080/0966369032000153331 Elwood S.A. Lesbian living spaces: multiple meanings of home J. Lesbian Stud. 4 1 2000 11 27 10.1300/J155v04n01_02 exile Flicker S. Nixon S.A. The DEPICT model for participatory qualitative health promotion research analysis piloted in Canada, Zambia and South Africa Health Promot. Int. 30 3 2015 616 624 10.1093/heapro/dat093 24418997 Forsberg H. Strandell H. After-school hours and the meanings of home: re-defining Finnish childhood space Child. Geogr. 5 4 2007 393 408 10.1080/14733280701631841 Gale A. Park N.K. Desired and achieved privacy and interaction in multigenerational homes Hous. Soc. 37 1 2010 25 41 10.1080/08882746.2010.11430579 Goldstein Flicker S Some things just won't go back”: teen girls’ online dating relationships during COVID-19 Girlhood Stud. 13 3 2020 64 78 10.3167/GHS.2020.130306 Goldstein A. Flicker S. It's been a good time to reflect on…who isn't worth keeping around”: COVID-19, adolescent relationship maintenance and implications for health education Health Educ. 2021 10.1108/HE-01-2021-0010 Vol. ahead-of-print No. ahead-of-print Grant R. Gorman-Murray A. Briohny Walker B. The spatial impacts of COVID-19 restrictions on LGBTIQ wellbeing, visibility, and belonging in Tasmania, Australia J. Homosex. 68 4 2021 647 662 10.1080/00918369.2020.1868188 33492203 Hankivsky O. Christoffersen A. Intersectionality and the determinants of health: a Canadian perspective Crit. Public Health 18 3 2008 271 283 10.1080/09581590802294296 Hankivsky Grace D Understanding and emphasizing difference and intersectionality in multimethod and mixed methods research The Oxford Handbook of Multimethod and Mixed Methods Research Inquiry 2015 Oxford University Press 10.1093/oxfordhb/9780199933624.013.8 Heinämaa S. Toward a Phenomenology of Sexual Difference: Husserl, Merleau-Ponty 2003 Rowman & Littlefield Publishers Beauvoir. Lanham, Maryland Hernes T. Studying composite boundaries: a framework of analysis Hum. Relat. 57 1 2004 9 29 10.1177/0018726704042712 (New York) Hopkins P. Social geography I: intersectionality Prog. Hum. Geogr. 43 5 2019 937 947 10.1177/0309132517743677 Hörschelmann K. Harker C. Hörschelmann K. Skelton T. ‘Violent geographies of childhood and home: the child in the closet eds. Conflict, Violence and Peace. Geographies of Children and Young People 2017 Springer 233 251 11 Johnson L. Bell D. Valentine G. Wherever I lay my girlfriend, that's my home: the performance and surveillance of lesbian identities in domestic environments Mapping Desire: Geographies of Sexuality 1995 Routledge 98 113 10.4324/9780203427897-13 ed. Kaestle C.E. Allen K.R. The role of masturbation in healthy sexual development: perceptions of young adults Arch. Sex. Behav. 40 5 2011 983 994 10.1007/s10508-010-9722-0 21293916 Kearney PRODUCTIVE SPACES: girls’ bedrooms as sites of cultural production J. Child Med. 1 2 2007 126 141 10.1080/17482790701339126 Lincoln S. Feeling the noise: teenagers, bedrooms and music Leis. Stud. 24 4 2005 399 414 10.1080/02614360500199544 Lindberg D.L. Bell D.L. Kantor L.M The sexual and reproductive health of adolescents and young adults during the COVID-19 pandemic Perspect. Sex. Reprod. Health 52 2 2020 75 79 10.1363/psrh.12151 32537858 Longhurst R. Dis)embodied geographies Prog. Hum. Geogr. 21 4 1997 486 501 Lorimer K. DeAmicis L. Dalrymple J. Frankis J. Jackson L. Lorgelly P. McMillan L. Ross J. A rapid review of sexual wellbeing definitions and measures: should we now include sexual wellbeing freedom? J. Sex Res. 56 7 2019 843 853 10.1080/00224499.2019.1635565 31335208 Marsh P. Diekmann L. Egerer M. Lin B. Ossola A. Kingsley J Where birds felt louder: the garden as a refuge during COVID-19 Wellbeing Space Soc. 2 2021 100055 10.1016/j.wss.2021.100055 McRobbie A. Garber J. Girls and subcultures Stuart Hall and Tony Jefferson Resistance Through Ritual- Youth Subcultures in Post-war Britain 1976 Harper Collins London 209 222 Mitchell K.R. Lewis R. O'Sullivan L.F. Fortenberry J.D. What is sexual wellbeing and why does it matter for public health? Lancet Public Health 6 8 2021 e608 e613 10.1016/S2468-2667(21)00099-2 34166629 Mitra R. Faulkner G.E. Buliung R.N. Stone M.R. Do parental perceptions of the neighbourhood environment influence children's independent mobility? Evidence from Toronto, Canada Urban Stud. 51 16 2014 3401 3419 10.1177/0042098013519140 Mittal S. Singh T. Gender-based violence during COVID-19 pandemic: a mini-review Front. Glob. Women's Health 1 2020 4 34816149 Mmeje O.O. Coleman J.S. Chang T. Unintended consequences of the COVID-19 pandemic on the sexual and reproductive health of youth J. Adolesc. Health 67 3 2020 326 327 10.1016/j.jadohealth.2020.06.019 32690467 Okafor C. Black feminism embodiment: a theoretical geography of home, healing, and activism Meridians Fem. Race Transnationalism 16 2 2018 373 381 https://link.gale.com/apps/doc/A542968077/AONE?u=yorku_main&sid=AONE&xid=21 f92dfe Paremoer L. Nandi S. Serag H. Baum F. COVID-19 pandemic and the social determinants of health BMJ 2021 372 Pratt G Geographies of identity and difference: marking boundaries Human Geography Today 1999 Polity Press Cambridge, U.K. 151 168 ed. D. Massey, J. Allen, and P. Sarre Risi E. Pronzato R. Di Fraia G. Everything is inside the home: the boundaries of home confinement during the Italian lockdown Eur. Soc. 23 1 2021 464 477 10.1080/14616696.2020.1828977 Rodó-de-Zárate M. Developing geographies of intersectionality with relief maps: reflections from youth research in Manresa, Catalonia Gend. Place Cult. 21 8 2014 10.1080/0966369X.2013.817974 Saad T. Carter P. The entwined spaces of “Race”, sex and gender Gend. Place Cult. 12 1 2005 49 51 10.1080/09663690500082927 Sang K. Gender, ethnicity and feminism: an intersectional analysis of the lived experiences feminist academic women in UK Higher Education J. Gend. Stud. 27 2 2018 192 206 10.1080/09589236.2016.1199380 Seedall R.B. Anthony J.C. Monitoring by parents and hypothesized male-female differences in evidence from a nationally representative cohort Re-sampled from age 12 to 17 years: an exploratory study using a "Mutoscope" approach Prev. Sci. 16 5 2015 696 706 25429727 Skeggs The forces that shape us: the entangled vine of gender, race and class Sociol. Rev. 67 1 2019 28 35 10.1177/0038026118821334 (Keele) Smith S.J. Performing the (Sound)world Environ. Plan D 18 5 2000 615 637 10.1068/d225t Taylor Complexities and complications: intersections of class and sexuality J. Lesbian Stud. 13 2 2009 189 203 10.1080/10894160802695361 19363764 Torresin S. Albatici R. Aletta F. Babich F. Oberman T. Stawinoga A.E. Kang J Indoor soundscapes at home during the COVID-19 lockdown in London – Part I: associations between the perception of the acoustic environment, occupantś activity and well-being Appl. Acoust. 183 2021 108305 10.1016/j.apacoust.2021.108305 – Usher K. Bhullar N. Durkin J. Gyamfi N. Jackson D. Family violence and COVID-19: increased vulnerability and reduced options for support Int. J. Ment. Health Nurs. 29 4 2020 549 552 10.1111/inm.12735 32314526 Valentine G. Boundary crossings: transitions from childhood to adulthood Child. Geogr. 1 1 2003 37 52 10.1080/14733280302186 Valentine G. Theorizing and researching intersectionality: a challenge for feminist geography Prof. Geogr. 59 10 2007 85 101	36466112	PMC9708619	NO-CC CODE	2022-12-06 23:15:06	no	Wellbeing Space Soc. 2023 Nov 30; 4:100117	utf-8	Wellbeing Space Soc	2,022	10.1016/j.wss.2022.100117	oa_other
==== Front J Stroke Cerebrovasc Dis J Stroke Cerebrovasc Dis Journal of Stroke and Cerebrovascular Diseases 1052-3057 1532-8511 Elsevier Inc. S1052-3057(22)00612-7 10.1016/j.jstrokecerebrovasdis.2022.106920 106920 Article Is a high chest CT severity score a risk factor for an increased incidence of long-term neuroimaging findings after COVID-19? Kaya Ahmet Turan Asst. Prof ⁎ Akman Burcu Asst. Prof Department of Radiology, Amasya University, Faculty of Medicine, Amasya, Turkey ⁎ Corresponding author. 30 11 2022 2 2023 30 11 2022 32 2 106920106920 15 11 2022 27 11 2022 © 2022 Elsevier Inc. All rights reserved. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objectives We aimed to determine the incidences of neuroimaging findings (NIF) and investigate the relationship between the course of pneumonia severity and neuroimaging findings. Materials and methods Our study was a retrospective analysis of 272 (>18 years) COVID-19 patients who were admitted between “March 11, 2021, and September 26, 2022". All patients underwent both chest CT and neuroimaging. The patient's chest CTs were evaluated for pneumonia severity using a severity score system (CT-SS). The incidence of NIF was calculated. NIF were categorized into two groups; neuroimaging positive (NIP) and neuroimaging negative (NIN). Consecutive CT-SS changes in positive and negative NIF patients were analyzed. Results The median age of total patients was 71; IQR, 57-80. Of all patients, 56/272 (20.6%) were NIP. There was no significant relationship between NIP and mortality (p = 0.815) and ICU admission (p = 0.187). The incidences of NIF in our patients were as follows: Acute-subacute ischemic stroke: 47/272 (17.3%); Acute spontaneous intracranial hemorrhage: 13/272 (4.8%); Cerebral microhemorrhages: 10/272 (3.7%) and Cerebral venous sinus thrombosis: 3/25 (10.7%). Temporal change of CT-SSs, there was a statistically significant increase in the second and third CT-SSs compared to the first CT-SS in both patients with NIP and NIN. Conclusion Our results showed that since neurological damage can be seen in the late period and neurological damage may develop regardless of pneumonia severity. Keywords COVID-19 CT severity score Brain CT Brain MRI Stroke Neuroimaging ==== Body pmcIntroduction From the outbreak of the Coronavirus disease 2019 (COVID-19) pandemic on 17 November 2019 in Wuhan to 2 November 2022, the total number of cases was reported as 617,879,854 and the total number of deaths was reported as 6,546,448 worldwide.1 In Turkey, the first case was reported on March 11, 2020. Until November 2, 2022, the total number of cases was 16,873,793 and the total number of deaths was 101,139.1 Today, there is a significant decrease in the number of cases and deaths as a result of vaccination and mutations in the virus. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often causes severe respiratory distress. In addition, a wide range of neurological symptoms has been reported during the disease (28.2%) and in the post-COVID-19 period (55%), from mental status changes to acute cerebrovascular disease.2 , 3 Acute-subacute ischemic stroke (ASIS) is the most common cause of neurological deficit detected in brain computed tomography (CT) and magnetic resonance imaging (MRI) in the post-COVID-19 period. Other neuroimaging findings are cerebral venous sinus thrombosis (CVST), acute-subacute spontaneous intracranial hemorrhage (ICH), and cerebral microhemorrhages (CMH).4 SARS-CoV-2 can enter cells with the angiotensin-converting enzyme 2 (ACE2) receptor expressed on neurological cell surfaces, including glial cells, endothelial cells, and circumventricular organ cells.5 , 6 Post-COVID-19 neurological damage can occur as a result of direct and/or indirect pathways.7 , 8 Direct neuronal cell invasion was shown by a positive reverse transcriptase polymerase chain reaction (RT-PCR) test applied to cerebrospinal the fluid (CSF) sample and the presence of intraneuronal viral inclusions at autopsy.9, 10, 11, 12, 13, 14 Even without neural invasion, stroke may occur due to coagulopathy secondary to vascular endothelial damage.15, 16, 17 Studies investigating neurological damage post-COVID-19 usually included the first year of the pandemic and patients were not followed for more than 2 years.18, 19, 20, 21, 22, 23 While these studies investigated neurological complications in inpatients, post-discharge patients were not investigated. The relationship between the course of pneumonia severity and neurological damage has not been investigated. Therefore, we aimed to determine the incidences of neuroimaging findings (NIF) in the post-COVID-19 period and investigate the relationship between the temporal changes in pneumonia severity in consecutive chest CTs and NIF. Methods Study population This is a single-center, retrospective analysis of patients admitted to our hospital between “March 11, 2021, and September 26, 2022”. This study was approved by the Ethical Committee of Amasya University Faculty of Medicine and was conducted according to the Declaration of Helsinki and Good Clinical Practice (02 December 2021, number: 12/155). Patient information was obtained from electronic records and censored. Since the study was retrospective, the ethics committee did not find it necessary to obtain written informed consent from the patients. Data collection In our study, we obtained electronic medical records of individuals who applied to other associated clinics in addition to the “COVID-19 clinic and/or post-COVID-19 follow-up clinic”. The first electronic data search resulted in a list of a total of 21,878 case records. Patients with positive RT-PCR were evaluated if they had chest CT scans in addition to neuroimaging (with or without contrast) scans. In patients with more than one COVID-19 positive, the date of the first RT-PCR positive was accepted. Patients with a time interval of more than 10 days between RT-PCR and first chest CT were excluded from the study. The first positive neuroimaging was enrolled in the study in patients with more than one neuroimaging. If sequential neuroimaging is negative, the last neuroimaging was enrolled in the study. As a result, 272 patients were included in the study (Fig. 1 ).Fig. 1 Workflow diagram of the study. Fig 1 Inclusion criteria Patients over 18 years of age, with positive RT-PCR test, and with neuroimaging (brain CT and/or MRI) and chest CT were included in the study. Exclusion criteria Patients with chronic-stage infarcts and hemorrhages confirmed by neuroimaging before the study time interval were excluded. Clinical and laboratory data Demographic information of the patients, comorbidities, history of hospitalization or intensive care unit (ICU) admission, and survival were recorded from electronic medical records. Imaging protocols Chest and brain imaging were performed in the routine protocols of our hospital. The chest and brain CT scans were performed using the multidetector CT (MDCT) scanner 128-slice GE Healthcare Revolution EVO CT (GE Medical Systems; Milwaukee, WI). All brain MRI examinations were performed on a 1.5 Tesla scanner (Avanto, Siemens Healthcare). Image analysis Two radiologists (ATK, BA) with 9 and 15 years of experience in general radiology evaluated chest and neuroimaging together. Firstly, neuroimaging findings were categorized into two groups; neuroimaging positive (NIP) and neuroimaging negative (NIN). After, NIP was categorized into four subgroups; 1) Acute-subacute ischemic stroke (ASIS) ( Fig. 2 ); 2) Acute spontaneous intracranial hemorrhage (ICH); 3) Cerebral microhemorrhages (CMH); 4) Cerebral venous sinus thrombosis (CVST) ( Fig. 3 ). Fig. 2 (A) 53-year-old male patient has a positive RT-PCR test for COVID-19. He was discharged after being treated in Non-ICU in our hospital. 464 days after the RT-PCR positive date, brain CT and DWI were performed in our hospital due to neurological complaints. Focal ground glass opacities were present in both lung peripheries. Chest CT-SS:6. (B) Axial DWI image showed hyperintense cerebellum in the right paramedian area, (C)ADC images had hypointense acute ischemic infarct with diffusion restriction (Open arrow). Fig 2 Fig. 3 A 48-year-old female patient had a positive RT-PCR test for COVID-19 (First CT-SS= 14; Second CT-SS= 15; Third CT-SS= 10). She was discharged after her treatment in the Non-ICU in our hospital. Brain CEMRI and MRV were performed in our hospital due to neurological complaints 24 days after the RT-PCR positive date. In the axial FLAIR (a) and coronal T2W (b) images, there were hyperintense edematous gray matter changes (GMCs) in both parietal lobe gyri (white arrows), and there was an increase in signal due to a filling defect in the superior sagittal sinus (SSS) in the T2W coronal image (magnified area). c) Coronal post-contrast T1W image showed hypointense edematous GMCs (white arrows) in both parietal lobe gyri, dilated veins due to venous congestion (open arrows), and no filling with contrast in the SSS lumen (magnified area). d-e) MRI was repeated 14 days later. Axial unenhanced T1W (d) and T2* GRE (e) images show a hyperintense hemorrhage area with a hypointense rim in the left parietal lobe gyrus. There were also millimetric hemorrhage areas in both lobes on the same images. f) Sagittal 2D TOF MRI shows no flow-related hyperintense in the SSS localization and dilatation due to congestion in the adjacent vein (open arrow). g) 647 days later, the control sagittal 2D TOF MRI shows contrast filling in the SSS localization (SS: straight sinus). Fig 3 In addition, the NIP group was divided into two groups “white matter changes” (WMC) and “grey matter changes (GMC)” according to the localization of the changes. Radiologists evaluated the patients' first and, if available, second and/or third chest CTs for pneumonia severity. They used the extent of parenchymal involvement per lobe using a computed tomography severity score (CT-SS) on a total 25-point scale (0 = 0.1% = 1% - 5.2% = 6% - 25%, 3 = 26% - 50%, 4 = 51%-75% and 5 => 75%). 24 Statistical analysis Statistical analyzes were performed by using IBM SPSS Statistics for Windows, Version 25.0 (IBM, Armonk, New York, USA). The normal distribution of the variables was examined using Kolmogorov-Smirnov. In the descriptive analysis, continuous and categorical variables were compared according to neuroimaging groups and subgroups. Pearson Chi-square or Fisher tests were used to comparing categorical variables. The Mann-Whitney U test was used to compare the neuroimaging groups. Median and interquartile ranges (IQR) were used for the results. p < 0.05 was considered statistically significant. The statistical significance of the temporal changes of CT-SSs according to neuroimaging groups was examined with the Friedman test. Pairwise comparisons were made using the Wilcoxon test. After Bonferroni's correction, p < 0.017 was considered statistically significant. Results Demographic results and frequencies of neuroimaging findings 272 patients (median age= 71; IQR, 57-80) were included in our study. Of these patients, 56/272 (20.6%) were neuroimaging positive (NIP). 144/272 (52.9%) of our patients were male and there was no significant relationship between patients with NIP and gender (p = 0.621). 60/272 (22.1%) of our patients died, and there was no significant relationship between patients with NIP and mortality (p = 0.815). 60/272 (22.1%) of our patients were admitted to the ICU. There was no significant relationship between patients with NIP and ICU admission (p = 0.187). The incidences of NIF in our patients were as follows: ASIS, 47/272 (17.3%); ICH, 13/272 (4.8%); CMH, 10/272 (3.7%) and CVST, 3/25 (10.7%). CVST was present in all 3/272 (1.1%) patients who underwent MRV. There was no significant relationship between patients with NIP and inpatient or ICU admission (p = 0.535; p = 0.187). In the patients with NIP, chronic cardiovascular diseases 48/56 (85.7%) and chronic neurological diseases 47/56 (83.9%) were significantly more common (p = 0.017; p < 0.001) (Tables 1 and 2 ). Of the 272 patients included in our study, 138 had second a chest CT and 45 had a third chest CT.Table 1 Frequencies of demographic data and neuroimaging findings. Table 1 Frequency Percent Gender Female 128 47.1 Male 144 52.9 Inpatients? Outpatients 72 26.5 Inpatients 200 73.5 ICU? Non-ICU 212 77.9 ICU 60 22.1 Survival Alive 212 77.9 Death 60 22.1 Neuroimaging Negative 216 79.4 Positive 56 20.6 Neuroimaging localizations WMC Negative 230 84.6 Positive 42 15.4 GMC Negative 234 86.0 Positive 38 14.0 Neuroimaging findings ASIS Negative 225 82.7 Positive 47 17.3 ICH Negative 259 95.2 Positive 13 4.8 CMH Negative 262 96.3 Positive 10 3.7 CVST Negative 25 9.2 89.3* Positive 3 1.1 10.7* Not performed⁎⁎ 244 89.7 Neuroimaging methods Brain CT Not performed 4 1.5 Performed 268 98.5 CECT Not performed 255 93.8 Performed 17 6.3 Brain MRI Not performed 214 78.7 Performed 58 21.3 CEMRI Not performed 257 94.5 Performed 15 5.5 Brain DWI Not performed 87 32 Performed 185 68 MRV Not performed 269 98.9 Performed 3 1.1 ⁎ Valid Percent ⁎⁎ MRV or Contrast-enhanced neuroimaging wasn't performed ICU: Intensive care unit; WMC: White matter changes; GMC: Grey matter changes; Acute-subacute ischemic stroke (ASIS); ICH: Acute – subacute spontaneous intracranial hemorrhage; CMH: Cerebral microhemorrhages; CVST: Cerebral venous sinus thrombosis; CECT: Contrast enhanced CT; CEMRI: Contrast enhanced MRI; DWI: Diffusion-weighted imaging; MRV: Magnetic Resonance Venography Table 2 Comparison of positive neuroimaging with demographic data and comorbidities. Table 2 Neuroimaging Negative Positive p value n (%) n (%) Gender Female 100 46.3 28 50 0.621 Male 116 53.7 28 50 Total 216 56 Inpatients or outpatients Outpatients 59 27.3 13 23.2 0.535 Inpatients 157 72.7 43 76.8 Total 216 56 ICU Non-ICU 172 79.6 40 71.4 0.187 ICU 44 20.4 16 28.6 Total 216 56 Survival Alive 169 78.2 43 76.8 0.815 Death 47 21.8 13 23.2 Total 216 56 Pulmonary diseases Absent 177 81.9 43 76.8 0.382 Present 39 18.1 13 23.2 Total 216 56 Cardiovascular disease Absent 65 30.1 8 14.3 0.017 Present 151 69.9 48 85.7 Total 216 56 Neurological diseases Absent 151 69.9 9 16.1 <0.001 Present 65 30.1 47 83.9 Total 216 56 Diabetes mellitus Absent 170 78.7 46 82.1 0.571 Present 46 21.3 10 17.9 Total 216 56 Kidney diseases Absent 204 94.4 49 87.5 0.069 Present 12 5.6 7 12.5 Total 216 56 Liver diseases* Absent 215 99.5 56 100 0.999 Present 1 0.5 0 0 Total 216 56 Chi-square or () Fisher tests were used to compare categorical variables according to neuroimaging groups The relationship between ICU admission and NIF The most common NIF was ASIS 13/47 (21.7%) in the ICU admission group, which was not statistically significant (p = 0.887). The rate of WMC in the ICU group compared to the non-ICU group was [25% (15/60) versus 12.7% (27/212)], which was statistically significantly higher (p = 0.02). There was a statistically insignificant increase in NIF (excluding CMH) rates in the ICU group compared to the non-ICU group (p > 0.05) (Table 3 ).Table 3 Comparison of neuroimaging findings with ICU admission and survival. Table 3 ICU Survival Non-ICU ICU Alive Death n (%) n (%) p value n (%) n (%) p value WMC Negative 185 87.3 45 75 0.02 182 85.8 48 80 0.268 Positive 27 12.7 15 25 30 14.2 12 20 Total 212 60 212 60 GMC Negative 185 87.3 49 81.7 0.27 182 85.8 52 86.7 0.872 Positive 27 12.7 11 18.3 30 14.2 8 13.3 Total 212 60 212 60 ASIS Negative 178 84 47 78.3 0.309 175 82.5 50 83.3 0.887 Positive 34 16 13 21.7 37 17.5 10 16.7 Total 212 60 212 60 ICH Negative 203 95.8 56 93.3 0.492 200 94.3 59 98.3 0.309 Positive 9 4.2 4 6.7 12 5.7 1 1.7 Total 212 60 212 60 CMH* Negative 204 96.2 58 96.7 0.999 204 96.2 58 96.7 0.999 Positive 8 3.8 2 3.3 8 3.8 2 3.3 Total 212 60 212 60 CVST* Negative 21 91.3 4 80 0.459 23 88.5 2 100 0.999 Positive 2 8.7 1 20 3 11.5 0 0 Total 23 5 26 2 WMC: White matter changes; GMC: Grey matter changes; ASIS: Acute-subacute ischemic stroke; ICH: Acute – subacute spontaneous intracranial hemorrhage; CMH: Cerebral microhemorrhages; CVST: Cerebral venous sinus thrombosis. Chi-square or () Fisher tests were used to compare categorical variables according to ICU admission and survival. Relationship between mortality and NIF There was no significant relationship between NIF and mortality (p > 0.05). In the ex-patients, the highest incidence of NIF was ASIS [10/50 (16.7%)]. But it was not statistically significant (p = 0.887). There was a statistically insignificant decrease in NIF rates in the ex-patients compared to the alive patients (p > 0.05) (Table 3). Relationship between NIF and CT-SS The median age of patients with NIP was 71.5 (IQR; 63.25 – 82.75), which was not statistically significant compared to the negative group (p = 0.098). There was no significant relationship between patients with NIP, and CT-SS values of the first, second, and third chest CT scans (p = 0.247; p = 0.832; p = 0.978). The time between RT-PCR and the first chest CT was 1.24±2.33 (0-9) days. The time interval between RT-PCR and neuroimaging was 94.62±172.37 (0-692) days. The time interval between the first chest CT and neuroimaging was 93.38±172.55 (0-692) days. There was no significant relationship between the patients with NIP and the time interval between RT-PCR and the first CT (p = 0.257). In the patients with NIP, the median time interval (days) between RT-PCR and neuroimaging was 22.24 (IQR, 4.44-141.23); the median time interval (days) between chest CT and neuroimaging was 20.81 (IQR, 2.37-141.23). These time intervals were statistically higher than in patients without NIP (p = 0.023; p = 0.01) (Table 4 ).Table 4 Comparison of positive neuroimaging with CT-SS's and time intervals. Table 4Neuroimaging N Mean SD Min. Max. Median 25th 75th p value Age Negative 216 66.68 16.20 22 94 71.00 56.00 79.00 0.098 Positive 56 71.20 13.72 32 94 71.50 63.25 82.75 Total 272 67.61 15.80 22 94 71.00 57.00 80.00 First CT-SS Negative 216 6.93 6.96 0 25 5.00 0.00 10.00 0.247 Positive 56 7.95 7.15 0 25 6.00 2.00 12.75 Total 272 7.14 7.00 0 25 5.00 0.00 11.00 Second CT-SS Negative 109 11.62 7.59 0 25 12.00 5.00 17.00 0.832 Positive 29 11.24 6.18 0 25 12.00 7.00 15.00 Total 138 11.54 7.30 0 25 12.00 6.00 16.25 Third CT-SS Negative 35 12.17 7.08 0 25 13.00 8.00 16.00 0.978 Positive 10 11.90 5.04 4 19 13.00 8.50 15.25 Total 45 12.11 6.63 0 25 13.00 8.00 15.50 Time from RT-PCR test to First Chest CT (days) Negative 216 1.32 2.36 0 9 0.00 0.00 2.00 0.257 Positive 56 0.94 2.18 0 9 0.00 0.00 0.75 Total 272 1.24 2.33 0 9 0.00 0.00 1.13 Time from RT-PCR test to Neuroimaging (days) Negative 216 87.39 164.79 0 681 7.67 1.80 73.00 0.023 Positive 56 122.52 198.07 0 692 22.24 4.44 141.23 Total 272 94.62 172.37 0 692 9.39 2.00 79.00 Time from First Chest CT to Neuroimaging (days) Negative 216 86.07 164.98 0 675 5.00 0.06 73.00 0.01 Positive 56 121.58 198.22 0 692 20.81 2.37 141.23 Total 272 93.38 172.55 0 692 7.95 0.41 79.00 ⁎ Mann-Whitney U test was used CT-SS: CT Severity Score SD: Standart Deviation; Min: Minimum; Max: Maximum. Relationship between follow-up chest CT-SS and NIF We compared the course of consecutive CT-SS values according to neuroimaging groups. There was a statistically significant increase between the first CT-SS and second CT-SS (p < 0.001; p < 0.001) and first CT-SS and third CT-SS (p=0.001; p=0.014) values in both patients with NIP and patients with NIN, respectively (Fig. 4 ) (Table 5 ).Fig. 4 Graph of temporal change of chest CT-SSs according to neuroimaging results. Fig 4 Table 5 Temporal change of consecutive CT-SSs in patients with positive and negative neuroimaging. Table 5 N Mean Rank Sum of Ranks p value⁎⁎ Neuroimaging Negative Second CT-SS- First CT-SS Negative Ranks 20a 25.78 515.50 <0.001 Positive Ranks 75b 53.93 4044.50 Ties 14c Total 109 Third CT-SS- First CT-SS Negative Ranks 9d 12.28 110.50 0.001 Positive Ranks 25e 19.38 484.50 Ties 1f Total 35 Third CT-SS- Second CT-SS Negative Ranks 20g 13.33 266.50 0.963 Positive Ranks 12h 21.79 261.50 Ties 3i Total 35 Neuroimaging Positive Second CT-SS- First CT-SS Negative Ranks 2a 5.25 10.50 <0.001 Positive Ranks 25b 14.70 367.50 Ties 2c Total 29 Third CT-SS- First CT-SS Negative Ranks 1d 3.50 3.50 0.014 Positive Ranks 9e 5.72 51.50 Ties 0f Total 10 Third CT-SS- Second CT-SS Negative Ranks 6g 3.75 22.50 0.607 Positive Ranks 4h 8.13 32.50 Ties 0i Total 10 ⁎⁎ Wilcoxon Signed Ranks Test was used. p < 0.017 was considered statistically significant. a Second CT-SS < First CT-SS b Second CT-SS > First CT-SS c Second CT-SS = First CT-SS d Third CT-SS < First CT-SS e Third CT-SS > First CT-SS f Third CT-SS = First CT-SS g Third CT-SS < Second CT-SS h Third CT-SS > Second CT-SS i Third CT-SS = Second CT-SS Discussion In our study, we investigated the incidences of post-COVID-19 neuroimaging findings (NIF) and the relationship between the temporal changes in pneumonia severity in consecutive chest CTs and acute-subacute neurological pathologies. The incidence of patients with neuroimaging positive (NIP) was 20.6% (56/272). In the patients with NIP, the highest incidence was acute-subacute ischemic stroke (ASIS) [47/272 (17.3%)], while the lowest incidence was cerebral microhemorrhages (CMH) [10/272 (3.7%)] in the subgroup analysis. ASIS had the highest incidence of NIF in patients who were admitted to ICU [13/60 (21.7%)] and in the ex-patients group [10/60 (16.7%)]. There was no significant relationship between patients with NIP and CT-SS values. When we analyzed the temporal change, there was a statistically significant increase in the second and third CT-SSs compared to the first chest CT in both patients with NIP and NIN. SARS-CoV-2 can cause brain damage through both direct and indirect pathways. Four different pathways are thought to be effective in direct damage. First, SARS-CoV-2, which reaches the brain tissue by a hematogenous route, may attach to the ACE-2 receptor, causing endothelial damage, slowing blood flow, and disrupting the blood-brain barrier.25 , 26 Secondly, it may be due to inflammatory damage of the virus that reaches the brain retrogradely from peripheral nerves.26 Third, the virus can enter via the neuronal pathway between the respiratory tract and the brain stem.27 Fourth, some authors claim that the virus enters the intestinal epithelial cells via ACE-2 receptors, which are abundant there, and reaches the brain by the neuronal spread.28 The indirect pathway can be divided into brain damage secondary to hypoxia, especially in critical COVID-19 patients, and severe inflammatory response syndrome (SIRS) due to an excessive immune response to the viruses. Increased IL-6 in the CSF samples is important evidence for cytokine storms.26 In addition, thromboembolism may be seen due to familial hypercoagulation disorder, multi-organ dysfunction secondary to SIRS, antiphospholipid antibody syndrome, viral myocarditis, and triggered atrial fibrillation.29, 30, 31, 32 Some studies hold hypertension and empirical anticoagulation therapy responsible for the development of ICH and CMH.33, 34, 35 They also reported that severe coagulopathy is effective in the pathogenesis of CMH.36 Neurological complications were reported by approximately 37% in studies and reviews conducted in the early period of the pandemic, and this rate has been reduced in the late period of the pandemic with the development of vaccination and appropriate treatments.3 For example, Ladopoulos et al. reported that the main factor in the etiology of ASIS is large vessel occlusion.19 This may be due to the lack of experience with the infection in the early period of the pandemic and inadequate anticoagulant therapy.19 In the studies that included in the early period of the pandemic, the incidence ranges were reported as ASIS: 1.76%-59.9%; ICH: 5.4%-69.2%; CMH: 0.8%-58.7% and CVST: 0.08%-5.5%, respectively.18, 19, 20, 21, 22, 23 In our study, NIF incidences were ASIS: 17.3%; ICH: 4.8%; CMH: 3.7%, and CVST: 10.7%, respectively. In a study of hospitalized patients in New York, the incidence of ASIS was reported as 0.9%.37 Yaghi et al. and Tan et al. reported that the reasons for the different results in neuroimaging incidence studies were severe patients who were intubated and sedated, incomplete imaging due to difficulty in mobilization due to isolation, and long MRI scans time.37 , 38 As a result of delays due to these reasons, ASIS may have a falsely low incidence because hemorrhagic transformation developing after ASIS is interpreted as ICH.37 , 38 Due to the small number of participants in reviews of CVST-positive COVID-19 patients, case reports are generally included rather than clinical trials.19 , 23 This reduces the reliability of the reported incidence of CVST. In our study, only 3 patients underwent MRV and all had CVST. In addition, since sinuses and veins can be evaluated in brain CECT and CEMRI, a total of 28 patients were analyzed in terms of CVST. MRV or any contrast-enhanced neuroimaging modality was not performed on 244 patients. Therefore, we calculated the valid percent as 2/28 (10.7%) while CVST was positive in 3/272 (1.1%). This was the reason for our higher prevalence compared to other studies. In another review, Choi et al. reported that the prevalence may vary depending on the difference in imaging modalities used in the studies.20 Choi et al.divided studies into using MR only and using CT and/or MRI. The incidences of NIF in these two groups were reported as ASIS: 20.0% [7.9–32.2] and 27.1% [16.4–37.7]; ICH: 3.9% [0.6–7.3] and 6.1% [3.3–8.9]; CMH: 13.8% [10.5–17.2] and 3.1% [1.0–5.2], respectively.20 As seen in this study, a higher prevalence was reported when CT and/or MRI was performed in ASIS and ICH compared to patients who only underwent MRI, while it was reported to be lower in CMH.20 Kim et al. reported that, unlike this study, there was no statistically significant difference in the rates of COVID-19 patients with NIP between the studies that used and did not use MRI.21 The long time interval in our study also included the vaccination program that started in the first half of 2021 in our country. Although complications especially ASIS and CVST have been reported post-COVID-19 vaccination, Rahming et al. reported in their review that there was no significant increase in the overall incidence of stroke in the population of individuals administered COVID-19 vaccines.39 According to our results, we thought that the main cause of positive neuroimaging in our patients who were COVID-19 positive before vaccination and who were vaccinated afterward was the infection itself. In our study, the incidence of NIP in patients admitted to the ICU [26.7% (16/60) vs 18.9% (40/212)] showed a statistically insignificant increase compared to the non-ICU group (p = 0.187). Like our study, Choi et al. also reported that the incidence of NIP in patients admitted to ICU (11.8 % vs. 3.2%) was higher compared to the non-ICU group.20 Kim et al. compared the incidence of NIP in studies that included critically ill patients with other studies. The incidence of NIP was 9.1% in studies that included critically ill patients, which was higher than in other studies (1.6%) 21. In three different reviews including critically or ICU admitted patients, the incidence ranges of NIF were reported as ASIS: 3.37%-17.2%; ICH: 6.2%-11.3%; CMH: 8.8%-14.8% and CVST: 1.8%-15.6%, respectively.20, 21, 22 The fact that most of the patients admitted to the ICU were intubated suggests that they may have a history of hypoxia. The increase in the incidence of NIP in this group may be due to this reason in our study and Choi et al.’s study.20 Since this group of patients has a low level of consciousness or is under sedation, neurological deficits of the patients may be hidden and examination may be difficult. Therefore, the need for neuroimaging should be kept in mind in clinically critical patients and patients admitted to the ICU. In our study, the incidence of NIF in patients admitted to ICU was similar to the literature, and ASIS: 21.7%; ICH 6.7%; CMH: 3.3%, and CVST: 20%. There was no statistically significant difference in NIF between the ICU and non-ICU groups. Kim et al. compared NIF with other studies in critically ill patients and reported that there was no significant difference between the two groups, similar to our study.21 In particular, they argued that the development of ASIS was due to an increased risk of thrombosis due to hypercoagulability, not due to the systemic inflammatory response secondary to acute respiratory distress syndrome (ARDS).21 This may explain the non-significant ASIS increase in the ICU admission group in our study. Mogensen et al. reported the highest rate of neuroimaging findings in patients who died, as ICH (49.7%). The incidence of ASIS was 30% in their study.18 In our study, the most common incidence of NIF in patients who died was ASIS: 16.7%; CMH: 3.3% and ICH: 1.7%; respectively. The low incidence in our study may be due to the increase in knowledge in diagnosis and treatment because the first case was seen late compared to other countries. Lang et al. reported a statistically insignificant increase in the mortality rate in the patients with NIP (21%) compared to patients with NIN (17%) in their study (p = 0 .945).40 Similarly, in our study, there was a statistically insignificant increase in the mortality rate in the patients with NIP (23.2%) compared to patients with NIN (21.8%) (p = 0.815). In our study, we investigated the effect of increased pneumonia severity on the incidence of NIP. We analyzed the temporal change of CT-SSs in three consecutive chest CTs. There was no significant increase in CT-SS in the patients with NIP compared to the patients with NIN. In addition, there was a statistically significant increase in the second and third CT-SS compared to the first CT-SS in both patients with NIP and NIN. This showed us that the increase in CT-SS was not only associated with NIP. Mahammedi et al. also investigated the effect of CT-SS on patients with NIP.41 While our study included inpatients, outpatients, and discharged patients, other studies included only inpatients. They analyzed the highest CT-SS value in patients with more than one chest CT and reported significantly higher CT-SS in the group with NIP, unlike our study.41 They reported a higher incidence of neurological symptoms in patients with severe respiratory disease.41 Lang et al. also reported that the CT-SS value was high in the patients with NIP, but there was no significant predictor of acute NIP in multivariate analysis.40 The difference in results in our study is consistent with the literature. It has been reported that not only high CT-SS but also silent hypoxia, metabolic disorder, intubation history, advanced age, history of ICU admission, cardiovascular diseases, autoimmune diseases, angiopathies, and proinflammatory cytokines are effective in brain damage.42, 43, 44 In addition, patients with NIP at low CT-SS have been reported, and the cause of neurological damage in these patients can be explained by direct damage to the SARS-CoV-2 virus, which reaches the brain in a retrograde way from peripheral nerves.45 Although the interval between positive neuroimaging after SARS-CoV-2 infection is not clear in the literature, Li et al. reported it as about 12 days.46 In our study, the median time between the first positive RT-PCR and the first positive neuroimaging was 22.24 days (IQR: 4.44-141.23, p=0.023). As seen in Table 4, the time interval are 0-692 days, and unlike the existing studies, our results were obtained over a much longer period. To our knowledge, our study was the first to investigate the incidence of ischemic and hemorrhagic strokes post-COVID-19 with the longest time interval of more than two years. It was also the first study to evaluate the effect of temporal variation of CT severity of pneumonia on neuroimaging findings. Our study had some limitations. First, it was a retrospective single-center study. Secondly, no analysis was performed to show the virus in the cerebrospinal fluid during the NIP period of the patients. Third, a histopathological examination of the brain tissue after mortality was not performed. Finally, it is difficult to definitively associate new neurological findings post-COVID-19 with this disease. In conclusion, our results showed that since neurological damage can be seen in the late period, careful follow-up should be done in risky groups and neurological damage may develop regardless of the severity of the disease. Ethics Committee Approval This retrospective and the single-center study was approved by the Ethical Committee of Amasya University Sabuncuoğlu Şerefeddin Education and Research Hospital (02 December 2021, number: 12/155). The procedures used in this study adhere to the tenets of the Declaration of Helsinki. Informed Consent The study is retrospective, patient information was obtained from electronic records and censored. Since the study was retrospective, the ethics committee did not find it necessary to obtain written informed consent from the patients. Data Availability Statement The data that support the findings of this study are available on request from the corresponding author, [ATK]. Authors’ contribution statements All of the authors declare that they have all participated in the design, execution, and analysis of the paper and that they have approved the final version CRediT authorship contribution statement Ahmet Turan Kaya: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Supervision. Burcu Akman: Methodology, Writing – original draft, Writing – review & editing, Supervision. Declaration of Competing Interest The authors declare they have no conflicts of interest Funding No funding was received to assist with the preparation of this manuscript. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors The type of manuscript: Original article ==== Refs References 1 Johns Hopkins Coronavirus Resource Center COVID-19 Map - Johns Hopkins Coronavirus Resource Center 2020 Johns Hopkins Coronavirus Resource Center 1 2 Lu Y Li X Geng D Cerebral Micro-Structural Changes in COVID-19 patients – an MRI-based 3-month follow-up study EClinicalMedicine 2020 25 3 Mao L Jin H Wang M Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China JAMA Neurol 77 2020 1 32614385 4 Gulko E Oleksk ML Gomes W MRI brain findings in 126 patients with COVID-19: initial observations from a descriptive literature review AJNR Am J Neuroradiol 41 2020 2199 32883670 5 Mahajan A, Of JH-AJ, 2020 undefined, Novel coronavirus: what neuroradiologists as citizens of the world need to know. Am Soc Neuroradiol. 6 Zhou P, Yang X, Wang X, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. nature.com. 7 Moonis G, Filippi CG, Kirsch CFE, et al. The Spectrum of neuroimaging findings on CT and MRI in adults with COVID-19. 102214/AJR2024839 2020;217:959–74. 8 Zubair A, McAlpine L, Gardin T, SF J, 2020 Undefined, Neuropathogenesis and neurologic manifestations of the coronaviruses in the age of coronavirus disease 2019: a review. jamanetwork.com. 9 Moriguchi T Harii N Goto J A First Case of Meningitis/Encephalitis Associated with SARS-Coronavirus-2 undefined 2020 Elsevier 10 Paniz-Mondolfi A Bryce C Grimes Z Central nervous system involvement by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) J Med Virol 92 2020 699 702 32314810 11 Zhou L, Zhang M, Wang J, Dis JG-TMI, 2020 undefined. Sars-Cov-2: underestimated damage to nervous system. text2fa.ir 2020. 12 Huang Y, Jiang D, Brain JH, Behavior undefined, immunity and, 2020 undefined. SARS-CoV-2 detected in cerebrospinal fluid by PCR in a case of COVID-19 encephalitis. ncbi.nlm.nih.gov. 13 Xu J Zhong S Liu J Detection of severe acute respiratory syndrome coronavirus in the brain: potential role of the chemokine MIG in pathogenesis Clin Infect Dis 41 2005 1089 1096 16163626 14 Zhang Q, Ding Y, Hou J, LH-D 1 jun yi da xue xue, 2003 undefined. Detection of severe acute respiratory syndrome (SARS)-associated coronavirus RNA in autopsy tissues with in situ hybridization. europepmc.org. 15 Libby P COVID-19 is, in the end, an endothelial disease Eur Heart J 2020 academic.oup.com 16 Oxley T Mocco J Majidi S Large-vessel stroke as a presenting feature of Covid-19 in the young Mass Med Soc 382 2020 e60 17 Helms J Tacquard C Severac F High risk of thrombosis in patients with severe SARS-CoV-2 infection: a multicenter prospective cohort study Intensive Care Med 46 2020 1089 1098 32367170 18 Mogensen MA Wangaryattawanich P Hartman J Special report of the RSNA COVID-19 task force: systematic review of outcomes associated with COVID-19 neuroimaging findings in hospitalized patients Br J Radiol 94 2021 20210149 19 Ladopoulos T Zand R Shahjouei S COVID-19: neuroimaging features of a pandemic J Neuroimaging 31 2021 228 33421032 20 Choi Y Lee MK. Neuroimaging findings of brain MRI and CT in patients with COVID-19: a systematic review and meta-analysis Eur J Radiol 133 2020 109393 21 Kim PH Kim M Suh CH Neuroimaging findings in patients with COVID-19: a systematic review and meta-analysis Korean J Radiol 22 2021 1875 34269530 22 Lu Y Zhao J-ji Ye M-fan The relationship between COVID-19’s severity and ischemic stroke: a systematic review and meta-analysis Neurol Sci 42 2021 2645 33954827 23 Baldini T Asioli GM Romoli M Cerebral venous thrombosis and severe acute respiratory syndrome coronavirus-2 infection: a systematic review and meta-analysis Eur J Neurol 28 2021 3478 33426733 24 Chang YC Yu CJ Chang SC Pulmonary sequelae in convalescent patients after severe acute respiratory syndrome: evaluation with thin-section CT Radiology 236 2005 1067 1075 16055695 25 Baig AM Khaleeq A Ali U Syeda H. Evidence of the COVID-19 virus targeting the CNS: tissue distribution, host–virus interaction, and proposed neurotropicmechanisms ACS Chem Neurosci 11 2020 995 32167747 26 Wu Y, Xu X, Chen Z, et al. Nervous System Involvement After Infection with COVID-19 and Other Coronaviruses. Elsevier. 27 Conde-Cardona G Quintana-Pájaro LD Quintero-Marzola ID Ramos-Villegas Y Moscote-Salazar LR Neurotropism of SARS-CoV 2: mechanisms and manifestations J Neurol Sci 412 2020 116824 28 Esposito G Pesce M Seguella L Sanseverino W Lu J Sarnelli G. Can the enteric nervous system be an alternative entrance door in SARS-CoV2 neuroinvasion? Brain Behav Immun 87 2020 93 32335192 29 Zhang L Yan X Fan Q D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19 J Thromb Haemost 18 2020 1324 32306492 30 Varga Z Flammer AJ Steiger P Endothelial cell infection and endotheliitis in COVID-19 Lancet (London, England) 395 2020 1417 32325026 31 Zhang Y Xiao M Zhang S Coagulopathy and antiphospholipid antibodies in patients with Covid-19 N Engl J Med 382 2020 e38 32268022 32 Deng Q Hu B Zhang Y Suspected myocardial injury in patients with COVID-19: evidence from front-line clinical observation in Wuhan, China Int J Cardiol 311 2020 116 121 32291207 33 Kvernland A Kumar A Yaghi S Anticoagulation use and hemorrhagic stroke in SARS-CoV-2 patients treated at a New York healthcare system Neurocrit Care 34 2021 748 32839867 34 Lioutas VA Goyal N Katsanos AH Clinical outcomes and neuroimaging profiles in non-disabled patients with anticoagulant-related intracerebral hemorrhage Stroke 49 2018 2309 30355114 35 Tsivgoulis G Wilson D Katsanos AH Neuroimaging and clinical outcomes of oral anticoagulant–associated intracerebral hemorrhage Ann Neurol 84 2018 694 704 30255970 36 Maas MB. Critical medical illness and the nervous system Continuum (Minneap Minn) 26 2020 675 694 32487902 37 Yaghi S Ishida K Torres J SARS2-CoV-2 and stroke in a New York healthcare system Stroke May 2020 2002 2011 32432996 38 Tan YK Goh C Leow AST COVID-19 and ischemic stroke: a systematic review and meta-summary of the literature J Thromb Thrombolysis 50 2020 587 32661757 39 Rahmig J Altarsha E Siepmann T Barlinn K. Acute ischemic stroke in the context of SARS-CoV-2 vaccination: a systematic review Neuropsychiatr Dis Treat 18 2022 1907 36065388 40 Lang M Li MD Jiang KZ Severity of chest imaging is correlated with risk of acute neuroimaging findings among patients with COVID-19 AJNR Am J Neuroradiol 42 2021 831 33541897 41 Mahammedi A Ramos A Bargalló N Brain and lung imaging correlation in patients with COVID-19: could the severity of lung disease reflect the prevalence of acute abnormalities on neuroimaging? A global multicenter observational study AJNR Am J Neuroradiol 42 2021 1008 33707278 42 Raabe A Wissing H Zwissler B. Brain cell and S-100B increase after acute lung injury Anesthesiology 102 2005 713 714 15791097 43 Mascia L. Acute lung injury in patients with severe brain injury: a double hit model Neurocrit Care 11 2009 417 426 19548120 44 Wenting A Gruters A van Os Y COVID-19 neurological manifestations and underlying mechanisms: a scoping review Front Psychiatry 11 2020 45 Conde-Cardona G Quintana-Pájaro LD Quintero-Marzola ID Ramos-Villegas Y Moscote-Salazar LR Neurotropism of SARS-CoV 2: mechanisms and manifestations J Neurol Sci 2020 412 46 Li Y Li M Wang M Acute cerebrovascular disease following COVID-19: a single center, retrospective, observational study Stroke Vasc Neurol 5 2020 279 284 32616524	36516593	PMC9708621	NO-CC CODE	2022-12-16 23:21:38	no	J Stroke Cerebrovasc Dis. 2023 Feb 30; 32(2):106920	utf-8	J Stroke Cerebrovasc Dis	2,022	10.1016/j.jstrokecerebrovasdis.2022.106920	oa_other
==== Front Int Immunopharmacol Int Immunopharmacol International Immunopharmacology 1567-5769 1878-1705 Elsevier B.V. S1567-5769(22)01019-0 10.1016/j.intimp.2022.109534 109534 Article The impacts of vaccination status and host factors during early infection on SARS-CoV-2 persistence: a retrospective single-center cohort study☆ Tian Xiangxiang aceg1 Zhang Yifan ace1 Wang Wanhai c Fang Fang d Zhang Wenhong ef Zhu Zhaoqin a⁎ Wan Yanmin bef⁎ a Department of Laboratory Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China b Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China c Clinical Laboratory, The First Affiliated Hospital of Zhengzhou University, Key Laboratory of Laboratory Medicine of Henan Province, Zhengzhou 450052, China d Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China e Department of Infectious Diseases, Shanghai Key Laboratory of Infectious Diseases and Biosafety Emergency Response, National Medical Center for Infectious Diseases, Huashan Hospital, Fudan University, Shanghai 200040, China f State Key Laboratory of Genetic Engineering, School of Life Science, Fudan University, Shanghai 200000, China g Department of Clinical Laboratory, The First People’s Hospital of Shangqiu, Shangqiu 476000, China ⁎ Corresponding authors. 1 Xiangxiang Tian and Yifan Zhang contributed equally to this work. 30 11 2022 1 2023 30 11 2022 114 109534109534 23 9 2022 13 11 2022 28 11 2022 © 2022 Elsevier B.V. All rights reserved. 2022 Elsevier B.V. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Background Viral persistence is a crucial factor that influences the transmissibility of SARS-CoV-2. However, the impacts of vaccination and physiological variables on viral persistence have not been adequately clarified. Methods We collected the clinical records of 377 COVID-19 patients, which contained unvaccinated patients and patients received two doses of an inactivated vaccine or an mRNA vaccine. The impacts of vaccination on disease severity and viral persistence and the correlations between 49 laboratory variables and viral persistence were analyzed separately. Finally, we established a multivariate regression model to predict the persistence of viral RNA. Results Both inactivated and mRNA vaccines significantly reduced the rate of moderate cases, while the vaccine related shortening of viral RNA persistence was only observed in moderate patients. Correlation analysis showed that 10 significant laboratory variables were shared by the unvaccinated mild patients and mild patients inoculated with an inactivated vaccine, but not by the mild patients inoculated with an mRNA vaccine. A multivariate regression model established based on the variables correlating with viral persistence in unvaccinated mild patients could predict the persistence of viral RNA for all patients except three moderate patients inoculated with an mRNA vaccine. Conclusion Vaccination contributed limitedly to the clearance of viral RNA in COVID-19 patients. While, laboratory variables in early infection could predict the persistence of viral RNA. Keywords COVID-19 Inactivated vaccine Laboratory variables mRNA vaccine Viral RNA shedding ==== Body pmc1 Introduction Real world studies suggest that the implementing of vaccination have dramatically reduced the rates of SARS-CoV-2 infection and SARS-CoV-2 related hospitalization, admission to intensive care unit and death [1], [2], [3]. In addition, retrospective estimations have also suggested that vaccines were effective at preventing the transmission of both the Alpha [4], [5] and the Delta variants [6]. It is believed that the vaccine mediated reduction of transmission relies on the prevention of infection, while the effect of vaccines in preventing onward transmission after breakthrough infections has not been adequately clarified. It is speculated that vaccines may help to restrain the onward transmission of SARS-CoV-2 [7] based on observations that COVID-19 vaccination can reduce viral loads in nasal mucosa [8], [9], [10]. However, contradictory evidence showed that the viral loads of breakthrough infections after full vaccination were similar with those of unvaccinated individuals [11], [12]. More recently, a preprint study carried out in a prison demonstrated that there was no significant difference in the duration of RT-PCR positivity and the kinetics of Ct values between fully vaccinated participants and those not fully vaccinated [13]. In addition to vaccination status, the onward transmission risk can also be driven directly by factors, such as closeness of social interactions, symptom status, the severity of illness, environment, and time of exposure [14]. Host defense mechanism has been shown to affect susceptibility to infection [15], but its impact on onward transmission has not been clearly elucidated. To reveal the impacts of vaccination status and the host factors during early infection on the potential of onward transmission, in this study, we retrospectively retrieved the clinical records of 377 hospitalized COVID-19 patients and analyzed the correlations between the host variables and the duration of viral RNA shedding. Given that the levels of viral RNA can reflect the shedding of infectious virus [16], factors that associate with pharyngeal viral RNA shedding may serve as indicators for the potential of onward transmission. The findings of this study may provide a rationale to optimize the management of hospitalized COVID-19 patients and the control measures for the pandemic. 2 Materials and methods 2.1 Ethical approval statement This study was approved by the Research Ethics Review Committee (Ethics Approval Number: YJ-2020-S080-02) of the Shanghai Public Health Clinical Center Affiliated to Fudan University. 2.2 Study design and participants 377 COVID-19 patients hospitalized in Shanghai Public Health Clinical Center during the period from January 2020 to mid-January 2022 were included in this study. The diagnosis and clinical management of COVID-19 patients were conducted following the Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia released by National Health Commission & National Administration of Traditional Chinese Medicine [17]. According to the protocol, mild patients refer to those who have mild the clinical symptoms and show no sign of pneumonia. Moderate patients refer to those who have fever and respiratory symptoms and show signs of pneumonia. The demographic and COVID-19 vaccination information were depicted in Table 1 . 49 laboratory variables detected at the immediate early stage of infection and the duration of viral RNA shedding were shown in Supplementary Table 1. The duration of viral RNA shedding was defined as the period from the initial nasal swab RT-PCR positivity to the final nasal swab RT-PCR positivity. Patients with hypertension, diabetes, hyperlipidemia and/or chronic obstructive pulmonary disease were excluded.Table 1 Demographical characteristics of the enrolled COVID-19 patients. Inactivated vaccine (N = 165) mRNA vaccine (N = 41#) Without vaccination (N = 171) P value Clinical category (n) Mild 133 38 110 <0.0001 Moderate 32 3 61 Gender (n) Male 126 21 105 0.0011 Female 39 20 66 Age, Median (IQR)(Years) 35.0 (25.0–44.0) 30.0 (25.0–46.5) 32.0 (24.0–44.0) 0.5651 Time-since-vaccination (Mean ± SD) (Days) 175.5 ± 90.83 146.0 ± 63.63 – 0.1183 Note: All the vaccinated patients received two doses of either an inactivated vaccine or an mRNA vaccine. Time-since-vaccination is defined as the period from the second dose of vaccination to the diagnosis of COVID-19. Among these patients, 129 received two doses of the inactivated vaccine manufactured by Sinopharm and 36 received two doses of the inactivated vaccine manufactured by Sinovac. # Among these patients, 6 received two doses of the mRNA vaccine manufactured by Moderna and 35 received two doses of the mRNA vaccine manufactured by Pfizer. 2.3 SARS-CoV-2 viral RNA detection Viral RNA extraction was performed using an automatic nucleic acid extractor (Bioperfectus technologies Co., ltd., Jiangsu, China). 2019-nCoV nucleic acid detection kit (Bioperfectus technologies Co., ltd., Jiangsu, China) and ABI 7500 real-time fluorescence quantitative PCR (qPCR) instrument (Thermo Fisher Technology Co., ltd., Shanghai, China) were used to detected SARS-CoV-2 genomic RNA. When the Ct values of both ORF1ab and N gene of 2019-nCoV are ≤ 37 and the amplification curve is “S-shaped”, the result is defined as positive. When the Ct values of both genes are > 40 or undetermined, the result is defined as negative. When the Ct value is between 37 ∼ 40, the sample would be re-examined. 2.4 Laboratory measurements All the laboratory tests were carried out following manufacturers' instructions in the department of laboratory medicine of Shanghai Public Health Clinical Center. All the tests had passed the ISO15189 accreditation. In this study, we retrospectively retrieved the values of 49 laboratory variables (Supplementary Table 1) for each patient, which were measured at a median of 3 days post diagnosis. 2.5 Statistical analysis Quantitative data were examined for normality using the Shapiro–Wilk test before all downstream analyses except the Chi square test. Means for variables with a normal distribution were compared using the two-tailed parametric t-test and with the two-tailed non-parametric t-test when distributions of data departed from normality. Pearson correlation was used for correlation analyses of normally distributed data and Spearman correlation was used for analyses of non-normally distributed data. Multivariate regression analyses were performed for variables with statistical significance by the correlation analysis. P ≤ 0.05 was considered as statistically significant. All the statistical analyses were conducted using Graphpad Prism 9 (GraphPad Software, USA). 3 Results 3.1 Demographical characteristics of COVID-19 patients In this study, we retrospectively retrieved the clinical records of 377 COVID-19 patients hospitalized in Shanghai Public Health Clinical Center during the period from January 2020 to mid-January 2022. Among these patients, 165 received two doses of an inactivated vaccine, 41 received two doses of an mRNA vaccine and 171 were not vaccinated (W/O vaccination) (Table 1). The median age was similar across all groups (P = 0.5651), and the mean time-since-vaccination of patients inoculated with an inactivated vaccine was comparable with that of patients inoculated with an mRNA vaccine (P = 0.1183) (Table 1). The gender composition of patients inoculated with an mRNA vaccine was significantly different from the other two groups, which contained more female (P = 0.0011) (Table 1), but it did not impact the duration of viral RNA shedding and the severity of disease (Supplementary Fig. 1). 3.2 Vaccines reduced the proportion of moderate cases and shortened the duration of viral RNA shedding among these patients Compared with unvaccinated patients, patients inoculated with either an inactivated vaccine (19.4 % vs 35.7 %, P = 0.0002) or an mRNA vaccine (7.3 % vs 35.7 %, P = 0.0001) significantly reduced the incidence of moderate cases (Fig. 1 A). More interestingly, we found that patients inoculated with an inactivated vaccine showed significantly shorter duration of viral RNA shedding in moderate patients compared with unvaccinated patients (17, IQR 12.25–19.5 vs 19, IQR 12–24.5, P = 0.0382) and patients inoculated with an mRNA vaccine (17, IQR 12.25–19.5 vs 22.33 ± 4.163, P = 0.0385) (Fig. 1B). However, the vaccines showed no effect on constraining the duration of viral RNA shedding in mild COVID-19 patients (Fig. 1C).Fig. 1 The impacts of vaccination on COVID-19 clinical manifestation and the duration of viral RNA shedding. (A) The proportions of mild and moderate COVID-19 cases were compared among unvaccinated patients and patients inoculated with an inactivated vaccine or an mRNA vaccine. (B) Comparisons of the duration of viral RNA shedding among moderate COVID-19 patients. (C) Comparisons of the duration of viral RNA shedding among mild COVID-19 patients. Statistical analyses were performed by the method of non-parametric t-test. 3.3 Common variables that correlated with the duration of viral RNA shedding were found in unvaccinated mild patients and mild patients inoculated with an inactivated vaccine, but not in those inoculated with an mRNA vaccine As aforementioned, the vaccines failed to constrain the duration of viral RNA shedding in mild COVID-19 patients (Fig. 1C), which implies that vaccine induced immune responses might not play a pivotal role in determining viral persistence in these patients. To identify physiological variables that are associated with the duration of viral RNA shedding in the mild COVID-19 patients, we performed correlation analyses between the duration of viral RNA shedding and 49 laboratory variables detected at the immediate early stage of infection. Our data showed that 16 variables in unvaccinated patients (Fig. 2 A) and 15 variables in patients inoculated with an inactivated vaccine (Fig. 2B) correlated with the duration of viral RNA shedding, respectively. Of note, 10 variables were shared by these two groups of patients, including 3 positively correlated variables (Fibrinogen concentration, Monocyte count and IL-17 concentration) and 7 negatively correlated variables (Neutrophil, Eosinophil, Basophil, CD4+, CD8+, CD19+ and CD16+CD56+ cell counts). In contrast, in patients inoculated with an mRNA vaccine, only one variable (eGFR, estimated glomerular filtering rate) was found to correlate negatively with the duration of viral RNA shedding (Fig. 2C). Next, we compared the laboratory variables among the three groups of mild COVID-19 patients and found that inoculations with an inactivated vaccine or an mRNA vaccine influenced the laboratory parameters differentially (Supplementary Table 1). Patients inoculated with an mRNA vaccine mounted highest median IL-8 and IL-17 responses at the immediate early stage of infection (Supplementary Table 1).Fig. 2 Correlation analyses between multiple laboratory variables and the duration of viral RNA shedding in different groups of mild COVID-19 patients. Data of 49 laboratory variables tested at the immediate early stage of infection were retrieved and correlations between each individual variable and the duration of viral RNA shedding were analyzed in unvaccinated mild patients (A), and mild patients inoculated with either an inactivated vaccine (B) or an mRNA vaccine (C), respectively. Variables that significantly correlated the duration of viral shedding (P < 0.05) were shown in red. Correlation analyses were done using Pearson correlation for normally distributed data and Spearman correlation for non-normally distributed data. Volcano plots were constructed using an online software (https://www.bioinformatics.com.cn). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 3.4 Variables correlating with the duration of viral RNA shedding in unvaccinated mild patients could predict the duration of viral RNA shedding in all patients except the moderate patients inoculated with an mRNA vaccine Although multiple individual parameters were found to correlate significantly with the duration of viral RNA shedding, the correlation coefficients were low (│r│<0.5) (Fig. 2), suggesting that the correlations were relatively weak. This notion was supported by the results of univariate logistic regression analyses, which showed that no individual variable could reliably discriminate between the short duration of viral RNA shedding (≤14 days) and the long shedding duration (>14 days) in unvaccinated mild patients (AUC < 0.80) (Supplementary Fig. 2). The count of CD19+ B cell is of the highest discriminating efficiency (AUC = 0.7556) followed by the count of CD4+ T cell (AUC = 0.7261) (Supplementary Fig. 2). To further characterize the relationships between host factors and viral persistence, we performed multivariate logistic regression analyses exploiting the significant factors identified in the correlation analyses for unvaccinated mild patients. Our results showed that the selected laboratory variables discriminated between the short (≤14 days) and the long (>14 days) viral RNA shedding duration quite well for all the three groups of mild COVID-19 patients. The area under the ROC curve (AUC) for unvaccinated patients, patients inoculated with an inactivated vaccine and patients inoculated with an mRNA vaccine were 0.9231, 0.8365 and 0.9508, respectively (Fig. 3 ). Moreover, we found that this multivariate regression model could also predict the duration of viral RNA shedding in moderate COVID-19 patients. The AUC for unvaccinated moderate patients and moderate patients inoculated with an inactivated vaccine were 0.9110 and 0.9514, respectively (Fig. 4 ). Due the limited sample size (only three patients), the multivariate regression analysis could not be applied to moderate patients inoculated with an mRNA vaccine.Fig. 3 Variables correlating with viral persistence in unvaccinated mild patients could discriminate between the short (≤14 days) and the long (>14 days) duration of viral RNA shedding for all mild COVID-19 patients. Multivariate regression model constructed using significant factors identified in unvaccinated mild patients discriminated between the short and the long duration of viral RNA shedding for unvaccinated mild patients (A) and mild patients inoculated with an inactivated vaccine (B) or an mRNA vaccine (C). NPV, negative predictive value; PPV, positive predictive value. Fig. 4 Variables correlating with viral persistence in unvaccinated mild patients could discriminate between the short (≤14 days) and the long (>14 days) duration of viral RNA shedding for moderate COVID-19 patients. A multivariate regression model constructed using factors identified in unvaccinated mild patients discriminated between the short and the long duration of viral RNA shedding for unvaccinated moderate patients (A) and moderate patients inoculated with an inactivated vaccine (B). 4 Discussion An important factor that influences the transmission of SARS-CoV-2 is the period of transmissibility, which may be influenced by many factors. To identify these factors, detections of live virus and viral RNA are frequently used as indicators of transmissibility, of which the live virus culture is thought to be more relevant to the transmissibility of SARS-CoV-2 infections [18], [19], [20]. However, the culture of live virus is time-consuming and might be affected by specimen qualities and the sensitivities of laboratory methods. Hence, as an alternative solution, the measurement of viral RNA shedding remains to be an important criterion for the diagnosis of COVID-19 and the discharge of hospitalized patients [21]. Multiple factors have been found to associate with prolonged viral RNA shedding in previous studies, such as old age [22], compromised immune status [23], treatment with corticosteroids [24], [25], [26] and disease severity [27], [28]. Meanwhile, biological sex and comorbidities, such as hypertension and diabetes, were found not to associate with the viral RNA shedding [29], [30], [31]. Unlike most previous works, our current study investigated the impacts of vaccination status and multiple laboratory variables during early infection on the duration of viral RNA shedding. First of all, we compared the ratios of moderate COVID-19 cases among unvaccinated patients and patients fully vaccinated with either an inactivated vaccine or an mRNA vaccine. Our data showed that both the inactivated and the mRNA vaccines significantly reduced the incidence of moderate cases, which is in consistence with the observation of a previous real-world study [32]. Next, we compared the duration of viral RNA shedding between unvaccinated and fully vaccinated patients. Quite unexpectedly, our results showed that vaccination significantly shortened the duration of virus shedding in moderate patients but not in mild patients, suggesting that vaccine induced specific immune responses did not contribute significantly to constraining viral persistence in mild patients. We were not able to elaborate the underlying mechanism in this retrospective study. However, as previous studies suggested that more severe COVID-19 could usually evoke stronger host immune responses [33], [34], we speculate that the memory immune responses established by vaccination could be more efficiently activated in moderate patients, which may control the in vivo virus replication better. Next, to characterize host factors that may affect viral persistence in mild COVID-19 patients, we retrieved 49 laboratory variables which were detected at the immediate early stage after infection (at a median of 3 days post diagnosis). Correlation analyses showed that the plasma levels of IL-17, fibrinogen and the counts of peripheral leucocytes were associated with the duration of viral RNA shedding in both unvaccinated patients and patients inoculated with an inactivated vaccine. Elevated levels of proinflammatory cytokines and lymphopenia have been demonstrated to be associated with prolonged viral RNA shedding by previous studies [35], [36], [37]. Here, we further showed that the counts of T, B, NK, neutrophil and eosinophil were negatively associated with the duration of viral RNA shedding, while the count of monocyte was positively associated with the duration of viral RNA shedding. More intriguingly, our data showed that all the significant individual variables identified in unvaccinated patients and patients inoculated with an inactivated vaccine were not significantly associated with the duration of viral RNA shedding in patients inoculated with an mRNA vaccine. This phenomenon implies that the mRNA vaccine may have unique impacts on host responses after break through infection. And these impacts might be deleterious, because the median duration of viral RNA shedding of patients inoculated with an mRNA vaccine tended to be longer than those of unvaccinated patients and patients inoculated with an inactivated vaccine. Insight into the underlying mechanisms will help to improve our understanding of the biological effect of mRNA vaccine. At the end of this study, we established a multivariate regression model using the individual factors correlating with the duration of viral RNA shedding in unvaccinated mild patients and found that the model could be applied to all groups of patients, including the mild patients inoculated with an mRNA vaccine and the moderate patients. This finding suggests that despite of the impacts of vaccination status and the difference of disease severity, the combined effect of host factors on viral persistence remains stable. Several limitations of the present study should be noted. First, antigen-specific immune responses were not detected in this retrospective work. Nonetheless, we speculate that the vaccine induce immunities might not be able to constrain viral RNA shedding in mild COVID-19 patients, since both an inactivated vaccine and an mRNA vaccine failed to shorten the duration of viral RNA shedding in these patients. Second, although a previous study showed that viral RNA load (>107 copies/mL) is an independent risk factor for live virus shedding [38], the detection viral RNA might not always indicate the presence of live viruses. Further investigations are necessitated to characterize host factors that associate with the shedding of viable viruses. Third, most cases enrolled in this study were infected by either the D614G variant or the Delta variant, but we were not able to define the infected variant for each case. Therefore, the impact of viral factor on the duration of viral RNA shedding is out-of-scope for this study. Despite of these limitations, our study showed for the first time that COVID-19 vaccines contributed to the clearance of viral RNA in moderate cases, while failed to shorten the duration of viral RNA shedding in mild patients. Moreover, we identified a set of laboratory variables in early infection that could predict the persistence of viral RNA. These findings may serve as a rationale to optimize the control measures for COVID-19 pandemic. 5 Conclusions Our study showed that COVID-19 vaccines contributed to the clearance of viral RNA in moderate cases, but not in mild patients. Moreover, we identified a set of laboratory variables in early infection that could predict the persistence of viral RNA. Data availability All the data generated during the current study are included in the manuscript. CRediT authorship contribution statement Xiangxiang Tian: Investigation, Writing – original draft. Yifan Zhang: Software, Writing – original draft. Wanhai Wang: Methodology, Supervision. Fang Fang: Validation. Wenhong Zhang: Writing – review & editing. Zhaoqin Zhu: Methodology, Validation, Supervision. Yanmin Wan: Funding acquisition, Methodology, Supervision. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary material The following are the Supplementary data to this article:Supplementary data 1 Supplementary data 2 Supplementary data 3 Supplementary data 4 Supplementary data 5 Supplementary data 6 Supplementary data 7 Supplementary data 8 Supplementary data 9 Data availability No data was used for the research described in the article. Acknowledgments This work was supported by a grant from the National Natural Science Foundation of China [81971559, 32270986], a grant from the major project of Study on Pathogenesis and Epidemic Prevention Technology System by the Ministry of Science and Technology of China [2021YFC2302500] and a grant from the Science and Technology Commission of Shanghai Municipality [21NL2600100]. We thank Dr. Liqiu Jia and Ms. Jing Wang for their help in retrieving the clinical records of the COVID-19 patients. ☆ Yanmin Wan will handle correspondence at all stages of refereeing and publication. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.intimp.2022.109534. ==== Refs References 1 Liu Q. Qin C. Liu M. Liu J. Effectiveness and safety of SARS-CoV-2 vaccine in real-world studies: a systematic review and meta-analysis Infectious Diseases of Poverty 10 1 2021 132 34776011 2 Zheng C. Shao W. Chen X. Zhang B. Wang G. Zhang W. Real-world effectiveness of COVID-19 vaccines: a literature review and meta-analysis, International journal of infectious diseases : IJID : official publication of the International Society for Infect. Dis. 114 2022 252 260 3 Hungerford D. Cunliffe N.A. Real world effectiveness of covid-19 vaccines 374 2021 n2034 4 Shah A.S.V. Gribben C. Bishop J. Hanlon P. Caldwell D. Wood R. Reid M. McMenamin J. Goldberg D. Stockton D. Hutchinson S. Robertson C. McKeigue P.M. Colhoun H.M. McAllister D.A. Effect of vaccination on transmission of SARS-CoV-2 N. Engl. J. Med. 385 18 2021 1718 1720 34496200 5 Harris R.J. Hall J.A. Zaidi A. Andrews N.J. Dunbar J.K. Dabrera G. Effect of vaccination on household transmission of SARS-CoV-2 in England N. Engl. J. Med. 385 8 2021 759 760 34161702 6 D.W. Eyre, D. Taylor, M. Purver, D. Chapman, T. Fowler, K.B. Pouwels, A.S. Walker, T.E. Peto, The impact of SARS-CoV-2 vaccination on Alpha & Delta variant transmission, (2021) 2021.09.28.21264260. 7 Vitiello A. Ferrara F. Troiano V. La Porta R. COVID-19 vaccines and decreased transmission of SARS-CoV-2 Inflammopharmacology 29 5 2021 1357 1360 34279767 8 Callaway E. Delta coronavirus variant: scientists brace for impact Nature 595 7865 2021 17 18 34158664 9 Levine-Tiefenbrun M. Yelin I. Katz R. Herzel E. Golan Z. Schreiber L. Wolf T. Nadler V. Ben-Tov A. Kuint J. Gazit S. Patalon T. Chodick G. Kishony R. Initial report of decreased SARS-CoV-2 viral load after inoculation with the BNT162b2 vaccine Nat. Med. 27 5 2021 790 792 33782619 10 Levine-Tiefenbrun M. Yelin I. Alapi H. Katz R. Herzel E. Kuint J. Chodick G. Gazit S. Patalon T. Kishony R. Viral loads of Delta-variant SARS-CoV-2 breakthrough infections after vaccination and booster with BNT162b2 Nat. Med. 27 12 2021 2108 2110 34728830 11 K.B. Pouwels, E. Pritchard, P.C. Matthews, N. Stoesser, D.W. Eyre, K.-D. Vihta, T. House, J. Hay, J.I. Bell, J.N. Newton, J. Farrar, D. Crook, D. Cook, E. Rourke, R. Studley, T. Peto, I. Diamond, A.S. Walker, t.C.-I.S. Team, Impact of Delta on viral burden and vaccine effectiveness against new SARS-CoV-2 infections in the UK, (2021) 2021.08.18.21262237. 12 K.K. Riemersma, B.E. Grogan, A. Kita-Yarbro, P.J. Halfmann, H.E. Segaloff, A. Kocharian, K.R. Florek, R. Westergaard, A. Bateman, G.E. Jeppson, Y. Kawaoka, D.H. O’Connor, T.C. Friedrich, K.M. Grande, Shedding of Infectious SARS-CoV-2 Despite Vaccination, (2021) 2021.07.31.21261387. 13 P.P. Salvatore, C.C. Lee, S. Sleweon, D.W. McCormick, L. Nicolae, K. Knipe, T. Dixon, R. Banta, I. Ogle, C. Young, C. Dusseau, S. Salmonson, C. Ogden, E. Godwin, T. Ballom, T. Ross, N.T. Wynn, E. David, T.K. Bessey, G. Kim, S. Suppiah, A. Tamin, J.L. Harcourt, M. Sheth, L. Lowe, H. Browne, J.E. Tate, H.L. Kirking, L.M. Hagan, Transmission potential of vaccinated and unvaccinated persons infected with the SARS-CoV-2 Delta variant in a federal prison, July—August 2021, (2021) 2021.11.12.21265796. 14 Cevik M. Baral S.D. Networks of SARS-CoV-2 transmission Science 373 6551 2021 162 163 34244400 15 Cevik M. Marcus J.L. Buckee C. Smith T.C. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission dynamics should inform policy Clin Infect Dis 73 Suppl 2 2021 S170 S176 32964919 16 Takahashi K. Ishikane M. Ujiie M. Iwamoto N. Okumura N. Sato T. Nagashima M. Moriya A. Suzuki M. Hojo M. Kanno T. Saito S. Miyamoto S. Ainai A. Tobiume M. Arashiro T. Fujimoto T. Saito T. Yamato M. Suzuki T. Ohmagari N. Duration of infectious virus shedding by SARS-CoV-2 omicron variant-infected vaccinees Emerg Infect Dis 28 5 2022 998 1001 35290176 17 Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Version 7), Chinese medical journal 133(9) (2020) 1087-1095. 18 Manzulli V. Scioscia G. Giganti G. Capobianchi M.R. Lacedonia D. Pace L. Cipolletta D. Tondo P. De Nittis R. Rondinone V. Serrecchia L. Parisi A. Galante D. Lo Caputo S. Santantonio T.A. Moschetta D. Dattoli V. Fasanella A. Foschino Barbaro M.P. Real time PCR and culture-based virus isolation test in clinically recovered patients: is the subject still infectious for SARS-CoV2? J. Clin. Med. 10 2 2021 309 33467628 19 Huang C.-G. Lee K.-M. Hsiao M.-J. Yang S.-L. Huang P.-N. Gong Y.-N. Hsieh T.-H. Huang P.-W. Lin Y.-J. Liu Y.-C. Tsao K.-C. Shih S.-R. McAdam A.J. Culture-based virus isolation to evaluate potential infectivity of clinical specimens tested for COVID-19 J. Clin. Microbiol. 58 8 2020 20 Jefferson T. Spencer E.A. Brassey J. Heneghan C. Viral cultures for coronavirus disease 2019 infectivity assessment: a systematic review Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America 73 11 2021 e3884 e3899 33270107 21 Jin Y.H. Zhan Q.Y. Peng Z.Y. Ren X.Q. Yin X.T. Cai L. Yuan Y.F. Yue J.R. Zhang X.C. Yang Q.W. Ji J. Xia J. Li Y.R. Zhou F.X. Gao Y.D. Yu Z. Xu F. Tu M.L. Tan L.M. Yang M. Chen F. Zhang X.J. Zeng M. Zhu Y. Liu X.C. Yang J. Zhao D.C. Ding Y.F. Hou N. Wang F.B. Chen H. Zhang Y.G. Li W. Chen W. Shi Y.X. Yang X.Z. Wang X.J. Zhong Y.J. Zhao M.J. Li B.H. Ma L.L. Zi H. Wang N. Wang Y.Y. Yu S.F. Li L.Y. Huang Q. Weng H. Ren X.Y. Luo L.S. Fan M.R. Huang D. Xue H.Y. Yu L.X. Gao J.P. Deng T. Zeng X.T. Li H.J. Cheng Z.S. Yao X. Wang X.H. Chemoprophylaxis, diagnosis, treatments, and discharge management of COVID-19: An evidence-based clinical practice guideline (updated version) Mil. Med. Res. 7 1 2020 41 32887670 22 Cevik M. Tate M. Lloyd O. Maraolo A.E. Schafers J. Ho A. SARS-CoV-2, SARS-CoV, and MERS-CoV viral load dynamics, duration of viral shedding, and infectiousness: a systematic review and meta-analysis The Lancet. Microbe 2 1 2021 e13 e22 33521734 23 Zhu L. Gong N. Liu B. Lu X. Chen D. Chen S. Shu H. Ma K. Xu X. Guo Z. Lu E. Chen D. Ge Q. Cai J. Jiang J. Wei L. Zhang W. Chen G. Chen Z. Coronavirus disease 2019 pneumonia in immunosuppressed renal transplant recipients: a summary of 10 confirmed cases in Wuhan, China European Urology 77 6 2020 748 754 32317180 24 Hu F. Yin G. Chen Y. Song J. Ye M. Liu J. Chen C. Song Y. Tang X. Zhang Y. Corticosteroid, oseltamivir and delayed admission are independent risk factors for prolonged viral shedding in patients with Coronavirus Disease 2019 Clin. Respir. J. 14 11 2020 1067 1075 32750201 25 Liu W. Liu Y. Xu Z. Jiang T. Kang Y. Zhu G. Chen Z. Clinical characteristics and predictors of the duration of SARS-CoV-2 viral shedding in 140 healthcare workers J. Intern. Med. 288 6 2020 725 736 32959400 26 Qi L. Yang Y. Jiang D. Tu C. Wan L. Chen X. Li Z. Factors associated with the duration of viral shedding in adults with COVID-19 outside of Wuhan, China: a retrospective cohort study International Journal of Infectious Diseases: IJID: Official Publication of the International Society for Infect. Dis. 96 2020 531 537 27 M. Park, C. Pawliuk, T. Nguyen, A. Griffitt, L. Dix-Cooper, N. Fourik, M. Dawes, Determining the period of communicability of SARS-CoV-2: A rapid review of the literature, (2020) 2020.07.28.20163873. 28 Fontana L.M. Villamagna A.H. Sikka M.K. McGregor J.C. Understanding viral shedding of severe acute respiratory coronavirus virus 2 (SARS-CoV-2): Review of current literature Infect. Control Hosp. Epidemiol. 42 6 2021 659 668 33077007 29 Stehlik P. Alcorn K. Jones A. Schlebusch S. Wattiaux A. Henry D.A. Repeat testing for SARS-CoV-2: persistence of viral RNA is common, and clearance is slower in older people Med. J. Aust. 214 10 2021 468 470 33899232 30 Xu K. Chen Y. Yuan J. Yi P. Ding C. Wu W. Li Y. Ni Q. Zou R. Li X. Xu M. Zhang Y. Zhao H. Zhang X. Yu L. Su J. Lang G. Liu J. Wu X. Guo Y. Tao J. Shi D. Yu L. Cao Q. Ruan B. Liu L. Wang Z. Xu Y. Liu Y. Sheng J. Li L. Factors associated with prolonged viral RNA shedding in patients with coronavirus disease 2019 (COVID-19) Clin. Infect. Dis. 71 15 2020 799 806 32271376 31 Zhou C. Zhang T. Ren H. Sun S. Yu X. Sheng J. Shi Y. Zhao H. Impact of age on duration of viral RNA shedding in patients with COVID-19 Aging 12 22 2020 22399 22404 33223506 32 Tenforde M.W. Self W.H. Adams K. Gaglani M. Ginde A.A. McNeal T. Ghamande S. Douin D.J. Talbot H.K. Casey J.D. Mohr N.M. Zepeski A. Shapiro N.I. Gibbs K.W. Files D.C. Hager D.N. Shehu A. Prekker M.E. Erickson H.L. Exline M.C. Gong M.N. Mohamed A. Henning D.J. Steingrub J.S. Peltan I.D. Brown S.M. Martin E.T. Monto A.S. Khan A. Hough C.L. Busse L.W. Ten Lohuis C.C. Duggal A. Wilson J.G. Gordon A.J. Qadir N. Chang S.Y. Mallow C. Rivas C. Babcock H.M. Kwon J.H. Halasa N. Chappell J.D. Lauring A.S. Grijalva C.G. Rice T.W. Jones I.D. Stubblefield W.B. Baughman A. Womack K.N. Rhoads J.P. Lindsell C.J. Hart K.W. Zhu Y. Olson S.M. Kobayashi M. Verani J.R. Patel M.M. Association between mRNA vaccination and COVID-19 hospitalization and disease severity JAMA 326 20 2021 2043 2054 34734975 33 Mariën J. Ceulemans A. Michiels J. Heyndrickx L. Kerkhof K. Foque N. Widdowson M.A. Mortgat L. Duysburgh E. Desombere I. Jansens H. Van Esbroeck M. Ariën K.K. Evaluating SARS-CoV-2 spike and nucleocapsid proteins as targets for antibody detection in severe and mild COVID-19 cases using a Luminex bead-based assay J. Virol. Methods 288 2021 114025 34 Reynolds C.J. Swadling L. Gibbons J.M. Pade C. Jensen M.P. Diniz M.O. Schmidt N.M. Butler D.K. Amin O.E. Bailey S.N.L. Murray S.M. Pieper F.P. Taylor S. Jones J. Jones M. Lee W.-Y. Rosenheim J. Chandran A. Joy G. Di Genova C. Temperton N. Lambourne J. Cutino-Moguel T. Andiapen M. Fontana M. Smit A. Semper A. O’Brien B. Chain B. Brooks T. Manisty C. Treibel T. Moon J.C. Noursadeghi M. Altmann D.M. Maini M.K. McKnight Á. Boyton R.J. Discordant neutralizing antibody and T cell responses in asymptomatic and mild SARS-CoV-2 infection Sci. Immunol. 5 54 2020 35 C. Gao, L. Zhu, C.C. Jin, Y.X. Tong, A.T. Xiao, S. Zhang, Proinflammatory cytokines are associated with prolonged viral RNA shedding in COVID-19 patients, Clinical immunology (Orlando, Fla.) 221 (2020) 108611. 36 Hao S. Lian J. Lu Y. Jia H. Hu J. Yu G. Wang X. Xu K. Ni Q. Li Y. Liu J. Zhao H. Zhang X. Yu L. Yu X. Xiang D. Chen Y. Zheng S. Qiu Y. Li L. Liang T. Yang Y. Sheng J. Decreased B cells on admission associated with prolonged Viral RNA shedding from the respiratory tract in coronavirus disease 2019: a case-control study J Infect Dis 222 3 2020 367 371 32474608 37 Mondi A. Lorenzini P. Castilletti C. Gagliardini R. Lalle E. Corpolongo A. Valli M.B. Taglietti F. Cicalini S. Loiacono L. Di Gennaro F. D’Offizi G. Palmieri F. Nicastri E. Agrati C. Petrosillo N. Ippolito G. Vaia F. Girardi E. Capobianchi M.R. Antinori A. Zito S. Abbonizio M.A. Abdeddaim A. Agostini E. Agrati C. Albarello F. Amadei G. Amendola A. Antinori A. Antonica M.A. Antonini M. Bartoli T.A. Baldini F. Barbaro R. Bartolini B. Bellagamba R. Benigni M. Bevilacqua N. Biava G. Bibas M. Bordi L. Bordoni V. Boumis E. Branca M. Buonomo R. Busso D. Camici M. Campioni P. Canichella F. Capobianchi M.R. Capone A. Caporale C. Caraffa E. Caravella I. Carletti F. Castilletti C. Cataldo A. Cerilli S. Cerva C. Chiappini R. Chinello P. Cianfarani M.A. Ciaralli C. Cimaglia C. Cinicola N. Ciotti V. Cicalini S. Colavita F. Corpolongo A. Cristofaro M. Curiale S. D’Abramo A. Dantimi C. De Angelis A. De Angelis G. De Palo M.G. De Zottis F. Di Bari V. Di Lorenzo R. Di Stefano F. D’Offizi G. Donno D. Evangelista F. Faraglia F. Farina A. Ferraro F. Fiorentini L. Frustaci A. Fusetti M. Galati V. Gagliardini R. Gallì P. Garotto G. Gaviano I. Tekle S.G. Giancola M.L. Giansante F. Giombini E. Granata G. Greci M.C. Grilli E. Grisetti S. Gualano G. Iacomi F. Iaconi M. Iannicelli G. Inversi C. Ippolito G. Lalle E. Lamanna M.E. Lanini S. Lapa D. Lepore L. Libertone R. Lionetti R. Liuzzi G. Loiacono L. Lucia A. Lufrani F. Macchione M. Maffongelli G. Marani A. Marchioni L. Mariano A. Marini M.C. Maritti M. Mastrobattista A. Mastrorosa I. Matusali G. Mazzotta V. Mencarini P. Meschi S. Messina F. Micarelli S. Mogavero G. Mondi A. Montalbano M. Montaldo C. Mosti S. Murachelli S. Musso M. Nardi M. Navarra A. Nicastri E. Nocioni M. Noto P. Noto R. Oliva A. Onnis I. Ottou S. Palazzolo C. Pallini E. Palmieri F. Palombi G. Pareo C. Passeri V. Pelliccioni F. Penna G. Petrecchia A. Petrone A. Petrosillo N. Pianura E. Pinnetti C. Pisciotta M. Piselli P. Pittalis S. Pontarelli A. Proietti C. Puro V. Ramazzini P.M. Rianda A. Rinonapoli G. Rosati S. Rubino D. Rueca M. Ruggeri A. Sacchi A. Sampaolesi A. Sanasi F. Santagata C. Scarabello A. Scarcia S. Schininà V. Scognamiglio P. Scorzolini L. Stazi G. Strano G. Taglietti F. Taibi C. Taloni G. Nardi T. Tonnarini R. Topino S. Tozzi M. Vaia F. Vairo F. Valli M.B. Vergori A. Vincenzi L. Visco-Comandini U. Vita S. Vittozzi P. Zaccarelli M. Zanetti A. Zito S. Abbonizio M.A. Abdeddaim A. Agostini E. Agrati C. Albarello F. Amadei G. Amendola A. Antinori A. Antonica M.A. Antonini M. Bartoli T.A. Baldini F. Barbaro R. Bartolini B. Bellagamba R. Benigni M. Bevilacqua N. Biava G. Bibas M. Bordi L. Bordoni V. Boumis E. Branca M. Buonomo R. Busso D. Camici M. Campioni P. Canichella F. Capobianchi M.R. Capone A. Caporale C. Caraffa E. Caravella I. Carletti F. Castilletti C. Cataldo A. Cerilli S. Cerva C. Chiappini R. Chinello P. Cianfarani M.A. Ciaralli C. Cimaglia C. Cinicola N. Ciotti V. Cicalini S. Colavita F. Corpolongo A. Cristofaro M. Curiale S. D’Abramo A. Dantimi C. De Angelis A. De Angelis G. De Palo M.G. De Zottis F. Di Bari V. Di Lorenzo R. Di Stefano F. D’Offizi G. Donno D. Evangelista F. Faraglia F. Farina A. Ferraro F. Fiorentini L. Frustaci A. Fusetti M. Galati V. Gagliardini R. Gallì P. Garotto G. Gaviano I. Tekle S.G. Giancola M.L. Giansante F. Giombini E. Granata G. Greci M.C. Grilli E. Grisetti S. Gualano G. Iacomi F. Iaconi M. Iannicelli G. Inversi C. Ippolito G. Lalle E. Lamanna M.E. Lanini S. Lapa D. Lepore L. Libertone R. Lionetti R. Liuzzi G. Loiacono L. Lucia A. Lufrani F. Macchione M. Maffongelli G. Marani A. Marchioni L. Mariano A. Marini M.C. Maritti M. Mastrobattista A. Mastrorosa I. Matusali G. Mazzotta V. Mencarini P. Meschi S. Messina F. Micarelli S. Mogavero G. Mondi A. Montalbano M. Montaldo C. Mosti S. Murachelli S. Musso M. Nardi M. Navarra A. Nicastri E. Nocioni M. Noto P. Noto R. Oliva A. Onnis I. Ottou S. Palazzolo C. Pallini E. Palmieri F. Palombi G. Pareo C. Passeri V. Pelliccioni F. Penna G. Petrecchia A. Petrone A. Petrosillo N. Pianura E. Pinnetti C. Pisciotta M. Piselli P. Pittalis S. Pontarelli A. Proietti C. Puro V. Ramazzini P.M. Rianda A. Rinonapoli G. Rosati S. Rubino D. Rueca M. Ruggeri A. Sacchi A. Sampaolesi A. Sanasi F. Santagata C. Scarabello A. Scarcia S. Schininà V. Scognamiglio P. Scorzolini L. Stazi G. Strano G. Taglietti F. Taibi C. Taloni G. Nardi T. Tonnarini R. Topino S. Tozzi M. Vaia F. Vairo F. Valli M.B. Vergori A. Vincenzi L. Visco-Comandini U. Vita S. Vittozzi P. Zaccarelli M. Zanetti A. Zito S. Risk and predictive factors of prolonged viral RNA shedding in upper respiratory specimens in a large cohort of COVID-19 patients admitted to an Italian reference hospital, International journal of infectious diseases : IJID : official publication of the International Society for Infect. Dis. 105 2021 532 539 38 van Kampen J.J.A. van de Vijver D. Fraaij P.L.A. Haagmans B.L. Lamers M.M. Okba N. van den Akker J.P.C. Endeman H. Gommers D. Cornelissen J.J. Hoek R.A.S. van der Eerden M.M. Hesselink D.A. Metselaar H.J. Verbon A. de Steenwinkel J.E.M. Aron G.I. van Gorp E.C.M. van Boheemen S. Voermans J.C. Boucher C.A.B. Molenkamp R. Koopmans M.P.G. Geurtsvankessel C. van der Eijk A.A. Duration and key determinants of infectious virus shedding in hospitalized patients with coronavirus disease-2019 (COVID-19) Nat Commun 12 1 2021 267 33431879	36476489	PMC9708622	NO-CC CODE	2022-12-15 23:19:38	no	Int Immunopharmacol. 2023 Jan 30; 114:109534	utf-8	Int Immunopharmacol	2,022	10.1016/j.intimp.2022.109534	oa_other
