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==== Front
BJA Open
BJA Open
Bja Open
2772-6096
Published by Elsevier Ltd on behalf of British Journal of Anaesthesia.
S2772-6096(22)00116-2
10.1016/j.bjao.2022.100117
100117
Original Research Article
Impact of the COVID-19 pandemic on anaesthesia specialty training – a single centre quantitative analysis
Hughes Lauren Registrar 1
Murphy Orla Registrar 1
Lenihan Martin Consultant 1
Mhuircheartaigh Róisín Ní Consultant 1
Wall Tom Consultant, Adjunct Clinical Lecturer 12∗
1 Department of Anaesthesia, Mater Misericordiae University Hospital, Dublin, Ireland
2 University College Dublin, Dublin, Ireland
∗ Corresponding author. , Department of Anaesthesia, Mater Misericordiae University Hospital, Eccles St, Dublin 7, Ireland.
5 12 2022
5 12 2022
10011712 9 2022
29 11 2022
30 11 2022
© 2022 Published by Elsevier Ltd on behalf of British Journal of Anaesthesia.
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
The COVID-19 pandemic disrupted healthcare services worldwide, with a consequent impact on the delivery of medical education and training in all acute care specialties. Anaesthesia training has been challenged by a combination of reduced elective theatre activity, redeployment of trainees to critical care units, and changes in standard anaesthetic practices.
Methods
The aim of this study was to quantify the impact of COVID-19 on specialist anaesthesia training at a tertiary level teaching hospital in Ireland via a retrospective analysis of data captured by electronic anaesthesia records. The anaesthetic caseloads of trainees in periods before and during the pandemic were analysed along with airway management practices, core procedural skills performed and critical care rostering. Data relating to 145 anaesthesia trainees were captured during the study periods: pre-pandemic (January 2018- January 2020) and pandemic (January 2020-January 2022).
Results
The mean number of theatre cases logged per trainee in a 6-month period reduced from 156.8 pre-pandemic to 119.2 during the pandemic (23.9% reduction, p <0.0001). While theatre caseload was reduced, trainees gained additional critical care experience with a significant increase in overall days spent staffing critical care wards. In the theatre setting, the number of arterial lines, central lines, neuraxial blocks and peripheral nerve blocks performed were significantly reduced during the pandemic.
Conclusions
While the COVID-19 pandemic significantly reduced anaesthesia training exposure and increased critical care exposure over an extended period, the overall long-term significance of this alteration in the anaesthesia training experience remains uncertain.
Keywords
Postgraduate medical education
anaesthesia
COVID-19
anaesthesia training
medical education
==== Body
pmcOn the 31st of December 2019 a cluster of pneumonia cases was detected that was later found to be caused by a novel coronavirus (COVID-19)1. Since that date the COVID-19 pandemic has had a profound effect on almost every aspect of medicine and numerous studies have investigated the implications of the pandemic on healthcare systems2 , 3. As well as changing the way healthcare professionals work, the pandemic has challenged medical education and training across all specialties. Much of the data regarding the impact of COVID-19 on training is qualitative, with themes of reduced clinical exposure, reduced case volume and disrupted educational activities quoted as common concerns from a systematic review on residency training during the pandemic4. In the field of anaesthesia, a survey by the Royal College of Anaesthetists in the UK reported that trainee anaesthetists were worried about decreased training opportunities during the pandemic5, while a survey across six continents identified reduced learning opportunities and reduced opportunities for solo practice amongst anaesthesia trainee’s concerns6. The pandemic has presented numerous challenges to the delivery of training in anaesthesia because of a reduction in elective theatre throughput7, trainee redeployment to intensive care, trainee illness and quarantine requirements and a change in many standard practices to adapt to the demands of the pandemic8. Few studies have attempted to quantify the impact of the pandemic on anaesthesia training. Assessing the nature and extent of this impact may enable medical educators to identify any deficiencies in training and to assist those affected with remedial measures if deemed necessary.
We undertook a retrospective analysis of data from electronic anaesthesia records with the aim of quantifying the impact of the pandemic on Specialist Anaesthesia Training (or SAT, as the 6-year anaesthesia training programme is known in Ireland) in the Mater Misericordiae University Hospital (MMUH). MMUH is a 700-bed teaching hospital in Dublin, Ireland which provides a wide range of adult emergency and elective surgical services, including the national cardiothoracic and spinal surgery centres. Data from electronic anaesthesia records were utilised as they provide a record of which trainee was present for each theatre case and detail any anaesthetic procedures performed. We hypothesised that anaesthesia training experiences were reduced during the pandemic. The primary outcome measure assessed was trainee theatre case load. Secondary outcomes measures included anaesthetic procedures performed and time spent staffing critical care areas.
Methods
This study was approved following review by a departmental clinical effectiveness and quality improvement committee. As a retrospective analysis of anonymised data presented in aggregate no institutional research ethics board review was required. All patient and trainee data were anonymised. In this teaching hospital, trainees in anaesthesia complete a six-month attachment from either January to July or July to January each year. The first training period included in this study was January 2018-January 2020, this was defined as the control, the period during which the trainees’ six-month rotations were not affected by the pandemic. The second period was January 2020-January 2022, the training period in which six-month rotations were affected by the COVID-19 pandemic. Selecting these time periods allowed the entire six-month training rotation to be analysed and allowed for any seasonal variation in workload or annual leave. Subgroup analysis by year of training was also performed for overall anaesthetic caseloads, with trainees grouped by their year of training on the specialist anaesthesiology training programme (SAT years 1 to 6).
Data analysis was performed by integrating electronic records extracted from the Centricity™ High Acuity anaesthesia information system (General Electric, CA, USA) with Power BI™ business intelligence software (Microsoft, WA, USA). Trainee caseload and associated numbers of several commonly performed anaesthetic procedures in theatre were analysed. These procedures were: 1) endotracheal intubation 2) supraglottic airway use 3) arterial line insertion 4) central venous access 5) epidural anaesthesia 6) spinal anaesthesia 7) peripheral nerve blocks. Departmental rota records were reviewed to determine the number of day shifts spent staffing critical care units. All data were analysed using Prism version 9 (GraphPad, CA, USA). Descriptive statistics are reported for anaesthetic caseloads and common procedures performed by each trainee in a six-month training rotation. Significance testing was undertaken using an unpaired Student’s t-test to compare normally distributed data between the control and pandemic periods. Welch’s correction was utilised when variances between groups were unequal. Non-normal data were compared using the Mann-Whitney U test. A 2-way ANOVA was used to compare data between trainee years. A p-value of <0.05 was considered statistically significant.
Results
After excluding data from non-trainee grade staff and trainees who did not complete an entire 6-month rotation, a total of 145 trainees were included in the analysis, comprising 76 trainees in the control period and 69 trainees in the pandemic period. A total of 20,003 theatre cases were recorded and analysed during this period. A comparative analysis was then performed between the two training periods of 1) control and 2) pandemic. Examination of overall anaesthesia caseload demonstrated a significant difference in the primary outcomes of theatre caseload between the two study periods, with the mean number of cases logged per trainee in a 6-month period reducing from 156.8 (SD 46.5) in the control period to 119.4 (SD 45.8) in the pandemic period, a 23.9% reduction (p <0.0001), see Figure 1 (a).Figure 1 (a) Box plot showing numbers of anaesthetic cases undertaken by SAT trainees in a six-month training rotation. Horizontal line, median; box, IQR, whiskers, range; cross, mean. White box, control period; grey box, pandemic period. (b) Box plot showing numbers of anaesthetic cases undertaken by SAT trainees in a six-month training rotation, broken down by year of training. Horizontal line, median; box, IQR, whiskers, range; cross, mean. White box, control period; grey box, pandemic period. (c) Box plot showing numbers of critical care day shifts worked by SAT trainees in a six-month training rotation. Horizontal line, median; box, IQR, whiskers, range; cross, mean. White box, control period; grey box, pandemic period.
Figure 1
Sub-group analysis by trainee year showed that trainees in each year experienced a reduction in their anaesthetic theatre caseload, see Figure 1(b) and Table 1 . Two-way ANOVA showed that while there was a significant effect of study period and stage of training on the trainee caseload, there was no interaction between these factors (Table 2 ). There was no significant difference in the impact of the pandemic on trainee caseload between stages of training.Table 1 Numbers of anaesthetic cases undertaken by trainees in a six-month training rotation. Values are mean (SD).
Table 1 Control Period
Jan 2018 - Jan 2020 Pandemic Period
Jan 2020 - Jan 2022 Percentage Change (%)
Year 1 Trainees 216 (35) 155 (44.9) -28%
Year 2 Trainees 151 (15) 129 (51) -15%
Year 3 Trainees 139 (34) 87 (44) -38%
Year 4 Trainees 111 (18) 81 (31) -27%
Year 5 Trainees 137 (35) 109 (34) -21%
Year 6 Trainees 160 (43) 142 (22) -11%
Table 2 ANOVA Results to assess for the variance in the number of cases carried out by different SAT years.
Table 2Source of Variation % of total variation F statistic P value
SAT Year 34.58% 19.47 <0.0001
Control Vs Pandemic 10.75% 30.24 <0.0001
Interaction 2.499% 1.406 0.2260
Analysis of the number of weekday critical care shifts worked showed that while trainees had reduced theatre case exposure, they gained significantly more experience in the critical care unit with the number of days spent per trainee in critical care increasing from a median (range) of 4 (0-30) to 12 (0-37) during a 6-month rotation (p<0.001) see Figure 1(c).
Examining the principal skill in anaesthesia training, airway management, a significant reduction was observed in both tracheal intubation and supraglottic airway placement undertaken by anaesthesia trainees during the pandemic, with the number of intubations reducing by 22.7% and the number of supraglottic airways used reducing by 44.4% (Figure 2 (a)). Further analysis of the intubations performed demonstrated a profound shift in laryngoscope use by trainees, with a large increase in use of the McGrathTM (Medtronic, MN, USA) video laryngoscope (VL) and a large reduction in use of the Macintosh laryngoscope for direct laryngoscopy observed during the pandemic (Figure 2(b)). In the control period, direct laryngoscopy using a Macintosh blade was used for 74% (n=6045) of laryngoscopy attempts, and only 10% (n=665) in the COVID-19 era. During the pandemic, 90% (n=5624) of intubations by SAT trainees utilised video laryngoscopy, compared to just 26% (n=665) pre-pandemic.Figure 2 (a) Box plot showing numbers of endotracheal intubations and supraglottic airway insertions performed by SAT trainees in a six-month training rotation. Horizontal line, median; box, IQR, whiskers, range; cross, mean. White box, control period; grey box, pandemic b) Box plot showing numbers of intubations divided into direct and indirect laryngoscopy by SAT trainees in a six-month training rotation, broken down by year of training. Horizontal line, median; box, IQR, whiskers, range; cross, mean. White box, control period; grey box, pandemic period.
Figure 2
Trainee exposure to other core procedural skills during the pandemic was reduced (Table 3 ). Overall, trainee exposure to arterial line and central venous line insertion, neuraxial anaesthesia and regional techniques was significantly reduced (online supplementary material).Table 3 Numbers of anaesthetic cases and commonly performed procedures undertaken by trainees in a six-month training rotation. Values presented as mean (SD) for normally-distributed data or median (range) for non-normal data (marked with an asterisk).
Table 3 Control Pandemic % Change p-value
Caseload 156.8(46.5) 119.4(45.8) -23.8% <0.0001
Intubations 122.2(34.4) 94.4(37.2) -22.7% <0.0001
Direct Laryngoscopy 77(31-159)* 4(0-40)* -94.8% <0.0001*
Video Laryngoscopy 27.7(13.6) 81.5(37.6) 194.2% <0.0001
Supraglottic Devices 27(3-91)* 15(1-38)* -44.44% <0.0001*
Arterial Access 56.0(19.9) 41.1(19.8) -26.6% <0.0001
Central Venous Access 35(9-161)* 29(2-170)* -17.14% 0.0044*
Neuraxial Technique 11.8(6.9) 7.8(5.3) -33.9% 0.0002
Peripheral Nerve Block 20(2-49)* 11(0-27)* -45% <0.0001*
Critical Care Dayshifts 4(0-30)* 12(0-37)* 200% <0.0001*
2 tailed p-values calculated by unpaired t-test on normally-distributed data, or Mann-Whitney U test on non-normal data (marked with an asterisk).
Discussion
In this study, we have quantified the effect of the COVID-19 pandemic on aspects of anaesthesia training in a tertiary level teaching hospital in Ireland by analysis of electronic anaesthesia records. Between the two time periods studied, there was a significant reduction in trainee anaesthetic caseload by 23.9% in the pandemic period, with a significant reduction in the number of the core procedural skills of arterial line insertion, central venous access and neuraxial/peripheral nerve blocks performed in the pandemic period. Trainees did gain a significant increase in exposure to critical care medicine during the pandemic. Analysis of airway practices during the two time periods showed a significant change from direct to video-laryngoscopy in the pandemic period.
To our knowledge, this is the first study to assess the impact of the COVID-19 pandemic on anaesthesia training in Ireland. In comparison, the only other quantitative study published to date examining anaesthesia training during the pandemic found a 35% reduction in trainee theatre caseload from a UK logbook analysis9. Training data from Irish surgical specialties during the pandemic has demonstrated caseload reductions of approximately 20%, similar to our own findings10. Reductions in trainee case load of up to 40% have been reported from some surgical specialties in other jurisdictions11. While we did not analyse theatre caseload by surgical subspecialties, a reduced operative experience in all surgical specialties was reported by a UK surgical logbook analysis12. Variations within training programmes and alterations in clinical exposure in residency training are not new phenomena. Substantial and rapid changes in anaesthesia training in Ireland in recent years have resulted from reductions in training programme length13 and legislation to reduce working hours to comply with the European Working Time Directive (EWTD) in 200314.
The aim of all anaesthesia training programmes is to produce competent and safe anaesthetists. Anaesthesia is a skills-intensive specialty and a key aspect of safe anaesthetic practice is the ability to perform certain procedural skills adeptly. The relationship between numbers and competence during training is complex, and data on the numbers required to attain competence in procedural skills in anaesthesia is scarce15. Competency in certain procedures can only be expected if adequate experience is gained by the trainee, with evidence suggesting that for the acquisition of procedural skills in anaesthesia the learning curve starts to plateau after 20 attempts for the core skills of endotracheal intubation, arterial access, nerve blocks, and spinal anaesthesia16. While a reduction in clinical exposure to core procedurals skills in theatre was observed during the pandemic in this study group, it is likely that these trainees still obtained appropriate exposure to the core anaesthetic procedural skills of tracheal intubation and vascular access. However, this study did not assess if trainees’ clinical competency was affected. While logbooks and procedural numbers can be used to confirm that a pre-determined amount of clinical exposure is provided by training programmes, they do not provide insight into the key factors that determine competence. Many national anaesthesia training schemes are moving towards competency-based training programmes17, with several competency measurement tools existing. Such programmes diverge from the traditional time-based apprenticeship model and instead promote an outcomes-based model with assessment of skills and clinical performance using well-defined curricula18 , 19. Competency based medical education embraces the assertion that trainees attain competency at different times and speeds, moving away from the traditional models of learning. As ‘assessments for learning’, they have been shown to help improve trainee performance by timely feedback and remediation with positive trainee feedback20. Trainees of this generation will inevitably have less clinical experience, but focused observation of trainees with meaningful feedback and coaching can help mitigate this reduction and maximise learning opportunities. Experience from previous pandemics has shown that the disruption in training can be prolonged21. This emphasizes the importance of anaesthesia training programmes investing in robust and reliable competency-based assessment tools for use across a wide range of tasks.
We identified a profound change in airway management practices, with a sustained shift toward widespread video laryngoscope use in this institution since the advent of the COVID-19 pandemic. Video laryngoscopy has been shown to be superior for teaching purposes compared to direct laryngoscopy22, and has been recommended as the first choice for intubation in COVID positive patients23 , 24. Our data on training in airway management during the pandemic are in keeping with findings from other institutions25 , 26. It is very possible that the COVID-19 pandemic is causing a permanent change in standard airway management practices, and that video laryngoscopy will become the preferred intubation method in the future. This identifies a point for anaesthesia training programmes to consider as trainees consequently may not be as proficient in conventional direct laryngoscopy, particularly those in the early years of training. The Intercollegiate Committee has accepted this for Acute Care Common Stem (ACCS) trainees in the UK, and has changed the initial assessment of competency criteria to include proficiency with the video or direct laryngoscope, with the caveat that competency with direct laryngoscopy must be shown in a simulated environment27. Other factors may influence changing patterns of use of video versus direct laryngoscopy, such as equipment availability and economic considerations of purchasing disposable video laryngoscope blades compared to Macintosh blade sterilisation.
While this study demonstrated a significant reduction in trainee theatre caseloads and procedural skills performed, they gained significantly more exposure to critical care medicine during the pandemic. Critical care experience is obviously beneficial for anaesthesia trainees, informing as it does the intra-operative and post-operative care of critically ill patients. Procedural skills gained by trainees in the intensive care unit were not captured in this review, however it is likely that procedural frequency was reduced compared to the theatre environment and was likely largely confined to airway management and vascular access. In addition, while trainees gained much experience in managing COVID-19 and its sequelae, much of their intensive care unit exposure was related to the management of this one illness. Therefore, the critical care educational experience of anaesthesia trainees seconded to the intensive care unit during the pandemic may not be as comparable in terms of diversity of pathology and patient mix as would be expected during non—pandemic rotations. Critical care exposure during this period may in fact have detrimental effects on trainees - there have been multiple documented impacts of the pandemic on intensive care staff indicating high levels of stress, depression, and burnout28 , 29.
There are several limitations to this study. We examined data from a single institution; training in other hospitals catering for different patient cohorts may have been affected to a greater or lesser extent (e.g. obstetric or paediatric centres). Additionally, the impact of the pandemic on hospitals in Ireland may not be comparable to those in other jurisdictions, as public health measures differed greatly between countries30. Using an electronic record to collect data has advantages but some inaccuracies may occur – the database does not record which person performed each individual procedure such that, for example, an initial failed attempt at epidural placement by a trainee may have been followed by successful placement by a consultant, or the first attempt may have in fact been by a consultant. As a training centre, we strive to prioritise first-hand trainee procedural experience where possible, however it is impossible to qualify precisely who performed (or who completed) each procedure. Finally, this study has only looked at objective data of caseload and procedural numbers and has not assessed the impact of COVID-19 on the anesthetists’ non-technical skills. While exposure to procedural skills and anaesthetic cases are important during training, they are not sufficient for the expert practice of anaesthesia, with an increasing awareness of the importance of the cognitive and social skills that complement technical skills31. Studies have documented an improvement in trainee’s non-technical skills in the post-COVID era32 , possibly driven by the increase in teamwork and crisis management skills that were required while working in the challenging environment of COVID-19 and personal protective equipment.
This retrospective analysis demonstrates that in our institution, the overall caseload and many core procedures undertaken by anaesthesia trainees during the pandemic period was significantly reduced. This appears consistent with data from other procedure-based specialties. Training time which would otherwise have been spent in anaesthesia was largely replaced by critical care duties. The COVID-19 pandemic has caused the greatest disruption to anaesthesia training in recent history, but whether this will translate into an overall negative impact on training outcomes remains unclear. Delivery of anaesthesia training in the ongoing pandemic period should hasten a move away from traditional models of learning and assessment and embrace competency based medical education. Careful evaluation of the impact on training and education with the development of a framework to deal with any training deficits is required to guide the delivery of anaesthesia training through and beyond the pandemic, to ensure training programmes remain fit for purpose as they experience real time challenges.
Author Contributions
Study Conception and Design: ML, TW.
Data acquisition: LH, OM.
Data analysis: LH, RN, TW.
Drafting of the manuscript: LH.
Critical revision of paper: TW.
Funding
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Declaration of Interests
The authors declare they have no conflicts of interest.
Appendix A Supplementary data
The following is the Supplementary data to this article:Supplementary Material: (a) Box plot showing numbers of vascular access procedures performed by SAT trainees in a six-month training rotation. Horizontal line, median; box, IQR, whiskers, range; cross, mean.White box, control period; grey box, pandemic period. (b) Box plot showing numbers of neuraxial procedures performed by SAT trainees in a six-month training rotation, broken down by year of training. Horizontal line, median; box, IQR, whiskers, range; cross, mean. White box, control period; grey box, pandemic period.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.bjao.2022.100117.
==== Refs
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| 36505902 | PMC9721281 | NO-CC CODE | 2022-12-12 23:20:59 | no | BJA Open. 2022 Dec 5;:100117 | utf-8 | BJA Open | 2,022 | 10.1016/j.bjao.2022.100117 | oa_other |
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J Med Imaging Radiat Sci
J Med Imaging Radiat Sci
Journal of Medical Imaging and Radiation Sciences
1939-8654
1876-7982
Published by Elsevier Inc. on behalf of Canadian Association of Medical Radiation Technologists.
S1939-8654(22)00672-5
10.1016/j.jmir.2022.11.012
Research Article
Two years on and four waves later: Johannesburg diagnostic radiographers’ experiences of COVID-19
Lewis Shantel ⁎
Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Johannesburg, Johannesburg, South Africa
⁎ Corresponding author at: University of Johannesburg, Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, DFC campus, JOB 6306a, Doornfontein, Johannesburg, 2000 South Africa.
5 12 2022
5 12 2022
© 2022 Published by Elsevier Inc. on behalf of Canadian Association of Medical Radiation Technologists.
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.
Introduction
At the onset of COVID-19 diagnostic radiographers from Gauteng, South Africa, shared their experiences of the new workflow and operations, their well-being and their resilience during this time. They experienced emotional, physical and financial fatigue. It is now over two years later, and South Africa has experienced four waves of COVID-19. Therefore, this study explored diagnostic radiographers' experience of COVID-19 after two years and four waves.
Methods
A qualitative explorative, descriptive and contextual study was conducted by collecting data through nine virtual individual in-depth interviews. Responses from the diagnostic radiographers in Johannesburg, Gauteng, South Africa, underwent thematic analysis.
Results
Thematic analysis revealed two themes and related categories. Theme one: participants shared synchronistic experiences with the four COVID-19 waves, the heterogeneous vaccination ideologies and their support and coping skills. Theme two: lessons learnt and the way forward.
Conclusion
Participants shared feeling overwhelmed at the onset of COVID-19 and feared infecting their family, friends and colleagues. However, their anxiety and fear decreased with time. They experienced the Delta variant as the worst and felt supported by their colleagues more than by management. They recounted observations of vaccine hesitancy but acknowledged that vaccination had alleviated some of the fear and anxiety. Participants' coping skills varied, and reflecting on their experience, they shared the lessons learnt and the way forward.
RÉSUMÉ
Introduction
Au début de la pandémie de COVID-19, des radiographes de diagnostic de Gauteng, en Afrique du Sud, ont partagé leurs expériences du nouveau flux de travail et des nouvelles opérations, leur bien-être et leur résilience pendant cette période. Ils ont ressenti une fatigue émotionnelle, physique et financière. Plus de deux ans plus tard, l'Afrique du Sud a connu quatre vagues de COVID-19. Par conséquent, cette étude a exploré l'expérience des radiographes de diagnostic de COVID-19 après deux ans et quatre vagues.
Méthodologie
Une étude qualitative exploratoire, descriptive et contextuelle a été menée pour recueillir des données par le biais de neuf entretiens individuels virtuels en profondeur. Les réponses des radiographes diagnosticiens de Johannesburg, Gauteng, Afrique du Sud, ont fait l'objet d'une analyse thématique.
Résultats
L'analyse thématique a révélé deux thèmes et des catégories connexes. Premier thème : les participants ont partagé des expériences synchronisées avec les quatre vagues de COVID-19, les idéologies de vaccination hétérogènes et leurs capacités de soutien et d'adaptation. Deuxième thème : les leçons apprises et la voie à suivre.
Conclusion
Les participants ont dit s'être sentis dépassés par les événements au début du COVID-19 et avoir craint de contaminer leur famille, leurs amis et leurs collègues. Cependant, leur anxiété et leur peur ont diminué avec le temps. Ils ont vécu la variante Delta comme la pire et se sont sentis soutenus par leurs collègues plus que par la direction. Ils ont relaté des observations d'hésitation à se faire vacciner, mais ont reconnu que la vaccination avait atténué une partie de leur peur et de leur anxiété. Les capacités d'adaptation des participants variaient et, en réfléchissant à leur expérience, ils ont partagé les leçons apprises et la voie à suivre.
Keywords
COVID-19
Diagnostic radiographers
Healthcare workers
South Africa
Vaccine hesitancy
Psychological support
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pmcIntroduction
A COVID-19 wave is described as periods of increased transmission [1], and the World Health Organization (WHO) classifies COVID-19 variants as variants of concern, variants of interest and variants under monitoring. The five variants of concern are Alpha, Beta, Gamma, Delta, and Omicron [2]. To date, four waves have been experienced in South Africa: the first wave dominated by the Alpha variant, the second wave dominated by the Beta variant, the third wave dominated by the Delta variant and the fourth wave dominated by the Omicron variant [3]. Weekly hospital admissions in Gauteng province during the third wave surpassed admissions during the peak of the second wave [4], and even though the fourth wave had the highest infection rate of all the waves, it had the lowest number of hospital admissions [5,6]. Gauteng is the smallest, most densely populated of the nine South African provinces and has the highest COVID-19 infection in South Africa [7]. Johannesburg is the largest city in Gauteng.
Now over two years since the first case of COVID-19 [8] was reported, and five variants of concern [2] later, medical imaging still plays a significant role in managing COVID-19 [9]. Central to the provision of medical imaging are radiographers [9]. Diagnostic radiographers (hereafter referred to as radiographers) in Gauteng province, South Africa, at the onset of the alpha variant [2], completed an online open-ended questionnaire that was qualitatively analysed to explore and describe their experiences of COVID-19 [10]. The findings of the study indicated that radiology departments changed their work-flow and operations; radiographers experienced pay cuts, worked longer shifts in specific teams and had to adapt to protocols that were continuously changing. Stringent infection control measures were affected by the varied provision of personal protective equipment (PPE) since radiographers were not considered frontline workers at the time [11]. Radiographers were emotionally, physically, mentally and financially fatigued. However, they realised the opportunity for learning and growth [10]. Globally, studies conducted in Australia, Canada, India, Ireland, the Middle East, Nigeria, Norway, North Africa, Lebanon, Portugal, the Republic of Cyprus, Singapore, South Africa, Spain and the United Kingdom evidence similar experiences and impacts of COVID-19 on radiographers [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24]. Since the Gauteng, South African study [10] was conducted in the early stages of COVID-19, understanding radiographers' longitudinal experiences of COVID-19 in this context remained unexplored.
Method
The study used an exploratory, descriptive, and contextual qualitative approach to understand radiographers' experiences of COVID-19 two years into the pandemic. Qualitative research involves a naturalistic inquiry to understand participants’ worlds through their frame of reference [25].
Population and sampling
The study population were radiographers from Johannesburg, Gauteng, South Africa. Convenience and snowball sampling were used. Information about the study was shared through radiographer community messaging groups, and those interested in participating in the study contacted the researcher either telephonically or via email as per the contact details shared in the study information sheet.
Ethics
Radiographers voluntarily agreeing to participate in the study signed and returned consent forms via email. Ethical clearance for the study was obtained from the university research ethics committee (REC-524-2020).
Data collection
Virtual one-on-one in-depth interviews were conducted by the researcher on Microsoft Teams and were recorded with the participants’ permission. By the ninth interview, data saturation was noted, resulting in nine in-depth interviews ranging from 30 to 60 minutes. At the start of the interview, participants provided their demographic details and were asked the broad question - ‘How they had experienced COVID-19?’ The conversation determined follow-up probing questions (Table 1 ) in keeping with a qualitative naturalistic inquiry [25]. Since the interviews were recorded, the researcher could take brief observational notes during the interviews.Table 1 Examples of probing questions used during the individual in-depth interviews.
Table 1:So when you say overwhelming, did you experience feeling overwhelmed in these two years?
When you say wave, what do you mean?
Can you elaborate on the challenges you mentioned?
Do you think there are any positives?
What has this taught you?
How were you able to cope with everything you were dealing with?
And when you speak about support, you mean support from your manager, the radiologist, who exactly?
You know, when we reflect, we often think about positives. Do you find there was any positive at all for you, or was it just all negative?
Do you think there was a transition or evolution in your coping skills from the beginning to now?
And for you, do you feel because you didn't get that support, you made sure you gave the support to the junior radiographers you work with?
Do you feel people are better able to cope now, or are they just tired like you said now?
Data analysis
The researcher transcribed the interviews verbatim, where participants’ names were replaced with codes to ensure privacy. The interviews were thematically analysed [26]. The transcripts were read and reread to become familiar with the data. The data were coded, and initial themes were generated. The themes were then developed, reviewed and refined [26].
Trustworthiness
Trustworthiness in the study was based on four criteria: credibility, dependability, transferability and confirmability [27]. Member checking ensured credibility; this was done during the interviews and by sharing the identified themes with participants after data analysis. Triangulation of the interviews, observational notes, and literature further contributed to the study's credibility. The study's dependability was achieved through a thick description of the research methodology and the path to the study's conclusions. Transferability was ensured by a sufficiently dense description of the setting and participants, allowing judgement of the transferability. Direct quotations from participants and detailed descriptions of the results further ensured transferability. Confirmability was established by an audit trail detailing data collection, analysis and interpretation.
Results
Participants' demographics are detailed in Table 2 . Participants’ ages ranged from 26 to 59 years, with an average of 17 years of experience as a radiographer. Participants represented both the private and public sectors. The healthcare sector in South Africa consists of the government-funded public sector and the private sector funded through medical aid or consumers. Radiographers in South Africa can work in private, public, or both concurrently [28]. Thematic analysis identified two themes and related categories shown in Table 3 .Table 2 Participants' demographics.
Table 2: Age Years of experience Sector of experience
P1 27 6 Public and private
P2 26 6 Public
P3 42 20 Public
P4 40 19 Private
P5 33 11 Public
P6 37 17 Private
P7 28 10 Private
P8 27 7 Public
P9 59 35 Public
Table 3 Themes and categories.
Table 3:Themes Categories
1. Participants shared synchronistic experiences with the four COVID-19 waves, heterogeneous vaccination ideologies and their support and coping skills. 1. Experiences synchronous with the
COVID-19 waves
• Overwhelmed at the onset
• Fear of infecting family, friends or colleagues
• Anxiety and fear decreased with time however varied through the waves. Delta wave was the worst.
2. Heterogenous vaccination ideologies
• Vaccine hesitancy
• Vaccine alleviating fear and anxiety
3. Support structures and coping strategies
• Greater support from colleagues than management
• Participants coping skills varied from blocking off their feelings to speaking to their colleagues. Others spent time in nature and exercising
2. Lessons learnt and the way forward 1. Lessons learnt
• Appreciation for life, family, friends and colleagues.
• Learning patience, better communication and interpersonal relationships.
• Now recognised as frontline workers.
• Development of independence and confidence in practice.
2. The way forward
• Psychological support and sharing conversations
• Importance of monitoring mental and emotional health.
Theme one: participants shared synchronistic experiences with the four COVID-19 waves, heterogeneous vaccination ideologies and their support and coping skills
Participants shared that their experiences fluctuated through the four COVID-19 waves [2], and heterogeneous vaccination ideologies were encountered. They also indicated that support from colleagues and hospital and radiology management varied. They explained ways they tried to cope. The following three categories were identified.
Category one: experiences synchronous with the COVID-19 waves
Participants felt overwhelmed at the onset of COVID-19:“It was very challenging at first, and it was very scary” P3
“My experience was very overwhelming.” P7
With time participants' anxiety, fear and nervousness surrounding COVID-19 lessened but participants shared diverse experiences through the COVID-19 waves in South Africa [2]. The second wave (Beta) was less overwhelming because there were established operational protocols, better personal protection equipment (PPE) provision, and a greater understanding of the virus. However, the third wave (Delta) was described as “horrific” P6 and analogous to death. The fourth wave (Omicron) was better as there were fewer admissions. Participants explained that their experiences were synchronous with the waves of the virus. When each wave peaked, they felt more significant anxiety and fear.“With the Beta wave, we had the practices from the previous wave. There was flow, and we were able to get through it. There are already systems in place, so it helps to get the job done. So definitely, as the waves progressed, they were set plans of action, so you knew more or less what to do, but if it weren't for those plans of action, I don't think we would have survived mentally because of the trauma we had in that delta wave.”P2
“..the first two waves were sort of manageable. But the delta wave for me personally was very emotional in the sense that we were surrounded by death 24/7. We would be called to the COVID ICU or ward for mobiles, and the five minutes it takes to get there, they would tell us no, it's too late. That would happen multiple times in a day. That was very demotivating and depressing.” P4
Some participants were not afraid of contracting COVID-19 themselves, but all participants shared concern about infecting their families, friends and colleagues:“I have never been scared about COVID, but I would say I was a bit worried about older family members.” P4
“It was so scary at the time, and you didn't want to take that home to somebody we were living with or your families.” P5
Category two: heterogenous vaccination ideologies
Participants experienced vaccination hesitancy among colleagues and family members but shared that vaccination reduced anxiety and fear overall.“Some of them are hesitant to take the vaccine, and now, they still haven't been vaccinated. …we don't know what is being introduced to our body.” P2
“…..less scared since knowing if you had contact, you wouldn't be so severe, so in that sense, vaccination has lessened the fear.” P5
Category three: support structures and coping strategies
Participants felt the most incredible support from their colleagues and little support from management.“I don't see the profession as I used to, largely because of the, you know, the company I worked for….the lack of support structure that was provided, the lack of, you know, emotional care or any sort of reassurance and you know there were salary cuts happening. You were working weekends without pay, and it was less than perfect situations.” P1
“…I had a panic attack, but it was in the ward, and I just called my colleague and was like listen, I'm not OK, I'm waiting to do a portable, and she came, and she sat next to me.”P6
Participants' coping skills varied. Some just went on to “autopilot” P1, blocking off emotions. Others spent time in nature and exercising. For most participants talking to their colleagues who had gone through the same experience helped. One participant did not want to burden their family and cried in the shower as a means to cope.“… we have survived, and we have learned we are able to deal with it now…..from the support that I got from all of my friends.” P9
“I suffer from depression anyway, so it definitely, escalated during the pandemic….being outside in nature and exercise, that helps me a lot to cope with the depression in general….a lot of hikes with the dogs … to get outside and exercise as much as possible just to cope and to breathe….and colleagues….lean on each other and share our experiences” P4
“I feel like I just blocked it out and just carried on with life, and like you know you deal with it because you know this is your profession. This is what you signed up for… there were moments I'd come home, I didn't really want to worry my parents or my family, I'll just have a good cry in the shower, and then I'll feel better, but you're not exactly dealing with it in a healthy way.” P7
Theme two: lessons learnt and the way forward
Participants reflected on the lessons they learnt and the way forward.
Category one: lessons learnt
Participants shared an appreciation for life, family, friends and colleagues. They shared learning patience, better communication, and interpersonal relationships with health care workers, as well as developing their independence and confidence in their practice.“..grateful for my family and friends, like even more so than before, grateful for my life.”P6
“It taught me to have better communication, interpersonal relationships and to start to have a bit more self-confidence. It taught me a lot of patience. It taught me that you know that we are also human, and we all get so tired….you just need to have a little bit more patience, a bit more tolerance and respect towards the next person.” P1
The participants shared that they felt they were now recognised as frontline workers.“We were not really recognised as frontline workers, even though we come in contact with the patient but still they didn't wanna recognise us fully as frontline workers….it's somewhat changed, I think they just needed to realise what we do.” P8
Category two: the way forward
Participants expressed that psychological support and sharing conversations are needed. They reflected on the importance of monitoring their mental and emotional health.“I don't think I've actually handled it. I just feel like numb. After this, I feel like I need to discuss all of this…it's been a lot. It's not just work it's your personal issues as well. Everything was just like it was too much, and I need to go for therapy I need to sort it out….. also mental health it was never something that bothered me before…now my friends we check on each other. That's something we never did. But we have become more aware of people's feelings and mental state.”P7
“We've lost colleagues in the broad hospital and within our X-ray department. Yeah, it's for a lot of people. It's been quite a roller coaster with a lot of downs.” P6
Participants at the end of the interview expressed gratitude for the conversation, listened to and thought of the interview as a debriefing session.“Thank you so much for listening. It felt like a little debriefing. So thank you so much. I really appreciate it.” P4
Discussion
Participants’ initial experience of being overwhelmed was echoed in the Gauteng, South African study [10] and confirmed in a national South African study where most participants' coronavirus anxiety scale scores indicated probable dysfunctional coronavirus-related anxiety [21]. Aligned to the current study participants’ emotional state, studies [15,20,24]. report a roller coaster of emotions, including fear, nervousness and anxiety. In addition, studies [12,14,23,29,30]. support the stress experienced by participants in the current study, reiterated in a systematic literature review of 31 articles on COVID-19’s impact on clinical radiographic practice evidencing that radiographers' well-being and mental health were affected [31].
Participants' anxiety increased during the third wave(delta), and they experienced less anxiety in the fourth wave. Similarly, radiographers in Ireland showed decreased anxiety levels over the six-week study period [15]. In comparison to the first study conducted in Gauteng [10], where radiographers found the evolving protocols during that time confusing, participants in the current study explained that since protocols and operations were established, anxiety levels surrounding work operations diminished. Tomlin et al. [32] acknowledge that uncertainty leads to anxiety and stress; removing the uncertainty of protocols would likely inhibit anxiety and stress.
In the current study, some participants were not afraid of contracting COVID-19 themselves, but all participants shared concern about infecting their families, friends and colleagues. In contrast, studies reported that radiographers and healthcare workers shared high-stress levels associated with contracting COVID-19 at work [10,17,23,30,33] Pereira et al. [18] noted that heightened fear of contracting the virus increased emotional exhaustion and depersonalisation. However, most healthcare workers from a Singapore hospital accepted contracting the virus as part of their occupation [34].
Sharing the current study participants' experiences, UK radiographers reported fear of transmission to family and friends [24], and Portuguese radiographers reported the impact of COVID-19 on the family as detrimental [18]. Aligned with the current study findings related to concern about infecting family members, radiographer studies from Australia, India, Lebanon, the Middle East, North Africa, and the UK reported the significant impact radiographer work-related stress had on family, friends or partners [12], [14], [17], [23].
The current study revealed heterogeneous vaccination ideologies where participants experienced vaccination hesitancy among colleagues and family members. Vaccine hesitancy is defined as the “delay in acceptance or refusal of vaccination despite the availability of vaccination services” (page 7) [35]. A study in Ghana [36] agreed with the participants in the current study, where radiographer vaccine hesitancy was attributed to a lack of education/information and religious beliefs. Ethiopian healthcare workers reported their vaccine hesitancy was due to a lack of belief in COVID-19 vaccines' benefits and safety and a lack of trust in the government [37]. Another Ethiopian study that included radiographers found that vaccine hesitancy could also be attributed to a negative attitude and low perception of COVID-19 vaccines [38]. A study found vaccination hesitancy in 41% of South African Cape Town healthcare workers [39]. In contrast, an Eastern Cape South Africa study showed a 90.1% overall acceptance of COVID vaccines by healthcare workers [40]. Participants shared that vaccination reduced anxiety and fear overall, and studies concur that decreased stress and anxiety have been reported post-COVID-19 vaccinations [41,42].
Participants shared that support from colleagues and hospital and radiology management varied. In the first Gauteng study [10], radiographers also found solace in their colleagues' support. The lack of managerial support is consistent with a study of radiographers in Gauteng, South Africa, indicating that working conditions were unsatisfactory, attributed to a lack of immediate supervisors and senior management support [43]. Similarly, Naylor et al.' s24 study shared that radiographers did not feel supported by radiography managers and obtained support from their team. Yasin et al. [44] found that department support was favoured over organisational support. Swedish radiographers attributed managers' lack of support to their lack of knowledge of this "new" pandemic [45]. The systematic review and qualitative meta-synthesis of 46 studies revealed that although healthcare workers wanted support from their organisation, often it was not received, and they relied on their colleagues for support [33]. Conversely, radiology staff in Iran felt that managerial support was given to them since the outbreak of COVID-19 increased [46]. Brooks et al. [47] suggest employers provide a supportive working environment to mitigate the psychological impact during infectious disease outbreaks. However, Colombian healthcare managers found it difficult to motivate employees [48]. A study in a Gauteng [49], South African radiology department recommended developing interpersonal relationships to motivate employees. Guidelines to support healthcare workers emphasise the importance of employers appreciating healthcare workers.
Participants shared varied coping skills. Coping is the dynamic process used to manage stressors [50]. COVID-19 as a catalytic stressor requires cognitive and behavioural changes where individuals may respond actively or try to avoid it. Avoidance as a coping mechanism is a cognitive and behavioural decision to avoid directly dealing with a stressor and is the most frequently used coping mechanism [51,52]. Nurses in Alabama and nursing students in Pune, Indian and Cyprus healthcare workers reported using avoidance as a coping strategy during COVID-19 [53], [54], [55]. A multi-country study of healthcare workers' coping strategies during COVID-19 rated that family support and positive thinking as the highest coping strategies [56]. Cook et al. [57] attributed positive coping strategies among healthcare workers at an ophthalmic practice in South Africa to organisational support. The lack of organisational support in the current study may have contributed to a participant's use of avoidance coping. A study reported that radiotherapists surveyed in Canada, coping strategies through COVID-19 included exercise, hobbies, and meditation/mindfulness [13].
Participants' sentiments express their growth from experiencing the trauma of being frontline workers during COVID-19. Even through the anxiety and stress, participants shared an appreciation for life, family, friends and colleagues, learning patience, better communication, and interpersonal relationships with healthcare workers, and developing their independence and confidence in their practice. These findings align with Tedeschi and Calhoun's [58] post-traumatic growth theory, which asserts that growth may emanate from a traumatic experience. A study [59] found that 39.3% of nurses experienced post-traumatic growth during COVID-19 surveyed through the Posttraumatic Growth Inventory-Short Form [13] subscales of relating to others, new possibilities, personal strength, spiritual change and appreciation of life. Similarly, healthcare workers in China [60,61], Greece [62] and New York City [63] evinced post-traumatic growth. Despite their anxiety, CT radiographers could still praise their adaptability and acknowledge the support of their team during COVID-19 [29].
Participants also shared that they now felt recognised as frontline workers and more valued among healthcare workers in contrast to the earlier study [10], where radiographers thought they were not accepted as frontline workers. De Guzman and Angcahan [64] argue that a lesson of COVID-19 should be a collaborative community of healthcare professionals who are equally essential and allied in providing care. However, Naylor et al.'s [24]. study of radiographers' experiences during COVID-19 still highlighted healthcare workers' limited understanding of the role of radiographers. Publications on mobile radiography [65] and computed tomography [66] attempt to clarify the role of radiographers and healthcare workers in radiographic examinations.
Participants in the current study reflected on the importance of monitoring their mental and emotional health and the need for psychological support and sharing conversations. In agreement with psychological support, Tomlin et al. [32] propose a model to support healthcare workers through the various waves of COVID-19. Although psychological services were available to radiographers, previous studies noted that many did not use the services [15,31,44]. Studies have reported the benefits of debriefing among healthcare workers [67], [68], [69], Further research on the value of debriefing for radiographers is suggested [24].
Limitations
The study is contextual to a single city in South Africa and is from the perspective of diagnostic radiographers; however, global literature does support the study findings.
Conclusion
Participants shared that their experiences mirrored the COVID-19 waves and their worst experience was the Delta variant. At the onset of COVID-19, participants felt overwhelmed and were afraid of infecting their family, friends and colleagues, but their anxiety and fear diminished with time. The vaccine helped alleviate some of the fear and anxiety; however, participants observed vaccine hesitancy among their colleagues and family. Participants coped by blocking off their emotions and others through conversations with their colleagues. Participants shared their positive reflections during this challenging time, recognising that monitoring radiographers' mental and emotional health is needed. Participants developed a greater appreciation for life and acknowledged their growth through learning patience, better communication and interpersonal relationships.
Contributors: All authors contributed to the conception or design of the work, the acquisition, analysis, or interpretation of the data. All authors were involved in drafting and commenting on the paper and have approved the final version.
Funding: This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Competing interests: All authors have completed the ICMJE uniform disclosure form and declare no conflict of interest.
Ethical approval: Ethical clearance for the study was obtained from the university research ethics committee (REC-524-2020).
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| 0 | PMC9721282 | NO-CC CODE | 2022-12-14 23:52:40 | no | J Med Imaging Radiat Sci. 2022 Dec 5; doi: 10.1016/j.jmir.2022.11.012 | utf-8 | J Med Imaging Radiat Sci | 2,022 | 10.1016/j.jmir.2022.11.012 | oa_other |
==== Front
Vaccine
Vaccine
Vaccine
0264-410X
1873-2518
The Authors. Published by Elsevier Ltd.
S0264-410X(22)01497-9
10.1016/j.vaccine.2022.11.073
Article
A case-crossover study of the effect of vaccination on SARS-CoV-2 transmission relevant behaviours during a period of national lockdown in England and Wales
Serisier Aimee a
Beale Sarah ab⁎
Boukari Yamina b
Hoskins Susan a
Nguyen Vincent ab
Byrne Thomas b
Fong Wing Lam Erica b
Fragaszy Ellen bc
Geismar Cyril ab
Kovar Jana a
Yavlinsky Alexei b
Hayward Andrew a
Aldridge Robert W. a
a Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK
b Centre for Public Health Data Science, Institute of Health Informatics, University College London, NW1 2DA, UK
c Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK
⁎ Corresponding author at: Institute of Epidemiology and Health Care, University College London, London WC1E 7HB, UK.
5 12 2022
5 12 2022
29 8 2022
28 11 2022
29 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
Studies of COVID-19 vaccine effectiveness show increases in COVID-19 cases within 14 days of a first dose, potentially reflecting post-vaccination behaviour changes associated with SARS-CoV-2 transmission before vaccine protection. However, direct evidence for a relationship between vaccination and behaviour is lacking. We aimed to examine the association between vaccination status and self-reported non-household contacts and non-essential activities during a national lockdown in England and Wales.
Methods
Participants (n = 1154) who had received the first dose of a COVID-19 vaccine reported non-household contacts and non-essential activities from February to March 2021 in monthly surveys during a national lockdown in England and Wales. We used a case-crossover study design and conditional logistic regression to examine the association between vaccination status (pre-vaccination vs 14 days post-vaccination) and self-reported contacts and activities within individuals. Stratified subgroup analyses examined potential effect heterogeneity by sociodemographic characteristics such as sex, household income or age group.
Results
457/1154 (39.60 %) participants reported non-household contacts post-vaccination compared with 371/1154 (32.15 %) participants pre-vaccination. 100/1154 (8.67 %) participants reported use of non-essential shops or services post-vaccination compared with 74/1154 (6.41 %) participants pre-vaccination. Post-vaccination status was associated with increased odds of reporting non-household contacts (OR 1.65, 95 % CI 1.31–2.06, p < 0.001) and use of non-essential shops or services (OR 1.50, 95 % CI 1.03–2.17, p = 0.032). This effect varied between men and women and different age groups.
Conclusion
Participants had higher odds of reporting non-household contacts and use of non-essential shops or services within 14 days of their first COVID-19 vaccine compared to pre-vaccination. Public health emphasis on maintaining protective behaviours during this post-vaccination time period when individuals have yet to develop full protection from vaccination could reduce risk of SARS-CoV-2 infection.
Keywords
COVID-19
Vaccine
Behaviour
Mitigations
Abbreviations
COVID-19, Coronavirus disease 2019
SARS-CoV-2, Severe acute respiratory syndrome coronavirus 2
UK, United Kingdom
IMD, Index of Multiple Deprivation
VoC, Variant of Concern
==== Body
pmc1 Introduction
The coronavirus disease 2019 (COVID-19) pandemic has had a devastating impact on global public health since the causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in late 2019 [1], [2]. SARS-CoV-2 is transmitted through direct or indirect contact with infected respiratory droplets or aerosols [3], [4], [5] and consequently public settings and activities that involve direct and indirect contact may promote SARS-CoV-2 transmission [4], [6]. These were the target of non-pharmaceutical interventions (NPIs) introduced by many governments worldwide to control the spread of the virus, including social distancing measures to minimise contacts with other people, face mask mandates and closure of non-essential public venues. In the UK, the most stringent levels of national restrictions included periods of ‘lockdown’, which broadly involved strict restrictions on non-household mixing, closure or restrictions on usage of non-essential public spaces, advice to stay at home and work from home where possible, and stringent isolation and quarantine protocols [7]. Lockdowns were introduced three times in England and Wales, between March and May 2020 (first national lockdown), October and November 2020 (depending on nation, second lockdown), and January to March 2021 (third national lockdown).
COVID-19 vaccination programmes are now a cornerstone of pandemic response in the UK and worldwide, allowing for relaxation of lockdown restrictions with varying degrees of remaining NPIs. In the UK, COVID-19 vaccinations began in December 2020 with the emergency licencing of first the Pfizer BioNTech and then AstraZeneca vaccines. Delivered as two doses with additional booster doses, first and subsequent doses are proven to be effective in reducing symptomatic and asymptomatic infections, hospitalisations, and deaths from COVID-19 [8], [9], [10], [11]. However, the protection provided by vaccines is not immediate and infection with SARS-CoV-2 after vaccination is possible [12]. Studies using data from Israel and the UK suggest an increased risk of symptomatic infection within 14 days of the first dose of a COVID-19 vaccine [13], [14]; the reduction in risk of symptomatic infection is seen after 14 days. It is possible that increased risk in the 14 days after vaccination may reflect changes in behaviour associated with SARS-CoV-2 transmission during the period of time in which immunologic protection is building [15], [16], [17], particularly given the extent to which human behaviour is known to influence infectious disease dynamics [18], [19], [20], [21]. Furthermore, concerns have been raised that a reduction in behaviours protecting against SARS-CoV-2 infection could be seen if the perceived risk of infection is reduced after vaccination against COVID-19 [22].
Empirical evidence regarding the effect of vaccination on infection prevention behaviour is limited. One study examining protective behaviours before and after vaccination against Lyme disease found that people who were vaccinated reduced protective behaviours and believed that they were at less risk of infection than unvaccinated people [23]. There is evidence in the context of the COVID-19 pandemic that suggests changes in protective behaviours may occur. In February 2021 the Office for National Statistics (ONS) reported that 41 % of over 80 s met a person who was not a part of their household or support bubble indoors within 21 days post-vaccination [24]. A December 2020 YouGov survey poll found that of the 1706 people surveyed, 29 % said that they would follow public health restrictions less strictly after receiving a vaccine [25]; this poll was conducted prior to widespread availability of COVID-19 vaccination in the UK. Reductions in compliance with mask use and handwashing post-vaccination were found amongst healthcare workers in Ethiopia after the first dose of a COVID-19 vaccine [26]. Contradictory to these findings, a longitudinal analysis investigating compliance with protective behaviours found increases in self-reported compliance with public health guidance and social distancing in vaccinated and unvaccinated individuals from October 2020 to February 2021 [27]. However, variation in context over this time could have influenced these results - for example, the introduction of the third UK-wide national lockdown in early January 2021 [7] or changes in the perceived risk of infection as case numbers rose.
This study aimed to quantify the effect of a change in COVID-19 vaccination status on transmission-relevant behaviours during a period of national lockdown using data from Virus Watch, a large prospective cohort study based in England and Wales. We set out to investigate whether participants’ self-reported levels of non-household contacts and retail and social activities classed as non-essential under lockdown restrictions (such as use of a hairdressers or other services for personal care, attending a party, or dining at a restaurant or café) changed within 14 days of their first dose of a COVID-19 vaccination compared to pre-vaccination.
2 Methods
2.1 Study design
A case-crossover design was used to examine the association between vaccination status (pre-vaccination versus ≤ 14 days post first dose) and self-reported contacts and activities within individuals. Case-crossover designs are appropriate to examine the association between transient exposures and acute outcomes [28] and eliminate measured and unmeasured time-invariant confounding when comparing within-person exposed and unexposed periods [28], [29], [30], [31].
To maximise the comparability of referent periods and reduce the potential for confounding by temporal or spatial trends in contacts and activities, the study timeframe was limited to include survey responses from within the third national lockdown in England and Wales (6 January–29 March 2021). Each survey was open for 7 days and concerned non-household contacts and activities in the week prior to its start date – surveys from 9 to 16 February 2021 and 9–16 March 2021 could therefore be included, and the pre-vaccination period comprised responses from 2 to 9 February 2021 and the post-vaccination period from 2 to 9 March 2021 (Fig. 1 ).Fig. 1 Participants reported contacts and activities in the previous week in monthly web surveys, which were open for completion for 7 days. Vaccination status was reported in weekly web surveys. The post-vaccination monthly web survey was therefore selected to cover a period of time within 7–14 days post-vaccination.
2.2 Study setting and population
Data analysed in this project were collected as part of Virus Watch, a household community cohort study of SARS-CoV-2 transmission and COVID-19 in England and Wales. Details of Virus Watch relevant to the present study are described briefly here, with further information on its full scope and methodology described in the study protocol [32].
Whole households who met the following inclusion criteria were recruited for voluntary participation through social media and General Practice supported postal and SMS recruitment campaigns: resident in England or Wales, household size 1–6 people (due to survey infrastructure limitations), internet and email access, and ability of at least one household member to complete online surveys in English [32]. All participants completed a baseline survey upon study registration that collected information on sociodemographic factors for each household member. Participants reported on changes in vaccination status in weekly web surveys, which were weekly completed by approximately 50 % of the recruited cohort [33]. Monthly web surveys collected health-related and behavioural/psychosocial factors, including information about participants’ activities and contacts, with responses to the monthly surveys varying, with responses to relevant monthly surveys reported in the results.
Inclusion criteria for the present study were having received the first dose of a COVID-19 vaccine and responded to a monthly survey regarding activities and contacts both pre-vaccination and post-vaccination (n = 1154). All surveys were administered online and survey completion was voluntary and not incentivised. Participants under 18 years old were excluded (n = 4) as this age group was not the focus of vaccinations during the study timeframe. Participants with missing data on sociodemographic covariates (see below) to be used in stratified analyses were excluded from the study sample following initial descriptive statistics (n = 23). The final study population for analysis included 1141 participants (Fig. 2 ).Fig. 2 Exclusion criteria for the analysis of the effect of vaccination status on contacts and activities.
2.3 Exposure
Self-reported vaccination status was the exposure of interest, defined as ‘pre-vaccination’ and ‘within 14-days post-vaccination’ of a first dose of a COVID-19 vaccine and derived from data on the date and dose of vaccinations reported in weekly surveys.
2.4 Outcome
The following contact and activity outcomes were derived based on responses to monthly surveys, and were binary coded (yes or no during survey week): [1] any close contact with non-household members (‘face-to-face contact with another person <1 m away, spending more than 15 min within 2 m of another person, or travelling in a car or other small vehicle with another person’ [34], [2] any social or leisure activity (attending a theatre, cinema, concert, or sports event; attending a party; going to a restaurant, café, or canteen; going to a bar, pub, or club; and/or use of a gym or indoor sports facility), and [3] using non-essential shops or services (retail venues or services not required to meet basic needs such as food and medicine, e.g., hairdressers, barbers, or beauty salons). Such behaviours were targeted by social distancing restrictions and/or public venue closures under lockdown restrictions in place in February–March 2021. Due to low numbers of participants reporting social activities in both pre- and post-vaccination surveys during this lockdown period, social and leisure activities could not be included in this analysis.
2.5 Demographic characteristics
Data on sociodemographic characteristics were collected from study baseline responses. Age group was defined according to reported age of participants at baseline: <60 years and 60 years or more. Participants were categorised as White British and Minority Ethnic according to self-reported ethnicity. Sex was categorised as male and female by self-reported sex. Household size was defined as households of 1 person, 2–3 people and 4–6 people. The Index of Multiple Deprivation (IMD) for each participant was categorised using household postcodes recorded on registration, comprising 1 (least deprived) through 5 (most deprived). Region was measured using participant’s postcodes and categorised according to ONS national regions. Observations with missing data on sex (1.12 %) and age (0.43 %) which were included as potential effect modifiers were excluded following descriptive statistics in order to perform a complete cases analysis.
2.6 Analysis
The frequency with which non-household contacts and use of non-essential shops or services were reported in surveys pre- and post-vaccination were calculated and stratified by age and sex only as group sizes amongst other participant subgroups were too small. Conditional logistic regression was used to assess the odds of reporting contacts and activity outcomes within 14 days after the first dose of a COVID-19 vaccine compared to the earlier pre-vaccination time point within individuals. This approach accounts for non-independence in responses within individuals over time by conditioning the participants responses using an individual-specific fixed effect [35]. Each participant contributes two observations occupying a single stratum in the model. As the model estimates within-individual changes, strata that do not vary (i.e., people who reported contacts and activities in neither time period, or in both) do not contribute to the model [30]. Stratification of conditional logistic regression models by age and sex was then performed. Sociodemographic covariates were collected at baseline and considered time-invariant confounders, and consequently were not entered into the models. Cluster robust standard errors were used to account for household-level clustering. The alpha level was set at p = 0.05.
2.7 Ethics
Ethical approval for Virus Watch was obtained from the Hampstead National Health Service (NHS) Health Research Authority Ethics Committee (ethics approval number 20/HRA/2320). All participants provided online informed consent. All analyses were conducted using the University College London Data Safe Haven.
3 Results
Table 1 presents the sociodemographic characteristics of Virus Watch participants in this analysis (n = 1154 from 1031 households). The median age in years was 60 (interquartile range: 56,62). The majority of participants were over 60 years of age (58.12 %). Participants were mostly white (95.93 %), from the East of England (24.61 %) or South East (20.10 %), lived in households with 2–3 people (62.48 %), in IMD 5 (least deprived, (31.63 %) or with an annual household income of £50,000 or more (38.88 %). There was a greater proportion of men in the sample (54.59 %) compared to women (44.37 %).Table 1 Characteristics of study participants.
Characteristic n %
Age
<60
≥60
Missing
480
673
<5
41.45
58.12
0.43
Sex
Male
Female
Missing
630
512
12
54.59
44.37
1.04
Ethnicity
White
Minority ethnic
Missing
1,069
72
13
92.63
6.24
1.13
Region
East Midlands
East of England
London
North East
North West
South East
South West
Wales
West Midlands
Yorkshire and the Humber
Missing
104
284
101
50
132
232
90
19
65
64
13
9.01
24.61
8.75
4.33
11.44
20.10
7.80
1.65
5.63
5.55
1.13
Household size
1 person
2–3 people
4–6 people
Missing
223
721
210
0
19.32
62.48
18.20
0
IMD
1 (most deprived)
2
3
4
5 (least deprived)
Missing
78
173
254
271
365
13
6.76
14.99
22.01
23.48
31.63
1.13
Household income
£0-£24,999
£25,000-£49,999
≥£50,000
Missing
249
342
376
187
25.75
35.37
38.88
16.20
Total 1,154
Table 2 shows the frequency of reporting non-household contacts and use of non-essential shops or services. Pre-vaccination surveys asked participants to record activities from 2 to 9 February 2021, with post-vaccination surveys recording activities from 2 to 9 March 2021. 371/1,154 (32.15 %) participants reported contact with a person outside of their household or support bubble pre-vaccination compared with 457/1,154 (39.60 %) participants in the 14 days after the first dose of a COVID-19 vaccine. 74/1154 (6.41 %) participants reported using non-essential shops or services pre-vaccination, while 100/1,154 (8.67 %) participants did so in the 14 days after their first COVID-19 vaccine.Table 2 Frequency of reporting non-household contacts and use of non-essential shops or services over 7 days in pre- and post-vaccination surveys (n = 1154).
Activity Pre-vaccination ≤14 days post-vaccination
No Yes No Yes
n (%) n (%) n (%) n (%)
Contacts 783 (67.85)
1,080 (93.59) 371 (32.15)
74 (6.41) 697 (60.40)
1,054 (91.33) 457 (39.60)
100 (8.67)
Use of non-essential shops or services
The pre-vaccination survey and post-vaccination surveys asked participants to report activities from 2 to 9 February 2021 and 2–9 March 2021 respectively.
Table 3 shows the frequency with which participants reported activities in pre-vaccination and post-vaccination surveys stratified by age group and sex. A greater proportion of participants reported non-household contacts in both pre-vaccination and post-vaccination surveys compared to use of non-essential shops and services, but both activities were reported more frequently post-vaccination. Post-vaccination, reporting of non-household contacts was greatest amongst male participants and those aged <60 (257/630, 40.79 % and 199/480, 41.46 % respectively), but the greatest increase in reported non-household contacts was seen when comparing post-vaccination with pre-vaccination surveys from male participants and those aged 60 years or more. Greater proportions of female participants (47/512, 9.18 %) and participants aged 60 years or more (70/673, 10.40 %) reported use of non-essential shops or services within 14 days post-vaccination. However, reported use of non-essential shops or services by male participants increased by more than female participants post-vaccination.Table 3 The frequency of reporting non-household contacts and use of non-essential shops or services over 7 days in pre-vaccination and post-vaccination surveys by participant characteristics (n = 1154).
Contacts Use of non-essential shops or services
Pre-vaccination ≤14 days post-vaccination Pre-vaccination ≤14 days post-vaccination
Characteristic n No Yes No Yes No Yes No Yes
n (%) n (%) n (%) n (%) n (%) n (%) n (%) n (%)
Age
<60
≥60
Missing
480
673
<5
310 (64.58)
472 (70.13)
<5
170 (35.42)
201 (29.87)
0
281 (58.54)
415 (61.66)
<5
199 (41.46)
258 (38.34)
0
457 (95.21)
622 (92.42)
<5
23 (4.79)
51 (7.58)
0
450 (93.75)
603 (89.60)
<5
30 (6.25)
70 (10.40)
0
Sex
Male
Female
Missing
630
512
12
425 (67.46)
351 (68.55)
7 (58.33)
205 (32.54)
161 (31.45)
5 (41.67)
373 (59.21)
317 (61.91)
7 (58.33)
257 (40.79)
195 (38.09)
5 (41.67)
597 (94.76)
472 (92.19)
11 (91.67)
33 (5.24)
40 (7.81)
<5
579 (91.90)
465 (90.82)
10 (83.33)
51 (8.10)
47 (9.18)
<5
The pre-vaccination survey and post-vaccination surveys asked participants to report activities from 2 to 9 February 2021 and 2–9 March 2021 respectively.
Table 4 presents the results of conditional logistic models for the within-individual effects of vaccination on non-household close contacts and use of non-essential shops or services. Odds of reporting non-household close contacts (OR 1.65, 95 % CI 1.31–2.06, p=<0.001) and use of non-essential shops or services (OR 1.50, 95 % CI 1.03–2.17, p = 0.032) were higher within 14 days post-vaccination compared to pre-vaccination.Table 4 Conditional logistic regression models for the effect of vaccinations status on contacts and use of non-essential shops or services.
Vaccination status n Paired OR Std. Err. 95 % CI P-value
Contacts
≤14 days post-vaccination
Pre-vaccination 352 1.65 0.19
- 1.31–2.06
- <0.001
-
Use of non-essential shops or services
≤14 days post-vaccination
Pre-vaccination 125 1.50 0.28
- 1.03–2.17
- 0.032
-
Models adjusted for household structure with cluster-robust standard errors.
Table 5 shows the results of stratified conditional logistic models examining heterogeneity of the within-individual effect of a change in vaccination status for contacts and use of non-essential shops or services by sociodemographic characteristics. All analyses compare time periods within 14 days post-vaccination to time periods pre-vaccination.Table 5 Conditional logistic analyses stratified by sociodemographic characteristics for the effect of vaccination status on self-reported contacts and use of non-essential shops or services within 14 days of the first dose of a COVID-19 vaccine compared to pre-vaccination.
Characteristic Contacts Use of non-essential shops or services
n Paired OR Std. Err. 95 % CI P-value n
Paired OR Std. Err. 95 % CI P-value
Sex
Male
Female
198
154
1.71
1.57
0.25
0.26
1.28–2.29
1.13–2.17
<0.001
0.007
60
65
1.86
1.24
0.51
0.31
1.08–3.19
0.76–2.03
0.025
0.39
Age group
<60
≥60
151
201
1.48
1.80
0.26
0.27
1.05–2.08
1.33–2.41
0.027
<0.001
45
80
1.37
1.58
0.41
0.36
0.75–2.47
1.01–2.48
0.30
0.046
Models adjusted for household structure with cluster-robust standard errors.
After stratification by sex, greater odds of reporting non-household close contacts within the 14 days post-vaccination compared to pre-vaccination were consistent for both men (OR 1.71, 95 % CI 1.28–2.29, p=<0.001) and women (OR 1.57, 95 % CI 1.13–2.17, p = 0.007). However, overlapping confidence intervals indicate a lack of effect heterogeneity by sex in the study population. There was evidence of effect of vaccination status on use of non-essential shops or services amongst male participants, who had greater odds of reporting these activities post-vaccination (OR 1.86 95 % CI 1.14–3.32, p = 0.015). Female participants were not at increased odds of reporting use of non-essential shops or services (OR 1.24, 95 % CI 0.76–2.03, p=<0.39).
The odds of reporting non-household contacts and use of non-essential shops or services were respectively 1.48 (95 % CI 1.05–2.08, p = 0.027) and 1.37 (95 % CI 0.75–2.47, p = 0.30) amongst participants aged <60 years. Elevated odds were seen in those aged 60 years or more, with greater odds for both non-household close contacts (OR 1.80, 95 % CI 1.33–2.41, p=<0.001) and use of non-essential shops or services (OR 1.58, 95 % CI 1.01–2.48, p = 0.046).
4 Discussion
Our findings indicate that within 14 days of their first dose of a COVID-19 vaccine, participants were more likely to report non-household close contacts (OR = 1.65, 1.31–2.06) and using non-essential shops and services (OR = 1.50, 1.03–2.17) compared to pre-vaccination. There was no substantial heterogeneity in the effect of vaccination status on non-household close contacts between men and women, but men had higher odds of reporting use of non-essential shops or services. Participants aged 60 years or more had greater odds of reporting both non-household close contacts and use of non-essential shops or services post-vaccination. Increased close contact with people outside of the household and in non-essential retail activities may contribute to the increased risk of infection seen within 14 days of a first dose of a COVID-19 vaccine [13], [14], although directly measuring the risk of infection associated with these behaviours was outside the scope of this analysis.
The greatest effect was seen for contacts with people outside of the household. Few participants reported use of non-essential shops or services or social activities, leading to the exclusion of social activities from this analysis due to small group sizes. This is largely due to closure of public venues (e.g., non-essential retail or indoor dining venues) under national regulations in England and Wales at the time of the survey. Results from stratified analyses must also be interpreted with caution due to small subgroup sizes (Table 5), which likely substantially reduced power for these analyses.
These results highlight the need to maintain protective behaviours within 14 days of the first dose of a COVID-19 vaccine while immunological protection builds. This could reduce the risk of infection in this time and is a relevant target for public health messaging in future outbreaks of infectious diseases for which vaccines are available. Although this analysis focused specifically on non-essential contacts and activities during the third national lockdown, it is possible that the effect of vaccination on activity levels at times when social distancing and other public health measures are relaxed may be even greater. Additionally, there is some evidence to suggest that older people are more likely to adopt or adhere to protective behaviours than younger people during pandemics [36]. As most participants in this project were older than 50 years of age, our findings may be different or magnified in younger age groups. Our findings may also be of relevance for current and future booster doses of COVID-19 vaccines considering reported reductions in vaccine effectiveness over time [37], [38], [39], [40]. This may be of particular importance to booster programmes given recent evidence for immune evasion of both natural and vaccine-acquired immunity by the Omicron Variant of Concern (VoC) [41], currently the most dominant strain in the UK [42]. Longer-term, quantitative data on behaviour related to vaccination could be used to parametrize and validate models of infectious disease dynamics used in public health decision making and policy [20], [21], assisting public health planning in response to future outbreaks of infectious diseases.
Our findings are supported by an analysis of social contacts between school children in the United States during the COVID-19 pandemic, which reported increased non-household contacts amongst children and adults in a household where at least one adult was vaccinated between February and April 2021 [43]. While we found no differences in the effect of vaccination status between men and women, previous research has suggested that women may be more likely to adopt protective behaviours than men during pandemics, which is potentially related to increased perceived risk [36]. A study examining adherence to public health guidance in the UK during the COVID-19 pandemic found that women reported making fewer trips outside of the home than men and less use of non-essential shops [44]. Although our findings are supported by evidence that a reduction in protective behaviours may be seen after the introduction of vaccines during the COVID-19 pandemic [26], [45], [46], they are not consistent with those from a Virus Watch study using GPS location data to examine changes in travel distance pre- and post- the first dose of a COVID-19 vaccine during the third national lockdown [47]. In this analysis, Nguyen et al. found no evidence for an increase in the rate of change in the distance travelled by participants post-vaccination in the 30 days before and after vaccination, suggesting that participants may not have altered behaviours leading to an increased distance travelled during this time. However, participants in the geolocation tracking arm of the study were aware that they were being monitored and so may have been more likely to modify their behaviour. Our results differ from those of Wright et al. [27], whose research showed that compliance with public health guidelines increased in both unvaccinated and vaccinated individuals from October 2020 to February 2021. Context-specific psychological predictors of behaviour could account for this difference. A scoping review of 149 studies of behaviour change during pandemics (including the COVID-19 pandemic) found higher perceived risk predicted greater levels of adherence to a number of protective behaviours, including social distancing and avoidance of non-essential shopping [48]. A number of contextual factors could have caused changes in perceived risk between late 2020 and early 2021. Perceived risk may have been greater during the national lockdown beginning 6 January 2021 at the peak of the second wave of COVID-19 cases [49], which was larger and associated with more deaths from COVID-19 than the first wave [50], [51]. It also occurred at a time when the SARS-CoV-2 Alpha VoC was rapidly expanding [52]. Comparison of longer-term changes in compliance with guidelines or protective behaviours post-vaccination with changes over a shorter period of time may therefore not be appropriate.
Alternatively, perceived risk may contribute to the changes in behaviour seen in this analysis independently of vaccination status. Following the peak in January 2021, COVID-19 cases decreased from February – March 2021 [48], and so over time participants may have felt at less risk of COVID-19 and gradually increased their contacts and activities. As our analysis did not directly examine the perceived risk of infection further investigation of the relationship between psychological predictors of behaviour, vaccination and infectious disease dynamics is warranted.
A strength of the case-crossover design is that self-matching controls for time-invariant confounders, which may be unknown or difficult to quantify. Furthermore, by selecting pre- and post-vaccination time periods to be during a national lockdown, temporal and spatial variation in behaviours was likely minimised. The potential for information bias is minimised as survey questions remained the same over time, and self-matching means that the same individuals reported information for both pre- and post-vaccination time periods.
An important limitation of this analysis is the lack of representativeness of the study population with the general population in England and Wales. Participants who responded to surveys were predominantly male, of White ethnicity, living in the East or South East of England and living in less deprived environments with high household income. This is significant given the unequal burden of disease and mortality from COVID-19 in the UK population. People living in overcrowded households are at greater risk of SARS-CoV-2 infection [53], and rates of diagnosis and death are higher in people who live in more deprived areas and are from minority ethnic backgrounds [54]. Most participants were over 60 years of age, likely due to the phases of COVID-19 vaccine delivery, with older age groups being prioritised in the early stages of the COVID-19 vaccination programme [55]. The age structure of the cohort may therefore have been subject to eligibility bias. It is possible that a change in vaccination status may have had a greater effect on participants aged 60 years or more who were less worried about COVID-19 than those under 60 years of age who were eligible for vaccination during the same time period. Confounders such as household income, IMD and region were only available at baseline, however these were unlikely to vary over the study period. Self-reported contacts and activities may have been affected by recall bias, although this may have been minimised by survey timing (i.e., the following week). Social desirability bias may have been reduced by recording survey responses online. Importantly, the infection risk of contacts and activities in this project could not be directly measured using survey responses – this limits inferences about the impact of changes in behaviour on infection risk and is recommended as a focus for future research.
This analysis provides quantitative evidence for an association between vaccination status and transmission-relevant behaviour using data gathered during the third national lockdown in England and Wales. Our findings suggest that changes in protective behaviours occur while immunological protection is building and may contribute to risk of infection in the 14 days after vaccination, and therefore interventions emphasising the need to maintain protective behaviours in the recently vaccinated could reduce the risk of infection during time periods while immunity is building. It is possible that such an effect could exist during outbreaks of other vaccine preventable infectious diseases, and therefore quantitative data on behaviour relevant to vaccination could be of use in policy-making for not only current and future booster doses of COVID-19 vaccines but also vaccines for emerging and future pathogens.
Funding
The Virus Watch study is supported by the MRC Grant Ref: MC_PC 19,070 awarded to UCL on 30 March 2020 and MRC Grant Ref: MR/V028375/1 awarded on 17 August 2020. The study also received $15,000 of Facebook advertising credit to support a pilot social media recruitment campaign on 18th August 2020. This study was also supported by the Wellcome Trust through a Wellcome Clinical Research Career Development Fellowship to RA [206602]. SB and TB are supported by an MRC doctoral studentship (MR/N013867/1). The funders had no role in study design, data collection, analysis and interpretation, in the writing of this report, or in the decision to submit the paper 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.
Data availability
We aim to share aggregate data from this project on our website and via a “Findings so far” section on our website - https://ucl-virus-watch.net/. We also share some individual record level data on the Office of National Statistics Secure Research Service. In sharing the data we will work within the principles set out in the UKRI Guidance on best practice in the management of research data. Access to use of the data whilst research is being conducted will be managed by the Chief Investigators (ACH and RWA) in accordance with the principles set out in the UKRI guidance on best practice in the management of research data. We will put analysis code on publicly available repositories to enable their reuse.
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| 36496282 | PMC9721283 | NO-CC CODE | 2022-12-12 23:20:58 | no | Vaccine. 2022 Dec 5; doi: 10.1016/j.vaccine.2022.11.073 | utf-8 | Vaccine | 2,022 | 10.1016/j.vaccine.2022.11.073 | oa_other |
==== Front
Journal of Hospitality and Tourism Management
1447-6770
1447-6770
The Authors.
S1447-6770(22)00189-9
10.1016/j.jhtm.2022.12.003
Article
Compensatory travel in the post COVID-19 pandemic era: How does boredom stimulate intentions?
Yao Yanbo a
Zhao Xinxin a∗
Ren Lianping b
Jia Guangmei a
a College of Tourism and Service Management, Nankai University, Tianjin, China
b Macao Institute for Tourism Studies, Colina de Mong-Ha, Macao
∗ Corresponding author.
5 12 2022
5 12 2022
28 3 2022
30 11 2022
3 12 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.
To promote tourism recovery in the post-COVID-19 pandemic era, it is critical to understand the psychological factors that either boost or suppress travel demands. However, little is known about the underlying psychological mechanism that affects compensatory travel intention. Therefore, by scrutinizing the roles that autonomous self-motivation, sensation seeking, and perceived susceptibility to COVID-19 play, this study conducted two scenario-based experiments (N = 223 + 200) to explore the psychological mechanism and boundary conditions behind the influence of boredom on compensatory travel intention. The findings reveal that people are more likely to generate compensatory travel intention when there is a higher level of boredom during the COVID-19 pandemic due to their desire for sensation seeking. This effect is magnified when people adopt autonomous self-motivating strategies. However, for people with high (vs. low) perceived susceptibility to COVID-19, a high level of boredom evokes lower compensatory travel intention through sensation seeking.
Keywords
Compensatory travel intention
Boredom
Sensation seeking
Autonomous self-motivation
Perceived susceptibility
COVID-19
==== Body
pmc1 Introduction
Aspects of the COVID-19 pandemic have brought about a new normal, suppressing travel demand since 2020 and causing unprecedented damage to travel and tourism worldwide. However, the relaxation of tourism restrictions and widespread vaccination have resulted in signs that people are interested in resuming travel (Williams et al., 2022). According to Airbnb's financial report, its net profit in the third quarter of 2021 was USD 834 million, the highest quarterly profit in the company's history, with a profit margin of 49%, 30% more than for the third quarter of 2019, before the COVID-19 pandemic (NetEase, 2021). Previous research has indicated that when the crisis is over, tourism industry will recover as travel demand increases (Novelli et al., 2018). Tourism companies now count on a post-pandemic travel boost, the industry's resilience, and people's compensatory travel intention. Therefore, to promote the recovery of tourism in the post-pandemic era, it is important to understand under what conditions people would have higher travel intentions.
Psychology literatures suggest that boredom, autonomous self-motivation, sensation seeking, and perceived susceptibility may influence behaviour (D'Souza et al., 2011; LePera, 2011; Waterschoot, 2021Waterschoot, 2021; Zuckerman, 1979). To avoid the spread of COVID-19, many countries have adopted the home isolation policy and travel restrictions to prevent transmission. The restrained activities and repetitive routines during the pandemic led to low motivation levels and boredom (Chao et al., 2020). Boredom is defined as an unwanted experience, a lack of desire to participate in fulfilling activities, resulting in feelings of dissatisfaction and meaninglessness (Fahlman et al., 2013). Boredom is a negative emotion that can cause physical and psychological harm in the long run (Williams et al., 2020), leading to a change in behaviours. But the other side of the coin is that boredom can create a need for novel stimuli and experiences and can arouse sensation and stimuli seeking (Reisenzein, 2017). From the self-determination theory perspective, when autonomously motivated, people seek alternative activities and novel experiences, which will reduce their boredom level (Waterschoot, 2021Waterschoot, 2021). However, in the post-pandemic era, the pandemic situation has seen rises and falls, and it is still continuing by the time of writing (Wang, 2020). There are still risks of COVID-19 infection during travel (Dryhurst et al., 2020). Therefore, people's perceived susceptibility to COVID-19 may pose hindrance in compensatory travel intention in the post-pandemic world.
Many studies have focused on the effect of the COVID-19 pandemic on tourism (Duro et al., 2021; Qiu, Park, Li, & Song, 2020), tourist behaviour (Bhati et al., 2021; Huo, 2021; Neuburger & Egger, 2021; Rather, 2021), and tourism recovery (Fotiadis et al., 2021; Kim & Liu, 2022). Few studies have paid attention to compensatory travel, and little is known about the psychological factors affecting post-pandemic potential demand and compensatory travel intention (Kim et al., 2021; Zhang et al., 2021; Zopiatis, 2021). Hence, this study attempts to fill this gap by exploring whether boredom from limited activities can stimulate positive results in compensatory travel intention. Furthermore, this study explores the psychological mechanism and boundary condition behind the influence of boredom from limited activities on compensatory travel intention by looking into the role that autonomous self-motivation, sensation seeking and perceived susceptibility to COVID-19 play through two experiments. The findings of this study enrich existing knowledge on compensatory travel in the post COVID-19 world and provide insights into the psychological mechanism behind such intentions. The results of this study also provide references for tourism marketers in their efforts to revive post COVID-19 tourism.
2 Theoretical background and hypotheses
2.1 Boredom and compensatory travel in post-pandemic world
As a result of the COVID-19 outbreak, many countries have issued travel bans and quarantine orders, forcing the suspension or cancellation of travel activities, which has ultimately resulted in a monotonous lifestyle with limited activities and repetitive routines. Limited activities cause negative emotions, psychological pressure, and different degrees of psychological problems (Qiu, Shen, et al., 2020; Xiang et al., 2020). Furthermore, the restrained activities and monotonous routines during the pandemic have caused a low arousal state and induced boredom (Chao et al., 2020; Williams et al., 2020). Boredom is a negative experience, a desire to engage in satisfactory activity, causing dissatisfaction and meaninglessness (Fahlman et al., 2013). It is directly linked to many negative states, such as depression, anxiety and stress (Lee & Zelman, 2019).
Compensatory consumption refers to purchases made to satisfy unmet psychological needs and to alleviate negative emotions (Grunert, 1994). Negative experiences make consumers' compensatory consumption intention and their travel budgets significantly higher than their budgets for regular consumption (Huo, 2021). The interruption and loss of travel experiences may cause psychological distress and stress. These negative states may lead to abnormal compensatory behaviour (Gabriel & Monaco, 1994). The essential feature of compensatory consumption is the need to address a psychological deficiency or to reach an ideal psychological state through consumption behaviour, a form of pure psychological consumption (Rucker & Galinsky, 2008; Woodruffe-Burton & Elliott, 2005). High boredom state can activate people's desire to seek out and to explore new experiences and alternative experiences (Reisenzein, 2017), while people in low boredom state, they are excited in this higher level of arousal (Picard et al., 2016). People with boredom are also more likely to choose high-risk and high-pleasure activities (Abramson & Stinson, 1977; LePera, 2011; Vodanovich, 2003). Tourism can provide people with novel experiences, and post-pandemic travel can be a form of risk-taking behaviour.
Based on the compensatory consumption theory, compensatory travel is conceptualized as traveling for a longer time, with a bigger budget, or travelling more frequently to satisfy unfulfilled and suppressed leisure desires arising from constraints (such as restrictions imposed due to COVID-19) (Kim et al., 2021). Many previous studies have demonstrated that stressful situations can cause negative experiences leading to unusual consumer behaviour, and consumer willingness to spend more to compensate for difficult times (e.g., Chen, 2021; Ruvio et al., 2014). Bench and Lench (2018) found that compared with low boredom state, high boredom people desired novel experiences. Therefore, H1 was developed:H1 Compared to low boredom, high boredom will trigger greater compensatory travel intention.
2.2 The mediating role of sensation seeking
Sensation seeking is conceptualized as a desire for novel, exciting sensations and experiences (Zuckerman, 1979). It contains four dimensions: (a) experience seeking, which means the demand to seek different experiences; (b) thrill and adventure seeking, meaning the need for exciting and stimulating activities and experiences; (c) avoiding repetition and monotony, meaning detesting endless repetition and monotony; and (d) disinhibition, which refers to the sense of having no inhibition (Hoyle et al., 2002; Litvin, 2008). Sensation seeking plays a vital role in predicting behaviour. For instance, it involves an aversion to structured, restrained, and repetitive situations (Nickerson & Ellis, 1991), and an interest in new and exciting experiences such as novel foods (Galloway & Lopez, 1999).
The outbreak of COVID-19 resulted in restrained activities and repetitive routines, causing low arousal states and feelings of boredom (Chao et al., 2020). The state of boredom will motivate the need for different and novel stimuli and experiences (Reisenzein, 2017). Bored tourists want to get involved in more activities and spend more than other tourists (Mauri & Nava, 2021). Although sensation seeking is associated with personal trait, situational factors lead to varying degrees of such behaviour (Tett & Burnett, 2003). Therefore, boredom as a situational factor may change the level of sensation seeking. The COVID-19 pandemic caused serious concerns about travel safety (WHO, 2020); however, the desire for sensation seeking may increase the willingness to take risks (Zuckerman, 1979). Therefore, we assume that boredom will arouse sensation seeking to increase sensations and stimuli, triggering travel intentions that offer stimuli and different experiences (Park & Stangl, 2020). The following hypothesis was developed:H2 Sensation seeking mediates the relationship between boredom and compensatory travel intentions.
2.3 The moderating role of autonomous self-motivation
Self-determination theory posits that people are inherently inclined to psychological growth and integration and prone to learning and connecting with others (Ryan & Deci, 2020). Controlled motivation involves feeling compelled or pressured by internal or external forces and acting in a specific way. However, autonomous motivation involves active and integrative tendencies and personal causation (Deci & Ryan, 1985). Activities arising from exploration and curiosity reflect intrinsic motivation because they do not rely on external motivation or pressure. They create their own satisfaction and fun. Autonomous self-motivation refers to the degree to which an individual actively try to increase the interest, pleasure and value of an activity (Smit et al., 2017). For example, such self-motivation includes involving game elements in work conditions (Skowronski, 2012) or seeking meaning in the current activity (Green-Demers et al., 1998). Autonomous self-motivating strategies are part of the self. They remain stable over time and in changing situations (Koestner et al., 1992).
Evidence shows that when people become bored, they seek escape by engaging in new activities (Bench & Lench, 2019). During the COVID-19 pandemic, limited activities and home isolation caused boredom (Chao et al., 2020). Autonomously motivated people show proactive cognition and behaviours. They seek novel exciting feelings and experiences in alternative activities, thereby reducing boredom and promoting life satisfaction (Waterschoot, 2021Waterschoot, 2021). Therefore, we assume that autonomous self-motivation moderates the indirect effect between boredom and compensatory travel intention due to sensation seeking. For people who have autonomous self-motivation, boredom may trigger greater compensatory travel intention than those who lack such self-motivation. Accordingly, we propose:H3 Boredom will trigger greater compensatory travel intention for people with (vs. lack of) autonomous self-motivation due to greater sensation seeking.
2.4 The moderating role of perceived susceptibility to COVID-19
Perceived susceptibility refers to an individual's susceptibility to specific diseases, which is a critical construct in the health belief model (Klohn & Rogers, 1991). The health belief model integrates value expectation theory, cognitive theory, and other related psychological theories. It is widely used to explain why individuals engage in health-related behaviours (Champion & Skinner, 2008). After the COVID-19 outbreak, the health belief model was applied to explain the changes in tourists' behaviours in the pandemic context (Suess, Maddock, Dogru, Mody, & Lee, 2022). In the present study, perceived susceptibility means tourists' belief about their susceptibility to contracting COVID-19. The COVID-19 pandemic caused major travel restrictions globally. Tourist's behaviour in the post-pandemic era depends on the perceived risk of infection and the safety of the travel destination (Dryhurst et al., 2020). Health risk perceptions in tourism significantly influence tourism intention in the post-pandemic era (Godovykh et al., 2021; Ram et al., 2021; Yu et al., 2021). On the other hand, vaccination tends to decrease the perceived susceptibility to contracting COVID-19 and promote the travel intention (Boto-García & Pino, 2022). Therefore, high perceived susceptibility to COVID-19 is likely to suppress compensatory travel intention, whereas low perceived susceptibility may increase compensatory travel intention. We assume that perceived susceptibility moderates the indirect effect between boredom and compensatory travel intention due to sensation seeking. People with low (vs. high) perceived susceptibility boredom will have greater compensatory travel intention due to sensation seeking. Therefore, we propose:H4 Boredom will induce greater compensatory travel intention for people with low (vs. high) perceived susceptibility because of sensation seeking.
3 Overview of the studies
The purpose of this study was to explore how boredom stimulates intention, as well as the psychological mechanism and boundary conditions behind the influence of boredom on compensatory travel intention. This was achieved via two studies. Study 1 tested the main effect between boredom and compensatory travel intention (H1), mediating effect of sensation seeking (H2) and the moderating effect of autonomous self-motivation (H3). Study 2 tested the moderating effect of perceived susceptibility to COVID-19 (H4). Fig. 1 illustrates the conceptual model.Fig. 1 Conceptual model.
Fig. 1
4 Study 1
Study 1 was a 2 (boredom: high vs. low) × 2 (autonomous self-motivation vs. lack of self-motivation) between-subjects experiment to examine the main effect of boredom on compensatory travel intention, the mediating effect of sensation seeking and moderating effect of autonomous self-motivation.
4.1 Method
4.1.1 Stimuli
This study manipulated the level of boredom. The experiment aimed to elicit a high boredom and low boredom state. Previous studies manipulated boredom by having participants engage in repetitive tasks (e.g., Gu et al., 2021; Van Tilburg & Igou, 2011). We designed the stimuli by referencing blogs describing activities and feelings resulting from COVID-19 lockdowns. We made textual descriptions of activities depicted in blogs (see Appendix 1). Two scenarios described two different states during the COVID-19 pandemic. In the high boredom scenario, the textual description addressed the repetitive activities and monotonous routines caused by the lockdown during the COVID-19 pandemic. In the low boredom scenario, although people's activities were restrained, they engaged in various interesting activities providing different sensations and stimuli during the COVID-19 pandemic.
A pretest was conducted to determine if the scenario could induce boredom. After reading the textual description, participants were required to assess the extent of boredom they felt, ranging from 1 to 7. Eighty-four participants were recruited. To compare the means of boredom in different groups, the independent sample t-test was conducted. Results showed participants in the high boredom scenario expressed significantly greater degree of boredom than the ones in the low boredom group (M high boredom = 4.61, M low boredom = 2.89, F (1,82) = 1.735, p < 0.001), verifying that the scenarios could induce boredom successfully.
4.1.2 Procedures
First, participants were randomly assigned to the high boredom scenario or the low boredom scenario. After reading the textual description, participants were required to assess the extent of boredom they felt. Next, the Motivational Self-Regulation Strategies questionnaire (MSRS, Waterschoot, 2021Waterschoot, 2021) was used to determine if participants had autonomous self-motivation or lacked self-motivation. MSRS contains three subscales: autonomous self-motivation, controlled self-motivation and lack of self-motivation. We adopted the autonomous self-motivation and lack of self-motivation subscales. Higher scores in one of the two subscales showed whether the participants had autonomous self-motivation or lacked self-motivation. For example, when a respondent who has scored 4 in the scale of autonomous self-motivation, but scored 6 in the scale of lacking self-motivation, he/she is categorized into lacking self-motivation group. Finally, participants completed the measurement of sensation seeking and compensatory travel intentions.
4.1.3 Participants
A total of 230 participants were recruited in Credamo (a survey company specializing in collecting responses in China with 1.5 million qualified users) on December 8, 2021. Based on the response time and attention check question (A scale instructed item “Test Question, please choose 1”), 7 samples were excluded. The final samples comprised 223 participants: 43% of the samples was male, 57% were female, 59% were 21–30 years old, and 33% were 31–40 (see Table 1 ).Table 1 Profile of respondents.
Table 1Items Study 1 Study 2
n % n %
Gender rowhead
Male 96 43.05 83 41.5
Female 127 56.95 117 58.5
Age
18–20 8 3.59 7 3.5
21–30 131 58.75 127 63.50
31–40 74 33.18 60 30
41–50 6 2.69 5 2.5
51–60 4 1.79 1 0.5
Education
High school 5 2.24 4 2
Vocational school 20 8.97 21 10.5
Undergraduate degree 170 76.23 148 74
Master's degree 25 11.21 22 11
PhD 3 1.35 5 2.5
Occupation
Student 33 14.8 24 12
State-owned enterprise staff 45 20.18 44 22
Government-affiliated institutions staff 21 9.42 12 6
Civil servant 3 1.34 5 2.5
Private enterprise staff 121 54.26 115 57.5
Monthly income (RMB)
≤5000 69 30.9 50 25
5001–10,000 127 57 112 56
10,001–15,000 20 8.97 25 12.5
≥15,001 7 3.14 13 6.5
4.2 Measurement
Taking cultural background into consideration, boredom was measured by the five items adapted from the Chinese version of the Multidimensional Boredom Scale (MSBS) (Liu et al., 2013). We added “during the pandemic” before the items, such as “During the pandemic, I feel bored”. The measure's Cronbach's α was 0.88. Sensation seeking was measured by the brief sensation seeking scale for Chinese (BSSS–C), containing eight items (Cronbach's α = 0.89; Chen et al., 2013a, Chen et al., 2013b). The measurement of compensatory travel intentions was adopted from Kim et al. (2021). The question is “I intend to travel in the near future with following features:”. And the features include: frequency (1 = less frequent, 7 = more frequent), length (1 = shorter, 7 = longer), and spending (1 = Less travel spending, 7 = more travel spending) (Cronbach's α = 0.89). All measures utilized a seven-point Likert scale (see Appendix 2 for details on the measurement items).
4.3 Results
4.3.1 Manipulation check
As expected, the participants assigned to the high boredom group perceived more boredom than the low boredom group (M high boredom = 5.26, M low boredom = 2.61, F (1,221) = 3.05, p＜0.001), implying the successful manipulation of boredom.
4.3.2 Main effect
One-way ANOVA was used to test the main effect. The results indicated that the effect of boredom on compensatory travel intention was significant (M high boredom = 5.11, M low boredom = 3.96, F(1,221) = 2.536, p＜0.001). High boredom will trigger greater compensatory travel intention compared to low boredom. Therefore, H1 was supported.
4.3.3 Mediating effect
When controlling the effect of gender, age, and income, bootstrapping tests with 5000 replications and 95% Confidence Interval (CI) were conducted to analyse the mediating role of sensation seeking. In PROCESS Macro for SPSS (mediation model 4), boredom and compensatory travel intentions were used as the independent variable and dependent variable respectively, while sensation seeking was the mediating variable. The mediating effect of sensation seeking was significant (Indirect Effect = 0.142, 95% CI: 0.075, 0.231), and the direct effect was also significant (Effect = 0.129, 95% CI: 0.003, 0.254), verifying the partial mediating role of sensation seeking (see Table 2 ). Therefore, H2 was supported.Table 2 Results of mediation analysis in Study 1.
Table 2Boot 95%CI
Value BootSE LLCI ULCI Percentage
Total effect 0.271 0.056 0.153 0.389
Direct effect 0.129 0.064 0.003 0.254 47.6%
Indirect effect 0.142 0.039 0.075 0.231 52.4%
4.3.4 Moderated mediation analysis
A moderated mediation analysis was conducted by using the PROCESS macro in SPSS (Hayes, 2013; model 7). Boredom functioned as independent variable (Low boredom = 0, High boredom = 1). Compensatory travel intention functioned as dependent variable. The mediator was sensation seeking. Autonomous self-motivation functioned as a moderator (Lack of self-motivation = 0, Autonomous self-motivation = 1). Control variables were gender, age and income. The significance of direct and indirect effects was evaluated by 5000 replications and 95% Confidence Interval (CI). The results showed that the effect of boredom on compensatory travel intention was significant (β = 0.162, p＜0.001). Boredom had significant impact on sensation seeking (β = 0.421, p＜0.001). The effect of sensation seeking on compensatory travel intention was also significant (β = 0.558, p＜0.001). Autonomous self-motivation moderated the relationship between boredom and sensation seeking such that for people who had autonomous self-motivation, the effect of boredom on sensation seeking was strengthened (β = 0.233, p＜0.001). The conditional indirect effects of boredom on compensatory travel intention at different levels of autonomous self-motivation were significant (see Table 3 ). The difference of indirect effects at different levels of autonomous self-motivation was significant (see Table 3). Specifically, for people with autonomous self-motivation (+1SD) the indirect effect of boredom on compensatory travel intention through sensation seeking was greater than it was for those who lacked self-motivation (-1SD), which means for people with autonomous self-motivation boredom triggered greater compensatory travel intention through sensation seeking. Therefore, H3 was supported.Table 3 Conditional indirect effects of X on Y at different moderator values in Study 1.
Table 3Moderator: Autonomous self-motivation Indirect effect se Boot 95%CI
LLCI ULCI
Low: −1SD 0.109 0.032 0.050 0.174
High: +1SD 0.383 0.065 0.262 0.519
Difference 0.274 0.061 0.165 0.406
5 Study 2
Study 2 was a 2 (boredom: high vs. low) × 2 (perceived susceptibility: high vs. low) between-subjects experiment to test the moderating effect of perceived susceptibility to COVID-19.
5.1 Method
5.1.1 Stimuli
This study manipulated the level of boredom and perceived susceptibility to COVID-19. The manipulation of boredom is the same as Study 1. As the manipulation of perceived susceptibility to COVID-19, we consulted COVID-19 prevention regulations in several scenic areas with strict COVID-19 prevention regulations to design the stimuli. In the high perceived susceptibility to COVID-19 scenario, the text (see Appendix 1) describes the absence of pandemic prevention regulations and the probability of infected people in the scenic area. In the low perceived susceptibility to COVID-19 scenario, the text stresses strict pandemic prevention regulations such as ‘only people who have not been to a city where COVID-19 cases have been identified within 14 days are allowed to visit’ and ‘all the people in this scenic area must wear mask’.
Similarly, a pretest was conducted to confirm if the scenario could elicit perceived susceptibility to COVID-19. The participants were encouraged to imagine they were travelling to a scenic area. They were randomly assigned to a high perceived susceptibility scenario or a low perceived susceptibility scenario. Participants were required to assess the perceived possibility of virus contraction in this scenario, ranging from 1 to 7. A total of 84 participants were recruited. Results also showed that the high perceived susceptibility scenario induced higher level of perceived susceptibility than the ones who were assigned to the scenario with low perceived susceptibility (M high susceptibility = 6.03, M high susceptibility = 3.02, F (1, 82) = 65.015, p < 0.001), verifying that the scenarios could induce perceived susceptibility successfully.
5.1.2 Procedures
Participants were randomly assigned to one of the four scenarios: high perceived susceptibility with low boredom group (n = 51), high perceived susceptibility with high boredom group (n = 50), low perceived susceptibility with low boredom group (n = 49), and low perceived susceptibility with high boredom group (n = 50). After reading the scenarios, participants were required to assess the extent of boredom and perceived susceptibility. Then participants completed the measurements of sensation seeking and compensatory travel intentions.
5.1.3 Participants
A total of 220 participants were recruited in Credamo on December 11, 2021. Based on the response time and attention check question (A scale instructed item “Test Question, please choose 1”), 20 samples were excluded. Of the 200 samples: 41.5% of the samples was male, 58.5% were female, 63.5% were 21–30 years old, and 30% were 31–40 (see Table 1).
5.1.4 Measurement
The measurement scales of boredom (Cronbach's α = 0.87), sensation seeking (Cronbach's α = 0.88) and compensatory travel intentions (Cronbach's α = 0.81) were the same as Study 1. Participants' perceived susceptibility to COVID-19 was accessed by three items adopted from Suess et al. (2022) (Cronbach's α = 0.83). All measurement scales utilized a seven-point Likert scale (see Appendix 2 for details on the measurement items).
5.2 Results
5.2.1 Manipulation check
As expected, the participants assigned to the high perceived susceptibility group perceived a greater possibility of contracting COVID-19 than the low perceived susceptibility group (M high perceived susceptibility = 6.21, M low perceived susceptibility = 2.74, F(3,196) = 6.05, p＜0.001). The participants assigned to the high boredom group perceived more boredom than the low boredom group (Mhigh boredom = 5.74, Mlow boredom = 2.40, F(3,196) = 14.83, p＜0.001), implying the successful manipulation of perceived susceptibility and boredom.
5.2.2 Moderated mediation analysis
A moderated mediation analysis was conducted by using the PROCESS macro in SPSS (Hayes, 2013; model 14) to estimate the moderating role of perceived susceptibility. Boredom functioned as independent variable (Low boredom = 0, High boredom = 1). Compensatory travel intention functioned as dependent variable. The mediator was sensation seeking. Perceived susceptibility functioned as a moderator (Low perceived susceptibility = 0, High perceived susceptibility = 1). Control variables were gender, age and income. The significance of direct and indirect effects was evaluated by 5000 replications and 95% Confidence Interval (CI). The results showed that the effect of boredom on compensatory travel intention was significant (β = 0.361, p＜0.001). Boredom had significant impact on sensation seeking (β = 0.716, p＜0.001). The effect of sensation seeking on compensatory travel intention was also significant (β = 0.425, p＜0.001). Perceived susceptibility moderated the relationship between sensation seeking and compensatory travel intention such that for people who had high perceived susceptibility to COVID-19, the effect of sensation seeking on compensatory travel intention was reduced (β = −0.336, p＜0.05). The conditional indirect effect of boredom on compensatory travel intention through sensation seeking at different levels of perceived susceptibility was significant (see Table 4 ). There was significant difference in indirect effect at different levels of perceived susceptibility (see Table 4). Specifically, for people with low level of perceived susceptibility (-1SD), the indirect effect of boredom on compensatory travel intention through sensation seeking was greater than it was for people with high level of perceived susceptibility (+1SD). Therefore, H4 was supported.Table 4 Conditional indirect effects of X on Y at different moderator values in Study 2.
Table 4Moderator: Perceived susceptibility Indirect effect se Boot 95%CI
LLCI ULCI
Low: 1SD 0.434 0.133 0.211 0.732
High: +1SD 0.193 0.091 0.017 0.382
Difference 0.241 0.135 0.012 0.544
6 Conclusion and discussion
The COVID-19 pandemic has caused unprecedented impacts on tourism, and prior studies have revealed tourist behaviour changes during the pandemic (Neuburger & Egger, 2021; Rather, 2021; Yang, 2021). For example, some studies found that health-protective behaviour and media engagement have an impact on post-COVID-19 travel (Bhati et al., 2021). Studies investigated people’ cognitive and emotional processes in forming compensatory travel demands in post-COVID-19 era (Kim et al., 2021), but little is known about how specific state like boredom (leading feeling during lockdown) (Chao et al., 2020; Williams et al., 2020) stimulate compensatory travel intention. This study fills this gap by exploring what psychological mechanism and boundary conditions are behind the influence of boredom on compensatory travel intention.
This study proposed a moderated mediation model that consists of the relationship between boredom, autonomous self-motivation, sensation seeking, perceived susceptibility to COVID-19 and compensatory travel intention. This study demonstrated that boredom from limited activities during the COVID-19 pandemic triggered compensatory travel intention in the post COVID-19 pandemic period. This was because, as a low arousal state, boredom triggered sensation seeking, which played partial mediating role between boredom and compensatory travel intention. Autonomous self-motivation and perceived susceptibility moderated the mediation. Specifically, boredom triggered greater compensatory travel intention for people with autonomous self-motivation (vs. lack of self-motivation) and low perceived susceptibility (vs. high perceived susceptibility) to COVID-19 due to sensation seeking.
6.1 Theoretical implications
This study has innovatively proposed a moderated mediation model to understand how the state of boredom influences travel intentions, and verified the applicability of compensatory consumption theory in the tourism context and provided insights into psychological factors affecting potential demands and compensatory travel intention in the post-pandemic world. First, boredom from limited activities during the COVID-19 pandemic was shown to influence compensatory travel intention in post COVID-19 pandemic significantly. In addition, people's sensation seeking tendency partially mediates this relationship. These findings corroborate previous studies, such as Bench and Lench (2018), Chen (2020) and Deng et al. (2020). Bench and Lench (2018) found that compared with the low boredom state, high boredom participants desired more novel experiences. Chen (2020) illustrated that boredom during the COVID-19 pandemic motivated online leisure crafting, contributing to individuals' thriving at home. Boredom induced the sensation seeking which in turn triggered purchase intentions (Deng et al., 2020).
Furthermore, our use of the self-determination theory addressed why people acted differently in a high boredom state. Autonomously motivated tourists showed more proactive cognition and behaviours. They sought alternative activities for novel, exciting feelings and experiences to reduce the boredom level and promote life satisfaction (Waterschoot et al., 2021). Therefore, for tourists with autonomous self-motivation, boredom triggered greater compensatory travel intention than for those lacking self-motivating strategies through sensation seeking. Additionally, this study advances the understanding of how the existing infection risk of COVID-19 shaped potential travel demands in the post-pandemic world. Perceived susceptibility moderated the indirect effect between boredom and compensatory travel intention. High perceived susceptibility suppressed the impact of boredom on compensatory travel intention through sensation seeking. This is because high perceived susceptibility results in the perception of health risk (Mermelstein & Riesenberg, 1991), and health-related crises suppress post-pandemic travel intention (Kim et al., 2021).
6.2 Managerial implications
The findings of this study provide valuable references for the DMOs to harness their product packaging and marketing practices in the post COVID-19 pandemic era. The COVID-19 pandemic has been lingering for almost three years, since its breakout in the end of 2019. People's travel and tourism activities have been disrupted and their needs have been severely suppressed, resulting in various negative emotions and psychological problems (Xiang et al., 2020). This situation is evident in the Chinese context. The country has been implementing dynamic zero-COVID practice since the outbreak of the pandemic (Gao, 2022). In some cities (e.g., Shanghai), the lockdown period was as long as two months, during which people were asked to stay at home. Extended lockdown leads to heightened boredom state, which in turn amplifies people's needs for sensation seeking and compensatory travel. During the past three years, when COVID was under good control, the tourism industry saw vibrant bounce-back of the tourist flow in various tourism cities, as it can be seen from various news report (e.g., NetEase, 2021). DMOs are recommended to gear toward welcoming tourists who have been suffering from boredom due to lockdown as well as travel restriction. Some tourists are even willing to make travel plans at the cost of quarantine, especially for destinations where tourism products are attractive. DMOs need to package their products in a more sensorially engaging manner, as the findings of this study showed that people in high boredom state tend to engage more in sensation seeking behaviour. Innovations in tourism provisions such as game-based tours, immersive tourism activities, and themed tourism offering may become more selected options.
As people with different traits respond differently to their state of boredom, DMOs are advised to make accurate marketing by targeting the right people with right products. Based on the findings of this study, it is recommended that DMOs dedicate more marketing efforts to people with higher level of autonomous self-motivation, as these people are active in seeking ways to alleviate their boredom. Marketers need to find a way to understand the mass tourists and achieve more with this segment. Additionally, DMOs in the post-COVID-19 pandemic era should focus the communication of marketing messages on strict pandemic prevention measures and travel safety to reduce tourists' perceived susceptibility to COVID-19. Social distancing, operational facilities and management measures need to be enforced to minimize human contact, thereby ensuring a safe tourism environment (e.g., service robots, virtual payment technology and online services). DMOs can use advanced technologies such as virtual reality and augmented reality to provide contactless service (e.g., virtual display system, virtual tourism marketing and augmented reality shopping). These measures will be helpful to reduce perceived susceptibility to COVID-19 during travel and improve tourists’ consuming confidence.
6.3 Limitations and future research
This research has several limitations. First, a scenario-based experiment was conducted only with Chinese participants. Cross-cultural participants would be needed to validate the findings. Second, from the self-determination theory perspective, we focus on whether having autonomous self-motivation strategies or lacking self-motivation strategies moderates the relationship between boredom and compensatory travel intention due to sensation seeking. There are other types of motivating strategies, such as control-oriented motivating strategies (Waterschoot, 2021Waterschoot, 2021). Future studies could explore the relationship between control-oriented motivating strategies and compensatory travel. Finally, compensatory travel was measured through retrospective reporting. Other methods could be utilized in future research, such as second-hand data from tourism enterprises and field experiments providing details about other factors that could influence post COVID-19 compensatory travel.
Uncited references
Curran, 2016; Oh et al., 2007; Walsh and Mitchell, 2010; Wijnand and van TilburgIgou, 2012; Yi et al., 2013; Zhai and Du, 2020; He et al., 2021.
Declaration of competing interest
None.
Yanbo Yao, Ph.D. is a professor in College of Tourism and Service Management, Nankai University. She is interested in tourism enterprise management, tourism and destination marketing, and tourism economy.
Xinxin Zhao is a Ph.D. student in College of Tourism and Service Management, Nankai University. Her research interests are mainly in consumer behaviour in tourism and sustainable tourism.
Lianping Ren, Ph.D. is an assistant professor at the Institute for Tourism Studies, Macao. Her research areas include consumer behaviour, traveler experience, hospitality education, and hotel strategic study.
Guangmei Jia is a Ph.D. student in College of Tourism and Service Management, Nankai University. She is interested in consumer behaviour in tourism, and tourism and destination marketing.
Appendix 1 Experimental stimuli.
High boredom condition Low boredom condition
People need to stay at home to reduce the risk of COVID-19 infection. Activities are severely restricted. We must stay at home every day with no social or recreational activities. Every day, all we can do is sleep, do housework, and work online. We repeat the same routine every day. People need to stay at home to reduce the risk of COVID-19 infection. Activities are severely restricted. However, this is a precious restful time. We can read books, listen to music, watch movies, grow flowers, make delicious food, and spend time with our family, things we never have time for during working days.
High perceived susceptibility to COVID-19 Low perceived susceptibility to COVID-19
Imagine that you are travelling to a scenic area with no pandemic prevention measures. People don't need to wear masks when entering the scenic spot. People from high virus infection risk areas are allowed to enter. People don't have to keep a one-metre social distance. It is likely that there are infected people in this scenic area. Imagine that you are travelling to a scenic spot in a low-risk area with rigorous pandemic prevention measures. All people must wear masks and only people who have not been to a city where there have been COVID-19 cases in the last 14 days are allowed to visit. People can only enter when their body temperature is normal. Everyone must keep a one-metre social distance.
Appendix 2 The measurement items.
Constructs Items
Boredom (Liu et al., 2013) During the pandemic, I feel bored.
During the pandemic, I feel depressed.
During the pandemic, I feel empty.
During the pandemic, everything is repetitive and boring.
During the pandemic, I want to do something interesting, but nothing attracts me.
Sensation seeking (Chen et al., 2013a, Chen et al., 2013b) I'm interested in almost everything that is new.
I always like to do things that no one else has done before.
I will feel very uncomfortable if I stay in the same place for too long.
I get restless if I do the same thing for a long time.
I would love to socialize with adventurous people.
Having adventures always make me happy.
I would do anything as long as it is exciting and stimulating.
To pursue new stimuli and excitement, I can go against rules and regulations.
Autonomous self-motivation (Waterschoot et al., 2021) (How did you deal with boredom in the current situation of the COVID-19 crisis?)
I looked for ways to make the current situation more interesting for me.
I looked for ways to make the present situation somewhat more enjoyable for myself.
I explored how the present situation could be more valuable to me.
I worked out how the present situation could be more personally meaningful.
Lack of self-motivation (Waterschoot et al., 2021) I couldn't think of anything to do.
I did not know how to deal with the boredom in the present situation.
Perceived susceptibility (Suess, 2022) I worry a lot about getting COVID-19 from travel.
The chances that I will get COVID-19 if I travel are great.
My physical health makes it more likely that I will contract COVID-19 if I travel.
Compensatory Travel Intention (Kim et al., 2021) (I intend to travel in the near future with following features).
Less frequently–more frequently
Shorter–longer
Less travel spending–more travel spending
==== Refs
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| 0 | PMC9721284 | NO-CC CODE | 2022-12-13 23:17:22 | no | 2023 Mar 5; 54:56-64 | utf-8 | null | null | null | oa_other |
==== Front
Biomed Pharmacother
Biomed Pharmacother
Biomedicine & Pharmacotherapy
0753-3322
1950-6007
Published by Elsevier Masson SAS.
S0753-3322(22)01472-X
10.1016/j.biopha.2022.114083
114083
Article
A potential host and virus targeting tool against COVID-19: Chemical characterization, antiviral, cytoprotective, antioxidant, respiratory smooth muscle relaxant effects of Paulownia tomentosa Steud
Magurano Fabio a⁎1
Micucci Matteo b1
Nuzzo Domenico c
Baggieri Melissa a
Picone Pasquale c
Gioacchini Silvia a
Fioravanti Raoul a
Bucci Paola a
Kojouri Maedeh a
Mari Michele b
Retini Michele b
Budriesi Roberta d
Mattioli Laura Beatrice d
Corazza Ivan e
Di Liberto Valentina f
Todaro Luigi g
Giuseppetti Roberto a
D’Ugo Emilio a
Marchi Antonella a
Mecca Marisabel h
D’Auria Maurizio h
a Department of Infectious Diseases, Istituto Superiore di Sanità (ISS), Rome, Italy
b Department of Biomolecular Sciences, University of Urbino Carlo Bo, Piazza Rinascimento 6, 61029 Urbino, PU, Italy
c Istituto per la Ricerca e l’Innovazione Biomedica, CNR, via U. La Malfa 153, 90146 Palermo, Italy
d Department of Pharmacy and Biotechnology, Food Chemistry and Nutraceutical Lab, Alma Mater Studiorum-University of Bologna, 40126 Bologna, Italy
e Department of Specialistic, Diagnostic and Experimental Medicine (DIMES), University of Bologna, S. Orsola-Malpighi University Hospital, Alma Mater Studiorum-University of Bologna, Bologna, Italy
f Department of Biomedicine, Neuroscience and Advanced Diagnostic, University of Palermo, 90128 Palermo, Italy
g Scuola di Scienze Agrarie, Forestali, Alimentari ed Ambientali, Università della Basilicata, Via dell’Ateneo Lucano 10, 85100 Potenza, Italy
h Dipartimento di Scienze, Università della Basilicata, Via dell’Ateneo Lucano 10, 85100 Potenza, Italy
⁎ Corresponding author.
1 These authors equally contribute to the work
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© 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.
COronaVIrus Disease 2019 (COVID-19) is a newly emerging infectious disease that spread across the world, caused by the novel coronavirus Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2). Despite the advancements in science that led to the creation of the vaccine, there is still an urgent need for new antiviral drugs effective against SARS-CoV-2. This study aimed to investigate the antiviral effect of Paulownia tomentosa Steud extract against SARS-CoV-2 and to evaluate its antioxidant properties, including respiratory smooth muscle relaxant effects. Our results showed that P. tomentosa extract can inhibit viral replication by directly interacting with both the 3-chymotrypsin-like protease and spike protein. In addition, the phyto complex does not reduce lung epithelial cell viability and exerts a protective action in those cells damaged by tert-butyl hydroperoxide , a toxic agent able to alter cells’ functions via increased oxidative stress. These data suggest the potential role of P. tomentosa extract in COVID-19 treatment, since this extract is able to act both as an antiviral and a cytoprotective agent in vitro.
Graphical Abstract
ga1
Keywords
COVID-19
Paulownia tomentosa Steud extract
Antiviral
Antioxydant
Phytocomplex
SARS-CoV-2
==== Body
pmc1 Introduction
The COronaVIrus Disease 2019 (COVID-19) pandemic has been caused by the enveloped Betacoronavirus Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), transmitted from person to person through respiratory droplets and direct contact, and potentially by indirect contact through fomites [1].
The clinical spectrum of COVID-19 ranges from asymptomatic or paucisymptomatic forms to clinical pathologies characterized by a wide spectrum of symptoms involving severe conditions such as acute respiratory failure requiring mechanical ventilation, septic shock, and multiple organ failure [2], [3], [4], [5], [6].
The disease resulted in high occurrences of fatal pneumonia with clinical symptoms resembling those of severe acute respiratory syndrome (SARS), a viral respiratory illness caused by the SARS-associated coronavirus (SARS-CoV), during the 2002–2004 SARS epidemic. Symptoms included persistent fever, chills/rigor, myalgia, malaise, dry cough, headache and dyspnoea [7].
Since its appearance, up to the end of October 2022 over 615 million cases have been reported worldwide, including over 6 million deaths [8].
Epidemiological studies suggest that COVID-19 severity overall correlates with several comorbidities, including diabetes, obesity, cardiovascular diseases, and immunosuppressive conditions. Patients with such comorbidities exhibit elevated levels of reactive oxygen species (ROS) and oxidative stress caused by an increased accumulation of angiotensin II and by the activation of the Nicotinamide Adenine Dinucleotide PHosphate (NADPH) oxidase pathway. Moreover, evidences suggest that oxidative stress coupled with the cytokine storm contributes to COVID-19 pathogenesis and immunopathogenesis causes endotheliitis and endothelial cell dysfunction and activates the blood clotting cascade that results in blood coagulation and microvascular thrombosis [9], [10].
According to the World Health Organization (WHO), about 80% of the world's population relies on medicinal plants or herbs to fulfil their medical needs [11]. Natural compounds with many biological activities involving antiviral, antioxidant, anti-inflammatory and respiratory smooth muscle relaxing properties were investigated in an attempt to contribute to global research for discovering effective therapeutic agents in the treatment of coronavirus infections and their clinical complications. Also concerning COVID-19, the identification of integrative therapies or new antiviral therapies is still important. In this context, the investigation of compounds from terrestrial and marine plants may also lead to the discovery of new bioactive molecules [12], [13], [14].
Based on the literature, antioxidant substances may offer a potential integrative option. Some plant secondary metabolites, including flavonoids and limonoids, exhibit both antiviral action in models of coronavirus infections and antioxidant effects [15], [16]. Several flavonoids, in addition to inhibiting SARS-CoV-2 in vitro, exert a plethora of biological activities resulting in cardiovascular system protection [17], [18]. For some flavonoids, the antiviral activity has been mainly attributed to the inhibition of the 3-chymotrypsin-like protease (3CLpro) of SARS-CoV-2, as this enzyme plays a major role during viral replication [19]. Also, clinical data suggest the efficacy of vegetal extracts rich in polyphenols as a coadjuvant in the treatment of COVID-19 [20].
Several compounds extracted from plant matrices, in addition to antioxidant and mildly anti-inflammatory activities, have antimicrobial effects against different viruses, including coronaviruses. It is the case of glycyrrhizin, withaferin A, curcumin, nigellidine and cordifolioside A, able to inhibit SARS-CoV-2 replication and reduce host inflammation response [21]. Similar observations may be reported for phyto complexes [22], [23]. The observation that the same substance is capable to inhibit viral replication and reducing the molecular phenomena, triggered by the infection that produces the inflammatory state, constitutes an element of novelty. This spectrum of activity could be explained by a multi-components-multitarget paradigm on the effects of different phyto complexes and compounds isolated from the plant kingdom. These data provided support to the investigation of phyto complexes and isolated compounds as potential nutraceutical approaches in the management of COVID-19 disease and its severe clinical complications, including long-COVID-related symptoms [24].
In this paper, we present a study on an extract from waste wood of Paulownia tomentosa Steud . This plant, with a long history of use for medical purposes in China belongs to the family of Paulowniaceae, and represents a rich source of biologically active secondary metabolites, such as flavonoids, lignans, phenolic glycosides, quinones, terpenoids, glycerides, phenolic acids, and other compounds [25], [26].
Several geranylated flavonoids isolated from this plant were shown to inhibit SARS-CoV papain-like protease [27], suggesting its antiviral activity against SARS-CoV-2 in vitro.
P. tomentosa extract was obtained by the treatment of wood wastes in an autoclave, in the presence of micrometric crystals of H3PMo12O40, according to the method previously described [28].
The aim of our study was to investigate the biological effects of P. tomentosa extract, including antiviral effects against SARS-CoV-2, antioxidant and respiratory smooth muscle modulation properties, using in vitro experimental models, that may result in clinical benefits in COVID-19 patients.
The results obtained represent the basis for a further investigation aimed at clinical applications of this phyto complex in COVID-19 and may lead to the development of medical devices.
2 Methods
2.1 Sample preparation and treatment
In this work, wooden boards of P. tomentosa (0.5 m 3; dimensions: 30 × 180 × 1500 mm) without defects, supplied by the local manufacturers, were used as raw materials. Boards were thermally modified with a vacuum plant, developed by WDE Maspell srl (Terni, Italy), located at the University of Basilicata. This method consists in placing the boards between two metal plates, which contain diathermic hot oil that provides conductive heat transfer to the boards. Pressure in the kiln can be regulated in the range of 60–1000 mbar. The vacuum is maintained through a water ring-type pump equipped with a heat exchanger. Under pressure, the plates provide a force on the boards that prevents potential deformation of the wood [29]. Drying and thermal treatment were applied in the same plant.
The thermal modification started after drying by gradually increasing the temperature to 210 °C (maintained for 3 h). Total treatment including the cooling phase lasted 15 h. More details regarding the Thermo-Vacuum process and its technical particularities were previously described [29], [30].
The mass loss, due to the thermal treatment, was determined by weighting each treated board immediately after the drying process (when the wood moisture content was 0%) and at the end of the thermal treatment.
2.2 Chemical characterization
Treated and untreated wood samples were randomly selected and reduced to a small size with a mill saw and subjected to Soxhlet extraction technique with 1:2 ethanol-toluene mixture for 7 h in a Soxhlet apparatus by using the TAPPI test method T204 [31]; 1 g of milled wood repeated three times was used.
The extraction apparatus consisted of a 500 ml flask, Soxhlet tube, and 300-mm Allihn condenser.
Samples were put in cellulose thimbles (33 × 80 mm) of medium porosity. After the extraction, the solution was dried in a previously weighed 25 ml flask, by using a rotary evaporator connected to a vacuum pump (Vacuumbrand PC3001). The percentage of extractives were determined gravimetrically by weighing the flask containing the residue and comparing the weight to that of the initial dry mass wood.
The obtained mixture was fractionated as follows: the mixture was treated with chloroform (20 ml) and filtered. The solvent was evaporated, and the residue was chromatographed by using tin layer chromatography. The eluent was 1:1 hexane-ethyl acetate. Qualitative and quantitative measurements of the extracts were then made by the analytical method using a Gas Chromatography–Mass Spectrometry (GC-MS) system. GC-MS analyses were performed on an HP 6890 (Agilent) GC system equipped with an HP 5963 MS selective detector, with a high-temperature capillary column (HP-5MS, 30 m × 0.25 mm I.D., 0.25-μm film thickness; J& W Scientific, CA, USA) and helium as carrier gas. Samples were injected directly into the column at the temperature of 80 °C. After injection, the temperature was held at 80 °C for 3 min, and then heated to 250 °C at a rate of 20 °C min − 1 and held for 20 min. Compounds were identified by computer comparison of the mass spectra with NIST libraries and by mass fragmentation patterns.
The lignin content was determined as follows. The sawdust was transferred to a 50 ml beaker, a cold H2SO4 solution (72%, 15 ml) was added, and the mixture was frequently stirred for 2 h at room temperature. The mixture was then diluted to 3% (w/w) with 560 ml of distilled water, heated under reflux for 4 h, filtered, and washed with 500 ml of water. The residue was dried at 105 °C to a constant mass. The holocellulose content was determined by the difference between the amount of residue after extraction and the lignin content.
2.3 Cell cultures and treatment
Lung epithelial cell line H292 (ATCC NCI-H292 [H292] CRL-1848) were cultured with Roswell Park Memorial Institute (RPMI) 1640 medium and supplemented with 10% fetal bovine serum (FBS), 100 U/ml penicillin and 100 U/ml streptomycin (Sigma) and 2 mM l-glutamine in a humidified atmosphere of 95% air and 5% CO2 at 37 °C. In cell viability time-course and dose-effect experiments the cells received the following treatment: 0.01, 0.1 and 1 mg/ml of P. tomentosa extract for 24 and 48 h. In Tert-Butyl Hydroperoxide (TBH, Luperox® TBH70X, Merck Life Science S.r.l., Italy) cell viability dose-effect experiments, TBH 50, 100 and 150 µM for 24 h; in protection experiments against TBH-induced toxicity and oxidative stress, TBH 150 µM for 24 h, P. tomentosa extract (0.1 mg/ml) +TBH (150 µM) for 24 h (co-treatment), P. tomentosa extract (0.1 mg/ml) for 24 h. The no-treatment control (Ctrl) received an equal volume of the medium.
Vero E6 (Cercopithecus aethiops derived epithelial kidney, C1008 ATCC CRL-1586) cells were grown in Minimum Essential Medium (MEM + GlutaMAX, Gibco) supplemented with 10% Fetal Calf Serum (FCS), 100 U/ml penicillin, 100 U/ml streptomycin, 1 mM sodium pyruvate, and 1% non-essential amino acids.
2.4 Cell viability and morphology
H292 cells were grown at a density of 2 × 104 cells/well on 96-well plates in a final volume of 100 µL/well. After the treatment, cell viability was assessed by 3-(4,5-dimethylthiazol-2-yl)− 2,5-diphenyltetrazolium bromide (MTT, Sigma-Aldrich) assay, as previously described [32]. After 2 h of incubation, after dissolving formazan crystals with dimethyl sulfoxide (DMSO) 100 µL/well, absorbance was measured at 570 nm with background subtraction. Cell viability was expressed as arbitrary units, with Ctrl set to 1. For the analysis of cell morphology, the cellular images were obtained using the Zeiss Axio Scope 2 microscope (Carl Zeiss, Oberkochen, Germany).
Vero E6 cells were grown at a density of 1 × 104 cell/well in 96-well plates and treated for 24 h with P. tomentosa extract (from 1 mg/ml to 0.01 mg/ml). Then, a XTT assay (Cell Proliferation Kit II, Roche) was performed as previously described [15]. The measured absorbance directly correlates with the number of viable cells.
2.5 Antioxidant effect against Reactive Oxygen Species (ROS)
In the measurement of ROS generation, H292 cells were plated at a density of 1 × 10 4 cells/well on 96-well plates in a final volume of 100 µL/well. TBH at 150 µM was used to induce the oxidative stress, alone or in combination whit 0.01 and 0.1 mg/ml of P. tomentosa extract. At the end of the treatments, dichlorofluorescein diacetate (DCFH-DA) was added to each sample at the final concentration of 1 mM (Invitrogen, Monza, Italy) and incubated for 30 min in the dark at 37 °C. After washing with PBS, the cells were analyzed by measuring the fluorescence intensity with a Microplate Reader GloMax fluorimeter (Promega Corporation, Madison, WI 53711 USA) at Ex/Em: ∼492–495/517–527 nm. In addition, the cells were analyzed by a fluorescence microscope (Axio Scope 2 microscope; Zeiss, Oberkochen, Germany).
2.6 Virus propagation
Viral isolate BetaCov/Italy/CDG1/2020|EPI ISL 412973|2020–02–20 (GISAID accession ID: EPI_ISL_412973), was propagated as previously described [33]. Virus propagation was conducted within biosafety-level-3 facilities at Istituto Superiore di Sanità (Rome, Italy).
2.7 Plaque reduction neutralization test
Plaque Reduction Neutralization Test (PRNT) assay was used to assess the virucidal potential of P. tomentosa extract against SARS-CoV-2. The natural compound was resuspended in 30% DMSO, to a final concentration of 1 mg/ml that did not affect the growth of the cells in vitro. Then, serial dilutions of the extract (from 0.1 to 0.001 mg/ml) were incubated with 80 PFU of SARS-CoV-2 at 4 °C overnight (∼16 h). The mixtures were added in triplicates to confluent monolayers of Vero E6 cells, grown in 12-well plates and incubated at 37 °C in a humidified 5% CO2 atmosphere for 60 min. Then, 4 ml/well of a medium containing 2% Gum Tragacanth + MEM 2X supplemented with 2.5% of heat-inactivated FCS was added. Plates were left at 37 °C with 5% CO2. After 3 days, the overlay was removed, and the cell monolayers were washed with PBS to completely remove the overlay medium. Cells were stained with a crystal violet 1.5% alcoholic solution. The presence of SARS-CoV-2 virus-infected cells was indicated by the formation of plaques. The half-maximal inhibitory concentration (IC50) was determined as the highest dilution of the substance resulting in a 50% (PRNT50) reduction of plaques as compared to the virus control and it was calculated using GraphPad Prism software.
2.8 Antiviral activity
Vero E6 cells were seeded in 24-well plates at a concentration of 200 000 cells/well. After 24 h of incubation at 37 °C, plates were infected with SARS-CoV-2 at 0.01 multeplicity of infection (MOI) and incubated 1 h at 37 °C. Then, 2 ml of P. tomentosa extract, diluted at the concentration of 0.1 mg/ml in medium, was added to each infected well in triplicate. Plates were left at 37 °C in a humidified 5% CO2 atmosphere. After 24 h, the cytopathic effect was observed, and each culture supernatant was collected for RNA extraction and virus titration for viral quantification. The 50% Tissue Culture Infectious Dose (TCID50) assay was performed on the harvested samples as previously described [33], and the RNAs extracted were tested by Real-time PCR to target the N gene of SARS-CoV-2 based on protocols developed by the Centers for Disease Control and Prevention [34].
2.9 Surface Plasmon Resonance
Binding studies were performed through Surface Plasmon Resonance (SPR) on Biacore X100 (Cytiva). 3CLpro (ProteoGenix, Schiltigheim, France) and SARS-CoV-2 (S) protein (Acc. # YP_009724390.1; R&D Systems, Minneapolis, USA) were immobilized on a CM5 sensor chip by the amine coupling method as per manufacturer instructions by using Acetate pH 5.0 and pH 4.0 respectively and final protein concentration of 50 μg ml-1 [35], [36]. PBS-P + (10 mM phosphate buffer, 150 mM NaCl, 0.05% surfactant P20) supplemented with 5% DMSO was used as running and dilution buffer per manufacturer instructions. Serial dilutions of analyte were injected at 25 °C with a flow rate of 30 µL min-1. The surface was regenerated between samples with a 70% ethylene glycol solution per manufacturer instructions. All data were zero adjusted and the reference (blank) was subtracted. The quality of the fitted results was evaluated by the χ2 parameter.
2.10 In vitro functional studies
2.10.1 Animals
Guinea-pig of either sex (200–400 g) obtained from Charles River (Calco, Como, Italy) were used. The animals were housed according to the ECC Council Directive regarding the protection of animals used for experimental and other scientific purposes. All procedures followed the guidelines of the animal care and use committee of the University of Bologna (Protocol PR 21.79.14). The animals were sacrificed by cervical dislocation. The trachea and lungs were set up rapidly under a suitable resting tension in 15 ml of an organ bath containing appropriate physiological salt solution consistently warmed (see below) and buffered to pH 7.4 by saturation with 95% O2 - 5% CO2 gas.
2.10.2 Guinea-pig trachea
The method described by Budriesi et al. [37] was modified as described above. The trachea was cut transversally between the segment of cartilage and four groups of the tracheal segments, each one made up of three rings, were tied together and mounted under a tension of 1 g at 37 °C in an organ bath containing Krebs-Ringer solution of the following composition (mM): NaCl 95, KCl 4.7, CaCl2 2.50, MgSO4 1.0, KH2PO4 1.17, NaHCO3 25, and glucose 10.6, equilibrated with 95% O2-5% CO2 gas at pH 7.4. Tissues were allowed to stabilize for 90 min. The tension was recorded isometrically. The rings were allowed to stabilize for 60 min. A constant tone level was induced by Carbachol (CCh) chloride (0.5 μM), and after 15 min, a cumulative concentration-response curve to isoprenaline, extracts, magnolol and honokiol was done. All responses to different concentrations of compounds and extracts were expressed as a percentage of the maximal relaxation recorded isometrically. Data of ß-receptor agonism is presented as mean ± S.E.M. The IC50 were calculated from concentration-response curves and were analyzed by the Student’s t-test and presented as means ± S.E.M.
2.10.3 Guinea-pig lung
The procedure was previously described [38]. Briefly, strips of peripheral lung tissue, approximately 15 × 2 x 2 mm, were cut either from the body of a lower lobe with the longitudinal axis of the strip parallel to the bronchus or from the peripheral margin of the lobe and set up under 0.3 g tension at 37 C in organ baths containing Krebs-Henseleit buffer solution (composition (mM): NaCl 118.78, KCI 4.32, CaCl2 2.52, MgSO4 1.18, KH2PO4 1.28, NaHCO3 25 and glucose 5.5). Tension changes were recorded isometrically.
2.10.4 Trachea ring and lung spontaneous contractility
The experimental design was previously described [39]. Briefly, for the trachea and lung the tracing graphs of spontaneous contractions were continuously recorded with the LabChart Software (ADInstruments, Bella Vista, New South Wales, Australia). After the equilibration period (about 30–45 min according to each tissue) cumulative concentration curves of extracts and reference compounds were constructed. At the end of every single dose, the following parameters of the Spontaneous Contraction (SC) recording were evaluated considering a 5 min stationary period: the Mean Spontaneous Contraction Amplitude (MCA), evaluated as the mean force value (g); the standard deviations of the force values over the period, as an index of the Spontaneous Contraction Variability (SCV); and Basal Spontaneous Motor Activity (BSMA), as the percentage (%) variation of each mean force value (g) with respect to the control period. The SCs were investigated in the frequency domain through a standard FFT analysis and a subsequent Power Spectral Density (PSD) plot. The absolute powers of the following frequency bands of interest—low [0.0,0.2[ Hz (LF), medium [0.2,0.6[ Hz (MF) and high [0.6,1.0] Hz (HF) [40] were then calculated. The PSD percentage (%) variations for each band of interest with respect to control were estimated.
2.10.5 β-agonist activity
The trachea was prepared as previously described [37], using the PSS described above. The rings were allowed to stabilize for 60 min. A constant tone level was induced by CCh chloride (0.5 μM), and after 15 min, a cumulative concentration-response curve to isoprenaline, extracts, magnolol and honokiol was done. All responses to different concentrations of compounds and extracts were expressed as a percentage of the maximal relaxation recorded isometrically. Data of ß-receptor agonism are presented as mean ± S.E.M. The IC50 were calculated from concentration-response curves and were analyzed by the Student’s t-test and presented as means ± S.E.M.
2.11 Statistical analysis
Data analysis was performed using GraphPad Prism 8.4.3 software (GraphPad Software, Inc, La Jolla, CA, USA). The results are presented as mean ± SE, and expressed as arbitrary units, with controls equal to 1. Statistical evaluations were performed by one-way ANOVA, followed by Tukey Post-Hoc test. Differences in a p-value less than 0.05 were considered statistically significant.
The IC50 value from the PRNT assay was calculated using GraphPad Prism version 9.0.0 (GraphPad Software, LLC., San Diego, USA) for Windows. The potency of extracts defined as IC50 were calculated from concentration-response curves Probit analysis using Litchfield and Wilcoxon [41] and GraphPad Prism® software [42], [43].
3 Results
3.1 Chemical characterization
The thermo-treated wood showed the following composition: the extractives represented 10.2% (w/w) of the wood, lignin was 39.5%, while holocellulose represents 50.3% of the weight of wood.
A major part of extractives is soluble in chloroform (87.2%). Table 1 collects the compounds found in the GC-MS analysis of chloroform soluble fraction of the extractives.Table 1 GC-MS analysis of the chloroform soluble fraction of P. tomentosa extractives. Total phenolic content was determined as 750 mg gallic acid equivalent (GAE)/g, while total flavonoid content was 1800 mg quercetin equivalent (QE)/g.
Table 1Compound R.T. [min.]
Piperonal 8.26
2,6-Dimethoxyphenol 8.30
5-(1-propenyl)− 1,3-benzodioxole 8.47
1-(3,4-methylenedioxy) phenyl-1,2-propanedione 9.57
4-hydroxy-3,5-dimethoxybenzaldehyde 10.31
3-(4-hydroxy-3-methoxy)− 2-propenal 10.82
Heptadecene 10.84
Methyl hexadecanoate 11.52
Methyl 14-methylpentadecanoid acid 11.55
Hexadecanoic acid 11.74
3,5-dimethoxy-4-hydroxycinnamaldehyde 12.11
Methyl 8-octadecenoate 12.54
1,13-Tetradecadiene 12.64
Methyl stearate 12.70
Octadecanoic acid 12.94
Butyl citrate 13.70
Pentacosane 13.97
Eicosane 16.19
Sesamin 16.28
Episesamin 17.15
3.2 Effects of P. tomentosa extract on H292 cell viability
A dose-effect study on cell viability was performed by MTT assay on lung epithelial cell H292, treated with P. tomentosa extract for 24 h. For this analysis, we investigated three different P. tomentosa extract doses: 0.01 mg/ml, 0.1 mg/ml and 1 mg/ml. The results in Fig. 1 show that 0.01 mg/ml and 0.1 mg/ml P. tomentosa extract treatment did not induce any significant change in cell viability (Fig. 1 A, C). Conversely, cell viability significantly decreased after 1 mg/ml P. tomentosa extract exposure (Fig. 1 B). When the treatment with P. tomentosa extract was prolonged up to 48 h, a slight but significant decrease in cell viability was associated with the 0.1 mg/ml dose.Fig. 1 Effects of P. tomentosa extract (Paulownia) treatments on cell viability of H292 cells for 24 and 48 h. A) Histogram of MTT cell viability assay for 24 h. B) Histogram of MTT cell viability assay for 48 h. C) Representative morphological images of untreated cells (Ctrl) and treated with 0.1 mg/ml of the P. tomentosa extract (Paulownia). Bar: 50 µm. Tukey test: ## p < 0.01, ### p < 0.001 as compared to Ctrl group; * p < 0.05, * ** p < 0.001.
Fig. 1
3.3 Effects of P. tomentosa extract on cell viability impaired by TBH treatment
We analyzed oxidative agent TBH sensitivity using MTT and DCHF-DA assays on H292 lung epithelial cells [44]. As the dose-effect shown in Fig. 2, H292 cells treated with TBH at 100 µM induce a 60% reduction in cell viability (Fig. 2 A). Based on these results we choose a concentration of 150 µM for the subsequent experiments. P. tomentosa extract was able to protect the H292 cell under stress conditions; when treated with TBH 150 µM in combination with increasing concentrations of P. tomentosa extract after 24 and 48 h. As detected by the MTT assay, the extract was able to inhibit the toxicity induced by TBH in a dose-dependent manner, reaching a viability value comparable to the control at the concentration of 0.1 mg/ml of P. tomentosa extract after 24 h of treatment (Fig. 2B). The long stimulation with TBH for 48 h is not able to revert the cell damage (Fig. 2 C).Fig. 2 Oxidative insult and effects of P. tomentosa extract (Paulownia) treatment on ROS production in H292 cells. A) Dose-effect of TBH treatment (24 h) on cell viability. B) Cytoprotective effect of P. tomentosa extract treatment (24 h) on cells treated with TBH (24 h, 150 µM). C) Cytoprotective effect of P. tomentosa extract treatment (48 h) on cells treated with TBH (24 h, 150 µM). D) Bright field, Fluorescence microscopy (FITC-DCFHDA) and merged images of untreated cells (Ctrl) and cells treated with TBH, or P. tomentosa extract or a combination of P. tomentosa extract and TBH for 24 h. E) FITC-DCFHDA fluorescence intensity quantification. Bar: 50 µm. Tukey test: # p < 0.05 as compared to Ctrl group; * p < 0.05.
Fig. 2
3.4 Effects P. tomentosa extract on ROS generation by TBH treatment
The ability of P. tomentosa extract to decrease ROS production induced by TBH treatment was analyzed by DCFH-DA assay. The result was evaluated by fluorescence microscopy observation (Fig. 2D) and fluorimeter analysis (Fig. 2E), in which control cells (untreated) or treated cells with P. tomentosa extract at different doses, did not show any fluorescence, indicating that the extract did not induce oxidative stress. In contrast, the treatment with P. tomentosa extract abolished the TBH-induced ROS production (Fig. 2 D, E).
3.5 Virucidal Activity on SARS-CoV-2
Concentrations of P. tomentosa extract from 0.1 to 0.001 mg/ml were chosen to test virucidal activity in Vero E6 monolayers. Cell viability XTT analysis was not affected by the extract up to the concentration of 1 mg/ml ( Fig. 3 A). The virucidal activity against SARS-CoV-2 was evaluated in vitro through PRNT50, tested in triplicates, and a concentration required to inhibit 50% of infection was used as the cut-off threshold of virucidal activity. The results demonstrated that P. tomentosa extract was effective against SARS-CoV-2, with an IC50 value of 0.0035 mg/ml, calculated using GraphPad Prism (V. 9.0.0), as shown in Fig. 3B.Fig. 3 A) Results of XTT assay on cells incubated with different concentrations of P. tomentosa extract (from 1 to 0.01 mg/ml). Cell viability was not affected by the P. tomentosa extract at the concentrations tested. B) Results of PRNT assay on serial dilutions of P. tomentosa extract (from 0.1 to 0.001 mg/ml) to assess the virucidal activity against SARS-CoV-2: P. tomentosa extract was effective against SARS-CoV-2 until the concentration of 0.005 mg/ml with an Ic50 value of 0.0035 mg/ml. C) SARS-CoV-2 plaques after fixing and staining with crystal violet at 72 hpi. D) Results of TCID50 assay performed on Vero E6 cells treated with P. tomentosa (PTS) extract at the concentration of 0.1 mg/ml and untreated cultures, infected at 0.01 MOI: there is an evident decrease in viral titer comparing infected cells treated with PTS and untreated ones. E) Results of Real-time RT-PCR by Ct values of SARS-CoV-2 N1 and N2 gene targets (mean of triplicates) on supernatants of cells treated at 0.1 mg/ml and untreated , harvested at 24 hpi: the same results are confirmed even by the reduction of the viral genome when supernatants are treated with PTS.
Fig. 3
3.6 Antiviral Activity
The antiviral activity against SARS-CoV-2 was measured by comparing infection levels in P. tomentosa extract -treated and untreated Vero E6 cultures. The 0.1 mg/ml concentration (that did not affect the growth of Vero E6 monolayers and showed a significant virucidal effect in our previous experiments) was chosen to investigate SARS-CoV-2 replication inhibition and the antiviral activity of this compound. The antiviral effects of P. tomentosa extract were evaluated using TCID50 assay in Vero E6 cells, to quantify the virus titer in the supernatants from infected cells in which the P. tomentosa extract was added.
The quantification of the antiviral activity was also evaluated by Real-Time RT-PCR in terms of Ct threshold of the amplification of the N gene of the virus.
The results show a remarkable antiviral effect on the infected cells at 24 h post-infection (hpi) as shown in Fig. 3D, E.
3.7 Results of SPR
The P. tomentosa extract was utilized in SPR measurements to investigate the interaction of its components with two major structural and non-structural proteins hallmarks of SARS-CoV-2 host recognition/insertion and viral replication, i.e. the S protein and the 3CLpro (also known as Main Protease Mpro), to explain its antiviral activity seen in in vitro studies. Given the complexity of the sample, the affinity evaluation was not possible due to the unknown concentration of the active compounds and the diversity of molecular weights. Qualitative analysis of the sensorgrams clearly shows that the extract interacts efficiently with 3CLpro as well as with S protein ( Fig. 4). The strength of the protein-inhibitor complex is clearly visible from the rate of the signal decay during the stability phase (t > 45 s) which is quite slow in comparison with common small molecules between 400 and 200 Da, for which the complete dissociation of the complex (the signal returns to the baseline value) is usually observed within 30–40 s. From the association phase is possible to deduce the stoichiometry of the interaction, which probably involves more than a single compound since the sensorgram presents discontinuities that suggest the participation of fast interactions added to another slower ones.Fig. 4 Results from SPR: sensorgrams show that P. tomentosa extract interacts efficiently with Mpro (A) as well as with Spike protein (B). The strength of the protein-inhibitor complex can be inferred from the rate of the signal decay during the stability phase (t > 45 s) which is quite slow in comparison with common small molecules.
Fig. 4
3.8 Effects of P. tomentosa extract on airways contractility
The P. tomentosa extract was studied on trachea and lung spontaneous contractility and cumulative concentration-response curves were realized.
The phyto complex, in the trachea, decreases tone up to the concentration of 0.5 mg/ml ( Fig. 5). At the same concentration, low-frequency waves undergo a significant increase.Fig. 5 The experimental original recording of the concentration-response curve of P. tomentosa extract (PTS), on spontaneous trachea and lung basal contractility. Spontaneous Contraction (SC)-Trachea: A) Signals for each concentration; B) Mean Contraction Amplitude (MCA) and Spontaneous Contraction Variability (SCV). All the comparisons are to be considered significant (p < 0.05). SC-Lung: D) Signals for each concentration; E) MCA and SCV. All the comparisons are to be considered significant (p < 0.05); C) and F) absolute powers (PSD) of the different bands of interest (LF: [0.0,0.2[ Hz; MF: [0.2,0.6[ Hz; HF: [0.6,1.0] Hz) and PSD% variations with respect to the control phase.
Fig. 5
In the lungs, the extract determines smooth muscle relaxation in a concentration-dependent manner, up to a maximum percentage of 16% (Fig. 5). In this tissue, P. tomentosa extract increases low-frequency waves up to a stable value.
4 Discussion
After more than two years into the pandemic, with over one million new infections and thousands of deaths around the world every day, COVID-19 continues to strain healthcare systems and exact a terrible human toll. Despite vaccines remaining the most important way to rein in the pandemic, there is still a desperate need for better therapies to treat people who cannot access vaccines, and whose immune systems cannot respond fully to vaccination, or who experience breakthrough infections. Moreover, new medications can intervene whenever vaccines cannot be administered.
P. tomentosa extract derives from wood wastes. It was chemically characterized and its composition includes several compounds such as episesamin and sesamin. The study described here confirms that P. tomentosa extract exerts a virucidal effect from 0.1 to 0.005 mg/ml, a concentration range not detrimental to cell viability (Fig. 3B). Given the results of our assays, at the concentration of 0.1 mg/ml, P. tomentosa extract clearly shows a relevant antiviral effect too. There is a strong decrease in viral titers when the infected Vero E6 cells are treated with P. tomentosa extract, confirmed also by the presence of the viral genome by Real-Time RT-PCR (Fig. 3E).
The inhibition of the viral replication seems to be attributable, at least in part, to the inhibition of the main protease, called 3CLpro, involved in viral RNA transcription [45]. P. tomentosa extract inhibition of this enzyme is likely due to the presence of flavonoids [27]. In silico studies suggest that also sesamin may exert a role in this activity [46], [47], [48].
In the nutraceutical field, several substances act along with a different paradigm that we name host and guest-oriented, meaning the ability of these mixtures or isolated compounds to both hit viral targets and affect host functions involved in viral entry into host cells, and functions related to viral infection-induced detrimental effects, such as increased oxidative stress, inflammation, thrombosis and multiorgan failure.
The lung is the primary site of SARS-CoV-2-induced immunopathology, and blood vessels were presumed to be one of the main targets for the infection. Therefore, COVID-19 may be considered a vascular disease occurring mainly in the lungs.
SARS-CoV-2 binds to angiotensin-converting enzyme 2, decreasing the conversion of Angiotensin II to Angiotensin-[1], [2], [3], [4], [5], [6], [7] and favouring the production of O2 .- and other radicals such as OH.- through the NADPH-oxidase, which increases along with the augment of neutrophils/lymphocytes ratio [49]. This increase in oxidative stress results in endothelium alterations and tissues damages. Furthermore, also S protein alone is able to damage endothelium cells as it enters mitochondria and inhibits endothelial nitric oxide (NO) synthase activity [50]. In this study, P. tomentosa extract was shown to inhibit viral replication interacting directly with 3CLpro and S protein, potentially reducing also S protein-induced damages. Despite the lack of the dissociation constant determination due to the complexity of the sample, the interaction of P. tomentosa extract with the two proteins mentioned above is clearly visible and significant. Indeed, from the association phase (Fig. 4) it is possible to deduce the stoichiometry of the interaction, that probably involves more than a single compound since the sensorgram presents discontinuities that suggest the participation of fast interactions added to other slower ones, encouraging the study of the activity of each component of the sample with 3CLpro and S protein.
In addition, P. tomentosa extract not only does not reduce lung epithelial cell viability at concentrations higher than those producing the antiviral effects (0.001–0.01 mg/ml), but it also exerts a protective action in those cells damaged by TBH, a toxic agent able to alter cell functions via increased oxidative stress. These data suggest a potential role of P. tomentosa extract in COVID-19 treatment, since its use in in vitro experimental models, can act both as an antiviral and a cytoprotective agent. The ability to abolish ROS production in H292 cells may be relevant in patients infected with SARS-CoV-2, decreasing the oxidative consequences of the viral infection. Moreover, airway smooth muscle contraction has a fundamental role in lung effective ventilation, and it is affected by some cytokines such as Interleukin 13 (IL-13) and Interleukin-17 [51] and it is related to inflammatory conditions. In fact, β2-agonist-induced response inhibition is associated with inflammatory conditions, characterized by an increase of inflammatory cytokines such as IL-13, Tumor Necrosis Factor (TNF)-α and Transforming Growth Factor (TGF)-β [52], [53], [54]. This event is due to, at least in part, the phosphorylation of β − 2 adrenergic receptors triggered by G protein-coupled receptor kinase 2 and 3 that are upregulated in inflammatory conditions [55]. These phenomena partially explain the reduced functionality of smooth muscle of the trachea and lungs during inflammation. For this reason, we evaluated the effects of P. tomentosa extract on the trachea and lungs smooth muscle contractility, and we observed that this phyto complex exerts a relaxant effect in both tissues. These effects may be due to the presence of sesamin for its ability to affect the levels of NO and endothelin-1 [56] that influences respiratory smooth muscle contractility [57].
In addition, the maintenance of low-frequency oscillations and the reduction of tone that may result in a better flow of the gas, favouring exchange processes and producing a desirable effect in the treatment of respiratory tract infections, such as COVID-19, were observed. Furthermore, the P. tomentosa extract β-agonist action may be relevant in view of the use of the phyto complex in a respiratory medical device.
This extract may improve not only the oxidative stress and inflammatory status, but also the respiratory functions that undergo critical alterations in COVID-19 patients, in particular in those suffering from comorbidities, whose respiratory functions may be further worsened by opportunistic infections, such as mucormycosi [58].
5 Conclusion
This study originated with the goal of testing the biological activities of P. tomentosa extract. The results obtained from these tests are very promising. They confirm that P. tomentosa extract is an effective antiviral against SARS-CoV-2 and could involve directly the interaction with 3CLpro and S protein. At the same time, they report cytoprotective, antioxidant and respiratory smooth muscle relaxant properties. The data suggest a potential role of P. tomentosa extract in COVID-19 treatment. However, this study has some limitations which we are aware of. Firstly, we focused on the treatment of cells after the infection of wild type of SARS-CoV-2 and not its major variants: we are planning to test P. tomentosa biological activities against Alpha, Beta, Delta and Omicron variants of SARS-CoV-2. Another limitation is the choice to use the Vero E6 cells only to assess virucidal and antiviral activities, although this model is commonly used to isolate, propagate and study SARS-CoV-like viruses and provide a suitable basis to perform antiviral compound screening [59].
In future studies, further experiments may be exerted in order to investigate the possibility to set up effective systems, such as nanotechnological systems, aimed at improving this vegetal extract’s pharmacokinetic features [60], [61]. Indeed, nanotechnology may exert a central role in the delivery of natural bioactive compounds and nutraceuticals in the management of COVID-19-related conditions.
In conclusion, this research confirms the relevant properties of P. tomentosa extract and paves the way for future studies in order to permit to confirm in vivo these significant results.
CRediT authorship contribution statement
Conceptualization: F. Magurano, M. Micucci, D. Nuzzo, M. D’Auria, Formal analysis and Investigati on: F. Magurano, M. Micucci, D. Nuzzo, P.Picone, M. Baggieri, S. Gioacchini, R. Fioravanti, P. Bucci, M. Kojouri, M. Mari, M. Retini, E. D’Ugo, A. Marchi, M. D’Auria, Data Curation and Writing - Original Draft: F. Magurano, M. Micucci, D. Nuzzo, M. Baggieri, S. Gioacchini, R. Fioravanti, A. Marchi, M. D’Auria; V. Di Liberto; Roberta Budriesi, Laura Beatrice Mattioli, Ivan Corazza, Luigi Todaro, Marisabel Mecca, Writing - Review & Editing: F. Magurano, D. Nuzzo, M. D’Auria, Approval of the version of the manuscript to be published: F. Magurano, M. Micucci, D. Nuzzo, M. Baggieri, P. Picone, S. Gioacchini, R. Fioravanti, P. Bucci, M. Kojouri, M. Mari, M. Retini, R. Budriesi, L. B. Mattioli, I. Corazza, V.Di Liberto, L. Todaro, E. D’Ugo, A. Marchi, M. Mecca, M. D’Auria, Supervision: F. Magurano, M. Micucci, D. Nuzzo, M. D’Auria.
Conflict of interest statement
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.
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| 36495668 | PMC9721285 | NO-CC CODE | 2022-12-16 23:19:43 | no | Biomed Pharmacother. 2023 Feb 5; 158:114083 | utf-8 | Biomed Pharmacother | 2,022 | 10.1016/j.biopha.2022.114083 | oa_other |
==== Front
Int J Infect Dis
Int J Infect Dis
International Journal of Infectious Diseases
1201-9712
1878-3511
The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
S1201-9712(22)00636-1
10.1016/j.ijid.2022.11.044
Article
Age-related seroprevalence trajectories of seasonal coronaviruses in children including neonates in Guangzhou, China
Luo Yasha 1a
Lv Huibin 2a
Zhao Shilin 2⁎
Sun Yuanxin 34
Liu Chengyi 1
Chen Chunke 34
Liang Weiwen 2
Kwok Kin-on 4
Teo Qi Wen 25
So Ray TY 2
Lin Yihan 2
Deng Yuhong 1
Li Biyun 1
Dai Zixi 2
Zhu Jie 2
Zhang Dengwei 6
Fernando Julia 2
Wu Nicholas C 5789
Tun Hein M. 234
Bruzzone Roberto 2
Mok Chris KP 34⁎
Mu Xiaoping 1⁎
1 Department of Clinical Laboratory, Guangdong Women and Children Hospital, Guangzhou, China
2 HKU-Pasteur Research Pole, School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
3 Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, Hong Kong SAR, China
4 The Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong SAR, China
5 Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
6 Department of Chemistry and The Swire Institute of Marine Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China
7 Department of Biochemistry, University of Illinois at Urbana-Champaign, Urbana, IL, 61801, USA
8 Center for Biophysics and Computational Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
9 Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
⁎ Corresponding authors.
a These authors contribute equally to the work
5 12 2022
5 12 2022
15 8 2022
30 11 2022
30 11 2022
© 2022 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases.
2022
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Background
Four seasonal coronaviruses, including HCoV-229E and HCoV-OC43, HCoV-NL63 and HCoV-HKU1 cause approximately 15–30% of common colds in adults. However, the full landscape of the immune trajectory to these viruses that covers the whole childhood period are still not well understood.
Methods
We evaluated the serological responses against the four seasonal coronaviruses in 1886 children who aged under 18-year-old by using Enzyme-linked immunosorbent assay (ELISA). The O.D values against each human coronavirus were determined from each sample. Generalized addictive models (GAM) were constructed to determine the relationship between the age and seroprevalence throughout the whole childhood period. The specific antibody levels against the four seasonal coronaviruses were also tested from the plasma samples of 485 pairs postpartum women and their newborn babies.
Results
The IgG levels of the four seasonal coronaviruses in mother and the newborn babies were highly correlated (229E: r=0.63; OC43: r=0.65; NL63: r=0.69; HKU1: r=0.63). The seroprevalences in children showed a similar trajectory that the levels of IgG in the neonates dropped significantly and reached to the lowest level after the age of around 1 year (229E: 1.18 years; OC43: 0.97 years; NL63: 1.01 years; HKU1: 1.02 years) and then resurgence in the children who aged older than 1 year old. Using the lowest level from the GAMs as our cutoff, the seroprevalences for HCoV-229E, HCoV-OC43, HCoV-NL63 and HCoV-HKU1 were 98.11%, 96.23%, 96.23% and 94.34% at the age of 16-18 years.
Conclusion
Mothers share HCoV-specific IgGs with their newborn babies and the level of maternal IgGs waned at around one year after birth. Resurgence of the HCoV-specific IgGs were found thereafter with the increase of the age suggesting repeated infection occurred in children.
Keywords
Coronaviruses
children
serology
IgG
==== Body
pmcIntroduction
SARS-CoV-2 has become highly transmissible since it was discovered in 2019 and is now persistence in the human population. It is reasonable to expect that most people will be exposed to the virus for the first time during their childhood. Understanding the development of acquired immunity against the seasonal coronaviruses (HCoV-NL63 and HCoV-229E, HCoV-OC43 and HCoV-HKU1) in young age group will thus give us a clue on the impact of SARS-CoV-2 to human in the post COVID-19 era. The human coronaviruses have been circulating in human population for many years and are accounted for approximately 15–30% of upper respiratory tract infection (Desforges et al., 2019). Infection of these viruses mainly cause self-limiting flu-like illnesses, but severe pediatric respiratory infections are not rare (Huang et al., 2020, Paloniemi et al., 2015, Talbot et al., 2009).
Children are not entirely immunological naïve when they are born (Langel et al., 2022). IgG antibodies in the neonates are transferred from their mother so as to provide a transient immune barrier against the potential infection (Albrecht and Arck, 2020, Shook et al., 2022). This transferred immunity plays a protective role before the infants establish their own specific adaptive immunity to the same pathogen. So far, there is paucity of data to describe the transition period from transferred immunity to acquired immunity for the seasonal coronavirus in children. Moreover, the accumulation of immune response to the seasonal coronaviruses in children is also not yet well understood. Longitudinal study showed that adults are repeatedly infected by the seasonal human coronaviruses for every 12 months (Edridge et al., 2020). Although it was found that the induction of antibodies after each infection is short-lasting, frequent reinfections lead to persistent levels of antibodies to the four seasonal coronaviruses in most of the adults (Gorse et al., 2010). These pre-existing antibodies against seasonal coronaviruses were recently found to be associated with the neutralizing antibody response against SARS-CoV-2 that may mitigate disease manifestations from SARS-CoV-2 infection (Sagar et al., 2021). In this study, we determined the serological response against four seasonal coronaviruses in the plasma samples of children and modelled the seroprevalence trajectories of the four viral subtypes during the whole childhood period.
Methods
Sample Collection. 1886 pediatric patients who aged under 18-year-old and without signs of influenza-like illness in non-respiratory diseases wards were recruited from January and March 2020 in our study. All plasma samples were obtained from the EDTA anti-coagulated peripheral blood samples in the Guangdong Women and Children Hospital, Guangzhou, China. Peripheral whole blood samples were centrifuged at 3000 x g for 10 minutes at room temperature for plasma collection. All plasma samples were kept in -80oC until used. Moreover, 485 plasma samples from healthy postpartum women were collected between January and March 2020 in the same hospital, with paired plasma samples collected from their healthy newborn babies. Of note, the blood samples, which from the pediatric patients recruited from different departments or the paired maternal and infant, were collected for routine examination in the Department of Clinical Laboratory. Neonate was defined as those who were 4 weeks old or younger and the definition of children covers neonates and male/female under 18-year-old. All study procedures were performed after informed consent. The study was approved by the Human Research Ethics Committee at the Guangdong Women and Children Hospital (Approval number: 202101231).
ELISA. The S1 subunits of spike protein (His tag) of HCoV-229E (Seattle/USA/SC1073/2016), HCoV-HKU1 (Hong Kong/isolate N5/2006), and HCoV-NL63 (Florida/UF-2/2015) and the hemagglutinin esterase protein (His Tag) of HCoV-OC43 (Seattle/USA/SC9741/2016) were purchased from Sino Biological (China). A 96-well enzyme-linked immunosorbent assay (ELISA) plate (Nunc MaxiSorp, Thermo Fisher Scientific) was first coated overnight with 100 ng per well of purified recombinant protein in PBS buffer. In the next day, plates were washed for three times with PBS containing 0.1% Tween 20 first. The plates were then blocked with 100 μl of Chonblock blocking/sample dilution ELISA buffer (Chondrex Inc, Redmon, US) and incubated at room temperature for 1 h. Each human plasma sample was diluted to 1:100 in Chonblock blocking/sample dilution ELISA buffer and then added into the ELISA plates for a two-hour incubation at 37°C. After three times of washing with PBS containing 0.1% Tween 20, each well in the plate was further incubated with the anti-human IgG secondary antibody (1:5000, Thermo Fisher Scientific) for 1 hour at 37°C. The ELISA plates were then washed five times with PBS containing 0.1% Tween 20. Subsequently, 100 μl of Goat anti-Human secondary IgG (HRP) substrate (Ncm TMB One; New Cell and Molecular Biotech Co. Ltd, Suzhou, China) was added into each well. After 10 min of incubation, the reaction was stopped by adding 50 μl of 2 M H2SO4 solution and analyzed on a Sunrise (Tecan, Männedorf, Switzerland) absorbance microplate reader at 450 nm wavelength.
Modelling. Generalized addictive models (GAM) was fitted to investigate the association between age and the ELISA results. The restricted cubic splines (smooth curve) with five knots were used to construct the model (Nieboer et al., 2015). Of note, percentile places knots at five spaced percentiles of the explanatory variable, which are the 5th, 27.5th, 50th, 72.5th and 95th percentile. R version 4.0.4 was used for the analysis.
Statistical Analysis. Significance between two groups was determined by the Mann-Whitney test, with a p-value smaller than 0.05 being considered statistically significant. Correlation between plasma samples were evaluated by using Pearson's correlation coefficients.
Results
We tested the seroprevalence to the four seasonal coronaviruses by the Enzyme-linked Immunosorbent Assay (ELISA) using the plasma samples collected from 1886 children (Female: 43.9%) with age ranging from 0 (Neonates) to 18 years old in Guangzhou, China between January and March in 2020. Among our cohort, 259 were under 6 months old, 161 were between >6 months to <12 months, 278 were >1 to <3 years old, 603 were >3 to <7 years old, 466 were >7 to <12 years old, 66 were >12 to <16 years old, 53 were >16 to <18 years old (Table 1 ). S1 domains of the spike protein were used for measuring the serological response to 229E, NL63 and HKU1. Although the homology of S1 protein between HKU1 and OC43 is low, they both bind to receptors that carry 9-O-acetylated sialic acid. Hemagglutinin esterase (HE) but not S1 of OC43 was thus used for the ELISA assay to further reduce the cross-reactive signal. HE acts as a receptor-destroying enzyme to facilitate the release of viral progeny from infected cells (Hulswit et al., 2019, Tortorici et al., 2019, Vlasak et al., 1988). It is expressed on the surface of the OC43 and is also highly immunogenic.Table 1 Prevalence of the seasonal coronaviruses in children
Table 1 Participants Number of Positive (%)
Age (years) Number 229E-S1 NL63-S1 OC43-HE HKU1-S1
Male <0.5 155 124 (80.00) 98 (63.22) 108 (69.68) 107 (69.03)
>0.5 to <1 99 22 (22.22) 31 (31.31) 25 (25.25) 18 (18.18)
>1 to <3 151 47 (31.13) 57 (37.75) 62 (41.06) 48 (31.79)
>3 to <7 332 185 (55.72) 256 (77.11) 281 (84.64) 245 (73.80)
>7 to <12 266 191 (71.80) 235 (88.35) 261 (98.12) 231 (86.84)
>12 to <16 32 31 (96.88) 28 (87.50) 31 (96.88) 28 (87.50)
>16 to <18 23 23 (100.00) 23 (100.00) 23 (100.00) 23 (100.00)
Female <0.5 104 92 (88.46) 53 (50.96) 74 (71.15) 86 (82.69)
>0.5 to <1 62 32 (51.61) 19 (30.65) 11 (17.74) 25 (40.32)
>1 to <3 127 42 (33.07) 43 (33.86) 52 (40.94) 55 (43.31)
>3 to <7 271 163 (60.15) 194 (71.59) 215 (79.33) 232 (85.61)
>7 to <12 200 157 (78.50) 159 (79.50) 193 (96.50) 189 (94.50)
>12 to <16 34 32 (94.12) 28 (82.35) 31 (91.18) 34 (100.00)
>16 to <18 30 29 (96.67) 27 (90.00) 29 (96.67) 28 (93.33)
Overall <0.5 259 228 (88.03) 155 (59.85) 182 (70.27) 193 (74.52)
>0.5 to <1 161 62 (38.51) 51 (31.68) 37 (22.98) 38 (23.60)
>1 to <3 278 100 (35.97) 97 (34.89) 114 (41.01) 98 (35.25)
>3 to <7 603 364 (60.36) 448 (74.30) 505 (83.75) 472 (78.28)
>7 to <12 466 365 (78.33) 401 (86.05) 456 (97.85) 423 (90.77)
>12 to <16 66 63 (95.45) 57 (86.36) 61 (92.42) 62 (93.94)
>16 to <18 53 52 (98.11) 51 (96.23) 51 (96.23) 50 (94.34)
The IgG levels to the four seasonal coronaviruses were determined from each plasma sample (Supplementary Figures 1 and 2). The association between the IgG level and the age in each seasonal coronavirus was constructed by generalized additive models (GAM) (Figure 1 ) (Nieboer et al., 2015). The restricted cubic splines (smooth curve) with five knots were used to visualize the association. We found that the seroprevalences of the four seasonal coronaviruses showed a similar trajectory from the GAMs. Compared to the entire childhood period, the levels of IgG in the neonates dropped significantly and reached to the lowest level of the GAM after the age of 1 year (1.18 years: HCoV-229E; 0.97 years: HCoV-OC43; 1.01 years: HCoV-NL63; 1.02 years: HCoV-HKU1) (p<0.001). The levels of IgG were then increased and accumulated when the children became older in age. The IgG levels against HCoV-OC43, HCoV-NL63 and HCoV-HKU1 were increased to the comparable levels of the children at the age of 8, 9 and 6 years respectively. However, it was intriguing to find that the IgG to the HCoV-229E was increased slower than the other seasonal coronaviruses and it reached to the comparable level of the children at the age of 16 years. The serological results of each coronavirus were further stratified into two sex groups (male/female) and were further compared (Figure 2 ). Importantly, we found that the IgG waning of all four seasonal coronaviruses in male children were much faster than that in female. The time required for dropping the IgG of each coronavirus to their lowest level of the GAMs in male children were 1.95 (HCoV-229E: 0.75(M) vs 1.46(F)), 1.84 (HCoV-OC43: 0.63(M) vs 1.16(F)), 1.69 (HCoV-NL63: 0.68(M) vs 1.15(F)), 1.71 (HCoV-HKU1: 0.67(M) vs 1.15(F)) folds faster than that of the female children (p<0.001).Figure 1 Seroprevalence trajectory of the four seasonal coronaviruses in children. The plasma samples were collected from 1886 children who aged from 0 (neonates) to 18 years old. Each sample was tested by ELISA against either S1 (HCoV-229E, HCoV-NL63 or HCoV-HKU1) or hemagglutinin-esterase (HCoV-OC43) protein. Generalized addictive models (GAM) was used to model the association between the serological data and the age. The black lines showed the fitted values and gray areas showed the 95% confidence intervals. Each sample was tested in duplicate, and the results were represented by the mean of the two values. The solid line represents the cutoff of the negative control (PBS). The dashed lines represent the cutoff of the lowest point in GAMs and the solid lines represent the background (PBS) of the assay.
Figure 1
Figure 2 Antibody levels against the four seasonal coronaviruses in different genders. The 1886 plasma samples which were collected from children were further stratified into female (n=828 samples) and male (n=1058 samples) for analysis. Each sample was tested by ELISA against either S1 (A: HCoV-229E, C: HCoV-NL63 or D: HCoV-HKU1) or hemagglutinin-esterase (B: HCoV-OC43) protein. Generalized addictive models (GAM) was used to model the association between the serological data and the age. The black lines showed the fitted values and gray areas showed the 95% confidence intervals. Each sample was tested in duplicate, and the results were represented by the mean of the two values. The dashed lines represent the cutoff of the lowest point in GAMs and the solid lines represent the background (PBS) of the assay.
Figure 2
The relatively high levels of IgG antibody against four seasonal coronaviruses in the children under 1-year-old suggested a vertical transfer of the maternal immune response. It has been recently shown that the passive immunity against SARS-CoV-2 of children was contributed by their mothers (Shook et al., 2022). We collected plasma samples from 485 pairs of postpartum women and their newborn baby for testing the levels of their IgG to the four seasonal coronaviruses using similar serological assays. We found that the maternal IgG level was linearly associated with their neonatal IgG levels in each seasonal coronavirus: HCoV-229E (r=0.63, 95% CI: 0.57-0.68, p<0.0001), HCoV-OC43 (r=0.65, 95% CI: 0.60-0.70, p<0.0001), HCoV-NL63 (r=0.69, 95% CI: 0.64-0.74, p<0.0001), HCoV-HKU1 (r=0.63, 95% CI: 0.58-0.69, p<0.0001) (Figure 3 ). Interestingly, when we compared the antibody levels between the mothers and their newborn babies using Wilcoxon pairwise test, the newborn babies showed lower level of 229E antibody than their corresponding mothers (Supplementary Figure 3). However, no significant differences were found in HCoV-OC43, HCoV-NL63 or HCoV-HKU1. While comparing to the previous report that maternally derived antibodies against SARS-CoV-2 could persist up to 6 months of age in their infant (Song et al., 2021), our results indicated that the passive transferred immunity against the seasonal coronaviruses in children can maintain longer time (1.25 years: HCoV-229E; 1 years: HCoV-OC43; 1.08 years: HCoV-NL63; 1.08 years: HCoV-HKU1) (Figure 1).Figure 3 Correlation between the maternal and neonatal IgG levels of the four seasonal coronaviruses. 485 paired of maternal and neonatal plasma samples were collected and tested by ELISA. Antibody levels against A) HCoV-229E-S1, B) HCoV-NL63-S1, C) HCoV-OC43-HE, and D) HCoV-HKU1-S1 were determined and the correlations between the paired samples in the four seasonal coronavirus groups were shown. The black lines showed the fitted values and gray areas showed the 95% confidence intervals. The r represented the correlation coefficient.
Figure 3
Prevalence of the seasonal coronaviruses in children is determined either by detecting the specific nucleic acids from the respiratory specimen or through serology test. However, it is difficult to define and collect true negative reference samples because the seasonal coronaviruses are highly circulating in children. Previous studies adopted an approach in which the cutoffs were determined from a small subset of reference samples who the children were between 1-2 years old, and the tested samples were defined as positive if the results were above the mean of the references (Galipeau et al., 2021, Lv et al., 2021). Here, we estimated the prevalence of the seasonal coronaviruses by using the lowest level in the GAMs as our negative reference (Table 1). As shown in the figure, the lowest point in GAMs for four seasonal coronaviruses are OD 0.13 (HCoV-229E), 0.18 (HCoV-OC43), 0.27 (HCoV-NL63) and 0.17 (HCoV-HKU1) respectively. We assumed that children with IgG level above this point indicate infection of the corresponding seasonal coronaviruses and thus defined it as seropositive. 91.12%, 82.24%, 79.92% and 84.17% of sero-positivity to HCoV-229E, HCoV-OC43, HCoV-NL63 and HCoV-HKU1 respectively were found in those under 6 months old. In infants with the age between 6 months and 12 months, the seropositive rates dropped to 44.72% (HCoV-229E), 43.48% (HCoV-OC43), 45.96% (HCoV-NL63) and 45.96% (HCoV-HKU1) (p<0.001). The sero-positivity of each seasonal coronavirus increased with age and was over 64.51% of prevalence in the children at their pre-school age (3-6 years). The seroprevalences for HCoV-229E, HCoV-OC43, HCoV-NL63 and HCoV-HKU1 kept increasing and were 98.11%, 96.23%, 96.23% and 94.34%, respectively, at the age of 16-18 years.
Discussion
Our study described the transition from passive to acquired immunity for seasonal coronaviruses in children. The established approach here provides a view to identify the waning period of immunity against coronavirus after birth, that will be useful to apply on SARS-CoV-2. The best timing to receive COVID-19 vaccine is still under being debated. Though US CDC suggested that COVID-19 vaccination is recommended for children aged 6 months or older, it is mainly based on the safety concern rather than aiming for better protection. Defining the waning period in SARS-CoV-2 using our approach will provide scientific evidence to determine the vaccination window for children in post-COVID era.
Full spike protein was used in the previous study to detect the antibody against the four seasonal coronaviruses in 218 children (Zhou et al., 2013). It is known that there are 63–98% of sequence similarity at the S2 among the seven human coronaviruses. Binding from the cross-reactive antibodies at the S2 domain may lead to an over-estimation of the seroprevalence. The homology of the S1 amino acid sequences among the four human coronaviruses is between 40-68% only. We expected that using S1 as the antigens for detection should represent the specific serological response. However, as HKU1 and OC43 both bind to receptors carrying 9-O-acetylated sialic acid, we used HE to replace the S1 in the assay for measuring the antibody to OC43. The homology of S1 and HE between the two viruses is 68% and 66% respectively. Interestingly, despite using S1 or HE domain to evaluate the specific antibody response for the seasonal coronaviruses, the trajectory of the seroprevalence of the four subtypes were still very similar.
Although seasonal coronaviruses are responsible for only 4%-6% of acute respiratory tract infections in children (Chiu et al., 2005, Gaunt et al., 2010, Kuypers et al., 2007), our serology study showed that the infections are indeed very common at this age group. The low detection rate may be due to the fact that most of the infections were either asymptomatic or very mild. We observed that the levels of IgG to the seasonal coronaviruses increased with the age of children. Among the four subtypes, the level of IgG to HCoV-229E rises comparatively slower than the HCoV-OC43, HCoV-NL63 and HCoV-HKU1. It was reported that HCoV-229E was less frequently detected in human including children (Monto et al., 2020). The recent study also showed that HCoV-229E evolves slower than the HCoV-OC43 over time (Kistler and Bedford, 2021) and the previous human-challenge study showed that individuals infected with HCoV-229E were resistant to re-infection with the same strain but partially susceptible to an antigenicity different strain (Reed, 1984).
Previous studies showed that maternal antibody levels are associated with the protection for neonates against bacteria and viruses (Fouda et al., 2018, Langel et al., 2022). In this study, we first reported that high levels of IgG to the four seasonal coronaviruses in neonates is linearly correlated with their corresponding maternal IgG levels and this suggested that the vertical transmission of coronavirus-specific antibodies from mothers to neonates. It is dropped within the first year after the babies born and male children showed a faster interesting that these antibodies quickly waning compared to the female. However, further investigation is needed to determine whether male children may have a higher risk for serious infection if they catch seasonal coronavirus during their early life. On the other hand, the previous results showed that though maternal antibody provide protection for infant, maternal antibody may also suppress the B cell response by epitope masking or inhibition of infant B cell activation by Fcγ-receptor mediated signaling (Edwards, 2015, Kim et al., 2011). Therefore, future studies should explore the positive and negative effect of the maternal antibodies, which will provide important insights into the antibody response with pre-existing maternal antibodies.
There were some limitations in our study. Firstly, the trajectories were illustrated using cross-sessional samples from population age groups, not in the longitudinal cohort. Secondly, the seroprevalences from our cohort were determined by ELISA only. The neutralizing effect to the seasonal coronaviruses was not evaluated. Thirdly, although the children were recruited from the non-respiratory ward or routine body check center, we did not collect their clinical background for analysis in this study.
In conclusion, we described that IgG antibody against four seasonal coronaviruses could be transferred from mother to their infant in a large-scale cohort. Importantly, we reported this transferred immunity waned for one year after birth and children could acquire immunity against four seasonal coronaviruses with the increase of the age. Overall, these results provide a comprehensive analysis of the antibody dynamic in the early life of the children.
Acknowledgements
This work was supported by The Medical Scientific Research Foundation of Guangdong Province of China (A2022196), Calmette and Yersin scholarship from the Pasteur International Network Association (H.L.), Guangdong-Hong Kong-Macau Joint Laboratory of Respiratory Infectious Disease (20191205) (C.K.P.M.) and Emergency Key Program of Guangzhou Laboratory (Grant No. EKPG22-30-6) (C.K.P.M.), RGC's Collaborative Research Fund (C6036-21GF) (C.K.P.M), and visiting scientist scheme from Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore (C.K.P.M.).
Author contributions
H.L., N.C.W. and C.K.P.M. conceived the research idea and designed the study. Y.L., Y.S. C.L., Y.D., B.L. and X.M coordinated and carried out cohort recruitment. H.L., S.Z., K.K., C.K.P.M. and H.M.T., analyzed the data. Y.L., H.L., C.C., W.L, Q.W.T, R.T.Y.S., Y.L, Z.D., J.Z., D.Z. and J.F. performed the experiments. H.L., R.B., H.M.T., and C.K.P.M. wrote the manuscript.
Competing Interests
The authors declare no competing interests.
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| 36481488 | PMC9721286 | NO-CC CODE | 2022-12-07 23:19:07 | no | Int J Infect Dis. 2022 Dec 5; doi: 10.1016/j.ijid.2022.11.044 | utf-8 | Int J Infect Dis | 2,022 | 10.1016/j.ijid.2022.11.044 | oa_other |
==== Front
Journal of Hospitality and Tourism Management
1447-6770
1447-6770
The Authors.
S1447-6770(22)00190-5
10.1016/j.jhtm.2022.12.004
Article
The impact of infection risk communication format on tourism travel intentions during COVID-19
Savadori Lucia a∗
Tokarchuk Oksana a
Pizzato Massimo b
Pighin Stefania c
a Department of Economics and Management, University of Trento, Via Inama 5, 38122, Trento, Italy
b Department of Cellular, Computational and Integrative Biology, University of Trento, Via Sommarive 9, 38123, Povo, Trento, Italy
c Center for Mind/Brain Sciences, University of Trento, Corso Bettini, 31, 38068, Rovereto, Trento, Italy
∗ Corresponding author. Dipartimento di Economia e Management, Via Inama 5, 38122, Trento, Italy.
5 12 2022
5 12 2022
30 6 2022
26 11 2022
3 12 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.
Perceived risks and safety concerns are strong predictors of travel intentions. This research examines the effectiveness of the COVID-19 infection rate presentation format in changing respondents' risk perceptions and travel intentions to a COVID-19-affected destination. In two experimental studies conducted during the COVID-19 pandemic, participants (N = 1219) received information on infection rates in one of four mathematically equivalent formats: raw numbers, percentages, N-in-NX ratio, and 1-in-X ratio. Three distinct components of risk perception were measured: affective, analytical, and experiential. Results show that the infection rate presented using percentages increased the intention to travel compared to that presented using an N-in-NX ratio and raw numbers. Moreover, the infection rate presented using a 1-in-X ratio decreased the intention to travel compared to that presented using an N-in-NX ratio and percentages. These findings are in line with two apparently inconsistent phenomena: the ratio bias, according to which ratios with larger numerators induce a higher perceived infection risk than ratios with smaller ones, and the 1-in-X effect, according to which ratios with “1” at the numerator induce a higher perceived infection risk than ratios with other numbers at the numerator. Additionally, the effect of numerical formats on travel intentions was fully mediated by affective and analytical risk perceptions but only partially by experiential risk perceptions. Overall, the findings show the importance of the format used to present infection rates on changing individuals' travel intentions.
Keywords
Tourism
COVID-19
Travel intentions
Communication
Numerical format
Risk perception
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pmc1 Introduction
The COVID-19 epidemic devastated the 2020 tourism industry. In the United States, the majority (70 percent) of the hotel staff was let off, with an estimated 4.6 million jobs lost (American Hospitalityand Lodging Association, 2020). In Europe, the tourism business witnessed a 61 percent (1.1 billion) drop between April 2020 and March 2021 (Eurostat, 2020) compared to the 12 months before the pandemic. It is, therefore, important to investigate what strategies can be adopted to foster a prompt industry recovery in the post-COVID-19 phase.
Perceived financial, physical, social, and health risks have been suggested as important factors in tourism decision-making as tourists tend to avoid high-risk destinations in favor of low-risk ones (e.g., Sönmez & Graefe, 1998). Risk perceptions during the COVID-19 pandemic made no exception, significantly influencing tourism travel intentions (Bae & Chang, 2021; Rather, 2021). At the same time, the format used to present the risk information is acknowledged to affect risk perceptions and behavioral intentions (for a review, see Ancker et al., 2022). We, therefore, can expect an effect of the format used to convey the COVID-19 infection rate on travel risk perceptions and, thus, travel intentions.
During the pandemic, the rate of COVID-19 infection in specific locations was provided daily and was readily available to potential tourists through internet websites. For example, the CDC issued travel recommendations by providing an updated list of countries where COVID-19 risk was high, moderate, or low (https://www.cdc.gov/coronavirus/2019-ncov/travelers/map-and-travel-notices.html), and websites as the "Our world in data" (https://ourworldindata.org/explorers/coronavirus-data-explorer) and Johns Hopkins Coronavirus Resource Center (https://coronavirus.jhu.edu/map.html) offered information on COVID-19 cases at specific locations. Therefore, the rate of COVID-19 infection in a location is among the information that can potentially influence tourists' travel decisions.
Several studies show that the type of message delivered to tourists can influence their behavioral intentions in the post-COVID-19 period. According to Feng, Liu, and Li (2022), for example, messages with emotional appeals can increase visit intention when the country is associated with a warmth stereotype, thus promoting tourism recovery following COVID-19. Likewise, retentive advertising messages that remind visitors of the destination image can significantly increase tourists' booking intentions (Volgger, Taplin, & Aebli, 2021), and a COVID-19 risk-attenuating message frame was found to increase post-COVID-19 travel intentions (Xie, Zhang, Sam, & Huang, 2022). However, no previous study has systematically examined the effect of the numerical format of risk messages on travel intentions in the post-COVID-19 period.
This study examines the effectiveness of the COVID-19 infection rate format in changing respondents' risk perceptions and travel intentions to a COVID-19-affected destination. It further contributes to the literature by addressing the mediating influence of perceived risk in explaining the format effects on travel intentions. It also adds to the existing knowledge by empirically studying the distinct mediating effect of three different risk perception components (affective, analytical, and experiential). Furthermore, this study also examines the moderating role of worry about COVID-19 on the relationship between numerical formats and perceived risk and intentions. Importantly, it examines format effects in the context of a disease (i.e., COVID-19) that is personally relevant to participants.
2 Conceptual background and research hypotheses
2.1 Risk perception components
Risk perception is the subjective evaluation of the riskiness of various activities, substances, phenomena, or technologies (Slovic, 1987). Individual risk perceptions are prevalently informed by feelings and affect (Loewenstein, Hsee, Weber, & Welch, 2001; Slovic, Finucane, Peters, & MacGregor, 2004). The affective component of risk perception is the instinctive, emotional reaction that reflects how positive or negative something makes one feel (Loewenstein et al., 2001; Slovic et al., 2004), which is then used to make fast and intuitive decisions in a reasoning process known as the affect heuristic (Finucane, Alhakami, Slovic, & Johnson, 2000). According to this heuristic, locations associated with positive feelings are considered safe; conversely, those associated with negative feelings are judged risky (Slovic et al., 1991). Notably, when predicting actual behavior, the affective dimension of risk perception is the most predictive among the different dimensions (Ferrer et al., 2018), even when studying vacation intentions (Bae & Chang, 2021).
The second component of risk perception is the analytical evaluation of the risk, also known as deliberate risk perception (Ferrer, Klein, Persoskie, Avishai-Yitshak, & Sheeran, 2016; Slovic et al., 2004). This component is represented by the perceived probability, such as the probability of getting infected. Usually, the analytical dimension is not immediately involved in determining actual individual behavior unless one is very motivated to do so, like when confronted with a new risk or a high-stakes decision (Slovic et al., 2004).
Risk perceptions are also informed by a distinct third dimension: experiential risk perception. Experiential risk perception tackles the experiential feeling of being vulnerable and is strongly linked to sensory and physical cues of being at risk (Ferrer et al., 2016). Deciding to travel to a tourist destination for vacation might be a very experiential task, as thinking about it might elicit mental images and feelings (Bogicevic, Seo, Kandampully, Liu, & Rudd, 2019), making the experiential dimension particularly predictive in the tourism context.
Extensive research has shown, however, that the format used to present risk information influences an individual's risk perceptions (for a review, see Ancker et al., 2022).
2.2 Risk communication format
The COVID-19 risk of infection can be communicated using different formats, such as the number of confirmed cases (e.g., "As of Friday, June 3, 2022, 8:02 p.m., there are 62,515 confirmed cases in Oregon, USA"). However, the number of infections makes sense only if provided relative to a population (e.g., 62,515 confirmed cases out of 4176 million residents). Different numerical formats can be used to convey this ratio, such as raw numbers (62,515 out of 4176 million residents), 1-in-X ratios (1 in 67 people), N-in-NX ratios (14,970 people per one million population), and percentages (1.5% of the population). Although different formats deliver the same information, they use numbers of different magnitudes, thus conveying different psychological meanings, which significantly affect risk perceptions and intentions (Ancker et al., 2022; Oudhoff & Timmermans, 2015; Pighin, Savadori, et al., 2011; Sirota, Juanchich, & Bonnefon, 2018).
In a well-known experiment, when asked to choose whether they preferred to draw a winning red bean from a jar that contained 100 beans, of which ten were red, or another that contained ten beans, of which one was red, individuals preferred the jar with more red beans in absolute terms (Kirkpatrick & Epstein, 1992). This irrational behavior has been termed the “ratio bias” (Denes-Raj, Epstein, & Cole, 1995; Kirkpatrick & Epstein, 1992). The ratio bias has been explained as resulting from two forms of reasoning: (1) participants do not combine the ratio components (component reasoning), and (2) the numerator receives more weight than the denominator (denominator neglect) (Denes-Raj et al., 1995; Stone, Parker, & Townsend, 2018). Given that the numerator is larger in “10 in 100” than in “1 in 10,” when comparing the two ratios, people sense that the event is more likely in the first instance (Denes-Raj et al., 1995). Even though the typical ratio bias is usually observed in the joint comparison of two ratios, the bias also occurs in evaluations of single ratios (although never with “1” at the numerator). For example, a hazard that kills "1286 out of 10,000" was judged riskier than one that kills "24.14 out of 100" (Yamagishi, 1997), and a disease that kills 36,500 people every year was perceived riskier than one that kills 100 people every day (Bonner & Newell, 2008). Also, a discount of $120 on an item priced at $480 was perceived as more valuable than a discount of 25% (González, Esteva, Roggeveen, & Grewal, 2016). The ratio bias explanation is based on the cognitive-experiential-self theory (CEST) hypothesis, which attributes the bias to an intuitive-experiential thinking style (Kirkpatrick & Epstein, 1992).
Although the ratio bias explanation accounts for many format effects, there is one exception: the 1-in-X effect (Pighin, Savadori, et al., 2011). The effect was first studied in a systematic way by Pighin, Bonnefon, and Savadori (2011). According to this effect, the ratios with “1” at the numerator, the 1-in-X ratios (e.g., 1 in 200), trigger a higher subjective probability than equivalent N-in-NX ratios (e.g., 5 in 1000) or percentages (Pighin, Savadori, et al., 2011) and influence intentions accordingly (Oudhoff & Timmermans, 2015; Sirota & Juanchich, 2019; Sirota, Juanchich, & Bonnefon, 2018). For example, a 1 in 13 chance to contract malaria during a trip to Kenya persuaded more people to cancel the trip than a 10 in 130 chance (Sirota & Juanchich, 2019). Also, a 1 in 4 chance of winning a lottery was deemed higher than an equivalent 25% and convinced more participants to buy a lottery ticket in a hypothetical decision (Oudhoff & Timmermans, 2015).
Although the 1-in-X effect conflicts with the ratio bias explanation, the effect is robust across different scenarios and populations (Oudhoff & Timmermans, 2015; Pighin et al., 2015; Pighin, Savadori, et al., 2011; Sirota, Juanchich, Kostopoulou, & Hanak, 2014; Sirota & Juanchich, 2019; Sirota, Juanchich, & Bonnefon, 2018) and is not affected by people's numerical ability or age and gender (Pighin et al., 2015; Pighin, Savadori, et al., 2011; Sirota et al., 2014). The 1-in-X effect is limited to those ratios with “1” at the numerator; indeed, it disappears when “1” is substituted with a value greater than 1 (e.g., 2 or 3) (Pighin, Savadori, et al., 2011). A consistent finding of the 1-in-X effect is that it overestimates the risk compared to the objective risk (Sirota, Juanchich, Petrova, et al., 2018). To explain the effect, some have proposed an overestimation of the risk magnitude because the format would convey higher severity (Sirota, Juanchich, Petrova, et al., 2018) or ease of imagination triggered by the “1” (Oudhoff & Timmermans, 2015).
2.3 Research hypotheses
2.3.1 Effect of format on risk perceptions
This study investigated the effect of four formats (raw numbers, percentages, N-in-NX, and 1-in-X) used to convey the infection rate, as shown in Table 1 . According to the ratio bias (e.g., Denes-Raj et al., 1995), formats with larger numerators will trigger a higher perceived risk than those with smaller numerators. This would imply that the rate of infection presented using raw numbers should foster a higher perceived risk than that presented using an N-in-NX ratio, which, in turn, should promote a higher perceived risk than that presented using percentages. In a recent taxonomy for classifying the evidence on ways to communicate numbers effectively, it was suggested to treat the affective perception and the perceived magnitude of the number as separate components of the perception process (Ancker et al., 2022). Consistently, we tested the format effects on the three components of risk perception separately. We hypothesized that the format effect would impact especially the affective component because this is believed to be the main element of risk perception judgments (Slovic et al., 2004). We also expected a significant role of the analytical component, given that the manipulated variable (i.e., the numerical format) is expressed through numbers, which tend to be naturally elaborated in quantities (Dehaene, 2011). Moreover, given that tourism is an experiential type of consumption (Le et al., 2019), the experiential component could also be relevant in explaining the format's effects on risk perception. Thus, we propose that:H1a The rate of COVID-19 infection presented using raw numbers will foster a higher (affective, analytical, and experiential) perceived risk than that presented using an N-in-NX ratio or percentages. Moreover, the rate presented using an N-in-NX ratio will exhibit greater (affective, analytical, and experiential) perceived risk than that presented using percentages.
According to the 1-in-X effect (Pighin, Savadori, et al., 2011), the infection rate presented using “1” at the numerator should foster a higher perceived risk than that presented using an N-in-NX ratio or percentages. This occurs perhaps because the “1” at the numerator triggers an overestimation of COVID-19 infections or increases the imagination of the “one infected person”. Thus we propose that:
H1b The reported rate of COVID-19 infection presented using a 1-in-X ratio will induce a higher (affective, analytical, and experiential) risk perception than that presented using an N-in-NX ratio or percentages.
Table 1 Risk communication messages.
Table 1Formats Risk message
Study 1
Raw numbers In the region where the site you're considering for vacation is located,
1170 people out of 4,459,000 currently test positive for coronavirus.
Percentages In the region where the site you're considering is located,
0.026 percent of persons currently test positive for coronavirus.
N-in-NX In the region where the site you are considering is located,
26 out of 100,000 people currently test positive for coronavirus.
1-in-X In the region where the site you are considering is located,
1 in 3811 people is currently positive for coronavirus testing.
Study 2
Raw numbers According to an authoritative website, in the country where the site you are considering is located, 2,367,166 people out of 125,800,000 have tested positive for coronavirus in the past month.
Percentages According to an authoritative website, in the country where the site you are considering is located, 1.9% of people tested positive for coronavirus in the past month.
N-in-NX According to an authoritative website, in the country where the site you are considering is located, 1887 people out of 100,000 have tested positive for coronavirus in the past month.
1-in-X According to an authoritative website, in the country where the site you are considering is located, 1 in 53 people tested positive for coronavirus in the past month.
2.3.2 Effect of format on travel intentions
According to the ratio bias explanation (e.g., Denes-Raj et al., 1995), the infection rate presented using larger numerators should trigger lower travel intentions than that presented with smaller numerators, given that people avoid high-risk destinations (Sönmez & Graefe, 1998). Following this reasoning, we propose the following hypothesis:H2a The rate of COVID-19 infection presented using percentages will increase travel intentions compared to that presented using the N-in-NX ratio and that presented using raw numbers. Moreover, the rate presented using the N-in-NX ratios will trigger higher travel intentions than raw numbers.
Consistent with previous studies showing a 1-in-X effect on intentions (Oudhoff & Timmermans, 2015; Sirota & Juanchich, 2019; Sirota, Juanchich, & Bonnefon, 2018), we also predicted that the 1-in-X format would decrease travel intentions compared to the N-in-NX and the percentages formats because it induces an overestimation of the risk value and increases the ease of imagination of the "single person infected." Thus, we propose that:
H2b The rate of COVID-19 infection presented using the 1-in-X ratio will decrease travel intentions compared to that presented using the N-in-NX ratio and that presented using percentages.
2.3.3 The mediating effect of risk perceptions
Why might different numerical formats elicit different travel intentions? One possibility is that numerical formats elicit different behavioral intentions because they induce different risk perceptions. Indeed, during the COVID-19 pandemic, news media exposure increased the perceived risk of COVID-19, influencing protective/preventive behaviors (Heydari et al., 2021). Perceived risk in tourism decision-making has been found to guide consumer behavior in times of crisis, such as under the risk of terrorism, natural disasters, and pandemics (George, 2003; Hall, 2002; Sonmez & Graefe, 1998). For example, when consumers evaluated destination alternatives, safety concerns directly influenced international vacation destination choices (Sönmez & Graefe, 1998).
Previous evidence of the mediating role of risk perception in explaining the effect of presentation format on intentions and behavior showed that the perceived probability of contracting malaria mediated the 1-in-X effect on the hypothetical decision to cancel a trip to Kenya (Sirota & Juanchich, 2019). Also, perceived safety and travel fear mediated the relationship between the risk message frame and travel intentions (Xie et al., 2022). Seemingly, risk perceptions mediated the effect of loss-vs. gain-framed messages on vaccination intentions (Gursoy, Ekinci, Can, & Murray, 2022).
To our knowledge, no study has examined the mediating role of COVID-19 risk perceptions in explaining format effects on tourism travel intentions. Importantly, no study has analyzed the distinct mediating role of the components of risk perceptions: affective, analytical, and experiential. In the present research, we hypothesized that the type of format used to present the rate of COVID-19 infection (X) influences travel intentions (Y) with the mediating role of affective, analytical, and experiential risk perceptions (M) (Fig. 1 ). All three components might have a mediating role in the case of COVID-19. The affective risk component is known to be the strongest predictor of behavior (Brewer et al., 2007); thus, we expect this component to mediate the relationship significantly. The perceived probability (analytical component) was found to fully mediate the effect of the ratio format on decisions (Sirota & Juanchich, 2019); therefore, we expect a significant role of the analytical component. Moreover, given that experiences shape behavior (Heydari et al., 2021), the experiential component could also be relevant in explaining this relationship. Our hypothesis, therefore, is as follows:H3 Perceived affective, analytical, and experiential risk mediates the effect of message numerical format in changing traveling intentions.
Fig. 1 Conceptual mediation models.
Fig. 1
3 Research design
Two studies are included in this research. Both studies employed an online experimental design in which the effect of the numerical format used to present the COVID-19 infection rate (raw numbers, percentages, N-in-NX, and 1-in-X) was manipulated between subjects and tested on perceived risk (affective, analytical, and experiential) and travel intentions. The University of Trento Research Ethics Committee approved the research protocol (N. 2020–020).
4 Study 1
4.1 Design and stimuli
Italian participants, contacted through a crowdsourcing platform (prolific.co), were administered an online questionnaire programmed on Qualtrics. The questionnaire comprised four main sections (travel intentions, affective risk perception, analytical risk perception, and experiential risk perception) and a personal characteristics section. The order of the main sections was randomized between participants and the items within each section as well. The section on personal characteristics was always last. At the time of the data collection (September 2, 2020), the first COVID-19 wave had just finished, and the second one was slowly starting, with 1009 new daily COVID-19 infections. A total of 2,07 million Italians had contracted the virus (3,4% of the population), and no one was vaccinated (vaccinations started on December 2020). Major restrictions had been lifted, and Italians could go to vacation sites and hotels. The only restriction was to wear a regular mask (not FFP2) in indoor environments and circumstances where social distance could not be observed.
All participants were instructed as follows: “Imagine you have to choose your summer vacation destination, and you are evaluating a specific location based on its safety with regard to coronavirus.” Then, they received information on the rate of COVID-19 infection according to the experimental condition they were randomly assigned to (see Table 1). The official reported rate of COVID-19 infection for a region (Emilia Romagna, Italy) at the time of the study was used.
4.2 Measures
The intention to travel to a tourist destination was measured using two items as in previous literature (Boulding, Kalra, Staelin, & Zeithaml, 1993; Xie et al., 2022). One item asked for the intention to travel: "Given the number of cases currently testing positive for coronavirus in this region and having the opportunity, how likely would you be to go on a 7-day vacation to this location in summer 2020?". A second item asked for the intention to recommend a vacation, "Given the number of cases currently testing positive for coronavirus in this region, how likely would you recommend to a relative or a friend to take a 7-day vacation to this location in summer 2020?". Answers were provided on a 7-point response scale ranging from 1 (not likely at all) to 7 (extremely likely). The two items were highly correlated (r = 0.844; p < .001), and a composite average measure was computed (Cronbach's alpha = .916) with higher values representing a higher intention to spend a vacation in that location (M = 3.79; SD = 1.59).
A total of 13 items were formulated to measure the three risk perception dimensions: affective, analytical, and experiential (Table 2 ). Because items were adapted from the medical literature, the three-factor model was subjected to confirmatory factor analysis (CFA) using the maximum likelihood estimation method. Results indicated that the measurement model fitted the data quite well (χ2 = 375, df = 62, χ2/df = 6.05, CFI = 0.951, TLI = 0.938, RMSEA = 0.090); however, two fit indices (χ2/df and RMSEA) were below the model fit standards suggested by Hair et al. (2010) (χ2/df < 3, CFI >0.90, TLI >0.90, RMSEA <0.07). Following the modification indices, we deleted two items (Q2 and Q3) and re-specified the model. The new model fits the data better (χ2 = 126, df = 41, χ2/df = 3.07, CFI = 0.984, TLI = 0.978, RMSEA = 0.058) with only one of the values of the fit indices (χ2/df) just below the model adaptability standard (χ2/df < 3). Table 3 shows the variable's mean, standard deviation, composite reliability, average variance extracted (AVE), and correlations. All items were significantly linked to their corresponding latent factor (p < .001), with factor loadings ranging from 0.550 to 0.945 (Table 1S). The composite reliability estimates of all the constructs ranged from 0.954 to 0.784, indicating good internal consistency and reliability of the items included in each variable. A mean composite score (Table 2S) of each risk perception variable was used in the subsequent analyses.Table 2 Risk perception items and dimensions.
Table 2Item Dimension Description Source (adapted from) Wording
Q2a Affective risk perception Perceived infection rate Pighin et al. (2015) How high do you rate the number of currently positive cases in this region
Q6 (Ferrer et al., 2016; Kaufman et al., 2020; Sheeran et al., 2014) Given the number of cases currently testing positive for coronavirus in this region, how concerned would you be about going on a 7-day vacation to this location?
Q7 Given the number of cases currently testing positive for coronavirus in this region, how afraid would you be to go on a 7-day vacation to this location?
Q8 Given the number of cases currently testing positive for coronavirus in this region, how nervous would you be about going on a 7-day vacation to this location?
Q9 General perceived risk (Peters et al., 2011) Given the number of cases currently testing positive for coronavirus in this region, how risky do you consider this vacation location to be?
Q1 Analytical risk perception Perceived probability (Kaufman et al., 2020; Pighin, Bonnefon, & Savadori, 2011) In your opinion, the probability of contracting coronavirus infection by going on vacation to this location for 7 days is:
Q3a Conditional Risk Perception Ferrer et al. (2016) Considering the way you take care of your health, in your opinion, the probability of you contracting coronavirus infection by going on vacation to this location for 7 days is:
Q4 (Dillard et al., 2012) If you did not follow precautions (such as wearing a mask, maintaining social distancing, etc.), in your opinion, the probability of you contracting coronavirus infection by going on vacation to this location for 7 days would be:
Q5 (Ferrer et al., 2016; Kaufman et al., 2020) Considering your lifestyle, in your opinion, the probability of you contracting coronavirus infection by going on vacation to this location for 7 days is:
Q10 Experiential risk perception Ferrer et al. (2016) How easy is it for you to imagine contracting coronavirus infection by vacationing in this location for 7 days?
Q11 How confident would you feel that you would not contract coronavirus infection by going to this location for your vacation? [REV]
Q12 You would be lying if you said, "There is no chance of me contracting coronavirus infection by going on vacation to that location for 7 days" [REV]
Q13 If you heard that someone contracted the coronavirus infection by going to that vacation resort, how much your first reaction would be, "that could be me"?
Notes.
a Denote those items eliminated from the final variables after the CFA; [REV] indicates those items that were reverse coded for analysis.
Table 3 Descriptive statistics, reliability estimates, and correlations for the study variables.
Table 3 Variables Mean S.D. 1 2 3 AVE
1 Affective risk perception 3.57 1.49 (.954) .84
2 Analytical risk perception 3.66 1.22 .765*** (.813) .62
3 Experiential risk perception 4.32 1.28 .713*** .696*** (.784) .49
Notes: n = 604; values along the diagonal in parentheses indicate the composite reliability estimate for the scale.
***p < .001.
4.3 Participants
The required sample size for an effect size of 0.17 (an average of the sizes obtained in previous studies), a probability of 0.05, and a power of 0.95 was 596 participants. We collected data from 611 participants to protect us from data losses, which fortunately did not occur. Participants were Italian residents (52% males; mean age of 27.6 years). Most of the participants were employed at the time of the survey, either with a full-time (21.5%) or part-time (17.2%) job. Education was as follows: Ph.D. (3.4%), university degree (47.3%), high school degree (48.1%), middle school (1.2%). Most of the sample had a yearly average income between €20,001 and €30,000 (24%) or between €30,001 and €50,000 (21.7%).
4.4 Data analysis strategy
Differences between conditions on the dependent variables were analyzed through between-subjects Analysis of Variance (ANOVA). When the analysis yielded significant results, Tukey's post hoc pairwise comparisons were used to test for significant differences between levels of the condition. The jAMM: jamovi Advanced Mediation Models (Version.2.0.0) was used to conduct the simple mediation analysis to explain the relationship between the independent and the dependent variables.
5 Results
5.1 Effect of numerical format on risk perceptions
The numerical format used to convey the infection rate significantly changed the perceived risk of traveling to a pandemic-affected touristic destination for vacation (Fig. 2 panels b, c, and d). The risk format significantly influenced affective, F (3, 607) = 17.4; p < .001, η2 = 0.079, analytical, F (3, 607) = 15.2; p < .001, η2 = 0.070, and experiential, F (3, 607) = 9.32; p < .001, η2 = 0.044, risk perceptions. Raw numbers increased affective, analytical, and experiential risk perceptions compared to percentages, thus confirming H1a (Table 4 ). However, raw numbers increased affective and analytical, but not experiential, risk perceptions compared to the N-in-NX ratio format, thus partially confirming H1a. The N-in-NX ratio format did not exhibit greater (affective, analytical, and experiential) perceived risk than percentages, contrary to H1a. The results are coherent with a ratio bias explanation which assumes that participants' risk perceptions are guided by the number's face value (the magnitude) of the numerator. In the raw-numbers format, the numerator (i.e., 1170) was greater than in the N-in-NX (i.e., 26) and percentages (i.e., 0.026) formats, and this seemed to increase affective and analytical risk perceptions. The N-in-NX format and percentages were perceived as equally risky because the respective magnitudes (i.e., 26 vs. 0.026) were likely not perceived as very different. Finally, the format effect did not affect experiential risk perception, suggesting that it may be a more robust mental construct immune to message effects.Fig. 2 Intention (a) and affective (b), analytical (c) and experiential (d) risk perceptions of taking a vacation at a pandemic-affected touristic site depending on the type of infection-risk communication format (raw numbers, percentages, N-in-NX and 1-in-X) in Study 1. Error bars represent the Standard Error (SE) of the mean.
Fig. 2
Table 4 Descriptive statistics of the variables in the four experimental conditions and Tukey's post hoc comparisons for Study 1.
Table 4Variables Condition N Mean SD SE t-value
Percentages N-in-NX 1-in-X
Intention to vacation Raw numbers 168 3.29 1.56 0.120 −6.54*** −4.12*** −1.37
Percentages 144 4.43 1.62 0.135 2.29 5.12***
N-in-NX 141 4.01 1.47 0.124 2.75*
1-in-X 158 3.53 1.49 0.118
Affective Risk Perception Raw numbers 168 4.06 1.51 0.117 6.54*** 4.64*** 1.58
Percentages 144 2.99 1.30 0.108 −1.80 −4.93***
N-in-NX 141 3.30 1.41 0.119 −3.06*
1-in-X 158 3.81 1.48 0.118
Analytical Risk Perception Raw numbers 168 3.98 1.22 0.094 5.97*** 3.56** 0.60
Percentages 144 3.18 1.07 0.089 −2.19 −5.30***
N-in-NX 141 3.49 1.20 0.101 −3.03*
1-in-X 158 3.90 1.21 0.096
Experiential Risk Perception Raw numbers 168 4.58 1.25 0.097 4.75*** 2.42 0.41
Percentages 144 3.90 1.33 0.111 −2.23 −4.29***
N-in-NX 141 4.23 1.24 0.105 −1.99
1-in-X 158 4.52 1.19 0.095
***p < .001; **p < .01; *p < .05.
Confirming H1b, the 1-in-X format increased affective, analytical, and experiential risk perceptions compared to percentages. Partially confirming H1b, the 1-in-X format also increased affective and analytical, but not experiential risk perceptions compared to the N-in-NX format. In line with the 1-in-X effect, the infection rate presented using a 1-in-X format seemed higher than the comparable N-in-NX format. However, this pattern was not observed for the experiential component, which was immune to the 1-in-X effect as it was to the ratio bias.
5.2 Effect of numerical format on travel intentions
The numerical format significantly changed the intention to travel to a pandemic-affected destination for vacation, F (3,607) = 16.8; p < .001, η2 = 0.077 (see Fig. 2, Panel a). As shown in Table 4, in partial confirmation of k, percentages increased travel intentions compared to raw numbers but not compared to the N-in-NX format. Also, the N-in-NX format triggered higher travel intentions than raw numbers. These results are coherent with a ratio bias explanation (e.g., Denes-Raj et al., 1995), as the intention to travel was higher when the risk information was presented using smaller numbers at the numerators, such as in the percentages (i.e., 0.026) and N-in-NX (i.e., 26) formats, than when it was presented using larger numbers at the numerator, such as in the raw-numbers format (i.e., 1170). Regarding the lack of difference between the N-in-NX format and percentages, we might suppose that 26 people were not perceived as significantly different from zero (i.e., 0,026), being a relatively low frequency, after all.
In support of H2b, the COVID-19 infection rate presented using the 1-in-X ratio significantly decreased travel intentions compared to that presented using the N-in-NX ratio and percentages. By the 1-in-X effect (Pighin, Savadori, et al., 2011), this format makes the risk value seem higher and reduces the intention to travel to a high-risk destination.
5.3 Mediating effects of risk perceptions
The mediating effects were tested for the four contrasts that showed a significant difference in the previous ANOVA analyses (see Tables 3S–14S). Confirming H3, results showed that affective risk perception fully mediated the effect of numerical format (raw numbers vs. N-in-NX) on travel intentions. Instead, analytical and experiential risk perceptions only partially mediated the effect of numerical format (raw numbers vs. N-in-NX) on potential tourist travel intentions (see Tables 3S–5S). Moreover, the affective, analytical, and experiential risk perceptions only partially mediated the effect of numerical format (raw numbers vs. percentages) on travel intentions (see Tables 6S–8S). Furthermore, risk perception's affective and analytical dimensions completely mediated the relationship between numerical format (1-in-X vs. N-in-NX) and travel intentions. The experiential risk dimension, instead, only showed a partial mediation (see Tables 9S–11S). Finally, risk perception's affective, analytical, and experiential components partially mediated the relationship between the numerical format (1-in-X vs. percentages) and travel intentions (see Tables 12S–14S).
Overall, the findings indicate that the affective component of risk perception explained the format effects in two out of four cases, confirming being the prime candidate for a likely explanation of format effects. The role of the affective component in explaining format effects further supports the ratio bias explanation, which assumes that people focus on the magnitude of the numerator disregarding the denominator because they follow an intuitive-experiential thinking style as opposed to a cognitive one (Pacini & Epstein, 1999). Our data confirm this assumption showing that a smaller ratio (26 out of 100,000) increased travel intentions compared to a larger ratio (1170 out of 4,459,000) because the former ratio induced less fear and worry (a feeling component).
Our findings also help to explain the 1-in-X effect, as they indicate that it was mediated by both the affective and the analytical components. The 1-in-X ratio influenced intentions by increasing fear and worry but also by increasing the subjective probability of infection. However, neither of the two format effects appeared to be based on the experiential component of risk perception.
6 Study 2
Study 2 aimed at confirming the format effects by generalizing them to a different infection rate, thus using different numbers. For this purpose, the official reported rate of COVID-19 infection in Japan in the month prior to the data collection (October 2022) was used (i.e., 0.019), which was higher than that used in Study 1 (i.e., 0.00026). We, therefore, hypothesized that:H4 The format effects on behavioral intentions and risk perceptions (affective, analytical, and experiential) found in Study 1 will be replicated with a higher rate of destination COVID-19 infection.
Study 2 further aimed at testing for boundary conditions. We investigated how worrying about COVID-19 might play a role in enhancing or attenuating the format effects. When the level of worry is high, people may be more sensitive to the numbers or certain numerical formats than when worry is low (Pighin, Bonnefon, & Savadori, 2011). Yet, the opposite might also hold true. Under strong emotional reactions, people ignore important numeric information such as probabilities (Rottenstreich & Hsee, 2001). Therefore, in Study 2, we measured people's level of worry about COVID-19 and examined the moderating effect of subjective worry in the relationship between numerical format and intentions. We hypothesized that:
H5 Subjective worry moderates the effect of numerical formats on risk perceptions and travel intentions.
6.1 Design and stimuli
Study 2 was conducted using the same procedure as in Study 1. The data was collected on October 2022 when the COVID-19 pandemic was in its third year, and many citizens had contracted the disease (38%). Most of the citizens were vaccinated (84%), and there were no restrictions. As in Study 1, participants were randomly assigned to one of four conditions that varied the numerical format: raw numbers, percentages, N-in-NX, and 1-in-X. The same introductory message was used except that the words “summer vacations” were substituted with “the next vacation”. Participants received information on the officially reported rate of COVID-19 infection in Japan in the month prior to the data collection (retrieved from the Johns Hopkins Coronavirus Resource Center online) according to the experimental condition they were randomly assigned to (see Table 1). As in Study 1, no reference to the real country or region was provided to participants.
6.2 Measures
The same measures were used as in Study 1, except that the words “summer vacations” were replaced with “the next vacation” to adapt items to the time of data collection. The two items measuring travel intentions were highly correlated (r = 0.787; p < .001), and a composite average measure was computed (Cronbach's alpha = .880). The same items as in Study 1 were used to create the affective (Cronbach's alpha = .955), analytical (Cronbach's alpha = .855), and experiential (Cronbach's alpha = .955) risk perceptions scales (descriptive statistics in Table 15S).
6.3 Participants
Participants were 608 Italian residents (59.6% male; mean age of 30 years) who did not participate in Study 1. Most participants were employed full-time (33.9%) or part-time (16.9%). Education was as follows: Ph.D. (4.1%), university degree (54.3%), high school degree (40.6%), middle school (1.0%). Most of the sample had a yearly average income between €20,001 and €30,000 (24.8%) or between €30,001 and €50,000 (24.3%).
6.4 Data analysis strategy
The same data analysis strategy was used as in Study 1, except that an ANCOVA analysis was performed to test for the moderation effect.
7 Results
7.1 Effect of format on risk perceptions
Confirming H4, the numerical format had the predicted effect on affective F (3, 602) = 14.2; p < .001, η2 = 0.066, analytical F (3, 602) = 16.1; p < .001, η2 = 0.074, and experiential F (3, 602) = 3.78; p = .010, η2 = 0.018 risk perceptions (see Fig. 3 panels b, c, and d). Similarly to Study 1 and coherently with a ratio bias explanation, raw numbers (i.e., 2,367,166 people out of 125, 800, 000) increased affective, analytical, and experiential risk perceptions compared to percentages (i.e., 1.9%), possibly because they convey the risk using larger numbers (see Table 5 ). Contrary to Study 1, only analytical risk perception was higher in the raw numbers than in the N-in-NX format (i.e., 1881 out of 100,000 people). The higher number of people (2,367,166) in the raw format numerator increased the subjective probability of getting infected compared to the smaller number of people (1,881) in the N-in-NX format, an instance of the ratio bias. The N-in-NX format also increased affective, analytical, and experiential risk perceptions compared to percentages, in line with a ratios bias explanation.Fig. 3 Intention (a) and affective (b), analytical (c) and experiential (d) risk perceptions of taking a vacation at a pandemic-affected touristic site depending on the type of infection-risk communication format (raw numbers, percentages, N-in-NX and 1-in-X) in Study 2. Error bars represent the Standard Error (SE) of the mean.
Fig. 3
Table 5 Descriptive statistics of the dependent variables in the four experimental conditions and Tukey's post hoc comparisons for Study 2.
Table 5Variables Condition N Mean SD SE t-value
Percentages N-in-NX 1-in-X
Intention to vacation Raw numbers 142 4.11 1.53 0.129 −5.213*** −1.742 0.0792
Percentages 170 4.97 1.36 0.104 3.394** 5.392***
N-in-NX 142 4.41 1.52 0.127 1.851
1-in-X 152 4.09 1.46 0.119
Affective Risk Perception Raw numbers 142 3.50 1.50 0.126 5.534*** 2.547 0.057
Percentages 170 2.58 1.28 0.098 −2.876* −5.577***
N-in-NX 142 3.06 1.51 0.127 −2.533
1-in-X 152 3.49 1.52 0.124
Analytical Risk Perception Raw numbers 142 4.06 1.24 0.104 5.919*** 2.926* 0.163
Percentages 170 3.22 1.22 0.093 −2.864* −5.858***
N-in-NX 142 3.63 1.25 0.105 −2.813*
1-in-X 152 4.04 1.30 0.105
Experiential Risk Perception Raw numbers 142 4.43 0.73 0.061 3.188** 1.437 0.776
Percentages 170 4.14 0.91 0.070 −1.688 −2.436
N-in-NX 142 4.30 0.78 0.066 −0.685
1-in-X 152 4.36 0.78 0.063
***p < .001; **p < .01; *p < .05.
In further support of the 1-in-X effect, the feelings of fear and worry about traveling were enhanced when the destination infection rate was communicated using the 1-in-X format (1 in 53) compared to percentages (1.9%) and, limited to the analytical component, also compared to the N-in-NX format (1881 out of 100,000 people).
Finally, the experiential component of risk perception was the least sensitive of the three components as it was influenced only by the raw numbers vs. percentages format effect, in the same direction as the other two risk perception components. A general higher experienced familiarity with the disease during this second data collection, as evidenced by the higher number of infected cases among the reference population, might have induced fewer people to be “experientially” influenced by the format in deciding to travel.
The ANCOVA models with worry as a covariate (H5) showed that affective risk perceptions were significantly higher in those potential tourists who were more worried about the disease, F (1,598) = 261.12; p < .001, η2 = 0.302, however, worry did not interact with the type of numerical format, F (3,598) = 1.83; p = .141, η2 = 0.006, showing no moderating role. Seemingly, analytical risk perceptions significantly increased with an increase in worry for the disease, F (1,598) = 124.47; p < .001, η2 = 0.170, but the interaction term with the type of numerical format was not significant, F (3,598) = 0.11; p = .953, η2 = 0.000. Finally, worry significantly increased experiential risk perceptions, F (1,598) = 118.77; p < .001, η2 = 0.164, but as for the previous components, it did not moderate the relationship between the type of numerical format and experiential risk perception, F (3,598) = 1.20; p = .310, η2 = 0.005.
7.2 Effect of numerical format on travel intentions
The numerical format had a significant effect on travel intentions, F (3,602) = 12.8; p < .001, η2 = 0.060 (see Fig. 3, Panel a). As shown in Table 5, confirming H4, the lowest travel intention was observed when the destination infection rate was conveyed using raw numbers (2,367,166 people out of 125, 800, 000), but the difference was statistically significant only for percentages (1.9%), whereas it was not for the N-in-NX format (1881 out of 100,000 people). Contrary to H4, the ratio bias effect was attenuated in the raw numbers vs. N-in-NX comparison because values at the numerators were quite high in both conditions. Confirming H4, the difference between N-in-NX and percentages was statistically significant, showing that more people intended to travel if the destination infection rate was communicated with ratios with small numerators (percentages) rather than larger ones (N-in-NX). Partially confirming the 1-in-X effect and previous literature (Oudhoff & Timmermans, 2015), findings show that when presented through a 1-in-X format, destination infection rates induce fewer people to travel than when presented through percentages, presumably because the 1-in-X format produces an overestimation of the magnitude of the risk (Sirota, Juanchich, & Bonnefon, 2018) or ease of imagination of the “one person” (Oudhoff & Timmermans, 2015).
To test H5, participants' worry was added to the model as a covariate (ANCOVA). Results showed that the intention to travel was significantly lower in those potential tourists who were more worried about the disease, F (1,598) = 101.18; p < .001, η2 = 0.143. However, worry did not interact with the type of numerical format, F (3,598) = 1.12; p = .342, η2 = 0.005. Therefore, findings disconfirmed H5 and the moderating role of worry.
8 Discussion and conclusions
People deciding whether to travel for vacation might consider looking for information about the risks they could encounter at the tourist location, such as infectious disease or crime rates. This information has to be communicated using numbers. Although our mind has adapted to perceive numbers as natural quantities (Dehaene, 2011), the meaning of such a value can become obscure when numbers are in the form of ratios, such as in the case of an infection rate. In our study, the rate of COVID-19 infection was presented using different formats, which induced different risk perceptions and travel intentions. In line with a ratio bias explanation (Denes-Raj et al., 1995), when the infection rate was presented using ratios with smaller numbers at the numerator, such as percentages or an N-in-NX format (Study 1), potential tourist's worry was reduced, and their intention to travel increased. Conversely, when the same risk information was presented using ratios with larger numbers at the numerator, such as in the case of raw numbers, people's fear of getting infected increased, and consequently, their travel intentions decreased. The affective component of perceived risk fully mediated the format effect on travel intentions.
In line with the 1-in-X effect, which induces people to believe that a 1-in-X value represents a higher risk (Oudhoff & Timmermans, 2015; Pighin, Savadori, et al., 2011; Sirota, Juanchich, & Bonnefon, 2018), in the present research, we found that when the infection rate was presented in the 1-in-X format, perceived fear and subjective probability increased, and travel intentions decreased compared to other formats, such as percentages or N-in-NX ratios. Intuitive-hot (affective) and systematic-cold (analytical) risk-perception reasoning equally mediated the format effect on intentions. Overall, these results imply that different format effects rely on distinct mechanisms involving affective and cognitive elaborations of risk information.
8.1 Practical implications
Presenting the same information about risk in different ways alters people's perspectives and actions accordingly. This has important implications for managerial marketing strategy. Suppose the goal is to restore the tourism attractiveness of the location in the post-pandemic period. In that case, our results recommend reporting the infection rate using small numbers at the numerator (percentages and N-in-NX formats) but avoiding using 1-in-X formats or raw numbers, which feature large numbers at the numerator.
Our results have important implications for public health policy managers as well. During a pandemic outbreak, it might be important to limit the circulation of people in areas affected by a virus. As our study shows, potential tourists' risk awareness in a particular tourist area affected by a virus can be increased by communicating infection rates using ratios with larger numerators rather than those with smaller ones or by using ratios with the number “1” at the numerator. These two formats, indeed, were found to increase participants' awareness of risk, albeit with the counter effect of reducing the location's attractiveness.
Confirming the results of Bae and Chang (2021), our study suggests that tourists' intention to visit a destination is mediated primarily by the affective dimension of risk perception. The communication strategy of health policy managers that aims to reduce tourist inflow to infected areas should underline the affective component of possible infections. On the contrary, post-pandemic marketing activities of destination managers should be directed toward reducing the perception of fear and worry related to possible infections.
Overall, it must be said that the numerical format used to convey health information is a matter of choice. Tourism managers and policymakers should hold themselves accountable for the communication strategies they adopt, being informed by results like those offered by the present research.
8.2 Limitations and directions for future research
We explored four information formats: raw numbers, percentages, N-in-NX fractions, and 1-in-X fractions. Both practical and theoretical considerations guided our choice. These formats are a standard way of communicating numerical information, and they were examined in previous studies. However, other reporting methods for viral infection exist that use other formats, such as probabilities (e.g., 0.1) or odds (e.g., 1 to 9 odds). Future studies could investigate the effect of these alternative formats on perceived risk and travel intentions.
An additional aspect needs to be considered primarily from a health policy perspective. A related and important question is whether different formats improve the comprehension of rational number concepts, such as fractions. Several studies have highlighted that people make consistent and systematic errors when processing rational numbers (Hurst & Cordes, 2016). Therefore, further studies should also aim to understand the most effective format to improve people's understanding of risk information and promote informed tourism decision-making.
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
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.jhtm.2022.12.004.
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| 0 | PMC9721287 | NO-CC CODE | 2022-12-10 23:14:58 | no | 2023 Mar 5; 54:65-75 | utf-8 | null | null | null | 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)00940-5
10.1016/j.bspc.2022.104486
104486
Article
COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold
Rao Yunbo a
Lv Qingsong a
Zeng Shaoning b⁎
Yi Yuling a
Huang Cheng c
Gao Yun d
Cheng Zhanglin e
Sun Jihong f
a School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China
b Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, 313000, China
c Fifth Clinical College of Chongqing Medical University, Chongqing, 402177, China
d Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
e Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
f Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310014, China
⁎ Corresponding author.
5 12 2022
3 2023
5 12 2022
81 104486104486
15 8 2022
23 11 2022
1 12 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 ground glass opacity (GGO) of the lung is one of the essential features of COVID-19. The GGO in computed tomography (CT) images has various features and low-intensity contrast between the GGO and edge structures. These problems pose significant challenges for segmenting the GGO. To tackle these problems, we propose a new threshold method for accurate segmentation of GGO. Specifically, we offer a framework for adjusting the threshold parameters according to the image contrast. Three functions include Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). The lung is divided into three areas using the attention mechanism threshold. Further, the segmentation parameters of the attention mechanism thresholds of the three parts are adaptively adjusted according to the image contrast. Only the segmentation regions restricted by the lung segmentation results are retained. Extensive experiments on four COVID datasets show that ACL can segment GGO images at low contrast well. Compared with the state-of-the-art methods, the similarity Dice of the ACL segmentation results is improved by 8.9%, the average symmetry surface distance ASD is reduced by 23%, and the required computational power FLOPs are only 0.09% of those of deep learning models. For GGO segmentation, ACL is more lightweight, and the accuracy is higher. Code will be released at https://github.com/Lqs-github/ACL.
Keywords
COVID-19
GGO segmentation
Attention mechanism
Adaptive threshold
Pneumonia
CT image
==== Body
pmc1 Introduction
The outbreak of COVID-19 has a significant impact on the whole world [1]. The World Health Organization Global Case Report (updated on 11 October 2022) showed that there were more than 619.16 million confirmed cases of COVID-19, including 6.54 million deaths in 200 countries and regions around the world [2]. Reverse transcriptase chain reaction (RT-PCR) is the medical gold standard for diagnosing COVID-19 [3]. However, the shortage of medical staff and testing equipment to handle a large number of suspected cases limits the speed and accuracy of medical diagnosis. In addition, with the spread of COVID-19 virus variants reported, the false-negative rate of RT-PCR detection is also high [4]. CT and other imaging techniques are a good supplement, and clinical trials have also proved the effectiveness of CT imaging in diagnosing COVID-19 [5]. Therefore, it is crucial to have an efficient segmentation solution of CT GGO images for automatic COVID-19 diagnosis [6]. As shown in Fig. 1, we can observe the three-layer structure of COVID-19 lung CT images with low contrast, including lung background (black area), normal tissue, and GGO. However, manually mapping the GGO area is a tedious task. Marked areas are also easily influenced by subjective factors and limited by personal experience and energy.
Deep learning has been widely used to segment GGO areas in COVID-19. For example, Inf-net uses the constraints between the interior and edges of GGO to segment GGO [7]. In addition, there are image pyramids to segment targets by improving image quality [8] and segmentation methods to improve network models such as U-net [9], [10]. A large number of network segmentation methods have been proposed to determine the GGO area [11]. Image analysis of pixels can implement a much faster segmentation which is rarely discussed.Fig. 1 The left is the CT image of COVID-19’s lung with low contrast, and the right is the segmentation result by ACL (ours) on the CT image. The red part is the GGO area.
In machine learning, threshold is commonly preferred for pixel-level segmentation tasks on images. The image pixel values are divided according to the threshold to get the segmentation target. Adaptive threshold tends to compute thresholds from global or local GGO CT image features or use multiple sets of thresholds to jointly determine the class of targets. The GGO CT image is more finely divided into multiple sub-image tasks for multi-threshold segmentation, facilitating a more accurate acquisition of segmentation targets [12]. However, it also faces the problem of coordinating the relationship between numerous block thresholds and uneven local contrast degree of the image, which resulted in segmented block image disconnection [13]. Further, local thresholds are constructed based on the class’s uncertainty and the region’s homogeneity with sub-regions of images at different scales co-associated. The details of the overall target structure are preserved, and the segmentation effect of the local threshold is obtained.
Segmentation of GGO can be summarized into four main problems. (1) The shape and texture of GGO regions are extremely variable, and it is not easy to find fixed features for segmentation. (2) The contrast between GGO regions and edge tissues is low, making it difficult to segment edges accurately. (3) Segmenting GGO blurred edges with a single fixed threshold is challenging. (4) The thresholds of values and their selection are problems that need to be solved. In addition, there are only a few methods to segment COVID-19 GGO based on the threshold. It is difficult to fix the threshold selection and parameter adjustment, and the segmentation effect needs to be improved [14].