==== Front Biomed Signal Process Control Biomed Signal Process Control Biomedical Signal Processing and Control 1746-8094 1746-8094 Elsevier Ltd. S1746-8094(22)00899-0 10.1016/j.bspc.2022.104445 104445 Article PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates Bhosale Yogesh H. ⁎ Patnaik K. Sridhar Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India ⁎ Corresponding author. 30 11 2022 3 2023 30 11 2022 81 104445104445 17 4 2022 10 10 2022 20 11 2022 © 2022 Elsevier Ltd. All rights reserved. 2022 Elsevier Ltd Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Graphical abstract Background and Objective In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the severity and mortality ratio of COVID-19 affected patients is at greater risk because of chronic pulmonary diseases. This study looks at radiographic examinations exploiting chest X-ray images (CXI), which have become one of the utmost feasible assessment approaches for pulmonary disorders, including COVID-19. Deep Learning(DL) remains an excellent image classification method and framework; research has been conducted to predict pulmonary diseases with COVID-19 instances by developing DL classifiers with nine class CXI. However, a few claim to have strong prediction results; because of noisy and small data, their recommended DL strategies may suffer from significant deviation and generality failures. Methods Therefore, a unique CNN model(PulDi-COVID) for detecting nine diseases (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-Pneumonia) using CXI has been proposed using the SSE algorithm. Several transfer-learning models: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 are trained on CXI of chronic lung diseases and COVID-19 instances. Given that the proposed thirteen SSE ensemble models solved DL's constraints by making predictions with different classifiers rather than a single, we present PulDi-COVID, an ensemble DL model that combines DL with ensemble learning. The PulDi-COVID framework is created by incorporating various snapshots of DL models, which have spearheaded chronic lung diseases with COVID-19 cases identification process with a deep neural network produced CXI by applying a suggested SSE method. That is familiar with the idea of various DL perceptions on different classes. Results PulDi-COVID findings were compared to thirteen existing studies for nine-class classification using COVID-19. Test results reveal that PulDi-COVID offers impressive outcomes for chronic diseases with COVID-19 identification with a 99.70% accuracy, 98.68% precision, 98.67% recall, 98.67% F1 score, lowest 12 CXIs zero-one loss, 99.24% AUC-ROC score, and lowest 1.33% error rate. Overall test results are superior to the existing Convolutional Neural Network(CNN). To the best of our knowledge, the observed results for nine-class classification are significantly superior to the state-of-the-art approaches employed for COVID-19 detection. Furthermore, the CXI that we used to assess our algorithm is one of the larger datasets for COVID detection with pulmonary diseases. Conclusion The empirical findings of our suggested approach PulDi-COVID show that it outperforms previously developed methods. The suggested SSE method with PulDi-COVID can effectively fulfill the COVID-19 speedy detection needs with different lung diseases for physicians to minimize patient severity and mortality. Keywords Biomedical engineering Convolution neural networks (CNN) Ensemble deep learning Chronic Obstructive Pulmonary Diseases (COPD) COVID-19 Diagnosis & Classification Transfer learning Medical Imaging ==== Body pmc1 Introduction An unexpected death and debilitating infection worldwide, are caused by SARS-CoV-2, which patients have detected with COVID-19. One-to-one COVID-19 transmission occurs quickly among two persons in intimate exposure via plumes or minute droplets produced by talk, coughing, and sneezing. After becoming infectious, patients commonly experience these signs: flu, coughing, shortness of breath, smell, and taste [1]. As of 07 January 2022, there are still more than 298,915,721 infected cases, 5,469,303 people have died, and 9,118,223,397 vaccine doses have been administered worldwide [5]. In general, the RT-PCR test is utilized to diagnose COVID-19. It can detect infectious RNA in a nasopharyngeal swab [15]. It necessitates specialized materials and kits that are not readily available. It requires at least 8–12 h, which is inconvenient because COVID-19 + Ve patients should be detected and followed as soon as feasible. Some investigations discovered that RT-PCR findings from many tests performed on the same individuals at different stages throughout the illness were varied, resulting in a significant FNR [16]. As a result, it may misclassify COVID-19 patients as uninfected. The Author proposed combining the RT-PCR test with additional clinical procedures such as radiography imaging [50]. In parallel to RT-PCR, medical imaging assessment is a powerful clinical method for identifying COVID-19 quickly. Clinicians study and evaluate CXI and CT scans to assess whether or not a suspectable subject has been infected with SARS-CoV-2 [27], [26]. Subsequent investigations have shown atypical abnormalities in radiographic imaging of COVID-19 patients, and it was frequently employed during the early stages of the global pandemic [11], [14], [24]. Although CXI is extremely efficient, it contains a professional radiologist to physically judge for COVID19 case detection, which is not a time-saving method that loads medical professional skills. However, the proportion of radiologists is far smaller than that of individuals undergoing monitoring. An AI-aided diagnosing tool is required to help the radiologist in detecting COVID19 occurrences in a much quicker, more immediate, and accurate manner. Otherwise, infected persons may not be recognized and isolated as fast as practicable and may not undergo appropriate treatments [25], [26]. Agreeing to American Lung Association [34], Lancet report [35], and Geng et al. [24], the severity and mortality rates are increased due to chronic pulmonary diseases in COVID-19-confirmed patients[23]. So, we consider this as a challenge and opportunity for further research. Moreover, the studies [36], [39], [41], [42], [43] reported higher performance, whereas their used COVID-19 samples size are small. So, to urge this need and minimize the mortality rates because of pulmonary diseases with COVID-19 in this work, we expanded dl-based automatic detection of pulmonary disease with COVID-19 (PulDi-COVID) using CXI for nine class classifications. Moreover, we also investigate how to use eight CNN for identifying pulmonary disease with COVID-19 cases with thirteen probable selective snapshots stacked ensemble (SSE) models. Our outcomes highlight the need to utilize proper computing tools to analyze COVID-19 concerns and assist associated decision-making. The following precise research questions (RQ) motivated our study:RQ1) How important chronic pulmonary diseases can affect the COVID-19 diagnosis? Are mortality rates increasing due to the existence of chronic pulmonary disorders along with COVID-19 diagnosis? RQ2) How can we obtain the highest performance with nine-class classification using CNN? RQ3) What is the comparative performance of various DL algorithms for pulmonary disease classification with COVID-19 cases detection, and which classification algorithm performs better? RQ4) Is there any common automated tool for detecting maximum diseases with the assistance of clinicians and radiologists? We focused on searching chronic pulmonary diseases with COVID-19–related datasets to address the above questions to achieve multi-class classification from online public repositories. Specifically, we used pulmonary diseases with COVID-19-related datasets from NIH, Kaggle, GitHub, and dl-based networks to model various issues related to COVID-19 from the radiographic domain. This paper's primary contributions are as follows:1) We used the eight most popular transfer learning CNN models based on state-of-the-art DL architectures. 2) Applied nine-class classification using chronic lung diseases with COVID-19 cases detection. 3) Proposed an SSE strategy with the awareness of varied class-level accuracies for different DL models. SSE models achieve superior performance by minimizing the variance of prediction errors to the competing base learners. 4) Evaluated individual DL models and proposed PulDi-COVID experimentally, showing the promising results of PulDi-COVID. The main aim of SSE is to reduce the error rate and enhance accuracy. 5) To evaluate the performance of this framework, three publicly available datasets of CXI have been used, which are both more widely available tests to perform, and more sensitive to COVID-19. The obtained results outperform the existing methods by a significant margin. 6) This research will be helpful for clinicians and radiologists to minimize the workload, severity, and deaths of COVID-19 patients because the mortality rate may increase as chronic lung diseases present in COVID-19 affected individuals. Our experiment results highlight the necessity of detecting chronic pulmonary disorders with COVID-19 as well as appropriate simulation tools for understanding COVID-19 concerns and guiding associated decision-making. The following is the general layout of the article. First, we'll give a quick overview of online healthcare forums. Section 2 discusses COVID-19-related concerns as well as some comparative studies. Section 3 describes the preprocessing data methods used in our research, and the DL approaches for detecting chronic pulmonary illness with COVID-19. Section 4 then describes the results. Lastly, sections 5 and 6 explore future studies and draw conclusions based on DL techniques for evaluating the COVID-19 radiology society. The set of acronyms used is shown in Table 1 .Table 1 List of abbreviations. Sr. No. Abbreviation Full Form 1. AI Artificial Intelligence. 2. AUC Area under the ROC Curve. 3. BM Base-Model. 4. CAD Computer-Aided Diagnostic. 5. CNN Convolution Neural Network. 7. CXI Chest-X-ray Images. 8. CL Convolution Layer. 9. CT Computed Tomography. 10. DL Deep Learning. 11. COVID-19 Coronavirus-Disease-2019. 12. FC Fully Connected Layer. 13. FN False Negative. 14. FNR False Negative Rate. 15. FP False Positive. 16. FPR False-Positive-Rate. 17. HPC High-Performance-Computing. 18. ML Machine Learning. 19. MM Meta-Model. 20. MSE Mean Squared Error. 21. PulDi-COVID Pulmonary Diseases with COVID-19. 22. SSE Selective-snapshots Stacked Ensemble. 23. TL Transfer Learning. 24. ResNet residual neural network. 25. ReLU Rectified Linear Unit. 26. RT-PCR Reverse Transcription–Polymerase Chain Reaction. 26. RNN Recurrent Neural Networks. 27. ROC Receiver Operating Characteristic. 28. ROC Receiver Operating Characteristic. 29. VGG Visual Geometry Group. 30. TN True Negative. 31. TP True Positive. 32. TPR True-Positive-Rate. 33. VGG Visual Geometry Group. 2 Background and related work For a clearer insight into the PulDi-COVID model, we describe the ensemble DL and CNN for pulmonary illnesses with COVID-19 instance identification. 2.1 Ensemble Deep learning Being a robust ML approach, DL is extensively used in various disciplines, including computer vision, voice translation, healthcare image assessment, pharmaceutical research, and so on [7], [8]. This includes a multi-layer neural network capable of extracting top-level characteristics from primary input, including images, and producing predictive outcomes depending on those characteristics. A DL modeling algorithm is divided into two steps: training and inference. Training is a random and repeated calculation that generates a model depending on training inputs. Different parameters must be initialized during the training process, particularly learning rate, epoch number, and batch size, with alternative setups resulting in models with varying accuracies. Inference involves the procedure of making predictions using the learned DL model. Other prominent DL approaches are accessible, including CNN and RNN. In contrast, CNN is widely used, and it is helpful for image identification, classification, and healthcare image analysis [6]. However, because of outliers in the dataset and unpredictability in the DL method, it is prone to high variation and generalization error [10]. Despite specific strategies, like data augmentation and regularisation [9], the difficulties with DL models are still not effectively tackled. Ensemble learning is a viable strategy for overcoming these issues for multiple ML models [11]. This offers a hybrid learning framework capable of producing highly accurate and resilient predicting outcomes than a single model by intelligently merging several ML models. There are other ensemble algorithms available, such as averaging[11], random forest[12], boosting[13], and stacking[14]. To render the ensemble victorious, we should ensure that the simulations included in the combination are varied. We investigate ensemble DL for pulmonary illnesses using COVID-19 case identification in the suggested project by integrating different DL models using an ensemble technique. 2.2 CNN-Related work The radiography imaging community makes significant contributions to medical data processing and studies, which eventually aids in advancing health technologies. Various AI-aided diagnosis techniques relying on DL have already been suggested to minimize the load of diagnosis from CT and CXI for radiologists [26], [28], [29], [30], [31], [32]. CXI has become a prominent and widely used source of information for COVID-19 early diagnosis because of various advantages of CXI, particularly mobility, affordability, ease of access, and quick screening compared to CT. It has been found that the majority of authors implemented binary [38], [39], [42], [45] and three [36], [37], [41], [43], [44], [48] and multi [40], [46] class classification with COVID instances. Wang et al. [41] combined ResNet-101 and ResNet-152 models to classify COVID-19 from pneumonia and normal CXI. Developed fusion system attained 96.1 % accuracy. However, used COVID-19 samples are small (i.e., 128). Narin et al. [42] compared CNN variants trained on CXI (ResNet50, Inception V3, and Inception-ResNetV2) for COVID-19 identification, finding that ResNet-50 leads the other two approaches with 98 % accuracy. Chowdhury et al. [12] conducted a comparison of various DL networks(AlexNet, ResNet18, DenseNet201, SqueezeNet) to binary classification (COVID-19, normal) for CXI, indicating that SqueezeNet outpaces with 98.3 % accuracy. Rahimzadeh et al. [37] made a chain of Xception + ResNet50V2 to classify 80 CXI of COVID-19, 6054 CXI of pneumonia, and 8851 CXI of normal instances and were able to achieve a 91.4 % accuracy. Alqudah et al.[38] developed AOCT-NET for binary classification (Covid19, normal) with a 95.2 % accuracy. Hemdan et al.[39] proposed COVIDX-Net(DenseNet201, Inceptionv3, VGG19, MobileNetv2, Xception, InceptionResNetv2 and ResNetv2) using 25 cases/class for COVID-19, no infections, attained F1-score of 0.89 % for normal and 91 % for COVID-19. Mishra et al. [45] developed binary classification of DL model (CovAI-Net) using CNN variants (Inception, DenseNet, Xception). CoviAI-Net attained 100 % precision and specificity for ‘COVID + Ve’ class. Asnaoui et al. [40] experimented comparative study of DL variants (IncpetionResNetV2, VGG16/19, DenseNet201, InceptionResNetV2, InceptionV3, Resnet50, MobileNetV2) using CXI of 2780 for bacterial, 1493 for viral, 231 of Covid19, and 1583 normal instances. IncpetionResNetV2 outperformed with 92.18 % accuracy, 96.06 % specificity, 92.38 % precision, and 92.07 % F1-score. Another study by Ucar and Korkmaz [43] implemented SqueezeNet-Bayes for Covid19(76), normal(1583), and pneumonia(4290) classification with an accuracy of 98.30 %. DarkCovidNet is CNN based DL model created by Ozturk et al. [44] for Covid19(1 2 5), normal(5 0 0), and pneumonia(5 0 0) classification with an accuracy of 87.02 %. Tang et al. [47] developed an ensemble dl-based(EDL-COVID) model for the classification of COVID(5 7 3) instances from pneumonia(6053) and normal(8851) from CXI. EDL-COVID attained 95 % accuracy and 96 % sensitivity. Zhou et al. [48] developed an ensemble dl-based model (EDL_COVID) to classify Covid19, lung tumors, and normal cases from CT scans. EDL_COVID utilized 2500 samples per class for experimentation. They achieved 99.05 % accuracy, 99.6 % specificity, and 98.59 % F1-score. 3 Materials and methods This section discusses the datasets, preprocessing, and the proposed PulDi-COVID, an SSE algorithm depending on eight cutting-edge DL architectures. The entire flowchart for PulDi-COVID, as shown in Fig. 1 , comprises two stages: snapshot DL models and ensembling. The training step of DL models is responsible for creating several snapshots, which are subsequently integrated for a meta-learner prediction in the SSE algorithm of the ensemble step (Section 3.3). The PulDi-COVID application source code is described in Appendix A.Fig. 1 Flow chart of proposed PulDi-Covid framework. 3.1 Dataset Until related pulmonary disorders are involved, measuring the efficiency of any classification method in identifying COVID-19 infection is critical. As a result, the entire dataset includes a subset of CXI associated with various lung illnesses with COVID-19 and healthy cases. The online public repository of NIH ChestX-ray8 [2] dataset initially contains fourteen class labels of 112,120 CXI from 30,805 unique patients with chronic lung diseases. Viral and bacterial pneumonia samples are collected from the Kaggle dataset [26]. At the same time, COVID-19 instances are collected from the Kaggle COVID-19 detection challenge [27]. Furthermore, the collected dataset was undergone data preprocessing, which is discussed in the next section. 3.2 Pre-processing For experimentation purposes, we choose six disorders from fourteen ChestX-ray8 [2] labels: atelectasis, cardiomegaly, effusion, infiltration, no-finding/healthy, and pneumothorax. The composed Kaggle dataset contains viral and bacterial pneumonia samples [3]. Simultaneously, COVID-19 cases from the COVID-19 detection challenge [4] are collected. The assembled dataset of CXI from three repositories contains noisy samples, which further need to preprocess. The noisy and blurry CXI has been discarded manually from the collected datasets. To avoid data imbalance, each class is retained with an equal number of samples. The assembled dataset has been partitioned to the 76 %:12 %:12 % ratios for train, val, and test sets. For nine-class detection, we used 10,800 observations from three repositories integrations. For each labeled class in training, 1000 CXIs are evaluated, 100 CXIs for each validation class, and 100 CXIs for each test set class. The pixel data of the given samples were normalized within 0 and 1 for normalization. The CXI used in the sets of data under examination were gray, and the rescale is done by converting 1./255 to adjacent pixels. The train set is augmented online with 'imagedatagenerator,' which increases the collection and adds robustness to the neural model, minimizing the likelihood of overfitting concerns. Shear-range to 0.2, zoom-range to 0.2 are augmentation approaches utilized for cardiomegaly and bacterial pneumonia. Equal sample sizes are allocated for each class after augmentation to avoid data imbalance. This contributes to the creation of the network and improves test imaging performance. Table 2 shows the label data distribution for each class. The major issue was with the database itself. We have programmatically selected those samples whose image id contains a single label instead of multi-labels. Our preferred database faced class imbalance issues, especially for certain categories such as ‘cardiomegaly’. There were no other large diverse databases suitable for real-time CAD implementable framework development.Table 2 Multisource dataset used after preprocessing. Source ChestX-ray8[2] Kaggle Pneumonia[3] Covid19[4] Labels Criteria Atelectasis Cardiomegaly Effusion Infiltration Healthy Pneumothorax Viral Bacterial Covid-19 Sample Size Actual 4215 1093 3955 9547 60,361 2194 2780 1493 6054 Eliminated 774 351 557 1346 4731 832 586 434 1893 Remaining 3441 742 3398 8201 55,630 1362 2194 1059 4161 Balanced class Real 1100 742 1100 1100 1100 1100 1100 1059 1100 Augmented 0 358 0 0 0 0 0 41 0 The creators of certain openly available data sets increase the dataset by augmenting it. Several photos in these databases may be duplicated. If we split those photographs in train, val, and test sets, the pictures in the tests and val-sets likely replicate. Assume a photograph is additionally augmented into 5 photos, three of them remain inside the train pool whereas the remaining percentage is divided among val and test sets. Performance may be misleading. Although it already previously recognizes the significant pictures in the training phase, the classifier can readily identify items. During this case, however, the simulation might collapse if evaluated using real-world photos. To deal with such a problem & verify that there is zero information leaks throughout network train and assessment, we employed a sourced dataset [2], [26], [27] in which verified there are no redundant CXR images. Furthermore, the dataset has been meticulously separated into train, val, and final testing/model assessment data. To prevent database leaks, we initially divided the dataset and augmented just the train set. To check the duplicate we used images translation to numeric data and compare each numeric value. Another technique using MSE, where sum of the squared difference between the two images should be lower to check similar images. 3.3 Proposed methodology Our framework includes a 2-phase transfer learning (TL): a training approach and an ensemble technique. We employed eight TL-CNNs for feature extraction in the first phase: VGG16[39], ResNet50[26], VGG19[39], DenseNet201[12], MobileNetV2, NASNetMobile, ResNet152V2[41], and DenseNet169 [51]. Custom build layers replace the classifier and fully connected layers. TL is the process of adopting the weights of a pretrained network and applying recently learned characteristics to decide on a unique class name. In TL, a model that has been pretrained on the 'imagenet' is employed, and this model has learned to detect top-level image features in the early layers [53]. A dense layer was appended to the CNN structure for TL. The model then determines which feature groupings will aid in identifying features in new data gathering. In this kind of case, the usage of pretrained networks based on the notion of TL might be beneficial. In TL, information obtained by a network trained on a massive dataset is utilized to tackle a similar problem using a lesser dataset. It assists in eliminating the requirement for an enormous dataset and a considerably more extended period, which are needed by DL algorithms that are learned from scratch [22], [23], [24]. Fig. 1 depicts the recommended architecture's block diagram. VGG16 or VGG19 consisting of 16 and 19 convolutional layers. It’s a standard CNN [52] architecture with multiple channels. The numbers “16″ and ”19″ represent the networks weight levels(i.e. CL). VGG19 thus has “3″ extra convolutional layers over VGG16. The image size of 224x224x3 set to first input layer. ReLU is employed in all of the VGG channel's hidden tiers. First CL set to 224x224 with kernel size 64. Followed by 128x128 with kernel size of 3. The remaining layers from VGG16/19 are set to trainable = false. A dense layer attained with a size of 25088-neurons. ResNet50 and ResNet152V2 designed to solve the problem of the disappearing gradient. Residual block networks accepts the input size with 224x224 and 299x299 pixels. The skip connection between 7x7x64 and 3x3x64 allow the network to fit undermapping. If any layer hurt the performance of architecture then it will be skipped by regularization. The filter size set to 64 and kernel set to 3 for the second layer. The remaining layers froze and global pooling and dense and softmax(9) for last layer. Form Fig. 2 MobileNet uses depthwise(DW) + pairwise(PW) separable-CL. In comparison to the structure with ordinary convolutions to the equal depths inside the networks, it greatly decreases the range of parameters. As a consequence, DNNs are compact. Filter parameters for DW and PW are 32 and PW 64 with a stride of 3. After that, we use a 1x1 filter to cover the depth and point dimension. The last flatten + dense contains 62,720 neurons for 9-class classification. DenseNet performs channelwise concatenations instead of passing individual layer results to next layer. It uses concatenated denseBlock + transitionBlock for each layer is receiving a “collective knowledge” from all earlier layers. The default input size of 224x224x3 set to first CL. First and second CLs are of size (112x112x64) and (56x56x64) followed by maxpooling. For both DenseNet169/201 initial 2 blocks are trained while remaining set to false for training. The last layer of flatten + dense deals with 94080-neurons for 9-disease classifications. The default input accepted by NasNetMobile with a size of 224x224x3. In CL(111x111x32) return a feature map of the same dimension. Whereas, reduction block(56x56x32) returns a feature map where the feature map height and width are reduced by a factor of two. The last flatten + dense contains 51,744 neurons for 9 class-classification.Fig. 2 Proposed PulDi-Covid SSE ensemble. The following are the drawbacks of utilizing a single model and the benefits of adopting multi-modal fusion in disease categorization. However a mono-shape permutation operation can decrease the number of dimensions and increase the model's operating acceleration, but this couldn't assure adequate mining of features, particularly in complicated environments, where crucial data is easily lost as well as the feature impact of the final disease detection is reduced. The aforesaid challenges can be efficiently solved using multi-scale information retrieved by different classifiers of convolutional units. However, the previous transmission of every convolutional network could simply verify that the features are transmitted to the outer filter and can't be fused with the model's shallower information. As a consequence, the illness-detection CXIs have inadequate explanations. Our multi-branch merger achieves pattern extraction using several parallel convolutional filters and emphasizes meaningful data using a feature fusing that achieves feature interactions without expanding the model's complexities. In comparison to serial fusing, parallel fusing across convolutions can take advantage of differing convolution kernel sizes, ensuring the complete retrieval of both higher and lower frequencies features and making the integration layered feature include additional important data. Fusion block contains 8 layers(individual feature-vectors), construction of stacked layers, and final fusion. To create a fusion layer in phase 2, we have collected all the 8 models features(8[9-lables64]) individually to make stacking ensemble; finally, all feature-vectors are embedded in stacked constructed layer(Fig. 2). This requires additional improvement in the extraction of desirable feature’s as well as the correlation of various aspects from BMs to MMs. We used the SSE technique with thirteen probable ensembles(i.e., base models-BM are preferred based on minimum error rate) for experimentation in the second phase. The possibility of generating ensembles from eight BMs is not only chosen thirteen but also 2n-1 possible ensembles. So, we try ensemble best BMs without skipping. After producing several DL networks & their weights from the main step, now we proceed to the proposed assembly step for developing PulDi-COVID by stacking various models, as shown in Fig. 1. Several ensembling ways exist for model ensemble, as detailed in section 2.1. Stacking is a prominent ensembling approach for snapshot ensemble learning [20]. It finds the maximum class probabilities from all models for each class for an input image. Let M denote the total classifiers (eight DL models). Let pm, x(si) is the class likelihood of xth class output by mth classifier concerning the source image si. Then the greatest predicated class predicted is =(max⋃m=1Mpm,xsi) for × ∈ [1, X] of the source image si, with X indicating the total classes. The conventional ensembling technique (stacking, bagging, boosting, and averaging) implies that almost all classifiers have equal weights. Furthermore, three introductory remarks are made.1) Overall testing accuracy rates for individual classes in a DL model are often varied. 2) Several DL models have varying levels of accuracy for every classification. 3) It shows that we cannot treat every model equivalently throughout the model assembly process. We developed an SSE technique for TL ensemble related to the above three findings, as shown in Algorithm-1. Let acci,j is the test accuracy of ith model for jth class for test CXI. The measured weights of ith model for jth class are represented as wi,j=max(⋃m=1Maccm,j). For each source image si, we begin by obtaining the results of each classification probabilities pm,xsi from mth model for ∀m ∈ M. The classification probabilities may then be assessed as pxsi of PulDi-COVID by a maximum of the stacked class likelihood of all M for ∀x ∈ X. Furthermore, we obtain the classification results by reporting the class number with the highest-class probabilities for every input image. Overview of the proposed PulDi-COVID model is presented in algorithm 1.Algorithm 1 Proposed dl-based SSE Algorithm for PulDi-COVID. a. Parameter: [train, test, valid] = split(Dataset), a = argmax, m1 = model, t = train, v = valid, tt = test, b = batch, w = weights, em = ensemble-model, si = ith sample input of CXI dataset, X = class. b. Input: Dataset D = sum ∑d=0n(td,vd,ttd) c. Output: ensemble meta-classifier, c(si): predicted class index for ith image with PulDi-COVID. Model Training, Validation, testing: 1. Action 1: Base model classification 2. for c = [VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, 3. DenseNet169] 4. for j = 1:150 // epoch range from 1 to 150 5. [t(j), v(j)] = partition(t, v) 6. for k=(t/b, v/b) // sample/batch 7. model(m1, c, j) = train(c(m1), a, t(j), v(j)) // training 8. valid(m1, c, j) = valid((c(m1), a, j), v(j)) // validation 9. end 10. end 11. tt(c(m1), a) = predict(max(test(c(m1), a, tt))) // prediction on test 12. c(si) = tt(c(m1), a) // predicted class label with index 13. end 14. acquireWeights = model(w) //wi,j=max(⋃m=1Maccm,j) 15. Action 2: Create SSE ensemble-classifier(meta-model) and load feature vectors. 16. for i = 1:8 17. em = model(a). append(c(i))// input as base model structure to create meta-ensemble-learner 18. em(w) = loadWeights(acquireWeights(c[1…8])) 19. Action 3: Test ensemble classifier 20. tt(em, a) = predict(max(test(em, a, tt))) // estimate the class probability 21. pxsi = tt(em, a) 22. c(si) = pxsi// predicted specific class label with index 23. end; 3.4 Stacking ensemble A set of features considered a group instead of individually is an ensemble. An ensemble technique generates various models and then merges them to address a problem. Ensemble approaches to aid in improving the model's strength. Some of the ensemble techniques are averaging [11], max voting, GBM, XGBM, adaboost[33], stacking[14], blending[17], bagging[18], and boosting[13]. A stacked ensemble is an ML algorithm that practices a MM/classifier to merge several base-classifiers(BMs). Stacking learns the optimum way to aggregate predictions from several high-performing models. The BMs are trained on the whole dataset, followed by the MM, which is trained upon features returned (as output) by the BM. In stacking, the BMs are often distinct. The MM aids in the extraction of features from BM to attain the highest level of accuracy. The advantage of stacking is that it may combine the expertise of several high-performing models on a classification/regression job to create recommendations that outperform any one model in the ensemble [17]. It employs a meta-learning strategy to find the optimum way to aggregate predictions from two or more underlying ML techniques. In contrast to boosting, multiple models are utilized in stacking to learn how to merge the predicted results from the participating models. A stacking model's framework consists of two or more BM, also known as 0th step models, and a MM that integrates the predictions of the BM, known as a 1st step model.Step-0 Models (Base-Models): Models fit the training data and collected predictions. Step-1 Model (meta-Model): Model that learns how to best combine the predictions of the base models. The MM is trained on the predictions made by BM on out-of-sample data. Data that was not utilized for training the BM is given to them; predictions are formed; these predictions, along with the expected outcomes, constitute the input and output sets of the training dataset required to fit the MM. In the case of regression, the results of the BM given as input to the MM may be actual values, and in the case of classification, they may be probability values and class names. 4 Experiments & results PulDi-COVID uses CNN to retrieve features from CXI and COVID-19 to classify chronic pulmonary illnesses using an ensemble of eight distinct DL classifiers(based on the identical input size, i.e., 224x224). The TL models were trained for a total of 150 epochs. The stacking approach was used to modify the hyperparameters of the DL classifiers. Individual models and the proposed SSE classifier's performance for each class label were evaluated using the confusion matrix, ROC curve, precision, recall, F1-score, accuracy, error-rate, AUC-ROC-score, and zero-one loss and test time per input image. 4.1 Simulation Requirements. The experiment was run-on HPC. The platform cast for the simulation experiment was an [email protected] GHz, 64.0 GB RAM, and a 48 TB hard disk with 16 child nodes and one central node. CentOS6, Python3.8, Tensorflow, Keras, Jupyter-notebook, matplotlib, numpy, and pandas are employed for conducting tests. It provides an optimized runtime for DL research and high-end computation access to a dependable GPU. 4.2 Experimental setup Experiments have been carried out in two-phase TL training and an SSE scheme. In the first stage, the individual DL models were tested using CXI datasets from the CXR-lung-disease [2], viral and bacterial pneumonia dataset [3], and COVID-19 dataset [4]. The input CXI were first set to 224 × 224 utilizing VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, and DenseNet169. From Table 3 , training for each network with target epochs is set to 150. Based on early-stoppage and callback to escape overfitting and constant performance [52] (if there is no improvement at the training and validation phase). The parameters applied to early stopping and callback are provided in Table 3. Initial ‘l_r’ assigned as ‘0.0001′. The epoch/iteration size will be decided automatically. Training operation of every DCNNs will be stopped robotically based on Table 3 criteria. The 'adam' optimizer was applied for training, and the ‘l_r’ was well-ordered internally. Whereas 64 batch size for train and val set and 32 for the test set. Lastly, 9-class expectation outcomes for each dl-BMs from the 'softmax' layer (Table 4 ). With the help of early stopping. Eventually, individual DL models experimental performance accumulated.Table 3 Early-stopping & callback hyperparamters. Sr. No. Parameters Value 1 Input Size 224x224 2 Target epoch 150 3 Patience. 10 4 Mindelta 0.0001 5 Verbose 1 6 Learning rate 0.0001 7 Mode “auto” 8 Monitor “val_loss” 9 Baseline “none” 10 min_lr 0.000001 11 Restoreweights “true” 12 Batch_size 64 13 Optimizer ‘adam’ 14 Dynamic_l_r ‘auto’ Table 4 Obtained architectural details and results of individual BM-CNNs. Model Trainable parameters Training Time(Hrs) Macro avg. Precision Macro avg. Recall Macro avg. F1-Score Macro avg. Accuracy Zero-one Loss (Out of 900 test samples) Error Rate (%) VGG16 225,801 18.54 77.10 77.11 76.71 94.91 206 22.88 ResNet50 903,177 26.10 44.72 44.44 41.31 87.65 500 55.55 VGG19 225,801 19.27 66.98 66.78 66.10 92.61 299 33.22 DenseNet201 846,729 31.42 88.11 86.44 85.92 96.98 122 13.55 MobileNetV2 564,489 25.27 92.15 92.00 91.97 98.22 72 8 NASNetMobile 465,705 23.36 65.92 57.33 52.89 90.51 384 42.66 ResNet152V2 903,177 28.53 93.46 93.22 93.15 98.49 61 6.77 DenseNet169 733,833 24.49 89.66 89.00 88.92 97.55 99 11 Note: Bold values are optimal results. In the second phase, we have composed the proposed stacking ensemble model (i.e., PulDi-COVID) to evaluate nine-class classification. The possible thirteen SSE models are prepared using received weights from the base learners. Ensemble models benefit from aggregating relevant data from multiple classification techniques to create a highly reliable model. Variance and bias are also minimized, resulting in a more minor anticipated error. Furthermore, a feature vector block that was wrongly learned by classification can still be successfully categorized by leveraging the pattern acquired by other classifiers, which the ensemble model exploits. Because of these properties, ensemble models are an excellent choice for tackling difficult classification and regression tasks [21]. All received base-learners weights files are united based on two to eight individual TL models to create a selective stacked ensemble model. Then we estimated the class probability of PulDi-COVID by the maximum of the stacked class probabilities of all models. Next, we identified the class by providing the class id with the highest-class possibility for every input image. We have executed these networks using TensorFlow and Keras. 4.3 Performance metrics The parameters employed in this article to evaluate the performance of PulDi-COVID were accuracy(Acc.), precision(Pre.), recall(Rec.), specificity(Spe.), F1score, zero-one loss(z), and error rate(e). Criteria for assessment were obtained from the confusion matrix concerning the CNNs' classification task as follows: (a) Positive instances that were properly recognized (TP), (b) Negative cases that were wrongly categorized (FN), (c) Negative cases that were correctly identified (TN), and (d) Positive cases that were misclassified (FP). Fig. 3 depicts the confusion matrix of all thirteen SSE ensembled models. The formulae used to calculate the values of these metrics are listed below:1. Acc. = (TP + TN)/(TP + FP + TN + FN) 2. Pre. = TP/(TP + FP) 3. Rec. = TP/(TP + FN) 4. Spe. = TN/(FP + TN) 5. F1-score = 2 × (Pre. × Rec.)/(Pre. + Rec.) 6. e =∑i=0n(predictedn≠truen)∑i=onall-clases(test-samplesn)// sum (not-equal (pred, true)) / sum(all-classes(total-test-samples)) 7. z(i, j)=∑c=1n(zerosn-onesn)0(zeros)i=j1(ones)i≠ji,j∊class-label Fig. 3 Confusion matrix attained for all SSE models at the test time. 4.4 Results Table 4, Table 5, Table 6 summarize the comprehensive classification outcomes achieved across all models regarding various metrics. The assessment methods mentioned in Table 4, Table 5, Table 6 were the more commonly used to measure classification efficiency. The performance for all individual DL networks has been shown in Table 4; we processed the PulDi-COVID performance for the separate classes in Table 5. The process is repeated for thirteen probable ensemble models. It can be observed that the ResNet152 attained the maximum performance followed by MobileNetV2 from individual CNNs (Table 4). ResNet152V2 reached the highest accuracy of 98.49 %, the precision of 93.46 %, recall of 93.22 %, F1-score of 93.15 %, zero-one loss of 61, and the lowest error rate of 6.77 % among 8 CNNs. From Table 5, chronic pulmonary diseases with nine-class classification the highest attained accuracy of 99.77 % for cardiomegaly, and pneumothorax class, 99.66 % for atelectasis and effusion class, 99.55 % for infiltration class by proposed ensemble model; for atelectasis, cardiomegaly, effusion, infiltration, and pneumothorax highest attained precision of 98.98 %, 98.03 %, 100 %, 100 % and 99 % respectively; and recall of 99 %, 100 %, 99 %, 97 %, and 100 % respectively; specificity of 99.87 %, 99.75 %, 100 %, 100 %, and 99.87 % respectively; F1-score of 98.49 %, 99.01 %, 98.47 %, 97.98 %, and 99 % respectively; and AUC of 99.31 %, 99.87 %, 99.31 %, 98.43 %, and 99.75 % respectively. However, for the comparison of pneumonia and COVID-19, the bacterial pneumonia class attained the uppermost 100 % performance for all metrics (this class may lead to overfit). However, the accuracy of 99.77 %, precision of 99 %, recall of 99 %, specificity of 99.87 %, F1-score of 99 %, and AUC of 99.43 % were attained by Covid19 and no-finding classes. For viral pneumonia class, achieved an accuracy of 99.77 %, precision of 100 %, recall of 100 %, specificity of 100 %, F1-score of 99 %, and AUC of 99.56 %.Table 5 PulDi-COVID MM-classification performance assessment based on each class label. Ensemble Model Metrics Atelecta-sis Bacterial pneumonia Cardio-megaly Covid19 Effusion Infiltrat-ion No-Finding Pneumo-thorax Viral Pneumonia VGG16 + VGG19 + DenseNet201 Accuracy (%) 95.66 97.66 95.22 98.55 96 94.44 99.33 97 97 Precision (%) 80.19 98.76 70.8 89.18 86.36 89.06 95.19 86.13 82.3 Recall (%) 81 80 97 99 76 57 99 87 93 Specificity (%) 97.5 99.87 95 98.5 98.5 99.12 99.37 98.25 97.5 F1-score (%) 80.59 88.39 81.85 93.83 80.85 69.51 97.05 86.56 87.32 AUC (%) 89.25 89.93 96 98.75 87.25 78.06 99.18 92.62 95.25 VGG16 + VGG19 Accuracy (%) 92.55 96.11 93.11 97.88 93.11 90.22 98.55 93.44 94.55 Precision (%) 69.87 90.12 64.39 85.84 70.21 56.38 89.18 73.56 74.28 Recall (%) 58 73 85 97 66 53 99 64 78 Specificity (%) 96.87 99 94.12 98 96.5 94.87 98.5 97.12 96.62 F1-score (%) 63.38 80.66 73.27 91.08 68.04 54.63 93.83 68.44 76.09 AUC (%) 77.43 86 89.56 97.5 81.25 73.93 98.75 80.56 87.31 VGG16 + DenseNet201 Accuracy (%) 96.22 98.66 95.66 98.77 96.55 94.77 99.66 97.44 98.44 Precision (%) 80 100 72.93 90.82 89.65 98.18 98.01 85.98 89.09 Recall (%) 88 88 97 99 78 54 99 92 98 Specificity (%) 97.25 100 95.5 98.75 98.87 99.87 99.75 98.12 98.5 F1-score (%) 83.81 93.61 83.26 94.73 83.42 69.67 98.5 88.88 93.33 AUC (%) 92.62 94 96.25 98.87 88.43 76.93 99.37 95.06 98.25 VGG19 + DenseNet201 Accuracy (%) 95.66 97.77 95.55 98.66 96.33 94.11 99.66 97.11 97.55 Precision (%) 77.47 100 72.05 90 90.36 92.72 98.01 84.9 83.05 Recall (%) 86 80 98 99 75 51 99 90 98 Specificity (%) 96.87 100 95.25 98.62 99 99.5 99.75 98 97.5 F1-score (%) 81.51 88.88 83.05 94.28 81.96 65.8 98.5 87.37 89.9 AUC (%) 91.43 90 96.62 98.81 87 75.25 99.37 94 97.75 VGG16 + VGG19 + DenseNet201 + ResNet50 Accuracy (%) 95.55 97.11 94.88 98.44 96.11 94.11 99.33 96.44 96.44 Precision (%) 77.77 96.25 69.28 88.39 90.12 87.3 95.19 84.69 79.82 Recall (%) 84 77 97 99 73 55 99 83 91 Specificity (%) 97 99.62 94.62 98.37 99 99 99.37 98.12 97.12 F1-score (%) 80.76 85.55 80.83 93.39 80.66 67.48 97.05 83.83 85.04 AUC (%) 90.5 88.31 95.81 98.68 86 77 99.18 90.56 94.06 VGG16 + VGG19 + DenseNet201 + ResNet50 + MobileNetV2 Accuracy (%) 97.77 97.88 97.44 99.22 97.88 96 99.66 98.44 97.66 Precision (%) 87.03 98.79 81.81 94.28 92.63 94.44 98.01 93 84.34 Recall (%) 94 82 99 99 88 68 99 93 97 Specificity (%) 98.25 99.87 97.25 99.25 99.125 99.5 99.75 99.125 97.75 F1-score (%) 90.38 89.61 89.59 96.58 90.25 79.07 98.5 93 90.23 AUC (%) 96.12 90.93 98.12 99.12 93.56 83.75 99.37 96.06 97.37 DenseNet201 + MobileNetV2 Accuracy (%) 98.33 99.11 98.66 99.33 98.66 97.66 99.77 99 99.22 Precision (%) 88.99 100 89.28 95.19 95.83 98.76 99 95.95 93.45 Recall (%) 97 92 100 99 92 80 99 95 100 Specificity (%) 98.5 100 98.5 99.37 99.5 99.87 99.87 99.5 99.12 F1-score (%) 92.82 95.83 94.34 97.05 93.87 88.39 99 95.47 96.61 AUC (%) 97.75 96 99.25 99.18 95.75 89.93 99.43 97.25 99.56 VGG16 + VGG19 + DenseNet201 + ResNet50 + MobileNetV2 + NASNetMobile Accuracy (%) 97.66 97.88 97.55 99.22 97.33 95.33 99.66 96.77 97.66 Precision (%) 89.89 98.79 82.5 94.28 93.18 95.31 98.01 78.4 84.34 Recall (%) 89 82 99 99 82 61 99 98 97 Specificity (%) 98.75 99.87 97.37 99.25 99.25 99.62 99.75 96.62 97.75 F1-score (%) 89.44 89.61 90 96.58 87.23 74.39 98.5 87.11 90.23 AUC (%) 93.87 90.93 98.18 99.12 90.62 80.31 99.37 97.31 97.37 VGG16 + VGG19 + DenseNet201 + ResNet50 + MobileNetV2 + NASNetMobile + ResNet152V2 + DenseNet169 Accuracy (%) 98.88 99.44 98.88 99.44 98.88 98 99.55 98.66 99.11 Precision (%) 95.91 100 90.9 96.11 98.91 97.67 97.05 89.28 95.09 Recall (%) 94 95 100 99 91 84 99 100 97 Specificity (%) 99.5 100 98.75 99.5 99.87 99.75 99.62 98.5 99.37 F1-score (%) 94.94 97.43 95.23 97.53 94.79 90.32 98.02 94.34 96.04 AUC (%) 96.75 97.5 99.37 99.25 95.43 91.87 99.31 99.25 98.18 ResNet152V2 + DenseNet169 Accuracy (%) 99.11 99.66 99.44 99.77 99.66 99.33 99.22 99 99 Precision (%) 97.91 98.01 95.23 99 100 98.95 94.28 92.52 98.92 Recall (%) 94 99 100 99 97 95 99 99 92 Specificity (%) 99.75 99.75 99.37 99.87 100 99.87 99.25 99 99.87 F1-score (%) 95.91 98.5 97.56 99 98.47 96.93 96.58 95.65 95.33 AUC (%) 96.87 99.37 99.68 99.43 98.5 97.43 99.12 99 95.93 DenseNet201 + MobileNetV2 + ResNet152V2 + DenseNet169 Accuracy (%) 99.55 99.88 99.55 99.77 99.44 99.55 99.77 99.77 99.77 Precision (%) 97.05 100 96.15 99 98.96 98.97 99 99 99 Recall (%) 99 99 100 99 96 97 99 99 99 Specificity (%) 99.62 100 99.5 99.87 99.87 99.87 99.87 99.87 99.87 F1-score (%) 98.02 99.49 98.03 99 97.46 97.98 99 99 99 AUC (%) 99.31 99.5 99.75 99.43 97.93 98.43 99.43 99.43 99.43 MobileNetV2 + ResNet152V2 + DenseNet169 Accuracy (%) 99.66 100 99.77 99.77 99.55 99.55 99.66 99.55 99.77 Precision (%) 98.98 100 98.03 99 98.97 98.97 98.01 96.15 100 Recall (%) 98 100 100 99 97 97 99 100 98 Specificity (%) 99.87 100 99.75 99.87 99.87 99.87 99.75 99.5 100 F1-score (%) 98.49 100 99.01 99 97.98 97.98 98.5 98.03 98.99 AUC (%) 98.93 100 99.87 99.43 98.43 98.43 99.37 99.75 99 MobileNetV2 + ResNet152V2 Accuracy (%) 99.66 99.66 99.22 99.66 99.55 99.44 99.66 99.44 99.44 Precision (%) 98.98 98.01 93.45 98.98 97.05 100 98.01 97.97 98.96 Recall (%) 98 99 100 98 99 95 99 97 96 Specificity (%) 99.87 99.75 99.12 99.87 99.62 100 99.75 99.75 99.87 F1-score (%) 98.49 98.5 96.61 98.49 98.02 97.43 98.5 97.48 97.46 AUC (%) 98.93 99.37 99.56 98.93 99.31 97.5 99.37 98.37 97.93 Table 6 Macro average(overall) results obtained on ensembling models. Ensemble Model Macro avg. Precision (%) Macro avg. Recall (%) Macro avg. F1-score (%) Macro avg. Accuracy (%) Zero-one Loss (Out of 900 test samples) AUC-ROC-Score (%) Error Rate (%) Test Time VGG16 + VGG19 + DenseNet201 86.45 85.44 85.11 96.76 131 91.81 14.55 4.130 s VGG16 + VGG19 74.87 74.78 74.39 94.39 227 85.81 25.22 0.569 s VGG16 + DenseNet201 89.41 88.11 87.69 97.35 107 93.31 11.88 3.518 s VGG19 + DenseNet201 87.62 86.22 85.70 96.93 124 92.24 13.77 3.458 s VGG16 + VGG19 + DenseNet201 + ResNet50 85.43 84.22 83.85 96.49 142 91.12 15.77 4.560 s VGG16 + VGG19 + DenseNet201 + ResNet50 + MobileNetV2 91.60 91.00 90.81 98 81 94.93 9 11.203 s DenseNet201 + MobileNetV2 95.17 94.89 94.83 98.86 46 97.12 5.11 3.918 s VGG16 + VGG19 + DenseNet201 + ResNet50 + MobileNetV2 + NASNetMobile 90.53 89.56 89.24 97.67 94 94.12 10.44 9.923 s VGG16 + VGG19 + DenseNet201 + ResNet50 + MobileNetV2 + NASNetMobile + ResNet152V2 + DenseNet169 95.66 95.44 95.41 98.98 41 97.43 4.55 18.254 s ResNet152V2 + DenseNet169 97.21 97.11 97.11 99.35 26 98.37 2.88 5.400 s DenseNet201 + MobileNetV2 + ResNet152V2 + DenseNet169 98.57 98.56 98.56 99.67 13 99.18 1.44 11.087 s MobileNetV2 + ResNet152V2 + DenseNet169 98.68 98.67 98.67 99.70 12 99.24 1.33 8.590 s MobileNetV2 + ResNet152V2 97.94 97.89 97.89 99.53 19 98.81 2.11 3.515 s TF: Bold values are observed as optimal results. Table 6 shows the overall(average) outcomes found for thirteen ensembling possibilities, comprising zero-one loss (all nine-class classification), AUCscore, e-rate, and testing time per CXI, etc. Among all thirteen ensembled, MobileNetV2 + ResNet152V2 + DenseNet169 achieved the overall highest outcomes. Proposed PulDi-COVID (MobileNetV2 + ResNet152V2 + DenseNet169) attained highest overall accuracy of 99.70 %, precision of 98.68 %, recall of 98.67 %, F1-score of 98.67 %, minimum zero-one loss to 12, and lowest error rate of 1.33 % among thirteen SSE. Even after ensembling the worst performance targeted by VGG16 + VGG19 with 227 samples of misclassification for nine class classifications. However, 94.39 % accuracy was attained by the VGG16 + VGG19 model but attained the lowest precision of 74.87 %, recall of 74.78 %, F1-score of 74.39 %, and the error rate of 25.22 %(which is the highest and it's not recommended). For testing individual images, 0.569 s were taken by VGG16 + VGG19 and 8.590 s taken by MobileNetV2 + ResNet152V2 + DenseNet169 models. Ensembling of (VGG16 + VGG19 + DenseNet201), (VGG16 + VGG19), (VGG16 + DenseNet201), (VGG19 + DenseNet201), (VGG16 + VGG19 + DenseNet201 + ResNet50), (VGG16 + VGG19 + DenseNet201 + ResNet50 + MobileNetV2) are used to forecast the significant misclassified instances, as shown in Fig. 3. The misconception was most likely caused by the comparable imaging findings of the disease cases. The ROC curves of all thirteen models are shown in Fig. 4 . ROC is a 2-D chart that compares the TPR as opposed to the FPRThe ROC curve illustrates the sensitivity and specificity. TPR on the y-axis and FPR on the x-axis are used to plot the ROCs. Higher AUC scores are important in medical diagnoses. As a result, its simulations in medical analytics aid data analysts in their diagnostic investigation predicting analysis. From Fig. 4, the highest ROCAUCscore of 99.24 % for all lung diseases attained by MobileNetV2 + ResNet152V2 + DenseNet169, followed by 99.18 % of DenseNet201 + MobileNetV2 + ResNet152V2 + DenseNet169.Fig. 4 ROC curves obtained for all ensembling models at the testing phase. 4.5 Comparing PulDi-COVID with cutting-edge systems Table 7 compares the results of PulDi-COVID with several other current studies for the automated identification of COVID-19. The methods suggested in [37], [39], [40], [44], [46] obtained accuracy of 91.40 %, 90 %, 92.18 %, 87.02 %, and 80.60 %, respectively. However, their utilized CXI is small[37], [39], [42], [43], [46] and applied for three-class classification only. The systems shown in [36], [41], [43] have enhanced the accuracy to 99.4 %, 96.10 %, and 98.30 %, respectively. The methods suggested in [48] show a COVID-19 detection accuracy of 99.05 %. PulDi-COVID beat all of these classifiers in terms of accuracy. PulDi-COVID obtained 99.70 % accuracy, indicating that it could be a useful tool for early diagnosis and nine-class classification for lung disease detection using CXIs. However, due to the multiheaded ensembles, the MobileNetV2 + ResNet152V2 + DenseNet169 model takes 8.59 s to test individual CXI.Table 7 Performance comparison of PulDi-COVID classifier based on an already developed system. Reference Model Class with sample size Performance Test Time/ Image(S) Zabirul [36] CNN-LSTM Covid19:613, Pneumonia:1252, Normal:1252. Accuracy:99.4 %, AUC:99.9 %, Specificity:99.2 %, Sensitivity:99.3 %, F1-score:98.9 %. 113 s Rahimzadeh et al. [37] Xception + ResNet50V2 Covid19:80, Pneumonia:6054, Normal:8851. Accuracy:91.4 %. – Alqudah et al. [38] AOCT-NET Covid19, Normal. Accuracy:95.2 %. 6.3 s Hemdan et al. [39] COVIDX-Net Covid19:25, Normal:25. Accuracy: 90.0 %. 4.0 s Asnaoui et al. [40] IncpetionRes NetV2 Bacterial: 2780, Virus:1493, Covid19:231, Normal:1583. Accuracy: 92.18 %, Sensitivity:92.11 %, Specificity: 96.06 %, Precision:92.38 %, F1-score: 92.07 %. 262 s Wang et al. [41] ResNet-101 + ResNet-152 Covid19:140, Pneumonia:8620, Normal:7966. Accuracy: 96.1 %. – Narin et al. [42] ResNet-50 Covid19:50, Normal:50. Accuracy:98.00 %. – Ucar and Korkmaz [43] SqueezeNet- Bayes Covid19:76, Normal:1583, Pneumonia: 4290. Accuracy: 98.30 %. – Ozturk et al. [44] DarkCovidNet Covid19:125, Normal:500, Pneumonia:500. Accuracy:87.02 %. – Mishra et al. [45] CovAI-Net Covid + Ve:369, Covid -Ve:309. Precision: 100 %, Sensitivity:96.74 %, Specificity:100 %. – Loey et al. [46] GoogleNet Covid19:69, Normal:79, Bacterial:79, virus:79. Accuracy:80.6 %. – Tang et al. [47] EDL-COVID Covid19:573, Pneumonia:6053, Normal:8851. Accuracy:95 %, Sensitivity:96 %, PPV:94.1 %. 450 s/100 image Apx. Zhou et al. [48] EDL_COVID Covid19:2500, Lung tumors:2500, Normal:2500. Accuracy:99.05 %, Specificity:99.6 %, F1-score:98.59 %. 2251 s Ilhan et al. [54] Deep Feature Fusion Covid19:125, Pneumonia:500, Normal:500. Accuracy:90.84 %, Precision:100 %, Recall:97.6 %. – Proposed PulDi-COVID (SSE) Atelectasis, Bacterial Pneumonia, Covid19, Cardiomegaly, Effusion, Infiltration, No-Finding, Pneumothorax, Viral Pneumonia. (1200 cxi/class = 10800 images). Accuracy:99.70 %, Specificity:99.91 %, Precision:98.68 %, Recall:98.67 %, F1-score:98.67 %, AUC-ROC-score:99.24 %, Error rate:1.33 %, Zero-one loss:12. 8.59 s 5 Discussion Despite the availability of datasets on online public platforms, the research of CXI for the accurate assessment of COVID-19 infection has attracted a lot of interest. Following that, various efforts were made to create an exact diagnostic model employing DL approaches. The notion of TL has been widely applied in CNNs. However, most of the older approaches were assessed using minimal data. The studies [37], [39], [42], [43], [46] attained remarkable performance but used a very tiny sample size for the COVID-19 class. Furthermore, in certain circumstances, the data is skewed. Grad-CAM graphical representations are also used to confirm the accuracy of the outcomes. Fig. 6 depicts the infographics that are equated to the predicted results/ class. The CAD's recommendations were examined by analyzing the binding of thorax illnesses as depicted in obtained heatmaps (Fig. 5 ) depict several examples of GradCAM mapping of image data from the set of data. A feature vector histogram is overlaid on the actual picture to show how the embedding design recognizes it and sheds more light on specific areas of the pixel. The framework pays more emphasis to the location outlined in orange-red (ROI), while the section featured in light-blue receives less recognition. This aesthetic depiction of focus engages end-users in identifying or confirming ROI where symptoms exist and can be confined.Fig. 5 Training-Validation accuracy and loss plots of each DCNNs. Fig. 6 Grad-CAM Visualisation of thoracic abnormalities with heatmaps (highlighting essential regions for the model prediction and its source CXRs.). In the first phase, we systematically assessed the eight most popular DL models' effectiveness: VGG16, ResNet50, VGG19, DenseNet201, MobileNetV2, NASNetMobile, ResNet152V2, DenseNet169 for the prediction of chronic pulmonary diseases with COVID-19 infections from CXI. Extensive tests were carried out on a rather big dataset, considering a variety of criteria to establish the best functioning model for automatic disease diagnosis. The CXI of the various lung disorders, COVID-19, pneumonia, and normals, were obtained from three different sources [2], [3], [4]. To address the issue of data imbalance, an equal size of samples was chosen for all classes. Experimented findings and extensive comparative analysis of all approaches revealed that the PulDi-COVID model outperformed eight models and state-of-the-art methods. This research aims to find the possible biomarkers from chronic lung disease with COVID-19 to minimize the mortality rates and provide assistance to healthcare staff. Also, reduce the error rate to enhance the accuracy. From the obtained experimented results of individual transfer learning, it has been observed that it attained the lowest error rate by ResNet152V2 of 6.77 %(61 misclassified CXI) and proposed PulDi-COVID(MobileNetV2 + ResNet152V2 + DenseNet169) by 1.33 %(12 misclassified CXI) for nine class classification. This difference error rate of 5.44 %(49 misclassified CXI) shows that the proposed PulDi-COVID model is robust and efficient for chronic pulmonary diseases with COVID-19 cases detection in this pandemic era using a developed GUI application (Fig. 7 ). Limitation of ensemble model deals with model overfitting as seen for bacterial pneumonia class (shows 100 % performance for all metrics. Further research will look at radiography pictures to discover COVID-19 variations such as Beta, Delta, Omicron, and IHU[49].Fig. 7 Deployed GUI Web-application for Pulmonary(Lung) disease detection and classification with COVID-19. 6 Conclusion COVID-19 has substantially detrimental influences on our daily lives, extending from public healthcare services to the entire economic system. This study introduced a PulDi-COVID model that uses CXI to diagnose chronic pulmonary disease with COVID-19. PulDi-COVID, the suggested model, effectively delivers accurate diagnoses for 9 class classifications (atelectasis, bacterial-pneumonia, cardiomegaly, covid19, effusion, infiltration, no-finding, pneumothorax, viral-pneumonia). The suggested framework has a classification accuracy of 99.70 %, a precision of 98.68 %, recall of 98.67 %, F1-score of 98.67 %, a minimum zero-one loss of 12, and the lowest error rate of 1.33 %, which is the maximum attained accuracy on the datasets utilized in the experimentations to the best of knowledge. Another addition to the study is the compilation of the largest dataset for the assessment of classification methods. In terms of accuracy and other metrics, the effectiveness of PulDi-COVID is proven to be superior to 14 current approaches. The result of our suggested technique demonstrates its improvement over previous methods. Our SSE model's empirical explanation is offered. The model's outcomes were described, and physicians may adopt it in the future. Our long-term objective is to combine COVID-19 cases with 14 illness classifications from the NIH [19] dataset (Chest X-ray Dataset of 14 Common Thorax Diseases). We also utilized large datasets to train our proposed PulDi-COVID approach and evaluate its efficiency with a broader range of current techniques. We hope that the purpose of this proposed dl-based model can benefit healthcare workers in detecting pulmonary diseases with COVID-19 to minimize severity and deaths. Also, this research with the deployment of a web application will be helpful to radiology assistance in spotting COVID19 and pulmonary illnesses. Future studies will identify different COVID19 variants using multimodal radiography imaging. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Data availability. No datasets were generated during the current study. The datasets analyzed during this work are made publicly available in this published article. Compliance with Ethical Standards.• No funding received for this work. • All methods in papers including individual subjects were carried out in line with the institutions and/or national scientific board's ethical principles, as well as the 1964 Helsinki statement and its subsequent revisions or equivalent ethical criteria. • We also affirm that any component of the study reported in this publication that involves human subjects was carried out with the appropriate permission of all applicable entities, and that such authorization is acknowledged in the article. Data availability The authors do not have permission to share data. ==== Refs References 1 Coronavirus Disease 2019. [Online]. Available: https://en.wikipedia.org/wiki/Coronavirus_disease_2019 . 2 Summers, Ronald (Lung Disease X-Ray Dataset). CXR8 - National Institutes of Health - Clinical Center. Accessed On 12 September 2021. Online Available https://nihcc.app.box.com/v/ChestXray-NIHCC/folder/37178474737. 3 Paul Mooney (Chest X-Ray Pneumonia Dataset). Accessed On 12 September 2021. [Online]. Available: https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia. 4 SIIM-FISABIO-RSNA COVID-19 Detection Challenge. Accessed On 12 September 2021. [Online]. Available: https://www.kaggle.com/c/siim-covid19-detection. 5 Who coronavirus disease (covid-19) dashboard, (https://covid19.who.int/). Accessed On 09 January 2022. 6 Albawi S. Mohammed T.A. Al-Zawi S. Understanding of a convolutional neural network Proc. Int. Conf 2017 Eng. Technol 1 6 7 Cao Z. Liao T. Song W. Chen Z. Li C. Detecting the shuttlecock for a badminton robot: A YOLO based approach Expert Systems with Applications 164 2021 113833 8 Ma C. Liu Z. Cao Z. Song W. Zhang J. Zeng W. Cost-sensitive deep forest for price prediction Pattern Recognition 107 2020 107499 9 S. Chen, E. Dobriban, and J. H. Lee, “Invariance reduces variance: Understanding data augmentation in deep learning and beyond,” 2019, arXiv:1907.10905. 10 D. Jakubovitz, R. Giryes, and M. R. Rodrigues, “Generalization error in deep learning,” in Compressed Sensing and Its Applications, Springer, 2019, pp. 153–193. 11 Polikar R. “Ensemble learning,” in Ensemble Machine Learning Zhang C. Ma Y. Ensemble Machine Learning 2012 Springer US Boston, MA 1 34 12 Chowdhury M.E.H. Rahman T. Khandakar A. Mazhar R. Kadir M.A. Mahbub Z.B. Islam K.R. Khan M.S. Iqbal A. Emadi N.A. Reaz M.B.I. Islam M.T. Can AI Help in Screening Viral and COVID-19 Pneumonia? IEEE Access 8 2020 132665 132676 13 Svetnik V. Wang T. Tong C. Liaw A. Sheridan R.P. Song Q. “Boosting: An ensemble learning tool for compound classification and QSAR modeling J. Chem. Inf. Model. 45 3 2005 786 799 15921468 14 F. Divina, A. Gilson, F. Goméz-Vela, M. García Torres, and J. F. Torres, “Stacking ensemble learning for short-term electricity consumption forecasting,” Energies, vol. 11, no. 4, 2018, Art. No. 949. 15 H. Wong et al., “Frequency and distribution of chest radiographic findings in COVID-19 positive patients”, Radiology, 2020. 16 Li Y. Yao L. Li J. Chen L. Song Y. Cai Z. Yang C. Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19 J. Med. Virol. 92 7 2020 903 908 32219885 17 Ensemble techniques in python. Accessed on: 15 December 2021. [Online]. Available: https://www.geeksforgeeks.org/ensemble-methods-in-python/. 18 Stacked Ensemble technique. Accessed on: 15 December 2021. [Online]. Available: https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/. 19 NIH Chest X-ray Dataset of 14 Common Thorax Disease Categories. Accessed On 2 December 2021. [Online]. Available: https://nihcc.app.box.com/v/ChestXray-NIHCC. 20 G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, and K. Q. Weinberger, “Snapshot ensembles: Train 1, get M for free,” 2017, arXiv:1704.00109. 21 Geron A. In: Hands-on machine learning with scikit-learn, keras, and TensorFlow second ed. 2019 O'Reilly Media, Inc 22 Ledezma C.A. Zhou X. Rodriguez B. Tan P.J. Diaz-Zuccarini V. A modeling and machine learning approach to ECG feature engineering for the detection of ischemia using pseudo-ECG PLoS ONE 14 8 2019 PMC6690680 https://doi:10.1371/journal.pone.0220294 23 Semenzato L. Botton J. Drouin J. Cuenot F. Dray-Spira R. Weill A. Zureik M. Chronic diseases, health conditions and risk of COVID-19-related hospitalization and in-hospital mortality during the first wave of the epidemic in France: a cohort study of 66 million people The Lancet Regional Health - Europe 8 2021 100158 34308411 24 Geng J. Yu X. Bao H. Feng Z. Yuan X. Zhang J. Chen X. Chen Y. Li C. Yu H. Chronic Diseases as a Predictor for Severity and Mortality of COVID-19: A Systematic Review with Cumulative Meta-Analysis Front. Med. 8 2021 588013 10.3389/fmed.2021.588013 25 H. S. Maghdid, A. T. Asaad, K. Z. Ghafoor, A. S. Sadiq, and M. K. Khan, “Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms,” 2020, arXiv:2004.00038. 26 Wang L. Lin Z.Q. Wong A. COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images Sci Rep 10 1 2020 27 Abdel-Basset M. Chang V. Hawash H. Chakrabortty R.K. Ryan M. FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection Knowledge-Based Systems 212 2021 106647 33519100 28 Hall L.O., Paul R., Goldgof D.B., Goldgof G.M.. Finding covid-19 from chest x-rays using deep learning on a small dataset. arXiv preprint arXiv:2004020602020. 29 Singh G.A.P. Gupta P. Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans Neural Computing Applications 31 10 2019 6863 6877 30 Klang E. Deep learning and medical imaging J Thorac Dis 10 3 2018 1325 1328 29708147 31 Shan F., Gao Y., Wang J., Shi W., Shi N., Han M., et al. Lung infection quantification of covid-19 in ct images with deep learning. arXiv preprint arXiv:2003046552020. 32 Liu J. Pan Y. Li M. Chen Z. Tang L. Lu C. Applications of deep learning to mri images: asurvey Big Data Min Anal 1 1 2018 1 18 33 Rajaraman S. Siegelman J. Alderson P.O. Folio L.S. Folio L.R. Antani S.K. Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-Rays IEEE Access 8 2020 115041 115050 32742893 34 American Lung Association. Accessed On 12 September 2021. [Online]. Available: https://www.lung.org/lung-health-diseases/lung-disease-lookup/covid-19/chronic-lung-diseases-and-covid. 35 Aveyard P. Gao M. Lindson N. Hartmann-Boyce J. Watkinson P. Young D. Coupland C.A.C. Tan P.S. Clift A.K. Harrison D. Gould D.W. Pavord I.D. Hippisley-Cox J. Association between pre-existing respiratory disease and its treatment, and severe COVID-19: a population cohort study The Lancet Respiratory Medicine 9 8 2021 909 923 33812494 36 Zabirul Islam, Milon Islam,, Amanullah Asraf. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. 37 Rahimzadeh M, Attar A. A new modified deep convolutional neural network for detecting COVID-19 from X-ray images. http://arxiv.org/abs/2004.08052; 2020. 38 Alqudah AM, Qazan S, Alquran HH, Alquran H, Qasmieh IA, Alqudah A. Covid-2019 detection using X-ray images and artificial intelligence hybrid systems. https://doi.org/10.13140/RG.2.2.16077.59362/1 ; 2020. 39 Hemdan EE-D, Shouman MA, Karar ME. COVIDX-net: a framework of deep learning classifiers to diagnose COVID-19 in X-ray images. http://arxiv.org/abs/2003.11055 ; 2020. 40 El Asnaoui K. Chawki Y. Using X-ray images and deep learning for automated detection of coronavirus disease J Biomol Struct Dyn 39 10 2021 3615 3626 32397844 41 Ningwei Wang, Hongzhe Liu*, Cheng Xu. Deep Learning for The Detection of COVID-19 Using Transfer Learning and Model Integration. IEEE Conference (ICEIEC); https://doi.org/10.1109/ICEIEC49280.2020.9152329 ; 2020. 42 Narin A. Kaya C. Pamuk Z. Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks Pattern Anal Applic 24 3 2021 1207 1220 43 F. Ucar, D. Korkmaz, COVIDiagnosis-Net: Deep Bayes-SqueezeNet based diag nosis of the coronavirus disease 2019 (COVID-19) from X-Ray images, Med.Hypotheses (2020) 109761. 44 Ozturk T. Talo M. Yildirim E.A. Baloglu U.B. Yildirim O. Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images Comput. Biol. Med. 121 2020 103792 32568675 45 Mohit Mishra, Varun Parashar, Rushikesh Shimpi. Development and evaluation of an AI System for early detection of Covid-19 pneumonia using X-ray. https://doi.org/10.1109/BigMM50055.2020.00051 . 46 Loey M. Smarandache F. Khalifa N.E.M. Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning Symmetry 12 2020 651 10.3390/SYM12040651. (2020) 651 47 Tang S. Wang C. Nie J. Kumar N. Zhang Y. Xiong Z. Barnawi A. EDL-COVID: Ensemble Deep Learning For COVID-19 Cases Detection From Chest X-ray Iimages. IEEE Transactions On Industrial Info 17 9 Sept. 2021 10.1109/TII.2021.3057683 48 T. Zhou, H.L. Lu, Z. Yang et al. The ensemble deep learning model for novel COVID-19 on CT images. https://doi.org/10.1016/j.asoc.2020.1068851568-4946/ . 49 Covid-19: Omicron less severe than Delta variant, UK studies find. Accessed On 12 September 2021. [Online]. Available: https://www.business-standard.com/article/current-affairs/covid-19-omicron-less-severe-than-delta-variant-uk-studies-find-121122300641_1.html. 50 Tahamtan A. Ardebili A. Real-time RT-PCR in COVID-19 detection: Issues affecting the results Expert Rev. Mol. Diagn. 10 5 2020 453 454 51 Bhosale YH, Patnaik KS. Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review. Neural Process Lett. 2022 Sep 16:1-53. DOI: 10.1007/s11063-022-11023-0. Epub ahead of print. PMID: 36158520; PMCID: PMC9483290. 52 Bhosale Y.H., Patnaik K.S, IoT Deployable Lightweight Deep Learning Application For COVID-19 Detection With Lung Diseases Using RaspberryPi; IEEE-ICIBT-2022, pp. 1-6. 53 Y. H. Bhosale, et al., Deep Convolutional Neural Network Based Covid-19 Classification From Radiology X-Ray Images For IoT Enabled Devices; IEEE-ICACCS-2022, pp. 1398-1402. 54 Ilhan H.O. Serbes G. Aydin N. Decision and feature level fusion of deep features extracted from public COVID-19 data-sets Applied Intelligence 52 8 2022 8551 8571 34764623	36466567	PMC9708623	NO-CC CODE	2022-12-15 23:15:05	no	Biomed Signal Process Control. 2023 Mar 30; 81:104445	utf-8	Biomed Signal Process Control	2,022	10.1016/j.bspc.2022.104445	oa_other