We propose a threshold-based attention mechanism to segment GGO regions in COVID-19 images. Three functions are designed: Attention mechanism threshold to segment regions, contour equalization, and lung segmentation (ACL). Our motivation is that clinicians use prior information first to determine the approximate location of lung infection. Furthermore, we compare inner lung tissues and the empirical relationship between internal lung tissues and normal lung contour [15]. The attention mechanism Threshold roughly separates the lung background, GGO area, and normal tissue. Then, the GGO area is determined according to the empirical relationship between image contrast quality and normal lung contour. Finally, balance the refined contour of the GGO area and normal tissue. In addition, to further verify the features of this method in CT image segmentation, the segmentation effect of various common pneumonia is also confirmed. Specifically, our adaptive threshold segmentation method has a fast and accurate segmentation ability in the specific case of lung GGO segmentation in CT images, which is better than some of the cutting-edge models and performs better.
To sum up, our contributions are as follows:
(1) We present a new efficient attention mechanism threshold method, including Attention mechanism threshold, Contour equalization, and Lung segmentation (ACL). It can accurately segment GGO without complicated training processes.
(2) The contour equalization in ACL can solve the situation of extreme contrast. Classifying CT images according to their quality can refine different contours and improve segmentation accuracy.
(3) ACL can also accurately segment ordinary pneumonia. The experimental results on four pneumonia segmentation data sets show that the segmentation effect is better than the frontier models.
2 Related work
This section discusses three types of work most relevant to our work. (1) GGO segmentation based on deep learning networks. (2) GGO segmentation without deep neural networks. (3) Artificial intelligence progresses for COVID-19 diagnosis.
2.1 Deep learning in segmentation of lung diseases
Since the outbreak of COVID-19, more and more deep neural networks have been proposed for the segmentation of lung GGO. Deep learning solutions can be classified into three categories: First is deep neural networks with an attention mechanism trained by a primary information constraint [16]. For example, Inf-net obtained the low-level semantic features of contours in advance and then performed a network training to segment the GGO areas of chest CT [7], [17]. It was also influential in connecting an attention-rejecting network with an interactive attention-thinning network to segment the infected regions of GGO [18]. Besides these, there are many other promising solutions, including extended convolution residual attention block, enhancement of receptive field, and improvement of feature map segmentation [19]. All these methods paid attention to the fact that the known information can be considered a constraint to limit the network training further [20]. However, the blurred edges make it difficult to segment the contour due to the problem of low contrast between the GGO area and the lung background.
The next group of deep segmentation methods was designed to improve the quality of CT images. For example, multi-scale feature image fusion and enhancement networks were applied to segment GGO [21]. A novel data augmentation based on Gabor filter and convolutional deep learning for improving COVID-19 images [22], and Dense GAN and multi-layer attention to segment GGO [23].Fig. 2 Flow chart of GGO segmentation and lung segmentation. The input is the CT lung slice of the patient, and the output is the map indicating the GGO area. The three functions of attention mechanism threshold (A), contour equalization (C), and lung threshold segmentation (L) in the graph are the method ACL.
Fig. 3 Attention mechanism threshold. T1, T2 and T3 divide the image pixels into three areas S1, S2 and S3.
The final design is combing the attention mechanism and image enhancement. For example, CNN based on Sugeno fuzzy integral was devoted to detecting GGO in COVID-19 [24]. Anam-net improved by a lightweight CNN was applied to segment GGO [25], [26]. In particular, detecting GGO can also be utilized feature selection based on particle swarm optimization algorithm, and ant colony algorithm [27]. However, deep neural networks are also trained on the premise of limiting adequate prior information and improving the image quality [28]. Therefore, when the prior information is enough [29], it is not necessary to train these intense networks using a great deal of computation.
2.2 Non-network in segmentation of lung diseases
The good news is that non-network methods showed their promising performance in the segmentation of lung GGO. Unlike deep neural networks, two separate tasks are usually involved in non-network models. One is the segmentation of the lung contour. Another is the final segmentation of the GGO. Segmentation of lung based on the morphological operation [30]. Combine Gabor filter for threshold segmentation [31]. Automatic multi-organ segmentation of abdominal CT volume based on locally linear embedding graph segmentation [32]. These methods all show fast and accurate results in lung contour segmentation. The other is to segment the GGO area: (1) The global task is split into local segmentation tasks, and the lung nodule regions are obtained and merged [33]. (2) Image features are enhanced by increasing the image resolution to segment lung nodules [34]. (3) Features are combined with support vector machines for lung cancer segmentation [35]. Moreover, distinguishing from deep learning methods, non-network methods usually apply two distinct algorithms for the two segmentation tasks, i.e., lung contour segmentation and GGO segmentation [36].
However, a general problem in these methods is that it is complicated to choose their optimal parameters [37]. For example, selecting the size of the operator in image preprocessing is challenging [33]. It is not easy to choose the appropriate segmentation points for the image when threshold segmentation also [38]. We observed that non-network methods have some advantages over deep learning. Most methods are based on fast pixel segmentation and do not require extensive training [39], threshold-based segmentation used in this work considers the change of the relationship between shape and position. It leads to a better performance of the non-network method than deep learning in lung contour segmentation.
CT image GGO is still not accurate enough by threshold segmentation. It is mainly due to the difficulty of parameter selection. It motivates us to explore a new schema. Let the segmentation automatically adjust its parameters subject to the quality of the images. Much preliminary information is available when segmenting the GGO area of lung CT images. For example, one-dimensional CT rapid localization of lung cavity [5]. The characteristic information of pneumonia [40] and so on are all useful prior information. We consider the corresponding parameters of various situations as preliminary information to generate a mapping relationship between the input images and the parameters.
2.3 AI progresses for COVID-19
AI progresses for COVID-19, especially the techniques represented by the aforementioned automatic segmentation methods, and has been widely used in COVID-19 diagnosis. The successful applications include diagnosis of suspected patient [7], [41], development of patient’s condition, as well as their follow-up cure [42]. Segmentation of GGO areas in CT images is beneficial for understanding a patient’s specific conditions. Deep neural evolution algorithms can directly analyse whether the patient is sick or not in diagnosing suspected patients. For example, there are three types of pneumonia diagnosis based on the architecture, including confirmed COVID-19, confirmed virus, and bacterial cases. Diagnosis of suspected patients can be implemented by Convolutional Neural Networks (CNN) and dynamic feature selection. The prediction effect of these methods on COVID-19 is similar to that of radiologists. In addition, quantitative results by segmentation can be used to quantify the infection status [43], [44]. Furthermore, for example, an intelligent analysis system based on local binary patterns and patient information cloud system doctors for a better diagnosis and follow-up treatment. In a word, supported by an efficient segmentation technique, AI can significantly facilitate the diagnosis of COVID-19 [45].
3 Methodology
In this section, we detail the implementation of the attention mechanism threshold segmentation of the lung cavity segmentation algorithm. As shown in Fig. 2, the process is divided into GGO segmentation and lung segmentation. (1) The GGO segmentation consists of two functions: attention mechanism threshold and contour equalization. (2) The lung lumen segmentation is obtained from the designed lung threshold segmentation function. The complete process threshold segmentation of GGO is realized.
3.1 Overview of ACL
Our segmentation method is shown in Fig. 2. The CT lung image of the patient first divides the lung region of the image into four equal parts. The CT image is divided into three areas by three threshold segmentation functions. Calculate the contrast of the image by using the designed contrast Eqs. (1), (2). According to different contrast values, the contour of the GGO area is refined. Finally, the segmentation result of the GGO area is combined with the segmentation result of the lung cavity, which is called the final labelling result of the GGO area. Only the segmentation region of the GGO in the lung cavity is retained. Notably, in this framework, we do not adjust the specific parameters. According to the quality of the image itself, we adopted a uniform threshold and segmentation standard. We use the GGO matrix and the standard tissue matrix in the intermediate process instead of operating on the original image. We only make the final colouring mark on the original image to display the predicted lung infection area. Next, we will introduce the key components of the GGO segmentation, attention mechanism threshold, contour equalization, lung segmentation and parameters of the algorithms.
3.2 GGO segmentation
3.2.1 Attention mechanism threshold
Most of the COVID-19 CT image histograms are regularly distributed through statistical analysis of segmented data sets. Therefore, threshold segmentation can roughly distinguish the specific tissue region and background of the GGO region. As shown in Fig. 3, we first use the OSTU threshold method to determine the threshold of the image and divide the image pixels into two parts. Then, the second threshold is divided for the lower grey value. We separate the parts more considerably than the second threshold in the whole image. Finally, the third threshold segmentation divides the lung into three parts. Both the GGO region (S2 or S3) and specific tissue region (S2 or S3) exist in the second threshold segmentation, and all pixels are more diminutive than the second threshold segmentation are the background regions S1.
In the process of obtaining the threshold, we used Global T3=max Ti3, but the actual effect was very poor. We realize that the dividing line of the histogram between normal tissue and the GGO area in the local area is constantly changing. Therefore, in theory, Global T3 cannot separate the GGO area from normal tissue. Then, as shown in the Alg. 1, it is a reasonable segmentation method to separate the GGO area from the normal tissue by the separation threshold Ti3 of each block of the image. At the same time, we find that the change of the second threshold between blocks is usually small, and the second threshold is used to distinguish the background from other features. Therefore, Global T1 and Global T2 are used to separate the background, and then combine the GGO areas of each block separated by Ti3. Finally, a GGO map of the whole lung is formed.
3.2.2 Contour equalization
After obtaining the regions of S1,S2,S3, the contour needs to be further refined. Threshold segmentation is essentially the classification of pixels [46], and the grey level of outline tends to decrease continuously as shown in Fig. 4, resulting in that even the GGO area will be segmented into normal tissues [47]. Therefore, we expand and erode the affected and the average regions to a certain extent. However, this operation will lead to pixel loss in the area, we use filtering to compensate. According to the image quality, the parameters such as swelling, corrosion, and filter compensation are set to balance the contour of the GGO area and normal tissue and obtain more accurate segmentation results. The above method can solve most cases well, but segmenting the whole image with low or high brightness is challenging. As shown in Fig. 5, this is because under highly high and external brightness conditions, the grey value of the GGO area is relatively consistent with that of normal tissue, and it is difficult to distinguish it through the threshold relationship directly. It is necessary to revise the shape of the results of threshold processing.
In this extreme case, our inspiration mainly comes from how clinicians judge the GGO area. Usually, we first locate the lung cavity, then we see a large size of highly dark grey or a large area of bright light in the lung. Then, it is judged that these two types of sites are pathological areas. Therefore, we classify the images into three categories according to the contrast quality of the images and discuss the parameters of swelling, corrosion and filtering compensation to balance the contours of the GGO area and normal tissue and obtain more accurate segmentation results. The contrast value is calculated as follows.
Image size is m×n. I(i,j) represents the pixel value at the image (i,j). The total variance (Var) of the image is: (1) Var=∑i=1m∑j=1n(I(i,j−1)−I(i,j))2+(I(i−1,j)−I(i,j))2+(I(i,j+1)−I(i,j))2+(I(i+1,j)−I(i,j))2,
and image contrast value is: (2) Contrast=Var4(m−1)(n−1)+3∗(2(m−1)+2(n−1))+4∗2.
3.3 Lung segmentation
Because of the similarity between the GGO area and tissue, as shown in Fig. 5, it is difficult to separate the lung cavity area from the whole image. Hence, we design a new lung segmentation algorithm with the adaptive threshold to divide the entire lung.
The lung tissues will be separated together. It is worth noting that in Alg. 1, Ti3 can be used to distinguish the GGO area from normal tissue. We can use the attributes of Ti3 to binarize the original image. At the same time, we can roughly distinguish the lung contour from the lung tissue. As shown in Fig. 6, S1 is the background part, S2 is the lung part, and S3 is the intrapulmonary part. Filtering the whole lung can separate the lung cavity well.Fig. 4 The grey value of GGO contour in different contrast CT images gradually decreases.
Fig. 5 GGO in extremely high brightness and extremely low brightness CT images. The left shows a CT GGO image with extremely high brightness and shallow contrast. The right CT GGO image has extremely low brightness and contrast.
Fig. 6 Lung segmentation process. (a) is the result of lung cavity filling, (b) denotes the binarization result using Ti3, (c) represents the result of the closed operation, and (d) expresses the result of lung segmentation.
Although there are burrs and depressions at the edges of the lung contour, as shown in Fig. 6(d), Alg. 1 and Alg. 3 only separate the pixels with the same grey value of the GGO, burrs will not be marked as the GGO area. In addition, since the lung cavity divided by the threshold of global Ti3 is adopted, it has been proved that the concave part is normal tissue, and the concave part will not be marked as the GGO area.
3.4 Parameters of the ACL
ACL algorithm parameters are divided into two types: fixed and adaptively computed.
Attention Mechanism Threshold: All parameters are obtained from the adaptive calculation of Alg. 1.Fig. 7 ACL actual effect comparison chart. The first column shows the original CT lung image from COV1, COV2, COV3 and COV4, which is classified into Contrast∈0,ContrastValue1, ContrastValue1,ContrastValue2, ContrastValue2,∞. The second to fifth columns are the GGO segmentation results of Gate-UNet, Inf, JCS, and ACL (ours). The last column is Ground True. The top right corner of each resulting image shows Dice (%) and ASD.
Fig. 8 Dice of ACL and three methods in different contrast CT images. The ordinate is the Dice index, and the abscissa is image contrast. The Dice of four methods of contrast image at every five units is tested. Orange shows Gate-UNet, purple represents Inf-Net, blue expresses JCS, and green is ACL.
Contour Equalization: Contrast Value 1 is set as 150. Contrast Value 2 is taken as 300, and the size of the empty matrix is 3 × 3 for disconnected domain and expansion operation. The mean filter template is 3 × 3. These parameters are the default preferred parameters built into the respective algorithms.
Lung Segmentation: The size of the closed operation is the empty matrix of 3 × 3. The binarization parameters are calculated by the algorithm adaptively.
4 Experiments
4.1 Segmentation dataset
The COVID CT segmentation datasets consists of four parts. Two sets of COVID-19 data sets are segmented by a radiologist using three labels, including ground glass, nodules, and pleural effusion. The COVID CT segmentation datasets were published on April 2, 2020, and April 13, 2020, respectively [48]. In the experiments, we only used CT images with GGO labels. Another group of COVID-19 is the COVID CT segmentation data set collected by Zhao on April 8, 2020 [49]. Zhao collected the non-COVID CT segmentation dataset from June 2011 to the present and applied it to other pneumonia segmentation experiments. Finally, we also use the Kaggle dataset [12] for GGO segmentation testing as shown in Table 1.
Since mixing COVID-19 virus datasets lead to performance bias [51], we independently conduct segmentation experiments on the four COVID datasets. The rest of the non-COVID-19 images were combined for extended testing. In total, the test results of the state-of-the-art COVID segmentation method and the state-of-the-art pneumonia segmentation method are compared in four COVID datasets.Table 1 Public CT image segmentation dataset of COVID-19 and the non-COVID CT image segmentation dataset.
Dataset #COV #Non-COV Cases Slices Download
COVID-19
(2020.4.2) COV1 110 3700 [48]
COVID-19
(2020.4.13) COV2 350 [48]
COVID-CT
(2020.4.8) COV3 216 349 [50]
COVID-CT
(2011.6.15) Non-COV 133 397 [50]
COVID-19
(Kaggle) COV4 – 100 [12]
The experimental environment of ACL is as follows. With Inter(R) Core(TM) i7-10700 CPU & GeForce RTX 3070 hardware and Matlab2020 software, ACL segmented 4149 CT GGO images in a total time of 387.655 s.
4.2 Evaluation indicators
We use three widely used evaluation indicators to subdivide specificity (SP), sensitivity (SE), and Dice similarity coefficient. We also introduce two golden indexes in medical image segmentation: relative volume difference (RVD) and average symmetrical surface distance (ASD). We can know the specific difference between the predicted and actual contour. The formula is as follows:
Relative Volume Difference (RVD) indicates the volume difference between the predicted and actual labels, (3) RVD=(VsegVgt−1)∗100%,
where Vseg represents the outline of actual segmentation, and Vgt expresses the outline of the actual label. Therefore, we know the overall gap between the predicted contour and the actual contour of the GGO area. ASD indicates the specific average distance between the predicted contour and the label contour. (4) Bpred=∀p1∈Apred,
Distanceclosest(p1,p2)|∃p2∈Agt,
(5) ASD=mean(Bpred,Bgt).
where Apred denotes the pixels of the boundary in the predicted Vpred, Agt is ground true. Bpred represents the nearest pixel to the prediction boundary of Apred, and Bgt refers to the set of pixels closest to the real contour.
Table 2 Comparison of indicators of various advanced methods.
Method FLOPs COV1 COV2
Dice SP SE RVD ASD Dice SP SE RVD ASD
U-Net [52] 38.1G 0.439 0.858 0.534 4.713 1.521 0.432 0.812 0.513 5.148 1.530
U-Net++ [53] 65.9G 0.581 0.902 0.672 4.152 1.208 0.548 0.874 0.607 4.424 1.225
At-UNet++ [25] 31.7G 0.583 0.921 0.637 3.831 1.204 0.548 0.885 0.593 4.105 1.293
Dense-UNet [54] 43.7G 0.515 0.840 0.594 4.376 1.386 0.474 0.788 0.586 4.628 1.430
Gate-UNet [55] 714.4G 0.623 0.826 0.658 3.192 0.913 0.615 0.816 0.625 3.317 0.945
Inf-Net [7] 13.9G 0.682 0.943 0.792 2.513 0.769 0.631 0.914 0.723 2.608 0.811
2D-UNet [19] 42.8G 0.768 0.935 0.842 1.834 0.624 0.699 0.849 0.781 1.929 0.680
JCS [41] 51.4G 0.785 0.937 0.814 1.741 0.620 0.746 0.872 0.778 1.789 0.654
LCOV-Net [56] 35.1G 0.776 0.929 0.821 1.761 0.617 0.740 0.921 0.812 1.890 0.647
LwMLA-NET [12] 2.1G 0.767 0.917 0.845 1.759 0.631 0.753 0.922 0.810 1.826 0.651
ACL (Ours) 13.2M 0.771 0.931 0.817 1.766 0.592 0.736 0.925 0.807 1.781 0.634
Method FLOPs COV3 COV4
Dice SP SE RVD ASD Dice SP SE RVD ASD
U-Net [52] – 0.362 0.762 0.490 5.482 1.593 0.367 0.768 0.478 5.610 1.799
U-Net++ [53] – 0.471 0.865 0.591 4.532 1.366 0.483 0.749 0.556 4.586 1.822
At-UNet++ [25] – 0.494 0.843 0.592 4.141 1.316 0.495 0.717 0.583 4.525 1.420
Dense-UNet [54] – 0.506 0.795 0.593 4.617 1.421 0.520 0.766 0.579 4.649 1.780
Gate-UNet [55] – 0.629 0.873 0.671 3.017 0.889 0.645 0.802 0.660 3.265 1.104
Inf-Net [7] – 0.657 0.916 0.741 2.585 0.793 0.673 0.729 0.703 2.784 1.272
2D-UNet [19] – 0.653 0.821 0.763 1.935 0.708 0.666 0.805 0.734 2.371 1.033
JCS [41] – 0.641 0.786 0.761 1.947 0.674 0.651 0.794 0.739 2.446 0.949
LCOV-Net [56] – 0.731 0.825 0.792 1.921 0.650 0.739 0.803 0.786 2.104 0.926
LwMLA-NET [12] – 0.728 0.871 0.779 1.918 0.669 0.757 0.739 0.742 2.215 0.871
ACL (Ours) – 0.734 0.812 0.784 1.819 0.639 0.711 0.704 0.722 2.071 0.747
4.3 COVID-19 GGO segmentation results
To compare the GGO segmentation performance of the ACL framework and the network method position of the equivalent effect of this framework, we systematically compared 9 advanced models, including U-Net [52], U-Net++ [53], Attention-UNet [25], Dense-UNet [54], Gate-UNet [55], Inf-Net [7], 2D-UNet [19], JCS [41] and LCOV-Net [56].
(1) Quantitative results. Each method index is shown in the Table 2. The part in bold indicates that the method corresponding to this indicator is the best. The amount of floating-point operations required by ACL is small. In the framework, ACL only needs computational power to calculate image contrast and pixel segmentation.
At the same time, it can be seen that other indicators of the ACL framework are equivalent to the [12], [19], [41], [56] method. ΔDice=±0.04, ΔSP=±0.1, ΔSE=±0.03, ΔRVD=±0.02, ΔASD=±0.02, The error of each index is only within 0.1. However, the ACL framework does not have an important division of network training. Furthermore, ASD shows that the GGO segmentation results of ACL frames have the smallest average distance from labels, more accurately limiting the GGO range.
(2) Qualitative results. The results of GGO segmentation are shown in Fig. 7. It can be seen that under extremely low brightness, the edge of the GGO segmented by other methods is not accurate enough. In contrast, ACL can still accurately segment the GGO area due to the balance between local threshold and contour segmentation by Ti3 according to image quality.Fig. 9 ACL without pulmonary cavity segmentation. (a) expresses the original CT image, (b) represents the directly binarized image with unlimited lung cavity range, (c) is S2 more prominent than the segmentation in T1, and (d) shows the remaining part S3.
Specifically, the subdivision results produced by ACL are closer to ground truth, and there are fewer wrong subdivision pixels. It can be seen that gate-UNet missed the GGO area on the edge of normal tissue, and Inf-net and joint classification segmentation (JCS) improved the results, but only a few GGO areas could be segmented. The main reason is that the two characteristics are similar in the GGO area next to normal tissue, and the boundary is not apparent. ACL can divide this area. All pixels will be classified and calculated in Alg. 1. Threshold segmentation is based on pixel classification [57], and even pixels with gradually reduced edges will be classified into different categories. After the contour of Alg. 2 is balanced, different segmentation results are obtained. Includes two cases, one is that S2 is the GGO area and S3 is the normal tissue. The other is that both S2 and S3 are pathological tissues. The specific meaning of Si is different due to different image contrast.
(3) Dice of ACL and three methods in different contrast CT images. Because the three methods of Gate-UNet, Inf-Net, and JCS perform better in some indicators, we chose these three methods to discuss the performance with ACL about contrast. As shown in Fig. 8, when each method is in ContrastValue≈120, Dice begins to decline. When Contrast Value≈410, Dice back to normal. It is roughly equivalent to Dice of ACL and JCS in contrast 120,410. However, when it in contrast, 0,120, 410,600, ACL is superior to other methods, and the extreme cases are also in these two contrast values. The overall GGO segmentation effect of ACL is equivalent to that of the JCS method. However, the error distance is stable under different contrast, indicating that the segmented GGO area of ACL is closer to the Ground Truth.Fig. 10 The effect of lung segmentation. The first row shows the original CT image, which contains the Contrast∈0,ContrastValue1, ContrastValue1,ContrastValue2, ContrastValue2,∞. The second row shows the Alg. 3 lung segmentation results. The third row is the GGO mask.
4.4 Ablation study
In this subsection, we conduct three experiments to verify the performance of each module of ACL.
(1) Attention mechanism threshold (A). In the ACL framework, we adopt Global T1, Global T2 and Ti3. As shown in Table 3, eight combinations are proposed to search for the best solution. ✓is Global Tj j∈Ti. X is Global Tij,i∈Blockj, j∈Ti, Global T1, Global T2 and Ti3 is the best combinations.
(2) Contour equalization (C). We divide images into three categories 0,ContrastValue1, ContrastValue1,ContrastValue2, ContrastValue2,∞, and carry out corresponding contour balancing operations respectively. The joint operation of three kinds of images is verified to verify that the scheme is the optimal contour balance. As shown in Table 4, classify the corresponding images into the corresponding categories, and the segmentation effect is the best. Better results can be obtained only by corresponding processing, minDice≥0.8.Table 3 Discussion schemes of the three thresholds.
GlobalT1 GlobalT2 GlobalT3 Dice
✕ ✓ ✕ 0.718
✕ ✓ ✓ 0.427
✕ ✕ ✓ 0.173
✕ ✕ ✕ 0.141
✓ ✕ ✕ 0.727
✓ ✕ ✓ 0.612
✓ ✓ ✓ 0.694
✓ ✓ ✕ 0.771(ACL)
(3) Lung segmentation (L). Before segmenting the GGO area, we need to get the lung cavity area to narrow the scope of GGO segmentation. When the whole graph is directly divided, the lack of restrictions leads to that GlobalT2 and Ti3 can no longer directly divide S2 and S3 as shown in Fig. 9(c) and (d). The threshold of the attention mechanism will consider all the pixels of the CT image. However, many black backgrounds and lung tissue pixels will cause the threshold to deviate, and the pixels in the lung cannot be accurately classified. Segmentation area Dice is only 0.13, hence cavity segmentation effectively improves the segmentation performance of GGO in ACL.Table 4 Dice of each type of contrast image in various operations.
Dice 0,Value1 Value1,Value2 Value2,∞
0,ContrastValue1 0.841 0.725 0.159
ContrastValue1,ContrastValue2 0.784 0.817 0.175
ContrastValue2,∞ 0.121 0.159 0.872
(4) Lung segmentation result. Lung segmentation is significant as the limiting condition of GGO segmentation. On the one hand, we need them to combine and restrain the effects of GGO segmentation. On the other hand, we need the results of lung segmentation to help reduce the scope of GGO segmentation. The results of adaptive threshold lung segmentation. Adaptive lung segmentation can still completely segment the lung cavity and provide a limited focusing range.
However, there are burrs in the segmented lung cavity. It can be seen from Fig. 10, redundant burrs will not affect the segmentation range of the GGO. The content of Lung segmentation includes all pixel positions in the Ground Truth. When Alg. 1 and Alg. 2 are finished, only by keeping the GGO area of Alg. 3. Get the final segmentation result. It can be seen that ACL can segment the normal tissue well when encountering holes, and the edge segmentation is closer to the real label. In extreme contrast, ACL can still retain detailed information about the GGO.
4.5 Other pneumonia segmentation results
In COVID-19 segmentation, we found that ACL’s attention mechanism threshold segmentation effectively uses the image’s information. In addition, ACL can also be used to segment common pneumonia. The characteristics of common pneumonia are similar to those of COVID-19 [58], such as a small ground glass shadow area and a small amount of pleural effusion [59].
In this way, we also segmented the other pneumonia [49], and the segmentation of three kinds of contrast images as shown in Fig. 11. Notably, the second column of CT images contained a piece of normal tissue within the lung. ACL can still accurately segment GGO. In the strict sense, our lung segmentation algorithm does not segment the boundaries of the lung. Instead, areas within the lung cavity that may contain GGOs are to be isolated. When finally merging the threshold results of the attention mechanism, the lung segmentation algorithm has already separated all the pixels of the normal tissue in this part of the lung. Similarly, there are two holes in the third column of images, and the lung segmentation algorithm does not separate the pixels within the holes. If the result of the attention mechanism threshold algorithm has GGO in the hole, then the final segmentation result will also be presented in the hole.
It can be seen that the ACL is also suitable for segmenting CT images of common pneumonia, with an average Dice=77.2%. Mainly because in the specific lung region of CT images, the block threshold segmentation combined with prior information [60] can be used to segment three area, including GGO region, normal tissue, and background. These areas combine the designed GGO contour balance to fine-tune the segmentation boundary further. ACL can show an efficient segmentation effect in lung image GGO segmentation. Therefore, the ACL framework can be applied to the segmentation of COVID-19 GGO and common pneumonia.Fig. 11 ACL’s segmentation effect on other pneumonia data sets. The first row shows CT image of common pneumonia, which contains the Contrast∈0,ContrastValue1, ContrastValue1,ContrastValue2, ContrastValue2,∞. The second row shows the Alg. 3 lung segmentation results. The third row is the Ground Truth.
ACL can be used well in CT images to group pixel points into many classes by a group of thresholds. The segmentation target is obtained after the pixels are grouped. Unfortunately, when the grey values of target pixels are no longer relatively concentrated as in CT images, threshold segmentation can hardly solve the problem of mixing grey-level pixel features.
5 Conclusion
We propose a new attention mechanism of threshold combination, which no longer classifies a single pixel into a specific category. With continuous segmentation and threshold selection, the individual pixels are classified most logically. Minor pixel adjustments on the edges further adjust the contour equalization. In addition, a threshold algorithm is designed to segment the lung cavity, thus proposing a complete adaptive threshold process. It can assist physicians in making fast and accurate diagnoses. ACL does not require a lot of tedious training processes compared to deep learning algorithms. GGO can perform the segmentation of CT images directly with pre-set parameters. The hardware overhead is small, the program execution is fast, and the GGO segmentation is accurate.
ACL benefits from the roughly fixed grey-level distribution of CT images of human tissues. ACL is also only applicable to segmenting regions of interest within CT images. For the natural environment, ACL is also challenging to segment the target by the threshold, which is extremely difficult to have generalizability. Therefore, targeted solutions can be found first when segmenting specific categories such as CT images. In addition, due to its small overhead and adaptive tuning parameters, ACL can be used as a pre-input for deep learning networks to achieve more advanced semantic tasks, e.g., ACL can guide consolidation segmentation or separate GGO from other tissue margins.
CRediT authorship contribution statement
Yunbo Rao: Writing – review & editing, Supervision, Project administration, Funding acquisition. Qingsong Lv: Conceptualization, Methodology, Software, Validation, Writing – original draft, Visualization. Shaoning Zeng: Writing – review & editing, Project administration, Data curation. Yuling Yi: Formal analysis, Resources. Cheng Huang: Formal analysis, Data curation. Yun Gao: Conceptualization, Methodology, Software, Resources, Visualization. Zhanglin Cheng: Methodology, Writing – review & editing, Supervision. Jihong Sun: Data curation, Supervision.
Declaration of Competing Interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Yunbo Rao reports financial support was provided by University of Electronic Science and Technology of China. Yunbo Rao reports a relationship with University of Electronic Science and Technology of China that includes: funding grants.
Data availability
Code will be released at https://github.com/Lqs-github/ACL.
Acknowledgements
This research was supported by the Science and Technology Project of Sichuan, China (Grant NOs. 2022ZHCG0033, 2021YFG0314, 2020YFG0459), and the 10.13039/501100001809 National Natural Science Foundation of China (Grant NO. U19A2078).
==== Refs
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| 36505089 | PMC9721288 | NO-CC CODE | 2022-12-14 23:54:32 | no | Biomed Signal Process Control. 2023 Mar 5; 81:104486 | utf-8 | Biomed Signal Process Control | 2,022 | 10.1016/j.bspc.2022.104486 | oa_other |
==== Front
J Environ Manage
J Environ Manage
Journal of Environmental Management
0301-4797
1095-8630
Elsevier Ltd.
S0301-4797(22)02460-4
10.1016/j.jenvman.2022.116887
116887
Research Article
Has the COVID-19 pandemic changed household food management and food waste behavior? A natural experiment using propensity score matching
Ananda Jayanath a∗
Karunasena Gamithri Gayana bc
Pearson David bc
a School of Business and Law, CQ University, 120 Spencer Street, Melbourne, VIC 3000 Australia
b School of Business and Law, CQ University, 400 Kent Street, Sydney, NSW, 2000, Australia
c Fight Food Waste Cooperative Research Centre, Wine Innovation Central Building, Level 1, Waite Campus, Urrbrae, SA, 5064, Australia
∗ Corresponding author.
5 12 2022
5 12 2022
1168876 10 2022
22 11 2022
25 11 2022
© 2022 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.
Household food management behavior changed considerably during the COVID-19 pandemic. A growing body of work has quantified the impact of lockdowns on household food waste. Yet, previous studies used a retrospective study design which undermines the accuracy of the causal effect on household food waste. This paper investigates the causal impact of the COVID-19 pandemic on household food management and food waste using a natural experiment approach. Using two large national-scale longitudinal data sets (n = 8157), this study quantifies the impact of COVID-19 on food waste and food behavior of Australian households. Propensity score matching (PSM) was carried out to address potential endogeneity issues and to select control and treatment groups for analysis. Findings reveal that Australian households reduced food waste by 9% on average in 2020 (during COVID-19) compared to the pre-pandemic (2019) level. The use of a grocery list, discount purchases, and ‘just-in-case’ purchases, and food refrigeration have recorded a marked increase during the pandemic compared to pre-pandemic times. The changes to food management and food discard behavior during the pandemic offer important insights for behavior change campaigns to reduce household food waste. Interventions to sustain good food planning and storage practices and involving food retailers are promising entry points in addressing household food waste. The study also highlights the considerable challenge in achieving SDG 12.3 target by 2030.
Graphical abstract
Image 1
Keywords
Food waste
Households
COVID-19 lockdown
Propensity score matching
Behavior change
Recall bias
==== Body
pmcNomenclature
ATE Average Treatment Effect
COVID-19 Coronavirus Disease of 2019
CO2e Carbon Dioxide equivalent
NSW New South Wales
PSM Propensity Score Matching
QLD Queensland
RCT Randomized Controlled Trails
SA South Australia
SD Standard Deviation
SDG Sustainable Development Goal
SMD Standardized Mean Differences
VIC Victoria
1 Introduction
Food systems in the world are under severe stress due to increasing population, disruptions to critical food supply chains, energy crises, and declining resource availability due to both man-made and natural disasters. The COVID-19 pandemic has further exacerbated the problem. Reducing global food loss and waste has been regarded as an urgent issue given its deleterious economic, social and environmental impacts (Amicarelli and Bux, 2021; FAO, 2014). In Australia alone, 7.3 million tonnes of food are wasted annually, which is valued over $20 billion (FIAL, 2019). In contrast, over 4 million experienced food insecurity in Australia according to 2021 Foodbank Hunger Report (Foodbank, 2021). Australian food waste generated a whopping 7.6 million tonnes of greenhouse gas emissions (CO2e) in 2014–15 over the life if its decay (FIAL, 2019).
The multi-faceted global impact of food waste prompted the United Nations to designate the Sustainable Development Goal Target 12.3: halving food waste of retail and household levels by 2030. Achieving this goal largely hinges on changing human behavior. As in many developed countries, food discarded from Australian household sector is the largest contributor to total food waste across all stages of the supply chain. The pandemic has altered many behaviors including household food provisioning, food management and food disposal behaviors (Armstrong et al., 2021; Babbitt et al., 2021; Principato et al., 2022).
Lockdowns imposed by countries around the world to suppress the COVID-19 pandemic provide a unique opportunity to investigate the influence of external shocks to the food system in general particularly the household food behavior. Such investigations can shed a light on the magnitude of behavior change needed to achieve nationally and globally set targets such as halving food waste by 2030, as enshrined in the United National Sustainable Development Goal 12.3. A plethora of literature has been emerging during post-lockdown period examining the impact of COVID-19 pandemic on household food behavior and food waste (see Iranmanesh et al. (2022) for a recent review of pandemic impacts on household food waste behavior). However, whilst useful, almost all studies that examined the causal impact of COVID-19 on household food waste behavior adopted a retrospective study design. In retrospective studies, the outcome of interest has already occurred, and participants must recall information extending over six months to years, leading to significant ‘recall bias’. Memory capacity coupled with ‘social desirability biases’ can affect the accuracy of food waste behavior (Music et al., 2021). More importantly, retrospective studies ignore both observed and unobserved confounding effects as they assume that except for the variable of interest (household food waste), all other factors are similar between two periods. This is a significant research gap in COVID-19 related food waste studies.
The present study reports the findings of a natural experiment (Ek and Miliute-Plepiene, 2018) based on observational data collected during pre- and post-pandemic periods. On the methodological side, the present study differs from other studies conducted on the topic by its research design and causal inference approach used. To the best of our knowledge, the present study is the first study to use a natural experiment and Propensity Score Matching approach to quantify COVID-19 impacts on household food waste. Previous studies took a retrospective perspective and did not contain a robust causal inference research design. Except for a handful of studies, most studies were underpowered and used small (n = 15) to medium (n < 1000) samples (Iranmanesh et al., 2022), which are not nationally representative. The present study uses a large nationally representative sample allowing us to make robust inferences. Past studies were conducted mostly in Europe, North America and Asia. However, no study has been conducted on the impact of COVID-19 pandemic on food waste behavior in Oceania, which is surprising since Australia ranks as the fourth largest food waster globally (Iranmanesh et al., 2022). To our best knowledge, the present study is the first study that examines the COVID-19 impacts on household food waste in Australia.
The paper is organized as follows. In the following section, a brief review on the impact of COVID-19 on household food waste is provided. Section 3 discusses the methods, the research design, survey questions and sampling strategies used. Findings are discussed in section 4. Finally, section 5 concludes the paper with several policy implications.
2 Literature review
2.1 Pandemic impacts on household food behavior
COVID-19 pandemic triggered food management behavior changes in households (Iranmanesh et al., 2022). These behavior changes were induced by lockdowns and self-isolation rules, they can be regarded as ‘forced behavior change’ as opposed to completely voluntary behavior change induced by some government intervention to reduce household food waste. The behaviors included panic buying (Berjan et al., 2022), skill improvements in home cooking (Berjan et al., 2022; Laila et al., 2022; Rodgers et al., 2021; Roe et al., 2021), the use of leftovers (Laila et al., 2022; Principato et al., 2022) online shopping and the use of meal box delivery services (Filimonau et al., 2022), inventory management (Ben Hassen et al., 2022; Li et al., 2022).
The pandemic lockdowns caused infrequent visits to shops, buy larger quantities of food than normal and panic buying (Berjan et al., 2022). High uncertainty avoidance behavior is linked to ‘just-in-case buying and over-provisioning food which was prominent during the COVID-19 pandemic (Ananda et al., 2021). Fewer impulse buying (Roe et al., 2021) reported during the pandemic is attributed to households being mindful of what they were purchasing, better meal planning and food shopping trips (Laila et al., 2022). An increase of food purchasing (Vargas-Lopez et al., 2022) and a slight reduction of food waste have been reported during the pandemic with these levels differing across household income categories (Babbitt et al., 2021).
Burlea-Schiopoiu et al. (2021) examined the impact of COVID-19 pandemic on food waste behavior of young people. They found that the pandemic has led to more people exhibiting food waste reduction behaviors and a heightened awareness of the ethical aspects and environmental consequences of food waste among young people. The pandemic made people more aware of food insecurity and what they had in inventory.
Increased meal planning has been reported during the pandemic. Over 60% of respondents from a US study stated that they have started meal planning during the pandemic (Babbitt et al., 2021). More frequent use of a shopping list and meal planning during the pandemic have been reported (Kubíčková et al., 2021; Principato et al., 2022). Young consumers, typically regarded as a high waste group (Karunasena et al., 2021), reduced food waste during the pandemic by good food management behaviors such as writing a shopping list, meal planning etc. (Principato et al., 2022).
2.2 Pandemic impacts on the type and amount of food wasted at home
A vast majority of studies reported reductions in household food waste during the pandemic (Principato et al., 2022; Vittuari et al., 2021). Laila et al. (2022) reported a decrease in avoidable per capita food waste in a Canadian study. The study argued that this decline could be attributed to increase in serving of leftovers or increase in meal planning and inventory management (Laila et al., 2022).
Conflicting results on the change of food wasted at home have also been reported. In a large Canadian study, Music et al. (2021) found that household self-reported food waste depicted an insignificant decrease during the pandemic. As a plausible explanation for this finding, the authors attribute consumers’ risk aversion and purchasing more non-perishable food during the pandemic. This assertion has been supported in the literature. For instance, Babbitt et al. (2021) reported that a relative increase in canned food and frozen food purchases during the pandemic. Music et al. (2021) also pointed out that the impact of child rearing, work-life balance and stress during the pandemic may have played a part and warrant further investigation. Another Canadian case study found that no difference in the amount of organic waste generated from residents during the pandemic (Ikiz et al., 2021).
Several studies examined the change of food wastage in terms of food categories. In a study of household food waste behaviors in Turkey, Çavuş et al. (2022) found a significant decrease in the purchase of milk and dairy products, bread and bakery products and packaged foods during the pandemic. The study also reported an increase in red meat consumption during the pandemic, which was attributed to its immune-boosting qualities (Çavuş et al., 2022). Armstrong et al. (2021) reported that green leaves, carrots, potatoes and sliced bread were the most wasted of purchased foods during the pandemic. Babbitt et al. (2021) reported a decline of wastage from unused ingredients, uneaten leftovers, spoilt or expired food items.
3 Methods
3.1 Study design
This study aims to investigate the impact of COVID-19 lockdowns on household food waste behavior using a study design that minimizes the recall biases and confounding effects. In Randomized Controlled Trials (RCTs), which are regarded as the ‘gold standard’ in causal inference, both treatment and control groups are pre-assigned. Thus, causal inferences can be drawn by directly comparing the results of treatment and control groups. The downside of RCTs is that they are expensive and highly restrictive, hence the widespread use of observational studies. In natural experiments, the assignment to the treatment is not generated by a purely random mechanism but some external phenomenon (a global pandemic, in our case) assigns different people to different treatment or conditions (Pokropek, 2016). Thus, on average, the treatment assignment in natural experiments is similar to RCTs so that differences in outcomes between groups can be attributed to the causal effects of the treatment or condition in this study (COVID-19 lockdowns).
The timeline of surveys and COVID-19 lockdowns in Australia are shown in Fig. 1 . This study was based on two large nationally representative surveys conducted independently in 2019 (n = 5272) and in 2020/2021 (n = 2885). The surveys were part of a large national study, commissioned by the Fight Food Waste Cooperative Research Centre which aims to improve the competitiveness, productivity and sustainability of Australian food industry. It is also tasked with monitoring food waste and contributing to the national goal of halving food waste in Australia by 2030, in line with the United National Sustainable Development Goal 12.3. The 2019 survey aimed at establishing a national baseline on community knowledge, attitudes and behaviors around household food management.Fig. 1 Timeline of surveys and COVID-19 lockdowns in Australia (SA = South Australia; VIC = Victoria; NSW = New South Wales; QLD = Queensland).
Fig. 1
The datasets are unique in several ways. First, they contained self-reported household food waste data for two time periods, 2019 and 2020/21. Second, both surveys are nationally representative datasets. Third, the first survey was completed just before the onset of COVID-19 pandemic providing the latest pre-pandemic household level food waste data. Finally, the combined dataset is comprised of large number of households (n = 8157) enhancing statistical power of the analysis. The data collection process involved several stages including a workshop to identify research target audiences and quotas, in-home interviews (n = 20) to test the survey instrument and online survey of respondents from 14–28 June 2019.
In 2020/21, a shortened survey extended the benchmark study by using multiple measures of household food waste (self-reported survey, electronic diary and physical bin audit). The data collection took place in 2 phases: 10 November – December 21, 2020 and 30 January to February 16, 2021. The sample was drawn from almost 10,000 invitations to participate in the survey using Australia's largest online research only panel. After applying several screening criteria, 2885 participants completed the survey. Although abbreviated, the 2020/21 survey contained several key questions from 2019 National Benchmark Survey, which made it possible to conduct a comparative analysis. These questions included self-reported food waste quantities (avoidable) and food management behaviors such as food planning, grocery shopping, storing, cooking and disposal.
It is noteworthy that many studies have identified that the social desirability bias, inherent to self-reported food waste surveys, which led to significant underestimations of household food waste (Elimelech et al., 2019; Everitt et al., 2022). However, in online studies, where participants do not have any face-to-face interaction with researchers or survey administrators, this is a much lesser concern. More importantly, social desirability bias and recall bias are less of a concern in comparisons across time (Secondi et al., 2015). This is because of longitudinal comparisons focus on the change in food waste rather than the absolute value. Nevertheless, self-reported surveys are a cost-effective method with a reasonable accuracy provided the participants are guided methodically to recall waste from various food categories incurred in the week prior to the survey.
3.2 Sampling strategy: propensity score matching
Without addressing the selection bias and confounding variables, it is not possible to make causal claims. The propensity score matching (PSM) method (Rosenbaum and Rubin, 1983) minimizes the above biases by allowing researchers to reconstruct counterfactuals using observational data (Li, 2012). Propensity score is defined as the probability of assignment to a particular treatment (COVID-19 lock down in our case) given a set of observed covariates (Rosenbaum and Rubin, 1983). The focus of the method is to select control and treatment groups so that the differences between the two groups are balanced, mimicking a pseudo-random assignment. PSM can handle multiple covariates by converting them to one dimensional probability values, balance the deviation between the treatment and control group effectively reducing the selection bias and incorporates a sufficient overlap between the groups ensuring the reliability of results (Zhang et al., 2022).
The PSM is applied by calculating propensity scores using the following equation:(1) PSi=P(Xi)=Pr[Ti=1|Xi]
where PSi is the propensity score of household i, Ti denotes whether or not the household i is assigned to the treatment (COVID-19 lockdown restrictions). If assigned, the value is equal to one or otherwise zero. Xi denotes a set of covariates that affects the outcome (household food waste). The logit of Ti is then used as the dependent variable along with j covariates of Xi to perform a logit regression as shown in equation (2):(2) Logit(Tij=1)=β0+βjXi,j+δij
Once PSi is computed, control and treatment group members (households) are matched. Several matching methods such as the greedy or nearest neighbor matching and hierarchical matching, etc. Are available. The final step involves the estimation of average treatment effect (ATE), in our case, the average change in household food waste between pre- and post-COVID lockdown periods.
3.3 Survey design and survey questions
Three areas of questions were chosen from the two surveys for the analysis: household food management behavior and responsibility, socio-demographic factors and self-reported avoidable food waste. The food management behavior included nineteen questions covering five specific areas: grocery planning, grocery shopping, food storing, food preparation and food disposal. These questions were based on the latest literature on food behavior measurement and included a 5-point Likert scale to express the frequency of behavior: ‘Rarely or Never’ (none of the time), ‘Sometimes’ (About quarter of the time), ‘About half the time’, ‘Most times’ (About three quarters of the time) and ‘Every time’ (100% of the time) (see section 3.5).
3.4 Selection of covariates
Covariates are variables influence the dependent variable (food waste) and are controlled by statistical techniques. The number of covariates to use is imperative as too many covariates make it difficult to match households in the treatment group with the control group resulting a small, matched sample. Contrarily, too fewer variables will yield an inaccurate matched sample affecting the estimation. A total of eleven covariates covering both socio-demographics and food management responsibility were used: age, gender, household composition, number of young children (<15 years) at home, resident type, household income, food planning responsibility, shopping responsibility, storing responsibility, cooking responsibility and disposal responsibility. Table 1 below provides description and measurement scales for each covariate.Table 1 Descriptions of covariates to match pre- and during pandemic samples.
Table 1Covariate Description
Age Age of respondent
1 = 18–24; 2 = 25–34; 3 = 35–44; 4 = 45–54; 5 = 55–64; 6 = 65–74; 7 = 75+ years
Gender 1 = Male; 2 = Female
Planning responsibility To what extent do you contribute to planning for food shopping?
1 = Mainly responsible; 2 = Equally responsible; 3 = Partly responsible; 4 = Not responsible/rarely responsible
Shopping responsibility To what extent do you contribute to doing food shopping?
1 = Mainly responsible; 2 = Equally responsible; 3 = Partly responsible; 4 = Not responsible/rarely responsible
Storing responsibility To what extent do you contribute to unpacking and storing?
1 = Mainly responsible; 2 = Equally responsible; 3 = Partly responsible; 4 = Not responsible/rarely responsible
Cooking responsibility To what extent do you contribute food preparation including cooking?
1 = Mainly responsible; 2 = Equally responsible; 3 = Partly responsible; 4 = Not responsible/rarely responsible
Disposal responsibility To what extent do you contribute to planning for food shopping?
1 = Mainly responsible; 2 = Equally responsible; 3 = Partly responsible; 4 = Not responsible/rarely responsible (0.70)
Resident type Which of the following best describes your residence?
1 = Separate house; 2 = Semi-detached terrace house/townhouse; 3 = Flat/Unit
Household composition Which of the following best describes your household?
1 = Single; 2 = Couple; 3 = Family with children <15 years; 4 = Family with children >15+ years; 5 = Shared household/Unrelated adults
No. Of young children No. Of children under 15 years
Household income Which of the following best describes your household income before tax?
1 = <A$1000; 2 = A$1000–1999; 3 = A$2000–2999; 4 = A$3000+ per week
3.5 Food management behavior variables
Table 2 summarizes the fifteen food management behavior variables included in the study. It is noteworthy that both surveys contained the same behavioral questions with the same 5-point Likert scale which ensured measurement consistency across time. Four questions were related to food planning at home: checking inventory before grocery shopping (Farr-Wharton et al., 2014; Schanes et al., 2018), meal planning (Babbitt et al., 2021; Jörissen et al., 2015; Principato et al., 2021; van Geffen et al., 2016), writing a basic grocery list and writing a complete grocery list (Graham-Rowe et al., 2014; Jörissen et al., 2015; Pearson and Perera, 2018).Table 2 Behavioral variables of household food planning, acquisition and management.
Table 2Covariate Description
Food planning
Check inventory Before going shopping for food, how often do you or another member of your household check what food is already available in the cupboard?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Meal planning Before going shopping for food, how often do you or another member of your household plan the meals to be cooked?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Write a basic list Before going shopping for food, how often do you or another member of your household write a list of basic essentials?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Write a complete list Before going shopping for food, how often do you or another member of your household write a complete list of everything needed?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Food purchasing
Sticking to the grocery list When doing the main food shopping, how often do you or other members of your household buy what is on the shopping list?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Set a budget When doing the main food shopping, how often do you or other members of your household buy food according to a set budget?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Buy specials When doing the main food shopping, how often do you or other members of your household buy food based on what is on ‘special’?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Check dates When doing the main food shopping, how often do you or other members of your household check the ‘use by’ or ‘best before’ dates before purchasing food items?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Buy just-in-case When doing the main food shopping, how often do you or other members of your household buy food for ‘just-in-case’?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Food storing
Use containers When storing food, how often do you or other members of your household use storage containers to keep food for as long as possible?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Fridge full When storing food, how often do you or other members of your household find it hard to fit the refrigerator/freezer because it is already full?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Refrigerate food When storing food, how often do you or other members of your household put food in the refrigerator/freezer, so it keeps for as long as possible?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Moving oldest items When storing food, how often do you or other members of your household move the oldest food items to the front or top so that they can be used first?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Food preparation
Oldest first When preparing food, how often do you or other members of your household try to use up the oldest food first?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Measured ingredients When preparing food, how often do you or other members of your household stick to ingredients in a recipe?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Portion control When preparing food, how often do you or other members of your household only prepare as much as is needed?
1 = Rarely or never; 2 = Sometimes; 3 = Half the time; 4 = Most times; 5 = Almost every time
Over-provisioning food is regarded as a major cause leading to superfluous food at home (Evans, 2011; Schanes et al., 2018). Food provisioning behaviors were examined using four questions: buying food according to a set budget, buying food on ‘specials’ or discounted (Farr-Wharton et al., 2014; Giordano et al., 2019; Graham-Rowe et al., 2014; Porpino et al., 2015), checking date labelling before purchasing (Secondi, 2019) and ‘just-in-case’ buying (Berjan et al., 2022; Giordano et al., 2019).
Food storing practices play a critical role in determining the level of household food waste (Schanes et al., 2018). We included four questions relating to food storage practices at home: using storage containers to keep food fresh (Aschemann-Witzel et al., 2015; Porpino et al., 2015), refrigerator/freezer practices (Waitt and Phillips, 2016), the difficulty of finding space to store food in the refrigerator/freezer (Schanes et al., 2018) and move or arrange food items in storage in order to display and use the oldest items first (Farr-Wharton et al., 2014). Finally, we included three questions pertaining to food preparation: using oldest food items first (Principato et al., 2021), stick to ingredients in a recipe (Karunasena et al., 2021) and portion control when cooking (Secondi et al., 2015). Fig. 2 illustrates the research flow of the study.Fig. 2 Research flowchart of the study.
Fig. 2
4 Results and discussion
Section 4.1 presents the details of PSM and associated summary statistics including standardized mean differences for control and treatment groups. Socio-demographic profile of the matched sample is discussed next. Section 4.3 analyses the key changes in behaviors related to food provisioning, storing and preparation. Section 4.4 quantifies the food waste impact of COVID-19 pandemic.
4.1 Empirical results of the matched sample
We used PSM to match 2019 survey participants with 2020/21 survey participants using multiple covariates and confounding factors. The cleaned dataset contained 8157 observations. Eleven covariates described in Table 1 were used for matching. Several matching strategies were used in the study. First, greedy matching on Mahalanobis distance (M-distance) was conducted (See Appendix Table 1). The matching performance was assessed using Standardized Mean Differences (SMDs) and visual analysis. This matching yielded a poor balance and most of the covariate SMDs were greater than the cut off, we carried out a much more robust propensity score matching on the data set with a logistic regression model (with a caliper) using MatchIt R package (Ho et al., 2011) and R version 4.1.1 (R Core Team, 2020), which resulted a control and treated samples of 1901 households each.
Table 3 summarizes the descriptive statistics and SMDs of covariates used for matching. This matching yielded the required balance as all SMDs of the matched sample are lower than the cut off of 0.1 in the unmatched pairing. Fig. 3 illustrates the SMDs by covariates for the matched and unmatched samples. Appendix Fig. 1 shows the propensity score plots and jitter plots of estimation. The long-jittered bars of Appendix Fig. 1 (a) indicate that the matched treated and control samples are reasonable.Table 3 Summary statistics and SMDs with propensity score matching (with a caliper of 0.2 of SD).
Table 3 Unmatched Matched
Pre-COVID COVID SMD Pre-COVID COVID SMD
N 5272 2885 1901 1901
Age 4.10 (1.66) 5.19 (1.58) 0.675 4.65 (1.50) 4.77 (1.54) 0.078
Gender 1.57 (0.50) 1.63 (0.48) 0.118 1.59 (0.49) 1.59 (0.49) <0.001
Planning responsibility 1.45 (0.71) 1.31 (0.52) 0.220 1.41 (0.68) 1.38 (0.56) 0.048
Shopping responsibility 1.43 (0.68) 1.32 (0.53) 0.168 1.41 (0.66) 1.38 (0.55) 0.047
Storing responsibility 1.47 (0.72) 1.32 (0.51) 0.241 1.42 (0.67) 1.40 (0.55) 0.033
Cooking responsibility 1.61 (0.89) 1.40 (0.68) 0.275 1.54 (0.84) 1.50 (0.73) 0.060
Disposal responsibility 1.50 (0.70) 1.28 (0.49) 0.351 1.41 (0.63) 1.37 (0.54) 0.070
Resident type 1.47 (0.79) 1.35 (0.69) 0.163 1.33 (0.68) 1.35 (0.69) 0.028
Household composition 2.39 (1.08) 3.06 (1.13) 0.610 2.83 (1.03) 2.74 (0.97) 0.098
No. Of young children 0.32 (0.82) 0.55 (0.93) 0.263 0.51 (1.06) 0.52 (0.83) 0.019
Household income 1.96 (0.90) 2.18 (1.05) 0.230 2.24 (0.97) 2.16 (1.00) 0.083
Fig. 3 Standardized Mean Differences (SMDs) of covariates for the matched and unmatched samples. (age = Age; gender = Gender; hh_comp = Household composition; hh_income = Household income; res_cook = Cooking responsibility at home; res_dispose = Food disposal responsibility; res_plan = Food planning responsibility; res_store = Food storing responsibility; res_type = Residential type; young_kids = Number of young children at home).
Fig. 3
4.2 Effects of COVID-19 lockdowns on household food planning behavior
Fig. 4 shows the impact of lockdowns on household food planning behavior. A considerable change was observed in grocery list writing pre- and during COVID-19 pandemic. During the pandemic, 43% households used a basic grocery list for shopping all the time compared to 10% pre-pandemic. The frequency of using a complete grocery list (all the time; blue color section of the bar chart) for shopping also increased from 27% (pre-pandemic) to 41% (during the pandemic). The above results echo the findings of Laila et al. (2022), a Canadian study, which reported that the proportion of families using a shopping list has increased from 39% (pre-COVID) to 56% during the pandemic. The proportion of households engaged in meal planning (all the time) also increased from 18% (pre-pandemic) to 26% (during the pandemic). Interestingly, there was virtually no effect on checking stocks and inventory between the two periods.Fig. 4 Grocery planning behavior: (a) pre-COVID and (b) during COVID19 lockdowns (the numbers inside bars are percentages).
Fig. 4
This is a good trend for practitioners who are interested in promoting such behaviors to pick up on for developing interventions. Getting households to change their behaviors is always a difficult task, now that they have trialed out meal planning and creating basic/complete shopping lists, behavior change interventions should focus on encouraging continuity of practice of these behaviors in order to form habits. Introducing consumers to tools that makes it easy for them to plan their meals and create lists such as APPs and online shopping lists and meal plans will also help continue their newly adopted behaviors.
4.3 Effects of COVID-19 lockdowns on grocery shopping
Lockdown restrictions forced households to change grocery shopping frequency and the largest change occurred in checking date labels when purchasing groceries. Prior to the pandemic, 34% of households checked date labels (all the time) before purchasing grocery items whereas 40% of households checked the ‘best before’ and/or ‘use by’ date (all the time) during the pandemic (Fig. 5 ). This is consistent with the nature of the lockdowns experienced in Australia and elsewhere. Since lockdowns were uncertain, households appeared to be paying more attention to ‘use by’ and/or ‘best before’ date labelling because of the uncertainty surrounding the next shopping trip and longer shelf-life food items might have been preferred. Picking up on this trend, interventions should focus on educating consumers on the meaning of ‘best before’ and ‘used by’ dates. Further emphasis must be put into educating consumers on how to use their senses such as smell, sight and taste to identify food that are still good to use.Fig. 5 Grocery shopping behavior: (a) pre-COVID and (b) during COVID-19 lockdowns.
Fig. 5
COVID-19 also caused certain food shortages and higher food prices and 18% of households bought food to a set budget (all the time) during the pandemic compared 14% during pre-pandemic period. Buying discounted food (‘buy_specials’) showed the greatest change in shopping behavior the two periods. Only 13% of households reported buying food items on ‘specials’ (all the time) before the pandemic while this proportion almost doubled (25%) during the pandemic. This may be driven by difficult economic circumstances experienced by households due to complete or partial loss of income during the pandemic.
4.4 Effects of COVID-19 lockdowns on food storing behavior
Fig. 6 shows substantial differences in food storing behavior between pre- and during COVID-19 pandemic periods. Interestingly, the most noticeable change occurred in food refrigeration. Prior to the pandemic, only 28% of households use refrigeration extensively (all the time) to keep food as fresh as possible. However, the proportion of households refrigerated food during the pandemic increased to 56%. The proportion of households that reported a full fridge (all the time) increased from 4% to 8% while moving oldest food items to the front increased from 28% to 37% between the two periods. Increase in panic buying/just-in-case buying and cost of food prices would have influenced more use of the fridge and adoption of better storage practices such as moving oldest items to the front. Advice on better storage practices in the freezer such as ‘use-it-up’ area in the fridge and cupboards could ensure the oldest food stored is visible in the fridge and cupboards, leading to a food waste reduction.Fig. 6 Grocery storing behavior: (a) pre-COVID and (b) during COVID-19 lockdowns.
Fig. 6
4.5 Effects of COVID-19 lockdowns on food preparation behavior
Fig. 7 shows the changes in food preparation behavior between the two periods. A considerable increase (15%) in using the oldest food items first when preparing meals was recorded during COVID-19 lockdowns. A doubling of the proportion of households who used portion control every time when preparing food was also observed.Fig. 7 Food preparation behavior: pre- and during COVID19 lockdowns.
Fig. 7
Appendix Tables 2–5 present descriptive statistics including standardized mean differences, confidence intervals and Pearson's Chi Squared statistical significance of Pre-COVID and during COVID food behaviors, which were all significant at 5% level.
4.6 Effect of COVID-19 lockdowns on the magnitude of household food waste
Fig. 8 shows the histograms of the outcome variable, total self-reported household food waste per week for the unmatched sample pre- and during-COVID pandemic. Pre-pandemic (control period) food waste average was 2.28 kg per household per week (SDPRE = 2.73) while it was 2.07 kg per household per week (SDPOST = 2.16) during the post-lockdown period, indicating a 9.2% decline of household food waste on average. A computation of standardized means of two periods revealed that average food waste during COVID pandemic was 5% of a standard deviation less than pre-COVID food waste and the mean difference was statistically significant (Welch two sample t-test: p < 0.000). Principato et al. (2022) reported a self-declared food waste difference of 3.6% between pre- and during COVID periods with a median reduction of 7%. It should be noted that, in the abovementioned study, participants self-assessed the percentages of food discarded not actual amounts of food discarded as in the present study. Laila et al. (2022) found a decrease of 0.222 kg per week on average on the total per capita avoidable food waste in a study conducted in Canada. This decline represents about 32% reduction of avoidable food waste. It should be noted that this study used an extremely small sample (19 households), hence it is not directly comparable to large nationally representative samples used in the present study.Fig. 8 Density graphs of food waste (matched sample) by pre- and during COVID-19 (the dotted lines indicate the sample means; covid: 0 = pre-COVID; 1 = During COVID).
Fig. 8
Using the matched sample, two sample t-test was conducted to ascertain whether the mean difference of food waste pre- and during COVID-19 is statistically significant. The test reported a statistically significant (p = 0.005) mean difference of −0.196 with confidence intervals of −0.373 and −0.066. This implies that COVID-19 has caused households to reduce approximately 219 g of food waste per week on average. This result is consistent with similar studies conducted elsewhere. For instance, Babbitt et al. (2021) reported that US consumers experienced a slight reduction of food waste generation albeit an increase in overall food purchases during the pandemic.
4.6.1 Average treatment effect on the treated (COVID-19)
To estimate average treatment effect (ATE) and standard errors, we fit a linear regression model with selected covariates. Table 4 presents the regression results of the impact of COVID-19 on household food waste. It shows the significant food waste reduction between pre- and during COVID-19 accounting for confounding by included variables. The coefficient on COVID-19 variable is taken as the estimate of the ATE. Accordingly, household food waste declined by about 212 g per week per household (SE = 0.076, p = 0.006).Table 4 Average treatment effects regression results.
Table 4Variable Estimate Std. Error t-value p
Intercept 3.24 0.256 12.66 0.000***
COVID-19 effect −0.212 0.076 −2.78 0.006**
Age −0.167 0.029 0–.69 0.000***
Gender −0.169 0.079 −2.14 0.033*
Presence of young children 0.290 0.045 6.51 0.000***
Household income 0.002 0.041 0.43 0.966
Significance codes: ‘***’ < 0.001 ‘**’ < 0.01 ‘*’ < 0.05.
Table 4 also shows the influence of age, gender and the presence of young children at home, which were all statistically significant. The influence of age on food behavior is widely known and the pandemic offered an opportunity to raise awareness among young cohorts of the population (Burlea-Schiopoiu et al., 2021). These factors have a moderating effect on food waste. As Iranmanesh et al. (2022) highlighted, the above factors may have a strong link to trust on government's ability ensure food availability and risk aversion behaviors during the pandemic.
5 Conclusions and policy implications
This paper presented a natural experiment of the impact of COVID-19 pandemic on household food management behavior and food waste in Australia. This observational study used a robust causal inference research design and was unique because the same survey was conducted before and during the pandemic without any reference to COVID-19, thus eliminating the participant ‘recall biases’ and other confounding effects. Findings indicated that Australian households reduced their food waste by 9% compared to pre-COVID-19 level. They also demonstrated improvements in desirable food management behaviors such as more frequent use of a grocery shopping list, meal planning, better storage usage and efficient food preparation and disposal skills during the pandemic.
This paper also provides evidence for practitioners on which behaviors leading to a reduction in food waste have been adopted during the pandemic. Given that households have acquired new habits of efficient use of food planning, storing and disposal during the pandemic, they would feel they could change those behaviors without much effort as opposed to pre-Covid times. As such, interventions focusing on promoting and improving those behaviors we identified could lead to better adoption rates and enduring changes in behaviors.
Developing consumer confidence in sticking to a grocery list and creating meal plans are two skills that need to be promoted given the increase in the proportion of people that made a complete shopping list and engaged in meal planning. The study also shows that storing food in fridges has increased resulting in more people claiming of having full fridges. As such, interventions focusing on fridge management would help consumers to reduce food waste caused by above behaviors. On the other hand, policy makers must also get the retailers such as supermarkets involved in creating confidence to reduce panic buying and just in case buying. Consumer promotions that allow consumers to use the promotion at a latter day such as ‘buy one and get one free later’, would reduce the waste induced by promotional buying of perishable products. Corporate communications assuring food security would help reduce panic buying induced food waste. Upskilling consumers on performing behaviors such as storing food correctly, using leftovers preserving food, and encouraging them to use their senses to identify if food is good to eat are other strategies to reduce waste resulting in panic buying and just in case buying.
Food wastage can occur due to unconsciously performed activities; also known as habits (Comber and Thieme, 2013). Behavior science advocates the use of a major incidents or change to an individual's routines and environment as points of entry to change a habit (Boudreaux et al., 2012). We argue COVID-19 pandemic is one such event that affected the lives of people to connect more with their food and engage in behaviors that could lead to reduction in food waste, understand the environmental and economic impact of food waste. The current post-COVID world, affected by tough economic conditions, offers an ideal opportunity to implement national food waste reduction interventions, which can leverage on the efficient food management routines and habits formed during the pandemic.
The findings of the present study indicated a 9% decline in household food waste on average due to COVID-19 pandemic, which is a significant volume overall. However, this behavior change was forced upon households due to lockdowns and self-isolation rules. It sheds a light on the enormity of the challenge in meeting the United Nation's Sustainable Development Goal 12.3 – to halve the per capita food waste by 2030 by voluntary means. Given that household food waste makes up the largest share of food waste in the food supply chain in developed countries, a concerted effort is required to achieve the abovementioned goal in these countries. Particularly, large scale behaviour changes are needed to achieve the abovementioned goal.
The findings of this study are useful for state and Federal government agencies and non-governmental agencies tasked with the challenge of meeting SDG 12.3 target. National food waste campaigns in particular can benefit from robust estimations of actual food waste reductions in the household sector. This paper highlighted the limitations of the retrospective study design and the need of better research design such as field experiments with control groups to quantify causal effects of food waste reduction interventions.
An important question arising from this research is whether there will be a lasting effect of good food management behavior experienced during the pandemic. Since household food management behavior is complex, it is not possible to forecast the continuation of COVID-19 acquired behavior change. However, there are indications that at least a proportion of population will continue good food management behaviors that would lead to reduced food waste at home. For instance, Babbitt et al. (2021) a study reported that 60% of respondents in a New York State, who started or increased efficient use of food use behaviors, intend to continue these activities after the pandemic. They also pointed out that it would be relatively more challenging to alter food waste separation behaviors compared to food use behaviors (Babbitt et al., 2021). If desirable food management behaviors translate into long-term behavior change, then it could make a significant contribution towards the goal of halving food waste by 2030.
Future research should focus on how governments and food industry could encourage and maintain favorable food use behaviors. In this regard, longitudinal studies that track not only household food behaviors but also the quantity of food waste changes would be beneficial. In addition, the future research could also benefit from taking a segmented approach to COVID-19 impacts on different consumers, which would enable practitioners to come up with more targeted interventions. Such research could also be extended to the types of food products and categories that are most wasted and the behaviors leading to such waste.
Credit author statement
Jayanath Ananda: Conceptualization, Methodology, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Software. Gamithri Gayana Karunasena:Project administration, Investigation, Writing – original draft, Writing – review & editing. David Pearson: Validation, Writing – review & editing, Supervision, Project administration.
Uncited references
FAO, 2015; Henningsen and Hamann, 2007.
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 1 Appendix Table 1 Summary statistics and standardized mean differences of greedy matching (M-distance)
Appendix Table 1 Unmatched Matched
Pre-COVID COVID SMD Pre-COVID COVID SMD
N 5272 2885 2885 2885
Age 4.10 (1.66) 5.19 (1.58) 0.675 4.73 (1.48) 5.19 (1.58) 0.303
Gender 1.57 (0.50) 1.63 (0.48) 0.118 1.63 (0.48) 1.59 (0.49) 0.046
Planning responsibility 1.45 (0.71) 1.31 (0.52) 0.220 1.39 (0.66) 1.31 (0.52) 0.136
Shopping responsibility 1.43 (0.68) 1.32 (0.53) 0.168 1.39 (0.66) 1.32 (0.53) 0.119
Storing responsibility 1.47 (0.72) 1.32 (0.51) 0.241 1.41 (0.67) 1.32 (0.51) 0.150
Cooking responsibility 1.61 (0.89) 1.40 (0.68) 0.275 1.53 (0.84) 1.40 (0.68) 0.176
Disposal responsibility 1.50 (0.70) 1.28 (0.49) 0.351 1.40 (0.62) 1.28 (0.49) 0.209
Resident type 1.47 (0.79) 1.35 (0.69) 0.163 1.35 (0.70) 1.35 (0.69) 0.004
Household composition 2.39 (1.08) 3.06 (1.13) 0.610 2.65 (1.07) 3.06 (1.13) 0.379
No. Of young children 0.32 (0.82) 0.55 (0.93) 0.263 0.46 (1.00) 0.55 (0.93) 0.096
Household income 1.96 (0.90) 2.18 (1.05) 0.230 2.11 (0.93) 2.18 (1.05) 0.072
Note: Mean values with SD in parentheses. SMD = Standardized Mean Difference.
Appendix Table 2 Grocery planning behavior summary statistics for matched samples
Appendix Table 2Variable Pre-COVID
N = 1,9021 During COVID
N = 1,9021 Difference2 95% CI2,3 p-value4
meal_plan 0.22 0.15, 0.28 <0.001
1 236 (12%) 158 (8.4%)
2 340 (18%) 295 (16%)
3 434 (23%) 427 (23%)
4 538 (28%) 521 (28%)
5 343 (18%) 485 (26%)
Missing values 11 16
inventory_check 0.12 0.06, 0.18 0.009
1 48 (2.5%) 62 (3.3%)
2 131 (6.9%) 159 (8.4%)
3 249 (13%) 292 (15%)
4 660 (35%) 576 (30%)
5 812 (43%) 809 (43%)
Missing values 2 4
comp_list 0.62 0.56, 0.69 <0.001
1 483 (26%) 233 (12%)
2 313 (17%) 170 (9.0%)
3 405 (22%) 267 (14%)
4 176 (9.4%) 440 (23%)
5 501 (27%) 783 (41%)
Missing values 24 9
basic_list 0.92 0.86, 1.0 <0.001
1 546 (29%) 198 (11%)
2 330 (18%) 179 (9.5%)
3 432 (23%) 264 (14%)
4 363 (20%) 421 (22%)
5 181 (9.8%) 813 (43%)
Missing values 50 27
1 n (%).
2 Standardized Mean Difference.
3 CI = Confidence Interval.
4 Pearson's Chi-squared test.
Appendix Table 3Grocery shopping behavior summary statistics for matched samples
Appendix TableVariable Pre-COVID
N = 1,9021 During COVID
N = 1,9021 Difference2 95% CI2,3 p-value4
stick_to_list 0.39 0.33, 0.46 <0.001
1 450 (24%) 269 (14%)
2 319 (17%) 240 (13%)
3 392 (21%) 407 (22%)
4 595 (32%) 635 (34%)
5 123 (6.5%) 309 (17%)
Missing values 23 42
set_budget 0.21 0.14, 0.27 <0.001
1 573 (30%) 454 (25%)
2 298 (16%) 221 (12%)
3 299 (16%) 332 (18%)
4 455 (24%) 501 (27%)
5 260 (14%) 340 (18%)
Missing values 17 54
check_dates 0.15 0.09, 0.22 <0.001
1 219 (12%) 182 (9.6%)
2 256 (14%) 194 (10%)
3 282 (15%) 310 (16%)
4 489 (26%) 458 (24%)
5 645 (34%) 752 (40%)
Missing values 11 6
buy_specials 0.41 0.35, 0.48 <0.001
1 154 (8.1%) 78 (4.1%)
2 473 (25%) 270 (14%)
3 455 (24%) 439 (23%)
4 562 (30%) 639 (34%)
5 247 (13%) 471 (25%)
Missing values 11 5
buy_just_in_case 0.33 0.26, 0.39 <0.001
1 485 (26%) 291 (15%)
2 726 (38%) 661 (35%)
3 365 (19%) 470 (25%)
4 237 (13%) 317 (17%)
5 74 (3.9%) 144 (7.6%)
Missing values 15 19
1 n (%).
2 Standardized Mean Difference.
3 CI = Confidence Interval.
4 Pearson's Chi-squared test.
Appendix Table 4 Grocery storing behavior summary statistics for matched samples
Appendix Table 4Variable 0, N = 1,9021 1, N = 1,9021 Difference2 95% CI2,3 p-value4
use_containers 0.16 0.10, 0.23 <0.001
1 81 (4.3%) 68 (3.6%)
2 206 (11%) 174 (9.2%)
3 273 (14%) 342 (18%)
4 645 (34%) 541 (29%)
5 686 (36%) 763 (40%)
Missing values 11 14
Refrigerate 0.63 0.56, 0.69 <0.001
1 97 (5.1%) 22 (1.2%)
2 240 (13%) 96 (5.1%)
3 347 (18%) 225 (12%)
4 671 (35%) 486 (26%)
5 539 (28%) 1070 (56%)
Missing values 8 3
move_items 0.25 0.19, 0.32 <0.001
1 271 (14%) 167 (8.9%)
2 256 (14%) 201 (11%)
3 293 (16%) 296 (16%)
4 547 (29%) 513 (27%)
5 522 (28%) 706 (37%)
Missing values 13 19
fridge_full 0.25 0.19, 0.31 <0.001
1 733 (39%) 577 (31%)
2 529 (28%) 491 (26%)
3 292 (15%) 364 (19%)
4 265 (14%) 301 (16%)
5 69 (3.7%) 146 (7.8%)
Missing values 14 23
1 n (%).
2 Standardized Mean Difference.
3 CI = Confidence Interval.
4 Pearson's Chi-squared test.
Appendix Table 5 Food preparation behavior summary statistics for matched samples
Appendix Table 5Variable 0, N = 1,9021 1, N = 1,9021 Difference2 95% CI2,3 p-value4
portion_control 0.54 0.47, 0.60 <0.001
1 262 (14%) 79 (4.2%)
2 365 (19%) 185 (9.8%)
3 414 (22%) 451 (24%)
4 610 (32%) 684 (36%)
5 238 (13%) 498 (26%)
Missing values 13 5
oldest_first 0.34 0.27, 0.40 <0.001
1 56 (3.0%) 20 (1.1%)
2 164 (8.7%) 85 (4.5%)
3 279 (15%) 233 (12%)
4 761 (40%) 655 (35%)
5 625 (33%) 905 (48%)
Missing values 17 4
measure_ingred 0.54 0.48, 0.61 <0.001
1 401 (21%) 123 (6.6%)
2 370 (20%) 217 (12%)
3 385 (20%) 446 (24%)
4 481 (26%) 711 (38%)
5 246 (13%) 361 (19%)
Missing values 19 44
1 n (%).
2 Standardized Mean Difference.
3 CI = Confidence Interval.
4 Pearson's Chi-squared test.
Appendix Fig. 1 Distribution and propensity scores of raw and matched samples: (a) jitter plot and (b) histograms
Appendix Fig. 1
Data availability
The data that has been used is confidential.
Acknowledgements
This work was supported by the Fight Food Waste Cooperative Research Centre, whose activities are funded by the Australian Government's Cooperative Research Centre Program.
==== Refs
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| 36502706 | PMC9721290 | NO-CC CODE | 2022-12-08 23:16:23 | no | J Environ Manage. 2023 Feb 15; 328:116887 | utf-8 | J Environ Manage | 2,022 | 10.1016/j.jenvman.2022.116887 | oa_other |
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J Biosaf Biosecur
J Biosaf Biosecur
Journal of Biosafety and Biosecurity
2588-9338
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S2588-9338(22)00024-3
10.1016/j.jobb.2022.11.003
News
The WHO and the ICTV rename viruses such as monkeypox
Gao Jiaxuan ⁎
State Key Laboratory for Infectious Disease Prevention and Control, ational Institute for Communicable Disease Control and Prevention, China CDC, Beijing, CHINA
⁎ Corresponding author.
5 12 2022
5 12 2022
22 11 2022
22 11 2022
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.
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pmcMonkeypox is a viral zoonosis caused by monkeypox virus, a DNA virus belonging to the genus Orthopoxvirus within the Poxviridae family. Monkeypox virus is “closely related” to smallpox virus, which has threatened human health for thousands of years. Monkeypox virus was first identified in captive monkeys in 1958, and the first human case of monkeypox was a nine-month-old boy in the Democratic Republic of the Congo in 1970. The main virus variants were named according to the known geographic regions of transmission. However, the naming of newly identified viruses, related diseases and viral variants has not taken into account the need to respect and protect cultural, social, national, regional, professional and ethnic groups and to minimize the negative impact on trade, travel, tourism and animal welfare.
The World Health Organization (WHO) has considered renaming “monkeypox virus” because of concerns about the stigma associated with this name. The naming of virus species is the responsibility of the International Committee on the Taxonomy of Viruses (ICTV), which is working on the naming of the monkeypox virus. The WHO, which is responsible for naming existing diseases under the International Classification of Diseases and the WHO Family of International Health Related Classifications (WHO-FIC), is holding an open consultation on a new disease name for monkeypox.
To accelerate the renaming process in the context of the current pandemic, the WHO organized a special meeting on August 8, 2022, to enable virologists and public health experts to reach a consensus on the new terminology. The phylogeny and nomenclature of known and new monkeypox virus variants or clades were reviewed by experts in pox virology, evolutionary biology, and representatives of research institutes from around the world. They discussed the characteristics and evolution of monkeypox virus variants, the apparent phylogenetic and clinical differences, and potential consequences for public health and future virological and evolutionary research.
The expert group reached a consensus on how the viral clades should be recorded and classified on genome sequence repository sites. The former Congo Basin (Central Africa) clade is now referred to as Clade one (I) and the former West African clade as Clade two (II). In addition, it was agreed that Clade II consists of two subclades. The proper naming structure will be represented by a Roman numeral for the clade and a lowercase English letter for the subclade. Thus, the new naming convention comprises Clade I, Clade IIa, and Clade IIb, with the latter mainly referring to the group of variants that were predominantly circulating during the 2022 global outbreak. The WHO stated that lineages will be named according to scientists’ recommendations as the outbreak evolves, and experts will be reconvened if necessary. Work continues on disease and virus names, while new names for each clade take effect immediately.
A Chinese expert involved in the research said, “It is absolutely necessary from the perspective of non-discrimination. In the past, people used to put places or specific populations in the names of diseases out of ‘convenience’, such as ‘Spanish flu’. By today’s standards, this naming method is not scientific, and is also likely to cause regional or racial discrimination. It is imperative to standardize the names of diseases and viruses to avoid discrimination and stigma.”
Li Zhenjun, Director of the Biosafety Laboratory, National Institute for Communicable Disease Control and Prevention, China CDC, stated, “Against the backdrop of the current global monkeypox outbreak, the naming of monkeypox virus is not only inaccurate, but also discriminatory and stigmatizing. The current term ‘monkeypox’ is misleading because monkeys are not the main animal host of the virus. Although the virus was first identified in macaques, many cases have been transmitted from rodents to humans. Therefore, it is very important to find a new name for monkeypox to make sure it is not offensive toward any specific people, animals, countries, regions, etc.”
| 36504725 | PMC9721292 | NO-CC CODE | 2022-12-08 23:18:11 | no | J Biosaf Biosecur. 2022 Dec 5; doi: 10.1016/j.jobb.2022.11.003 | utf-8 | J Biosaf Biosecur | 2,022 | 10.1016/j.jobb.2022.11.003 | oa_other |
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Biochem Pharmacol
Biochem Pharmacol
Biochemical Pharmacology
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S0006-2952(22)00465-8
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115370
Review
Counter-regulatory renin-angiotensin system in hypertension: review and update in the era of COVID-19 pandemic
Chen Hongyin a
Peng Jiangyun bc
Wang Tengyao bc
Wen Jielu bc
Chen Sifan bc
Huang Yu d⁎
Zhang Yang a⁎
a School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen 518000, Guangdong, China
b Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, Guangdong, China
c Nanhai Translational Innovation Center of Precision Immunology, Sun Yat-sen Memorial Hospital, Foshan 528200, Guangdong, China
d Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
⁎ Corresponding authors.
5 12 2022
5 12 2022
11537012 10 2022
26 11 2022
29 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.
Graphical abstract
Cardiovascular disease is the major cause of mortality and disability, with hypertension being the most prevalent risk factor. Excessive activation of the renin-angiotensin system (RAS) under pathological conditions, leading to vascular remodeling and inflammation, is closely related to cardiovascular dysfunction. The counter-regulatory axis of the RAS consists of angiotensin-converting enzyme 2 (ACE2), angiotensin (1-7), angiotensin (1-9), alamandine, proto-oncogene Mas receptor, angiotensin II type-2 receptor and Mas-related G protein-coupled receptor member D. Each of these components has been shown to counteract the effects of the overactivated RAS. In this review, we summarize the latest insights into the complexity and interplay of the counter-regulatory RAS axis in hypertension, highlight the pathophysiological functions of ACE2, a multifunctional molecule linking hypertension and COVID-19, and discuss the function and therapeutic potential of targeting this counter-regulatory RAS axis to prevent and treat hypertension in the context of the current COVID-19 pandemic.
Keywords
Counter-regulatory renin-angiotensin system
ACE2
Hypertension
COVID-19
Abbreviations
ADAM17, A Disintegrin and Metalloproteinase 17
ACEI, ACE inhibitors
AC, Adenylate cyclase
AGT, Angiotensinogen
Alamandine-HPβCD, Alamandine/2-hydroxypropyl-β-cyclodextrin
atRA, All-trans retinoic acid
AMPK, AMP-activated protein kinase
AT1R, Ang II type-1 receptor
ARB, Ang II type-1 receptor blockers
AT2R, Ang II type-2 receptor
Ang I, Angiotensin I
Ang II, Angiotensin II
ACE, Angiotensin-converting enzyme
ACE2, Angiotensin-converting enzyme 2
AD, Aspartate decarboxylase
ANP, Atrial natriuretic peptide
BP, Blood pressure
BK2R, Bradykinin B2 receptor
Bdkrb2-/-, Bradykinin B2 receptor-deleted
CaMKII, Calcium/calmodulin-dependent protein kinase II
CCB, Calcium channel blockers
CPA, Carboxypeptidase A
CVD, Cardiovascular disease
CTSL, Cathepsin L
CNS, Central nervous system
CSF, Cerebrospinal fluid
C21, Compound 21
COVID-19, Coronavirus disease 2019
DOCA, Deoxycorticosterone acetate
DES, Diethylstilbestrol
DIZE, Diminazene aceturate
D-Pro, D-ProD-Pro7-Ang (1-7)
DUSP, Dual specificity phosphatase
eNOS, Endothelial nitric oxide synthase
EDR, Endothelium-dependent relaxation
eQTL, Expression quantitative trait loci
GPCRs, G protein-coupled receptors
GI, Gastrointestinal
HAECs, Human aortic endothelial cells
long COVID, Long-term consequence of COVID-19
MrgD, MAS-related GPR family member D
MAP, Mean arterial pressure
mACE2, Membrane-bound ACE2
MKP1, Mitogen-activated protein kinase-phosphatase 1
MrgDR, MrgD receptors
MI, Myocardial infarction
NHE1, Na+/H+ exchanger-1
NET, NE transporter
NAM, Negative allosteric modulator
NRLP1, Neuropilin-1
NEP, Neutral endopeptidase
NO, Nitro oxide
NE, Norepinephrine
NFAT, Nuclear factor of activated T-cell
PVN, Paraventricular nucleus
PDGF-BB, Platelet-derived growth factor-BB
PRCP, Prolyl carboxypeptid ase
PEP, Prolyl oligopeptidase (prolyl endopeptidase)
PLZF, Promyelocytic zinc finger protein
PGI2, Prostacyclin
PAH, Pulmonary arterial hypertension
ROS, Reactive oxygen species
RBD, Receptor-binding domain
rACE2, Recombinant ACE2 protein
rACE2-Fc, rACE2 and the immunoglobulin fragment Fc segment
rhACE2, Recombinant human ACE2 protein
RSNA, Renal sympathetic nerve activity
RAS, Renin-Angiotensin System
RVLM, Rostral ventrolateral medulla
SARS, Severe acute respiratory syndrome
sc-RNAseq, Single-cell RNA-seq
SM, Smooth muscle
sACE2, Soluble ACE2
sGC, Soluble guanylate cyclase
SHRs, Spontaneously hypertensive rats
SHP1, Src homology 2-containing protein-tyrosine phosphatase-1
THOP1, Thiocyanate oligopeptidase
TMPRSS2, Transmembrane protease serine isoform 2
2K1C, Two-kidney one-clip
VSMCs, Vascular smooth muscle cells
XNT, Xanthone
β-AIBA, β-aminoisobutyric acid
==== Body
pmc1 Introduction
Globally, cardiovascular disease (CVD) is the major cause of mortality and disability, with hypertension being the most prevalent risk factor [1]. The Renin-Angiotensin System (RAS) is one of the critical hormonal systems in human cardiovascular homeostasis and an essential player in the regulation of blood pressure, fluid and electrolyte balance, and systemic vascular resistance [2]. Excessive activation of the RAS under pathological conditions leads to vascular remodeling and inflammation, which is closely associated with cardiovascular dysfunction. RAS consists of several important enzyme-mediated conversions and ligand-receptor binding pathways. Since the discovery of renin in 1898, research on RAS components has been a hot topic over the past 100 years, including their participation in the regulation of both cardiovascular function and pathologies, and also as effective therapeutic targets in the prevention and treatment of CVD [3].In the classical RAS, renin cleaves liver-derived angiotensinogen into decapeptide angiotensin I (Ang I) which is subsequently converted to the highly physiologically active octapeptide angiotensin II (Ang II) by angiotensin-converting enzyme (ACE). By binding to angiotensin receptors, Ang II exerts pathophysiological effects such as elevating blood pressure, promoting cardiac and vascular remodeling, and interrupting renal water-sodium balance, which in turn leads to hypertension, end-stage organ failure, atherosclerosis, and heart failure [4].
The discovery of the core non-canonical RAS component, angiotensin-converting enzyme 2 (ACE2), opened a new window for studying the counter-regulatory arm of the RAS [5]. There is substantial evidence that the products of ACE2, including angiotensin (1-7), angiotensin (1-9) and alamandine, generate opposite effects of Ang II, suggesting the balance of the RAS and its counter-regulatory arm is critical in cardiovascular physiopathology. Moreover, the severe acute respiratory syndrome (SARS) outbreak in 2003 and the Coronavirus disease 2019 (COVID-19) outbreak in 2019 brought to the attention of scientists another vital role of ACE2, the binding receptor for coronaviruses SARS-CoV and SARS-CoV2 entering cells. In particular, to date (Oct 1, 2022), there have been over 600 million confirmed cases of COVID-19 worldwide, including 6.5 million deaths, reported to WHO (https://covid19.who.int/), while variants of the virus are still rampant. Hypertension and COVID-19 are closely associated, as hypertension aggravates the negative consequence of viral infection, while the long-term consequence of COVID-19 (Long COVID) also includes new-onset hypertension [6], [7]. Therefore, the counter-regulatory RAS may have important pharmacological potential as a therapeutic target against viral infection and for treating Long COVID.
In light of the emergence of numerous studies evaluating the pathophysiological effects and signaling pathways elicited by the counter-regulatory RAS, in this review, we aimed to summarize and provide an update on the current understanding of the complex regulation of the non-canonical RAS in hypertension. We highlighted the roles of ACE2, a multifunctional molecule linking hypertension and COVID-19, and discussed the pharmacological investigation of the counter-regulatory RAS axis against hypertension in the context of the ongoing COVID-19 pandemic.
2 Counter regulatory RAS components and pathways in hypertension
2.1 Angiotensin-converting enzyme 2 (ACE2)
ACE2, a type 1 integral membrane glycoprotein mainly expressed in vascular endothelial cells and renal tubular epithelium, is the first recognized homolog of human ACE [8]. The catalytic metallopeptidase unit located in the extracellular domain of ACE2 shares 42% sequence identity (61% sequence similarity) with the catalytic domain of ACE [5]. Nevertheless, ACE2 differs from ACE in that it works as a carboxypeptidase rather than a dipeptidase; thus, ACE2 activity cannot be inhibited by typical ACE inhibitors [9]. Ang II appears to be the major substrate for ACE2 [5], [9], [10], while other peptides, such as Ang I, vasoactive bradykinin (1-8), des-Arg-kallidin, apelin-13 and apelin-36 as well as other possible substrates might also have a relatively low affinity to be degraded by ACE2 [5], [11], [12]. So far, as a decarboxylase, ACE2 has been known to catalyze the following three reactions: a) cleavage of Ang II to Ang (1-7), b) breakdown of Ang I to Ang (1-9), and c) cleavage of Ang A (an analog of Ang II) to alamandine. Figure 1 illustrates the relationships among the major members of the counter-regulatory RAS, which consists of core enzyme-mediated peptide conversions and ligand-receptor binding pathways.Fig. 1 Classical and counter-regulatory renin-angiotensin system (RAS). Left: The classical RAS pathway. Angiotensinogen (AGT) can be cleaved by renin to form angiotensin I (Ang I). Ang I is converted by angiotensin-converting enzyme (ACE) to generate angiotensin II (Ang II). Ang II activates the Ang II type-1 receptor (AT1R) and the Ang II type-2 receptor (AT2R). The activation of AT1R leads to increased blood pressure, cardiac hypertrophy and fibrosis, vascular inflammation and remodeling, end-stage organ failure, decreased nitric oxide (NO) bioavailability and interrupted renal water-sodium balance. Moreover, Ang II can also produce angiotensin A (Ang A) with the action of aspartate decarboxylase (AD). Right: The counter-regulatory RAS pathway. Angiotensin-converting enzyme 2 (ACE2) is considered as the core component of the counter-regulatory arm of the RAS, catalyzing the following three reactions: a) cleavage of Ang II to Ang (1–7); b) breakdown of Ang I to Ang (1–9); and c) cleavage of Ang A to alamandine. Alternatively, Ang(1-7) can be formed from Ang (1-9) cleavage by ACE, neutral endopeptidase (NEP), prolyl endopeptidase (PEP) and thiocyanate oligopeptidase (THOP1). Alamandine can also be generated from Ang (1-7) catalyzed by AD. Ang (1-7) binds to Mas receptor (MasR) to elicit vasoprotective effects, including lowered blood pressure, reduced cardiac hypertrophy and fibrosis, thrombosis, inflammation, cell proliferation, inhibits the sympathetic nervous system and osteogenic transition of vascular smooth muscle cells (VSMCs), but promotes vasodilatation, the release of atrial natriuretic peptide, and NO production. By binding to AT2R and Mas-related G protein-coupled receptor member D (MrgDR), respectively, Ang (1-9) and alamandine exhibit cardioprotective effects, such as enhanced vasodilatation, reduced blood pressure, and improved cardiac hypertrophy and fibrosis. Illustration was created with BioRender.com.
The relationship between ACE2 gene variations and the risk of hypertension in different ethnic populations has been the focus of numerous genetic association studies. One of the first studies to show a link between ACE2 polymorphisms and hereditary hypertension is the Leeds Family Study [13]. Variants rs2285666 (genotype GG, AA), rs1978124 (genotype GG) and rs2106809 (genotype TT, T) have been reported to be associated with a higher risk of hypertension in at least two different ethnic groups, but these studies did not sufficiently represent the generalizability of the link across ethnicity [14], [15], [16], [17], [18], [19], [20], [21], [22], [23].
ACE2 is dynamically expressed across the progression of hypertension associated with RAS activation. In spontaneously hypertensive rats (SHRs), renal tubular expression of ACE2 decreases with the onset of hypertension and remains low compared to control rats, while it is highly expressed at birth in these rats [24]. Although there are no appreciable differences in cardiac ACE2 activity between neonatal SHRs and WKY rats, adult SHRs have much lower cardiac ACE2 expression and activity than age-matched WKY rats did [25], [26], [27], [28], [29]. However, salt loading has no effects on the cardiac ACE2 gene or activity in SHRs or salt-sensitive Sabra rats [30], [31]. When assessing the involvement of ACE2 in hypertension, the discrepancies in observed alterations bring attention to the importance of the strain and model used.
The ACE2 gene corresponds to a specific quantitative trait locus on chromosome X that was previously found as a quantitative locus for blood pressure in preclinical models of hypertension [32]. Moreover, in clinical studies, patients with hypertension had higher serum ACE2 activity compared to healthy individuals, which indicates the association between increased blood pressure and elevated ACE2 activity due to a possible compensatory response [33], [34]. Indeed, several preclinical models of hypertension have shown that the upregulation of ACE2 has an anti-hypertensive effect [25], [35], [36], [37], [38]. The elevated blood pressure can be decreased by lentiviral or adenoviral overexpression of ACE2 via increasing the expression of the components of the counter-regulatory RAS, such as Ang (1-7), MasR and Ang II type 2 receptor (AT2R) [25], [36], [39]. Likewise, recombinant human ACE2 protein (rhACE2) administration attenuated acute Ang II infusion-induced hypertension and was associated with the rebalance of plasma Ang II and Ang (1-7) levels [38], [40]. It also reduced plasma and renal cortex levels of Ang II in diabetic Akita mice, which was linked to reduced blood pressure and postponed development of diabetic nephropathy [41]. Although the effects of intravenous rhACE2 administration in healthy subjects were well tolerated with low immune response and increased plasma ACE2 without affecting blood pressure or heart rate [42], studies examined the efficacy of this genetic ACE2 overexpression to reduce blood pressure in hypertensive patients are currently still lacking.
Reduced expression of ACE2 has been linked to CVDs in a variety of animal models, which is largely attributed to the imbalance of the production of Ang II and Ang (1-7). ACE2 deficiency is associated with the upregulation of putative atherosclerotic mediators, such as cytokines and adhesion molecules [43]. In ApoE-/- mice, ACE2 depletion is associated with increased plaque accumulation [44], [45], [46]. Low levels or complete knockout of ACE2 can lead to elevated Ang II levels, which eventually results in hypertension. Downregulation of ACE2 has also been implicated in the pathogenesis of cardiac dysfunction and central hypertension. A severe cardiac contractility defect, an increased Ang II level, and the upregulation of hypoxia-induced genes in the heart were observed in mice with targeted ACE2 disruption [32], [47]. In rat brain areas, ACE2 gene deletion leads to impaired baroreflex sensitivity and autonomic function [48]. In particular, the downregulation of ACE2 in the rostral ventrolateral medulla (RVLM) [37], the periventricular nucleus (PVN) [39], the hypothalamus as well as the cerebrospinal fluid (CSF)[49], [50], were reportedly associated with elevated blood pressure in different hypertensive animal models including SHRs, Ang II-induced and deoxycorticosterone acetate (DOCA) salt-induced hypertension.
In addition to the previously described membrane-bound ACE2 (mACE2) that is expressed in a tissue- or cell-specific manner, another ACE2 in the circulation, soluble ACE2 (sACE2), can be formed by cleavage of the extracellular domain of the full-length ACE2 catalyzed by A Disintegrin and Metalloproteinase 17 (ADAM17), a member of the exonuclease family. sACE2 has a similar carboxymonopeptidase function to mACE2 as part of the protective branch of RAS, which cleaves the carboxyl-terminal amino acid phenylalanine Ang II and hydrolyses it into the vasodilator Ang (1-7). sACE2, found in plasma and urine, is considered as a biomarker of cell death since it is released from the cytoplasmic membrane into the circulation when tissues are injured in CVDs, such as coronary artery disease [51], [52], myocardial infarction (MI) [52], heart failure [53], [54], atrial fibrillation [52], [55], and aortic stenosis [56], [57], [58]. Moreover, aging, metabolic syndromes like obesity, hypertension, insulin resistance, and dyslipidemia are also closely associated with elevated plasma sACE2 level or activity [34], [58], [59], implying that sACE2 assessment could be a valuable diagnostic and prognostic indicator for patients with cardiometabolic diseases. Although the increase in sACE2 may also signify a compensatory response to harmful stimuli, it is yet unclear whether this process was driven by increased local synthesis or enhanced tissue shedding. By contrast, the increases in serum ACE2 level and activity following therapeutic intervention in patients with acute decompensated heart failure or hypertension were linked to positive clinical outcomes, providing more evidence in favor of therapeutic strategies to raise ACE2 in a variety of diseases [34], [60]. However, additional mechanistic investigation into the dynamics of the sACE2 in physiopathological progress is needed in both animals and humans.
2.2 Ang (1-7)-MasR
In 1988, Santos et al. first discovered that Ang (1-7) could be converted from Ang I or Ang II in an ACE-independent manner [61], suggesting that Ang (1-7) is a new bioactive product of the RAS. In light of the discovery that ACE2 is the key enzyme for the hydrolysis of Ang II to form Ang (1-7) [8], the pathways of Ang (1-7) production to date are: (1) cleavage of Ang I by endopeptidases including thiocyanate oligopeptidase (THOP1), neutral endopeptidase (NEP) and prolyl oligopeptidase (also known as prolyl endopeptidase, PEP); (2) hydrolysis of Ang II by carboxypeptidases, including ACE2, carboxypeptidase A (CPA) and prolyl carboxypeptidase (PRCP); (3) cleavage of Ang (1-9) by ACE, NEP and PEP [9], [62], [63], [64], while Ang (1-9) is the intermediate product generated by cleavage of Ang I by ACE2. Furthermore, among those pathways, ACE2-mediated hydrolysis of Ang II is the predominant one to produce Ang (1-7) with the highest catalytic efficiency.
The binding receptor for Ang (1-7) is Mas receptor (MasR), a bioactive peptide encoded by the MAS1 gene. In the 1980s, MAS1 was initially identified as a human oncogene based on its ability to induce tumorigenicity of NIH 3T3 cells in nude mice [65], [66]. MasR belongs to a member of G protein-coupled receptors (GPCRs) with a predicted seven transmembrane segment structure. MasR is predominantly expressed in the brain and testis, while moderate levels are detected in the heart, kidney, and blood vessels [67].
Through the stimulation of MasR, Ang (1-7) activates the NO-soluble guanylate cyclase (sGC) pathway, subsequently triggering vasoprotective effects both in vitro and in vivo. For example, in human endothelial cells which express MAS, Ang (1-7) mediates endothelial nitric oxide synthase (eNOS) activation and NO production via a PI3K/AKT-dependent pathway, which was confirmed in Mas-transfected Chinese hamster ovary cells [68]. Additionally, in different vasculature of different species, Ang (1-7) exerts a vasodilatory effect via the release of endothelium-derived NO [69], [70], [71], [72], [73], [74], [75], [76], [77], [78], [79], but it is still confined to certain vascular beds [80], [81]. The cellular effects of Ang (1-7) are diminished in MAS-KO human endothelial cells, and the vasodilatory function of Ang (1-7) is also lost in aortic rings and mesenteric arteries from Mas-KO mice [82], [83], [84], [85]. Apparent endothelial dysfunction in FVB/N Mas-deficient mice suggests a crucial role of the Ang (1-7)-Mas axis in the regulation of endothelial function [86], [87]. Interestingly, a phosphoproteomics study showed that Ang (1-7) produces an antiproliferative action in human aortic endothelial cells (HAECs) by activating the downstream FOXO1 transcription factor, a well-known negative regulator of the AKT signaling pathway [88]. Nevertheless, other studies indicate that Ang (1-7) may function through the hetero-oligomeric complex of MasR and Ang II type-1 receptor (AT1R), and MasR can antagonize the actions of AT1R [89], [90].
In addition to the vasodilator effect, the Ang (1-7)-MasR axis also activates other downstream cascades to inhibit cardiovascular pathological processes, including antihypertrophic, antiproliferative and antithrombotic actions. The vast majority of the research on Ang (1-7) or the MasR agonists in the heart focuses on its cardioprotective effects, such as improved arrhythmias and post-ischemic cardiac function [91], [92], [93], [94], [95]. In cardiac fibroblasts, signaling molecules, such as Src homology 2-containing protein-tyrosine phosphatase-1 (SHP1) [96] and dual specificity phosphatase (DUSP) [97], [98], are found to be the targets of the Ang (1-7)-MasR activation, which consequently suppresses the activities of mean arterial pressure (MAP) kinases (ERK1/2 and p38) and attenuates the Ang II-induced synthesis of mitogenic prostaglandins. At high atrial pacing, Ang (1-7)-MasR activation also stimulates the secretion of atrial natriuretic peptide (ANP) via the PI3K-Akt pathway and the activation of Na+/H+ exchanger-1 (NHE1) and calcium/calmodulin-dependent protein kinase II (CaMKII) to decrease cardiac hypertrophy [99]. Treatment of cardiomyocytes with Ang (1-7) prevented Ang II-induced hypertrophy through inhibiting calcineurin/nuclear factor of activated T-cell (NFAT) signaling cascade via PI3K-AKT-NO-cGMP-dependent pathway [92]. The increased NO release promoted by Ang (1-7) was mediated through the activation of eNOS and nNOS [100], [101].
In vascular smooth muscle cells (VSMCs), Ang (1-7) inhibits cell proliferation through the release of prostacyclin (PGI2), which subsequently increases the production of cAMP and the activation of cAMP-dependent protein kinase (PKA) and attenuates ERK1/2 activation [102]. The Ang (1-7)-MasR axis also restores the reduced expression of lineage markers, including smooth muscle (SM) α-actin, SM22α, calponin and smoothelin, and inhibits the osteogenic transition of VSMCs [103].
As for the antithrombotic effect, Ang (1-7)-MasR decreases thrombosis in bradykinin B2 receptor-deleted (Bdkrb2-/-) mice by increasing plasma NO and PGI2 to reduce platelet spreading and glycoprotein VI activation [104]. Acute or chronic oral treatment with Ang-(1-7)-CyD, a modified Ang (1-7) in the oral formulation, can increase plasma Ang (1-7) concentration, promotes an antithrombotic effect in SHRs [105], and this striking effect is absent in Mas-/- mice [106].
Apart from the local protective effects in the cardiovascular system, Ang (1-7) functions as an important neuromodulator, particularly in the hypothalamus, dorsomedial and ventral medulla, and areas involved in the tonic and reflex control of arterial pressure and heart rate [107], [108], [109], [110], [111]. In situ central Ang (1-7) infusion decreases blood pressure in transgenic hypertensive rats, DOCA salt-induced hypertensive rats, aldosterone/NaCl induced hypertensive rats, fructose-induced metabolic syndrome rats, two-kidney one-clip (2K1C)-operated rats with renovascular hypertension, and stress-induced hypertensive rat [112], [113], [114], [115], [116], [117]. The Ang (1-7)-MasR axis regulating blood pressure in the nervous system is linked to several central effects, such as elevated NOS activity, increased NO generation in the brain, enhanced release of arachidonic acid and vasopressin, decreased norepinephrine (NE) bioavailability, and suppression of oxidative stress. For example, Ang (1-7) upregulates hypothalamic NOS as a compensatory and protective mechanism to combat hypertension in SHRs [117]. It also acts as a neuromodulator to balance the stimulatory effects of Ang II by reducing presynaptic NE synthesis and release in the hypothalamus [118], which is mediated through the bradykinin/NO-dependent mechanism [118], [119]. Ang (1-7)-MasR activation induces a chronic stimulatory effect on neuronal NE transporter (NET) expression via PI3K/Akt and Erk1/2-dependent pathways, indicating that Ang (1-7)-MasR axis regulates a presynaptic mechanism in maintaining appropriate synaptic NE levels under hypertensive conditions [120], [121]. Figure 2 (left) illustrates the signaling transduction cascades mediated by Ang (1-7)-MasR.Fig. 2 Signal transduction mechanisms of the counter-regulatory RAS and process of SARS-CoV-2 entry into the host cell. Left: Signal transduction cascades of the three principal axes of the counter-regulatory RAS in the heart, vasculature, and brain. a) ACE2-Ang (1-9)-AT2R: Stimulation of AT2R is coupled with Gi/s, leading to the activation of Src homology 2-containing protein-tyrosine phosphatase-1 (SHP1) /mitogen-activated protein kinase-phosphatase 1 (MKP1) to inhibit extracellular signal-regulated kinase 1/2 (ERK1/2). Moreover, stimulation of AT2R triggers the activation of transcription factor promyelocytic zinc finger protein (PLZF), thereby promoting the expression of ribosomal protein S6 kinase β1 (p70S6K) and PI3K regulatory subunit p85α. b) ACE2-Ang (1-7)-MasR: MasR activation stimulates SHP1 and dual specificity phosphatase (DUSP), subsequently suppressing ERK1/2 and p38. In addition, the PI3K-Akt pathway triggered by Ang (1-7)-MasR activates Na+/H+ exchanger-1 (NHE1) and calcium/calmodulin-dependent protein kinase II (CaMKII), leading to the increased secretion of atrial natriuretic peptide (ANP). The norepinephrine transporter (NET) in the central nervous system can be inhibited by the PI3K-Akt pathway. Moreover, Ang (1-7)-MasR activation can inhibit calcineurin/nuclear factor of activated T-cell (NFAT) signaling via PI3K-AKT-NO-cGMP-dependent pathway. c) ACE2-Alamandine-MrgDR: Alamandine-MrgDR activation functions through the Gi/s dependent cAMP-PKA pathway to induce NO production in the heart and vasculature. Right: The process of SARS-CoV-2 invasion of the host cell via a membrane-bound ACE2 receptor. The Spike glycoprotein (S protein) of SARS-CoV-2 binds to human ACE2 on the cell membrane through the S1 subunit containing the receptor-binding domain (RBD). A disintegrin and metalloproteinase 17 (ADAM17) mediates a proteolytic shedding of ACE2 to form sACE2 that can be released into extracellular cellular space. Viral membrane fusion with the host cell is activated upon binding through two distinct pathways: a) The intact ACE2 or its transmembrane structural domain is internalized along with the virus by clathrin-dependent endocytosis; b) In the presence of transmembrane proteins and transmembrane protease serine isoform 2 (TMPRSS2) and other co-transmembrane proteins, such as vimentin, neuropilin-1 (NRLP1), cathepsin L (CTSL), furin-like proteases (Furin), the S protein of SARS-CoV-2 is cleaved to trigger membrane fusion and cellular uptake of the virus. The host cell machinery promotes the release of the viral RNA into the cytoplasm for replication and translation. Illustration was created with BioRender.com
2.3 Ang (1-9)-AT2R
Ang I is cleaved by ACE2 and NEP to produce Ang (1-9) and Ang (1-7), respectively [122]. However, Ang (1-9) is less abundantly investigated compared to Ang (1-7). Ang (1-9) is hydrolyzed more slowly than Ang (1-7), and Ang (1-7) can also be synthesized from Ang (1-9) by the action of ACE [5]. Ang (1-9) is thought to reduce Ang II levels because it competes with Ang I as a substrate for the active site of ACE, thereby increasing Ang (1-7) levels and stimulating bradykinin release in endothelial cells [123]. The receptor for Ang (1-9) is now found to be the AT2R which is another binding receptor for Ang II. Thus, Ang (1-9) can compete with Ang II to activate AT2R to trigger urinary natriuretic response and NO production, thereby regulating vasodilatory effects and lowering blood pressure [124], [125]. This peptide has several cardioprotective effects, such as protecting cardiomyocytes from cell death induced by ischemia-reperfusion injury and attenuating inflammation, cardiac hypertrophy, and fibrosis [124], [126].
Although AT1R mediates most of the recognized Ang II actions, AT2R-mediated actions in the cardiovascular system are complicated. AT2R is a member of the GPCR superfamily that functions by coupling Gi [122]. There is evidence that AT2R-mediated actions are distinct from and often opposite to those of AT1R. Since AT2R is less expressed in healthy adult tissues than AT1R, the role and cellular signaling of AT2R are less characterized than those of AT1R [127], [128], [129], [130]. Most of the studies performed in the last decade show that activation of AT2R leads to a protective response to prevent the development of pathological processes such as inflammation, activation of the sympathetic nerve, cell apoptosis, autophagy, cardiac fibrosis, and arterial stiffness [130]. Meanwhile, AT2R stimulation activates defense mechanisms in the heart, including cardiac regeneration, vasodilation of coronary microvessels and compensatory hypertrophy of cardiomyocytes [130]. AT2R-deficient mice show elevated basal blood pressure and elevated blood pressure responsiveness after Ang II infusion [131], [132]. In addition, in transgenic mice overexpressing AT2R in VSMCs, long-term infusion of Ang II completely abolished the AT1R-induced elevated blood pressure [133].
Signaling through AT2R can directly inhibit the activation of AT1R triggered by Ang II, which is the first example of a GPCR acting as a receptor-specific antagonist [134]. Stimulation of AT2R triggers protein synthesis via activating the transcription factor promyelocytic zinc finger protein (PLZF) and subsequently promoting the expression of ribosomal protein S6 kinase β1 (p70S6K) and p85α subunit of PI3K [135]. In VSMCs, cardiomyocytes, neuronal cells and fibroblasts, AT2R activation also inhibits ERK1/2 by activating SHP1 [136] and mitogen-activated protein kinase-phosphatase 1 (MKP1) [137]. In addition, the PI3K/AKT-eNOS-NO-cGMP pathway can be activated to induce vasodilation via Ang (1-9) binding to AT2R [135], [138], [139], or via the heterodimerization between AT2R and bradykinin B2 receptor (BK2R) [140]. The vasodilating effect mediated by NO-cGMP triggered by AT2R activation has been reported in the aorta, coronary, cerebral, mesenteric, uterine, and renal arteries [141], [142], [143], [144]. Moreover, Ang (1-9)-AT2R activation can also inhibit platelet-derived growth factor-BB (PDGF-BB)-induced VSMC dedifferentiation via the AKT/FOXO1 pathway [145].
However, the signaling upon activation of the Ang (1-9)-AT2R axis remains incompletely understood. Crystal structural study on AT1R and AT2R suggested that, rather than binding to G proteins or β-arrestins [146], activation of this axis forms different heterodimers between AT2R and other receptors, including BK2R [140], AT1R [147] and MasR [148]. This may also account for the tissue specificity of the signaling mediated by the Ang (1-9)-AT2R axis. For instance, Ang (1-9)-dependent activation of AT2R in cardiac myocytes increases AKT phosphorylation, whereas it is reduced in VSMCs [145], [149]. The administration of Ang (1-9) reduces blood pressure in several hypertensive animal models [124], [145], [150]; however, whether it is applicable in hypertensive patients remains unknown. The mechanisms of Ang (1-9)-AT2R activation involved in blood pressure reduction mainly include increased endothelium-dependent vasodilatation [124], [150], improved renal function [124], elevated ANP release [139], restored natriuresis, as well as reduced vascular remodeling and inflammation [145]. Figure 2 (left) summarizes the signaling transduction cascades mediated by Ang (1-9)-AT2R.
2.4 Alamandine-MrgDR
Differing from Ang (1-7) only in the N-terminal alanine instead of aspartate residue, the recently discovered heptapeptide alamandine can be produced via two pathways: (1) Ang (1-7) by decarboxylation of its N-terminal aspartate to alanine and (2) Ang II is processed by aspartate decarboxylase (AD) to produce Ang A, an octapeptide with only one amino acid difference from Ang II [151]. Ang A can be further cleaved to alamandine by ACE2 [152]. Alamandine and Ang (1-7) have many similarities in their physiological functions, such as vasodilatory and anti-hypertensive actions by activating the NO pathway [153]. For example, both induce endothelium-dependent relaxation (EDR) in aortic rings isolated from FVB/N mice [152]. Unlike Ang (1-7), alamandine counteracts the vasoconstriction induced by its precursor Ang A without affecting Ang II-induced vasoconstriction [154], indicating that the first alanine residue of alamandine is critical for competitor recognition. The vasoreactivity induced by alamandine also varies in different blood vessels. In the rabbit, alamandine enhanced EDR in the thoracic aorta and iliac artery, but it inhibited EDR in the renal artery [154].