==== Front Hematol Transfus Cell Ther Hematol Transfus Cell Ther Hematology, Transfusion and Cell Therapy 2531-1379 2531-1387 Associação Brasileira de Hematologia, Hemoterapia e Terapia Celular. Published by Elsevier España, S.L.U. S2531-1379(22)01462-6 10.1016/j.htct.2022.11.001 Review Article Correlation between ABO blood type, susceptibility to SARS-CoV-2 infection and COVID-19 disease severity: A systematic review Soares Danyela Martins Bezerra a Araújo David Augusto Batista Sá b de Souza Jorge Luiz de Brito a Maurício Rebeca Bessa a Soares Emanuela Martins Bezerra c Neto Franklin de Castro Alves a Pinheiro Maria Suelly Nogueira b Gama Vitor Carneiro de Vasconcelos d Braga-Neto Pedro a Nóbrega Paulo Ribeiro a⁎ Aragão Gislei Frota a a Universidade Estadual do Ceará (UECE), Fortaleza, CE, Brazil b Universidade Federal do Ceará (UFC), Fortaleza, CE, Brazil c Universidade Federal do Cariri (UFCA), Barbalha, CE, Brazil d Hospital Geral de Fortaleza (HGF), Fortaleza, CE, Brazil ⁎ Corresponding author: R. Pastor Samuel Munguba, 1290, Rodolfo Teófilo, Fortaleza - CE, CEP: 60430-372. Phone: +55(85) 33366-8590. 30 11 2022 30 11 2022 16 8 2022 7 11 2022 © 2022 Associação Brasileira de Hematologia, Hemoterapia e Terapia Celular. Published by Elsevier España, S.L.U. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Objectives To verify the association between the ABO blood type and the risk of SARS-CoV-2 infection and COVID-19 disease severity. Methods This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), using the 2020 PRISMA Checklist and flow diagram, and articles selected for review were analyzed using the Newcastle-Ottawa Quality Rating Scale. The research question was: “Would the ABO blood group influence the risk of infection and clinical course of patients infected with SARS-CoV-2?”, The following databases were used: Embase, PubMed, Virtual Health Library (VHL), Web of Science, ScienceDirect and Scopus. The protocol for this review was registered in the Prospective Register of Systematic Reviews (PROSPERO), number CRD42021245945. Results We found 798 articles across PubMed, Embase, Scopus, Web of Science, Science Direct and Virtual Health Library and 54 articles were included in the final analysis. Among 30 studies evaluating the risk of COVID-19 infection, 21 found significant correlations with ABO blood groups, 14 of them revealing an increased risk in blood group A and 15 studies showing a decreased risk in blood group O. Most studies found no significant correlation with disease severity or mortality. Conclusion The qualitative assessment of available information suggests that blood group A may be a risk factor for COVID-19 infection and that blood group O may have a protective effect. We were unable to determine a clear association between the ABO blood group and mortality. These conclusions are based on highly heterogenous evidence. Keywords COVID-19 SARS-CoV-2 ABO blood group system Antibodies ==== Body pmcIntroduction Coronavirus Disease 19 (COVID-19) is an infectious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which may present with fever, cough and dyspnea or asymptomatic infection in some cases. The SARS-CoV-2 belongs to the Coronaviridae family, being an enveloped RNA virus, which contains Spike (S) proteins on its surface that make it look like a crown (Corona). It has high homology to the coronavirus responsible for the SARS outbreak of 2003, Severe Acute Respiratory Syndrome Coronavirus 1 (SARS-CoV-1) (1). Although most cases present with mild symptoms, the COVID-19 infection may lead to acute respiratory failure and death. This disease has caused worldwide health and economic impact, leading the international community to unite in trying to contain the virus through restrictive measures, research in potential drugs and mass vaccination. Despite these efforts, there is still a large number of new cases of the disease. Thus, it is important to continue searching for risk factors that might be associated with worse outcomes in persons infected by SARS-CoV-2 (1). Individuals with comorbidities and the elderly are susceptible to severe COVID-19 infection, but one of the intriguing aspects of this disease is that healthy young people may sometimes present severe respiratory failure, while many people with comorbidities may be asymptomatic (2). One of the hypotheses to explain susceptibility to SARS-CoV-2 is linked to the ABO blood group system, represented by ABH oligosaccharide antigens A and B and antibodies against these antigens (anti-A and anti-B). These antigens are coded by autosomal codominant genes corresponding to alleles A and B in the ABO locus. The H antigen, coded by FUT1/FUT2/SEC1 genes, is responsible for exhibiting A and B antigens with a α-1,3-glycosidic bond. When the A allele is present, one N-acetyl-D-galactosamine (GalNAc) or, when the B allele is present, a D-galactose (Gal) is transferred to the H antigen (1). These antigens are mainly expressed on the surface of red blood cells, on the surface of endothelial and epithelial cells and in mucins secreted by exocrine glandules. The main objectives of this systematic review were to investigate the association between the ABO blood group and the risk of COVID-19 infection and the association between the ABO blood group and the risk of suffering complications and death related to COVID-19. Materials and Methods This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines through the 2020 PRISMA Checklist and 2020 PRISMA Flow diagram. The protocol for this review was registered in the Prospective Register of Systematic Reviews (PROSPERO) under registration number CRD42021245945. The research question was structured and refined using the PICO method (Population, Intervention/exposure, Comparison and Outcome), presented as follows: “Would the ABO blood group influence the risk of infection and clinical course of patients infected with SARS-CoV-2?”, as detailed in Tables 1 and 2 . Table 1 PICO strategy used to construct the research. Table 1PICO criteria for Objective 1 Description Population Population at risk for SARS-CoV-2 infection (entire population) Intervention (exposure) ABO blood group Comparison Different blood groups by ABO classification system Outcome COVID-19 infection PICO criteria for Objective 2 Description Population Population at risk for SARS-CoV-2 infection (entire population) Intervention (exposure) ABO blood group Comparison Different blood groups by ABO classification system Outcome COVID-19 severe infection, hospitalization, admission to intensive care unit (ICU), need for mechanical ventilation, cardiovascular injury and mortality Table 2 Search strategy of the articles used in this research. Table 2Databases Descriptors utilized Date of Search Number of articles found Embase “blood group ABO system” AND “coronavirus disease 2019” 06/02/2022 256 PubMed “COVID-19” AND “ABO blood-group system” 06/02/2022 132 Science Direct “COVID-19” AND “ABO blood-group system” 06/02/2022 41 Scopus “coronavirus disease 2019” and “blood group ABO system” 06/02/2022 216 Virtual Health Library (VHL) “COVID-19” and “ABO Blood-Group system” 06/02/2022 135 Web of Science “COVID-19” and “ABO blood-group system” 06/02/2022 18 PICO strategy for objectives 1 and 2 are as follows:PICO strategy for objective 1 (COVID-19 infection): P: Population at risk for SARS-CoV-2 infection (entire population); I: ABO blood group; C: Different blood groups by ABO classification system, and; O: COVID-19 infection. PICO strategy for objective 2 (COVID-19 severe infection): P: Population at risk for SARS-CoV-2 infection (entire population); I: ABO blood group; C: Different blood groups by ABO classification system, and; O: COVID-19 severe infection, hospitalization, admission to intensive care unit (ICU), need of mechanical ventilation, cardiovascular injury and mortality. Source of information and search strategy The following databases were used: Embase, PubMed, Virtual Health Library (VHL), Web of Science, ScienceDirect and Scopus. The search strategy was based on paired combinations of the following descriptors, using the Boolean operator “AND”: “COVID-19”, “Coronavirus disease 2019”, “ABO blood-group system” and “blood group ABO system”. Articles in English, Spanish and Portuguese were included. The search was limited to articles published from January 2020 to December 2021. Details on the article collection strategy are in Table 3 .Table 3 Evaluation of included studies by Newcastle-Ottawa Scale (NOS). Table 3Authors (year) Type of study Selection Comparability Outcome RAY et al. (2020) (14) Cohort ** * ZIETZ et al. (2020) (15) Cohort * * SARDU et al. (2020) (13) Cohort * BARNKOB et al. (2020) (16) Cohort * * * MUÑIZ-DIAZ et al. (2020) (5) Cohort * TEVATIA et al. (2020) (17) Cohort ** * NAUFFAL et al. (2021) (18) Cohort * NALBANT et al. (2022) (19) Cohort * AMOROSO et al. (2021) (20) Cohort ** * * SZYMANSKI et al. (2021) (21) Cohort * * * BADEDI et al. (2021) (22) Cohort * * * DE FREITAS DUTRA et al. (2021) (23) Cohort * DOMÈNECH-MONTOLIU et al. (2021) (24) Cohort * * TAMAYO-VELASCO et al. (2021) (25) Cohort ISHAQ et al. (2021) (26) Cohort ** * * KABRAH et al. (2021) (59) Cohort * * * MATZHOLD et al. (2021) (27) Cohort * * MULLINS et al. (2021) (28) Cohort * GRECO et al (2021) (29) Cohort AL‐YOUHA et al (2020) (30) Cohort * FAN et al. (2020) (31) Case control ** * AD'HIAH et al. (2020) (32) Case control * ZHAO et al. (2020) (33) Case control ** * * SAIFY et al. (2020) (34) Case control * WU et al (2020) (35) Case control ** * * KHALIL et al. (2020) (36) Case control BADEDI et al. (2020) (37) Case control * * BEHBOUDI et al. (2021) (38) Case control ** * TORRES- ALARCÓN et al. (2021) (39) Case control ** * KİRİŞCİ et al. (2021) (40) Case control KOLIN et al (2020) (41) Case control ** GAMBOA-AGUILAR et al. (2022) (42) Case control * * LATZ et al. (2020) (43) Cross-sectional LIU et al. (2020) (6) Cross-sectional * HOILAND et al. (2020) (44) Cross-sectional * GÖKER et al. (2020) (45) Cross-sectional * KOTILA et al. (2020) (46) Cross-sectional * ** YAYLACI et al. (2020) (47) Cross-sectional * SOLMAZ et al. (2020) (48) Cross-sectional *** * AL KUWARI et al. (2020) (49) Cross-sectional * ALMADHI et al. (2021) (50) Cross-sectional * AKTIMUR et al. (2020) (51) Cross-sectional * ESREF et al. (2020) (52) Cross-sectional *** * MUÑOZ-CULLA et al. (2021) (53) Cross-sectional * * KHASAYESI et al. (2021) (54) Cross-sectional * ** PASANGHA et al. (2020) (55) Cross-sectional * KOMAL et al. (2021) (56) Cross-sectional ** * * HALIM et al. (2021) (57) Cross-sectional *** * HERMEL et al. (2021) (58) Cross-sectional * Note: Quality assessment using the NOS scale for Cohort and Case Control Studies revealed that all included studies scored from 6 (ranging from 0 to 9 points) and for Cross-sectional Studies, using the adapted NOS, all scored from 5 (ranging from 0 to 7 points) (7). Therefore, all studies had good scientific quality. Outcome variables The following outcome was considered for the first objective (risk of COVID-19 infection):• COVID-19 infection determined by either polymerase chain reaction (PCR), antigen or antibody tests. For the second objective (COVID-19 severity) the following outcomes were considered:• Mortality • Admission to intensive care unit (ICU) • Need for mechanical ventilation Study selection We included studies with humans of any age and of both sexes who manifested symptoms of SARS-CoV-2 infection, confirmed by laboratory tests, and that have reported ABO blood group types. Case–control, cohort and cross-sectional studies were eligible for inclusion. Studies were required to provide data for at least one pre-established outcome variable in order to be eligible for inclusion. We then excluded review articles, short communications, commentaries and editorial and opinion papers. Published studies with coronavirus infected animals, treatment with convalescent plasma, women in a gestational state (due to physiological changes typical of that period) and symptomatic patients without laboratory confirmation were also excluded. Strategy for data extraction First, three reviewers (DMBS, JLBS and RBM) performed a literature search using the descriptors mentioned above. The reviewers then removed the duplicates and all remaining titles and abstracts were evaluated for eligibility. If this was insufficient to determine the eligibility, full-text articles were retrieved. The full texts of the articles that met the inclusion criteria were analyzed separately by six reviewers (DMBS, DABSA, EMBS, FCAN, MSNP and VCVG). Any disagreement among them about the eligibility of certain studies was resolved through discussion with two other reviewers (GFA and PRN). Risk quality assessment and bias report The methodological quality of the studies was assessed by two reviewers and, in the case of a disagreement, another reviewer was asked for further analysis. Data provided for the systematic review were verified using the Newcastle-Ottawa Quality Rating Scale (3) by previously trained and qualified reviewers. The methodological quality scores of the cross-sectional, cohort and case-control studies were calculated in three components: selection of groups (0 - 4 points), quality of fit for confounding factors (0 - 2 points) and assessment of exposure after outcome (0 - 3 points). The maximum score was 7 points for cross-sectional studies and 9 points for cohort and case-control studies (3), which represents high methodological quality. The results of this analysis are contained in Table 4 .Table 4 Studies that show correlation between ABO blood group and COVID infection. Table 4Authors, year, place of study Type of study / Newcastle score Sample size (n) Outcomes of interest Main results SOLMAZ et al., 2020, Turkey. (48) Cross-sectional / 6 128,758 - 1,667 cases* - 127,091 controls2 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients AL KUWARI et al., 2020, Pakistan. (49) Cross-sectional / 7 3,870 - 1,935 cases* - 1,935 controls1 COVID-19 infection - Group B was more prevalent in COVID patients compared to controls, Group AB was less prevalent in patients KOTILA et al., 2020, Nigeria. (46) Cross-sectional / 5 9,440 - 302 cases** - 9,138 controls1 COVID-19 infection - Group B and AB were more prevalent in COVID patients compared to controls, Group O was less prevalent in patients LATZ et al., 2020, USA. (43) Cross-sectional / 6 7,648 - 1,289 cases* - 6,359 controls2 COVID-19 infection - Groups A, B and AB were more prevalent in COVID patients compared to controls, Group O was less prevalent in patients HOILAND et al., 2020, Canada. (44) Cross-sectional / 7 62,341 - 95 cases* - 62,246 controls2 COVID-19 infection - No significant difference GÖKER et al., 2020, Turkey. (45) Cross-sectional / 7 2,068 - 186 cases* - 1,882 controls2 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients AKTIMUR et al., 2020, Turkey. (51) Cross-sectional / 7 5,379 - 179 cases* - 5,200 controls2 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients KOMAL, 2021, Pakistan. (56) Cross-sectional / 4 1,599 - 305 cases - 1,294 controls1 COVID-19 infection - Group AB was more prevalent in COVID patients compared to controls, Group B was less prevalent in patients ALMADHI et al., 2021, Bahrain. (50) Cross-sectional / 7 7,319 - 2,334 cases - 4,985 controls2 COVID-19 infection - Group B was more prevalent in COVID patients compared to controls, Group AB was less prevalent in patients ZHAO et al., 2020, China. (33) Cross sectional / 8 29.253 - 2,173 cases* - 27,080 controls2 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients WU et al., 2020, China. (35) Case control / 6 2,178 - 187 cases* - 1,991 controls2 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients SAIFY et al., 2020, Afghanistan. (34) Case control/ 7 1,340 - 301 cases* - 1,039 controls1 COVID-19 infection - No significant difference AD'HIAH et al., 2020, Iraq. (32) Case control / 9 1,915 - 1,014 cases* - 901 controls1 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls. KİRİŞCİ et al.., 2021, Turkey. (40) Case control / 6 8,299 - 455 cases** - 7,844 controls2 COVID-19 infection - No significant difference FAN et al., 2020, China. (31) Case control / 9 208 - 105 cases* - 103 controls2 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients KHALIL et al., 2020, Lebanon. (36) Case control / 6 6,643 - 146 cases* - 6,497 controls2 COVID-19 infection - No significant difference RAY et al., 2020, Canada. (14) Cohort / 9 225,556 - 7,071 cases* - 218,485 controls2 COVID-19 infection - Group AB was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients. BARNKOB et al., 2020, Denmark. (16) Cohort / 7 2,211,894 - 7,422 cases - 2,204,472 controls2 COVID-19 infection - Groups A, B and AB were more prevalent in COVID patients compared to controls, Group O was less prevalent in patients MUÑIZ-DIAZ et al., 2020, Spain. (5) Cohort / 9 76.724 - 854 cases - 75,870 controls1 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients. NALBANT et al., 2022, Turkey. (19) Cohort / 9 313 - 220 cases* - 93 controls2 COVID-19 infection - No significant difference AMOROSO et al., 2021, Italy. (20) Cohort / 8 40,904 - 219 cases** - 40,685 controls2 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls MUÑOZ-CULLA et al., 2021, Basque. (53) Cohort / 7 18,208 - 412 cases* - 17,796 controls2 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients. DE FREITAS DUTRA et al., 2021, Brazil. (23) Cohort / 5 2,642 - 430 cases* - 2,212 controls1 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients. KOLIN et al., 2020, United Kingdom. (41) Cohort / 8 397.064 - 968 cases** - 396,096 controls2 COVID-19 infection - Group A was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients. MATZHOLD, et al., 2021, Austria. (27) Cohort / 6 250,636 - 338 cases* - 250,298 controls1 COVID-19 infection - Group AB was more prevalent in COVID patients compared to controls, Group O was less prevalent in patients. TEVATIA et al., 2020, India. (17) Cohort / 7 998 - 116 cases* - 882 controls1 COVID-19 infection - No significant difference DOMÈNECH-MONTOLIU et al.., 2021, Spain. (24) Cohort / 6 1,821 - 483 cases* - 1,338 controls2 COVID-19 infection - No significant difference 1 Blood donor controls 2 Non-blood donor controls; Study sample 1 (hospitalized); *Study sample 2 (outpatients) Data analysis Outcome variables were expressed as the proportion of all selected patients with at least one event. We did not perform a meta-analysis due to the high heterogeneity of data. Results The initial search resulted in 798 articles across the following databases: PubMed, Embase, Scopus, Web of Science, Science Direct and Virtual Health Library. Then automation tools were applied (detailed in Figure 1 ), resulting in the exclusion of 195 articles. The next step was duplicate removal, which further excluded 340 articles. Subsequently, 263 studies were selected for title and abstract reading and 178 of those were excluded for lacking pertinence to the subject of our research, not fulfilling inclusion criteria or for matching any of the exclusion criteria, resulting in 85 manuscripts for full-text reading. Fifty-three matched the research question of this review and were included in the final analysis. Figure 1 details the search and selection flow-chart. Three study types were analyzed: Cohort studies (n = 22), Cross-sectional studies (n = 20) and case-control studies (n = 11).Figure 1 ▒. Figure 1 For objective 1 (risk of COVID-19 infection) 30 articles were analyzed (Table 4). Sample sizes ranged from 208 to 2,211,894 participants, totaling 2,212,102 participants. These studies were conducted in 19 countries and the majority were from Turkey (6 studies), China (3 studies) and Spain (3 studies). The mean age ranged from 5.8 years (5 months - 13.3 years) to 82 years (6.9 years) (4, 5). Regarding the type of studies found, 11 were cohort, 12, cross-sectional, and 7, case control. Different types of samples were found. Some involved hospitalized or discharged patients, while others evaluated outpatients. Among 30 studies evaluating the risk of COVID infection associated with the ABO blood type, 21 studies found significant correlations. Most studies (14 studies), involving 105 to 7,422 patients, found an increased proportion of blood group A in patients, compared to controls. Fewer studies showed an increased proportion of patients with blood group B (6 studies) or AB (6 studies). Most studies also found a reduced proportion of blood group O (15 studies, 105 – 7,422 patients) in infected patients. A total of 10 studies found no statistically significant correlation between ABO status and COVID-19 infection. Among studies with blood donors as controls (10 studies), most studies found a reduced proportion of patients with blood group O (4 studies, 302 - 854 patients). Fewer studies showed an increased proportion of patients with blood group A (3 studies), B (2 studies) or AB (2 studies). Two out of 10 studies found no statistically significant correlation between the ABO status and COVID-19 infection using blood donors as controls. The majority of the studies (10 studies with 105 – 7,422 patients) with non-blood donor controls (21 studies) found an increased proportion of blood group A in patients, compared to controls. Fewer studies showed an increased proportion of patients with blood group B (3 studies) or AB (3 studies). Most studies also found a reduced proportion of patients with blood group O (11 studies, 105 – 7,422 patients). Seven studies using non-blood donors as controls found no statistically significant correlation between the ABO status and COVID infection. For objective 2 (COVID infection severity), 35 articles were analyzed (Table 5 ). Sample sizes ranged from 43 to 5,391,149 participants. The studies were conducted in 17 countries and the majority were from United States (6 studies), Turkey (5 studies) and Saudi Arabia (4 studies). Regarding the type of studies found, 16 were cohort, 13, cross-sectional, and 5, case control. We assessed studies including any of the three selected outcomes of interest (mortality, ICU and mechanical ventilation).Table 5 Studies that show correlation between ABO blood group and COVID severe infection. Table 5Authors, year, place of study Type of study / Newcastle score Sample size (n) Outcomes of interest Main results LATZ et al., 2020, USA. (43) Cross-sectional / 6 1,289 Mortality - No significant difference LIU et al., 2020, Germany. (6) Cross-sectional / 5 5,391,149 Mortality - Increased mortality in blood group A, decreased mortality in blood group B BADEDI, 2020, Saudi Arabia. (37) Case control / 8 323 Mortality - No significant difference - 108 cases - 215 control AD'HIAH et al., 2020, Iraq. (32) Case control / 9 895 Mortality - Increased mortality in blood groups A and AB - 300 cases - 595 controls BEHBOUDI et al., 2021, Iran. (38) Case control / 5 398 Mortality - No significant difference - 148 cases - 250 controls GAMBOA-AGUILAR et al.., 2022, Mexico (42) Case control / 8 7,416 Mortality - No significant difference - 2,416 cases - 5,000 controls SARDU et al., 2020, Italy. (13) Cohort / 7 164 cases Mortality - Increased mortality in non-O blood types - 72 type O - 92 non-O BARNKOB et al., 2020, Denmark. (16) Cohort / 7 2,211,894 Mortality - No significant difference - 7,422 cases - 2,204,472 controls RAY et al., 2020, Canada. (14) Cohort / 9 225,556 Mortality - Increased mortality in blood group B, decreased in blood group O - 7,071 cases - 218,485 controls NAUFFAL et al., 2021, USA. (18) Cohort / 9 810 Mortality - No significant difference - 401 cases - 409 controls SZYMANSKI et al., 2021, USA. (21) Cohort / 7 4,968 Mortality - Increased mortality in blood group A NALBANT et al., 2022, Turkey. (19) Cohort / 9 313 Mortality - No significant difference - 220 cases - 93 controls AMOROSO et al., 2021, Italy. (20) Cohort / 8 40,904 Mortality - No significant difference - 219 cases - 40,685 controls MATZHOLD, et al., 2021, Austria. (27) Cohort / 6 338 Mortality - No significant difference HERMEL et al., 2021, USA. (58) Cross-sectional / 7 473 ICU admission and mortality - No significant difference AKTIMUR et al., 2020, Turkey. (51) Cross-sectional / 7 5,379 ICU admission and mortality - No significant difference - 179 cases - 5,200 controls YAYLACI et al., 2020, Turkey. (47) Cross-sectional / 5 397 ICU admission and mortality - No significant difference PASANGHA et al.., 2020, India. (55) Cross-sectional / 5 370 ICU admission, mechanical ventilation and mortality - No significant difference MULLINS, et al., 2021, USA. (28) Cohort / 5 227 ICU admission and mechanical ventilation - No significant difference GRECO et al., 2021, Italy. (29) Cohort / 8 330 ICU admission and mortality - Increased ICU admission in blood group AB KABRAH et al., 2021, Saudi Arabia. (59) Cohort / 7 285 ICU admission and mortality - No significant difference HOILAND et al., 2020, Canada. (44) Cross-sectional / 7 95 Mechanical ventilation - Increased need for mechanical ventilation in blood groups A and AB HALIM et al., 2021, Blangadesh. (57) Cross-sectional / 6 771 Mechanical ventilation and mortality - Increased mortality and need for mechanical ventilation in blood group A GÖKER et al., 2020, Turkey. (45) Cross-sectional / 7 2,068 Mechanical ventilation, ICU admission and mortality - No significant difference - 186 cases - 1,882 controls TORRES-ALARCÓN et al., 2021, Mexico. (39) Case control / 5 125 Mechanical ventilation, ICU admission and mortality - Increased mortality in blood group A - 73 cases - 52 controls AL‐YOUHA et al., 2020, Kuwait. (30) Cohort / 7 3,703,305 Mechanical ventilation - No significant difference - 3,305 cases - 3,700,000 controls BADEDI, 2020, Saudi Arabia. (22) Cohort / 7 404 Mechanical ventilation - No significant difference TAMAYO-VELASCO et al., 2021, Spain. (25) Cohort / 6 136 Mechanical ventilation and mortality - Increased mortality and need for mechanical ventilation in blood groups A, B and AB - 108 cases - 28 controls ZIETZ et al., 2020, USA. (15) Cohort / 8 14,112 Mechanical ventilation and mortality - Increased mortality and need for mechanical ventilation in blood group AB ISHAQ et al., 2021, Pakistan. (26) Cohort / 6 1,067 Mechanical ventilation and mortality - No significant difference The mortality was evaluated in 30 studies. Seven studies found increased mortality in blood group A patients, 2, in blood group B patients, and 4, in blood group AB patients. One study also found reduced mortality in blood group O. The majority (21 studies) found no significant difference in mortality among ABO blood groups. Admission to the ICU was evaluated in 10 studies. Most (9 studies) found no significant correlation between the ABO blood group and need for ICU admission. One study (with 330 patients) found an increased need for ICU admission in blood group AB patients. Mechanical ventilation was analyzed in 10 studies. Most (6 studies) also found no significant difference between the ABO blood group and need for mechanical ventilation. Three studies found an increased need for mechanical ventilation in blood group A patients, 3, in blood group AB patients, and 1, in blood group B patients. Analyzing all three severity outcomes, the majority of the studies found no significant difference in either mortality, ICU admission or need for mechanical ventilation associated with the ABO blood group in COVID-19 patients. Only 12 of the 35 studies found a significant correlation, mostly showing an increased mortality and need for mechanical ventilation in blood group A patients. Discussion We performed a systematic review of the correlation between the ABO blood type and susceptibility to COVID-19 infection, both in terms of risk of infection and in terms of disease severity. The results of this review point to a possible increased risk of COVID-19 infection associated particularly with blood type A and a possible decreased risk of infection in blood type O, as most high-quality studies including either outpatients or hospitalized patients and with blood donors or non-blood donors as controls replicated this finding. The relationship between the ABO blood group and risk of COVID-19 infection has been previously investigated by other review studies (6, 7, 8, 9). Most reviews showed similar findings of an increased risk in group A patients and a decreased risk in group O. However, the present review includes a larger number of studies and more recently published papers, which might better reflect the present phase of the COVID-19 disease pandemic. As for disease severity, results were conflicting, as most studies found no statistically significant correlations. Those that have found differences mostly reported increased mortality in group A patients. Admission to the ICU was mostly similar among ABO blood groups in all except one study and the need for mechanical ventilation was higher in blood group A and B in only 3 in 11 studies (4, 5). Previously published reviews also bring conflicting results on this subject. One review has found increased mortality in group A versus non-A (6). Another study found lower risk of death in ABO group B patients (8). However, most reviews failed to demonstrate an increased mortality associated with any ABO blood group (4, 9), similar to the findings of the present review, albeit with fewer articles included in the analysis. A definitive association of the ABO blood group distribution with mortality or severity outcomes remains to be proven. Studies utilizing blood donors as controls carry a potential risk of bias, as the frequency of ABO blood group subtypes may be different between blood donors and the general population (1, 2). Studies with blood transfusions could also influence outcomes due to different profiles between donors and transfusion recipients. One study by Muñiz-Diaz et al. (2020) reported 2 different samples, one with blood donor controls and another with non-blood donor controls and found different results, namely a significant difference (increased A and decreased O blood groups in patients) when comparing patients to blood donor controls and no significant difference when comparing transfusion recipients with COVID-19 infection to recipients without COVID-19 infection (non-blood donor controls) (5). However, when summarizing all studies with blood donor controls and those with non-blood donor controls, results were largely similar (an increased risk in blood group A patients and a decreased risk in blood group O patients in most studies). Patient assessment methods varied among studies. We only extracted data regarding the ABO group distribution among infected and non-infected subjects for objective 1 (the risk of COVID-19 infection). For objective 2 (the severe COVID-19 infection), we extracted data regarding mortality, admission to the ICU and the need for mechanical ventilation for analysis, regardless of other clinical outcomes evaluated by the selected studies. This was an attempt to draw more homogenous conclusions based on heterogenous studies. The mechanisms responsible for this correlation between ABO blood groups and the susceptibility to COVID-19 infection are still unclear. Guillon et al. (2008) demonstrated that viral S proteins co-localize with the ABO group A antigens and that anti-A antibodies could inhibit the adhesion between the S protein and host cell ACE2 receptors that initiate viral invasion (10). This could explain why type O patients (who have both anti-A and anti-B antibodies) might be protected from infection and also why type A patients (who do not possess anti-A antibodies) might be at greater risk. Deleers et al. (2021) reported that COVID-19-infected patients had significantly lower levels of anti-ABO antibodies, reinforcing the hypothesis that these antibodies might have a protective role. Indeed, lower susceptibility to infection in group O patients has been previously reported for SARS-CoV, another member of the Coronavirus family (11). Another hypothesis to explain a relationship between the ABO blood group and COVID-19 disease outcomes involves the risk of thromboembolic complications. Group O individuals have lower levels of pro-thrombotic von Willebrand factor (vWF) and may be partially protected against COVID-associated thrombosis, an important mechanism in many complications leading to worse outcomes (12). This is in line with the findings from Ladikou et al. (2020) and Sardu et al. (2020), included in the present review, which have shown higher levels of vWF in non-O patients associated with worse outcomes (12, 13). The present study has several limitations. We have found high heterogeneity among the studies, regarding different populations, study designs, sample sizes, control groups, follow-up periods and outcome measures. For this reason, we did not perform a meta-analysis of the results. Limiting the analysis to less heterogeneous studies with a low risk for bias might have reduced sample size significantly. Also, the studies analyzed lacked important information, such as participant vaccination status and SARS-CoV2 variant reported, which might impact the outcomes. Due to the time in which our assessment was performed, most patients were not infected with the Omicron variant, that is presently the dominant variant worldwide. Results with the Omicron variant, including BA.1 and BA.2 variants, might have been different. This review summarizes all available studies to date regarding the association between the ABO blood group and susceptibility to COVID-19 infection and disease severity. A rigorous methodology was used to analyze and synthesize data and the risk for bias and quality of evidence were systematically assessed. Many previous reviews were published in an earlier time during the pandemic and thus, included fewer studies. Another strength of our review is that we only included studies with laboratory-confirmed SARS-CoV-2 infection. We have also tried to approach the susceptibility to infection and disease severity as different objectives, selecting studies that utilized these specific outcomes for each analysis. Understanding patient susceptibility to COVID-19 infection according to the ABO blood group could help us in identifying new risk factors to be incorporated in prognostic tools and triage algorithms. Comprehension of the mechanisms by which this susceptibility is manifested might also open new treatment possibilities by providing novel therapeutic targets or utilizing naturally occurring protective factors associated with ABO blood group antigens or antibodies. Future studies specifying the vaccination status and identifying particular SARS-CoV-2 variants, especially the dominant Omicron variants, and the susceptibility of these patients according to the ABO blood group might further optimize the COVID-19 risk stratification and treatment. Conclusion Qualitative assessment of available information suggests that the blood group A may be a risk factor for the COVID-19 infection and that the blood group O may have a protective effect. Based on all evidence available at this point, we were unable to determine a clear association between the ABO blood group and COVID-19 associated mortality. These conclusions were based on highly heterogenous evidence and furthermore, contain several biases. Moreover, they do not reflect the predominance of omicron variants and of vaccinated subjects, which characterize the present moment of the pandemic. Further studies are needed to assess the actual impact of the ABO blood group on the risk and severity of the COVID-19 infection. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials Not applicable. Funding Not applicable. Credit authorship contribution statement DMBS, DABSA, PRN, JLBS and DABSA contributed to the article conception and wrote the manuscript; DMBS, DABSA, RBM, EMBS, FCSN, MSNP and VCVG contributed to the data collection and analysis. DMBS, DABSA, MSNP, PRN, PBN and GFA read, revised and approved the final version of the manuscript. Conflicts of interest The authors have no conflicts of interest to declare. Acknowledgements We offer our thanks to the Study Group on Neuroinflammation and Neurotoxicology at the State University of Ceará (GENIT/UECE). The authors are grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for the funding of the Productivity scholarship to the author Pedro Braga Neto and to the Coordination for the Improvement of Higher Education Personnel – Brazil (CAPES) for the funding of Pedro Braga Neto (88881.505364/2020-01). ==== Refs References 1 Silva-Filho JC de Melo CGF de Oliveira JL. The influence of ABO blood groups on COVID-19 susceptibility and severity: A molecular hypothesis based on carbohydrate-carbohydrate interactions Medical Hypotheses 144 2020 110155 2 Callaway E Ledford H Mallapaty S. Six months of coronavirus: the mysteries scientists are still racing to solve Nature 2020 178 179 3 Wells GA Shea B O'Connell D Peterson J Welch V Losos M The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses 2000 Oxford 4 Bhattacharjee S Banerjee M Pal R. ABO blood groups and severe outcomes in COVID-19: a meta-analysis Postgraduate medical journal 98 e2 2022 e136 e1e7 35232874 5 Muñiz-Diaz E Llopis J Parra R Roig I Ferrer G Grifols J Relationship between the ABO blood group and COVID-19 susceptibility, severity and mortality in two cohorts of patients Blood Transfusion 19 1 2021 54 33196417 6 Liu N Zhang T Ma L Zhang H Wang H Wei W The impact of ABO blood group on COVID-19 infection risk and mortality: A systematic review and meta-analysis Blood reviews 48 2021 100785 7 Franchini M Cruciani M Mengoli C Marano G Candura F Lopez N ABO blood group and COVID-19: an updated systematic literature review and meta-analysis Blood Transfusion 19 4 2021 317 34059188 8 Wu B-B Gu D-Z Yu J-N Yang J Shen W-Q Association between ABO blood groups and COVID-19 infection, severity and demise: A systematic review and meta-analysis Infection, Genetics and Evolution 84 2020 104485 9 Pourali F Afshari M Alizadeh-Navaei R Javidnia J Moosazadeh M Hessami A. Relationship between blood group and risk of infection and death in COVID-19: a live meta-analysis New microbes and new infections 37 2020 100743 10 Guillon P Clément M Sébille V Rivain J-G Chou C-F Ruvoën-Clouet N Inhibition of the interaction between the SARS-CoV spike protein and its cellular receptor by anti-histo-blood group antibodies Glycobiology 18 12 2008 1085 1093 18818423 11 Deleers M Breiman A Daubie V Maggetto C Barreau I Besse T Covid-19 and blood groups: ABO antibody levels may also matter International Journal of Infectious Diseases 104 2021 242 249 33326874 12 Ladikou EE Sivaloganathan H Milne KM Arter WE Ramasamy R Saad R Von Willebrand factor (vWF): marker of endothelial damage and thrombotic risk in COVID-19? Clinical Medicine 20 5 2020 e178 32694169 13 Sardu C Marfella R Maggi P Messina V Cirillo P Codella V Implications of AB0 blood group in hypertensive patients with covid-19 BMC Cardiovascular Disorders 20 1 2020 1 10 31910809 14 Ray JG Colacci M Schull MJ Vermeulen MJ Park AL. Non-ABO red cell antibodies and risk of COVID-19 International Journal of Infectious Diseases 108 2021 179 182 34022334 15 Zietz M Zucker J Tatonetti NP. Associations between blood type and COVID-19 infection, intubation, and death Nature communications 11 1 2020 1 6 16 Barnkob MB Pottegård A Støvring H Haunstrup TM Homburg K Larsen R Reduced prevalence of SARS-CoV-2 infection in ABO blood group O Blood advances 4 20 2020 4990 4993 33057631 17 TEVATIA MS SIDHU A KALRA DK BARANWAL AK PANDIT P GULERIA P Does PCR Cycle Threshold for SARS-CoV-2 have Association with ABO Blood Group Types? Journal of Clinical & Diagnostic Research 14 10 2020 18 Nauffal V Achanta A Goldhaber SZ Piazza G. Association of ABO blood group type with cardiovascular events in COVID-19 Journal of thrombosis and thrombolysis 51 3 2021 584 586 33452583 19 Nalbant A Aydın A Yaylacı S Kaya T Wermeulen CL Cinemre H. Association of ABO blood group and age with COVID-19 positive test Revista da Associação Médica Brasileira 67 2021 46 50 20 Amoroso A Magistroni P Vespasiano F Bella A Bellino S Puoti F of Italian network. Regional transplant coordinating C. HLA and AB0 Polymorphisms May Influence SARS-CoV-2 Infection and COVID-19 Severity Transplantation 2020 21 Szymanski J Mohrmann L Carter J Nelson R Chekuri S Assa A ABO blood type association with SARS-CoV-2 infection mortality: A single-center population in New York City Transfusion 61 4 2021 1064 1070 33665881 22 Badedi M Alnami A Darraj H Alrajhi A Mutawwam D-A Somaily M Clinical characteristics and ABO blood groups in COVID-19 patients, Saudi Arabia Medicine 100 30 2021 23 de Freitas Dutra V Bonet-Bub C Yokoyama APH Achkar R Machado RRG Assunção M Anti-A and SARS-CoV-2: an intriguing association Vox Sanguinis 116 5 2021 557 563 33650690 24 Domènech-Montoliu S Puig-Barberà J Pac-Sa MR Vidal-Utrillas P Latorre-Poveda M Rio-González AD ABO blood groups and the incidence of complications in COVID-19 patients: A population-based prospective cohort study International journal of environmental research and public health 18 19 2021 10039 34639344 25 Tamayo-Velasco A Penarrubia Ponce MJ Álvarez FJ Gonzalo-Benito H de la Fuente I Perez-Gonzalez S Can the cytokine profile according to ABO blood groups be related to worse outcome in COVID-19 patients? yes, they can Frontiers in immunology 2021 4088 26 Ishaq U Malik A Malik J Mehmood A Qureshi A Laique T Association of ABO blood group with COVID-19 severity, acute phase reactants and mortality PloS one 16 12 2021 e0261432 27 Matzhold EM Berghold A Bemelmans MKB Banfi C Stelzl E Kessler HH Lewis and ABO histo-blood types and the secretor status of patients hospitalized with COVID-19 implicate a role for ABO antibodies in susceptibility to infection with SARS-CoV-2 Transfusion 61 9 2021 2736 2745 34151460 28 Mullins J Al-Tarbsheh AH Chieng H Chaukiyal P Ghalib S Jain E The association of ABO blood type with the risk and severity of COVID-19 infection American journal of blood research 11 1 2021 53 33796389 29 Greco S Bella A Bonsi B Fabbri N Califano A Morrone S SARS-CoV-2 infection and H1N1 vaccination: does a relationship between the two factors really exist? A retrospective analysis of a territorial cohort in Ferrara, Italy Eur Rev Med Pharmacol Sci 2021 2795 2801 33829464 30 Al-Youha SA Alduaij W Al-Serri A Almazeedi SM Al-Haddad M Jamal MH The impact of ABO blood groups on clinical outcomes and susceptibility to COVID-19: a retrospective study in an unselected population Transfusion 61 5 2021 1631 1641 33682150 31 Fan Q Zhang W Li B Li D-J Zhang J Zhao F. Association between ABO blood group system and COVID-19 susceptibility in Wuhan Frontiers in cellular and infection microbiology 10 2020 404 32793517 32 Ad'hiah AH Abdullah MH Alsudani MY Shnawa RM Al-Sa'ady AJ Allami RH Association between ABO blood groups and susceptibility to COVID-19: profile of age and gender in Iraqi patients Egyptian Journal of Medical Human Genetics 21 1 2020 1 10 33 Zhao J Yang Y Huang H Li D Gu D Lu X Relationship between the ABO blood group and the coronavirus disease 2019 (COVID-19) susceptibility Clinical Infectious Diseases 73 2 2021 328 331 32750119 34 Saify K Alborz MS Saadat M. Susceptibility to the novel coronavirus disease (COVID-19) is associated with ABO and Rh blood groups: a case-control study from Afghanistan Egyptian Journal of Medical Human Genetics 22 1 2021 1 5 35 Wu Y Feng Z Li P Yu Q. Relationship between ABO blood group distribution and clinical characteristics in patients with COVID-19 Clinica chimica acta 509 2020 220 223 36 Khalil A Feghali R Hassoun M. The Lebanese COVID-19 cohort; A challenge for the ABO blood group system Frontiers in Medicine 7 2020 585341 37 Badedi M Makrami A Alnami A. Co-morbidity and blood group type risk in coronavirus disease 2019 patients: a case–control study Journal of Infection and Public Health 14 4 2021 550 554 33756193 38 Behboudi E Hamidi V Gholizadeh F Grala EM Ghelmani Y Nakhaie M Association between ABO blood groups and rhesus antigen and susceptibility to COVID-19 in the Yazd hospital New Microbes and New Infections 44 2021 100934 39 Torres-Alarcón CG García-Ruíz A Cañete-Ibáñez CR Morales-Pogoda II Muñoz-Arce CM BE Cid-Domínguez Blood system ABO antigens as risk factor for severity of SARS-CoV-2 infection Gaceta médica de México 157 2 2021 181 187 34270532 40 KİRİŞCİ Ö ÖZLÜK S TOPALCA ÜS KÖPRÜLÜ ND Relationship of ABO Blood Groups to SARS-COV-2 Infection Causing COVID-19 Disease Konuralp Medical Journal 13 1 2021 18 23 41 Kolin DA Kulm S Christos PJ Elemento O. Clinical, regional, and genetic characteristics of Covid-19 patients from UK Biobank PLoS One 15 11 2020 e0241264 42 Gamboa-Aguilar J Zamorano-Montaño ÁC Enriquez-Osorio A Torres-Cubillas W López-Arroyo JL Chapol JAM Abo Blood Group, Atherothrombotic Comorbidities, and COVID-19: A Case-Control Study of their Association in the Mexican Population Archives of Medical Research 53 1 2022 100 108 34649737 43 Latz CA DeCarlo C Boitano L Png C Patell R Conrad MF Blood type and outcomes in patients with COVID-19 Annals of hematology 99 9 2020 2113 2118 32656591 44 Hoiland RL Fergusson NA Mitra AR Griesdale DE Devine DV Stukas S The association of ABO blood group with indices of disease severity and multiorgan dysfunction in COVID-19 Blood advances 4 20 2020 4981 4989 33057633 45 Göker H Karakulak EA Demiroğlu H Ceylan ÇMA Büyükaşik Y Inkaya AÇ The effects of blood group types on the risk of COVID-19 infection and its clinical outcome Turkish journal of medical sciences 50 4 2020 679 683 32496734 46 Kotila TR Alonge TO Fowotade A Famuyiwa OI Akinbile AS. Association of the ABO blood group with SARS-CoV-2 infection in a community with low infection rate Vox Sanguinis 116 8 2021 910 915 33529391 47 Yaylacı S Dheir H İşsever K Genc AB Şenocak D Kocayigit H The effect of abo and rh blood group antigens on admission to intensive care unit and mortality in patients with COVID-19 infection Revista da Associação Médica Brasileira 66 2020 86 90 48 Solmaz İ Araç S. ABO blood groups in COVID-19 patients; Cross-sectional study International journal of clinical practice 75 4 2021 e13927 33296536 49 Al Kuwari HM Rahim HFA Abu-Raddad LJ Abou-Samra A-B Al Kanaani Z Al Khal A Epidemiological investigation of the first 5685 cases of SARS-CoV-2 infection in Qatar, 28 February–18 April 2020 BMJ open 10 10 2020 e040428 50 Almadhi MA Abdulrahman A Alawadhi A Rabaan AA Atkin S AlQahtani M. The effect of ABO blood group and antibody class on the risk of COVID-19 infection and severity of clinical outcomes Scientific reports 11 1 2021 1 5 33414495 51 Aktimur SH Ahmet S Yazicioglu B Gunes AK Serhat G The assessment of the relationship between ABO blood groups and Covid-19 infection International Journal of Hematology and Oncology 31 3 2020 121 125 52 Esref A Solmaz I Akkoc H Donmezdil S Karahan Z Safak K Association between the Rh blood group and the Covid-19 susceptibility International Journal of Hematology and Oncology 32 2 2020 081 086 53 Muñoz-Culla M Roncancio-Clavijo A Martínez B Gorostidi-Aicua M Piñeiro L Azkune A O group is a protective factor for COVID19 in Basque population Plos one 16 4 2021 e0249494 54 KHASAYESI M KASHI Z ILALI NM GHASEMIAN R ESLAMIJOUBARI M HOSSEINI-KHAH Z Association of ABO and Rhesus Blood Groups with the Severity of SARS-CoV-2 Infection: A Cross-sectional Analytical Study Journal of Clinical & Diagnostic Research 15 12 2021 55 Pasangha E Dhali A D'Souza C Umesh S Are blood groups related to the distribution and severity of COVID-19? A cross-sectional study in a tertiary care hospital in South India Qatar medical journal 2021 3 2021 63 34888199 56 Komal A Noreen M Akhtar J Imran M Jamal M Atif M Analyses of ABO blood groups with susceptibility and symptomatic variations of COVID-19 infection, a questionnaire-based survey Apmis 129 10 2021 579 586 34342074 57 Halim MR Saha S Haque IU Jesmin S Nishat RJ Islam AA ABO blood group and outcomes in patients with COVID-19 admitted in the intensive care unit (ICU): a retrospective study in a tertiary-level hospital in Bangladesh Journal of Multidisciplinary Healthcare 14 2021 2429 34511926 58 Hermel DJ Spierling Bagsic SR Costantini CL Mason JR Gahvari ZJ Saven A ABO phenotype and clinical correlates of COVID-19 severity in hospitalized patients Future Science OA 7 8 2021 FSO735 34290882 59 Kabrah SM Abuzerr SS Baghdadi MA Kabrah AM Flemban AF Bahwerth FS Susceptibility of ABO blood group to COVID-19 infections: clinico-hematological, radiological, and complications analysis Medicine 100 52 2021	36467112	PMC9708632	NO-CC CODE	2022-12-05 23:15:32	no	Hematol Transfus Cell Ther. 2022 Nov 30; doi: 10.1016/j.htct.2022.11.001	utf-8	Hematol Transfus Cell Ther	2,022	10.1016/j.htct.2022.11.001	oa_other
==== Front Travel Behav Soc Travel Behav Soc Travel Behaviour & Society 2214-367X 2214-3688 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. S2214-367X(22)00139-9 10.1016/j.tbs.2022.11.011 Article Understanding Mobility Change in Response to COVID-19: A Los Angeles Case Study Lu Yougeng Giuliano Genevieve ⁎ Department of Urban Planning and Spatial Analysis, University of Southern California, Los Angeles, CA, USA ⁎ Corresponding author. 30 11 2022 30 11 2022 28 12 2021 5 10 2022 24 11 2022 © 2022 Hong Kong Society for Transportation Studies. Published by Elsevier Ltd. All rights reserved. 2022 Hong Kong Society for Transportation Studies Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. The COVID-19 pandemic has affected people’s lives throughout the world. Governments have imposed restrictions on business and social activities to reduce the spread of the virus. In the US, the pandemic response has been largely left to state and local governments, resulting in a patchwork of policies that frequently changed. We examine travel behavior across income and race/ethnic groups in Los Angeles County over several stages of the pandemic. We use a difference-in-difference model based on mobile device data to compare mobility patterns before and during the various stages of the pandemic. We find a strong relationship between income/ethnicity and mobility. Residents of low-income and ethnic minority neighborhoods reduced travel less than residents of middle- and high-income neighborhoods during the shelter-in-place order, consistent with having to travel for work or other essential purposes. As public health rules were relaxed and COVID vaccines became available, residents of high-income and White neighborhoods increased travel more than other groups, suggesting more discretionary travel. Our trip purpose model results show that residents of low-income and ethnic minority neighborhoods reduced work and shopping travel less than those of White and high-income neighborhoods during the shelter-in-place order. Results are consistent with higher-income workers more likely being able to work at home than lower-income workers. In contrast, low-income/minorities apparently have more constraints associated with work or household care. The consequence is less capacity to avoid virus risk. Race and socioeconomic disparities are revealed in mobility patterns observed during the COVID-19 pandemic. Keywords COVID-19 Mobility Race/Ethnicity Income ==== Body pmc1 Introduction The COVID-19 pandemic has affected economies and people around the world. The first reported COVID-19 case was confirmed in China in December 2019, and the virus then spread quickly to the rest of the world. On March 11, 2020, the World Health Organization (WHO) officially declared COVID-19 a pandemic (WHO, 2020). The first confirmed case of COVID-19 in the US occurred in Snohomish County, Washington on January 21, 2020. The virus spread quickly across the country (Gao et al., 2020). By March 17, 2020, COVID-19 had been reported in all 50 states in the US (ABC News, 2020). Given the absence of pharmacological control measures (e.g., medical treatment or vaccine) in the early stages of the pandemic, governments and health officials relied on non-pharmaceutical interventions to control the spread of the virus, including social distancing, use of face coverings, and shelter-in-place orders (Davies et al., 2020, Flaxman et al., 2020, Lai et al., 2020). In the US, social distancing was the primary strategy to limit the spread of COVID-19 until vaccinations became available in December 2020. In the absence of a strong federal response, managing the pandemic was largely left to state and local governments. Many states, counties, and cities began issuing shelter-in-place or similar mitigation measures that required residents to reduce movement and stay home as early as March 2020. Rules were relaxed as vaccinations became more available. Variation in COVID-19 policies provides an opportunity to examine travel behavior in response to shelter-in-place and other COVID-19 prevention policies. Our interest is on disparities in mobility changes across socioeconomic groups. Many papers have now been written on the travel impacts of the pandemic. Evidence suggests that travel patterns of low-income and minority populations changed less than those of higher income populations. These results were linked to the greater likelihood that low-income and minority populations work in jobs that cannot be done remotely (Atchison et al., 2020, Brough et al., 2021, Clay and Rogus, 2021, Goldman et al., 2021). Our research contributes to this emerging literature in the following ways. First, we use smartphone mobility data to explore differences and responses across policy stages, from the first shelter-at-home order to the widespread availability of vaccines.. Second, we directly test the relationship between travel response and occupation by segmenting travel by trip purpose. Third, we use data at the census block level; previous studies of socio-economic effects have been conducted at census tract or larger geographies. Our data covers Los Angeles County, one of the epicenters of COVID-19 in the US, from January 1, 2020, to April 16, 2021. This period includes a few months before the COVID surge of March 2020, three distinct policy stages and a period of vaccine availability. We use a difference-in-difference (DID) model to examine travel behavior across the various stages. We have two research questions: Do different race/ethnic or income groups respond differently to COVID-19 and related travel restriction policies? If so, what explains these differences? Prior research indicates that residents of low-income and minority-dominant neighborhoods are less able to comply with shelter-in-place orders relative to other groups due to differences in occupation (e,g, work in essential jobs that require physical presence) as well as racial and social segregation (Clay and Rogus, 2021, Hu et al., 2020, Khanijahani, 2022). However, these possible explanatory factors have not been formally tested. We compare travel behavior impacts across income and race/ethnic groups using two measures of mobility: total time spent at home, and average number of unique locations visited per day. We develop measures of trip purpose to examine whether differences across race/ethnic/income groups are associated with work or other more discretionary activities. The remainder of the paper is organized as follows. Section 2 presents a brief review of the literature to date on COVID-19 travel impacts. Section 3 describes our data and methodology. Results are presented in Section 4. We provide conclusions and policy implications in Section 5. 2 Literature review In this section we discuss the literature on (1) travel behavior changes in previous public health crises, and (2) travel behavior and COVID-19. 2.1 Travel behavior changes in previous health crises Although there is no historical precedent for COVID-19 since the Spanish Flu of 1918, there is a literature on the effects of prior health crises such as SARS in 2003 and the H1N1 pandemic in 2009. Wen et al. (2005) analyzed the impacts of the 2003 SARS outbreak on the travel behavior of Chinese domestic tourists and found that the outbreak led to a dramatic decrease in travel and tourism. Liu et al. (2010) studied the effects of the SARS outbreak on air travel between the US and three destinations: mainland China, Hong Kong, and Taiwan. Their study showed that the impact and life cycle of SARS effect on air travel varied by location and health regulations. Both studies indicated that the decrease in travel was associated with a combination of travelers’ internal motivations (e.g., perceived risks) and external enforced measures (e.g., travel bans, stay-at-home orders, disclosure of epidemic information). Kim et al. (2017) analyzed transit smart card data in Seoul before and after the 2015 MERS outbreak. Individuals living in neighborhoods with higher land prices decreased their transit trips more than others, suggesting some influence of socio-demographics. 2.2 COVID-19 research The scale and duration of COVID-19 may generate more dramatic and longer lasting impacts than have been observed with prior health crises. Research to date on COVID has addressed travel in various ways. Many papers have demonstrated the effectiveness of social distancing and shelter-in-place orders in reducing virus spread (Courtemanche et al., 2020; Fazio et al., 2021; Kwon et al., 2021). However, compliance varies across ethnicity and income levels (Barnett-howell and Mobarak, 2020, Chiou and Tucker, 2020, Wright et al., 2020). Recent literature shows that low-income and minority populations are less likely to shelter-in-place and have higher COVID infection and mortality than higher income populations and Whites (Chen et al., 2020, Hu et al., 2020, Jay et al., 2020, Khanijahani, 2022, Lou et al., 2020, Torrats-espinosa, 2021). Explanations for these differences include financial constraints and occupation (Atchison et al., 2020, Blau et al., 2020, Clay and Rogus, 2021, Garfield et al., 2020, Goldman et al., 2021, Kar et al., 2021), racial segregation in residential neighborhoods, workplaces, and daily-life space (Hu et al., 2020, Khanijahani, 2022, Torrats-espinosa, 2021), political preferences (Gatwood, 2021, Motta, 2020), and lack of trust in the medical profession (Stoler et al., 2021, Willis et al., 2021). Lower income workers are less likely to be able to work remotely and more likely to have essential jobs in services, medical care, or retail that require physical presence and face-to-face contact (Atchison et al., 2020, Blau et al., 2020, Clay and Rogus, 2021, Garfield et al., 2020, Goldman et al., 2021). The lesser response to social distancing orders early in the pandemic is documented by Jay et al., 2020, Lou et al., 2020 using smartphone mobility data. Both found that residents of low-income neighborhoods were more likely to work outside the home than residents of high income neighborhoods, supporting the explanation of occupation. Businesses deemed essential (e.g., grocery store, health facility) are staffed predominantly by low-wage and ethnic minority workers (Blau et al., 2020, Clay and Rogus, 2021, Garfield et al., 2020, Goldman et al., 2021). Another way to reduce travel is to change shopping behavior. Kar et al. (2021) found that while many higher income people switched to online shopping and did more stocking up during the COVID-19 pandemic, most low-income people did not have the means to shop online or stock up to reduce trips. To the extent that lower-income and ethnic minority workers continue to travel at higher rates, especially in modes frequently necessitating interaction with other people (e.g., public transit), higher viral transmission rates among those groups would be expected (Brough et al., 2021). Other factors may also contribute to less compliance with COVID-19 policies among low-income and minority communities. Studies highlight the existing intersection of income and unequal access to information (Norris, 2001), differences in political preferences that may influence how information is processed (Feldman and Johnston, 2014, Gatwood, 2021, Motta, 2020) and attitudes toward risk (Milosh et al., 2020, Painter and Qiu, 2020, Yesuf and Bluffstone, 2009). In addition, there is evidence of lack of trust in the medical profession among people of color as a result of discrimination and poor quality medical care (Bogart et al., 2021, Jamison et al., 2019, Quinn et al., 2021). There is a growing literature on mobility responses during the pandemic. However, gaps continue to exist. In the US, the pandemic response has been largely left to state and local governments (Gupta et al., 2021), yet most of the research has been conducted at the national level, making it difficult to examine responses to specific policies. Most existing literature concentrates on assessing how income disparities impact responses to the stay-at-home order (Jay et al., 2020, Lou et al., 2020, Weill et al., 2020) but does not consider the role of race and ethnicity. Most recent studies examined mobility change in response to non-pharmaceutical interventions (e.g., social distancing, face covering) during the pandemic (Brough et al., 2021; Fazio et al., 2021; Kwon et al., 2021, Mukherjee and Jain, 2022), but less is known about how people’s travel behavior and mobility change when COVID vaccines became available. We extend the literature by considering both income and race/ethnicity, using a time period that includes vaccine availability, examining mobility across different trip purposes, and using highly disaggregate data. 3 Methodology 3.1 Study Area Our study area is Los Angeles County, California, the most populous county in the US, with over 10 million residents. It also has the highest average population density, 815 people/km2 as of 2019. Los Angeles County is one of the most racially diverse in the US. There is no majority population. Hispanics 1 (49% of the total population) are the single largest group followed by Whites 2 (26%), Asians (15%) and African Americans 3 (9%). Los Angeles County reported its first COVID-19 case in late January 2020 and first COVID-19 death on March 11, 2020. As of July 27, 2022, there were more than three million confirmed cases and 32,691 COVID-related deaths in the County (Department of Public Health – Los Angeles, 2022). COVID cases revealed large disparities across race/ethnicity and income. Hispanics accounted for 58.1% and Whites accounted for 12.2% of COVID cases. More than 75% of COVID-19 cases were from the poorest neighborhoods (Los Angeles County, 2022). 3.2 Data 3.2.1 Mobility metrics We assembled a longitudinal dataset of daily mobility measures from January 1, 2020, to April 16, 2021 (469 days), for residents of Los Angeles County from SafeGraph, a data company that aggregates anonymized location data from mobile phone applications (SafeGraph, 2021). SafeGraph made the data available for COVID related research, but reduced availability after April 16, 2021. Therefore our time period is limited to that date. Availability of the data led to many studies COVID-19 pandemic (Gao et al., 2020, Huang et al., 2020, Jay et al., 2020, Kar et al., 2021, Weill et al., 2020). SafeGraph aggregates data using a panel of GPS points from anonymous mobile devices and determines the home location as the common nighttime location of each mobile device over six weeks to a Geohash-7 granularity (∼153m × ∼ 153m). All mobility data are anonymized and aggregated to the census block group (CBG) level 4. There are 6150 CBGs in the urbanized portion of the county, which accounts for 99% of the population. Using origin and destination information, the SafeGraph data allow us to construct high-frequency mobility measures among individuals. The SafeGraph data has more than 850,000 devices for the county, roughly equivalent to an 8.5% sample. The average number of devices is 135 per CBG, but the distribution is not even. There is a less than 4% sample in 176 CBGs (representing 2.8% of county population), and 204 CBGs have a sample of more than 20% (representing 3.1% of the county population). There is no direct way to test the representativeness of the SafeGraph data. We compared overall trends with the Apple Mobility Trends Reports and found a high degree of consistency despite the differences in sources. 5 The Apple data are drawn from Apple product users, while SafeGraph is drawn from both Android and Apple devices. The SafeGraph data has been widely used in COVID-19 studies, and no substantive problems with the data have been identified (Gao et al., 2020; Holtz et al., 2020; Huang et al., 2020, Jay et al., 2020, Kar et al., 2021). We generate two mobility measures: the average number of CBGs visited each day per device for a given CBG, and the proportion of devices in a given CBG that spent all day at home each day. SafeGraph also provides data on the number of visits to distinct points of interest (POI) each week in every CBG. The visits data is given at the POI level and categorized to a specific location category (e.g., restaurant, grocery store, park). The POI data makes it possible to classify trips by purpose, which then allows us to examine mobility patterns by trip purpose. We developed trip categories based on definitions of the U.S. Department of Transportation Federal Highway Administration (FHWA) (2019). FHWA assigns trip purpose in five categories based on 2017 National Household Travel Survey data. Safegraph is aggregate data and there is no information on each respondent’s (cellphone) activity. To impute a trip purpose, we use the POI’s North American Industry Classification System (NAICS) code, time of day, and duration of stay. Trip categories and their relationship to POI data are presented in Table 1 .Table 1 Categories of POI visit purpose. Category Trip Purpose Categories POI type Shopping Buy goods (e.g., groceries, clothes, appliances, or gas). NAICS sector 44-45 (Retail Trade);NAICS sector 53 (Real Estate and Rental and Leasing). Family/personal business Volunteer activities (not paid).Drop off/pick up someone.Attend adult care.Buy services (e.g., dry cleaners, service a car, or pet care).Other general errands (e.g., post office or library). NAICS sector 51 (Information);NAICS sector 52 (Finance and Insurance);NAICS sector 54 (Professional, Scientific, and Technical Services);NAICS sector 81 (Other Services [except Public Administration]);NAICS sector 92 (Public Administration). Social/recreational Perform recreational activities (e.g., visit parks, movies, bars, or museums).Exercise (e.g., go for a jog, walk, walk the dog, or go to the gym).Buy meals (e.g., go out for a meal, snack, or carry-out). NAICS sector 71 (Arts, Entertainment, and Recreation);NAICS sector 72 (Accommodation and Food Services). Medical/dental Make a health care visit (e.g., medical, dental, or therapy). NAICS sector 62 (Health Care and Social Assistance). Work Trips undertaken for work or business purposes. NAICS sector 11 (Agriculture, Forestry, Fishing and Hunting);NAICS sector 21 (Mining, Quarrying, and Oil and Gas Extraction);NAICS sector 22 (Utilities);NAICS sector 23 (Construction);NAICS sector 31-33 (Manufacturing);NAICS sector 48-49 (Transportation and Warehousing);NAICS sector 56 (Administrative and Support and WasteManagement and Remediation Services).NAICS sector 42 (Wholesale Trade); 3.3 Socio-demographic characteristics CBG level income and ethnicity data were obtained from American Community Survey (ACS) data (2014–2018, 5-year pooled). It is well known that travel behavior is related to demographic and socioeconomic characteristics (e.g., Giuliano and Hanson, 2017). We therefore segment CBGs into groups by household income levels and share of minority populations to test for differences across these attributes. We follow the method proposed by Turner and Rawlings (2009) to classify CBGs according to their ethnic composition. If a CBG has more than 50% minority residents (i.e., non-White), it is categorized as a majority minority neighborhood. If not, it is categorized as non-Hispanic White. If one specific minority accounts for more than 60% of the population, the CBG is categorized as specific minority dominant. The minority dominant categories are Hispanic (of all races), African American, and other (e.g., Asian, Native American). It is worth noting that more than 90% of the other neighborhoods are Asian dominant neighborhoods. We use three categories for household income. The categories are based on household income quartiles for Los Angeles County (Census Bureau, 2021). Low income is the lowest quartile (< $46,429), medium is the second and third quartiles, and high income is the highest quartile (> $92,358). The intersection of ethnicity and income categories generates twelve population groups. See Table 2 and Figure 1 . The county’s large Hispanic population is evident; 3054 CBGs are Hispanic. Lower-income Hispanic CBGs are concentrated in the central area, and higher-income Hispanic CBGs extend east to the county border. Other concentrations are located in the San Fernando Valley and to the far north in Lancaster/Palmdale. There are few CBGs with predominantly African American populations. The large areas of mixed minorities are generally Asian and Hispanic or Hispanic and African American.Table 2 Number of CBGs by race/ethnicity and income categories Low income Medium income High income White 37 616 931 Hispanic 1150 1646 258 African American 76 88 26 Other 275 729 318 Figure 1 Spatial distribution of population groups: a. Low-income; b. Middle-income; c. High-income. 3.4 Other variables Travel on any given day may be affected by the day of the week and weather. Weekday patterns are different from weekends, and discretionary travel is less frequent during inclement weather. We include weekday and holiday binary variables to account for daily variation. Daily average precipitation and daily maximum temperature data were accessed from the Global Historical Climatology Network (GHCN) (Global Historical Climatology Network, 2021). The meteorological parameters were then interpolated to each CBG using the inverse distance weighting (IDW) method. Finally, we want to know if travel behavior is affected by intensity of the pandemic. We might expect that as the case rate increases, travel would decline as people avoid the increased risk of exposure. The pandemic severity level was measured through COVID-19 infections data provided by the Los Angeles Times (Los Angeles Times, 2021). The data are collected from California's Department of Public Health and local public health departments. Cumulative COVID-19 cases and mortality data for Los Angeles County are provided at the neighborhood level within the City of Los Angeles and at city level outside the City of Los Angeles. Table 3 gives descriptive statistics for income and race/ethnicity for our 12 population groups. It can be seen that for low- and middle-income groups, spatial segmentation is greatest for Hispanic and African American groups.Table 3 Descriptive statistics by population group7. Population group Median Income ($) %White % Hispanic % African American % Other Low-income White 35,522 64.9% 20.6% 4.9% 9.6% Low-income Hispanic 36,376 7.4% 79.0% 7.8% 5.8% Low-income African American 31,517 7.8% 21.6% 67.9% 2.7% Low-income Other 34,380 10.9% 38.2% 22.4% 28.5% Middle-income White 73,203 66.8% 18.2% 4.3% 10.8% Middle-income Hispanic 64,158 14.4% 71.9% 5.0% 8.8% Middle-income African American 67,165 8.4% 15.1% 72.2% 4.4% Middle-income Other 68,345 20.1% 31.7% 11.3% 36.9% High-income White 135,957 71.4% 13.7% 2.6% 12.3% High-income Hispanic 108,076 25.6% 59.6% 2.7% 12.1% High-income African American 115,510 8.5% 10.1% 76.9% 4.5% High-income Other 117,280 27.9% 21.9% 5.8% 44.5% 7 American Community Survey (ACS) 5-Year Data (2015-2019): https://data.census.gov/cedsci/table?q=&g=0500000US06037.150000&y=2019&d=ACS%205-Year%20Estimates%20Detailed%20Tables&tid=ACSDT5Y2019.B01003 3.5 Methods There were distinct periods during the pandemic, mainly defined by policy actions. We use these periods to compare mobility patterns under different conditions over the course of the pandemic. We define a total of 5 stages:• Stage 0 before COVID: 1/1/20 to 3/18/20. • Stage 1 stay at home: 3/19/20 to 5/7/20. California issues statewide shelter in place order on 3/19; residents to stay at home except for essential activities, non-essential businesses closed, public school shift to online. • Stage 2 re-opening: 5/6/20 to 6/30/20: Los Angeles County allows selected businesses to reopen with restrictions (e.g., mask mandate, limits on indoor space occupation), some public schools reopen in person or hybrid; Los Angeles Unified School District (LAUSD) online. • Stage 3 business restrictions: 7/1/20 to 12/13/20, California re-imposes business restrictions in response to COVID resurgence, non-essential business (e.g., indoor restaurants, movie theaters) closed again; LAUSD remains online. • Stage 4 vaccinations: 12/14/20 – 4/16/21, vaccines become available, first to health care workers, then elderly and those with health problems (January 19, 2021), then all adults; non-essential business remains closed; LAUSD remains online. We use difference-in-difference (DID) regression to estimate the impact of the various COVID policy stages across our population groups:(1) Yb,d=α+γ·staged+∑s∈S′βs·staged·Tb,s+ρ·VCOVIDd,PRCPb,d,TMAXb,d+ω·Holidayd+λ·City+φ·Weekdayd+λb+λd+εb,d Where: Yb,d = mobility measure at CBG b on day d. staged= the treatment variable, which takes value one for each respective stage. s∈S′= race/ethnic/income category s in the set of S’ categories. Tb,s = one if CBG b is in group s. V= vector of control variables: COVID case rate, precipitation, maximum temperature. Holidayd= dummy variable for holiday. City= dummy variables for cities of Pasadena and Long Beach. Weekdayd= dummy variable for day of week. λb= individual CBG fixed effects. λd= day/week fixed effects. εb,d= standard errors. We use the natural logarithm form for the dependent variables due to their distribution. We use low-income White as the reference group for the race/ethnicity/income categories. The coefficients of interest are the interaction terms policyd∙Tb,s to examine the differential effect of the various policy stages. Dummy variables are created for City of Pasadena and Long Beach as these cities have their own department of public health and thus may have different health care resources, stringency of shelter-in-place order enforcement, and other factors that could affect travel behavior. For DID regression, the parallel trend assumption must be met between the treatment group and the control groups to control for the influence of time-variant factors. It is hard to test the parallel trend assumption in the post-treatment period. Thus, we plot the daily average mobility measures and weekly average POI visits by group and stage to compare the pre-treated trends between the treatment group and the control groups. We find that the pre-treated trends between groups are generally parallel in in all stages. Trends are further discussed in the next section. 4 Results 4.1 General mobility change by population group 4.1.1 Descriptive results As noted above, we use two measures of mobility to estimate general mobility change by population groups: the average number of CBGs visited each day per device, and the proportion of mobile phone users that spent the entire day at home. Table 4, Table 5 give descriptive results for each measure, respectively. The first three columns give the group means and the last three columns give the difference between the previous stage. All differences are statistically significant at p<0.001. It can be seen that the two measures of mobility are quite consistent; CBGs visited drops the most during Stage 1 and the gradually increases. The average share of devices remaining at home increases the most during Stage 1 and then gradually decreasesTable 4 Change in CBGs visited by social class. Population group Average CBGs visited by device each day Absolute difference* Stage 0 Stage 1 Stage 2 Stage 3 Stage 4 Between Stage 0& Stage 1 Between Stage 1& Stage 2 Between Stage 2& Stage 3 Between Stage 3& Stage 4 Low-income Hispanic 2.32 1.69 1.89 2.01 2.05 -0.63 +0.20 +0.12 +0.05 Low-income African American 2.17 1.57 1.71 1.81 1.77 -0.60 +0.14 +0.10 -0.04 Low-income White 2.17 1.57 1.75 1.92 1.98 -0.60 +0.18 +0.17 +0.06 Low-income Other 2.30 1.60 1.77 1.90 1.93 -0.70 +0.17 +0.13 +0.03 Middle-income Hispanic 2.39 1.70 1.92 2.03 2.07 -0.69 +0.22 +0.11 +0.04 Middle-income African American 2.27 1.60 1.75 1.79 1.77 -0.67 +0.15 +0.05 -0.03 Middle-income White 2.31 1.52 1.76 1.89 1.91 -0.81 +0.24 +0.13 +0.02 Middle-income Other 2.40 1.60 1.80 1.93 1.96 -0.80 +0.20 +0.13 +0.04 High-income Hispanic 2.48 1.65 1.91 2.02 2.06 -0.83 +0.26 +0.11 +0.04 High-income African American 2.31 1.54 1.73 1.78 1.73 -0.77 +0.19 +0.05 -0.05 High-income White 2.45 1.50 1.80 1.93 1.94 -0.95 +0.30 +0.13 +0.01 High-income Other 2.47 1.55 1.79 1.91 1.94 -0.93 +0.24 +0.12 +0.03 All within group differences are significant at p < 0.001 Table 5 Change in proportion of mobile phone users spending entire day at home. Population group Average proportion of completelystay at home device Absolute difference Stage 0 Stage 1 Stage 2 Stage 3 Stage 4 Between Stage 0& Stage 1 Between Stage 1& Stage 2 Between Stage 2& Stage 3 Between Stage 3& Stage 4 Low-income Hispanic 28% 44% 39% 34% 34% +16% -5% -4% -1% Low-income African American 32% 45% 42% 37% 36% +13% -3% -5% -1% Low-income White 30% 46% 42% 35% 34% +16% -4% -7% -2% Low-income Other 27% 45% 42% 37% 36% +18% -3% -5% -1% Middle-income Hispanic 25% 44% 38% 34% 33% +19% -6% -4% -1% Middle-income African American 28% 45% 40% 37% 37% +17% -5% -3% -1% Middle-income White 24% 47% 39% 34% 33% +23% -8% -6% -1% Middle-income Other 25% 48% 42% 37% 36% +23% -6% -5% -1% High-income Hispanic 23% 45% 37% 33% 32% +22% -8% -4% -1% High-income African American 25% 47% 42% 38% 38% +22% -5% -4% 0% High-income White 20% 47% 38% 32% 32% +27% -9% -6% 0% High-income Other 22% 50% 42% 37% 36% +28% -8% -5% -1% All within group differences are significant at p < 0.001 Table 4, Table 5 show that the average number of CBGs visited before the pandemic is consistent with the literature. In general people in high income neighborhoods travel the most and people in low-income neighborhoods travel the least. Within each income category, people in Hispanic neighborhoods travel the most and those in African American neighborhoods travel the least. In Stage 1 there is a large drop in travel across all income groups, but the drop is greatest for the high-income neighborhoods. There are differences within income categories. Among high income neighborhoods the drop is greatest for white neighborhoods. Hispanic CBGs reduce travel the least in every income category. While there is a clear trend of increasing mobility for all groups through Stages 2 and 3, Hispanic neighborhoods have the highest rates of mobility within each income category. Low-income African American neighborhoods have the lowest mobility in Stage 0 and remain at the bottom of the trend throughout the period. The general mobility stays quite stable between Stages 3 and 4. Interestingly, we see the same pattern for the middle-income neighborhoods for CBGs visited, but there is less variation across race/ethnic groups for staying at home. The figures suggest that people living in Hispanic neighborhoods systematically travel more during the pandemic. These comparisons suggest that people who live in higher income neighborhoods were more likely to adhere to the shelter-in-place order than those from lower income neighborhoods. Higher income neighborhoods also responded more positively to the business re-opening policy. These observations suggest that both income and race/ethnicity matter. The averages given in Table 4, Table 5 do not reveal day to day variation within each stage. Figure 2 shows daily mobility trends by income level. Figure 2a shows the average number of CBGs visited per device per day, and Figure 2b shows the share of devices staying at home each day. The color blocks in the figures denote stages. The pattern in Stage 0 is consistent with known travel patterns: high income CBGs have more travel; low income CBGs have the least travel. Figures 2a and b are quite consistent. Both show that reduced mobility began shortly before the actual shelter-in-place order. Travel increases consistently through stages 1 and 2. The renewed restrictions of stage 3 appear to stop the increase in travel but not reduce travel. The appearance of vaccines generates another surge in mobility, but activity levels do not reach pre-pandemic levels.Figure 2 Mobility change by time: a. CBGs visited per device per day by income group; b. Proportion of devices that stayed at home all day. 4.2 Difference-in-difference model We use the DID analysis to test for effects of stage, income and race/ethnicity. Table 6, Table 7 present results for the CBGs visits and stay-at-home variables, respectively. The baseline group is low-income white neighborhoods. The columns give results for each stage. We begin with some general observations. First, variable coefficients are highly significant; this is due to the large sample size. We therefore focus more on direction and magnitude than significance. Second, the explanatory level of the models declines with each stage. The random variability of travel appears to increase with the recovery of travel.Table 6 Difference-in-differences regression estimates, number of CBGs visited (1) Log(CBGs Visited) (2) Log(CBGs Visited) (3) Log(CBGs Visited) (4) Log(CBGs Visited) Stage 1: Shelter-in-Place Order Stage 2: Business Reopen Stage 3: Business Restriction Stage 4: Vaccination Post-policy -0.386 0.032* 0.048* 0.102* Post × Low-income White Ref. Ref. Ref. Ref. Post × Low-income Hispanic 0.057* 0.021* 0.012* 0.009* Post × Low-income African American 0.011 -0.057* -0.064* -0.100* Post × Low-income Other 0.007 -0.013* -0.012* -0.023* Post × Middle-income White -0.048* 0.018* -0.015* -0.028* Post × Middle-income Hispanic 0.039* 0.026* 0.006* 0.002 Post × Middle-income African American 0.017* -0.031* -0.065* -0.086* Post × Middle-income Other -0.011* 0.014* 0.001 -0.007* Post × High-income White -0.082 0.044* -0.001* -0.023* Post × High-income Hispanic -0.013* 0.015* -0.016* -0.019* Post × High-income African American -0.027* -0.030* -0.074* -0.106* Post × High-income Other -0.061* 0.013* -0.012* -0.025* New confirmed COVID case growth rate -0.018* -0.021* -0.014* 0.052 Precipitation 0.018* -0.027* -0.230* 0.103 Max temperature 0.004* 0.001* -0.001* 0.003 Holiday -0.068* -0.022* -0.005* -0.083 Pasadena dummy -0.104* -0.008* -0.060* -0.087* Long Beach dummy -0.135* 0.049* -0.039* -0.089* Day of week dummy Yes Yes Yes Yes Neighborhood fixed effect Yes Yes Yes Yes Time fixed effect Yes Yes Yes Yes Obs. 786,418 638,917 1,344,823 1,773,152 Adj. R2 0.63 0.33 0.21 0.22 * p < 0.05, p < 0.01, * p < 0.001 Table 7 Difference-in-differences regression estimates, staying at home (1)Log(% Stay Home) (2)Log(% Stay Home) (3)Log(% Stay Home) (4)Log(% Stay Home) Stage 1: Shelter-in-Place Order Stage 2: Business Reopen Stage 3: Business Restriction Stage 4: Vaccination Post-policy 0.731* -0.031* -0.130*** -0.021* Post × Low-income White Ref. Ref. Ref. Ref. Post × Low-income Hispanic -0.062* -0.031* 0.008 0.015* Post × Low-income African American -0.070* 0.056* 0.075* 0.074* Post × Low-income Other -0.036* 0.015* 0.034* 0.043* Post × Middle-income White 0.121* -0.060* -0.019*** 0.005 Post × Middle-income Hispanic -0.012* -0.054*** -0.007* -0.000 Post × Middle-income African American -0.066* -0.009 0.054* 0.045* Post × Middle-income Other 0.100* -0.001 0.028* 0.042* Post × High-income White 0.217* -0.108* -0.057* -0.022* Post × High-income Hispanic 0.067* -0.074* -0.028* -0.022* Post × High-income African American 0.005 0.020 0.054* 0.061* Post × High-income Other 0.201* -0.038* 0.010* 0.024* New confirmed COVID case growth rate 0.021* -0.030* -0.026* -0.002 Precipitation 0.088* 0.060* 0.071* 0.115 Max temperature -0.008* -0.000 0.001* -0.001 Holiday 0.171* -0.005 -0.057* 0.221 Pasadena dummy 0.030 -0.015 -0.091* -0.187* Long Beach dummy 0.211* -0.058 -0.115* -0.168* Day of week dummy Yes Yes Yes Yes Neighborhood fixed effect Yes Yes Yes Yes Time fixed effect Yes Yes Yes Yes Obs. 786,418 638,917 1,344,823 1,773,152 Adj. R2 0.51 0.20 0.13 0.12 * p < 0.05, p < 0.01, * p < 0.001 We turn now to variable coefficients. For visits, the stage coefficient is strongly negative, then turns positive and increases in each stage. For stay at home, the stage coefficient is strongly positive, then turns negative and becomes increasingly negative. The models give consistent results and suggest that after the first stay at home order COVID health policies had little impact on behavior. Some possible explanations are: 1) less public confidence in public policies that seemed to change arbitrarily, 2) deferring some types of travel (e.g. medical care) became more difficult as the pandemic continued; 3) fatigue or stress of staying at home that intensified over time. Income/race/ethnicity coefficients are mostly significant but often of small magnitude. Higher income and whiter neighborhoods reduced travel more than lower income and majority minority neighborhoods in stage 1. High-income White neighborhoods increased travel the most in Stage 2, while low-income African American neighborhoods increased travel the least. Results for stage 3 are mixed. Only low-income Hispanic neighborhoods show relatively more travel. Staying at home decreases less for middle- and high-income Whites as well as high income Hispanics. In stage 4 travel increases the most in low-income Hispanic neighborhoods while staying at home decreases the most for high income whites and Hispanics. With respect to control variables, the new confirmed COVID case rate coefficient for visits is negative and of similar magnitude in stages 1 through 3 and becomes positive in stage 4. For stay at home, the coefficient is positive for stage 1, negative for stages 2 and 3, and not significant for stage 4, suggesting that case rates had little effect on travel. Residents of Pasadena reduced mobility more than other areas in Los Angeles County while residents of Long Beach reduced mobility less, likely reflecting differences in local travel restriction policies. Our results on income are consistent with prior studies (Atchison et al., 2020, Brough et al., 2021, Clay and Rogus, 2021, Goldman et al., 2021). High-income households engage in more discretionary travel and are more likely to have jobs that can be conducted from home. They therefore have both greater abilities to reduce travel when necessary and increase travel when restrictions are relaxed. In contrast, low-income households engage in less discretionary travel, and are more likely to have jobs that require traveling to the job site (Blau et al., 2020, Garfield et al., 2020). Our results show that race/ethnicity also has significant effects, as for example low-income Hispanic neighborhoods showing the smallest decrease in travel in stage 1. 4.3 Mobility changes by trip purpose Our results show there was less reduction in travel in low-income minority neighborhoods. Why might this be the case? As discussed in Section 2, low-income and minority workers are more likely to be employed in sectors that are deemed essential and require physical presence. Higher-income workers are more likely to be employed in jobs that can be conducted remotely. Higher-income households also have more capacity to purchase services (e.g. grocery deliveries) to avoid travel (Kar et al., 2021). We use trip purpose to examine whether changes in travel are related to changes in trip purpose. 4.3.1 Descriptive results We used the POIs to impute trip purpose, as shown in Table 1. The resulting categories are only approximate. For example, spending time at a restaurant could be social/recreational (purchasing a meal) or work. To determine the most likely trip purpose, we compared the POI’s location (CBG), the visitor’s primary daytime location (i.e. where he/she stays for the longest time between 9 AM and 5 PM), and the visitor’s home location. If a visitor’s primary daytime location is at a POI different from his/her home location, we consider the visit a work trip. We generated O-D matrices for each trip purpose by identifying the origins of devices visiting each POI. Figure 3 shows average trip frequency per device per week by purpose (rows), income group (columns), and race/ethnic group (color codes). The Y axis for each trip purpose is scaled to frequency. There are several observations to be drawn from Figure 3: 1) each trip purpose has a different temporal pattern, but all reflect the steep decline at the beginning of Stage 1 and gradual recovery thereafter, even before COVID vaccines became available; 2) non-work travel declines more steeply at the beginning of Stage 1 than work travel; 3) social/recreational and shopping travel increases more than other travel until Stage 3, when many businesses were again closed; 4) all travel purposes rebound in Stage 4; 5) medical-related visits tick up in Stage 3, perhaps because households are no longer able to defer needed medical care.Figure 3 Origin-destination analysis categorized by trip purpose for different population groups. Figure 3 also shows that travel varies across income and racial groups. In general, lower-income neighborhoods reduce work travel less relative to pre-pandemic levels, while patterns for other trip purposes are relatively consistent across income groups. Within low-income neighborhoods, Hispanic neighborhoods retain the highest rate of work travel throughout the pandemic stages. The higher rate of work travel is consistent with less ability to work remotely in low wage jobs (Atchison et al., 2020, Blau et al., 2020, Garfield et al., 2020). Low-income Hispanic neighborhoods also show the highest rate of social/recreational and shopping travel; these differences tend to dissipate with income level. 4.3.2 DID model results We use the same model form and control variables to examine the effects of income and race/ethnicity on the frequency of visits by trip purpose. We present results for work, social/recreational, and shopping in Table 8, Table 9, Table 10 respectively. 6 All tables have the same structure as Table 6.Table 8 Difference-in-differences regression estimates, visits to work per device per week (1)Log(POIs Visited) (2)Log(POIs Visited) (3)Log(POIs Visited) (4)Log(POIs Visited) Stage 1: Shelter-in-Place Order Stage 2: Business Reopen Stage 3: Business Restriction Stage 4: Vaccination Post-policy -1.066* -0.046* 0.144* 0.068 Post × Low-income White Ref. Ref. Ref. Ref. Post × Low-income Hispanic 0.182* 0.031* 0.056 0.038* Post × Low-income African American -0.038 -0.171 -0.064 -0.078* Post × Low-income Other -0.045 -0.093* 0.009 -0.057 Post × Middle-income White -0.144* 0.019 0.027 0.071* Post × Middle-income Hispanic 0.136* 0.031 0.043 0.055 Post × Middle-income African American -0.063 -0.158* -0.069* 0.056 Post × Middle-income Other 0.008 -0.016 0.072 -0.009 Post × High-income White -0.284* -0.131* 0.020** 0.038* Post × High-income Hispanic -0.027 -0.036* -0.005 0.122* Post × High-income African American -0.139 -0.208 -0.017 -0.023 Post × High-income Other -0.158* -0.009 0.034 0.039 New confirmed COVID case growth rate -0.051* 0.034* -0.011 -0.004 Precipitation 0.018* 0.015* -0.202 0.013 Max temperature 0.003* -0.006* 0.007* 0.001 Holiday 0.048* -0.055* -0.037* -0.027* Pasadena dummy -0.171 1.090* 0.316 -0.060 Long Beach dummy -0.227 1.167* 0.388 -0.143 Day of week dummy Yes Yes Yes Yes Neighborhood fixed effect Yes Yes Yes Yes Time fixed effect Yes Yes Yes Yes Obs. 116,044 84,835 129,989 135,251 Adj. R2 0.39 0.08 0.09 0.09 * p < 0.05, p < 0.01, * p < 0.001 Table 9 Difference-in-differences regression estimates, social and recreational trips. (1)Log(POIs Visited) (2)Log(POIs Visited) (3)Log(POIs Visited) (4)Log(POIs Visited) Stage 1: Shelter-in-Place Order Stage 2: Business Reopen Stage 3: Business Restriction Stage 4: Vaccination Post-policy -1.226* 0.049 -0.027* 0.053* Post × Low-income White Ref. Ref. Ref. Ref. Post × Low-income Hispanic 0.220* 0.025 0.095* 0.075* Post × Low-income African American 0.074 -0.181* 0.003 -0.164* Post × Low-income Other 0.055 -0.056 0.038 0.016 Post × Middle-income White -0.070* 0.067* 0.090* 0.012 Post × Middle-income Hispanic 0.159* 0.044 0.088* 0.065* Post × Middle-income African American 0.060 -0.033 0.042 -0.085* Post × Middle-income Other 0.064* 0.053 0.107* 0.044* Post × High-income White -0.184* 0.132* 0.149* 0.061* Post × High-income Hispanic 0.035* 0.059 0.087* 0.083* Post × High-income African American -0.049 0.016 0.034 -0.132* Post × High-income Other -0.061* 0.069* 0.081* 0.059* New confirmed COVID case growth rate -0.269* 0.187* 0.062* 0.007 Precipitation -0.263* -0.081* -0.102* -0.097 Max temperature 0.012* 0.003* 0.005* 0.005* Holiday 0.184* 0.052* 0.037* -0.019* Pasadena dummy 0.918* 0.709* 0.914* -0.297 Long Beach dummy 1.006* 0.991* 1.052* -0.466 Day of week dummy Yes Yes Yes Yes Neighborhood fixed effect Yes Yes Yes Yes Time fixed effect Yes Yes Yes Yes Obs. 116,586 85,745 189,812 256,773 Adj. R2 0.69 0.20 0.12 0.13 * p < 0.05, p < 0.01, * p < 0.001 Table 10 Difference-in-differences regression estimates, shopping trips. (1)Log(POIs Visited) (2)Log(POIs Visited) (3)Log(POIs Visited) (4)Log(POIs Visited) Stage 1: Shelter-in-Place Order Stage 2: Business Reopen Stage 3: Business Restriction Stage 4: Vaccination Post-policy -0.977* 0.050 -0.019 0.071* Post × Low-income White Ref. Ref. Ref. Ref. Post × Low-income Hispanic 0.201* 0.092* 0.072* 0.053* Post × Low-income African American 0.021 -0.125* -0.049 -0.219* Post × Low-income Other 0.031* -0.024 -0.032 -0.038 Post × Middle-income White -0.008 0.064* 0.047 -0.022 Post × Middle-income Hispanic 0.188* 0.105* 0.077* 0.002 Post × Middle-income African American 0.079* 0.022 -0.001 0.046* Post × Middle-income Other 0.098* 0.091* 0.089* -0.105* Post × High-income White -0.070* 0.099* 0.090* 0.020 Post × High-income Hispanic 0.095* 0.077* 0.055 -0.170* Post × High-income African American -0.036 -0.025 -0.096 -0.010 Post × High-income Other -0.015 0.080* 0.052 0.065* New confirmed COVID case growth rate -0.125* 0.109* 0.072* 0.041 Precipitation -0.091* -0.087* -0.068* -0.102 Max temperature 0.010* 0.002* 0.001 0.003* Holiday 0.225* -0.132* 0.043* 0.004 Pasadena dummy -0.247 -0.772* 0.254 0.597* Long Beach dummy -0.325 -0.743*** 0.226* 0.544*** Day of week dummy Yes Yes Yes Yes Neighborhood fixed effect Yes Yes Yes Yes Time fixed effect Yes Yes Yes Yes Obs. 116,604 85,785 189,752 256,603 Adj. R2 0.64 0.33 0.29 0.26 * p < 0.05, p < 0.01, * p < 0.001 Work trips decline significantly at the beginning of Stage 1. Even when businesses re-open, work trips continue to decline until Stage 3. There are distinct income and race/ethnicity effects; work trips from low-income Hispanic neighborhoods decline less, while work trips from high-income White, high income Other, and middle-income White neighborhoods reduce work trips more. These patterns remain in Stage 2 and reverse in Stages 3 and 4. More people living in high-income neighborhoods reduce their work trips even after business re-opens. The COVID case rate has a depressive effect in Stage 1, but not in the other stages. Table 9, Table 10 give the DID results for social/recreational and shopping visits, respectively. These visits are more strongly affected by both Stage 1 and Stage 2 than work trips, as expected. For both trip purposes, the average per device/week drops by about one full trip. Stage 2 results in a slight increase, and Stage 3 results in a slight decrease, likely because of business closures. Stage 4 is associated with increases in both trip purposes. For social/recreational visits, low-income Hispanic neighborhoods reduce visits the least, and high-income white neighborhoods reduce visits the most. In the later stages, high-income white neighborhoods increase visits the most and low-income African American neighborhoods increase visits the least. The pattern is similar for shopping, except that middle-income Hispanic neighborhoods reduce shopping visits the least in Stage 1. The COVID case rate control variable has the same effect as in the previous models. We summarize the trip purpose results as follows. The shelter-in-place order results in travel reductions for all purposes but with different magnitude. Trips for all purposes were found to have the greatest decline in Stage 1 when compared to the following stage (trip reduction rates range from 98 percentage points (shopping trips) to 122 percentage points (social/recreational trips). Stage 2, which re-opened restaurants and bars, has a positive effect on trips for social/recreational and shopping activities (5 percentage points), but not for work trips. Stage 3 resulted in further trip declines for social/recreational and shopping trips (3 percentage points and 2 percentage points), but not for work trips (14 percentage points increase). Stage 4 led to increase in work, social/recreational, and shopping trips (7 percentage points, 5 percentage points and 7 percentage points). Within these general trends, differences between income and race/ethnic groups show generally less reduction in travel for low-income groups, and greater later stage increases for high-income groups. When controlling for COVID-19 cases and other covariates, Whites tend to reduce more work trips during the shelter-in-place period. In other words, people in ethnic minority neighborhoods, whether high-income or low-income, appear to have less opportunity to work remotely than those in otherwise-similar White neighborhoods. Similarly, low-income and middle-income groups reduce work trips less than otherwise-similar high-income groups during the shelter-in-place period. When COVID vaccines became available, residents of White neighborhoods show the largest increase in discretionary trips, while residents of otherwise-similar African American neighborhoods show less increase or even a decrease in trips for the same purpose. 5 Conclusions Our analysis may be summarized as follows. First, our results on travel responses to the various pandemic stages track with traffic observations. The first stay at home order had a strong effect; levels of travel plummeted in the early stage of the pandemic. Travel began to gradually recover before the stay at home order was lifted and continued through the later stages. The second period of restriction (stage 3) slowed or stopped increases for some travel purposes (shopping, social/recreational) but not for others. The availability of vaccines allowed the travel recovery to resume, despite no policy changes for business activities. Within these general trends, income level and ethnicity explain differences across neighborhoods. High-income groups, notably high-income White neighborhoods, have the strongest response to travel restriction policy implementation and vaccination availability. In contrast, low-income groups, and in particular low-income minority neighborhoods responded less; these groups reduced mobility less in Stage 1 and increased mobility less in Stage 2. More limited response to Stage 1 does not appear to be a matter of compliance. Our trip purpose models support the argument of differences in occupation and associated opportunities for working at home. Evidence suggests that although people perceived increased health risks at their work location, they would not significantly reduce work trips if being present at the workplace is required (Hotle et al., 2020). Our trip purpose DID models also demonstrate that low-income neighborhoods had relatively higher mobility for shopping trips during the shelter-in-place period. The possible explanation is that low-income households engage in less discretionary travel due to budget constraints (Giuliano, 2005, Murakami and Jennifer, 1997). They therefore have less flexibility to reduce such travel. Lower income households also have less capacity for stocking up or purchasing online (Kar et al., 2021). Response to vaccine availability also differs across population groups. Residents of White neighborhoods had the greatest increase in discretionary trips, while residents of African American neighborhoods had the least increase or even reduction. These differences might be explained by unequal access to vaccines and different levels of trust in vaccines across various population groups (Siegel et al., 2021, Wong et al., 2022). Compared to other population groups, African American and low-income populations have the highest vaccine hesitancy and least confidence in traveling (Stoler et al., 2021, Willis et al., 2021). Overall, our findings provide evidence of social injustice during the COVID-19 pandemic. The same populations – low income, predominantly minority – disadvantaged in other circumstances (e.g., financial and medical resources) are also disadvantaged in the COVID-19 pandemic. These populations have fewer options to avoid exposure and risk and are suffering the highest rates of both health and economic impacts (Ahmad et al., 2020, Brough et al., 2021, Jacobs, 2011). Given the prevalence of comorbidity factors and poor health insurance coverage among low-income and ethnic minority dominant communities, the risks of less physical distancing and lack of access to vaccines are substantial. These communities are also more likely to lack sufficient testing, vaccination, and contact tracing capacity to monitor and thwart COVID-19 outbreaks (Cardona et al., 2021, Maroko et al., 2020, Schmidt et al., 2021, Thakore et al., 2021). Our findings suggest the need for more responsive public policy to protect marginalized populations from the effects of COVID. Now well into the third year of the pandemic, low income, minority populations continue to be disproportionately represented in the COVID hospitalization and death statistics. Policy strategies include more education and vaccine access as well as broad distribution of testing kits in high vulnerability neighborhoods; more social distancing, frequent handwashing, and extensive cleaning at the worksite; more flexible work arrangements; and greater availability of paid sick leave. Finally, financial mechanisms to facilitate online purchases and home deliveries should be considered. There are some limitations to our work. First, our data include only Los Angeles County. Given that the response to the pandemic has largely been left to states and counties, every county is to some extent a unique case. The question is how much this affects the generalizability of results. We would argue that results are generalizable to the extent that socioeconomic conditions hold in other places. High-income, highly educated workers are more likely to be able to work at home, whether in Los Angeles or any other metropolitan area. Minorities make up a large portion of service industry jobs across the US. Low-income households travel less due to income constraints. We therefore expect future studies of other states or metro areas to be broadly consistent. Second, we followed Turner and Rawlings (2009)’s method to categorize ethnic neighborhoods and used income quartiles to distinguish relative income levels, which allows for large enough groups for both high- and low-income race/ethnicity categories. However, there are various methods to define minority and low-income neighborhoods and there is no “gold standard” for these measures. Different ethnic and income category definitions might lead to different results. Sensitivity analysis could be applied in further research to examine the effect of different ethnic and income category definitions on mobility changes. Third, the global pandemic continues to evolve as this paper is written. We know from studies of earthquakes and other disasters that people can make dramatic changes in travel behavior for a short period of time but then quickly revert to regular behavior. Anecdotal evidence suggests that people have become increasingly resistant to behavior changes that have economic or other costs. As the pandemic wears on, more people likely have become fatigued with staying at home, while others may have found it increasingly difficult to defer activities requiring travel. COVID-19 presents a unique opportunity to explore how people react and respond to a long-term crisis. Unlike floods or earthquakes, the pandemic is global and the timeframe for defeating the virus is uncertain. The duration of the pandemic has gone on far longer than temporary adjustments can be sustained; the question is, how much of the adjustments made will continue beyond the pandemic. Working from home and online shopping are of particular interest in this context. COVID-19 also illustrates how social injustice feeds on itself. The most vulnerable neighborhoods are the places where people have the least options for avoiding travel and hence the greatest risk of exposure. COVID-19 is one more illustration of social disparities that need to be addressed. Uncited references Fazio, 2021, U.S. Department of Transportation Federal Highway Administration, 2019. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors are grateful to SafeGraph for providing mobile device data. Comments from anonymous reviewers helped to improve the analysis and paper organization. This research was supported by the METRANS Transportation Consortium at University of Southern California. All errors and omissions are the responsibility of the authors. 1 “Hispanic” is defined as Hispanic or Latino of any race. 2 “White” is defined as non-Hispanic or Latino White alone. 3 “African Americans” is defined as non-Hispanic or Latino Black alone. 4 Users of mobile devices have given permission for their location to be tracked by a variety of mobile apps. The SafeGraph traces a device user’s GPS trajectory from the apps. 5 https://covid19.apple.com/mobility. 6 Full results for other trip purposes are available upon request. ==== Refs References ABC News. (2020). Timeline: How coronavirus got started. available at: https://abcnews.go.com/Health/timeline-coronavirus-started/story?id=69435165 (Accessed on 23 December 2021) Ahmad K. Erqou S. Shah N. Nazir U. Morrison A.R. Choudhary G. Wu W.C. Association of poor housing conditions with COVID-19 incidence and mortality across US counties PLoS One 15 2020 1 13 10.1371/journal.pone.0241327 Atchison, C., Bowman, L., Vrinten, C., Redd, R., Pristera, P., Eaton, J., Ward, H., 2020. Perceptions and behavioural responses of the general public during the COVID-19 pandemic: A cross-sectional survey of UK Adults 1–21. 10.1101/2020.04.01.20050039 Barnett-howell, Z., Mobarak, A.M., 2020. The Benefits and Costs of Social Distancing in Rich and Poor Countries 1–14. Blau F.D. Koebe J. Meyerhofer P. Who Are the Essential and Frontline Workers? SSRN Electron. J. 2020 10.2139/ssrn.3686432 Bogart L.M. Ojikutu B.O. Tyagi K. Klein D.J. Mutchler M.G. Dong L. Lawrence S.J. Thomas D.R. Kellman S. COVID-19 Related Medical Mistrust, Health Impacts, and Potential Vaccine Hesitancy Among Black Americans Living With HIV J. Acquir. Immune Defic. Syndr. 86 2021 200 207 10.1097/QAI.0000000000002570 33196555 Brough R. Freedman M. Phillips D.C. Understanding socioeconomic disparities in travel behavior during the COVID‐19 pandemic Journal of Regional Science 61 4 2021 753 774 34230690 Cardona S. Felipe N. Fischer K. Sehgal N.J. Schwartz B.E. Vaccination Disparity: Quantifying Racial Inequity in COVID-19 Vaccine Administration in Maryland J. Urban Heal. 98 2021 464 468 10.1007/s11524-021-00551-0 Chen J.T. Waterman P.D. Krieger N. COVID-19 and the unequal surge in mortality rates in Massachusetts, by city/town and ZIP Code measures of poverty, household crowding, race/ethnicity, and racialized economic segregation 2020 HCPDS Work Pap 19 Chiou, L., Tucker, C., 2020. Social Distancing, Internet Access and Inequality. Clay, L.A., Rogus, S., 2021. Impact of Employment , Essential Work , and Risk Factors on Food Access during the COVID-19 Pandemic in New York State. Courtemanche C. Garuccio J. Le A. Pinkston J. Yelowitz A. Strong social distancing measures in the united states reduced the covid-19 growth rate Health Aff. 39 2020 1237 1246 10.1377/hlthaff.2020.00608 Davies, N.G., Kucharski, A.J., Eggo, R.M., Gimma, A., Edmunds, W.J., Jombart, T., O’Reilly, K., Endo, A., Hellewell, J., Nightingale, E.S., Quilty, B.J., Jarvis, C.I., Russell, T.W., Klepac, P., Bosse, N.I., Funk, S., Abbott, S., Medley, G.F., Gibbs, H., Pearson, C.A.B., Flasche, S., Jit, M., Clifford, S., Prem, K., Diamond, C., Emery, J., Deol, A.K., Procter, S.R., van Zandvoort, K., Sun, Y.F., Munday, J.D., Rosello, A., Auzenbergs, M., Knight, G., Houben, R.M.G.J., Liu, Y., 2020. Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study. Lancet Public Heal. 5, e375–e385. 10.1016/S2468-2667(20)30133-X Fazio, R.H., Ruisch, B.C., Moore, C.A., Granados, J.A., Boggs, S.T., Ladanyi, J.T., 2021. Social distancing decreases an individual ’ s likelihood of contracting COVID-19. 10.1073/pnas.2023131118 Feldman S. Johnston C. Understanding the determinants of political ideology: Implications of structural complexity Polit. Psychol. 35 2014 337 358 10.1111/pops.12055 Flaxman S. Mishra S. Gandy A. Unwin H.J.T. Mellan T.A. Coupland H. Whittaker C. Zhu H. Berah T. Eaton J.W. Monod M. Perez-Guzman P.N. Schmit N. Cilloni L. Ainslie K.E.C. Baguelin M. Boonyasiri A. Boyd O. Cattarino L. Cooper L.V. Cucunubá Z. Cuomo-Dannenburg G. Dighe A. Djaafara B. Dorigatti I. van Elsland S.L. FitzJohn R.G. Gaythorpe K.A.M. Geidelberg L. Grassly N.C. Green W.D. Hallett T. Hamlet A. Hinsley W. Jeffrey B. Knock E. Laydon D.J. Nedjati-Gilani G. Nouvellet P. Parag K.V. Siveroni I. Thompson H.A. Verity R. Volz E. Walters C.E. Wang H. Wang Y. Watson O.J. Winskill P. Xi X. Walker P.G.T. Ghani A.C. Donnelly C.A. Riley S. Vollmer M.A.C. Ferguson N.M. Okell L.C. Bhatt S. Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe Nature 584 2020 257 261 10.1038/s41586-020-2405-7 32512579 Gao, S., Rao, J., Kang, Y., Liang, Y., Kruse, J., Doepfer, D., Sethi, A.K., Reyes, J.F.M., Patz, J., Yandell, B.S., 2020. Mobile phone location data reveal the effect and geographic variation of social distancing on the spread of the COVID-19 epidemic 586. Garfield R. Rae M. Claxton G. Orgera K. Double Jeopardy: Low Wage Workers at Risk for Health and Financial Implications of COVID-19 2020 Kaiser Family Foundation Gatwood, J., 2021. Factors influencing likelihood of COVID-19 vaccination : A survey of Tennessee adults 78, 879–889. 10.1093/ajhp/zxab099 Giuliano G. Low income, public transit, and mobility Transp. Res. Rec. 63–70 2005 10.3141/1927-08 Giuliano G. Hanson S. The geography of urban transportation 2017 The Guilford Press New York Goldman N. Pebley A.R. Lee K. Andrasfay T. Pratt B. Racial and ethnic differentials in COVID-19- related job exposures by occupational standing in the US PLoS One 16 2021 1 17 10.1371/journal.pone.0256085 Gupta S. Nguyen T. Raman S. Lee B. Lozano-Rojas F. Bento A. Simon K. Wing C. Tracking public and private responses to the covid-19 epidemic evidence from state and local government actions Am. J. Heal. Econ. 7 2021 361 404 10.1086/716197 Hotle S. Murray-Tuite P. Singh K. Influenza risk perception and travel-related health protection behavior in the US: Insights for the aftermath of the COVID-19 outbreak Transp. Res. Interdiscip. Perspect. 5 2020 100127 10.1016/j.trip.2020.100127 Hu, T., Yue, H., Wang, C., She, B., Ye, X., Liu, R., Zhu, X., Guan, W.W., Bao, S., 2020. Racial Segregation , Testing Site Access , and COVID-19 Incidence Rate in Massachusetts , USA Huang, X., Li, Z., Lu, J., Wang, S., Wei, H., Chen, B., Bloustein, E.J., 2020. Time-series clustering for home dwell time during COVID-19: what can we learn from it? medRxiv 2020.09.27.20202671. Jacobs D.E. Environmental health disparities in housing Am. J. Public Health 101 2011 115 122 10.2105/AJPH.2010.300058 Jamison A.M. Quinn S.C. Freimuth V.S. “You don’t trust a government vaccine”: Narratives of institutional trust and influenza vaccination among African American and white adults Soc. Sci. Med. 221 2019 87 94 10.1016/j.socscimed.2018.12.020 30576982 Jay J. Bor J. Nsoesie E.O. Lipson S.K. Jones D.K. Galea S. Raifman J. Neighbourhood income and physical distancing during the COVID-19 pandemic in the United States Nat. Hum. Behav. 4 2020 10.1038/s41562-020-00998-2 Kar A. Motoyama Y. Carrel A.L. Miller H.J. Le H.T.K. COVID-19 exacerbates unequal food access Appl. Geogr. 134 2021 102517 10.1016/j.apgeog.2021.102517 Khanijahani, A., 2022. Socioeconomic and Racial Segregation and COVID-19 : Concentrated Disadvantage and Black Concentration in Association with COVID-19 Deaths in the USA 367–375. Kim C. Cheon S.H. Choi K. Joh C.H. Lee H.J. Exposure to fear: Changes in travel behavior during MERS outbreak in Seoul KSCE J. Civ. Eng. 21 2017 2888 2895 10.1007/s12205-017-0821-5 Kwon S. Joshi A.D. Lo C. Drew D.A. Nguyen L.H. Guo C. Ma W. Mehta R.S. Shebl F.M. Warner E.T. Astley C.M. Merino J. Murray B. Wolf J. Ourselin S. Steves C.J. Spector T.D. Hart J.E. Song M. Vopham T. Association of social distancing and face mask use with risk of COVID-19 Nat. Commun. 1–10 2021 10.1038/s41467-021-24115-7 Lai, S., Ruktanonchai, N.W., Zhou, L., Prosper, O., Luo, W., Floyd, J.R., Wesolowski, A., Santillana, M., Zhang, C., 2020. outbreak in China 1–29. Lou J. Shen X. Niemeier D. Are stay-at-home orders more difficult to follow for low-income groups ? J. Transp. Geogr. 89 2020 102894 10.1016/j.jtrangeo.2020.102894 Maroko A. Nash D. Pavilonis B. Covid-19 and Inequity: A comparative spatial analysis of New York City and Chicago hot spots J. Urban Heal. 461–470 2020 10.1101/2020.04.21.20074468 Milosh M. Painter M. Van Dijcke D. Wright A.L. Unmasking Partisanship: How Polarization Influences Public Responses to Collective Risk SSRN Electron. J. 2020 10.2139/ssrn.3664779 Motta, M., 2020. How Right-Leaning Media Coverage of COVID-19 Facilitated the Spread of Misinformation in the Early Stages of the Pandemic in the U . S . 335–342. 10.1017/S0008423920000396 Mukherjee S. Jain T. Impact of COVID-19 on the mobility patterns: An investigation of taxi trips in Chicago PLoS One 17 2022 1 19 10.1371/journal.pone.0267436 Murakami E. Jennifer Y. Daily Travel by Persons with Low Income Daily Travel by Persons with Low Income African Am. Mobil. Symp. 1997 1 27 Norris, P., 2001. Digital divide: Civic engagement, information poverty, and the Internet worldwide. Painter M. Qiu T. Political Beliefs affect Compliance with COVID-19 Social Distancing Orders SSRN Electron. J. 2020 10.2139/ssrn.3569098 Quinn S.C. Lama Y. Jamison A. Freimuth V. Shah V. Willingness of Black and White Adults to Accept Vaccines in Development: An Exploratory Study Using National Survey Data Am. J. Heal. Promot. 35 2021 571 579 10.1177/0890117120979918 Schmidt H. Weintraub R. Williams M.A. Miller K. Buttenheim A. Sadecki E. Wu H. Doiphode A. Nagpal N. Gostin L.O. Shen A.A. Equitable allocation of COVID-19 vaccines in the United States Nat. Med. 27 2021 1298 1307 10.1038/s41591-021-01379-6 34007071 Siegel M. Jain I.C. Boykin M. Owens A. Muratore R. Nunn T. Oh J. Racial / Ethnic Disparities in State - Level COVID - 19 Vaccination Rates and Their Association with Structural Racism J. Racial Ethn. Heal. Disparities. 2021 10.1007/s40615-021-01173-7 Stoler J. Enders A.M. Klofstad C.A. Uscinski J.E. The limits of medical trust in mitigating COVID-19 vaccine hesitancy among black Americans J. Gen. Intern. Med. 36 2021 3629 3631 34021446 Thakore N. Khazanchi R. Orav E.J. Ganguli I. Association of Social Vulnerability, COVID-19 vaccine site density, and vaccination rates in the United States Healthcare 9 2021 100583 10.1016/j.hjdsi.2021.100583 Torrats-espinosa, G., 2021. Using machine learning to estimate the effect of racial segregation on COVID-19 mortality in the United States. 10.1073/pnas.2015577118/-/DCSupplemental.y Turner, M.A., Rawlings, L., 2009. Promoting Neighborhood Diversity: Benefits, Barriers, and Strategies 18. U.S. Department of Transportation Federal Highway Administration, 2019. Trends in discretionary travel: 2017 National Household Travel Survey. Weill J.A. Stigler M. Deschenes O. Springborn M.R. Social distancing responses to COVID-19 emergency declarations strongly differentiated by income Proc. Natl. Acad. Sci. U. S. A. 117 2020 19658 19660 10.1073/PNAS.2009412117 32727905 Wen Z. Gu H. Kavanaugh R.R. The impacts of SARS on the consumer behaviour of Chinese domestic tourists Curr. Issues Tour. 8 2005 22 38 10.1080/13683500508668203 Willis D.E. Andersen J.A. Bryant-Moore K. Selig J.P. Long C.R. Felix H.C. Curran G.M. McElfish P.A. COVID-19 vaccine hesitancy: Race/ethnicity, trust, and fear Clin. Transl. Sci. 14 2021 2200 2207 34213073 Wong C.A. Alzuru C. Kinsley K. Jury R. Sauer M.L. Jones T. Armstrong V. COVID-19 Reflections: COVID-19 Vaccination in North Carolina: Promoting Equity by Partnering with Communities and Health Care Providers N. C. Med. J. 83 2022 197 202 35504705 Wright A.L. Sonin K. Driscoll J. Wilson J. Journal of Economic Behavior and Organization Poverty and economic dislocation reduce compliance with COVID-19 shelter-in-place protocols R J. Econ. Behav. Organ. 180 2020 544 554 10.1016/j.jebo.2020.10.008 33100443 Yesuf M. Bluffstone R.A. Poverty, risk aversion, and path dependence in low-income countries: Experimental evidence from Ethiopia Am. J. Agric. Econ. 91 2009 1022 1037 10.1111/j.1467-8276.2009.01307.x	36467712	PMC9708633	NO-CC CODE	2022-12-16 23:21:36	no	Travel Behav Soc. 2023 Apr 30; 31:189-201	utf-8	Travel Behav Soc	2,022	10.1016/j.tbs.2022.11.011	oa_other
==== Front J Funct Foods J Funct Foods Journal of Functional Foods 1756-4646 2214-9414 The Authors. Published by Elsevier Ltd. S1756-4646(22)00426-1 10.1016/j.jff.2022.105356 105356 Article Randomized placebo-controlled trial of oral tannin supplementation on COVID-19 symptoms, gut dysbiosis and cytokine response Molino Silvia a Pisarevsky Andrea b Badu Shyam cd Wu Qinglong cd Mingorance Fabiana López e Vega Patricia b Stefanolo Juan Pablo f Repetti Julieta e Ludueña Guillermina b Pepa Pablo g Olmos Juan Ignacio g Fermepin Marcelo Rodriguez h Uehara Tatiana i Viciani Elisa j Castagnetti Andrea j Savidge Tor cd⁎ Piskorz María Marta i⁎ a Departamento de Nutrición y Bromatología, Instituto de Nutrición y Tecnología de los Alimentos, Centro de Investigación Biomédica, Universidad de Granada, Granada, Spain b Universidad de Buenos Aires, Hospital de Clínicas José de San Martin, Departamento de Medicina Interna, Buenos Aires, Argentina c Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX, USA d Texas Children’s Microbiome Center, Department of Pathology, Texas Children’s Hospital, Houston, TX, USA e Universidad de Buenos Aires/ IBIMOL, Hospital de Clínicas José de San Martin, Programa de Estudios Pancreáticos, Buenos Aires, Argentina f Hospital de Gastroenterología Dr Carlos Bonorino Udaondo, Buenos Aires, Argentina g Universidad de Buenos Aires, Hospital de Clínicas José de San Martin, Servicio de Gastroenterología, Buenos Aires, Argentina h Universidad de Buenos Aires, Hospital de Clínicas José de San Martin, Facultad de Farmacia y Bioquímica, Departamento de Bioquímica Clínica, Buenos Aires, Argentina i Universidad de Buenos Aires, Hospital de Clínicas José de San Martin, Sector Neurogastroenterología del Servicio de Gastroenterología, Buenos Aires, Argentina j Wellmicro Srl, Bologna, Italy ⁎ Corresponding authors at: Universidad de Buenos Aires, Hospital de Clínicas José de San Martin, Sector Neurogastroenterología del Servicio de Gastroenterología, Buenos Aires, Argentina (M.M. Piskorz). 30 11 2022 12 2022 30 11 2022 99 105356105356 27 8 2022 2 11 2022 29 11 2022 © 2022 The Authors. Published by Elsevier Ltd. 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Graphical abstract The clinical study aim was to investigate whether a tannin-based dietary supplementation could improve the efficacy of standard-of-care treatment of hospitalized COVID-19 patients by restoring gut microbiota function. Adverse events and immunomodulation post-tannin supplementation were also investigated. A total of 124 patients receiving standard-of-care treatment were randomized to oral tannin-based supplement or placebo for a total of 14 days. Longitudinal blood and stool samples were collected for cytokine and 16S rDNA microbiome profiling, and results were compared with 53 healthy controls. Although oral tannin supplementation did not result in clinical improvement or significant gut microbiome shifts after 14-days, a reduction in the inflammatory state was evident and significantly correlated with microbiota modulation. Among cytokines measured, MIP-1α was significantly decreased with tannin treatment (p = 0.03) where it correlated positively with IL-1β and TNF- α, and negatively with stool Bifidobacterium abundance. Keywords COVID-19 Tannins Gut microbiota Dysbiosis Serum cytokines Abbreviations AUC, area-under-curve COVID-19, Coronavirus disease 2019 HC, Non COVID-19 healthy controls LEFSe, Linear discriminant analysis effect size SARS-CoV-2, severe acute respiratory syndrome coronavirus 2 ==== Body pmc1 Introduction The major organs affected in individuals infected with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) generally include the respiratory and cardiovascular systems. Less well appreciated in COVID-19 (Coronavirus disease 2019) patients is the dysfunction evident in other organs, including the gastrointestinal tract or onset of multiple organ failure (Chen et al., 2020, Gao et al., 2020). Disrupted crosstalk between the respiratory system and gut microbiota, termed the “gut-lung axis”, is reported in COVID-19 patients who generally display depletion of symbionts (i.e., Bifidobacterium spp. and Lactobacillus spp.) with enrichment of opportunistic pathobionts that correlate with disease severity (Xu et al., 2020, Zuo et al., 2020). Moreover, the exacerbated immune response in COVID-19 is strongly linked to gastrointestinal dysfunction and dysbiosis, which in turn can lead to production of pro-inflammatory cytokines (Villapol, 2020). Thus, the gut microbiota represents a potential target for therapy and diet could have a significant impact on the health status of the host. Several reports have highlighted the influence of different diets on COVID-19 severity, which merits deeper investigation of therapeutic options based on nutritional support favoring restoration of gut microbiota function (Kalantar-Zadeh et al., 2020, Liang, 2020). Tannins are natural bioactive compounds that positively impact gut microbiota function, in part by promoting expansion of probiotic bacteria (Molino et al., 2021, Molino et al., 2022). These polyphenol substrates are actively metabolized by gut microbes with end products, such as short chain fatty acids, urolithins and quercetin that can regulate local and systemic immune responses (Fanos et al., 2020, Molino et al., 2018). Despite the rollout of vaccination and booster campaigns against SARS-CoV-2, the threat of infection is still present, with a need for long-term clinical management of post-infectious symptoms becoming apparent. With the reported ability of oral tannin extract to modulate the gut microbiota composition and inflammatory response, this could potentially supplement COVID-19 standard therapies as a nutraceutical approach to mitigate SARS-CoV-2 infection. The aim of this study was to investigate the efficacy and adverse events of a tannin-based dietary supplement added to standard-of-care treatment in patients with non-life-threatening COVID-19. The impact of oral tannin on dysbiosis and the systemic inflammatory response was also investigated. Furthermore, at baseline, the inflammation and gut microbiota of COVID-19 patients were compared with a cohort of non-COVID-19 controls. 2 Materials and methods 2.1 Study design The study was approved by the ethics committee of the University Hospital of Universidad de Buenos Aires “Hospital de Clínicas José de San Martín”, Argentina, and adhered to ethical principles outlined in the declaration of Helsinki convention. Registered on ClinicalTrials.gov (NCT04403646), all protocol details are shown in Supplementary Figure S1 and reported previously by us (Molino, Pisarevsky, et al., 2021). Eligibility of study participants was assessed within 24 h of randomization, with patients and clinicians blinded to treatment. Computer-generated randomization (1:1) assigned tannin supplement or placebo, with sample size estimated at 140 (70 patients per group) based on a calculated alpha error of 5 % and power of 80 %. All COVID-19 participants continued to receive standard-of- care treatment based on the Argentina Ministry of Health and hospital treatment guidelines (Ministerio de Salud Argentina, n.d.). Such treatments included antiviral medications, antibacterial medications, steroids, supplementary oxygen, and convalescent plasma. Participants in the treatment arms received 2 capsules per day of tannin (240 mg of quebracho and chestnut tannin extract blend + 0.72 µg B12 vitamin; Arbox Microbiota, Indunor S.A.) or identical capsules of placebo for 14 days (see Fig. 1 ).Fig. 1 Study design and treatment allocation. During screening, patients continued to receive standard-of- care treatment. At baseline, blood and stool samples were collected for COVID-19 patients and HC, for serum cytokine measurement and 16S rDNA microbiome profiling. 124 COVID-19 patients were randomized to receive tannin supplementation or placebo for 14 days. At day 14, blood and stool samples were collected only for COVID-19 patients. Non COVID-19 healthy controls (HC) were age and sex-matched individuals with no medical history or use of antibiotic or corticosteroid within 3 months and tested negative for SARS-CoV-2. All authors had access to the study data and reviewed and approved the final manuscript. 2.2 Trial outcome endpoints The primary outcome measure was time to hospital discharge within 28-days (T28) of receiving the first oral treatment. Secondary outcome measures included: 28-day all-cause mortality, invasive ventilation at T28, measurement of systemic inflammatory cytokine levels, and fecal microbiota composition between baseline T0 and 14 days of intervention (T14). 2.3 Sample collection Blood and stool samples were collected at T0 for both COVID-19 patients and HC, while sample collections were repeated at T14 for COVID-19 patients only. Venous blood samples were drawn into Vacutainer tubes containing clot activator and serum obtained by centrifugation (3,000 g for 15 min, 4 °C). Stool samples were collected in μb-eNAT preservation tubes (COPAN®, Italy) and stored at − 80 °C. 2.4 Serum cytokines Cytokines were quantified using the commercial Bio-Plex Pro™ Human Cytokine 27-plex following kit instructions. 2.5 Microbiome sequencing and analysis Microbial DNA was extracted from 100 mg of homogenized fecal samples using the MagMAX™ Microbiome Ultra Nucleic Acid Isolation Kit according to the manufacturer’s instructions and was sequenced as described by Gaibani et al. (2021). DADA2 package (version 1.8) was used for processing de-multiplexed raw sequencing reads, with minor modifications (Klindworth et al., 2013). Specifically, raw sequencing reads were trimmed while maintaining the overlap regions for merging paired-end reads; sequencing primers retained in forward and reverse reads were removed. IdTaxa function in DECIPHER package (version 2.6.0) (Callahan et al., 2016) and its pre-built training set (LearnTaxa function) of SILVA database (release 132) (Murali et al., 2018) was used for taxonomy assignment (threshold: 0.5) of amplicon sequence variants (ASVs) from DADA2 output. ASVs that were not classified as bacteria and chimeras were removed. Samples with less than 1,000 reads were excluded and total sum scaling method was applied to normalize feature data prior to downstream analysis. 2.6 Statistical analyses Data were analyzed using the independent t-test, Mann–Whitney U test or chi2 test depending on their distribution. Comparison of cytokine levels between COVID-19 patients and controls (with and without adjustment for age, sex, and BMI by linear regression) was analyzed by linear regression with Stata software (Stata Corp., College Station, Texas, USA) version 17.0. Time-to-event data were analyzed using the Kaplan-Meier method. Hazard ratios were calculated using Cox proportional hazards models while treatment effects for secondary outcome measures applying odds ratios. Random forest algorithm in the random Forest R package together with 5-fold cross-validation sampling was used to generate a supervised classification model using serum cytokine profiles to differentiate COVID-19 patients from HC. Alpha diversity analysis and Bray-Curtis dissimilarity distance were calculated at the ASV level using QIIME 1.9.1. Mann-Whitney test was used to compare alpha diversity metrics and ANOSIM non-parametric test was used to establish dissimilarity between groups. Relative abundances at each taxonomic level were compared by non-parametric Kruskal-Wallis test. Spearman correlation analysis between serum cytokines and stool microbiome features at ASV and genus levels was performed using the R based cor.test() function, Benjamini-Hochberg procedure was applied as multiple testing corrections. 3 Results 3.1 Study cohort A total of 124 out of 143 screened COVID-19 patients were enrolled and randomized to receive oral tannin-based supplementation or placebo, added to standard-of-care treatment. Demographics of the study cohort are summarized in Supplementary Table S1. The mean age of the HC group was 52 ± 8 years (62 % women), while the COVID-19 patients were 54 ± 15 years (50 % women). Four COVID-19 patients in the interventional arm and one patient in the placebo group withdrew from the study, due to protocol violation and adverse event respectively. The median interval between the onset of symptoms and randomization was 5 days (IQR, 4–7 days) overall. 3.2 Primary outcome Clinical characteristics did not present any significant differences between the interventional and placebo arms (Supplementary Table S2). Similarly, the primary (time to hospital discharge), and secondary outcomes (mortality, and invasive ventilation over a 28-day period from the start of the study) did not show significant differences between subjects supplemented with tannins or placebo (Supplementary Figure S2 and Table S3). No adverse events were recorded in the tannin group, whereas two patients reported diarrhea and abdominal pain in the placebo group. 3.3 Cytokine production in treatment-naïve COVID-19 patients versus post-treatment At baseline, 17 cytokines were identified as differentially regulated in COVID-19 patients compared with HC (Supplementary Table S4). Hierarchical clustering and supervised learning accurately grouped COVID-19 patients from controls based on serum cytokine profiles, with an area-under-curve (AUC) value of 0.933 (Fig. 2 A & B). After adjusting for age ≥ 60, sex and body mass index ≥ 30, significantly higher pro-inflammatory cytokines levels were found in COVID-19 patients at baseline (T0) compared to HC: IL-1ra (p = 0.001), IL-2 (p < 0.001), IL-6 (p < 0.001), IL-7 (p < 0.001), IL- 8 (p = 0.05), IL-13 (p < 0.001), IP-10 (p < 0.001), PDGF-bb (p < 0.001). In particular, COVID-19 patients with severe disease had significantly higher levels of IL-1ra (p < 0.001), IL-13 (p < 0.001) and IFN-γ (p = 0.002), when compared to mild and moderate disease.Fig. 2 (A) Heatmap representation of selected cytokines that are significant after group comparison between COVID (N = 101) and control (N = 50) subjects. (B) ROC plot corresponding to Random Forest Classification model. (C) Heatmap of cytokines stratified by subject with different treatment and time. (D) Abundance of MIP-1α cytokine stratified by treatment and time. (Mann Whitney test; significant p values for different groups are illustrated). A reduction in IL-1β, IL-1ra, IL-2, IL-8, IL-9, IL-13, MCP-1, MIP-1β, PDGF-β, TNF-α and RANTES was measured in the tannin-treated COVID-19 arm, but these differences did not meet statistical significance (Fig. 2C). MIP-1α was the only cytokine that was significantly decreased in patients post tannin treatment (Fig. 2D) (Supplementary Table S5). Strong positive correlations between MIP-1α and IL-1β, rho = 0.58 (p < 0.001) and rho = 0.31 (p < 0.01), and between MIP-1α and TNF-α, rho = 0.23(p < 0.01) and rho = 0.41 (p < 0.01) were found in placebo and tannin arms, respectively. 3.4 Gut microbiome composition at baseline T0 is associated with disease severity Differences in gut microbiota composition between HC and COVID-19 patients at baseline T0 was evaluated in 152 subjects (50 HC and 102 COVID-19 subjects). COVID-19 patients receiving antibiotics with standard of care treatments did not present a distinct gut microbiota composition (Data not shown). COVID-19 patients possessed a lower α-diversity (Shannon diversity index, p < 0.001) (Fig. 3 A) and richness (Chao1, p < 0.001) (Fig. 3B) compared to HC. The largest reduction in Shannon diversity index was evident in the most severe COVID-19 cases (p = 0.032). β-Diversity was calculated using Bray–Curtis dissimilarity, which measures differences in the relative abundance of ASVs across all T0 subjects. Although HC subjects clustered distinctly from COVID-19 patients (p = 0.001), no significant separation in β-diversity was evident with increasing disease severity (Fig. 3C) and may be reflective of large microbiome variation in this cohort (Bray-Curtis dissimilarity index, PCo1 and PCo2 contributed 5.23 and 4.84 % of the total variation).Fig. 3 Per sample alpha diversity at ASV level for HC vs COVID-19 subjects at various levels of severity of symptoms (A) Shannon index and (B) Chao1 richness index. (C) Bray Curtis dissimilarity metric for profiling samples stratified by severity of COVID-19 symptoms. Analysis of similarities non-parametric test results are R = 0.203 and p = 0.001 (D) Relative abundance of top 20 taxa at family level. (E) LEFSe analysis for group comparison of the features in COVID and HC subjects at family level. (F) Correlation heatmap for the Cytokines having spearman correlation significant with Shannon index. Analysis of family-level abundances at baseline demonstrated a significant increase in Bacteroidaceae in COVID-19 patients compared with HC (Fig. 3D). LEFSe (Linear discriminant analysis Effect Size) analysis was used to illustrate specific microbiota changes associated with COVID-19. In particular, besides Bacteroidaceae (LDA score 4.723), also Clostridiales (LDA score 3.681), Pseudomonadiaceae (LDA score 2.859) and Atopobiaceae (LDA score 2.799) were significantly enriched in COVID-19 patients (Fig. 3E). Spearman correlations of alpha-diversity and serum cytokine levels demonstrated that IL-15, MIP-1b, IFN-γ, IL-1rα, FGF-β, IL-9, IP-10, IL-6, IL-13, PDGF-ββ and IL-2 were statistically associated with Shannon diversity index after multiple testing correction. Out of these, six cytokines negatively and five positively correlated with alpha diversity (Fig. 3F). 3.5 Gut microbiome composition pre and post treatment Shannon and Chao1 alpha-diversity indices were not altered in COVID-19 patients after tannin intervention (Fig. 4 A & B). Similarly, no significant changes were evident in beta-diversity clustering based on Bray-Curtis dissimilarity-based analysis (Fig. 4C). Burkholderiaceae was significantly (p = 0.0015) elevated at T14 in patients after 14-days tannin treatment (Fig. 4D), as was Enterococcus (LDA score 3.095) and Allisonella (LDA score 2.733) whereas Lachnospiraceae (LDA score 2.676) demonstrated reduced abundance in the tannin-arm (Fig. 4E).Fig. 4 Gut microbiota analysis of fecal samples at time T0 and T14 in COVID patients administered placebo (N = 46) or tannins (N = 56). (A) Shannon index (B) Chao1 index. (C) Bray Curtis beta diversity (D) Stacked bar plots to show the abundance of top 20 gut microbiota at family-rank to compare tannins vs placebo treatment arms at T14 (E) LEFSe analysis at genus-rank of placebo vs tannin treatment arms at T14. (F) Spearman correlation between serum MIP-1α levels and Bifidobacterium relative abundance for HC, placebo, and tannins arms. When assessing microbiome composition with cytokine levels, a significant negative correlation between serum MIP-1α and stool Bifidobacterium was noted, especially with ASV103 which showed 100 % identity to Bifidobacterium longum (Fig. 4F). 4 Discussion Continuously emerging SARS-CoV-2 variants contributing to COVID-19 being endemic in several countries, indicates that we must learn to coexist with this disease. Prevention and control measures other than vaccination are therefore needed. Dietary intervention represents one of the most realistic approaches to broadly manage the body's exacerbated inflammatory state to COVID-19 by maintaining a well-balanced gut microbiota. In the present study, we investigated this concept by testing the efficacy of oral tannin supplementation in COVID-19 patients with mild-to-severe disease severity. Although our nutraceutical-intervention failed to improve clinical outcomes in acute COVID-19 patients receiving standard-of-care treatment, some notable findings were made that may direct treatment towards future management of acute and chronic illness linked to SARS-CoV-2 infection. Before analyzing the effects of tannin supplementation on gut microbiota composition and inflammatory status of COVID-19 patients, we assessed how these features differed from a cohort of 53 healthy adult controls (HC). This comparison made it possible for us to confirm the patients' COVID-19 disease-associated features with other reports in geographically distinct locations. Notably, the high level of systemic cytokines recorded by us in COVID-19 patients compared to HC reconfirms the findings reported by other authors (Chi et al., 2020). Indeed, eleven of the significantly increased proinflammatory cytokines (namely IL-1β, IL-1rα, IL-2, IL-6, IL-7, IL-8, IL-9, IL-10, IL-13, GM-CSF, IFN-γ) are in good agreement with Chi et al. (2020). This type of cytokine-mediated inflammatory response is shared by different infections (i.e. MERS-CoV, SARS, and more recently with SARS-CoV-2), which typically presents with pulmonary inflammation and acute lung injury (Huang et al., 2020, Mahallawi et al., 2018, Wong et al., 2004). IL-1ra, IL-13, and IFN-γ were identified as cytokines that significantly discriminate severe from mild and moderate cases. These cytokines markers were not included in previous studies and could serve as new predictors of morbidity (Chi et al., 2020, Huang et al., 2020, Qin et al., 2020). When compared with HC, the gut microbiota composition was significantly altered in COVID-19 patients. As previously reported by other authors, in COVID-19 patients a similar dysbiosis was evident regardless of whether anti-microbials were prescribed and this is probably due to the severe impairment caused by the disease (Zuo et al., 2020). An altered gut microbiome has already been associated respiratory viral infections, and this could lead to more severe clinical course due to secondary bacterial infections (Groves et al., 2018, Wang et al., 2014). The significant reduction in α-diversity (Shannon diversity) and richness (Chao1) in COVID-19 patients has been extensively described by other authors, who similarly to us have found an exacerbation of the situation in severe cases (Hilpert & Mikut, 2021). The alteration of gut microbiota balance was reflected also in a significant increase in the abundance of families containing opportunistic pathogens and the loss of beneficial symbionts. In particular, Zuo et al. (2020) reported an increase of taxa belonging to Bacteroidaceae (namely Bacteroides nordii) and Clostridiaceae family (namely Clostridium hathewayi), which were also found to be significantly enriched in our study (Zuo et al., 2020). Our study also found a correlation between the disruption of gut microbiota composition due to SARS-CoV-2 infection and the accentuated inflammatory state, suggesting that the intestinal microbial ecosystem could play a role in regulating the immune response in the host. In particular, the reduction of α-diversity in the COVID-19 cohort was linked to increased concentrations of IL-15, MIP-1b, IFN-γ, IL-1rα, FGF-β, IL-9, IP-10, IL-6, IL-13, PDGF-ββ and IL-2, consistent with immunological studies conducted by Yeoh et al., (2021). As regards to the oral tannin supplementation, it led to a reduced inflammatory state, with the most significant inhibition being evident in the MIP-1α response. This chemokine plays a pivotal role in directing appropriate immune responses towards infection and inflammation. Moreover, several authors reported the impact of this mediator on lung defense (Driscoll, 1994, Jøntvedt Jørgensen et al., 2020) and its correlation with COVID-19 severity (Chi et al., 2020). MIP-1α acts by activating human granulocytes, with a consequent release of other pro-inflammatory cytokines (i.e. IL-6 and TNF-α) (Ren et al., 2010). The relatively short tannin intervention could represent one explanation why we did not observe significant shifts in α-diversity nor β-diversity, or in tannin-induced compositional shifts which we have previously been reported for Bacteroidaceae, Lachnospiraceae and Ruminococcaceae families (Molino et al., 2022, Molino et al., 2021). Members of these families are recognized for being producers of short chain fatty acids (SCFAs), interesting metabolites with a powerful effect in counteracting the inflammatory state (Akhtar et al., 2022). The correlation study highlighted an inverse correlation between the MIP-1α serum levels and the abundance of Bifidobacterium species in the stool samples of tannin-supplemented COVID-19 patients. This finding suggests that specific gut bacteria, such as Bifidobacterium could regulate aspects of the gut-lung axis, through modulating systemic levels of cytokines. Reinforcing these results, previous studies on oral tannins showed similar effects in less dysbiotic individuals, where the anti-inflammatory effect could be related to the gut microbiota-derived metabolites known to be effective in inflammatory events (Molino et al., 2018, Molino et al., 2022). In particular, the authors highlighted that the inmunomodulatory effect, through the microbiota modulation could be due to both the production of SCFAs and the release of metabolites such as quercetin and urolithins (A and B) (Molino et al., 2018). These compounds are powerful antioxidants, able to decrease the inflammatory state and, especially, to inhibit the release of MIP-1α (Alexander Haslberger et al., 2020, Noh et al., 2014). A major limitation of our study is related to the short tannin intervention time (14 days) restricted to the hospitalization of COVID-19 patients. In a previous study we showed that supplementation with a tannin blend led to an increase in SCFA release after the two weeks, whereas significant changes in microbiota composition emerged only after 4 weeks of treatment (Molino et al., 2022). This might explain why in our study we could begin to observe a reduction in cytokines after 14 days of tannin supplementation, but shifts in microbiota composition were not readily evident. Thus, the duration of the oral tannin intervention may have been suboptimal. In addition, it should be considered that the composition of the microbiota of COVID-19 patients at T0 was highly affected by the virus infection, reporting a decreased alpha diversity and an altered composition compared to the HC. Thus, probably the gut microbiota community structure was too compromised to observe significant beneficial tannin modulation. Taking all these results together, they indicate that tannin supplementation may be more amenable to chronic illness such as long-COVID where longer treatment duration is merited, and patients are less likely to marked dysbiosis. 5 Conclusions In this study, a tannin supplementation was administered to 124 COVID-19 patients receiving standard-of-care treatment. Comparison of the patients with 53 healthy volunteers showed that the COVID-19 patients were characterized by a pronounced intestinal dysbiosis and high inflammatory status. Although the short course of oral tannin supplementation (14 days) failed to provide significant clinical improvement or favorable hospital discharge rate after 28 days, oral tannins were associated with a decrease in systemic inflammation correlated with fecal Bifidobacterium abundance. Our study sets the stage for future clinical investigation of prophylactic nutraceutical management of COVID-19 symptoms, through precision restoration of gut microbiota members that modulate adverse host immune responses to infection. Ethics statement The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of the Hospital de Clínicas Jose de San Martin, Buenos Aires University, Argentina and approved on 12/06/2020. The study protocol was also registered with ClinicalTrials.gov under the number NCT04403646, on May 27th, 2020. Informed consent was obtained from all subjects involved in the study. The study was approved by the ethics committee of the University Hospital of Universidad de Buenos Aires “Hospital de Clínicas José de San Martín”, Argentina, and adhered to ethical principles outlined in the declaration of Helsinki convention. All participants gave written informed consent, and they were able to withdraw from the survey at any time without giving a reason. Funding This work was supported in part by Silvateam/ Indunor SA and 10.13039/100000002 NIH grants from NIDDK P30-DK56338, NIAID U01-AI24290 and P01-AI152999. Financial disclosures SM, AP, FLM, PV, JPS, JR, GL, PP, JIO, MRF, TU, SB, QW, EV, AC and MMP have no financial disclosures. TS received research funding from Merck, Nivalis, Cubist, Mead Johnson, Rebiotix, BioFire, Assembly BioSciences, and has served on the advisory board for Rebiotix and BioFire. Transparency Statement The data sets, including the redacted study protocol, redacted statistical analysis plan, and individual participant data supporting the results reported in this article, will be made available within 3 months from initial request to researchers who provide a methodologically sound proposal. The data will be provided after its de-identification, in compliance with applicable privacy laws, data protection, and requirements for consent and anonymization. Sequencing data were deposited in the European Nucleotide Archive (ENA) at EMBL-EBI (Project: PRJNA856489). CRediT authorship contribution statement Silvia Molino: Conceptualization, Investigation, Formal analysis, Writing – review & editing. Andrea Pisarevsky: Conceptualization, Investigation, Formal analysis, Supervision, Visualization, Writing – original draft, Writing – review & editing, Resources. Shyam Badu: Investigation, Formal analysis, Data curation, Data curation, Software, Supervision, Writing – original draft, Writing – review & editing. Qinglong Wu: Investigation, Formal analysis, Data curation, Software, Writing – original draft, Writing – review & editing. Fabiana López Mingorance: Investigation, Formal analysis, Data curation, Writing – review & editing. Patricia Vega: Investigation, Formal analysis, Writing – review & editing. Juan Pablo Stefanolo: Investigation, Formal analysis, Visualization, Writing – review & editing. Julieta Repetti: Investigation, Formal analysis, Writing – review & editing. Guillermina Ludueña: Investigation, Formal analysis, Writing – review & editing. Pablo Pepa: Investigation, Formal analysis, Writing – review & editing. Juan Ignacio Olmos: Investigation, Formal analysis, Writing – review & editing. Marcelo Rodriguez Fermepin: Investigation, Writing – review & editing, Resources. Tatiana Uehara: Investigation, Writing – review & editing. Elisa Viciani: Investigation, Software, Writing – original draft, Writing – review & editing. Andrea Castagnetti: Investigation, Software, Writing – review & editing. Tor Savidge: Investigation, Formal analysis, Visualization, Writing – original draft, Writing – review & editing, Resources, Funding acquisition. María Marta Piskorz: Conceptualization, Formal analysis, Data curation, Supervision, Visualization, Writing – original draft, Writing – review & editing, Funding acquisition. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A Supplementary material The following are the Supplementary data to this article:Supplementary data 1 Supplementary data 2 Supplementary data 3 Supplementary data 4 Data availability Data will be made available on request. Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.jff.2022.105356. ==== Refs References Akhtar M. Chen Y. Ma Z. Zhang X. Shi D. Khan J.A. Liu H. Gut microbiota-derived short chain fatty acids are potential mediators in gut inflammation Animal Nutrition 8 2022 350 360 10.1016/J.ANINU.2021.11.005 35510031 Alexander Haslberger G. Jacob U. Hippe B. Karlic H. Mechanisms of selected functional foods against viral infections with a view on COVID-19: Mini review Functional Foods in Health and Disease 10 4 2020 195 209 10.31989/ffhd.v10i5.707 Callahan B.J. McMurdie P.J. Rosen M.J. Han A.W. Johnson A.J.A. Holmes S.P. DADA2: High-resolution sample inference from Illumina amplicon data Nature Methods 13 7 2016 581 583 10.1038/nmeth.3869 27214047 Chen N. Zhou M. Dong X. Qu J. Gong F. Han Y. Qiu Y. Wang J. Liu Y. Wei Y. Xia J. Yu T. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China : A descriptive study The Lancet 395 10223 2020 507 513 10.1016/S0140-6736(20)30211-7 Chi Y. Ge Y. Wu B. Zhang W. Wu T. Wen T. Liu J. Guo X. Huang C. Jiao Y. Zhu F. Zhu B. Cui L. Serum Cytokine and Chemokine Profile in Relation to the Severity of Coronavirus Disease 2019 in China The Journal of Infectious Diseases 222 5 2020 746 754 10.1093/INFDIS/JIAA363 32563194 Driscoll K.E. Macrophage inflammatory proteins: Biology and role in pulmonary inflammation Experimental Lung Research 20 6 1994 473 490 10.3109/01902149409031733 7882902 Fanos, V., Pintus, M. C., Pintus, R., & Marcialis, M. A. (2020). Lung microbiota in the acute respiratory disease: From coronavirus to metabolomics. In Journal of Pediatric and Neonatal Individualized Medicine (Vol. 9, Issue 1). Hygeia Press di Corridori Marinella. https://doi.org/10.7363/090139. Gaibani, P., Viciani, E., Bartoletti, M., Lewis, R. E., Tonetti, T., Lombardo, D., Castagnetti, A., Bovo, F., Horna, C. S., Ranieri, M., Viale, P., Re, M. C., & Ambretti, S. (2021). The lower respiratory tract microbiome of critically ill patients with COVID-19. Scientific Reports 2021 11:1, 11(1), 1–11. https://doi.org/10.1038/s41598-021-89516-6. Gao Q.Y. Chen Y.X. Fang J.Y. 2019 Novel coronavirus infection and gastrointestinal tract Journal of Digestive Diseases 21 3 2020 125 126 10.1111/1751-2980.12851 32096611 Groves H.T. Cuthbertson L. James P. Moffatt M.F. Cox M.J. Tregoning J.S. Respiratory Disease following Viral Lung Infection Alters the Murine Gut Microbiota Frontiers in Immunology 9 FEB 2018 12 10.3389/FIMMU.2018.00182 29416537 Hilpert K. Mikut R. Is There a Connection Between Gut Microbiome Dysbiosis Occurring in COVID-19 Patients and Post-COVID-19 Symptoms? Frontiers in Microbiology 12 2021 2564 10.3389/FMICB.2021.732838/BIBTEX Huang C. Wang Y. Li X. Ren L. Zhao J. Hu Y. Zhang L. Fan G. Xu J. Gu X. Cheng Z. Yu T. Xia J. Wei Y. Wu W. Xie X. Yin W. Li H. Liu M. …Cao B. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China The Lancet 395 10223 2020 497 506 10.1016/S0140-6736(20)30183-5 Jøntvedt Jørgensen, M., Holter, J. C., Christensen, E. E., Schjalm, C., Tonby, K., Pischke, S. E., Jenum, S., Skeie, L. G., Nur, S., Lind, A., Opsand, H., Enersen, T. B., Grøndahl, R., Hermann, A., Dudman, S., Muller, F., Ueland, T., Mollnes, T. E., Aukrust, P., … Dyrhol-Riise, A. M. (2020). Increased interleukin-6 and macrophage chemoattractant protein-1 are associated with respiratory failure in COVID-19. Scientific Reports 2020 10:1, 10(1), 1–11. https://doi.org/10.1038/s41598-020-78710-7. Kalantar-Zadeh K. Ward S.A. Kalantar-Zadeh K. El-Omar E.M. Considering the Effects of Microbiome and Diet on SARS-CoV-2 Infection: Nanotechnology Roles ACS Nano 14 5 2020 5179 5182 10.1021/acsnano.0c03402 32356654 Klindworth A. Pruesse E. Schweer T. Peplies J. Quast C. Horn M. Glöckner F.O. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies Nucleic Acids Research 41 1 2013 1 11 10.1093/nar/gks808 23143271 Liang, T. (2020). Handbook of COVID-19 Prevention and Treatment. Handbook of Covid-19, Prevention and Treatment, 68. https://covid-19.alibabacloud.com. Mahallawi W.H. Khabour O.F. Zhang Q. Makhdoum H.M. Suliman B.A. MERS-CoV infection in humans is associated with a pro-inflammatory Th1 and Th17 cytokine profile Cytokine 104 2018 8 13 10.1016/J.CYTO.2018.01.025 29414327 Ministerio de Salud Argentina. (n.d.). Tratamiento de sostén \| Argentina.gob.ar. Retrieved October 21, 2022, from https://www.argentina.gob.ar/salud/coronavirus/tratamiento-sosten. Molino S. Fernández-Miyakawa M. Giovando S. Rufián-Henares J.Á. Study of antioxidant capacity and metabolization of quebracho and chestnut tannins through in vitro gastrointestinal digestion-fermentation Journal of Functional Foods 49 May 2018 188 195 Molino S. Lerma-aguilera A. Jiménez-hernández N. Ángel J. Henares R. Francino M.P. Evaluation of the Effects of a Short Supplementation With Tannins on the Gut Microbiota of Healthy Subjects Frontiers in Microbiology 13 April 2022 1 10 10.3389/fmicb.2022.848611 Molino S. Lerma-aguilera A. Jiménez-Hernández N. Gosalbes M.J. Rufián-Henares J.Á. Francino M.P. Enrichment of food with tannin extracts promotes healthy changes in the human gut microbiota Frontiers in Microbiology 12 March 2021 1 13 10.3389/fmicb.2021.625782 Molino S. Pisarevsky A. Mingorance F.L. Vega P. Stefanolo J.P. Repetti J. Ludueña G. Pepa P. Olmos J.I. Fermepin M.R. Uehara T. Villapol S. Savidge T. Treangen T. Viciani E. Castagnetti A. Piskorz M.M. Natural tannin extracts supplementation for COVID-19 patients (TanCOVID): A structured summary of a study protocol for a randomized controlled trial Trials 22 310 2021 21 23 10.1186/s13063-021-05281-x 33407781 Murali A. Bhargava A. Wright E.S. IDTAXA: A novel approach for accurate taxonomic classification of microbiome sequences Microbiome 6 1 2018 1 14 10.1186/S40168-018-0521-5/FIGURES/6 29291746 Noh H.J. Kim C.S. Kang J.H. Park J.Y. Choe S.Y. Hong S.M. Yoo H. Park T. Yu R. Quercetin suppresses MIP-1α-induced adipose inflammation by downregulating its receptors CCR1/CCR5 and inhibiting inflammatory signaling Journal of Medicinal Food 17 5 2014 550 557 10.1089/jmf.2013.2912 24325454 Qin C. Zhou L. Hu Z. Zhang S. Yang S. Tao Y. Xie C. Ma K. Shang K. Wang W. Tian D.S. Dysregulation of Immune Response in Patients With Coronavirus 2019 (COVID-19) in Wuhan, China Clinical Infectious Diseases : An Official Publication of the Infectious Diseases Society of America 71 15 2020 762 768 10.1093/CID/CIAA248 32161940 Ren M. Guo Q. Guo L. Lenz M. Qian F. Koenen R.R. Xu H. Schilling A.B. Weber C. Ye R.D. Dinner A.R. Tang W.-J. Polymerization of MIP-1 chemokine (CCL3 and CCL4) and clearance of MIP-1 by insulin-degrading enzyme The EMBO Journal 29 2010 3952 3966 10.1038/emboj.2010.256 20959807 Villapol, S. (2020). Gastrointestinal symptoms associated with COVID-19: impact on the gut microbiome. In Translational Research (Vol. 226, pp. 57–69). Mosby Inc. https://doi.org/10.1016/j.trsl.2020.08.004. Wang J. Li F. Wei H. Lian Z.X. Sun R. Tian Z. Respiratory influenza virus infection induces intestinal immune injury via microbiota-mediated Th17 cell–dependent inflammation The Journal of Experimental Medicine 211 12 2014 2397 10.1084/JEM.20140625 25366965 Wong C.K. Lam C.W.K. Wu A.K.L. Ip W.K. Lee N.L.S. Chan I.H.S. Lit L.C.W. Hui D.S.C. Chan M.H.M. Chung S.S.C. Sung J.J.Y. Plasma inflammatory cytokines and chemokines in severe acute respiratory syndrome Clinical and Experimental Immunology 136 1 2004 95 103 10.1111/J.1365-2249.2004.02415.X 15030519 Xu K. Cai H. Shen Y. Ni Q. Chen Y. Hu S. Li J. Wang H. Yu L. Huang H. Qiu Y. Wei G. Fang Q. Zhou J. Sheng J. Liang T. Li L. Translation: Management of Coronavirus Disease 2019 (COVID-19): Experience in Zhejiang Province, China Infectious Microbes & Diseases 2 2 2020 55 63 10.1097/IM9.0000000000000023 Yeoh Y.K. Zuo T. Lui G.C.Y. Zhang F. Liu Q. Li A.Y.L. Chung A.C.K. Cheung C.P. Tso E.Y.K. Fung K.S.C. Chan V. Ling L. Joynt G. Hui D.S.C. Chow K.M. Ng S.S.S. Li T.C.M. Ng R.W.Y. Yip T.C.F. …Ng S.C. Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19 Gut 70 4 2021 698 706 10.1136/GUTJNL-2020-323020 33431578 Zuo T. Zhang F. Lui G.C.Y. Yeoh Y.K. Li A.Y.L. Zhan H. Wan Y. Chung A.C.K. Cheung C.P. Chen N. Lai C.K.C. Chen Z. Tso E.Y.K. Fung K.S.C. Chan V. Ling L. Joynt G. Hui D.S.C. Chan F.K.L. …Ng S.C. Alterations in Gut Microbiota of Patients With COVID-19 During Time of Hospitalization Gastroenterology 159 3 2020 944 955.e8 10.1053/J.GASTRO.2020.05.048 32442562	36467850	PMC9708634	NO-CC CODE	2022-12-05 23:15:31	no	J Funct Foods. 2022 Dec 30; 99:105356	utf-8	J Funct Foods	2,022	10.1016/j.jff.2022.105356	oa_other