The binding receptor of alamandine is the MAS-related GPR family member D (MrgD). MrgD receptor (MrgDR), widely expressed in tissues throughout the body, was originally identified in primary injurious sensory neurons in rodents and humans to regulate neuropathic pain [155], [156], [157]. In the cardiovascular system, MrgDR is found to be expressed in atherosclerotic plaques, SM cells and endothelial cells [154]. MrgDR is a natural endogenous receptor for alamandine both in vitro and ex vivo. The stimulation of MrgDR-transfected cells with alamandine produces and releases NO [152]. Meanwhile, MrgDR has been shown to have other ligands, including β-alanine, β-aminoisobutyric acid (β-AIBA), and diethylstilbestrol (DES) [158], [159]. Interestingly, MrgDR can be activated by Ang (1-7) as its second receptor involving adenylate cyclase (AC), cAMP, and protein kinase A (PKA) signal cascade [160], [161]. However, except alamandine and Ang (1-7), the presence of other MrgDR ligands like β-alanine fails to elicit any vasoactive response and even competitively inhibits alamandine-induced vasodilatation [152].
Like Ang (1-7), alamandine has been found to attenuate hypertension in SHRs and renal vascular hypertensive rats, which involves local vascular tone regulation as well as a central regulatory effect [152], [162], [163], but the mechanism of its anti-hypertensive effect is complicated. Alamandine also appears to exert several beneficial effects, including anti-hypertrophy, anti-remodeling, anti-fibrosis, anti-oxidation, and anti-inflammation [164], [165], [166], [167], [168], [169], [170]. Long-term administration of alamandine to isoproterenol-treated Wistar rats is associated with reduced accumulation of collagens and fibronectin in the heart [171]. A recent study showed that alamandine alleviated high-salt diet-induced hypertension and renal dysfunction via inhibiting the PKC/reactive oxygen species (ROS) signaling pathway, thereby suppressing apoptosis of renal tubular cells [172]. Different types of G proteins, such as Gs, Gq and Gi, can be coupled to MrgDR, which is dependent on ligand selectivity and cell specificity [173]. High-dose almandine increases cAMP concentrations in primary mesangial and endothelial cells via MrgDR coupling Gs protein [174]. Interestingly, in aortic rings isolated from New Zealand white rabbits, almandine can reverse vascular dysfunction induced by hyperhomocysteine, which is through the Gi-coupled PKA pathway [175]. In PVN, Alamandine-MrgDR activation functions through the Gi dependent cAMP-PKA pathway, subsequently increases blood pressure and sympathetic outflow [163]. However, activation of this axis prevents Ang II-induced cardiac hypertrophy via LKB1 dependent-AMP-activated protein kinase (AMPK)-NO pathway [176]. Alamandine also stimulates cardiomyocytes from hypertensive rats to contract vigorously by activating CaMKII in a NO-dependent manner [177]. Figure 2 (left) demonstrates the signaling transduction cascades mediated by alamandine-MrgDR.
3 The role of counter-regulatory RAS to link hypertension and COVID-19
3.1 ACE2 in SARS-CoV-2 infection
COVID-19 is an acute respiratory disease caused by SARS-CoV-2. Entry into the host cell is the initial step of a viral infection. The structure of the coronavirus consists of the viral envelope (E), membrane (M), nucleocapsid (N), and spike (S) proteins. The S-glycoprotein on the viral envelope can bind to ACE2, which is recognized as the specific functional receptor for SARS-CoV, on the host cell membrane [178]. SARS-CoV-2 has been confirmed to bind to ACE2 as a cellular receptor in a similar way but with a 10- to 20-fold higher binding affinity than SARS-CoV, which leads to a higher pathogenic effect [179], [180]. The S glycoprotein of SARS-CoV-2 binds to human ACE2 on the cell membrane through the S1 subunit containing the receptor-binding domain (RBD) [180], [181], [182]. Viral membrane fusion with the host cell is activated upon binding. The intact ACE2 or its transmembrane structural domain is internalized along with the virus by endocytosis, and viral RNA is subsequently released into the cytoplasm for replication and translation by the host cell machinery [183]. Several transmembrane proteins, including A disintegrin and metalloprotease 17 (ADAM17), clathrin, transmembrane protease serine isoform 2 (TMPRSS2), cathepsin L (CTSL), vimentin, furin-like proteases, neuropilin-1 (NRLP1) and other newly identified factors are potentially involved in the binding and membrane fusion process [184], [185], [186], [187], [188], [189], [190], [191], [192], [193]. Interestingly, the crystal structure demonstrates that the receptor function of ACE2 and S-glycoprotein binding process is independent with the peptidase domain of ACE2 [181], which is also observed in other coronavirus-receptor binding models, such as MERS-CoV to DPP4, HCoV-229E to APN [194], [195], [196]. Figure 2 (right) displays the process of SARS-CoV-2 virus infection of host cells.
Although COVID-19 is primarily a respiratory disease, it is well-known that its progression can cause acute or subacute cardiovascular damage and inflammation. The variation of ACE2 expression levels in different organs could reflect the potential risk of SARS-CoV-2 infection. Immunohistochemical and single-cell RNA-seq (sc-RNAseq) analysis reveals the abundant expression of ACE2 in the lung, heart, esophagus, kidney, bladder, vasculature, and ileum, and located specific cell types (i.e., type II alveolar cells, enterocytes, proximal renal tubules, ileum and esophagus epithelial cells, placental trophoblasts, ductal cells, cardiomyocytes, endothelial cells, and bladder urothelial cells) [197], [198]. In the respiratory system, ACE2-enriched nasal ciliated cells are the main targets for SARS-CoV-2 replication in the early stage of infection [199], [200]. While in the lower lung, ACE2 is predominantly expressed on type II alveolar epithelial cells, indicating that the lungs are vulnerable to SARS-CoV-2 infection, which leads to severe respiratory symptoms [197], [199]. Through blood circulation, the cell-free and macrophage phagocytosis-associated virus can spread from the lungs to other organs with high ACE2 expression. Autopsy studies confirmed the SARS-CoV-2 presence within the myocardium, colon, and kidney [201], [202]. Most patients with severe COVID-19 also have multi-organ damage, including acute lung injury, acute kidney injury, heart injury, liver dysfunction, and pneumothorax [203], suggesting that organ involvement and injury are closely related to receptor distribution in vivo.
However, it remains debatable whether ACE2 expression level links to a risk factor or the cause of the severity of COVID-19 due to the dynamics of ACE2 expression profile in different genetic backgrounds, ethnicity, organs, genders, ages, degrees of obesity, medication, and several comorbidities, such as CVD and metabolic syndrome [204]. Expression quantitative trait loci (eQTL) analysis for ACE2 variants across different populations suggests higher allele frequencies associated with higher ACE2 tissue expression level in East Asian compared to European populations [205]. Genome-wide association meta-analysis also showed a causal effect or positive association of elevated liver or circulating ACE2 levels on COVID-19 susceptibility, severity, and clinical outcomes [206], [207]. The relatively lower ACE2 expression in nasal and bronchial epithelial cells in children may account for the lower incidence and milder symptoms of COVID-19 in the youngest [208], [209]. However, there has been evidence of both positive and negative correlations between ACE2 expression and advanced age [210], [211], [212].
Many COVID-19 comorbidities, such as hypertension, T2DM and chronic obstructive pulmonary diseases, are characterized by a shift in the ratio of ACE/ACE2 in both directions, as well as either increased or decreased ACE2 expression or activity, so the relationship between ACE2 expression/activity in these comorbidities and the COVID-19 susceptibility, severity, and clinical outcomes seems paradoxical. Although ACE2 overexpression has shown a protective effect in a lung injury model in mice [213], ACE2 mRNA expression is increased unexpectedly in the lungs of patients with comorbidities associated with severe COVID-19 [214]. Several epidemiological studies showed that hypertension was moderately associated respectively with severity and mortality for COVID-19 [215], [216], [217], [218]; however, ACE2 expression levels in patients with hypertension decrease as the disease progresses as previously mentioned. To address this controversy, some investigators have used theoretical models to account for the severity of COVID-19 based on different ACE2 thresholds [219], [220]; some researchers propose that the limited expressed local ACE2 in the lung predominately serves as the SARS-CoV-2 receptor and is dysregulated in COVID-19, while highly expressed proinflammatory prolyl oligopeptidase, which has a significantly lower efficiency in converting Ang II to Ang (1-7), contributes to vascular inflammation and dysfunction induced by viral infection [221], [222]. However, more clinical and experimental investigations are still needed to confirm this causal relationship.
Studies of ACE2 concentration and activity in circulation are gradually converging from the initial controversy. The ACE2 mRNA level in circulating blood cells (mainly monocytes) and sACE2 concentration from COVID-19 patients were initially found to be lower in prolonged viral shedders than that in healthy controls [223]. By contrast, a few trials did not find evidence for altered ACE2 as well as other RAS components [224]. However, more recent studies demonstrate that sACE2 level or activity is significantly positively associated with the severity of COVID-19, indicating that sACE2 could be a useful biomarker for predicting the risk of severe disease [225], [226], [227], [228], [229], [230]. The infection of lung epithelial cells by various SARS-CoV-2 variants can be neutralized by soluble rhACE2 [231]. However, Yeung et al. revealed a new mechanism of sACE2-mediated cell entry of SARS-CoV-2 via interaction with virus dependency factors such as AT1R or AVPR1B [232]. Based upon that, rACE2 has been explored in clinical trials as a possible treatment for COVID-19 [231], [233], [234].
Given that ACE2, as one of the key counter-regulators of the RAS in regulating cardiovascular physiology, is also an important host receptor that mediates viral binding and triggers the infection, it is obvious that it can become a straightforward and rational research target. However, the causal relationship between the changes in ACE2 levels and the severity and prognosis of COVID-19 is still controversial to some extent. More investigation of the differences between transcriptional and translational levels of ACE2 is needed, as well as the development of specific antibodies against ACE2 isoforms of different lengths.
3.2 Hypertension and COVID-19
The relationship between hypertension and COVID-19 is bidirectional. On the one hand, initial studies have shown that hypertension is one of the most commonly associated comorbidities seen in patients with severe COVID-19 pneumonia during the early outbreak of COVID-19 [216], [235]. Reports from China, Europe, and the United States have validated the positive association of arterial hypertension with increased COVID-19-related mortality [216], [236], [237], [238], [239], [240], although this association does not necessarily imply a causal relationship between hypertension and COVID-19 or its severity. On the other hand, as the pandemic continues and the population of patients recovering from acute COVID-19 grows, the syndromes of Post-acute COVID-19 or Long COVID have been characterized by persistent symptoms and delayed or long-term complications beyond four weeks from the onset of symptoms [241], [242]. The new onset or the aggravating pre-existing cardiometabolic syndromes caused by COVID-19 are attracting the attention of scientists and physicians.
Many large-scale epidemiological studies have revealed that hypertension is an independent risk factor for the SARS-CoV-2 infection and severe COVID-19 outcome. The first large-scale data analysis on 1590 laboratory-confirmed hospitalized patients from 575 hospitals found that hypertension was the most prevalent comorbidity (16.9%) [236]. The case fatality rate is 6% in patients with hypertension compared to the overall 2.3% [243]. Many other single-center and multicenter cohort studies also provided clinical evidence of the effect of hypertension on the progression of severe COVID-19 [236], [243], [244], [245], [246], [247], [248]. In the COVID-19 diagnosed cohort, hypertensive patients had more hospitalizations and higher mortality rates than those without hypertension; in the COVID-19 hospitalized cohort, hypertensive patients were more likely to develop acute respiratory distress syndrome, severe inflammatory response, acute cardiac injury, and arrhythmias with higher mortality rate [249], [250], [251], [252]. Notably, almost all epidemiological studies have shown that the mortality of COVID-19 and the prevalence of hypertension increase with advancing age.
Furthermore, long-term hypertension and COVID-19 can damage multiple target organs, such as exacerbated myocardial injury, which implies that the mechanisms of the interaction between hypertension and COVID-19 are complex and may be linked with comorbidities. The pathological mechanisms that link hypertension and COVID-19 are yet to be fully elucidated, but they could be potentially related to imbalanced RAS, dysregulated immunoinflammation, and gut microbiome dysfunction.
First, the dysregulation of the RAS, which is characterized by the overactivated ACE-Ang II-AT1R axis in parallel with the inhibition of the counter-regulatory arm of RAS, is proposed to be the underlying mechanism leading to severe COVID-19 outcome in hypertension. The internalization of membrane-bound ACE2, which occurs as SARS-CoV-2 binds to the host cell membrane, leads to the concurrent loss of ACE2's catalytic activity [253]. This results in not only the upregulation of Ang II and overactivity of the conventional ACE-Ang II-AT1R axis but the decrease in Ang (1-7) as well as the protective effect of the unconventional ACE2-Ang (1-7)-MasR axis. Excessive Ang II can promote endothelial dysfunction and cytokine storm, which in turn contribute to the pulmonary, inflammatory, and hematological complications of COVID-19 [254], [255], [256].
Second, extensive experimental and clinical evidence has shown that hypertension is associated with inflammation and immune cell activation, although it has not been elucidated whether this association is causally linked through a direct or indirect regulation [257], [258]. It has been shown that innate immune cells (macrophages, microglia, monocytes, dendritic cells, and myeloid-derived suppressor cells), as well as adaptive immune cells (CD8+ T cells, CD4+ cells, T cells, and B cells), play significant roles in the development of hypertension [259]. For example, both CD4+ and especially CD8+ cells are dysregulated and tend to overproduce proinflammatory cytokines in hypertension, and also show less efficiency in antiviral defense [260], [261]. So far, a small number of cytokine mediators, such as interleukins (IL-6, IL-7, IL-17), TNF-α, INF-γ, have been discovered in regulating immunoinflammatory responses in hypertension as well as the accelerated end-organ damage [260], [262], [263], [264], [265]. On the other hand, SARS-CoV-2 infection activates both innate and adaptive immune responses and triggers the release of proinflammatory factors, which leads to hyperinflammation or “cytokine storms” [266], [267]. The multi-organ damage caused by this uncontrolled innate response and impaired adaptive immune response determines the severity of COVID-19 and the risk for clinical deterioration of patients. The immunoinflammatory status of hypertensive patients could exacerbate COVID-19 severity has been studied in several epidemiological and experimental research. A limited-size of cohort study demonstrated a distinct inflammatory predisposition of immune cells in patients with hypertension that correlated with critical COVID-19 progression [268]. The dynamic immunological profile analysis of hypertensive COVID-19 patients found that T lymphopenia, in particular the prolonged activation and exhaustion of CD8+ T cells, is associated with critical or severe COVID-19 cases [269].
Lastly, ACE2 is most abundantly expressed in the gastrointestinal (GI) tract at both mRNA and protein levels; it also serves as a key regulator of intestinal homeostasis [270]. GI symptoms, including diarrhea, nausea, and vomiting, are very common in COVID-19 patients [271], [272], [273]. As the secondary site of viral infection, ACE2 expression on surface cells of the small intestine may mediate the invasion and amplification of the virus and activation of GI inflammation [274], [275], [276]. Interestingly, hypertension is also associated with GI inflammation and increased intestinal permeability [277], and intestinal dysbiosis is thought to be one of the causal factors for hypertension in both animal models and patients [278], [279]. In addition, the distribution of the gut microbiota in hypertensive and COVID-19 patients also indicates some intriguing correlations. The fecal abundance of the Bacteroidetes species was inversely correlated with COVID-19 severity as well as the fecal viral load of SARS-CoV-2 [275], [280], and the subjects with pre-existing hypertension and other cardiometabolic diseases were characterized by a low abundance of Bacteroides species and had the highest COVID-19 mortality and morbidity [281], [282], [283]. As a result of gut leakiness and wall pathology brought on by hypertension, SARS-CoV-2 infection can become more severe. In turn, SARS-CoV-2 infection deteriorates the function of the gastrointestinal tract.
Although hypertension is linked to an increased risk of severe COVID-19 and higher mortality, the effects of COVID-19 on pre-existing and newly diagnosed hypertension are still incompletely understood. Elevated BP and even spontaneous hypertension were observed in COVID-19 patients with respiratory failure during the hospitalization [284], [285], [286], and individuals with pre-existing hypertension were also found to have worsening BP outcomes, severe respiratory symptoms and excessive inflammatory response [218], [287]. A recent prospective case-control study in hospitalized patients also shows that COVID-19 pneumonia individuals have a higher probability of a persistent elevated BP [288]. These studies suggest that the SARS-CoV-2 infection-triggered respiratory failure and modulation of the RAS may increase BP, which is likely explained by the positive correlation between plasma Ang II level and COVID-19 severity [289]. However, a retrospective and prospective cohort study showed that COVID-19 had no lasting effects on systolic or diastolic BP [290]. Additionally, an unexpected rise in plasma Ang (1-7) and a parallel decline in plasma Ang II were observed in another cohort study [291]. It is important to note that all of those controversial findings came from research with a small sample size of hospitalized COVID-19 patients representing a small portion of the total infected population. Another major challenge in determining the relationship between hypertension and COVID-19 is the lack of blood pressure information prior to hospital admission. Although a large-scale longitudinal study in the US showed that blood pressure among US adults increased during the COVID-19 pandemic, the rise in the number of cases of hypertension was probably related to stress or alterations in lifestyle during the pandemic [287], [292].
SARS-CoV-2 is prone to mutations due to continuous transmission. As the pandemic continues, SARS-CoV-2 variants of B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta) and B.1.1529 (Omicron) alternated as the dominant pathogen in the world accompanied by increasing transmissibility. Hypertension remains a significant risk factor for COVID-19 severity brought on by different SARS-CoV-2 variants, but only limited studies have investigated the relationship between hypertension and infection with these variants. Although the COVID-19 severity and mortality rate associated with Omicron infections declined significantly compared to the previous variants, a recent study showed those infected with variant Omicron were older and had significantly more frequent comorbidities, including hypertension [293], which is probably due to the remarkably higher transmissibility and immune evasion ability.
The cardiometabolic complications of Long COVID have been reported widely [294], [295], [296], [297], [298], [299], [300], [301], [302], [303], [304], [305], [306], [307]. We mechanistically investigated that COVID-19 induces new-onset metabolic disorders and found that SARS-CoV-2 infection modulated several secreted metabolic factors contributing to the perturbation of glucose and lipid metabolism [308], [309]. Nevertheless, the majority of the studies in patients who had recovered from acute symptoms for more than a month, which were limited to case reports and retrospective observational studies, paradoxically displayed the link between COVID-19 and aggravated hypertension or an elevated risk of new-onset hypertension. In two confirmed COVID-19 patients enrolled cohort studies, BP was significantly higher in the post-COVID-19 period than upon admission [285], [286], and Ang II signaling upregulation driven by SARS-CoV-2 infection might play a critical role in the progression of new-onset hypertension [285]. Another retrospective study also demonstrated that arterial hypertension following SARS-CoV-2 infection, either newly verified or worsened existing, was a relatively common occurrence [310]. However, some cross-sectional research showed that COVID-19 did not influence blood pressure, particularly in young subjects [311], [312], [313], [314]. To resolve these discrepancies and clarify the pathophysiological effect on the BP of Long COVID, additional clinical and animal investigations are required, and exploring the mechanisms underlying cardiovascular manifestations among patients with Long COVID is warranted. [294], [315].
In the context of COVID-19, blood pressure control and anti-hypertensive medication in hypertensive patients warrant special consideration. The safety and efficacy of the first-line BP-lowering drugs, including beta-blockers [316], thiazide diuretics [317], RAS blockers [318], [319], [320], [321] and calcium channel blockers [322], have been evaluated in COVID-19 patients with hypertension. After a brief controversy during the early days of the COVID-19 outbreak, a consensus has been reached that patients with coexisting COVID-19 and hypertension should be actively treated with anti-hypertensive drugs to control their blood pressure. In addition to the blood pressure-lowering benefits, the findings from retrospective and preclinical studies suggested that these reagents may reduce the morbidity and mortality of COVID-19 patients. For example, alpha- and beta-adrenergic receptor antagonists may reduce the risk of hyperinflammation characterized by profound elevation of many proinflammatory cytokines in patients with severe COVID-19 [316], [323], [324]. The use of calcium channel blockers nifedipine and amlodipine was associated with improved survival and decreased risk of intubation and mechanical ventilation in elderly patients hospitalized for COVID-19; the underlying mechanisms may include inhibition of post-entry replication of SARS-CoV-2 [322], [325]. The diuretic spironolactone may protect against COVID-19 by inhibiting TMPRSS2, reducing the cytokine storm and lessening organ injury [317], [326], [327]. Overall, there is growing observational evidence that the application of anti-hypertensive agents in patients with COVID-19 and hypertension morbidities is safe and even has additional benefits, but the mechanisms underlying this protective response remain largely unknown.
3.3 RAS and counter-regulatory RAS in COVID-19
Although SARS-CoV-2 infection may lead to RAS imbalance, it is still imperative to understand whether all RAS component-related pathways are affected by COVID-19. However, the existing results remain controversial. Ang II was shown to facilitate SARS-CoV-2 infection in human bronchial cells by upregulating ACE2 [328]. Nevertheless, in COVID-19 patients’ plasma, there was no change in the levels of Ang I, Ang II, and Ang (1-7) [329]. Consistently, Rieder et al. also demonstrated that ACE2, Ang II and aldosterone were not altered in SARS-CoV-2-positive patients [224]. Paradoxically, Wang et al. found that the plasma levels of Ang I and Ang (1-7) or the Ang (1-7)/Ang II ratio were elevated during SARS-CoV-2 infection related to the reduced ACE activity at baseline [330]. Many recent clinical trials have confirmed that there is no significant correlation between the application of RAS inhibitors (ACEIs and ARB) and COVID-19 [331]. These data suggest that different RAS peptides may not be involved in SARS-CoV-2 infection, but further studies are needed to elucidate whether other counter-regulatory peptides have beneficial effects on the sequelae of COVID-19.
The application of RAS inhibitors (ACEIs and ARBs), RAS modulators like AT2R agonists (C21), soluble rhACE2, Ang (1-7), and MasR agonists has been known to prevent organ damage via the rebalancing of the overactivated RAS in hypertension [332]. However, as discussed above, in some COVID-19 cases, the immune response to viral infection is augmented due to the upregulation of the ACE-Ang II-AT1R axis, accompanied by ACE2 downregulation. This imbalance of the RAS results in the overproduction of proinflammatory cytokines, leading to cytokine storm syndrome, which is associated with severe cases and death, as well as with multi-organ damage. Moreover, ACE2 expression is also highly correlated with the use of RAS inhibitors. Therefore, it has been focused on the relationship between COVID-19 and RAS counter-regulation, which is summarized in Table 1 .Table 1 Effect of RAS inhibitor treatment on clinical outcome of COVID-19 patients with hypertension
No. Region Participants RAS inhibitors used (% of population) Description & NCT ID Main outcomes PMID
1 Hubei, China 1128 adult inpatients with confirmed COVID-19 and hypertension from 9 hospitals ACEIs (2.75%, 31/1128) /ARBs (14.0%, 158/1128) Not mentioned Reduce risk of all-cause mortality in COVID-19 patients with hypertension 32302265
2 Lombardy, Italy 6272 patients with confirmed COVID-19 ACEIs (23.9%, 1502/6272)/ARBs (22.2%, 1394/6272) ≥1 prescription during 2019 No association between ACEI/ARB treatment and risk of COVID-19 infection 32356627
3 NY, USA Patients with COVID-19 records in the New York University (NYU) Langone Health electronic health record ACEIs (8.32%, 214/2573); ARBs (10.5%, 270/2573); ACEIs/ARBs (18.4%, 473/2573) Within 18 months before COVID-19 diagnosis No association between ACEI/ARB treatment and susceptibility to or severity of COVID-19 infection 32356628
4 Wuhan, China 2877 COVID-19 patients admitted to the Huo Shen Shan Hospital ACEIs/ARBs (6.36%, 183/2877) Not mentioned No association between ACEI/ARB treatment and risk of mortality in COVID-19 patients 32498076
5 Wuhan, China 1178 patients with COVID-19 hospitalized in Wuhan Central Hospital ACEIs/ARBs (31.8%, 115/362) Not mentioned No association between ACEI/ARB treatment and severity or mortality of COVID-19 patients with hypertension 32324209
6 Tarragona area, Spain 34936 hypertensive patients under 50 years of age ACEIs (37.6%, 77/205)/ARBs (16.1%, 33/205) Not mentioned No association between ACEI/ARB treatment and risk of COVID-19 infection in hypertensive patients 32710674
7 Italy 1761 patients aged 18 to 101 years with confirmed COVID-19 ACEIs (21.9%, 348/1591) /ARBs (19.3%, 307/1591) NCT04331574 No contribution to mortality from COVID-19 32564693
8 Kanagawa Prefecture, Japan 151 patients with COVID-19 in 6 hospitals ACEIs (1.99%, 3/151); ARBs (12.6%, 19/151) Not mentioned Reduce poor outcomes in COVID-19 patients with hypertension 32820236
9 4 regions of Italy Patients >50 years hospitalized for COVID-19 ACEIs (21.9%, 171/781) /ARBs (17.0%, 133/781) Not mentioned Reduce risk of mortality in hospitalized COVID-19 patients who previously taken ACEIs/ARBs 33023356
10 A French geriatric unit, France 201 patients hospitalized for COVID-19 ACEIs/ARBs (31.3%, 63/201) ≥1 week before the onset of infection Reduce mortality rate in COVID-19 patients 33138935
11 Shenzhen, China 417 COVID-19 patients admitted to the Shenzhen Third People’s Hospital, ACEIs/ARBs (40.5%, 17/42) >1 year Improve clinical outcomes of COVID-19 patients with hypertension 32228222
12 Tehran, Iran 636 patients with COVID-19 referred to Sina Hospital ARBs (48.0%, 122/254) ≥7 days after hospital admission Not worsen clinical outcomes in hypertensive patients during COVID-19 infection 32920644
13 Spain 545 hypertensive patients hospitalized for COVID-19 in a Spanish hospital ACEI/ARB (30.8%, 168/545) ≥1 month before hospital admission Reduce risk of death in COVID-19 hypertensive patients during hospitalization 32949380
14 Italy 4069 patients with SAPS-CoV-2 infection and hospitalized in 34 clinical centers ACEIs (13.5%, 549/4069); ARBs (13.3%, 541/4069); ACEIs & ARBs (0.393%, 16/4069) NCT04318418 No association between ACEI/ARB usage and severity or mortality in all COVID-19 patients 32992048
15 Wuhan, China 650 COVID-19 patients admitted to the Public Health Treatment Center of Changsha and Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology ACEIs/ARBs (29.4%, 37/126) Not mentioned No contribution to mortality in COVID-19 patients with hypertension 33322988
16 Italy 351 COVID-19 patients in Naples who did not require hospitalization and were treated in an outpatient setting ACEIs/ARBs (26.8%, 94/351) Not mentioned No causal relationship between hypertension and treatment of hypertension in critically ill patients with COVID-19 33375676
17 USA 1449 hospitalized and non‐hospitalized patients from a large comprehensive health care system serving central ACEIs/ARBs (16.0%, 84/525) Within 12 months before COVID-19 test No effect on severity of COVID-19 infection 33220171
18 Brazil 659 patients hospitalized for mild or moderate COVID-19 ACEIs/ARBs (49.3%, 325/659) NCT04364893 No association with the number of days alive and out of the hospital in mild COVID-19 patients 33464336
19 Italy 575 consecutive patients with laboratory-confirmed COVID-19 ACEIs (14.3%, 56/391)/ARBs (12.3%, 48/391) Not mentioned Reduce mortality risk in COVID-19 patients with ACEI not ARB 32980434
20 Italy 2446 charts of Italian patients admitted for certified COVID-19 in 27 hospitals ACEIs (35.4%, 530/1498)/ARBs (29.7%, 445/1498) NCT04331574 No effect on chance of recovery in COVID-19 patients with hypertension or heart failure 33186327
21 USA, Canada, Mexico, Sweden, Peru, Bolivia, and Argentina 152 patients admitted to hospital (20 large referral hospitals) with a clinical presentation consistent with COVID-19 and prescribed ACEI or ARB therapy as an outpatient before the hospital admission ACEIs/ARBs (49.3%, 75/152) NCT04338009 No effect on severity and mortality of COVID-19 33422263
22 France Almost 2 million patients from the French national health data system ACEIs (30.1%, 566023/1882556)/ARBs (50.9%, 958227/1882556) ≥3 months before study conduction Reduce risk of hospitalization and death with COVID-19 patients 33423528
23 Italy 2377 charts of Italian COVID-19 patients in hospital ACEIs/ARB (59.0%, 827/1402) NCT04331574 No association between ACEI/ARB administration and anticoagulant treatment effect of COVID-19 34055928
24 USA Data from the national Veterans Health Administration ACEIs/ARBs (49.9%, 2482/4969) ≥3 months before study conductionNCT04467931 Association with a 15% lower relative risk of COVID-19-related outcomes 33891615
25 Iran 64 patients with COVID-19 Not mentioned IRCT20151113025025N3 No association between ACEI/ARB treatment and risk of COVID-19 infection in hypertensive patients 34265044
26 Minnesota, USA 117 participants included symptomatic outpatients with COVID-19 not already taking ACE-inhibitors or ARBs, enrolled within 7 days of symptom onset Not mentioned NCT04311177 No effect on hospitalization and viralload with COVID-19 patients 34195577
27 Argentina Patients from 18 years of age hospitalized with COVID-19 ARBs (49.4%, 78/158) NCT04355936 Reduce morbidity and mortality in hospitalized COVID-19 patients 34189447
Animal studies have found that ACEI therapy can increase plasma Ang (1-7) levels, decrease plasma Ang II levels, and increase cardiac ACE2 expression, whereas ARBs can increase the plasma levels of both Ang II and Ang (1-7) as well as the cardiac expression and activity of ACE2 [333]. Therefore, as the COVID-19 pandemic is spreading rapidly, concerns about the medical management of hypertension involving RAS inhibition are raised due to the speculation that the use of ACEIs and ARBs may also increase the risk of infection with SARS-CoV-2 [282]. The initial small sample study (113 hypertensive COVID-19 patients) did show that the patients on ACEIs/ARBs therapy had a higher incidence of in-hospital death than those who were not [334]. Although the later cohort study by Mehta et al. demonstrated that there seemed to be a higher risk of hospitalization admission, ICU admission, or mechanical ventilation required in patients with COVID-19 infection taking ACEIs/ARBs, after using overlap propensity score weighting to adjust for underlying confounding factors such as underlying chronic disease conditions, they concluded that there is no association between ACEIs/ARBs use and COVID-19 test positivity [335]. The subsequent large-scale studies have shown that the RAS inhibition does not contribute to SARS-CoV-2 infection and COVID-19 exacerbation, which suggests the application of ACEIs/ARBs appears to be safe in the setting of SARS-CoV-2 infection and should not be discontinued [254], [335], [336], [337], [338]. Moreover, randomized trials, such as BRACE CORONA, REPLACE COVID and ACEI-COVID19, also concluded an absence of effect of chronic RAS blockade on the course of COVID-19, as previously observed in observational studies [318], [339], [340]. Notably, in COVID-19 patients, rather than an increase in the risk of death or serious adverse events, inhibition of classic RAS displayed protective effects, which were confirmed in emerging results. Two retrospective multicenter studies, including 15,504 and 2190 participants hospitalized due to the diagnosed COVID-19, demonstrated that ACEIs/ARBs use was associated with lower in-hospital mortality and improved clinical outcomes [341], [342]. Additional observational studies, as well as meta-analysis, revealed lower incidences of severe disease among the patients using RAS inhibitors, which further supported these findings [321], [343], [344], [345], [346]. This discrepancy could be explained by the presence of a number of confounding factors, including a) patients’ missing information (lifestyle, body mass index, history of smoking, age>80); b) the dose and period of ACEIs/ARBs intake; c) the degree and duration of hypertension; d) adherence to anti-hypertensive medication during the various COVID-19 pandemic periods; and e) combined use of ACEIs/ARBs and other anti-hypertensive medication [347]. Moreover, the mechanism of the beneficial outcomes induced by RAS inhibition in COVID-19 is still largely unknown, partly due to the lack of appropriate animal models, which can reflect hypertension caused by the imbalance of ACE/ACE2, exhibit a similar susceptibility to SARS-CoV-2 and comparable pathology as humans, including fever, pulmonary infection, and antibody response and pneumonia symptoms [348].
Since ACE2 blocks the proinflammatory AT1R-mediated action of Ang II, studies have been conducted to see if patients with COVID-19 infection and severe lung injury can benefit from using rhACE2 [42], [349]. Monteil et al. have shown that soluble rhACE2 markedly reduced the SARS-CoV-2 load by a factor of 1000-5000 and directly neutralized the virus in engineered human organoids [350]. Moreover, the result of phase II clinical trials NCT04335136 showed an inspiring benefit of APN01 (alunacedase alfa, a clinical grade rhACE2) for severe Ill COVID-19 patients due to the infection by various SARS-CoV-2 variants. It led to significant improvement in several specific areas, including the increase in mechanical ventilator-free days, a reduction in viral RNA load, and a reduced plasma Ang II level accompanied by an enhanced Ang (1-7) and Ang (1-5) level [351], [352].
4 Targeting counter-regulatory RAS in hypertension
Over the past decade, there appears to be a shift in the strategies for intervening in RAS activity. The growing evidence on the beneficial effects of counter-regulatory RAS is encouraging researchers to explore the potential of the new therapeutic targets, ACE2 and its relevant signaling pathways, in CVD therapy, rather than to block the classical RAS as has been done with well-known renin inhibitors, ACEIs and ARBs. Based on the knowledge of the core enzyme ACE2 and related three counter-regulation axes, the upregulation of ACE2 and the application of analogs of Ang (1-7)/Ang (1-9)/alamandine or agonists of the corresponding receptors can be a potential therapeutic strategy against hypertension. Figure 3 illustrates the pharmacological treatment for hypertension in the context of the COVID-19 pandemic, which includes first-line antihypertensive medications and counter-regulatory RAS stimulation.Fig. 3 Hypertension pharmacological treatment in the context of the COVID-19 pandemic. SARS-CoV-2 infection-induced cytokine storm, intravascular coagulation, and multi-organ dysfunction are the leading cause of severe COVID-19. The relationship between hypertension and COVID-19 is bidirectional. Numerous observational studies have confirmed that hypertension is an independent risk factor for SARS-CoV-2 infection and a severe COVID-19 outcome. However, the effects of COVID-19 on both pre-existing and newly diagnosed hypertension are still disputable. The safety and efficacy of the first-line BP-lowering drugs, including beta-blockers, thiazide diuretics, RAS blockers (ACEIs and ARBs) and calcium channel blockers (CCB) have been confirmed in COVID-19 patients with hypertension, some of these agents even provide additional benefits for treating COVID-19, but the mechanisms underlying this protective response remain largely unknown. Pharmacological agents/strategies targeting the counter-regulatory RAS to treat hypertension are highlighted in pink rectangle (artificial agents) and green cycle (natural ligands), including a) ACE2 upregulation: all-trans retinoic acid (atRA), xanthone (XNT), diminazene aceturate (DIZE), recombinant ACE2 protein (rACE2), and the immunoglobulin fragment Fc segment infused rACE2 (rACE2-Fc) etc.; b) MasR agonists: Ang (1-7), cAng (1-7), AVE0991, CGEN-856S; c) AT2R agonists: Ang (1-9), CGP42112, Novokinin, Compound 21 (C21) etc.; d) MrgDR agonist: Alamandine, and alamandine-HPβCD. The priming of ACE2 in activity or expression may also provide protection against severe COVID-19. Illustration was created with BioRender.com.
4.1 ACE2 as a potential therapeutic target for hypertension
It is known that several pharmaceutical substances can either activate or inhibit ACE2 activity. Given that ACE2 plays a protective role in the cardiovascular system, seeking out new ACE2 activators is a straightforward route for drug discovery, and boosting ACE2 activity might be a useful therapeutic strategy in the treatment of hypertension. Compounds that can activate ACE2, such as all-trans retinoic acid (atRA), xanthone (XNT), and diminazene aceturate (DIZE, Berenil®, an FDA-approved drug), are employed in current pharmacochemical approaches to boost ACE2 activity [29], [353], [354]. In vivo treatment with atRA, a biologically active metabolite of vitamin A, can increase cardiac and renal expression of ACE2, reduce blood pressure, and attenuate myocardial damage in SHR [29]. XNT is an ACE2 activator discovered by a virtual screening approach. In vitro studies showed that XNT triggered a conformational change of ACE2 to increase its activity in a dose-dependent manner [354]. Both acute and chronic in vivo administration of XNT can induce a decrease in blood pressure, while chronic infusion increases cardiac ACE2 expression and Ang (1-7) production and reverses cardiac and renal fibrosis in SHRs [28], [354]. Another ACE2 activator DIZE decreased the infarct area, attenuated LV remodeling post-MI, and restored the normal balance of RAS in ischemia-induced cardiac injury, whose mechanism is probably related to the increased circulating endothelial progenitor cells, increased engraftment of cardiac progenitor cells, and decreased inflammatory cells in peri-infarct cardiac regions [353], [355]. ACE2 priming triggered by XNT and DIZE improved endothelial function, which is characterized by endothelial-dependent vasorelaxation response in hypertensive and diabetic rodents by attenuation of oxidative stress [35], [356]. However, a few studies questioned the specificity of the known ACE2 activators, as many of the beneficial effects induced by XNT can also be observed in ACE2 knockout animals. Moreover, in a model of Ang II-induced acute hypertension, neither renal nor plasma ACE2 activity was affected by XNT treatment, though it could induce a significant reduction of blood pressure [357]. Recently, by using virtual screening and bioinformatic analysis plus experimental confirmation, we found three FDA-approved drugs, imatinib, methazolamide, and harpagoside, as direct enzymatic activators of ACE2. Unlike previously known ACE2 activators, they remarkably ameliorate COVID-19-induced metabolic complications via elevating ACE2 enzymatic activity and inhibiting viral entry [358].
The application of recombinant ACE2 protein (rACE2) is an alternative direct approach to prime ACE2 in the anti-hypertension investigation. Zhong et al. demonstrated that rhACE2 infusion attenuated Ang II-induced hypertension and pressure-overload-induced myocardial hypertrophy, fibrosis, and diastolic dysfunction without affecting basal systolic blood pressure in control mice [359]. Likewise, sustained elevations in serum ACE2 activity followed by attenuated blood pressure can be accomplished with murine rACE2 administration in Ang II-induced mice [40], [360]. In SHRs, diabetic Akita and db/db mice, rhACE2 or adenovirus-mediated ACE2 overexpression lowered blood pressure in association with reduced plasma Ang II levels and increased Ang (1-7) [35], [38], [41]. Moreover, rACE2 also produces a therapeutic effect on pulmonary injury and diabetic nephropathy [41], [361]. Administration of rhACE2 was well tolerated by healthy and acute respiratory distress syndrome human subjects [42], [349], and the safety and efficacy were confirmed by the non-detectable antibodies to rhACE2 at different time points after the last dose of rhACE2, a dose-dependent increase in plasma ACE2 and no change in blood pressure or heart rate [42]. The clinical trial NCT01597635 results show a single infusion of rhACE2 (GSK2586881, 0.2 or 0.4 mg·kg-1 intravenously) was well tolerated with significant improvement in cardiac output and pulmonary vascular resistance, associated with improved pulmonary hemodynamics and reduced markers of oxidant and inflammatory mediators in a small number (N = 5) of patients with pulmonary arterial hypertension (PAH) [362]. However, in a recent Phase IIa study (NCT03177603), no consistent or sustained effect on acute cardiopulmonary hemodynamics was observed in participants with PAH receiving background PAH therapy (N = 23). This discrepancy suggests that additional clinical investigation is required to determine the efficacy of rACE2.
The therapeutic potential of chronic use of rACE2is is still restricted partially due to the short plasma half-life of the recombinant protein. Thus, a chimeric fusion protein of rACE2 and the immunoglobulin fragment Fc segment (rACE2-Fc) was constructed to increase its plasma stability but retained full peptidase activity and exhibited a greatly extended plasma half-life in mice [363]. The weekly injections of rACE2-Fc effectively lowered plasma Ang II and blood pressure, associated with meliorated albuminuria and reduced kidney and cardiac fibrosis [363]. Recently, in a stringent K18-hACE2 mouse model challenged with various SARS-CoV-2 variants, rACE2-Fc counteracts murine lethal SARS-CoV-2 infection through direct neutralization and Fc-effector activities with a broadly effective therapeutics capability [364].
The inhibition of ACE2 by pharmacological inhibitors such as MLN-4760 and DX600 aggravated cardiopathy in many animal models. The specific and potent ACE2 inhibitor MLN-4760 (IC50 = 0.44 nM) worsens the glomerular injury in streptozotocin-induced diabetic mice in vivo, associated with increased ACE expression [365], [366]. In (mRen2)27 transgenic hypertensive rats, chronic treatment with MLN-4760 exacerbated cardiac hypertrophy and fibrosis [367]. Of note, MLN-4760 increased local cardiac Ang II accumulation while having no effect on plasma Ang II or Ang (1-7) levels [367]. The other ACE2 inhibitor, DX600, a peptide inhibitor with high affinity (Kd = 10.8 nM) and efficacy (IC50 = 10.1 nM), is regarded as a partial negative allosteric modulator (NAM) of ACE2 due to its mixed inhibitory characteristics [368]. In vitro, DX600 caused a reciprocal increase in autocrine Ang II and a corresponding decrease in Ang (1-7) in cell culture media [369], and it also increased thrombus formation in SHRs [370], [371]. Importantly, ACE2 inhibitors can also increase the risk of SARS-CoV-2 infection and exacerbate COVID-19 outcomes since the conformational change of ACE2 triggered by MLN-4760 increased the affinity of the viral S protein’s binding to [372].
4.2 Ang (1-7) and MasR agonists
The crucial function of Ang (1-7) in hypertension is to regulate renal blood flow by inducing vasodilatation and counterbalancing the Ang II-induced vasoconstriction. However, both experimental and preclinical studies on the anti-hypertensive effect of Ang (1- 7) yielded heterogeneous results, which are related to the drug administration procedure and hypertensive animal models [373], [374].
Numerous studies have demonstrated that acute systemic infusion of Ang (1-7) has no effect on blood pressure in either normotensive rats or hypertensive animals, including SHRs and renal hypertensive rats [80], [110], [375]. Intriguingly, in some of the models, Ang (1-7) synergistically enhanced the hypotensive effect of bradykinin [375], [376]. In Ang II-induced hypertensive mice, Ang (1-7) had no effect on elevated blood pressure or on the restoration of blood pressure after cessation of Ang II infusion [40]. However, in Dahl salt-sensitive rats, a decreased MAP and an increased release of PGI2 and nitric oxide were seen after Ang (1-7) infusion [377], [378]. Chronic administration of Ang (1-7) was associated with transient or sustained reductions in blood pressure in SHRs and Dahl salt-sensitive rats but not in DOCA salt-induced hypertensive rats [378], [379], [380]. The effects of Ang (1-7) on blood pressure in 2K1C and subtotal-nephrectomy-induced hypertensive rats are inconsistent across different studies [381], [382], [383]. Moreover, paradoxical results were obtained in the hypertensive rats administrated with Ang (1-7) to the central nervous system (CNS), which is probably due to the differences in injection sites, administration concentrations, and hypertensive models [83], [110], [112], [384], [385], [386].
The clinical trials with Ang (1-7) have also produced conflicting results. Kono et al. first reported that supraphysiologic doses of intravenous Ang (1-7) administration produced a weak pressor response and renin-suppressing actions in healthy men [387]. Nevertheless, in the other study, using the same delivery route, a 3-h infusion of Ang (1-7) (3 pmol/kg/min) did not affect blood pressure or heart rate in healthy people [388]. Intrabrachial Ang (1-7) caused vasodilation in the forearm circulation of normotensive subjects and patients with essential hypertension through a NO-independent pathway [389]. It also antagonized Ang II-induced vasoconstriction and potentiated bradykinin-mediated vasodilation in healthy male subjects [390], [391]. Interestingly, intrabrachial Ang (1-7) infusion not only improved insulin-stimulated endothelium-dependent vasodilation but also blunted endothelin-1-dependent vasoconstriction in obese subjects [392]. However, there are two studies showing no significant change in the forearm blood flow in healthy and hypertensive subjects [393], [394]. Moreover, intrarenal Ang (1-7) infusion showed an improved renal blood flow as well as glomerular filtration in hypertensive subjects, but those benefits could be attenuated by renal artery stenosis, low sodium intake, and Ang II co-infusion [395], [396], [397]. The controversies among those reported preclinical findings call for more clinical investigations to better understand the local and systemic hemodynamic effects of Ang (1-7) in human. There are several ongoing (pre)clinical trials examining the cardiovascular effects of Ang (1-7) pathway activation as shown in Table 2 .Table 2 Pharmacological agents/strategies to treat hypertension targeting the counter regulatory RAS
Pharmacological approach Types In vivo model Used Clinical Status Effects References
atRA ACE2 agonist SHRs N/A Increase ACE2 protein; reduce blood pressure; attenuate myocardial damage 29
XNT ACE2 agonist SHRs; db/db mice; streptozotocin-induced diabetic rats N/A Increase ACE2 expression/activity; reduce blood pressure; increase Ang (1-7) production; reverse cardiac and renal fibrosis; promote vasorelaxation response by attenuation of oxidative stress 28, 35, 354, 356
DIZE ACE2 agonist db/db mice; MI rats; ischemic stroke rats; SHRs; streptozotocin-induced diabetic rats N/A Decrease the infarct area; attenuate LV remodeling post-MI; restore the normal balance of RAS; promote vasorelaxation response by attenuation of oxidative stress 35, 353, 355, 356
imatinib ACE2 agonist high-fat-diet-induced insulin-resistant mice; hACE2 transgenic mice Phase II NCT04416750 Enhance ACE2 enzyme activity; reduce Ang II/Ang (1–7) ratio; exhibit anti-inflammatory effects; improve aortic glucose and lipid metabolism; ameliorate COVID-19-induced metabolic complications; inhibit spike protein binding to ACE2 358
methazolamide ACE2 agonist high-fat-diet-induced insulin-resistant mice; hACE2 transgenic mice N/A Enhance ACE2 enzyme activity; reduce Ang II/Ang (1–7) ratio; exhibit anti-inflammatory effects; improve aortic glucose and lipid metabolism; ameliorate COVID-19-induced metabolic complications; inhibit spike protein binding to ACE2 358
rACE2 ACE2 protein Ang II infused mice; K18-hACE2 mice; healthy participants Phase II NCT00886353 Attenuate hypertension, myocardial hypertrophy, fibrosis, and diastolic dysfunction; reduce plasma Ang II levels and increase plasma Ang (1-7) levels; sustain elevations in serum ACE2 activity to attenuate blood pressure; improve cardiac output and pulmonary vascular resistance; neutralize SARS-CoV-2 infection 40, 231, 359, 360
rACE2-Fc ACE2 protein Ang II infused mice; K18-hACE2 mice N/A Improve the pharmacokinetic properties of rACE2; reduce plasma Ang II levels and blood pressure; reduce cardiac fibrosis; counter lethal SARS-CoV-2 infection 363, 364
MLN-4760 ACE2 inhibitor streptozotocin-induced diabetic mice N/A Exacerbate cardiac hypertrophy and fibrosis; increase ACE expression; increase local cardiac Ang II accumulation; increase the risk of SARS-CoV-2 infection; exacerbate COVID-19 outcomes 365, 366, 372
DX600 ACE2 inhibitor SHRs N/A Increase autocrine Ang II and decrease Ang (1-7); increase thrombus formation 369, 370, 371
Ang (1-7) Ang (1-7) analog renal hypertensive rats; SHRs; anesthetized rats; Dahl salt-sensitive rats; obese patients; hypertensive patients; Phase INCT05189015, NCT03001271 Induce vasodilation and counterbalance the vasoconstriction induced by Ang II; enhance the hypotensive effect of bradykinin; decrease mean arterial pressure; increase release of prostacyclin and nitric oxide; cause vasodilation in the forearm circulation of normotensive subjects and patients with essential hypertension; blunt vasoconstrictor tone; improve renal blood flow and glomerular filtration. 375, 376, 377, 378, 389, 390, 391, 392, 395, 396, 397
cAng (1-7) Ang (1-7) analog infarcted rats; isoproterenol-treated rats; MI rats N/A Exhibit vasodilatory effects; improve cardiac remodeling and endothelial function 408, 409
AVE0991 MasR agonist streptozotocin-treated rats; infarcted male Wistar rats; Ang II induced myocardial hypertrophy rats; ApoE-/- mice N/A Mimic the effects of Ang (1-7); restore vasodilatation; attenuate heart failure; diminish pathological cardiac remodeling; improve baroreflex sensitivity; prevent Ang II-induced myocardial hypertrophy; exhibit anti-atherosclerotic and anti-inflammatory effects 400, 401, 402, 403, 404, 405
CGEN-856S MasR agonist SHRs N/A Decrease mean arterial pressure; elicit vasodilation; exhibit anti-arrhythmogenic effects 410
A-779 MasR inhibitor Mas-deficient mice N/A Attenuate Ang (1-7) depressor response in hypertension 84
D-Pro Mas/MrgD antagonist Mas-deficient micecirrhosis and portalhypertension rats N/A Block vasorelaxation effects produced by Ang (1–7); compete for the binding of Ang-(1–7) to the cortical supramedullary region; Improve portal hypertension 84, 406, 437
Ang (1-9) Ang (1-9) analog Ang II infusion rats; MI rats; C57BL/6 mice with permanent left anterior descending coronary artery ligation; CKD II-III patients with hypertension NCT01832558 Reduce Ang II levels and increase Ang (1-7) levels; activate AT2R to trigger urinary natriuretic response and NO production; reduce blood pressure; reduce cardiomyocyte hypertrophy; attenuate inflammation, cardiac hypertrophy, and fibrosis; lessen myocardial injury 123, 124, 125, 126, 414
CGP42112 AT2R agonist obese Zucker rats; conscious SHRs N/A Induce a depressor effect together with generalized vasodilatation; reduce blood pressure by increasing urinary sodium excretion 418, 419, 420, 421
C21 AT2R agonist SHRs; AT2R knockout mice; Ang II infused rats; AT2R-eGFP mice and heterozygous transgenic eGFP-AVP Wistar rats; healthy participants Phase I NCT05277922 Reduce blood pressure by enhancing natriuresis, increasing vasodilation, and inhibiting vasopressin release 421, 425, 426, 427, 428
Novokinin AT2R agonist SHRs; L-NAME plus salt-induced hypertensive rats N/A Exhibit anti-hypertensive effects 429, 430, 431
β-Ile(5)-Ang II AT2R agonist conscious SHRs N/A Reduce blood pressure with evoked vasorelaxation by selectively binding to AT2R 432
β-Tyr(4)-Ang II AT2R agonist SHRs N/A Reduce blood pressure with evoked vasorelaxation by selectively binding to AT2R 432
alamandine alamandine analog SHRs; LPS-induced myocardial dysfunction mice N/A Reduce the long-term blood pressure; attenuate hypertension; alleviate cardiac hypertrophy; improve left ventricular function 152, 164, 165
alamandine-HPβCD alamandine analog SHRs; mice with transverse aortic constriction N/A Exhibit long-term anti-hypertensive effects; attenuate aorta remodeling 152, 434
Abbreviations: atRA: all-trans retinoic acid; XNT: xanthone; DIZE: diminazene aceturate; rACE2: recombinant ACE2 protein; rACE2-Fc: a chimeric fusion protein of rACE2 and the immunoglobulin fragment Fc segment; D-Pro:D-Pro7-Ang (1-7); C21: compound 21; alamandine-HPβCD: alamandine/2-hydroxypropyl-β-cyclodextrin; cAng (1-7): peptidase-resistant thioether-bridged Ang (1-7); SHRs: spontaneously hypertensive rats; MI rats: myocardial infarction rats; ApoE-/- mice: apolipoprotein E-/- mice; CKD: chronic kidney disease patients; L-NAME: L-NG-Nitro arginine methyl ester.
Ang (1-7) has several unfavorable pharmacokinetic properties, particularly a very short half-life (∼10 seconds) due to rapid cleavage by peptidases [398], [399]. Therefore, biochemical modifications to this heptapeptide were developed to increase its stability and affinity for its receptors. A few MasR agonist and Ang (1-7) formulations have been tested in animals and in preclinical research. The first MasR agonist, AVE0991, a nonpeptide compound that can be used orally, was initially synthesized in 2002 [400]. AVE0991 efficiently mimics the effects of Ang (1-7) on the endothelium, causing significant kinin-mediated activation of eNOS and higher NO release, which subsequently restore vasodilatation in various vascular beds of hypertensive and diabetic animals [401], [402]. AVE0991 can also moderate pathological cardiac remodeling and improve baroreflex sensitivity in renovascular hypertensive rats [402], attenuate MI-induced heart failure [403], and prevent Ang II-induced myocardial hypertrophy [404]. It also inhibits monocyte/macrophage differentiation and monocyte transendothelial migration during early stages of atherosclerosis in ApoE-/- mice, displaying anti-atherosclerotic and anti-inflammatory properties [405]. In addition, the results from MasR-deficient mice and the blockade of AVE 0991 with Ang (1-7) antagonists (A-779 and D-Pro7-Ang (1-7) (D-Pro) suggest that its actions are mediated at least in part through MasR [84], [406]. To date, the clinical trial of AVE0991 has not been initiated yet [407]. Meanwhile, some other metabolically stable Ang (1-7) analogs, like peptidase-resistant thioether-bridged Ang (1-7) called cAng (1-7), have also been shown to exert vasodilator effects in rat aortic rings and improve cardiac remodeling and endothelial function after MI [408], [409]. More recently, another novel nonpeptide MasR agonist, CGEN-856S, was discovered by a computational drug discovery platform [410]. This specific MasR agonist, binding neither to AT1R nor to AT2R, is shown to produce a shallow dose-dependent decrease in MAP of conscious SHRs and to elicit vasodilation in aortic rings and anti-arrhythmogenic effects in the isolated rat heart [410].
4.3 Ang (1-9) and AT2R agonists
Ang (1-9) produces potent protective effects in several cardiopathy animal models, but the dosage of the Ang (1-9) used in animal studies is critical to the anti-hypertensive effect. The administration of Ang (1-9) via osmotic micropump at doses of 600 ng/kg/min or higher reduces blood pressure in SHRs [145], and similar effects were also observed in rodents treated with DOCA salt, Ang II-infusion, and in the Goldblatt rats with renal artery clip [124], [150]. By contrast, a low dose (100 ng/kg/min) of Ang (1-9) was unable to achieve a hypotensive effect in SHRs [411].
However, the pharmacological application of Ang (1-9) as an anti-hypertensive agent is still limited due to its unstable pharmacokinetic properties as Ang (1-7). The actual plasma half-life for Ang (1-9) is still unknown. Likewise, the results obtained from animals and preclinical studies are also specious. However, the Ang (1-9)-AT2R axis remains a potential therapeutic target for the treatment of hypertension if the current obstacles can be overcome by focusing on the local synthesis of Ang (1-9), enhancing its stability, or synthesizing small-molecule AT2R agonists [412].
First, engineered adenoviral Ang (1-9) vector transduction boosted local Ang (1-9) levels, reduced cardiomyocyte hypertrophy in vitro, and lessened myocardial injury after MI in vivo [413], [414]. However, it has not been demonstrated that this approach reduces blood pressure in hypertensive models yet. Second, the nanoparticle technique is an ideal method for stabilizing Ang (1-9) since it preserves the biological activity of Ang (1-9) after modification with Eudragit® E/alginate or gold nanoparticles [415], [416], [417], but this strategy has not yet been evaluated in vivo. Third, studies on various animal models suggest that synthetic AT2R agonists, including that CGP42112, C21, Novokinin, and β-Ile(5)-Ang II, are functionally protective against hypertension. CGP42112 is the first available AT2R agonist that functions both in vitro and in vivo [418], [419]. In obese Zucker rats, 2-week subcutaneous osmotic minipump delivery of CGP42112 caused a decreased blood pressure associated with increased urinary sodium excretion [420]. Similarly, intravenous infusion of CGP42112 induces a marked depressor effect together with generalized vasodilatation that was abolished by the co-infusion of the AT2R antagonist PD123319 [421]. C21, a non-peptide small molecule agonist (Vicore Pharma, Gothenburg, Sweden), is another synthetic AT2R agonist that has been studied in both hypertensive animals such as the obese Zucker rat, DOCA salt-induced hypertension rats and SHRs [420], [422], [423]. The mechanism of C21-mediated blood pressure reduction includes enhancing natriuresis, increasing vasodilation, and inhibiting vasopressin release [424], [425], [426], [427], [428]. Additionally, there are a few clinical trials ongoing to evaluate vasodilatation in humans. The short peptide Nnovokinin showed anti-hypertensive effects in SHR and L-NAME (NOS inhibitor) plus salt-induced hypertensive rats [429], [430], [431] . By selectively binding to AT2R, the Ang II derivatives β-Tyr(4)-Ang II and β-Ile(5)-Ang II triggered a reduced blood pressure in conscious SHRs with evoked vasorelaxation [432].
4.4 Alamandine and MrgD agonists
As a new member of the angiotensin family, alamandine has been investigated in a few hypertensive animal models, including SHRs, 2K1C rats, Dahl salt-sensitive rats, and Ang II infusion mice [152], [162], [164], [165], [172], [433]. Alamandine was firstly found to lower long-term blood pressure by a single dose in SHRs [152]. Moreover, the administration of alamandine via oral and subcutaneous injection attenuates hypertension, alleviates cardiac hypertrophy, and improves LV function in SHRs [164], [165]. In renal vascular hypertensive rats, alamandine exhibits a biphasic hemodynamic effect after infusion into different sites of the brain. Microinjection of alamandine at picomole level into the caudal ventrolateral medulla caused a hypotensive effect in 2K1C hypertensive rats [162]. However, microinjection of alamandine into the hypothalamic PVN increased MAP and renal sympathetic nerve activity (RSNA) in both WKY and SHR rats but to a greater extent in SHRs [163]. Interestingly, oral treatment of an inclusion compound of alamandine/2-hydroxypropyl-β-cyclodextrin (alamandine-HPβCD) produced a long-term anti-hypertensive effect in SHRs and attenuated aorta remodeling in the transverse aortic constriction mouse model [152], [434].
Cryo-EM has recently revealed the structural insight into the activation mechanism of MrgDR coupled with Gi-protein after β-alanine stimulation [435], but the conformational change of MrgD coupled Gs triggered by alamandine is still unknown. Alamandine competes with β-alanine for binding to MrgDR in vivo, suggesting that they share a similar binding site; however, MrgDR ligands other than alamandine failed to elicit any vasoactive response and even competitively inhibit alamandine-induced vasodilation [152], which indicates that the particular dynamic properties of the receptor-ligand interactions are critical for the downstream signal cascade. Recently, the alamandine glycoside analogs were synthesized and evaluated for their ability to antagonize the MrgDR to produce antinociceptive effects and neuropathic pain modulation in vitro [436]. The blockade of MrgDR by D-Pro improved portal hypertension in cirrhotic rats, indicating the major role of MrgDR in the development of cirrhotic portal hypertension [437]. However, due to the absence of a specific structure to explicate the activation process in the cardiovascular system, research on the activation of MrgDR by alamandine and its mimetics to combat hypertension is currently limited.
5 Conclusion
The current evidence supporting the cardiometabolic protective role of the counter-regulatory RAS is encouraging but incomplete, particularly in this COVID-19 era when the dual properties of the core enzyme ACE2 are well recognized. Targeting the counter-regulatory pathways with agonists or stimulators to rescue functional ACE2 and effector peptides may effectively promote the function of this regulatory arm and be a promising therapeutic strategy for the treatment of hypertension and COVID-19. Anti-hypertensive, antiviral, and anti-inflammatory treatments for hypertension and COVID-19 could be personalized based on the commonalities and associations between these two diseases. However, better phenotypic analysis of animal studies and more well-designed interventional trials are needed to understand the Yin and Yang of this complex system in blood pressure regulation as well as in viral infection.
CRediT authorship contribution statement
Hongyin Chen: Investigation, Writing – original draft, Visualization. Jiangyun Peng: Investigation, Writing – original draft. Tengyao Wang: Investigation, Writing – original draft. Jielu Wen: Investigation, Writing – original draft. Sifan Chen: Investigation, Supervision, Funding acquisition, Writing – review & editing. Yu Huang: Supervision, Funding acquisition, Writing – review & editing. Yang Zhang: Conceptualization, Supervision, Funding acquisition, Writing – original draft, 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.
Data availability
No data was used for the research described in the article.
Acknowledgment
This work was supported by the National Natural Science Foundation of China (82000462) (Y.Z.), Research Grants Council of Hong Kong (SRFS2021-4S04) (Y.H.), and Guangdong Basic and Applied Basic Research Foundation (2021B1515020005) (S.C.).
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| 36481346 | PMC9721294 | NO-CC CODE | 2022-12-07 23:19:07 | no | Biochem Pharmacol. 2023 Feb 5; 208:115370 | utf-8 | Biochem Pharmacol | 2,022 | 10.1016/j.bcp.2022.115370 | oa_other |
==== Front
J Am Coll Cardiol
J Am Coll Cardiol
Journal of the American College of Cardiology
0735-1097
1558-3597
by the American College of Cardiology Foundation. Published by Elsevier.
S0735-1097(22)07107-8
10.1016/j.jacc.2022.09.049
Original Investigation
Prognosis of Myocarditis Developing After mRNA COVID-19 Vaccination Compared With Viral Myocarditis
Lai Francisco Tsz Tsun PhD ab∗
Chan Edward Wai Wa MPH ab∗
Huang Lei MSc ab
Cheung Ching Lung PhD ab
Chui Celine Sze Ling PhD bcd
Li Xue PhD be
Wan Eric Yuk Fai PhD abf
Wong Carlos King Ho PhD abf
Chan Esther Wai Yin PhD ab
Yiu Kai Hang MD, PhD gh∗∗
Wong Ian Chi Kei PhD abij∗
a Centre for Safe Medication Practice and Research, Department of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
b Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong Science and Technology Park, Hong Kong Special Administrative Region, China
c School of Nursing, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
d School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
e Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
f Department of Family Medicine and Primary Care, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, China
g Cardiology Division, Department of Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen City, China
h Cardiology Division, Department of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong Special Administrative Region, China
i School of Pharmacy, University College London, London, United Kingdom
j Aston School of Pharmacy, Aston University, Birmingham, United Kingdom
∗ Address for correspondence: Dr Ian Chi Wong, 2/F Laboratory Block, 21 Sassoon Road, Pok Fu Lam, Hong Kong Special Administrative Region, China.
∗∗ Dr Kai Hang Yiu, Cardiology Division, Department of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong Special Administrative Region, China.
∗ Dr Lai and Mr Chan contributed equally as joint first authors.
5 12 2022
13 12 2022
5 12 2022
80 24 22552265
13 6 2022
23 9 2022
30 9 2022
© 2022 by the American College of Cardiology Foundation. Published by Elsevier.
2022
American College of Cardiology Foundation
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
Association between messenger RNA (mRNA) COVID-19 vaccines and myocarditis has aroused public concern over vaccine safety.
Objectives
The goal of this study was to compare the prognosis of this condition with viral infection–related myocarditis over 180 days.
Methods
A territory-wide electronic public health care database in Hong Kong linked with population-based vaccination records was used to conduct a retrospective cohort study. Since the roll-out of BNT162b2 (Pfizer-BioNTech), patients aged ≥12 years hospitalized with myocarditis within 28 days after BNT162b2 vaccination were compared against viral infection–related myocarditis recorded before the pandemic (2000-2019), over a 180-day follow-up period (starting from diagnosis of myocarditis). All-cause mortality, heart failure, dilated cardiomyopathy, heart transplant, and postdischarge health care utilization were examined with Cox proportional hazards models.
Results
A total of 866 patients were included for analysis. Over the follow-up period, 1 death (1.0%) of 104 patients with postvaccination myocarditis and 84 deaths (11.0%) of 762 patients with viral infection–related myocarditis were identified. One case (1.0%) of dilated cardiomyopathy and 2 cases (1.9%) of heart failure were identified in the postvaccination group, compared with 28 (3.7%) and 93 (12.2%) in the viral infection–related myocarditis group, respectively. Adjusted analysis showed that the postvaccination myocarditis group had a 92% lower mortality risk (adjusted HR: 0.08; 95% CI: 0.01-0.57). No significant differences in other prognostic outcomes were seen.
Conclusions
This study found a significantly lower rate of mortality among individuals with myocarditis after mRNA vaccination compared with those with viral infection–related myocarditis. Prognosis of this iatrogenic condition may be less severe than naturally acquired viral infection–related myocarditis.
Central Illustration
Key Words
adverse events of special interest
immunization
myopericarditis
perimyocarditis
SARS-CoV-2
Abbreviations and Acronyms
A&E, accident and emergency department
CDARS, clinical data analysis and reporting system
cMRI, cardiac magnetic resonance imaging
EHR, electronic health record
ICD-9-CM, International Classification of Diseases-9th Revision-Clinical Modification
ICU, intensive care unit
mRNA, messenger ribonucleic acid
==== Body
pmcWith accruing evidence showing an association of messenger RNA (mRNA) COVID-19 vaccines with myocarditis worldwide,1, 2, 3, 4, 5, 6 myocarditis is increasingly acknowledged as one of the rare potential adverse events that follow COVID-19 vaccination.7 An Israeli retrospective cohort study of 0.9 million individuals found a 3-fold elevated risk of myocarditis associated with the use of BNT162b2, an mRNA vaccine developed by Pfizer and BioNTech.1 Subsequent research studies from the United Kingdom,5 Nordic countries,2 , 8 and Hong Kong3 all suggest similar associations.
Despite this evident relationship, it has been observed that the demographic characteristics and prognosis of patients presenting with this condition are noticeably different from previously seen cases of myocarditis arising from pathomechanisms other than mRNA vaccines.9 , 10 In general, there are more male and younger patients, particularly adolescents, identified with myocarditis than those of other demographic characteristics.11 The prognosis of postvaccination myocarditis has been reported to be mostly mild and self-limiting compared with previously seen cases of otherwise acquired myocarditis, such as those arising from an influenza infection.11 , 12 Although the younger age of the patients is one of the obvious reasons for the better prognosis, there may also be a distinct etiology underlying this post–mRNA vaccination iatrogenic acute condition.
Amid the pandemic, nonpharmaceutical infection control strategies such as social/physical distancing measures have significantly suppressed the circulation of other pathogens that may induce myocarditis such as influenza.13 For adolescents and children, in particular, the incidence of otherwise acquired myocarditis, mainly from a viral infection, has essentially dropped to near zero in many developed countries with strict measures.13 , 14 Hence there are few data to facilitate a fair comparison of the prognosis between myocarditis cases following mRNA vaccination and those related to a viral infection. Using a territory-wide electronic public health care database in Hong Kong, the aim of the current study was to examine the potential differences in a range of prognostic outcomes between patients with myocarditis following the use of BNT162b2 and a historical cohort of patients with viral infection–related myocarditis.
Methods
Study design and data source
We adopted a retrospective cohort study design following the statement on Strengthening the Reporting of Observational Studies in Epidemiology.15 Routine health care records provided by the Hospital Authority of Hong Kong were linked with population-based vaccination records at the Department of Health to identify myocarditis cases following mRNA vaccination. Matching between inpatient and vaccination records was based on the pseudo-identification numbers provided by the Hospital Authority and the Department of Health.
The Hospital Authority serves as the sole provider of public inpatient services and a major provider of outpatient services in Hong Kong, with a comprehensive electronic health record (EHR) system in the facilitation of clinical management. Each Hong Kong resident has a unique Hong Kong Identity Number that allows the Hospital Authority to create a unique EHR for each patient to link attendances to all health care facilities. Data from the Hospital Authority’s EHR are de-identified and transferred daily to the Clinical Data Analysis and Reporting System (CDARS), a nonclaims-based clinical management database with the EHRs of all patients who used the Hospital Authority’s health care services. The EHRs in CDARS include demographic characteristics, diagnoses, medication dispensing records, outpatient and primary care clinics, accident and emergency department (A&E) attendances, laboratory tests, and hospitalization details. The database has frequently been used for high-quality pharmacovigilance studies to evaluate the safety of medicines and vaccines at the population level.3 , 16, 17, 18, 19, 20, 21, 22 A previous study showed high coding accuracy for major cardiovascular diagnosis in CDARS, with positive predictive values estimated at >85% based on the International Classification of Diseases-9th Revision-Clinical Modification (ICD-9-CM).23
Ethical considerations
This study was approved by the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West (UW 21-149 and UW 21-138) and the Department of Health Ethics Committee (LM 21/2021).
Cohort identification and follow-up
Starting from March 6, 2021, the day on which BNT162b2 was first rolled out in Hong Kong for emergency use, all patients diagnosed with myocarditis (ICD-9-CM: 422.x, 429.0) aged ≥12 years within 28 days after receiving BNT162b2 (first, second, or booster doses) were examined for inclusion in the BNT162b2-related myocarditis cohort (up to March 31, 2022). All other patients aged ≥12 years with a myocarditis diagnosis from January 1, 2000, to December 31, 2019, were examined for inclusion in the viral infection–related myocarditis cohort for comparison. The adopted ICD-9-CM codes represent conditions most typically induced by a viral infection in the Hospital Authority setting. Those patients with a COVID-19 infection indicated by a positive polymerase chain reaction test result within the current episode were removed. Cases without elevated troponin levels during indexed hospitalization were excluded; Supplemental Table 1 provides details of troponin reference.3 Patients with a history of any examined disease outcome before the respective index date were also excluded. Concerning multiple episodes within individuals, only the most recent episode for each individual was selected for inclusion. Stratified according to age group (12-17, 18-59, and ≥60 years), patients were followed up from the date of the myocarditis diagnosis (index date) until the occurrence of the outcome of interest, death (for outcomes other than all-cause mortality), 180 days of follow-up, or March 31, 2022 (ie, the end of data availability), whichever came first. For A&E attendance, intensive care unit (ICU) admission, and subsequent hospitalization outcomes, the date of discharge, instead of the date of myocarditis diagnosis, was used as the index date, with those who died within the current episode excluded. This approach was to ensure everyone included in the cohort belonged with the population at risk at the commencement of observation.
Prognostic outcomes
The incidence of a variety of prognostic outcomes was investigated,24 including all-cause mortality, heart failure (ICD-9-CM: 398.91, 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428; International Classification of Primary Care, 2nd edition: K77), dilated cardiomyopathy (ICD-9-CM: 425.4),25 heart transplant (ICD-9-CM: 37.51), ICU admission, A&E attendance, and subsequent hospitalization. All ranks of diagnosis (ie, regardless of primary or secondary diagnoses) were considered for ICD-9-CM–operationalized outcomes.
Covariates
These covariates were included for multivariable adjustment: sex, age, Charlson Comorbidity Index,26 health care utilization in the past year (including the number of outpatient visits, the number of A&E attendances, and the number of hospitalizations), and cardiovascular disease medications prescribed within the past year before the current episode, which were treated as a composite binary variable. These medications included anticoagulants, antiplatelet medications, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, beta-blockers, calcium-channel blockers, digoxin, diuretics, and statins. Supplemental Table 2 presents the specific medications included.
Statistical analysis
Descriptive statistics, including 180-day cumulative incidence and incidence rate, were generated for a comparison between the 2 types of myocarditis stratified according to age group (12-17, 18-59, and ≥60 years). Upon confirming no violation of the assumption of proportional hazard, multivariable Cox proportional hazards models were used to examine the association of post–mRNA vaccination myocarditis (vs viral infection–related myocarditis) with the aforementioned prognostic outcomes. Adjusted HRs were estimated considering the included covariates as specified earlier. For sensitivity analysis, we confined the BNT162b2-related myocarditis patients to those only diagnosed within 14 days after receiving BNT162b2 and repeated the analysis. We also repeated the analysis with this interval extended to 56 days.
All statistical tests were 2-sided, and P values <0.05 were considered statistically significant. Statistical analysis was conducted using R version 4.0.3 (R Foundation for Statistical Computing). Two investigators (E.W.W.C. and L.H.) conducted the statistical analyses independently for quality assurance.
Results
Up to March 31, 2022, a total of 8,896,843 doses of BNT162b2 were administered in 3,979,103 individuals aged ≥12 years in Hong Kong. There were 119 cases of myocarditis within 28 days after the use of BNT162b2 according to electronic diagnostic codes. From January 1, 2000, to December 31, 2019, there were 1,483 cases of myocarditis. After application of the aforementioned exclusion criteria, our eventual cohort included a total of 866 patients. One hundred four (12.0%) were patients with myocarditis following mRNA vaccination, and 762 (88.0%) were viral infection related. Incidence of myocarditis following mRNA vaccination according to our adopted operational definition was 2.61 (95% CI: 2.14-3.17) per 100,000 vaccinated persons. Details of the cohort selection are described in Figure 1 . The frequencies of each unique myocarditis ICD-9-CM code in each group are tabulated in Supplemental Table 3.Figure 1 Flowchart of Cohort Selection
Arrows indicate the narrowing selection of participants from earlier to later stages. Dark blue boxes represent selected participants or potential participants for selection. Light blue boxes represent excluded individuals. mRNA = messenger RNA.
Cohort characteristics
In the cohort of myocarditis cases following mRNA vaccination, 96 (92.3%) patients had received at least 1 of the 2 priming doses (68 [65.4%] received the second dose), whereas only 8 (7.7%) individuals had received a booster third dose. There were more male patients than female patients in both the post–mRNA vaccination and viral infection–related myocarditis groups except for older adults. Across age groups, a higher proportion of cardiovascular medication use, higher average Charlson Comorbidity Index scores, and higher health care utilization in the past year (including hospitalization, outpatient consultation, and A&E attendances) were observed among patients with viral infection–related myocarditis than among the patients with myocarditis after mRNA vaccination. Notably, among adults aged ≥18 years, more patients with viral myocarditis had been using a statin typically for lowering cholesterol, beta-blocker and angiotensin-converting enzyme inhibitors most often used for abnormal heart rhythm and hypertension, respectively, and antiplatelets for preventing blood clots and strokes. Specifically, among older people, those with a viral myocarditis were more often found using digoxin, typically for atrial fibrillation, and calcium-channel blocker most often used for hypertension. Across all age groups, diuretics used for hypertension were more commonly used by patients with viral myocarditis. More cohort characteristics are presented according to age group in Table 1 .Table 1 Characteristics of Viral Infection–Related Cases and Myocarditis Cases After mRNA Vaccination According to Age Group
Aged 12 to 17 Years Aged 18 to 59 Years Aged ≥60 Years
Viral Infection–Related (n = 105) Post–mRNA Vaccination (n = 47) SMDa Viral Infection–Related (n = 548) Post–mRNA Vaccination (n = 52) SMDa Viral Infection–Related (n = 109) Post–mRNA Vaccination (n = 5) SMDa
Male 79 (75.2) 42 (89.4) 0.14 327 (59.7) 39 (75.0) 0.15 51 (46.8) 1 (20.0) 0.27
Age at diagnosis, y 15.30 ± 1.54 14.79 ± 1.47 0.34 36.36 ± 12.37 31.42 ± 10.40 0.43 70.70 ± 7.79 68.80 ± 5.85 0.28
Statin 0 0 0.00 105 (19.2) 2 (3.8) 0.15 48 (44.0) 0 0.44
Angiotensin-converting enzyme inhibitors 5 (4.8) 0 0.05 156 (28.5) 0 0.29 63 (57.8) 0 0.58
Angiotensin receptor blockers 0 0 0.00 18 (3.3) 1 (1.9) 0.01 11 (10.1) 1 (20.0) 0.10
Digoxin 2 (1.9) 0 0.02 18 (3.3) 0 0.03 11 (10.1) 0 0.10
Diuretics 11 (10.5) 0 0.11 108 (19.7) 1 (1.9) 0.18 57 (52.3) 0 0.52
Anticoagulants 0 0 0.00 18 (3.3) 0 0.03 4 (3.7) 0 0.04
Antiplatelets 6 (5.7) 0 0.06 259 (47.3) 0 0.47 80 (73.4) 0 0.73
Beta-blockers 2 (1.9) 0 0.02 148 (27.0) 2 (3.8) 0.23 54 (49.5) 0 0.50
Calcium-channel blockers 3 (2.9) 0 0.03 46 (8.4) 3 (5.8) 0.03 36 (33.0) 0 0.33
Cardiovascular medicationb 19 (18.1) 0 0.18 364 (66.4) 6 (11.5) 0.55 99 (90.8) 1 (20.0) 0.71
Charlson Comorbidity Index 0.15 ± 0.48 0.00 ± 0.00 0.45 0.25 ± 0.69 0.12 ± 0.43 0.23 0.69 ± 1.14 0.00 ± 0.00 0.85
Hospitalization in the past year 1.05 ± 0.86 0.09 ± 0.35 1.47 1.36 ± 2.20 0.19 ± 1.01 0.69 2.11 ± 2.73 0.20 ± 0.45 0.98
Outpatient visit in the past year 3.03 ± 6.05 0.60 ± 1.19 0.56 2.55 ± 6.18 2.08 ± 4.70 0.09 6.46 ± 6.78 3.00 ± 2.92 0.66
A&E attendance in the past year 1.20 ± 0.92 0.28 ± 0.50 1.24 1.48 ± 1.09 0.38 ± 0.72 1.18 1.88 ± 2.09 1.00 ± 0.71 0.56
Values are n (%) or mean ± SD.
A&E = accident and emergency department; mRNA = messenger RNA.
a Standardized mean difference (SMD) for continues variables; proportion difference for categorical variables.
b Statin, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, digoxin, anticoagulants, antiplatelets, beta-blockers, calcium-channel blockers, and diuretics.
Incidence of prognostic outcomes
Over the follow-up period (tabulated in Supplemental Table 4 for each outcome), 1 death (1.0%) was identified among the patients with myocarditis after mRNA vaccination, and 84 (11.0%) among those with viral infection–related myocarditis died. A zero incidence of heart transplant surgery was seen in both groups. One case (1.0%) of dilated cardiomyopathy and 2 cases (1.9%) of heart failure were identified in the post–mRNA vaccination myocarditis group, compared with 28 (3.7%) and 93 (12.2%) in the viral infection–related myocarditis group, respectively. For health care utilization outcomes, 21 (20.4%), 33 (32.0%), and 1 (1.0%) patient with myocarditis after mRNA vaccination had at least 1 subsequent A&E attendance, hospitalization, and ICU admission respectively, compared with 196 (28.4%), 224 (32.5%), and 6 (0.9%) patients among those with viral infection–related myocarditis.
Overall, a lower incidence of adverse prognostic outcomes (including mortality, heart failure, and dilated cardiomyopathy) was observed among the patients with myocarditis following mRNA vaccination, but the rates of subsequent health care utilization were similar. Similar patterns across age groups were also identified, with the frequencies and incidence estimates according to age group illustrated in Table 2 and Figure 2 .Table 2 Incidence Rates (per 100 Person-Days) and HRs of Prognostic Outcomes Among All Examined Cases
Post–mRNA Vaccination Group Viral Infection–Related Group HR (95% CI)a
No. of Events Incidence Rate No. of Events Incidence Rate Crude Adjustedb
Overall
Mortality 1 (1.0) 0.01 84 (11.0) 0.07 0.08 (0.01-0.61)c 0.08 (0.01-0.57)c
Heart failure 2 (1.9) 0.01 93 (12.2) 0.08 0.15 (0.04-0.60)d 0.34 (0.08-1.45)
Dilated cardiomyopathy 1 (1.0) 0.01 28 (3.7) 0.02 0.26 (0.04-1.94) 0.38 (0.05-3.14)
Heart transplant surgery 0 0.00 0 0.00 – –
Subsequent A&E attendance 21 (20.4) 0.16 196 (28.4) 0.20 0.80 (0.51-1.25) 1.38 (0.84-2.27)
Subsequent ICU admission 1 (1.0) 0.01 6 (0.9) 0.00 1.22 (0.15-10.11) 1.12 (0.11-11.26)
Subsequent hospitalization 33 (32.0) 0.30 224 (32.5) 0.24 1.12 (0.77-1.61) 1.40 (0.92-2.11)
Aged 12-17 y
Mortality 0 0.00 3 (2.9) 0.02 – –
Heart failure 0 0.00 6 (5.7) 0.03 – –
Dilated cardiomyopathy 0 0.00 3 (2.9) 0.02 – –
Heart transplant surgery 0 0.00 0 0.00 – –
Subsequent A&E attendance 8 (17.0) 0.13 25 (24.5) 0.16 0.77 (0.35-1.70) 0.62 (0.23-1.67)
Subsequent ICU admission 0 0.00 0 0.00 – –
Subsequent hospitalization 22 (46.8) 0.54 41 (40.2) 0.34 1.32 (0.79-2.22) 1.25 (0.65-2.41)
Aged 18 to 59 y
Mortality 1 (1.9) 0.01 56 (10.2) 0.06 0.18 (0.02-1.30) 0.13 (0.02-0.98)c
Heart failure 2 (3.8) 0.03 67 (12.2) 0.08 0.30 (0.07-1.22) 0.62 (0.14-2.72)
Dilated cardiomyopathy 1 (1.9) 0.01 22 (4.0) 0.03 0.48 (0.06-3.56) 1.00 (0.11-8.88)
Heart transplant surgery 0 0.00 0 0.00 – –
Subsequent A&E attendance 12 (23.5) 0.19 136 (27.3) 0.19 0.98 (0.54-1.77) 0.59 (0.94-3.47)
Subsequent ICU admission 1 (2.0) 0.01 5 (1.0) 0.01 2.10 (0.25-18.02) 1.91 (0.18-20.32)
Subsequent hospitalization 10 (19.6) 0.16 114 (22.8) 0.21 0.98 (0.54-1.77) 1.12 (0.56-2.26)
Aged ≥60 y
Mortality 0 0.00 25 (22.9) 0.16 – –
Heart failure 0 0.00 20 (18.3) 0.15 – –
Dilated cardiomyopathy 0 0.00 3 (2.8) 0.02 – –
Heart transplant surgery 0 0.00 0 0.00 – –
Subsequent A&E attendance 1 (20.0) 0.24 35 (39.3) 0.30 0.66 (0.09-4.81) 0.51 (0.05-4.81)
Subsequent ICU admission 0 0.00 1 (1.1) 0.01 – –
Subsequent hospitalization 1 (20.0) 0.24 39 (43.8) 0.37 0.49 (0.07-3.60) 0.56 (0.06-5.44)
Values are n (%) unless otherwise indicated.
ICU = intensive care unit; other abbreviations as in Table 1.
a Viral infection–related group as reference group; HRs involving zero events were not estimated.
b Covariates include age, sex, Charlson Comorbidity Index, prior health care utilization, and dichotomized history of cardiovascular disease medication use (composite binary indicator).
c P < 0.05.
d P < 0.01.
Figure 2 Bar Chart Showing the Proportion of the Incidence of Prognostic Outcomes
The left panel shows the frequencies of prognostic outcomes, including dilated cardiomyopathy, heart failure, and mortality, in each of the 2 myocarditis groups; that is, viral infection–related myocarditis vs myocarditis following messenger RNA vaccination. Heart transplant was zero for both myocarditis groups and was omitted. The right panel shows the postdischarge frequencies of service utilization. A&E = accident and emergency department; ICU = intensive care unit.
Cox regressions
Results of the adjusted Cox model (Table 2) showed that the post–mRNA vaccination myocarditis group had a 92% lower mortality risk (adjusted HR: 0.08; 95% CI: 0.01-0.57) compared with the viral infection–related myocarditis group. Analysis conducted specifically among the patients aged 18 to 59 years yielded a similar result (adjusted HR: 0.13; 95% CI: 0.02-0.98), but nonsignificant results were obtained for analyses among those aged 12 to 17 years and those aged ≥60 years. No significant differences in the occurrence of the other prognostic outcomes were identified.
Results of sensitivity analyses (Supplemental Tables 5 and 6) indicated that if myocarditis after mRNA vaccination was confined to those cases occurring within 14 days after receiving BNT162b2, a significant adjusted association with increased hospitalization was observed (adjusted HR: 1.77; 95% CI: 1.13-2.77), but the association with a lower mortality rate became inestimable due to zero mortality recorded. When extending this period to 56 days, the mortality results were highly similar to those of the main analysis (adjusted HR: 0.21; 95% CI: 0.06-0.70).
Discussion
In this territory-wide retrospective cohort study, we found a significantly lower rate of mortality among the myocarditis cases after mRNA vaccination within 180 days of diagnosis, compared with viral infection–related cases in a historical cohort. We observed very low incidence rates (<1 per 10,000 person-days) of mortality, heart failure, and dilated cardiomyopathy following myocarditis after mRNA vaccination, in contrast with incidence rates of 7, 8, and 2 per 10,000 person-days, respectively, among the viral infection–related myocarditis patients. Zero incidences of heart transplant surgery were recorded for both groups. The Central Illustration exemplifies the findings graphically with a cumulative incidence plot of mortality to highlight the significant risk difference between the 2 groups.Central Illustration Myocarditis Prognosis Post-BNT162b2 COVID-19 Vaccination May Be Less Severe Than Viral Infection–Related Myocarditis
This illustration visualizes the design and findings of the study, showing that the prognosis of myocarditis after BNT162b2 COVID-19 vaccination (symbolized by the virus logo) may be less severe than viral infection–related myocarditis (symbolized by the syringe logo). The plot of cumulative incidence with 95% CIs represented by the shaded areas indicates the mortality risk difference between the 2 groups.
Among nearly 4 million individuals having been vaccinated with BNT162b2 in Hong Kong, a total of 104 myocarditis cases (2.61 per 100,000 persons) were captured, constituting a highly comparable estimate with previous research in other populations such as Israel (ie, 2.13; 95% CI: 1.56-2.70).27 In addition, this study’s findings are consistent with the existing literature in 2 other main aspects. First, the demographic features of the cohort of patients with myocarditis following mRNA vaccination were characterized by a higher proportion of male subjects and were generally younger, although we identified many more cases from those aged 18 to 59 years than those aged 12 to 17 years, likely due to the fact that the total population of the latter is much smaller to begin with.7 Second, the prognosis was typically mild among those with myocarditis following mRNA vaccination, with very few deaths and other adverse prognostic outcomes recorded.28 For instance, a study examining myocarditis cases from the United States and Canada reported on 139 adolescents and young adults with vaccine-related myocarditis who typically had abnormal cardiac magnetic resonance imaging (cMRI) findings but rapid resolution of symptoms.29 Nevertheless, this study is the first, to the best of our knowledge, comparing the prognostic outcomes of myocarditis related to mRNA vaccines and viral infection–related ones.
Importantly, a significant and substantially lower rate of mortality (ie, >90%) was found among the patients with myocarditis following mRNA vaccination compared with those with viral infection–related myocarditis. Because this difference has already been adjusted for a certain variety of clinical histories and medication use, as well as demographic information, this finding may suggest a potentially distinct etiology of myocarditis conditions related to mRNA vaccines as distinguished from those otherwise acquired such as viral infection. Considering immunology, it may be sensible to anticipate a milder prognosis because of typically brief exposure to stimuli that triggered the immune responses (ie, vaccine vs viruses). In fact, for viral infection–related myocarditis, there may be a direct invasion of myocardial cells, constituting a direct cardiomyocyte injury caused by the infection.30 A greater extent of damage and inflammation to the heart would then be anticipated, and this may explain our observation of a potentially more severe prognosis in the current study.
We also observed that the underlying health conditions, particularly cardiovascular health status, of the patients with myocarditis following mRNA vaccination were apparently better than the patients with viral infection–related myocarditis, across the age groups. This finding potentially implies that the incidence of myocarditis related to viral infections is typically higher among those with an underlying medical condition. It therefore also reflects the iatrogenicity of the myocarditis cases that were related to mRNA vaccines,22 which could apparently occur in otherwise healthy individuals. The low incidence of mortality, heart failure, and cardiomyopathy may be partly explained by this observation.
There are clear strengths to the current study. First, it is the first study comparing myocarditis prognosis after mRNA COVID-19 vaccination vs viral myocarditis. We made use of comprehensive clinical data from a large sample with a follow-up period of up to 180 days. To the best of our knowledge, no comparable data of a similar quality and quantity have been published. Second, all diagnoses in our database were made and entered by registered hospital doctors based on standard clinical work-up, including cMRI, with previous validation studies suggesting high accuracy of the data.23 , 31 Third, the selection of a historical cohort of viral myocarditis ensured that the cases were neither related to COVID-19 nor mRNA vaccination.
Study limitations
First, we were not able to double confirm the myocarditis diagnoses with clinical investigative data such as cMRI, which are unavailable in our database.32 Second, there was also the possibility of overdiagnosis of myocarditis in mRNA vaccine recipients due to the increased awareness (ie, surveillance bias) about myocarditis as an adverse event after COVID-19 vaccination. Third, the sample may be biased toward more severe cases because the included patients were all hospitalized rather than treated in the community, although typically all suspected myocarditis cases are admitted in the public health care setting in Hong Kong. Fourth, the etiology of myocarditis cases categorized as viral infection related was not all confirmed by laboratory test results although viral infection has been the most common cause of myocarditis.25 Fifth, we did not investigate the impacts of potentially different specific treatments between the 2 cohorts in our study. For instance, the treatment of viral myocarditis would typically involve the use of antiviral agents upon confirmation of a viral infection. Nevertheless, such treatment is highly collinear with the type of myocarditis (ie, viral infection– vs vaccine-related myocarditis) and thus would be very challenging to delineate. Sixth, troponin levels were measured with various kits of different technologies that were independently calibrated. Thus, these levels could not be directly compared between the groups. Seventh, the follow-up time was confined by the data availability of the databases. Further studies should investigate longer term outcomes. Eighth, we were not able to further compare vs patients with myocarditis following a COVID-19 infection due to a much lower infection rate than most other populations in 2020 to 2021. Also, partly due to the less severe infections and complications associated with the Omicron variant, which dominated the large outbreak in 2022, there was a very low incidence of COVID-19–related myocarditis.33 In addition, we did not have a sufficient follow-up period amid the Omicron outbreak starting from January 2022,34 with more than two-thirds of the cases occurring in March. Ninth, because the event rate of some of the outcomes was low, we have only been able to include a limited number of covariates in the model, and cardiovascular medications were operationalized as some vs none. Tenth, because the Hong Kong population is predominantly Chinese, this comparison should be conducted in other populations to test for the generalizability of the results to other ethnicities. Eleventh, the timing of viral infection is less clear than that of mRNA vaccination. It is possible that the viral condition resolved in some patients without hospitalization but grew more severe in the hospitalized sample over this unknown duration, thus potentially causing a selection bias. Last but not least, given the nature of the disease, the rare incidence number of myocarditis, especially among the exposure group, also limited our sample size and small numbers of events, resulting in substantially wide CIs for some estimates.
We observed a very low incidence of mortality, heart failure, heart transplant, dilated cardiomyopathy, and similar postdischarge health care utilization among patients with myocarditis following mRNA vaccines in contrast with those with viral infection–related myocarditis. If this potential difference is substantiated by future data, it will affect the risk–benefit assessment of the use of mRNA vaccines for both society and individuals. Specifically, if proven to be milder in prognosis, less weight should be given to this particular risk, and mRNA vaccination could be more strongly encouraged given a lesser impact of this established side effect of the vaccines. In addition, as the prognosis of myocarditis after vaccination is apparently mild, our data favor the current clinical management approaches for myocarditis to be largely appropriate for most people. Nevertheless, further longer-term follow-up data need to be accumulated to inform the postdischarge care in the medium to long term. Close monitoring of patients with myocarditis after mRNA vaccination, therefore, is still strongly recommended for clinicians worldwide.
Conclusions
This study found a significantly lower rate of mortality among the myocarditis cases following mRNA vaccination compared with those with viral infection–related myocarditis. The incidence of mortality, heart failure, heart transplant, and dilated cardiomyopathy within 180 days of myocarditis diagnosis among the former group is very rare.Perspectives COMPETENCY IN PATIENT CARE AND PROCEDURAL SKILLS: Compared with cases of myocarditis before the COVID-19 pandemic, cases arising within 28 days of mRNA COVID-19 vaccination have been associated with a generally milder course and lower risk of mortality.
TRANSLATIONAL OUTLOOK: Longitudinal data from a larger number of cases with longer follow-up in varied populations are needed to better assess the prognosis associated with myocarditis following mRNA vaccination.
Funding Support and Author Disclosures
This study was funded by a research grant from the Food and Health Bureau, the Government of the Hong Kong Special Administrative Region (reference COVID19F01). Drs Lai and I.C. Wong are partially supported by the Laboratory of Data Discovery for Health (D24H) funded by AIR@InnoHK administered by the Innovation and Technology Commission. Dr Lai has been supported by the RGC Postdoctoral Fellowship under the Hong Kong Research Grants Council; and has received research grants from the Food and Health Bureau of the Government of the Hong Kong Special Administrative Region, outside the submitted work. Dr Chui has received grants from the Food and Health Bureau of the Government of the Hong Kong Special Administrative Region, Hong Kong Research Grant Council, Hong Kong Innovation and Technology Commission, Pfizer, IQVIA, and Amgen; and has received personal fees from PrimeVigilance, outside the submitted work. Dr Li has received research grants from the Food and Health Bureau of the Government of the Hong Kong Special Administrative Region; has received research and educational grants from Janssen and Pfizer; has received internal funding from the University of Hong Kong; and has received consultancy fees from Merck Sharp & Dohme, unrelated to this work. Dr Wan has received research grants from the Food and Health Bureau of the Government of the Hong Kong Special Administrative Region, and the Hong Kong Research Grants Council, outside the submitted work. Dr Chan has received honorarium from the Hospital Authority; and has received grants from the Hong Kong Research Grants Council, Research Fund Secretariat of the Food and Health Bureau, National Natural Science Fund of China, Wellcome Trust, Bayer, Bristol Myers Squibb, Pfizer, Janssen, Amgen, Takeda, and Narcotics Division of the Security Bureau of the Hong Kong Special Administrative Region, outside the submitted work. Dr Wong has received research funding outside the submitted work from Amgen, Bristol Myers Squibb, Pfizer, Janssen, Bayer, GSK, Novartis, the Hong Kong Research Grants Council, the Food and Health Bureau of the Government of the Hong Kong Special Administrative Region, National Institute for Health Research in England, European Commission, and the National Health and Medical Research Council in Australia; has received speaker fees from Janssen and Medice in the previous 3 years; and is an independent non-executive director of Jacobson Medical in Hong Kong. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Appendix
Supplemental Tables 1–6
Acknowledgments
The authors thank colleagues from the Department of Health and from the Hospital Authority for their provision of data and support.
Listen to this manuscript's audio summary by Editor-in-Chief Dr Valentin Fuster onwww.jacc.org/journal/jacc.
The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.
Appendix
For supplemental tables, please see the online version of this paper.
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22 Li X. Lai F.T.T. Chua G.T. Myocarditis following COVID-19 BNT162b2 vaccination among adolescents in Hong Kong JAMA Pediatr 176 6 2022 612 614 35212709
23 Wong A.Y. Root A. Douglas I.J. Cardiovascular outcomes associated with use of clarithromycin: population based study BMJ 352 2016 h6926 26768836
24 Caforio A.L. Calabrese F. Angelini A. A prospective study of biopsy-proven myocarditis: prognostic relevance of clinical and aetiopathogenetic features at diagnosis Eur Heart J 28 11 2007 1326 1333 17493945
25 Imanaka-Yoshida K. Inflammation in myocardial disease: from myocarditis to dilated cardiomyopathy Pathol Int 70 1 2020 1 11 31691489
26 Kieszak S.M. Flanders W.D. Kosinski A.S. Shipp C.C. Karp H. A comparison of the Charlson Comorbidity Index derived from medical record data and administrative billing data J Clin Epidemiol 52 2 1999 137 142 10201654
27 Witberg G. Barda N. Hoss S. Myocarditis after Covid-19 vaccination in a large health care organization N Engl J Med 385 23 2021 2132 2139 34614329
28 Jaiswal V. Jaiswal A. Batra N. Symptomatology, prognosis and clinical findings of myocarditis as an adverse event of COVID-19 mRNA vaccine: a systematic review Eur Heart J 43 suppl 1 2022 ehab849109
29 Truong D.T. Dionne A. Muniz J.C. Clinically suspected myocarditis temporally related to COVID-19 vaccination in adolescents and young adults: suspected myocarditis after COVID-19 vaccination Circulation 145 5 2022 345 356 34865500
30 Rezkalla S.H. Kloner R.A. Viral myocarditis: 1917-2020: from the influenza A to the COVID-19 pandemics Trends Cardiovasc Med 31 3 2021 163 169 33383171
31 Lau W.C. Chan E.W. Cheung C.L. Association between dabigatran vs warfarin and risk of osteoporotic fractures among patients with nonvalvular atrial fibrillation JAMA 317 11 2017 1151 1158 28324091
32 Cunningham K.S. Veinot J.P. Butany J. An approach to endomyocardial biopsy interpretation J Clin Pathol 59 2 2006 121 129 16443725
33 Bouzid D. Visseaux B. Kassasseya C. IMProving Emergency Care (IMPEC) FHU Collaborators Group Comparison of patients infected with delta versus omicron COVID-19 variants presenting to Paris emergency departments: a retrospective cohort study Ann Intern Med 175 6 2022 831 837 35286147
34 Taylor L. Covid-19: Hong Kong reports world’s highest death rate as zero covid strategy fails BMJ 376 2022 o707 35302043
| 36480967 | PMC9721305 | NO-CC CODE | 2022-12-07 23:22:08 | no | J Am Coll Cardiol. 2022 Dec 13; 80(24):2255-2265 | utf-8 | J Am Coll Cardiol | 2,022 | 10.1016/j.jacc.2022.09.049 | oa_other |
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J Adolesc Health
J Adolesc Health
The Journal of Adolescent Health
1054-139X
1879-1972
Society for Adolescent Health and Medicine.
S1054-139X(22)00695-4
10.1016/j.jadohealth.2022.09.031
Adolescent Health Brief
Gender Differences in Routine Health Maintenance Examinations Before and During the COVID-19 Pandemic
Vargas Gabriela M.D., M.P.H. ab∗
Prunier Lee BS a
Borus Joshua M.D., M.P.H. ab
a Division of Adolescent/Young Adult Medicine, Boston Children's Hospital, Boston, Massachusetts
b Department of Pediatrics, Harvard Medical School, Boston, Massachusetts
∗ Address correspondence to: Gabriela Vargas, M.D., M.P.H., Division of Adolescent/Young Adult Medicine, Boston Children's Hospital, 300 Longwood Avenue, Mailstop 306, Boston, MA 02115.
5 12 2022
5 12 2022
20 4 2022
17 9 2022
© 2022 Society for Adolescent Health and Medicine. All rights reserved.
2022
Society for Adolescent Health and Medicine
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
Adolescent and young adult (AYA) males historically have lower healthcare utilization than their female peers.
Methods
Electronic health record data from an Adolescent/Young Adult Medicine outpatient practice were reviewed to assess gender differences in routine health maintenance examinations before and during the COVID-19 pandemic.
Results
Routine health maintenance examinations decreased for both males and females during the pandemic. However, a two-proportion z-test demonstrated that established male patients were statistically less likely (p < .01) to have a routine health maintenance examination from December 2020 to December 2021 than their female counterparts.
Discussion
AYA males are at a higher risk for persistent disengagement in healthcare and exacerbates future gender gaps in healthcare utilization. Primary care providers need to focus efforts on re-engaging all young people in preventive care, with specific efforts tailored to AYA males.
Keywords
Adolescence
Young adults
Males
Health maintenance examination
==== Body
pmc Implications and Contribution
The COVID-19 pandemic negatively affected routine health maintenance examinations among adolescent and young adults. However, this impact was more significant among adolescent and young adult males. This is concerning as once males are disengaged from healthcare, they are less likely to re-engage in the future.
The COVID-19 pandemic has impacted primary care access and utilization patterns, but research is still emerging regarding the distribution of these impacts. A recent retrospective study of four large academic institutions showed a significant decrease for routine pediatric healthcare visits and chronic condition follow-up between April 2019 and April 2020 [1]. Disruptions have also been noted among receipt of sexual and reproductive healthcare by adolescents in the primary care setting [[2], [3], [4], [5]]. These findings highlight the acute and potential long-term health implications surrounding missed care during the pandemic but do not address discrepancies due to gender.
Although young people face multiple barriers to care, masculinity norms may create an additional barrier for young males. Those with stronger masculine values are less likely to seek healthcare [6,7]. Historically, adolescent and young adult (AYA) males are less likely than females to have a usual source of healthcare or to have had a primary care visit within the last 12 months [8,9]. Thus, the decreases in healthcare utilization seen during the pandemic are particularly concerning for males as they already have a more tenuous connection to care [[10], [11], [12]].
In this report, we aim to describe gender differences of the COVID-19 pandemic's impact on AYAs' routine health maintenance examinations (HMEs). We use documented gender, which in the majority of cases is reflective of biological gender.
Methods
We retrospectively extracted electronic health record (EHR) data from a large academic center's Adolescent/Young Adult Medicine outpatient practice. The practice's primary care patients' demographics include age 12–26 years (primarily); insurance: 43% private and 53% public; ethnicity: 25% Latinx; and race: 38% Black, 37% unknown/declined, and 20% White. As the practice provides primary and specialty care, the primary care population was defined as AYAs with a routine HME within the three years prior to January 2019 through December 2021. A patient was considered to have an up-to-date routine HME if they had an HME within the last 18 months; time frame was established by our Accountable Care Organization. A patient was only counted once per 18-month period. Routine HMEs were identified via diagnostic codes. HMEs included physical examination, psychosocial screening, and immunizations. During the pandemic, some HMEs were offered via telehealth, with an in-person component offered as needed. Gender was defined in binary terms of male and female as documented in the EHR. Gender is reported at registration; it can subsequently be updated by patient or staff. Due to EHR limitations, “male” may include cisgender males, transgender males, nonbinary individuals who were assigned male gender at birth, and transgender females who may not have their gender updated in the EHR; “female” may also include a similar array of gender identities.
Descriptive analyses were conducted to determine the monthly number of males and females with up-to-date routine HMEs. Proportions of males and females with up-to-date routine HMEs were compared by month using Z-tests. A two-sided p value < .01 was considered statistically significant for analyses to be conservative for multiple comparisons.
The Boston Children's Hospital Institutional Review Board determined this study not to be human subjects’ research and thus was an Institutional Review Board exempt.
Results
During January 2019 to December 2021, there were 6,853 routine HMEs performed. During this span, females accounted for a larger proportion of patients with an up-to-date HME (59.2% females). Following the onset of the COVID-19 pandemic in March 2020, the absolute number of routine HMEs declined for both female and male patients (Figure 1 ). The proportion of patients with an up-to-date HME also decreased for both females and males (Figure 2 ). Pre-pandemic young males engaged in care were not statistically different from females to have a routine HME. During the 13-month period of December 2020 (60.5% vs. 65.4%) to December 2021 (63.5% vs. 68.7%), AYA females were more likely to have an up-to-date HME than AYA males. There was a statistically significant (p < .01) widening of the gap between males and females from December 2020 to December 2021.Figure 1 Routine health maintenance examinations by gender. Monthly number of males and females with an up-to-date routine health maintenance examination (i.e., within the prior 18 months) during 2019–2021.
Figure 2 Proportion of patients with an up-to-date health maintenance examination by gender. Comparison of proportion of males and females attending an adolescent and young adult practice with an up-to-date routine health maintenance examination (i.e., within the prior 18 months) during 2019–2021. p values shown for Z-tests comparing proportions of routine health maintenance examinations by documented gender in electronic health record.
Discussion
Our data highlight a general decrease in routine HMEs during the COVID-19 pandemic for males and females. This general decrease is consistent with other findings regarding health visits and preventive services during the pandemic [[1], [2], [3], [4], [5]]. Routine HMEs are associated with higher rates of preventive services for adolescents and young adults, such as anticipatory guidance, blood pressure checks, and influenza immunization [13]. This reduction in HMEs demonstrates a loss of these services at an age when many chronic conditions are first diagnosed [13].
In addition, our study highlights gender differences in healthcare utilization that predated the pandemic. Males have lower healthcare utilization than their female peers [[10], [11], [12]]. As of December 2020, male patients who had previously been engaged in primary care were statistically less likely than their female counterparts to have an up-to-date HME. From a health equity lens, any difference in care is concerning, especially as these young men are at risk to remain disengaged in care in the future. A recent study of well-care use patterns from childhood through adolescence found that nearly half (48%) of males were persistently disengaged in healthcare with only 18% gradually re-engaging [14]. COVID-19 may serve as the initial rupture in care which exacerbates future gender gaps in healthcare utilization and health outcomes without steps to re-engage those AYA male patients back into healthcare.
Our results have limitations. The definition of gender was limited due to EHR constraints. We also defined an up-to-date HME as 18-months rather than the 12-month ideal, due to an accountable care organization-defined measure and recognizing several potential barriers to care, especially in the setting of the pandemic. We were unable to assess whether patients had a routine HME elsewhere. Urgent care or problem-based follow-up visits were not examined. Based on prior literature, it is likely gender disparities would be exacerbated including these other visit types as AYA females have higher healthcare utilization overall. Finally, our data include patients in Massachusetts and may limit generalizability.
In conclusion, there was a decline in routine HMEs following the onset of the COVID-19 pandemic. This has disproportionately affected AYA males and may lead to future disengagement in an already vulnerable population. Thus, it is imperative that primary care providers and community stakeholders work to re-engage all young people in preventive care, with specific efforts focused on AYA males.
Funding Sources
This article was supported in part by the 10.13039/100000102 Health Resources and Services Administration (HRSA) of the U.S. Department of Health and Human Services (HHS) as part of an MCHB T71MC00009 LEAH training grant. The contents are those of the authors and do not necessarily represent the official views of nor an endorsement, by HRSA, HHS, or the U.S. Government (HRSA.gov). The funders/sponsors did not participate in the work.
Acknowledgments
The authors thank Elizabeth R. Woods, M.D., for her guidance.
Conflicts of interest: The authors have no conflicts of interest to declare.
==== Refs
References
1 Brown C.L. Montez K. Amati J.B. Impact of COVID-19 on pediatric primary care visits at four academic institutions in the Carolinas Int J Environ Res Public Health 18 2021 5734 34071783
2 Steiner R.J. Zapata L.B. Curtis K.M. COVID-19 and sexual and reproductive health care: Findings from primary care providers who serve adolescents J Adolesc Health 69 2021 375 382 34301467
3 Ott M.A. Bernard C. Wilkinson T.A. Clinician perspectives on ethics and COVID-19: Minding the gap in sexual and reproductive health Perspect Sex Reprod Health 52 2020 145 149 32945616
4 Lindberg L.D. 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 2020 75 79 32537858
5 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 2020 326 327 32690467
6 Marcell A.V. Ford C.A. Pleck J.H. Sonenstein F.L. Masculine beliefs, parental communication and male adolescents’ health care use Pediatrics 119 2007 e966 e975 17403834
7 Tyler R.E. Williams S. Masculinity in young men’s health: Exploring health, help-seeking and health service use in an online environment J Health Psychol 19 2014 457 470 23493865
8 Adams S.H. Newacheck P.W. Park M.J. Health insurance across vulnerable ages: Patterns and disparities from adolescence to the early 30s Pediatrics 119 2007 e1033 e1039 17473076
9 Kirzinger W.K. Cohen R.A. Gindi R.M. Health care access and utilization among young adults aged 19–25: Early release of estimates from the National Health Interview Survey, January -September 2011 Natl Cent Health Stat 2012 Available at: www.cdc.gov/nchs/nhis/releases.htm
10 Lau J.S. Adams S.H. Boscardin W.J. Young adults’ health care utilization and expenditures prior to the affordable care act J Adolesc Health 54 2014 663 671 24702839
11 Callahan S.T. Cooper W.O. Gender and uninsurance among young adults in the United States Pediatrics 113 2004 291 297 14754940
12 Fortuna R.J. Robbins B.W. Haterman J.S. Ambulatory care among young adults in the United States Ann Intern Med 151 2009 379 385 19755363
13 Adams S.H. Park M.J. Twietmeyer L. Increasing delivery of preventive services to adolescents and young adults: Does the preventive visit help? J Adolesc Health 63 2018 166 171 29929838
14 Van Eck K. Thakkar M. Matson P.A. Adolescents' patterns of well-care use over time: Who stays connected Am J Prev Med 60 2021 e221 e229 33648787
| 36473777 | PMC9721310 | NO-CC CODE | 2022-12-13 23:16:31 | no | J Adolesc Health. 2022 Dec 5; doi: 10.1016/j.jadohealth.2022.09.031 | utf-8 | J Adolesc Health | 2,022 | 10.1016/j.jadohealth.2022.09.031 | oa_other |
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J Am Coll Cardiol
J Am Coll Cardiol
Journal of the American College of Cardiology
0735-1097
1558-3597
Published by Elsevier on behalf of the American College of Cardiology Foundation
S0735-1097(22)07109-1
10.1016/j.jacc.2022.10.010
Original Investigation
Editorial Comment
COVID-19 Vaccine Myocarditis∗
Cautious Reassurance in an Era of Dynamic Uncertainty
Liu Peter P. MD ∗
Kafil Tahir S. MD
University of Ottawa Heart Institute, University of Ottawa, Ottawa, Ontario, Canada
∗ Address for correspondence: Dr Peter P. Liu, University of Ottawa Heart Institute, 40 Ruskin Street, Room H2238, Ottawa, Ontario K1Y 4W7, Canada.
∗ Editorials published in the Journal of the American College of Cardiology reflect the views of the authors and do not necessarily represent the views of the Journal of the American College of Cardiology or the American College of Cardiology.
5 12 2022
13 12 2022
5 12 2022
80 24 22662268
© 2022 Published by Elsevier on behalf of the American College of Cardiology Foundation.
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.
Corresponding Author
Key Words
COVID-19
mRNA vaccine
myocarditis
pericarditis
prognosis
SARS-CoV-2
vaccination
==== Body
pmc “Reports have appeared of changes in the ECG in connection with vaccination against small-pox.”
Ahlborg et al1
Reports of COVID-19 mRNA vaccine–associated myocarditis (“vaccine myocarditis”) first emerged in the spring of 2021. For myocarditis researchers, this was reminiscent of previous vaccines that have historically been associated with myocarditis, including the smallpox vaccine.2 , 3 With large-scale vaccination efforts ongoing around the world, there is continually accumulating perspective on this rare adverse event of mRNA COVID-19 vaccination. Reassuringly, this has contributed to an increase in research on the topic of myocarditis in the literature (Figure 1 ).4 Further information on the outcomes in patients with vaccine myocarditis is critically important to allow for ongoing risk-benefit analyses at both individual and population levels.Figure 1 Trends in Myocarditis Research
A PubMed search for myocarditis-related publications as of September, 2022. (A) Search results for the term “myocarditis.” The earliest publication was in 1869. (B) Search results for the terms “vaccine myocarditis.” The earliest publication was in 1947.
Strengths of Finding
In this issue of the Journal of the American College of Cardiology, Lai et al5 report retrospective data comparing the 6-month outcomes in 104 cases of COVID-19 mRNA vaccine-associated myocarditis after BNT162b2 (Pfizer-BioNTech) exposure in the Hong Kong Territory national health registry with the outcomes in a historical control group of 762 non-COVID-19 viral myocarditis cases between 2000 and 2019. They report 6-month outcomes measures showing a 92% lower mortality risk in the vaccine myocarditis group compared with the earlier viral myocarditis group. This included 1 death in the vaccine myocarditis group (1%) vs 84 deaths (11%) in the viral myocarditis group. Similarly, there was 1% dilated cardiomyopathy and 1.9% heart failure in the vaccine myocarditis group vs 3.7% and 12.2%, respectively, in the earlier viral myocarditis group. Overall, the results are reassuring for patients hospitalized with vaccine myocarditis due to BNT162b2.5
The strengths of the study included the following: 1) the data were extracted from the entire single national health database of Hong Kong for hospitalized patients, thus minimizing selection or self-referral bias; 2) the analysis adopted a common inclusion criteria, using ICD-9 codes and hospital clinical records submitted by the same health care provider teams; and 3) the complication endpoints were captured by the system at a common time interval of 6 months, with validated criteria.
Challenges in Case Definition and Comparator Group
The study does have several limitations, some of which have already been identified by the investigators.5 The first is the absence of standardized case definition criteria. Vaccine-associated myocarditis is currently defined worldwide by either the Brighton Collaboration or the Centers for Disease Control (CDC) criterion.6 Both include objective findings of myocarditis and exclusion of alternative causes of symptoms. There are 5 levels of certainty in the Brighton criterion (definitive, probable, possible, insufficient evidence, and not myocarditis) and 3 levels of certainty in the CDC criteria (confirmed, probable, and suspected).6 Neither criterion was reported in this study.5 The advantage of the standardized Brighton Collaboration or the CDC criterion is the ability to compare data from cohorts globally. The CDC and Brighton criteria include cardiac magnetic resonance (CMR) imaging for their higher levels of certainty.6 Both draw from the Lake Louise criteria for myocarditis, which were revised in 2018 to incorporate novel CMR mapping techniques.7 In the study by Lai et al,5 the authors report they were not able to confirm the myocarditis diagnoses with clinical investigative data such as CMR because they were not available in their database. Further, the inability to confirm exclusion of other potential causes of myocarditis can lead to heterogeneity of disease inclusion and subsequent prognosis.
Although this study found significant assurance in terms of the relatively better outcomes of patients hospitalized with vaccine myocarditis compared with that of prepandemic viral myocarditis, the latter is not an ideal comparator group. Viral myocarditis is a very heterogeneous group of conditions that are influenced by local seasonal viral patterns, underlying population comorbidities, and the availability of gold standard diagnostic criteria such as analysis of endomyocardial biopsy specimens to definitively diagnose the cause of myocarditis. A potential better comparator that is also relevant for risk assessment is to use COVID-19–induced myocarditis. The PCORNet has examined records of 15,215,178 patients from 40 health care systems and found that the risk of adverse cardiac events such as myocarditis/pericarditis after COVID-19 infection, compared with that after the second vaccine dose in young males, is still 1.8 to 5.6 times higher after COVID-19 infection than from COVID-19 vaccination.8
Challenges in Pathophysiology and Biological Mechanisms of Vaccine Myocarditis
The full pathophysiology of vaccine myocarditis is not yet understood. Multiple mechanisms by which COVID-19 mRNA vaccines contribute to myocarditis have been raised, each influenced by sex hormones, age, and genetic HLA factors.4 First, mRNA itself might induce immune reactivity if it is detected as an antigen. Although this may help explain multisystem inflammatory syndrome, it does not explain isolated myocarditis specifically. Second, SARS-CoV-2 spike protein may have cross reactivity with cardiac contractile proteins and induce autoimmunity. Third, sex hormones such as testosterone may promote certain inflammatory responses, and estrogen may decrease certain responses.4 Fourth, there is the possibility that the delivery mRNA lipid nanoparticle vector itself may be contributing to the immunogenicity. Further study is needed into all of these hypotheses.
The Future of Vaccine Myocarditis
Given the anticipated need for regular COVID-19 booster vaccinations and advancements in mRNA technologies for various other medical indications, vaccine myocarditis will continue to be an ongoing challenge into the foreseeable future. It is exciting to see the global enthusiasm and renaissance in myocarditis research (Figure 1). We will need to work collaboratively to capture the longer-term outcomes in these patients, to identify the specific individual risk factors leading to the development of myocarditis, and mitigation strategies for those who are affected. Joining global collaborations will be critical for our collective success. Whereas this present study provides a reassuring initial look at 6-month outcomes data after BNT162b2 vaccination, it is not yet time to roll down our sleeves.
Funding Support and Author Disclosures
This work is supported by the Canadian Institutes of Health Research, Public Health Agency of Canada and the Myocarditis Foundation. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
The authors attest they are in compliance with human studies committees and animal welfare regulations of the authors’ institutions and Food and Drug Administration guidelines, including patient consent where appropriate. For more information, visit the Author Center.
==== Refs
References
1 Ahlborg B. Linroth K. Nordgren B. ECG-changes without subjective symptoms after smallpox vaccination of military personnel Acta Med Scand Suppl 464 1966 127 134 5229008
2 Jacobson I.G. Smith T.C. Smith B. Wells T.S. Reed R.J. Ryan M.A. US military service members vaccinated against smallpox in 2003 and 2004 experience a slightly higher risk of hospitalization postvaccination Vaccine 26 2008 4048 4056 18586364
3 Casey C.G. Iskander J.K. Roper M.H. Adverse events associated with smallpox vaccination in the United States, January-October 2003 JAMA 294 2005 2734 2743 16333009
4 Heymans S. Cooper L.T. Myocarditis after COVID-19 mRNA vaccination: Clinical observations and potential mechanisms Nat Rev Cardiol 19 2022 75 77 34887571
5 Lai F.T.T. Chan E.W.W. Huang L. Prognosis of myocarditis developing after mRNA COVID-19 vaccination compared with viral myocarditis J Am Coll Cardiol 80 2022 2255 2265
6 Sexson Tejtel S.K. Munoz F.M. Al-Ammouri I. Myocarditis and pericarditis: Case definition and guidelines for data collection, analysis, and presentation of immunization safety data Vaccine 40 2022 1499 1511 35105494
7 Ferreira V.M. Schulz-Menger J. Holmvang G. Cardiovascular magnetic resonance in nonischemic myocardial inflammation: Expert recommendations J Am Coll Cardiol 72 2018 3158 3176 30545455
8 Block J.P. Boehmer T.K. Forrest C.B. Cardiac complications after SARS-CoV-2 infection and mRNA COVID-19 vaccination - PCORnet, United States, January 2021-January 2022 MMWR Morb Mortal Wkly Rep 71 2022 517 523 35389977
| 36480968 | PMC9721358 | NO-CC CODE | 2022-12-07 23:19:08 | no | J Am Coll Cardiol. 2022 Dec 13; 80(24):2266-2268 | utf-8 | J Am Coll Cardiol | 2,022 | 10.1016/j.jacc.2022.10.010 | oa_other |
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J Diabetes Complications
J Diabetes Complications
Journal of Diabetes and Its Complications
1056-8727
1873-460X
Elsevier Inc.
S1056-8727(22)00291-4
10.1016/j.jdiacomp.2022.108379
108379
Article
Type 1 diabetes outpatient care and treatment effectiveness during COVID-19: A single-center cohort study
Kania Michał ab
Suduł Paulina ab
Mazur Konrad a
Chaykivska Zlata a
Fiema Mateusz a
Kopka Marianna ab
Kostrzycka Małgorzata a
Wilk Magdalena ab
Hohendorff Jerzy ab
Kieć-Wilk Beata ab
Klupa Tomasz ab
Witek Przemysław ab
Katra Barbara ab
Malecki Maciej T. ab⁎
a University Hospital, Krakow, Poland
b Department of Metabolic Diseases, Jagiellonian University Medical College, Krakow, Poland
⁎ Corresponding author at: Jagiellonian University Medical College, Department of Metabolic Diseases, 2 Jakubowskiego Str, 30-688 Krakow, Poland.
5 12 2022
1 2023
5 12 2022
37 1 108379108379
24 8 2022
30 11 2022
1 12 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.
Purpose
COVID-19 has brought many challenges for providing quality healthcare for type 1 diabetes (T1DM). We evaluated the impact of the COVID-19 pandemic on the medical care, glycemic control, and selected outcomes in T1DM patients.
Methods
We retrospectively analyzed medical records from 357 T1DM adults enrolled in the Program of Comprehensive Outpatient Specialist Care at the University Hospital in Krakow, and assessed differences in patient data from before the COVID-19 period (March 2019–February 2020) and after it started COVID-19 (March 2020–February 2021).
Results
The median HbA1c levels and the percentage of patients within the HbA1c target of <7 % (53 mmol/mol) were similar in both periods: before and after the beginning of the pandemic (6.86 % [51.5 mmol/mol], IQR 6.23–7.58 % [44.6–59.3 mmol/mol] vs. 6.9 % [51.9 mmol/mol], IQR 6.2–7.61 % [44.3–59.7 mmol/mol]; p = 0.50 and 56.3 % vs. 57.1 %, p = 0.42, respectively). However, we observed a rise in BMI and body weight (median 24.25, IQR 21.97–27.05 vs. 24.82, IQR 22.17–27.87 and median weight 71.0 IQR 61–82 vs. 72.55, IQR 55–85; p < 0.001 for both comparisons). There was no reduction in the numbers of total diabetes-related visits (median 4, IQR 4–5 vs. 5, IQR 4–5; p = 0.065), but the frequency of other specialist consultations decreased (2, IQR 0–2 vs. 1, IQR 0–2). During the pandemic, telehealth visits constituted of 1191 out of 1609 (71.6 %) total visits.
Conclusions
In this single-center observation, the COVID-19 pandemic did not have a negative impact on glycemic control in T1DM patients, but the patients' weight did increase. Telemedicine proved to be a valuable tool for T1DM care.
Keywords
Type 1 diabetes
COVID-19
Glycemic control
Outpatient care
==== Body
pmc1 Introduction
The coronavirus disease 2019 (COVID-19) pandemic has caused a major healthcare crisis worldwide.1., 2. Diabetes has been identified as a risk factor for severe COVID-19 infection course and increased mortality.3 In many countries, lockdown strategies have been implemented as a measure to control the COVID-19 outbreak, but for millions of people, including patients with chronic diseases such as diabetes, this brought significant changes to many aspects of everyday life, such as levels of physical activity and degree of stress. People with type 1 diabetes (T1DM) were, similarly to those with type 2 diabetes (T2DM), severely affected by many aspects by COVID-19, which include the effects of limited access to healthcare, especially during periods of lockdown. For people with T1DM, this could lead to changes in adherence to schedules for meals and insulin administration.4., 5. When the strictest lockdown measures were applied, these patients had also very limited opportunities to exercise and were prone to unhealthy habits, such as overeating and sedentary lifestyle.6 Additionally, increased incidence of infectious complications and mortality was reported in COVID-affected adults with T1DM (compared with peers without diabetes).7
The COVID-19 pandemic has also influenced the healthcare provided for patients with diabetes. Multiple services have been canceled, including face-to-face outpatient visits and planned hospitalizations. Healthcare has been delivered remotely by phone or video calls, emails, or smartphone applications.8 Telehealth guidelines and legal instruments were introduced by the Polish authorities in late 2019, enabling medical practitioners to provide this type of care.9
However, patients and medical staff have faced challenges in becoming accustomed to constant use of technologies and tools, such as possibility to download patients' results and data-sharing apps for insulin pumps, which had not been routinely used in diabetes care.10 Thus, inevitable gaps in healthcare continuity occurred and posed challenges for adequate maintenance of glycemic control and other clinical outcomes. Preliminary reports from the World Health Organization (WHO) showed that during the pandemic, patients with non-communicable diseases, including diabetes, have faced obstacles such as treatment delays or even temporary disruption of care.11 Therefore, the aim of this paper is to evaluate the impact of the COVID-19 pandemic on the medical care, glycemic control, and selected clinical outcomes of T1DM patients in a university diabetic outpatient clinic.
2 Methods
2.1 Study design and participants
We performed a retrospective, real-world data (RWD) analysis involving T1DM patients participating in the Polish National Health Fund Program of Comprehensive Outpatient Specialist Care (KAOS) at the University Hospital in Krakow. This tertiary-level hospital is an academic referral center for diabetes in Southeastern Poland. KAOS is mainly dedicated to people suffering from T1DM and requires diabetology patients' visits to be performed regularly. To warrant the cost coverage of each individual patient by the National Health Fund, a list of clinical consultations and laboratory tests done in pre-specified time frames is used. Specifically, every patient must be consulted by an ophthalmologist and neurologist at least once a year. Visits to diabetologists take place regularly every three months. Each visit, the patient's weight is checked, blood pressure is measured, and data on the daily insulin requirements are collected.
T1DM patients attending the clinic in the time period between 2019 and 2020 were eligible for the study. We included participants that attended at least 1 visit in 2019 and were followed up in the program in 2020. We excluded female patients who were pregnant in 2019, 2020, or 2021.
Patients were defined as having T1DM if, at diagnosis, they met WHO criteria for diabetes diagnosis, had typical clinical symptoms, an insulin therapy requirement from the beginning of the disease, and diagnosed established before 30 years of age.2 Demographic and clinical data was collected based on medical records. The data included: age at study entry, T1DM duration, presence of diabetes complications (defined as any form of diabetic polyneuropathy/autonomic neuropathy, diabetic retinopathy, diabetic foot syndrome, diabetic nephropathy, or cardiovascular disease), HbA1c level (blood serum using methods certified by the National Glycohemoglobin Standardization Program and point-of-care measurements were recorded), method of insulin therapy (continuous subcutaneous insulin infusion (CSII) or multiple daily injections (MDI)), use of continuous blood glucose monitoring systems (CGMS) or self-monitoring of blood glucose (SMBG), body mass index (BMI), weight, and numbers of stationary healthcare visits, telehealth visits, and specialist consultations (only appointments with medical doctors were counted).
Weight and BMI were only recorded and analyzed if measured during a face-to-face visit, and self-reported values were excluded. For each study period, only the latest available weight and BMI measurements were recorded. The frequency of HbA1c measurements for each study period was recorded. The mean HbA1c for each study period was calculated. Based on the calculated mean HbA1c, the percentage of participants meeting the HbA1c target of <7 % (53 mmol/mol) for each study period was calculated.
The following outcomes were compared between the study periods: mean HbA1c, the percentage of participants meeting the HbA1c target of <7 % (53 mmol/mol), frequency of HbA1c measurements, BMI, body weight, total number of diabetology visits, number of telehealth visits, and number of other specialist consultations. Comparisons were performed when data from both study periods was available for all the patients. We compared the outcomes between the two periods. Period 1 was the control period before the COVID-19 pandemic from March 2019 to February 2020. Period 2 was the investigated period during the COVID-19 period from March 2020 to February 2021.
The study had sufficient power of 80 % to detect HbA1c and weight differences of 0.5 % (±0.11 %) and 1.0 kg (±2.38 kg), respectively, values considered by us to be clinically meaningful. To achieve this, it was required to include 105 and 183 patients, respectively.
2.2 Statistical analysis
For normally distributed data, means and standard deviations (SDs) are presented. For non-normally distributed data, medians and interquartile ranges (IQRs) are presented. Paired data clusters were analyzed using the Wilcoxon test, and Friedman test, and unpaired data was analyzed using the Mann–Whitney U test. Categorical unpaired data was analyzed using the chi-squared test, and paired data were analyzed using the McNemar test. No sample-size calculation was performed. A p value <0.05 was regarded as statistically significant, and all analyses were performed using SPSS.
2.3 Ethics
The study was entirely based on retrospective analysis of patients' medical records, and ethics approval was not required. This retrospective analysis did not impact neither any diagnostic procedures, nor treatment methods. This type of research is exempted from obtaining the informed consent. The authors were granted the permission to access and analyze the patients' data by the Hospital Board.
3 Results
There were 413 people with T1DM in the KAOS program in 2019, however, 56 did not attend any visit in 2020, thus, they were considered dropouts. Eventually, data from 357 T1DM participants was included in the analysis. The mean age of study participants was 35.2±9.9 years, the mean T1DM duration was 17.9±10.2 years, and the rate of occurrence of any diabetes complications was 18.8 %. Women constituted 70.9 % of the study population. The baseline characteristics of the study population are presented in Table 1 .Table 1 Characteristics of the study population and study outcomes.
Table 1Characteristic N
Age (years) 357 35,2 (9.9)
34 (28–41)
Diabetes duration (years) 357 17.9 (10.2)
17 (10–24)
Age at diabetes diagnosis (years) 357 17.1 (9.8)
16 (9–23)
Sex, women (%) 357 253 (70.9 %)
Diabetes complications (%) 355 63 (18.8 %)
N, data available (period 1/period 2) Period 1 Period 2 P value Period 1 vs. 2
Diabetes treatment (%) 355/340 0.73
MDI 141 (39.7 %) 135 (39.7 %)
CSII 214 (60.3 %) 205 (60.3 %)
BGM 345/327 0.25
SMBG 277 (80.3 %) 259 (79.2 %)
CGMS 68 (19.7 %) 68 (20.8 %)
BMI (kg/m2) 300/157 24.87 (4) 25.3 (3.88) 0.001
24.25 (21.97–27.05) 24.82 (22.17–27.87)
Body weight (kg) 346/176 72.2 (13.82) 74 (14.7) P < 0.001
71 (61–82) 72.55 (55–85)
HbA1c (%) 317/238 6.98 (1) 7.01 (1.03) 0.50
6.86 (6.23–7.58) 6.9 (6.2–7.61)
HbA1c (mmol/mol) 317/238 52.8 (11) 53.1 (11) 0.50
51.5 (44.6–59.3) 51.9 (44.3–59.7)
Patients meeting target HbA1c (<7 %, <53 mmol/mol), (%) 317/238 179 (56.3 %) 136 (57.1 %) 0.43
Frequency of HbA1c measurements 357/357 3.17 (1.9) 1.06 (1) <0.001
3 (2–5) 1 (0–2)
Number of total visits 353/354 4.51 (1.4) 4.55 (1.7) 0.06
4 (4–5) 5 (4–5)
Number of specialist consultations 347/346 1.43 (1.06) 1.1 (1,14) <0.001
2 (0–2) 1 (0–2)
Number of telehealth visits 352/352 0 3.35 (1.7) –
Data are presented as mean, standard deviation (SD), median interquartile range (IQR). For categorical variable numbers and percentage were used.
MDI – multiple daily injections, CSII - continuous subcutaneous insulin infusion, BGM – blood glucose measurements, SMBG - Self-Monitoring Blood Glucose, CGMS - continuous glucose monitoring system, BMI – body mass index.
We did not identify significant differences in glycemic control (expressed as median HbA1c), and the percentages of participants meeting general the treatment target of HbA1c <7 % (<53 mmol/mol) were similar in the pre-COVID-19 and pandemic periods (median 6.86 % [51.5 mmol/mol], IQR 6.23–7.58 % [44.6–59.3 mmol/mol] vs. 6.9 % [51.9 mmol/mol], IQR 6.2–7.61 % [44.3–59.7 mmol/mol]; p = 0.505; percentage: 56.3 % vs. 57.1 %, p = 0.429, respectively). However, we observed a significant reduction in the frequency of HbA1c measurements (median 3, IQR 2–5 vs. median 1, IQR 0–2; p < 0.001, respectively).
Additionally, increases in BMI and body weight were observed between the two study periods (median BMI 24.25 kg/m2, IQR 21.97–27.05 vs. 24.82 kg/m2, IQR 22.17–27.87; median weight 71.0 kg, IQR 61–82 vs. 72.55 kg, IQR 55–85; p < 0.001). There was no reduction in the number of total visits (face-to-face and televisits) in our diabetes clinic (median 4, IQR 4–5 vs. median 5, IQR 2–4; p = 0.065), but a decrease in frequency of other specialist consultations was observed when comparing the two study periods (median 2, IQR 0–2 vs. median 1, IQR 0–2, p < 0.001).
There were no telehealth visits in the pre-COVID-19 period in our clinic. During the pandemic, they constituted of 1191 of the 1609 (71.6 %) total visits.
Study outcomes are reported in Table 1. We also investigated the impact of telehealth medical care on diabetes control. Participants with deterioration of metabolic control defined as HbA1c <7 % (53 mmol/mol) in period 1 and >7 % in period 2 (n = 17, 6.3 % of the total study group) were compared with those who had stable or better results (252, 93.7 %). No differences were recorded in age, BMI, weight, diabetes duration, number of visits, televisits, and specialist consultations.
4 Discussion
COVID-19 has caused an unprecedented health emergency and led to severe disruption of many aspects of life worldwide. According to a survey conducted by the World Health Organization during the COVID-19 pandemic, diabetes patients have been at risk of treatment delays or even temporary disruption of care. In some measure, care was disrupted in 49 % of 155 analyzed countries (data from May 2020).11 We report retrospective data concerning T1DM outpatient care during the COVID-19 pandemic from a single-center cohort study. Interestingly, the pandemic did not have a negative impact on glycemic control in the examined cohort of T1DM patients, however their weight increased during this period. It partially negatively affected the quality of healthcare provided to the patients, as the number of in-person diabetes visits as well as specialist consultations were reduced, but there was no change in the total number of diabetology clinic visits.
Research effort has been made to investigate the impact of the pandemic on the glycemic control of T1DM patients, but in most cases so far, it has been limited to the lockdown period during the first wave of COVID-19 in the first half of 2020.12., 13., 14., 15., 16., 17. Reports from the Western European countries, including the United Kingdom, Italy, and Spain, surprisingly revealed no detriment. Sometimes, even some improvement was observed in selected populations of T1DM patients regarding time in range, glucose variability, frequency of hypoglycemia, and estimated HbA1c (for example, using flash or continuous glucose monitoring systems).13., 14., 16. It was hypothesized that individuals who were on home-office work mode during lockdown experienced improvements in glycemic control, suggesting that standardization of daily routine and more time for self-care were responsible for this beneficial effect.12., 13., 15., 16., 17. In contrast, a report from India showed a significant deterioration in glycemic control assessed by SMBG in T1DM, very likely due to problems in availability of insulin and test strips,18 which are factors that are unlikely to affect our study population.
We also analyzed a subgroup of participants that showed deterioration in glycemic control to identify factors that could have contributed to this result. As no differences were found when comparing available data, this deterioration could have resulted from other factors, such as less motivation, social deprivation, etc., which were highlighted in other studies.6., 14., 18. In our study, we only analyzed medical records, so we were not able to approach the patients and gather additional input.
Nevertheless, with the majority of visits delivered remotely, a decrease in the frequency of HbA1c measurements requiring travel to the clinic was expected. In patients using CGMS, estimated HbA1 can be used to monitor long-term metabolic control, but the remaining part of the patient population rely on the traditional method. The relatively large amount of lacking data concerning HbA1c could have potentially influenced our results, but this is an unlikely scenario.
The impact of the COVID-19 pandemic on weight, dietary habits, and psycho-social aspects has already been investigated. Similar to our results, two studies originating from the Netherlands and Italy reported weight gain and linked it to an increase in stress levels and anxiety, as well as less physical exercise. Interestingly, there was no deterioration in glycemic control in both cohorts.6., 19. Patients who gained more weight reported lower frequency of exercise, social deprivation, increased anxiety, depression states, change of dietary habits such as consumption of unhealthy snacks and food, or generally eating more.6 Another study from the Middle Eastern region revealed that COVID-19 lockdown-associated lifestyle changes, such as decreased physical activity, were more prevalent in individuals with T1DM as compared to control group, potentially explaining undesirable weight gain.20
The total number of diabetology visits during the pandemic was not lower than in the pre-COVID-19 period. This resulted from the fact that T1DM participants required scheduled medical attention - i.e., for prescribing insulin, test strips, or insulin pump equipment. Moreover, our participants could have been more motivated to remain enlisted, because of the requirements of the Program of Comprehensive Outpatient Specialist Care. Finally, in 2019, Polish authorities introduced numerous e-health systems that enabled telehealth visits with remote prescriptions of drugs or insulin pump kits, thus providing tools for telehealth outpatient care.9
The number of telehealth visits was substantial and is generally comparable with data published on this subject from other countries. One study showed that during lockdown for COVID-19, telemedicine positively impacted glycemic control in the T1DM population, though it was not shown in our study.18 Notably, in this mentioned Italian study only patients using CGMS were evaluated. Another study showed that the telemedicine approach negatively affected older diabetes patients, which was probably due to their weaker compliance with new technologies. This subpopulation also tends to need more attention during typical face-to-face consultations.19 Our participants were relatively young, so they probably had superior computer operating skills and were better adapted to the new reality. Nevertheless, during the COVID-19 pandemic, telemedicine proved effective for patients with diabetes and should be offered as an alternative or integrated with the current approach. Unfortunately, the number of other specialist consultations decreased in our population. For example, many ophthalmologist visits were canceled due to the close face-to-face contact with patients and the risk of infection.
A key strength of our study is the homogeneity of the study group, its size, and the continuous care of the participants for many years in one center. This is also probably one of very few reports so far to cover most of the COVID-19 pandemic period encompassing 2020 and 2021. However, this report also has some limitations. For example, our cohort was characterized by an over-representation of female T1DM participants. This is related to the fact that young woman are attracted to our department by a special program dedicated to pregnancy planning and care. These women usually remain under our care after the delivery. Additionally, diagnosis of T1DM in our cohort was not based on specific autoantibodies or C-peptide level. Our results could have been affected by incomplete data from medical records, missing input, and inability to follow-up due to the dropping out of some participants. We did not gather data concerning lifestyle factors, i.e. dietary habits, that could have impacted the results. We were limited to HbA1c monitoring as available data from SMBG reports and CGMS download were hardly comparable or representative for adequate time periods. Lastly, we did not assess the psychological impact of the pandemic on T1DM participants.
5 Conclusions
In this single-center observational study, the COVID-19 pandemic did not have a negative impact on glycemic control in T1DM participants. However, their weight increased during this period. Telemedicine proved to be a valuable tool for T1DM diabetes care.
Funding
The authors were supported by the Polish 10.13039/501100011755 National Center for Research and Development grant (grant number SZPITALE-JEDNOIMIENNE/18/2020).
CRediT authorship contribution statement
Michał Kania: Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing. Paulina Suduł: Data curation, Investigation, Writing – review & editing. Konrad Mazur: Data curation, Investigation, Writing – review & editing. Zlata Chaykivska: Data curation, Investigation, Writing – review & editing. Mateusz Fiema: Data curation, Investigation, Writing – review & editing. Marianna Kopka: Data curation, Investigation, Writing – review & editing. Małgorzata Kostrzycka: Data curation, Investigation, Writing – review & editing. Magdalena Wilk: Data curation, Investigation, Writing – review & editing. Jerzy Hohendorff: Conceptualization, Formal analysis, Methodology, Project administration, Supervision, Validation, Writing – review & editing. Beata Kieć-Wilk: Conceptualization, Investigation, Methodology, Writing – review & editing. Tomasz Klupa: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing. Przemysław Witek: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing. Barbara Katra: Conceptualization, Investigation, Methodology, Writing – review & editing. Maciej T. Malecki: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – original draft, 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.
Data availability
The data associated with the paper is not publicly available but is available from the corresponding author on reasonable request.
Acknowledgements
The authors would like to express sincere gratitude to the entire medical staff of the University Hospital providing care for patients with diabetes during the COVID-19 pandemic.
==== Refs
References
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2. Schlesinger S. Neuenschwander M. Lang A. Pafili K. Kuss O. Herder C. Risk phenotypes of diabetes and association with COVID-19 severity and death: a living systematic review and meta-analysis Diabetologia 64 2021 1480 1491 33907860
3. Chang D.J. Moin T. Coronavirus disease 2019 and type 1 diabetes mellitus Curr. Opin. Endocrinol. Diabetes Obes. 28 2021 35 42 NLM (Medline) 33278126
4. Zhang J.Y. Shang T. Ahn D. Chen K. Coté G. Espinoza J. How to best protect people with diabetes from the impact of SARS-CoV-2: report of the international COVID-19 and diabetes summit J Diabetes Sci Technol 15 2021 478 514 33476193
5. Pellegrini M. Ponzo V. Rosato R. Scumaci E. Goitre I. Benso A. Changes in weight and nutritional habits in adults with obesity during the “lockdown” period caused by the COVID-19 virus emergency Nutrients 12 2020 1 11
6. Bashshur R. Doarn C.R. Frenk J.M. Kvedar J.C. Woolliscroft J.O. Telemedicine and the COVID-19 pandemic, lessons for the future Telemed e-Health 26 2020 571 573
7. DZIENNIK USTAW RZECZYPOSPOLITEJ POLSKIEJ Warszawa, dnia 16 kwietnia r Poz 711 2021
8. Iyengar K. Upadhyaya G.K. Vaishya R. Jain V. COVID-19 and applications of smartphone technology in the current pandemic Diabetes Metab Syndr 14 2020 733 737 32497963
9. Dyer O. Covid-19: pandemic is having “severe” impact on non-communicable disease care, WHO survey finds BMJ 369 2020 m2210
10. Aragona M. Rodia C. Bertolotto A. Campi F. Coppelli A. Giannarelli R. Type 1 diabetes and COVID-19: the "lockdown" effect Diabetes Res Clin Pract 2020 170
11. Capaldo B. Annuzzi G. Creanza A. Giglio C. de Angelis R. Lupoli R. Blood glucose control during lockdown for COVID-19: cgm metrics in Italian adults with type 1 diabetes Diabetes Care 43 2020 e88 e89 32540921
12. Dover A.R. Ritchie S.A. McKnight J.A. Strachan M.W.J. Zammitt N.N. Wake D.J. Assessment of the effect of the COVID-19 lockdown on glycaemic control in people with type 1 diabetes using flash glucose monitoring Diabet Med 38 2021 e14374
13. Fernández E. Cortazar A. Bellido V. Impact of COVID-19 lockdown on glycemic control in patients with type 1 diabetes Diabetes Res Clin Pract [Internet] 166 2020 108348 [cited 2021 Jun 13]
14. Maddaloni E. Coraggio L. Pieralice S. Carlone A. Pozzilli P. Buzzetti R. Effects of COVID-19 lockdown on glucose control: continuous glucose monitoring data from people with diabetes on intensive insulin therapy Diabetes Care 43 2020 e86 e87 32503838
15. Bonora B.M. Boscari F. Avogaro A. Bruttomesso D. Fadini G.P. Glycaemic control among people with type 1 diabetes during lockdown for the SARS-CoV-2 outbreak in Italy Diabetes Ther 11 2020 1369 1379 32395187
16. Verma A. Rajput R. Verma S. Balania V.K.B. Jangra B. Impact of lockdown in COVID 19 on glycemic control in patients with type 1 diabetes mellitus Diabetes Metab Syndr 14 2020 1213 1216 32679527
17. Ruissen M.M. Regeer H. Landstra C.P. Schroijen M. Jazet I. Nijhoff M.F. Increased stress, weight gain and less exercise in relation to glycemic control in people with type 1 and type 2 diabetes during the COVID-19 pandemic BMJ Open Diabetes Res Care 9 2021
18. Boscari F. Ferretto S. Uliana A. Avogaro A. Bruttomesso D. Efficacy of telemedicine for persons with type 1 diabetes during COVID19 lockdown Nutr. Diabetes 11 2021
19. Fadini G.P. Bonora B.M. Morieri M.L. Avogaro A. Why diabetes outpatient clinics should not close during pandemic crises J Endocrinol Invest 2021 1 4
20. Al-Daghri N.M. Almiman A.A. Wani K. Khattak M.N.K. Aljohani N.J. Alfawaz H. COVID-19 lockdown and lifestyle changes in Saudi adults with types 1 and 2 diabetes Front Public Health 8 2022 912816
| 0 | PMC9721371 | NO-CC CODE | 2022-12-14 23:45:36 | no | J Diabetes Complications. 2023 Jan 5; 37(1):108379 | utf-8 | J Diabetes Complications | 2,022 | 10.1016/j.jdiacomp.2022.108379 | oa_other |
==== Front
Biochem Biophys Res Commun
Biochem Biophys Res Commun
Biochemical and Biophysical Research Communications
0006-291X
1090-2104
Elsevier Inc.
S0006-291X(22)01677-1
10.1016/j.bbrc.2022.12.011
Article
Spike protein receptor-binding domains from SARS-CoV-2 variants of interest bind human ACE2 more tightly than the prototype spike protein
Charles Jermilia
McCann Nathan
Ploplis Victoria A.
Castellino Francis J. ∗
W.M. Keck Center for Transgene Research and Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, IN, 46556, USA
∗ Corresponding author.
5 12 2022
22 1 2023
5 12 2022
641 6166
24 11 2022
30 11 2022
3 12 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.
Several SARS-CoV-2 variants of interest (VOI) have emerged since this virus was first identified as the etiologic agent responsible for COVID-19. Some of these variants have demonstrated differences in both virulence and transmissibility, as well as in evasion of immune responses in hosts vaccinated against the original strain of SARS-CoV-2. There remains a lack of definitive evidence that identifies the genetic elements that are responsible for the differences in transmissibility among these variants. One factor affecting transmissibility is the initial binding of the surface spike protein (SP) of SARS-CoV-2 to human angiotensin converting enzyme-2 (hACE2), the widely accepted receptor for SP. This step in the viral replication process is mediated by the receptor binding domain (RBD) of SP that is located on the surface of the virus. This current study was conducted with the aim of assessing potential differences in binding affinity between recombinant hACE2 and the RBDs of emergent SARS-CoV-2 WHO VOIs. Mutations that affect the binding affinity of SP play a dominant initial role in the infectivity of the virus.
Keywords
COVID-19
Spike protein
Receptor binding domain
Protein expression
Protein binding
Analytical ultracentrifugation
Isothermal calorimetry
==== Body
pmc1 Introduction
SARS-CoV-2 infections have resulted in over a million deaths in the United States alone since the introduction of the virus in the winter of 2020. SARS-CoV-2 is a member of the Coronaviridae family, several of which have been shown to infect humans [1]. Attachment and entry of coronaviruses into susceptible host cells are facilitated via their surface-oriented spike protein (SP). This is the first stage of the infection process and requires the expression of specific host cell receptors for attachment and entry. According to the Centers for Disease Control and Prevention (CDC), seven coronaviruses that infect humans have been identified, four of which cause common infections, e.g., the common cold. A human cell receptor for the SP of SARS-CoV-2, viz, angiotensin-converting enzyme-2 (ACE2), has been identified as the receptor, not only for the SARS-CoV-2 virus, but also for two other human coronaviruses: HCoV-NL63 and SARS-CoV [1]. Human aminopeptidase N (APN) and dipeptidyl peptidase 4 (DPP4) have been shown to serve as the receptors for two other human coronaviruses, HCoV-229E and MERS-CoV, respectively [1].
ACE2 is a metalloproteinase [2] which is expressed in several human organs, including the lungs, small intestine, heart, testis, and kidneys [3]. Physiologically, ACE2 acts to reduce blood pressure by catalyzing the cleavage of angiotensin II (Ang II) into Ang 1–7 [4]. This cleavage can ultimately lead to increased vasodilation, anti-inflammatory, antifibrinolytic, and anti-apoptotic effects. Competition between Ang II and SARS-CoV-2 for ACE2 can result in a reduction in vasodilation effects originating from Ang 1–7.
In addition to ACE2, various other host components have been reported to be involved in the initial stages of the viral infection process. One such protein is the serine protease, transmembrane serine protease 2 (TMPRSS2) [5], which cleaves the SP prior to entry of SARS-CoV-2 into cells [6]. One study showed that viral entry was blocked by a TMPRSS2 inhibitor, indicating the indispensable nature of this protein [6]. Another study identified heparan sulfate as a cofactor in ACE2-mediated SARS-CoV-2 entry into cells [7]. Additionally, vimentin, a protein expressed in cells of mesenchymal origin, has been reported to enhance the efficiency of SARS-CoV-2 entry into cells [8].
Several other variants have emerged since the original was identified in 2019. Some of these variants were at some point identified by the World Health Organization (WHO) (https://www.who.int/activities/tracking-SARS-CoV-2-variants) as variants of concern (VOC) and/or variants of interest (VOI). These variants presented with varying levels of virulence or transmissibility, with increased transmissibility not necessarily being an indication of increased virulence.
The current study was aimed at an investigation of the effects on binding to recombinant hACE2 of specific mutations in the RBDs of six known variants of SP. Certain mutations on their own had previously been reported to result in an increase in binding affinity whereas others had the opposite effect. Herein we compare the binding affinities of six different variants, mostly past and present VOIs mutated from the original Wuhan-Human Coronavirus-1 (WHCV; Gene Bank # MN908947) [9,10].
2 Materials and methods
2.1 Protein expression and purification
Receptor binding domains (RBDs) from SPs of SARS-CoV-2 variants, in addition to the original WHCV-SP, were PCR-amplified from pcDNA3.1-SARS2spike (Addgene). The SP of the Omicron variant was synthesized by Twist Bioscience (San Francisco, CA) and used as a template for the construction of its RBD. The RBD regions spanned amino acid positions 320–541 of SP (Fig. 1 ). Mutations made in WHCV-SP to generate the first five variants were introduced using primers carrying the specific nucleotide changes for each variant (Table 1 ).Fig. 1 Modules of WHCV-SP. The 1273-amino acid mature SP consists of S1 (residues 1–685) and S2 (residues 686–1273) subunits. S1 contains sequentially, from the 5′-terminus, a 16-residue signal peptide (SP) and a N-terminal domain that extends from amino acid residues 17–319. Next, a receptor binding domain (RBD) spans residues 320–541, within which is contained a receptor binding module (RBM) at residues 438–506. The S2 subunit (residues 542–1273) contains the TMPRSS cleavage site (CS) at residues 685–686 which is cleaved to liberate S1 prior to the SP entering the cells via S2. Next, a transmembrane spanning domain (TMD) is encompassed by residues 1213–1236, followed by a cytoplasmic region at residues 1237–1273.
Fig. 1
Table 1 Locations of mutations in RBD variants.
Table 1Variant Mutations in RBD GenBank accession
Alpha-RBD N501Y UFQ05186
Beta-RBD K417N, E484K, N501Y UFQ05198
Delta-RBD L452R, T478K UAL04647
Delta + -RBD K417N, L452R, T478K OX011815
Gamma-RBD K417T, E484K, N501Y UJW42419
Omicron-RBD G339D, S371L, S373P, S375F, K417N, N440K, G446S, S477N, T478K, E484A, Q493R, G496S,Q498R, N501Y, Y505H 7QO9_B
WHCV-RBD YP_009724390
RBD inserts containing a SpeI restriction sites at the 5′ end and a MluI restriction site at its 3′ end were cloned from pcDNA3.1SARS2spike into pMT-puro (pND879). pND879 [pMTpuro-Bip signal peptide-MCS-V5-Mlu1-(His)6] is an efficient in-house S2-cell expression vector backbone, with the original backbone being from the pMT/BiP/V5-His vector (Invitrogen) and pCoPuro (Invitrogen). The puromycin resistance gene from pCoPuro was inserted into the pMT/BiP/V5-His vector, along with a multi-cloning site containing SpeI, were engineered 3’ of the BiP-SS to generate pND879 [11]. This latter pMT-puro plasmid allowed a single plasmid to have both protein expression and antibiotic selection functions in Drosophila melanogaster Schneider 2 (S2) cells. Use of this plasmid also reduced antibiotic-resistance selection time from three weeks (time necessary for traditional co-transfections) to 3–5 days [11].
Human angiotensin-converting enzyme 2 (hACE2) was amplified by PCR from plasmid pcDNA3.1-hACE2 (Addgene, plasmid #145033) and cloned into the MCS of pMTpuro-MBP (pND1705) [5′-pMTpuro-Bip signal peptide-MBP-(His)6-thrombin cleavage site-TEV-MCS-V5-Mlu1-(His)6–3’]. The vector was constructed via the modification of pND879 using a pET28-MBP-TEV plasmid [12] (Addgene plasmid #69929; http://n2t.net/addgene:69929; RRID:Addgene_69929). This plasmid contains a tobacco etch virus (TEV) protease cleavage site in addition to a maltose-binding protein (MBP) tag, which is useful for purifying the expressed protein.
All constructs were transfected into S2 cells using TransIT-Insect Transfection Reagent (Mirus Bio, Madison, WI) and selection with puromycin (Research Products International, Mount Prospect, IL) completed within four days of transfection. Cells were grown in EX-CELLTM 420 (Millipore Sigma) media initially with 10% FBS. After transfection, FBS was gradually reduced. The cells were eventually transferred to spinner flasks with 0% FBS/0.1% final concentration Pluronic™ F-68 Non-ionic Surfactant (Gibco)/EX-CELL media (Millipore Sigma) and induced with copper sulfate (600 μM final concentration). Aprotinin protease inhibitor (DSM Nutritional Products, Parsippany, NJ) was added upon induction and again at harvest. Proteins were purified using Ni+-Sepharose (Cytiva) and eluted with 250 mM imidazole. The samples were stored in PBS.
2.2 Isothermal titration calorimetry (ITC)
Isothermal Titration Calorimetry (ITC) was conducted at room temperature titrating with 100 μM RBD or SP into 7μM hACE2 in PBS. A total of 50 injections were accomplished per run. The data were analyzed with NITPIC [13] followed by SEDPHAT (https://sedfitsedphat.github.io/sedphat/default.htm) and plotted using Graphpad (Graphpad Software, San Diego, CA). Experiments were performed in duplicate.
2.3 Analytical ultracentrifugation (AUC)
Sedimentation velocity (SV) experiments were performed at 0.1, 0.3, and 0.5 absorbance units at 280 nm (A280) for hACE2 samples and 0.3 for each RBD variant. A preliminary run aimed at investigating the occurrence of concentration-dependent aggregation was also performed at 0.1, 0.3, and 0.5 absorbance units at 280 nM (A280) for WHCV-SP. Runs were accomplished at rotor speeds of 46,000 rpm and 30,000 rpm for RBDs and hACE2, respectively. For WHCV-SP and Omicron-SP, SV experiments were performed at A280 values of 0.25, 0.5, and 1.0. WHCV-SP and Omicron-SP were run at 30,000 rpm. Data were analyzed with SEDFIT (https://sedfitsedphat.github.io/) and plotted using Graphpad.
2.4 Matrix-assisted laser desorption/ionization time-of-flight (MALDI/TOF) mass spectrometry
MALDI-TOF mass spectra were acquired using the Bruker UltrafleXtreme equipped with a Nd:YAG laser operating at a repetition rates between 50 and 200 Hz for linear-mode acquisition of protein mass spectra and between 200 and 2,000 Hz for reflectron-mode acquisition of peptide mass spectra. A supersaturated solution of sinapinic acid in 50:50 water-acetonitrile with 0.5% trifluoroacetic acid was chosen as the matrix. Images were processed with flexImaging 4.1 software (Bruker Daltonics, Bremen, Germany) to generate ion maps with a semiquantitative color scale bar normalized to total ion count. MALDI-TOF mass spectra represent the summation of 1000 to 10,000 laser pulses.
2.5 Deglycosylation
Deglycosylation of RBDs was performed under denaturing conditions using PNGaseF (New England BioLabs) before analyzing with SDS-PAGE and Western blots (WB). WBs were completed using 12% tris-glycine gels. Blots were blocked using 5% milk in PBST (blocking buffer) before incubating in blocking buffer containing 5 μg/mL of Galanthus Nivalis Lectin (GNL) Biotinylated (Vector Laboratories, Burlingame, CA) for 90 min at 37 °C. Blots were washed with PBS before incubation in 2 μg/mL streptavidin-horseradish peroxidase (HRP) (ThermoFisher Scientific) in blocking buffer for 1 hr. The blots were developed with ECL substrate (Bio Rad).
2.6 ELISA
ELISA plates were coated with 8 μg per well. Untreated control samples, as well as WHCV-RBD treated with PNGaseF were included on plates. Samples were treated under native conditions. Control samples were processed under similar conditions but lacked PNGaseF. Plates were then incubated overnight at 4 °C. Recombinant hACE2 was added at a concentration range of 0-450 nM and allowed to bind to the plate at room temperature for at least 1 hr. ACE2 antibody was added at a dilution of 1:1000 (ThermoFisher Scientific). This was followed by incubation with HRP-conjugated goat-anti-rabbit IgG at a 1:2000 dilution (BioRad). Substrate (Fisher Scientific) was added to each well and reactions were stopped with sulfuric acid. All dilutions from the coating to the secondary antibody step were done in PBS. PBS was also used for washes in between steps.
3 Results and discussion
SARS-CoV-2, the virus that causes COVID-19, was first identified in 2019 when an epidemic broke out in Wuhan, China. Since then, viral spread has advanced to pandemic-level proportions, claiming millions of lives worldwide. Several VOC have emerged in the last two years since the original strain was first identified. Some of these appeared to be more virulent and/or transmissible than others. For this study, we expressed and characterized the receptor binding domains (RBDs; of WHCV-SP (Fig. 1) and compared its properties to that of six SARS-CoV-2 VOC/VOI as defined by the WHO (summarized in Table 1), as well as the human angiotensin converting enzyme-2 (hACE2) receptor. The recombinant (r) RBDs were then utilized for the assessment of differences in binding affinity to rhACE2.
AUC was used to determine sedimentation coefficients for these RBDs and hACE2 ( Supplementary Fig. 1 ). RBDs of all SARS-CoV-2 variants showed S0 20,w values ranging from 2.5 to 2.6S (Table 2 ), consistent with the expected values for monomers of the predicted size of the RBDs. Other groups have reported the occurrence of heterogenous monomer/dimer populations and employed various strategies to combat this problem [14,15]. It has been suggested that the occurrence of a free cysteine residue at position 538 on the SP encourages dimer formation in rRBDs [16]. However, it is interesting to note that although our RBD constructs did retain C538, dimer formation was not observed except for WHCV-RBD and the Alpha-RBD. Even when dimer formation was observed, the percentage was <10% of the total protein. Both WHCV-SP and Omicron-SP were also analyzed using AUC. WHCV-SP existed as a mixture of approximately 60% monomers and 25% trimers whereas the ratio of monomer to trimer ratio differed only slightly for the Omicron-SP. For this variant, a mixture of 44% monomer and 30% trimer was observed. The Omicron-SP variant also precipitated to a large extent in PBS, which was the storage buffer used for all the proteins reported here. For this reason, we were unable to conduct binding studies using isothermal titration calorimetry (ITC) as was accomplished with WHCV and all RBD variants. Sedimentation velocity (SV) experiments also revealed the dimeric nature of hACE2 which had an S0 20,w value of 9.2 S (Supplementary Fig. 1 and Table 2).Table 2 Analytical ultracentrifugation analysis of SP/RBD variants.
Table 2Protein S020,w Mol. Wt. Expt'l kDaa Mol. Wt. Calc'd kDa
Alpha-RBD 2.55 ± 0.01 30.0 26,817
Beta-RBD 2.54 ± 0.01 30.0 26,802
Delta-RBD 2.58 ± 0.01 29.2 26,838
Delta + -RBD 2.60 ± 0.02 30.1 26,824
Gamma-RBD 2.48 ± 0.01 29.3 26,789
Omicron-RBD 2.53 ± 0.01 30.5 26,901
WHCV-RBD 2.49 ± 0.04 29.9 26,768
hACE2-MBP 9.15 ± 0.02 137 129,160
WHCV-SP 6.89 ± 0.01 159 135,035
Omicron-SP 7.05 ± 0.06 158 135,185
a Average of two determinations.
Matrix-assisted laser desorption/ionization-time-of-flight (MALDI-TOF) mass spectrometry was used to determine the molecular weights of each variant RBD, WHCV-SP, and hACE2. The molecular weight of hACE2 (with the MBP attached) was found to be ∼ 137 kDa, which was as expected. The molecular weights of WHCV-SP and Omicron-SP were determined as 159 kDa and 15.4 kDa, respectively (Table 2). The molecular weights of the RBDs ranged from 29 to 30 kDa with minor variations between the variants. SDS-PAGE analysis of purified recombinant RBDs revealed the presence of two glycoforms in each variant analyzed (Fig. 2 ). To confirm that the proteins present were glycoforms of the same initial protein, each variant was treated with Peptide-N-Glycosidase F (PNGaseF) and analyzed via SDS-PAGE and Western blot (Fig. 2). Treatment with PNGaseF resulted in the generation of the same single band of lower molecular weight in each variant (Fig. 2), implying that the two bands observed were indeed two glycoforms of the same protein. This was confirmed by Western blot, using a biotinylated lectin, Galanthus Nivalis Lectin (GNL), that binds to mannose residues. Treatment with PNGaseF resulted in a drastic reduction in size reflective of the of elimination of N-linked glycans compared to the untreated control. The products of the PNGaseF-treated RBDs did not stain in Western blots with GNL ((Fig. 2B), showing the complete removal of N-linked carbohydrate from each of the RBDs and further demonstrate that the two bands in WHCV-RBD were glycosylation variants of this protein. There are two N-linked glycosylation consensus sequences in the RBDs at N331 and N343, which are fully occupied when the RBD is expressed in HEK293 cells [17]. Our more indirect evidence provides support for this conclusion when the RBDs are expressed in S2 cells. We do not have evidence to comment on the single O-linked glycosylation site found in RBD at T323 expressed in HEK293 cells [17].Fig. 2 SDS gel electrophoretograms of RBD variants before and after, respectively, treatment with PNGaseF. A. Coomasie-blue stained gels showing each RBD variant before and after deglycosylation with PNGaseF: Lanes 1, 10, 11 and 19 are molecular weight controls; Lanes 2 and 3, Alpha-RBD before and after deglycosylation; Lanes 4 and 5, Beta-RBD before and after deglycosylation; Lanes 6 and 7, Delta-RBD before and after deglycosylation; Lanes 8 and 9, Delta + -RBD before and after deglycosylation; Lanes 12 and 13, Gamma-RBD before and after deglycosylation; Lanes 14 and 15, Omicron-RBD before and after deglycosylation; Lanes 16 and 17, WHCV-RBD before and after deglycosylation; Lane 18, PNGaseF alone. B. Western analysis of the same samples as in A and in the same order as in A. The gels were stained with biotinylated-Galanthus Nivalis Lectin (GNL) lectin, washed with PBS, and incubated with streptavidin-HRP for visualization after addition of the chemiluminescent HRP substrate, ECL.
Fig. 2
Fig. 3 Binding affinity of glycosylated versus deglycosylated RBD of SARS-CoV-2 to hACE2. (A) ELISA using WHCV-RBD treated with PNGaseF and a control WHCV-RBD which was not deglycosylated. (B) Gel showing -PNGaseF (-PF) and + PNGaseF (+PF) RBD samples used for the ELISA experiment.
Fig. 3
Binding affinities of RBD mutants to hACE2 were assessed using ITC. Supplementary Fig. 2 shows representative data for each variant as compared to WHCV-SP and WHCV-RBD. These experiments were repeated at least once to confirm dissociation constants. The KD values for each variant ranged from2 nM, as observed for Alpha-RBD to over 30 nM as observed for the Delta + -RBD (Table 3 ). All but one of the variants determined showed tighter binding to the hACE2 receptor as compared to WHCV-RBD which showed a dissociation constant of ∼14 nM. It is not clear why Delta + -RBD exhibited such a large difference from the pattern observed for the other variants. The closest sequence to Delta + -RBD, viz., Delta-RBD, differs by only one mutation-K417N, yet its binding affinity was found in our studies to be 6X that of Delta + -RBD. This can be explained by the fact that this mutation does reduce binding to the hACE2 receptor and that its presence might therefore result in a net reduction in overall affinity. One study showed that the K417N mutation alone led to a 5.6-fold reduction in affinity to hACE2 as compared to the WHCV-SP [18]. This K417N mutation, although also present in Beta-RBD, did not have the same effect on binding affinity. Accompanying the K417N mutation in this variant, are two other mutations, E484K and N501Y, which were shown in the aforementioned study to have opposite effects on binding affinity. Although the effect is rather modest for E484K (1.2-fold), an 11-fold increase in affinity is seen for the N501Y mutation when existing without any other mutations. The effects of this mutation combination could explain the tighter binding observed in our studies in Beta-RBD. Another study also reported a reduced affinity of the K417N mutant to hACE2 [19]. In that investigation, the authors used a rSP ectodomain with the D614G mutation as their WHCV-SP and found that there was a 0.6-fold change in binding to hACE2 upon introduction of the K417N mutation into the RBD of this recombinant SP. Although both of the aforementioned studies reported reduced binding in the K417N mutant, one result was more dramatic that the other. This may be due to the fact that the first study used the RBD for binding whereas the second study used the SP ectodomain. The methods used between the two groups were also different and could have led to differences in sensitivity. The first group used surface plasmon resonance (SPR), whereas the second used biolayer interferometry.Table 3 Dissociation constants of the variant RBD/SP-hACE2 complex.
Table 3Protein KD (nM)
Alpha-RBD 1.8 ± 0.4
Beta-RBD 5.0 ± 0.4
Delta-RBD 5.6 ± 2.1
Delta + -RBD 33 ± 2
Gamma-RBD 2.7 ± 0.2
Omicron-RBD 3.0 ± 0.3
WHCV-RBD 14 ± 1
WHCV-SP 44 ± 6
Glycosylation is a post-translational modification that can have important implications for the cell. N-linked glycosylation contributes to protein folding, quality control, signal transduction, and viral attachment [20]. Glycosylation of the HIV virus assists in the evasion of the immune response via shielding of immunogenic epitopes [21]. Glycosylation patterns also vary based on the organism in which proteins are expressed. In an effort to determine whether the glycosylation pattern affected binding affinity between hACE2 and WHCV-RBD, this protein was treated with PNGaseF under native conditions. Samples were then analyzed using SDS-PAGE to confirm that deglycosylation was successful. These samples were later used to coat ELISA plates for the determination of binding affinity to hACE2. The binding affinities of PNGase-treated WHCV-RBD was compared to that of a control sample that was not treated with PNGase and was found to be essentially the same (Fig. 3), indicating that N-linked glycosylation did not affect the binding affinity.
Various groups have conducted investigations aimed at determining the binding affinity of the Omicron variant to hACE2. However, the results of comparisons between their WT-SP and Omicron-variants are inconsistent. A variety of methods including, but not limited to, microscale thermophoresis (MST) [22], flow cytometry [23], SPR [[23], [24], [25]] and ELISA [26] were employed in these assessments. This variant, unlike the others that emerged before it, presented with a large number of mutations in the RBD (Table 1). According to the CDC, the Omicron variant was detected in every U.S. state and territory by December 2021 and is currently the dominant variant in the U.S. For our studies, we used ITC to determine the KD of Omicron-RDB to hACE2 in solution and found that it was almost 5-fold less than the WHCV, but similar to most of the other variants. The binding affinity did not therefore appear to account for the differences in transmissibility between Omicron and the other variants, although it may be partly responsible for the changes seen when compared to WHCV-RBD.
Alpha- and Gamma-RBDs both demonstrated a greater affinity for hACE2 as compared to WHCV-RBD, consistent with previous findings [22,26,27]. The Beta- and Delta-RBDs also had lower dissociation constants than WHCV-RBD as reported by other studies. One report showed no difference in hACE2 binding affinity between Delta-RBD and WT-RBD (18). Another study comparing the binding affinity of Beta-RBD to their prototype RBD also failed to demonstrate any difference in binding to hACE2 [24].
In summary, our results show similar binding affinities among the various SP RBDs of seven different SP variants, most of which were at one point VOC. All but one of the variants assessed had a greater affinity for hACE2 than WHCV-RBD. This suggests that the difference in transmissibility observed may be in part due to mutations that encourage tighter binding. Overall, this study shows that the binding of the virus with its receptor is consistent with the infectivity of the parent virus and provides a framework for studies examining other important aspects of the SARS-CoV-2 infectious mechanism.
Funding
These studies were supported by internal funds from the 10.13039/100008109 University of Notre Dame, United States .
Author contributions
J.C. and N.M performed the experiments, V.A.P. assisted in drafting the final manuscript, and F.J.C. conceived the project and wrote the initial and final drafts of the manuscript.
Declaration of competing interest
None for any author.
Appendix A Supplementary data
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Acknowledgments
We thank Zhong Liang for his assistance with the cloning of the RBD variants.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbrc.2022.12.011.
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| 0 | PMC9721372 | NO-CC CODE | 2022-12-14 23:52:22 | no | Biochem Biophys Res Commun. 2023 Jan 22; 641:61-66 | utf-8 | Biochem Biophys Res Commun | 2,022 | 10.1016/j.bbrc.2022.12.011 | oa_other |
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Antiviral Res
Antiviral Res
Antiviral Research
0166-3542
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The Authors. Published by Elsevier B.V.
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10.1016/j.antiviral.2022.105481
105481
Article
Phenothiazines inhibit SARS-CoV-2 cell entry via a blockade of spike protein binding to neuropilin-1
Hashizume Mei a
Takashima Ayako a
Ono Chikako b
Okamoto Toru cd
Iwasaki Masaharu ad∗
a Laboratory of Emerging Viral Diseases, International Research Center for Infectious Diseases, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Osaka, 565-0871, Japan
b Laboratory of Virus Control, Center for Infectious Disease Education and Research, Osaka University, Suita, Osaka, Japan
c Institute for Advanced Co-Creation Studies, Research Institute for Microbial Diseases, Osaka University, Suita, Osaka, Japan
d Center for Infectious Disease Education and Research (CiDER), Osaka University, 2-8 Yamadaoka, Suita, Osaka, 565-0871, Japan
∗ Corresponding author. Laboratory of Emerging Viral Diseases, International Research Center for Infectious Diseases, Research Institute for Microbial Diseases, Osaka University, 3-1 Yamadaoka, Suita, Osaka, 565-0871, Japan.
5 12 2022
5 12 2022
10548115 9 2022
2 12 2022
4 12 2022
© 2022 The Authors. Published by Elsevier B.V.
2022
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters cells using angiotensin-converting enzyme 2 (ACE2) and neuropilin-1 (NRP-1) as the primary receptor and entry co-factor, respectively. Cell entry is the first and major step in initiation of the viral life cycle, representing an ideal target for antiviral interventions. In this study, we used a recombinant replication-deficient vesicular stomatitis virus-based pseudovirus bearing the spike protein of SARS-CoV-2 (SARS2-S) to screen a US Food and Drug Administration-approved drug library and identify inhibitors of SARS-CoV-2 cell entry. The screen identified 24 compounds as primary hits, and the largest therapeutic target group formed by these primary hits was composed of seven dopamine receptor D2 (DRD2) antagonists. Cell-based and biochemical assays revealed that the DRD2 antagonists inhibited both fusion activity and the binding of SARS2-S to NRP-1, but not its binding to ACE2. On the basis of structural similarity to the seven identified DRD2 antagonists, which included six phenothiazines, we examined the anti-SARS-CoV-2 activity of an additional 15 phenothiazines and found that all the tested phenothiazines shared an ability to inhibit SARS2-S-mediated cell entry. One of the phenothiazines, alimemazine, which had the lowest 50% effective concentration of the tested phenothiazines, exhibited a clear inhibitory effect on SARS2-S–NRP-1 binding and SARS-CoV-2 multiplication in cultured cells but not in a mouse infection model. Our findings provide a basis for the development of novel anti-SARS-CoV-2 therapeutics that interfere with SARS2-S binding to NRP-1.
Keywords
SARS-CoV-2
COVID-19
Phenothiazine
Neuropilin-1
==== Body
pmc1 Introduction
The rapid global spread of coronavirus disease 2019 (COVID-19) caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) resulted in an unprecedented pandemic with more than 639 million cases and more than 6 million deaths to date (https://www.who.int/emergencies/diseases/novel-coronavirus-2019). Several COVID-19 vaccines, chiefly mRNA-based vaccines, have been used worldwide and have significantly contributed to the prevention of SARS-CoV-2 infection and COVID-19 progression to severe disease (National Center for, 2020). However, the continuous emergence of SARS-CoV-2 variants with accumulated mutations that attenuate vaccine efficacy and the incompatibility of vaccine strategies for immunocompromised patients underscore the need for safe, effective, and readily available antivirals against SARS-CoV-2. Remdesivir, a nucleoside analog prodrug, originally developed as an Ebola virus disease treatment, is the first US Food and Drug Administration (FDA)-approved anti-SARS-CoV-2 drug (Beigel et al., 2020; Rubin et al., 2020). Remdesivir, which needs to be administrated intravenously, may be used for COVID-19 patients who require oxygen supplementation or have a pre-existing disease that could increase their risk of serious illness (Schooley et al., 2021). Baricitinib, a Janus kinase (JAK) inhibitor, is an oral medication initially approved to treat rheumatoid arthritis (Al-Salama and Scott, 2018). Recently, baricitinib was approved by the FDA for the treatment of COVID-19 (Rubin, 2022). However, use of baricitinib is limited to hospitalized adults with COVID-19 requiring oxygen supplementation, non-invasive or invasive mechanical ventilation, or extracorporeal membrane oxygenation (ECMO). These limitations further emphasize the importance of expanding the number of potential anti-SARS-CoV-2 drug candidates. Given that the COVID-19 pandemic is an ongoing crisis, the repositioning of already existing, clinically used drugs with known safety profiles is the most practical approach for the rapid development of COVID-19 treatments (Riva et al., 2020).
The SARS-CoV-2 life cycle can be divided into several steps: cell entry, genome translation, subgenomic transcription, genome replication, and progeny virion formation. Of those distinct steps of the virus life cycle, cell entry is the first, and this critical step in the initiation of the viral life cycle represents an ideal target for antiviral interventions. The spike protein of SARS-CoV-2 (SARS2-S) is responsible for both receptor recognition and membrane fusion. SARS2-S is proteolytically processed in-cis by the host protease furin at the S1/S2 boundary, generating the mature virion surface glycoprotein complex composed of noncovalently associated S1 and S2. Subsequently, S2 further undergoes priming at the S2’ site in-trans by host serin proteases, which include TMPRSS2 at the plasma membrane and cathepsins in the endosomes, to expose the fusion peptide of S2; this process is required for membrane fusion (Hoffmann et al., 2020a). S1 binds to the cell surface angiotensin-converting enzyme 2 (ACE2) (Yu et al., 2022), which was identified as the primary receptor for SARS-CoV as well (Li et al., 2003). In contrast with the spike protein of SARS-CoV, SARS2-S can be cleaved by furin, generating an Arg-Arg-Ala-Arg (RRAR) sequence at the C-terminus end, and this sequence matches an [R/K]XX[R/K] motif, called the C-end Rule (CendR). As with other proteins that conform to the CendR, the furin-cleaved S1 can bind to neuropilin-1 (NRP-1), and the S1–NRP-1 interaction facilitates ACE2-dependent cell entry (Cantuti-Castelvetri et al., 2020; Daly et al., 2020).
In the present study, we have used a recombinant replication-deficient vesicular stomatitis virus (VSV)-based pseudovirus bearing SARS2-S to screen a US FDA-approved drug library and identify novel drug candidates that specifically inhibit SARS2-S-mediated cell entry.
2 Materials and methods
2.1 ELISA-based SARS2-S–NRP-1 binding assay
The effects of the test compounds on SARS2-S binding to NRP-1 were analyzed with a RayBio COVID-19 Spike-NRP1 binding assay kit (RayBiotech, Peachtree Corners, GA, USA). Compounds were separately added to the recombinant NRP-1 (rNRP-1)-coated microplate in the presence of recombinant SARS2-S S1 domain (rS1), and the microplate was incubated at room temperature for 2.5 h. The microplate was then washed four times to remove unbound rS1, and rNRP-1-bound rS1 was reacted with a mouse anti-S1 IgG detection antibody, followed by an HRP-conjugated anti-mouse secondary IgG. TMB (3,3′, 5,5′-tetramethyl benzidine) substrate was used to detect HRP activity, which was halted by the addition of the Stop Solution (0.2 M sulfuric acid). The absorbance at 450 nm was measured using a multi-mode microplate reader (SpectraMax iD5, Molecular Devices, San Jose, CA, USA).
2.2 Statistical analysis
GraphPad Prism 9 (GraphPad, San Diego, CA, USA) was used for all the statistical analyses. Statistical significance was analyzed by one-way ANOVA, and statistically significant differences were determined by a Dunnett's multiple comparisons test unless otherwise indicated.
The detailed materials and methods are described in Appendix A, SI Materials and Methods.
3 Results
3.1 Screening of a US FDA-approved library to identify inhibitors of SARS2-S-mediated cell entry
We conducted VSV pseudotype-based screening using a US FDA-approved drug library containing 1061 compounds to identify novel inhibitors of SARS2-S-mediated cell entry. We used a VSV pseudotype encoding green fluorescent protein (GFP) instead of the VSV glycoprotein (VSVG) and bearing a SARS2-S (rVSVΔG-GFP/SARS2-S) from the original strain of SARS-CoV-2 (Wu et al., 2020) containing a D614G substitution. The D614G substitution contributes to increased viral transmissibility and has been retained in all SARS-CoV-2 variants of concern (VOCs) (Korber et al., 2020). We also used a VSV pseudotype bearing VSVG (rVSVΔG-GFP/VSVG), which allowed us to identify the compounds that had non-specific or VSV replication-specific inhibitory effects. VSV pseudotypes were used to inoculate HEK293T cells constitutively expressing human ACE2 (HEK293T/ACE2) that had been treated with each compound at 10 μM. At 16 h post-infection (hpi), the GPF-positive (VSV pseudotype-infected) cell and nuclei numbers were determined by high-content imaging analysis (Fig. 1 A). The number of nuclei was determined as an initial assessment of the cytotoxicity of the test compounds. Primary hits were selected on the basis of a strong reduction in the rVSVΔG-GFP/SARS2-S-infected cell number (<25%), but limited impacts on rVSVΔG-GFP/VSVG-infected cell (>75%) number and number of nuclei (>75%) compared with vehicle treatment (Table 1 and Appendix B, Dataset S1).Fig. 1 Screening of a US FDA-approved drug library to identify inhibitors of SARS-CoV-2 spike protein (SARS2-S)-mediated cell entry. (A) Screening flow chart. (B) Dose-dependent inhibitory effects of dopamine receptor D2 (DRD2) antagonists on pseudovirus infection. HEK293T/ACE2 cells that had been treated with three-fold serial dilutions of each of the DRD2 antagonists or with vehicle (0.1% DMSO) for 90 min were inoculated with vesicular stomatitis virus (VSV)-based pseudovirus bearing SARS2-S (rVSVΔG-GFP/SARS2-S, SARS2-S) or the VSV glycoprotein (rVSVΔG-GFP/VSVG, VSVG). The DRD2 antagonists and DMSO were present throughout the experimental period. At 16 h post-inoculation, the cells were fixed, and their nuclei were stained with Hoechst 33,342. The GFP-positive (virally infected) cells were quantified with a high-content imaging system. The mean value of vehicle-treated, VSV-based pseudovirus-inoculated cells was set to 100%. The cell viability of HEK293T/ACE2 cells treated with three-fold serial dilutions of each of the DRD2 antagonists or with vehicle (0.1% DMSO) for 17.5 h was determined with CellTiter 96 AQueous One Solution Reagent. The mean value of vehicle-treated, uninfected cells was set to 100%. The presented data are the mean ± SD of the results of four replicates. EC50 values for the rVSVΔG-GFP/SARS2-S-inoculated cells are indicated in each graph.
Fig. 1
Table 1 Inhibitory potencya of 24 primary hits against pseudovirus infection identified in the US FDA-approved drug library screening and their pharmacological target molecules.
Table 1Compound Target rVSVΔG-GFP/SARS2-S rVSVΔG-GFP/VSVG
GFP (%) Hoechst (%) GFP (%) Hoechst (%)
Bazedoxifene acetate NR3A (ESR) 12.26 88.96 115.01 102.51
Toremifene NR3A (ESR)
IGF1R (CD221) 21.77 101.27 142.24 90.44
Tamoxifen citrate NR3A (ESR) 16.27 99.67 105.72 97.14
Toremifene citrate NR3A (ESR)
IGF1R (CD221) 8.32 99.98 109.35 98.74
Raloxifene hydrochloride NR3A (ESR) 9.66 101.70 97.55 103.53
Vandetanib (ZD6474) VEGFR2
EGFR
RET 20.29 104.11 107.38 99.04
AZD-9291 mesylate EGFR 5.36 98.67 112.06 98.67
Prochlorperazine Maleate DRD2 16.56 97.05 131.30 93.26
Thioridazine hydrochloride DRD2 15.80 98.59 118.64 95.38
Fluphenazine hydrochloride DRD2 19.31 103.35 106.20 100.59
Perphenazine DRD2 17.90 104.54 113.49 86.64
Trifluoperazine dihydrochloride DRD2 8.63 107.56 129.52 97.29
Chlorpromazine hydrochloride HTR2
HRH1
ADRA2
DRD2
CHRM 24.88 98.41 95.70 97.98
Asenapine Maleate HTR1A
HTR2A
HTR2C
HTR6
HTR7
DRD2 14.81 98.20 150.43 95.98
Sertraline hydrochloride SLC6A4 (HTT) 15.85 101.27 104.62 97.33
Trimipramine maleate SLC6A2 (NAT1)
SLC6A4 (HTT) 23.16 103.55 90.44 99.90
Nortriptyline hydrochloride SLC6A2 (NAT1)
SLC6A4 (HTT) 17.44 81.23 91.71 76.77
Amoxapine SLC6A4 (HTT)
SLC6A2 (NAT1) 23.68 102.06 116.48 99.31
Paroxetine hydrochloride SLC6A4 (HTT) 21.00 102.73 112.39 98.03
Clemastine fumarate HRH1 12.46 106.94 104.53 101.47
Dronedarone hydrochloride CACNA1-L
KCND3
KCNH2
KCNQ1
KCNJ3
KCNJ5
KCNJ11
ADRA1
ADRB1 10.87 99.72 138.68 101.34
Azithromycin dihydrate 50S ribosomal subunit 18.36 100.22 101.10 101.30
Cinacalcet hydrochloride CASR 23.68 97.33 131.09 95.94
Imatinib Mesylate (STI571) BCR-ABL
FIP1L1-PDGFRA
KIT (CD117) 20.79 96.53 103.15 94.49
a The mean value of vehicle-treated cells was set to 100%.
The classification of the 24 primary hits by their target molecules uncovered dopamine receptor D2 (DRD2) as the most common therapeutic target of the primary hits. One of the DRD2 antagonists, asenapine (maleate salt), was also identified as one of four top hits in another US FDA-approved drug library screening (hydrochloride salt version in a previous study) (Xiong et al., 2020), supporting the robustness of our screening system. These seven DRD2 antagonists were selected for further study, and experiments to validate their specificity and assess the correlation between the efficacy and cytotoxicity were conducted with freshly prepared compounds. All seven DRD2 antagonists exhibited dose-dependent inhibitory effects on rVSVΔG-GFP/SARS2-S with 50% effective concentrations (EC50s) ranging from 2.93 to 6.06 and selective indexes (SIs) [SI = 50% cytotoxic concentration (CC50)/EC50] ranging from 2.34 to 24.74 at concentrations that did not cause a significant reduction in rVSVΔG-GFP/VSVG infectivity (Fig. 1B and Table S1).
3.2 The DRD2 antagonists inhibit SARS2-S-mediated membrane fusion without affecting SARS2-S–ACE2 binding
SARS-CoV-2 enters the cell via the cell surface pathway or the endocytic pathway depending on the location where the S2’ priming takes place (Hoffmann et al., 2020a). In either mode of cell entry, membrane fusion is a final and critical process, by which genome RNA is released from the viral particle into the cell cytoplasm for the initiation of genome RNA translation. To determine the effect of the seven DRD2 antagonists on SARS2-S-mediated membrane fusion, we examined whether treatment with the DRD2 antagonists prevented SARS2-S-mediated cell fusion following the co-culture of SARS2-S-expressing HEK293T and HEK293T/ACE2 cells (Ou et al., 2020). HEK293T transfectants expressing SARS2-S and ZsGreen were added onto HEK293T/ACE2 cells treated with the DRD2 antagonists at a concentration of 10 μM. ZsGreen was expressed to fluorescently visualize the area of cytoplasm. After a 4 h co-culture, the magnitude of membrane fusion was evaluated by the average number of nuclei in each syncytium. Consistent with the VSV-pseudotype infection assay results, all the DRD2 antagonists strongly inhibited SARS2-S-mediated membrane fusion (Fig. 2 A). One of the DRD2 antagonists, chlorpromazine, has been reported to inhibit clathrin-mediated endocytosis (Daniel et al., 2015), and could have affected the cell surface expression of host cell proteins including ACE2 during pretreatment. To address this issue, we examined the effects of the DRD2 antagonists on the cell surface expression of ACE2 by detecting the ACE2 levels in biotinylated cell surface proteins (Fig. S1A) and by flow cytometry using an antibody that recognizes the extracellular domain of ACE2 (Fig. S1B). We confirmed that treatment with any of the DRD2 antagonists did not affect the cell surface expression of ACE2. In agreement with these results, we confirmed the inhibitory effect of the DRD2 antagonists in a fusion assay without pretreatment (Fig. S1C).Fig. 2 The dopamine receptor D2 (DRD2) antagonists inhibit the SARS-CoV-2 spike protein (SARS2-S)-mediated membrane fusion but not SARS2-S binding to ACE2. (A) Effect of the DRD2 antagonists on SARS2-S-mediated membrane fusion. HEK293T cells transfected with plasmids expressing SARS2-S and ZsGreen (ZsG) were detached with trypsin and overlaid on HEK293T/ACE2 cells that had been pretreated with 0.1% DMSO or one of the DRD2 antagonists (10 μM) for 90 min. DMSO and the DRD2 antagonists were present throughout the experimental period. After a 4-h incubation, the cells were fixed, and their nuclei were stained with Hoechst 33,342. Fluorescent images of the cells were captured (left), and the average number (ave. #) of nuclei in each syncytium was determined with a high-content imaging system (right). The presented data are the mean ± SD of the results of four independent experiments. Statistical significance was determined by comparing the average nuclei numbers of the DRD2 antagonist-treated samples with that of the DMSO-treated samples. **p < 0.01. (B) Inhibitory effect of the DRD2 antagonists on the binding of SARS2-S to ACE2. An ELISA-based SARS2-S–ACE2 binding assay was performed in the presence of 0.1% DMSO, the neutralizing antibody anti-SARS2-S S1 domain (anti-S1 Ab, 5 μg/mL), or one of the DRD2 antagonists (10 μM or 100 μM). The mean chemiluminescent signal intensity of DMSO-treated samples was set to 1. The presented data are the mean ± SD of the results of three replicates. ND, not detected.
Fig. 2
Several of the previously reported inhibitors of SARS-CoV-2 cell entry were suggested to exert their inhibitory effect by disturbing the interaction between SARS2-S and the primary cell entry receptor, ACE2 (Fu et al., 2021; Taha et al., 2022; Wang et al., 2021). We then examined whether the DRD2 antagonists interrupted the SARS2-S–ACE2 interaction by performing an enzyme-linked immunosorbent assay (ELISA)-based SARS2-S–ACE2 binding assay. Recombinant SARS2-S receptor-binding domain (rRBD)-coated microplate was incubated with recombinant ACE2 fused with a His tag (rACE2-His) in the presence of a DRD2 antagonist, an anti-S1 neutralization antibody (Anti-S1 Ab), or the treatment vehicle (DMSO). After the microplate was extensively washed, the rRBD-bound rACE2-His was detected using HRP-conjugated anti-His antibody. As expected, anti-S1 Ab strongly blocked rRBD binding to rACE2-His (Fig. 2B). However, the DRD2 antagonists did not affect the interaction between rRBD and rACE2-His at concentrations as high as 100 μM, indicating that the DRD2 antagonists disrupt the SARS2-S binding to a cell host protein other than ACE2.
3.3 The DRD2 antagonists block SARS2-S–NRP-1 interaction
SARS-CoV-2 uses NRP-1 to facilitate ACE2-mediated cell entry. Although there are conflicting results regarding NRP-1 expression in HEK293T cells (Cantuti-Castelvetri et al., 2020; Daly et al., 2020), the inability of the DRD2 antagonists to block SARS2-S binding to ACE2 led us to explore the possibility that the SARS2-S–NRP-1 interaction was disrupted by the DRD2 antagonists. NRP-1 was readily detected in HEK293T/ACE2 cell lysates by western blotting, and its expression was significantly reduced by NRP-1-specific small interfering RNA (siRNA) treatment (Fig. 3 A). In the fusion assay, the formation of syncytia was suppressed when SARS2-S- and ZsGreen-expressing HEK293T cells and HEK293T/ACE2 cells were treated with siRNA against NRP-1 as compared with that when these cells were treated with non-specific siRNA (siControl) (Fig. 3B), indicating that NRP-1 plays an important role in SARS2-S-mediated cell entry in our experimental system. We then asked whether the DRD2 antagonists could inhibit the binding of SARS2-S to NRP-1. For this, we employed an ELISA-based SARS2-S–NRP-1 binding assay, where a rNRP-1-coated microplate was incubated with rS1 in the presence of the DRD2 antagonists and rNRP-1-binding rS1 was detected using anti-S1 Ab. The DRD2 antagonists inhibited the interaction between SARS2-S and NRP-1 at the concentrations as low as 10 μM (Fig. 3C). EG00229 is a small compound that is reported to bind to the b1 domain of NRP-1 preventing SARS2-S–NRP-1 binding (Daly et al., 2020), and can be a benchmark to evaluate the inhibitory potency of the DRD2 compounds on SARS2-S–NRP-1 binding. In line with the previous observation that EG00229 inhibited SARS-CoV-2 infection at 100 μM (Daly et al., 2020), EG00229 required a higher concentration (1 mM) to inhibit SARS2-S–NRP-1 binding (Fig. S2), compared with the DRD2 antagonists, suggesting a correlation between inhibitory effects of the compounds on SARS2-S–NRP-1 binding and SARS2-mediated cell entry.Fig. 3 The dopamine receptor D2 (DRD2) antagonists block binding of the spike protein of SARS-CoV-2 (SARS2-S) to neuropilin-1 (NRP-1). (A and B) Role of NRP-1 in SARS2-S-mediated membrane fusion with HEK293T/ACE2 cells. HEK293T/ACE2 cells were transfected with siRNA against NRP-1 (siNRP-1) or with non-targeting siRNA (siControl). At 48 h post-transfection, total cell lysate was prepared, and the protein levels of NRP-1 and GAPDH in the cell lysates were examined by western blotting (A). siNRP-1-treated HEK293T cells transfected with plasmids expressing SARS2-S and ZsGreen (ZsG) were detached with trypsin and overlaid on siNRP-1-treated HEK293T/ACE2 cells. For a control, siControl was used instead of siNRP-1. After a 4-h incubation, the cells were fixed, and their nuclei were stained with Hoechst 33,342. Fluorescent images of the cells were captured (B, left), and the average number (ave. #) of nuclei in each syncytium was determined with a high-content imaging system (B, right). The presented data are the mean ± SD of the results of four independent experiments. Statistical significance was determined by a Student's t-test. **p < 0.01. (C) Effects of the DRD2 antagonists on the binding of SARS2-S to NRP-1. An ELISA-based SARS2-S-NRP-1 binding assay was performed in the presence of 0.1% DMSO or one of the DRD2 antagonists (10 μM or 100 μM). The mean absorbance value of the DMSO-treated samples was set to 1. The presented data are the mean ± SD of the results of three replicates. Statistical significance was determined by comparing the absorbance values of the DRD2 antagonist-treated samples with the value for the DMSO-treated samples. **p < 0.01.
Fig. 3
Taken together, these results indicate that the tested DRD2 antagonists inhibit SARS2-S-mediated cell entry by blocking the SARS2-S–NRP-1 interaction.
3.4 Alimemazine inhibits SARS-CoV-2 infection
Phenothiazines are a group of compounds that include several antipsychotic drugs already used in clinics. Notably, six out of the seven DRD2 antagonists identified by our screen are phenothiazine derivatives, indicating that phenothiazines may share the ability to inhibit SARS2-S-mediated cell entry. To investigate this possibility, the EC50s and CC50s for an additional 15 commercially available phenothiazines were determined. As expected, all of these additional phenothiazines specifically inhibited SARS2-S-mediated cell entry with variable potencies (EC50 = 2.42–14.1 μM) but did not affect VSVG-mediated cell entry (Fig. 4 and Table S2). Among the compounds we tested, alimemazine (tartrate salt) exhibited the lowest EC50 (2.42 μM) and had a high SI (12.2); thus, we selected it for use in downstream validation assays. Using the fusion assay, we confirmed that alimemazine treatment reduced the formation of syncytia induced by the expression of SARS2-S as well as by a mutant SARS2-S containing a deletion in aa positions 69 to 70 (Δ69–70) and the substitutions of N501Y and E484K, the characteristic mutations found in SARS-CoV-2 VOCs, suggesting that alimemazine could have an inhibitory effect on cell entry by a broad range of SARS-CoV-2 VOCs (Fig. 5 A). In addition, alimemazine inhibited the binding activity of SARS2-S to NRP-1 but not to ACE2 (Fig. 5B and C), suggesting that alimemazine inhibits SARS2-S-mediated cell entry through a mechanism similar to that used by the DRD2 antagonists. The serine protease TMPRSS2 plays a critical role in SARS-CoV-2 infection in vivo (Iwata-Yoshikawa et al., 2022a). We then assessed the inhibitory effect of alimemazine on VSV-based pseudovirus cell entry in Vero E6 cells expressing TMPRSS2 (VeroE6/TMPRSS2). Alimemazine significantly inhibited the SARS2-S-mediated, but not VSVG-mediated, cell entry in VeroE6/TMPRSS2 cells (Fig. S3A) at the concentration (10 μM) that did not affect the VeroE6/TMPRSS2 cell viability (Fig. S3B).Fig. 4 Dose-dependent inhibitory effects of phenothiazines on pseudovirus infection. HEK293T/ACE2 cells that had been treated with three-fold serial dilutions of a phenothiazine or with vehicle (0.1% DMSO) for 90 min were inoculated with vesicular stomatitis virus (VSV)-based pseudovirus bearing the spike protein of SARS-CoV-2 (rVSVΔG-GFP/SARS2-S, SARS2-S) or the VSV glycoprotein (rVSVΔG-GFP/VSVG, VSVG). The tested phenothiazine or DMSO was present throughout the experimental period. At 16 h post-inoculation, the cells were fixed, and their nuclei were stained with Hoechst 33,342. The number of GFP-positive (virally infected) cells was determined with a high-content imaging system. The mean value of vehicle-treated, VSV-based pseudovirus-inoculated cells was set to 100%. The cell viability of HEK293T/ACE2 cells treated with three-fold serial dilutions of each of the phenothiazines or with vehicle (0.1% DMSO) for 17.5 h was determined with CellTiter 96 AQueous One Solution Reagent. The mean value of the vehicle-treated, uninfected cells was set to 100%. The presented data are the mean ± SD of the results of four replicates. EC50 values for the rVSVΔG-GFP/SARS2-S-inoculated cells are indicated in each graph.
Fig. 4
Fig. 5 Antiviral effect of alimemazine on SARS-CoV-2 multiplication. (A) Effect of alimemazine on SARS2-S-mediated membrane fusion. HEK293T cells transfected with a plasmid expressing SARS2-S (left) or SARS2-S containing 69–70 deletion and the N501Y and E484K substitutions in addition to the D614G substitution (right) together with a plasmid expressing ZsGreen (ZsG) were overlaid on HEK293T/ACE2 cells that had been pretreated with 0.1% DMSO or 10 μM alimemazine for 90 min. DMSO and alimemazine were present throughout the experimental period. After a 4-h incubation, fluorescent images of the cells were captured (left), and the average number (ave. #) of nuclei in each syncytium was determined with a high-content imaging system (right). The presented data are the mean ± SD of the results of four independent experiments. Statistical significance was determined by a Student's t-test. **p < 0.01. (B and C) Effect of alimemazine on SARS2-S binding to ACE2 (B) or to NRP-1 (C). ELISA-based SARS2-S–ACE2 and SARS2-S–NRP-1 binding assays were performed in the presence of DMSO (0.1%), the neutralizing antibody anti-SARS2-S S1 domain (anti-S1 Ab; 5 μg/ml, used only in B), or alimemazine (10 μM or 100 μM). The mean chemiluminescence (B) and absorbance (C) values of DMSO-treated samples were each set to 1. The presented data are the mean ± SD of the results of three replicates. Statistical significance was determined by comparing the chemiluminescence (B) and absorbance (C) values of the experimentally treated samples with the values for the DMSO-treated samples. ns, p > 0.05, not significant; **p < 0.01; ND, not detected. (D) Effect of alimemazine on SARS-CoV-2 multiplication in cultured cells. HEK293T/ACE2 cells that had been pretreated with 0.1% DMSO or 10 μM alimemazine for 90 min were inoculated (MOI = 0.1) with SARS-CoV-2 TY7-501 strain. After 24 h, the cells in the tissue culture supernatant were collected, and the virus titers were determined by a TCID50 assay. The presented data are the mean and SD of the results of three independent experiments. Statistical significance was determined by a Student's t-test. **p < 0.01. (E) Effect of alimemazine on SARS-CoV-2 multiplication in mice. Eight-week-old BALB/c mice were treated with alimemazine (10 mg/kg) or saline by oral gavage 1 h prior to and 24 h after intranasal inoculation with SARS-CoV-2 TY7-501 strain (2×104 TCID50 per mouse). At 2 days post-viral inoculation, lung tissues were harvested, and the tissue titers were determined by a TCID50 assay. The presented data are the mean and SD of the results from four mice per group. Statistical significance was determined by a Student's t-test.
Fig. 5
Next, we assessed the effect of alimemazine on SARS-CoV-2 multiplication in cultured cells. HEK293T/ACE2 or VeroE6/TMPRSS2 cells were inoculated with a gamma SARS-CoV-2 variant, strain TY7-501, in the presence of alimemazine. Viral titers in the tissue culture supernatants were determined at 24 hpi. Consistent with the inhibitory effect of alimemazine on rVSVΔG-GFP/SARS2-S infectivity, alimemazine significantly reduced the production of infectious SARS-CoV-2 progeny (Fig. 5D and Fig. S3C). This clear inhibitory effect of alimemazine on SARS-CoV-2 multiplication in HEK293T/ACE2 and VeroE6/TMPRSS2 cells motivated us to investigate the efficacy of alimemazine in vivo. For this, we employed a mouse infection model of SARS-CoV-2 gamma variants to gain an assessment of the in vivo efficacy of alimemazine. SARS-CoV-2 gamma variants contain an N501Y mutation in the spike protein, which significantly increases the affinity to mouse ACE2 (Imai et al., 2021; Li et al., 2021; Winkler et al., 2022). Accordingly, intranasal inoculation of a SARS-CoV-2 gamma variant to a wildtype (WT) mouse results in a productive infection in the lungs, albeit without developing systemic manifestations, such as bodyweight loss (Imai et al., 2021). To investigate the antiviral effect of alimemazine in vivo, WT BALB/c mice were intranasally inoculated with SARS-CoV-2 strain TY7-501, and the lung viral titers were determined at 2 days post-inoculation. Treatment with two doses of 10 mg/kg alimemazine (at 1 h prior to and 24 h after virus inoculation, respectively) did not decrease the lung viral titer (Fig. 5E).
4 Discussion
Substantial drug repurposing efforts have resulted in the identification of several candidate drugs that inhibit SARS-CoV-2 cell entry. The mechanisms of action of those identified cell entry inhibitors involve disruption of the SARS2-S–ACE2 interaction (Fu et al., 2021; Taha et al., 2022; Wang et al., 2021) and reduction of the host protease activity (Chen et al., 2021; Hoffmann et al., 2020b; Yu et al., 2022) but are otherwise not well defined. In the present study, we showed that phenothiazines inhibit SARS2-S-mediated cell entry and block SARS2-S–NRP-1 binding. This novel mechanism of action may permit the use of phenothiazines not only as monotherapy options but also as drugs to use in combination with other antivirals that have distinct mechanisms of action to ideally produce synergistic antiviral effects. One advantage of combination therapy lies in its use of lower doses of each drug, thus reducing the risk of severe side effects and emergence of drug-resistant viral variants. Additionally, phenothiazines can be used as a molecular probe to facilitate the investigation of NRP-1-dependent cell entry by SARS-CoV-2.
The phenothiazine moiety has been observed to exhibit versatile biological properties, which led to the synthesis of its derivatives. Many such drugs are now used in clinics for the treatment of a variety of diseases, including psychotic diseases, targeting several neurotransmitter receptors and transporters, such as dopamine and histamine receptors (Jaszczyszyn et al., 2012; Pluta et al., 2011). Furthermore, several phenothiazines have been reported to have antiviral potential against hepatitis B virus, influenza virus, measles virus, human immunodeficiency virus, John Cunningham virus, Japanese encephalitis virus, bronchitis virus, hepatitis C virus, mouse hepatitis virus, chikungunya virus, SARS-CoV, Middle East respiratory syndrome coronavirus (MERS-CoV), Zika virus, dengue virus, Ebola virus, and Epstein-Barr virus (EBV) (Anderson et al., 2019; Chu et al., 2006; Nugent and Shanley, 1984; Otreba et al., 2020; Varga et al., 2017). On the basis of the diverse biological activities of phenothiazines, their anti-SARS-CoV-2 activities have been explored in other work (Lu et al., 2021). Phenothiazines were previously shown to associate with the membrane fraction of ACE2-expressing HEK293T cells by cell membrane chromatography (CMC) (Lu et al., 2021). In contrast to our results, trifluoperazine, chlorpromazine, and thioridazine reportedly bound to purified ACE2 with dissociation constant (KD) values of 33.3, 13.8, and 7.88 μM, respectively, indicating that these phenothiazines exert an antiviral effect through their binding to ACE2. However, the relatively high KD values compared with EC50 values determined in this study suggest that the contribution of the association of phenothiazines with ACE2 to the inhibition of SARS-CoV-2 cell entry may be limited. Moreover, the membrane fraction used in the CMC may have contained NRP-1 and, therefore, the strong retention of phenothiazines in the column could have been due to their association with NRP-1 rather than with ACE2.
HEK293T cells expressing SARS2-S were reported to fuse to ACE2-expessing HEK293T cells without trypsin treatment or the presence of TMPRSS2, but those expressing the SARS-CoV spike protein (SARS1-S) were unable to fuse (Ou et al., 2020). While the relevance of trypsin-uncleaved SARS2-S-mediated membrane fusion in SARS-CoV-2 cell entry remains to be determined, insertion of the multibasic furin cleavage site in SARS2-S is responsible for the marked phenotypic difference between SARS2-S and SARS1-S in the fusion assay (Hornich et al., 2021; Xia et al., 2020). SARS2-S-mediated cell entry requires proteolytic cleavage of SARS2-S by cellular proteases including cathepsins and TMPRSS2 (Hoffmann et al., 2020a). To mimic SARS2-S processing by cellular proteases, we treated SARS2-S-expressing cells with trypsin in the fusion assay. Previous reports showed that trypsin treatment did not significantly promote SARS2-S-mediated membrane fusion in some cell lines expressing TMPRSS2, indicating that trypsin treatment can be a functional surrogate of SARS2-S cleavage by TMPRSS2 (Koch et al., 2021; Ou et al., 2020). In our fusion assay, SARS2-S-expressing HEK293T cells were treated with trypsin and co-cultured with HEK293T/ACE2 for 4 hours prior to cell fixation to evaluate the levels of membrane fusion. It should be noted that SARS2-S newly expressed on the cell surface during the 4-hour co-culture was not exposed to trypsin, and trypsin-uncleaved SARS2-S may have partially contributed to membrane fusion in the fusion assay. As such, the substantial reduction in syncytia formation observed following treatment with the DRD2 antagonists, siNRP-1, or alimemazine indicates that the reduction was due to the inhibition of membrane fusion mediated by trypsin-cleaved and trypsin-uncleaved SARS2-S.
Several cationic amphiphilic drugs (CADs) have been reported to exhibit antiviral activity against diverse enveloped viruses, including Ebola and Marburg (MARV) viruses, Lassa virus, hepatitis C virus, Japanese encephalitis virus, SARS-CoV, MERS-CoV, and EBV (Klintworth et al., 2015; Nemerow and Cooper, 1984; Salata et al., 2017). A comprehensive physicochemical analysis of CADs in MARV cell entry revealed that the antiviral activity of CADs is associated with their ability to induce cellular phospholipidosis (Gunesch et al., 2020). In line with this study, Tummino et al. demonstrated a correlation, in the same concentration range, between the drug-induced phospholipidosis and anti-SARS-CoV-2 effects of CADs (Tummino et al., 2021). Phospholipidosis may disrupt lipid homeostasis, causing the disruption of double membrane vesicles (DMVs) and therefore suppressing the propagation of viruses whose life cycle is dependent on DMVs. The cationic amphiphilic nature of phenothiazines raises the possibility that phenothiazines exert their anti-SARS-CoV-2 activity via the induction of phospholipidosis. However, we found that phenothiazines blocked the interaction between purified recombinant SARS2-S and NRP-1, strongly suggesting that they have specific target-based antiviral activity. The diverse structural features of anti-SARS-CoV-2 phenothiazines will facilitate structure-activity relationship (SAR) approaches to the development of novel antivirals with reduced risks of undesirable pharmacological actions, including phospholipidosis.
The availability of small animal models that mimic the natural course of infection is a critical factor in promoting the rapid development of antivirals. A mouse infection model in particular would be ideal because vast resources for physiological, biochemical, immunological, and genetic analyses in mice already exist. However, mice are refractory to infection with the original strain of SARS-CoV-2, which can be explained mainly by the low binding affinity of SARS2-S to mouse ACE2 (mACE2) (Zhou et al., 2020). To address this issue, genetically engineered mice in which human ACE2 (hACE2) is systemically expressed (hACE2-transgenic mice) or is expressed in place of mACE2 (hACE2-knock-in mice) have been developed (Bao et al., 2020; Jiang et al., 2020; Winkler et al., 2022). In addition, mouse-adapted strains of SARS-CoV-2 have been generated by the serial passage of SARS-CoV-2 in mice (Gu et al., 2020; Huang et al., 2021; Iwata-Yoshikawa et al., 2022b; Leist et al., 2020). Genetic and biochemical analyses of the mouse-adapted SARS-CoV-2 strains have identified several mutations in the spike protein that contribute to an enhanced binding of SARS2-S to mACE2 and thereby promote productive SARS-CoV-2 infection in mice. Those identified mutations include the N501Y substitution that is also present in SARS-CoV-2 lineages B.1.1.7, B.1.351 (alpha variants), and P.1 (gamma variant). In this study, we used a lineage P.1 TY7-501 gamma variant of SARS-CoV-2, which has the N501Y mutation, to examine the in vivo efficacy of alimemazine. However, the clear in vitro antiviral effect of alimemazine did not translate in this mouse infection model. Interestingly, three amino acid differences, between human NRP-1 (hNRP-1) and NRP-2 (hNRP-2), located in the L1 loop of b1 domain, dictate the binding capacity for VEGF-A, which shares the same binding site as SARS2-S (Parker et al., 2012a, 2012b). Likewise, mouse NRP-1 (mNRP-1) contains in its L1 loop an amino acid that is different from the one at the corresponding position of hNRP-1. This indicates that SARS2-S binds to mNRP-1 in a manner distinct from that of SARS2-S binding to hNRP-1. Therefore, possible explanations for the lack of in vivo efficacy of alimemazine are that alimemazine cannot interfere with the SARS2-S–mNRP-1 interaction or that SARS-CoV-2 enters the cells via a mNRP-1-independent manner in the mouse infection system. The development of a novel mouse model that can reproduce human NRP-1-mediated cell entry by SARS-CoV-2 will significantly facilitate research on phenothiazine-based, and other novel, antivirals against SARS-CoV-2 that disrupt the SARS2-S–NRP-1 interaction.
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
The following are the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Multimedia component 2
Multimedia component 2
Data availability
Data will be made available on request.
Acknowledgements
We thank Yoshiharu Matsuura for providing us with a pseudotyped VSV system. We also thank Katie Oakley, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. This research was supported in part by the 10.13039/100009619 Japan Agency for Medical Research and Development (10.13039/100009619 AMED ) (JP20nk0101602 and JP20nf0101623) (M.I.).
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.antiviral.2022.105481.
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| 36481388 | PMC9721373 | NO-CC CODE | 2022-12-13 23:16:44 | no | Antiviral Res. 2023 Jan 5; 209:105481 | utf-8 | Antiviral Res | 2,022 | 10.1016/j.antiviral.2022.105481 | oa_other |
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Can J Cardiol
Can J Cardiol
The Canadian Journal of Cardiology
0828-282X
1916-7075
Published by Elsevier Inc. on behalf of the Canadian Cardiovascular Society.
S0828-282X(22)01051-0
10.1016/j.cjca.2022.11.014
Review
Healthcare implications of the COVID-19 pandemic for the cardiovascular practitioner
McAlister Finlay A. MD MSc 12∗
Parikh Harsh MD 3
Lee Douglas S. MD PhD 34
Wijeysundera Harindra C. MD PhD 456
1 The Division of General Internal Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
2 The Alberta Strategy for Patient Oriented Research Support Unit, Edmonton, Canada
3 Peter Munk Cardiac Center, Ted Rogers Centre for Heart Research, University of Toronto
4 ICES (formerly Institute for Clinical Evaluative Sciences), Toronto, Canada
5 Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto
6 Schulich Heart Program, Sunnybrook Health Sciences Center, University of Toronto, Toronto, Ontario
∗ Correspondence: Dr. Finlay A. McAlister, 5-134C Clinical Sciences Building, University of Alberta, 11350 83 Avenue, Edmonton, Alberta, Canada T6G 2G3 Tel: (780) 492-9824 Fax: (780) 492-7277
5 12 2022
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© 2022 Published by Elsevier Inc. on behalf of the Canadian Cardiovascular Society.
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.
There has been substantial excess morbidity and mortality during the COVID-19 pandemic, not all of which was directly attributable to SARS-CoV-2 infection, and many non-COVID-19 deaths were cardiovascular. The indirect effects of the pandemic have been profound, resulting in a substantial rise in the burden of cardiovascular disease and cardiovascular risk factors, both in individuals who survived SARS-CoV-2 infection and in people never infected. In this manuscript, we review the direct impact of SARS-CoV-2 infection on cardiovascular and cardiometabolic disease burden in COVID-19 survivors as well as the indirect impacts of the COVID-19 pandemic on the cardiovascular health of people who were never infected with SARS-CoV-2. We also examine the pandemic impacts on healthcare systems and particularly the care deficits caused (or exacerbated) by healthcare delayed or foregone during the COVID-19 pandemic. We review the consequences of (i) deferred/delayed acute care for urgent conditions, (ii) the shift to virtual provision of outpatient care, (iii) shortages of drugs and devices, and reduced access to (iv) diagnostic testing, (v) cardiac rehabilitation, and (vi) homecare services. We discuss the broader implications of the COVID-19 pandemic for cardiovascular health and cardiovascular practitioners as we move forward into the next phase of the pandemic.
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pmcWhile the official death toll from Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) infection was approximately 6 million by the summer of 2022, studies of all-cause mortality rate trends over time suggest that more than 18 million people died prematurely during the first two years of the pandemic.[1] Many of these deaths were cardiovascular,[1] and while the absolute number of in-hospital cardiovascular deaths decreased during the pandemic, cardiovascular deaths at home and in long-term care facilities increased substantially.[2] In fact, deaths attributed to cardiac events or strokes have risen more than deaths for any other non–COVID-19 diagnosis during the pandemic.[3,4]
Despite assumptions that excess mortality during the COVID-19 pandemic was largely amongst older individuals, even in developed nations more than 50% of quality-adjusted life years lost have been in people younger than 65.[5] Moreover, less than half of those who died had evidence of SARS-CoV-2 infection.[1,6,7] Fortunately, the Omicron variant, although far more transmissible than earlier variants, causes less severe disease than prior variants of concern.[8] Thus, we appear to have entered an endemic phase with SARS-CoV-2, but we will have to continue to factor the numerous impacts of the COVID-19 pandemic into health care projections and planning for many years to come. In this manuscript, we examine the direct, secondary, and tertiary impacts of the COVID-19 pandemic on healthcare systems (Table 1 ), highlight some of the care deficits that have arisen, and discuss the broader implications for cardiovascular health and cardiovascular practitioners as we move forward.Table 1 The impacts of the COVID-19 pandemic for cardiovascular patients and practitioners
Direct (Primary) Impact of SARS-CoV-2 infection on cardiovascular disease burden in COVID-19 survivors -acute cardiovascular complications from COVID-19 (myocarditis, acute coronary syndromes, microvascular thromboses, arrhythmias, pericarditis)
-therapies for COVID-19 with cardiovascular side effects and/or potentially interact with cardiovascular medications
-increased long-term risks for various chronic cardiovascular conditions (heart failure, scar-related arrhythmias, myocardial fibrosis, accelerated atherosclerosis)
-increased frequency of cardiometabolic diseases (such as diabetes mellitus) and chronic kidney disease
Secondary Impacts: Care deficits caused (or exacerbated) by healthcare delayed or foregone during the COVID-19 pandemic -Reduced outpatient care and shift to virtual care resulting in:
-deferred/delayed cardiovascular risk factor optimization
-suboptimal chronic disease management
-Deferred/delayed acute care for urgent conditions resulting in:
-increased out-of-hospital events
-worsened in-hospital outcomes
-Reduced diagnostic testing resulting in:
-shrinking procedural waiting lists but poorer outcomes
-Reduced access to homecare and long-term care services resulting in system backlogs
-Reduced access to cardiac rehabilitation services resulting in deferred/delayed
secondary prevention
-Shortages of drugs and devices resulting in sub-optimal treatment
Indirect (Tertiary) Impacts: Increased future cardiovascular disease burden in people who didn’t have COVID-19 -pandemic-related stressors,
-socioeconomic upheaval,
-increased social isolation and mental health issues,
-worsened physical activity profiles,
-increased alcohol consumption
-exacerbation of existing deficiencies and inequities in healthcare,
-exacerbation of health human resource shortages
-more cases of influenza and Respiratory Syncytial Virus infections in coming seasons due to immunity debt
Direct Impact of SARS-CoV-2 infection on future cardiovascular disease burden
Early in the COVID-19 pandemic it became apparent that SARS-CoV2 infection was associated with a wide range of acute cardiovascular complications, including myocarditis, acute coronary syndromes, microvascular thromboses, arrhythmias, and pericarditis.[10] In addition, a number of the therapies used to treat COVID-19 have cardiovascular side effects and/or potentially interact with cardiovascular medications.[10] Further, there is now increasing recognition of elevated long-term risks for a variety of chronic cardiovascular conditions (both ischemic and non-ischemic) in survivors of SARS-CoV-2 infection, even in those with no prior cardiovascular disease or comparatively mild COVID-19 symptoms.[10]
The relative risks of chronic cardiovascular conditions developing in COVID-19 survivors are similar across patient subgroups defined by baseline characteristics (absolute risks are obviously higher in those with higher baseline risk); however, relative risks are higher in those patients who had more severe COVID-19 disease.[11,12] The current best estimate is that those who survive their acute SARS-CoV-2 infection exhibit an approximately 55% relative increase in the risk of major adverse cardiovascular events in the next year: an extra 23.5 deaths, myocardial infarcts, or strokes per 1000 COVID-19 survivors.[11] As well, there are subsequent increases in atrial fibrillation (71% relative increase, or 11 more cases per 1000 survivors), ventricular arrhythmias (84% relative increase, 4 extra events per 1000), and heart failure (72% increase, 12 additional cases per 1000 patients) after SARS-CoV-12 infection of any severity.[11]. Notably, the risks are even higher in patients who survive a COVID-19 hospitalization, even after adjusting for demographics, CV risk factors, and established cardiovascular disease (45% increase in HF with 23 additional cases per 1000 patients in the US National COVID Cohort Collaborative study). Of course, this is undoubtedly an underestimate of myocardial damage since ongoing myocardial inflammation on cardiac magnetic resonance imaging is commonly reported after COVID-19.[14] It has been estimated that there have been an additional 30,000 extra strokes and up to 110,000 extra AMI in the United States in COVID survivors in 2020 and 2021.[12]
SARS-CoV-2 targets multiple cells that express ACE2, including pulmonary alveolar epithelial cells, nasal goblet secretory cells, pancreatic β-cells, gastrointestinal epithelial cells, astrocytes in the brain, and renal proximal tubules and podocytes. Many of these non-cardiac targets are important in cardiometabolic health, and organ dysfunction induced by SARS-CoV-2 does result in an increased frequency of cardiometabolic diseases in survivors.[9] For example, new diabetes (mostly type 2) has been documented in 0.8% of non-hospitalized COVID-19 survivors, 5.7% of hospitalized patients, and 8.9% of those with COVID-19 who survived ICU.[15] Of the numerous other manifestations of post-acute COVID syndrome that have been described so far, the increased frequency of chronic kidney disease due to direct renal injury from SARS-CoV-2 infection in survivors is also of particular relevance to cardiovascular practitioners.[16]
Thus, healthcare systems around the world must be prepared to deal with a substantial rise in the burden of cardiovascular disease in individuals who survived SARS-CoV-2 infection.
Indirect Impacts of the COVID-19 pandemic on future cardiovascular disease burden
We are likely to see higher than normal rates of influenza and other respiratory viruses in the coming seasons (assuming universal masking and social distancing protocols remain discontinued) due to the “immunity debt” accrued over the past two seasons (ie. low exposure rates in the general population to these other viral pathogens leading to a paucity of protective immunity). As we already know that influenza or respiratory virus outbreaks are associated with upswings in cardiovascular hospitalizations and deaths, this immunity debt is likely to further exacerbate the increasing burden from cardiovascular disease in the near future.[17]
Furthermore, we must also prepare for an increased burden of cardiovascular disease even in those who never had COVID-19 due to pandemic-related stressors (a systematic review of 58 studies confirmed that natural disasters were often followed by upswings in cardiometabolic risks and events)[18] and the numerous unintended consequences of the pandemic public health restrictions.[19] These unintended consequences included socioeconomic upheaval, increased social isolation and mental health issues, decreased physical activity but increased caloric and alcohol consumption resulting in exacerbation of the obesity epidemic,[20] and care deficits caused (or exacerbated) by the pandemic.
As a result of public health advice to reduce contacts, patient fear of exposure, and restricted access to health care providers, there was also a substantial drop in healthcare interactions during the COVID-19 pandemic, especially in the first year. Although volumes have returned to (and in some cases now exceed) pre-pandemic levels, there are a myriad of consequences arising from the healthcare delayed or forgone during the COVID-19 pandemic. An analysis of data from the US Coronavirus Tracking Survey found that 33% of American adults with one chronic health condition and 46% of those with multiple chronic conditions reported delayed or forgone health care during the first year of the pandemic.[19] Of those reporting delayed/forgone care, 23% felt it had worsened their health condition(s), 15% felt it caused new limitations in their abiity to work, and 21% described new limitations in their ability to do other daily activities (Figure 1 ).[19] In an earlier report in this journal,[21] we argued that these secondary and tertiary impacts of the pandemic on cardiovascular health were likely to far exceed the primary impacts directly related to SARS-CoV-2 infection (Figure 2 ) and in the remainder of this manuscript we will explore the emerging evidence on pandemic-induced care deficits relevant to the cardiovascular practitioner.Figure 1 Impact of delayed or foregone health care during the COVID-19 pandemic, as reported by American adults in September 2020 (adapted from reference 19 with permission)
Figure 2 Effects of the COVID-19 pandemic on cardiovascular morbidity and mortality (reproduced from Lau and McAlister[21] with permission from Elsevier)
1 Impact of the COVID-19 pandemic on outpatient care: deferred/delayed cardiovascular risk factor optimization and suboptimal chronic disease management
In addition to the initial reduction in volume, outpatient care shifted from an almost exclusively in-person model pre-pandemic to a mixed model very rapidly after the onset of the COVID-19 pandemic,[22, 23, 24, 25, 26, 27, 28, 29, 30, 31] and the implications of this shift are still being investigated. While two studies[26,29] reported that virtual visits for patients with a variety of cardiovascular diagnoses were associated with fewer subsequent ED visits and hospitalizations, another[30] found higher rates of ED visits, hospitalization, or death for HF patients after virtual visits compared to in-person visits. All 3 of those studies were observational and thus could only demonstrate association and not causation, and without randomized trial evidence it is impossible to declare one type of outpatient encounter superior to another. However, it does seem clear that virtual visits are associated with less risk factor screening, diagnostic testing, or medication intensification than in-person visits.[26, 27, 28, 29, 30, 31]
Delayed detection or deferred management of cardiovascular risk factors (such as hypertension or dyslipidemia) and the economic and psychosocial upheaval associated with the pandemic and public health responses has undoubtedly led to poorer cardiovascular risk-factor control which will cause future increases in the frequency of cardiovascular events. While early evidence has demonstrated a marked reduction in cardiovascular risk factor screening and management, as well as medication intensification for chronic conditions such as hypertension or heart failure, during the pandemic,[26, 27, 28, 29, 30] outcome differences resulting from these care patterns are yet to fully manifest. Of course, this problem is not unique to cardiovascular medicine and many other preventative services (such as vaccinations or cancer screening) were deferred during the pandemic, and evidence is already beginning to emerge of adverse clinical outcomes as a result.[32, 33, 34]
It should be noted that training future cardiovascular health care professionals to use telehealth technologies, while of paramount importance, poses unique challenges due to variations between institutions in communication platforms that need to be addressed.[35]
2 Impact of the COVID-19 pandemic on acute care: deferred/delayed acute care for urgent conditions and worsened in-hospital outcomes
A number of studies in other jurisdictions confirm reports from the Canadian Institute for Health Information[36] that total emergency department visits,[37] even those for acute cardiovascular diagnoses,[38] and hospitalizations for non-COVID conditions and surgeries dropped markedly during the pandemic.[37,39, 40, 41]. The median decrease in cardiovascular service utilization in a systematic review of 33 studies (64 services) was 29% in the first two quarters after onset of the pandemic.[40] Concerningly, the hesitation to seek care was not limited to elective care; Czeisler and colleagues estimated that approximately 41% of U.S. adults avoided medical care during the pandemic because of concerns about COVID-19, including 12% who avoided urgent or emergency care.[42] There was substantial inequity in terms of this care deficit, with a disproportionate burden on women, ethnic minorities, those in lower socio-economic strata specifically without health insurance, and those with greater medical comorbidity and disability.[42]
Multiple lines of evidence suggest that outcomes in patients with cardiovascular disease have worsened during the pandemic due to this reluctance to go to acute care centres. For example, studies have shown a 2 fold increase in the proportion of patients with acute myocardial infarctions or ischemic stroke who refuse emergency service transportation to the hospital,[43,44] and the most alarming upstream consequence are rates of out-hospital cardiac arrest that have increased by almost 50%, indicative of patients ignoring even very severe symptoms.[45, 46, 47] Although data from multiple jurisdictions, including Canada, have shown a decrease in cardiovascular hospitalization volumes during the pandemic, outcomes have also been worse for those patients who are hospitalized, with in-hospital mortality increases of >50%, even independent of possible COVID-19 coinfection.[48, 49, 50]
3 Impact of the COVID-19 pandemic on diagnostic testing: shrinking procedural waiting lists but poorer outcomes
Above and beyond acute conditions, the delays in ambulatory care as well as reduced access to diagnostic testing have also impacted chronic cardiovascular conditions such as heart failure. In the National Health System in the United Kingdom, ∼1/3 of heart failure patients reported subjective deterioration during the pandemic.[51] The magnitude of the reductions in cardiovascular diagnostic capacity has been substantial - a study of 909 institutions across 108 counties found a >60% decrease in all non-invasive cardiac studies (such as stress ECG, echocardiography and SPECT).[52]
However, these upstream barriers have had a counter-intuitive impact on procedural waitlists for percutaneous coronary intervention or coronary artery bypass grafting.[53] Although volumes of procedures performed were low in the early part of the pandemic, due to closure of facilities from redeployment of health human resources or reallocation of beds for COVID-19 patients, even when cardiovascular capacity was restored, the actual wait-lists were surprisingly shorter.[53] These patients were “missing” due to the upstream care deficit. For example, a patient who has not been diagnosed by their family physician/cardiologist, nor completed testing due to barriers at those stops of care, cannot get on the procedural wait-list. This has implications for how to address the decrease in procedure volumes from the pre-pandemic era as measures to increase procedural capacity without similar efforts to address upstream barriers in diagnostic testing and access to ambulatory care will not be successful.[54] This is an important contradistinction to the strategies currently being proposed by the Canadian Medical Association and policy makers to address waitlists for orthopedic, cancer, or cataract surgeries.[55]
The cumulative impact of each of these care deficits is excess cardiovascular mortality and morbidity, particularly amongst our frailest and sickest patients.[56] Given the poor outcomes for untreated or partially treated cardiovascular disease, the estimates of excess mortality from delays in treatment are sobering. For example, estimates for England suggest between 49 932 to 99 865 excess cardiovascular deaths just in the first year of the pandemic due to indirect pandemic effects from reductions in referrals, diagnostic testing and treatment services.[57] The substantial magnitude of these indirect impacts is reinforced when evaluating temporal trends of overall excess mortality and that of COVID-related deaths, which show persistent excess mortality during the periods in between pandemic waves, even when direct COVID-19 deaths were comparatively low.[56]
4 Impact of the COVID-19 pandemic on homecare and long-term care services: reduced access leading to system backlogs
Deaths from SARS-CoV-2 infection disproportionately affected residents of long-term care (LTC) homes, especially early in the pandemic. In Canada, LTC residents accounted for 3% of all COVID-19 cases but 43% of COVID-19 deaths (Figure 3 ),[36] although vaccinations have significantly helped reduce these numbers. This population is at high risk due to their advanced age and multiple comorbidities, but also socio-economic factors including lack of access to testing, less personal protective equipment, difficulty maintaining social distancing, and a precariously employed workforce that can transmit the virus across LTC sites.[58,59] Additionally, these nursing home residents were at even greater risk of delayed/deferred acute care than community-dwelling elderly: transfers from LTC to hospitals were substantially reduced for COPD (by 58%), pneumonia (by 52%), and heart failure (by 41%), when compared to pre-pandemic rates (Figure 4 ).[36] Despite this, the wait times for LTC beds and home care services, which were problems well before the pandemic, have worsened and the number of patients requiring altered level of care (ALC) in hospital has risen.[60] Moreover, because of the negative impact of COVID-19 on LTC facilities, an increasing proportion of Canadians are expressing the wish to avoid long-term care for themselves and their loved ones, which will exacerbate this issue further.[61] The resulting shortage in acute care beds will undoubtedly have negative consequences for patients with cardiovascular disease who require hospitalization for evaluation or management in the future.Figure 3 Canadian Long Term Care resident COVID-19 deaths versus COVID-19 community deaths to August 15, 2021. Reproduced from https://www.cihi.ca/en/covid-19-resources/impact-of-covid-19-on-canadas-health-care-systems/long-term-care with permission of the Canadian Institute for Health Information
Figure 4 Changes in transfers of Long Term Care residents to hospital during the pandemic, by reason for transfer, March 2020 to June 2021. Reproduced from https://www.cihi.ca/en/covid-19-resources/impact-of-covid-19-on-canadas-health-care-systems/long-term-care with permission of the Canadian Institute for Health Information
With regards to home care services, the volume of home care assessments plummeted early in the pandemic, with more than 60,000 deferred new assessments (44% decline) between March and June 2020.[55] Correcting for this backlog and other negative impacts of the pandemic on the frequency and intensity of home care service provision will likely take years to address and in the meantime frail cardiovascular patients will exhibit poorer outcomes and our ability to manage them in their home setting will be impaired.