==== Front Soc Sci Humanit Open Soc Sci Humanit Open Social Sciences & Humanities Open 2590-2911 The Author(s). Published by Elsevier Ltd. S2590-2911(22)00132-2 10.1016/j.ssaho.2022.100378 100378 Regular Article COVID 19 fatalities burden in Asian countries: An analysis of pattern and determinants Panda Prasant Kumar a∗ Varkey Rittu Susan b Priyaranjan c Meher Ashish Kumar d Panda Soumyaranjan e a Department of Economics, Pondicherry University, Puducherry, 605014, India b Department of Economics, CHRIST (Deemed to be University), Bengalure, 560029, India c Department of Economics, Pondicherry University, Puducherry, 605014, India d Department of Economics, Central University of Tamil Nadu, Thiruvarur, Tamil Nadu, 610101, India e Department of Computer Science and Information Technology, Mahatma Gandhi Central University, Motihari, Bihar, 845401, India ∗ Corresponding author. 30 11 2022 30 11 2022 10037818 9 2021 18 10 2022 18 11 2022 © 2022 The Author(s) 2022 Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. Covid 19 pandemic has severe implications on health and life of people. Asia being the most populous region has higher fatalities burden. Health infrastructure, stringent preventive measures by the government and public participation through adhering to social distancing have influence to check on fatalities' burden. The level of Social capital as well as voters' participation in a particular country can have influence on containment of COVID cases and fatalities. In this context, the main objectives of this study are to analyse pattern and trend of death burden for 45 Asian countries and impact of stringency measures by government, and voters’ turnout ratio on death burden. However, for regression analysis only 32 countries are taken into account considering the availability of data for all variables. Multiple linear regression analysis is employed in a cross-sectional framework and Ordinary least square estimation technique with heteroscedastic adjusted standard errors have been used for estimation of coefficients. The results show that southern Asia contributes the highest share of fatality cases in total fatality cases of Asia with 71.43% share. It also has the highest share of confirmed cases in total confirmed cases of Asia with 71.72%. However, when we take the population into account, Western Asia leads in the share of confirmed COVID-19 cases and its associated fatality cases per million populations in Asia as compared to other Asian regions. The factors like health infrastructure and voters’ turnover ratio are found to be significant and potential in reducing the new deaths per million populations. Though the coefficient of Stringency index has been negative and it did not emerge to be significant in Asian countries. The COVID related fatalities in Asian region are urban centric and urbanization proxy is found to be positive and significant. Diabetes prevalence rate has some heterogeneous result and in the present study its coefficient is not in the hypothesized direction. . The Countries should ramp up health infrastructure and necessary preparedness to deal with the subsequent waves and COVID related fatalities. Importance need to be given people's participation and their shared responsibilities in dealing with COVID cases and checking on fatalities. The realisation of social responsibility among the masses can lead to community participation and adhering to the protocols imposed by the government and helps in checking on spread of virus and associated death. Keywords COVID 19 death burden Socio economic determinants Social capital Social distancing stringent measures Voters' turnout ratio Per capita GNI Poverty Co-morbidity Ageing ==== Body pmc1 Introduction The outbreak of SARS coronavirus-2 (SARS-CoV-2), popularly known as Corona Virus Disease 2019 (COVID-19) has caused a severe health crisis in the World. As on November 1, 2020, 218 countries and territories have been affected by the novel corona virus with more than 40.68 million cases causing death of more than 1.2 million deaths globally (world meter 2020). According WHO, the total number of corona virus cases in Asian countries is more than 10.37 million with around 2.45 lakhs deaths as on November 1, 2020 and the number is increasing every hour. As most of the countries were not prepared to face such a health emergency, the pandemic escalated the mortality rate due to lack of health infrastructure. In the present scenario, India has been the most affected country in the world followed by United States of America in terms of corona virus cases. The other countries which have higher numbers of COVID-19 cases as well as death tolls are Brazil, Italy, Spain, Russia, and UK. India is in the top among Asian countries to be hit hard by the pandemic followed by Iran, Iraq, Indonesia, Bangladesh and Philippines (World meter 2020). The increase in the incidence of COVID-19 cases and mortality has been observed on a daily basis in Asian countries due to their high population density. Being the largest and most populous continent on the earth, Asia reported around one fourth of the total COVID cases and high incidence of death. Though the deadly corona virus originated from one of the Asian countries i.e. China, it has been able to manage and control the perilous disease in the due course of time. But other Asian countries have been affected terribly as half of the world's population is in Asia. Though the recovery rate is more than 89% still the death burden has become alarming for Asia. The global economy is dependent on the Asian countries to a large extent as many fastest growing market economies have been emerging in Asia. This has a greater influence on the world economy due to its major share of contribution to the growth rate in terms of production and consumption. As the trend of COVID-19 cases and deaths in Asia has been showing steeply increasing, it is indeed significant to study the factors determining the COVID deaths from a social and economic perspective. Worldwide studies have been continuing to analyse the trend, pattern and determinants of COVID-19 cases and fatalities. Despite many such studies, factors determining fatalities among nations and the methods to control the disease have not been much explored. Some studies have verified the importance of clinical factors such as old age, lack of immunity, obesity, smoking, inadequate hospital care etc. on COVID related fatalities (Wang et al., 2020). However, apart from clinical characteristics, there are other socioeconomic determinants such as, health care infrastructure, size of urban population, social capital in terms of public participation and magnitude of poverty which can determine COVID-19 related mortality across regions. Social determinants of health have crucial role to play in explaining differential mortality rates of COVID-19. There is a need to consider resource allocations and policy decisions on operational needs at county levels (Paul et al., 2021). Social factors contributed to COVID-19 death significantly for Black Americans at the county level (Dalsania et al., 2022). Yanagisawa, Kawachi, Scannell, Oronce, and Tsugawa (2021) observed the importance of social and emotional support and individuals’ commitment to social institutions on lowering COVID related deaths. Given this background, the present study is an attempt to look into the trend and pattern of death burden of COVID 19 and important factors which have rampantly contributed to the corona virus cases and deaths leading to serious health concerns and economic trouble. The rest of the work is structured in four sections. Review of relevant literature pertaining to COVID-19 related death burden and its associated issues is provided in section two. Data and empirical framework of study are explained in section three. Section four presents the empirical findings and discussions with a conclusive remarks and policy suggestions provided in section five. 2 Review of selected literature Studies pertaining to COVID-19 cases, fatalities and its implications are growing across the countries. An attempt has been made in this context to examine the relevant literature on COVID-19 cases, death burden and its determinants. Varkey et al. (2020) studied about the determinants of COVID-19 positive cases in Asian countries and found out that net migration rate, higher per capita gross national income and high incidence of poverty have positive impact on the increase of new COVID cases in Asian countries. Richardson et al. (2020) from a study among COVID patients in New York City, found obesity, diabetics and hypertension were the common comorbidities. Demographic, environmental, and healthcare factors are very important to explain the COVID deaths. However, population aging, air pollution, humidity, COVID-19 prevalence, ordinary beds saturation, and critical care are positively associated with the fatality rate (Perone, 1920). Factors that positively contribute to the mortality rate include larger share of ageing population above 65 years of age, high obesity rate, and urbanization (Squalli, 2020, pp. 1–14). Adults aged over 70 and those with comorbidities such as respiratory and cardiovascular diseases and cancer are at high risk to death in the United Kingdom. Jordan et al. (2020) have observed that age with comorbidities has increased risk of COVID mortality. The population thickness and unhygienic living conditions are uncritically associated with the spread of COVID-19 infections and deaths in South Asia (Altaf, 2020). The COVID-19 mortality in India is under reported. However, the major factors associated with mortality rate in India are high rate of diabetes, inadequate preparedness and the suboptimal level of healthcare (John & Seshadri, 2020). Panneer et al. (2022) highlighted on the impact of COVID-19 and lockdowns in India through a systematic review. Ranjan and Muraleedharan (2020) observed that the elderly population in India are more vulnerable to COVID-19. Chronic non-communicable diseases, diabetes and hypertension are highly prevalent among the elderly population which further increases their vulnerability to COVID-19 death. Along with this, the social determinants are more crucial in determining the vulnerability of the elderly people during the pandemic. In urban counties, population density is significantly correlated with high death rate. In non-urban counties with more agricultural workers, high levels of poverty and more elderly people have significantly higher levels of mortality (Fielding-Miller et al., 2020). COVID-19 related fatality in United Kingdom was highly related with male patients, ageing and deprivation. Similarly patients with comorbidities are at a higher risk (Williamson et al., 2020). Holman et al. (2020) observed higher risk factor and COVID-19 fatality rate among people with type 1 and type 2 diabetes in England. In the first few months of the pandemic, counties having higher bonding in terms of social capital experienced lower mortality per one lakh population. Lesser excess deaths were seen by the communities having strong close-knit social ties (Fraser et al., 2021). Lower perceived stress was associated with greater social support. Social capital in different forms can effectively reduce stress which will be helpful to maintain a healthy and better lifestyle away from unexpected or unavoidable stressful life events such as the on-going COVID-19 pandemic (Jean-Baptiste et al., 2020). Social capital can be helpful in designing right strategies to address effects of technological disasters on community (Ritchie & Gill, 2007). Social capital has a significant association on the number of infections and spread of virus and therefore increasing investment in this sectors would help in checking negative shocks (Makridis & Wu, 2020). High social capital in communities better ensure that the entire community is supported in the most efficient way. It facilitates for community level monitoring to check transmission (Borgonovi et al., 2020). COVID-19 deaths are affected both positively and negatively by dynamics of social capital related factors (Imbulana Arachchi & Managi, 2021). Ding et al. (2020) analyzed the role of community engagement and individual commitment to social institutions in designing health policies. Yanagisawa et al. (2021) highlighted on the differential impacts of social and emotional support, and that of voter turnout ratio on COVID burden. Panda et al (2022) in their volume tried to address the impact of COVID-19 on health, economy and society and various factors associated with COVID through seminal contributions by various authors. Besides, health infrastructure in terms of hospital beds, manpower in health sector and availability of critical supplies including medicines and vaccines are important in preventing death. Rout and Panda (2007) have highlighted on multi-dimensional household specific and government specific determinants of health including Public investment and social practices. From the above literature, it is observed that the factors like comorbidities, old age, un-hygienic living condition of the people, obesity and urbanization are highly associated with COVID-19 mortality rate. The role of social capital in minimizing coronavirus fatalities has also been studied but mostly they are limited to United States. However, there are many other factors that are responsible for determining the COVID-19 deaths which are not highlighted in the above studies. The factors like health infrastructure, Government's approach in stringency measures, and peoples' participation in terms of adhering lockdown, social distancing and sanitary practices in health care management significantly contribute in determining COVID-19 related deaths. Not many studies have explored the factors associated with death burden in Asian countries which is the largest populous continent in the world. So, this paper tries to look into these issues for a comprehensive study to determine the factors responsible for death burden of COVID-19 in the Asian countries. 3 Data and methodology Secondary data have been used from various sources like our world in Data, United Nations Development Programme and the Institute for Demographic and Electoral Assistance for analysis. The study includes 45 Asian counties for graphical analysis based on the availability of data. The countries include Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei, Cambodia, China, Georgia, India, Indonesia, Iran, Iraq, Israel, Japan, Jordan, Kazakhstan, Kuwait, Kyrgyzstan, Laos, Lebanon, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Philippines, Qatar, Saudi Arabia, Singapore, South Korea, Sri Lanka, Palestine, Syria, Tajikistan, Thailand, Timor, Turkey, United Arab Emirates, Uzbekistan, Vietnam and Yemen. But, for the regression analysis only 32 countries such as Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Georgia, India, Indonesia, Iran, Iraq, Israel, Japan, Jordan, Kazakhstan, Kuwait, Kyrgyzstan, Laos, Lebanon, Myanmar, Nepal, Oman, Pakistan, Philippines, Singapore, South Korea, Sri Lanka, Tajikistan, Thailand, Turkey, Uzbekistan, Vietnam and Yemen have been included considering the availability of data for all variables. The time period for the pattern analysis (May to October 2020) was decided specifically to cover the first wave of the pandemic. For the graphical analysis we used data of six months to show a time series plot of the fatalities in Asia. It was during this period that many Asian countries reached a peak with a maximum number of cases and later on the cases started declining. This paper has been developed in the lines of Squalli (2020, pp. 1–14). In order to understand the death burden for Asian countries, we have used total death per million and total death as a percentage of total cases as the dependant variable. Based on daily data up to 25th October 2020 pattern analysis has been performed. The study has also employed regression technique in a cross-sectional framework to examine the determinants of COVID-19 fatalities in Asian countries. The socio-economic variables used in the study are per capita GDP, net migration, old age population covering age group of 65 and above, number of hospital beds, stringency index, and voter's turnover ratio. In addition, a co-morbidity factor like the diabetes prevalence rate has been used in the analysis. The variables have been described in the table −1. The reasons for the selection of variables were based on its relevance in determining the mortality rate. Health infrastructure has played an important role in reducing the mortality rates associated with the virus. Better infrastructure would help tackle the crises and it is hypothesized to be negatively associated with COVID related mortality. Country with larger size of aged population is more vulnerable to COVID infections and death. Old age dependency ratio is used as a proxy to capture the size of aged population and is included in the model. Social capital through community participation and adhering to the lockdown norms have been important in reducing the COVID positive cases and mortality rate associated with the virus. Very specifically, voters' turnout ratio (VTR) has been used as a proxy for people's participation and collective action. In the absence of direct data for social capital, VTR is used as a proxy for Social capital. It measures the level of people's participation in democracy, public activities and awareness. This may also help to indicate people's participation in creating awareness about Covid appropriate behaviour in the society. But countries in the Asian countries have varied social setup and different political systems starting from full scale democracies to semi-dictatorships. However, main purpose of including VTR is to draw preliminary inferences of social capital on Covid related mortality. For the countries where, VTR data are not available, they are excluded from regression analysis. Though VTR is used as crude proxy for social capital in the present study, future study may try to construct an appropriate index for social capital in order to overcome the weaknesses and see its influence on managing on health emergencies and related mortality. Comorbidity is another factor which may possibly influence fatalities associated with COVID. One of the important comorbidities is prevalence of diabetic. Diabetic prevalence rate is used to control for comorbidity factor in explaining COVID related mortality. The study has also used a stringency index that is calculated by “Our world in data” to account for the restrictions implemented to check the spread of the disease and its fatality rate. In order to understand the degree of association across the independent variables the correlation matrix has been computed and the same is reported in Table 4. It is observed that per capita gross national income has high degree of correlation with both the diabetes prevalence rate and urbanisation; as a result the same has been omitted from the model estimating the determinants of COVID related fatalities. The cross-sectional model in its general form has been given below(1) lnTDCPMi=HBi+NMi+ODRi+VTRi+CVDi+DPRi+SIi+URi+Ui (2) NDPMi=HBi+NMi+ODRi+VTRi+CVDi+DPRi+SIi+URi+Ui The description of all variables mentioned in equation (1) and equation (2) is provided in Table 1 and descriptive statistics of the variables used are provided in table-5. While equation-1 explains the determinants of total deaths per million, equation-2 finds the factors for new deaths per million populations. The first model is used in a log-linear (Semi-log) model, whereas in the second one we couldn't take log because for some countries, the new deaths per million population were zero. Ordinary least square technique is adopted to estimate the regression coefficients. The correlation matrix and variance inflating factor have been used to detect the influence of multicollinearity among the independent variables (Table 8). From the table-6 and table-7, it is observed that there is presence of heteroscedasticity in the data set. To correct for the existence of heteroscedasticity, robust standard error has been used.Table 1 Description of variables. Table 1Variable Abbrevi ation Description Unit Source Total death cases per million TDCPM Total COVID19 death cases per million population Ratio Our world in data Death cases as a share of total cases DCTC Total death cases as a share of total cases Ratio Our world in data Per capita gross national income PCGNI The per capita income divided by the total population in a country Ratio United Nations Development Program(UNDP) Old age dependency ratio ODR the number of population in the age group 65 and above to the total population Ratio Our World in Data Voters' turnout ratio VTR Number of voters casted vote as a percentage of the total eligible voters Ratio Institute for Demographic and Electoral Assistance(IDEA). Diabetic prevalence rate DPR Diabetes prevalence refers to the percentage of people ages 20–79 who have type 1 or type 2 diabetes Rate Our World in data Number of hospital beds HB Total number of hospital beds per thousand Number Our World in Data Stringency Index SI An index scaled to 0 to 100 that includes travel bans, school closure and work place closure Index Our World in Data Urbanisation UR Urban population share in total population Percentage Our World in Data Source: Compiled by Authors from Our World in data, UNDP and IDEA 4 Results and discussion In this section an attempt is made to analyse the COVID-19 related death burden of each region of Asia visa-vis Asian region as a whole. Asian countries are segregated in the regions in lines with Asian Country Research @ Pitt: Regions of Asia. It is also in similar lines to the United Nations statistics division's scheme of sub-regions. The details of classification are provided in appendix table-9. Besides, factors determining the COVID-19 fatalities have been identified in subsection-2. Figure-1 shows the share of each regions's fatality cases to total fatality cases in Asia caused by COVID-19.Fig. 1 Share of each Asian region COVID-19 fatality cases in total fatality in Asia. Fig. 1Source: Compiled by authors from “Our world in Data” as on October 25, 2020 It is observed from the figure-1 that the fatality share of the regions such as Central Asia, Eastern Asia, South-Eastern Asia, Southern Asia, Southwest Asia, and Western Asia were 1.69%, 2.96%, 8.77%, 71.43%, 0.26%, 14.89%, respectively. The fatality burden in Southern Asia is considerably higher, followed by Western Asia, and South-Eastern Asia. Figures for Eastern Asia, Central Asia, and Southwest Asia are negligible. The main possible reasons of.relatively higher death vulnerability in Southern Asia to COVID-19 are population density, poor health infrastructure, and challenges of implementation of COVID- appropriate behaviour. The study has examined the fatality burden of each Asian region, giving due weightage to the population. Fig. 2 shows the share of each Asian region in fatalities per million populations to total fatalities per million populations in Asia.Fig. 2 Share of each Asian region fatality cases per million populations to total fatality cases per million population in Asia. Fig. 2Source: Compiled by authors from “Our world in Data” as on October 25, 2020 Fig. 2 shows the shares of each region's fatality cases per million population to total fatality cases per million populations. The percentage for Central Asia, Eastern Asia, South-Eastern Asia, Southern Asia, Southwest Asia, and Western Asia is 9.1%, 0.74%, 3.79%, 20%, 0.58%, and 65.79%, respectively. Western Asia has a vast fatality burden, followed by Southern Asia, Central Asia, South-Eastern Asia, Eastern Asia, and Southwest Asia when weightage for the population is concerned. While taking population into account to examine the fatality burden of COVID-19, the study found different results. South Asia had the highest burden of fatality, but while taking the share of the population, Western Asia had a more fatality burden. Fig. 3 highlights the shares of confirmed cases to total confirmed cases of Asia for Central Asia, Eastern Asia, South-Eastern Asia, Southern Asia, Southwest Asia, and Western Asia, which are 2.13%, 1.63%, 6.42%, 71.72%, 0.02%, 18.09% respectively. Southern Asia is the highest contributor to COVID-19 cases in Asia, followed by Western Asia, South-Eastern Asia, Central Asia, Eastern Asia, and Southwest Asia. The study has tried to examine the same per million population shown in Fig. 4 to understand the fatality burden of COVID-19 on the Asian region in relative terms.Fig. 3 Share of confirmed case to total confirmed cases of Asia. Fig. 3Source: Compiled by authors from “Our world in Data” as on October 25, 2020 Fig. 4 Share of confirmed cases per million population to total confirmed cases per million population of Asia. Fig. 4Source: Compiled by authors from “Our world in Data” as on October 25, 2020 Fig. 4 shows the shares of confirmed cases per million population to total confirmed cases per million population of Asian regions like Central Asia, Eastern Asia, South-Eastern Asia, Southern Asia, Southwest Asia, and Western Asia are 5.34%, 0.39%, 4.37%, 12.46%, 0.02%, 77.42% respectively. When the population is considered, the share of COVID cases in Western Asia is extremely high, followed by Southern Asia, Central Asia, South-Eastern Asia, Eastern Asia, and Southwest Asia. Fig. 5 depicts the percentage share of Asian region fatality cases to total fatality cases of Asia from 17th May 2020 to 25th October 2020. There is a decline in the share of fatality cases to total fatality cases in Asia for Central Asia, Eastern Asia, Southwest Asia, and Western Asia. Further, there is an increasing pattern in South-Eastern Asia and Southern Asia for the same period. However, it has been observed that there is a sharp decline in Eastern Asia and Southwest Asia in percentage share.Fig. 5 Share of Asian region fatality cases to total fatality cases of Asia for the period of 17th May 2020 to 25th October 2020. Fig. 5Source: Compiled by authors from ‘Our World In data’ for the period of 17th May 2020 to 25th October 2020 Fig. 6 shows the percentage share of fatality cases to total positive cases in Asia and total fatality cases to total positive cases in Asia from 17th May 2020 to 25th October 2020. There is a declining share of total fatality cases to total positive cases in Asia. Eastern Asia and Southern Asia follow the same pattern. Central Asia has a lesser percentage share of fatality cases than the total positive cases of Asia between 17th May to 17th July 2020. There is an increase in fatality cases' percentage share to total positive cases in Asia, but eventually, it stagnates. Furthermore, in the case of South-Eastern Asia, Southwest Asia, and Western Asia the percentage share were constant throughout. However, Southwest Asia shares a considerable percentage of fatality cases to total positive cases of Asia compared to other Asian regions.Fig. 6 Share of fatality cases to total confirmed cases of Asia for the period of 17th May 2020 to 25th October 2020. Fig. 6Source: Compiled by authors from ‘Our World In data’ for the period of 17th May 2020 to 25th October 2020 Fig. 7 shows the fatality case per million population for the Asian region from 17th May 2020 to 25th October 2020. There is a fluctuation in fatality cases per million for Central Asia, South-Eastern Asia, Southwest Asia, and Western Asia initially, and later a steady pattern for the same. Furthermore, there is a sharp decline in the fatality cases per million population in Eastern Asia. South Asia shows an upward trend in fatality cases per million population.Fig. 7 Fatality case per million population for the Asian region. Fig. 7Source: Compiled by authors from ‘Our World In data’ for the period of 17th May 2020 to 25th October 2020 Fig. 8 shows Asia's fatality cases per million population from 17th May 2020 to 25th October 2020. The fatality case per million population in Asia is increasing at an increasing rate over the study period.Fig. 8 Fatality case per million populations for Asia as a whole. Fig. 8Source: Compiled by authors from ‘Our World In data’ for the period of 17th May 2020 to 25th October 2020 Fig. 9 shows the share of fatality cases in selected countries to total fatality cases in Asia. Fig. 9 calculates the percentage share of the country's death cases to total death cases in Asia on 25th October 2020. It has been observed that India, Iran, Iraq, Turkey, and the Philippines are five countries that had a significant percentage share of day-wise death cases to total death cases in Asia. Together, these countries contribute 76.24% of death cases to total death cases in Asia as of 25th October 2020.Fig. 9 Share of fatality cases to total fatality cases in Asia (Five Most affected countries by Covid-19, death burden). Fig. 9Source: Compiled by authors from ‘Our World In data’ for the period of 17th May 2020 to 25th October 2020 Fig. 10 shows the share of confirmed cases of selected countries in total confirmed cases of Asia. Fig. 10 shows the percentage share of the country's confirmed cases day-wise to the total confirmed cases in Asia on 25th October 2020. India, Iran, Iraq, Bangladesh, and Indonesia are five countries that have the largest percentage share of day-wise confirmed cases to total confirmed cases in Asia. These countries accounted for 73.61% of confirmed cases to the total confirmed cases in Asia on 25th October 2020.Determinants of death burden.Fig. 10 Share of confirmed cases to total confirmed cases of Asia (Five Most infected countries by Covid-19). Fig. 10Source: Compiled by authors from ‘Our World In data’ for the period of 17th May 2020 to 25th October 2020 In the lines of methodology and variables outlined in the section −3, regression coefficients of new deaths per million populations and total deaths per million populations are shown in Table 2 and Table 3 respectively.Table 2 New Deaths Per million as the Dependant Variable(Model 1). Table 2Variables Coefficients HB −0.2713509b (−1.74) UR 0.0538392a (2.01) VTR −0.0369079b (−1.79) SI −0.0445875 (−0.97) DPR −0.3088225b (−1.94) C 6.44b (1.77) R squared 34.61 F statistics 3.04a No of observation 32 @1% level of significance. a @5% level of significance and. b @10%level of significance. Source: Authors' calculation using Stata using data from “Our world in data”, UNDP and IDEA as on 25th October 2020 Table 3 Total deaths per million populations (Model 2). Table 3Variables Coefficients (P value) HB −0.3108965 (−0.99) UR 0.0177438 (1.16) VTR −0.0581178a (−3.14) SI 0.0215584 (1.12) DPR 0.0065915 (0.07) C 5.27a (3.28) R squared 0.4680 F statistics 3.05b No of observation 32 **@10%level of significance. a @1% level of significance. b @5% level of significance. Source: Authors calculation using Stata Authors' calculation using Stata using data from “Our world in data”, UNDP and IDEA as on 25th October 2020 Table 4 Correlation matrix. Table 4 PCGNI HB VTR SI UR ODR DPR PCGNI 1.00 0.26 0.22 0.16 0.77 0.29 0.68 HB 0.26 1.00 −0.01 −0.15 0.29 0.67 −0.06 VTR 0.22 −0.01 1.00 0.06 −0.17 0.05 0.33 SI 0.16 −0.15 0.06 1.00 0.30 −0.23 −0.01 UR 0.77 0.29 −0.17 0.30 1.00 0.29 0.36 ODR 0.29 0.67 0.05 −0.23 0.29 1.00 0.15 DPR 0.68 −0.06 0.33 −0.01 0.36 0.15 1.00 Source: Authors' calculation using Stata Table 5 Descriptive statistics. Table 5 TDCPM NDCPM HB UR VTR SI DPR Obs 32 32 32 32 32 32 32 Mean 88.6 0.99 2.88 62.06 65.34 55.73 9.78 Min 0.36 0.00 0.3 18.5 36.13 18.56 3.78 Max 398.21 11.8 13.05 100 99.26 0 4.61 Std. Dev. 105.33 2.23 2.78 25.31 15.99 85.19 17.72 Source: Authors' calculation using Stata Table 6 Breush Pagan test for Model 1. Table 6Ho: constant variance Variables: fitted values of new death per million Chi square 33.85 Prob > chi square 0.0000 Source: Authors' calculation using Stata Table 7 Breush Pagan test for Model 2. Table 7Ho: constant variance Variables: fitted values of total death per million Chi square 10.60 Prob > chi square 0.0011 Source: Authors' calculation using Stata Table:8 Variance inflating factor. Table:8Variable VIF 1/VIF UR 2.12 0.472176 DPR 1.76 0.569696 HB 1.54 0.649981 SI 1.22 0.820682 VTR 1.08 0.928963 Source: Authors' calculation using Stata Table 9 Classification of Asian countries. Table 9Central Asia Kazakhstan Kyrgyzstan Tajikistan Uzbekistan Eastern Asia China Japan Mongolia South Korea South-Eastern Asia Brunei Cambodia Indonesia Laos Malaysia Philippines Singapore Thailand Timor Vietnam Southern Asia Afghanistan Bangladesh Bhutan India Iran Maldives Myanmar Nepal Pakistan Sri Lanka Southwest Asia Yemen Western Asia Armenia Azerbaijan Bahrain Georgia Iraq Israel Jordan Kuwait Lebanon Oman Palestine Qatar Saudi Arabia Syria Turkey United Arab Emirates Source: Compiled by authors from Asian Country Research @ Pitt: Regions of Asia Table 2 shows the model with new death per million populations as the dependant variable as on 25th October 2020. As heteroscedasticity is present in the data set, heteroscedasticity corrected robust SE is used. The coefficient estimate of heath infrastructure represented by the number of hospital beds found to be negative and statistically significant. The countries in Asia having better health infrastructure have been able to reduce daily new deaths per million populations. Another potential predictor of new death is found to be voter's turnover ratio (VTR) which is used as a proxy for social capital. The coefficient of VTR is negative and significant. It shows how people's participation through community development programmes and increased awareness has helped in reducing the fatality rate of COVID 19. Higher social capital is associated with higher social responsibility. These countries therefore experience voluntary measures like hand sanitising and social distancing to help reduce the incidence and the fatality rate of the virus. Stringency Index did not emerge to be significant though the coefficient is negative. In populous continent like Asia, stringency measures such as the policies like school closure, travel ban and work place closure are important to deal with check the spread of COVID virus and its related death. School closure was a major factor that influenced the transmission rates during pandemics Jackson et al. (2013). But, in Asian region, proper implementation of stringency is a bigger challenge considering its population and size of informal activity. As a result, the coefficient of stringency index seems to be insignificant in predicting new death per million populations in Asian countries. Urbanisation has a positive significant relationship on the death burden. Countries in Asian region with larger urban population have higher daily new death cases. Similar results are found in earlier studies. Countries having a higher proportion of urban population had a higher disease burden (Gupta et al., 2020). 90% of the cases reported are in urban areas that are epicentre of the pandemic (United Nations,2020). A greater risk of COVID-19 is seen in patients who are diabetic. The fatality rate was much higher for patients that are diabetic than those patients who do not have any comorbidity factors. In the present study result is contrary and diabetic prevalence rate (DPR) has negatively significant relationship on the fatality rate of the virus. Some studies have confirmed for the heterogenous outcomes that the pandemic has bought forward and therefore difficult to give a conclusive relationship between the fatality rate and COVID-19 Barrera et al. (2020). An alternate model has been specified with total death per million population as the dependant variable as on 25th October 2020.As heteroscedasticity is present in the data set, heteroscedasticity corrected robust SE is used Results are shown in Table 3. The only variable that was significant in the analysis is the voter's turnover ratio. Public participation tends to have a negatively significant relationship with total deaths per million. 5 Conclusion The study has analyzed the pattern of COVID-19 death burden in Asian region and different factors that contribute to the fatality rates of the virus. It has been observed that among different regions in Asia, Southern Asia contributes the highest share of fatality cases in total fatality cases of Asia with 71.43%. It also has the highest share of confirmed cases in total confirmed cases of Asia with 71.72%. However, when we take the population into account, then Western Asia leads fatality cases per million population to total fatality cases per million population of Asia as well as share of confirmed cases per million population to total confirmed cases per million of population in Asia as compared to other Asian regions. The factors like health infrastructure and voters’ turnover ratio are found to be significant and potential in reducing the new deaths per million populations. Though the coefficient of Stringency index has been negative and it did not emerge to be significant in Asian countries. The COVID related fatalities in Asian region are urban centric and urbanization proxy is found to be positive and significant. Diabetes prevalence rate has some heterogeneous result and in the present study its coefficient is not in the hypothesized direction. The Countries should ramp up health infrastructure and necessary preparedness to deal with the subsequent waves and COVID related fatalities. Importance needs to be given people's participation and their shared responsibilities in dealing with COVID cases and checking on fatalities. The realisation of social responsibility among the masses can lead to community participation and adhering to the protocols imposed by the government and help in checking on spread of virus and associated death. Funding No funding support received for this research. Conflicts of interest No conflict of interest. Uncited references Balakrishnan et al., 2019; CCO and Ontario Agency for Health Protection and Promotion, 2019; Chatterjee, 2020; Delk and Hayes, 2018; Knudsen et al., 2018; Mensah and Brown, 2007; Pitas and Ehmer, 2020; Prabhakaran et al., 2018; Roy, 2020. CRediT authorship contribution statement Prasant Kumar Panda: Over all planning, thoughts, analysis, content development and write up, revision of work. Rittu Susan Varkey: Estimation, and data accumulation, revision of work. Priyaranjan: Preparation of charts and graphs, write up. Ashish Kumar Meher: Review of literature, charts and tables. Soumyaranjan Panda: Review of literature, charts and tables. ==== Refs References Altaf A. COVID-19 and population density A methodological note Economic and Political Weekly 55 36 2020 21 23 Balakrishnan K. Dey S. Gupta T. Dhaliwal R.S. Brauer M. Cohen A.J. …Sabde Y. The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: The global burden of disease study 2017 The Lancet Planetary Health 3 1 2019 e26 e39 30528905 Barrera F.J. Shekhar S. Wurth R. Moreno-Pena P.J. Ponce O.J. Hajdenberg M. …Porter F. Prevalence of diabetes and hypertension and their associated risks for poor outcomes in Covid-19 Patients Journal of the Endocrine Society 4 9 2020 bvaa102 Borgonovi F. Andrieu E. Subramanian S.V. Community level social capital and COVID-19 infections and fatality in the United States Covid Economics 32 2020 110 126 CCO and Ontario Agency for Health Protection and Promotion (Public Health Ontario) The burden of chronic diseases in Ontario: Key estimates to support efforts in prevention 2019 Queen’s Printer for Ontario Toronto Chatterjee P. Is India missing COVID-19 deaths? The Lancet 396 10252 2020 657 Dalsania A.K. Fastiggi M.J. Kahlam A. Shah R. Patel K. Shiau S. …DallaPiazza M. The relationship between social determinants of health and racial disparities in COVID-19 mortality Journal of racial and ethnic health disparities 9 1 2022 288 295 33403652 Delk R. Hayes T.O. Understanding the social determinants of health. Primer 2018 American Action Forum Ding W. Levine R. Lin C. Xie W. Social distancing and social capital: Why US counties respond differently to COVID-19 (No. w27393) 2020 National Bureau of Economic Research Fielding-Miller R.K. Sundaram M.E. Brouwer K. Social determinants of COVID-19 mortality at the county level 2020 medRxiv Fraser T. Aldrich D.P. Page-Tan C. Bowling alone or distancing together? The role of social capital in excess death rates from COVID19 Social Science & Medicine 284 2021 114241 Gupta S. Kumar Patel K. Sivaraman S. Mangal A. Global epidemiology of first 90 Days into COVID-19 pandemic: Disease incidence, prevalence, case fatality rate and their association with population density, urbanisation and elderly population Journal of Health Management 22 2 2020 117 128 10.1177/0972063420932762 Holman N. Knighton P. Kar P. O'Keefe J. Curley M. Weaver A. …Sattar N. Risk factors for COVID-19-related mortality in people with type 1 and type 2 diabetes in England: A population-based cohort study Lancet Diabetes & Endocrinology 8 10 2020 823 833 32798471 Imbulana Arachchi J. Managi S. The role of social capital in COVID-19 deaths BMC Public Health 21 1 2021 1 9 33388037 Jackson C. Vynnycky E. Hawker J. Olowokure B. Mangtani P. School closures and influenza: Systematic review of epidemiological studies BMJ Open 3 2 2013 Jean-Baptiste Ogolla C. Patti Herring R. Lawrence Beeson W. Hildemar Dos Santos Banta J.E. Stressful life events and social capital during the early phase of COVID-19 in the US Social Sciences & Humanities Open 2 1 2020 100057 John T.J. Seshadri M.S. Covid-19 mortality trends and reporting comparing India and Italy Economic and Political Weekly 55 37 2020 13 17 Jordan R.E. Adab P. Cheng K.K. Covid-19: Risk factors for severe disease and death BMJ 2020 1 2 Knudsen A.K. Tollånes M.C. Haaland Ø.A. Kinge J.M. Skirbekk V. Vollset S.E. Disease burden in Norway 2015. Results from the global burden of diseases, injuries, and risk factors study 2015 (GBD 2015) 2018 Public Health Institute Norway Oslo 2017 Makridis C. Wu C. Ties that bind (and social distance): How social capital helps communities weather the COVID-19 pandemic 2020 Available at SSRN 3592180 Mensah G.A. Brown D.W. An overview of cardiovascular disease burden in the United States Health Affairs 26 1 2007 38 48 17211012 Panda P.K. Srinivasan P. Chittaranjan S. Dickson A. COVID-19 impact on health, economy and society 2022 Neelkamal Publications New Delhi Panneer S. Kantamaneni K. Akkayasamy V.S. Susairaj A.X. Panda P.K. Acharya S.S. Rice L. Liyanage C. Pushparaj R.R.B. The great lockdown in the wake of COVID-19 and its implications: Lessons for low and middle-income countries International Journal of Environmental Research and Public Health 19 2022 610 35010863 Paul R. Arif A. Pokhrel K. Ghosh S. The association of social determinants of health with COVID-19 mortality in rural and urban counties The Journal of Rural Health 37 2 2021 278 286 33619746 Perone G. The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors Science of the Total Environment 755 2021 1920 142523 1-11 Pitas N. Ehmer C. Social capital in the response to COVID-19 American Journal of Health Promotion 34 8 2020 942 944 10.1177/0890117120924531 32394721 Prabhakaran D. Jeemon P. Sharma M. Roth G.A. Johnson C. Harikrishnan S. …Dhaliwal R.S. The changing patterns of cardiovascular diseases and their risk factors in the states of India: The Global Burden of Disease Study 1990–2016 Lancet Global Health 6 12 2018 e1339 e1351 30219317 Ranjan A. Muraleedharan V.R. Higher disease burden in India's elderly Economic and Political Weekly 55 35 2020 13 16 Richardson S. Hirsch J.S. Narasimhan M. Crawford J.M. McGinn T. Davidson K.W. …Cookingham J. Presenting characteristics, comorbidities, and outcomes among 5700 patients hospitalized with COVID-19 in the New York City area JAMA 323 20 2020 2052 2059 32320003 Ritchie L.A. Gill D.A. Social capital theory as an integrating theoretical framework in technological disaster research Sociological Spectrum 27 1 2007 103 129 Rout H.S. Panda Kumar P. Health economics in India 2007 New Century Publications New Delhi Roy M.P. Socio-demographic factors of COPD mortality in India Clinical Epidemiology and Global Health 8 2020 939 942 Squalli J. Evaluating the determinants of COVID-19 mortality: A cross-country study 2020 medRxiv Varkey R.S. Joy J. Sarmah G. Panda P.K. Socioeconomic determinants of COVID-19 in Asian countries: An empirical analysis Journal of Public Affairs 2020 e2532 Wang D. Hu B. Hu C. Zhu F. Liu X. Zhang J. …Peng Z. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus–infected pneumonia in Wuhan, China JAMA 323 11 2020 1061 1069 32031570 Williamson E.J. Walker A.J. Bhaskaran K. Bacon S. Bates C. Morton C.E. …Cockburn J. Factors associated with COVID-19-related death using OpenSAFELY Nature 584 7821 2020 430 436 32640463 Yanagisawa M. Kawachi I. Scannell C.A. Oronce C.I.A. Tsugawa Y. Association between county-level social capital and the burden of COVID-19 cases and deaths in the United States Annals of Epidemiology 2021	36466378	PMC9708635	NO-CC CODE	2022-12-10 23:15:26	no	Soc Sci Humanit Open. 2023 Nov 30; 7(1):100378	utf-8	Soc Sci Humanit Open	2,022	10.1016/j.ssaho.2022.100378	oa_other
==== Front Phys Med Rehabil Clin N Am Phys Med Rehabil Clin N Am Physical Medicine and Rehabilitation Clinics of North America 1047-9651 1558-1381 Elsevier Inc. S1047-9651(21)00006-1 10.1016/j.pmr.2021.01.006 Article Logistics of Rehabilitation Telehealth Documentation, Reimbursement, and Health Insurance Portability and Accountability Act Chan Anne H. DPT, MBA Department of Physical Medicine and Rehabilitation, Virginia Commonwealth University, 1223 East Marshall Street, PO Box 980677, Richmond, VA 23298, USA 1 4 2021 5 2021 1 4 2021 32 2 429436 © 2021 Elsevier Inc. All rights reserved. 2021 Elsevier Inc. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. As a result of the COVID-19 public health emergency, the Centers for Medicare & Medicaid Services expanded its telehealth benefit on a temporary and emergency basis. Effective March 6, 2020, Medicare will pay for Medicare telehealth services at the same rate as regular, in-person visits. Medicare has prescribed specific guidance on the billing and coding of such services, having an impact on reimbursement for qualified providers. Additional guidance also exists on acceptable telehealth communication platforms and patient privacy. Keywords Telehealth Rehabilitation Telehealth documentation Telehealth billing Telehealth coding Telehealth privacy ==== Body pmcKey points • As a result of the COVID-19 public health emergency (PHE), the Centers for Medicare & Medicaid Services expanded its telehealth benefit on a temporary and emergency basis. • Effective March 6, 2020, Medicare began to pay for Medicare telehealth services at the same rate as regular, in-person visits. • To bill telehealth claims for services delivered during the PHE, providers should include the place of service in which the service would have been furnished if the visit was in person, and providers should add modifier 95 to denote the service took place via telehealth. • Non–public-facing remote communications like Apple FaceTime®, Zoom®, and Skype® can be used for the delivery of Medicare telehealth services. Introduction The Centers for Medicare & Medicaid Services (CMS) defines telehealth, telemedicine, and related terms as “the exchange of medical information from one site to another through electronic communication to improve a patient’s health.”1 Additionally, the Health Resources and Services Administration of the US Department of Health and Human Services (HHS) describes telehealth as “the use of electronic information and telecommunication technologies to support long-distance clinical health care, patient and professional health-related education, public health, and health administration. Technologies include videoconferencing, the internet, store-and-forward imaging, streaming media, and landline and wireless communications.”2 With the COVID-19 public health emergency (PHE), the CMS expanded its telehealth benefit on a temporary and emergency basis. Effective March 6, 2020, Medicare began to pay for office, hospital, and other visits furnished via telehealth. Prior to this expansion, Medicare “could only pay for telehealth on a limited basis: when the person receiving the service is in a designated rural area and when they leave their home and go to a clinic, hospital, or certain other types of medical facilities for the service.”1 Furthermore, prior to the COVID-19 PHE telehealth expansion, Medicare was making payments for virtual check-ins, and Medicare Part B was paying for e-visits, which are “non-face-to-face patient-initiated communications through an online patient portal.”1 Note that guidance from the CMS and other agencies are evolving, and the most current policies can be found on the CMS Web site. Also note that coverage for telehealth varies by payer and by state. Medicare classifies 3 main types of virtual services that physicians and other health care professionals can provide to Medicare beneficiaries:1. Medicare telehealth visits: these visits are furnished using telecommunications technology that must be real-time, 2-way interactive, and with audio and visual capabilities. Effective March 6, 2020, and for the duration of the PHE, Medicare will pay for Medicare telehealth services at the same rate as regular, in-person visits. Additional details on Medicare telehealth visits are discussed later. 2. Virtual check-ins: these visits are briefer than Medicare telehealth visits and do not require audio and visual capabilities for real-time communication. Examples include “synchronous discussion over a telephone or exchange of information through video or image.”1 Virtual check-ins are done with established patients, are patient-initiated, and require verbal patient consent. 3. E-visits: using online patient portals, Medicare patients initiate communications with their physicians. These are established patients, and “the Medicare coinsurance and deductible would apply to these services.”1 Medicare telehealth visits: expansions due to the COVID-19 public health emergency On March 30, 2020, the CMS issued an Interim Final Rule with Comment Period, which, in addition to its March 17, 2020, announcement, expanded telehealth by waiving its geographic and place of service (POS) restrictions and by adding more flexibility in the use of telehealth. The key elements of this expansion are as follows:• The Medicare telehealth expansions are effective for March 6, 2020, through the period of the PHE. • The geographic and POS restrictions are waived, such that Medicare pays for Medicare telehealth visits in patient locations, such as their homes and outside of designated rural areas. • Medicare telehealth visits must be furnished using telecommunications technology that is real-time, 2-way interactive, and with audio and visual capabilities. An exception to this rule is that “CMS has used its waiver authority to allow, beginning on March 1, 2020, telephone evaluation and management codes and certain counseling behavior health care and educational services, to be furnished as telehealth services using audio-only communications technology (telephones or other audio-only devices).”3 • Medicare telehealth visits can be furnished by distant site practitioners from their home during the PHE. The POS code should be reported as the place where the service would have been reported if the service had been provided in person. • Providers may see both new and established Medicare patients via telehealth. • Medicare telehealth visits must be reasonable and necessary. • The types of telehealth services covered by Medicare during the COVID-19 PHE can be found at https://www.cms.gov/Medicare/Medicare-General-Information/Telehealth/Telehealth-Codes. • The HHS Office of Inspector General (OIG) will not sanction health care providers for reducing or waiving cost-sharing obligations for telehealth services paid for by Medicare. More information can be found at the following:▪ https://oig.hhs.gov/fraud/docs/alertsandbulletins/2020/policy-telehealth-2020.pdf ▪ https://www.oig.hhs.gov/fraud/docs/alertsandbulletins/2020/factsheet-telehealth-2020.pdf ▪ https://oig.hhs.gov/fraud/docs/alertsandbulletins/2020/telehealth-waiver-faq-2020.pdf • The CMS has allowed providers to deliver telehealth services across state lines. Providers are subject, however, to the requirements established by the states involved. The following resources are available:○ Federation of State Medical Boards: this link provides a list of US states and territories that have modified their telehealth requirements in response to the COVID-19 PHE: https://www.fsmb.org/siteassets/advocacy/pdf/states-waiving-licensure-requirements-for-telehealth-in-response-to-covid-19.pdf. ○ Federation of State Medical Boards: this link provides a list of US states and territories that have modified licensure requirements for physicians in response to the COVID-19 PHE: https://www.fsmb.org/siteassets/advocacy/pdf/state-emergency-declarations-licensures-requirementscovid-19.pdf. ○ Federation of State Medical Boards: this link provides a list of US states and territories that have expedited licensure for inactive/retired licensees in response to the COVID-19 PHE: https://www.fsmb.org/siteassets/advocacy/pdf/states-expediting-licensure-for-inactive-retired-licensees-in-response-to-covid19.pdf. ○ The National Telehealth Policy Resource Center: this link provides information by state on changes states have made to remove policy barriers to telehealth in response to the COVID-19 PHE: https://www.cchpca.org/covid-19-related-state-actions. ○ Interstate Medical Licensure Compact (IMLC): the IMLC, created by state medical boards, executives, and administrators, significantly streamlines the licensure process for physicians wanting to practice in multiple states. The intent of the IMLC was to increase access to health care (eg, geographic access and access to specialists), and the benefits to physicians include faster licensure and fewer administrative burdens: https://www.imlcc.org/a-faster-pathway-to-physician-licensure/. ○ Medicare: this link provides information on Medicare’s recognition of IMLCs: https://www.cms.gov/files/document/SE20008.pdf. ○ IMLCs also exist for other health care providers:▪ Emergency medical services workers: https://www.emscompact.gov/ ▪ Nurses: https://www.ncsbn.org/nurse-licensure-compact.htm ▪ Physical therapists: http://ptcompact.org/ ▪ Psychologists: https://psypact.org/page/PracticeUnderPSYPACT ▪ Speech language pathologists/therapists: https://aslpcompact.com/ • As a result of the COVID-19 PHE, the Drug Enforcement Administration (DEA) has made the following key changes:○ The DEA has allowed DEA-registered practitioners to prescribe schedule II–V controlled substances via telehealth. The following 3 conditions must be met:▪ “The prescription is issued for a legitimate medical purpose by a practitioner acting in the usual course of his/her professional practice; ▪ The telemedicine communication is conducted using an audio-visual, real-time, two-way interactive communication system; and ▪ The practitioner is acting in accordance with applicable Federal and State laws.”4 ○ The DEA, through the Controlled Substances Act, “allows practitioners to dispense narcotic drugs, including buprenorphine, to individuals with OUD [opioid use disorder] for maintenance or detoxification treatment if the practitioners separately register with DEA as an opioid treatment program.”5 ○ The DEA has granted an exception to DEA-registered practitioners, such that DEA-registered practitioners are not required to register with the DEA in additional states where they dispense controlled substances. Further detail is available at https://www.deadiversion.usdoj.gov/GDP/(DEA-DC-018)(DEA067)%20DEA%20state%20reciprocity%20(final)(Signed).pdf. ○ The DEA has granted an exception to requirements for paper delivery of an emergency oral prescription. Further detail is available at https://www.deadiversion.usdoj.gov/GDP/(DEA-DC-021)(DEA073)%20Oral%20CII%20for%20regular%20CII%20scirpt%20(Final)%20+Esign%20a.pdf. Medicaid coverage for telehealth Because Medicaid coverage differs by state, the National Telehealth Policy Resource Center has compiled a resource on COVID-19–related state actions: https://www.cchpca.org/covid-19-related-state-actions. The link provides state-by-state information on the allowance of telehealth, changes to geographic restrictions, and coverage for telehealth services. Uninsured patients: federal COVID-19 reimbursements Health care providers and health centers can be reimbursed by the federal government for testing and treating uninsured individuals for COVID-19. Claims can be submitted to the Health Resources and Services Administration COVID-19 Uninsured Program Portal (https://coviduninsuredclaim.linkhealth.com/). Claims generally are paid at Medicare rates, upon availability of funding. Medicare billing, coding, and reimbursement The types of telehealth services covered by Medicare during the COVID-19 PHE can be found at https://www.cms.gov/Medicare/Medicare-General-Information/Telehealth/Telehealth-Codes. Medicare reimburses a telehealth visit at the same fee-for-service-rate as an in-person visit during the PHE. To bill telehealth claims for services delivered during the PHE, providers should include the POS in which the service would have been furnished if the visit were in person, and providers should add modifier 95 in order to denote the service took place via telehealth. The catastrophe/disaster-related (CR) modifier is not needed when billing for telehealth services. The CMS requires modifiers, however, for Medicare telehealth professional claims for the following 2 scenarios:• “Furnished as part of a federal telemedicine demonstration project in Alaska and Hawaii using asynchronous (store and forward) technology, use GQ (Via an asynchronous telecommunications system when reporting telehealth services) modifier.”6 • “Furnished for diagnosis and treatment of an acute stroke, use G0 (used to identify Telehealth services furnished for purposes of diagnosis, evaluation, or treatment of symptoms of an acute stroke) modifier.”6 For office/outpatient evaluation and management (E/M) services furnished via telehealth, level selection can be based on medical decision making or time. Medical decision making refers to the existing definition of “the complexity of establishing a diagnosis and/or selecting a management option, which is determined by considering these factors: the number of possible diagnoses and/or the number of management options that must be considered; the amount and/or complexity of medical records, diagnostic tests, and/or other information that must be obtained, reviewed, and analyzed; [and] the risk of significant complications, morbidity, and/or mortality as well as comorbidities associated with the patient’s presenting problem(s), the diagnostic procedure(s), and/or the possible management options.”7 Additional information on time can be found at https://www.aap.org/en-us/professional-resources/practice-transformation/getting-paid/Coding-at-the-AAP/Pages/Using-Time-to-Report-Outpatient-EM-Services.aspx. Examples of Healthcare Common Procedure Coding System/Current Procedural Terminology codes: Medicare telehealth visitsService Healthcare Common Procedure Coding System/Current Procedural Terminology Code New patient office/outpatient E&M services 99201 99202 99203 99204 99205 Under new or established patient office/outpatient E&M services 99221 99222 99223 99224 99225 Telehealth consultations, emergency department, or initial inpatient G0425 G0426 G0427 Follow-up inpatient consultation, furnished via telehealth to beneficiaries in hospitals or skilled nursing facilities G0406 G0407 G0408 Examples of Healthcare Common Procedure Coding System/Current Procedural Terminology codes: virtual check-insService Healthcare Common Procedure Coding System/Current Procedural Terminology Code Brief virtual check-in (5-10 minutes of medical discussion) with an established patient, not resulting from a service provided within the past 7 days nor leading to a service within the next 24 hours or next available appointment G2012 Remote evaluation with interpretation of recorded video or images submitted by an established patient, inclusive of follow-up with the patient within 24 business hours, not resulting from a service provided within the past 7 days nor leading to a service within the next 24 hours or next available appointment G2010 Examples of Healthcare Common Procedure Coding System/Current Procedural Terminology codes: e-visitsService Healthcare Common Procedure Coding System/Current Procedural Terminology Code Under non–face-to-face on-line digital E/M service 99421 99422 99423 Qualified nonphysician health care professional online assessment and management service, for an established patient, for up to 7 days, cumulative time during the 7 days G2061 G2062 G2063 • Residents furnishing services at primary care centers may provide an expanded set of services to beneficiaries, including levels 4-5 of an office/outpatient Evaluation and Management (E/M) visit, telephone E/M, care management, and some communication technology-based services • This expanded set of services at CPT® codes 99204-99205, 99214-99215, 99495-99496, 99421-99423, 99452, and 99441-99443 and HCPCS codes G2010 and G2012 • Teaching physicians may submit claims for these services furnished by residents in the absence of a teaching physician using the GE modifier.”6 Health information portability and accountability act During the COVID-19 PHE, the Office for Civil Rights at HHS has issued a Notification of Enforcement Discretion and “will not impose penalties for noncompliance with the regulatory requirements under the HIPAA [Health Insurance Portability and Accountability Act of 1996] Rules against covered health care providers in connection with the good faith provision of telehealth.”8 Non–public-facing remote communications like Apple FaceTime®, Zoom®, and Skype® can be used. Public-facing applications like Facebook Live®, Twitch®, and TikTok® are not allowed to be used. Clinics care points • The CMS defines telehealth, telemedicine, and related terms as “the exchange of medical information from one site to another through electronic communication to improve a patient’s health.”1 • Guidance from the CMS and other agencies is evolving, and the most current policies can be found on the CMS Web site. • Coverage for telehealth varies by payer and by state. • Providers may see both new and established Medicare patients via telehealth. • Medicare telehealth visits must be reasonable and necessary. • The CMS has allowed providers to deliver telehealth services across state lines. Providers are subject, however, to the requirements established by the states involved. • The DEA has allowed DEA-registered practitioners to prescribe schedule II–V controlled substances via telehealth, with specific required conditions. Disclosure The author has nothing to disclose. ==== Refs References 1 Centers for Medicare and Medicaid Services Medicare telemedicine health care provider fact sheet Available at: https://www.cms.gov/newsroom/fact-sheets/medicare-telemedicine-health-care-provider-fact-sheet 2020 Accessed November 3, 2020 2 U.S. Department of Health and Human Services, Office for Civil Rights FAQs on telehealth and HIPAA during the COVID-19 nationwide public health emergency Available at: https://www.hhs.gov/sites/default/files/telehealth-faqs-508.pdf Accessed November 3, 2020 3 Centers for Medicare and Medicaid Services COVID-19 frequently asked questions (FAQs) on Medicare fee-for-service (FFS) billing Available at: https://www.cms.gov/files/document/medicare-telehealth-frequently-asked-questions-faqs-31720.pdf 2020 Accessed November 3, 2020 4 U.S. Department of Justice, Drug Enforcement Administration, Diversion Control Division COVID-19 information page Available at: https://deadiversion.usdoj.gov/coronavirus.html?inf_contact_key=410e6a45f5ef27deb85e6b6a8b284664 Accessed November 4, 2020 5 U.S. Department of Justice, Drug Enforcement Administration Use of telephone evaluations to initiate buprenorphine prescribing Available at: https://www.deadiversion.usdoj.gov/GDP/(DEA-DC-022)(DEA068)%20DEA%20SAMHSA%20buprenorphine%20telemedicine%20%20(Final)%20+Esign.pdf 2020 Accessed November 4, 2020 6 Centers for Medicare and Medicaid ServicesMedicare Learning Network Medicare fee-for-service (FFS) response to the public health emergency on the coronavirus (COVID-19) Available at: https://www.cms.gov/files/document/se20011.pdf 2020 Accessed November 3, 2020 7 Centers for Medicare and Medicaid Services, Medicare Learning Network Evaluation and management services guide Available at: https://www.cms.gov/outreach-and-education/medicare-learning-network-mln/mlnproducts/downloads/eval-mgmt-serv-guide-icn006764.pdf 2020 Accessed November 4, 2020 8 U.S. Department of Health and Human Services, Health Information Privacy Notification of enforcement discretion for telehealth remote communications during the COVID-19 nationwide public health emergency Available at: https://www.hhs.gov/hipaa/for-professionals/special-topics/emergency-preparedness/notification-enforcement-discretion-telehealth/index.html 2020 Accessed November 4, 2020	33814067	PMC9709306	NO-CC CODE	2022-12-01 23:23:03	no	Phys Med Rehabil Clin N Am. 2021 May 1; 32(2):429-436	utf-8	Phys Med Rehabil Clin N Am	2,021	10.1016/j.pmr.2021.01.006	oa_other
==== Front Psychiatr Q Psychiatr Q The Psychiatric Quarterly 0033-2720 1573-6709 Springer US New York 10009 10.1007/s11126-022-10009-4 Original Paper Assessing the Feasibility, Acceptability, and Preliminary Effectiveness of a School-Aged Program that Supports Physical Activity and Wellness Lebby Stephanie R. 1Stephanie R. Lebby is currently getting a master’s of science in physical activity and wellness at the University of Vermont. She is a graduate of Dartmouth College with a degree in psychology. Her current research focuses on improving mental health and chronic health conditions through physical activity, education, awareness, and preventative medicine. Myers Amanda 2Amanda Myers, MPH is a researcher with skills and expertise in health policy, user-centered design, usability, user experience, mental health, and community engagement. She has 14 publications on these topics and is also a member of the Digital Peer Support Team. Bohm Andrew R. 3Dr. Andrew R. Bohm is an adjunct instructor in The Dartmouth Institute. He has an extensive background in Healthcare Research and received his M.S. in Healthcare Research at Geisel School of Medicine at Dartmouth. Fortuna Karen L. [email protected] [email protected] 4Dr. Karen L. Fortuna is an Assistant Professor of Psychiatry at Dartmouth College. Through the Collaborative Design for Recovery and Health, Dr. Fortuna works in equal partnership with patient partners in co-producing and empirically testing digital peer support technologies and trainings. Dr. Fortuna has over 85 peer-reviewed publications co-produced with patient partners. Dr. Fortuna serves on the American Psychiatric Association’s Expert Advisory panel on Smartphone App Development and co-Chairs PCORI’s Advisory Panel on Patient Engagement. She serves as editor of the Journal of Participatory Medicine and the Shared Wisdom feature in the American Journal of Geriatric Psychiatric. 1 grid.59062.38 0000 0004 1936 7689 College of Nursing and Health Sciences, The University of Vermont, Burlington, VT 05405 USA 2 grid.253264.4 0000 0004 1936 9473 Heller School for Social Policy and Management at Brandeis University, 415 South St, Waltham, MA 02453 USA 3 grid.254880.3 0000 0001 2179 2404 The Dartmouth Institute for Health Policy and Clinical Practice, Geisel School of Medicine at Dartmouth, Hanover, NH USA 4 grid.254880.3 0000 0001 2179 2404 Department of Psychiatry, Geisel School of Medicine at Dartmouth, Hanover, NH USA 30 11 2022 18 20 11 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The prevalence of anxiety symptoms in children and adolescents aged 4 to 18 years has nearly doubled after the first year of the pandemic. However, only one in five adolescents diagnosed with anxiety is treated. We R H.O.P. E. is a school-based mental health program that includes evidence-based principles designed to engage children and adolescents in anxiety treatment, including wellness and emotional regulation, and the emotional CPR method. We R H.O.P. E. augments traditional services provided by school administrators, school social workers, school teachers, and school nurses. The purpose of this study was to examine the feasibility, acceptability, and preliminary effectiveness of We R H.O.P. E. Keywords Adolescent anxiety eCPR Physical activity Wellness ==== Body pmcIntroduction Before the COVID-19 pandemic, 11.6% of children and adolescents were diagnosed with generalized anxiety disorder [1]. Generalized anxiety disorder is defined by the Diagnostic and Statistical Manual V as excessive distress out of proportion with a given situation and manifested by physical symptoms [2]. However, the prevalence of children and adolescent anxiety symptoms has nearly doubled to 20.5% after the first year of the pandemic [3]. Currently, 1 in 5 children and adolescents are experiencing clinically elevated anxiety symptoms [3], likely due to disruption of their everyday lives. School closures and quarantine orders have led to social isolation, missed milestones, increased family stress, and decreased peer interactions [4]. However, only 1 in 5 children and adolescents with anxiety symptoms are treated for anxiety disorders due to lack of outward signs and difficulty of children and adolescents to identify when anxiety is not normal (2018 Children’s Mental Health Report, n.d.). As such, there is a critical need to ensure the other 80% are treated. Childhood and adolescent anxiety disorders can lead to additional psychiatric issues such as anxiety disorders and depressive disorders, as well as suicidal ideation later in life [5]. Anxiety disorders in childhood and adolescence are also associated with less life satisfaction, poor family relationships, educational underachievement, poor adjustment at work, and alcohol use disorders [6, 7]. Additionally, adolescent anxiety predicts physical health issues such as coronary heart disease [8] and adult-onset asthma [9]. It is currently estimated there is a $5890 direct cost and $4658 indirect cost annually for families of children and adolescents with anxiety [10]. Cognitive behavior therapy (CBT) combined with antidepressant medication is considered the gold standard of treatment and is the most effective for treating separation anxiety, generalized anxiety, and social anxiety disorders in children and adolescents [11]. However, despite empirical evidence supporting specific anxiety treatments for children and adolescents stigma, negative beliefs toward mental health services and professionals, lack of knowledge, unavailability of services, and structural barriers decrease help-seeking behavior [12, 13]. As such, non-clinical school-based services provided by lay professionals or “coaches” may be an effective method to offset barriers associated with accessing evidence-based services and augment services provided by school administrators, school social workers, school teachers, and school nurses. This study was guided by the social ecological model, which conceptualized health broadly. The social ecological model focuses on multiple factors that affect health including physical, social, mental, and social well-being (World Health Organization, 1947). The model acknowledges health to be affected by the interaction between the individual, group/community, and the physical, social, and political environments [14, 15]. The purpose of this study was to examine the feasibility, acceptability, and preliminary effectiveness of We R H.O.P. E., a school-based mental health program that includes evidence-based principles designed to engage children and adolescents in anxiety treatment, including wellness and emotional regulation, and the emotional CPR method. Methods A single-arm pre/post study was conducted in collaboration with We R H.O.P.E, a school-based individual coaching service. We R H.O.P. E. has created a youth mental health support program and coaching through school-based anxiety coaching services that provide daily individualized coaching sessions for children and adolescents who need support, regardless of financial resources. The school-based anxiety coaching services focus on normalizing anxiety within the school community and providing children and adolescents with skills and tools to manage anxiety. We R H.O.P. E. incorporates psychoeducation, emotional regulation, and emotional CPR (eCPR). eCPR teaches people how to assist others and work through emotional crises through connecting, empowering, and revitalizing. eCPR is based on the recovery care model of mental health and the principles of recovery: trauma-informed care, counseling after disasters, peer support, emotional intelligence, suicide prevention, and cultural attunement. eCPR was developed using an iterative design process by an expert panel of peer support specialists, nonprofit leaders, and people with a lived experience of mental health conditions. eCPR is delivered by a lay professional (non-clinically trained) using a manualized workbook. Empirically eCPR is associated with increased empowerment, hope, and quality of life and decreased incidence of psychiatric symptoms and loneliness [16]. Study instruments were administered at baseline and 90 days. Both the baseline and 90-day assessments were administered in person in a private setting. No incentives were provided. The study was approved by the (blinded for review) Institutional Review Board. A secondary data analysis was conducted. Participants The study included N = 191 children ages 6 through 16 years from 14 (blinded for review) schools. Of the 197 participants, 77 completed both the pre and post-instruments. Of the 56 participants who completed the pre and post–instrument, 49 identified as female, 27 identified as male, and 1 responded as “other”. Instruments Study instruments were administered in person at baseline and 90 days by a trained rater. A trained master-level individual entered the data into excel and then transferred to STATA for analyses. The General Anxiety Disorder-7 (GAD-7) was used to measure generalized anxiety disorder, which has shown reliability and validity in measuring anxiety in children and adolescents [17]. The GAD-7 asks participants, “Over the last two weeks, how often have you been bothered by the following problems?” Sample problems include “Feeling nervous, anxious, or on edge,” “Not being able to stop or control worrying,” “Worrying too much about different things,” and “Trouble relaxing.” Response options are on a zero to three scale, including “Not at all,” “Several days,” “More than half the days,” and “Nearly every day.” GAD-7 scores range from 0 to 21 with low scores indicating minimal anxiety and high scores indicating severe anxiety (0–4 minimal anxiety, 5–9 mild anxiety, 10–14 moderate anxiety, 15–21 severe anxiety). Fidelity Assessment We R H.O.P. E. research team monitored intervention fidelity through (1) biweekly discussions between coaches and supervisors; and (2) supervisors also observed a minimum of one in-person session over the 90 day intervention with each coach and provided an evaluation of their coaching work. Informed Consent As this is a secondary data analysis of de-identified data, consent was not requested from participants. This secondary data analysis involved no more than minimal risk to the participants, did not adversely affect the rights and welfare of the participants, and could practically be carried out without the waiver of consent. Statistical Analyses Descriptive statistics were used to describe the demographic characteristics of the study sample. A 2-tailed paired sample t-tests was conducted to assess the difference between pre scores and post scores for statistical significance at 90 days as well as 30 and 60 days. Repeated measure ANOVA analysis was performed to evaluate performance improvements over time. Incomplete survey responses were excluded from the analyses. STATA was used to compute descriptive statistics and analyses. Results Sociodemographic Characteristics of the Study Sample Of the 197 total people who participated, 77 participants completed the GAD-7 survey at baseline and at 90-days. Among the 77 participants assessed, 64% were female (n = 49), 36% were male (n = 27), and 1 responded “other”. Participants ranged in age from 6 years old to 16 years old and had an average age of 9.9 years old. This study population heavily favored white respondents as they accounted for 92% of all races represented in the data (See Table 1).Table 1 Characteristics of included study participants (n = 77) Age, years Mean (SD) 9.9 (2.6) Range 10 Sex, n (%) Female 49 (64) Male 27 (36) Other 1 (1.3) Race, n (%) American Indian or Alaska Native 0 (0) Asian 1 (1.3) Black / African American 1 (1.3) Native Hawaiian or Pacific Islander 0 (0) White 71 (92) Two or more races 4 (5.2) Feasibility and Acceptability Of the 197 participants, 39% (n = 77) completed both the intake and 90-day Generalized Anxiety Disorder-7 (GAD-7), indicating a low level of survey responses. Low levels of pre/post measures also indicate data collection procedure was not feasible or acceptable. Data collection should be modified to ensure participants complete both pre and post-measures. Recommendations to enhance data collection involve providing incentives to the participants, making post-measures easy and fast to complete, and tracking participants to ensure they complete both the pre and post-measures. Preliminary Effectiveness A paired sample t-test was run on a sample of 77 participants of the We R. H.O.P.E. intervention. The purpose of this study was to determine whether there is a statistically significant mean difference between GAD-7 scores from baseline to 90 days (Table 2). Participants who went through the study had a mean intake GAD-7 score of 10.2 with a standard deviation of 5.4. At 90 days the mean score went down to 6.7 with a standard deviation of 4.7; a statistically significant decrease of 3.2 (95% CI 2.1—4.4, p < 0.001).Table 2 Changes in Outcomes from Baseline to 90 Days for Study Participants—Mean (SD) Measure Baseline 90 Days Score Δ pValue GAD-7 10.2 (8.9,11.3) 6.7 (5.9,8.0) -3.2 (-2.1,-4.4) < 0.001 As data was collected during the study at different time points as well, change in GAD-7 from baseline was also analyzed at 30 and 60 days using paired sample t-tests. (Table 3) Statistically significant decreases in GAD-7 score also occurred at point time points with a 1.8 (95% CI 0.5–3.0, p = 0.006) and 2.4 (95%CI 1.3–3.5, p = 0.001) mean score difference at 30 and 90 days respectively. These findings were confirmed with a repeated measure ANOVA test which confirms there are statistically significant different scores at each time point, with each one showing marked improvement (F(4,68) = 9.69, p < 0.001).Table 3 Changes from baseline at other time intervals Timepoint Baseline Score at timepoint Score Δ n Missing p Value 30-Days 10.3 (9.1,11.5) 8.5 (7.4,9.6) -1.8 (-0.5,-3.0) 1 0.006 60-Days 10.1 (8.9,11.4) 7.7 (6.6,8.8) -2.4 (-1.3,-3.5) 2 0.001 90-Days 10.2 (8.9,11.3) 6.7 (5.9,8.0) -3.2 (-2.1,-4.4) 0 < 0.001 Further repeated measure ANOVA test confirms there is a statistical significance in performance between time points F(4,68) = 9.69, p < 0.001 Discussion The purpose of this study was to examine the feasibility, acceptability, and preliminary effectiveness of We R H.O.P. E., a school-aged program that supports physical activity and wellness. Preliminary effectiveness was assessed using the General Anxiety Disorder-7 (GAD-7). The study demonstrated that the school-based We R H.O.P. E. program is a promising approach to decrease anxiety in children and adolescents in everyday environments that interact in. The We R H.O.P.E. program was associated with a significant decrease in participant anxiety levels after 90 days. As such, school-based interventions that offer lay professional support services, in combination with traditional school services, may be an ideal approach for dissemination and uptake of evidence-based mental health treatment. Feasibility and acceptability by We R H.O.P. E coaches was demonstrated through their capacity to deliver evidence-based components of the program with fidelity. The We R H.O.P. E manual and supervision sessions enabled coaches to link students' needs and preferences to standardized evidence-based intervention components. This study highlights promising findings that manualized school-based interventions provided by coaches may facilitate the delivery of evidence-based practices. Understanding the impact of We R H.O.P.E. individually and in combination with services provided by school social workers, school teachers, and school nurses may help delineate the impact of We R H.O.P.E. as a stand-alone or augmentative service. The feasibility and acceptability of We R H.O.P. E was established through participants’ attendance. Specifically, participants met with a coach a total of 5–7 times. These findings suggest We R H.O.P. E represents a promising strategy to evidenced-based anxiety treatment outside of a clinical setting. However, feasibility and acceptability of data collection was not obtained as less than half of the participants completed the post measure at 90 days. This indicates either a low level of engagement by the participants over a sustained amount of time or infeasibility of data collection. Data collection should be modified to ensure participants complete both pre and post-measures. Providing incentives to the participants, making post-measures easy and fast to complete, and tracking participants to ensure they complete both the pre and post measures are recommended to increase student engagement and data collection. Due to a decrease in anxiety levels associated with the We R H.O.P.E intervention, schools may provide the ideal setting to help decrease anxiety because they are already accessed daily by children and adolescents. This makes it easy for students and mental health providers to stay in continuous contact. Additionally, school-based mental health services may reduce anxiety levels by reducing stigma around mental health services [18]. When students see their classmates using mental health services, they may feel more comfortable also receiving help, normalizing the use of treatment services. When stigma is reduced, it also leads to more positive attitudes towards mental health services and professionals making students more likely to seek treatment. School-based mental health programs may also reduce anxiety levels by increasing academic outcomes, providing earlier identification and intervention for mental health issues, creating a more positive school climate, increasing psychosocial outcomes, and encouraging engagement between peers, families, and educators [19]. Limitations This study is not without limitations and results should be interpreted with caution. Of the 197 students who participated, only 77 completed the pre and post-test. Thus, creating a potential sampling bias. Additionally, the pre/post-test design does not allow for a causal relationship between the We R H.O.P.E. intervention and reduced anxiety scores. However, as this is the first study of the We R H.O.P.E. intervention, this design is aligned with pilot studies to assess the feasibility and acceptability of our approach [20]. Also, the lack of a control group makes it impossible to conclude that the decrease in anxiety scores was due to the intervention itself and not the passage of time. However, these findings are promising, and examining We R H.OP.E. with a fully-powered sample and a rigorous design is an important next step. Implications for Practice This pilot study added promising evidence to the potential effectiveness of school-based programs that support physical activity and wellness. School-based interventions meet children and adolescents in their socio-cultural environments and offer additional (non-traditional) support services. School administrators, school social workers, school teachers, and school nurses can have an important role in supervising coaches delivery of services and support a comprehensive social ecological approach to support children and adolescents in schools. Further research with a fully-powered sample and a rigorous design may advance our understanding of school-based programs and their impact on systems and children and adolescents. Funding The authors received no financial support for the research, authorship, or publication of this article. Declarations Conflict of Interest Dr. Fortuna provides consulting through Social Wellness and Emissary Health, Inc. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Tiirikainen K Haravuori H Ranta K Kaltiala-Heino R Marttunen M Psychometric properties of the 7-item Generalized Anxiety Disorder Scale (GAD-7) in a large representative sample of Finnish adolescents Psychiatry Res 2019 272 30 35 10.1016/j.psychres.2018.12.004 30579178 2. Rakel RE Differential Diagnosis of Anxiety Psychiatr Ann 1981 11 11 11 14 10.3928/0048-5713-19811102-08 3. Racine N McArthur BA Cooke JE Eirich R Zhu J Madigan S Global Prevalence of Depressive and Anxiety Symptoms in Children and Adolescents During COVID-19: A Meta-analysis JAMA Pediatr 2021 175 11 1142 1150 10.1001/jamapediatrics.2021.2482 34369987 4. Larsen L, Helland MS, Holt T. The impact of school closure and social isolation on children in vulnerable families during COVID-19: A focus on children’s reactions. Eur Child Adolesc Psychiatry. 2021;1–11. 10.1007/s00787-021-01758-x. 5. Doering S Lichtenstein P Gillberg C Boomsma DI van Beijsterveldt TCEM Ligthart L Willemsen G de Geus E Middeldorp CM Bartels M Kuja-Halkola R Lundström S NTR Anxiety at age 15 predicts psychiatric diagnoses and suicidal ideation in late adolescence and young adulthood: Results from two longitudinal studies BMC Psychiatry 2019 19 1 363 10.1186/s12888-019-2349-3 31727035 6. Essau CA Lewinsohn PM Olaya B Seeley JR Anxiety disorders in adolescents and psychosocial outcomes at age 30 J Affect Disord 2014 163 125 132 10.1016/j.jad.2013.12.033 24456837 7. Woodward LJ Fergusson DM Life Course Outcomes of Young People With Anxiety Disorders in Adolescence J Am Acad Child Adolesc Psychiatry 2001 40 9 1086 1093 10.1097/00004583-200109000-00018 11556633 8. Janszky I Ahnve S Lundberg I Hemmingsson T Early-Onset Depression, Anxiety, and Risk of Subsequent Coronary Heart Disease J Am Coll Cardiol 2010 56 1 31 37 10.1016/j.jacc.2010.03.033 20620714 9. Scott KM Von Korff M Alonso J Angermeyer MC Benjet C Bruffaerts R de Girolamo G Haro JM Kessler RC Kovess V Ono Y Ormel J Posada-Villa J Childhood Adversity, Early-Onset Depressive/Anxiety Disorders, and Adult-Onset Asthma Psychosom Med 2008 70 9 1035 1043 10.1097/PSY.0b013e318187a2fb 18941133 10. Pella JE Slade EP Pikulski PJ Ginsburg GS Pediatric Anxiety Disorders: A Cost of Illness Analysis J Abnorm Child Psychol 2020 48 4 551 559 10.1007/s10802-020-00626-7 32078089 11. Walkup JT Albano AM Piacentini J Birmaher B Compton SN Sherrill JT Ginsburg GS Rynn MA McCracken J Waslick B Iyengar S March JS Kendall PC Cognitive Behavioral Therapy, Sertraline, or a Combination in Childhood Anxiety N Engl J Med 2008 359 26 2753 2766 10.1056/NEJMoa0804633 18974308 12. Aguirre Velasco A Cruz ISS Billings J Jimenez M Rowe S What are the barriers, facilitators and interventions targeting help-seeking behaviours for common mental health problems in adolescents? A systematic review BMC Psychiatry 2020 20 1 293 10.1186/s12888-020-02659-0 32527236 13. Hansen AS Telléus GK Mohr-Jensen C Lauritsen MB Parent-perceived barriers to accessing services for their child’s mental health problems Child Adolesc Psychiatry Ment Health 2021 15 1 4 10.1186/s13034-021-00357-7 33514400 14. Israel BA Schulz AJ Parker EA Becker AB Minkler M Wallerstein N Critical Issues in Developing and Following Community-Based Participatory Research Principles Community-Based Participatory Research for Health 2008 Jossey-Bass 47 62 15. Sallis JF Owen N Fisher EB Glanz K Rimer BK Viswanath K Ecological models of health behavior Health behavior and health education: Theory, research, and practice 2008 Jossey-Bass 465 485 16. Myers AL Collins-Pisano C Ferron JC Fortuna KL Feasibility and Preliminary Effectiveness of a Peer-Developed and Virtually Delivered Community Mental Health Training Program (Emotional CPR): Pre-Post Study Journal of Participatory Medicine 2021 13 1 e25867 10.2196/25867 33661129 17. Spitzer RL Kroenke K Williams JBW Löwe B A Brief Measure for Assessing Generalized Anxiety Disorder: The GAD-7 Arch Intern Med 2006 166 10 1092 1097 10.1001/archinte.166.10.1092 16717171 18. Herzig-Anderson K Colognori D Fox JK Stewart CE Warner CM School-Based Anxiety Treatments for Children and Adolescents Child Adolesc Psychiatr Clin N Am 2012 21 3 655 668 10.1016/j.chc.2012.05.006 22801000 19. Hoover S, Lever N, Sachdev N, Bravo N, Schlitt J, Acosta Price O, Sheriff L, Cashman J. Advancing Comprehensice School mental Health: Guidance From the Field. Baltimore: National Center for School Mental Health, University of Maryland School of Medicine; 2019. 20. Fortuna KL DiMilia PR Lohman MC Bruce ML Zubritsky CD Halaby MR Walker RM Brooks JM Bartels SJ Feasibility, Acceptability, and Preliminary Effectiveness of a Peer-delivered and Technology Supported Self-Management Intervention for Older Adults With Serious Mental Illness Psychiatry Q 2018 89 2 293 305 10.1007/s11126-017-9534-7	36449253	PMC9709352	NO-CC CODE	2022-12-01 23:23:04	no	Psychiatr Q. 2022 Nov 30;:1-8	utf-8	Psychiatr Q	2,022	10.1007/s11126-022-10009-4	oa_other