5 Impact of the COVID-19 pandemic on cardiac rehabilitation services: deferred/delayed secondary prevention
Despite the well established benefits of cardiac rehabilitation for secondary prevention in patients with coronary disease or heart failure,[62] at the start of the COVID-19 pandemic many cardiovascular rehabilitation programs were closed, which resulted in negative outcomes for the vulnerable population.[63,64] While in-person rehabilitation is the gold standard, even pre-COVID the European Society of Cardiology guidelines raised the possibility of home-based rehabilitation with or without telemonitoring.[62] Systematic reviews have proven that tele-rehabilitation is superior to no rehabilitation and non-inferior to in-person rehabilitation for improving functional capacity and quality of life for all patients, including those with heart failure and coronary disease.[65,66]
While some have enthusiastically advocated for that option during the pandemic,[67] it should still be acknowledged that there is no definitive data on the effectiveness of home-based rehabilitation programs vs centre-based ones, and this is a clear research need that the COVID-19 pandemic has highlighted.[68]
6 Impact of the COVID-19 pandemic on shortages of drugs and devices: under-treatment
While drug shortages were common around the world even prior to 2019, the COVID-19 pandemic exacerbated many of these concerns due to increased demand for drugs used for COVID-19 therapies (such as steroids, hydroxychloroquine, anti-virals) and drugs for supportive care (especially anesthetics such as propofol, midazolam, fentanyl, rocuronium), as well as decreased supply as drug-supplying countries decreased their exports to prioritize their citizens, and pharmacies & hospitals began stockpiling medications.[69] Each of these factors contributed to scarcity of essential, lifesaving medications, impacting many countries around the world, but especially low-income countries in Africa and Asia [70,71]. Thankfully, with massive global collaboration efforts including the four-level mitigation strategy established by the World Health Organization (WHO)], as well as local efforts at government and pharmacy levels, many of these shortages have been addressed.[72,73]
As a result of decreased supply, increased financial constraints, increased stress, decreased outpatient visits, and misinformation (the infodemic), adherence to many chronic medications has significantly decreased since the start of the pandemic.[74] As healthcare providers, it would behoove us to inquire about adherence disruptions and provide prompt, nonjudgmental, re-initiation of medications where necessary.[74]
In addition to drugs, the COVID-19 pandemic also caused a shortage in devices including personal protective equipment, ventilators, COVID-19 testing supplies, and even blood collection tubes.[75,76] This was due both to increased demand as well as decreased supply from major global suppliers such as China due to factory shutdowns. Many countries around the globe were particularly impacted by this, especially Italy, Spain, and parts of the US.[75,77] While the device shortage issue has abated to a large degree, it did trigger the need for international cooperation in the trade of medical supplies, a proactive backup approach at first signs of shortages, and innovative approaches such as 3D printing of medical supplies, all of which are likely to stay for the long term.[78]
7 Impact of the COVID-19 pandemic on exacerbating existing deficiencies in healthcare and health human services
The COVID-19 pandemic also highlighted the inequities in health care access, particularly with respect to primary care for disadvantaged communities, and the structural deficits in the healthcare workforce that already existed pre-pandemic.[79] Indeed, all of the care deficits outlined earlier are more pronounced in disadvantaged groups. Several recent studies have demonstrated the negative impacts of the pandemic on health care workers and have raised concerns about further increases in shortages of physicians and nurses. The Canadian Medical Association’s 2021 National Physician Health survey[80] indicated that physician burnout nearly doubled (up to 53%) during the pandemic and nearly half of physicians are planning to reduce their workload in the near future, mirroring reports from the Canadian Nurses Association[81] and the Association of American Medical Colleges[82].
Concluding thoughts and looking forward:
During the pandemic, COVID-19 was the third leading cause of death in Canada, behind only cancer and cardiovascular disease. Although much attention has been focused on COVID-19 entering an “endemic phase”, as recently pointed out by Professor Katzourakis in Nature: “a disease can be endemic and both widespread and deadly” (he cited the example of two endemic infectious diseases – malaria and tuberculosis- that killed more than 2 million people in 2020).[83]. In addition to the direct morbidity and mortality attributable to SARS-CoV-2 infection, the indirect effects of the pandemic have been profound, and cardiovascular conditions are one of the early bellweathers for these effects. The background incidence of cardiovascular disease has at the very least been constant over this period, but has more likely increased given the increased burden of cardiovascular risk factors in COVID-19 survivors and pandemic-related stressors. While Canada performed better than most other countries in the first 2 years of the pandemic on metrics such as number of infections, number of deaths, or proportion fully vaccinated,[84] our ability to address the care deficits described in this paper will determine how we fare in the next phase of the pandemic. To do so, we need to learn from the natural experiment of reduced care induced by the pandemic to identify which elements of deferred care proved unnecessary and prioritize only those interventions of proven efficacy and cost effectiveness.[85,86] As pointed out by Abraham Maslow, “to a man with a hammer, everything looks like a nail” and thus choosing which services to prioritize will require input from a broader constituency than just physicians. Indeed, the need for timely and explicit communication between health care providers, government and other policy makers, industry, and the public cannot be understated.
Uncited reference
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Disclosures: DSL is the Ted Rogers Chair in Heart Function Outcomes, University Health Network. HCW is a Canada Research Chair in Structural Heart Disease Policy and Outcomes.
Funding: None for this manuscript.
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| 36481398 | PMC9721374 | NO-CC CODE | 2022-12-07 23:19:08 | no | Can J Cardiol. 2022 Dec 5; doi: 10.1016/j.cjca.2022.11.014 | utf-8 | Can J Cardiol | 2,022 | 10.1016/j.cjca.2022.11.014 | oa_other |
==== Front
Comput Biol Med
Comput Biol Med
Computers in Biology and Medicine
0010-4825
1879-0534
Published by Elsevier Ltd.
S0010-4825(22)01100-3
10.1016/j.compbiomed.2022.106392
106392
Article
Neohesperidin and spike RBD interaction in omicron and its sub-variants: In silico, structural and simulation studies
Singh Jaikee Kumar a
Dubey Saumya a
Srivastava Gaurava b
Siddiqi Mohammad Imran b
Srivastava Sandeep Kumar a∗
a Department of Biosciences, Manipal University Jaipur, Dehmi Kalan, Off Jaipur-Ajmer Expressway, Jaipur, Rajasthan, 303007, India
b Division of Biochemistry and Structural Biology, CSIR-Central Drug Research Institute, Jankipuram Extension, Sitapur Road, Lucknow, Uttar Pradesh, 226031, India
∗ Corresponding author. Department of Biosciences, Manipal University Jaipur, Jaipur-Ajmer Express Highway, Jaipur, Rajasthan, 303007, India.
5 12 2022
5 12 2022
1063926 6 2022
29 10 2022
29 11 2022
© 2022 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-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged first around December 2019 in the city of Wuhan, China. Since then, several variants of the virus have emerged with different biological properties. This pandemic has so far led to widespread infection cycles with millions of fatalities and infections globally. In the recent cycle, a new variant omicron and its three sub-variants BA.1, BA.2 and BA.3 have emerged which seems to evade host immune defences and have brisk infection rate. Particularly, BA.2 variant has shown high transmission rate over BA.1 strain in different countries including India. In the present study, we have evaluated a set of eighty drugs/compounds using in silico docking calculations in omicron and its variants. These molecules were reported previously against SARS-CoV-2. Our docking and simulation analyses suggest differences in affinity of these compounds in omicron and BA.2 compared to SARS-CoV-2. These studies show that neohesperidin, a natural flavonoid found in Citrus aurantium makes a stable interaction with spike receptor domain of omicron and BA.2 compared to other variants. Free energy binding analyses further validates that neohesperidin forms a stable complex with spike RBD in omicron and BA.2 with a binding energy of −237.9 ± 18.7 kJ/mol and −164.1 ± 17.5 kJ/mol respectively. Key residual differences in the RBD interface of these variants form the basis for differential interaction affinities with neohesperidin as drug binding site overlaps with RBD-human ACE2 interface. These data might be useful for the design and development of novel scaffolds and pharmacophores to develop specific therapeutic strategies against these novel variants.
Graphical abstract
Image 1
Keywords
Omicron
BA.2
Spike RBD
Neohesperidin
Molecular docking
Molecular dynamics simulations
==== Body
pmcAbbreviations
COVID-19 Coronavirus disease of 2019
SARS-CoV-2 Severe Acute Respiratory Syndrome Corona Virus-2
S-RBD Spike- Receptor Binding Domain
ACE2 Angiotensin-Converting Enzyme 2
VOC Variants of Concern
FDA Food and Drug Administration
TMPRSS2 Trans Membrane Protease, Serine 2
PDB Protein Data Bank
NCBI National Center for Biotechnology Information
PRODIGY Protein Binding Energy
PDBSum Protein Database Summaries
MGL Molecular Graphics Laboratory
GROMACS Groningen Machine for Chemical Simulations
MD Molecular Dynamics
LINCS Linear Constraint Solver
ED Essential Dynamics
PCA Principal Component Analysis
FEL Free-Energy Landscapes
MM-PBSA Molecular Mechanics Poisson–Boltzmann Surface Area
RMSD Root Mean Square Distance
RMSF Root Mean Square Fluctuations
Rg Radius of gyration
hACE2 Human Angiotensin-Converting Enzyme 2
WHO World Health Organization
COOT Crystallographic Object-Oriented Toolkit
1 Introduction
B.1.1.529 also known as ‘Omicron’ is a newly emerged SARS-CoV-2 variant which has been detected in early November 2021 in Botswana, South Africa. It is rapidly spreading in an exponential manner leading to spike in infections in several countries. WHO designated it as Voice of Concern (VOC) on November 26, 2021 [1]. Interestingly, five sub-lineages of omicron designated as BA.1 (1.1.529.1), BA.2 (1.1.529.2) and BA.3 (1.1.529.3), BA.4 (1.1.529.5) and BA.5 (1.1.529.5) have also been reported from different regions (https://www.ecdc.europa.eu/en/news-events/epidemiological-update-sars-cov-2-omicron-sub-lineages-ba4-and-ba5) [2,3]. Although BA.1 was the first of the omicron sub-lineages to spread quickly over the world, the proportion of BA.2 infections have increased in recent weeks. New variants show a host of new mutations compared to previous SARS-CoV-2 variants. A total of 39 mutations have been reported in omicron of which spike-RBD alone harbours thirty-two mutations which interfaces with human ACE2 and has been documented as a potential drug target. These numbers are relatively much higher than sixteen mutations present in highly infectious delta strain [4]. Spike receptor directly interacts with human ACE2 (Angiotensin converting enzyme 2) which plays an important role in viral infection and transmission. Presence of large portion of these mutations in spike receptor of different variants affect their conformational flexibility and complexity influencing affinity and interaction with human ACE2 which in turn influences a particular strain's transmission rate, immune evasion and virulence. This has led to variation in responses of therapeutics and vaccines against these strains so far [5]. Omicron sub-variants BA.1, BA.2 and BA.3 contain 39, 31 and 34 mutations respectively with 21 shared mutations among them [6]. In spike RBD, BA.2 has four unique mutations and while twelve mutations are shared with BA.1 [7]. The increase of immune evasion activity in omicron family is a more prominent feature compared to previous lineages which makes them highly infectious and alarming. Thus, it is important to investigate newer therapeutic molecules and drugs which can have significant affinity for spike-RBD to destabilize its interaction with human ACE2 thus disrupting the viral attachment and subsequent entry. Recent reports suggest that several of the commonly used COVID-19 vaccinations offer little or no protection against the omicron or it's variants [8]. As a result, omicron and its sub-lineage BA.2 could jeopardise worldwide attempts to contain the COVID-19 pandemic by posing a public health threat. Apart from improving vaccines efficacies against these variants, we need to expedite the development of novel antivirals. In the present study, we have evaluated affinity and energetics of FDA approved drug neohesperidin (ZINC ID: 8234302) using computational drug discovery and simulation against omicron spike RBD and its variant BA.2 aiming to save both time and resources. We have also presented atomistic details of their interaction pattern and role of mutants in binding. Neohesperidin is a natural flavanone and a subclass of flavonoids found in bitter orange Citrus aurantium. It is present in a variety of other plants and portions of plants growing in many parts of the world [9]. Neohesperidin belong to many bioactive compounds that regulate the innate immune system and avoid cytokine storms by modulating the toll-like signalling pathway. Despite preliminary evidence of synergistic benefits, more research is required to strengthen COVID-19-fighting strategies [10]. Neohesperidin has also been reported for inhibition of Vero E6 cells and are also capable of interaction with the spike-ACE2 complex [11,12]. Neohesperidin has also been found to be promising against the TMPRSS2 [13]. Our interaction data suggest greater stability and structural compactness of neohesperidin-RBD complex in omicron and BA.2 variant particularly. Binding free energy, residue contribution and free energy landscape analysis further validate stable interaction efficacy against omicron and BA.2 spike RBDs. We have also performed the molecular docking of previously reported inhibitors cefuroxime and arbidol to compare and validate interaction parameters and structural results and precede our analysis further [14]. These agents have been used as a primary treatment recipe in China against infected patients.
2 Materials and methods
2.1 Structure retrieval, modeling and alignment of spike S RBD in omicron, BA.2 and SARS-CoV-2
The three-dimensional crystal structures of the spike receptors of omicron (B.1.1.529) (PDB: 7WPB); BA.1 (B.1.1.529.1) (PDB: 7U0N); BA.2 (B.1.1.529.2) (PDB ID: 7UB0) and SARS-CoV-2 (PDB: 6LZG) were retrieved from the Protein Data Bank (https://www.rcsb.org). FASTA sequence of SARS-CoV-2 S RBD (333–527) was retrieved from Uniprot Knowledgebase (ID: P0DTC2) while BA.2 (B.1.1.529.2) (UFO69279.1) and BA.3 (B.1.1.529.3) (UNH00890.1) sequences were retrieved from NCBI GenBank. Sequences were aligned using program Clustal Omega (https://www.ebi.ac.uk/Tools/msa/clustalo/) [15]. Mutations were identified using multiple sequence alignments. Alignment figures were created using ESPript 3.0 program (https://espript.ibcp.fr/ESPript/ESPript/) [16]. PyMol [17] and COOT [18] graphic interfaces were used for structural alignment and mutational analysis of S RBD of different variants.
2.2 Protein-protein docking analysis
HADDOCK program [19] was used to investigate the interaction between BA.2 S RBD and ACE2. Additionally, the PRODIGY (PROtein binding enerGY) online suite (https://wenmr.science.uu.nl/prodigy/) were used to determine the binding affinity (ΔG) and dissociation constant values of S RBD-ACE2 complexes in omicron, BA.2 and SARS-CoV-2 with default temperature of 25 °C [20]. The interaction networks were visualized using the PDBsum [21].
2.3 System preparation and molecular docking
The S RBDs (residues 333–527) of omicron and BA.2 were used as a docking target. Targets were prepared by stripping off water molecules from structure coordinates files and hydrogen atoms were added by MGL tools and converted in PDBQT format as per the protocol. All the ligand structures were retrieved from ZINC database (https://zinc15.docking.org). Arbidol and cefuroxime, earlier reported as S RBD-ACE2 inhibitors [22], were used in this study as docking controls. Molecular docking experiments were performed using Autodock Vina [23] using a grid size of 60 × 60 × 60 xyz points with a 0.375 Å grid spacing and exhaustiveness value of 32. The grid centre was chosen around binding interface as reported previously with dimensions (x, y, and z) 72.298, 87.385, and 108.833 and dimensions (x, y, and z) −39.646, 22.727 and 6.860 in omicron and BA.2 respectively. Hundred docked conformations were generated for each ligand. Ligands were ranked based on their docking scores. Ligand confirmations and scores were compared with control drugs. 2D interactions between ligand and binding sites were plotted using Ligplot [ 24 ].
2.4 Molecular dynamics (MD) simulations
Prior to the simulations, energy minimization and system equilibration were carried out to eliminate the unfavourable interactions between the solute and solvent water molecules [25]. Production simulations of the complexes were carried by GROMACS 2018.1 package running on Intel Xeon 3106 1.7 GHz 2133 MHz workstation on 12 Core CPU equipped with NVIDIA Quadro P1000 4 GB (4) mDP GFX graphics card. GROMOS43a1 force field was applied in all the runs [26]. Ligand topology files were generated using PRODRG server [27]. The system has been solvated by adding a cubic box of TIP3P water up to a distance of 10 Å from the solute, and neutralised by adding the appropriate quantity of Cl− ions. All the MD simulations were carried out in periodic boundary conditions. The system has been relaxed by optimizing the geometry of hydrogens, ions, and water molecules through 5000 cycles of steepest descent. Particle Mesh Ewald with an interpolation order of 4 was utilized for long-range electrostatic interactions within 1.2 nm. The solvent box was equilibrated at 300 K by simulating at 100 ps of constant volume and temperature (NVT), constant pressure and temperature (NPT) and a pressure of 1 bar using modified Berendsen thermostat (tau_T = 0.1 ps) and a Parrinello –Rahman barostat (tau_P = 2 ps). For van der Waals interactions, a Verlet scheme was employed with interactions switched above 1.0 nm. The LINCS algorithm was used to constrain the bond length. The production run, performed under the previously equilibrated conditions, consisted of 100 ns. Integration time steps of 2fs were employed during the equilibration and production processes. Energies and compressed coordinates were saved at every 100 ps. Three independent production runs were carried out for each of the systems using randomized velocities. All the plots were generated using XMGRACE software package [28].
2.5 Principal component analysis (PCA) and free energy landscape (FEL)
Essential dynamics (ED), also known as principal component analysis (PCA), were examined by diagonalizing a covariance matrix of Cα atoms of the system. The movements in proteins and protein-ligand complexes were characterized using MD simulation trajectory files. 2D projections were plotted using first principal component (PC1) and second principle component (PC2) to depict the motions. Eigenvectors were utilized to define the directions of coordinated motions, with eigenvalues representing the motion's extent and eigenvector representing the motion's direction [29]. The 2D representation of the free energy landscapes (FEL) were computed by the gmx sham package using the first two eigenvectors retrieved from PCA to analyze the conformational changes in the omicron and BA.2 complexes [30,31].
2.6 MM/PBSA binding free energy calculation
Binding free energies of neohesperidin-S RBD complexes in omicron and BA.2 were calculated from molecular mechanics Poisson–Boltzmann Surface Area (MM-PBSA) using g_mmpbsa package [32] for the last 10 ns (90–100 ns) of the run time with time frame (dt) of 1000. The binding energy (ΔGbinding) of proteins and ligands were computed as:ΔGbinding =Gcomplex- (Gprotein + Gligand)
where ΔGcomplex, ΔGprotein, and ΔGligand represent the total free energies of the protein, the ligand, and the protein-ligand complex, respectively.
3 Results and discussion
3.1 Structure retrieval, modelling and alignment of spike S RBD in omicron, BA.2 and SARS-CoV-2
The sequences and structures of omicron, BA.1, BA.2 and BA.3 S RBD proteins and its alignment with the SARS-CoV-2 S RBD are shown in (Fig. 1 ). Alignment shows 15 mutations n omicron RBD, residues G339, S371, S373, S375, K417, N440, G446, S477, T478, E483, Q493, G496, Q498, N501 and Y505 replace with D339, L371, P373, F375, N417, K440, S446, N477, K478, A484, R493, S496, R498, Y501 and H505 whereas BA.2 has 16 mutations compared to SARS-CoV-2. It shares twelve mutations with omicron while four mutations F371, A376, N405 and S408.Fig. 1 Sequence and structural alignment of receptor binding domains (A) Interacting residues of SARS-CoV-2 RBD to ACE2 are labelled with blue triangles. The alignment is performed by CLUSTAL-Omega and visualized by ESPript 3.0. (B) Superimposed structures of the SARS-CoV-2 RBD (green) and Omicron S RBD (B.1.1.529) (red) (RMSD = 0.36 Å) (C) Omicron S RBD (B.1.1.529) (red) and BA.2 S RBD (blue) (RMSD = 0.59 Å). Mutation points in omicron and BA.2 are shown in sticks.
Fig. 1
3.2 Interaction analysis of S RBD-human ACE2 in omicron, BA.2 and SARS-CoV-2
We evaluated binding energies of omicron and BA.2 S RBD with human ACE2 to see if this transmissibility may be changed into a greater ligand-protein interaction. The binding energy and dissociation constant (KD) of omicron S RBD-hACE2 interaction was −11.8 kcal/mol, 2.3 × 10−9 and that of BA.2 was −12.6 kcal/mol and 5.3. x10−10. The analysis shows that BA.2 variant has higher affinity with ACE2 as compared to omicron. The structural details of the interacting residues in the S RBD-ACE2 complex in SARS-CoV-2, omicron, BA.2 have been represented in (Fig. 2 , Table 1 ). In SARS-CoV2, S RBD-ACE2 interface residue pairs Ala475-Ser19, Asn487-Tyr83, Asn487-Gln24, Tyr449-Asp38, Tyr449-Gln42, Gln498-Gln42, Thr500-Tyr41, Gly496-Lys353 and Gly502-Lys353 are engaged in hydrogen bonding interactions. Phe486-Tyr83 makes stacking interactions. There is only one salt bridge interaction between Lys417 and Asp30. While some interactions viz. Asn487-Tyr83, Thr500-Tyr41, Gly502-Lys353, Asn487-Gln24 are common in omicron and SARS-CoV-2. Differential omicron S-RBD dynamics due to mutations have generated new interacting pairs of Asn477-Ser19, Tyr449-Asp38, Arg498-Gln42, Ser494-His34 as well with hACE2. Also, mutation of Gln at 493 and 498 positions to Arg in omicron have led to formation of two new salt bridges between Asp38-Arg498 and Glu35-Arg493. There are 12 H-bonding interactions in BA.2 complex compared to 9 in omicron. Several of these interactions are novel due to high rate of mutations viz. Ser494-Lys31, Tyr495-Lys31, Tyr449-Asp30, Tyr453-Glu35, Asn487-Tyr41, Asp487-Asp355 and Val455-Gln24. Four salt bridge interactions viz. Lys478-Glu329, Arg403-Glu75, Arg498-Asp38 and Arg498-Asp30 further strengthens complex affinity in BA.2 compared to omicron which has two such interactions. Along with this, omicron and BA.2 complexes show 116 and 123 non-bonded contacts which reveal stronger binding affinity of BA.2 S RBD with host ACE2 compared to omicron or SARS-CoV-2 (Fig. S1).Fig. 2 Structural representation of the interface residues of the Native SARS-CoV-2, omicron S, BA.2 S RBD and ACE2 complexes. The interface of the ACE2 (cyan) and (A) Native SARS-CoV-2 spike RBD (orange) (B) Omicron spike RBD (green) (C) BA.2 spike RBD (magenta).
Fig. 2
Table 1 The interface statistics in S RBD-hACE2 interaction in SARS-CoV-2, Omicron, BA.2
Table 1Variants Number of Interface
Residues Interface Area (Å2) Salt Bridges Disulfide Bonds H-bond Non-bonded contacts
Omicron S ACE2 19 836 2 0 9 116
RBD 19 854
BA.2 S ACE2 24 1139 4 0 14 123
RBD 26 1138
SARS-CoV-2 S ACE2 20 853 1 0 11 112
RBD 19 902
3.3 Molecular docking-based interaction analysis of neohesperidin complexes
Herein molecular docking was performed on the selected drug dataset of 80 compounds and ranked based on docking scores. Hundred docked poses were generated for each complex using Autodock programme. This was followed by pose compatibility and manual curation for stearic clashes etc. and ranked based on docking energy values and binding pose at S RBD-hACE2 binding interface to choose the initial conformations for further investigation (Fig. S2, Table S2). We also performed molecular docking of the reference drug cefuroxime and arbidol to compare the binding affinity of these drugs with the dataset. The neohesperidin showed highest dock score of −7.8 kcal/mol and −7.5 kcal/mol with BA.2 and omicron. In comparison, arbidol (−5.3 kcal/mol, −6.2 kcal/mol) and cefuroxime (−5.8 kcal/mol, −6.1 kcal/mol) energies were much less in omicron and BA.2 respectively (Table 2 ). Initial conformation of neohesperidin-S RBD in omicron have six hydrogen bonds linked by Arg403, Asn417, Tyr453, Ser496, Tyr501 and His505 and three hydrophobic interactions linked with Ser494, Tyr495 and Arg498 while neohesperidin complex in BA.2 includes seven hydrogen bonds linked by Arg403, Tyr453, Ser494, Gly496, Arg498, Tyr501 and His505 and hydrophobic contacts with residues Tyr449, Leu452, Phe490, Leu492, Arg493 and Tyr495 (Fig. 3 ). In comparison, arbidol-S RBD has only one and two and cefuroxime-S RBD complex show two and six H-bond patterns in omicron and BA.2 respectively ( Fig. S3 ). We have also performed neohesperidin docking on omicron-ACE2, BA.2-ACE2 and SARS-CoV-2-ACE2 spike RBD complex represented in ( Fig. S4 ) to confirm our findings. The docking score of neohesperidin at omicron, BA.2 and SARS-CoV-2-ACE2 spike RBD interfaces are −6.9 kcal/mol, −7.1 kcal/mol and −6.3 kcal/mol respectively as compared to arbidol which binds and stabilize with binding affinity of −5.7 kcal/mol at the SARS-CoV-2-ACE2 S RBD interface [33]. The analysis shows that, neohesperidin could strongly inhibit and disrupt the spike-ACE2 complex of BA.2 variant as compared to omicron. Stability and structural coherency of the neohesperidin were further validated through molecular dynamics simulation and thermodynamics calculations.Table 2 Binding affinities (kcal/mol) between different drugs and spike RBDs of different variants.
Table 2Compound-spike RBD Interacting residues and distance (Å) Docking Score (kcal/mol)
Neohesperidin-Omicron Arg403(3.18), Asn417(2.81), Tyr453(3.12), Ser496(2.85), Tyr501(3.20), His505(3.18) −7.5
Neohesperidin-BA.2 Arg403(2.80), Tyr453(3.27), Ser494(3.08), Gly496(3.17), Arg498(2.82), Tyr501(3.05), His505(3.09) −7.8
Arbidol-Omicron Pro463(3.20) −5.8
Arbidol-BA.2 Arg498(3.23), Tyr501(3.09) −5.3
Cefuroxime-Omicron Tyr453(2.99), Ser496(3.06), Arg498(3.11), Tyr501(2.96) −6.2
Cefuroxime-BA.2 Arg403(2.83), Tyr449(3.08), Tyr453(3.26), Gly496(3.02), Arg498(3.24), Tyr501(2.84) −6.1
Fig. 3 Stable receptor ligand interactions over the course of MD simulations. (A) Binding modes of neohesperidin in Omicron S RBD (Green) (B) Binding modes of neohesperidin in BA.2 S RBD (Cyan) (C, D) Ligplots of the interactions in Omicron and BA.2 respectively. Hydrogen bonds are shown as black dashed lines.
Fig. 3
3.4 MD simulation analysis
Accuracy and reliability of the docking poses in omicron and BA.2 S RBD were further studied by molecular dynamics simulations in three independent runs for 100ns ( Fig. S5 ). RMSDs of the protein backbone and ligand heavy atom in omicron and BA.2-neohesperidin complexes were computed in comparison to the initial conformation (3.5 Å and 3.2 Å) over the course of simulation ( Fig. 4 ). It showed little variation in comparison to their respective ligand-free states (3.8 Å and 4.1 Å) (Table S3), indicating that each complex attained an equilibrium state. As shown all simulated complexes exhibited conformation modifications (RMSD< 4 Å) [34]. The average RMSD of binding site backbone atoms are 3.4 Å in omicron and 3.6 Å in BA.2 RBD (Table S3). Ligand RMSDs in omicron and BA.2 are 2.7 Å and 2.9 Å respectively (Fig. S6). It reflects compatibility of predicted poses with binding interface in both the variants. Other trajectory parameters, root mean square fluctuations (RMSF), radius of gyration (Rg) and ligand protein hydrogen bonding showed comparable binding profiles for both the complexes. The structural flexibilities evaluated by computing the per-residue RMSF of the neohesperidin-omicron and BA.2 complex are 1.1 Å and 1.0 Å respectively ( Fig. 5 (A-B)). In BA.2 S RBD the RMSF of mutated residues promoting high fluctuations which is stabilized by neohesperidin as compared to omicron where fluctuations tend to increase after ligand binding. This indicates stable binding of neohesperidin to BA.2 S RBD. RMSF fluctuations are summarized in Table 3 . Compactness of complexes were similar in both as reflected by Rg values of 1.79 nm and 1.83 nm in omicron and BA.2 respectively (Fig. 5(C-D)). Hydrogen bonding plots show continuous network of five or more bonds at any particular time frame indicating stable interactions in both (Fig. 5(E-F)).Fig. 4 RMSD plots of neohesperidin-S RBD interaction during the course of MD simulations (A) Omicron and (B) BA.2. Panel I, II and III represents r.m.s.d. of protein backbone, ligand binding site and ligand respectively as a function of time.
Fig. 4
Fig. 5 RMSF, Rg plots and hydrogen bonding analysis of omicron and BA.2 neohesperidin complexes Omicron (black) and neohesperidin complex (green). BA.2 (blue) and neohesperidin complex (magenta).
Fig. 5
Table 3 Root Mean Square Fluctuations of the mutated residues in Omicron, BA.2 and their neohesperidin complexes during 100ns MD simulations.
Table 3Residues RMSF fluctuation (nm)
Omicron S RBD Neohesperidin-omicron S RBD BA.2 S RBD Neohesperidin-BA.2 S RBD
Lys440 0.33 0.21 0.33 0.24
Asn477 0.35 0.55 0.75 0.36
Lys478 0.31 0.42 0.67 0.48
Ala484 0.29 0.44 0.38 0.20
Arg493 0.17 0.27 0.26 0.14
Arg498 0.23 0.16 0.31 0.24
Tyr501 0.56 0.21 0.48 0.39
3.5 Neohesperidin conformations in omicron and BA.2 complexes
Representative snapshots of omicron and BA.2 complexes from the equilibrated trajectories to their corresponding initial conformations were shown in (Fig. 6 ). Slight shift is observed in the simulated poses of omicron and BA.2-neohesperidin complexes compared to initial docking conformations. Equilibrated trajectories in the 90–100ns range were retrieved to estimate the thermodynamic parameters of these complexes, a significant parameter for stability of the complexes. In the representative conformations of omicron complex, a shift in the ligand towards loop 2 (residues 497–505) creates new hydrogen bonds with residues Phe497 and Arg498 at distances of 3.4 Å and 2.9 Å respectively compared to initial docked pose. Additionally, His505 forms salt bridges compared to initial conformation. In BA.2 representative complex, a slight shift in the orientation of the ligand from docked pose and inward movement of the receptor binding ridge loop1 (residue 477–487) creates a compact and closed conformation to preserve the ligand stability. This allows new hydrophobic contacts by residues Tyr449, Tyr453 and Tyr 495 at 3.8 Å, 3.8 Å and 3.6 Å respectively. Hydrophobic interactions formed by Arg 493 in initial docked pose were lost due to this shift. H-bonding partners remain same in initial and final poses except increase in distances of Arg 403 (3.1 Å), Gly496 (3.4 Å), Ser494 (3.4 Å), Tyr453 (3.3 Å) and His505 (3.3 Å) due to shift in the orientation of the ligand. Tyr449 and Tyr453, which initially were engaged in H-bonding at ∼3.0 Å, are now engaged in the hydrophobic interactions at 3.8 Å, in representative conformation. Random measurements of distances between binding pocket residues and ligand show a noticeable decrease in the distances between the amino acids in the representative structure compared to the initial conformation, a plausible reason for increase affinity of the neohesperidin towards these new variants preventing its strong interaction with hACE2 modulating its entry to host cells. The interaction surface in spike protein, consists of two significant loop regions from 477 to 487 and from 497 to 505 which with the support of nearby residues face the first two helices of hACE2 [35] and are stabilized forming several intermolecular H-bonds. Neohesperidin makes significant contacts with loop 2 in omicron and loop 1 in BA.2, thus destabilizing S RBD-hACE2 interaction. Overall simulation statistics and ligand conformations at different time intervals (Fig. S7(A-B)) indicate that neohesperidin could form a stable structure with omicron and BA.2 S RBD.Fig. 6 Comparison of the representative MD simulation snapshots for neohesperidin binding to (A) Omicron (yellow) (B) BA.2 (orange) with corresponding docking conformations.
Fig. 6
3.6 Principle component and free energy analysis
PCA is a useful tool for understanding the correlation of atomic movements in enzyme-substrate interactions, which are caused by the collective motion of atoms regulated by protein secondary structures. The crucial subspace, in which most protein dynamics occur, is usually defined by the greatest related eigenvalues. For this objective, the cluster of stable PCA states for the omicron, BA.2 and their neohesperidin bound complexes were visualized and analyzed. The highly coordinated mode of fluctuations for these complexes were sorted using essential dynamics (ED). Covariance matrices were generated using complex's Cα atoms (N = 195). The trace of the covariance matrices was 4.56 nm2, and 7.29 nm2 respectively in omicron and BA.2 S RBDs. The trace of the covariance matrices of neohesperidin bound complexes in both were 6.11 nm2 and 5.78 nm2 respectively, suggesting more compact BA.2-neohesperidin complexes [Fig. 7 (A-B)]. A larger trace value suggests high degree of flexibility. In free energy plots based on fluctuations of PC1 and PC2, the ΔG values of omicron and its complex ranged from −12.4 kJ/mol to −13.4 kJ/mol and that of BA.2 from −14.2 kJ/mol to −12.3 kJ/mol indicating energetically favourable structural changes for stable ligand interactions [Fig. 8 ].Fig. 7 Projection of Cα atoms along the first two eigenvectors of (A) Omicron S RBD (black) and neohesperidin-complex (green). (B) BA.2 (blue) and Neohesperidin complex (magenta) show different projection spaces. The per-residue energy contributions based on MM-PBSA binding affinity calculations (C) Omicron, (D) BA.2 S RBD-neohesperidin complexes.
Fig. 7
Fig. 8 The free energy landscapes of (A) Omicron S RBD (B) Neohesperidin complex (C) BA.2 S RBD (D) Neohesperidin-BA.2 complex from MD simulations at 300 K. The free energies are represented by -kBT ln P(PC1, PC2), with P(PC1, PC2) being the distribution probability calculated using the structures sampled at 300 K. The blue, green, and cyan colors represent the metastable conformations with low-energy states, while the red color signifies the high-energy protein conformations.
Fig. 8
3.7 Analysis of binding free energies of omicron and BA.2-neohesperidin complexes
Binding free energies were evaluated using MM-PBSA method to further investigate the stability of the omicron and BA.2 S RBD-neohesperidin complexes. Trajectories from 90 to 100ns were extracted to determine binding affinities. Binding affinities of BA.2 and omicron complexes were −164.1 ± 17.5 kJ/mol and −237.9 ± 18.7 kJ/mol suggesting stable binding interactions. Furthermore, higher contributions of additional energy components such as ΔGnon-polar, ΔGelectrostatic, and ΔGVander−Waals confirmed this conclusion shown in Table 4 . The contribution of polar solvation energy of neohesperidin was high in BA.2 as compared to omicron variant. Binding free energy of neohesperidin complex in omicron was higher due to more favourable interactions with the surrounding key residues. The details of MM-PBSA calculation of the complex are summarized Table 4. We identified the key residues in the spike RBD that favourably and unfavourably contributes in neohesperidin binding through per-residue energy decomposition analysis. The contribution energies of individual residues of omicron S RBD showed that Asn439, Tyr449, Tyr495, Phe497, Tyr501 and His505 contributed significantly as interacting partners whereas in BA.2 S RBD residues Tyr449, Lys455, Ala484, Tyr495 and His505 contributed maximally to complex interactions. [Fig. 7(C-D)]. These analyses provide insight into the key residues in omicron S and BA.2 S RBD crucial for neohesperidin binding.Table 4 Binding free energies of neohesperidin-S RBDs in omicron and BA.2
Table 4Protein–ligand complexes Van der Waal energy (kJ/mol) Electrostatic energy (kJ/mol) Polar solvation energy (kJ/mol) SASA energy (kJ/mol) Binding energy (kJ/mol)
Neohesperidin-omicron S RBD −279.9 ± 15.1 −20.7 ± 9.0 85.1 ± 15.5 −22.4 ± 1.0 −237.9 ± 18.7
Neohesperidin-BA.2 S RBD −210.4 ± 18.3 −26.5 ± 8.2 92.1 ± 15.0 −19.2 ± 1.5 −164.1 ± 17.5
4 Conclusions
Due to substantial costs and slow pace of new drug discovery especially due to fast infection rate of this pandemic, drug repurposing offers an alternative and viable screening option against potential SARS-CoV-2 new variants structural components to test de-risked compounds involving potentially lower overall development costs and timelines. As a result of the large number of mutations observed in the spike RBD protein of the SARS-CoV-2, omicron and its sub-lineage BA.2 also known as “stealth” omicron raises serious concerns. Our findings demonstrated that the BA.2 variant has a higher binding affinity for ACE2, perhaps facilitating increased transmission. In the following work, we used in silico computational drug designing strategies to identify an effective drug molecule against BA.2 which could be further validated using in vitro and in vivo experiments. Free energy profile and energy decomposition analysis suggest high and stable affinity of neohesperidin for BA.2. Neohesperidin disrupt the salt bridges formed between BA.2 S RBD and ACE2, by forming hydrogen bonds with the residues Arg403 and Arg498 participating in salt bridge formation with Glu75 and Asp30 residue of hACE2 thus weakening the binding and interaction of BA.2 and hACE2. The binding of neohesperidin causes aberrance in the structural properties of the BA.2 spike protein as revealed from molecular dynamic simulation and MM-PBSA analysis. Engagement of neohesperidin-RBD chemical space is structurally stable which could be a promising pharmacophore to search for similar drug candidates showing specific and greater affinity with omicron sub-variants.
Funding
The work was financially supported by Science and Engineering Research Board (SERB), India Grant CRG/2020/002008 awarded to S.K.S.
Author contributions
“formal analysis, investigation, JKS, SD, SKS, MIS; writing—review and editing, JKS, SD, GS, MIS, SKS; conceptualization, supervision, funding acquisition, SKS. All authors have read and agreed to the published version of the manuscript.”
Declaration of competing interest
The authors declare no conflict of interest.
Appendix A Supplementary data
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Acknowledgements
The authors acknowledge the support of Multiscale Simulation Research Center (MSRC), 10.13039/501100004843 Manipal University Jaipur for computational work.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.compbiomed.2022.106392.
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Literature Reviews
Top 100 Most Cited Neurosurgical Articles on COVID-19: A Bibliometric Analysis
Al-Habsi Jehad MD 1
Al-Hatmi Afaf MD 2
Al-Saadi Tariq MD 3∗
1 College of Medicine, Sultan Qaboos University, Muscat, Sultanate of Oman
2 Department of Neurosurgery, Khoula Hospital, Muscat, Sultanate of Oman
3 Department of Neurology & Neurosurgery - Montreal Neurological Institute, Faculty of Medicine, McGill University, QC, Canada. 3801 Rue University, Montreal, Quebec, H3A 2B4, Department of Neurosurgery, Khoula Hospital, Muscat, Sultanate of Oman
∗ Corresponding author: Tariq Al-Saadi, MD , Department of Neurology & Neurosurgery - Montreal Neurological Institute, Faculty of Medicine, McGill University, QC, Canada. 3801 Rue University, Montreal, Quebec, H3A 2B4, Department of Neurosurgery, Khoula Hospital, Muscat, Sultanate of Oman.
5 12 2022
5 12 2022
8 11 2022
29 11 2022
© 2022 Elsevier Inc. All rights reserved.
2022
Elsevier Inc.
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INTRODUCTION
The first case of coronavirus disease 2019 (COVID-19) was reported in December 2019 in Wuhan, China. This study uses a bibliometric analysis of the top 100 most cited neurosurgical COVID-19-related articles to date to identify and determine their characteristics.
METHODS
The Scopus library was searched for all published articles on neurosurgery and COVID-19. The main keywords were used for the search “neurosurgery, neurosurgical, and COVID-19”. English language articles reporting on neurosurgical aspects during COVID-19 were included in the study. The retrieved top 100 articles were analyzed, and the following characteristics were noted for each article: 1) article title, 2) year of publication, 3) citations, 4) first author, 5) corresponding author, 6) names of other authors, 7) journal name 8) article type, 9) study focus and 10) involvement of the patient.
RESULTS
Our search obtained articles published from December 2019 until 29 March 2022. It was observed that 93% of the documents were published in 2020. The top 100 articles have been cited 2649 times in total. The most cited article was “Factors Associated with Surgical Mortality and Complications among Patients with and without Coronavirus Disease 2019 (COVID-19) in Italy” by Doglietto F. et al., published in JAMA Neurology in June 2020, with 124 citations.
CONCLUSIONS
This analysis facilitated making evidence-based clinical decisions and drawing the attention of researchers to identify and contribute to the increasing scientific work by identifying the top 100 most cited neurosurgical COVID-19-related articles published.
Keywords
Coronavirus disease 2019
COVID-19
Neurosurgical
neurological surgery
Abbreviations
COVID-19, Coronavirus disease 2019
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pmcOn behalf of all the contributors I will act and guarantor and will correspond with the journal from this point onward.
Prior publication: No
Conflicts of interest: No
Permissions N/A
Yours’ sincerely, Tariq Al-Saadi, MD
McGill University, Montreal, Canada
Montreal Neurological Institute, Montreal, Canada
| 36481440 | PMC9721376 | NO-CC CODE | 2022-12-07 23:19:08 | no | World Neurosurg. 2022 Dec 5; doi: 10.1016/j.wneu.2022.11.133 | utf-8 | World Neurosurg | 2,022 | 10.1016/j.wneu.2022.11.133 | oa_other |
==== Front
SSM Qual Res Health
SSM Qual Res Health
Ssm. Qualitative Research in Health
2667-3215
The Authors. Published by Elsevier Ltd.
S2667-3215(22)00169-X
10.1016/j.ssmqr.2022.100207
100207
Article
Negotiation of collective and individual candidacy for long Covid healthcare in the early phases of the Covid-19 pandemic: Validated, diverted and rejected candidacy
Maclean Alice a1
Hunt Kate a∗1
Brown Ashley a
Evered Jane A. b
Dowrick Anna c
Fokkens Andrea d
Grob Rachel e
Law Susan fg
Locock Louise h
Marcinow Michelle g
Smith Lorraine i
Urbanowicz Anna j
Verheij Nientke d
Wild Cervantee c
a Institute for Social Marketing and Health, University of Stirling, Scotland, FK9 4AL, UK
b University of Wisconsin, Madison, Madison, WI, USA
c Health Experiences Research Group, Nuffield Department of Primary Care Health Sciences, University of Oxford, OX2 6GG, UK
d University of Groningen, University Medical Center Groningen, Department of Health Sciences, Applied Health Research, the Netherlands
e Department of Family Medicine and Community Health, University of Wisconsin, Madison, WI, USA
f Institute of Health Policy, Management & Evaluation, University of Toronto, M5T 3M6, Canada
g Institute for Better Health, Trillium Health Partners, L5B 1B8, Canada
h University of Aberdeen, Foresterhill, Aberdeen, AB25 2ZD, UK
i Sydney Pharmacy School, Faculty of Medicine and Health, University of Sydney, Australia
j Social and Global Studies Centre, School of Global, Urban and Social Studies, RMIT University, School of Health and Social Development, Faculty of Health, Deakin University, Australia
∗ Corresponding author. Institute for Social Marketing and Health, University of Stirling, Scotland, FK9 4AL, UK.
1 Joint first authors.
5 12 2022
6 2023
5 12 2022
3 100207100207
20 9 2022
2 12 2022
4 12 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 analysis of people's accounts of establishing their need and experiences of healthcare for long Covid (LC) symptoms draws on interview data from five countries (UK, US, Netherlands, Canada, Australia) during the first ∼18 months of the Covid-19 pandemic when LC was an emerging, sometimes contested, condition with scant scientific or lay knowledge to guide patients and professionals in their sense-making of often bewildering constellations of symptoms. We extend the construct of candidacy to explore positive and (more often) negative experiences that patients reported in their quest to understand their symptoms and seek appropriate care. Candidacy usually considers how individuals negotiate healthcare access. We argue a crucial step preceding individual claims to candidacy is recognition of their condition through generation of collective candidacy. “Vanguard patients” collectively identified, named and fought for recognition of long Covid in the context of limited scientific knowledge and no established treatment pathways. This process was technologically accelerated via social media use. Patients commonly experienced “rejected” candidacy (feeling disbelieved, discounted/uncounted and abandoned, and that their suffering was invisible to the medical gaze and society). Patients who felt their candidacy was “validated” had more positive experiences; they appreciated being believed and recognition of their changed lives/bodies and uncertain futures. More positive healthcare encounters were described as a process of “co-experting” through which patient and healthcare professional collaborated in a joint quest towards a pathway to recovery. The findings underpin the importance of believing and learning from patient experience, particularly vanguard patients with new and emerging illnesses.
Keywords
Long Covid
Candidacy
Help-seeking
Patient experience
Cross-national comparison
Covid-19
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pmc1 Introduction
1.1 The “making” of long Covid
When the World Health Organisation declared Covid-19 a pandemic in March 2020, professional and public understandings, whilst rapidly developing, were that Covid-19 caused either symptoms which rapidly resolved or potentially life-threatening illness requiring hospitalisation (especially amongst the elderly and clinically vulnerable). This was enshrined in public messaging during ‘lockdowns’ to limit virus transmission, when the public was asked to stay home, save lives and protect healthcare systems in the UK, Canada, Australia and elsewhere. The threat of premature mortality and catastrophic strain on healthcare systems was reflected internationally in media reports of escalating cases and deaths, makeshift Covid wards and overworked healthcare staff.
However, soon another picture began to emerge, of people with a bewildering array of longer-term symptoms, even after initially mild symptoms of (assumed) infection. Within six months, Callard and Perego (2021) described long Covid as the first illness to be collectively made by patients finding one another through various social media (e.g. Twitter, www.facebook.com/groups/longcovid), as first-person accounts rendered their persisting and heterogenous symptoms visible and challenged earlier assumptions about the severity and longevity of symptoms. Roth and Gadebusch-Bondio (2022, p2) also noted the importance of collective online advocacy by people with long Covid symptoms, including in the naming of the condition, and identified this as a form of “bio-digital citizenship”. They suggest that “online mobilisation of subjective evidence” (p4) by people with longer-term symptoms, including the active engagement of healthcare professionals and “medically literate academics” (p2) who were themselves affected, led to a more rapid and wider acceptance of long Covid than for other contested conditions. Furthermore, the “digital interconnectedness of sufferers” online facilitated a “collective gathering” of patient experience in a “heterogenous and global community” (p6).
Many (e.g., Altmann & Boyton, 2021) have emphasised the unpredictable, relapsing, remitting and diverse symptoms of long Covid which can affect respiratory, cardiovascular, urological, neurological, and gastrointestinal systems; and the lack of information about management and prognosis. A dynamic review highlighted an urgent need for research on (and rapid access to) treatment and management, with “expert by experience” patients as equal partners in setting the agenda (Maxwell, 2021). A review by Macpherson et al. (2022) of the then limited evidence on patient experience (two international surveys and three qualitative studies from the UK) highlighted the lack of knowledge and understanding about long Covid among healthcare professionals and the confusion and anxiety this could create for patients. They noted multiple perceived barriers to healthcare which could make accessing care complex, difficult and exhausting. These findings have been reinforced by more recent papers on patient experience of long Covid (e.g. Rushforth et al., 2021).
1.2 Theoretical framing
When scientific knowledge of a new condition is limited, deciphering patient experience is paramount. Aptly, in relation to an emergent and contested condition such as long Covid, Lian et al. (2021, p7) observe that, in the face of medical uncertainty:“… [g]iving people a medical name for their health problems … is the starting point for defining, explaining and acting on illness and for predicting future developments. Nameless ailments … remain indecipherable. Diagnostic uncertainty renders patients incapable of making sense of what is happening to them, what to do, and what to expect, and it prevents clinicians from predicting future developments, which patients often expect and sometimes ask for.”
Models of illness behaviour are founded on people's experiences of sense-making of painful and/or disruptive symptoms. In their review of sociological and psychological models, Wyke et al. (2013, p85) suggest the purpose of such behaviour is to “(re)achieve normality in physical or social functioning”, drawing on interactions with others, past experiences of symptoms and treatment systems, and knowledge of social norms or expectations of treatments. Healthcare seeking actions, they argue, are “continually evaluated in the light of changes in knowledge, resources or embodied experience.” Davison et al. (1991) coined the term “lay epidemiology” for the process by which people make sense of their (risk of) illness, assessing the possibility and probability of becoming ill with particular conditions - for example, the “kind of person who gets heart trouble”. Their coronary candidacy theory suggested four outcomes, two explicable (“candidate” who develops heart disease; someone who is not a candidate who never develops heart disease) and two apparently inexplicable (people who do “all the wrong things” yet survive to a ripe old age; those who appear to have no risk factors but succumb to heart disease as “the last person you'd expect”). Others extended this theory to show how factors such as gender are an integral part of the structuring of candidacy (Emslie et al., 2001). A distinct construct of candidacy (Dixon-Woods et al., 2006) was subsequently formulated in relation to healthcare utilisation, and describes how people's eligibility for healthcare is a “continually negotiated property” subject to micro- and macro-level influences (e.g. configuration of services). Dixon-Woods et al. argue that:“Health services are continually constituting and seeking to define the appropriate objects of medical attention and intervention, whilst at the same time people are engaged in constituting and defining what they understand to be appropriate objects of medical attention and intervention. Access represents a dynamic interplay between these simultaneous, iterative and mutually reinforcing processes” (p1).
But what happens when, as in the case of long Covid in the early years of the pandemic, knowledge is absent or scarce, interactions with others are limited or changed, healthcare resources are subject to unprecedented strain, and expectations of the healthcare system's response to symptoms may be less certain because provision of care is disrupted?
This is the theoretical framing for our analysis of experiences of people who experienced long Covid early in the pandemic – who we refer to as “vanguard patients” here - in their quest for support and treatment from healthcare professionals. Our analysis covers a time in the pandemic when long Covid was a newly emergent illness, with a scant lay or professional epidemiological evidence-base for making sense of often bizarre and life-changing constellations of symptoms, including when long Covid was unrecognised or contested by the medical establishment. We argue that, of the aspects or features (Liberati et al., 2022) of candidacy, four of the seven outlined by Dixon-Woods et al. (2006) are particularly salient: identification of candidacy, navigation of services, permeability of services and adjudication by healthcare professionals. We follow others in recognising the collective nature of the “patient-making” of long Covid, arguing that this can be understood as the generation of “collective candidacy”.
We then analyse the experience of our participants, many of whom were amongst the earliest vanguard patients, as they tried to define and assert their own individual candidacy for healthcare in the early stages of the pandemic. Using data from five countries, at various stages of the unfolding of the pandemic in 2020/1 and with differing healthcare systems, we expected many similarities in experiences cross-nationally, in part because of the global interconnectedness engendered by the collective online “making” of long Covid. We argue negative experiences of healthcare interactions of long Covid when it was emerging and contested can be understood as “diverted” or “rejected” candidacy, whereas when candidacy is “validated and affirmed” healthcare interactions are more positive, even in the face of extreme uncertainty about the causes, consequences and prognosis for long Covid. We also draw on developments of the candidacy construct by others (e.g. Macdonald et al., 2016, Kirkpatrick et al., 2018) and an application of the construct to understand access to secondary mental health services in the UK during the pandemic (Liberati et al., 2022).
We conclude by considering whether understanding the making of collective and individual candidacy, and the professional adjudication of candidacy (as rejected, diverted or validated) alongside the accessibility and permeability of services in pandemic times, has cross-national policy and practice implications for healthcare use and interactions for long Covid and other emerging and contested conditions.
2 Methods
This paper draws on ongoing research on experiences of participants with (long) Covid in the UK (n = 30), US (n = 20), Netherlands (n = 10), Canada (n = 6) and Australia (n = 6). Analysis was conducted in 2022; some country teams are continuing to collect data for online platforms for patient experiences of long Covid based on analysis of narrative interviews (see, for example, Long Covid In Adults - Symptoms of long Covid (healthtalk.org)).
2.1 Data collection and sampling
The interviews (n = 72) included in this analysis were undertaken between November 2020 and March 2022 using a comparable narrative and semi-structured approach. All researchers received the same training and support through the DIPEx International collaboration (Ziebland et al., 2020). Specific studies of long Covid are ongoing in the UK, US, Netherlands and Canada; data from Australia and Canada (and additional UK and US data) were from interviews with people with longer term symptoms in broader studies of Covid-19. Convenience sampling was undertaken in Australia given the very low number of cases in 2020. Sampling within most countries will ultimately aim for maximum variation (Coyne, 1997) when country-specific studies complete in 2022/3, with diversity in location, occupational social class, ethnicity, gender and age. Table 1 shows participant characteristics, including when they first became ill with Covid-19 and number of months affected by subsequent symptoms.Table 1 Participant characteristics grouped by country (ordered by sample size).
Table 1UK (n = 30) ID Gender Ethnicity Age Month of infection Month of interview Time affected by symptoms (months)
UKLC01 M White British 30–39 Apr-20 Apr-21 12
UKLC02 F White British 40–49 Apr-20 Apr-21 13
UKLC03 M White British 40–49 May-20 Apr-21 11
UKLC04 F White British 50–59 Mar-20 Apr-21 13
UKLC05 M White British 20–29 Mar-20 May-21 14
UKLC06 F White British 30–39 Mar-20 May-21 14
UKLC07 F White British 30–39 Mar-20 Jun-21 6
UKLC08 F White British 30–39 Dec-20 Jun-21 15
UKLC09 F White British 30–39 Mar-20 Aug-21
Oct-21 19
UKLC10 M White British 50–59 Mar-20 Aug 21 17
UKLC11 F Asian British 30–39 Mar-21 Sep-21 6
UKLC12 M White British 60–69 Mar-20 Oct-21 10
UKLC13 F White British 60–69 Mar-20
May-20 Jan-22 20
UKLC14 F White British 50–59 Sep-21 Feb-22 5
UKLC15 F Bangladeshi 30–39 Dec-20 Mar-21 3
UKLC16 F Black African 50–59 Feb-21 Apr-21 3
UKLC17 F Pakistani 30–39 Oct-20 Mar-21 3
UKLC18 F Malaysian 40–49 Aug-20 May-21 9
UKLC19 F White Welsh 50–59 Aug-20 May-21 4
UKLC20 F Black British 50–59 Sep-20 May-21 8
UKLC21 F Black British 60–69 Dec-20 May-21 5
UKLC22 F Black Pakistani 30–39 Jan-21 Jul-21 6
UKLC23 F White American/British 50–59 Jan-20 Sep-21 21
UKLC24 F Black Caribbean 60–69 Dec-20 Sep-21 3??
UKLC25 F White English 30–39 Mar-20 Oct-21 18
UKLC26 F Black British 50–59 Jan-20 Nov-21 25
UKLC27 F Ashkenazi Jewish 30–39 Mar-20 Nov-21 19
UKLC28 F White English 20–29 Oct-20 Nov-21 14
UKLC29 F White British 40–49 Mar-20 Dec-21 21
UKLC30 M White British 20–29 Mar-20 Mar-21 24
US (n = 20)
ID Gender Ethnicity Age Month of infection Month of interview Time affected by symptoms (months)
USLC01 F Mixed Race: White/Hispanic 20–29 Mar-20 Jan-21 10
USLC02 M Hispanic or Latino 40–49 Nov-20 Jan-21 3
USLC03 F Black or African American 40–49 Dec-20 Feb-21 2
USLC04 F White 50–59 Mar-20 Apr-21 12
USLC05 F Hispanic or Latino 30–39 Mar-20 (first) May-21 3 (second infection)
Dec 20 (second)
USLC06 M Hispanic or Latino 60–69 Jun-20 May-21 6
USLC07 F White 50–59 Nov-20 Jul-21 6
USLC08 F White 40–49 Dec-20 Jul-21 4
USLC09 F White 60–69 Oct-20 21-Jul 9
USLC10 F Arab-American Latina 50–59 Sep-20 Jul-21 6
USLC11 F White 50–59 Dec-20 Jul-21 4
USLC12 F White 50–59 21-Jan Jul-21 6
USLC13 F White 30–39 Aug-20 Sep-21 6
USLC14 F White 40–49 Nov-20 Oct-21 5
USLC15 F White 40–49 Oct-20 Oct-21 6
USLC16 F White 40–49 Mar-20 Oct-21 18
USLC17 M White 60–69 May-20 Nov-21 6
USLC18 F White 50–59 Feb-20 Nov-21 6??
USLC19 F White 50–59 Oct-20 Dec-21 8??
USLC20 F Asian 40–49 Mar-20 Jan-22 7
NL (n = 10)
ID Gender Ethnicity Age Month of infection Month of interview Time affected by symptoms (months)
NLLC01 F NL 50–59 Dec-19 Feb-22 25
NLLC02 F NL 60–69 May-20 Mar-22 22
NLLC03 F NL 20–29 Mar-20 Jan-22 22
NLLC04 M NL 40–49 Apr-21 Feb-22 10
NLLC05 M NL 40–49 Apr-20 Feb-22 10
NLLC06 F NL 40–49 Mar-21
Dec-21 Feb-22 13
NLLC07 F Other 40–49 Mar-20 Feb-22 24
NLLC08 F Other 30–39 Apr-21 Feb-22 10
NLLC09 M NL 70–79 Apr-21 Feb-22 10
NLLC10 F NL 60–69 Apr-21 Feb-22 10
CAN (n = 7)
ID Gender Ethnicity Age Month of infection Month of interview Time affected by symptoms (months)
CANLC01 (02) F White 40–49 Mar-20 Nov-20 8
CANLC02 (04) F White 40–49 Mar-20 Nov-20 8
CANLC03 (05) F Caucasian/British Isle 60–69 Apr-20 Nov-20 7
CANLC04 (06) F White 40–49 Mar-20 Nov-20 8
CANLC05 (07) F Caucasian 40–49 Mar-20 Nov-20 8
CANLC06 (08) M Jewish 40–49 Mar-20 Dec-20 9
CANLC07 (11) F South-Asian 30–39 Jun-20 Dec-20 6
AUS (n = 6)
ID Gender Ethnicity Age Month of infection Month of interview Time affected by symptoms (months)
AUSLC01 F British 40–49 Mar-20 Dec-20 8
AUSLC02 M Western European 30–39 Jul-20 Nov-20 4
AUSLC03 F Eastern European 30–39 Aug-20 Dec-20 4
AUSLC04 F Australian peoples 60–69 Mar-20 Feb-21 11
AUSLC05 F Australian peoples 40–49 Jul-20 Mar-21 8
AUSLC06 M Southern European 40–49 Aug-20 Mar-21 6
Recruitment was through various routes, including clinicians, social media, support groups and snowballing to facilitate diversity in experiences and perspectives. Interviews were conducted online (except in the Netherlands where they were face-to-face) and recorded on audio and/or video according to participant preference. Interviews typically lasted 60–90 min, although some were shorter and/or conducted over multiple sessions if the participant preferred (e.g. due to fatigue); the longest totalled 4.5 h. The first part of the interview invited participants to relate how they first became aware of (long) Covid and their experience of the illness. The second part drew on semi-structured topic guides with various prompts, including questions around their experiences of help-seeking.
Relevant research ethics review and approval was undertaken in each country before data collection.
2.2 Data analysis
Interviews were transcribed verbatim. Transcripts were checked for accuracy and imported into specialist computer software (NVivo [UK, Australia]; ATLAS-ti [NL]; MAXQDA [US, Canada]) for organising textual data for coding/analysis. Our multistage analytical approach was as follows. First, interview accounts were independently analysed by each country team for narrative themes that structured participant experiences. Second, a descriptive write up of initial themes in the UK and a proposed analytical framework based on the UK data were shared with researchers in the four other countries. The UK team met separately with each country team, to allow detailed discussion of each country's data and minimise meetings at antisocial hours. These discussions established many strong commonalities across the data from the five countries. Third, the US, Dutch, Canadian and Australian teams applied the UK conceptual structure to code their own data and shared relevant data extracts in response to the UK team's initial formulation of influences on help-seeking for long Covid; selected extracts from the Dutch data were translated to English. The Dutch extracts were forward and backward translated by two Dutch researchers (NV and AF). Fourth, we undertook another series of online bilateral discussions and face to face meetings between members of the authorship team at a DIPEx International collaborators meeting in May 2022. Following this, the analysis was reworked using the theoretical lens of candidacy, as described above, and the amended analytical framework was shared for critical comment. In the final step, authors from all countries interrogated the redrafted text and analytical framework and confirmed it fully resonated with their data. Subsequent refinements to the line of argument were elaborated, which included searching for any examples that contradicted the main findings. During this iterative process, the data were thus compared and discussed, and the analytical framework was iteratively tested against each country's data and through ongoing dialogue within the author team, in light of existing knowledge, relevant theory and internal peer review. Country teams were asked to highlight any differences in health systems and policies, circumstances of the participants/phase of the pandemic, if relevant for any country-specific observations. Illustrative interview extracts are included in the main text, supplemented by examples from all five countries in Supplementary Table 1.
3 Findings
3.1 Overview
Fig. 1 presents the analytical framework we developed to make sense of people's accounts of having and seeking help for symptoms of long Covid in the relatively early stages of the Covid-19 pandemic, when long Covid was novel and often contested and patients and healthcare professionals alike had limited lay or professional epidemiological knowledge to draw on. In 3.2 below, we give an overview of the types of symptoms experienced, and how a “collective candidacy” was established, principally through vanguard patients' digital interactions through social media. We then discuss people's individual articulation of candidacy and need for healthcare. In 3.3–3.5, we demonstrate how healthcare professionals' adjudication (Dixon-Woods et al., 2006), based on a spectrum of professional knowledge and experience of long Covid, can lead to patients experiencing their claims to candidacy for healthcare as “rejected”, “diverted” or “validated/affirmed”, which in turn affects whether their medical encounters are experienced as negative and undermining or positive and enabling. The most positive healthcare interactions can be experienced as an affirming partnership of discovery in which patient and healthcare professional “co-expert” to learn more about (treatment for) long Covid together. We note here and in Fig. 1 that, as a consequence of these processes, those with the most negative experiences may only be able to find support from health professionals for pathways to recovery and adequate management through resorting to other (e.g. privately-provided) sources of care. This has the potential to further exacerbate health inequality. Although we present these three forms of candidacy as discrete in sections 3.3-3.5 below, it is important to note that there was at times a degree of overlap between rejected and diverted candidacy and between diverted and validated/affirmed candidacy.Fig. 1 Patient negotiation and recognition of collective and individual candidacy for healthcare for long Covid, and impact of professional adjudication (rejected, diverted, heard) on patient experience and pathway to treatment and recovery.
Fig. 1
3.2 Making sense of symptoms and negotiating collective and individual candidacy in the context of the pandemic
Experiences of initial infection varied considerably. Some described mild illness, whereas others described being “bedridden”, "completely exhausted" or "trapped in my own body" (AUSLC04). In line with other research on experiences of long Covid (see e.g., Callard & Perego, 2021; Macpherson et al., 2022; Rushforth et al., 2021), participants described how their symptoms fluctuated. Some continued, whilst others disappeared, reappeared, worsened or were compounded by new symptoms. Longer term symptoms included extreme fatigue, “brain fog”, breathlessness, cardiac symptoms (e.g., palpitations), dizziness, joint/muscle aches, anosmia, difficulty controlling body temperature, headaches, numbness/pins and needles, problems with vision and many more. Participants, especially the vanguard patients who were affected early in the pandemic when long Covid was new, unrecognised and unknown, said it was hard for them to make sense of their symptoms, not least because of the variety they experienced and their fluctuating occurrence and severity. One described this fluctuation as a “stinking teaser […] it crushes you because it's like you just want to be you again” (USLC16). Dissonance with past experience of acute illnesses or infections that followed a linear pathway to recovery could be particularly confusing, causing people to doubt their existing understandings of illness and recovery. For example:[I] would think, ‘Okay I’m better now’, because this is my experience of being ill […] of so-called respiratory viruses […] So, our experience of being sick […] was that you feel like shit for a few days, and then you get better. Not that you feel like shit for a few days [with Covid-19], you start to feel a little bit better, then you feel like shit again […] it’s about a whole new experience of illness that very few people understood […] that suddenly lots of us were going through [and] you’re doubting your experience all the time. You come to doubt your knowledge of illness and recovery. (UKLC23)
Participants commonly referred to their constellation of symptoms as “life-changing”. For example, UKLC10 described long Covid as a “completely new condition, that's come out of nowhere, that's turned my life on its head”. The array and severity of symptoms made continuation of people's pre-Covid daily life challenging or impossible (“pretty much everyday activities suddenly became almost impossible” AUSLC05). For example, some described disturbing and confusing disruption to taste and smell, crippling fatigue which prevented them from getting out of bed, or how routine tasks (e.g., driving, showering, hanging out washing) could cause their heart rate to soar. Brain fog could render people unable to do their job or day-to-day tasks.
For those affected earliest in the pandemic, the struggle to make sense of their symptoms only began to resolve as they heard of others' suffering. This process was accelerated by people's access to online technologies and media. As vanguard patients began to share their stories and information about symptoms, treatment and support in medical, mainstream and social media, they collectively came to understand their illness as long Covid through a technologically accelerated process of “patient-making” of this novel condition, as others have described (e.g. Callard et al., 2021; Rushforth et al., 2021). As one participant said, “most of our help has come from online groups which I find extraordinary because I'm not a Facebook user but I've spent a lot of time on there because that's where a lot of the long Covid groups are” (AUSLC04). As a nascent lay epidemiology (Davison et al., 1991) of long Covid began to coalesce online, inevitably ahead of a scientific understanding given the emergent nature of long Covid as an illness category, people around the world could begin to make sense of their confusing experiences through a shaping of “collective candidacy”.I was questioning my sanity. Am I just making this up, am I just depressed? … [J]oining the slack [online] group was huge, because I discovered that I wasn’t alone and there were so many people worldwide that were experiencing the same thing. (CANLC07)
I had no idea what was happening to me … [then] there started to be reports about people having post-Covid syndromes or having episodes of Covid that just seemed to go on and on and on which was just amazing, like a light in the dark because I thought I was going mad and I thought it was something about me. It was just really difficult and then reading more about it, I was like, ‘Okay this is something that happens to people, this is not just about me!’ (UKLC25)
As an understanding of a “collective candidacy” began to gain traction internationally, this could enable a sense-making process through which people could reinterpret their own experience and articulate their individual candidacy, as someone needing support for long Covid symptoms. This process involved recognising they were seriously unwell and both in need, and worthy, of medical care. It was a complex and bewildering process, even for those (like UKLC06 and USLC07) who were doctors themselves:[Y]ou can probably get a sense that a lot of this illness, because it’s all been new, a lot of it has just been me trying to work out what on earth is going on. And as a doctor, that has been really, really difficult. I can’t imagine what it would be like as a lay person - the neuro stuff, I thought had gotten better, so you know, the phantom smells, the tinnitus, I'd had some strange nerve pain, like a poker in my ear, really bizarre, but it was very, very painful. Strange kind of scalp sensations, as well, but all, all such vague and strange things, I didn’t even mention them to, to my GP, because I thought she might think I was completely mad [laugh]. UKLC06
“[i]t is scary to me as a healthcare professional, the stuff that people are not telling their docs […] people are so frustrated with their health care, you know health care systems not knowing what to do with […] that they just live at home with these symptoms […] it is really sad”. (USLC07)
This selective “editing-out” of what people understood to be the most bizarre and implausible symptoms in medical encounters plausibly slowed the development of professional epidemiological understandings of long Covid and contributed to continuing medical scepticism about some symptoms. This scepticism, and people's struggles to assert their individual candidacy, could be compounded for those affected before the widespread availability of testing for Covid-19, as initial infection may never have been confirmed. For example, UKLC23 said: “Doctors could not make sense [of my symptoms] [… or] help me process what I was going through, because a) they didn't have the time, b) they didn't know what was going on [laughs], and c) I wasn't a valid patient because I didn't have a [positive] test”.
Hence, even as collective candidacy for long Covid took root internationally (perhaps not as quickly as implied by Roth and Gadebusch-Bondio (2022)), some found their individual candidacy even more difficult to assert in the face of organisational changes to restrict virus transmission or pressures on health systems during the pandemic. As some wrestled with the validity of their candidacy, participants often emphasised their previous good health, their infrequent past resort to medical care or time off work for illness, pandemic-related pressures on the healthcare system or the futility of help-seeking for a novel illness. For example, a UK participant said:I’m not someone […] who’s been off sick, not someone who’s actually suffered with ill health particularly in my life. And then to be back and forth to the GP is really uncomfortable. Just because I don’t want to put any pressure on the … the health service [… it] is quite awful to come and approach a GP and say, ‘I’m really tired’ […] I just feel like I’m becoming a hypochondriac […] an annoying patient that is saying ‘I don’t feel well’ and there’s no real … and they’ll be like, ‘What’s wrong?’ And I’m like, ‘Well I don’t really know’ […] because you do feel like, well what are they going to do? They’ve already done [some tests] […] What else could there be that could be offered? (UKLC09)
Analyses of participants' accounts in some countries revealed the ways that they were managing the task of balancing responsible healthcare service use (i.e., not “wasting” the doctor's time, not using scarce resources without just cause) against taking responsibility for their own health (seeking investigations into possible long-term damage to their bodies caused by long Covid) and a sometimes desperate need for explanations, support and treatment. The morally charged nature of this balancing act could be intensified by the fact that the pandemic was adding pressure to already stretched health services and the knowledge that Covid was causing life-threatening illness or death for others. Such considerations caused some participants to repeatedly question if their own healthcare service demands were justifiable. It was common for participants to follow up ways in which they felt they had not been supported with a comment which downplayed their need for help as compared to others with more severe illness (although this was not evident to the same extent in data from the Netherlands):There wasn’t any screening by your GP, by the surgery nurse, nothing. Nothing at all. [erm] And obviously I don’t wanna, I don’t wanna say in-depth of care as some […] had to be induced into a coma and had to be hospitalised. They are the priority. Even with all of that, if some people we know have died. But it just felt as though there was a gap. (UKLC21)
However, while acknowledging that those experiencing more acute illness should be prioritised, participants emphasised the importance of acknowledging, and planning for, those with long Covid too; one described the lack of support as “galling” (UKLC10). This desire to fill the support void for long Covid can be seen as part of their efforts to draw on and shape a collective candidacy for long Covid.
Across the five countries, there were many accounts of negative and unsatisfying, frustrating or unhelpful healthcare encounters, but also some positive accounts. We now go on to discuss how these can be understood in relation to rejected, diverted and validated/affirmed candidacy.
3.3 Rejected candidacy
The data included many examples of participants' claims to individual candidacy being adjudicated then rejected, at least initially, by healthcare professionals. Participants' accounts suggest this happened in various ways, from health professionals telling patients that long Covid did not exist, to patients being left with the impression that healthcare professionals, certainly during the early stages of the pandemic, were at a loss as to how to deal with their claims to candidacy. Many participants recounted feeling disbelieved by some of the healthcare professionals they encountered. For example, CANLC06 said one junior hospital doctor told her “it was all in my head and it was anxiety”. She challenged the doctor who called in a superior who said “it was not in my head, that it was very real, they just couldn't prove it”. UKLC05 reported presenting to the emergency department with severe chest pain, after being encouraged to do so at an earlier visit, and being told it was likely a symptom of his long Covid and he should just take Paracetamol [a commonly used pain killer]. This left him feeling “very let down […] I don't feel believed”. UKLC10 was told by a respiratory specialist “I don't believe in long Covid” and was then frustrated when subsequently a neurology specialist said: “I think you have long Covid, and when it does turn out to be long Covid, I really don't know what we can do about it”.
Often participants described a compounding of their symptoms and suffering in their struggle to establish their need for, and get, healthcare. AUSLC06 was told by his primary care doctor to seek help from Covid doctors at the hospital who, in turn, told him “Your [primary care doctor] should be able to look after you”. He found this “frustrating […] you've burnt up more of your energy”. Participants who felt their symptoms had not been sufficiently investigated spoke of ongoing fears that Covid had inflicted lasting damage to their body and their struggle to have their fears allayed by diagnostic testing. For example, USLC07 said “There are times when I was like [asking healthcare professionals] ‘[do] You just want to CAT scan? Like, I just want my brain checked. I want to make sure that my brain is okay’”.
Some participants (including some who were medically trained) said they understood the difficult position that healthcare professionals found themselves in when faced with patients whose symptoms they could neither explain nor effectively treat. UKLC06, a doctor herself, said “it was difficult for the [primary care doctor] to know what to do with people like me, because it was a new condition, there was no service set up specifically for it”. However, there were many participants who experienced a sense of abandonment when pathways to healthcare seemed impermeable (Dixon-Woods et al., 2006), when their claims to candidacy were rejected (adjudicated as not credible) or when their accounts of symptoms were believed but there was no clear investigative or treatment pathway.[The process of searching for pathways to care] was difficult and frustrating […] I called [names four cities]. I left messages. I got no responses. Finally, with the [city] free clinic, I was able to get in with them, but it took over a week […] [W]hen I was there, I had two people [nurse and doctor] in the room […] they looked at my chart […] and x-rays [and I asked] ‘When can I get back to work?’ And they just basically laughed and were like, ‘You can't work. You're on oxygen.’ And I didn't really get any like, any plans to help […] I was supposed to hear follow-up from them in about a week [… but] I still have not, to this day, heard from them. (USLC13)
[T]hey sent me home because they said everything, all my tests were normal. And you’re like well I know I’m far from normal here [laughs] but equally I know that you can’t help me […] it was just really weird. So you’re kind of abandoned basically. (UKLC29)
Many participants presented themselves as frustrated, angry or defeated. Their accounts suggest that they were particularly angry at being left alone to understand their condition and find ways to recover their former health. One participant called this “DIY GP-ing [um] and I think for the people that don't have any medical knowledge it must be awful” (UKLC13).I went to a neurologist […] he says, ‘Yes, you just have to learn to live with it.’ And you are kicked out and yes then I became very angry with, ‘Yes, you should learn to live more with that pain you have’ or whatever. And then I had a second opinion from a second neurologist, he says well he says, […]’Yes, you have to learn to live with it […] but also learn to deal with it.’ […] But then you still want to know, where does it come from? (NLLC02)
I would literally like want to bang my head when I left my doctor’s office. Like what a waste of my time […] they had no clue […] don’t feel like they’re very educated, and they’re not very compassionate. Like this is new to everybody […] I went from being a perfectly healthy person […] to being so sick […] they’re like ‘I don’t know what to tell you. Everything looks good. You look, physically you look great.’ But I don’t feel great. There’s something going on inside […] she’s like, ‘Well, do you think I missed something?’ ‘I don’t know. You’re the doctor […] You’re not helping me. You’re not informing me. You know, I don’t understand’ […] I go online. I’m like […] ‘I should have that checked’, you know. And so, but she just doesn’t understand why I want to do that […] they’re [medical profession] all like, ‘Ooh, long haulers, no way, we don’t know anything.’ [LAUGHING] Their hands are up in the air […] I kind of feel like, you know, back in the ‘80s when HIV first came out […] it’s like, ‘Oh, my God, you got some like weird disease going on. Like what’s wrong with you?’ You know, and nobody wants to understand or take the time to figure it out. (USLC16)
Thus, those who experienced rejected candidacy felt either overlooked, invisible and discounted, or actively disbelieved and challenged (“I don't believe in long Covid”) when their strange, often fluctuating, constellations of symptoms defied some healthcare professionals' understandings of how Covid-19 could manifest after the acute stage of infection.
3.4 Diverted candidacy
As part of their care, symptoms often required investigation and tests, to rule out alternative explanations, which was often portrayed by participants as reassuring because these diagnostic explorations allayed nagging fears that another serious illness, such as cancer, underlay their symptoms. This can be understood as an appropriate part of the process of adjudication of candidacy by the healthcare professional. Sometimes, however, this testing for other illnesses was felt to undermine or invalidate participants’ lived experiences as people with long Covid:I needed – it would have been good for [the doctor] to say to me ‘I want you to understand that even though this may all be attributable to Covid, we still have to go through the process of ruling out other serious things, like heart disease or whatever is known already within the medical system’. That would have gone a long way to validate my experience, to foster a greater sense of trust when my trust in the medical system had already been so compromised because of the way that I had been treated all along. (CANLC02)
You know a lot of people in the long Covid community, we’ve all been through this thing of going for the tests that are available, the chest x-rays and ECGs, doctors turn around and say, ‘There’s nothing wrong with you, you’re fine’ because […] they don’t-test for the right things. They don’t-test for the kinds of damage, they cannot reveal the kinds of damage that are going on in your body […] they’re not calibrated to show the sorts of things that would indicate disease, then then, the doctors are telling you you’re fine […] But those are the only tests that are available […] in primary care, and the system’s not geared up for finding it. (UKLC23)
Others argued that the investigation of symptoms was frustrating because they rarely felt any further forward in understanding their symptoms and were exhausted by the burdensome process of seeking help and investigations.[A] lot of people were looking for medical answers, getting testing, you know, a lot of it. And I think [laughs] 99% of the people didn’t get any good answers. They were already exhausted and went to get this testing, and these testings made them even more exhausted. But they didn’t get any answer [or …] any good medication that worked. (USLC18)
For some, clinician-directed investigations of symptoms were interpreted as a curtailed exercise in ruling out a limited number of alternative diagnoses rather than a continuing exploration or quest that led to a greater understanding of their long Covid symptoms and future management. In such cases, participants said they felt their symptoms were “put down to” other conditions; or as one participant said, healthcare professionals would “put [symptoms] in boxes that exist” (UKLC06), and further investigation could then cease. This was the case for UKLC10, who said that “some of the medical professionals I engaged with were very nervous about, labelling [my symptoms] as long Covid, but really comfortable labelling [them] as chronic fatigue [syndrome].” CANLC04 said that her symptoms were attributed to allergies by her doctor and she was told not to worry. An alternative diagnosis may not even have been clearly communicated, as for USLC14: “it wasn't until I had actually gone back into my notes to realize that he actually had diagnosed me with something but never even bothered telling me what that was.”
By far the most common way in which participants' claims to candidacy was diverted, was by healthcare professionals attributing the cause of their sometimes complex, bizarre and changing symptoms to psychological origins, such as anxiety. The extracts below demonstrate how participants presented themselves as frustrated and dismayed by their individual candidacy being diverted in this way. They also implied that this was a challenge to collective candidacy, in some cases. Not only did this response from healthcare professionals make participants’ efforts to assert their candidacy more of a struggle, it deterred some participants from actively seeking medical help:[T]hat’s what’s happened to other people [symptoms put down to psychological origins] […] ‘Well, it’s just anxiety, [um], stop watching the news, stop being in these groups, you know, you’re not going to make yourself better with that’ But it’s not anxiety. I mean, they may well have anxiety. A lot of people with long Covid have anxiety and depression and all sorts of issues, but that’s because they’ve got long Covid and they’re not being listened to and there’s nobody helping us. So, [laughs] you know, most people would be anxious and depressed. It’s, it’s, come … it’s part of it, it’s not instead of it. (UKLC04)
"And, ah, I think during that process, because there was nothing wrong in my tests, it was implicated to me on multiple occasions that it’s just anxiety. And, um, I do appreciate that anxiety is a real condition, and many people are suffering from it, and it’s horrible. However, I don’t think this really applies to me, because there was no reason for me to feel stress, other than not getting help, and not being really treated like this is a physical condition that is happening to me. (AUSLC05)
[A] lot of people were getting, like you know, answers like, ‘It’s all in your head’ [laughs]. It’s like you’re too, you're like, you know, anxiety is causing this kind of phantom symptoms. But like I knew that myself, I was the last person, one of the last person I think would create phantom symptoms, because I try to ignore my pain [laughs], and I try to power through with everything. So, yeah, after reading that [I didn’t try to find a doctor]. (USLC20)
Analysis of participants' accounts suggested that when they were subject to doctors diverting their (or other long Covid sufferers’) claims to candidacy in this way, it served to compound their suffering, could act as a barrier to further help-seeking and was referred to as medical “gaslighting” by some participants.
3.5 Validated or affirmed candidacy
Although examples of negative healthcare interactions were plentiful across participants' accounts, there was evidence of more positive help-seeking experiences from participants' perspectives. Participants placed great value on interactions where their claims to candidacy were validated and affirmed by healthcare professionals, even when people with long Covid acknowledged the lack of their own or collective medical knowledge of causes, prognosis and appropriate treatment pathways for long Covid. The following extracts demonstrate the strength of participants’ responses to feeling they had been listened to, believed and taken seriously in healthcare encounters:My GP is fantastic. He listens to me, he knows that I’m a healthcare worker myself […] he doesn’t have any answers, like he doesn’t know what’s going on either. But, but at least he hasn’t told me it’s all in my head, and that it’s all anxiety, and he, you know, has been very clear that if I’m talking about it, then it’s a real thing, and hasn’t tried to dismiss me. (AUSLC03)
I can’t say enough about how amazing [appointment at post-Covid clinic] was. The doctor […] is probably the best provider I’ve ever been in contact with. He just, he listened to absolutely everything I had to say, every weird symptom that I thought was unrelated. You know, he listened to everything and then would tell me, ‘Nope, that’s all part of it’. (USLC10)
Participants also valued their candidacy being affirmed and validated by healthcare professionals referring to other patients with long Covid. For example, UKLC04 said a breathing specialist had told her she had worked with “loads of long Covid” patients with similar symptoms.
Alongside the importance of their claims to candidacy being listened to, validated and affirmed, participants' accounts suggested they also valued feeling that healthcare professionals were professionally curious about long Covid and engaged in trying to understand its mechanisms and how to treat it. UKLC04 said she had asked specifically to see a doctor at her primary care practice who was keeping up to date with emerging long Covid research. She stressed the need “to have an open mind.” UKLC02 said it was important that honesty about what healthcare professionals did and did not know, was “accompanied by an earnest wish to help me with it and keep helping me with it […] and she's not going to abandon me halfway through”. Some participants indicated that they felt part of a joint quest for understandings and solutions alongside their healthcare professional. UKLC05 described this as “co-experting” and accounts provide evidence of the value of being treated as partners as their healthcare professional(s) tried to navigate a pathway to recovery:[My GPs] have been absolutely brilliant in terms of responsiveness, supportive, kind of taking what I’m saying and acting on it but also kind of if I’m asking for, for stuff to be considered, like I say, I’m on that, that [social media] group, if I see something else the doctor isn’t particularly aware of […] and I propose that, she takes that away, validates it and comes back […] there was no … no need to convince her, like I explained honestly and frankly what I was experiencing. She could come back to me and say, ‘Yeah, this is what I've heard from you. Do you agree?’ […] So, yeah, really kind of co-experting essentially, you're the expert on your own body, the doctor's the expert on what we can do about that. (UKLC05)
[When I finally saw the long-haul doctor] that was so wonderful because it was just validation. Like, yeah, this is happening. You know, and he knew the right tests, and he knew the right meds to treat my symptoms. And, you know, why it was happening, and we even had conversation like because […] there’s lots of theories out there about why this is happening, you know, scientists and doctors and things that are addressing it. […] So, you know, it was good to talk to him about it and him to understand. You know, that was, [um] helped with the fear. (USLC17)
Participants' accounts also suggest that validated and affirmed candidacy should be reflected in the ways that healthcare services are provided for long Covid patients so that the burden of pursuing investigations and care does not fall on the patient. In particular, they felt it important that care pathways should be better coordinated and easier for people with debilitating fatigue and brain fog to navigate. UKLC02 suggested “one person maybe having ownership over a patient's case” would serve to smooth experiences for long Covid patients, and other participants from the Netherlands said they would have been helped by having support from a healthcare professional who acted as a “case manager”. Similarly, UKLC06 suggested ways in which existing long Covid clinics could be improved:… some of the clinics that have popped up […] have an [occupational therapist], and a physio[therapist], and maybe a psychologist. And whilst those things can be helpful, you know, people [with long Covid] have acute medical problems. So having the different specialties, that are primarily affected [a neurologist, cardiologist and respiratory physician] would be really helpful, with a knowledge of, of what’s going on, and how to treat.
4 Discussion
4.1 Summary of main findings in context
In this paper, we revisit and extend Dixon-Woods et al.’s (2006) concept of candidacy, by arguing that preceding patients' individual claims to candidacy is recognition of their condition through the generation of a collective candidacy by vanguard patients. We suggest that, whilst long Covid is a novel, emergent condition, the most salient aspects of candidacy are identificiation, navigation, permeability and particularly adjudication. Using data collected in 2020-2 from participants from five countries which were at various stages of the pandemic, we have shown many similarities across these countries in people's accounts of their experience of help-seeking for long Covid. Our participants' experiences of often bewildering, disabling, life-changing and fluctuating but persistent constellations of symptoms align with the findings of other studies and the growing understanding and recognition of long Covid (Subramanian et al., 2022). Vanguard patients' sense of reassurance as they experienced the realisation that they were neither alone nor ”going mad” when they discovered others' similarly bizarre and unexpected experiences, largely through online communities, reflects other accounts of the early pandemic. Roth and Gadebusch-Bondio (2022, p1) describe the importance of a “collective gathering” of experience which transformed “patients' .. subjective experience [of long Covid] into a collective one”; Callard and Perego (2021) heralded the collective nature of the “patient-making” of long Covid; and Rushforth et al. (2021, p7) describe how their participants articulated “a rich description of the diverse manifestations of a grave new illness [and] a shared account of rejection by the healthcare system”.
Here, we have argued that the experience of participants across several studies and countries can be understood as not just the making and naming of an emergent condition, but its transition from the unknown and invisible to the decipherable (Lian et al., 2021). Most prior work on candidacy (defined by Dixon-Woods et al. (2006, p1) as the process through which “people's eligibility for healthcare is determined between themselves and health services”) has been conducted on known or established conditions (e.g. cancer and heart disease (Macdonald et al., 2016)), hence our highlighting of the generation of a collective candidacy which precedes individual candidacy. Long Covid has provided a real-time example of how this happens with new and emergent conditions, in this case a largely technologically enabled or accelerated patient-led generation exemplifying the increasing importance of digital interconnectedness of an emerging community of people with long Covid across social and national boundaries.
In considering how doctors negotiate uncertainty in clinical encounters, Lian et al. (2021, p7) note that “Illness is a life-changing experience that deprives the ill person from taken-for-granted routines and habits and reveals aspects of human existence that often go unnoticed. These experiences put us in a state of vulnerability”. Our participants' accounts powerfully recount the disruption of the taken-for-granted because of their long Covid symptoms and the vulnerability that both vanguard and later patients with long Covid experienced as they attempted to navigate various healthcare systems in the midst of the pandemic, when healthcare systems (as many aspects of day-to-day life at a societal level) were themselves overwhelmed and disrupted. Lian et al. go on to suggest that in the face of medical uncertainty, “the main source of patient contention is the ways in which doctors engage with patients, not the lack of biomedical knowledge per se” (p7, emphasis added). Given the uncertainty inherent when emergent conditions have limited foundations in lay and professional epidemiological evidence, and the difficulties that many described in navigating what could seem like impermeable systems, at a time when many with long Covid have a dearth of physical, mental and cognitive resources, it is perhaps not surprising that their experience of healthcare professionals' adjudication of their candidacy (Dixon-Woods et al., 2006) is so strongly aligned with how negative or positive patients' experience of healthcare was. The people who felt dismissed, unheard, unworthy or invisible in their dealings with healthcare professionals (i.e., those whose candidacy was perceived to have been rejected after adjudication), described the most negative reactions (e.g., anger, frustration, hopelessness, dejection). The experience of “diverted” candidacy could be interpreted as an appropriate quest for alternative explanations, or a different kind of dismissal of people's experience. This had perhaps its most negative connotations when people felt that their symptoms were seen as being ”just” a manifestation of anxiety or other mental health conditions. Being listened to, believed, validated and affirmed was experienced as positive by those who encountered this in their medical interactions - even in the face of healthcare professionals' professed uncertainty about the causes, consequences, best management and prognosis for long Covid. Its importance was strongly emphasised by the majority. The similarities internationally, in symptom stories and the impact of healthcare professionals' responses, underlie the particular importance of active and attentive listening and response in healthcare interactions for patients where care pathways, like the underlying conditions, are themselves emergent or medically unexplained.
It is pertinent to note, that our participants' accounts relate to times when many health systems were under extreme strain due to the demands of caring for hospitalised patients whose acute manifestations of Covid symptoms were life-threatening. For this reason, we suggest that it may make sense to interrogate other aspects of Dixon-Woods’ candidacy framework (appearances, offers and resistance, and operating conditions and the local production of candidacy) when the acute disruption and overwhelming of health systems by Covid-19 has largely abated. In their study of the application of the construct of candidacy to understand access to secondary mental health services in the UK during the pandemic, Liberati et al. (2022, p1) discuss how macro-level changes affected identification of candidacy:“Macro-level changes, including an increased emphasis on crisis and risk management and adapted risk management systems, produced effects that went far beyond restrictions in the availability of services: they profoundly re-structured service users' identification of their own candidacy, including perceptions of what counted as a problem worthy of attention and whether they as individuals needed, deserved, and were entitled to care”.
Arguably all negotiations of illness and health care are imbued with some moral undertone, and our data included many examples of patients acknowledging the strains on healthcare systems and staff imposed at the height of the Covid pandemic, and the undoubted needs (candidacy) of those who were suffering from life-threatening symptoms. These featured across the data, including in accounts (e.g. from the UK and US) where healthcare systems are very differently structured. This will be an important area to revisit in accounts of patients diagnosed with long Covid later in the pandemic.
5.2 Strengths and limitations of the study
A significant strength of our study is the use of identical rigorous methods of data collection in several countries (Ziebland et al., 2020). This has enabled the analysis of a large number of robustly collected and analysed qualitative interviews (n = 72). To our knowledge, this is the first study that has examined experiences of help-seeking amongst people with long Covid that has drawn on data from multiple countries, albeit four of them anglophone countries. We were also able to interview an ethnically diverse sample. Another strength is our iterative approach to analysis and verification of the analytical framework that we developed. As the analytical framework is the result of critical input from authors in countries with varied healthcare systems, it is likely to be generalisable to other high income countries. We recognise the need for comparable work on patient experience in low and middle income countries. A further strength is the identification and empirical substantiation of concepts, which has enabled us to contribute to theorisations of health and illness (e.g. vanguard patients, adjudication, collective candidacy, and individual candidacy as validated, diverted or rejected), including in relation to new and emergent illness.
One limitation of the paper is the differing number of interviews from participating countries. Covid as an emergent illness penetrated some countries (e.g. UK and USA) faster and more broadly than others (e.g. Australia), reflecting differences in public health control measures during early parts of the pandemic. Also, the nature of illness narratives is that they are, of necessity, retrospective and others have noted the ways in which narratives may be “honed” (Rushforth et al., 2021) as people with long Covid (as with other conditions) will have ”rehearsed” their journey with long Covid through multiple recantations for both medical and non-medical audiences. On one hand, this could be interpreted as a limitation; alternatively, this can also be understood as the product of a long period of sense-making by participants. We acknowledge too that what patients take away from healthcare encounters may differ from the perspective or intent of the care provider. We also acknowledge that people who choose to take part in qualitative interview studies may not be representative of the wider population in various ways.
5 Conclusion
Paying close heed to the experience of vanguard patients and how they generate collective candidacy is paramount in the context of new, emerging and contested health conditions, as exemplified by the experience of people who developed long Covid at the earliest stages of the pandemic. This can not only alleviate the alienation and suffering of those with bewildering and disruptive constellations of symptoms, but may accelerate pathways to lay and medical knowledge about aetiology, management and recovery through a process of “co-experting”. At the very least, professional adjudication of patient accounts that leads to them feeling listened to and believed (i.e. validates and affirms candidacy) may significantly improve patients’ experiences, particularly whilst the science on pathways to recovery is being developed.
Author contribution statement
Alice Maclean: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing - original draft; Kate Hunt: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Supervision, Validation, Writing - original draft; Ashley Brown: Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing - review & editing; Jane Evered: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Validation, Writing - review & editing; Anna Dowrick: Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing - review & editing; Andrea Fokkens: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing - review & editing; Rachel Grob: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing - review & editing; Susan Law Supervision, Validation, Writing - review & editing; Louise Locock: Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Writing - review & editing; Michelle Marcinow: Formal analysis, Methodology, Project Administration, Resources, Writing - review & editing; Lorraine Smith: Conceptualization, Formal analysis, Methodology, Project Administration, Resources, Writing - review & editing; Anna Urbanowicz: Formal analysis, Methodology, Project Administration, Resources, Writing - review & editing; Nientje Verheij: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing - review & editing; Cervantee Wild: Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Writing - review & editing.
Ethical review
UK: NHS Health Research Authority [IRAS 112111; minor amendments 22].
USA: The study was determined exempt by the University of Wisconsin-Madison Institutional Review Board.
Netherlands: Approved by the Central Review Committee of the Medical Ethical Committee (Research Registration Number 202100710).
Australia: Approved by University of Sydney Human Research Ethics Committee #2020/401.
Canada: Reviewed by Trillium Health Partners Research Ethics Board (Mississauga, Canada) ID#1003.
Declaration of competing interest
The authors have no competing interests to declare.
Appendix A Supplementary data
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Acknowledgements
We would like to thank all of the participants who took part in the interviews, especially as many of our participants were still very affected by their long Covid symptoms and had limited physical and cognitive resources.
We would like to thank the funders of our research (listed below) and our colleagues at the Health Experiences Research Group in Oxford. Thank you to Professor Sue Ziebland, Professor Sarah Nettleton for comments on an earlier draft, and Professor Trish Greenhalgh and an anonymous reviewer for their helpful observations.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.ssmqr.2022.100207.
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| 36507117 | PMC9721377 | NO-CC CODE | 2022-12-15 23:17:52 | no | SSM Qual Res Health. 2023 Jun 5; 3:100207 | utf-8 | SSM Qual Res Health | 2,022 | 10.1016/j.ssmqr.2022.100207 | oa_other |
==== Front
Physica A
Physica A
Physica a
0378-4371
0378-4371
Elsevier B.V.
S0378-4371(22)00941-4
10.1016/j.physa.2022.128383
128383
Article
Dynamics of a fractional order mathematical model for COVID-19 epidemic transmission
Arshad Sadia a
Siddique Imran b⁎
Nawaz Fariha a
Shaheen Aqila c
Khurshid Hina c
a Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan
b Department of Mathematics, University of Management and Technology, Lahore 54770, Pakistan
c School of Mathematics, Minhaj University, Lahore, Pakistan
⁎ Corresponding author.
5 12 2022
1 1 2023
5 12 2022
609 128383128383
3 7 2022
24 8 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.
To achieve the aim of immediately halting spread of COVID-19 it is essential to know the dynamic behavior of the virus of intensive level of replication. Simply analyzing experimental data to learn about this disease consumes a lot of effort and cost. Mathematical models may be able to assist in this regard. Through integrating the mathematical frameworks with the accessible disease data it will be useful and outlay to comprehend the primary components involved in the spreading of COVID-19. There are so many techniques to formulate the impact of disease on the population mathematically, including deterministic modeling, stochastic modeling or fractional order modeling etc. Fractional derivative modeling is one of the essential techniques for analyzing real-world issues and making accurate assessments of situations. In this paper, a fractional order epidemic model that represents the transmission of COVID-19 using seven compartments of population susceptible, exposed, infective, recovered, the quarantine population, recovered–exposed, and dead population is provided. The fractional order derivative is considered in the Caputo sense. In order to determine the epidemic forecast and persistence, we calculate the reproduction number R0. Applying fixed point theory, the existence and uniqueness of the solutions of fractional order derivative have been studied . Moreover, we implement the generalized Adams–Bashforth–Moulton method to get an approximate solution of the fractional-order COVID-19 model. Finally, numerical result and an outstanding graphic simulation are presented.
Keywords
Fractional calculus
COVID-19 model
Stability analysis
Existence and uniqueness
Adams–Bashforth Moulton method
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pmc1 Introduction
COVID-19, was identified as a pandemic by WHO on January 22, 2020 a syndrome that was not particularly uncommon at the time. The antagonistic mechanism of infection used by this pandemic is its most important feature. The COVID-19 disease affects most patients who are elderly or have long-term health problems like diabetes, high blood pressure, or cardiovascular disease. Due to the COVID-19 outbreak, the entire world is experiencing a number of problems, including the breakdown of medical systems, a decrease in social relations between people, a collapse in the economy, and most importantly the escalation of acute depression in society. Numerous researchers are working very hard to examine and analyze this disease from all perspectives to assist in dominating the spread of this outbreak [1], [2], [3], [4], [5].
COVID-19 is spread from person to person when an infected person sneezes, coughs, or releases tiny droplets from their mouth or nose. This transmission may have happened directly through contact with the infected person or through touching objects that had the virus. Many safety measures have been established globally to stop this virus spread, including social isolation, mask usage, lockdowns, blocking national borders, cleanliness, and frequent hand washing and sanitizing. Infected individuals are also advised to confine themselves under quarantine for at least 14 days after receiving positive test reports. The 8-scale feelings (Anxiety, Curiosity, Anger, Afraid, Happiness, Grief, Surprise, and Believe) are examined in [6] in a variety of domains, including ecology, lockdown, health, education, the business, and democracy. Using the B-cells dataset in [7] focuses on the prediction of SARS-CoV and SARS-CoV-2. The dataset is predicted and analyzed using a variety of machine learning models, including support vector machine (SVM), Naive Bayes, K-nearest neighbors, AdaBoost, Gradient boosting, XGBoost, Random forest, ensembles, and neural networks. The suggested algorithm produced the most accurate results, with a 0.919 AUC value and an 87.248% validation data for SARS-CoV and a 0.923 AUC and an 87.7934% validation data for SARS-CoV-2 viral prediction. In [8] author studied that the immunity cannot defend persons against a second infection, herd immunity is not the greatest strategy in this scenario to fight SARS-Cov-2 since we are unsure of how long immunity lasts after an infection.
Data analysis and mathematical modeling can play an important role in evaluating the effects of mitigation techniques in order to properly understand the dynamics of the disease [9]. Mathematical frameworks established on the basis of infectious disease transmission are the most effective methods for predicting, measuring, and controlling current outbursts [10].
Generally, a system of differential equations is employed to describe how infectious diseases spread [11]. The four compartments of the epidemic models that are most frequently used are susceptible (S), exposed (E), infected (I), and recovered (R). This model is also called the SEIR model. This model might be expanded by include more compartments depending on the available data and the situation being studied. A system of differential equations in an SEIR model represents the rate of change of the compartments that are susceptible (S), exposed (E), infected (I), and recovered (R) with time [12], [13], [14], [15]. Furthermore, Fanelli et al. provided a more complicated SIRD model with a death (D) class [16], and Ndairou et al. introduced a model SEIPAHRF with further compartments, including new super-spreaders (P), asymptomatic (A), hospitalized (H), and fatality (F) [17]. A cellular automata (CA) model based on artificial intelligence methodologies was constructed by Medrek and Pastuszak in [18] to simulate the COVID-19 diffusion. By extending the SEIR framework, they were able to evaluate the behavior of the pandemic in Poland, France, and Spain. They approximated new parameters into their model to show the real dependency on interaction rates and age-dependent mortalities. By implementing the methods of non-linear analysis, Ali et al. [19] considered an SEIR model and identified the requirements and Ulams type stability for recommended categorization. They investigated the asymptotic local and global stabilities of the epidemic model under the conditions of disease-free, endemic equilibrium, and reproductive number, and they proposed that a good influence of the virus can be achieved by bringing down the transmission rate including by raising the rate of treatment. Kamal et al. [20] analyze the SIR model in which a fractal fractional mathematical model of the propagation and control of the COVID-19 of an affected area by divided into different categories of susceptible, infected, and recovered individuals and conclude that reducing the transmission rate and enforcing the rules regulations for safety will have the best advantageous effects on preventing or decreasing the COVID-19 spread. In [21] analyzes a basic non-autonomous SIR model to fit the COVID-19 data in the state of New York. The model shows and quantifies how we may flatten the curve of infected individuals over time by changing the control. In [21] analyzes a basic non-autonomous SIR model to fit the COVID-19 data in the state of New York. The model shows and quantifies how we may flatten the curve of infected individuals over time by changing the control.
Fractional calculus is a generalization of the classical calculus integer-order. In the eighteenth century Liouville, Riemann, Fourier, and Euler struggled to generate significant conclusions in classical calculus, Additionally, there was also a significant advancement in the area of fractional calculus. In the context of mathematical modeling, where calculus was unable to properly represent a number of inherited materials and memory processes, this was due to the multiple uses of fractional calculus [22]. The cumulative advantages of fractional differential equations (FDE) over integer-order differential (IDEs) equations for modeling complex real-world issues are due to their unique features. In contrast to (IDEs), which are local by origin, FDEs are made up of the memory effects that make them more efficient. As a result, depending on the circumstances, the future state model may depend not just on its current state but also on its past. Numerous scholars have recently examined the dynamics of some infectious diseases as the fractional model, including measles dynamics [23], chickenpox [24], dengue fever [25], HIV [26], rubella disease [27], and tuberculosis [28].
As fractional-operator derivatives add entire flexibility to the system and infinite consciousness of fractional operators also gives multiple advantages over restricted memory of integer models, fractional-order derivatives gives more precise analysis for modeling COVID-19 outbreak using the combination of memory and highly infectious properties [29]. The long and short term cognitive effects on pandemic transmission were integrated in [30] by authors who created a SIRD model which is based on fractional order differential equations. The authors modified the SEIPAHRF model proposed in [17] with fractional-order derivative in order to highlight the significance of assuming the Caputo fractional difference and to more exactly reflect the number of cases reported in the regions of Spain, Portugal, and Galicia [31]. The COVID-19 epidemic was studied using a general incidence rate function and a nonlinear recovery rate using a fractional order SIR model that was developed in [32]. In [33] the human population is studied using a nonlinear delayed coronavirus outbreak model by using Routh Hurwitz criterion, Volterra Lyapunov function, and Lasalle invariance principle are applied to analyze the model stability. The COVID-19 mathematical model was studied in the Caputo sense by Gao et al. [34]. An optimal control dynamical model is designed in [35] to examine how well each tactic works at decreasing the severity of COVID-19. Asamoah et al. [36] developed a mathematical model considering the environmental virus load and suggested some effective intervention techniques using an optimal control technique. Zamir et al. [37] uses sensitivity analysis of the model of parameter values to examine the impact of various interventions on disease transmission. This study based on the earlier study of COVID-19 using mathematical frameworks, we are inspired to employ the Caputo fractional-order operators to model and analyze COVID-19 outbreaks for transmission of disease. The Caputo derivative has the advantage of providing natural modeling over other fractional derivatives in that the Caputo derivative of a constant is zero. As a result, local initial conditions can be included in the model derivation using the Caputo fractional differential equation (FDO) [38].
The content of this article is as follows. In Section 3, the Caputo fractional operator is used to construct the COVID-19 model. We present the stability analysis of our proposed fractional COVID model in Section 4, along with the positivity and boundedness of the solution, and we also obtain the basic reproduction number R0. In Section 5, we demonstrate the existence and uniqueness of the solution suggested fractional model. To find the general solution of the model, the Adams–Bashforth–Moulton method is used in Section 6. We use graphical representation to further explain our conclusions.
2 Preliminaries
This section provides some basic definitions and notations of fractional differential calculus that will be used in the remaining of the paper.
Definition 2.1 [39]
The Riemann–Liouville (R–L) of the continuous fractional integral function ψ:(0,∞)→R of the order ν>0 with t is describe as a (1) Dtνψ(t)=1Γ(ν)∫0t(t−u)ν−1ψ(u)du,t>0.
Definition 2.2 [40]
The Caputo derivative of fractional order ν>0 of a function ψ:(0,∞)→R is expressed as (2) 0cDtνψ(t)=1Γ(n−ν)∫0t(t−u)n−1−νψ(n)(u)du,n−1≤ν<n,dnψ(t)dtn,n=ν.
where 0<ν≤1, we have 0cDtνψ(t)=1Γ(n−ν)∫0t(t−u)νψ′(u)du.
Definition 2.3 [41]
If ν>0 then a gamma function is defined as (3) Γ(ν)=∫0∞xν−1e−xdx.
3 Generalized mathematical model
The classic model SIR (susceptible–infected–recovered) in epidemiology [42], [43] makes it possible to explain the critical condition of the development of the disease inside the population, independently of the total size of the population for a short period. The simplest ODE system of SIR considered as the following (4) dSpdt=−μMSpIp,
dIpdt=μMSpIp−βIp,
dRpdt=βIp.
The term μMSpIp describes the rate of transmission of disease suitable for interaction between susceptible and infected persons, and βIp indicates the rate of heal of infected person. The recovered individuals are now considered not to enter the susceptible class, i.e. the recovered individuals are resistant to the disease; they cannot be re-infected, and susceptible individuals cannot be infected.
The demonstration of mathematical methods of calculation makes it possible to simulate in a numerical way the infections contained by the inhabitants. Mathematical models of dynamic disease capture have a deep history of more than 100 a year. In an audience of “compartments” that model the individual infection situation with irregularly significant precision in defining individual progression, the most common mathematical formulations are used. These classified disease patterns dispersed people into groups, allowing us to know the infectious status of all individuals and the interdependent population sizes over time.
In the following, We will take it that we have a constant population M distributed into seven epidemiological classes, namely:
1. susceptible (Sp),
2. exposed (Ep),
3. infective (Ip),
4. recovered-exposed (Erp),
5. recovered (Rp),
6. dead population (Dp),
7. the quarantine population (Qp).
In [43] formulated a mathematical model of the coronavirus versus people have been provided ordinary differential equations from the following system (5) dSpdt=−μ1MSpEp−μ2MSpIp−μ3Sp,
dEpdt=μ1MSpEp+μ2MSpIp−(α1+α2)E,
dIpdt=α1Ep−(β1+β2)Ip,
dErpdt=α2Ep,
dRpdt=β1Ip,
dDpdt=β2Ip,
dQpdt=μ3Sp.
A dynamic field of inquiry is fractional calculus and fractional differential equations, and it is sufficient to provide process history in a few cases [10]. The previous ordinary differential model (5) is expanded under the caputo operator to a fractional order system (6) 0cDtνSp=−μ1νMSpEp−μ2νMSpIp−μ3νSp,
0cDtνEp=μ1νMSpEp+μ2νMSpIp−(α1ν+α2ν)Ep,
0cDtνIp=α1νEp−(β1ν+β2ν)Ip,
0cDtνErp=α2νEp,
0cDtνRp=β1νIp,
0cDtνDp=β2νIp,
0cDtνQp=μ3νSp,
with non-negative initial conditions (7) Sp(0)=Sp0≥0,Ep(0)=Ep0≥0,Ip(0)=Ip0≥0,Erp(0)=Erp0≥0,
Rp(0)=Rp0≥0,Dp(0)=Dp0≥0,Qp(0)=Qp0≥0.
In which we have the following parameters in Table 1:
In this matrix form, considering the non-linear FODEs: (8) 0cDtνZ´(t)=A(Z)+f(Z),
whereTable 1 Parameters and description.
Parameters Description
μ1ν The contact rate with Sp and Ep
μ2ν The contact rate with Sp and Ip
μ3ν The home quarantine rate of Sp
M The total population Sp+Ep+Ip+Erp+Rp+Dp+Qp
α1ν The incubation rate
α2ν The recover rate of Ep
β1ν The recover rate of Ip
β2ν The fatality rate
A(Z)=(Sp,Ep,Ip,Erp,Rp,Dp,Qp) for all situations, it is the fractional order derivative operator.
We make this matrix from model (6) A(Z)=−μ3ν0000000−(α1ν+α2ν)000000α1ν(β1ν+β2ν)00000α2ν0000000β1ν000000β2ν0000μ3ν000000,
f(Z)=(−μ1νMSpEp−μ2νMSpIpμ1νMSpEp−μ2νMSpIp00000)T.
4 Stability analysis
We will dispute the positivity of the suggested fractional model (6) in this part. Similarly, the basic reproduction number R0 will be computed using the next generation matrix and the disease free equilibrium points will be determined.
4.1 The positivity and boundness of the solution
We discuss the positive nature of this epidemiological model (6) in this subsection. We recall the following lemma to proof the main theorem on the positivity of the solution of fractional model (6).
Lemma 1 Generalized Mean Value Theorem
Let ψ(t)∈ℂ[a,b] and Caputo operator for fractional derivative 0cDtνψ(t)∈ℂ(a,b] for 0<ν≤1 , then ψ(t)=ψ(u)+1Γ(ν)∫0t(t−u)ν0cDtνψ(u)du,
with 0≤u≤t,∀t∈(a,b] .
Remark 1 Suppose that ψ(t)∈C[0,b] and the Caputo derivative of a 0cDtν∈(0,b) for 0<ν≤1. Lemma 1 show that if 0cDνψ(t)≥0,∀t∈(0,b], then the ψ(t) function is non-decreasing if 0cDνψ(t)≤0,∀t∈(0,b] then ψ(t) function is non-increasing for all t∈[0,b].
Theorem 1 The solution to the initial condition (7) of the proposed fractional order model (6) is unique and limited in R+7 .