==== Front Anal Bioanal Chem Anal Bioanal Chem Analytical and Bioanalytical Chemistry 1618-2642 1618-2650 Springer Berlin Heidelberg Berlin/Heidelberg 4396 10.1007/s00216-022-04396-7 Research Paper Modeling study of long-term stability of the monoclonal antibody infliximab and biosimilars using liquid-chromatography–tandem mass spectrometry and size-exclusion chromatography–multi-angle light scattering Legrand Pauline [email protected] 12 Dufaÿ Sophie 2 Mignet Nathalie 1 Houzé Pascal 13 Gahoual Rabah 1 1 grid.464146.5 0000 0004 0371 0921 Université Paris Cité, Faculté de sciences pharmaceutiques et biologiques, Unité de Technologies Chimiques et Biologiques pour la Santé (UTCBS), CNRS UMR8258, Inserm U1267, 4, avenue de l’observatoire, 75270 Paris Cedex 06, France 2 grid.50550.35 0000 0001 2175 4109 Département Recherche Et Développement Pharmaceutique, Agence Générale Des Equipements Et Produits de Santé (AGEPS), Assistance Publique–Hôpitaux de Paris, (AP-HP), Paris, France 3 grid.50550.35 0000 0001 2175 4109 Laboratoire de Toxicologie Biologique, Hôpital Lariboisière, Assistance Publique – Hôpitaux de Paris (AP-HP), Paris, France 30 11 2022 114 25 4 2022 7 10 2022 20 10 2022 © Springer-Verlag GmbH Germany, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Monoclonal antibodies (mAbs) represent a dynamic class of biopharmaceutical products, as evidenced by an increasing number of market authorizations for mAb innovator and biosimilar products. Stability studies are commonly performed during product development, for instance, to exclude unstable molecules, optimize the formulation or determine the storage limit. Such studies are time-consuming, especially for mAbs, because of their structural complexity which requires multiple analytical techniques to achieve a detailed characterization. We report the implementation of a novel methodology based on the accelerated stability assessment program (ASAP) in order to model the long-term stability of mAbs in relation to different structural aspects. Stability studies of innovator infliximab and two different biosimilars were performed using forced degradation conditions alongside in-use administration conditions in order to investigate their similarity regarding stability. Thus, characterization of post-translational modifications was achieved using liquid-chromatography–tandem mass spectrometry (LC-MS/MS) analysis, and the formation of aggregates and free chain fragments was characterized using size-exclusion chromatography–multi-angle light scattering (SEC-MALS-UV/RI) analysis. Consequently, ASAP models were investigated with regard to free chain fragmentation of mAbs concomitantly with N57 deamidation, located in the hypervariable region. Comparison of ASAP models and the long-term stability data from samples stored in intravenous bags demonstrated a relevant correlation, indicating the stability of the mAbs. The developed methodology highlighted the particularities of ASAP modeling for mAbs and demonstrated the possibility to independently consider the different types of degradation pathways in order to provide accurate and appropriate prediction of the long-term stability of this type of biomolecule. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s00216-022-04396-7. Keywords Monoclonal antibody Biosimilar Stability study Stability modeling Mass spectrometry Multi-angle light scattering ==== Body pmcIntroduction Monoclonal antibodies (mAbs) and their related therapeutic agents such as fusion proteins, bispecific antibodies (BsAbs) [1] or antibody–drug conjugates (ADC) [2] are meeting with unprecedented success as biopharmaceutical products. Currently, more than 100 therapeutic mAbs are approved worldwide, with 10 newly authorized products reported in 2020 alone [3]. Their therapeutic applications have been focused mainly in oncology and for the treatment of immune disorders; however, their application is continuously broadening, as recently illustrated with the development of treatments for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [4]. Also, with several patents reaching the public domain, the development of biosimilars that define an equivalent product containing the active substance of an original biopharmaceutical product is gaining growing interest [5]. MAbs represent tetrameric glycoproteins based on immunoglobulin G, which naturally exhibit a wide variety of variants. Because of their structural complexity, extensive research activity has been devoted to developing analytical methods able to provide a detailed characterization of their structure [6, 7], which requires a panel of analytical techniques in order to analyze the different aspects of the protein [8, 9]. In addition, mAbs can undergo different types of post-translational modification (PTM) and/or structural alterations, for instance asparagine deamidation, methionine oxidation or aggregation. The modifications potentially altering the properties, the quality and the safety of mAbs are referred to as critical quality attributes (CQA) [10, 11]. Therefore, they need to be characterized in order to maintain modification levels within appropriate limits during the production process and also during stability studies. In the same manner, biosimilarity assessment requires a complete characterization of the biosimilar candidate to provide a comprehensive comparison with the innovator product over the different levels defining their structure. The comparison should also demonstrate the absence of significant differences between the two products regarding CQA; otherwise, the absence of impact in terms of clinical activity and toxicity should be demonstrated [5]. Several studies have described the comparison between innovator mAbs and biosimilar candidates; however, they focused on assessing the biosimilar produced [12–14]. Recently, interest has increased in the implementation of forced degradation studies in order to evaluate the biosimilarity of mAbs with regard to their stability [15, 16]. Early during the drug development process, important efforts are made to develop the most stable formulation. Indeed, it is important to limit the risk of stability issues and endogenous degradation. The conventional methodology for investigating their stability consists of subjecting mAbs to various stress conditions including temperature, oxidation, light or extreme pH. This enables the evaluation of major degradation pathways and selection of the most stable formulation [17]. This methodology is used for small chemical drugs [18–20] and for the development of therapeutic mAbs [21]. Nevertheless, the application of forced degradation does not allow the precise prediction of degradation during the shelf-life of the product. To address these limitations, different modeling approaches, referred to as risk-based predictive stability (RBPS) or accelerated stability assessment program (ASAP), have been recently developed [22]. These modeling approaches are based on accelerated stability studies and statistical modeling in order to predict the long-term stability of the drug [23]. Because this type of study necessitates extensive computational modeling [24], Waterman et al. [25, 26] helped to popularize ASAP studies with the recent introduction of a software program (ASAPprime®, FreeThink Technologies) enabling the implementation of this approach. The ASAP methodology is based on a modified Arrhenius equation which links the degradation rate of a compound to the temperature and the relative humidity. Typically, during ASAP studies, the pharmaceutical product is exposed to different temperatures, typically between 50 °C and 80 °C, and various relative humidity levels for 3 to 4 weeks. ASAP studies have generally been used to predict the stability of small chemical drugs [27, 28], and the approach has recently been used to predict the stability of peptides [29, 30]. It has also been used in regulatory submissions for advanced shelf-life prediction in support of stability studies, especially for clinical trials [31]. In this work, the innovator product corresponding to infliximab was characterized alongside two different types of biosimilar products in the context of forced degradation in order to assess their biosimilarity with respect to stability. Several PTM hotspots including methionine oxidation and asparagine deamidation were characterized using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). In addition, size-exclusion chromatography coupled to multi-angle light scattering analysis (SEC-MALS-UV/RI) was used to characterize aggregates and free chain fragmentation. Consequently, the ASAP approach was applied in order to perform the modeling of asparagine deamidation and free chain fragmentation of infliximab concomitantly. The quality of the models generated for the different alterations of the mAbs was evaluated in order to investigate the possibility of obtaining long-term prediction regarding the stability of mAbs. Finally, the ASAP models developed for the different types of alterations were compared with experimental data generated from stability studies. The stability studies were performed using in-use conditions by reconstitution of infliximab in IV bags followed by storage for 3 months in order to show the adequacy of this approach for long-term prediction of therapeutic mAbs. Materials and methods Chemicals Chemicals used for the experiments were systematically of analytical grade or high-purity grade. Ultrapure water used to prepare buffers and sample solutions was obtained using a Milli-Q Reference A+ water purification system purchased from Merck Millipore (Billerica, MA, USA). LC-MS grade H2O and acetonitrile (ACN) used for ultrahigh-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) experiments were purchased from VWR Chemicals (Fontenay-sous-Bois, France), respectively. Commercial products of infliximab innovator Remicade® (Merck Sharp and Dohme) and the respective EMA/FDA-approved biosimilars Remsima® (Celltrion Healthcare) and Flixabi® (Biogen) were purchased from their respective manufacturers. Dithiothreitol (DTT) and iodoacetamide (IAM) were purchased from Sigma-Aldrich (Breda, the Netherlands). Trypsin enzyme was purchased from Promega (Madison, WI, USA). Infliximab reconstitution and stability study First, the vials of infliximab were reconstituted using water for injection followed by gentle shaking, based on the guidelines of the manufacturers which were common for Remicade®, Remsima® and Flixabi®. Reconstitution was performed to a final concentration of 10 mg/mL. Consequently, the stability samples were prepared from the reconstituted solution by dilution of infliximab in an intravenous bag containing NaCl 0.9% (Freeflex, Fresenius Kabi or Ecoflac, B. Braun) to a final concentration of 1 mg/mL. Several intravenous bags were prepared and stored under the following stability conditions for 3 months: 4 °C, 25 °C protected from light and at 25 °C exposed to light. Forced degradation study Forced degradation samples of infliximab were prepared and characterized in order to identify the major degradation product and to evaluate biosimilarity between the three references of infliximab. High-temperature stress was performed as follows: 1 mL of reconstituted infliximab of each reference (1mg/mL) was conditioned in 1.5 mL vials, hermetically closed, and stored at 40 °C in temperature stability chambers (Binder, Tuttlingen, Germany) for 1, 2 and 3 months. Oxidative stress was performed as follows: 1 mL of reconstituted infliximab of each reference (1mg/mL) was diluted to 0.5 mg/mL with H202 0.1% (final concentration: 0.05%). After 24 or 48 h, filtration of the samples was performed using a 30 kDa Amicon centrifugal filter (Merck, Molsheim, France) to remove the H2O2 and to concentrate the sample to a final concentration of 1 mg/mL. Stability prediction of Remicade® using ASAP modeling Remicade® solution was conditioned in 1.5 mL vials, hermetically closed, and stored as described in Table 1 in order to perform the degradation study. Remicade® accelerated degradation was performed in temperature stability chambers (Binder, Tuttlingen, Germany). For each condition, all vials were placed in the chambers at the same time, and were removed after appropriate time of stress, to be stored at 4 °C before analysis. Temperature of the chambers was monitored in real time in order to avoid any excursion of temperature. Statistical and stability predictions were performed using the software ASAPprime® version 5.0.3 (FreeThink Technologies, Branford, CT, USA). Stability prediction was realized for the following CQA: deamidation and fragmentation. For deamidation, the default fit method was used. For fragmentation, the diffusion method was used. Confidence intervals were calculated through the Monte Carlo approach: the number of Monte Carlo simulations was set at 2500. The limit of specification for deamidation was 5.0%, and 1.0% for fragmentation.Table 1 Storage conditions used for the ASAP modeling of infliximab Temperature (°C) Storage duration (days) 30 3, 7, 10, 15, 30 35 3, 7, 10, 15, 30 40 3, 7, 10, 15, 30 45 3, 7, 10, 15 Tryptic digestion of monoclonal antibody samples For one sample, a volume equivalent to a theoretical quantity of 50 μg of mAbs was sampled and desalted against milli-Q H2O using a 30 kDa Amicon centrifugal filter (Merck, Molsheim, France) in order to remove salts. Afterward, 20 μL of ammonium bicarbonate 50 mM (pH 8.0) was added to the sample. The sample was incubated with mild stirring at 40 °C for 10 min. DTT was added to a final concentration of 10 mM and incubated at 80 °C for 20 min. After cooling down to room temperature, IAM was further added to the sample mixture to a concentration of 10 mM followed by incubation of the sample for 20 min at room temperature in the dark in order to enable alkylation of the thiol residues. A 1 μL volume of trypsin (0.5 μg/μL) was added to the mixture which was left for incubation at room temperature for 3 h. Another volume of 1 μL was added and digestion was performed overnight by incubation at 37 °C. Following digestion, a volume of 24 μL of formic acid (FA) 0.1 M was added and the sample was left at RT for 1 h. Finally, the samples were diluted to a final concentration of 0.5 μg/μL using H2O (0.1% FA). Digested samples were stored at 5 °C prior to UPLC-MS/MS analysis. Ultrahigh-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) analysis The described UPLC-MS/MS method was previously developed by our research group [32, 33]. Peptide mixtures obtained from tryptic digestion were separated by UPLC (ACQUITY, Waters, Manchester, UK) using a reversed-phase C18 stationary phase (BEH C18 1.7 μm, 2.1 × 150 mm) from Waters (St Quentin-en-Yvelines, France) directly coupled to an LTQ Orbitrap XL mass spectrometer (Thermo Scientific, Bremen, Germany). The mobile phases were composed of 0.1% formic acid (FA) in water (mobile phase A) and 0.1% FA in acetonitrile (mobile phase B). Peptide separation was carried out using a gradient from 5% to 80% B over 38 min and maintained at 80% B for 3 min, at a flow rate of 100 μL/min. The sample volume used for UPLC-MS/MS experiments was systematically 10 μL. The LTQ Orbitrap XL MS was equipped with a heated electrospray ionization source (HESI-II) from Thermo Scientific (Bremen, Germany). ESI source parameters were set as follows: ESI voltage of 4.0 kV, sheath gas flow rate of 40 and an auxiliary gas flow rate value of 12. ESI nebulizer temperature was set to 300 °C. Capillary voltage and tube lens were set to 35 V and 90 V, respectively. MS/MS experiments were performed in a Top5 data-dependent acquisition (DDA) composed of one full MS scan over the mass/charge (m/z) range of 150–2000 followed by five sequential MS/MS scans realized on the five most intense ions detected at a minimum threshold of 500 counts. Full MS scans were collected in profile mode using the high-resolution FTMS analyzer (R = 60,000) with a full-scan AGC target of 1E6 and microscans = 1. The ion trap was used in centroid mode at normal scan rate to analyze MS/MS fragments. The MSn AGC target was set to 1E4 with microscans = 3. Ions were selected for MS/MS using an isolation width of 2 Da, then fragmented by collision-induced dissociation (CID) using a normalized CID energy of 35, an activation Q of 0.25 and an activation time of 30 ms. The default charge state selected was z = 2. Using these parameters, the total duty cycle was determined to be 0.65 s. Parent ions were excluded from MS/MS experiments for 60 s in case ions triggered an event twice in 15 s using an exclusion mass width of ±1.5 Th. The instruments were controlled using Xcalibur 2.1.0 SP1 Build 1160 (Thermo Scientific, Bremen, Germany). MS/MS data analysis Data obtained from UPLC-MS/MS experiments were analyzed using Xcalibur Qual Browser 2.2 SP1.48 (Thermo Scientific, Bremen, Germany). Purely proteolytic peptides (no miscleavages) were determined in silico considering the tryptic digestion of the amino acid sequence of infliximab. For each proteolytic enzyme, conventional cleavage rules were applied and carbamidomethylation of cysteine (+57.0215 Th) considered as a systematic modification. With regard to the occurrence of PTMs, asparagine deamidation was characterized as a potential modification considering a mass shift of +0.9840 Da, and methionine oxidation by taking into account a mass shift of +15.9949 Da. Peptide identification and PTM characterization were achieved manually from the conjunction of intact peptide mass measurements in full MS and MS/MS peptide fragments attribution using a mass tolerance of less than 5 ppm in MS and 0.05 Th in MS/MS. Relative modification levels were determined for PTM using the peak areas corresponding to the MS signal for the intact peptide and the modified equivalent. Size-exclusion chromatography–multi-angle light scattering (SEC-UV/RI-MALS) The described SEC-UV/RI-MALS method was previously developed by our research group [32]. Intact mAb samples were characterized by size-exclusion liquid chromatography (SEC) using a Biozen SEC column (300 × 4.6 mm, 1.8 μm) purchased from Phenomenex (Le Pecq, France). Experiments were performed using a Prominence HPLC-UV system (Shimadzu; Marne-la-Vallée, France) equipped with an RID-20A refractive index detector (Shimadzu, Marne-la-Vallée, France) and a miniDAWN TREOS II multi-angle light scattering instrument (MALS) acquired from Wyatt Technology (Santa Barbara, CA, USA). The MALS instrument was equipped with a 658 nm laser and light scattering measurements were performed simultaneously at 49°, 90° and 131°. The MALS was also equipped with a COMET ultrasonic actuator (Wyatt Technology) which enables sonication of the MALS flow cell between each analysis in order to prevent any deposit formation. The mobile phase used for SEC-UV/RI-MALS analysis was composed of 50 mM phosphate buffer (pH 6.8) and 300 mM NaCl using a flow rate of 200 μL/min. An injection volume of 20 μL was used, the UV absorbance detection was performed at a wavelength of 280 nm and the analysis time was 35 min. Data were collected and processed using ASTRA® version 7.2 software (Wyatt Technology). Relative levels of aggregation and free chain fragmentation were estimated from 280 nm absorbance chromatograms using the peak area compared to the sum of all the peak areas corresponding to the different forms of the mAbs. Results and discussion Infliximab biosimilarity assessment in relation to stability In order to study the biosimilarity between the infliximab innovator (Remicade®) and the corresponding biosimilar products (Remsima®, Flixabi®) in terms of stability, the products were subjected to different stress conditions. The primary structure of the mAbs was then systematically characterized using LC-MS/MS analysis (see “Materials and methods”) in order to identify PTM hotspots and estimate the level of modification. In addition, the aggregation and/or chain fragmentation of the mAbs was characterized using SEC-MALS-UV/RI analysis. For the freshly reconstituted products, LC-MS/MS analysis enabled sequence coverage of 94.9% to be systematically achieved (Fig. S1). This shows the robustness of the method and the possibility for near complete characterization of the primary structure, providing the opportunity to identify all the PTM hotspots described for infliximab. Therefore, LC-MS/MS data enabled the unambiguous identification of one deamidation and nine different oxidation hotspots. As emphasized in Fig. 1A, the level of oxidation initially ranged from 0.7% to 5% depending on the residue. Levels of oxidation were found to be similar for the different residues except for heavy chain (HC) M18 and M255 residues in addition to light chain (LC) M55, which exhibited higher modification levels for the biosimilar products. The residue M18 is located in the mAb variable domain, and the oxidation of the amino acid M255 is reported to lead to lower affinity with the FcRn receptor [34]. Therefore, the occurrence of such modifications can alter the biological properties of the mAbs. It is important to characterize the occurrence and the proportion of PTMs in the context of biosimilarity assessment, which in this case showed only slight differences. The residue N57 is located in the complementarity-determining region (CDR) of infliximab HC, and therefore deamidation into aspartic acid would have a major impact on the epitope interaction. Also, this residue has been described as being sensitive to deamidation [12]. The level of N57 deamidation was between 1.5% and 2.4% initially for the different products (Fig. 1B). Note that the PTM levels observed for the reconstituted products are consistent with those in other studies [12, 14, 35, 36]. Similarly, the reconstituted products were characterized using SEC-MALS-UV/RI. SEC-MALS results presented in Fig. 1C show that initially the proportion of aggregates represents less than 1% of the total forms, whereas the proportion of aggregates is slightly higher in the case of the two biosimilar products.Fig. 1 Proportions of oxidation (A) and N57 deamidation (B) determined from LC-MS/MS data and levels of aggregation (C) measured from SEC-MALS-UV/RI experiments in the case of Remicade® (red), Remsima® (green) and Flixabi® (blue) present after reconstitution Consequently, forced degradation was performed on the three references of infliximab in order to identify potential degradation of the mAbs and to assess their biosimilarity with regard to their stability. To accelerate the oxidation of residues, the different samples were incubated in the presence of H2O2, whereas mAbs were exposed to relatively high temperature in order to generate deamidation and aggregation. Results of LC-MS/MS experiments demonstrated an increase in oxidation in the case of M55, M18, M255 and M431, as presented in Fig. 2A, whereas no significant modification could be observed for other residues characterized (Fig. S2). Thus, the oxidation levels increased rapidly, with modification levels ranging from 39.8% to 76.0% after incubation for 24 h, depending on the residue, and systematically above 62.5% after 48 h of incubation. Regarding deamidation in the CDR, the residue N57 exhibited modification levels that gradually increased over time, with values ranging from 1.9% to 3.8% after 5 days of exposure to a temperature of 40 °C, and from 21.9% to 24.5% after 90 days of incubation (Fig. 2C). The tridimensional structure of infliximab described in the literature was compared to the LC-MS/MS data in order to characterize the differences between the modified residues and the amino acids which remained intact. As emphasized in Fig. 2B, the amino acid residues prone to modification were located at the periphery of the protein, and therefore significantly exposed to the environment. In addition, this observation suggests that amino acids buried deeper inside the tertiary structure of the protein may be less sensitive to endogenous modification. Regarding the oxidation and deamidation hotspots characterized, the data showed similar modification levels among the different products corresponding to infliximab. Therefore, LC-MS/MS experiments showed the relevant similarity of the different infliximab products with regard to their stability under stress conditions in addition to the similarity of their tertiary structure. Indeed, the modified residues are consistent between the different samples, indicating similar exposure to the environment. In the case of SEC-MALS analysis, the proportion of mAb aggregates remained constantly below 1% irrespective of the duration of the stress (Fig. 2D). In contrast, exposure to a temperature of 40 °C led to the fragmentation of infliximab in free heavy chains and light chains, with a proportion of 5% of fragments after 90 days (Fig. 2E). In this case as well, the innovator and biosimilar products corresponding to infliximab demonstrated similar levels of fragmentation throughout the duration of the temperature stress. Consequently, the fragmentation into free HC and LC was determined to be the major degradation pathway regarding size variants of infliximab.Fig. 2 Proportions of (A) oxidation for methionine and tryptophane hotspots after incubation in 0.05% H2O2 for Remicade® (red), Remsima® (green) and Flixabi® (blue). (B) Schematic representation of the structure of infliximab showing the localization of oxidized methionine. Proportions of (C) N57 deamidation, (D) infliximab aggregation and (E) free chain fragmentation after incubation at 40 °C for 90 days Infliximab stability and biosimilarity assessment for in-use conditions Following the assessment of the different infliximab products with respect to stress stability, the in-use stability of infliximab was evaluated using different conditions, this time after reconstitution in intravenous (IV) bags. The conditions were selected to reflect different scenarios regarding storage temperature, the type of IV bag or the exposure to light (see “Materials and methods”) over a period of 3 months. In addition to assessing the stability of the different products corresponding to infliximab in real-life conditions, this study aimed to provide evidence regarding the possibility of anticipated preparation in a hospital unit in order to manage a continuously growing number of patients. The results obtained from LC-MS/MS experiments concerning the residues sensitive to oxidation are presented in Fig. 3. At a storage temperature of 4 °C, oxidation appeared globally limited, with levels ranging from 0.5% to 4% depending on the residue. Moreover, the oxidation levels did not show a significant increase upon storage at 4 °C even for an extended time (Fig. 3A). Similarly, when the IV preparations were maintained at 25 °C in the dark, oxidation levels did not exhibit any increase, and the modification levels were comparable to the data obtained at 4 °C (Fig. 3B). In the case of IV bags kept at 25 °C exposed to light, the sensitive methionine previously identified showed a gradual increase in oxidation levels, with values ranging from 0.5% to 12.9% in the case of M255, which showed the highest oxidation levels (Fig. 3C). The oxidation of residue M255 is reported to significantly decrease the affinity between the mAbs and the FcRn receptor which is responsible for the preservation of IgG proteins in the endosome [37]. Therefore, the results obtained show that prolonged exposure to light is likely to impact the serum half-life of infliximab. Interestingly, the methionine and tryptophan residues, which were not modified during the stress degradation, also did not exhibit any modification in the IV bag samples. Therefore, this comparison shows that H2O2 represents a relevant oxidation stress assay capable of indicating residues prone to modification. For the different conditions, the biosimilar products generally exhibited similar behavior regarding oxidation hotspots regardless of the type of IV bag. However, in the case of the infliximab innovator, LC-MS/MS data showed that M55 and M255 demonstrated increased oxidation when the mAbs were reconstituted in IV bag B. For instance, in the case of M255, the level of oxidation was 5% after incubation when the mAbs were reconstituted in IV bag A, while the level was 13% if the product was solubilized in IV bag B (Fig. 3C). IV bag A is composed of polypropylene whereas IV bag B is constituted of low-density polyethylene. Therefore, this result could be linked to an interaction between the additives of the IV bag and the mAbs.Fig. 3 Oxidation levels measured from LC-MS/MS analysis for the infliximab products reconstituted in IV bag A (continuous line) and IV bag B (dotted line) after (A) conservation at 4 °C, (B) storage at 25 °C protected from light and (C) storage at 25 °C exposed to light. Remicade® (red), Remsima® (green) and Flixabi® (blue) With regard to the deamidation of the amino acid hotspot N57, the results presented in Fig. 4A show that the modification of the residue was not significantly increased when the sample was stored at 4 °C, with modification levels ranging from 1.5% to 2.7%. On the contrary, the samples subjected to a temperature of 25 °C demonstrated a gradual increase in the modification level from 1.5% to 5%. In the case of the deamidation, light exposure did not influence the kinetics of the modification, indicating that temperature is the major factor in this case. Meanwhile, the study of the higher order structure using SEC-MALS-UV/RI analysis did not demonstrate infliximab fragmentation for the samples maintained at 4 °C, whereas the samples subjected to a temperature of 25 °C over an extended period exhibited fragmentation of the proteins in free chains (Fig. 4B).Fig. 4 (A) Deamidation of amino acid N57 determined from LC-MS/MS analysis and (B) free chain fragmentation determined from SEC-MALS-UV/RI for the infliximab products reconstituted in IV bag A (continuous line) and IV bag B ( dotted line) for the different conservation conditions. Remicade® (red), Remsima® (green) and Flixabi® (blue) As a consequence, the experiments performed concomitantly using LC-MS/MS analysis and SEC-MALS-UV/RI allowed us to investigate the stability of the different products corresponding to infliximab with respect to different PTM hotspots and aggregation/fragmentation. The study using stressed conditions allowed us to identify the residues prone to modification, especially for oxidation and deamidation. Thus, the study demonstrated that the occurrence of oxidation is driven by the media solubilizing the mAbs. In addition, data demonstrated that residues accessible to the solvent are rapidly modified, in contrast to the amino acid buried in the structure of the protein, which remains intact. For deamidation and free chain fragmentation, the degradation of the protein was attributed to the effect of temperature. The investigation of the infliximab products reconstituted using in-use conditions demonstrated that the residues from modification during real-life conditions were the same as those modified using stressed conditions. The results showed that no significant modification was observed when samples were stored at 4 °C for a period of 3 months. The innovator product and the corresponding biosimilar demonstrated important similarity in terms of stability; however, it is important to note that the innovator samples exhibited significantly higher oxidation for the residues M55, M18, M431 and M255 when the product was reconstituted in IV bags composed of polyethylene, which has not been described before. Such results clearly emphasize that biosimilarity assessment should not be restricted to a simple structural characterization but should also investigate the stability of biosimilar candidates over the different levels characterizing the structure of the mAbs, if possible in conditions as close as possible to real-world use conditions. Indeed, to our knowledge, this is the first study evaluating the evolution of PTM hotspots during hospital in-use conditions of infliximab. Also, it is the first time that biosimilarity assessment was performed in the context of hospital in-use conditions. Infliximab stability prediction using ASAP modeling After the stability study, an accelerated stability assessment program (ASAP) was realized for infliximab in order to evaluate the possibility of employing this type of approach in predicting the long-term stability of therapeutic mAbs. Experimentally, the ASAP model is built by subjecting the studied product to different temperature conditions and various relative humidity levels in the case of a solid formulation. The level of the degradation product (DP) generated depending on the conditions is then used to determine the modified Arrhenius equation parameters as illustrated in Eq. 1:1 Ln(k)=Ln(A)-EaRT+B×RH where k represents the degradation rate, Ln (A) the pre-exponential factor, Ea the activation energy, T the temperature, R the gas constant, B the moisture sensitivity factor and RH the relative humidity. Thus, the model is established by artificial generation of the considered DP, close to the specification limit in each stress condition [26], which makes it possible to overcome the heterogeneous kinetics of degradation [25]. The specification limit corresponds to the maximum amount of degradation product allowed for the therapeutic product. For small chemical synthetic drugs, the limit of specification is generally defined from 0.05% to 1.0% by the regulatory authorities depending on the posology of the molecule [38]. For biotherapeutic products, the limit is defined on a case-by-case basis for each drug based on the regulatory guidelines or the information described in the scientific literature [39]. The time necessary to reach the specification limit is referred to as the isoconversion time. The model developed can then be used to predict the level of DP that would be observed during long-term stability study in the envisaged storage temperature and relative humidity. Note that in order to be able to perform ASAP modeling, it is essential to characterize the DP beforehand and benefit from an analytical method which allows it to be analyzed without any interference. Infliximab and mAbs in general are extremely complex macromolecules which may undergo several modifications simultaneously [9]. Thus, it is usually difficult to predict the long-term stability for biotherapeutic products because of their inherent complexity and the non-Arrhenius behavior for quality attributes such as aggregation [40]. However, the aggregation pathways seem to be significantly mAb-dependent [41]; for instance, a recent study was able to model aggregation using a thermodynamic equation with other types of mAbs [42]. For the long-term stability study of infliximab, the ASAP approach was performed concomitantly for different degradation processes in order to investigate the possibility of modeling the stability of the mAbs regarding different aspects defining the structure of the protein. As illustrated in Eq. 1, the implementation of the modified Arrhenius equation makes it possible to study the degradation influenced by temperature and/or humidity. The results obtained previously for stability studies showed that oxidation of methionine hotspots were influenced by the level of oxygen and the exposure to light, whereas the storage temperature had no influence on the level of their oxidation when increased from 4 °C to 25 °C (Fig. 3). On the contrary, the stability study showed that for the different infliximab products, the deamidation of N57 and the fragmentation in free chains are impacted by the temperature (Fig. 4). Therefore, the ASAP modeling was envisaged only for these two types of degradation. Thus, a vial of infliximab corresponding to the marketed formulation was reconstituted and consequently split into equal-volume samples, which were subjected to temperature ranging from 30 °C and 45 °C for up to 30 days. During the incubation, the samples were regularly characterized regarding N57 deamidation using LC-MS/MS analysis and concomitantly regarding chain fragmentation of mAbs by the intermediate of SEC-MALS-UV/IR analysis. Note that the incubation temperature was limited, because above 45 °C, rapid precipitation generating non-soluble particles could be observed. The specification limits were fixed at a value of 5.0% in the case of deamidation and 1.0% for free chain fragmentation. The monograph of the pharmacopoeia for infliximab did not mention the limits in terms of acceptable PTM levels; therefore, the specification limits were determined using minimal values considering modification levels previously described in the literature. In addition, 5% modification was considered as a maximum acceptable level of PTMs, in order to maintain 95% of the original form as is commonly acceptable [16, 43]. As emphasized in Fig. 5A, the deamidation of the residue N57 demonstrated a gradual increase from 0.7% initially up to 8.6% for incubation at 45 °C for 15 days. Results also showed an increase in the kinetics of the reaction when the incubation temperature was increased. With regard to the fragmentation of infliximab in free chains, no fragments were initially detected (Fig. 5B). Subsequently, the proportion of free chains increased over time when the mAbs were exposed to increasing temperatures up to 1.6% and incubated at 40 °C for 30 days. In this case as well, the kinetics of the free chain fragmentation increased when the temperature was higher, showing that the degradation process is impacted by the temperature. For each temperature condition, the isoconversion time was calculated or extrapolated from the experimental data. A linear regression was used in the case of N57 deamidation (Fig. S3) and a diffusion regression (Fig. S4) for mAb free chain fragmentation, which is consistent, as the two processes of degradation showed different evolutions (Fig. 5). The isoconversion times and isoconversion ratios obtained for the different conditions are detailed in Table 2. The isoconversion ratio corresponds to the ratio between the latest measurement time and the calculated isoconversion time. It enables us to estimate the extent of the extrapolation required to determine the isoconversion time. Thus, it tends to be closer to 0.0 when an important extrapolation is required to calculate the isoconversion time. The value is above 1 when the specification limit is reached during the experiment and extrapolation is not necessary. Therefore, the isoconversion ratio makes it possible to limit excessive extrapolation in order to build a valid model. Generally, isoconversion ratios lower than 0.1 are considered out of range, and the data should be excluded from the model. For the N57 deamidation hotspot, the lowest isoconversion ratio calculated was 0.62 at 30 °C, whereas for the free chain fragmentation, the lowest isoconversion ratio was 0.41 (Table 2). Thus, the data obtained using the different conditions were compatible with the model for infliximab N57 deamidation and free chain fragmentation.Fig. 5 (A) Proportion of N57 deamidation estimated from LC-MS/MS analysis and of (B) infliximab chain fragmentation determined from SEC-MALS-UV/RI analysis for the ASAP stress samples. The red discontinuous lines indicates the specification limit in each case Table 2 Isoconversion time and isoconversion ratio calculated from ASAP stress samples for deamidation of the amino acid N57 and infliximab chain fragmentation for the different temperature conditions Temperature condition Deamidation Fragmentation Isoconversion time (days) Isoconversion ratio Isoconversion time (days) Isoconversion ratio 30 °C 48.4 ± 12.2 0.6 72.9 ± 14.9 0.4 35 °C 35.6 ± 3.5 0.8 43.0 ± 8.1 0.7 40 °C 21.4 ± 2.8 1.4 10.9 ± 2.0 2.8 45 °C 8.3 ± 1.5 1.8 5.8 ± 1.1 2.6 Using the experimental data from the different conditions, the parameters of the Arrhenius equation were calculated for the deamidation of the amino acid N57 and the fragmentation of the mAbs in free chains, as summarized in Table 3. Different parameters were considered in order to evaluate the adequacy of the various ASAP models. The correlation coefficient R2 between the model and the experimental data was 0.934 for the deamidation and 0.968 in the case of the free chain fragmentation, thus demonstrating validity (Table 3). Indeed, regarding adequacy, the ASAP model is considered to be valid when R2 > 0.9 [44]. The cross-validation coefficients Q2 were also calculated yielding a value of 0.576 for deamidation and 0.898 for free chain fragmentation. Due to their respective characteristics, the value of Q2 is expected to be lower than R2; however, it is the difference between the two values which makes it possible to further demonstrate the adequacy of the model compared to the experimental data. For the two types of degradation considered, the values of both R2 and Q2 proved to be satisfactory. In addition, as presented in Fig. 6, the residual plots corresponding to the DP formation rate ln (k) were satisfactory compared to other studies using the ASAP methodology [29], with residues of ln k systematically lower than ±0.4.Table 3 ASAP model parameters obtained from infliximab stress samples for the deamidation of N57 hotspot and free chain fragmentation and correlations calculated between the present model and the experimental data Model parameters Deamidation Fragmentation Ln A 34.05 ± 6.22 52.43 ± 5.80 Ea (kcal/mol) 22.06 ± 3.85 34.25 ± 3.58 B NA NA R2 0.934 0.968 Q2 0.576 0.898 Fig. 6 Residual plot obtained from the ASAP model (A) for the deamidation of residue N57 and (B) in the case of infliximab free chain- fragmentation Previous stability studies of infliximab using forced degradation enabled the identification of different alterations of the mAbs. In addition, they identified the degradation processes which could potentially be modeled using an ASAP approach. Thus, the characterization of the samples subjected to different temperature conditions showed the possibility for concomitant modeling of the deamidation of the residue N57 and the fragmentation of the mAbs in free chains. The ASAP models established for the different degradation types were shown to be in agreement with the experimental data, confirming the validity of the model based on common practice regarding ASAP prediction. In addition, the relevance of the models clearly indicates that N57 deamidation and free chain fragmentation are thermodynamically driven alterations of infliximab. The ASAP models further highlighted the possibility of performing mAb stability prediction using the ASAP approach over several degradation processes which may occur concomitantly on the structure of the mAbs. From that perspective, the methodology developed in this work is particularly interesting because it provides the opportunity to perform comprehensive stability prediction regardless of the structural complexity of the mAbs. To the best of our knowledge, this is the first time that ASAP modeling was performed simultaneously with success for degradation over different levels defining the structure of the mAbs, primary structure for the deamidation and tertiary structure in the case of free chain fragmentation. Comparison between stability prediction and long-term stability data In order to evaluate the performance of the model, the stability prediction generated using the described ASAP models was compared to the data obtained during the in-use stability study of infliximab with respect to the deamidation of the residue N57 and the free chain fragmentation of the mAbs (Fig. 4). The elaboration of the ASAP model takes into account the variability of the generated model and eventually the variability of the analytical method in order to predict the minimum and maximum quantity of DP generated over time for a designated condition (Fig. 7). Regarding deamidation of the residue N57, at 4 °C the level of deamidation predicted using the model was systematically below 2% and the prediction described a near absence of increase concerning the modification level. However, the level of deamidation determined during the in-use stability study was systematically over the predicted level, even in the initial conditions directly after reconstitution (Fig. 7A). Nevertheless, the predicted and experimental evolution appears similar. This observation was attributed to the fact that infliximab vials used to perform the ASAP modeling and the in-use stability study came from different batches. Indeed, after reconstitution the two batches exhibited different initial deamidation levels of N57: 0.7 % ± 0.1 for the ASAP batch and 1.5 % ± 0.1 for the batch used for in-use stability samples (Fig. 7A). If a correction of the ASAP prediction is applied based on the initial mismatch concerning the deamidation level, all the experimental points of the in-use stability study are comprised in the prediction showing an outstanding correlation. For the storage conditions at 25 °C and 40 °C, the predictions described a gradual increase of the deamidation level after 90 days up to 3% for a temperature of 25 °C (Fig. 7B) and 4% at 40 °C (Fig. 7C). The predictions generated from the ASAP model demonstrated different slopes depending on the temperature applied. The experimental data are showing a relevant correlation with the prediction for the different case in term of deamidation levels and evolution over the duration of the study. Therefore, the correlation of the results obtained from the ASAP prediction and experimental data demonstrated that the ASAP modeling is able to predict the evolution of the modification level of the residue characterized.Fig. 7 ASAP model prediction concerning the deamidation of the amino acid N57 (A) at 4 °C, (B) 25 °C and (C) 40 °C. ASAP model prediction regarding free chain fragmentation at (D) 4 °C, (E) 25 °C and (F) 40 °C. The mean predicted value is represented by the blue line, the maximum and minimum predicted values are shown in green and red lines, respectively. Experimental data are represented as black dots Concerning free chain fragmentation, the prediction generated for a temperature of 4 °C indicated a calculated level < 0.02% after 6 months (Fig. 7D) which, considering the performance of the SEC-MALS-UV/RI method, corresponds to an absence of fragmentation. Experimentally, when stored at a temperature of 4 °C, free chain fragmentation of infliximab was not observed, which is in agreement with the prediction of the model. For the conservation conditions at 25 °C, the prediction achieved from the model initially exhibited an absence of fragmentation followed by a constant increase over the duration of the experiment to a maximum value of 0.1% after 6 months. From the comparison with the experimental data, the fragmentation appears to be slightly overestimated by the model prediction (Fig. 7E). The difference in terms of the proportion of free chains was attributed to the extremely low levels generated during the first 60 days of the study, ranging from 0% to 0.015%, which can be difficult to accurately estimate from SEC-MALS-UV/RI chromatograms. When the proportion of free chains was further increased, the experimental data correlated with the model prediction. In addition, the larger discrepancy between the prediction and the experimental data represented 0.01%, which remained negligible and placed the prediction on the safer side compared to the level of free chains effectively characterized (Fig. 7E). Williams et al. reported a similar conclusion in a study using ASAP for small chemicals drugs [27]. As illustrated in Fig. 7F, for a temperature of 40 °C, the model prediction showed a steeper increase in the proportion of free chains above 0.25% after a period of 6 months. The experimental data showed a consistent correlation with the prediction provided by the model regarding the proportion of free chains and the evolution over time. Note that the experimental results from the in-use condition samples exhibited minor differences compared to the prediction of the model, which in this case as well represented a maximal difference of 0.01%, which is lower than the standard deviation provided by the analytical method. Indeed, in addition, the experimental data showed significant variability between the sample triplicates (Fig. 7F). As a consequence, the correlation of the model developed from the ASAP approach with the data obtained from the analysis for the samples stored using in-use conditions was found to be relevant for the long-term fragmentation of the infliximab in free chains. Finally, the ASAP approach was successfully used to model two different types of alteration occurring on the infliximab mAbs. Thus, the required experiments were performed over a period of 30 days and allowed for the concomitant modeling of the deamidation of the residue N57 and the fragmentation of infliximab into free chains, for a period longer than 6 months (Fig. 7). For the two different alteration processes, the comparison between the prediction generated from the ASAP model and the experimental data obtained from long-term storage of infliximab reconstituted in IV bags demonstrated a relevant correlation. Also, the evolution of mAb degradation over time was relevant with the modeling. Therefore, the results achieved showed the possibility of using an ASAP model in order to predict the stability of mAbs under conventional storage conditions. In addition, the long-term stability prediction generated from the ASAP model could be eventually considered in order to optimize the composition of the formulation regarding mAb stability and help to determine the expiration time of the product. The implementation of the ASAP approach for the long-term stability study of infliximab highlighted the crucial requirements. Indeed, before the development of the model, it is essential to perform a complete characterization of the different degradation products generated by the therapeutic protein investigated. This makes it possible to identify the major degradation products as a preliminary approach, in addition to identifying the degradation processes which can be modeled by a modified Arrhenius equation due to the impact of temperature and/or humidity. In a similar manner, the implementation of the ASAP approach requires an analytical method able to unambiguously separate the investigated DP and accurately estimate the level. In particular, results achieved for the ASAP modeling concerning infliximab demonstrated that the accuracy, sensitivity and variability of the analytical method is crucial for providing a valid model and may influence the correlation with the experimental data. ASAP models could be established concomitantly for two different types of structural alterations of infliximab. Therefore, the workflow implemented in this study showed the methodology required to perform ASAP modeling of therapeutic mAbs. It may be possible to extend the ASAP modeling of mAbs to a wider range of modifications known to influence the characteristics of the protein such as aspartic acid isomerization and N-terminal glutamic acid cyclization in order to provide a comprehensive stability model regarding these complex macromolecules. As such, capillary zone electrophoresis coupled to tandem mass spectrometry (CE-MS/MS) represents a relevant technique for characterization of a large number of PTMs simultaneously [45]. Conclusion In the present study, an innovator product corresponding to infliximab and two approved biosimilars were characterized during forced degradation and stability studies after reconstitution of the products in IV bags, in order to assess their similarity in the context of degradation. The characterization of several PTM hotspots, including deamidation and oxidation, was realized using LC-MS/MS, and SEC-MALS-UV/RI was used to characterize aggregates and free chain fragmentation. For oxidative stress and temperature stress conditions, results demonstrated high similarity between the reference and the biosimilar products. During the stability study, experimental data for the most part exhibited similar evolution. However, the innovator infliximab demonstrated significantly higher levels of oxidation when the product was stored in IV bags composed of polyethylene and exposed to light. This result underlines the need to consider different possible impacts of content–container interaction when performing such stability study and biosimilarity comparison. Consequently, the ASAP approach was implemented in order to perform the simultaneous modeling of infliximab N57 deamidation and mAb fragmentation in free chains. The application of the ASAP methodology allowed us to successfully model the evolution of infliximab concerning the different degradation processes over a period of up to 6 months. Finally, the results obtained from the long-term stability study of infliximab reconstituted in IV bags were compared to the evolution of infliximab predicted from ASAP models to determine the relevant correlation between the evolution of mAbs predicted from the ASAP models and the long-term stability results in the case of deamidation and free chain fragmentation. The methodology developed in this study showed the possibility of elaborating the ASAP model of different degradation processes concomitantly, over the different levels defining the structure of the mAbs, in order to address the structural complexity of the protein. The present study helps to demonstrate the possibility of using such an approach to predict the stability not only of small molecules but also of biopharmaceutical products. The results highlight the current limitations of modeling based on a modified Arrhenius equation. In addition, this study allowed us to define the consecutive steps required for the implementation of an ASAP model for mAbs. Supplementary information ESM 1 (DOCX 872 KB) Declarations Conflicts of interest The authors declare no conflict of interest. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Duivelshof BL Beck A Guillarme D D'Atri V Bispecific antibody characterization by a combination of intact and site-specific/chain-specific LC/MS techniques Talanta 2022 236 122836 10.1016/j.talanta.2021.122836 34635226 2. Beck A Wurch T Bailly C Corvaia N Strategies and challenges for the next generation of therapeutic antibodies Nat Rev Immunol 2010 10 5 345 352 10.1038/nri2747 20414207 3. Kaplon H Reichert JM Antibodies to watch in 2021 MAbs 2021 13 1 1860476 10.1080/19420862.2020.1860476 33459118 4. Weinreich DM Sivapalasingam S Norton T Ali S Gao H Bhore R REGN-COV2, a Neutralizing Antibody Cocktail, in Outpatients with Covid-19 N Engl J Med 2021 384 3 238 251 10.1056/NEJMoa2035002 33332778 5. Guideline on similar biological medicinal products. Eur Med Agency. 2014. 6. Gahoual R Busnel JM Beck A Francois YN Leize-Wagner E Full antibody primary structure and microvariant characterization in a single injection using transient isotachophoresis and sheathless capillary electrophoresis-tandem mass spectrometry Anal Chem 2014 86 18 9074 9081 10.1021/ac502378e 25141158 7. Lakayan D Haselberg R Gahoual R Somsen GW Kool J Affinity profiling of monoclonal antibody and antibody-drug-conjugate preparations by coupled liquid chromatography-surface plasmon resonance biosensing Anal Bioanal Chem 2018 410 30 7837 7848 10.1007/s00216-018-1414-y 30328504 8. Fekete S Guillarme D Sandra P Sandra K Chromatographic, Electrophoretic, and Mass Spectrometric Methods for the Analytical Characterization of Protein Biopharmaceuticals Anal Chem 2016 88 1 480 507 10.1021/acs.analchem.5b04561 26629607 9. Beck A D'Atri V Ehkirch A Fekete S Hernandez-Alba O Gahoual R Cutting-edge multi-level analytical and structural characterization of antibody-drug conjugates: present and future Expert Rev Proteomics 2019 16 4 337 362 10.1080/14789450.2019.1578215 30706723 10. Alt N Zhang TY Motchnik P Taticek R Quarmby V Schlothauer T Determination of critical quality attributes for monoclonal antibodies using quality by design principles Biologicals 2016 44 5 291 305 10.1016/j.biologicals.2016.06.005 27461239 11. Beck A Wagner-Rousset E Ayoub D Van Dorsselaer A Sanglier-Cianferani S Characterization of therapeutic antibodies and related products Anal Chem 2013 85 2 715 736 10.1021/ac3032355 23134362 12. Pisupati K Tian Y Okbazghi S Benet A Ackermann R Ford M A Multidimensional Analytical Comparison of Remicade and the Biosimilar Remsima Anal Chem 2017 89 9 4838 4846 10.1021/acs.analchem.6b04436 28365979 13. Lee C Jeong M Lee JJ Seo S Cho SC Zhang W Glycosylation profile and biological activity of Remicade(R) compared with Flixabi(R) and Remsima(R) MAbs 2017 9 6 968 977 10.1080/19420862.2017.1337620 28640663 14. Kim J Chung J Park S Jung S Kang D Evaluation of the physicochemical and biological stability of reconstituted and diluted SB2 (infliximab) Eur J Hosp Pharm 2018 25 3 157 164 10.1136/ejhpharm-2016-001085 29732144 15. Dyck YFK, Rehm D, Joseph JF, Winkler K, Sandig V, Jabs W, et al. Forced Degradation Testing as Complementary Tool for Biosimilarity Assessment. Bioeng (Basel). 2019;6(3). 10.3390/bioengineering6030062. 16. Pisupati K Benet A Tian Y Okbazghi S Kang J Ford M Biosimilarity under stress: A forced degradation study of Remicade(R) and Remsima MAbs 2017 9 7 1197 1209 10.1080/19420862.2017.1347741 28787231 17. Nowak C Cheung JK Dellatore SM Katiyar A Bhat R Sun J Forced degradation of recombinant monoclonal antibodies: A practical guide MAbs. 2017 9 8 1217 1230 10.1080/19420862.2017.1368602 28853987 18. Blessy M Patel RD Prajapati PN Agrawal YK Development of forced degradation and stability indicating studies of drugs-A review J Pharm Anal 2014 4 3 159 165 10.1016/j.jpha.2013.09.003 29403878 19. Narang AS Desai D Badawy S Impact of excipient interactions on solid dosage form stability Pharm Res 2012 29 10 2660 2683 10.1007/s11095-012-0782-9 22707362 20. Singh S Junwal M Modhe G Tiwari H Kurmi M Parashar N Forced degradation studies to assess the stability of drugs and products. TrAC, Trends Anal Chem 2013 49 71 88 10.1016/j.trac.2013.05.006 21. Tamizi E Jouyban A Forced degradation studies of biopharmaceuticals: Selection of stress conditions Eur J Pharm Biopharm 2016 98 26 46 10.1016/j.ejpb.2015.10.016 26542454 22. Qiu F Wu Y Hahn D McMahon M Orr R Webb D Risk-Based Predictive Stability–An Industry Perspective Pharm Technol 2017 41 3 52 57 23. Qiu F Scrivens G Qiu F Scrivens G Accelerated Predictive Stability Accelerated Predictive Stability 2018 Boston Academic Press xxiii xiv 24. Evers A, Clenet D, Pfeiffer-Marek S. Long-Term Stability Prediction for Developability Assessment of Biopharmaceutics Using Advanced Kinetic Modeling. Pharmaceutics. 2022;14(2). 10.3390/pharmaceutics14020375. 25. Waterman KC Carella AJ Gumkowski MJ Lukulay P MacDonald BC Roy MC Improved protocol and data analysis for accelerated shelf-life estimation of solid dosage forms Pharm Res 2007 24 4 780 790 10.1007/s11095-006-9201-4 17372701 26. Waterman KC The application of the Accelerated Stability Assessment Program (ASAP) to quality by design (QbD) for drug product stability AAPS PharmSciTech 2011 12 3 932 937 10.1208/s12249-011-9657-3 21748541 27. Williams HE Bright J Roddy E Poulton A Cosgrove SD Turner F A comparison of drug substance predicted chemical stability with ICH compliant stability studies Drug Dev Ind Pharm 2019 45 3 379 386 10.1080/03639045.2018.1542707 30395722 28. Tan ZJ, Wu ZS. ASAP Application in Suspension, Liquid, Lyophilized, and Controlled-Release Drug Products. Accelerated Predictive Stability. 2018. p. 323-40; 10.1016/b978-0-12-802786-8.00014-0 29. Waterman R Lewis J Waterman KC Accelerated Stability Modeling for Peptides: a Case Study with Bacitracin AAPS PharmSciTech 2017 18 5 1692 1698 10.1208/s12249-016-0635-7 27714699 30. Legrand P Gahoual R Houze P Dufay S Accelerated Stability Assessment Program to Predict Long-term Stability of Drugs: Application to Ascorbic Acid and to a Cyclic Hexapeptide AAPS PharmSciTech 2021 22 7 234 10.1208/s12249-021-02121-8 34498167 31. Colgan ST, Mazzeo T, Orr R. Regulatory Expectations and Industry Practice on Stability Testing. Accelerated Predictive Stability2018. p. 15-32; 10.1016/b978-0-12-802786-8.00002-4. 32. Legrand P Dembele O Alamil H Lamoureux C Mignet N Houze P Structural identification and absolute quantification of monoclonal antibodies in suspected counterfeits using capillary electrophoresis and liquid chromatography-tandem mass spectrometry Anal Bioanal Chem 2022 414 8 2699 2712 10.1007/s00216-022-03913-y 35099584 33. Gahoual R Bolbach G Ould-Melha I Clodic G Francois YN Scherman D Kinetic and structural characterization of therapeutic albumin chemical functionalization using complementary mass spectrometry techniques J Pharm Biomed Anal 2020 185 113242 10.1016/j.jpba.2020.113242 32169787 34. Gahoual R Heidenreich AK Somsen GW Bulau P Reusch D Wuhrer M Detailed Characterization of Monoclonal Antibody Receptor Interaction Using Affinity Liquid Chromatography Hyphenated to Native Mass Spectrometry Anal Chem 2017 89 10 5404 5412 10.1021/acs.analchem.7b00211 28398745 35. Mart√≠n. SMn, Zaborowska I, Jakes C, Carillo S, Bones J. Comparability study for the determination of post-translational modifications of infliximab innovator and biosimilars by automated high-throughput peptide mapping analysis. Thermoscientific application note 21849. 36. Giorgetti J Beck A Leize-Wagner E Francois YN Combination of intact, middle-up and bottom-up levels to characterize 7 therapeutic monoclonal antibodies by capillary electrophoresis - Mass spectrometry J Pharm Biomed Anal 2020 182 113107 10.1016/j.jpba.2020.113107 32004767 37. Wang W Vlasak J Li Y Pristatsky P Fang Y Pittman T Impact of methionine oxidation in human IgG1 Fc on serum half-life of monoclonal antibodies Mol Immunol 2011 48 6-7 860 866 10.1016/j.molimm.2010.12.009 21256596 38. ICH Q3B(R2) : Impurities in new drug products. International Conference On Harmonisation Of Technical Requirements For Registration Of Pharmaceuticals For Human Use. 2003. 39. ICH Q5C: Stability testing of biotechnological/biological products. International Conference On Harmonisation Of Technical Requirements For Registration Of Pharmaceuticals For Human Use. 1995. 40. Wang W Roberts CJ Non-Arrhenius protein aggregation AAPS J 2013 15 3 840 851 10.1208/s12248-013-9485-3 23615748 41. Ito T Tsumoto K Effects of subclass change on the structural stability of chimeric, humanized, and human antibodies under thermal stress Protein Sci 2013 22 11 1542 1551 10.1002/pro.2340 23963869 42. Kuzman D Bunc M Ravnik M Reiter F Zagar L Boncina M Long-term stability predictions of therapeutic monoclonal antibodies in solution using Arrhenius-based kinetics Sci Rep 2021 11 1 20534 10.1038/s41598-021-99875-9 34654882 43. Tokhadze N Chennell P Le Basle Y Sautou V Stability of infliximab solutions in different temperature and dilution conditions J Pharm Biomed Anal 2018 150 386 395 10.1016/j.jpba.2017.12.012 29287266 44. Williams H. Predictive Stability Testing Utilizing Accelerated Stability Assessment Program (ASAP) Studies. Methods for Stability Testing of Pharmaceuticals. Methods in Pharmacology and Toxicology2018. p. 213-32; 10.1007/978-1-4939-7686-7_10. 45. Gahoual R Leize-Wagner E Houze P Francois YN Revealing the potential of capillary electrophoresis/mass spectrometry: the tipping point Rapid Commun Mass Spectrom 2019 33 Suppl 1 11 19 10.1002/rcm.8238 30022554	36449030	PMC9709354	NO-CC CODE	2022-12-01 23:23:04	no	Anal Bioanal Chem. 2022 Nov 30;:1-14	utf-8	Anal Bioanal Chem	2,022	10.1007/s00216-022-04396-7	oa_other
==== Front Ecotoxicology Ecotoxicology Ecotoxicology (London, England) 0963-9292 1573-3017 Springer US New York 2608 10.1007/s10646-022-02608-5 Article High mercury concentrations in steelhead/rainbow trout, sculpin, and terrestrial invertebrates in a stream-riparian food web in coastal California http://orcid.org/0000-0002-9881-2626 Rundio David E. [email protected] 1 Rivera Roberto 23 http://orcid.org/0000-0001-9407-5860 Weiss-Penzias Peter S. 2 1 grid.3532.7 0000 0001 1266 2261 Fisheries Ecology Division, Southwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, 110 McAllister Way, Santa Cruz, CA 95060 USA 2 grid.205975.c 0000 0001 0740 6917 Department of Microbiology and Environmental Toxicology, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95064 USA 3 grid.266093.8 0000 0001 0668 7243 Present Address: Department of Materials Science and Engineering, University of California, Irvine, 544 Engineering Tower, Irvine, CA 92697 USA 30 11 2022 114 20 11 2022 © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. Stream and riparian food webs are connected by cross-habitat exchanges of invertebrate prey that can transfer contaminants including mercury. Marine fog has been identified as a source of methylmercury (MeHg) to some terrestrial food webs in coastal California, suggesting that terrestrial invertebrates might have elevated MeHg relative to stream invertebrates and might lead to higher mercury exposure in fish that consume terrestrial subsidies. As an initial step to examine this possibility, we analyzed mercury concentrations in terrestrial and aquatic invertebrates and two fish species, steelhead/rainbow trout (Oncorhynchus mykiss) and coastrange sculpin (Cottus aleuticus), in a small watershed. Mean MeHg and total mercury (THg) concentrations in terrestrial invertebrates were three to four times higher than in aquatic invertebrates of the same trophic level. MeHg was >1000 ng/g dw in some individual centipede and scorpion samples, and also relatively high (100–300 ng/g dw) in some terrestrial detritivores, including non-native isopods. Mean THg in age 0 trout was 400 ng/g dw compared to 1200–1300 ng/g dw in age 1+ trout and sculpin, and the largest trout sampled had THg >3500 ng/g dw. However, the similar mercury concentrations between age 1+ trout and sculpin, despite different diet types, indicated that Hg concentrations in fish were not related simply to differences in consumption of terrestrial invertebrates. The high mercury concentrations we found in terrestrial invertebrates and fish suggest that further research on the sources and bioaccumulation of mercury is warranted in this region where O. mykiss populations are threatened. Keywords Mercury Steelhead/rainbow trout Sculpin Invertebrates Terrestrial-aquatic subsidies Non-native species ==== Body pmcIntroduction Mercury is a global contaminant of concern for many fish and wildlife populations due to its persistence in the environment and potentially serious effects as a toxin (Driscoll et al. 2013; Eagles-Smith et al. 2016a; Chételat et al. 2020). Human activities have increased mercury levels in the atmosphere by more than 3-fold in the past 150 years (Streets et al. 2017). Most mercury in the environment is from atmospheric deposition of inorganic mercury, while methylmercury (MeHg) is the biologically available organic form that bioaccumulates in organisms and biomagnifies with increasing trophic level within food webs (Driscoll et al. 2013; Eagles-Smith et al. 2016a; Chételat et al. 2020). Methylation of inorganic mercury to MeHg primarily occurs through microbial activity under reducing conditions typically present in aquatic environments such as wetlands, sediments in freshwater and coastal habitats, and in the mid and upper ocean (e.g., MeHg maxima occur around 300 m depth off California; Coale et al. 2018), leading to high MeHg concentrations in many aquatic food webs and consumers of aquatic organisms (Driscoll et al. 2013; Eagles-Smith et al. 2016a; Chételat et al. 2020). Aquatic and terrestrial food webs are connected by reciprocal fluxes of materials and organisms across habitat boundaries (Polis et al. 1997; Nakano and Murakami 2001; Baxter et al. 2005), which can also result in transfer of mercury and other contaminants between ecosystems (Walters et al. 2008; Kraus et al. 2020). Because of high concentrations of MeHg and other contaminants in aquatic habitats, most attention regarding transfers between ecosystems has focused on the export of contaminants in aquatic organisms to consumers in adjacent terrestrial food webs (Sullivan and Rodewald 2012; Kraus et al. 2020). For example, stream and riparian food webs are closely linked by reciprocal subsidies of invertebrate prey (Nakano and Murakami 2001; Baxter et al. 2005), and emerging adult aquatic insects transfer MeHg to terrestrial predators including birds, bats, and spiders (Cristol et al. 2008; Tsui et al. 2012; Becker et al. 2018; Jackson et al. 2021). However, terrestrial invertebrate subsidies are a major energy source to fish and some aquatic invertebrate predators in many freshwater habitats, especially small streams and rivers in forest ecosystems (Nakano and Murakami 2001; Baxter et al. 2005), and may influence mercury dynamics in stream-riparian food webs depending on the relative concentrations of mercury in aquatic and terrestrial prey taxa. For example, subsidies of terrestrial invertebrates reduced the mercury burdens of fishes in streams in northeastern North America where aquatic insects had high mercury concentrations (Jardine et al. 2012; Ward et al. 2012). In contrast, terrestrial subsidies transferred MeHg to aquatic predators (water striders and juvenile steelhead trout) in small headwater streams in northern California, although concentrations in both fish and invertebrates were relatively low (Tsui et al. 2012, 2014). Marine fog has recently been identified as an important source of MeHg to terrestrial food webs in coastal California, through a pathway where inorganic mercury is methylated in the midwater zone of the ocean and then brought by strong seasonal upwelling to the surface boundary layer where it enters the atmosphere through air-sea mixing and bubble burst and is subsequently transported to land as MeHg in fog and marine aerosols (Weiss‐Penzias et al. 2012; Coale et al. 2018). This ocean-derived MeHg in fog appears to be the source of elevated mercury concentrations in coastal mountain lion populations, where fog MeHg enters the food web through lichen and bioaccumulates in deer and ultimately mountain lions, leading to mercury concentrations three times higher in coastal than inland populations in California (Weiss-Penzias et al. 2019). Mercury concentrations in several terrestrial invertebrate taxa also were higher along the coast of central California (Ortiz et al. 2015) than in an interior-facing, fog-sheltered basin in the coastal mountains of northern California (Tsui et al. 2019), suggesting that predators that consume terrestrial invertebrates in areas with fog might have increased exposure to mercury. Terrestrial invertebrates are a major prey source for stream-dwelling steelhead/rainbow trout (Oncorhynchus mykiss) in watersheds on the Big Sur coast of central California, providing 15–20% of the annual energy in the diet of age 0 (young-of-year) trout and up to 60% of the energy in age 1+ trout (Rundio and Lindley 2008, 2019, 2021). Steelhead populations in this region are listed as threatened under the U.S. Endangered Species Act (U.S. Office of the Federal Register 2014), however the level of exposure to mercury and potential for harm have not been assessed for these populations in coastal streams. The recent studies on fog transfer of marine-derived MeHg in coastal California suggest that terrestrial invertebrates might have elevated MeHg concentrations relative to aquatic invertebrates in streams in this region and might expose trout that consume terrestrial subsidies to elevated mercury levels as well. As an initial step to assess this possibility, we analyzed mercury concentrations of terrestrial and aquatic invertebrate prey and fish predators, O. mykiss and coastrange sculpin (Cottus aleuticus), in a riparian-stream food web in a small basin on the Big Sur coast. Our objectives were to determine the range of mercury concentrations among consumers within the food web and to test two basic hypotheses: (1) that concentrations would be higher in terrestrial invertebrates than aquatic invertebrates of the same trophic level; and (2) that, based on differences in diet, concentrations would be higher in age 1+ O. mykiss than in age 0 trout or sculpin, which consume primarily aquatic insects (Rundio and Lindley 2019; Moyle 2002). Among terrestrial invertebrates, we were particularly interested in mercury concentrations in non-native isopods, as they are a major prey item for trout in Big Creek (accounting for 20–30% of the annual energy in the diet, more than any other prey taxon; Rundio and Lindley 2008) and other Big Sur streams (Rundio and Lindley 2021) and are known to bioaccumulate heavy metals including mercury (Hopkin et al. 1986; Dallinger et al. 1992 Pedrini-Martha et al. 2012). Methods Sampling design For this study, we collected invertebrate samples in summer 2020 and used muscle tissue from fish specimens collected in 2005–2019 from previous sampling in the watershed that were archived in a −20 °C freezer. We used these existing fish specimens to avoid needing to sacrifice new fish, particularly for threatened O. mykiss. Trout samples were incidental mortalities from backpack electrofishing surveys from a long-term study of population dynamics in Big Creek, which limited the number and sizes of archived specimens available. Sculpin were collected in 2017 as a proxy for threatened O. mykiss for some analyses in a study involving otolith (ear stone) chemistry. Workplace restrictions during the covid-19 pandemic restricted both field and laboratory activities and limited the number of samples we could collect and analyze. In addition, a large wildfire burned the study basin in August 2020, preventing additional invertebrate sampling and all fish sampling for the long-term population study that year. In January 2021, a post-wildfire debris flow caused massive disturbance to the stream and riparian zone, preventing replication of sampling across years. Despite these limitations on our dataset, the samples allowed for a general assessment of the range of mercury concentrations in fish and invertebrate consumers in the stream-riparian food web in this system and testing for basic differences between groups of consumers. Study area We conducted the study at the University of California (UC) Landels-Hill Big Creek Reserve (36.0708°, −121.5991°) within the Santa Lucia Mountains on the Big Sur coast of central California (Supplementary Information [SI] Fig. 1). The region is characterized by steep mountains, which reach 1600 m within 5 km of the coast, and a coastal Mediterranean climate, with a warm but foggy dry season and a cool and rainy wet season. The coastal marine layer and fog typically occur from sea level to 400–500 m elevation. Big Creek (watershed area 58 km2) is typical of the many small coastal basins that drain the Santa Lucia Mountains, with narrow stream channels and riparian zones confined in steep hillsides. The basin is in relatively natural condition. Limited farming, livestock grazing, and logging occurred in some areas 70–130 years ago, but since the late 1970s the basin has been protected with the lower portion within the UC Reserve and the upper portion within the Los Padres National Forest Ventana Wilderness Area. Our study area was the lower portion of Big Creek within 1.8 stream km from the ocean and elevation < 100 m, and included the mainstem of Big Creek (0–1200 m from the mouth) and the lower 600 m of the two main branches, upper Big Creek and Devils Creek (SI Fig. 1), overlapping with previous fish and food web studies in the basin. This area was within the zone with highest fog exposure in the basin, and fog penetrated up through the study reaches in both tributaries. Stream habitat was a mixture of pools and rapids and the channel gradient was 3–8%. Stream width averaged 5–6 m during summer base flow, with mean water depth of about 35 cm and maximum depth of about 1.75 m in pools. The streams had perennial flow, cool water temperatures (daily mean typically 8–10 °C during the winter wet season and 15–17 °C during the summer dry season), and were alkaline (pH typically 8.3–8.6). The riparian forest was primarily coast redwood (Sequoia sempervirens), white alder (Alnus rhombifolia), and bigleaf maple (Acer macrophyllum), with an understory of shrubs, and the canopy over the stream channel was almost fully closed from spring through fall. Riparian habitat on the streambanks included narrow gravel and cobble bars, boulders, soil, logs, and litter from leaves and redwood needles. Steelhead/rainbow trout (O. mykiss) and coastrange sculpin (C. aleuticus) were the only fish species present in the stream. The O. mykiss population in Big Creek is partially migratory, consisting of both anadromous (steelhead) and nonanadromous (resident rainbow trout) individuals (Rundio et al. 2012). Adult O. mykiss spawn in the stream during winter and spring. After hatching, juveniles rear for 2–3 years before either migrating to the ocean (steelhead) or maturing in the stream (resident rainbow trout). Adult steelhead typically return to spawn after 1–2 years in the ocean, while resident trout may reach 6–7 years total age in the stream. Thus, O. mykiss present in the stream year-round include juveniles of both life-history types and mature resident trout (typically > 150 mm in length), but hereafter we will refer to all as ‘trout’ for simplicity. Adult coastrange sculpin also spawn in the stream during winter and spring, and after hatching larvae drift downstream for a brief (3–5 week) marine planktonic stage before returning to freshwater as juveniles to rear for 2–3 years until maturing (Moyle 2002; Rundio, unpublished data). Coastrange sculpin in California appear to reach 35–45 mm in length after their first year and can live up to 8 years and reach 145 mm (Moyle 2002). Sample collection We collected aquatic and terrestrial invertebrates using hand searches on two dates in June and August 2020, targeting larger taxa from a range of taxonomic groups and from both primary (detritivore/herbivore) and secondary/tertiary (predator) consumer levels. The sampling method and target taxa were chosen under the logistical constraints imposed by covid-19 workplace restrictions, where our goal was to sample representative taxa from the two trophic levels (including important trout prey such as terrestrial isopods) likely to span the range of mercury concentrations in the food web and that could be collected efficiently by a single person during limited visits. Invertebrates were collected throughout the study area in the lower basin, including mainstem Big Creek and both major branches, to overlap with collection locations of the archived fish samples; given the similar habitat conditions and close proximity within this area, no differences in mercury concentrations were expected among stream reaches. Aquatic insects were captured by searching on and under large gravel and cobble and through accumulations of leaf/needle litter. We included large specimens of the largest-bodied and longest-lived predatory aquatic insects present in the stream (Perlidae stoneflies, Corydalidae dobsonflies, and Aeshnidae dragonflies), which we expected would reflect maximum mercury concentrations in aquatic invertebrates. Terrestrial invertebrates were captured by searching on and under gravel, cobbles, boulders, logs, and leaf/needle litter within 2 m of the stream. Samples were collected following clean techniques (gloved hands and clean forceps), placed into polypropylene vials, and stored on ice until being placed in a −20 °C freezer within 8 h. The number of specimens per sample ranged from one to 28 (mean = 6) depending on size of taxa and availability during searches, and we collected replicate samples for most taxa. Sample information is summarized in SI Table 1. With respect to the overlap between invertebrate samples and fish diets, all invertebrate orders sampled except scorpions were found in O. mykiss diets in previous studies (Rundio and Lindley 2008, 2019). The main prey taxa not included in this study were a few families of small-bodied aquatic insects (Baetidae mayflies and Chironomidae and Simuliidae true flies) and terrestrial hymenoptera, but we assume that mercury concentrations in these taxa were likely to fall within the range of values of sampled taxa. The fish samples used in this study were taken from specimens captured during prior studies and stored in a −20 °C freezer. We selected 10 samples each for age 0 trout, age 1+ trout, and sculpin. The ten age 0 trout (72–90 mm) were selected from mortalities from 2010 when samples were available from all three stream reaches in the study area. For age 1+ trout, a lower incidental mortality rate and a desire to include specimens across the full range of sizes/ages present in the stream necessitated taking fish across a range of years from 2005 to 2019. The 10 age 1+ trout were 125 to 300 mm and ranged in age from probably 1.5 years (based on size) to one individual that appeared to be 7 years old (based on otolith annuli). Sculpin were collected in 2017 and were 86 to 128 mm, indicating that they were probably at least 2+ years old and likely included multiple ages (Moyle 2002). All fish were captured by backpack electrofishing, placed into individual plastic bags, and stored on ice before being frozen within 12 h. To obtain muscle samples for mercury analysis, fish were partially thawed to the point where tissues still contained ice crystals but 1–3 g (wet) skinless samples could be dissected from the dorsal muscle using a stainless steel scalpel. Fish were dissected using new gloves and scalpel blades between fish, and samples were placed in polypropylene vials and refrozen until analysis. Sample information is summarized in SI Table 2. Laboratory analysis Laboratory analyses were conducted at the University of California Santa Cruz (UCSC). Frozen fish and invertebrate samples were homogenized with 10% HCl-cleaned mortar and pestle and liquid nitrogen and then transferred to a 20 mL glass scintillation vial and lyophilized overnight to obtain dried sample for analysis of mercury concentration as ng/g dry weight. In fish, it generally appears that nearly all mercury (> 90–95%) is in the form of MeHg (Bloom 1992; but see Lescord et al. 2018 for exceptions), so we followed previous studies and guidelines and analyzed fish muscle samples only for total Hg (THg) under the assumption that this represented MeHg (U.S. EPA 2000; Peterson et al. 2007). To facilitate comparison with other studies and health benchmarks in which mercury concentrations in fish are often reported on the basis of wet weight in muscle or whole-body samples (see Discussion below), we assumed dry weight was 25% of wet weight (Reinitz 1983; Ciancio et al 2007) and used the muscle to whole-body regression for mercury concentration in Peterson et al. (2007). In invertebrates, the fraction of total mercury as MeHg can be more variable among taxa and feeding groups (Tsui et al. 2019; Riva-Murray et al. 2020), so we analyzed invertebrate samples for both THg and MeHg. For THg analysis, 0.1–0.5 g of dried sample was weighed into quartz boats and analyzed in a Milestone® DMA 80 direct mercury analyzer according to EPA Method 7473 (U.S. EPA 2007). The instrument was calibrated with a liquid standard according to the manufacturer’s instructions, and certified reference materials (CRMs) were run alongside the samples daily. Recoveries of CRMs (mean ± SD) were 107.0 ± 11.6% for BCR-320R (n = 9), 105.8 ± 13.2% for DOLT-3 (n = 4), 114.4 ± 8.5% for DORM-3 (n = 4), 98.0 ± 1.5% for DORM-4 (n = 3), and 96.7 ± 9.4% for IAEA-407 (n = 14); the mean recovery across all CRMs was 102.7 ± 11.4% (n = 34). For MeHg analysis of invertebrate samples, 0.1–0.5 g of dried sample was weighed into 50 mL glass centrifuge tubes. Two mL of a 20% KOH solution in methanol was then added and heated to 60 °C for 4 h (Bloom 1989). After heating, deionized water (18.2 MΩ) was added, giving a final volume of 10 mL, and the sample was centrifuged at 1200 rpm for 10 min. The supernatant was withdrawn into a 20 mL glass scintillation vial. To convert dissolved MeHg to a volatile form so it can be detected, 0.1 mL of the digested sample was added to a purge vessel containing acetate buffer, to which sodium tetraethylborate was added according to EPA Method 1630 (U.S. EPA 1998). MeHg was analyzed with gas chromatography separation (DB-1 capillary column, 60–120 °C temperature ramp), 800 °C pyrolysis on quartz wool, and detection with cold-vapor atomic fluorescence spectroscopy (Tekran® 2500). A control standard sample was run between every 4th sample, which consisted of 100 μL of a 1.0 ppb standard, diluted from a 1.0 ppm stock solution of CH3HgCl (Brooks Rand Laboratories). The CRM used for MeHg analysis was DORM-4, and mean recovery was 90.3 ± 14.5% (n = 6). For both the THg and MeHg analyses, we initially ran two replicate aliquots per sample. The relative percent difference (RPD) of these initial sample replicates was 12.3 ± 11.7% (mean ± SD) for THg and 23.2 ± 22.2% for MeHg. While this variability was comparable to some previous studies (e.g., Jardine et al. 2012), it was higher than others (e.g., RPD < 10%: Riva-Murray et al. 2011, Ward et al. 2012; Lescord et al 2018; Eagles-Smith et al. 2020). We suspect that this variability was due to imperfect homogenization of samples for THg and to error inherent to the manual MeHg extraction and analysis method. Therefore, we decided to run an additional aliquot for samples where the RPD was ≥ 40% to improve the accuracy of estimates of mean concentration per sample (based on all replicates); this applied to 27% of fish THg samples, 25% of invertebrate THg samples, and 28% of invertebrate MeHg samples. For a small number of invertebrate MeHg samples, a fourth (n = 3) or fifth (n = 3) aliquot was run if the RPD between the new aliquot(s) and both of the original replicates still was ≥40% or if the percent MeHg based on the means of the THg and MeHg replicates for the sample was >115%. These criteria for running additional aliquots were selected ad hoc based on inspection of the data to increase the number of replicates for samples where high variability among runs indicated possible analytical error. To formally screen the data for outliers, we calculated the RPD for all pairwise combinations of replicates per sample for both THg and MeHg. These RPD values were highly left-skewed, so to test for outliers we used the function LocScaleB in the R package univOut (D’Orazio 2021), as this method applies robust estimates of location and scale that are suitable for skewed data. Based on the outlier tests, we dropped a MeHg replicate from four invertebrate samples and a THg replicate from one invertebrate sample (out of 61 total invertebrate samples) that had extreme (and likely erroneous) values; excluding these outliers, the mean (± SD) relative standard deviation (RSD) of sample replicates was 9.4 ± 7.8% for THg and 16.2 ± 11.9% for MeHg. We took the mean of the remaining replicates per sample as our estimates of mercury concentrations for analysis (invertebrates, Table 1; fish, SI Table 2). Removing outlier replicates from the few invertebrate samples did not affect results of the statistical analyses below, and sample means with and without outliers are shown in SI Table 1.Table 1 Taxonomic information and mean THg, MeHg, and %MeHg for aquatic and terrestrial invertebrate samples from Big Creek, California Class Order Family Genus/species Common Name Consumer Level Consumer Type n THg (ng/g) MeHg (ng/g) %MeHg Aquatic Insecta Ephemeroptera Heptageniidae Epeorus, Ironodes Mayfly 1° H 1 69.70 55.30 79 Insecta Plecoptera Pteronarcyidae Pteronarcys californica Stonefly 1° D 4 17.94 15.97 89 Insecta Trichoptera Limnephilidae Dicosmoecus gilvipes Caddisfly 1° H 5 35.00 28.63 82 Insecta Trichoptera Limnephilidae Psychoglypha bella Caddisfly 1° H 2 43.69 30.10 69 Insecta Trichoptera Thremmatidae Neophylax rickeri Caddisfly 1° H 4 13.25 12.22 92 Insecta Ephemeroptera Ephemerellidae Drunella coloradensisa Mayfly 2°/3° P 1 120.03 123.16 103 Insecta Megaloptera Corydalidae Dobsonfly 2°/3° P 1 140.57 108.26 77 Insecta Odonata Aeshnidae Aeshna Dragonfly 2°/3° P 2 82.18 79.62 97 Insecta Plecoptera Perlidae Calineuria californica Stonefly 2°/3° P 4 97.57 96.66 99 Terrestrial Diplopoda Callipodida Tynommatidae Millipede 1° D 2 363.88 299.51 82 Diplopoda Polydesmida Xystodesmidae Harpaphe haydeniana Millipede 1° D 3 512.55 41.00 8 Gastropoda Stylommatophora Arionidae Prophysaon andersoni Slug 1° H/D 1 86.13 46.76 54 Gastropoda Stylommatophora Helminthoglyptidae Helminthoglypta Snail 1° H/D 1 56.08 26.75 48 Gastropoda Stylommatophora Polygyridae Vespericola Snail 1° H/D 3 65.07 29.10 45 Malacostraca Isopoda Armadillidiidae Armadillidium vulgare Pillbug 1° D 4 139.18 112.52 81 Malacostraca Isopoda Porcellionidae Porcellio scaber Sowbug 1° D 5 329.65 285.56 87 Arachnida Araneae Zoropsidae Anachemmis Spider 2°/3° P 3 777.94 678.10 87 Arachnida Scorpiones Vaejovidae Vaejovis Scorpion 2°/3° P 3 777.90 757.27 97 Chilopoda Scolopendromorpha Scolopocryptopidae Scolopocryptops Centipede 2°/3° P 4 685.77 609.50 89 Insecta Coleoptera Carabidae Pterostichus Beetle 2°/3° P 4 128.90 117.43 91 Insecta Coleoptera Carabidae Scaphinotus Beetle 2°/3° P 4 97.77 78.83 81 Primary consumers (1°) are detritivores (D) and herbivores (H), and secondary/tertiary consumers (2°/3°) are predators (P). The number of samples per taxon is indicated by n. THg and MeHg concentrations are ng/g dry weight aLarge, late instar nymphs are predatory (Hawkins 1985, 1990; Merritt and Cummins 1996) As an additional quality assurance step, we had samples from five age 1+ trout independently analyzed for THg concentration by the Marine Pollution Studies Laboratory (MPSL) at Moss Landing Marine Laboratory. We dissected new dorsal muscle tissue samples (3–5 g wet) from the five fish, and provided the frozen samples to MPSL for analysis following their standard protocol. The mean (± SD) RPD between our original measurements of THg concentrations and those from MPSL was 14 ± 6%, and the slope of the linear regression between measurements did not differ from one (slope = 0.9997, R2 = 0.99, p = 0.99) and the intercept did not differ from zero (p = 0.09) (SI Figure 2). Data analysis For invertebrates, we used linear mixed models (LMM) to determine whether mercury concentrations differed with respect to source (aquatic versus terrestrial) and trophic level (detritivore/herbivore versus predator). Models were fit with THg or MeHg concentration as the response, with source, trophic level, and their interaction as fixed-effect factors, and with taxon as a random effect to account for correlations among samples from the same taxon. THg and MeHg concentrations were log-transformed to meet assumptions of normality. Models were fit in the R package nlme (Pinheiro et al. 2021). Examination of residuals from simple linear models indicated heterogeneity of variances, so we used the varIdent function to allow variance to differ by the combination of trophic level and source. The significance of fixed-effect factors was determined by likelihood ratio tests between nested models, fit by maximum likelihood, with and without a specific factor. Models were re-fit by restricted maximum likelihood for estimation of mean mercury concentrations by source and trophic level. Invertebrate percent MeHg values failed to meet the assumption of normality even after transformation, so we used the nonparametric Brunner-Munzel test (Brunner and Munzel 2000) to determine whether %MeHg differed with respect to source and trophic level. The Brunner-Munzel test is a rank order test that is robust to unequal variances between groups, which was true of our data. To avoid lack of independence of samples from the same taxon, we calculated mean %MeHg by taxon and then ran permuted Brunner-Munzel tests to determine whether %MeHg differed between aquatic versus terrestrial invertebrates of the same trophic level (e.g., aquatic predators vs. terrestrial predators) or between trophic levels within the same source (e.g., aquatic detritivores/herbivores vs. aquatic predators). We ran Brunner-Munzel tests in the R package rankFD (Konietschke et al 2021), with p-values determined by a studentized permutation test appropriate for small samples (Neubert and Brunner 2007). For fish, our primary interest was whether THg concentrations differed among age 0 trout, age 1+ trout, and sculpin (age 1 +) as hypothesized based on differences in diet. Mercury concentrations also often increase with fish age and size due to bioaccumulation; however, we were unable to assess size and age class relationships for both species simultaneously (i.e., in a single model) due to the lack of samples from age 0 sculpin and the very different size ranges and size-at-age relationships between species. Similarly, samples for the different groups were collected in different years but we could not test for a year effect in a single model because only age 1+ trout were collected over multiple years. We therefore analyzed THg in fish in three steps. First, we tested whether THg concentrations differed among the three groups using one-way analysis of variance (ANOVA) followed by post-hoc pairwise tests using the package emmeans (Lenth 2021), recognizing that any differences between groups were confounded with size and age and collection year. Second, we used linear regression to determine whether THg concentration was related to fish length, with separate models for trout and sculpin. For trout, we included age class in the model to determine whether age classes differed after accounting for length. Finally, we used age 1+ trout samples to test whether THg concentrations were related to collection year, after accounting for fish length. We fit a linear regression model to test for a trend with year as a continuous variable, and fit LMMs with and without year as a categorical random effect to test for variation among years without a trend using a likelihood ratio test between models; both types of models included length as a fixed effect. THg values were log-transformed for all three analyses to meet assumptions of normality and equal variance. All analyses were conducted in the program R version 4.1.1 (R Core Team 2021), and all plotting was done using the package ggplot2 (Wickham 2016). For reporting results, estimated means and effect sizes (i.e., parameter estimates for fixed-effects factors) from models on log-transformed Hg concentrations were back-transformed to the original scale where they are equivalent to geometric means and multiplicative differences of geometric means, respectively. Results For invertebrates, concentrations of both THg and MeHg differed significantly by source and by trophic level (Table 2). Mean THg was estimated to be 4.3 (95% CI: 2.0–9.1) times higher in terrestrial invertebrates than in aquatic invertebrates, and mean MeHg 3.1 (95% CI: 1.4– 6.9) times higher (Table 3, Fig. 1), and these differences were the same regardless of trophic level (i.e., no source by trophic level interaction; Table 2). Mean THg in predators was estimated to be 2.6 (95% CI: 1.2–5.4) times higher than in detritivores/herbivores, and MeHg 3.9 (95% CI: 1.7–8.6) times higher, for both terrestrial and aquatic invertebrates (Table 3, Fig. 1). There also was very high variability in mercury concentrations among taxa, with the variance associated with the random effect for taxa of nearly the same magnitude as the parameter estimates for trophic level and source (Table 3). Terrestrial centipedes, scorpions, and spiders had the highest mercury levels measured, with mean THg and MeHg of 600–775 ng/g dw and with individual samples having values up to 1000–1500 ng/g dw (Table 1, Fig. 2). In contrast, predatory ground beetles (Carabidae) had much lower concentrations (mean THg and MeHg of 80–130 ng/g dw) that were more similar to predatory aquatic insects (Table 1, Fig. 2). Terrestrial isopods and millipedes had high mercury concentrations relative to other terrestrial detritivores/herbivores (gastropods) and most aquatic taxa, although there also was considerable variability among isopod and millipede taxa (Table 1, Fig. 2). For non-native isopods, mean THg and MeHg were 110–140 ng/g dw for Armadillidium vulgare compared with 285–330 ng/g dw for Porcellio scaber. For millipedes, mean THg was about 510 ng/g dw for H. haydeniana and 365 ng/g dw for Tynommatidae, but mean MeHg was only about 40 ng/g dw for H. haydeniana compared to 300 ng/g dw in Tynommatidae (Table 1, Fig. 2).Table 2 Results of significance tests from linear mixed models of THg and MeHg as a function of source (aquatic versus terrestrial) and trophic level (detritivore/herbivore versus predator) as fixed effects THg MeHg Factor L-ratio p-value L-ratio p-value Source 13.767 <0.001 8.473 0.004 Trophic Level 6.873 0.009 11.304 <0.001 SourceTrophic Level 0.715 0.398 0.001 0.976 Models were fit on log-transformed data, and included a random effect for taxon. Significance was determined from likelihood ratio tests between nested models with and without a particular factor. All tests have 1 degree of freedom Table 3 Parameter estimates from linear mixed models of THg and MeHg as a function of source and trophic level as fixed effects and taxon as a random effect Factor THg MeHg Fixed Effects (Intercept) 3.519 ± 0.309 3.188 ± 0.327 Source (Terrestrial) 1.459 ± 0.355 1.133 ± 0.378 Trophic level (Predator) 0.940 ± 0.357 1.352 ± 0.380 Random effect Taxon (Intercept) 0.779 0.822 Residual 0.349 0.476 Reference levels for the fixed effects are source = aquatic and trophic level = detritivore/herbivore. For the fixed effect parameters, values are estimates ± one standard error (SE). For the random effect parameter, values are estimates of variance expressed as standard deviations (SD). Models were fit on log-transformed data Fig. 1 Mean invertebrate THg and MeHg concentrations (with 95% confidence intervals) by source and trophic level. Estimates are back-transformed means from linear mixed models on log-transformed data, which are equivalent to geometric means on the original scale Fig. 2 Invertebrate THg and MeHg concentrations by individual taxa. Dots are individual samples, and bars are means by taxon. Taxa are listed by common name, with genus/species or family indicated in parentheses (see Table 1) Percent MeHg in invertebrates was around 70–100% in aquatic taxa and 80–100% in terrestrial predator taxa (Table 1, Fig. 3). In contrast, terrestrial detritivores/herbivores had much greater variability in %MeHg among taxa, ranging from about 10% in H. haydeniana millipedes to about 50% in snails and slugs to about 80–90% in isopods and Tynommatidae millipedes (Table 1, Fig. 3). At the group level, terrestrial detritivores/herbivores had significantly lower %MeHg than terrestrial predators (Brunner-Munzel test, t = 11.667, p = 0.004) but did not differ significantly from aquatic detritivores/herbivores (t = 2.182, p = 0.082). Percent MeHg also did not differ between aquatic and terrestrial predators (t = 0.396, p = 0.722) or between aquatic detritivores/herbivores and aquatic predators (t = 0.939, p = 0.407).Fig. 3 Invertebrate % MeHg by individual taxa. Dots are individual samples, and bars are means by taxon. Taxa are listed by common name, with genus/species or family indicated in parentheses (see Table 1) For fish, mean THg was estimated to be three times lower in age 0 trout (399 ng/g dw; 95% CI: 298–534) than in age 1+ trout (1326 ng/g dw; 95% CI: 992–1772) or sculpin (1199 ng/g dw; 95% CI: 897–1620) (pairwise post-hoc tests, t > 5.50, p < 0.001), which did not differ from one another (post-hoc test, t = 0.458, p = 0.89) (Fig. 4a). For trout, a linear regression model with both length and age class indicated that THg increased significantly with length (F = 71.569, p < 0.001) but also differed between age classes (F = 10.066, p = 0.006), with THg in age 1+ trout estimated to be 2.0 times higher (95% CI: 1.3–3.2) than in age 0 trout after accounting for length. There was no relationship between length and THg for sculpin (F = 0.182, p = 0.681) (Fig. 4b). For age 1+ trout, there was no relationship between THg and collection year, either as a linear trend (F = 0.924, p = 0.368) or as random categorical effect (L = 0.575, p = 0.448), after accounting for length. Among samples from individual fish, THg was as high as 2100–2400 ng/g dw in some sculpin and nearly 3600 ng/g dw in the largest and oldest age 1+ trout (Fig. 4, SI Table 2).Fig. 4 Fish THg concentrations. A THg in age 0 trout, age 1+ trout, and sculpin. Boxplots indicate median and interquartile range (IQR), and whiskers are 1.5IQR. Points are individual samples. B Relationship between THg and fish length by species. Line and shading are linear regression with 95% confidence interval Discussion Mercury concentrations in terrestrial invertebrates and fish (steelhead/rainbow trout and coastrange sculpin) in this small coastal basin were very high for habitats without point-source contamination, with THg and MeHg > 1000 ng/g dw in some individual terrestrial invertebrate predator samples and THg > 3500 ng/g dw in the oldest trout sampled. Mercury concentrations were about 3 to 4 times higher (for MeHg and THg, respectively) in terrestrial invertebrates than aquatic invertebrates of the same trophic level, consistent with our hypothesis. However, THg concentrations in fish in Big Creek did not support our hypothesis that higher consumption of terrestrial invertebrates by age 1+ trout would lead to higher mercury levels relative to age 0 trout or sculpin that feed primarily on aquatic insects. While THg was twice as high (after accounting for length) in age 1+ trout as in age 0 trout, age 1+ trout and sculpin had similar THg, indicating that mercury concentrations in fish in this stream are not related simply to differences in diet and that other mechanisms of bioaccumulation are also important. Therefore, although the elevated levels of mercury we found in terrestrial invertebrates relative to aquatic invertebrates in this basin suggest that cross-habitat prey subsidies may potentially increase mercury exposure to threatened O. mykiss, additional studies using mercury stable isotopes (e.g., Tsui et al. 2012, 2014) will be needed to determine the sources of mercury to different consumers in this coastal stream-riparian food web. Comparison of mercury concentrations in Big Creek to values reported by Tsui et al. (2012, 2014, 2019) from a fog-sheltered study area on the inland side of the coast range in northern California appears to provide some support that terrestrial invertebrates may have higher MeHg in fog-exposed basins. The studies by Tsui et al. were conducted in the University of California Angelo Coast Reserve in the forested headwaters of the South Fork Eel River. The Angelo Reserve is approximately 12–15 km from the ocean but is blocked from marine fog by a high ridge of the coastal mountains (https://angelo.berkeley.edu/about-angelo/geo-context/). Mercury concentrations of aquatic invertebrates were similar in Big Creek and the Angelo Reserve (Tsui et al. 2012). In contrast, terrestrial invertebrates in Big Creek had higher MeHg concentrations than in the Angelo: the difference was greatest for spiders, centipedes, and scorpions (about 3- to 6-fold) but occurred in other taxa (ground beetles, slugs, and millipedes) as well. MeHg concentrations reported by Ortiz et al. (2015) for spiders and ground beetles from fog-exposed sites near Monterey Bay also were higher than the Angelo Reserve and more similar to levels in Big Creek. While very limited, this apparent difference in MeHg levels in terrestrial invertebrates between fog-exposed and fog-sheltered sites in California is similar to the pattern seen in deer and mountain lions (Weiss-Penzias et al. 2019) and consistent with observations of high MeHg levels in marine fog and aerosols (Weiss‐Penzias et al. 2012; Coale et al. 2018). However, studies from additional sites varying in fog exposure are needed before conclusions about the influence of fog on mercury concentrations in terrestrial invertebrates can be reached. With respect to broader geographic patterns, MeHg concentrations in terrestrial invertebrates also appear to be higher in Big Creek than in forests in the eastern United States, whereas concentrations in aquatic invertebrates in Big Creek appear to be lower than in eastern streams. For example, mean MeHg concentrations in terrestrial predators such as spiders, centipedes, and scorpions in Big Creek were 2–3 times higher, or more, than in studies from the eastern U.S. (Rimmer et al. 2010; Rodenhouse et al 2019; Tsui et al. 2019). Further, MeHg levels in some detritivores in Big Creek (Tynommatidae millipedes and Porcellionidae isopods) exceeded levels in predators in those studies, and were about 10 times greater than millipedes at the locations studied in Tsui et al. (2019). In addition, %MeHg in terrestrial invertebrates also was higher in Big Creek than the sites in Tsui et al. (2019). In contrast, for aquatic invertebrates, MeHg concentrations in predators (for which the most taxa are in common across studies) in Big Creek were 2–3 times lower than in eastern streams (Riva-Murray et al. 2011; Jardine et al. 2012; Broadley et al. 2019). Mercury concentrations in aquatic organisms tend to be highest in acidic streams (Ward et al. 2010; Jardine et al. 2013), so the higher pH (> 8) in Big Creek may partly explain the lower mercury concentrations in aquatic insects relative to streams in eastern North America with lower pH (Riva-Murray et al. 2011; Jardine et al. 2012; Broadley et al. 2019). Mercury concentrations in trout and sculpin in Big Creek were higher than average levels in these species across freshwater habitats in western North America (Peterson et al. 2007; Eagles-Smith et al. 2016b). For example, mean THg in age 1+ trout in Big Creek was four times higher than the mean concentration for age 1+ rainbow trout across streams and rivers in the western United States (approximately 325 ng/g dw after converting from whole-body wet weight, Peterson et al. 2007) and 4–5 times higher than age 1+ trout in the Angelo Reserve in northern California (Tsui et al. 2014). Likewise, mean THg in age 0 trout in Big Creek was more than five times higher than age 0 trout in the Angelo Reserve, despite similar mercury concentrations in aquatic insects between the sites (Tsui et al. 2012); in fact, age 0 trout in Big Creek had higher concentrations than most age 1+ trout in Peterson et al. (2007) and Tsui et al. (2014). The high mercury concentrations in trout and sculpin in Big Creek were more similar to levels in salmonids and blacknose dace (a benthic insectivore) in acidic streams in eastern North America where aquatic invertebrates have high mercury concentrations (Riva-Murray et al. 2011; Jardine et al. 2012; Ward et al. 2012; Broadley et al. 2019). However, the relatively low mercury concentrations in aquatic invertebrates in Big Creek suggest that the sources or processes driving biomagnification and bioaccumulation in these systems differ. Mercury concentrations in some age 1+ trout (at sizes corresponding to both juvenile steelhead prior to ocean migration and juvenile and resident trout) and sculpin in Big Creek are in the range where studies have found negative health effects in fish, although assessing the potential consequences is complicated by several uncertainties. In reviews that involved primarily laboratory experiments but also included some field studies, Beckvar et al. (2005) suggested that sublethal effects on growth, behavior, and reproduction may occur in fish with muscle concentrations greater than about 1400 ng/g dw (0.2 μg/g whole-body ww). Similarly, Sandheinrich and Weiner (2011) concluded that changes in biochemical processes, damage to cells and tissues, and reduced reproduction occur at muscle concentrations of about 2200–5400 ng/g dw (0.3–0.7 μg/g whole body ww) based on lab experiments, and that these effects were supported by correlations between mercury levels and health biomarkers (such as gene and hormone activity, tissue histology, and condition factor) in field studies. Beyond sublethal effects, Dillon et al. (2010) suggested that low rates (3–13%) of severe injuries related to lethality (survival, spawning and hatching success, and severe developmental abnormalities) may occur in juvenile and adult fish at muscle tissue concentrations of about 700–3800 ng/g dw (0.1–0.5 μg/g whole body ww) from a dose-response curve based on lab experiments, although the 95% confidence intervals for injury rates included zero at 700–1400 ng/g dw. However, in addition to the considerable ranges in health effect thresholds in the above reviews, interpreting the potential for negative health effects is further complicated by uncertainty and debate around how lab studies apply to fish under natural conditions and how selenium may interact with mercury to influence toxicity. With respect to the applicability of lab studies, Harris et al. (2003) showed that the form of MeHg in fish tissue is methylmercury cysteine (MeHgCys) and appears to be less toxic than the form of MeHg used in most lab exposure studies (methylmercury chloride, MeHgCl), leading them and Peterson et al. (2009) to suggest that lab experiments may overestimate toxicity. However, Sandheinrich and Weiner (2011) argued that the correspondence between limited field studies or lab studies using natural diets and lab studies using MeHgCl suggested that this is not the case, and Depew et al. (2012) concluded that there is insufficient evidence to determine whether dietary MeHgCl and MeHgCys differ in toxicity. In addition, Dillon et al. (2010) speculated that their dose-response model based on lab studies probably underestimated injury rates in wild fish due to the additional stressors, such as predation, competition, and environmental conditions, that occur in nature. With respect to selenium, its role in mediating mercury toxicity is another potentially important but poorly understood factor that was not addressed in the above reviews that developed mercury health thresholds. Some studies, primarily in mammals and birds, have been used to suggest that selenium may have protective effects against mercury toxicity when Se:Hg molar ratios in fish tissues are > 1 (Peterson et al. 2009). However, recent reviews have argued that conclusions about the potential protective effects of selenium to fish and other aquatic organisms are still unclear due to limited and variable results among studies, the complexity of biochemical pathways and effects involving selenium and mercury within organisms, and the toxicity of selenium itself (Eagles-Smith et al. 2018; Gerson et al. 2020). In spite of this uncertainty, after finding high mercury levels in fish in Big Creek, we analyzed selenium concentrations from a small number of age 1+ trout and invertebrates to provide additional context for interpreting potential health effects. This preliminary analysis indicated that Se:Hg molar ratios were > 1 in age 1+ trout and aquatic and terrestrial invertebrates (SI Table 3), and also that selenium concentrations in trout (1.7–4.2 μg/g dw muscle; SI Table 3) were below levels associated with reproductive harm and juvenile mortality in fish due to toxicity of selenium itself (8–11 μg/g dw muscle: Lemly 2002; U.S. EPA 2021). In sum, the limitations and debates involving toxicity studies described above make it unclear whether the high mercury concentrations in trout and sculpin in Big Creek are potentially leading to negative health effects, but this topic appears to warrant further research for threatened O. mykiss. Non-native species occur in many ecosystems and can have strong ecological effects, yet there has been very little research on their role in mercury dynamics in food webs or invertebrate subsidies to aquatic and terrestrial predators (Eagles-Smith et al. 2018; Rundio and Lindley 2021). In Big Creek, non-native terrestrial isopods (A. vulgare and P. scaber) appear to be a potentially important source of mercury to trout due to the combination of their abundance in the diet (Rundio and Lindley 2008, 2019) and high mercury concentration relative to most aquatic invertebrate taxa and many terrestrial taxa (Table 1, Fig. 2). Non-native isopods have become established in temperate regions around the world and often reach very high densities (summarized in Rundio and Lindley 2021), and isopods are known to bioaccumulate heavy metals including mercury (Hopkin et al. 1986; Dallinger et al. 1992; Pedrini-Martha et al. 2012), suggesting that they, and potentially other non-native species, may be important to mercury bioaccumulation and transfer in many areas. Our study had several limitations that restricted our dataset and some of the analyses and inferences we could draw. Our sample sizes were relatively small due to constraints collecting and analyzing samples from covid-19 restrictions, the wildfire and debris flow in the study basin, and using archived specimens of threatened O. mykiss. We were also unable to replicate sampling across years due to the fire and debris flow, which necessitated comparing samples types collected in different years. Although concentrations in age 1+ trout (for which we had samples from multiple years) did not differ among collection years, this test likely had low power due to the small sample. Given the different size ranges and size-at-age relationships for trout and sculpin, lack of samples from age 0 sculpin, and lack of age information for age 1+ fish, we were unable to evaluate relationships between THg and fish size and age rigorously or between species. For invertebrates, we sampled large-bodied individuals and taxa due to logistical constraints and omitted some small aquatic taxa that are important in fish diets as well as canopy and aerial terrestrial taxa such as Lepidoptera, Diptera, and Hymenoptera, and also lacked replication for some sampled taxa. There also was high variability in mercury concentrations both among samples within some taxa and among taxa within the same trophic level. Despite these caveats, our dataset allowed for a general assessment of the range of mercury concentrations in fish and invertebrate consumers in the food web and testing for basic differences between groups of consumers. In conclusion, our study documented high mercury concentrations in terrestrial invertebrates and fish in this coastal basin, but the relationship between cross-habitat subsidies of terrestrial prey and mercury concentrations in fish remains unclear. Nevertheless, concentrations in both age classes of trout and sculpin were high relative to other locations in the western U.S., reaching levels in some fish potentially associated with negative health effects, which suggests that mercury may be a possible stressor for fish populations in this region where O. mykiss is listed as threatened. These initial results indicate that further research is warranted to determine the sources and transport of mercury, including the possible role of fog, leading to high concentrations in coastal food webs at the intersection of marine, terrestrial, and freshwater ecosystems in this region. Supplementary information Supplementary Information Supplementary information The online version contains supplementary material available at 10.1007/s10646-022-02608-5. Acknowledgements We thank Wes Heim (Marine Pollution Studies Laboratory at Moss Landing Marine Laboratory) for analyzing samples for quality control comparisons; Bob Wisseman (Aquatic Biology Associates), Dessie Underwood (California State University, Long Beach), William Shear (Hampden-Sydney College), and James Labonte (Oregon Department of Agriculture) for identifying voucher specimens of invertebrates; Martin Tsui (University of North Carolina, Greensboro) for information on interference issues with sample digestions methods for MeHg; and Chris Russo (W.M. Keck Collaboratory for Plasma Spectrometry, Oregon State University) for analyzing samples for selenium. We thank Mark Readdie for support for field sampling at the University of California Landels-Hill Big Creek Reserve. We are grateful to Darren Ward (Humboldt State University) and three anonymous reviewers for helpful comments that improved the manuscript. This study was supported by NOAA Fisheries and a Keeley Coastal Scholars undergraduate scholarship at the University of California, Santa Cruz, to RR. Reference to trade names or manufacturers is for descriptive purposes only and does not imply U.S. Government endorsement of commercial products. Author contributions DR conceived of the study, and PSW-P provided input to study design and supervised laboratory analysis. DR collected field samples, and RR, DR, and PSW-P performed laboratory analysis. DR analyzed the data and wrote the first draft of the manuscript. PSW-P and RR commented on previous versions of the manuscript, and all authors read and approved the final manuscript. Funding This study was supported by NOAA Fisheries and by a Keeley Coastal Scholars undergraduate scholarship at the University of California, Santa Cruz, to RR. Compliance with ethical standards Competing interests The authors declare no competing interests. Ethics approval Capture and handling of fish was conducted under National Marine Fisheries Service Section 10(a)(1)(A) Scientific Research Permits 1044 and 17219 and California Department of Fish and Wildlife Scientific Collection Permit 13029. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References Baxter CV Fausch KD Saunders WC Tangled webs: reciprocal flows of invertebrate prey link streams and riparian zones Freshw Biol 2005 50 201 220 10.1111/j.1365-2427.2004.01328.x Becker DJ Chumchal MM Broders HG Korstian JM Clare EL Rainwater TR Platt SG Simmons NB Fenton MB Mercury bioaccumulation in bats reflects dietary connectivity to aquatic food webs Environ Pollut 2018 233 1076 1085 10.1016/j.envpol.2017.10.010 29042136 Beckvar N Dillon TM Read LB Approaches for linking whole‐body fish tissue residues of mercury or DDT to biological effects thresholds Environ Toxicol Chem 2005 24 2094 2105 10.1897/04-284R.1 16152984 Bloom N Determination of picogram levels of methylmercury by aqueous phase ethylation, followed by cryogenic gas chromatography with cold vapour atomic fluorescence detection Can J Fish Aquat Sci 1989 46 1131 1140 10.1139/f89-147 Bloom NS On the chemical form of mercury in edible fish and marine invertebrate tissue Can J Fish Aquat Sci 1992 49 1010 1017 10.1139/f92-113 Broadley HJ Cottingham KL Baer NA Weathers KC Ewing HA Chaves-Ulloa R Chickering J Wilson AM Shrestha J Chen CY Factors affecting MeHg bioaccumulation in stream biota: the role of dissolved organic carbon and diet Ecotoxicology 2019 28 949 963 10.1007/s10646-019-02086-2 31410744 Brunner E Munzel U The nonparametric Behrens‐Fisher problem: asymptotic theory and a small‐sample approximation Biom J 2000 42 1 17 25 10.1002/(SICI)1521-4036(200001)42:1<17::AID-BIMJ17>3.0.CO;2-U Chételat J Ackerman JT Eagles-Smith CA Hebert CE Methylmercury exposure in wildlife: a review of the ecological and physiological processes affecting contaminant concentrations and their interpretation Sci Tot Environ 2020 711 135117 10.1016/j.scitotenv.2019.135117 Ciancio JE Pascual MA Beauchamp DA Energy density of Patagonian aquatic organisms and empirical predictions based on water content Trans Am Fish Soc 2007 136 1415 1422 10.1577/T06-173.1 Coale K Heim WA Negrey J Weiss-Penzias P Fernandez D Olson A Chiswell H Byington A Bonnema A Martenuk S Newman A Beebe C Till C The distribution and speciation of mercury in the California current: Implications for mercury transport via fog to land Deep Sea Res Part II: Topical Studies Oceanogr 2018 151 77 88 10.1016/j.dsr2.2018.05.012 Cristol DA Brasso RL Condon AM Fovargue RE Friedman SL Hallinger KK Monroe AP White AE The movement of aquatic mercury through terrestrial food webs Science 2008 320 335 335 10.1126/science.1154082 18420925 Dallinger R Berger B Birkel S Terrestrial isopods: useful biological indicators of urban metal pollution Oecologia 1992 89 32 41 10.1007/BF00319012 28313392 Depew DC Basu N Burgess NM Campbell LM Devlin EW Drevnick PE Hammerschmidt CR Murphy CA Sandheinrich MB Wiener JG Toxicity of dietary methylmercury to fish: derivation of ecologically meaningful threshold concentrations Environ Toxicol Chem 2012 31 1536 1547 10.1002/etc.1859 22549893 Dillon T Beckvar N Kern J Residue‐based mercury dose–response in fish: An analysis using lethality‐equivalent test endpoints Environ Toxicol Chem 2010 29 2559 2565 10.1002/etc.314 20886502 D’Orazio M (2021) univOutl: detection of univariate outliers. R package version 0.3. https://CRAN.R-project.org/package=univOutl. Driscoll CT Mason RP Chan HM Jacob DJ Pirrone N Mercury as a global pollutant: sources, pathways, and effects Environ Sci Technol 2013 47 4967 4983 10.1021/es305071v 23590191 Eagles-Smith CA Wiener JG Eckley CS Willacker JJ Evers DC Marvin-DiPasquale M Obrist D Fleck JA Aiken GR Lepak JM Jackson AK Webster JP Stewart AR Davis JA Alpers CN Ackerman JT Mercury in western North America: A synthesis of environmental contamination, fluxes, bioaccumulation, and risk to fish and wildlife Sci Tot Environ 2016 568 1213 1226 10.1016/j.scitotenv.2016.05.094 Eagles-Smith CA Ackerman JT Willacker JJ Tate MT Lutz MA Fleck JA Stewart AR Wiener JG Evers DC Lepak JM Davis JA Pritz CF Spatial and temporal patterns of mercury concentrations in freshwater fish across the Western United States and Canada Sci Tot Environ 2016 568 1171 1184 10.1016/j.scitotenv.2016.03.229 Eagles-Smith CA Silbergeld EK Basu N Bustamante P Diaz-Barriga F Hopkins WA Kidd KA Nyland JF Modulators of mercury risk to wildlife and humans in the context of rapid global change Ambio 2018 47 170 197 10.1007/s13280-017-1011-x 29388128 Eagles-Smith CA Willacker JJ Nelson SJ Flanagan Pritz CM Krabbenhoft DP Chen CY Ackerman JT Grant EHC Pilliod DS A national-scale assessment of mercury bioaccumulation in United States national parks using dragonfly larvae as biosentinels through a citizen-science framework Environ Sci Technol 2020 54 8779 8790 10.1021/acs.est.0c01255 32633494 Gerson JR Walters DM Eagles-Smith CA Bernhardt ES Brandt JE Do two wrongs make a right? Persistent uncertainties regarding environmental selenium–mercury interactions Environ Sci Technol 2020 54 9228 9234 10.1021/acs.est.0c01894 32633495 Harris HH Pickering IJ George GN The chemical form of mercury in fish Science 2003 301 1203 10.1126/science.1085941 12947190 Hawkins CP Food habits of species of ephemerellid mayflies (Ephemeroptera: Insecta) in streams of Oregon American Midland Naturalist 1985 113 343 352 10.2307/2425580 Hawkins CP (1990) Relationships between habitat dynamics, food availability, and growth patterns of Ephemerellid mayflies from western North America. In: Campbell IC (ed) Mayflies and stoneflies: life histories and biology. Series Entomologica, vol 44. Springer, pp 35–42. 10.1007/978-94-009-2397-3_5. Hopkin SP Hardisty GN Martin MH The woodlouse Porcellio scaber as a ‘biological indicator’ of zinc, cadmium, lead and copper pollution Environ Pollut 1986 11 271 290 10.1016/0143-148X(86)90045-5 Jackson AK Eagles‐Smith CA Robinson WD Differential reliance on aquatic prey subsidies influences mercury exposure in riparian arachnids and songbirds Ecol Evol 2021 11 7003 7017 10.1002/ece3.7549 34141271 Jardine TD Kidd KA Rasmussen JB Aquatic and terrestrial organic matter in the diet of stream consumers: implications for mercury bioaccumulation Ecol Appl 2012 22 843 855 10.1890/11-0874.1 22645815 Jardine TD Kidd KA O’Driscoll N Food web analysis reveals effects of pH on mercury bioaccumulation at multiple trophic levels in streams Aquat Toxicol 2013 132 46 52 10.1016/j.aquatox.2013.01.013 23454309 Konietschke F, Friedrich S, Brunner E, Pauly M (2021) rankFD: rank-based tests for general factorial designs. R package version 0.1.0. https://CRAN.R-project.org/package=rankFD. Kraus JM Walters DM Mills MA Contaminants and ecological subsidies: the land-water interface 2020 Switzerland Springer Nature Lemly AD A procedure for setting environmentally safe total maximum daily loads (TMDLs) for selenium Ecotoxicol Environ Saf 2002 52 123 127 10.1006/eesa.2002.2176 12061828 Lenth R (2021) emmeans: estimated marginal means, aka least-squares means. R package version 1.7.0. https://CRAN.R-project.org/package=emmeans. Lescord GL Johnston TA Branfireun BA Gunn JM Percentage of methylmercury in the muscle tissue of freshwater fish varies with body size and age and among species Environ Toxicol Chem 2018 37 2682 2691 10.1002/etc.4233 30024049 Merritt RW Cummins KW An introduction to the aquatic insects of North America 1996 3rd edn. Dubuque IA Kendall Hunt Moyle PB Inland fishes of California: revised and expanded 2002 Berkeley CA University of California Press Nakano S Murakami M Reciprocal subsidies: dynamic interdependence between terrestrial and aquatic food webs Proc Natl Acad Sci 2001 98 166 170 10.1073/pnas.98.1.166 11136253 Neubert K Brunner E A studentized permutation test for the non-parametric Behrens–Fisher problem Comput Stat Data Anal 2007 51 5192 5204 10.1016/j.csda.2006.05.024 Ortiz C Weiss-Penzias PS Fork S Flegal AR Total and monomethyl mercury in terrestrial arthropods from the central California coast Bull Environ Contamin Toxicol 2015 94 425 430 10.1007/s00128-014-1448-6 Pedrini-Martha V Sager M Werner R Dallinger R Patterns of urban mercury contamination detected by bioindication with terrestrial isopods Arch Environ Contam Toxicol 2012 63 209 219 10.1007/s00244-012-9766-3 22534742 Peterson SA Van Sickle J Herlihy AT Hughes RM Mercury concentration in fish from streams and rivers throughout the western United States Environ Sci Technol 2007 41 58 65 10.1021/es061070u 17265927 Peterson SA Ralston NVC Whanger PD Oldfield JE Mosher WD Selenium and mercury interactions with emphasis on fish tissue Environ Bioindic 2009 4 318 334 10.1080/15555270903358428 Pinheiro J, Bates D, DebRoy S, Sarkar S, et al (2021) nlme: linear and nonlinear mixed effects models. R package version 3.1-152, URL: https://CRAN.R-project.org/package=nlme. Polis GA Anderson WB Holt RD Toward and integration of landscape and food web ecology: the dynamics of spatially subsidized food webs Annu Rev Ecol Syst 1997 28 289 316 10.1146/annurev.ecolsys.28.1.289 R Core Team. (2021) R: A language and environment for statistical computing. Version 4.1.1. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Reinitz G Relative effect of age, diet, and feeding rate on the body composition of young rainbow trout (Salmo gairdneri) Aquaculture 1983 35 19 27 10.1016/0044-8486(83)90067-4 Rimmer CC Miller EK McFarland KP Taylor RJ Faccio SD Mercury bioaccumulation and trophic transfer in the terrestrial food web of a montane forest Ecotoxicology 2010 19 697 709 10.1007/s10646-009-0443-x 19960247 Riva-Murray K Chasar LC Bradley PM Burns DA Brigham ME Smith MJ Abrahamsen TA Spatial patterns of mercury in macroinvertebrates and fishes from streams of two contrasting forested landscapes in the eastern United States Ecotoxicology 2011 20 1530 1542 10.1007/s10646-011-0719-9 21743999 Riva-Murray K Bradley PM Brigham ME Methylmercury–total mercury ratios in predator and primary consumer insects from Adirondack streams (New York, USA) Ecotoxicology 2020 29 1644 1658 10.1007/s10646-020-02191-7 32180092 Rodenhouse NL Lowe WH Gebauer RLE McFarland KP Bank MS Mercury bioaccumulation in temperate forest food webs associated with headwater streams Sci Tot Environ 2019 665 1125 1134 10.1016/j.scitotenv.2019.02.151 Rundio DE Lindley ST Seasonal patterns of terrestrial and aquatic prey abundance and use by Oncorhynchus mykiss in a California coastal basin with a Mediterranean climate Trans Am Fish Soc 2008 137 467 480 10.1577/T07-076.1 Rundio DE Lindley ST Diet variability of steelhead/rainbow trout in a coastal basin in central California: relative importance of seasonal, spatial, and ontogenetic variation Trans Am Fish Soc 2019 148 88 105 10.1002/tafs.10121 Rundio DE Lindley ST Importance of non‐native isopods and other terrestrial prey resources to steelhead/rainbow trout Oncorhynchus mykiss in coastal streams in Big Sur, California Ecol Freshw Fish 2021 30 419 432 10.1111/eff.12594 Rundio DE Williams TH Pearse DE Lindley ST Male-biased sex ratio of nonanadromous Oncorhynchus mykiss in a partially migratory population in California Ecol Freshw Fish 2012 21 293 299 10.1111/j.1600-0633.2011.00547.x Sandheinrich MB Wiener JG Beyer WN Meador JP Methylmercury in freshwater fish: recent advances in assessing toxicity of environmentally relevant exposures Environmental contaminants in biota: interpreting tissue concentrations 2011 2nd edn Boca Raton, FL CRC/Taylor and Francis 169 190 Streets DG Horowitz HM Jacob DJ Lu Z Levin L ter Schure AFH Sunderland EM Total mercury released to the environment by human activities Environ Sci Technol 2017 51 5969 5977 10.1021/acs.est.7b00451 28448134 Sullivan SMP Rodewald AD In a state of flux: the energetic pathways that move contaminants from aquatic to terrestrial environments Environ Toxicol Chem 2012 31 1175 1183 10.1002/etc.1842 22605620 Tsui MTK Blum JD Kwon SY Finlay JC Balogh SJ Nollet YH Sources and transfers of methylmercury in adjacent river and forest food webs Environ Sci Technol 2012 46 10957 10964 10.1021/es3019836 23033864 Tsui MTK Blum JD Finlay JC Balogh SJ Nollet YH Palen WJ Power ME Variation in terrestrial and aquatic sources of methylmercury in stream predators as revealed by stable mercury isotopes Environ Sci Technol 2014 48 10128 10135 10.1021/es500517s 25105808 Tsui M Liu S Brasso RL Blum JD Kwon SY Ulus Y Nollet YH Balogh SJ Eggert SL Finlay JC Controls of methylmercury bioaccumulation in forest floor food webs Environ Sci Technol 2019 53 2434 2440 10.1021/acs.est.8b06053 30727732 U.S. EPA (1998) Method 1630: methyl mercury in water by distillation, aqueous ethylation, purge and trap, and cold vapor atomic fluorescence spectrometry. https://www.epa.gov/sites/default/files/2015-08/documents/method_1630_1998.pdf U.S. EPA (2000) Guidance for assessing chemical contaminant data for use in fish advisories, volume 1: fish sampling and analysis, third edition. https://www.epa.gov/sites/default/files/2018-11/documents/guidance-assess-chemical-contaminant-vol1-third-edition.pdf U.S. EPA (2007) Method 7473: Mercury in solids and solutions by thermal decomposition, amalgamation, and atomic absorption spectrophotometry. https://www.epa.gov/sites/default/files/2015-12/documents/7473.pdf U.S. EPA (2021) 2021 revision to: aquatic life ambient water quality criterion for selenium –freshwater 2016. https://www.epa.gov/system/files/documents/2021-08/selenium-freshwater2016-2021-revision.pdf U.S. Office of the Federal Register (2014) Endangered and threatened wildlife: final rule to revise the code of federal regulations for species under the jurisdiction of the National Marine Fisheries Service. Federal Register 79:71(14 April 2014):20802–20817. Walters DM Fritz KM Otter RR The dark side of subsidies: adult stream insects export organic contaminants to riparian predators Ecol Appl 2008 18 1835 1841 10.1890/08-0354.1 19263881 Ward DM Nislow KH Folt CL Bioaccumulation syndrome: identifying factors that make some stream food webs prone to elevated mercury bioaccumulation Ann NY Acad Sci 2010 1195 62 83 10.1111/j.1749-6632.2010.05456.x 20536817 Ward DM Nislow KH Folt CL Do low-mercury terrestrial resources subsidize low-mercury growth of stream fish? Differences between species along a productivity gradient PLoS One 2012 7 e49582 10.1371/journal.pone.0049582 23166717 Weiss‐Penzias PS Ortiz C Acosta RP Heim W Ryan JP Fernandez D Collett JL Flegal AR Total and monomethyl mercury in fog water from the central California coast Geophys Res Lett 2012 39 L03804 10.1029/2011GL050324 Weiss-Penzias PS Bank MS Clifford DL Torregrosa A Zheng B Lin W Wilmers CC Marine fog inputs appear to increase methylmercury bioaccumulation in a coastal terrestrial food web Sci Rep 2019 9 17611 10.1038/s41598-019-54056-7 31772229 Wickham H (2016) ggplot2: elegant graphics for data analysis. Version 3.3.5. Springer-Verlag, New York	36449122	PMC9709357	NO-CC CODE	2022-12-08 23:16:06	no	Ecotoxicology. 2022 Nov 30; 31(10):1506-1519	utf-8	Ecotoxicology	2,022	10.1007/s10646-022-02608-5	oa_other
==== Front Multimed Tools Appl Multimed Tools Appl Multimedia Tools and Applications 1380-7501 1573-7721 Springer US New York 14173 10.1007/s11042-022-14173-4 Article Survey on user perceived system factors influencing the QoE of audiovisual calls on smartphones http://orcid.org/0000-0002-5604-2205 Vučić Dunja [email protected] 1 Baraković Sabina [email protected] 2 Skorin-Kapov Lea [email protected] 3 1 grid.424640.0 Ericsson Nikola Tesla d.d., Krapinska 45, Zagreb, 10000 Croatia 2 grid.11869.37 0000000121848551 Faculty of Traffic and Communications, University of Sarajevo, Zmaja od Bosne 8, Sarajevo, 71000 Bosnia and Herzegovina 3 grid.4808.4 0000 0001 0657 4636 University of Zagreb, Faculty of Electrical Engineering and Computing, Unska 3, Zagreb, 10000 Croatia 30 11 2022 126 31 3 2022 15 9 2022 27 10 2022 © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. With the widespread use of applications and services supporting audiovisual calls via smartphones, both in business and leisure contexts, a key challenge for service providers is meeting end user Quality of Experience (QoE) expectations and requirements. To successfully meet this challenge, there is a need to identify and analyze the key system-related factors impacting user perceived quality. In this paper, we contribute beyond state-of-the-art by conducting a large scale web-based questionnaire survey to investigate the system-related factors that subjects identify as most influential in contributing to their overall experience and quality perception. We focus in particular on leisure audiovisual calls, established via mobile devices. Our initial survey (Phase 1) was conducted in Feb. 2020, just prior to the outbreak of the COVID-19 pandemic (272 participants). To investigate if the importance of factors has changed due to increased usage of the service caused by the pandemic among the general population, we conducted a second survey (Phase 2) in October 2021 with 249 participants. Based on obtained results, we identify key system-related QoE influence factors belonging to three categories: media quality, functional support, and usability and service design. We observe no significant differences in user opinions and expectations prior to and during the period of increased service usage, despite different participant demographics and study time frames, thus contributing to generalizability of obtained results. Study results contribute to providing insights for designing future user studies investigating QoE, in terms of key factors that should be considered. Keywords QoE Influence factors Audiovisual calls Smartphones User perception https://doi.org/10.13039/501100004488 Hrvatska Zaklada za Znanost P-2019-04-9793 (Q-MERSIVE) Skorin-Kapov Lea ==== Body pmcIntroduction In the past decade, video transmission over the Internet has experienced significant rise, enabled by technological advancements such as higher network transmission rates, improved video coding capabilities, and the widespread availability of high quality displays, cameras, speakers, and microphones on heterogeneous end user devices. Mobile devices, services, and applications have become an inseparable part of our daily lives, affecting relationships, social norms, communication, and interaction methods, even before global outbreak of the COVID-19 pandemic. Trends in the increasing use of audiovisual communication services, both in business and private contexts, have stemmed from evolving life dynamics and accelerated lifestyles, as well as recent distancing and lockdown measures [14]. A recent Sandvine report [39] highlighted the impact of the pandemic on dramatic increases in traffic corresponding to applications supporting video telephony such as Zoom and MS Teams from mid-March 2020 onward. While the global video conferencing market size in 2018 was USD 3.02 billion, estimated growth by 2026 was set to USD 6.37 billion [42]. Given end user needs and expectations, modern video conferencing (or telemeeting) services are expected to be reliable and available across heterogeneous access networks, devices, and usage contexts, with underlying platforms and protocols secure and easy to manage. The term telemeeting is defined by ITU-T Recommendation P.1301 as a meeting in which participants are located in at least two different locations and the communication takes place via a telecommunication system [21]. Telemeetings held in a private/leisure context have the primary objective of experiencing a sense of presence or social connection. Considering that the term audiovisual call is commonly used among the general population and is usually associated with the leisure context, we have opted to use the term audiovisual call instead of telemeeting in our study. Technologies such as WebRTC (Web Real-Time Communications) have contributed to making many video conferencing services free and available to the wider public. Regardless of the system complexity, the service itself should be simple and participants should be able to use it without intense training. Features and functions must be useful and offer seamless access, both for when used in a business and leisure context. Video conferencing used in a business context generally has a specific objective, with a set of tasks that must be completed [36]. On the other hand, audiovisual calls used in the private/leisure context generally have the primary objective to experience a sense of presence or social connection. Due to the different objectives of the meeting or call, the quality expected by the participants may be different, with participants likely being less critical when it comes to the private context [18, 47]. Going beyond conversational services between two participants, users are increasingly using video conferencing/call services in both business and leisure contexts (e.g., social interactions via Skype, Viber, Whatsapp, Google Meet, Zoom, Microsoft Teams, Whereby, etc.). Such audiovisual settings impose a wide range of challenges with respect to identifying and quantifying the impact of various factors influencing end user Quality of Experience (QoE), in particular in the context of calls established via mobile devices. In [37], the authors summarize the challenges in properly assessing the QoE of such systems, and highlight mobility aspects, device and encoding interoperability, ease of use, and additional collaboration possibilities (e.g., exchanging pictures, files, chatting). With the processing power of mobile devices such as smartphones and tablets becoming sufficient to simultaneously encode and decode video at a high spatial and temporal resolution during real-time communication, mobile video communication service use has grown rapidly [42]. A wide range of smartphone models available on the market, along with heterogeneous access networks, can create numerous different asymmetric scenarios, with video calls imposing strict low latency and high volume requirements on the underlying network. Service architectures, such as those relying on a centralized Selective Forwarding Unit (SFU) or Multipoint Control Unit (MCU) are thus commonly deployed to optimize resource utilization and ensure high service quality, in particular in situations with a large number of simultaneous users. Designing and managing video conferencing services requires an understanding of the key underlying QoE influence factors. One of the key challenges faced by service providers lies in configuring the video encoding parameters so as to maximize participant QoE while meeting resource (network and mobile device) availability constraints. Currently developed QoE models can for the most part be applied to two interlocutors and in desktop environments. However, there is a lack of studies that focus on modeling and optimizing QoE for such services when using mobile devices. In our previous work, we have conducted numerous subjective user studies involving three-party video calls established via smartphone devices in both laboratory and field settings, with the aim being to study the impact of different video encoding parameters (encoding bitrate, resolution, and frame rate) on user perceived QoE [43–46]. In this paper, we aim to complement our earlier work and provide insights into designing future user studies by providing an in-depth investigation of users’ opinions and expectations related to audiovisual calls on mobile devices, focusing on the leisure/private context. We conducted an extensive web-based questionnaire survey to investigate the system-related factors that subjects identify as most influential in contributing to their overall experience and quality perception. We highlight that we make complete survey results (anonimized) publicly available to the research community to foster reproducible research (link: https://muexlab.fer.hr/muexlab/research/datasets). Our aim is not to quantify the impact of certain QoS factors on QoE (as has been done in a number of previous empirical studies), but rather to obtain up-to-date feedback from a large number of users on their opinions with respect to a wide range of potential QoE system influence factors. Therefore, our study is different in both focus and scope than previous dedicated studies focused on modeling QoE or MOS based on a limited set of chosen factors. We stress that our approach fills the gap of existing studies (especially those focusing on the mobile context) which do not explicitly ask users for their opinions on whether certain influence factors should be examined further, but rather focus on evaluating the impact of a limited set of previously chosen factors on QoE. Aiming to obtain insights into a wider range of potential factors that need to be addressed, we asked users which system-related factors are important to them in audiovisual calls on smartphones. Our aim is to obtain feedback that can be used as input for designing future user studies investigating QoE, whereby our results provide novel contributions in terms of input on what are the key QoE system influence factors that should be considered in future studies. Our study was conducted in two phases, so as to further investigate differences in user opinions potentially triggered by the global outbreak of the COVID-19 pandemic. Phase 1 included 272 participants and was conducted in February 2020, just prior to the global outbreak of the pandemic. Given the drastic increase in video communication services resulting from lockdown measures [39], we repeated the survey in Phase 2 (October 2021) which included 249 participants. We address the following research questions: RQ1: What do users consider to be the most important system influence factors in terms of their importance and impact on QoE in the context of audiovisual calls established for leisure purposes on smartphones? RQ2: Are there significant differences in user opinions when comparing survey results reported prior to global outbreak of the pandemic and results obtained via an independent survey conducted 20 months into the pandemic? The paper is organized as follows: Section 2 gives an overview of standards and related work, focusing on system-related QoE influence factors as pertaining to audiovisual calls established via smartphones. Our research methodology is described in Section 3, providing an overview of survey items and participant demographics. Results across both conducted surveys (Phase 1 and Phase 2) are analyzed in Section 4, summarizing opinions related to media quality, functional support of the service, and usability, service design, and resource consumption. The key influence factors, derived based on user opinions and analyzed results, are outlined in Section 5. Finally, Section 6 provides concluding remarks and an outlook for future research challenges in this area. The questionnaire used in both surveys is given in Appendix. Related work International bodies including ITU-T and ETSI have specified definitions of QoE over the years. However, to establish definitions and methods for the quantitative assessment of QoE for multimedia content and services in a given situation and system configuration, such as audiovisual calls on smartphones, the COST IC 1003 Action Qualinet defines QoE as: “the degree of delight or annoyance of the user of an application or service. It results from the fulfillment of his or her expectations with respect to the utility and/or enjoyment of the application or service in the light of the user’s personality and current state” [27]. The multidimensional nature of QoE stems from a number of different influence factors and perceived features comprising the overall QoE. The Qualinet white paper further defines an influence factor (IF) as “any characteristic of a user, system, service, application, or context whose actual state or setting may have influence on the Quality of Experience for the user” and groups them into three categories [23, 24, 27]: 1) Human IFs (HIF) as any variant or invariant property or characteristic of a human user. The characteristic can describe the demographic and socio-economic background, the physical and mental constitution, or the user’s emotional state; 2) Context IFs (CIF) factors that embrace any situational property to describe the user’s environment in terms of physical, temporal, social, economic, task, and technical characteristics; and 3) System IFs (SIF) referring to the properties and characteristics that determine the technically produced quality of an application or service. They are related to media capture, coding, transmission, storage, rendering, and reproduction/display, as well as to the communication of information itself from content production to user. Usually, they are grouped into network, media, content, and device categories. Factors affecting the QoE of different components of audiovisual conferences/calls or the service as a whole have been an increasing topic of interest to the research community before pandemic. Those research studies report the existence of the impacts of various human, context, or system IFs on QoE. However, most studies have focused on the impact of various system factors, while context and human factors have been addressed to a limited extent. Traditionally, addressed network related SIFs in terms of QoE are (wireless) channel characteristics and capacity (coverage, bandwidth, etc.), signal strength, and transmission impairments (delay, jitter, loss, etc.) [1, 4, 6, 9–13, 16, 26, 28, 29, 33, 34, 40, 41, 48]. Media and content related SIFs addressed so far cover content type and quality, synchronization, audiovisual features and quality, spatial and temporal artifacts [2, 7, 15, 19, 22, 30, 31, 35, 49]. In terms of application and device related SIFs, modern mobile users are looking to access and reliably utilize demanding services regardless of context or system influence factors, such as location, time, network conditions, service topology, or mobile device processing capabilities. Mobile end user devices such as smartphones used to take part in audiovisual calls often represent possible bottlenecks in the service delivery chain . Fortunately, each new generation of devices has brought more advanced hardware in terms of memory, processor power, camera, and battery cycle. Rapid development in the smartphone hardware industry in the last several years implies that the majority of recently released smartphones should be able to provide acceptable QoE for audiovisual calls with adapted video quality streams. Trends have shown increases in smartphone screen sizes, aiming to accommodate higher screen resolutions. High resolution displays impose additional load on the processing unit, particularly on the graphics processor, needed to render high definition images faster. Smartphone screen sizes will most likely not be much bigger in the future, since carrying devices with displays larger than 6” and noticeable weight is not particularly convenient. Thus, an important feature of any service is the possibility to adapt the layout and content to viewing contexts and devices. Even though smartphone displays are relatively small, with limited options for manipulating the design layout, results from studies focusing on desktop video conferencing could be taken into consideration and further extended to mobile devices [17]. The authors in [37] identified mobility, device and encoding interoperability, ease of use, and additional collaboration possibilities (e.g., exchanging pictures, files, chatting) as the most important aspects for audiovisual call services. However, mobility and device capacity, alone and together, can create asymmetry, which is a realistic and common case. Thus, the impact of the mobility and device, depending on the number of the participants, can greatly differ due to the numerous possible combinations of connection type between locations and type of equipment being used. In the context of mobile networks, characterized by variable resource availability, challenges arise with respect to meeting the QoE requirements of conversational real-time, media rich, and multiparty services [22]. In addition to network requirements, audiovisual calls impose high requirements on end user device processing capabilities, with the need for real-time encoding and decoding of multiple media streams. Recently, after the COVID-19 outbreak, Skowronek et al. [38] has provided an extensive, detailed, and comprehensive study on factors affecting QoE of videoconferencing. This study, among others, systematically analyzed the impact of system, human, context, and mixed factors on QoE in fixed audiovisual calls context. Addressed SIFs were grouped into: ones related to signal transmission over the system (media richness, processing, network and topology, and time) and technical aspects related to user interaction with the system (setting up a call and management of one ongoing). Recognized IFs are: communication mode, audiovisual presentation of environment and participant, audio and visual mixing paradigm and signal processing, audiovisual synchronization, end-to-end delay, call setup and management of ongoing call, participation registration, network access behavior and computation distribution, installation complexity, user interface, etc. Although it offers a novel systematization and taxonomy approach to influence factors, this study together with several others from this period refer to older references. There are also several studies that addressed the impact of various factors on QoE in services similar to audiovisual teleconferences, such as unified communications [5], video consultations [32], or video streaming [3]. While it is clear that a wide range of IFs affect QoE in audiovisual calls, questions remain as to the level and importance of the impact of particular factors, especially in a mobile context. For example, the question of whether certain impairments cause strong, noticeable, or imperceptible quality degradation commonly depends on the particular scenario, context, as well as the individual involved users. However, although the existing studies analyzed these various impacts of IFs, what they lack is the user’s opinion on whether those IFs should have been analyzed, i.e., do they matter to the end users of audiovisual calls on smartphones. That is why in this paper our primary focus has been on collecting user opinions pertaining to the impact of various system-related QoE influence factors, so that the most important ones (according to users’ opinions) can be used afterwards as input in empirical studies examining QoE of audiovisual calls on smartphones. Based on standards and related work, Table 1 provides a summary of QoE SIFs for audiovisual calls on smartphones to be considered according to our proposed categories. The categories are: media quality, functional support, and usability, service design, and resource consumption. These categories are used in the scope of our survey questionnaire which will be described in the next Section. It is important to emphasize that the media quality group is related to factors falling into network, media, and content SIFs. The functional support group as well as usability, service design, and resource consumption are related to device and application SIFs. They are divided into two groups, to ensure clearer distinction by participants. Table 1 System influence factors to be considered when assessing and modeling QoE for audiovisual calls on smartphones Category Influence factors Media Speech intelligibility Uninterrupted interaction Longer video freezes Perceptible audio delay Perceptible video delay Short video freezes Audio-video synchronization Image blurriness Voice naturalness Image sharpness Smooth movement Color accuracy Functional support Speaker identification Audio mute File transfer Texting Adaptive layout Audiovisual call recording Video pausing Applying filters Usability, Service reliability service design, Security resource consumption Device/browser interoperability Duration of call establishment time Service price Ease of use Installation complexity Noise free environment User interface aesthetics Low battery consumption Smooth simultaneous use of other apps In the following Section, we outline our survey methodology, focusing on contributing to state-of-the-art knowledge by collecting and analyzing user opinions and expectations related to various QoE SIFs, with a focus on audiovisual calls established on smartphones. Methodology Given the wide range of factors that may impact end user expectations and quality ratings, we conducted a web-based questionnaire survey with the goal being to investigate users’ opinions and expectations related to audiovisual calls on mobile devices. Furthermore, we highlighted and clearly stated that reported answers should be considered in a leisure context, i.e., when communicating and interacting with family or friends. The aim of this questionnaire was to investigate the factors that users identify as most influential in contributing to their overall experience and quality perception. As previously stated, we conducted our research in two phases. The first survey (S1) was conducted as a part of phase 1, just prior to the global outbreak of the COVID-19 pandemic, and reflects the views and opinions of users at that time. Due to the increase in video communication services resulting from lockdown measures, in phase 2 we repeated the survey 20 months later (Survey S2), so as to assess whether or not there are any differences in user opinions. The surveys were prepared using the Google Docs service and distributed via email to acquaintances, colleagues, and students. A total of 272 participants took part in S1, with responses collected over a period of thirteen days, from February 13 until February 26, 2020. The second survey, S2, was conducted during the period between October 5 and November 15, 2021, with 249 participants successfully having completed the questionnaire. The majority of participants involved in the first study are from Croatia (88.97%), while 6.62% and 4.41% of participants are from Serbia and Bosnia and Herzegovina, respectively. More than half of the participants included in the second study are from Bosnia and Herzegovina (53.82%), followed by 42.97% of Croats, while the remaining 3.21% are from Serbia. To gather user feedback on the perceived quality of audiovisual calls, two aspects of service delivery were considered: call initiation, and service operation once the audiovisual call is established. Both aspects are comprised of multiple dimensions that contribute to the overall QoE: effort required by the user, responsiveness of the service, fidelity of information, security, and availability. The questionnaire covered ratings of the impacts and importance of considered factors referring to the application, resources, and context. Selected factors belong to the quality features that can be evaluated by a wider audience from a perceptual perspective. Questions were divided into the following four groups which address SIFs from Table 1: general information - referring to the subject’s demographic data and previous experiences with taking part in audiovisual calls; media quality - referring to the quality of the speech (audio) and the image (video) in terms of perceivable impairments (e.g., delay, blurriness); functional support - referring to the additional functionalities supported by audiovisual call/conferencing services, beyond only basic support for audiovisual call; usability, service design, and resource consumption - referring to the ease of use, aesthetic design, as well as various service design features, such as service reliability, security, price, and battery consumption. In terms of network factors, participants are asked to rate their impact through quality and perceivable impairments. We offered participants only closed-ended questions that provided a fixed set of options to choose from. Closed-ended response choices were comprised of yes/no options, multiple choice options, and rating scales. The impact of each factor was rated on a 5-point scale. One set of questions used the following rating scale to collect user opinions with respect to the importance of certain factors: 5 - “Very Important”, 4 - “Important”, 3 - “Moderately Important”, 2 - “Slightly Important”, 1 - “Not Important”. The other set of questions used the following scale to collect feedback on the extent to which users considered certain factors to impact perceived quality: 5 - “To a great extent”, 4 - “To a moderate extent”, 3 - “To some extent”, 2 - “To a small extent”, 1 - “Not at All”. Further details and concrete questionnaire items are given in Appendix. When creating the questionnaire, we adhered to the principle of simplicity. In other words, all questions were formulated in a simple and clear manner in order to avoid confusion. In cases where it was necessary, we added an explanation in the questionnaire for further clarification. Furthermore, prior to each question set, we provided a short explanation for the survey participants to know what they are rating. The short descriptions used in the questionnaire are included in the annexes. The questionnaire was written in the Croatian language, while its English translation is provided in Appendix. Given the similarities between the Croatian, Bosnian and Serbian languages, the choice of words and phrases used in the questionnaire ensured that all respondents (regardless of nationality) were able to interpret the questions in the correct manner. Furthermore, we conducted a pilot study prior to the two reported studies, involving 15 participants who were later on not included in the actual survey, in order to confirm that the questions were clearly formulated and comprehensible to people that are non experts in the AV field. Given that we used no constructs in our evaluation questionnaire, there was no need to conduct the convergent and discriminant validity. Also, since the research does not include criterion, there was no need to assess criterion validity (concurrent and predictive). Finally, content validity has been addressed. The content validity assesses whether a test is representative