Proof The existence and uniqueness of the model solution (6)–(7) can be achieved in the time interval (0,∞) by Wei [44]. The non-negative region R7+ would seem to be a positive invariant region that must be represented. We observe from the model (6) (9) 0cDtνSp|Sp=0=0,
0cDtνEp|Ep=0=μ2νMSpIp≥0,
0cDtνIp|Ip=0=α1νEp≥0,
0cDtνErp|Erp=0=α2νEp≥0,
0cDtνRp|Rp=0=β1νIp≥0,
0cDtνDp|Dp=0=β2νIp≥0,
0cDtνQp|Qp=0=μ3ν≥0.
From Remark 1 and system (9) the solution will remain (Sp(0),Ep(0),Ip(0),Erp(0),Rp(0),Dp(0),Qp(0))∈R+7. Also in each line bounding the non-negative octant, the vector field points will remain in R+7. Therefore, the fractional model (6) of a solution (Sp(t),Ep(t),Ip(t),Erp(t),Rp(t),Dp(t),Qp(t)) is not negative if the initial condition is set positively to invariant. □
4.2 Disease free equilibrium
In the disease-free equilibrium state, we have a disappearance of infection. Therefore, all infected groups are null and only susceptible individuals can make up the entire population. Which implies that, Ep=Ip=Rp=Dp=Qp=Erp=0.
Thus, the disease-free equilibrium points of our model is E0=(Sp0,Ep0,Ip0,Erp0,Rp0,Dp0,Qp0)=(M,0,0,0,0,0,0).
Theorem 2 The disease free equilibrium E0 of the model (7) is locally asymptotically if R0<1 otherwise unstable.
Proof The following Jacobian matrix J(E0) obtain for the disease free equilibrium E0 J(E0)=−μ3ν−μ1ν−μ2ν00000μ1ν−α1ν−α2νμ2ν00000α1ν(β1ν−β2ν)00000α2ν0000000β1ν000000β2ν0000μ3ν000000.
The Jacobian matrix J(E0) has the eigenvalues λ4=λ5=λ6=λ7=0. The rest of (two) eigenvalues are the roots of the following equation Aλ2+Bλ+C=0,
with the coefficients given by A=1,
B=x+y−μ1ν,
C=xy−μ1ν−α1ν−μ1νβ2ν,
where x=α1ν+α2ν and y=β1ν+β2ν.
The remaining two eigenvalues are λ6=−12(x+y−μ1ν)+(x−y−μ1ν)2+4xyR0,
λ7=−12(x+y−μ1ν)−(x−y−μ1ν)2+4xyR0.
This shows that for R0<1, we have λ6<0 and λ7<0, this yields the disease free equilibrium point is asymptotically stable. □
4.3 Basic reproduction number
The number of secondary infections that a single primary infection can induce in an entirely susceptible population is known as reproduction number written as R0. Physically, if R0<1, the infection will vanish, but if R0>1, the infection is confirmed and the disease persists [20], [35].
To prove the reproduction number, using the second and third equation of system (6) as X=(Ep,Ip)t. 0cDtν(X)=0cDtνEp=μ1νMSpEp+μ2νMSpIp,
0cDtν(X)=0cDtνIp=α1νEp−(β1ν+β2ν)Ip,
(10) 0cDtν(X)=F(X)−V(X),
where F(X)=[μ1νMSpEp+μ2νMSpIp,0,0]t
V(X)=[α1νEp−(β1ν+β2ν)Ip]t.
Here, F is the linearity-infected term and V is the non-linearity-infected term. Furthermore, the next generation matrix is FV−1. Using the Jacobian matrix of Eq. (10) at the disease-free equilibrium point Ep=Erp=Ip=Rp=Qp=Dp=0,andSp=M, we obtain F=μ1νμ2ν00,
and V=(α1ν+α2ν)0α1ν(β1ν+β2ν).
Then FV−1=μ1ν(β1ν+β2ν)+μ2να1ν(α1ν+α2ν)(β1ν+β2ν).
The basic reproduction number is equal to the largest eigenvalue of FV−1, thus R0=μ1ν(β1ν+β2ν)+μ2να1ν(α1ν+α2ν)(β1ν+β2ν).
It can be seen that the basic reproduction number depends on the fractional order ν which is not the case in standard calculus.
5 Existence and uniqueness of the solution
This part shows that the system has a unique solution. First, we construct the system (6) as follows c0DtνSp(t)=F1(t,Sp(t)),c0DtνEp(t)=F2(t,Ep(t)),c0DtνIp(t)=F3(t,Ip(t)),c0DtνErp(t)=F4(t,Erp(t)),c0DtνRp(t)=F5(t,Rp(t)),c0DtνDp(t)=F6(t,Dp(t)),c0DtνQp(t)=F7(t,Qp(t)).
F1(t,Sp(t))=−μ1νMSpEp−μ2νMSpIp−μ3νSp,F2(t,Ep(t))=μ1νMSpEp+μ2νMSpIp−(α1ν+α2ν)Ep,F3(t,Ip(t))=α1νEp−(β1ν+β2ν)Ip,F4(t,Erp(t))=α2νEp,F5(t,Rp(t))=β1νIp,F6(t,Dp(t))=β2νIp,F7(t,Qp(t))=μ3νSp.
Taking the both sides of the previous equations as an integral form, we get (11) Sp(t)−Sp(0)=1Γ(ν)∫0t(t−κ)ν−1F1(κ,Sp)dκ,
Ep(t)−Ep(0)=1Γ(ν)∫0t(t−κ)ν−1F2(κ,Ep)dκ,
Ip(t)−Ip(0)=1Γ(ν)∫0t(t−κ)ν−1F3(κ,Ip)dκ,
Erp(t)−Erp(0)=1Γ(ν)∫0t(t−κ)ν−1F4(κ,Erp)dκ,
Rp(t)−Rp(0)=1Γ(ν)∫0t(t−κ)ν−1F5(κ,Rp)dκ,
Dp(t)−Dp(0)=1Γ(ν)∫0t(t−κ)ν−1F6(κ,Dp)dκ,
Qp(t)−Qp(0)=1Γ(ν)∫0t(t−κ)ν−1F7(κ,Qp)dκ.
We show that the kernel Fi, for i=1,2,3,4,5,6,7 follows the condition of Lipschitz and contraction.
Theorem 3 If the following inequality holds 0≤ri<1 , then the function Fi for i=1,2,3,4,5,6,7 fulfill with the condition of Lipschitz and contraction mapping as well.
Proof We have for Sp and Sp1
where there are positive constants z1,z2,z3,z4,z5,z6,z7 such as ‖Sp(t)‖≤z1;‖Ep(t)‖≤z2;‖Ip(t)‖≤z3;‖Erp(t)‖≤z4;‖Rp(t)‖≤z5;‖Dp(t)‖≤z6;‖Qp(t)‖≤z7
and r1=(μ1νMz2+μ2νMz3+μ3ν) are non-negative bounded functions.
Hence (12) ‖F1(t,Sp)−F1(t,Sp1)‖≤r1‖(Sp(t)−Sp1(t))‖.
Similarly, we can prove that Fi, for i=2,3,4,5,6,7 fulfill the Lipschitz condition as following: ‖F2(t,Ep)−F2(t,Ep1)‖≤r2‖(Ep(t)−Ep1(t))‖,‖F3(t,Ip)−F3(t,Ip1)‖≤r3‖(Ip(t)−Ip1(t))‖,‖F4(t,Erp)−F4(t,Erp1)‖≤r4‖(Erp(t)−Erp1(t))‖,‖F5(t,Rp)−F5(t,Rp1)‖≤r5‖(Rp(t)−Rp1(t))‖,‖F6(t,Dp)−F6(t,Dp1)‖≤r6‖(Dp(t)−Dp1(t))‖,‖F7(t,Qp)−F7(t,Qp1)‖≤r7‖(Qp(t)−Qp1(t))‖.
Therefore, Fi satisfies the Lipschitz condition. Furthermore, under the condition 0≤ri<1, the functions are contractions. □
Depending on the system (6), consider the following recursive forms: Spn(t)=1Γ(ν)∫0t(t−κ)ν−1F1(κ,Spn−1)dκ,
Epn(t)=1Γ(ν)∫0t(t−κ)ν−1F2(κ,Epn−1)dκ,
Ipn(t)=1Γ(ν)∫0t(t−κ)ν−1F3(κ,Ipn−1)dκ,
Erpn(t)=1Γ(ν)∫0t(t−κ)ν−1F4(κ,Erpn−1)dκ,
Rpn(t)=1Γ(ν)∫0t(t−κ)ν−1F5(κ,Rpn−1)dκ,
Dpn(t)=1Γ(ν)∫0t(t−κ)ν−1F6(κ,Dpn−1)dκ,
Qpn(t)=1Γ(ν)∫0t(t−κ)ν−1F7(κ,Qpn−1)dκ.
The difference between two terms can be expressed as follows: φ1n(t)=Spn(t)−Spn−1(t)=1Γ(ν)∫0t(t−κ)ν−1(F1(κ,Spn−1)−F1(t,Spn−2))dκ,
φ2n(t)=Epn(t)−Epn−1(t)=1Γ(ν)∫0t(t−κ)ν−1(F2(κ,Epn−1)−F2(t,Epn−2))dκ,
φ3n(t)=Ipn(t)−Ipn−1(t)=1Γ(ν)∫0t(t−κ)ν−1(F3(κ,Ipn−1)−F3(t,Ipn−2))dκ,
φ4n(t)=Erpn(t)−Erpn−1(t)=1Γ(ν)∫0t(t−κ)ν−1(F4(κ,Erpn−1)−F4(t,Erpn−2))dκ,
φ5n(t)=Rpn(t)−Rpn−1(t)=1Γ(ν)∫0t(t−κ)ν−1(F5(κ,Rpn−1)−F5(t,Rpn−2))dκ,
φ6n(t)=Dpn(t)−Dpn−1(t)=1Γ(ν)∫0t(t−κ)ν−1(F6(κ,Dpn−1)−F6(t,Dpn−2))dκ,
φ7n(t)=Qpn(t)−Qpn−1(t)=1Γ(ν)∫0t(t−κ)ν−1(F7(κ,Qpn−1)−F7(t,Qpn−2))dκ,
using the initial condition Sp(t)=Sp(0),Ep(t)=Ep(0),Ip(t)=Ip(0),Erp(t)=Erp(0),Rp(t)=Rp(0),Dp(t)=Dp(0),Qp(t)=Qp(0). We continue the first equation of the preceding method with the norm and through Lipschitz condition (12), we get ‖φ1n(t)‖=‖Spn(t)−Spn−1(t)‖=‖1Γ(ν)∫0t(t−κ)ν−1(F1(κ,Spn−1)−F1(κ,Spn−2))dκ‖,≤1Γ(ν)∫0t‖(t−κ)ν−1(F1(κ,Spn−1)−F1(κ,Spn−2))‖dκ,
(13) ‖φ1n(t)‖≤r1Γ(ν)∫0t‖φ1(n−1)(κ)‖dκ.
Likewise, we get (14) ‖φ2n(t)‖≤r2Γ(ν)∫0t‖φ2(n−1)(κ)‖dκ,‖φ3n(t)‖≤r3Γ(ν)∫0t‖φ3(n−1)(κ)‖dκ,‖φ4n(t)‖≤r4Γ(ν)∫0t‖φ4(n−1)(κ)‖dκ,‖φ5n(t)‖≤r5Γ(ν)∫0t‖φ5(n−1)(κ)‖dκ,‖φ6n(t)‖≤r6Γ(ν)∫0t‖φ6(n−1)(κ)‖dκ,‖φ7n(t)‖≤r7Γ(ν)∫0t‖φ7(n−1)(κ)‖dκ.
Then we can write that Spn(t)=∑i=1nφ1i(t);Epn(t)=∑i=1nφ2i(t);Ipn(t)=∑i=1nφ3i(t);Erpn(t)=∑i=1nφ4i(t);Rpn(t)=∑i=1nφ5i(t);Dpn(t)=∑i=1nφ6i(t);Qpn(t)=∑i=1nφ7i(t).
In the following theorem, we prove the existence of a solution.
Theorem 4 If there exists t1>1 such that riΓ(ν)t1≤1 , for i=1,2,3,4,5,6,7 , then, there exist at least one solution of system given by the fractional COVID-19 SpEpIpErpRpDpQp .
Proof Suppose their exist t such that riΓ(ν)t1≤1.
From the recursive scheme and from Eq. (13) as well as Eq. (14), We have obtained that ‖φ1n(t)‖≤r1Γ(ν)∫0t‖φ1(n−1)(κ)‖dκ.
Replacing n by n−1 in the above inequality ‖φn−1(t)‖≤r1Γ(ν)∫0t‖φ1(n−2)(κ)‖dκ,≤r1Γ(ν)2∫0t‖φ1(n−2)(κ)‖dκ.
Again replacing n by n−2 in the given inequality ‖φn−2(t)‖≤r1Γ(ν)∫0t‖φ1(n−3)(κ)‖dκ,≤r1Γ(ν)3∫0t‖φ1(n−3)(κ)‖dκ.
If we keep substituting in this way and use the initial condition, we obtain ‖φ1n(t)‖≤‖Spn(0)‖r1Γ(ν)tn.
Similarly, we get ‖φ2n(t)‖≤‖Epn(0)‖r2Γ(ν)tn,‖φ3n(t)‖≤‖Ipn(0)‖r3Γ(ν)tn,‖φ4n(t)‖≤‖Erpn(0)‖r4Γ(ν)tn,‖φ5n(t)‖≤‖Rpn(0)‖r5Γ(ν)tn,‖φ6n(t)‖≤‖Dpn(0)‖r6Γ(ν)tn,‖φ7n(t)‖≤‖Qpn(0)‖r7Γ(ν)tn.
This system has a solution, so it is also continuous. We will show that φ1n(t),φ2n(t),φ3n(t),φ4n(t),φ5n(t),φ6n(t) and φ7n(t) converge to system of solution (6).
Consider D1n(t),D2n(t),D3n(t),D4n(t),D5n(t),D6n(t),D7n(t), as fixed point iteration method, so that (15) Sp(t)−Sp(0)=Spn(t)−D1n(t),
Ep(t)−Ep(0)=Epn(t)−D2n(t),
Ip(t)−Ip(0)=Ipn(t)−D3n(t),
Erp(t)−Erp(0)=Erpn(t)−D4n,(t)
Rp(t)−Rp(0)=Rpn(t)−D5n(t),
Dp(t)−Dp(0)=Dpn(t)−D6n(t),
Qp(t)−Qp(0)=Qpn(t)−D7n(t).
Using the triangular inequality with the condition of Lipschitz F1, we are getting:
‖D1n(t)‖=‖1Γ(ν)∫0t(F1(κ,Spn)−F1(κ,Spn−1))dκ‖,≤1Γ(ν)∫0t‖F1(κ,Spn)−F1(κ,Spn−1)‖dκ,≤1Γ(ν)r1‖Spn−Spn−1‖t.
By applying the above process recursively, we obtain ‖D1n(t)‖≤r1Γ(ν)tn+1K.
Here K is the Lipschitz constant.
As a result, the sequence is valid and follows the described conditions as ‖D2n(t)‖→0;‖D3n(t)‖→0 ‖D4n(t)‖→0;‖D5n(t)‖→0 ‖D6n(t)‖→0;‖D7n(t)‖→0 as n→∞.
‖Spn+r(t)−Spn(t)‖≤∑i=n+1n+rY1i=Y1n+1−Y1n+r+11−Y1,‖Epn+r(t)−Epn(t)‖≤∑i=n+1n+rY2i=Y1n+1−Y2n+r+11−Y2,‖Ipn+r(t)−Ipn(t)‖≤∑i=n+1n+rY3i=Y1n+1−Y3n+r+11−Y3,‖Erpn+r(t)−Erpn(t)‖≤∑i=n+1n+rY4i=Y1n+1−Y4n+r+11−Y4,‖Rpn+r(t)−Rpn(t)‖≤∑i=n+1n+rY5i=Y1n+1−Y5n+r+11−Y5,‖Dpn+r(t)−Dpn(t)‖≤∑i=n+1n+rY6i=Y1n+1−Y6n+r+11−Y6,‖Qpn+r(t)−Qpn(t)‖≤∑i=n+1n+rY7i=Y1n+1−Y7n+r+11−Y7.
By hypothesis riΓ(ν)t1≤1. Sp,Ep,Ip,Erp,Rp,Dp,Qp are Cauchy sequences. For this reason, it can be deduce that they are uniformly convergent. Hence, the limit of the sequences is the unique solution of the fractional system (6). □
Theorem 5 If the condition (1−riΓ(ν)t)>0 , for i=1,2,3,4,5,6,7 , holds then the SpEpIpErpRpDpQp model of COVID-19 is unique solution.
Proof We assume that another solution is possible for the system to highlight the uniqueness of the solution, such as Sp1(t),Ep1(t),Ip1(t),Erp1(t),Rp1(t),Dp1(t) and Qp1 then we have Sp(t)−Sp1(t)=1Γ(ν)∫0t(F1(κ,Sp)−F1(κ,Sp1))dκ.
Now, we take the norm of above equation ‖Sp(t)−Sp1(t)‖=‖1Γ(ν)∫0t(F1(κ,S)−F1(κ,Sp1))‖dκ,≤1Γ(ν)∫0t‖(F1(κ,Sp)−F1(κ,Sp1))‖dκ.
From the Lipschitz condition (13) it follows that ‖Sp(t)−Sp1(t)‖≤1Γ(ν)r1t‖Sp(t)−Sp1(t)‖,
consequently ‖Sp(t)−Sp1(t)‖−1Γ(ν)r1t‖Sp(t)−Sp1(t)‖≤0,
(16) ‖Sp(t)−Sp1(t)‖[1−1Γ(ν)r1t]≤0.
By the hypothesis (1−riΓ(ν)t)>0 the previous Eq. (16) become the form ‖Sp(t)−Sp1(t)‖=0.
This means that Sp(t)=Sp1(t). Apply similar technique to all solution for i=2,3,…...,7, we get ‖Ep(t)−Ep1(t)‖=0;‖Ip(t)−Ip1(t)‖=0;
‖Erp(t)−Erp1(t)‖=0;‖Rp(t)−Rp1(t)‖=0;
‖Dp(t)−Dp1(t)‖=0;‖Qp(t)−Qp1(t)‖=0,
Hence, the theorem is proved. □
6 Numerical scheme for the solution
For our proposed fractional order epidemic model SpEpIpErRpDpQp, this section provides the numerical solution. Subsequently, a systematic solution is not proposed for the non-linear fractional system (6), We use the scheme known as the [45] generalized Adams–Bashforth–Moulton approach to establish the system’s numerical solution (6). In this process, to find the numerical solution of nonlinear FDEs, the predictor–corrector system is introduced. Assume the following fractional differential nonlinear equation to provide the expected solution using this technique. (17) 0cDtνψ(t)=g(t,ψ(t)),0≤t≤T,
by the following initial condition (18) ψ(j)=ψ0j,j=0,1,2,….....[ν]−1.
Currently, from Eq. (17), we can get the solution ψ(t) by solving the following equation: (19) ψ(t)=∑j=0[ν−1]ψ0jj!tj+1Γ(ν)∫0t(t−u)(ν−1)f(u,ψ(u))du.
The above Eq. (19) is known as the Volterra integral equation. For the integration of (19) [46], [47], [48] used the predictor–corrector formula dependent on the Adams–Bashforth–Moulton integration algorithm. Arrangement h=TN,tn=nh and n=0,1,2,….N∈Z+, that Eq. (19) can be discretized as obeys: (20) ψ(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+hνΓ(ν+2)f(tn+1,ψPr(tn+1))+hνΓ(ν+2)∑i=0nai,n+1f(ti,ψ(ti)),
where (21) ai,n+1=nν+1−(n−ν)(n+1)ν,ifi=0,(n−i+2)ν+1+(n−i)ν+1−2(n−i+1)ν+1,if0<i≤n,1ifi=n+1.
Then ψPr(tn+1) is calculated through (22) ψPr(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+1Γ(ν)∑i=0nbi,n+1f(ti,ψh(ti)),
with (23) bi,n+1=hνν((n+1−i)ν−(n−i)ν).
6.1 Numerical scheme for COVID-19 Caputo fractional model
In this part, the nonlinear fractional model SpEpIpEpErRpDpQp with the suggested scheme is numerically solved. For the generalized Adams–Bashforth–Moulton method, the numerical structure of the model suggested (6) is known as [46], [47], [48]. Consider ψ=SpEpIpErpRpDpQp,ϕ=ϕSpϕEpϕIpϕErpϕRpϕDpϕQp
in scheme (20). Sp(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+∑i=0nai,n+1ϕSp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))+hνΓ(ν+2)ϕSp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
Ep(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+∑i=0nai,n+1ϕEp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))+hνΓ(ν+2)ϕEp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
Ip(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+∑i=0nai,n+1ϕIp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))+hνΓ(ν+2)ϕIp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
Erp(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+∑i=0nai,n+1ϕErp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))+hνΓ(ν+2)ϕErp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
Rp(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+∑i=0nai,n+1ϕRp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))+hνΓ(ν+2)ϕRp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
Dp(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+∑i=0nai,n+1ϕDp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))+hνΓ(ν+2)ϕDp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
Qp(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+∑i=0nai,n+1ϕQp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))+hνΓ(ν+2)ϕQp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
where SpPr(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+1Γ(ν)∑i=0nbi,n+1ϕSp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
EpPr(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+1Γ(ν)∑i=0nbi,n+1ϕEp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
IpPr(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+1Γ(ν)∑i=0nbi,n+1ϕIp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
ErpPr(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+1Γ(ν)∑i=0nbi,n+1ϕErp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
RpPr(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+1Γ(ν)∑i=0nbi,n+1ϕRp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
DpPr(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+1Γ(ν)∑i=0nbi,n+1ϕDp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
QpPr(tn+1)=∑j=0[ν−1]ψ0jj!tn+1j+1Γ(ν)∑i=0nbi,n+1ϕQp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)).
Moreover, the quantities ϕSp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
ϕEp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
ϕIp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
ϕErp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
ϕRp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
ϕDp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
and ϕQp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti)),
are calculated from the following functions, (24) ϕSp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))=−μ1νMSpEp−μ2νMSpIp−μ3νSp
(25) ϕEp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))=μ1νMSpEp+μ2νMSpIp−(α1+α2)νEp
(26) ϕIp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))=α1νEp−(β1+β2)νIp
(27) ϕErp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))=α2νEp
(28) ϕRp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))=β1νIp
(29) ϕDp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))=β2νIp
(30) ϕQp(ti,Sp(ti),Ep(ti),Ip(ti),Erp(ti),Rp(ti),Dp(ti),Qp(ti))=μ3νSp
In accumulation, the quantities ϕSp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
ϕEp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
ϕIp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
ϕErp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
ϕRp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
ϕDp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
ϕQp(tn+1,SpPr(tn+1),EpPr(tn+1),IpPr(tn+1),ErpPr(tn+1),RpPr(tn+1),DpPr(tn+1),QpPr(tn+1)),
are calculated from Eqs. (24)–(30) correspondingly at the point tn+1, n=1,2,3,…..,k.
This section provides a comparison analysis between curves from the fractional model (6) and the actual data for a COVID-19 outbreak. The proposed COVID-19 model has been numerically simulated using the parametric values listed in Table 2 and shown in Fig. 1, Fig. 2. Note that all these simulations had been performed over a 100 days. Fig. 2 show the approximations for Sp(t),Ep(t),Ip(t),Erp(t),Rp(t), and Qp(t) obtained using the suggested algorithm for various values of ν=1,0.9,0.8,0.7. The exposed population is smaller after 50 days, and the infected population has reached its peak and is rapidly declining. Therefore, the Tunisian government can relax the precautions it adopted in response to the epidemic breakout, such as the general quarantine. Based on information about COVID-19 cases data collected as of March 14th, we approximated the basic reproduction number R0 and made predictions about how the epidemic will develop. In the simulation curve of the classical model, the number of confirmed infected people Ip(t) matches the official data curve for first 10 days but in the time period of 10 to 20 days, fractional model gives more accurate results as compared to classical model (see Fig. 1). The simulations shown in this figure are comparable with those mentioned in [49], but it can be seen in Figure 4 [49] that there exist error between actual data and model, however, in our model we can reduce the error by varying the fractional order. Fractional calculus may enhance our study of biological processes due to the ability of fractional differential equations to model previous evolution of function. During COVID-19 growth, COVID-19 patients exhibit a variety of tendencies that are challenging for an integer order derivative to capture. As different COVID-19 models progress is different, the fractional derivative can be changed to best fit the actual data according to the progressions. In latest years numerous researches have been done on the application of fractional derivatives in the modeling of natural phenomena, and the results suggest that the fractional derivative works better.Fig. 1 Comparison of simulation curves and real date on different fractional order for infected population.
Fig. 2 Simulations curves of COVID model governed by Caputo fractional operator and initial condition (10999782,200,18,0,0,0,0).
Table 2 Model parameters values [43].
Parameters Values
μ1ν 0.8ν(day−1)
μ2ν 0.02ν(day−1)
μ3ν 0.166ν(day−1)
M 11∗106 person
α1ν 0.0109ν(day−1)
α2ν 0.1ν(day−1)
β1ν 0.003ν(day−1)
β2ν 0.0037ν(day−1)
R0 7.5
7 Conclusion
In the current study, we used the mathematical modeling of based on Caputo-type fractional model to investigate the dynamics of the COVID-19 SpEpIpErpRpDpQp model in the human population. Our results show that the presented model might be use as the reference model [43] to conclude the infected impact on the population. The dynamical features of this model, including equilibrium points, the invariant region, stability analysis, and the basic reproduction number were derived and discussed. Furthermore, using data-fitting the values of the model parameters are estimated in accordance with the real COVID-19 cases in Tunisia. The only effective way to limit or slow the spread of the COVID-19 is to reduce the transmission rate and execute the rules and regulations for precaution. Moreover, it has been seen that the system used to treat the high infection with minimizing contact with other people. An effective approximation method namely the Adams–Bashforth–Moulton method and a sequential substitution method are employed for the numerical simulations.
CRediT authorship contribution statement
Sadia Arshad: Conceptualization, Writing – review & editing, Supervision, Software. Imran Siddique: Methodology, Investigation, Writing – review & editing, Validation. Fariha Nawaz: Conceptualization, Writing – original draft. Aqila Shaheen: Methodology, Writing – original draft, Investigation. Hina Khurshid: Writing – original draft, Data curation.
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
Data will be made available on request.
Acknowledgments
All authors approved version of the manuscript to be published.
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| 36506918 | PMC9721378 | NO-CC CODE | 2022-12-15 23:15:46 | no | Physica A. 2023 Jan 1; 609:128383 | utf-8 | Physica A | 2,022 | 10.1016/j.physa.2022.128383 | oa_other |
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Int J Biol Macromol
Int J Biol Macromol
International Journal of Biological Macromolecules
0141-8130
1879-0003
Published by Elsevier B.V.
S0141-8130(22)02876-8
10.1016/j.ijbiomac.2022.11.311
Article
Inhibitory activities of alginate phosphate and sulfate derivatives against SARS-CoV-2 in vitro
Yang Cheng a1
Li Dan a1
Wang Shixin ac
Xu Meijie a
Wang Dingfu a
Li Xin a
Xu Ximing ac⁎
Li Chunxia abc⁎
a Key Laboratory of Marine Drugs of Ministry of Education, Shandong Key Laboratory of Glycoscience and Glycoengineering, School of Medicine and Pharmacy, Ocean University of China, Qingdao 266003, China
b Laboratory for Marine Drugs and Bioproducts of Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
c Laboratory of Marine Glycodrug Research and Development, Marine Biomedical Research Institute of Qingdao, Qingdao 266071, China
⁎ Corresponding authors at: School of Medicine and Pharmacy, Ocean University of China, 5 Yushan Road, Qingdao, Shandong Province, China.
1 These authors contributed equally to this work
5 12 2022
5 12 2022
28 7 2022
27 11 2022
29 11 2022
© 2022 Published by Elsevier B.V.
2022
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Alginate derivatives have been demonstrated remarkable antiviral activities. Here we firstly identified polymannuronate phosphate (PMP) as a highly potential anti-SARS-CoV-2 agent. The structure-activity relationship showed polymannuronate monophosphate (PMPD, Mw: 5.8 kDa, P%: 8.7 %) was the most effective component to block the interaction of spike to ACE2 with an IC50 of 85.5 nM. Surface plasmon resonance study indicated that PMPD could bind to spike receptor binding domain (RBD) with the KD value of 78.59 nM. Molecular docking further suggested that the probable binding site of PMPD to spike RBD protein is the interaction interface between spike and ACE2. PMPD has the potential to inhibit the SARS-CoV-2 infection in an independent manner of heparan sulfate proteoglycans. In addition, polyguluronate sulfate (PGS) and propylene glycol alginate sodium sulfate (PSS) unexpectedly showed 3CLpro inhibition with an IC50 of 1.20 μM and 1.42 μM respectively. The polyguluronate backbone and sulfate group played pivotal roles in the 3CLpro inhibition. Overall, this study revealed the potential of PMPD as a novel agent against SARS-CoV-2. It also provided a theoretical basis for further study on the role of PGS and PSS as 3CLpro inhibitors.
Graphical abstract
Unlabelled Image
Abbreviations
SARS-CoV-2, severe acute respiratory syndrome coronavirus 2
COVID-19, corona virus disease 2019
ACE2, angiotensin-converting enzyme 2
RBD, receptor-binding domain
3CLpro, 3C-like protease
PLpro, papain-like protease
RdRp, RNA-dependent RNA polymerase
TMPRSS2, transmembrane protease serines
HSPG, heparan sulfate proteoglycans
T2DM, type 2 diabetes mellitus
PGS, polyguluronate sulfate
PSS, propylene glycol alginate sodium sulfate
PM, polymannuronate
PG, polyguluronate
PMP, phosphate of polymannuronate
HTRF, homogeneous time resolved fluorescence
FRET, fluorescent resonance energy transfer
SPR, surface plasmon resonance
PMPD, polymannuronate monophosphate
PMGS/TGC161, sulfated polymannuroguluronate
KD, equilibrium dissociation constant
LMWA, low-molecular-weight alginate
PMS, polymannuronate sulfate
PGP, polyguluronate phosphate
Keywords
SARS-CoV-2
Alginate derivatives
Spike protein
3CLpro
==== Body
pmc1 Introduction
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since the outbreak in 2019 and continues to spread at an unprecedented rate. According to the data of SARS-CoV-2 global tracking system [1], as of May 27, 2022, >527 million diagnosed patients and 6.28 million deaths had been reported. Although the most effective ways to resist SARS-CoV-2 infection are vaccination and maintaining social distance, multiple iterations of SARS-CoV-2 mutant strains have challenged the effectiveness of vaccination [[2], [3], [4], [5]]. Therefore, the development of anti-virus drugs for the corona virus disease 2019 (COVID-19) therapy continues to be of great public health importance.
SARS-CoV-2 belongs to the betacoronavirus and is closely related to the SARS-CoV branch [6]. It is mainly composed of spike protein, envelope protein, membrane protein, nucleocapsid protein, and non-structural proteins [7,8]. All proteins could be used as antigens for vaccine development or targets for antiviral drugs therapy. Notably, the transmembrane spike protein is a homotrimer that undergoes a significant change in its protein conformation during the early stages of infection, thereby facilitating binding to angiotensin-converting enzyme 2 (ACE2), initiating the fusion of the virus with the host cell membrane [[9], [10], [11]]. The spike protein contains two main subunits, S1 and S2. The S1 subdomain of the spike mainly contains the receptor-binding domain (RBD), while the S2 component mediates the fusion of virus. During viral infection, the spike protein initially binds to the ACE2 via the RBD site of the S1 component. Furthermore, the S1 domain detaches from the surface, prompting the fusion of the S2 region with the cell membrane [12]. Multiple studies have shown that the RBD region of the SARS-CoV-2 spike is the primary binding site to the ACE2 protein [13,14].
Up to now, a collection of potential targets of anti-SARS-CoV-2 drugs have been identified and applied to drug development, such as spike protein, 3C-like protease (3CLpro) [15], papain-like protease (PLpro) [16], RNA-dependent RNA polymerase (RdRp) [17], transmembrane protease serines (TMPRSS2) [18,19]. In addition, a variety of drugs for treatment of SARS-CoV-2 infection have been approved for marketing. Besides antibody drugs targeting the spike protein, 3CLpro and RdRp have become the primary targets for drug development due to their broad-spectrum properties. Remdesivir was the first drug approved for therapeutic use worldwide by intravenous injection, while Molnupiravir was the first approved oral drug, and both of them targeted RdRp to inhibit SARS-CoV-2 replication [20,21]. Paxlovid was another novel oral drug approved for treating infections through inhibiting 3CLpro [22]. The phase III clinical trial of VV116 for mild to moderate COVID-19 reached the primary study endpoint and will be marketed shortly [23,24].
Glycoconjugates attracted a lot of attention during the global pandemic. Clinical studies have found that venous thrombosis and pulmonary thrombosis are the most important causes of death in the early stage of SARS-CoV-2 outbreaks [[25], [26], [27], [28]]. Surprisingly, the widely available and inexpensive heparin could significantly ameliorate symptoms in COVID-19 patients [29]. Meanwhile, Robert J. Linhardt's group found that the fucoidan extracted from seaweed Saccharina japonica, unfractionated heparin, and two chemical modified heparins exhibited well anti-SARS-CoV-2 activity [30]. Beiwei Zhu's group also found that three marine-derived sulfated polysaccharides, including fucoidan, carrageenan, and shark chondroitin sulfate C (CS), exhibited inhibitory activity against SARS-CoV-2 [31]. Moreover, various research groups have illustrated the potential of sulfated polysaccharides against infection in vitro [[32], [33], [34], [35]]. With further depth, a study corroborated that SARS-CoV-2 required the involvement of heparan sulfate proteoglycans (HSPGs) during infection to transform the spike protein structure into an open conformation to facilitate the binding of ACE2 [36]. The above results provided insight into the anti-SARS-CoV-2 mechanism of heparin-like sulfated polysaccharides. However, due to the anticoagulant effect of sulfated polysaccharides, this property limits their use in antiviral applications.
Alginate is formed with polymannuronate (PM) and polyguluronate (PG), where polymannuronate is a linear polymer composed of 1 → 4-linked β-d-mannuronic acid, and polyguluronate is composed of 1 → 4-linked α-l-guluronic acid [37]. In recent years, our group has prepared different kinds of alginate derivatives (Scheme 1 ) for functional studies and applications. Kan Ding's group found that algae-derived polysaccharide 37,502 exhibited favorable anti-SARS-CoV-2 activity, inhibiting 3CLpro activity at low concentration and interfering with interactions between SARS-CoV-2-S1 and ACE2 [38]. Simultaneously, there is mounting evidence that after an acute period of SARS-CoV-2 infection, patients will likely experience a range of sequelae, such as hypertension and diabetes mellitus [39]. Multiple retrospective cohort studies have revealed that SARS-CoV-2 infection might lead to a significantly higher incidence of new-onset type 2 diabetes mellitus (T2DM) [[40], [41], [42]]. Furthermore, there is considerable evidence that diabetes is highly correlated with the severity of the infection and that mortality is significantly higher in patients with COVID-19 and concomitant diabetes [43,44]. In our previous study, the phosphate of polymannuronate (PMP) showed a certain anti-diabetic activity as a novel PTP1B inhibitor [45]. Given the close relationship between T2DM and COVID-19 infection and the fact that PMP has certain antidiabetic effects, we would like to investigate further whether PMP derivatives have inhibitory effect on SARS-CoV-2.Scheme 1 The synthesis of alginate derivatives
Scheme 1
This study found that PMP exhibited favorable spike/ACE2 inhibitory activity. Unlike the anti-SARS-Cov-2 mechanism of sulfated polysaccharides that have been widely studied previously, PMP could directly inhibit spike/ACE2 binding independent of HSPGs. Meanwhile, we found that the sulfate derivatives of alginate, PGS and PSS, displayed favorable 3CLpro inhibitory activity with IC50 of 1.20 μM and 1.42 μM, respectively. The structure-activity relationships were preliminarily explored and found that the polyguluronate backbone and sulfate played essential roles in the inhibition of 3CLpro. The above findings might provide novel insights and options for the research and development of drugs for the therapy of SARS-CoV-2.
2 Materials and method
2.1 Materials
PM and PG (purity ≥80 %) were provided by the National Engineering Research Center of Marine Drug (Qingdao, China). ACE2/Spike (WT) assay kit was purchased from PerkinElmer Cisbio (Cat. no. 63ADK000CB24PEG/H). Spike RBD protein (40592-V08B), ACE2 protein (10108-H08B), and wild-type spike neutralizing antibody (40592-MM57) were provided by Sino Biological Inc. The CM5 Sensor Chip was purchased from GE Healthcare Life Sciences. Coronavirus Mpro/3CLpro activity fluorometric assay kit was purchased from Shanghai Beyotime Biotechnology. The PMGS, PSS, PGS, PMS, PGP, and LMWA polysaccharides were prepared in our laboratory previously. All other reagents were analytical grade.
2.2 Preparation of polymannuronate phosphate
PMP was synthesized using the phosphoric acid/urine method [46,47]. Briefly, the phosphorylation reagent was prepared by dropping 85 % phosphoric acid into a reaction flask containing N, N-dimethylformamide (DMF) through a constant pressure funnel and stirring uniformly on ice for 20 min. Subsequently, the LPM, urea, and DMF were added into the three-necked reaction flask, stirred thoroughly, and heated in an oil bath. When the temperature rose to 110 °C, the prepared phosphorylation reagent was added into the reaction system through a constant pressure-dropping funnel and refluxed for 4 h at 130 or 150 °C in an oil bath. Next, the precipitate of the mixture was collected by filtration, washed it three times with 85 % ethanol, and re-dissolved in distilled water. The pH was adjusted to 9–10 with 1 M NaOH and stirred for 2– 3 h at 40 °C. Eventually, the reaction mixture was desalted with 1 kDa dialysis bag for 72 h, vacuum concentrated and freeze-dried to afford PMP1 ~ 3. PMPD was obtained by dialyzing PMP2 using a 3.5 kDa dialysis bag for 72 h. The detailed reaction parameters of PMP-1/PMP-2/PMP3 were shown in Table S1.
2.3 Analytical methods
The molecular weight (Mw) of PM and PMP was measured by high-performance gel permeation chromatography (HPGPC). The assay was performed on an Agilent 1260 Infinity II HPLC (USA, Agilent) system equipped with a TSK gel G3000 PWXL column (5 μm, 7.8 × 300 mm) and elution with 0.1 mol/L Na2SO4 at a flow rate of 0.5 mL/min. Sample signals were measured using a refractive index detector. The molecular weight was estimated by reference to a calibration curve made by dextran standards. The content of organic phosphorus was analyzed by the phosphorus molybdenum blue method [48]. Furthermore, the structure of PM and PMP was determined by nuclear magnetic resonance (NMR) and fourier transform infrared spectroscopy (FT-IR) analysis. Briefly, FT-IR was recorded on a Nicolet Nexus 470 FT-IR spectrometer using KBr to make sample pellets. The 1H NMR, 13C NMR and two-dimensional spectra were recorded on a JNM-ECP 600 MHz spectrometer using D2O as the solvent and acetone‑d 6 as the internal standard. The 31P NMR spectra were recorded on Bruker AVANCE NEO 400 MHz spectrometer with D2O as the solvent and 85 % H3PO4 as the external standard.
2.4 Spike/ACE2 binding assay
The homogeneous time resolved fluorescence (HTRF) SARS-CoV-2 spike/ACE2 binding assay was designed to evaluate the binding between the SARS-CoV-2 spike and human ACE2 protein. In the HTRF assay, the interaction between Tag1-SARS-CoV-2 spike and Tag2-ACE2 was detected by anti-Tag1-Europium (donor) and anti-Tag2-d2 (acceptor). When the donor and acceptor antibodies are brought into proximity, the excitation of the donor antibody triggers fluorescent resonance energy transfer (FRET) toward the acceptor antibody, which is emitted at 665 nm. This specific signal was directly proportional to the extent of the SARS-CoV-2 spike/ACE2 interaction.
2 μL of test compound, 4 μL of Tag1-SARS-CoV-2 spike protein (25 nM), and 4 μL of Tag2-ACE2 protein (75 nM) were added and then incubated at room temperature for 15 min. And then, 10 μL of Anti-Tag1-Eu3+ and Anti-Tag2-d2 premix reagent (1:1) were added and incubated at room temperature for 3 h. At last, the data were read using microplate reader. Three replications of each group were performed to ensure accuracy. The inhibition rate was calculated based on the following formula.Ratio=Signal665nm/Signal620nm
x=1−Rs−RnRp−Rn×100%
Rs is the ratio of 665/620 nm of samples, Rp is the ratio of 665/620 nm of positive control group, Rn is the ratio of 665/620 nm of negative control group.
2.5 The surface plasmon resonance (SPR) experiment
The SPR experiment was performed by the Biacore T200 SPR spectrometer. Briefly, a 1:1 aqueous mixture of 0.2 M EDC [1-(3-Dimethylaminopropyl)-3-ethylcarbodiimide hydrochloride] and 0.05 M NHS [N-hydroxysuccinimide] was flowed through the chip at a flow rate of 10 μL/min for 420 s to complete the activation of the CM5 chip. Then, the spike and ACE2 protein were diluted to 250 μg/mL using 10 mM sodium acetate buffer. Subsequently, the protein coupling procedure was initiated to immobilize approximately 1000 RU of spike protein and 2700 RU of ACE2 protein on the chip. Finally, 1 M ethanolamine (pH 8.5) was flowed through the chip at 10 μL/min to elute excess active esters, thereby reducing non-specific binding. The immobilization sensorgrams was shown in Fig. S1.
Furthermore, the samples were analyzed at 25 °C in hydroxyethyl piperazine ethyl sulfonic acid buffered saline (Hepes) using multi-cycle kinetics. Spike protein or ACE2 was immobilized onto separate cells of the CM5 sensor chip using an amine coupling kit. A blank immobilization was used as a reference on all chips. 2.5 μM of PMPD was diluted to 0.3125 μM by a triple concentration gradient of Hepes solution. And then injected various concentrations of PMPD into the immobilized protein and reference flow cell for 240 s, and the dissociation time allocated was 600 s. Kinetic evaluation of binding responses was performed with the Biacore T200 Evaluation software, and the curve-fitting was performed (global fitting, 1:1 model).
2.6 Molecular docking studies
Docking studies were performed using watvina software, which is a branch of vina optimized (https://github.com/biocheming/watvina, accessed on 13 July 2022, Ximing Xu, Qingdao, China). The crystal structure of spike RBD (PDB: 6m0j) and 3CLpro (PDB: 6w63) were searched and obtained from the Protein Data Bank (PDB). The tetrasaccharide molecular structure of PMPD, PGS, and PSS was built, and the MMFF94 force field was used for minimization. Protein receptor was prepared by Chimera software, adding the hydrogen and minimizing structure [49]. Docking results were rendered with Pymol.
2.7 3CLpro enzymatic activity and inhibition assays
The 3CLpro inhibitors screening was performed using coronavirus Mpro/3CLpro activity fluorometric assay kit (Beyotime Biotechnology, Shanghai, China). Since 3CLpro protease can cleave natural peptide substrate (KTSAVLQSGFRKME), its enzymatic activity could be assayed using the FRET technique, which enables high-throughput screening of inhibitors [50]. In brief, the sample was diluted to the designated concentrations, and the assays were performed by adding 5 μL of sample solution and 93 μL of assay solution per well. Subsequently, added 2 μL of the substrate to each well and mixed separately, incubated at 37 °C for 5 min, and detected the fluorescence signal at 340 nm (excitation)/490 nm (emission) with Spark 10 M plate reader immediately. Eventually, the inhibition rate of the samples was calculated using the following equation, and then the half-inhibition concentration (IC50) values were determined at three independent experiments.Inhibition rate%=RFU100%enzymatic control−RFUSamples/RFU100%enzymatic control−RFUblank control
3 Results and discussion
3.1 The preparation and characteristic analysis of PMP
Polymannuronate phosphate derivatives with different molecular weights were prepared and named PMP1, PMP2, and PMP3. According to the previous studies, the phosphate was mainly located at the C-2 and C-3 of glycosyl residues (Fig. 1A) [45,46]. PMP2 was further dialyzed using 3.5 kDa dialysis bag, and the product obtained was named PMPD. The molecular weight, phosphorus content, and phosphate type of PMPs were determined by HPGPC, phosphorus molybdenum blue method [48], and 31P NMR spectra, respectively. The chemical properties of PMPs were shown in Table 1 .Fig. 1 Chemical structure and properties of PMPD. (A) The schematic structure of PMPD; (B) 31P NMR spectrum of PMPD and PMP2; (C) 1H NMR spectra of PM and PMPD; (D) 13C NMR spectra of PM and PMPD.
Fig. 1
Table 1 The physico-chemical properties of PMPs.
Table 1Samples Phosphorus content (%) Phosphate type Mw(kDa)
PMP1 9.5 -P2O63− 3.4
PMP2 12.2 -P2O63−, -PO32− 5.6
PMP3 8.2 -PO32− 6.8
PMPD 8.7 -PO32 5.8
Compared to the different phosphate types, we found that PMP1 (3.4 kDa) was bisphosphate, PMP3 (6.8 kDa) was a monophosphate, while PMP2 (5.6 kDa) was a mixture of monophosphate and bisphosphate. Mainly, the 31P NMR spectrum of PMPD only showed characteristic peaks of monophosphate (~0 ppm) and no peaks for pyrophosphate (~−10 ppm) and triphosphate (~−20 ppm). The results indicated that the diphosphate was removed from PMP2 after the dialysis (Fig. 1B). Furthermore, it could be inferred from the molecular weights of PMPs that higher molecular weights tend to yield monophosphate such as PMP3, which was consistent with our previous report [51]. Meanwhile, we also found that PM at relatively low molecular weights would yield bisphosphonates after phosphorylation modifications, such as PMP1. Since the PMPD exhibited optimal inhibitory activity in the follow-up experiments, we further characterized its structure.
The 1H NMR and 13C NMR data assignments of PM and PMPD were given in Table 2 . In the 1H NMR spectrum, a new signal peak at 4.20 ppm appeared. Based on the previous experimental results, we speculated that it was due to the shift of the H3 signal to the low-field caused by C3-OH substitution with phosphate group, and the proton hydrogen was marked as H3' [51] (Fig. 1C). Next, in the 13C NMR spectrum of PM, the peaks of 175.21, 99.91, 77.74, 75.76, 71.31, and 69.94 ppm were attributed to C6, C1, C4, C5, C3, and C2, respectively. Moreover, the peaks at 73.79/74.84 ppm could be assigned to C3-P/C2-P substituted with phosphates in the 13C NMR spectrum of PMPD (Fig. 1D). The correlation signal analysis in the 1H— 1H COSY and HSQC spectra showed that the carbon and hydrogen signals were attributed reasonably (Fig. 2 ). The signal assignment of HSQC was shown in Table 3 .Table 2 The 13C NMR and 1H NMR signal assignment of PM and PMPD.
Table 2Samples 13C NMR data (ppm)
C1 C2 C3 C4 C5 C6 C3-P C2-P
PM 99.91 69.94 71.31 77.74 75.76 175.21 / /
PMPD 100.04 70.05 71.26 77.70 75.91 175.29
173.12 73.79 74.84
Samples 1H NMR data (ppm)
H1 H2 H4 H3/H5 H3'
PM 4.69 4.06 3.93 3.78 /
PMPD 4.69 4.01 3.92 3.73–3.77 4.20
Fig. 2 Two dimensional spectra of PMPD. 1H—1H COSY and HSQC spectrum detection using 600 MHz NMR. (A) 1H—1H COSY overall spectrum; (B) 1H—1H COSY partial spectrum; (C) HSQC overall spectrum; (D) HSQC partial spectrum.
Fig. 2
Table 3 The HSQC signal assignment of PMPD.
Table 3Chemical shift Signal assignment
4.69, 100.04 H1-C1
4.04, 70.05 H2-C2
4.20, 73.79 H3'-C3-P
3.74, 71.26 H3-C3
3.92, 77.70 H4-C4
3.74, 75.91 H5-C5
The structure of PMPD was further characterized by FT-IR (Fig. 3 ). Based on the results, the broad and intense bands at 3384 cm−1 was due to the stretching vibration of O—H. The signals at 1604 cm−1 and 1413 cm−1 were assigned to the asymmetric and symmetric stretching vibration of COO−, respectively. The band at 820 cm−1 was the C1 —H absorption peak of polymannuronate. Furthermore, the peak at 1074 cm−1 and 923 cm−1 derived from stretching vibration of P-O-C and P-O(Na). The above results indicated that the phosphorylation modification of PM occurred.Fig. 3 The FT-IR spectra of PMPD and PM.
Fig. 3
3.2 Inhibition of PMPD for the spike/ACE2 interaction
The ACE2/Spike (WT) binding assay kit was used to assay the binding of the SARS-CoV-2 spike and ACE2. As shown in Table 4 , bisphosphate (PMP1) showed no inhibitory activity, while monophosphate-dominated components (PMP2, PMP3) displayed good inhibitory activity of 82.33 % and 62.44 %. PMPD was the most effective component, and its activity increased from 82.33 % (PMP2) to 89.10 % (PMPD) after removing diphosphate. The above results demonstrated that monophosphate played a vital role in inhibitory activity. Furthermore, the inhibition of PMP3 (6.8 kDa) and PMPD (5.8 kDa) were 62.44 % and 89.10 %, which indicated the molecular weight had a considerable impact on the inhibitory activity. In addition, PMPD has a relatively lower molecular weight and a relatively higher phosphorus content compared to PMP3, and it has better inhibitory activity, indicating that the phosphorus content of PMP is also an identically influential factor affecting its biological activity.Table 4 Inhibitory activity of samples (10 μM).
Table 4Samples Inhibition ratio(%)
PMP1 9.88
PMP2 82.33
PMP3 62.44
PMPD 89.10c
Heparin /
TGC161a /
PGSb /
a TGC161 is sulfated polymannuroguluronate.
b PGS represents polyguluronate sulfate.
c Calculated from the IC50 nonlinear fitting equation.
From the data shown in Fig. 4A, the IC50 value of the spike neutralizing antibody was 3.85 nM, which was similar to the manufacturer's result (3.694 nM), proving the reliability of the experimental method. Additionally, PMPD could reduce the binding of spike protein and ACE2 with an IC50 value of 85.5 nM (Fig. 4B).Fig. 4 Determination of IC50 values for spike/ACE2 inhibition. Non-linearly fitted inhibition curves for wild-type spike neutralizing antibody (A) and PMPD (B).
Fig. 4
With further comprehensive studies on the mechanism of SARS-CoV-2 invasion, T.M. Clausen et al. found that SARS-CoV-2 infection depended on HSPGs on the cell membrane and that acetyl heparin sulfate was essential for the conformational opening of the spike protein and viral infection [36]. And heparin-like sulfated polysaccharides as analogs of acetyl heparan sulfate could attenuate the binding of spike to ACE2 receptors and thus block the internalization of SARS-CoV-2 [31,35,52,53]. Therefore, we further assayed heparin-like polysaccharides such as PGS [54], TGC161 [55] synthesized previously, and heparin (Table 4). However, the above sulfated polysaccharides did not exhibit inhibitory activity under this assay method, which was inconsistent with previous reports [30,32,56]. We speculated that this might be due to the limitation of the assay kit, which was only applicable to the detection of inhibitors for spike/ACE2 binding directly. Meanwhile, due to the inhibition of SARS-CoV-2 infection by heparin being mediated by competition with the cell membrane HSPG, this assay method did not apply to the heparin and heparin-like sulfated polysaccharides assay. However, this result also suggested that the anti-SARS-CoV-2 mechanism of PMPD was different from heparin-like polysaccharides, and PMPD could directly inhibit the spike/ACE2 interaction.
3.3 Interactions of PMPD with spike and ACE2
To further investigate the mechanism of inhibition by PMPD, we used the SPR method to evaluate the interaction of PMPD with spike or ACE2. Briefly, about 1000 RU of spike or 2700 RU of ACE2 were immobilized on a CM5 sensor chip. PMPD and spike neutralizing antibody at different concentrations flowed over the biosensor chip. The wild-type spike protein could bind to ACE2 with an equilibrium dissociation constant (KD) value of 2.74 nM (Fig. 5A). As shown in Fig. 5B, the KD value was 103.7 pM using a spike neutralizing antibody as the positive control. PMPD showed significant binding to spike with a KD value of 78.59 nM, implicating a high affinity of PMPD with spike protein (Fig. 5C). In addition, we found that PMPD also exhibited a lower affinity than the spike for ACE2 (156.2 nM, Fig. 5D). These findings further verified that PMPD could block the interaction of spike protein to ACE2 and was comparable to the heparin analogs reported previously [52,56,57]. The above results demonstrate that PMPD has massive potential for application in inhibiting SARS-CoV-2 infection.Fig. 5 SPR sensorgrams of interactions: spike protein with ACE2 (A); spike protein with wide-type neutralizing antibody (B); PMPD with spike protein (C); and PMPD with ACE2 (D).
Fig. 5
3.4 Molecular docking for interactions of PMPD and spike
Moreover, we performed a molecular docking study to predict the interaction of PMPD with the spike RBD domain. Since PMPD was a low-molecular-weight polysaccharide composed of repeated mannuronic acid units, the flexibility of the glycosidic bond would lead to a significant decrease in accuracy. Hence, we performed docking studies using the tetrasaccharide fragment of PMPD and attempted further to explain the interaction between PMPD and spike protein. The conformation with the highest score was shown in Fig. 6 , and its binding site was exactly the site of spike protein to ACE2 (Fig. 6B). In addition, the phosphate group of PMPD was oriented toward the RBD pocket, and we speculated that the phosphate groups played a crucial role to interfere the binding of spike protein to ACE2.Fig. 6 Molecular docking of PMPD with SARS-CoV-2 spike RBD (PDB: 6m0j). (A) A molecular model of SARS-CoV-2 spike protein and ACE2 generated by Pymol. ACE2 is shown in blue and the RBD is shown in green. (B) The highest scoring conformation for the docking of the tetrasaccharide of PMPD with the crystal structure of spike RBD. Blue and red surfaces indicate electropositive and electronegative surfaces. The phosphorus is orange, oxygen is red, and carbon is blue. (C) 2D interactions between PMPD and RBD, analyzed by LigPlot+ software [60,61]. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 6
Furthermore, the analysis of the docking results revealed that PMPD could interact with Asn501, Tyr449, Lys417, Gln493 and Gln498 of the RBD by hydrogen bonds (Fig. 6C). According to the data of molecular dynamics studies, Lys417, Gln493 and Gln498 residues played a vital role in hydrogen bonds between spike proteins and ACE2 proteins. The Asn501 and Tyr449 are relatively conserved on the spike protein and have a significant function in the recognition of ACE2 [58]. The above results suggested that PMPD could interact with key amino acid residues of RBD through hydrogen bonds and obstructed the interaction of spike protein and ACE2.
In the meantime, one critical study was reported and attracted our attention. The Lauren Byrd-Leotis group used natural glycan microarray to identify N-glycans in human lungs that could be recognized by various human and animal coronaviruses [61]. They reported that three coronaviruses, including SARS-CoV-2, MERS-CoV and SARS-CoV, could interact with a range of human lung phosphorylated high mannose glycans and specific sialylated structures. The phosphorylated mannose might play an essential role in virus invasion. This surprising finding provided a clue for the binding polymannuronate monophosphates with spike protein for our research. Interestingly, we also screened the inhibition of polyguluronate phosphate (PGP) in our previous study and found that PMP had better inhibitory activity (data not shown), which might indirectly explain why PMPD has a superior inhibitory activity.
3.5 PGS and PSS exhibit 3CLpro inhibitory activities
Kan Ding's group found that the algal-derived crude polysaccharide 37,502 (Mw: 27.9 kDa) could inhibit the activity of 3CLpro and block the binding of the spike protein to the ACE2 with IC50 of 135.67 μg/mL [38]. More importantly, the chemical structure of polysaccharide 37,502 contained the repetitive sequence of mannuronic acid, which was similar to the basic structure of PMP. Furthermore, the structural characterization showed that the molar ratio of mannuronic acid (M) to guronuronic acid (G) residues of polysaccharide 37,502 was 9:1, and we speculated that polymannuronate played a primary role in 3CLpro inhibition. It motivated us that PMPD might be an inhibitor of 3CLpro, which led us to assay the inhibitory activity of a series of alginate derivatives using the 3CLpro inhibitor screening kit.
As shown in Table 5 , PM, polymannuronate sulfate (PMS), PMPD, and low-molecular-weight alginate (LMWA) (Scheme 1) did not exhibit satisfactory 3CLpro inhibitory activity at 10 μM. It was reported that the molecular weight of polysaccharide 37,502 was 27.9 kDa. We speculated that the molecular weight of the polysaccharide had a significant effect on inhibitory activity. Since polysaccharide 37,502 contains a proportion of polyguluronate, we further examined the 3CLpro inhibitory activity of polyguluronate and its derivatives. The results showed that PG and PGP did not show well inhibitory effects, but their 3CLpro protease inhibitory effects were better than those of polymannuronate and its phosphate derivatives. These results indicated that the PG fragment is more significant for the inhibition of 3CLpro than the PM block.Table 5 The results of 3CLpro inhibitor screening (10 μM).
Table 5Samples Mw (KDa) Sulfate/phosphorus content (%) Inhibition rate (%)
Polymannuronate (PM) 5.6 / 1.2
Polymannuronate sulfate (PMS) 8.6 7.2 58.5
Polymannuronate phosphate (PMPD) 5.8 8.7 26.0
Polyguluronate (PG) 6.0 / 24.2
Polyguluronate sulfate (PGS) 7.6 12.1 91.9
Polyguluronate phosphate (PGP) 5.9 17.7 41.4
Propylene glycol alginate sodium sulfate (PSS) 9.4 10.6 82.0
Polymannuroguluronate sulfate (PMGS) 44.8 29.1 84.2
Polymannuroguluronate sulfate (PMGS) 10.0 10.4 77.9
Low-molecular-weight alginate (LMWA) 10.0 / 19.6
Meanwhile, we also unexpectedly found that PGS and PSS showed excellent 3CLpro inhibitory activities. Previous studies reported that PGS had anti-HBV viral effects, and we considered PGS as a heparin-like polysaccharide which antiviral effects were mainly mediated by its interaction with spike protein [54]. Nevertheless, the present study also identified PGS as a potential 3CLpro inhibitor with an IC50 of 1.20 μM for the first time (Fig. 7A). PSS is a marine drug that has been marketed for the treatment of hyperlipidemia [62], which is derived from the esterification of alginate by propylene glycol at the C-6 position, followed by sulfonation at the C-2 and C-3 positions. PSS also displayed favorable 3CLpro inhibitory activity with an IC50 of 1.42 μM (Fig. 7B). In addition, PMGS as a sulfated alginate derivative also exerted an excellent inhibitory effect on 3CLpro.Fig. 7 Determination of the IC50 value of PGS and PSS against 3CLpro. Ebselen was used as a positive control. The fluorescence signal was assayed at 340 nm (excitation)/490 nm (emission) with Spark 10 M plate reader. Three parallel experiments were performed in each group, and the inhibition curves of PGS (A) and PSS (B) were fitted nonlinearly, and the IC50 was calculated by the nonlinear fitting equation.
Fig. 7
We initially summarized certain structure-activity relationships. Firstly, the polysaccharide backbone of polyguluronate was more critical for the inhibitory effect of 3CLpro compared to the glycan backbone of polymannuronate. Furthermore, the sulfate group exhibited a promising 3CLpro inhibitory activity compared to the phosphorylated modification. Several known 3CLpro small molecule inhibitors contain sulfate groups, and the sulfated polysaccharide previously reported by Kan Ding's group also showed a favorable 3CLpro inhibitory effect [[63], [64], [65], [66]]. Our experimental results also indicated that the presence of sulfate played a vital role in the inhibition of 3CLpro.
Given that both PSS and PGS are composed of repetitive monosaccharide sequences, we try to further analyze the interaction of PGS and PSS fragments with the 3CLpro through molecular docking studies. 3CLpro is a homodimer with two homologous monomers aligned vertically with each other. Its monomer consists of three structural domains (Fig. 8A). According to the catalytic mechanism studies of 3CLpro, the interaction at the dimer and dimer interface is essential for catalytic function [[67], [68], [69]]. There are two potential allosteric sites for 3CLpro, as marked by the red dashed line in Fig. 8A. By interacting with the above two sites, the dimer interface interaction of 3CLpro can be interfered and thus inhibit the catalytic activity of 3CLpro. Both the allosteric pocket II and the catalytic pocket are negative charge-intensive regions (Fig. 8B), and PGS and PSS polysaccharides contain a large amount of sulfate groups, which also carry negative charges. In view of these, we think that the two sulfated polysaccharides could not interact in the above region. In contrast, the allosteric pocket I has a certain amount of positive charge enrichment, and it is also a large hydrophilic pocket (Fig. 8C). So, we speculated that PGS and PSS could most likely interact in this region. The molecular docking studies showed a scoring value of −9.6 for PGS tetrasaccharide and −13.1 for PSS tetrasaccharide with the allosteric pocket I (Fig. 8D). Based on the above results, we speculated that PGS and PSS might exert inhibition effect by inhibiting the dimerization of 3CLpro.Fig. 8 Molecular docking analysis of PGS and PSS tetrasaccharide molecules with 3CLpro non-catalytic pocket. (A) The overall structure of SARS-CoV-2 3CLpro was rendered by Pymol (PDB: 6w63). Domain I was shown in blue, domain II was presented in green, and domain III was colored in cyan; (B) The electrostatic potential of 3CLpro surface. Blue and red indicate electropositive and electronegative surfaces; (C) Hydrophilic and hydrophobic domains on protein surfaces analyzed by Chimera [50]. The blue indicates hydrophilic and yellow indicates hydrophobic. (D) The highest scoring conformation for the docking of 3CLpro with the tetrasaccharide of PGS (left) and PSS (right). The sulfur is yellow, oxygen is red, carbon is blue, and the 3CLpro protein was rendered in gray. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 8
In summary, we found that polymannuronate monophosphate showed anti-SARS-CoV-2 activity in vitro. PMPD had a strong binding ability to spike protein and inhibited the binding between spike protein and ACE2 with an IC50 of 85.5 nM. Furthermore, the molecular docking study predicted that the binding site of PMPD to spike protein was the region where the RBD of SARS-CoV-2 interacts with ACE2. The above results demonstrated that the PMPD could weaken the binding of spike protein to ACE2 and had the potential as an anti-SARS-CoV-2 candidate drug. In addition, we unexpectedly identified PGS and PSS as novel 3CLpro inhibitors with IC50 of 1.20 μM and 1.42 μM. Preliminary structure-activity studies revealed that the polyguluronate backbone and sulfate were essential for their inhibitory activity. It may affect the catalytic activity by inhibiting the dimerization interface interactions of 3CLpro.
This study also had some limitations. The PMPD exhibits favorable spike/ACE inhibitory activity at the molecular protein level in vitro, but its antiviral infection effect needs further clarification. Firstly, the antiviral effect of PMPD needs to be elucidated in the SARS-CoV-2 pseudovirus model in vitro. Next, the antiviral effect of PMPD on the SARS-CoV-2 mouse model needs clarification [70]. In addition, we identified PGS and PSS as potential novel 3CLpro inhibitors, but the relationship between their physicochemical properties and inhibitory activity are still unclear. We expect to elucidate the effect of molecular weight and sulfate content on their inhibitory activity in subsequent study to optimize the active components.
4 Conclusions
Our study identified that a novel alginate derivative PMPD possessed spike binding ability, which could inhibit spike/ACE2 binding in an HSPG-independent manner. The initial structure-activity relationship study revealed that the phosphate type of PMPs played a crucial role in the inhibition. The molecular docking study revealed that PMPD could interact via hydrogen bonds with the spike RBD site. Interestingly, we found that sulfated polysaccharides PGS and PSS possessed 3CLpro inhibition with IC50 of 1.20 μM and 1.42 μM, respectively. Molecular docking studies hinted that they may affect the catalytic activity by inhibiting the interaction at the dimerization interface of 3CLpro. In conclusion, this study laid the foundation for developing sodium alginate derivatives as anti-SARS-CoV-2 drugs and provided new ideas and options for treating COVID-19 infection.
CRediT authorship contribution statement
Cheng Yang: Investigation; Formal analysis; Writing - original draft.
Dan Li: Investigation; Formal analysis; Writing - original draft.
Meijie Xu: Resources.
Dingfu Wang: Resources.
Quancai Li: Resources; Investigation.
Shixin Wang: Supervision; Resources.
Ximing Xu: Methodology; Software.
Chunxia Li: Conceptualization; Writing - review & editing; Funding acquisition; Project administration; Supervision.
Uncited reference
[59]
Declaration of competing interest
The authors declare no conflict of interest.
Appendix A Supplementary data
Supplementary material
Image 1
Acknowledgments
This work was supported by the Shandong Major Science and Technology Project (2021ZDSYS22), 10.13039/501100001809 National Natural Science Foundation of China (Grant No. U21A20297, 82104058), National Science and Technology Major Project for Significant New Drugs Development (2018ZX09735004), Marine S&T Fund of Shandong Province for Pilot National Laboratory for Marine Science and Technology (Qingdao) (No. 2018SDKJ0404-2).
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijbiomac.2022.11.311.
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==== Front
J Infect
J Infect
The Journal of Infection
0163-4453
1532-2742
The British Infection Association. Published by Elsevier Ltd.
S0163-4453(22)00690-9
10.1016/j.jinf.2022.12.001
Letter to the Editor
Breakthrough monkeypox infection among individuals previously immunized with smallpox or monkeypox vaccination
Raccagni Angelo Roberto a⁎
Candela Caterina a
Mileto Davide b
Bruzzesi Elena a
Canetti Diana c
Bertoni Costanza a
Castagna Antonella ac
Nozza Silvia c
a Vita-Salute San Raffaele University, Milan, Italy
b Laboratory of Clinical Microbiology, Virology and Bioemergencies, Ospedale Sacco, Milan, Italy
c Infectious Diseases Unit, San Raffaele Scientific Institute, Milan, Italy
⁎ Corresponding author at: Via Stamira D'Ancona 20, Milano, 20127, Italy.
5 12 2022
5 12 2022
1 12 2022
© 2022 The British Infection Association. Published by Elsevier Ltd. All rights reserved.
2022
The British Infection Association
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
Vaccination
Vaccine
Breakthrough
==== Body
pmcDear Editor,
recently in this journal Moschese D et al. and Orviz E et al. described the characteristics of the natural history of human monkeypox virus infection.1 , 2 Although it has been suggested that previous smallpox vaccination could be effective in preventing monkeypox infection, some cohorts support the evidence of breakthrough infections.3, 4, 5, 6, 7 Moreover, individuals belonging to key-populations are currently receiving vaccines licensed against monkeypox, but preliminary data suggest that administration of the second dose is crucial for the effective development of neutralizing antibodies.8, 9
In this case-series monkeypox breakthrough infections following previous smallpox vaccination or recent single-dose monkeypox vaccination are presented.
Overall, 23 individuals diagnosed with monkeypox infection between June and September 2022 at the Infectious Diseases Unit of San Raffaele Scientific Institute, Milan, Italy, were included in this case-series: 20/23 (87%) previously received smallpox vaccination in their youth, 3/23 (13%) were recently vaccinated with one dose of monkeypox vaccination and were scheduled to receive a second one after 28 days.
Real-time (RT) PCR (RealStar® Orthopoxvirus PCR Kit 1.0 – altona DIAGNOSTICS) targeting the variola virus and the non-variola Orthopoxvirus species (cowpox, monkeypox, raccoonpox, camelpox, vaccinia virus) was used to detect non-variola DNA on swabs and serum, plasma, seminal fluids and urines samples and a specific RT-PCR targeting monkeypox virus DNA (Liferiver - SHANGHAI ZJ BIO-TECH CO., LTD) subsequently confirmed monkeypox infections.
Three individuals were taking HIV pre-exposure prophylaxis (PrEP), 18 were living with HIV, receiving antiretroviral therapy and with a CD4+ lymphocytes count >500 cells/microL and two were receiving immunosuppressive agents for other comorbidities.
Overall, 22/23 (96%) were men who have sex with men, 1/23 (4%) a transgender woman.
All reported high-risk sexual behaviours, most >10 partners in the three months prior to their monkeypox diagnosis and a past medical history of sexually transmitted infections and often chemsex use. Individuals’ characteristics among those who previously received smallpox or monkeypox vaccinations are presented in Table 1 .Table 1 Individuals’ characteristics among those who previously received smallpox or monkeypox vaccination.
Table 1Characteristics Previous smallpox vaccination (n = 20) Recent single-dose monkeypox vaccination (n = 3)
Age (years, IQR) 53 (50–57) 30 (28–32)
Living with HIV 16 (80%) 1 ((34%)
PrEP user 2 (10%) 1 (34%)
Previous STIs 18 (90%) 3 (100%)
Concurrent STIs 3 (15%) 2 (67%)
Number of sexual partners^(IQR) 10 (6–19) 10 (5–15)
Chemsex 6 (30%) 1 (34%)
Sexual contact with MPX case 8 (40%) 1 (34%)
Legend.
^ in the 3 months before MPX diagnosis; Abbreviations. IQR: interquartile; MPX: monkeypox; STI: sexually transmitted infection; PrEP: pre-exposure prophylaxis.
Clinical presentation and course of disease were mild among all cases; only one individual living with HIV required hospitalization and antiviral treatment with cidofovir. All individuals achieved clinical resolution of symptoms and virologic clearance of infection, without negative outcomes.
The median clinical duration of symptoms was 15 days (interquartile, IQR=11–21), which was apparently longer among individuals who previously received smallpox vaccination (16, IQR=12–22) than among who recently received a single-dose monkeypox one (6, IQR=4–8). The median number of lesions was 5 (IQR=2–12), which was similar among those who previously received smallpox vaccination (5, IQR=2–12) and those who recently received a single-dose monkeypox one (5, IQR=5–5). Presence of fever was reported among 14/23 (61%) individuals and lymphadenopathy among 12/23 (52%). Cutaneous involvement was recorded among 16/23 (70%), pharyngitis among 7/23 (30%) and proctitis among 12/23 (52%). Full details on monkeypox clinical presentations and course of disease are described in Table 2 . Among individuals who recently received a single-dose monkeypox vaccination, the median time between vaccination administration and onset of clinical symptoms was 10 days (IQR=8–12).Table 2 Clinical characteristics of monkeypox infection among individuals who previously received smallpox (Cases 1–20) or recent single-dose monkeypox (Cases 21–23) vaccination.
Table 2Cases Lesions (n) Fever Lymphadenopathy Pharyngitis Cutaneous Proctitis Clinical duration (days)
Case 1 3 – – – + + 24
Case 2 1 – + + – – 20
Case 3 5 – + + – – 14
Case 4 6 + + – + – 17
Case 5 11 + + – + – 23
Case 6 32 + + – + + 23
Case 7 1 – – – + – 5
Case 8 3 + + + + + 21
Case 9 12 + + – – + 16
Case 10 22 + + + + – 15
Case 11 1 + – – – – 19
Case 12 5 + – – – + 12
Case 13 4 + – – + + 14
Case 14 3 – + – + – 16
Case 15 5 + – – + – 12
Case 16 20 – – + + – 32
Case 17 1 + – – + + 11
Case 18 20 + + – + + 12
Case 19 1 + – + + + 23
Case 20 5 – – – + + 8
Case 21 5 – + – – + 6
Case 22 5 + + + – + 4
Case 23 5 – – – + – 8
This case-series corroborates the idea that breakthrough monkeypox infection can occur among individuals who previously received in their youth smallpox vaccination. Although it has been suggested that smallpox vaccination could be effective in preventing monkeypox infection, it is possible that the neutralizing antibodies which could grant this cross-protection likely diminish following several years from vaccination. For instance, the median age of the individuals who received smallpox vaccination in their youth was over 50 years. Notably, more than ¾ of individuals was living with HIV; we suggest that HIV-related immune-senescence and immunosuppression likely contributed in easing these breakthrough infections. Moreover, other individuals were taking immunosuppressive agents for co-morbidities, which also could have also played a role. Furthermore, we witnessed three monkeypox infections among individuals who were recently immunized with a single-dose monkeypox vaccination, with a scheduled second dose. These breakthrough infections are possibly caused by the short time between vaccination administration and infection, given the low titer of neutralizing antibodies expected following a single-dose administration.9 All in all, this case-series reinforces the idea that individuals previously vaccinated against smallpox require a booster dose and the need for administration of a full two-doses monkeypox vaccination for unimmunized individuals, together with providing adequate counselling, in a scenario of limited available data, on the possible risk of breakthrough infections.10
Declaration of Interest
None.
Appendix Supplementary materials
Image, application 1
Contributorship statement
A.R.R., S.N. and C.C., visited the individual and contributed to writing the article. D.C., C.B. and E.B. visited the individuals and contributed to the reviewing of the article. A.C. coordinated clinical activities and contributed to the reviewing of the article. D.M. coordinated virologic activities and performed PCR tests for MPX. All authors have read and agreed to the published version of the manuscript.
Funding
None.
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jinf.2022.12.001.
==== Refs
References
1 Moschese D. Pozza G. Giacomelli A. Mileto D. Cossu M.V. Beltrami M. Natural history of human monkeypox in individuals attending a sexual health clinic in Milan, Italy J Infect 2022 10.1016/j.jinf.2022.08.019 S0163-4453(22)00503-5Epub ahead of print. PMID:36007659; PMCID: PMC9628937
2 Orviz E. Negredo A. Ayerdi O. Vázquez A. Muñoz-Gomez A. Monzón S. Monkeypox outbreak in Madrid (Spain): clinical and virological aspects J Infect 85 4 2022 412 417 10.1016/j.jinf.2022.07.005 Epub 2022 Jul 10. PMID:35830908; PMCID: PMC9534097 35830908
3 Thornhill J.P. Barkati S. Walmsley S. Rockstroh J. Antinori A. Harrison L.B. Monkeypox virus infection in humans across 16 countries - April-June 2022 N Engl J Med 387 8 2022 679 691 10.1056/NEJMoa2207323 Epub 2022 Jul 21. PMID:35866746 35866746
4 Girometti N. Byrne R. Bracchi M. Heskin J. McOwan A. Tittle V. Demographic and clinical characteristics of confirmed human monkeypox virus cases in individuals attending a sexual health centre in London, UK: an observational analysis Lancet Infect Dis 22 9 2022 1321 1328 10.1016/S1473-3099(22)00411-X Epub 2022 Jul 1. PMID:35785793; PMCID: PMC9534773 35785793
5 Moschese D. Farinacci D. Pozza G. Ciccullo A. Cossu M.V. Giacomelli A. Is smallpox vaccination protective against human monkeypox? J Med Virol 2022 10.1002/jmv.28077 Epub ahead of print. PMID:35993271
6 Edghill-Smith Y. Golding H. Manischewitz J. King L.R. Scott D. Bray M. Smallpox vaccine-induced antibodies are necessary and sufficient for protection against monkeypox virus Nat Med 11 7 2005 740 747 10.1038/nm1261 Epub 2005 Jun 12. PMID: 15951823 15951823
7 Bragazzi N.L. Kong J.D. Mahroum N. Tsigalou C. Khamisy-Farah R. Converti M. Epidemiological trends and clinical features of the ongoing monkeypox epidemic: a preliminary pooled data analysis and literature review J Med Virol 2022 10.1002/jmv.27931 Epub ahead of print. PMID: 35692117
8 Gruber M.F. Current status of monkeypox vaccines NPJ Vaccines 7 1 2022 94 10.1038/s41541-022-00527-4 PMID:35977979; PMCID: PMC9385639
9 Zaeck L.M. Lamers M.M. Verstrepen B.E. Bestebroer T.M. van Royen M.E. Götz H. Low levels of monkeypox virus-neutralizing antibodies after MVA-BN vaccination in healthy individuals Nat Med 2022 10.1038/s41591-022-02090-w Epub ahead of print. PMID:36257333
10 Siddiqui M.O. Syed M.A. Tariq R. Mansoor S. Multicounty outbreak of monkeypox virus-challenges and recommendations J Med Virol 2022 10.1002/jmv.27966 Epub ahead of print. PMID:35773978
| 36481365 | PMC9721380 | NO-CC CODE | 2022-12-14 23:52:35 | no | J Infect. 2022 Dec 5; doi: 10.1016/j.jinf.2022.12.001 | utf-8 | J Infect | 2,022 | 10.1016/j.jinf.2022.12.001 | oa_other |
==== Front
Soc Sci Med
Soc Sci Med
Social Science & Medicine (1982)
0277-9536
1873-5347
Pergamon
S0277-9536(22)00905-4
10.1016/j.socscimed.2022.115599
115599
Article
COVID-related social determinants of substance use disorder among diverse U.S. racial ethnic groups
Tao Xiangyu a
Liu Tingting bc
Fisher Celia B. ad
Giorgi Salvatore be
Curtis Brenda b∗
a Department of Psychology, Fordham University, Bronx, NY, United States
b National Institutes of Health, National Institute on Drug Abuse, Baltimore, MD, United States
c Positive Psychology Center, University of Pennsylvania, Philadelphia, PA, United States
d Center for Ethics Education, Fordham University, Bronx, NY, United States
e Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, United States
∗ Corresponding author.Biomedical Research Center, 251 Bayview Blvd, Suite 200, Baltimore, MD, 21224, United States.
5 12 2022
1 2023
5 12 2022
317 115599115599
15 2 2022
1 9 2022
2 12 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
Black, Asian, and Hispanic/Latino people are disproportionately impacted by the COVID-19 pandemic and were more likely to experience coronavirus-related racial discrimination. This study examined the association between pandemic-related stressors, including employment and housing disruptions, coronavirus-related victimization distress, and perceptions of pandemic-associated increase in societal racial biases, and substance use disorder (SUD) risk among Asian, Black, Hispanic/Latino, and non-Hispanic White adults in the U.S.
Methods
Data were collected as part of a larger national survey on substance use during the pandemic. Eligible participants for the current study were 1336 adults self-identified as Asian (8.53%), Black (10.55%), Hispanic/Latino (10.93%), and non-Hispanic White (69.99%). Measures included demographic and COVID-19-related employment, housing, and health items, the coronavirus victimization distress scale (CVD), the coronavirus racial bias scale (CRB), and measures of substance use risk.
Results
Across racial/ethnic groups, employment disruption distress and housing disruption due to the pandemic were associated with SUD risk. Binary logistic regression analyses controlling for demographic variables indicated CVD was associated with higher odds of tobacco use risk (AOR = 1.36, 95% CI [1.01, 1.81]) and polysubstance use risk (AOR = 1.87, 95% CI [1.14, 3.06]), yet CRB was unrelated to any SUDs. Logistic regressions for each racial/ethnic group found different patterns of relationships between stressors and risk for SUDs.
Conclusions
Results highlight the significance of examining how the current pandemic has exacerbated racial/ethnic systemic inequalities through COVID-19 related victimization. The data also suggest that across all racial/ethnic groups employment and housing disruptions and perceptions of pandemic instigated increases in societal racial bias are risk factors for SUD. The study calls for further empirical research on substance use prevention and intervention practice sensitive to specific needs of diverse populations during the current and future health crises.
Keywords
COVID-19
Racial bias
Substance use
Victimization distress
NIDA-ASSIST
==== Body
pmc1 Introduction
Prior to the COVID-19 pandemic, Asian, Black, and Hispanic/Latino adults reported similar or lower rates of alcohol and other drug use than their non-Hispanic White counterparts, with one exception: Black adults reported higher rates of cannabis use than other groups (SAMSHA, 2020; SAMSHA, 2020). The pandemic appears to have begun to reverse these trends with Black, Indigenous, and people of color (BIPOC) reporting greater increases in substance use during the pandemic than non-Hispanic White adults (Czeisler et al., 2020; Acuff et al., 2021). This is worrisome, since despite lower levels of intake, Asian, Black, and Hispanic/Latino adults have suffered from inadequate substance use disorder (SUD) treatment and negative SUD outcomes (Camplain et al., 2020; SAMSHA, 2020). From 2015 to 2019, substance use service utilization rates of those who need such treatment were significantly lower among Asians (5.9%) and Hispanic/Latino (12.6%), compared to Black (15.8%) and non-Hispanic White adults (14.9%; SAMSHA, 2020). Despite equivalent rates of use, Black and Hispanic/Latino individuals were more likely than non-Hispanic White individuals to be incarcerated for nonviolent substance-related offenses and imprisoned for drug charges (Camplain et al., 2020).
The association between substance use and stress is well-established (Koob and Kreek, 2007; Sinha, 2001). Substance use has been considered a coping strategy for stress in many major theories, including the stress-coping model (Shiffman and Sinha, 1982) and the self-medication model (Khantzian, 1997). These models are supported by recent research including national representative samples across U.S. and Canada indicating that individuals have used alcohol and other substances to cope with coronavirus infection-related worry and fear (Emery et al., 2021; McKnight-Eily et al., 2021; Nyman et al., 2021; Rodriguez et al., 2020; Taylor et al., 2021). However few studies have examined the association between other sources of stress during the pandemic. For example, during the pandemic across racial/ethnic groups, increases in psychological distress have been associated with pre-existing socioeconomic disadvantages, financial and housing disruptions, and in addition to disease stigma against groups associated with the infection (American Psychological Association, 2020; Emery et al., 2021, Di Gessa et al., 2021; ; Posel et al., 2021). Compared to non-Hispanic Whites, BIPOC individuals are at higher risk of distress during the pandemic due to their disproportional higher risk of COVID-19 infections and financial disruptions rooted in long-term health disparities and systemic economic inequalities (American Psychiatric Association, 2017; American Psychological Association, 2017; Webb Hooper et al., 2020). Asian Americans in particular as well as other BIPOC groups have experienced increases in racism, xenophobia, and victimization related to COVID-19 (Croucher et al., 2020; Wu et al., 2021).
Discrimination and social prejudices are other sources of stress during the pandemic. Research on prior infectious disease epidemics (e.g., Emery et al., 2021) suggests that COVID-19-related discrimination, including both individual victimization and exposure to societal bias, may increase substance use among populations assumed to carry infection. Asian Americans, Black, and Hispanic/Latino persons have experienced increases in racism, xenophobia, and victimization related to COVID-19 (Croucher et al., 2020; Pew Research Center, 2020; Wu et al., 2021). Studies have also found that COVID-19-specific individual victimization and related distress were associated with anxiety, depression and substance use risk among Black young adults more than non-Hispanic White peers (; Fisher et al., 2022, Tao et al., 2022).
1.1 The current study
The primary aim of this study was to examine the relationship between a range of substance use disorders and pre-existing health and socio-economic disparities, employment and housing disruptions, coronavirus victimization distress, and perceived societal racial bias among Asian, Black, Hispanic/Latino, and non-Hispanic White adults during the COVID-19 pandemic. Substances of interest included alcohol, tobacco, cannabis, and other substances (i.e., cocaine, hallucinogens, inhalants, meth). We tested the following hypotheses: (1) Asian, Black and Hispanic/Latino compared to non-Hispanic White participants would report higher levels of pre-existing socio-economic disadvantages, employment, and housing disruptions, and coronavirus victimization distress and racial bias than non-Hispanic Whites; and (2) pandemic-related stressors would be associated with increased substance use risk among each racial/ethnic group, with the exception that coronavirus racial bias would not be a stressor associated with substance use among non-Hispanic White adults.
2 Methods
2.1 Participants
Participants were part of a larger longitudinal survey study on mental health and substance use during COVID-19. They were recruited through Qualtrics Panel with monetary compensation for participation in baseline demographic information collection ($30), in a 30-day ecological momentary assessment (EMA) session after baseline ($1 for each day, $30 in total), and in a 2nd wave data collection at 60 days after baseline ($40; see timeline in Fig. 1 ). Eligible participants for the larger study are individuals who are over 18-years-old, U.S. residents, and active Facebook users. Participants who finished baseline demographic information and completed at least 21 days of EMA data collection were invited to the 2nd wave of data collection at 60 days. In total, 2270 participants were invited for the 2nd wave data collection from December 5, 2020 to June 3, 2021, and 2111 participated in the survey (response rate = 92.9%). Of those, 1727 completed the survey (81.8%). We removed 33 records that had duplicate IDs or no research IDs. We then removed 156 participants who failed to pass all four attention check questions. Among the remaining 1538 participants, individuals who self-identified as Hispanic/Latino regardless of the racial category were categorized as Hispanic/Latino, and those who identified as non-Hispanic Asian, Black, or White were separated as Asian, Black, and White groups. Individuals who had missing values in race/ethnicity (N = 2), or endorsed other or more than one racial category (N = 67) were not included in the current study. For this study we wanted to examine whether COVID-19 societal stereotypes involving racial/ethnic groups were associated with acts of COVID-19 victimization and subsequent distress. Consequently, we excluded 135 participants who actually had been infected since their experiences might include, but were not solely based on societal stereotypes. The final sample for this study consisted of 1336 adults (M age = 39.34, SD age = 12.99, range = 18–78) self-identified as Asian (8.53%), Black (10.55%), Hispanic/Latino (10.93%), and non-Hispanic White (69.99%) (See in Table 1 ). The missing values in EMA responses were treated as missings at random and were computed as 0 for analysis. A smaller sample of Asian, Black, and Hispanic/Latino participants were also included in a prior study examining the impact of employment and housing, coronavirus-specific forms of victimization and racial bias on depression and anxiety (for details see Fisher et al., 2021). The current study is distinct in its examination of daily EMA questions on different forms of substance use disorders (e.g., alcohol, tobacco, and cannabis) and its inclusion of non-Hispanic White participants.Fig. 1 Timeline of questionnaires.
Fig. 1
Table 1 Demographics, employment and housing disruptions during the COVID-19 pandemic, substance use disorders, and chi-square tests results by race/ethnicity.
Table 1 Non-Hispanic Asian Non-Hispanic Black Hispanic/Latino Non-Hispanic White Total χ2 (df) p
N = 114 (8.53%) N = 141 (10.55%) N = 146 (10.93%) N = 935 (69.99%) N = 1336
Frequency (%) Frequency (%) Frequency (%) Frequency (%) Frequency (%)
Age 37.06 (3) < .001
18–25 37 (32.46) 16 (11.35) 31 (21.23) 118 (12.62) 202 (15.12)
>25 77 (67.54) 125 (88.65) 115 (78.77) 817 (87.38) 1134 (84.88)
Gender 2.51 (6) .87
Male 31 (27.19) 37 (26.24) 49 (33.56) 273 (29.20) 390 (29.19)
Female 81 (71.05) 102 (72.34) 95 (65.07) 644 (68.88) 922 (69.01)
Gender minority 2 (1.75) 2 (1.42) 2 (1.37) 18 (1.93) 24 (1.79)
Household income 19.91 (6) .004
<$20,000 16 (14.04) 21 (14.89) 15 (10.27) 102 (10.91) 154 (11.53)
$20,000-$50,000 21 (18.42) 52 (36.88) 37 (25.34) 223 (23.85) 333 (24.93)
>50,000 77 (67.54) 68 (48.23) 94 (64.38) 610 (65.24) 849 (63.55)
Education 37.23 (9) < .001
High school or technical/vocational school or less 4 (3.51) 20 (14.18) 12 (8.22) 91 (9.73) 127 (9.51)
Some college 12 (10.53) 45 (31.91) 45 (30.82) 223 (23.85) 325 (24.33)
Bachelor's degree 65 (57.02) 47 (33.33) 47 (32.19) 374 (40.00) 533 (39.90)
Graduate degree 33 (28.95) 29 (20.57) 42 (28.77) 247 (26.42) 351 (26.27)
COVID-19 health risk
Obesity 9 (7.89) 39 (27.66) 34 (23.29) 209 (22.35) 291 (21.78) 16.13 (3) .001
High blood pressure 9 (7.89) 35 (24.82) 18 (12.33) 157 (16.79) 219 (16.39) 15.19 (3) .002
Lung disease 3 (2.63) 12 (8.51) 8 (5.48) 106 (11.34) 129 (9.66) 12.61 (3) .006
Diabetes 6 (5.26) 7 (4.96) 5 (3.42) 53 (5.67) 71 (5.31) 1.30 (3) .73
Heart or artery diseases 4 (3.51) 2 (1.42) 1 (0.68) 22 (2.35) 29 (2.17) 3.00 (3) .39
Cancer 2 (1.75) 2 (1.42) 1 (0.68) 37 (3.96) 42 (3.14) 7.03 (3) .071
HIV or AIDS 0 (0.00) 2 (1.42) 1 (0.68) 5 (.53) 8 (.60) 2.36 (3) .5
At least one of the above medical problems 21 (18.42) 66 (46.81) 50 (34.25) 376 (40.21) 513 (38.40) 25.82 (3) < .001
Employment changes due to pandemic 85 (74.56) 96 (68.09) 106 (72.60) 598 (63.96) 885 (66.24) 8.57 (3) .036
Housing changes due to pandemic 29 (25.44) 46 (32.62) 54 (36.99) 222 (23.74) 351 (26.27) 14.71 (3) < .001
Alcohol Use Disorder 4.23 (6) .64
Moderate risk 66 (57.89) 84 (59.57) 87 (59.59) 581 (62.14) 818 (61.23)
High risk 4 (3.51) 10 (7.09) 9 (6.16) 63 (6.74) 86 (6.44)
Tobacco Use Disorder 14.29 (6) .027
Moderate risk 14 (12.28) 25 (17.73) 16 (10.96) 134 (14.33) 189 (14.15)
High risk 1 (0.88) 8 (5.67) 7 (4.79) 78 (8.34) 94 (7.04)
Cannabis Use Disorder 5.89 (6) .43
Moderate risk 22 (19.30) 39 (27.66) 28 (19.18) 207 (22.14) 296 (22.16)
High risk 2 (1.75) 5 (3.55) 4 (2.74) 18 (1.93) 29 (2.17)
Other Substance Use Disorders
Cocaine 6.43 (6) .37
Moderate risk 2 (1.75) 5 (3.55) 3 (2.05) 40 (4.28) 50 (3.74)
High risk 0 (0.00) 0 (0.00) 1 (0.68) 1 (0.11) 2 (0.15)
Prescription stimulants 1.55 (6) .95
Moderate risk 4 (3.51) 6 (4.26) 7 (4.79) 40 (4.28) 57 (4.27)
High risk 0 (0.00) 0 (0.00) 0 (0.00) 3 (0.32) 3 (0.22)
Methamphetamine 8.83 (6) .18
Moderate risk 3 (2.63) 3 (2.13) 3 (2.05) 26 (2.78) 35 (2.62)
High risk 0 (0.00) 1 (0.71) 0 (0.00) 0 (0.00) 1 (0.07)
Inhalants 1.11 (3) .78
Moderate risk 2 (1.75) 3 (2.13) 1 (0.68) 17 (1.82) 23 (1.72)
High risk 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00)
Sedatives or sleeping pills 14.82 (6) .023
Moderate risk 8 (7.02) 7 (4.96) 16 (10.96) 105 (11.23) 136 (10.18)
High risk 0 (0.00) 2 (1.42) 1 (0.68) 1 (0.11) 4 (0.30)
Hallucinogens .61 (3) .90
Moderate risk 1 (.88) 2 (1.42) 14 (1.50) 3 (2.05) 20 (1.50)
High risk 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00)
Street opioids 1.36 (3) .72
Moderate risk 1 (.88) 4 (2.84) 21 (2.25) 4 (2.74) 30 (2.25)
High risk 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00)
Prescription opioids 5.11 (3) .16
Moderate risk 1 (.88) 3 (2.13) 35 (3.74) 2 (1.37) 41 (3.07)
High risk 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00) 0 (0.00)
Other 3.47 (6) 75
Moderate risk 6 (5.26) 9 (6.38) 7 (4.79) 73 (7.81) 95 (7.11)
High risk 0 (0.00) 0 (0.00) 0 (0.00) 2 (0.21) 2 (0.15)
At least one type of Other Substance Use Disorders 11.75 (6) .068
Moderate risk 16 (14.04) 21 (14.89) 31 (21.23) 200 (21.39) 268 (20.06)
High risk 0 (0.00) 3 (2.13) 2 (1.37) 5 (0.53) 10 (0.75)
More than one type of SUDs (Polysubstance use disorder) 7.41 (6) .28
Moderate risk 33 (28.95) 51 (36.17) 47 (32.19) 339 (36.26) 470 (35.18)
High risk 1 (0.88) 7 (4.96) 4 (2.74) 29 (3.10) 41 (3.07)
2.2 Measures
2.2.1 Demographic variables and pre-existing health and socioeconomic disadvantages
Demographic information included self-reported race/ethnicity and gender. Participants' self-reported household income and education level were assessed to identify socioeconomic backgrounds. Self-reported COVID-19 health risks included seven medical conditions identified by the CDC (2021a) as associated with risks of severe illness from COVID-19, including obesity, high blood pressure, lung disease, diabetes, heart or artery diseases, cancer, and HIV.
2.3 Pandemic-related stressors
2.3.1 COVID-19 related employment and housing disruptions
Changes in employment due to the pandemic were assessed via a single check-all-that-apply question (“Which of the following employment changes have you experienced due to the COVID-19 (coronavirus) pandemic? Please check all that apply”.) with 11 options reflecting possible changes (e.g., “None,” “Furloughed,” “Switched to remote work”). Answers to this question were further dichotomized into 0 if they only selected “None” and 1 if they selected one or more other options reflecting changes. And if they selected one of the changes in the above employment change question, they would be directed to the question assessing the distress associated with employment changes. This distress was assessed using a single 5-point Likert-type scale question (“How troubled or bothered have you been as a result of these employment changes?”) with response options ranging from 1 = “not at all troubled” to 5 = “extremely troubled.” The housing disruptions due to the pandemic were measured by one check-all-that-apply question (“Did you experience any of the following as a result of the COVID-19 pandemic? Please check all that apply”), which provided 11 options reflecting different changes in housing including no changes (e.g., “None,” “Didn't pay the full amount of rent or mortgage,” “Had to move,” etc). Answers to this housing change question were also dichotomized into 0 if they only selected “None” and 1 if they selected one or more other options reflecting changes in housing.
2.3.2 Coronavirus victimization distress (CVD)
The Coronavirus Victimization Distress Scale (CVDS; Fisher and Yip, 2020a) assessed five coronavirus victimization experiences and associated distress. Items include being teased or bullied, physically threatened, mistreated, verbally taunted or called bad names, or cyberbullied because someone thought the respondent had the coronavirus. Responses were scored on a 5-point Likert-type scale (1 = “It never happened” to 5 = “It happened and upset me quite a bit”). Prior research involving Asian, Black, Indigenous, and Hispanic/Latino young adults (Fisher et al., 2022) reported high inter-item reliability (Cronbach's α = 0.91), and the scale had good reliability for the current study (α = 0.89).
2.3.3 Coronavirus racial bias (CRB)
The 9-item Coronavirus Racial Bias Scale (CRBS; Fisher and Yip, 2020b) assessed participants' beliefs about how the coronavirus is negatively affecting societal attitudes toward one's race/ethnicity (e.g., “I believe the country has become more dangerous for people in my racial/ethnic group because of fear of the coronavirus”). Response options ranged from 1 (Strongly disagree) to 4 (Strongly agree). A prior study (Fisher et al., 2022) with factor analyses identified CVDS and CRBS scale items loaded on distinct dimensions with one exception: there was a significant correlation error between item 7 in the CRBS (i.e., “Due to the coronavirus I have been cyberbullied because of my race/ethnicity”) and item 5 in the CVDS (i.e., “I have been cyberbullied because someone thought I was infected with the coronavirus”); the revised 8-item scale had good reliability among BIPOC young adults (α = 0.87). Accordingly, item 7 was removed from the CRBS for the current study. The scale had excellent reliability for the current study (α = 0.91).
2.4 Risk for substance use disorders (SUDs)
We calculated the substance use risk based on the NIDA-modified ASSIST (National Institute on Drug Abuse modified alcohol, smoking, and substance involvement screening test; NIDA, 2012) but for the past 30 days. The NIDA-modified ASSIST includes eight questions about the patient's use of, desire for, and problems related to the use of a wide range of substances. We replaced the original self-report frequency question in the NIDA-modified ASSIST (Q2) with the manually calculated frequency score from a 30-day EMA assessment. These EMA questions were sent daily at 6 p.m. local time for the 30 days after the baseline (see in Fig. 1), including a binary question on drinking (“Did you drink yesterday?” Yes = 1, No = 0), and a check-list question on other substance use (“Did you use cigarettes or any drugs yesterday? 1 = cigarettes/nicotine/tobacco/vape juice; 2 = cannabis; 3 = cocaine; 4 = prescription stimulants; 5 = methamphetamine; 6 = inhalants; 7 = sedatives or sleeping pills; 8 = hallucinogens; 8 = street opioids; 9 = prescription opioids; 10 = other; 11 = none"). We then added up the 30-day frequency score for each type of substance and divided this total score by 4 to calculate the relative frequency score (0 times per week = 0 - never; less than one time per week = 2 - once or twice per 3 months; one to five times per week = 4 - weekly; more than 5 times per week = 6 - daily or almost daily) to match the NIDA-modified ASSIST scoring format. We kept the original Q3-Q7 from NIDA-modified ASSIST but changed the time frame to the past 30 days (e.g., Q3: “In the past 30 days, how often have you had a strong desire or urge to use?"), and for Q3-Q5, we removed the original response option “Monthly” as it repeats the “Once or Twice” in a 30-day time frame. For each substance type, we added up the substance involvement (SI) score we calculated from EMA questions and received from NIDA-modified ASSIST Q3-Q7. We then use this SI score to identify respondents' risk level (0–3: lower risk, 4–26: moderate risk, 27+: high risk).
2.5 Data analysis plan
All analyses were conducted with SPSS (IBM Corp, 2020). We first calculated the descriptive statistics for demographic variables and employment and housing disruptions, coronavirus victimization distress, racial bias, and risk for SUDs for all and each racial/ethnic group; and then examined racial/ethnic differences on all these variables through Chi-square analyses and analysis of variance (ANOVA) tests with Bonferroni post hoc comparison. Spearman's rank correlations were conducted to examine the bivariate associations between employment and housing disruption, coronavirus victimization, and racial bias and risk for SUDs across all racial/ethnic groups. A binary logistic regression was then used to predict risk for SUDs based on significant results of tests. To further examine the risk for SUDs in each racial/ethnic group, four separate binary logistic regressions were conducted for Asian, Black, Hispanic/Latino, and non-Hispanic White adults. For logistic regression power analysis, we adopted the events per variable (EPV, i.e., the number of events divided by the number of degrees of freedom required to represent all the variables in the model) > 20 standard as suggested by Austin and Steyerberg (2017). Since our research questions included potential 7–14 predictors in the final model, the minimum sample size requirement for participants who were at risk for a specific type of SUD ranged from 140 to 280. Asian, Black, and Latinx groups had an overall sample size less of than N = 140, thus the results of separate binary logistic regressions for each racial/ethnic group should be interpreted with caution.
3 Results
3.1 Demographics and pre-existing socioeconomic and health disadvantages
Demographic data and Chi-Square analyses by racial/ethnic group and the total sample are provided in Table 1. Most respondents were female. More than half reported household income greater than $50,000 per year, and 11.53% reported below $20,000. The sample was highly educated, with over 90% of respondents reporting at least some college and 66.17% having completed college. Thirty-eight percent of respondents had at least 1 COVID-19 health risk listed by the CDC (2021a). ANOVA test yielded significant age differences across race/ethnicity, F (3, 1332) = 21.56, p < .001. A Bonferroni post-hoc comparison indicated that Asians were significantly younger than Black (p = .002) and non-Hispanic White (p < .001) groups and Hispanic/Latino were significantly younger than non-Hispanic Whites (p < .001). Chi-square tests indicated that Black respondents reported lower income and education levels than other racial/ethnic groups. Asians were significantly less likely to report at least one COVID-19 health risk.
3.2 COVID-19 related employment and housing disruption
As shown in Table 1, 66.24% of participants reported employment changes due to the pandemic, and 51.95% reported being at least slightly troubled by employment disruption (score ≥ 2; M = 1.77, SD = 1.64). Twenty-six percent of participants reported housing disruptions due to the pandemic. Results of Chi-square tests show that Asian and non-Hispanic White adults were less likely to report pandemic-related employment or housing disruptions. However, there were no racial/ethnic differences in employment disruption distress.
3.3 Coronavirus victimization distress (CVD) and coronavirus racial bias (CRB)
Frequencies, percentages, and χ2 statistics across racial/ethnic groups are provided in Table 2 . Black and non-Hispanic White participants were least likely to experience coronavirus victimization (9.93% and 9.51%, respectively) compared with Hispanic/Latino (14.38%) and Asian respondents. Asian respondents (19.30%) were significantly more likely to experience distress in response to these experiences than other groups (2–8%, p < .001). Most BIPOC respondents reported at least one coronavirus racial bias belief, compared to approximately one-third of non-Hispanic Whites.Table 2 Frequencies, percentages, means (SDs), and bivariate test results for coronavirus victimization distress (CVD) and coronavirus racial bias beliefs (CRB) by race/ethnicity.
Table 2 Asian Black Hispanic/Latino Non-Hispanic White Total
N = 114 (8.53%) N = 141 (10.55%) N = 146 (10.93%) N = 935 (69.99%) N = 1336
Frequency (%) Frequency (%) Frequency (%) Frequency (%) Frequency (%) Chi-square (df) p
At least one coronavirus victimization experience 31 (27.19) 14 (9.93) 21 (14.38) 61 (6.52) 127 (9.51) 55.19 (3) < .001
Reported CVD (at least one item score >2) 22 (19.30) 11 (7.80) 8 (5.48) 24 (2.57) 65 (4.87) 64.72 (3) < .001
Endorsed at least one CRB 100 (87.72) 130 (92.20) 123 (84.25) 295 (31.55) 648 (48.50) 360.22 (3) < .001
Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) F (df1, df2) p
CVD 1.32 (.78) 1.11 (.48) 1.14 (.54) 1.04 (.25) 1.08 (.40) 18.34 (3, 1332) < .001
CRB 2.48 (.70) 2.28 (.63) 2.14 (.62) 1.35 (.47) 1.63 (.69) 300.42 (3, 1332) < .001
3.4 Risk for substance use disorders
Table 1 presents levels of substance use disorder risks across racial/ethnic groups. More than half of respondents were at moderate risk for alcohol use disorder and 6% were at high risk, about a quarter were at moderate risk and 7% were at high risk for tobacco use disorder, 22% at moderate risk and 2% at high risk for cannabis use disorder. Around 10% reported moderate to high risk of sedatives or sleeping pill use disorder, and 2–7% of participants were at moderate or high risk for all other drug types. Due to the small sample sizes in these substance use disorders, they were combined as “at least one type of other substance use disorder (hereafter other substance use disorder)” in further analyses. Approximately 20% were at moderate risk for at least one other substance use disorders, and less than 1% were at high risk. For polysubstance use, around 35% were at moderate risk for at least two substances, and 3% were at high risk. Since less than 140 participants were at high risk for any SUDs, moderate to high risk were combined in further analyses. Black and non-Hispanic White adults reported significantly higher risk for tobacco and non-Cannabis substances compared to Asian and Hispanic/Latino respondents.
3.5 Associations among stressors and SUD risk across race/ethnicity
Spearman's rank correlations among studied variables and demographics are illustrated in Table 3 . CVD and CRB were positively associated with each other (r = 0.25, p < .001). Risk for tobacco, cannabis, and other SUDs (rs = 0.078, p < .001) was positively associated with CVD. None of the SUD risks were related to CRB after the Bonferroni adjustment of p values. Age was negatively associated with alcohol, cannabis, and other SUD risk. Household income and education level were positively correlated with alcohol risk and negatively correlated with risk of other SUDs and polysubstance use disorder and individuals with at least one COVID-19 health risk were significantly at greater risk for disorders related to tobacco and other substances. Employment and housing disruptions were significantly correlated with risk for cannabis and other substances, and polysubstance use disorders, while employment disruption was associated with alcohol disorder risk and disruption in housing with tobacco risk.Table 3 Spearman's correlations between CVD, CRB, SUDs, demographics, and COVID-19 related employment and housing disruptions across race/ethnicity.
Table 3 1 2 3 4 5 6 7 8 9 10 11 12 13
1. CVD 1
2. CRB .25*** 1
3. Alcohol use disorder .012 .005 1
4. Tobacco use disorder .078** .034 .24*** 1
5. Cannabis use disorder .078** .048 .25*** .64*** 1.
6. Other substance use disorder .078** .044 .09 *** .26 *** .23 *** 1
7. Polysubstance use disorder .05 .045 .10*** .29*** .19 *** .16*** 1
8. Age −.088** −.101** −.075** .051 −.13*** −.079* 0.016 1
9. Household income −.048 −.076** .10** −.16*** −.11** −.075 * −.055* .13*** 1
10. Education level −.028 −.085** .15*** −.19*** −.074* −.067 * −.067* .033 .33*** 1
11. At least one COVID-19 health risk −.015 −.010 −.028 .11** .023 .13 ** .038 .31*** −.068* −.12*** 1
12. Employment disruption distress .11*** .11*** .21*** .066 .11*** .10*** .12** −.14*** −.065 .077* −.058* 1
13. Housing disruption .10*** .11*** .052 .14*** .12*** .15 *** .12** −.08** −.22*** −.10*** .078** .32*** 1
Note. *p < .05, **p < .01, ***p < .001.
A binary logistic regression was then conducted to examine low-risk versus moderate/high-risk SUD based on COVID-19-related employment and housing disruptions, CVD, and CRB controlling for race/ethnicity, age, and pre-existing socioeconomic and health disadvantages (i.e., education and household income level and at least one COVID-19 health risk) across racial/ethnic groups (See Table 4 ). Results indicated that coronavirus victimization distress was only associated with higher odds of tobacco use risk (AOR = 1.36, 95% CI [1.01, 1.81]) and polysubstance use risk (AOR = 1.87, 95% CI [1.14, 3.06]). CRB was unrelated to risk of any SUDs. Employment disruption distress was associated with higher odds of alcohol (AOR = 1.26, 95% CI [1.16, 1.37]), cannabis (AOR = 1.10, 95% CI [1.02, 1.20]), other drug (AOR = 1.15, 95% CI [1.05, 1.25]) use risk, and polysubstance use risk (AOR = 1.34, 95% CI [1.10, 1.63]). Housing disruption due to the pandemic was associated with higher odds of tobacco (AOR = 1.50, 95% CI [1.11, 2.01]), other substance (AOR = 1.71, 95% CI [1.25, 2.34]), and polysubstance (AOR = 2.19, 95% CI [1.07, 4.48]) use risk.Table 4 Association between coronavirus victimization distress (CVD), coronavirus racial bias (CRB) and risk for SUDs.
Table 4 Alcohol use disorder risk Tobacco use disorder risk Cannabis use disorder risk Other substance use disorder risk Polysubstance use disorder risk
AOR 95% CI AOR 95% CI AOR 95% CI AOR 95% CI AOR 95% CI
CVD 0.97 (0.71,1.32) 1.37 (1.01,1.87) 1.18 (0.86,1.61) 1.15 (0.84,1.95) 1.87 (1.14,3.06)
CRB 1.02 (0.81,1.29) 1.11 (0.87,1.43) 1.14 (0.89,1.47) 1.28 (0.98,1.67) 1.25 (.69,2.29)
Race
Asian (Reference)
Black 1.8 (1.04,3.1) 1.89 (1,3.55) 1.8 (0.98,3.31) 0.97 (0.46,2.1) 5.34 (.59,48.72)
Hispanic/Latino 1.45 (0.85,2.49) 1.14 (0.59,2.19) 1.1 (0.58,2.05) 1.55 (0.78,3.12) 5.80 (.64,52.54)
Non-Hispanic White 1.89 (1.15,3.11) 1.92 (1.05,3.52) 1.68 (0.94,3.01) 1.99 (1.03,3.85) 2.67 (.27,26.32)
At least one COVID-19 health risk 1.01 (0.78,1.32) 1.45 (1.11,1.89) 1.28 (0.97,1.7) 1.69 (1.26,2.26) 1.35 (.68,2.70)
Age 0.99 (0.98,1) 1.01 (1,1.02) 0.98 (0.97,0.99) 1.01 (0.99,1.02) 1.00 (.98,1.03)
Household income
<$20,000 (Reference)
$20,000-$50,000 0.84 (0.55,1.28) 1.14 (0.75,1.73) 1.03 (0.67,1.59) 0.81 (0.51,1.28) 0.98 (.37,2.63)
>50,000 1.22 (0.81,1.82) 0.78 (0.52,1.18) 0.82 (0.54,1.25) 0.75 (0.45,1.17) 0.88 (.33,2.36)
Education level
High school or technical/vocational school or less (Reference)
Some college 1.11 (0.72,1.7) 0.95 (0.62,1.47) 0.94 (0.59,1.51) 0.86 (0.57,1.14) 1.46 (.46,4.63)
Bachelor's degree 1.68 (1.1,2.57) 0.61 (0.4,0.95) 0.86 (0.54,1.37) 0.67 (0.49,1.17) 0.84 (.25,2.81)
Graduate degree 1.87 (1.19,2.95) 0.42 (0.26,0.67) 0.7 (0.42,1.17) 0.75 (0.3,0.88) 0.61 (.16,2.39)
Employment disruption distress 1.26 (1.16,1.37) 1.06 (0.97,1.14) 1.1 (1.02,1.2) 1.15 (1.05,1.25) 1.33 (1.10,1.63)
Housing disruption 0.79 (0.59,1.07) 1.5 (1.11,2.01) 1.28 (0.95,1.74) 1.71 (1.25,2.34) 2.19 (1.07,4.48)
Note. Significant findings at p < .05 are in bold.
3.6 Associations for each racial/ethnic group
To further evaluate the relationship of race/ethnicity to the relationship between different SUDs and stressors, we conducted logistic regressions for each racial/ethnic group (See Table 5 ). Due to small sample sizes of individuals with moderate to high risk of polysubstance use disorder in each racial/ethnic group, only alcohol, tobacco, cannabis, and other substance use disorders were examined. For Black individuals, coronavirus racial bias belief was associated with higher odds of alcohol use risk (AOR = 1.94, 95% CI [1.01, 3.71]), employment disruption distress was associated with higher odds of cannabis use risk (AOR = 1.44, 95% CI [1.06, 1.95]) and other substance use risk (AOR = 1.43, 95% CI [1.01, 2.01]) For Hispanic/Latino, higher levels of coronavirus victimization distress were associated with higher odds of tobacco use risk (AOR = 2.59, 95% CI [1.07, 6.25]), and employment disruption distress was associated with higher odds of alcohol use risk (AOR = 1.43, 95% CI [1.10, 1.86]). For non-Hispanic White adults, higher levels of coronavirus victimization distress were associated with higher odds of other substance use risk (AOR = 1.84, 95% CI [1.05, 3.24]), coronavirus racial bias beliefs were associated with higher odds of tobacco use risk (AOR = 1.36, 95% CI [1.00, 1.86]). Moreover, employment disruption distress was associated with higher odds of alcohol (AOR = 1.24, 95% CI [1.13, 1.37]), while housing disruption was associated with higher odds of tobacco (AOR = 1.64, 95% CI [1.14, 2.34]), cannabis (AOR = 1.66, 95% CI [1.15, 2.39]), and other substance (AOR = 1.80, 95% CI [1.23, 2.64]) use risk. Despite the highest levels of coronavirus victimization distress and racial bias among Asians, CVD and CRB were unrelated to their SUD risk, and employment disruption distress was related to higher odds of at least one type of other substance use risk (AOR = 2.14, 95% CI [1.23, 3.75]).Table 5 Association between selected stressors and SUD risk adjusted for variables for each race/ethnicity.