of all aspects. There is no direct measure of content validity so it was tested by relevant ICT experts who reviewed and rated the survey questions, removing the ones that were marked as irrelevant and accepting and adjusting the ones that were relevant. Reliability considers the extent to which the questions used in a survey instrument consistently elicit the same results each time it is asked in the same situation on repeated occasions. Reliability is a statistical measure of how reproducible the survey instrument’s data is. A survey instrument is said to have high reliability if it produces similar results under consistent conditions, and any change would be due to a true change in the attitude, as opposed to changing interpretation (i.e., a measurement error). In our case, the survey instrument produces similar results, which proves survey reliability. Participant demographics Information related to age, gender, and education was collected to identify participant demographics (Table 2). In the first survey (S1), the majority of users (49.6%) fit into the category 36-45 years old, while in S2 the largest percentage fit into the 18-25 years category with a share of 53.82%. Fairly equal gender representation was recorded in both surveys, with 51.1% females in S1 and 50.2% in S2. The highest response rate for educational level in S1 was a University degree (71%), in contrast to S2 where 61.05% were younger adults with only a high school diploma. Table 2 Demographic information about participants that completed the survey in February 2020 and October 2021 Survey February 2020 (S1) October 2021 (S2) No. of participants 272 249 Age group 18–25 11.00% 53.82% 26–35 25.00% 5.62% 36–45 49.60% 28.92% 46–55 11.00% 10.84% > 55 3.40% 0.80% Gender Female 51.10% 50.20% Male 48.90% 49.80% Educational level High school degree 19.10% 61.05% University degree 71.00% 32.93% PhD degree 9.90% 6.02% To investigate possible generational differences in perception we grouped participants into two categories (considering uneven age distribution in both surveys): young adults (covering the age from 18-35) and middle-aged adults and older (age 36 and older). In S1, the average calculated difference in mean values between the two age groups and considering all IFs was 4.16%, while the biggest difference (13.84%) in average ratings between the two age groups was observed for the importance of the functionality video pausing (we note that in the context of audiovisual calls, this refers to turning off the video while maintaining audio communication), where young adults rated the impact on average with 3.43, and adults (older than 35) with 2.95. In S2, the average calculated difference in mean values between the two age groups and considering all IFs was 4.8%, while the biggest difference (9.9%) between average ratings was observed for video delay, where young adults rated the impact with an average score of 4.16, and adults (older than 35) with an average score of 4.57. Given that in both surveys, average ratings for the majority of IFs did not significantly differ between young adults and middle-aged to older adults, we refrain from further analyzing the impact of age group in the scope of the results analysis given in the following section. Analysis of survey results Following the collection of demographic data, the remainder of the questionnaire focused on collecting information on service usage habits, followed by user opinions with respect to the importance of certain factors and the extent to which users considered certain factors to impact perceived quality. Details are given in the remainder of this section, focused also on comparing S1 and S2 results. The factors considered by users to have the greatest impact on QoE are further summarized and compared across both studies in Section 5. Service usage Following the collection of demographic data, the aim of the following set of questions was to identify users’ habits associated with audiovisual calls. In terms of application frequency usage, we categorized participants per user type as: very frequent user (uses audiovisual call applications on a daily basis), frequent user (uses audiovisual call applications 2 to 3 times per week), occasional user (uses audiovisual call applications 4 to 7 times per month), and light user (uses audiovisual call applications rarely, 3 or less times per month). Of the 272 participants from S1, 16.2% reported participation in audiovisual calls in the last 30 days on a daily basis, while in S2 this increased to 21.3% (out of 249 participants) (Table 3). The biggest increase can be observed in the category of frequent usage (2-3 times per week), where the percentage of participants more than doubled and rose to 34.1% in October 2021. The percentage of participants that did not participate in any audiovisual call in the last 30 days dropped from 17.6% in 2020 to 3.6% in 2021. Table 3 Frequency of users that reported having participated in an audiovisual call during the last 30 days Frequency of participation February 2020 (S1) October 2021 (S2) Very frequently 16.2% 21.3% Frequently 14.7% 34.1% Occasionally 16.9% 20.9% Rarely 34.6% 20.1% Never 17.6% 3.6% The question addressing device usage was a multiple choice question type that allowed participants to select one or multiple answers from a defined list. Out of 272 participants from S1, 94.9% reported a smartphone as the device used to make audiovisual calls, 12.1% reported using a tablet, 52.2% a computer/laptop, while 1.5% responded they used some other device (Fig. 1). Participants in S2 reported similar usage of smartphones (94%) and tablets (10.1%), while the biggest difference can be observed in the computer/laptop category, where 81.9% participants reported using such devices for participating in audiovisual calls. Fig. 1 Percentage of participants that reported using a given device when taking part in audiovisual calls With respect to previous experiences with applications, participants were allowed to choose multiple predefined answers. In survey S1, Whatsapp and Skype were the two most commonly used applications in the audiovisual call context, with a share of 89.3% and 85.7%, respectively. This was followed by Viber (70.6%) and Google Hangouts (22.8%). Appear.in (renamed to Whereby in 2019) was used by 3.7%, while 26.5% of subjects used other apps (Fig. 2). Fig. 2 Percentage of applications used when making an audiovisual call reported in 2020 When conducting survey S2, we added additional popular applications, such as Microsoft Teams, Zoom, and FaceTime. In contrast to the first survey, Whatsapp (73.5%) reached fourth place, while Viber (83.5%) and Teams (75.9%) were the two most commonly used apps for establishing audiovisual calls as reported in our 2021 survey (Fig. 3). Fig. 3 Percentage of applications used when making an audiovisual call reported in 2021 Opinions related to media quality Questions regarding the importance of media quality factors and impact on the overall perceived quality of audiovisual calls were comprised of questions including a predefined list of five answer options (Appendix). Questions were explicitly related to audiovisual calls established via a mobile smartphone in a leisure context. Rating distributions and descriptive statistics for both S1 (2020) and S2 (2021) are given in Fig. 4. Fig. 4 Distribution of ratings and descriptive statistics for IFs belonging to the Media quality group. For each factor, the first bar corresponds to 2020 and the second bar to 2021 Ratings of the impact of speech intelligibility on overall audiovisual call quality had the highest mean value in both surveys, corresponding to 4.69 (S1) and 4.77 (S2). Corresponding values of standard deviation were the lowest within the media quality group. Speech intelligibility is a measure of the effectiveness of speech communication usually defined as the percentage of speech units (syllables, words, or sentences) correctly perceived by listeners. Reduced intelligibility occurs due to the nature of the spoken material (unfamiliarity with the speaker, possible abnormal speech characteristics, or unfamiliarity with the conversation topic) and the context of transmission [8]. It also depends on audio bandwidth, channel impairments, input (microphone) and output (speaker) of end user device characteristics and its placement in relation to the speaker/listener, acoustical properties of the room, sound pressure level, and background noise level [20]. If the cause of poor intelligibility does not lie in human characteristics, yet involves system components, there is a possibility to isolate the cause of the reduced quality and prevent further degradation. The ability to interact in the presence of interruptions can be difficult even in face to face communication. Participants in video mediated communication can be severely affected by transmission delays, where comprehension can be distorted by mutual silence or double talk [25]. Further considering media quality, image blurriness, image sharpness, voice naturalness and smooth movement in the video showed high percentages of 4 - “Important” and 3 - “Moderately Important” ratings. Color accuracy was the influence factor that showed the highest dispersion among ratings, with the greatest standard deviation (0.99 for 2020 and 1.07 for 2021), as well as having the highest number of reported 1 - “Not Important” ratings in both surveys within the media quality group. Seven out of twelve influence factors gained slight importance from February 2020 to October 2021, however there was no significant difference. In order to quantify the change from mean value (of factor importance reported in S1) to mean value (of that same factor reported in S2) and express the change as an increase or decrease we calculated the percentage change. Percentage change equals the change in mean value (S2-S1) divided by the value of the initial S1 mean value, multiplied by 100. The importance of color accuracy rose the most (on average by 3.66%), while the importance of voice naturalness decreased by 2.56%. Overall, a very strong negative correlation coefficient − 0.9 (calculated for all factors belonging to the media quality group) between mean value and standard deviation shows that for factors rated as having higher importance in terms of impact on quality, there was less diversity in user ratings. Summary of key findings Speech intelligibility and uninterrupted interaction were the two factors with the highest mean ratings in terms of importance. Factor analyses showed that the perceived importance of 8 IFs (speech intelligibility, uninterrupted interaction, longer video freezes (i.e., longer than 15 seconds), perceptible audio delay, perceptible video delay, short video freezes (lasting a few seconds), audio-video synchronization, image blurriness, voice naturalness, image sharpness, smooth movement in the video and color accuracy) on media quality did not significantly differ between S1 and S2, despite increased usage of audiovisual call services. Furthermore, user opinions with respect to the importance of various media quality factors did not differ greatly with respect to the frequency of video conferencing service usage patterns. We thus conclude that even in cases of occasional service use, participants provided very similar opinions as did users who more frequently used such services. Due to space limitations, we refrain from further analysis, however interested researchers are referred to our publicly available survey results. Opinions related to functional support Questions regarding additional functionalities supported by conferencing services (beyond only audiovisual calls) and corresponding importance were comprised of closed-ended questions including a predefined list of five answer options. Rating distributions and descriptive statistics for both S1 (2020) and S2 (2021) are given in Fig. 5. Active speaker identification, i.e., being able to identify the speaker who is currently talking, was the influence factor with the highest mean importance ratings in both surveys, 4.11 (S1) and 4.17 (S2), and lowest standard deviation values within the functional support group, namely 0.88 and 0.9, respectively. Fig. 5 Distribution of ratings and descriptive statistics for IFs belonging to the Functional support group. For each factor, the first bar corresponds to 2020 and the second bar to 2021 On the other hand, being able to apply make-up/filters/overlay items was the functionality perceived as least important in both surveys, whereas importance in 2021 decreased to a mean value of 1.81, even though the majority of participants were of a younger age in S2. More than half of the participants perceived the possibility to apply filters as “Not Important”. In case of additional functionalities, seven out of eight influence factors gained in mean importance ratings from February 2020 to October 2021, with mean ratings for file transfer and audiovisual call recording increasing by more than 0.5. Standard deviation values ranged from 0.96 (for file transfer in 2021) to 1.16 (for audiovisual call recording in 2020). Overall results show a strong negative correlation (with Pearson correlation coefficient − 0.66) between perceived importance of IFs and corresponding standard deviation values, meaning that with increased perceived importance, the dispersion of reported values does not decrease significantly, as was the case with the media quality factors. Summary of key findings The most significant differences between S1 and S2 were observed in factors belonging to the functional support group. The increased importance of the IFs belonging to the functional support group is an important change indicating that pure audio and video communications is not necessarily sufficient anymore. Additional features are needed to enhance the meeting quality in terms of collaboration, engagement, and interaction, ultimately making the communication easier and more effective. Opinions related to usability, service design, and resource consumption Questions regarding usability, service design, and resource consumption referred to the ease of use of the application, the extent to which users feel they are able to conduct audiovisual calls, and to the mobile context, encompassing usability, portability (in terms of efficiency with which the audiovisual call application can be transferred from one operational or usage environment to another), and resource consumption (battery consumption and CPU utilization). The final set of questions included also a predefined list of five answer options. The descriptive statistics for the collected ratings are given in Fig. 6. With respect to usability, ease of use and installation complexity both had mean ratings greater than 4, indicating the high importance of such factors to end users. Fig. 6 Distribution of ratings and descriptive statistics for IFs belonging to the Usability, service design, and resource consumption group. For each factor, the first bar corresponds to 2020 and the second bar to 2021 On the other hand, user interface aesthetics were deemed less important, with an average rating of 3.42 (S1) and 3.53 (S2). With respect to resource consumption, and given that the questions were specified in the context of mobile device use, rating distributions clearly show the importance of low battery consumption and smooth simultaneous use of other applications. Comparing the results between S1 and S2, we observe the biggest difference in smooth simultaneous use of other applications, with a higher mean score in 2021 (4.28) as compared to 2020 (3.79). This is followed by the importance of having a noise free environment, where the mean score increased by 7.42%. Finally, rating distributions clearly show that users are highly concerned with service reliability and security (in this case referring to having an encrypted connection during the audiovisual call). In total, even though ten out of eleven influence factors gained importance when comparing mean values of S1 and S2, a greater difference was found only for smooth simultaneous use of other applications and noise free environment. For this given group of influence factors, Pearson’s correlation coefficient between mean values and corresponding standard deviation showed a very strong negative correlation (− 0.84). Summary of key findings Rating distributions showed high importance of ease of use, resource consumption, security, service reliability, and price. As expected, and potentially due to increased service usage, factors related to the usability were rated as being more important in S2 as compared to S1. Impact of usage frequency on perceived importance of considered factors In Study 1, we compared the mean values with all participant ratings included, with the mean values obtained when excluding participants that had not used a video call service in the last 30 days (we note that this corresponds to 17.6% of participants in Study 1). Results showed that there is no significant difference between these mean values. The greatest difference (0.12) is identified for the functionality audio muting, where the reported mean value for all participants included is 3.64, and with excluded participants that had not used the service in the last 30 days 3.76. Most importantly, we found that there was no impact on our final conclusions in terms of factor importance, i.e., the list of factors identified as being the most important ones to consider remained the same, even when excluding ratings provided by participants with no recent experience in using audiovisual calls. We performed the same analysis for Study 2 (where only 3.6% of participants had not taken part in a video call in the past 30 days), where the greatest difference in mean values was found for the factor adaptive layout where all participants combined rated adaptive layout importance as 3.43, while the group with excluded participants that did not use the video call service in the last 30 days rated with 3.45 in average. Based on the results, we can conclude that frequency of usage did not have a significant impact on perceived importance of considered factors. Impact of educational level on perceived importance of considered factors To assess whether or not there are any differences in user opinions regarding educational level, we compare the mean ratings of both surveys, S1 and S2. In the first survey (S1), the majority of participants (71%) fit into the category University degree, while in S2 the largest percentage fit into the High school degree category with a share of 61.05%. The greatest change (in mean ratings) can be noticed within three factors belonging to the Functional Support group: file transfer, texting and call recording, and one factor smooth simultaneous use of other applications belonging to the Usability, service design, and resource consumption group. The importance of file transfer rose the most from 3.42 in S1 to 4.21 in S2, followed by texting 3.36 (S1) to 4.03 (S2), audiovisual call recording 3.32 (S1) to 3.89 (S2), and smooth simultaneous use of other applications from 3.81 (S1) to 4.36 (S2). All those factors help to elevate the audiovisual call experience, and the gained importance may possibly be attributed to increased usage of audiovisual calls. The importance of all other evaluated factors did not change greatly, the mean rating difference between two studies was lower than 0.29. For illustration purposes, we compare mean results (per factor) of participants who reported in study S1 level of education University degree, and in the second study High school degree (Fig. 7). Fig. 7 Mean ratings (per factor) of participants who reported in study S1 level of education University degree, and in second study S2 High school degree Factors considered by users as most important for audiovisual calls Following the analysis of rating distributions across different groups of factors, we refer back to RQ1 and identify those factors rated by users as being the most important in terms of their importance for services offering audiovisual calls and in terms of their impact on QoE. The rationale for identifying such “key influence factors” lies in providing valuable input for service designers in terms of factors to consider and optimize so as to increase their customer base, prevent customer churn, and maintain high customer satisfaction. Referring further to RQ2, we compare key factors between our two studies conducted in 2020 and 2021. To identify key factors, we consider IFs from all three groups, media quality, functional support and usability, service design, and resource consumption. We sorted IFs in descending order (by mean value) and selected the first ten factors that are considered to be most influential (Fig. 8). Selected key factors are bounded by coefficient of variation under 21%, mean ratings higher than 4.2, and total percentage of given ratings 4 (Important) and 5 (Very important) combined representing over 85% of ratings. Mean IFs ratings from S1 in 2020 are color coded in gray, while mean factor ratings from S2 in 2021 are color coded in red and green. If the factor gained in importance in 2021 as compared to the previous year, bars are colored green. On the other hand, if importance was reduced, the bar is colored red. Fig. 8 Key influence factors, selected by the users, in February 2020 and October 2021 The results of both surveys show that users perceived the same factors as being most important both before the pandemic and nearly two years after the outbreak, despite significantly increased usage of video conferencing services. Furthermore, the list of key factors is the same despite different participant demographics. The greatest positive change in 2021 in perceived factor importance (in terms of mean values) was found for ease of use of the application, while the factor uninterrupted interaction during communication showed the greatest decrease in importance. However, observed changes are not significant in terms of mean values, distribution, and variation of ratings. Reported data indicates that perceived importance of key influence factors did not change drastically, which might be expected due to the increased usage of audiovisual call services which could possibly lead to the higher or changed user perception and expectations. Based on the surveys, we identify relevant areas impacting QoE as pertaining to both the application and network domains: quality of real-time media from the user perspective (speech intelligibility, uninterrupted interaction, long (i.e., longer than 15 seconds) and short (lasting a few seconds) video freezes, perceptible audio and video delay), quality of service in terms of reliability, and application management (service price, security in terms of privacy, and ease of use of the application). Key influence factors that should be considered can be controlled, at least to some extent from an application point of view, by video encoding parameters, impacting the system as a whole. Namely, on application level it is possible to adapt video quality level (e.g., resolution, bitrate, and frame rate) to avoid CPU overload which can lead to congestion, prevent packet loss and delay, and save bandwidth needed for transmission, resulting at the end with acceptable QoE. Concrete recommendations in terms of video encoding parameters for three-party audiovisual calls established via mobile devices can be found in our earlier work [44, 45]. Conclusion With increasing use of audiovisual communication services, in particular using mobile devices, this paper aims to contribute to insights with respect to user opinions on the impact of various system factors on QoE, in terms of their importance. The advantage of this study is that it fills the gaps of the existing research in this field and takes user-oriented approach in identifying the importance of different SIFs in terms of QoE for audiovisual calls on smartphones. We report on two large scale surveys conducted both before and during the COVID-19 pandemic. In total, 521 participants took part in an online questionnaire designed to investigate users’ opinions and expectations related to audiovisual calls on mobile devices in the leisure/private context, with the main goal being to identify key system influence factors grouped in three categories. The second survey results confirmed initial findings derived based on the first survey in terms of key influence factors, with just slight differences in user opinions reported, in order and mean value of importance. Given that the two surveys were conducted at different time frames and involving different participants, differing to a certain extent in terms of demographics, this contributes to the generalizability of obtained results. Furthermore, it can be concluded that increased and more intense usage of audiovisual calls did not impact greatly perceived importance of key influence factors. Therefore, the contribution of the paper is three-fold. We have proposed a categorization of QoE SIFs for audiovisual calls on smartphones in the following groups: media quality, functional support, and usability, service design, and resource consumption. Further, we have identified the important system influence factors for QoE in this context. Finally, we have approached the topic by taking a complementary approach as compared to existing empirical user studies, i.e., we have determined the importance of SIFs by asking the users for their opinions. Obtained results can provide valuable input for service providers in terms of factors to consider and optimize so as to increase their customer base and maintain high satisfaction. Selected factors can be controlled to some degree by adaptation (in accordance with available resources such as mobile devices processing power or network conditions) of video encoding parameters on application layer (video bitrate, resolution, and frame rate). Additionally, identified factors can serve as input when deriving QoE models for audiovisual calls on mobile devices. In future research, we aim to design and conduct ecologically valid studies further quantifying the impact of identified key influence factors in both leisure and business contexts. Appendix: Questionnaire The questionnaire collects general demographic information, users’ habits, and ratings of the impacts and importance of considered factors referring to the application, resources, and context. Table 4 General information and users’ habits associated with audiovisual calls How old are you? 18–25 26–35 36–45 46–55 More than 55 What is your gender? Female Male What is your country of origin? a) Croatia b) Bosnia and Herzegovina c) Serbia d) Other What is your education level? a) High school degree b) University degree c) Higher University degree (PhD) Please indicate which of the following applications you have used? (Multiple choices are allowed) Feb. 2020: a) Skype b) G Hangouts c) Viber d) Whatsapp e) Whereby f) Other Oct. 2021: a) Skype b) Google Meet c) Viber d) Whatsapp e) Whereby f) Zoom g) Microsoft Teams h) FaceTime i) Other How often have you participated in the listed applications during the last 30 days? a) Very Frequently (on a daily basis) b) Frequently (2–3 times per week) c) Occasionally (4–7 time per month) d) Rarely (1–3 time per month) e) Never Which of the following devices have you used in the past to make audiovisual calls? (Multiple choices are allowed) a) Smartphone b) Tablet c) Computer/laptop d) Other The second part of the questionnaire is focused on quality aspects (in terms of user opinions with respect to the importance of certain factors) of audiovisual calls established via smartphones in a leisure context. Two different rating questions were asked, with a scale of answer options where participants can select the number/word that represents their opinion. Type 1 How important do you consider the following factor for overall audiovisual call quality? This set of questions used the following rating scale to collect user opinions with respect to the importance of certain factors: 5 -“Very Important”, 4 -“Important”, 3 -“Moderately Important”, 2 -“Slightly Important”, 1 -“Not Important”. Type 2 To what extent do you consider the following factor to impact overall audiovisual call quality? This set of questions used the following scale to collect feedback on the extent to which users considered certain factors to impact perceived quality: 5 -“To a great extent”, 4 -“To a moderate extent”, 3 -“To some extent”, 2 - “To a small extent”, 1 -“Not at All”. We specifically note before each group of factors that the following questions apply to calls established via smartphones in a private/leisure context (e.g., calls with friends, relatives, etc.) and not to business conference calls. Table 5 Questions related to Media Quality IFs. Media quality refers to the quality of the sound (audio) and the image (video) in terms of perceivable impairments (e.g., perceptible audio delay, image blurriness). Please evaluate how important you consider the following factors of audiovisual calls Rating scale 5 4 3 2 1 How important do you consider the following factor for overall audiovisual call quality?a Speech intelligibility ○ ○ ○ ○ ○ Voice naturalness ○ ○ ○ ○ ○ Uninterrupted interaction during communication ○ ○ ○ ○ ○ Audio-video synchronization ○ ○ ○ ○ ○ Image sharpness ○ ○ ○ ○ ○ Smooth movement in the video ○ ○ ○ ○ ○ Color accuracy (colors do not differ ○ ○ ○ ○ ○ significantly from real colors) To what extent do you consider the following factor to impact overall audiovisual call quality?b Perceptible audio delay ○ ○ ○ ○ ○ Perceptible video delay ○ ○ ○ ○ ○ Short and occasional video freezes ○ ○ ○ ○ ○ (lasting a few seconds) Longer video freezes (i.e., longer than 15 seconds) ○ ○ ○ ○ ○ impact overall audiovisual call quality, if the audio quality remains good for the duration of the call Image blurriness ○ ○ ○ ○ ○ a 5 - “Very Important”, 4 - “Important”, 3 - “Moderately Important”, 2 - “Slightly Important”, 1 - “Not Important” b 5 - “To a great extent”, 4 - “To a moderate extent”, 3 - “To some extent”, 2 - “To a small extent”, 1 - “Not at All” Table 6 Questions related to Functional Support IFs. Nowadays, many audiovisual call applications offer additional functionalities, beyond only audiovisual calls, such as image sharing or exchanging text messages. Please evaluate how important you consider the following factors of audiovisual calls Rating scale 5 4 3 2 1 How important do you consider the following factor for overall audiovisual call quality?a File transfer (image, video, document sharing) ○ ○ ○ ○ ○ Texting (sending text messages) ○ ○ ○ ○ ○ Active speaker identification (i.e., the participant who ○ ○ ○ ○ ○ is currently talking is highlighted/marked in some way) Applying make-up filters/overlay items (e.g., hat, mask) ○ ○ ○ ○ ○ Adaptive layout (e.g., movable participant’s preview ○ ○ ○ ○ ○ window, display zooming) Video pausing while in the call ○ ○ ○ ○ ○ Audio muting while in the call ○ ○ ○ ○ ○ Audiovisual call recording ○ ○ ○ ○ ○ a 5 - “Very Important”, 4 - “Important”, 3 - “Moderately Important”, 2 - “Slightly Important”, 1 - “Not Important” Table 7 Questions related to Usability, Service Design, and Resource Consumption IFs. Usability, service design, and resource consumption factors are related to the primary service, such as extent to which you feel you are able to make and conduct audiovisual calls. Please answer the following questions regarding how important you consider the following factors Rating scale 5 4 3 2 1 How important do you consider the following factor for overall audiovisual call quality?a Device/browser interoperability (meaning that ○ ○ ○ ○ ○ participant can use audiovisual call application regardless of the participant’s smartphone model or software installed) Duration of audiovisual call establishment time ○ ○ ○ ○ ○ Ease of use of the application (i.e., how easily ○ ○ ○ ○ ○ you can use an application to communicate) Installation complexity ○ ○ ○ ○ ○ User interface aesthetics (visual appearance) ○ ○ ○ ○ ○ Reliability of the service (i.e., being able to use ○ ○ ○ ○ ○ the service - audiovisual call - correctly the first time) Security in terms of privacy ○ ○ ○ ○ ○ (i.e., information transmitted is encrypted) Low battery consumption during the audiovisual call ○ ○ ○ ○ ○ Smooth simultaneous use of other applications ○ ○ ○ ○ ○ (enabled uninterrupted use of other applications at the same time) Noise free environment ○ ○ ○ ○ ○ Service price ○ ○ ○ ○ ○ a 5 - “Very Important”, 4 - “Important”, 3 - “Moderately Important”, 2 - “Slightly Important”, 1 - “Not Important” Author Contributions All authors contributed to the study conception and design equally. Material preparation, data collection and analysis were performed by Dunja Vučić, Sabina Baraković and Lea Skorin-Kapov. All authors read and approved the final manuscript. We confirm that the order of authors has been approved by all named authors. Funding This work has been supported by the Croatian Science Foundation under the project IP-2019-04-9793 (Q-MERSIVE). Declarations Ethics Approval The survey was conducted in the scope of the Croatian Science Foundation project IP-2019-04-9793, which received approval of the Ethics Committee of the University of Zagreb Faculty of Electrical Engineering and Computing. Consent for Publication All authors agree with the content and give explicit consent to submit the paper. Consent was also obtained from the responsible authorities at the institute/organization where the work has been carried out, before the work was submitted. Conflict of Interests The authors confirm there are no known conflicts of interest/competing interests associated with this paper that could inappropriately influence, or be perceived to influence, this work. Sabina Baraković and Lea Skorin-Kapov contributed equally to this work. Availability of Data and Materials The anonymized datasets are publicly available via an open repository (link: https://muexlab.fer.hr/muexlab/research/datasets). Consent to Participate The authors confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. Ammar D De Moor K Heegaard P An experimental platform for qoe studies of webrtc-based multi-party video communication Int J New Comput Archit Appl 2018 8 2 89 95 2. Ammar D, De Moor K, Skorin-Kapov L, Fiedler M, Heegaard PE (2019) Exploring the usefulness of machine learning in the context of WebRTC performance estimation. In: 2019 IEEE 44th conference on local computer networks (LCN). IEEE, pp 406–413 3. Baraković Husić J, Baraković S (2022) Multidimensional modelling of quality of experience for video streaming. In: Computers in human behavior, vol 129 4. Balihodžić M, Husić JB, Baraković S (2020) The influence of system factors on QoE for WebRTC video communication. In: International symposium on innovative and interdisciplinary applications of advanced technologies. Springer, pp 255–267 5. Baraković Husić J Baraković S Cero E Slamnik N Oćuz M Dedović A Zupčić O Quality of experience for unified communications: a survey Int J Netw Manag 2020 30 3 2083 10.1002/nem.2083 6. Bouraqia K, Sabir E, Sadik M, Ladid L (2020) Quality of experience for streaming services: measurements, challenges and insights. IEEE Access 8 7. Carofiglio G, Grassi G, Loparco E, Muscariello L, Papalini M, Samain J (2021) Characterizing the relationship between application QoE and network QoS for real-time services. In: Proceedings of the ACM SIGCOMM 2021 workshop on network-application integration, pp 20–25 8. Coppens-Hofman MC Terband H Snik AF Maassen BA Speech characteristics and intelligibility in adults with mild and moderate intellectual disabilities Folia Phoniatr Logop 2016 68 4 175 182 10.1159/000450548 28118637 9. Dasari M, Sanadhya S, Vlachou C, Kim K-H, Das SR (2018) Scalable ground-truth annotation for video qoe modeling in enterprise wifi. In: 2018 IEEE/ACM 26th international symposium on quality of service (IWQoS). IEEE, pp 1–6 10. Dasari M, Vargas S, Bhattacharya A, Balasubramanian A, Das SR, Ferdman M (2018) Impact of device performance on mobile internet qoe. In: Proceedings of the internet measurement conference 2018, pp 1–7 11. De Masi A, Wac K (2022) Less annoying: quality of experience of commonly used mobile applications. In: Proceedings of the 13th ACM multimedia systems conference, pp 86–95 12. De Moor K, Arndt S, Ammar D, Voigt-Antons J-N, Perkis A, Heegaard PE (2017) Exploring diverse measures for evaluating QoE in the context of WebRTC. In: 2017 ninth international conference on quality of multimedia experience (QoMEX). IEEE, pp 1–3 13. Dobrian F Awan A Joseph D Ganjam A Zhan J Sekar V Stoica I Zhang H Understanding the impact of video quality on user engagement Commun ACM 2013 56 3 91 99 10.1145/2428556.2428577 14. Feldmann A et al (2021) Implications of the covid-19 pandemic on the internet traffic. In: Broadband coverage in Germany; 15th ITG-symposium. VDE, pp 1–5 15. García B Gallego M Gortázar F Bertolino A Understanding and estimating quality of experience in WebRTC applications Computing 2019 101 11 1585 1607 10.1007/s00607-018-0669-7 16. Gulliver SR Ghinea G Defining user perception of distributed multimedia quality ACM Trans Multimed Comput Commun Appl (TOMM) 2006 2 4 241 257 10.1145/1201730.1201731 17. Holub J Isabelle S Krylová A Avetisyan H Subjective influence of camera-gaze angular offset IEEE Access 2022 10 9321 9327 10.1109/ACCESS.2022.3143814 18. Hoßfeld T, Biedermann S, Schatz R, Platzer A, Egger S, Fiedler M (2011) The memory effect and its implications on web QoE modeling. In: 2011 23rd international teletraffic congress (ITC). IEEE, pp 103–11 19. Husić JB, Baraković S, Veispahić A (2017) What factors influence the quality of experience for WebRTC video calls?. In: 2017 40th international convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, pp 428–433 20. ITU-T Rec (2016) P.807 Subjective test methodology for assessing speech intelligibility. Technical report, ITU-T 21. ITU-T Rec (2017) P.1301 Subjective quality evaluation of audio and audiovisual multiparty telemeetings. Technical report, ITU-T 22. Jana S Chan A Pande A Mohapatra P QoE prediction model for mobile video telephony Multimed Tools Appl 2016 75 13 7957 7980 10.1007/s11042-015-2711-5 23. Jumisko-Pyykkö S (2011) User-centered quality of experience and its evaluation methods for mobile television. Tampere University of Technology 12 24. Jumisko-Pyykkö S Vainio T Framing the context of use for mobile hci Int J Mob Human Comput Inter (IJMHCI) 2010 2 4 1 28 25. Kale V Digital transformation of enterprise architecture 2019 Boca Raton CRC Press 26. Laghari AA Channam MI He H Measuring effect of packet reordering on quality of experience (qoe) in video streaming 3D Res 2018 9 3 30 10.1007/s13319-018-0179-6 27. Le Callet P, Möller S, Perkins A (2013) Qualinet white paper on definitions of quality of experience (2012) version 1.2. In: Proceeding European network quality experience multimedia system services (COST Action IC), pp 1–23 28. Lee I, Lee J, Lee K, Grunwald D, Ha S (2021) Demystifying commercial video conferencing applications. In: Proceedings of the 29th ACM international conference on multimedia, pp 3583–3591 29. Liu M, Joskowicz J, Sotelo R, Hu Y, Chen Z, Yang L (2022) Subjective quality assessment of one-to-one video-telephony services. In: 2022 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB). IEEE, pp 1–6 30. Matulin M, Mrvelj Š, Abramović B, Šosštarić T, Čejvan M (2021) User quality of experience comparison between skype, microsoft teams and zoom videoconferencing tools. In: International conference on future access enablers of ubiquitous and intelligent infrastructures. Springer, pp 299–307 31. Mrvelj Š Matulin M Modeling the level of user frustration for the impaired telemeeting service using user frustration susceptibility index (ufsi) Electronics 2021 10 18 2202 10.3390/electronics10182202 32. Øie EB, Koniuch K, Cieplińska N, De Moor K (2021) Factors influencing qoe of video consultations. In: 2021 13th international conference on quality of multimedia experience (QoMEX). IEEE, pp 137– 140 33. Rao N, Maleki A, Chen F, Chen W, Zhang C, Kaur N, Haque A (2019) Analysis of the effect of QoS on video conferencing QoE. In: 2019 15th international wireless communications & mobile computing conference (IWCMC). IEEE, pp 1267–1272 34. Schmitt M, Gunkel S, Cesar P, Bulterman D (2014) The influence of interactivity patterns on the quality of experience in multi-party video-mediated conversations under symmetric delay conditions. In: Proceedings of the 3rd international workshop on socially-aware multimedia, pp 13–16 35. Silva AFD Mylène C Perceptual strengths of video impairments that combine blockiness, blurriness, and packet-loss artifacts Electronic Imaging 2018 2018 12 234 1 36. Skowronek J (2017) Quality of experience of multiparty conferencing and telemeeting systems. PhD thesis, Ph. D. thesis, Technical University of Berlin 37. Skowronek J, Schoenenberg K, Berndtsson G (2014) Multimedia conferencing and telemeetings. In: Quality of experience. Springer, pp 213–228 38. Skowronek J, Raake A, Berndtsson G, Rummukainen OS, Usai P, Gunkel SN, Johanson M, Habets EA, Malfait L, Lindero D et al (2022) Quality of experience in telemeetings and videoconferencing: a comprehensive survey. IEEE Access 39. The global internet phenomena report covid-19 spotlight. Available at https://www.sandvine.com/phenomena. Accessed 15 Nov 2022 40. Usman MA Shin SY Shahid MLövström B A no reference video quality metric based on jerkiness estimation focusing on multiple frame freezing in vide streaming IETF Technical Review 2017 54 3 309 320 10.1080/02564602.2016.1185975 41. Vega Torres MT Perra C Liotta A Resilience of video streaming services to network impairments IEEE Trans Broadcast 2018 64 2 220 234 10.1109/TBC.2017.2781125 42. Video conferencing market size, share and industry analysis. Available at https://www.fortunebusinessinsights.com/industry-reports/video-conferencing-market-100293. Accessed 15 Nov 2022 43. Vučić D, Skorin-Kapov L (2019) The impact of packet loss and google congestion control on QoE for WebRTC-based mobile multiparty audiovisual telemeetings. In: International conference on multimedia modeling. Springer, pp 459–470 44. Vučić D, Skorin-Kapov L (2019) QoE evaluation of WebRTC-based mobile multiparty video calls in light of different video codec settings. In: 2019 15th international conference on telecommunications (ConTEL). IEEE, pp 1–8 45. Vučić D Skorin-Kapov L QoE assessment of mobile multiparty audiovisual telemeetings IEEE Access 2020 8 107669 107684 10.1109/ACCESS.2020.3000467 46. Vucǐć D Skorin-Kapov L Sužnjević M The impact of bandwidth limitations and video resolution size on qoe for WebRTC-based mobile multi-party video conferencing Screen 2016 18 19 47. Wac K, Ickin S, Hong J-H, Janowski L, Fiedler M, Dey AK (2011) Studying the experience of mobile applications used in different contexts of daily life. In: Proceedings of the first ACM SIGCOMM workshop on measurements up the stack, pp 7–12 48. Yu C, Xu Y, Liu B, Liu Y (2014) Can you see me now? A measurement study of mobile video calls. In: IEEE INFOCOM 2014-IEEE conference on computer communications. IEEE, pp 1456–1464 49. Zeng K, Zhao T, Rehman A, Wang Z (2014) Characterizing perceptual artifacts in compressed video streams. In: Human vision and electronic imaging XIX, vol 9014. International Society for Optics and Photonics, p 90140	36467436	PMC9709358	NO-CC CODE	2022-12-01 23:23:04	no	Multimed Tools Appl. 2022 Nov 30;:1-26	utf-8	Multimed Tools Appl	2,022	10.1007/s11042-022-14173-4	oa_other
==== Front Intern Emerg Med Intern Emerg Med Internal and Emergency Medicine 1828-0447 1970-9366 Springer International Publishing Cham 3164 10.1007/s11739-022-03164-w Ce-Systematic Reviews and Meta-analysis Incidence of long COVID-19 in people with previous SARS-Cov2 infection: a systematic review and meta-analysis of 120,970 patients Di Gennaro Francesco 1 Belati Alessandra 1 Tulone Ottavia 2 Diella Lucia 1 Fiore Bavaro Davide 1 Bonica Roberta 2 Genna Vincenzo 2 Smith Lee 3 Trott Mike 4 Bruyere Olivier 5 Mirarchi Luigi 2 Cusumano Claudia 2 Dominguez Ligia Juliana 26 Saracino Annalisa 1 Veronese Nicola [email protected] 2 Barbagallo Mario 2 1 grid.7644.1 0000 0001 0120 3326 Department of Biomedical Sciences and Human Oncology, Clinic of Infectious Diseases, University of Bari, Aldo Moro, Bari, Italy 2 grid.10776.37 0000 0004 1762 5517 Department of Internal Medicine and Geriatrics, University of Palermo Geriatric Unit, Via del Vespro 141, 90127 Palermo, Italy 3 grid.5115.0 0000 0001 2299 5510 Centre for Health, Wellbeing, and Performance, Anglia Ruskin University, Cambridge, UK 4 grid.4777.3 0000 0004 0374 7521 Centre for Public Health, Queen’s University Belfast, Belfast, UK 5 grid.4861.b 0000 0001 0805 7253 WHO Collaborating Centre for Public Health Aspects of Musculoskeletal Health and Aging, Division of Public Health, Epidemiology and Health Economics, University of Liège, Liège, Belgium 6 Faculty of Medicine and Surgery, University Kore of Enna, Enna, Italy 30 11 2022 19 26 9 2022 21 11 2022 © The Author(s), under exclusive licence to Società Italiana di Medicina Interna (SIMI) 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. The long-term consequences of the coronavirus disease 19 (COVID-19) are likely to be frequent but results hitherto are inconclusive. Therefore, we aimed to define the incidence of long-term COVID signs and symptoms as defined by the World Health Organization, using a systematic review and meta-analysis of observational studies. A systematic search in several databases was carried out up to 12 January 2022 for observational studies reporting the cumulative incidence of long COVID signs and symptoms divided according to body systems affected. Data are reported as incidence and 95% confidence intervals (CIs). Several sensitivity and meta-regression analyses were performed. Among 11,162 papers initially screened, 196 were included, consisting of 120,970 participants (mean age: 52.3 years; 48.8% females) who were followed-up for a median of six months. The incidence of any long COVID symptomatology was 56.9% (95% CI 52.2–61.6). General long COVID signs and symptoms were the most frequent (incidence of 31%) and digestive issues the least frequent (7.7%). The presence of any neurological, general and cardiovascular long COVID symptomatology was most frequent in females. Higher mean age was associated with higher incidence of psychiatric, respiratory, general, digestive and skin conditions. The incidence of long COVID symptomatology was different according to continent and follow-up length. Long COVID is a common condition in patients who have been infected with SARS-CoV-2, regardless of the severity of the acute illness, indicating the need for more cohort studies on this topic. Keywords COVID-19 Long COVID Systematic review SARS-CoV-2 http://dx.doi.org/10.13039/501100004913 Università degli Studi di Palermo ffr ffr Veronese Nicola Barbagallo Mario ==== Body pmcIntroduction Since the beginning of the COVID-19 pandemic on 8 March 2020, more than 500 million cases of SARS-CoV-2 infection have been reported worldwide with a daily global increase of approximately 500,000 cases per day [1]. While global health strategies, vaccines, antivirals and new monoclonal antibodies have significantly reduced COVID-19 mortality and severe illness, long consequences after the acute phase of the disease remain an unresolved issue. During the first pandemic wave, several articles highlighted the possible medium-to long-term devasting consequences of SARS-CoV2 infection, for patients and healthcare systems [2, 3]. Article conclusions were based on follow-up studies of people who had coronavirus infections including SARS-CoV-1 in 2003 and MERS-CoV in 2012 [4, 5] and who, after months and years of follow-up, still had symptoms and signs linked to previous infection. There is an increasing body of global literature reporting the long-term sequelae of patients with previous SARS-CoV-2 infection [2, 3]. Reported symptoms vary and include, for example, dyspnea, hair loss, anxiety, depression, asthenia, fatigue and loss of appetite [2, 3]. Furthermore, the terminology relating to long COVID in the literature is not standardized. Researchers have used different terms to describe the prolonged symptoms following COVID-19 disease, for example: Long COVID, Long-haulers, Post-acute COVID-19 syndrome, and Chronic COVID-19. Moreover, different time cut-offs have been used (from 2–3 weeks to months after COVID-19) [4]. In October 2021, the World Health Organization (WHO) defined the long COVID as “a condition that occurs in individuals with a history of probable or confirmed SARS-CoV-2 infection, usually 3 months from the onset of COVID-19 with symptoms that last for at least 2 months and cannot be explained by an alternative diagnosis” [5]. The real number of people living with long COVID is unknown, as well as the real incidence and which organs or systems are most frequently involved. Knowing the real incidence of long COVID is critical for addressing the problem and examining possible therapeutic approaches, preventative efforts, and global health policy. The definition and inclusion criteria of previous studies on long/post COVID conditions may have masked the true burden. However, to explain the real incidence without the confounding influence of different follow-up lengths, we used the WHO definition. Importantly, our study is the first to include exclusively papers using the WHO proposed period to define long COVID [6–9]. Given this background, we carried out a systematic review and meta-analysis regarding the incidence of signs and symptoms typical of long COVID, with a minimum follow-up time longer than at least 3 months and according to the WHO definition. Materials and methods Protocol registration This study was conducted following the recommendations in the Cochrane handbook for systematic literature reviews to conduct the screening and selection of studies [10]. The original protocol was registered in https://osf.io/5b2tv. This systematic review and meta-analysis was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, updated version to 2021 [11]. Research question The research question for this systematic review is as follows: “What is the incidence of long COVID signs and symptoms?” To guide the identification of adequate keywords to build search strategies to search bibliographic databases, the research question was framed into the PICO(S) (Participants, Intervention, Comparison, Outcome, Study design) format: (P) laboratory confirmed and/or clinically diagnosed COVID-19: long COVID was defined as the presence of signs and/or symptoms after three months and lasting at least two months and that cannot be explained by other medical conditions, in agreement with the indications of the WHO [5]; (I): none; (C) none; (O) incidence of signs and symptoms of long COVID; (S) observational studies. Information sources and search strategies We searched Medline (via Ovid) and Web of Science from database inception to 12 January 2022, through OVID. The search for individual studies in these bibliographic databases was supplemented by a manual search of references included in relevant systematic reviews already published regarding this topic. Considering the main PICOS elements, we built the following search strategy for Medline: “(“COVID-19” OR “Novel Coronavirus–Infected Pneumonia” OR “2019 novel coronavirus” OR “2019-nCoV” OR “SARS-CoV-2”) AND (“lingering symptoms” OR “persistent symptoms” OR “long-term symptoms” OR "long-term Covid" OR “long-term” OR “long term” OR “long”)”. Then we adapted the search strategy for Web of Science. The management of potentially eligible references was carried out using the Rayyan website (https://www.rayyan.ai/). Eligibility criteria Inclusion criteria comprised the following: (1) observational studies (case–control, cohort, longitudinal studies); (2) studies that investigated the diagnosis of long COVID according to the criteria mentioned previously; (3) presence of long COVID for at least 12 weeks [5]. Only articles written in English were included. Studies with a follow-up shorter than 12 weeks or with an unclear follow-up, case series and case reports were excluded. Study selection We followed the recommendations reported in the Cochrane handbook for Systematic reviews to select studies that were finally included in this review [10]. The selection of the articles was performed independently by six authors (OT, AB, LD, DFB, RB, VG), in couples. The agreement within the couples, evaluated with the K was 0.85 in couple 1, 0.81 in couple 2 and 0.86 in couple 3. Consensus meetings were held with all reviewers to discuss the studies for which divergent selection decisions were made. Two additional senior members (NV, FDG) of the review team were involved, when necessary. The studies selection process involved, first, a selection based on title and/or abstracts, then a selection of studies retrieved from this first step based on the full-text manuscripts. Data collection and data items We collected the following information: data regarding the identification of the manuscript (e.g., first author name and affiliation, year of publication, journal name, title of the manuscript), data on the characteristics of the population considered (e.g., sample size, mean age, location, gender, etc.), setting (e.g., hospital, intensive care unit, etc.), method of follow-up visit, follow-up in months, type of diagnosis of COVID-19 and signs and symptoms recorded during the follow-up period. These data were collected using a standard data extraction form. The data extraction was carried out independently by the six authors, in couples, with one author for each couple extracting the data and the other checking, with the senior authors checking the quality of the data extraction. Risk of bias evaluation The Newcastle–Ottawa Scale (NOS) was used to assess the study quality/risk of bias [12]. The NOS assigns a maximum of 9 points based on following three quality parameters: selection, comparability, and outcome. The evaluation was made by one author and checked by another, independently. The risk of bias was then categorized as high (< 5/9 points), moderate (6–7) or low (8–9) [13]. The investigators solved any discrepancies by jointly re-assessing an article (NV, AB and FDG). Data synthesis Signs and symptoms were grouped into anatomical clusters, i.e., neurological, dermatological, and psychiatric conditions. The cumulative incidence of symptoms and 95% confidence intervals (CIs) were estimated using a meta-analysis, under a random-effect model [14]. Heterogeneity between estimates was assessed using the I2 statistic. In case of an I2 over 50% a series of meta-regression analyses (taking as moderators if the participants were hospitalized, the percentage of females, and the mean age of the sample size) was conducted. Several sensitivity analyses (continent, mean age, using the WHO classification in children, adults, older people [15], follow-up period, stayed in intensive care unit, hospitalized, type of follow-up, and risk of bias) were also conducted [16]. Moderators and strata were chosen based on clinical judgment. Publication bias was assessed by visually inspecting funnel plots and using Egger bias test, with a p-value < 0.05 indicative of possible publication bias [17]. All analyses were performed using “metaprop”, a command available in STATA 14.0 Results Search results As shown in Supplementary Fig. 1, among 11,167 records initially screened, 346 full-texts were retrieved, with a final selection of 196 articles (see the list in Supplementary Table 1). Descriptive characteristics As shown in Supplementary Table 2, the 196 studies included 120,970 participants (median per study: 190 participants, range 17–31,013) with a mean age of 52.3 years. The participants were more frequently males (percentage of females = 48.8%) (p < 0.0001, Chi Square test). The majority of the studies took place in Europe (n = 126, 64.3%) and used the polymerase chain reaction for the identification of SARS-CoV-2 (n = 185, 94.4%). Furthermore, most studies considered only hospitalized patients (n = 128, 65.3%) including people admitted to intensive care unit (n = 101, 51.5%). Follow-up with a median of six months (range 3–12 months) was predominantly conducted via outpatients’ visits (n = 86, 43.9%). Among the 196 articles included, only two reported data on the vaccination status against SARS-CoV-2. Risk of bias As reported in Supplementary Table 2, the risk of bias, evaluated with the NOS, was overall low in 129 (65.8) studies and moderate for the other works included. No study was at high risk of bias evaluated as a NOS score less than 5. Incidence of long COVID signs and symptoms Figure 1 and Table 1 show the incidence of long COVID signs and symptoms. In the 196 studies included, comprising 120,970 people, the cumulative incidence of any long COVID symptomatology was 56.9% (95% CI 52.2–61.6).Fig. 1 Incidence of long COVID signs and symptoms Table 1 Cumulative incidence of long COVID signs and symptoms System Number of cohorts Total sample size Cumulative incidence 95% CI Any 196 120,970 56.9 52.2–61.6 Neurological 156 106,284 19.7 17.4–22.1 Headache 104 87,599 12.4 10.5–14.4 Taste disorder (ageusia or dysgeusia) 116 62,510 12.8 10.7–15.0 Smell disorder (anosmia) 117 93,929 13.1 11.1–15.3 Cognitive impairment 44 21,300 13.5 10.5–16.8 Memory deficits 48 18,348 13.5 10.5–16.9 Difficulty concentrating 58 30,380 14.6 11.7–17.9 Dizziness 46 27,737 10.8 8.3–13.7 Tremors 8 4078 3.4 1.4–6.2 Seizures 4 9325 0.6 0.0–2.1 Cramps 6 790 12.0 5.2–21.0 Visual impairment 16 9963 4.6 2.5–7.2 Psychiatric 117 65,156 20.3 17.4–23.3 PTSD 26 13,167 13.6 8.9–19.3 Depression 74 43,789 16.1 12.8–19.8 Sleep disorders 81 50,757 17.8 14.8–21.0 Anxiety 85 46,762 18.9 15.2–22.2 Respiratory 154 101,849 24.5 21.3–27.9 Cough 108 86,809 13.1 11.0–15.5 Dyspnea 142 97,065 24.1 20.5–27.9 Oxygen use 4 400 4.3 2.4–6.7 Nasal congestion 36 48,592 6.3 5.0–7.7 Voice change 14 10,352 3.7 2.0–5.9 Mobility impairment 34 19,747 13.7 10.6–17.2 Decreased exercise tolerance 12 6431 16.6 11.2–22.8 Mobility decline 19 13,177 11.3 7.7–15.6 Functional impairment 10 6544 7.6 3.1–13.9 Heart 95 54,056 11.0 8.9–13.3 Palpitations 55 32,784 11.2 8.7–14.1 Chest pain 71 45,894 10.6 8.2–13.3 Flushing 3 2349 3.1 0–11.2 Hypertension (new onset) 4 2136 6.4 1.5–14.3 Digestive 99 80,701 7.7 6.4–9.1 Abdominal pain 47 61,445 5.2 4.0–6.5 Diarrhea 77 72,024 5.9 4.9–7.1 Vomit 40 28,238 3.0 2.0–4.0 Loss of appetite 52 27,034 7.1 5.2–9.4 Skin 63 34,224 8.5 6.8–10.3 Rash 34 25,796 4.1 2.9–5.5 Hair loss 52 28,816 8.8 6.8–11.1 General 166 113,802 31.0 27.1–35.1 Weight loss 16 11,234 7.2 5.1–9.6 Myalgia 103 84,678 15.5 13.0–18.3 Pain 48 28,230 19.9 14.7–25.6 Flulike symptoms 1 97 16.5 9.7–25.4 Fever 45 55,310 7.9 5.2–11.0 Fatigue 142 104,766 31.4 27.1–35.8 Arthralgia 64 34,941 15.0 11.6–18.9 Sore throat 49 63,400 7.6 6.2–9.2 Sweats 9 9079 5.8 4.4–7.4 Poor QoL 7 3995 16.0 9.0–24.7 Conjunctivitis 14 7256 3.1 1.1–6.0 Data are reported as cumulative incidence with their 95% confidence intervals PTSD post-traumatic stress disorder, QoL quality of life By grouping into anatomical clusters, we observed that in 156 cohorts (106,284 participants), the overall incidence of neurological signs/symptoms was 19.7% (95% CI 17.4–22.1). In this cluster the most frequent sign/symptom was difficulty in concentrating (14.6%), and the least frequent was seizures (0.6%). The incidence of headache, taste and smell disorders, cognitive impairment, memory deficits, dizziness, and cramps were over 10%. Psychiatric conditions affected 20.3% of the participants (95% CI 17.4–23.3), in 117 cohorts and for a total of 65,156 people. All the four signs and symptoms considered in this cluster (post-traumatic stress disorder [PTSD], depression, sleep disorder, anxiety) had an incidence over 10%. Respiratory conditions affected approximately one quarter of the participants with long COVID (154 cohorts, 101,849 participants, 24.5%; 95% CI 21.3–27.9). Among the respiratory signs or symptoms, the most frequent was dyspnea (142 cohorts, 97,065 participants, incidence of 24.1%). Mobility impairment disorders affected 13.7% (10.6–17.2) of the 19,747 participants included in 34 different cohorts, with a decreased exercise tolerance (incidence of 16.6%), being the most frequent. Heart conditions were also particularly frequent, affecting 11.0% of the participants. Palpitations were identified in 11.2% of the 32,784 participants considered. Among the clusters considered, digestive (incidence: 7.7%; 95% CI 6.4–9.1) and skin disorders (incidence: 8.5%, 95% CI 6.8–10.3) were the least represented. Finally, general signs and symptoms, i.e., not includible in any of the clusters cited before, affected approximately one-third of the 113,802 people included in 166 cohorts. Of particular interest, fatigue affected 31.4% (95% CI 27.1–35.8) of the people included, being the most common symptom in the general cluster. Meta-regression analyses Considering the incidence of signs and symptoms clusters, all were affected by a high heterogeneity (I2 = 99%). Therefore, we tried to explain the heterogeneity observed using a series of meta-regression and sensitivity analyses. Supplementary Table 3 shows the meta-regression analyses. Higher percentage of females moderated the onset of any, neurological, general, and cardiovascular long COVID symptomatology. Each increase in one percent of females in the sample size was associated with a small increase in any long COVID symptomatology (beta = 0.02 ± 0.01; p = 0.047), neurological (beta = 0.003 ± 0.0009; p = 0.001), general (beta = 0.02 ± 0.01; p = 0.05), and cardiovascular (beta = 0.003 ± 0.0009; p = 0.001) signs and symptoms. However, this moderator explained only a small proportion of the heterogeneity of the various outcomes (less than 10%, except for cardiovascular outcomes) (Supplementary Table 3). Finally, higher mean age of the cohorts included was associated with higher incidence of psychiatric (beta = 0.003 ± 0.001; p = 0.007), respiratory (beta = 0.004 ± 0.001; p = 0.009), general (beta = 0.004 ± 0.002; p = 0.03), digestive (beta = 0.002 ± 0.0009; p = 0.04) and skin conditions (beta = 0.002 ± 0.0009; p = 0.02) (Supplementary Table 3). Again, except for the last outcome, higher mean age explained only a small proportion of the heterogeneity found in the various outcomes. Sensitivity analyses Supplementary Table 4 shows the cumulative incidence stratified by some potential factors, i.e., continent, mean age and follow-up. Overall, the incidence of any long COVID was significantly higher in studies carried out in Oceania (63.4%) vs. Europe (48.5%) (p for the interaction < 0.0001), whilst no significant differences were observed by mean age or by follow-up. When considering neurological conditions, the incidence was, again, significantly higher in Oceania and in Europe compared to North America (with an incidence almost doubled). Moreover, the incidence of neurological conditions was significantly higher in adults than in children (p for interaction = 0.03) and in studies having a follow-up of 3 months compared to those with a longer follow-up (Supplementary Table 4). Similarly, psychiatric conditions affected more frequently African participants than Asians (p for the interaction < 0.0001) and participants older than 60 years, with an incidence approximately four times higher than children/youth. Similarly, respiratory conditions were more frequent in Europe than in the other continents and in the studies with a follow-up of 3 months. Another point of importance is that the incidence of mobility issues was significantly higher in adults than the other ages considered and in studies having a follow-up over six months. Finally, general and cardiovascular symptomatology was higher in studies carried out in Africa than in other continents and in adults (Supplementary Table 4). Finally, Supplementary Table 5 reports the data stratified by ICU admission, hospitalization status, type of follow-up and presence of risk of bias. Overall, patients previously admitted in ICU reported a significantly lower incidence of neurological conditions and mobility issues than their counterparts. Similarly, patients not hospitalized reported a significantly higher presence of neurological and psychiatric conditions. When considering the type of follow-up method used for evaluating long COVID symptomatology patients interviewed in person usually reported lower incidence of several long COVID signs and symptoms. Finally, considering the presence of risk of bias, we observed a significantly higher incidence of neurological, psychiatric, respiratory, cardiovascular, digestive, skin conditions and mobility issues in studies having a moderate risk of bias compared to low. Discussion According to the WHO definition for long COVID, we carried out a systematic review of all the studies reporting data on long COVID symptomatology including 196 studies for a total of 120,970 patients with a previous SARS-CoV-2 infection. A key finding of this study was that more than half of the patients previously having COVID-19 had some form of long COVID symptomology, further strengthening the importance of this emergent condition. Comparing our results with those reported in three previously published systematic reviews with meta-analyses [3, 18, 19], we observed that the incidence of any sign or symptom of long COVID remained high when only including studies having a follow-up of at least 3 months according to the new WHO definition [5]. Respiratory symptomatology, such as dyspnea, and general signs and symptoms, such as fatigue, may affect between one quarter and one-third of all long CVOID patients. Moreover, different inclusion/exclusion criteria indicated that long COVID is a long-term condition that will likely be experienced over coming years and with current limited therapeutical options [20]. These findings support the idea that COVID-19 could lead to persistence of symptoms even after the end of acute infection, as has already been demonstrated for SARS-CoV-1 and MERS-CoV. In 2003, after the end of the outbreak of SARS-CoV-1, Herridge et al. evaluated the respiratory function of 109 survivors at 3, 6 and 12 months after discharge, reporting a relevant reduction in respiratory function and quality of life [21]. Most patients had also extrapulmonary conditions, with muscle wasting and fatigue being the most frequent, similar to long COVID [21]. In addition, Ahmed et al. conducted a systematic review and meta-analysis investigating persistent symptoms of both SARS-CoV-1 and MERS-COV, demonstrating that up to 6 months after discharge impaired respiratory function was present in 27% of patients, PTSD in 39%, depression in 33%, and anxiety in 30%. Moreover, a reduction in exercise capacity was noted with a mean 6-min walking distance of 461 m in the cohort of patients analysed [22]. It is important to remark that some studies demonstrated the persistence of symptoms for several years from SARS-CoV-1 infection. In particular, Ngai et al. performed a respiratory function-test 2 years after discharge on 55 SARS-CoV-1 infected patients, showing a significant impairment of diffusing capacity of the lungs for carbon monoxide (DLCO), exercise capacity and health status, with a more marked adverse impact among health care workers [23]. Moldofsky et al., evaluated the neuropsychiatric disorders that occurred in SARS-CoV-1-infected patients, demonstrating that chronic fatigue, pain, weakness, depression, and sleep disturbance, were still present over a 20-month follow-up [24]. This evidence suggests that for COVID-19 we should expect similar long-term consequences. Another result of importance of our systematic review and meta-analysis was that long COVID signs and symptoms, and particularly general, neurological and cardiovascular symptoms, were more frequent in females than in males supporting other literature which found that females appear to be at higher risk of long COVID than males, even though females are less represented in the present systematic review [25]. Moreover, higher mean age also represents an important risk factor to develop long COVID symptoms, particularly general, psychiatric, respiratory, digestive and skin issues indicating that long COVID could be of epidemiological importance in older people. Sudre et al. in a cohort of 558 patients described a greater risk for people aged over 70 years of developing ongoing symptoms. Indeed, 22% of people aged over 70 reported symptoms lasting 4 weeks or more, compared to 10% of patients aged 18 to 49 years [26]. Notably, in our systematic review, there was not an increased risk of long COVID for patients who had been hospitalized or had stayed in intensive care units, contrary to what is reported by Jovanoski et al., who described an increased risk of respiratory, cardiovascular, and mental health outcomes up to 6 months after discharge in patients hospitalized with severe/critical COVID-19 [27]. Overall, these findings indicate that people living in the community and not hospitalized can have a similar incidence of long COVID symptomatology, demonstrating the importance of follow-up among these patients. Furthermore, the incidence of any and general signs and symptoms was significantly higher in Oceania, whilst respiratory symptoms were more commonly reported in Europe and Africa. North America reported the lowest incidence among all categories of symptoms. Even if a definitive conclusion cannot be drawn, we can hypothesize that genetic and environmental factors can justify these different incidences. We can also report that this difference is partially ascribable to the process of symptoms’ definition and perception, and data collection across countries that could greatly vary. However, future studies are needed to better understand these significant differences. Among all the results reported in the sensitivity analyses, we would like to underline the importance of mobility issues that were more frequent in adults than in the other ages. Since mobility issues are often a precursor to disability, our meta-analysis further indicates the need to approach long COVID with non-pharmacological approaches, such as promoting physical activity [28]. When stratifying patients for mean age, it is interesting that children and adolescents presented long COVID symptoms, particularly respiratory and general symptoms: taken together, these significant findings encourage follow-up of children previously affected by COVID-19 for better understanding of the long-term sequalae of this condition. In the opinion of the present authors, long COVID represents a major public-health problem, both because of its incidence in patients with SARS-CoV-2 infection and because of the lack of effective therapeutic strategies to date [20]. Published literature regarding the possible treatment is still limited, and studies published until now were limited by lack of homogeneity owing to varying study designs, settings, populations, follow-up period and symptoms description. Potentially, mass vaccination and the use of new therapies aimed at rapidly reducing viral load and limiting disease progression could play a crucial role in preventing long COVID and long-term symptoms persistence, but future studies are urgently needed. In addition to characteristics of patients, the SARS-CoV-2 variant of concern involved in acute infection is often missing, but it may also play an important role in the type of symptoms that occur in long COVID. The results of our systematic review with meta-analysis must be interpreted within its limitations. First, some long COVID symptoms may be missing because they were not identified and not investigated in patient questionnaires. This limitation determines the need to standardize questionnaires and to better define some symptoms as follows: for example, the symptom “fatigue” may be exaggerated by some patients or underestimated by others. The use of objective and precise scales, such as the Visual Analogue Scale for pain or the Fatigue Assessment Scale for fatigue would facilitate harmonization of symptom descriptions. The studies included in this meta-analysis often used only self-reported information or physical examination. Second, all the outcomes were characterized by a high heterogeneity, only partly explained by our meta-regression or sensitivity analyses. These findings suggest that other factors are probably important in determining a higher or lower incidence of long COVID. Unfortunately, we were not able to explore the role of vaccinations on the incidence of long COVID: further studies are urgently needed in this sense. Another important problem is the presence of publication bias in our findings, likely owing to the choice to screen papers written only in English and the fact that only two databases were screened [29]. Finally, the maximum follow-up reported by the studies included in our systematic review was only one year. Future studies are needed regarding long-term consequences of COVID-19. In conclusion, our systematic review and meta-analysis indicates that long COVID is a common condition in patients who have been infected with SARS-CoV-2 and often regardless of the severity of the acute illness. Therefore, more long-term studies are needed to understand the real long-term impact on quality of life, but also to develop optimal therapeutic and long COVID prevention strategies. Acknowledgements We acknowledge the illustrators Marco Rossetti and Giuseppina Maria Cozzolino for the drawing provided to us. Author contributions FDG and NV conceived the study topic and design. AB, LD, DFB, FDG, OT, VG and RB carried out the study selection and data extraction. The data were analysed by NV and the manuscript drafted by FDG, AB and NV. LS, MT, OB, LM, CC, MB, LJD, AS contributed significantly to the revision of the manuscript. All authors approved the final version of the text. All authors have read and agreed to the published version of the manuscript. Data availability The database is available upon reasonable request to the corresponding author. Declarations Conflict of interest The authors declare no conflict of interest. Ethical Statements Not needed. Human and animal rights Not needed since this study did not involve any human or animal. Informed consent Not needed. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. ==== Refs References 1. World Health Organization (2022) Weekly operational update on COVID-19; Issue No. 97. https://www.who.int/publications/m/item/weekly-operational-update-on-covid-19---30-march-2022#.YkwrvaHCh8g.link. Accessed 03/30/2022 2. Sanchez-Ramirez DC Normand K Zhaoyun Y Torres-Castro R Long-term impact of COVID-19: a systematic review of the literature and meta-analysis Biomedicines 2021 9 900 10.3390/biomedicines9080900 34440104 3. Lopez-Leon S Wegman-Ostrosky T Perelman C Sepulveda R Rebolledo PA Cuapio A Villapol S More than 50 long-term effects of COVID-19: a systematic review and meta-analysis Sci Rep 2021 11 1 12 10.1038/s41598-021-95565-8 33414495 4. Baig AM Chronic COVID syndrome: need for an appropriate medical terminology for long-COVID and COVID long-haulers J Med Virol 2020 10.1002/jmv.26624 33289144 5. Organization WH A clinical case definition of post COVID-19 condition by a Delphi consensus, 6 October 2021 2021 World Health Organization 6. Chen C Haupert SR Zimmermann L Shi X Fritsche LG Mukherjee B Global prevalence of post COVID-19 condition or long COVID: a meta-analysis and systematic review J Infect Dis 2022 10.1093/infdis/jiac136 36408618 7. Stavem K Ghanima W Olsen MK Gilboe HM Einvik G Prevalence and determinants of fatigue after COVID-19 in non-hospitalized subjects: a population-based study Int J Environ Res Public Health 2021 18 2030 10.3390/ijerph18042030 33669714 8. Natarajan A Shetty A Delanerolle G Zeng Y Zhang Y Raymont V Rathod S Halabi S Elliot K Phiri P A systematic review and meta-analysis of long COVID symptoms medRxiv 2022 10.1101/2022.03.08.22272091 36172122 9. Han Q Zheng B Daines L Sheikh A Long-term sequelae of COVID-19: a systematic review and meta-analysis of one-year follow-up studies on post-COVID symptoms Pathogens 2022 11 269 10.3390/pathogens11020269 35215212 10. Higgins JP Thomas J Chandler J Cumpston M Li T Page MJ Welch VA Cochrane handbook for systematic reviews of interventions 2019 John Wiley & Sons 11. Sarkis-Onofre R Catalá-López F Aromataris E Lockwood C How to properly use the PRISMA statement Syst Rev 2021 10 1 3 10.1186/s13643-021-01671-z 33388080 12. Luchini C Stubbs B Solmi M Veronese N Assessing the quality of studies in meta-analyses: advantages and limitations of the Newcastle Ottawa scale World J Meta-Anal 2017 5 80 84 10.13105/wjma.v5.i4.80 13. Luchini C Veronese N Nottegar A Shin JI Gentile G Granziol U Soysal P Alexinschi O Smith L Assessing the quality of studies in meta-research: review/guidelines on the most important quality assessment tools Pharm Stat 2021 20 185 195 10.1002/pst.2068 32935459 14. DerSimonian R Laird N Meta-analysis in clinical trials Control Clin Trials 1986 7 177 188 10.1016/0197-2456(86)90046-2 3802833 15. Dyussenbayev A Age periods of human life Adv Soc Sci Res J 2017 10.14738/assrj.46.2924 16. Higgins JP Thompson SG Quantifying heterogeneity in a meta-analysis Stat Med 2002 21 1539 1558 10.1002/sim.1186 12111919 17. Egger M Smith GD Schneider M Minder C Bias in meta-analysis detected by a simple, graphical test BMJ 1997 315 629 634 10.1136/bmj.315.7109.629 9310563 18. Michelen M Manoharan L Elkheir N Cheng V Dagens A Hastie C O'Hara M Suett J Dahmash D Bugaeva P Characterising long COVID: a living systematic review BMJ Glob Health 2021 6 e005427 10.1136/bmjgh-2021-005427 34580069 19. Groff D Sun A Ssentongo AE Ba DM Parsons N Poudel GR Lekoubou A Oh JS Ericson JE Ssentongo P Short-term and long-term rates of postacute sequelae of SARS-CoV-2 infection: a systematic review JAMA Netw Open 2021 4 e2128568 e2128568 10.1001/jamanetworkopen.2021.28568 34643720 20. Veronese N Bonica R Cotugno S Tulone O Camporeale M Smith L Trott M Bruyere O Mirarchi L Rizzo G Interventions for improving long COVID-19 symptomatology: a systematic review Viruses 2022 14 1863 10.3390/v14091863 36146672 21. Herridge MS Cheung AM Tansey CM Matte-Martyn A Diaz-Granados N Al-Saidi F Cooper AB Guest CB Mazer CD Mehta S One-year outcomes in survivors of the acute respiratory distress syndrome N Engl J Med 2003 348 683 693 10.1056/NEJMoa022450 12594312 22. Ahmed H Patel K Greenwood DC Halpin S Lewthwaite P Salawu A Eyre L Breen A O'Connor R Jones A Long-term clinical outcomes in survivors of severe acute respiratory syndrome and Middle East respiratory syndrome coronavirus outbreaks after hospitalisation or ICU admission: a systematic review and meta-analysis J Rehabil Med 2020 10.2340/16501977-2694 32449782 23. Ngai JC Ko FW Ng SS To KW Tong M Hui DS The long-term impact of severe acute respiratory syndrome on pulmonary function, exercise capacity and health status Respirology 2010 15 543 550 10.1111/j.1440-1843.2010.01720.x 20337995 24. Moldofsky H Patcai J Chronic widespread musculoskeletal pain, fatigue, depression and disordered sleep in chronic post-SARS syndrome; a case-controlled study BMC Neurol 2011 11 1 7 10.1186/1471-2377-11-37 21208452 25. Stewart S Newson L Briggs TA Grammatopoulos D Young L Gill P Long COVID risk-a signal to address sex hormones and women's health Lancet Regional Health-Eur 2021 11 100242 10.1016/j.lanepe.2021.100242 26. Sudre C Murray B Varsavsky T Graham M Penfold R Bowyer R Pujol JC Klaser K Antonelli M Canas L Attributes and predictors of long-COVID: analysis of COVID cases and their symptoms collected by the COVID symptoms study app Nat Med 2020 27 4 626 631 10.1038/s41591-021-01292-y 27. Jovanoski N Chen X Becker U Zalocusky K Chawla D Tsai L Borm M Neighbors M Yau V Severity of COVID-19 and adverse long-term outcomes: a retrospective cohort study based on a US electronic health record database BMJ Open 2021 11 e056284 10.1136/bmjopen-2021-056284 34893488 28. Fernández-Lázaro D González-Bernal JJ Sánchez-Serrano N Navascués LJ Ascaso-del-Río A Mielgo-Ayuso J Physical exercise as a multimodal tool for COVID-19: could it be used as a preventive strategy? Int J Environ Res Public Health 2020 17 8496 10.3390/ijerph17228496 33212762 29. Gilbody SM Song F Eastwood AJ Sutton A The causes, consequences and detection of publication bias in psychiatry Acta Psychiatr Scand 2000 102 241 249 10.1034/j.1600-0447.2000.102004241.x 11089723	36449260	PMC9709360	NO-CC CODE	2022-12-01 23:23:04	no	Intern Emerg Med. 2022 Nov 30;:1-9	utf-8	Intern Emerg Med	2,022	10.1007/s11739-022-03164-w	oa_other

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.