Table 5 Alcohol use disorder risk Tobacco use disorder risk Cannabis use disorder risk Other drug use disorder risk
AOR 95% CI AOR 95% CI AOR 95% CI AOR 95% CI
Asian CVD 0.75 (0.4,1.4) 0.99 (0.41,2.36) 1.04 (0.68,3.55) 1.16 (.53,2.58)
CRB 0.74 (0.36,1.5) 0.6 (0.24,1.48) 1.48 (0.42,4.02) 1.47 (.49,4.36)
Employment disruption distress 1.35 (0.97,1.88) 1.15 (0.75,1.75) 1.14 (0.69,1.89) 2.14 (1.23,3.75)
Housing disruption 0.32 (0.11,0.93) 0.28 (0.05,1.53) 0.37 (0.09,3.11) 1.44 (.37,5.65)
Black CVD 2.92 (0.26,33.42) 1.76 (0.72,4.3) 1.64 (0.63,3.62) .47 (.10,2.23)
CRB 1.94 (1.01,3.71) 0.5 (0.24,1.03) 1.17 (0.3,1.39) 2.41 (.97,5.99)
Employment disruption distress 1.16 (0.88,1.53) 1.27 (0.96,1.68) 1.44 (0.9,1.63) 1.43 (1.01,2.01)
Housing disruption 0.94 (0.37,2.43) 2.41 (0.95,6.09) 1.28 (1.37,9.96) 1.53 (.50,4.69)
Hispanic/Latino CVD 1.06 (0.5,2.23) 2.59 (1.07,6.25) 1.8 (0.44,2.34) 1.06 (.48,2.34)
CRB 0.8 (0.41,1.54) 1.12 (0.52,2.41) 0.76 (0.64,3.29) 1.25 (.57,2.70)
Employment disruption distress 1.43 (1.1,1.86) 1.12 (0.85,1.48) 1.15 (0.68,1.23) 1.26 (.95,1.67)
Housing disruption 0.6 (0.25,1.44) 1.65 (0.66,4.13) 0.7 (0.53,3.86) 2.02 (.80,5.13)
Non-Hispanic White CVD 1.01 (0.55,1.86) 1.43 (0.82,2.48) 1.09 (0.93,2.88) 1.84 (1.05,3.24)
CRB 0.94 (0.68,1.29) 1.36 (1,1.86) 1.12 (1.05,2.04) 1.10 (.78,1.55)
Employment disruption distress 1.24 (1.13,1.37) 1.02 (0.93,1.12) 1.08 (1.03,1.26) 1.09 (.99,1.21)
Housing disruption 0.89 (0.62,1.29) 1.64 (1.14,2.34) 1.66 (0.91,1.97) 1.80 (1.23,2.64)
Note. Significant findings at p < .05 are in bold.
4 Discussion
In addition to increases in susceptibility to infection and hospitalization, the COVID-19 pandemic has led to employment and housing disruptions and exacerbated racial/ethnic discrimination in the U.S. This study examined the association of specific COVID-19-related employment, housing, and discrimination stressors with SUD risks among a large national sample of Asian, Black, Hispanic/Latino, and non-Hispanic White adults aged 18–78 years old. Different stressors emerged as risk factors for substance use disorders across racial/ethnic groups. Results add to the growing body of research on coronavirus-related stressors and associated health and mental) health problems among U.S. racial/ethnic groups (Fisher et al., 2022, Rodriguez et al., 2020)
Pre-existing health and socioeconomic disadvantages were found among BIPOC adults. Black adults reported the highest rates of COVID-19 health risks and lowest education and income levels. Although Asians had the highest education levels and the lowest rates of COVID-19 health risk, they were more likely to have an annual income of less than $20,000. Consistent with past research, health and socioeconomic disadvantages were both associated with higher SUD risk, except that higher education levels were associated with higher risk for alcohol use disorder (Luther et al., 2020; Wu et al., 2018; Rodriguez et al., 2020). Our results expanded the prior research by identifying significant associations between employment and housing disruptions related distress with higher SUD risk across different racial/ethnic groups. More than half of our sample reported employment and housing disruptions during the pandemic, and consistent with previous research, BIPOC adults were significantly more likely to report such disruptions (Di Gessa et al., 2021). Despite no significant racial/ethnic differences in employment disruption distress, we found differential associations between such distress and SUDs across racial/ethnic groups. Distress from employment disruption was associated with other substance use risk among Asian and Black samples, alcohol use risk among Hispanic/Latino samples, and alcohol and cannabis use risk among non-Hispanic White samples. Likewise, housing disruption was associated with cannabis use risk only among Black individuals, and tobacco and other substance use risk among non-Hispanic White individuals. Such differential associations may speak to diverse substance preferences as coping strategies for distress for different racial/ethnic groups that future research should focus on.
BIPOC adults overall reported significantly higher levels of CVD and CRB than non-Hispanic Whites, which parallels their disproportionately higher rates of coronavirus infection and mortality (CDC, 2021b; Chae et al., 2021; Di Gessa et al., 2021; Posel et al., 2021). Asians reported the highest levels of distress and racial bias beliefs than all other groups. This finding is consistent with reports on the spike in racial discrimination against Asians during the pandemic motivated by racism, xenophobia, and the presumptive origin of COVID-19 (Croucher et al., 2020; Wu et al., 2021). Racial/ethnic differences were found in the association between CVD and CRB with different types of substance use risk, highlighting the significance of examining culturally specific stressors experienced by BIPOC populations. Coronavirus racial bias belief, but not individually targeted victimization distress, was associated with higher odds of alcohol use risk only among Black adults, indicating that the perception of long-standing societal racism experienced by the Black population in the US has more detrimental effects than individual discrimination exposure against an infectious disease identity (Tao et al., 2022). In contrast, although coronavirus racial bias beliefs were not associated with substance use risk among Hispanic/Latino, coronavirus victimization distress was associated with their tobacco use risk, which is consistent with their higher rates of COVID-19 infections and being employed in essential industries during the pandemic (Blau et al., 2020; CDC, 2021b). Results also suggest that mental health providers should attend to not only direct experiences with racial bias during pandemics, but also a social climate that increasingly stigmatizes BIPOC populations (Pew Research Center, 2020). For example, they should be aware of the impact of historical trauma and long-term social oppression experienced by the Black population on their internalized beliefs about racial bias related to new infectious diseases, and provide culturally adaptive treatment that addresses their internalized racism beliefs.
Although CVD and CRB were not associated with substance use risk among Asians, the pandemic has significantly elevated their risk of substance use. Prior data suggested that Asians had the lowest risk for all types of substance use in 2019 (SAMSHA, 2020). The current study found Asian reports of SUDs to be similar to those of Hispanic/Latino participants. Moreover, similar at-risk rates of cannabis use were found between Asians and all other racial/ethnic groups after adjusting for CVD, CRB, and demographics. Such high substance use risk during the pandemic may be related to a sense of insecurity experienced by all Asians due to housing and employment disruptions and the increase in pandemic-related anti-Asian sentiment in the U.S.
One-third of non-Hispanic White adults endorsed at least one item reflecting a belief that bias (CRB) against their race/ethnicity had increased since the pandemic. Contrary to our hypothesis, higher levels of CRB were associated with this population's tobacco and other SUD risk. This highlights the importance of not limiting studies on the impact of perceived racial bias to groups identified as BIPOC in the U.S. For example, media attention to resistance to masking and other COVID-19 safety procedures in some majority non-Hispanic White regions (e.g., Steineck, 2021), may have been perceived as racial discrimination and placed these populations at SUD risk.
4.1 Strength, limitations, and future directions
Identifying the associations between pandemic-related stressors and risk for substance use disorders across different racial/ethnic groups supports the importance of research that can inform culturally tailored prevention and intervention approaches to reduce SUD during national emergencies. One major limitation of the current study is that participants were asked to complete Q3-7 of the NIDA-ASSIST 30 days after their last substance use-focused EMA, thus the substance use risk score may not reflect the actual substance use in a 30-day range. Future studies should use time-consistent measures of substance use risks. Meanwhile, the separate logistic regression results for each racial/ethnic group should be interpreted with caution due to the small sample sizes in each BIPOC group. Also, recruitment was addressed to Facebook users, limiting the generalization of results to other social media users or people who do not use social media platforms. Individuals who did not have access to the internet were unable to take the survey, yet they may be at higher risk of SUD due to disadvantaged socioeconomic backgrounds (NIDA, 2020). We also noted that our sample has a higher percentage of females and more educated individuals, which are sample characteristics that are frequently seen in online survey recruitment (Weinberg et al., 2014). This may further prevent us from generalizing our findings to broader populations. In addition, we only used single items to measure participants’ disruptions in employment and housing, limiting our abilities to conduct in-depth investigations of these two important topics. Future studies should include more comprehensive assessments to fully capture the variances in employment and housing disruptions. Finally, the current study found differential patterns of relationship between stressors and different types of SUD risk across racial/ethnic groups (e.g., substance use was not associated with either CVD or CRB in the Asian group, while CRB was associated with alcohol use risk among Black and non-Hispanic White groups). This finding highlights the significance of future research as well as clinical efforts on identifying and addressing culturally-specific risk factors for different substance uses across race/ethnicity.”
5 Conclusions
Substance use risk has significantly increased in the U.S. during the COVID-19 pandemic (Czeisler et al., 2020). The current study adds to a small but growing body of research on COVID-19-specific employment and housing disruptions and racial/ethnic discrimination on substance use risk across diverse racial/ethnic adults. Results indicate that although Asian, Black and Hispanic/Latino adults were more likely to experience pre-existing health and socioeconomic disadvantages, employment and housing disruptions, and coronavirus victimization distress and racial bias, coronavirus-related perceptions of increased racial bias also affected non-Hispanic Whites with consequences for SUD risk. The findings underscore the importance of examining how the current pandemic has exacerbated racial/ethnic systemic inequalities and contributed to racial bias anxieties among majority of adults and implications for culturally tailored interventions for the current and future national health emergencies.
Credit author statement
Xiangyu Tao: Conceptualization, Writing – original draft preparation, Formal analysis, Tingting Liu: Methodology, Visualization, Writing – original draft preparation, Celia B. Fisher: Conceptualization, Supervision, Writing- Reviewing and Editing, Salvatore Giorgi: Methodology, Data curation, Writing- Reviewing and Editing, Brenda Curtis: Conceptualization, Methodology, Supervision, Funding acquisition, Writing- Reviewing and Editing
This study was funded by the Intramural Research Program of the NIH, National Institute on Drug Abuse (NIDA; ZIA-DA000632). The authors report no financial relationships with commercial interests. The corresponding author, Dr. Brenda Curtis, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Data availability
The data that has been used is confidential.
Acknowledgement
This study was funded by the Intramural Research Program of the NIH, National Institute on Drug Abuse (NIDA; ZIA-DA000632). The authors report no financial relationships with commercial interests. The corresponding author, Dr. Brenda Curtis, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
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Emery R.L. Johnson S.T. Simone M. Loth K.A. Berge J.M. Neumark-Sztainer D. Understanding the impact of the COVID-19 pandemic on stress, mood, and substance use among young adults in the greater Minneapolis-St. Paul area: findings from project EAT Soc. Sci. Med. 276 2021 113826
Fisher C.B. Tao X. Yip T. The Effects of COVID-19 Victimization Distress and Racial Bias on Mental Health Among AIAN, Asian, Black, and Latinx Young Adults Cultur Divers Ethnic Minor Psychol 2022 10.1037/cdp0000539
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Fisher C.B. Tao X. Liu T. Giorgi S. Curtis B. COVID-related victimization, racial bias and employment and housing disruption increase mental health risk among U.S. Asian, black and Latinx adults Front. Public Health 9 2021 772236 10.3389/fpubh.2021.772236
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| 0 | PMC9721390 | NO-CC CODE | 2022-12-14 23:52:21 | no | Soc Sci Med. 2023 Jan 5; 317:115599 | utf-8 | Soc Sci Med | 2,022 | 10.1016/j.socscimed.2022.115599 | oa_other |
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Med J Armed Forces India
Med J Armed Forces India
Medical Journal, Armed Forces India
0377-1237
2213-4743
Director General, Armed Forces Medical Services. Published by Elsevier, a division of RELX India Pvt. Ltd.
S0377-1237(22)00193-9
10.1016/j.mjafi.2022.10.009
Case Report
Enigma of otologic immunoglobulin G4–related disease, COVID-19, and uncontrolled diabetes: The unholy trinity
Jain Ashish a
Patnaik Uma b∗
Singh Kamalpreet c
Jayadevan Jijesh a
a Resident, Department of ENT, Armed Forces Medical College, Pune, India
b Professor & Head, Department of ENT, Armed Forces Medical College, Pune, India
c Associate Professor, Department of ENT, Armed Forces Medical College, Pune, India
∗ Corresponding author.
5 12 2022
5 12 2022
10 6 2022
19 10 2022
© 2022 Director General, Armed Forces Medical Services. Published by Elsevier, a division of RELX India Pvt. 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.
Otologic immunoglobulin G4–related disease (IgG4-RD) is a rare and a relatively newer pathology. COVID-19, IgG4-RD, and type 2 diabetes, all have independent capabilities to considerably affect the immune system. The immunological effects of COVID are a global conundrum; consequently, the association of this trio (otologic IgG4-RD, COVID-19, and type 2 diabetes), the only reported case in literature, paves the way for a fascinating interplay to explore the knowledge of which would help optimize treatment and improve disease outcomes.
Keywords
Immunoglobulin G4–related disease
COVID-19
Type 2 diabetes mellitus
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pmcIntroduction
COVID-19, immunoglobulin G4–related disease (IgG4-RD), and type 2 diabetes mellitus are all known to affect the immune system, but the complex interplay between these entities together has not been reported yet.
IgG4-RD is a systemic fibro-inflammatory condition that can affect multiple organs such as the pancreas, lacrimal glands, and salivary glands and it mainly affects the elderly male population. The revised guidelines for the diagnosis of this disease were given in 2021.1 Although this disease entity has been extensively studied with respect to autoimmune pancreatitis, its knowledge and manifestations in the field of otology are still obscure, as the presentation is rare and atypical and requires a very high clinical suspicion.
COVID-19 has the disrepute to gravely disrupt the immune system. Furthermore, an immunological state predisposed to preferential IgG4 production has recently been established as a predictor of mortality in hospitalized COVID-19 patients.2 Type 2 diabetes has been linked to poor healing and aggravation of nearly all infections in the human body, including ear infections.3 , 4
This article illustrates an enigma we encountered with a 78-year-old diabetic patient who presented with concomitant acute otitis media with sudden onset facial palsy and COVID-19, with rapid progression of disease, poor response to conventional treatments, and later diagnosed as IgG4-RD on immunohistochemistry of the surgical specimen.
An extensive literature search of peer-reviewed articles using the MEDLINE, Embase, Cochrane Library, and the PubMed search engine revealed no prior case reports comprising the three disease entities together.
Case report
A 78-year-old male patient with poorly controlled type II diabetes and bilateral retinitis pigmentosa arrived with a 4-day history of otorrhea and otalgia in the right ear, as well as a 6-h history of sudden onset right-sided facial weakness. The external auditory canal was normal, but the tympanic membrane was inflamed and retracted, with an air-fluid level in the middle ear. There was House-Brackmann (HB) grade V lower motor neuron (LMN) type facial palsy. Urgent high-resolution computed tomography revealed disease limited to the middle ear cleft (Fig. 1 a) with an eroded/dehiscent fallopian canal. A diagnosis of complicated acute otitis media with facial palsy was contemplated, and patient was planned for admission and further management.Fig. 1 (a) Computed tomography of temporal bone at initial presentation showing disease limited to middle ear cleft. (b) Repeat computed tomography of temporal bone showing extensive disease after 1 month (post-COVID-19 recovery).
Fig. 1
Unfortunately, the patient tested positive for COVID-19 during admission procedure, and as per government regulatory guidelines, was referred to an ENT center at a dedicated COVID facility. After recovering from COVID-19 a month later, the patient reported back to us and was re-evaluated. Severe otalgia, copious otorrhea, and persistent facial palsy were his major complaints. The external auditory canal (EAC) was markedly edematous with profuse mucopurulent discharge when examined. Tympanic membrane (TM) was inflamed, and HB Gd V facial palsy persisted.
A repeat urgent computed tomography scan indicated that the illness had expanded well beyond the confines of the TM. There were homogenous hypodense opacities filling the middle ear and mastoid with extension to infratemporal fossa, parapharyngeal space, and prevertebral space. There was erosion of the posterior and anterior canal wall and defect in supratubal region. There was also erosion of fallopian canal, tegmen tympani, and thinning of lateral wall of carotid canal (Fig. 1b). Blood investigations revealed grossly deranged glycemic levels, leukocytosis with neutrophilia, and raised C-reactive protein. Swab for pus culture revealed Pseudomonas aeruginosa.
The patient was hospitalized and started on injectable antibiotics and insulin. At this junction, given the rapid development of the disease, differential diagnoses of complicated acute otitis media, skull base osteomyelitis, and malignancy were kept in mind. Owing to the patient's substantial illness dissemination, a modified infratemporal Fisch type B technique was proposed for disease clearance. However, intraoperatively, there were significant granulations, mucosal hypertrophy, and mucopurulent discharge, unlike in a malignancy; therefore, a conscious decision was taken to optimize the surgical resection, taking into account the comorbidities, age, and poor overall general health of the patient. The disease was completely cleared from middle ear cleft, but the infratemporal fossa was only partially cleared through the anterior canal wall defect. Tissue was sent for histopathological evaluation.
After the surgery, the patient received injectable antibiotics and insulin to achieve strict glycemic control. Otalgia reduced significantly, and the facial palsy improved to HB Gd II. Such unusual intraoperative findings prompted us to consider rare disorders, such as granulomatous disease and IgG4-RD.
Histomorphology revealed predominant lymphoplasmacytic infiltration, extensive fibrosis, and necrotic bone in an inflammatory infiltrate. Malignancy and granulomatous pathology were ruled out. Immunohistochemistry revealed >10% IgG4-positive cells per high-power field, with ratio of IgG4-positive plasma cells to IgG-positive cells of >40% confirming IgG4-RD. Furthermore, his serum IgG4 levels were 246 mg/dL, validating his diagnosis of IgG4-RD. A rheumatology opinion was sought, and the patient showed rapid clinical recovery after the treatment of IgG4 RD was initiated in concurrence with targeted antibiotics and strict glycemic control.
Discussion
The first case of otologic manifestations of IgG4-RD was reported by Masterson et al 5 in 2010. The diagnosis of IgG4-RD is as per 2020 Revised Comprehensive Diagnostic (RCD) criteria for IgG4-RD (Table 1 ).1 After an extensive literature review on otological IgG4-RD, we found 23 reported cases.5, 6, 7, 8, 9, 10, 11, 12, 13 According to these case studies, otalgia, worsening hearing loss, tinnitus, and vertigo constitutes otologic manifestations. Acute recurrent mastoiditis,7 serous otitis media,12 otorrhea,10 and facial weakness11 are the main clinical presentations. If the lesion is confined to the middle ear cleft, it typically only causes conductive hearing loss whereas spreads to inner ear or intracranially, leads to add-on symptoms of sensorineural hearing loss, vertigo, tinnitus, and persistent headache. Only five of the 23 cases involved the inner ear, with three affecting the lateral semicircular canal and two involving the cochlea.Table 1 The 2020 Revised Comprehensive Diagnostic (RCD) criteria for IgG4-RD.1
Table 1Sr no Diagnosis methodology Features
1 Clinical and radiological One or more organs show diffuse or localized swelling or a mass or nodule characteristic of IgG4-RD. In single organ involvement, lymph node swelling is omitted.
2 Serological diagnosis Serum IgG4 levels greater than 135 mg/dL.
3 Pathological diagnosis Positivity for two of the following three criteria:
(1) Dense lymphocyte and plasma cell infiltration with fibrosis.
(2) Ratio of IgG4-positive plasma cells/IgG-positive cells greater than 40% and the number of IgG4-positive plasma cells greater than 10 per high powered field
(3) Typical tissue fibrosis, particularly storiform fibrosis, or obliterative phlebitis
Diagnosis: Definite: 1 + 2 + 3
Probable: 1 + 3
Possible: 1 + 2
IgG4-RD, immunoglobulin G4–related disease.
Concomitant immunosuppressive drugs such as prednisolone and rituximab, as well as comorbidities such as type II diabetes, have been shown to enhance the incidence of COVID-19 disease–related adverse effects in patients with inflammatory rheumatic illnesses. In addition, elevation of serum IgG4 has been recently identified as a predictor of mortality in hospitalized COVID-19 patients, raising the possibility that an immunological background prone to preferential IgG4 production may favor life-threatening SARS-CoV-2 infection.2
Type II diabetes has been linked to poor healing and aggravation of nearly all infections in the human body, ear infections included. It has also been independently associated with a significant increased odds of in-hospital death with COVID-19.3 , 4 Although there is a strong correlation between IgG4-related autoimmune pancreatitis and diabetes mellitus, there is no research available, which evaluates a possible causal relationship between diabetes mellitus and otologic IgG4-RD. There are also no reports studying the association between the severity of diabetes and features of IgG4-RD.14
Our patient presented with sudden onset otitis media with facial palsy in the setting of poorly controlled diabetes and COVID-19 disease with its rapid progression far beyond the confines of temporal bone within a month. The uniqueness in this case is the likely contribution by each of IgG4-RD, COVID-19, and diabetes, which lead to the spread of disease out of proportion with respect to the time. Also, the likelihood of COVID-19 playing part in aggravation of IgG4-RD, which had been dormant for so many years. Hence, ostensibly, what appears to be a simple case of skull base osteomyelitis secondary to diabetes, on scrutiny, is an enigma of the interaction of these three different disease processes, each causing colossal immunological disturbance in its own unique way, and one needs to have a high degree of clinical suspicion to identify and diagnose such cases.
Conclusion
IgG4-RD is an excellent mimicker of numerous neoplastic, inflammatory, and infectious disorders because of its complex presentation. As there are no reliable biomarkers, histopathology remains the gold standard for diagnoses.15 It is curable and responds well to glucocorticoids, but if left untreated, it can progress to end-stage organ failure and even death. The most challenging aspect of this disease is firstly, to have a high clinical suspicion and include it in the differentials and secondly, to attain diagnosis. Optimal surgery is the key along with targeted treatments for associated conditions. The association of this trio, the only reported in literature, paves the way for a fascinating interplay to explore, the knowledge of which would help optimize treatment and improve disease outcomes.
Disclosure of competing interest
The authors have none to declare
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One Health
One Health
One Health
2352-7714
The Authors. Published by Elsevier B.V.
S2352-7714(22)00100-8
10.1016/j.onehlt.2022.100468
100468
Article
Current research and future directions for realizing the ideal One-Health approach: A summary of key-informant interviews in Japan and a literature review
Andoh Kiyohiko a⁎1
Hidano Arata b1
Sakamoto Yoshiko c1
Sawai Kotaro a
Arai Nobuo a
Suda Yuto a
Mine Junki a
Oka Takehiko d
a National Institute of Animal Health, National Agriculture and Food Research Organization, 3-1-5 Kannondai, Tsukuba, Ibaraki 305-0856, Japan
b Communicable Diseases Policy Research Group, Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, United Kingdom
c National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
d World Fusion Co., Ltd., 1-38-12 Nihonbashi Kakigara-cho, Yusho-kaikann 2F, Chuo-ku, Tokyo, 103-0014, Japan
⁎ Corresponding author at: National Institute of Animal Health, National Agriculture and Food Research Organization, 3-1-5 Kannondai, Tsukuba, Ibaraki 305-0856, Japan.
1 These authors contributed equally to this work.
5 12 2022
5 12 2022
10046827 7 2022
30 11 2022
1 12 2022
© 2022 The Authors. Published by Elsevier B.V.
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 COVID-19 pandemic has highlighted the importance of the One Health (OH) approach, which considers the health of humans, animals, and the environment in preventing future pandemics. A wide range of sustainable interdisciplinary collaborations are required to truly fulfill the purpose of the OH approach. It is well-recognized, however, that such collaborations are challenging. In this study, we undertook key-informant interviews with a panel of stakeholders from Japan to identify their perceived needs and challenges related to OH research. This panel included scientists, government officials, journalists, and industry stakeholders. By combining a thematic analysis of these interviews and a literature review, we summarized two key themes pertinent to the effective implementation of OH research: types of required research and systems to support that research. As a technological issue, interviewees suggested the importance of research and development of methodologies that can promote the integration and collaboration of research fields that are currently fragmented. An example of such a methodology would allow researchers to obtain high-resolution metadata (e.g. ecological and wildlife data) with high throughput and then maximize the use of the obtained metadata in research, such as in environmental DNA analysis, database construction, or the use of computational algorithms to find novel viral genomes. In terms of systems surrounding OH research, some interviewees stressed the importance of creating a sustainable research system, such as one that has continuous budget support and allows researchers to pursue their academic careers and interests. These perceptions and challenges held by Japanese stakeholders may be common to others around the world. We hope this review will encourage more researchers and others to work together to create a resilient society against future pandemics.
Keywords
Pandemic
Metadata
Wildlife
Planetary health
Cross-sector
Interdisciplinary research
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pmc1 Introduction
How can we prepare for and mitigate the impact of future pandemics? The COVID-19 pandemic has highlighted not only the enormous damage that emerging diseases can cause to individuals and society but also the urgency for society to identify areas of actionable science that will enhance our pandemic preparedness. One such arena is improved surveillance systems of humans, livestock, and wildlife [1]. Given that some recent pandemics have an origin in wildlife, scientists have called for a One Health (OH) approach to better understand the spillover risk from wildlife populations [1,2]. The importance of wildlife surveillance has been stressed since the early 2000s [3], but this call for action has not spurred sufficient interdisciplinary collaboration in the past two decades.
While surveillance is critical, there are also many challenges in implementing surveillance in human, livestock, and wildlife populations, especially in low- and middle-income countries. A fundamental lack of resources means that the surveillance needs to be narrow, prioritizing subsets of the population. This need has spurred major efforts to predict the spillover risk of unknown pathogens. For example, the United States Agency for International Development Emerging Pandemic Threats PREDICT program has discovered approximately 1000 novel viruses and has attempted to rank the spillover risk of these viruses using risk factors identified through literature reviews and from expert opinions [4]. Some prominent virologists, however, have been skeptical about the feasibility of such predictions [5,6], arguing that this approach suffers from multiple problems. The first problem is the lack of scalability. Newly discovered viruses require genetic sequencing and laboratory analyses to assess their pathogenesis and transmissibility in humans. Nevertheless, the number of undiscovered viruses in nature is estimated to be tremendous, suggesting that substantial resources and time would be required to complete this task. The evolution of viruses also undermines the usefulness of these data, which have been collected at a single point in time in the past. The second problem is the significant knowledge gap about ecological factors that facilitate spillover and transmission of emerging microorganisms [7]. Previous spillover events suggest that sustained transmission of emerging viruses in new hosts is the exception rather than rule, even though the viruses have genetic characteristics required to infect specific species. Spillover and sustained onward transmission probably require specific ecological and epidemiological conditions for different viruses [[7], [8], [9]], and these remain poorly understood.
Some scientists argue that technological innovation may overcome these challenges and enable zoonotic risk prediction [10]. Although we acknowledge that such predictions are one of the key desired scientific outputs, it is important to recognize that these aforementioned challenges themselves highlight that previous OH studies were often concerned solely with microbiological aspects of zoonoses. We contend this focus needs to shift towards a more integrated approach in which complex interactions between ecology, epidemiology, biology, and evolution are studied (e.g. evolutionary epidemiology [11]). Currently, we have little understanding of the mechanism behind spillover transmission from wildlife even for known diseases, such as Ebola and severe fever with thrombocytopenia syndrome (SFTS). Moreover, disease dynamics may change in response to alterations in ecology induced by factors such as climate change, biodiversity loss, and land-use changes [12]. Hence, effective control of these known diseases requires a deeper understanding about how the ecology of host wildlife species and environments shape disease dynamics and evolution. We believe this understanding will also facilitate better risk predictions of unknown zoonoses.
These knowledge gaps can be filled only by OH studies that are committed to gathering metadata associated with microorganisms. These studies require truly interdisciplinary collaborations that enable researchers to identify what kind of data are required to achieve the objectives mentioned above and to determine how the data should be collected. However, as highlighted in previous studies, many challenges exist in initiating, designing, and implementing truly interdisciplinary OH studies [13]. These challenges include a lack of sustainable funding, underrepresentation of some actors, unsustained efforts, difficulty in promoting sustained interdisciplinary collaborations, and institutional and systemic fragmentation. Without solving these challenges, studies may be driven by stakeholders from limited disciplines and risk having insufficient access to data sources and analyses.
While momentum for OH approach is growing in Japan, interdisciplinary collaboration is still limited. To identify specific issues for accelerating interdisciplinary collaboration, we undertook key-informant interviews with stakeholders from a wide range of disciplines and sectors in Japan. We asked each interviewee how we can build a society that will be resilient to disease outbreaks and what needs to be done to accomplish this task, as well as how they could contribute to it. We then conducted a thematic analysis of these interviews and a literature review and summarized obtained insight into two thematic areas: types of required research and development (R&D) and the systems needed to support them.
2 Methods
From September to December 2020, we conducted an initial literature review of the challenges and opportunities related to OH research, the findings from which guided our selection of disciplines and individuals for interview. Key-informant interviews with stakeholders in Japan were conducted in January to July 2021 (interviewees are listed in Supplemental Table 1). These stakeholders included not only scientists who have been actively involved in OH studies but also others who are highly relevant but not always involved in OH studies, such as data management specialists, artificial intelligence (AI) scientists, journalists, and representatives of industry and government, and we asked each interviewee about their perceived needs, challenges, and opportunities related to OH research and discussed key themes pertinent to the effective implementation of OH research (see Supplementary material). Each interview was transcribed and analyzed through an iterative process of open/selective coding with a constant comparison with findings from our literature review. These results were summarized into two overarching themes: types of required R&D and the systems needed to support this R&D. This study was judged to be low risk by National Institute of Animal Health and JST Moonshot R&D –MILLENNIA Program, Japan, and further ethics approval was not required. Verbal consents were obtained from all the participants to participate in this study. Upon the completion of drafting the manuscript, a written consent was obtained from the participants to use their quotes after they have read the manuscript. The participants were given an option to be anonymized or reveal their names.
3 Results
3.1 Required R&D for realizing the ideal OH approach
3.1.1 Importance of conducting interdisciplinary studies
Many current surveillance systems need to start collecting additional metadata, such as data about wildlife ecology or vectors and environmental factors, because these are crucial for understanding pandemic risk [12]. Wildlife ecology affects viral dynamics in nature, and the habitat of animals drastically changes depending on soil and vegetation conditions. The distribution of vectors such as arthropods or blood-sucking insects is also affected by the relationship between the environment and animals, and the contact frequency between wildlife and livestock animals depends on land utilization. Analysis of such metadata requires an interdisciplinary approach integrating disciplines such as virology, bacteriology, parasitology, molecular biology, immunology, population genetics, ecology, and epidemiology. Our key-informant interviews highlighted the need for technologies for collecting, connecting, and analyzing all related information, including the genomes of pathogens, wildlife ecology, local ecosystems, and seroprevalence of specific pathogens in wildlife or livestock animals, to accelerate interdisciplinary research.
3.1.2 Technologies for collecting information about pathogens in nature
A new methodology for efficiently collecting viral genomes from field samples is required because surveillance for unknown viruses harbored by wildlife is an essential and effective countermeasure against future pandemics [1,14]. Recent advances in high-throughput sequencing (HTS) technology have dramatically improved genome analysis in terms of both quality and quantity; however, our key-informant interviews indicated that detecting a novel virus from nature is still a challenge. Viral genomes are extremely small and diverse compared to the host genome, and enrichment and separation methods are required for different viral species before HTS technology can be applied. In fact, the PREDICT program employed a conventional PCR method and not HTS, despite the large-scale nature of the research [14]. Recently, a novel technique called Fragmented and primer Ligated dsRNA Sequencing (FLDS) was developed by Urayama et al. [15]. The FLDS method can extract virus-derived double-stranded RNA, which is produced during the replication process of an RNA virus, making it easy to enrich and discover novel viral genomes. Thus, advanced technologies that allow the efficient isolation of viral genomes would foster breakthrough improvements in collecting viral genome information with far greater efficiency than is currently possible.
In addition, the scientists we interviewed mentioned that a new methodology needs to be developed that can identify completely unknown viral genomes from enormous amounts of metagenome data. Currently, most viral sequences in the metagenome data are identified based on their similarities to known viral species. For example, analyzing sequence similarity using the Basic Local Alignment Search Tool (BLAST) is a common method to detect unknown viral genomes. However, this method may fail to detect completely unknown viruses. To overcome this problem, Hie et al. [16] reported a method using natural language processing, which can identify a hidden viral genome by considering the viral sequence as a language and interpreting its grammar and meaning. This research area is underexplored and one of the interviewees mentioned that there are also ample opportunities to apply existing methods such as hidden Markov models [17] to identify hidden viral genomes.
The scientists interviewed also emphasized that isolation of infectious viruses is a prerequisite for determining the characteristics of viruses; therefore, we need innovative technologies that can efficiently culture novel viruses. For example, isolated infectious viruses are required for evaluating their host range, tissue tropism, virulence, and the efficacy of antiviral drugs, but it remains difficult to isolate an infectious virus with high throughput because cell culture techniques are laborious and time-consuming. Therefore, isolating viruses in a timely manner is one of the primary bottlenecks in interdisciplinary research. Recently, human induced pluripotent stem cells and human-derived organoids have been used to cultivate viruses that are hard to maintain in vitro [18,19], which can address this bottleneck. Further developments in reverse genetics technology may allow us to bypass this issue by artificially creating infectious viruses from a viral genome sequence.
3.1.3 Evaluating viral dynamics in nature
Knowledge of viral dynamics in nature is fundamental for effective disease prevention and control. The presence of multiple host animal species in nature may hamper the acquisition of this knowledge. For example, in the COVID-19 pandemic, the true transmission route and missing link between reservoirs remain unsolved [20]. The scientists we interviewed suggested that such knowledge gaps can be filled through continuous surveillance by an interdisciplinary consortium; in Japan, SFTS is a prime example of a disease that warrants such surveillance.
SFTS is an emerging infectious disease that was first reported in China, and a total of 402 cases were identified in Japan between 4 March 2013 and 31 March 2019 [21]. SFTS is caused by the SFTS virus (SFTSV), which is maintained and circulated among ticks and other wildlife [[22], [23], [24]]. Several research teams in Japan, some members of which were included in our interviews, have conducted surveillance in humans, animals, and ticks to identify the dynamics of SFTSV and the risk of human infections. One study reported that the seroprevalence of SFTSV antibodies in deer exceeds 40% [25]. SFTS cases in Japan have mainly been reported in western regions where the seroprevalence of SFTSV in deer was greater than that in other areas, suggesting a positive correlation between the seroprevalence in deer and the number of human cases [25]. On the other hand, surveillance of SFTSV in ticks has shown that SFTSV is widely distributed across Japan, including in regions where no cases of SFTS have yet been reported [25,26]. The apparent disparity between the regions where human cases have occurred and the distribution of the virus suggests that other ecological factors may affect the contact frequency between humans, ticks, and deer or other wildlife. To predict the spillover risk to humans, these factors should be identified by detecting the SFTS virus in nature, conducting serosurveys in wildlife, and monitoring the ecology and environmental factors in each region. Accumulating such efforts is crucial to showcase the feasibility of predictions of zoonosis risk in the future. One of the scientists interviewed also pointed out that serosurveys of potentially susceptible livestock as sentinels [[27], [28], [29]] may indicate the extent to which a virus has spread around the human population.
Because any animal species may be a potential reservoir of unknown pathogens, the interviewees agreed that it is valuable to monitor the ecology of as many wildlife species as possible. However, they also pointed out that such monitoring is not feasible with the methods currently available, such as conducting long-term field observations or using fixed-point cameras, both of which are laborious and time-consuming. Hence, it is crucial to develop new monitoring technologies with high throughput and high resolution. For example, an environmental DNA (eDNA) and DNA barcoding technique could be useful for obtaining ecological information. Because eDNA comprises a range of substances from various origins, such as tissue fragments and feces dropped from organisms living in a habitat, HTS of eDNA would provide an enormous amount of information on the ecology of wildlife in a given area (e.g. distributions of habitats, numbers of individuals, and biomass of organisms) with a higher throughput, greater scale, and higher resolution than existing techniques [30]. The eDNA technique has been already applied to the collection of ecological information and to the study of infectious diseases [31,32]. These attempts might contribute to building an ideal risk management system, but some scientists have emphasized the limitations of eDNA technology. eDNA has mainly been used to monitor aquatic ecosystems and rarely been used in terrestrial ecosystems [33]. Furthermore, precise information such as age, sex, and blood relationship cannot be obtained. However, if eDNA technology becomes more applicable to terrestrial animals, and if improvements are made in terms of collecting more precise information, it should strongly promote interdisciplinary research across zoonoses and ecology.
To fully utilize the eDNA technique in zoonosis research, the experts interviewed mentioned that the genomic data of wildlife needs to be expanded. The current genome database does not contain all information required to identify all animal species. For example, while bats are considered to be natural hosts of several notorious viruses, only a limited number of bat species have had their genomes determined [34].
An improved genome database might also be useful when conducting biological experiments to characterize novel viruses. If the genomic information of all animals could be deposited in a database, all candidate receptors of novel viruses that exist in animals could be examined in vitro (e.g. by creating recombinant receptors and analyzing their affinity against viral ligands) or in silico (e.g. by predicting the structure of receptors and conducting docking simulations).
3.1.4 Future prospects of interdisciplinary research in infectious diseases, epidemiology, ecology, and computer science
While recent major developments in computer science such as AI should be highly beneficial for research on zoonoses [10], these technologies are not yet readily applicable. For example, current AI technology requires a large amount of high-quality data as a training input, but both the quantity and quality of data available in current zoonosis research do not meet this requirement. An informatician we interviewed said that, “Zoonosis research is attractive, but only limited data is available to utilize our expertise”. Additionally, several interviewees argued that ontology and annotation are inconsistent and incomplete across different research fields, which also hinders the use of AI technology. Therefore, we urgently need to develop a standardized format for zoonosis data as well to reformat existing data.
Another requirement highlighted during the interviews was that the collected metadata must also be adequately linked and curated. Such linkage systems need to be well developed before we accumulate a huge amount of diverse data, such as genome sequences, ecological numeric data, and descriptive documents. One scientist said that resource description frameworks can be a useful methodology that connects several different types of data in a public network. The integration of multilayered data on ecosystems, soil, vegetation, climate, and viral metagenomes may enable researchers to conduct analyses using AI techniques and identify hidden associations between virus dynamics and ecosystems that are critical for pandemic prevention.
Other AI technologies such as those developed for image recognition algorithms and large data-driven research projects are also highly relevant for the research topics described in the above sections. Several interviewees said that image recognition algorithms may also be applicable for obtaining ecological data of wildlife in combination with camera traps and remote-sensing devices. Additionally, current popular topics in AI research, such as explainable, unsupervised learning and context-driven AI, may also be applicable to risk-management systems. For example, explainable and context-driven AI may be able to predict and explain a risk factor of future pandemics based on integrated information from descriptive documents, such as epidemiological reports and ecological metadata. Alternatively, AI may be able to predict a mutation pattern of a viral genome and consequent changes of receptor affinity based on the viral and animal genome database. Thus, these data-driven applications might be useful in decision-making and policy implementation as well as for scientific research [10].
Taken together, the interviews with experts in computer science highlighted the R&D required for improving interdisciplinary collaboration among zoonosis researchers and computer scientists. Continuous R&D can help fill the knowledge gap through interdisciplinary research and make it possible to predict the risk of pandemics, although perfect predictions may still be impossible.
3.2 Creating a sustainable system to control zoonoses
3.2.1 Sustained and continuing zoonosis research
A common view among many interviewees was that securing long-term budgets is crucial for continuous zoonosis research. Although the importance of measures against outbreaks of unknown zoonoses is recognized, it is difficult to evaluate the monetary value generated by such activities, and this has undeniably limited the level of R&D. The COVID-19 pandemic has highlighted once again that research investment in infectious diseases is an extremely important security issue. Dobson et al. [35] calculated that the cost of 10 years of pandemic prevention research would amount to just 2% of the economic losses incurred by the COVID-19 pandemic. Thus, continued investment in preventive R&D is wholly rational in the long term. The top priority for some countries, including Japan, is therefore to gain acceptance from relevant stakeholders and the public so that they can invest in research and basic technologies related to infectious diseases as a national security policy. This will in turn strengthen countries' capacity to deal with national and international crises in a future pandemic.
One interviewee argued that it is crucial to maintain the motivation of researchers, and hence research topics should be chosen to meet the intellectual and academic interests of all the researchers involved. It is neither sustainable nor ethical to request the voluntary cooperation of researchers based entirely on goodwill, so we need to build a system in which everyone participating in a project or consortium can conduct research towards a shared goal while maximizing their own benefits.
3.2.2 Social and economic issues: Stopping destruction of the natural environment to prevent future pandemics
There is a complex interplay of factors behind the spillover of zoonoses from wildlife to humans. A United Nations Environment Programme report [36] states that various social activities of humans are a major factor that is potentially responsible for spillover. Some of these activities are pressing issues, particularly in developing countries, including increased consumption of animal protein, the expansion of unsustainable agriculture, increased trapping and use of wildlife, and changes in the food supply chain. Other global factors, such as climate change, natural resource issues, and increased international human movement, are also relevant in the emergence and spread of zoonoses. Our interview analysis strongly indicated that not only scientific R&D but also research to overcome social and economic challenges is needed.
In the field of economics, the Dasgupta Review [37] contains many important recommendations to enable sustainable zoonoses management. Dasgupta considers biodiversity and its inherent associated ecosystem services as natural capital on which the economy is built. The review proposes the use of an inclusive measure of wealth, rather than gross domestic product to allow us to measure the well-being of current and future generations by considering all assets, including natural ones. Environmental degradation may contribute to pandemics such as COVID-19, which affect human economic activity. We therefore need a holistic approach that seeks to resolve environmental problems to reduce the risk of future outbreaks of zoonoses.
Both our interviews and literature survey [36,37] suggest that institutional reforms are crucial to sustain natural capital. We reason that we need a new measure of value that is mindful not only of traditional commercial value in terms of money, but also of environmental impact and sustainable development. As an initial goal, each country should aim to reduce investments in projects that degrade nature or involve its unsustainable use; facilitating this goal may well give rise to industrial re-structuring.
The management of global public health requires transnational institutions. Given that pandemics can affect all countries, global health initiatives should provide greater support for countries that are considered hot spots. A simulation study that combined data on infectious diseases with environmental factors suggested a higher risk of zoonotic disease emergence for areas with broadleaf evergreen forests, mammalian species richness, and significant changes in land use [12]. The same study suggested that all these environmental factors may also increase the opportunities for contact between humans and wildlife, which can lead to spillover of unknown pathogens into human populations. Our interviewees emphasized that no single field surveillance can cover regions across the entire world to search for unknown pathogens. Cooperation across countries and institutions are therefore essential. Systematic capacity-building will maximize the effectiveness of such global cooperation.
3.2.3 Ethical, legal, and social issues
Several stakeholders who have participated in zoonosis surveillance in Japan highlighted the need to reform the vertical administrative system within authorities relevant to human, animal, and environmental health, as well as the laws, regulations, and systems that have hindered research and information-sharing [13]. For example, sampling of wildlife requires various administrative procedures, which can be a barrier to an effective response and implementation of control measures against zoonoses. In the future, a new administrative system, such as a department that collectively manages zoonosis projects, may be required to maximize the efficiency of zoonotic research and the efficacy of infectious disease control.
The interviewees who are currently involved in the fields of epidemiology, policy making, and mass communication said that a great number of issues remain to be addressed in terms of handling information such as the genetic sequences of pathogens. For example, in Japan, there is resistance to establishing a nationwide system for banking samples from livestock. Although it is well-recognized that such systems are useful for epidemiological studies that monitor the pathogen infiltration status among livestock animals, many people are concerned about the potential reputational damage to livestock producers that may be imposed by these studies. We need to manage such conflict of interests and develop cooperative relationships between stakeholders, including veterinarians, hunters, breeders, and farmers, which will enable sustainable and effective surveillance. Measures such as risk communication, informed consent, and the formulation of rules for handling samples are required to solve the issues that currently hinder epidemiological studies in animals.
From an international perspective, concerns about biological resources and dual use were also mentioned during the interviews. Access and Benefit-Sharing (ABS), a system under public international law that aims to fairly distribute benefits arising from genetic resources, regulates transboundary movement of biological samples on zoonosis research. While complying with ABS, it is necessary to take care so that sample movement between nations or data collection by local researchers can be carried out smoothly.
4 Conclusion
Here we conducted a thematic analysis of key-informant interviews with Japanese experts and a literature review of the OH approach, and two key themes were identified: types of required R&D and the systems needed to support them. These challenges may be common throughout the world, and this review attempted to integrate the experts' perspectives as a convergence of knowledge in an effort realize an ideal OH approach. Although this review is mainly focused on viral zoonoses, these insights are expected to contribute to other topics such as global threat of bacterial antimicrobial resistance. Researchers, policy makers and health officials are encouraged to incorporate these two themes to successfully design and implement OH projects in order to create a resilient society against future pandemics.
The following are the supplementary data related to this article.Supplementary Table 1.
Supplementary Table 1
Supplementary material
Image 1
Declaration of Competing Interest
The authors declare that they have no competing interests.
Data availability
No data was used for the research described in the article.
Acknowledgements
The authors are grateful to all interviewees listed in Supplemental Table 1 for their participation in the interviews. The affiliations listed are those at the time of the interviews, and the opinions expressed by the interviewees do not represent the official view of their affiliated institutions. This work was supported by the JST Moonshot R&D –MILLENNIA Program, Grant Number JPMJMS20M2, Japan.
The authors are grateful to all interviewees listed in Supplemental Table 1 for their participation in the interviews. The affiliations listed are those at the time of the interviews, and the opinions expressed by the interviewees do not represent the official view of their affiliated institutions. This work was supported by the JST Moonshot R&D –MILLENNIA Program, Grant Number JPMJMS20M2, Japan.
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| 36507073 | PMC9721418 | NO-CC CODE | 2022-12-09 23:15:04 | no | One Health. 2023 Jun 5; 16:100468 | utf-8 | One Health | 2,022 | 10.1016/j.onehlt.2022.100468 | oa_other |
==== Front
Gene
Gene
Gene
0378-1119
1879-0038
Elsevier B.V.
S0378-1119(22)00917-9
10.1016/j.gene.2022.147097
147097
Article
In silico analysis of genomic landscape of SARS-CoV-2 and its variant of concerns (Delta and Omicron) reveals changes in the coding potential of miRNAs and their target genes
Saini Sandeep ab⁎
Khurana Savi a
Saini Dikshant a
Rajput Saru a
Thakur Chander Jyoti a
Singh Jeevisha a
Jaswal Akanksha a
Kapoor Yogesh c
Kumar Varinder a
Saini Avneet b⁎
a Department of Bioinformatics, Goswami Ganesh Dutta Sanatan Dharma College, Sector 32, Chandigarh 160030, India
b Department of Biophysics, Panjab University, Sector 25, Chandigarh 160014, India
c Department of Engineering and Technology, Shoolini University, Solan, Himachal Pradesh, India
⁎ Corresponding authors at: Department of Bioinformatics, Goswami Ganesh Dutta Sanatan Dharma College, Sector 32, Chandigarh 160030, India (S. Saini).
5 12 2022
15 2 2023
5 12 2022
853 147097147097
11 9 2022
24 11 2022
29 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.
Graphical abstract
COVID-19 related morbidities and mortalities are still continued due to the emergence of new variants of SARS-CoV-2. In the last few years, viral miRNAs have been the centre of study to understand the disease pathophysiology. In this work, we aimed to predict the change in coding potential of the viral miRNAs in SARS-CoV-2′s VOCs, Delta and Omicron compared to the Reference (Wuhan origin) strain using bioinformatics tools. After ab-intio based screening by the Vmir tool and validation, we retrieved 22, 6, and 6 pre-miRNAs for Reference, Delta, and Omicron. Most of the predicted unique pre-miRNAs of Delta and Omicron were found to be encoded from the terminal and origin of the genomic sequence, respectively. Mature miRNAs identified by MatureBayes from the unique pre-miRNAs were used for target identification using miRDB. A total of 1786, 216, and 143 high-confidence target genes were captured for GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis. The GO and KEGG pathways terms analysis revealed the involvement of Delta miRNAs targeted genes in the pathways such as Human cytomegalovirus infection, Breast cancer, Apoptosis, Neurotrophin signaling, and Axon guidance whereas the Sphingolipid signaling pathway was found for the Omicron. Furthermore, we focussed our analysis on target genes that were validated through GEO’s (Gene Expression Omnibus) DEGs (Differentially Expressed Genes) dataset, in which FGL2, TNSF12, OGN, GDF11, and BMP11 target genes were found to be down-regulated by Reference miRNAs and YAE1 and RSU1 by Delta. Few genes were also observed to be validated among in up-regulated gene set of the GEO dataset, in which MMP14, TNFRSF21, SGMS1, and TMEM192 were related to Reference whereas ZEB2 was detected in all three strains. This study thus provides an in-silico based analysis that deciphered the unique pre-miRNAs in Delta and Omicron compared to Reference. However, the findings need future wet lab studies for validation.
Keywords
miRNA
SARS-CoV-2
Delta
Omicron
Variants
Target gene
Pathway
Abbreviations
ABCs, Age-associated B cells
BMP11, Bone Morphogenetic Protein
CNS, Central Nervous System
COPD, Chronic Obstructive Pulmonary Disease
COVID-19, Coronavirus Disease-2019
DAVID, Database for Annotation, Visualization and Integrated Discovery
DEGs, Differentially Expressed Genes
FGL2, Fibrinogen-like 2
GDF11, Growth Differentiation Factor 11
GEO, Gene Expression Omnibus
GO, Gene Ontology
ICU, Intensive Care Unit
JEV, Japanese Encephalitis Virus
KEGG, Kyoto Encyclopedia of Genes and Genomes
MFE, Minimum Free Energies
miRNAs, MicroRNAs
MMP14, Matrix Metalloproteinase-14
NCBI, National Centre for Biotechnology Information
NP, Nucleoprotein
nt, Nucleotides
ORFs, Open Reading Frames
PBMCs, Peripheral Blood Mononuclear Cells
pre-miRNAs, precursor miRNA
pri-miRNAs, primary miRNAs
PseDPC, Pseudo-Distance-Pair Composition
PTGS, Post-Transcriptional Gene Silencing
RNAa, RNA activation
RNAi, RNA interference
RSU1, Ras Suppressor-1
RT, Reverse Transcriptase
SARS-CoV-2, Severe Acute Respiratory Syndrome Coronavirus-2
SGMS1, Sphingomyelin Synthase 1
SM, Sphingomyelin
SVM, Support Vector Machine
TMEM192, Transmembrane protein 192
TNFRSF21, TNF Receptor Superfamily Member 21
TNSF, Tumor Necrosis Factor Superfamily
UCSC, University of California Santa Cruz
UTR, Untranslated region
VOCs, Variants of Concerns
Edited by Shitao Li
==== Body
pmc1 Introduction
Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), the causative agent of infectious and deadly Coronavirus Disease-2019 (COVID-19), initially originated in Wuhan, China, in December 2019 and has impacted many countries worldwide (Guo, 2020, Wang et al., 2020). There have been 603,711,760 confirmed cases of COVID-19, including 6,484,136 deaths around the world as of 11 September 2022 (https://covid19.who.int/). SARS-CoV-2 is an enveloped, positive-sense, single-stranded RNA virus that belongs to the family Coronaviridae and the genus Betacoronavirus (Huang et al., 2019).
Infection with highly pathogenic SARS-CoV-2 causes severe ‘flu’-like symptoms that include fever, cough, and shortness of breath, along with other symptoms like chills, myalgias, headache, sore throat, fatigue, dyspnea, and the loss of taste (ageusia) and smell (anosmia) (Dixon, 2021, Gavriatopoulou, 2021). Respiratory failure owing to alveolar damage might occur as the COVID-19 progresses, followed by renal failure, septic shock, heart failure, and hemorrhage, resulting in fatality (Gibson et al., 2020, Struyf et al., 2022). In addition to the above common symptoms, the patients also experience other long-term ramifications, such as myocardial inflammation and neurological manifestations (Harrison et al., 2020). Furthermore, older adults and people with hypertension, diabetes, and chronic obstructive pulmonary disease (COPD) were found to be more prone to intensive care unit (ICU) hospitalization and thus resulting in higher rates of mortality (Sanyaolu, 2020).
The genome surveillance strategies adopted by various countries result in tracing the different pathogenic variants of SARS-CoV-2 that originated due to adaptive mutations. Among them, variants of concern (VOCs) are utmost significant due to their escalating speed of transmission, enhanced virulence and decreased effectiveness towards vaccines, treatments, or diagnostics methods (Petersen, 2022, Aleem et al., 2022, SARS-CoV-2, xxxx). Till date, five variants were designated as VOCs. Among them, Alpha (lineage-B.1.1.7) and Beta (lineage-B.1.351) were declared VOCs on 18 December 2020 whereas the Gamma (lineage-P.1) and Delta (lineage-B.1.617.2) on 11 January 2020 and 11 May 2020, respectively and Omicron (lineage-B.1.1.529) was considered under VOCs on 24 November 2021 (Duong, 2022, Petersen, 2022). Of these five VOCs, the Delta (till date 7 June 2022) and Omicron variants caused the rapid spread of infection, higher mortality, and immune evasion. Additionally, the challenges of evading vaccine protection (Del Rio and Malani, 2022, Jassat, 2022, Salehi-Vaziri, 2022) and large number of observed mutations in these two strains secured their place in the list of VOCs relative to others (Araf, 2022, Malik, 2022, Mohapatra, 2022).
MicroRNAs (miRNAs) are short (∼22 nucleotides (nt)) non-coding RNA that regulates post-transcriptional gene expression in many organisms. Biogenesis of miRNAs-initiated from miRNAs genes with the formation of long primary miRNAs (pri-miRNAs) transcripts by the action of RNA pol-II, these transcripts are then cleaved by a ribonuclease named, Drosha to yield precursor miRNA (pre-miRNAs). The pre-miRNAs are transported from the nucleus to the cytoplasm by a nuclear transporter protein, exportin-5/RanGTP complex and there it eventually transforms into short mature miRNAs by the action of endonuclease enzyme, Dicer (He and Hannon, 2004, O'Brien et al., 2018). The process of post-transcription gene regulation is mediated through the interaction of mature miRNAs and target mRNAs. This interaction can occur in two ways, in the first type, if the mature miRNAs are hybridizing with mRNAs through full complementary base-pairing then it results in mRNA cleavage whereas, in the second type, the insufficient complementarity between the molecules represses mRNAs translation (He and Hannon, 2004, Wahid et al., 2010).
A number of different biological processes such as development (Cirillo et al., 2020, Ivey and Srivastava, 2015, Wahid et al., 2010), apoptosis (Taghavipour, 2020), cell proliferation (Hwang and Mendell, 2006), hematopoiesis (Kim et al., 2019), immune responses (Hirschberger et al., 2018), and viral pathogenesis (Bruscella et al., 2017, Ojha et al., 2016) were found to be controlled by miRNAs. Additionally, the viral miRNAs-mediated host genes regulation has been well documented in the number of previous literature that highlight the significance of these small RNAs in host organisms (Kincaid and Sullivan, 2012, Mishra et al., 2019, Nanbo et al., 2021). Moreover, the identification of miRNAs in different DNA and RNA viruses has been done by many researchers over the years to decode the potential effects of these molecules on host machinery (Demey, 2022, Diallo, 2022).
The miRNAs identification by wet lab method such as direct cloning usually are affected by the lower expression levels and stability of miRNAs in samples and thus the in-silico methods such as homology and ab-initio based can be used to predict the miRNAs (Prabu and Mandal, 2010). The two widely used computational approaches are based on homology and ab-initio prediction. The latter has more significant as it does not depend on the availability of homolog hits in the database and has the potential of predicting novel miRNAs in genome sequence based on sequence features such as hairpin or stem-loop structures (Tempel and Tahi, 2012, Allmer et al., 2014).
Over the years, various efforts were put on by the research community to decipher the role of viral miRNAs in controlling the host gene expressions (Baruah and Bose, 2019, Islam et al., 2019, Islam and Khan, 2021, Noor, 2021, Noor, 2022, Ospina-Bedoya et al., 2014, Saini, 2018, Shi, 2014, Teng, 2015). Several studies have been conducted during the last two years for tracing the miRNA's coding potential and their effects on the target genes of humans (Aydemir et al., 2021, Khan et al., 2020, Rahaman et al., 2021, Roy, 2021, Saini et al., 2020, Sarma et al., 2020, Saçar Demirci and Adan, 2020). But owing to considerable mutations or variations in the SARS-COV-2 genome the coding potential of miRNAs could be changed and may affect new targets.
Hence, in this study, we have performed an in-silico analysis to decipher the change in miRNA coding potential of current VOCs (Delta and Omicron) compared to the SARS-CoV-2-Reference (Wuhan origin) genome. Our work also compares SARS-CoV-2 and VOCs encoded miRNAs and their targeted genes in the human genome. The associated GO (Gene Ontology) terms and microarray expression dataset was used to validate the study. We believe that this is the first study that highlighted the change in the miRNAs coding potential of SARS-CoV-2′s VOCs.
2 Materials and methods
2.1 Retrieval of genome sequences and alignment
The genome sequences for SARS-CoV-2 (accession number: NC_045512) and its VOCs, Delta (accession number: MW931310) and Omicron (accession number: OL672836, sublineage: B.1.1.529), were downloaded from National Centre for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/) (NCBI\accessed 17 March 2022). We considered the SARS-CoV-2 genome as a reference in this study and represented it as SARS-CoV-2-Reference. Furthermore, the University of California Santa Cruz (UCSC) genome browser (https://genome.ucsc.edu/) (Navarro Gonzalez et al., 2021) was used for the visualization of the mutational landscape in the VOCs.
2.2 Prediction of pre-miRNAs by Vmir
An ab-initio based pre-miRNA prediction program, Vmir software package (v2.2) was used to predict and visualize pre-miRNAs in the genome of SARS-CoV-2 and its variants. Vmir consists of two modules: a Vmir Analyzer for the prediction of pre-miRNAs and other, Vmir Viewer for viewing and filtering top-scoring pre-miRNAs (Grundhoff et al., 2006). The parameters in Vmir Analyzer for analysis i.e., window size, conformation, orientation, and step count were set at 500-nt, linear, both, and 1-nt, respectively. In Vmir viewer, parameters to filter out top scorer pre-miRNAs were taken as a minimum hairpin score: ≥150, minimum hairpin size:70, and window count: ≥35, as described in previous literature (Grundhoff et al., 2006, Noor, 2022, Sarma et al., 2020, Shi, 2014).
Furthermore, CD-HIT-EST-2D (https://weizhong-lab.ucsd.edu/cdhit-web-server/cgi-bin/index.cgi?cmd=cd-hit-est-2d) (Huang et al., 2010) webserver was used to find the intra or inter sequence identity among the predicted pre-miRNAs. The sequence identity cut-off value was set to 1 (for identification of 100 % identical pre-miRNAs).
2.3 Validation of pre-miRNAs
To identify real pre-miRNAs, three web-based tools, iMiRNA-PseDPC, iMiRNA-SSF, and iMcRNA were used. iMiRNA-PseDPC (http://bioinformatics.hitsz.edu.cn/iMiRNA-PseDPC/) uses a novel feature vector called PseDPC (Pseudo-Distance-Pair Composition) to identify real and pseudo miRNA precursors (Liu et al., 2016). iMiRNA-SSF(http://bioinformatics.hitsz.edu.cn/iMiRNA-SSF/) is an SVM (support vector machine) based miRNA predictor which predicts the pre-miRNAs based upon structure and sequence features (Chen et al., 2016). iMcRNA (https://bioinformatics.hitsz.edu.cn/iMcRNA/) consists of two predictors iMcRNA-PseSSC and iMcRNA-ExPseSSC that can be used to identify real pre-miRNAs (Liu et al., 2015).
Additionally, the RNA structure folding webserver, Mfold (https://www.unafold.org/mfold/applications/rna-folding-form.php), was used with default parameters to predict the secondary hairpin-loop structures of the validated pre-miRNAs along with their minimum free energies (MFE) (Zuker, 2003).
2.4 Mature miRNA prediction
The prediction of mature miRNAs was performed with the help of the MatureBayes web server (https://mirna.imbb.forth.gr/MatureBayes.html). This web tool incorporates a Naïve Bayes classifier which is used to identify mature miRNAs based on both the sequence and secondary structure information of the predicted miRNA precursors (Gkirtzou et al., 2010).
2.5 Target gene prediction using miRDB
miRDB (http://mirdb.org/), the online database, facilitates customizable miRNAs target prediction. It predicts target genes based on MirTarget, a SVM-based algorithm that is trained on high-throughput training datasets (Chen and Wang, 2020). In miRDB, the mature miRNA sequences were submitted to the miRDB custom prediction module for the identification of potential human target genes.
2.6 GO and KEGG pathway analysis
Gene ontology and KEGG pathway analysis were performed using DAVID (Database for Annotation, Visualization and Integrated Discovery), (https://david.ncifcrf.gov/home.jsp) (Dennis, 2003) to gain insight into biological pathways, molecular functions, biological processes, and cellular components of the predicted target genes.
Next, we compare the target genes of each GO and KEGG pathway terms using Genevenn (https://www.bioinformatics.org/gvenn/) (Pirooznia et al., 2007) webserver to retrieve the unique and common target genes among the strains. Similarly, we also analysed the GO and KEGG pathways terms of each strain target genes using the Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) webserver (Oliveros, 2022).
2.7 Gene expression analysis
GEO (https://www.ncbi.nlm.nih.gov/geo/) is an NCBI curated, public repository for microarray gene expression database (Barrett, 2013). The expression dataset of the GSE164805 series based on the GPL26963 platform (Agilent-085982 Arraystar human lncRNA V5 microarray), which contained the whole genome transcriptome data from peripheral blood mononuclear cells (PBMCs) taken from 5 severe, 5 mild COVID-19 patients and 5 healthy controls was used to evaluate the expression level of predicted target genes (Zhang, 2021). The inbuilt GEO2R (based on the Limma R package) was used to find out significant differentially expressed genes (DEGs).
The complete methodology used in the study is outlined in Fig. 1 . Fig. 1 The steps of the overall methodology used in the study. Flowchart describes the steps of computational approach used for prediction of miRNAs and validation of target genes using different databases, ab-intio tools and webservers. The tools used in the methodology has been shown in the bold text.
3 Results
3.1 Sequence data characterization and mutation pattern visualization
The genomes of the reference, delta, and omicron strains are single-stranded RNAs and have linear topologies with approximately 29,000 nt in each genome. Mutations visualization was done by the UCSC genome browser to locate the mutational sites (Fig. 2 ) in the genomic landscape of VOCs. Mutational sites were found to be present mostly in the coding region of the structural proteins of the VOCs in the context to reference genome of SARS-CoV-2. But a large number of mutational sites were also located in the ORFs (Open Reading Frames) that could change the coding potential of VOCs.Fig. 2 Mutational sites of VOCs in UCSC genome browser. The mutations in Delta and Omicron genomic regions were shown to be mapped onto the genomic landscape of SARS-CoV-2 reference genome (accession number: NC_045512.2). Blue color bars show the positions of mutations in Delta, red color bars show the positions of mutations in Omicron and black color bars show the Uniprot amino acid mutations. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
3.2 pre-miRNAs identification using Vmir
Vmir software package was used to analyze the genomes of reference and VOCs. Vmir analyzer predicted a total of 1957, 1968, and 1927 candidate pre-miRNAs in reference, delta, and omicron genomes, respectively. Vmir viewer filtered out the top-candidate pre-miRNAs with the use of filtering parameters (min. hairpin size: 70, min. score: ≥150, and window count: ≥35) and resulted in 36 (Reference), 38 (Delta), and 36 (Omicron) pre-miRNAs respectively. Supplementary file s 1, 2, and 3 contain the complete sequence and output data of Vmir prediction for Reference, Delta, and Omicron.
To precisely locate the novel or unique pre-miRNAs in VOCs CD-HIT-EST-2D was used to compare the pre-miRNA sequences with themselves or with Reference pre-miRNAs sequences. At cut-off score 1 (100 % sequence identity), 10 Delta and 10 Omicron pre-miRNAs sequences were retrieved whereas the duplicate pre-miRNAs were not considered for further analysis. The unique pre-miRNAs of Delta and Omicron were highlighted (bold letters) in Supplementary file s 2 and 3 respectively.
3.3 Identification of real pre-miRNAs
A consensus-based approach was used to identify the real pre-miRNAs. The identification was done using three web-based tools namely, iMiRNA-PseDPC, iMiRNA-SSF, and iMcRNA (incorporates two predictors- iMcRNA-PseSSC and iMcRNA-ExPseSSC). Only those pre-miRNAs were taken into account for further studies which were predicted to be real in all the four aforementioned predictors as shown in Table 1 .Table 1 Validated real pre-miRNAs with minimum free energy (MFE): pre-miRNAs were validated with the help of three different tools i.e., iMiRNA-SSF, iMcRNA, and iMiRNA-PseDPC and for further validation, MFE was also calculated using mfold.
SARS-CoV-2 Reference
Sr. No. Predicted pre-miRNAs iMiRNA-SSF iMcRNA iMiRNA-PseDPC MFE, kcal/mol
iMcRNA-PseSSC iMcRNA-ExPseSSC
1 SARS-CoV-2-MD32 REAL REAL REAL REAL −40.8
2 SARS-CoV-2-MD172 REAL REAL REAL REAL −36.9
3 SARS-CoV-2-MD398 REAL REAL REAL REAL −24.7
4 SARS-CoV-2-MD308 REAL REAL REAL REAL −38.7
5 SARS-CoV-2-MD387 REAL REAL REAL REAL −39.5
6 SARS-CoV-2-MR305 REAL REAL REAL REAL −45.3
7 SARS-CoV-2-MR45 REAL REAL REAL REAL −26.8
8 SARS-CoV-2-MR386 REAL REAL REAL REAL −28.4
9 SARS-CoV-2-MR189 REAL REAL REAL REAL −27.3
10 SARS-CoV-2-MD23 REAL REAL REAL REAL −30.3
11 SARS-CoV-2-MD147 REAL REAL REAL REAL −34.7
12 SARS-CoV-2-MR345 REAL REAL REAL REAL −25
13 SARS-CoV-2-MR49 REAL REAL REAL REAL −27.2
14 SARS-CoV-2-MD37 REAL REAL REAL REAL –23.7
15 SARS-CoV-2-MD255 REAL REAL REAL REAL −28.8
16 SARS-CoV-2-MD391 REAL REAL REAL REAL −26.3
17 SARS-CoV-2-MR313 REAL REAL REAL REAL −28.4
18 SARS-CoV-2-MD346 REAL REAL REAL REAL −34.7
19 SARS-CoV-2-MD15 REAL REAL REAL REAL −27.3
20 SARS-CoV-2-MD38 REAL REAL REAL REAL −40.1
21 SARS-CoV-2-MR230 REAL REAL REAL REAL –33
22 SARS-CoV-2-MD261 REAL REAL REAL REAL –32
SARS-CoV-2-DELTA
Sr. No. Predicted pre-miRNAs iMiRNA-SSF iMcRNA iMiRNA-PseDPC MFE, kcal/mol
iMcRNA-PseSSC iMcRNA-ExPseSSC
1 SARS-CoV-2-Delta- MD404 REAL REAL REAL REAL −26.8
2 SARS-CoV-2-Delta-MR324 REAL REAL REAL REAL −27.4
3 SARS-CoV-2-Delta-MD40 REAL REAL REAL REAL −40.7
4 SARS-CoV-2-Delta-MD394 REAL REAL REAL REAL −40.2
5 SARS-CoV-2-Delta-MR346 REAL REAL REAL REAL −25
6 SARS-CoV-2-Delta-MD340 REAL REAL REAL REAL −27.1
SARS-CoV-2-OMICRON
Sr. No. Predicted pre-miRNAs iMiRNA-SSF iMcRNA iMiRNA-PseDPC MFE, kcal/mol
iMcRNA-PseSSC iMcRNA-ExPseSSC
1 SARS-CoV-2-Omicron-MD30 REAL REAL REAL REAL −41.4
2 SARS-CoV-2-Omicron-MD339 REAL REAL REAL REAL –22.6
3 SARS-CoV-2-Omicron-MD36 REAL REAL REAL REAL −40.7
4 SARS-CoV-2-Omicron-MD67 REAL REAL REAL REAL −38.6
5 SARS-CoV-2-Omicron-MD114 REAL REAL REAL REAL −24.2
6 SARS-CoV-2-Omicron-MR227 REAL REAL REAL REAL −34.5
Further validation was done by calculating the MFEs of secondary structures of pre-miRNAs. All the real pre-miRNAs were found to be above the threshold MFE ≤ -20 kcal/mol (shown in Table 1) as discussed in previous literature (Noor, 2021, Roy, 2021). All the predicted fold secondary structures of Reference, Delta, and Omicron real pre-miRNAs can be found in supplementary file 4. The unique predicted pre-miRNAs of Delta and Omicron strains were mapped on genomic positions (Fig. 3 ) and it was found that most of the unique pre-miRNAs of the Delta strain were coded through genomic coordinates 22 K to 27 K (i.e., from the terminal of the genome) whereas the most pre-miRNAs of Omicron strain were clustered around 2 K to 6 K (i.e., from the origin of the genome). This finding correlates to the number of mutations in these strains as can be seen in Fig. 2, the number of mutations in the starting coordinates of the Delta strain is very few as compared to Omicron that contains most mutations in the terminal of the genome.Fig. 3 Genomic landscape of novel pre-miRNAs of Delta and Omicron strains. (A) Mapped positions of pre-miRNAs of Delta strain. (B) Mapped positions of pre-miRNAs of Omicron strain. Most of the predicted pre-miRNAs of Delta strain were encoded from the terminal region of the genome whereas the most pre-miRNAs of Omicron strain were mapped on the starting genomics coordinates.
3.4 Mature miRNAs identification by MatureBayes
MatureBayes server identifies the mature miRNAs within the real pre-miRNAs based on the sequence and structural information of their precursor miRNAs. One or both strands can serve as mature miRNA (Guo and Lu, 2010), hence, we took both strands for further study as described in previous studies (Islam et al., 2019, Teng, 2015). We got a total of 44, 12, and 12 mature miRNAs from 22, 6, and 6 (Table 2 ) pre-miRNA sequences on 5′ and 3′ stem locations.Table 2 Mature miRNAs identification: The mature miRNAs were predicted using the MatureBayes server. Each pre-miRNAs provides 2 mature miRNAs on 5′ and 3′ stems.
SARS-CoV-2 or Reference
Sr. No. Predicted miRNAs MATURE 5′STEM MATURE 3′ STEM
POSITION SEQUENCE POSITION SEQUENCE
1 SARS-CoV-2-MD32 POSITION 37 CUGCCUAUACAGUUGAACUCGG POSITION 69 AAUGAGUUCGCCUGUGUUGUGG
2 SARS-CoV-2-MD172 POSITION 43 AUACUAGUUUGUCUGGUUUUAA POSITION 75 UGUGUUAUGUAUGCAUCAGCUG
3 SARS-CoV-2-MD398 POSITION 46 AUAGUGUUUAUAACACUUUGCU POSITION 81 AAAGACAGAAUGAUUGAACUUU
4 SARS-CoV-2-MD308 POSITION 15 UUGGAGGUUCCGUGGCUAUAAA POSITION 61 UGAUCUUUAUAAGCUCAUGGGA
5 SARS-CoV-2-MD387 POSITION 50 AUGUGGCUCAGCUACUUCAUUG POSITION 66 UCAUUGCUUCUUUCAGACUGUU
6 SARS-CoV-2-MR305 POSITION 24 AGAGUAAGCAACUGAAUUUUCU POSITION 124 UGAUAACUAGCGCAUAUACCUG
7 SARS-CoV-2-MR45 POSITION 40 UUUUGUUCAACUUGCUUUUCAC POSITION 73 AAAAAGCUUGAAACAAGUUUGU
8 SARS-CoV-2-MR386 POSITION 16 AGAGUAGACUAUAUAUCGUAAA POSITION 54 UUUAUAUAGCCCAUCUGCCUUG
9 SARS-CoV-2-MR189 POSITION 45 UUUUGUAUAUGCGAAAAGUGCA POSITION 61 AGUGCAUCUUGAUCCUCAUAAC
10 SARS-CoV-2-MD23 POSITION 27 ACUCAAACCCGUCCUUGAUUGG POSITION 64 AAGGAAGGUGUAGAGUUUCUUA
11 SARS-CoV-2-MD147 POSITION 33 AUGUAUCUAAAGUUGCGUAGUG POSITION 99 UAUAAUAAGUACAAGUAUUUUA
12 SARS-CoV-2-MR345 POSITION 33 UCAACAAUUUUAUUGUAGAUGA POSITION 51 AUGAAGAAGGUAACAUGUUCAA
13 SARS-CoV-2-MR49 POSITION 33 AAAGCUUUCGCUAGCAUUUCAG POSITION 48 AUUUCAGUAGUGCCACCAGCCU
14 SARS-CoV-2-MD37 POSITION 35 AUGGCUACAUACUACUUAUUUG POSITION 47 UACUUAUUUGAUGAGUCUGGUG
15 SARS-CoV-2-MD255 POSITION 37 UUUGGUUUAUGAUAAUAAGCUU POSITION 57 UUAAAGCACAUAAAGACAAAUC
16 SARS-CoV-2-MD391 POSITION 32 ACUGUUGCUACAUCACGAACGC POSITION 72 GAGCUUCGCAGCGUGUAGCAGG
17 SARS-CoV-2-MR313 POSITION 29 UGCAAGUGUCACUUUGUUGAAA POSITION 61 CAAUAAAUGACCUCUUGCUUGG
18 SARS-CoV-2-MD346 POSITION 15 UAUCAGACUCAGACUAAUUCUC POSITION 78 UACACUAUGUCACUUGGUGCAG
19 SARS-CoV-2-MD15 POSITION 13 UAUGUUGGUUGCCAUAACAAGU POSITION 50 CACGUGCUAGCGCUAACAUAGG
20 SARS-CoV-2-MD38 POSITION 16 CUGGUGAGUUUAAAUUGGCUUC POSITION 105 UUUGAGCCAUCAACUCAAUAUG
21 SARS-CoV-2-MR230 POSITION 29 CAACACAUAAACCUUCAGUUUU POSITION 64 ACACUGAGGUGUGUAGGUGCCU
22 SARS-CoV-2-MD261 POSITION 4 UUGCAUAAUGUCUGAUAGAGAC POSITION 78 CAACUUUACAAGCUGAAAAUGU
SARS-COV-2-DELTA
Sr. No. PREDICTED miRNAs MATURE 5′ STEM MATURE 3′ STEM
POSITIONS SEQUENCE POSITIONS SEQUENCE
1 SARS-CoV-2-Delta- MD404 POSITION 46 AUAGUGUUUAUAACACUUUGCU POSITION 81 AAAGAUAGAAUGAUUGAACUUU
2 SARS-CoV-2-Delta-MR324 POSITION 19 AUUGGAGCUAAGUUGUUUAACA POSITION 57 GUGCAUUUUGGUUGACCACAUU
3 SARS-CoV-2-Delta-MD40 POSITION 16 CUGGUGAGUUUAAAUUGGCUUC POSITION 73 AUGAAGAAGAAGGUGAUUGUGA
4 SARS-CoV-2-Delta-MD394 POSITION 50 AUGUGGCUCAGCUACUUCAUUG POSITION 66 UCAUUGCUUCUUUCAGACUGUU
5 SARS-CoV-2-Delta-MR346 POSITION 33 UCAACAAUUUUAUUGUAGAUGA POSITION 51 AUGAAGAAGGUAACAUGUUCAA
6 SARS-CoV-2-Delta-MD340 POSITION 33 CUAAUCUCAAACCUUUUGAGAG POSITION 54 GAGAUAUUUCAACUGAAAUCUA
SARS-COV-2-OMICRON
Sr. No. PREDICTED miRNAs MATURE 5′STEM MATURE 3′ STEM
POSITIONS SEQUENCE POSITIONS SEQUENCE
1 SARS-CoV-2-Omicron-MD30 POSITION 37 CUGCCUAUACAGUUGAACUCGG POSITION 69 AAUGAGUUCGCCUGUGUUGUGG
2 SARS-CoV-2-Omicron-MD339 POSITION 14 CAGAUGAUUUUACAGGCUGCGU POSITION 66 UAAGGUUAGUGGUAAUUAUAAU
3 SARS-CoV-2-Omicron-MD36 POSITION 16 CUGGUGAGUUUAAAUUGGCUUC POSITION 73 AUGAAGAAGAAGGUGAUUGUGA
4 SARS-CoV-2-Omicron-MD67 POSITION 21 UUGUGCACUUAUCUUAGCCUAC POSITION 81 AACAAUGAGUUACUUGUUUCAA
5 SARS-CoV-2-Omicron-MD114 POSITION 26 AGCAGCUCGGCAAGGGUUUGUU POSITION 42 UUUGUUGAUUCAGAUGUAGAAA
6 SARS-CoV-2-Omicron-MR227 POSITION 29 CAACACAUAAACCUUCAGUUUU POSITION 64 ACACUGAGGUGUGUAGGUGCCU
3.5 miRNAs target genes analysis
All the mature miRNAs were screened using a target identification webserver, miRDB to identify the target genes of Reference, Delta, and Omicron in human 3′UTR (Untranslated region). In total 1786, 216, and 143 target genes were taken having a target score > 95. The high-scoring criteria were set to minimize the false positive and maximize the true positive number of predicted target genes. The target genes were listed in Supplementary file 5.
3.6 GO and KEGG pathway analysis of targeted human genes
DAVID functional annotation analysis of miRNA targeted genes was performed to better understand the functional enrichment of the target genes. The targeted genes were analyzed for the KEGG pathway and GO terms (Biological process, Cellular Component, and Molecular function). A P-value ≤ 0.05 was used to retrieve strongly enriched GO and KEGG pathway terms for the target genes as mentioned in the previous works (Islam and Khan, 2021, Noor, 2022). The complete data generated in DAVID analysis was provided in Supplementary file 6 (for Reference), Supplementary file 7 (for Delta), and Supplementary file 8 (for Omicron).
3.6.1 KEGG pathways analysis:
The KEGG pathways analysis (Fig. 4 -IA) of Reference revealed the involvement of the Ras signaling pathway, Glycerophospholipid metabolism, Gap junction, Adipocytokine signaling pathway, and Malaria etc. Moreover, the enriched target genes of the Delta strain (Fig. 4 -IIA) were found to be involved in Human cytomegalovirus, Breast Cancer, Apoptosis, Neurotrophin signaling pathway, and Axon guidance. Only the Sphingolipid signaling pathway (Fig. 4 -IIIA) was found for the enriched target genes of the Omicron strain.Fig. 4 (IA-D, IIA-D, IIIA-D) DAVID functional annotation to retrieve GO and KEGG pathways terms for enriched (P-value ≤ 0.05) target genes. The KEGG pathways (4-IA-4-IIIA), Molecular function (4-IB-4IIIB), Cellular component (4-IC-4-IIIC), and Biological process (4-ID-4-IIID) show the enriched GO and KEGG pathways terms for the Reference, Delta, and Omicron strains. Only top ten terms for each GO and KEGG pathways terms are shown.
3.6.2 GO terms analysis
The GO term, molecular function shows (Fig. 4 -IB) that the target genes of Reference were involved in the protein domain specific binding, ubiquitin-protein transferase activity, inositol hexakisphosphate 5-kinase activity and beta-1 adrenergic receptor binding etc. GO molecular terms (Fig. 4 -IIB), guanyl-nucleotide exchange factor activity, RNA polymerase II core promoter proximal region sequence-specific DNA binding, and semaphorin receptor binding were enlisted for the Delta strains. In the Omicron strain (Fig. 4 -IIIB), GPI-linked ephrin receptor activity, tau protein binding, inositol heptakisphosphapte, and GDP and DNA binding molecular function involvement of target genes were noted. Cellular components GO terms like membrane raft, terminal web, fibrillar center, etc. comes under Delta strains whereas terms like ubiquitin ligase complex, nuclear membrane and chromatin, nucleus and dendrite etc. were evident for the Reference and Omicron respectively, as shown in Fig. 4 -IC, 4-IIC & 4-IIIC. In the biological process, the Reference target genes were identified in nerve growth factor signaling pathway, inhibitor synapse assembly and glycerolipid metabolic process etc. (Fig. 4 -ID). In the Delta and Omicron strains, the target genes were identified as follows: chromatin organization, negative regulation of synapse assembly, etc. (Fig. 4 -IID) and ephrin receptor signaling pathway, ureter development, and neural crest cell migration, etc. (Fig. 4 -IIID), respectively.
The Genevenn analysis of target genes (Fig. 5 ) revealed the number of unique and common target genes of GO and KEGG pathway terms. In the biological process, cellular component, molecular function, and KEGG pathway the Reference, Delta, and Omicron share 18, 13, 12, and 5 genes in common respectively. The number of unique and common genes shared among studied strains can be seen in Fig. 5 . The unique genes of GO and KEGG pathway terms for Delta and Omicron strains are listed in below Table 3 .Fig. 5 Venn diagram showing the number of unique and shared enriched target genes of Reference, Delta and Omicron among: (5A) Biological process, (5B) Cellular component (5C) Molecular function, and (5D) KEGG pathway. In each GO and KEGG pathway terms Delta and Omicron predicted novel miRNAs were found to target unique genes as compared to Reference. The value in the parenthesis indicates the total number of enriched target genes of each strain used for analysis.
Table 3 Unique target genes of the VOCs, Delta and Omicron after comparison with Reference.
DELTA OMICRON
Biological process Cellular component Molecular function KEGG pathway Biological process Cellular component Molecular function KEGG pathway
ABR
ADAM23
ATXN1L
BPY2
BPY2B
BPY2C
CAPN2
CDK6
DYDC2
ERBIN
INSM1
MRTFB
NEUROD6
NFYC
NPR3
PCDH18
PHLDA2
PICALM
PLAGL1
RIF1
ROBO2
RPS6KB1
RSU1
SGIP1
SMAD7
SNAI2
TNFSF11
ZNF84 ABCA1
ABR
ASB5
ATXN1L
BEX3
CAPN2
CAPZA2
CCDC43
CDK6
COBL
DUSP6
DYNC1LI2
ERBIN
FMNL1
INSM1
KLHL15
MRTFB
NEUROD6
NFYC
OSBPL8
PICALM
PLAGL1
RIF1
RPS6KB1
RSU1
SLF2
SMAD7
SNAI2
STRN
UBE2N
VCL
ZNF84 ABR
ATXN1L
INSM1
KMT2A
NEUROD6
NFYC
PLAGL1
RGL2
SMAD7
SMARCA5
SNAI2
ZBTB11
ZNF84 BEX3
CAPN2
CDK6
DUSP6
ITPR1
ROBO2
RPS6KB1
TNFSF11 ADGRL2
AFDN
ASAP2
ATP9A
DOP1B
FYN
KLF10
KMO
NFIA
NFIL3
PAX2
RERG
RGL1
RSPO2
SMAD9
SNX18
UBE2A
VEZF1
WAC ADGRL2
AHNAK
ANKRD34B
CLIP1
FYN
KLF10
NFIA
NFIL3
NPTXR
PAX2
PDS5B
PSMB5
PURB
RAD23B
RERG
SMAD9
UBE2A
UBE2D2
USP24
VEZF1
WAC
ZNF207
ZNF532 ARL8B
BEND4
FYN
GNAI1
KLF10
NFIA
NFIL3
PAX2
PDS5B
PURB
RERG
SMAD9
VEZF1
ZNF207
ZNF268
ZNF532 FYN
Furthermore, the GO and KEGG pathway terms analysis (Fig. 6 ) was done by Venny 2.1 webserver server which revealed the presence of unique and common terms for each strain. For the Delta, 9 biological process and 3 cellular components terms were found to be unique compared to Reference and Omicron whereas 3 biological process and 4 molecular function unique terms were observed for the Omicron compared to other two. No KEGG pathway terms were found to be unique for the Delta and Omicron which show that the enriched pathway terms were shared with Reference. But our GeneVenn analysis results (Fig. 5, Table 3) show that unique target genes were present in KEGG pathway terms of Delta and Omicron compared to Reference. Thus, it might be possible that the predicted miRNAs of VOCs were targeting novel genes in the same KEGG pathways terms of Reference.Fig. 6 Venn diagram showing the number of unique and shared GO and KEGG pathways terms of Reference, Delta and Omicron among: (6A) Biological process, (6B) Cellular component (6C) Molecular function, and (6D) KEGG pathway. Unique terms for biological process and cellular component were observed for the Delta and biological process and molecular function for the Omicron as compared to Reference. The value in the parenthesis indicates the total number of terms of each strain used for analysis.
The unique and common GO and KEGG pathway terms found among the Reference, Delta and Omicron was provided in Supplementary file 9 .
3.7 GEO microarray gene expression analysis
Finally, validation of enriched target genes was examined by using the GSE164805 microarray dataset which was obtained from the transcriptional profiles of severe COVID-19 that consist of 15 samples (5 healthy, 5 mild, and 5 severe patients). Samples were compared using 5 samples each of Healthy vs Mild, Healthy vs Severe and Mild vs Severe to obtain the DEGs with P-value ≤ 0.05 and logFC values ± 1, if the logFC value is negative then genes were considered as down-regulated and a gene with positive logFC value marked as up-regulated (Islam and Khan, 2021, Khan et al., 2020, Noor, 2022).
A total of 1127 genes were discovered by profiling the healthy vs mild patients in which 97 were down-regulated and 67 were up-regulated (Fig. 7 A), while 1172 genes were obtained by comparing healthy vs severe patients (Fig. 7 B) that contains 126 down-regulated and 199 up-regulated genes. A comparison between mild vs severe patient samples (Fig. 7 C) gave a total of 2941 genes in which 58 and 62 genes were found to be in criteria of up-regulated and down-regulated, respectively.Fig. 7 Volcano plots of DEGs obtained by GEO2R analysis. Differential analysis of transcriptomics data of COVID-19 patients showing cluster of differentially expressed genes (P-value ≤ 0.05 and logFC values ± 1) for (7A) Healthy vs Mild, (7B) Healthy vs Severe and (7C) Severe vs Mild samples. The blue spots represented the down-regulated genes, while the red spots represented the up-regulated genes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
The down-regulated and up-regulated genes so obtained (Supplementary file s 10, 11, and 12) were compared with the enriched target genes. We found FGL2 and TNFSF12 (Healthy vs Mild), HIPK3, OGN and SCML2 (Healthy vs Severe), and GDF11, YAE1, and IPO9 (Severe vs Mild) target genes of the Reference in the down-regulated gene list. Comparison of Delta enriched target genes with down-regulated GEO gene set provided two genes, YAE1, and RSU1 in Severe vs Mild sample whereas no target genes were found for Delta for sample Healthy vs Mild and Healthy vs Severe. The enriched target genes of Omicron strains were not found in any samples. Table 4 summarizes the validated target genes in each strain.Table 4 Validated target genes validated from the GEO dataset.
DOWN-REGULATED GENES
VARIANT HEALTHY VS MILD HEALTHY VS SEVERE SEVERE VS MILD
REFERENCE FGL2
TNFSF12 HIPK3
OGN
SCML2 GDF11
YAE1
IPO9
DELTA NIL NIL YAE1
RSU1
OMICRON NIL NIL NIL
UP-REGULATED GENES
VARIANT HEALTHY VS MILD HEALTHY VS SEVERE SEVERE VS MILD
REFERENCE MMP14
MOSMO
BOLL
HOXB5
P3H2
TNFRSF21
SGMS1
TMEM192 ZEB2
PPIL4
DELTA NIL RSU1 ZEB2
RPS6KB1
OMICRON NIL NIL ZEB2
In up-regulated DEGs, for Reference following target genes were validated, MMP14 (Healthy vs Mild), MOSMO, BOLL, HOXB5, P3H2, TNFRSF21, SGMS1, and TMEM192 (Healthy vs Severe), and ZEB2 and PPIL4 (Severe vs Mild). In Delta strain, RSU1 (Healthy vs Severe) and ZEB2 and RPS6KB1 (Severe vs Mild) target genes were retrieved. In Omicron only one gene, ZEB2 (Severe vs Mild) was predicted.
4 Discussion
A never-ending effort must be needed to win the battle against COVID-19. The periodic emergence of SARS-CoV-2 strains posed serious concerns in front of the research community. Moreover, due to a myriad of post-COVID complications that are affecting multiple organs, the disease is now considered a systemic illness and results in increased morbidity (Chippa et al., 2022). The evidence of SARS-CoV-2 encoded miRNAs in virus pathogenesis and disease etiology has been deciphered by many recent works (Li, 2022, Merino, 2021, Zhao, 2022, Zhu, 2021). However, due to considerable mutations in the SARS-CoV-2 genome, the miRNAs coding potential may be altered and thus may affect the pathophysiology. Thus, in this work, we used in-silico methods to predict the change in miRNAs coding potential of SARS-CoV-2 VOCs (Delta and Omicron) and their target genes compared to SARS-CoV-2 reference.
In the present study, genome sequences of SARS-CoV-2 Reference (Wuhan origin) and VOCs (Delta and Omicron) were analyzed by an ab-intio based software package, Vmir. After filtering through the stringent parameters, we got 36, 38, and 36 pre-miRNAs for Reference, Delta, and Omicron, respectively. The redundancy in pre-miRNAs among the strains was analyzed using CD-HIT-EST-2D which provides the 10 unique pre-miRNAs for each Delta and Omicron as compared to Reference. Additionally, the validation of unique pre-miRNAs was done by three web-based servers and through calculation of MFEs that reduced the number of pre-miRNAs to 22 for Reference and 6 each for Delta and Omicron, respectively.
The validated unique pre-miRNAs of Delta were mapped onto their predicted genomic locations revealed that except for one pre-miRNA (MD-40) the remaining pre-miRNAs were encoded from the nucleotide position 22 K to 27 K, i.e., from the terminal of the genome. Furthermore, a closer analysis revealed that two pre-miRNAs, MD-340 and MR-324 were encoded from the genomic region which encodes for the spike protein whereas the other pre-miRNAs, MR-346, MD-394, and MD-404 were transcribed from the region spanning ORF3a, ORF6, ORF7a, and ORF7b. In contrast to this, Omicron codes two pre-miRNAs (MD-339 and MD-227) from the spike protein region whereas the remaining (MD-30, MD-36, MD-114, and MD-67) pre-miRNAs were found to be mapped from the region of ORF1ab. These observations do not seem to be surprising because there is well-established previous evidence that the miRNA coding genes can be located in protein-coding regions (Bavia et al., 2016, Olena and Patton, 2010). For instance, in a study by Diallo et al. (Diallo, 2022) two miRNAs, miR-MAY-251 and miR-MAK-403 in the Ebola virus genome were found to be encoded from the genomic coordinates of Nucleoprotein (NP) and RNA-dependent RNA polymerase (L), respectively. Furthermore, in another study by Zhang et al. (Zhang, 2014), HIV-1 encoded a miRNA (miR-H3) from the mRNA region spanning the active center of reverse transcriptase (RT). Moreover, it is worth mentioning that recent studies during the COVID-19 pandemic also identified the miRNAs in the coding regions of the genome, for example, in a study by Pawlica et al. (Pawlica, 2021), a novel miRNA attributed as CoV2-miR-O7a was discovered from the ORF7a. Besides this, in another study by Singh et al. (Singh, 2022), ORF7a encodes a miRNA, SARS-CoV-2 miR-O7a that was found to target the immune response genes. In one more study, Meng et al. (Meng, 2021), used deep sequencing strategies to identify three miRNAs (v-miRNA-N-28612, v-miRNA-N-29094, and v-miRNA-N-29443) encoded by nucleocapsid (N) gene. Thus, the above-mentioned pieces of evidence support our findings.
Next, we predicted the mature miRNAs from the unique pre-miRNAs of Reference, Delta, and Omicron. These mature miRNAs were used for target prediction against the human genome. The predicted target genes were functionally enriched using the DAVID webserver. The GO and KEGG pathway analysis of enriched target genes revealed the unique terms for Delta and Omicron strains. KEGG pathways like human cytomegalovirus infection, breast cancer, apoptosis, axon guidance, neurotrophin signaling pathway, and acute myeloid leukemia were found to be unique for Delta whereas the sphingolipid signaling pathway was found for the Omicron. The predicted miRNAs may affect genes that cause the coinfection or reactivation of Cytomegalovirus infection, which is reported widely during the COVID-19 and thought to be related to overlapped innate immune pathways and reactivation by inflammation (Alanio, 2022, Aldehaim et al., 2022). The other important unique pathway found in our study is the axon guidance pathway, the genes of which might be regulated by predicted miRNAs. The axon guidance pathway aid in guiding the axons to their particular target for the formation of synaptic connections (Russell and Bashaw, 2018). El-Aarag et al. (El-Aarag et al., 2022) in their differential gene expression study using transcriptome data also identified axon guidance as a potential pathogenic and highly enriched pathway in COVID-19. Moreover, the authors found two genes, SEMA7A and EPHA4 which are up and down-regulated respectively in the axon guidance pathway. In our study, we also found two genes, SEMA5A and EPHA7 of the same families that were targeted by predicted miRNAs and may result in neurological complications associated with COVID-19.
Another pathway related to the neuronal system found in our study is the neurotrophin signaling pathway which regulates the neuron's survival, growth, and differentiation during development by the action of secreted proteins known as neurotrophins or nerve growth factor (NGF) (Mitre et al., 2017). Impairment of central nervous system (CNS) functions during different viral infections including COVID-19 can lead to neurological disorders has been documented previously with special emphasis on the neurotrophin signaling malfunction (Bohmwald, 2022). Besides this, a recent study also specified the significance of viral miRNAs in targeting the gene related to the neurotrophin signaling pathway (Arisan, 2020). We found 5 enriched genes, BEX3, FRS2, KRAS, SOS1, and SH2B3 associated with this pathway. For the Omicron strain, we got 4 enriched genes (ROCK2, KRAS, FYN, and GNAI1) related to the sphingolipid signaling pathway. The role of sphingolipids in COVID1-19 related lung damage has been recently reviewed by Khan et al. (Khan et al., 2021) shows that disruption in the sphingolipid pathway induces the hyperinflammatory response known as cytokine storm.
Specifically, the enriched target genes of Delta and Omicron were matched with the Reference with the help of Genevenn which provides the unique target genes (Table 3) in each GO term and KEGG pathway. These genes were specifically targeted by predicted miRNA of Delta and Omicron in comparison to Reference. Furthermore, the GEO dataset was used for validation of predicted targets which revealed both up and down-regulated related to target genes. Usually, it is believed that the miRNAs downregulate the gene expression by the process called RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) but due to the emergence of knowledge about a new phenomenon, known as RNA activation (RNAa) which involves upregulation of gene expression, these short molecules thought to be controlling the expression of many target genes (Kwok et al., 2019).
In our validation study, we found target genes tend to be distributed among the both down-regulated and up-regulated gene set. For example, in Reference, ectopic expression of the down-regulated target gene, Fibrinogen-like 2 (FGL2) has been found to be linked with the pathogenesis of viral infections such as HIV, hepatitis B, C, and SARS due to its immunoregulatory and anti-inflammatory role (Yang and Hooper, 2013). TNSF12 gene encodes a protein that belongs to the tumor necrosis factor superfamily (TNSF), members of which play role in the activation, proliferation, and migration of immune cells into the CNS (Sonar and Lal, 2015). The target gene OGN also known as mimecan has a role in bone formation, tumor suppressor, and T-cell recruitment (Dimberg, 2018). GDF11 (Growth Differentiation Factor 11) also known as Bone Morphogenetic Protein (BMP11) plays a vital role in several physiological processes such as proliferation, differentiation, and apoptosis. The activation of the BMPs signaling pathway helps in reducing the deleterious effects of TGF-β signaling and thus assists in preventing inflammation, the main reason behind multiple organ failure in COVID-19 (Carlson et al., 2020). RSU1 (Ras Suppressor-1) gene was validated for Delta in the Severe vs Mild sample. This gene act as a tumor suppressor gene for various cancer types such as breast cancer, liver cancer, and glioblastoma (Louca et al., 2020).
We observed some validated target genes were up-regulated in the DEG gene set. Among those, MMP14 (Matrix Metalloproteinase-14) was also found to be up-regulated in lung biopsy samples of COVID-19 patients in a proteomic study (da Silva-Neto, 2022). Furthermore, TNFRSF21 (TNF Receptor Superfamily Member 21) also known as death receptor might be up-regulated in response to virus infection. This receptor tends to increase the inflammatory reaction and cellular apoptosis by activation of the JNK and NF-kappaB pathways (Benschop et al., 2009). SGMS1 (Sphingomyelin Synthase 1) was identified as an effector of interferon signaling in an epigenome-wide association study in severe COVID-19 patients (Castro de Moura, 2021). Besides this, the role of sphingomyelin (SM) (produced by SGMS1) in promoting Japanese encephalitis virus (JEV) attachment and subsequent infection has been previously deciphered (Taniguchi, 2016). TMEM192 (Transmembrane protein 192), is a novel protein of the lysosomal membrane, the deficiency of which increased apoptosis whereas the upregulation might contribute to the decrease in apoptosis of virus-infected cells (Nguyen, 2017). Interestingly, ZEB2, a zinc-finger E homeobox-binding protein was found to be a common validated target among all three strains in the Severe vs Mild sample. ZEB2 function as a transcription factor and is overexpressed in many types of cancers (Skrypek, 2018). Moreover, the role of ZEB2 has been elucidated in the stimulation of Age-associated B cells (ABCs) and cytokine receptor signaling in response to infection (Gu, 2021). The lack in the number of validated target genes among the Delta and Omicron strains could be due to the novel target genes that may be targeted by unique miRNAs of these two VOCs.
To conclude, the new miRNAs encoded by Delta and Omicron strains were found to target novel genes, the protein products of those involved in various types of cancers, and cytokine signaling.
Overall, our study provides insight into the change in coding potential of miRNAs in the Delta and Omicron strains that might occur due to a large number of mutations in the Reference strain. Additionally, the unique target genes of the Delta and Omicron miRNAs were retrieved and explored for GO and KEGG pathways terms that can help in understanding the COVID-19 related complications and comorbidities. The study also paves the path for future surveillance of the SARS-COV-2 genome for change in the coding potential of miRNAs due to adaptable mutations. The tracing of the novel miRNAs in the COVID-19 VOCs might prove helpful in the early clinical diagnostics of the VOCs because these unique small molecules can act as biomarkers for the different VOCs. Besides this, the unique target genes of novel miRNAs of each VOC could be used for understanding the diseased etiology and thus can be proved biologically significant for the health and survival of the infected population.
However, we have acknowledged some study limitations because the work is based on the in-silico concepts and methodology and needs further validation using in-vitro or in-vivo methods to validate the potential encoding of miRNAs and their target genes. Apart from that, the current study used the genomic sequence of Omicron sublineage B.1.1.529, and thus the predicted miRNAs might not be applicable to other sublineages like BA.2 or BA.3 that contain different unique mutations compared to BA.1. Thus, the future studies will be needed for those sublineages therefore we suggested above the continuous genomic surveillance for change in coding potential of miRNAs.
To the best of our knowledge, our study constitutes the first analysis of the impact of mutations on the change of coding potential of miRNAs in VOCs (Delta and Omicron).
5 Conclusion
Nowadays, it becomes in demand to decipher the effect of viral miRNAs on host target genes to understand the disease etiology. Since the onset of the COVID-19 pandemic, many studies unraveled the effect of SARS-CoV-2 miRNAs on human target genes. In this study, we predicted the viral miRNAs from SARS-CoV-2 Reference, Delta, and Omicron strains. A sequence alignment analysis of pre-miRNAs of the strains revealed unique pre-miRNAs encoded by Delta and Omicron strain as compared to Reference. Furthermore, the target genes of the mature miRNAs were retrieved for gene ontology and KEGG pathway analysis uncovered unique target genes of Delta and Omicron strains as compared to Reference. The pathways such as Human cytomegalovirus infection, Breast cancer, Apoptosis, Neurotrophin signaling, and Axon guidance were found for the enriched target genes of the Delta strain whereas the Sphingolipid signaling pathway was found for the Omicron. Moreover, GEO validation of enriched target genes revealed some genes such as FGL2, TNSF12, OGN, GDF11 for reference, and RSU1 for Dela strains in the down-regulated gene set. We also observed a few target genes of Reference like MMP14, TNFRSF21, SGMS1, and TMEM192 in the up-regulated gene set. ZEB2, a transcription factor was validated for all three strains in the up-regulated gene set.
This study has been performed using various in-silico tools for predicting the change in miRNAs coding potential of VOCs (Delta and Omicron) and hence needs validation by laboratory works that could further validate these predicted miRNAs and their target genes. Moreover, this work constitutes a contribution towards the future surveillance of change in the coding potential of miRNAs and their target genes that may occur due to adaptable mutational changes in SARS-CoV-2 strains.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
CRediT authorship contribution statement
Sandeep Saini: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, Writing – review & editing. Savi Khurana: Writing – original draft, Data curation. Dikshant Saini: Data curation. Saru Rajput: Data curation. Chander Jyoti Thakur: Data curation. Jeevisha Singh: Data curation. Akanksha Jaswal: Data curation. Yogesh Kapoor: Data curation. Varinder Kumar: Data curation. Avneet Saini: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, 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.
Appendix A Supplementary material
The following are the Supplementary data to this article:Supplementary data 1
SARS-CoV-2-Reference pre-miRNAs sequence data.
Supplementary data 2
SARS-CoV-2-Delta pre-miRNAs sequence data.
Supplementary data 3
SARS-CoV-2-Omicron pre-miRNAs sequence data.
Supplementary data 4
Secondary structures of predicted pre-miRNAs.
Supplementary data 5
Target gene list of Reference, Delta and Omicron.
Supplementary data 6
GO and KEGG pathway data of Reference target genes.
Supplementary data 7
GO and KEGG pathway data of Delta target genes.
Supplementary data 8
GO and KEGG pathway data of Omicron target genes.
Supplementary data 9
Unique and Common GO and KEGG pathway terms.
Supplementary data 10
GEO gene list of Healthy vs Mild sample.
Supplementary data 11
GEO gene list of Healthy vs Severe.
Supplementary data 12
GEO gene list of Severe vs Mild.
Acknowledgement
The authors would like to acknowledge the scientific community for depositing and sharing the genome sequence data of SARS-COV-2 and its variants at NCBI. The authors are also thankful to DST (Department of Science and Technology) and DBT (Department of Biotechnology), Government of India for providing the infrastructural facility to the Department of Bioinformatics, Goswami Ganesh Dutta Sanatan Dharma College, Chandigarh.
Data availability
All data generated and analyzed during this study are included in the main manuscript and supplementary files.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.gene.2022.147097.
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| 36470485 | PMC9721428 | NO-CC CODE | 2022-12-07 23:22:08 | no | Gene. 2023 Feb 15; 853:147097 | utf-8 | Gene | 2,022 | 10.1016/j.gene.2022.147097 | oa_other |
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Biochim Biophys Acta Gen Subj
Biochim Biophys Acta Gen Subj
Biochimica et Biophysica Acta. General Subjects
0304-4165
1872-8006
Elsevier B.V.
S0304-4165(22)00206-9
10.1016/j.bbagen.2022.130288
130288
Article
Ferritin nanocages as efficient nanocarriers and promising platforms for COVID-19 and other vaccines development
Reutovich Aliaksandra A. a
Srivastava Ayush K. a
Arosio Paolo b
Bou-Abdallah Fadi a⁎
a Department of Chemistry, State University of New York, Potsdam, NY 13676, USA
b Department of Molecular and Translational Medicine, University of Brescia, 25121 Brescia, Italy
⁎ Corresponding author.
5 12 2022
5 12 2022
13028823 10 2022
23 11 2022
28 11 2022
© 2022 Elsevier B.V. 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.
Background
The development of safe and effective vaccines against SARS-CoV-2 and other viruses with high antigenic drift is of crucial importance to public health. Ferritin is a well characterized and ubiquitous iron storage protein that has emerged not only as a useful nanoreactor and nanocarrier, but more recently as an efficient platform for vaccine development.
Scope of review
This review discusses ferritin structure-function properties, self-assembly, and novel bioengineering strategies such as interior cavity and exterior surface modifications for cargo encapsulation and delivery. It also discusses the use of ferritin as a scaffold for biomedical applications, especially for vaccine development against influenza, Epstein-Barr, HIV, hepatitis-C, Lyme disease, and respiratory viruses such as SARS-CoV-2. The use of ferritin for the synthesis of mosaic vaccines to deliver a cocktail of antigens that elicit broad immune protection against different viral variants is also explored.
Major conclusions
The remarkable stability, biocompatibility, surface functionalization, and self-assembly properties of ferritin nanoparticles make them very attractive platforms for a wide range of biomedical applications, including the development of vaccines. Strong immune responses have been observed in pre-clinical studies against a wide range of pathogens and have led to the exploration of ferritin nanoparticles-based vaccines in multiple phase I clinical trials.
General significance
The broad protective antibody response of ferritin nanoparticles-based vaccines demonstrates the usefulness of ferritin as a highly promising and effective approaches for vaccine development.
Graphical abstract
Unlabelled Image
Keywords
Ferritin
Vaccine
Antigen
Drug delivery
Nanoparticles
COVID-19
Bioengineering
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pmc1 Introduction
The COVID-19 pandemic has had devastating consequences on the population, economic, social, and health systems. According to the WHO statistics [1], there have been over 600 million confirmed cases worldwide including ~6.6 million deaths, as of November 2022. Despite the fact that a large number of nations have employed non-pharmaceutical interventions such as personal protective equipment, social distancing, wide-spread testing, contact tracing, and shutdown measures to contain the spread of the virus, it became evident that long-term control of the virus would require effective vaccines. The causative agent, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a single-stranded positive-sense RNA (+ssRNA) virus containing four major structural proteins including spike (S) glycoprotein, small envelope (E) glycoprotein, membrane (M) glycoprotein, and nucleocapsid (N) protein, in addition to several accessory proteins. The spike (S) glycoprotein mediates entry into host cells and has been the major target of vaccine approaches [2,3]. A great effort of scientific research and global coordination resulted in an unprecedented rate of vaccine development and rollout leading to more than 40 vaccines being approved for general or emergency use [4] and over 12 billion doses given worldwide as of mid-2022 [5,6]. This success in vaccine development, however, has been impeded by the emergence of SARS-CoV-2 variants including alpha (B.1.17.7), beta (B.1351), epsilon (B1429), delta (B.1.617) and omicron (B.1.529) that evade vaccine-induced immunity [7,8], posing issues for viral transmission and global vaccination efforts. Consequently, there is a critical need for vaccines that offer high levels of immunogenicity, safety, and cross-protection between viral variants for SARS-CoV-2 and future pandemics.
A vast array of vaccine platforms of different generations such as virus-based (first generation), subunit-based (second generation) and RNA- or DNA- based (third generation) technologies have been employed in the development of a safe and more effective SARS-CoV-2 vaccine, with over 160vaccines in clinical development and 200 in pre-clinical development as of August 2022 [6]. Despite the advantages of RNA/DNA-based vaccines, considerable hurdles are associated with their efficacy including premature degradation of molecules, and adjuvant and booster-shot requirements [9,10]. The last few decades have seen a considerable amount of research in the field of nanocarrier based vaccine delivery technologies including inorganic, lipid, polymeric, virus-like, micelle, and protein nanoparticles, which have been shown to improve antigen structure and stability [10] as well as provide broader [9,11,12] and more efficacious immune responses [13]. Among the multitude of nanocarriers, protein nanoparticles are particularly sophisticated and attractive targets for vaccine nanobiotechnology due to their biocompatibility, and flexibility of design by protein engineering [14,15]. Protein nanoparticles possess three distinct components that make them suitable for vaccine delivery: (i) a hollow interior cavity that can be loaded with vaccine antigens or nucleic acid cargos; (ii) an exterior surface that can be engineered to display vaccine antigens; and (iii) interfaces between subunits that may be engineered to allow controlled release [14,16,17]. One such protein nanoparticle that has emerged as a promising platform for the SARS-CoV-2 vaccine is ferritin, a ubiquitous iron storage and detoxification protein that protects cells from iron-induced oxidative damage [18]. Indeed, ferritin's remarkable chemical and thermal stability, reversible assembly and disassembly processes, and ability for engineering to display antigens has made the protein an attractive vaccine platform among other nanobiotechnology applications [19].
In this review, the structural and functional properties of ferritin is discussed, followed by an overview of ferritin nanoparticles production, purification, and functionalization and their applications to the field of nanomedicine, especially as a vaccine platform to augment immune response and enhance variant cross-protection.
2 Structural/functional, thermostability, and self-assembly properties of ferritin
2.1 Structural/functional
Ferritins are a family of highly conserved supramolecular nanostructures that play a critical role in iron homeostasis through sequestering thousands of Fe(III) atoms, protecting the cell from reactive oxygen species that may form from labile ferrous ions, and storing the oxidized iron in a mineralized core that is available for biological use [18,[20], [21], [22], [23], [24]]. The classical ferritin found in bacteria, plants, and animals is composed of 24subunits, each of which is a 4-helical bundle, that assemble in octahedral (4/3/2) symmetry and feature a hollow spherical structure with an outer diameter of 12 nm and an internal cavity with a diameter of 8 nm [18,25] (Fig. 1 ). While these ubiquitous ferritins have highly stable 24-mer protein nanostructures, the hyperthermophilic obligate anaerobe ferritin from Thermotoga maritima exists naturally as a dimer that can reversibly associate into a 24-mer nanocage at high protein concentrations or in the presence of iron or dissociate back into dimers at low ionic strengths [26]. Mammalian ferritin exists largely as heteropolymers (apart from serum and mitochondrial ferritins) consisting of two distinct subunit types, H (heavy, ~21 kDa) and L-type (light, ~19 kDa), which co-assemble in various ratios (isoferritins) with a tissue-specific distribution [18,[20], [21], [22], [23], [24], [25]]. On the other hand, plant and bacterial ferritins are composed of one type of subunits (i.e. H- or H-like subunits) [18]. In addition, some bacteria and archaea produce other types of ferritins such as bacterioferritin where a heme moiety is found at the interface between subunit dimers, and/or a smaller 12-subunit mini-ferritin named DNA-binding proteins from starved cell [25].Fig. 1 (A) Computer model of a human heteropolymer ferritin with 70% H subunits (cyan) and 30% L subunits (red). (B, C, D) Ferritin view through the 4-fold (B), the 3-fold (C) and the 2-fold (D) channels. (E) Schematic view of an individual ferritin subunit showing the five-helices (A, B, C, D, and E) and the di‑iron ferroxidase center residues. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 1
The H-subunit possesses a dinuclear ferroxidase center (Fig. 1E) that rapidly catalyzes the oxidation of Fe(II) to Fe(III), whereas the L-subunit lacks such center and consequently oxidizes iron at a much slower rate and are thought to play a role in iron mineral formation [18,[20], [21], [22], [23], [24], [25], [26], [27]]. Generally speaking, L-rich ferritins are characteristic of organs that store relatively large amounts of iron (≥ 1500 Fe atoms/molecule), while H-rich ferritins are found in organs with a low average iron content (≤ 1000 Fe atoms/molecule) [21]. While the mechanism through which ferritin H and L subunits co-assemble remains elusive, such assembly is likely a specific phenomenon since random interactions between H- and L-subunits would have led to the formation of isoferritin mixtures [28] in each tissue or organ, ranging from homopolymer H and H-rich ferritins, to L-rich and homopolymer L-ferritins. In vitro, heteropolymer ferritin reconstitution is a tedious process that yields very low amounts of functional proteins and may not represent naturally occurring ferritins; it occurs through denaturation and unfolding of recombinant homopolymers H- and L-subunits in high urea concentration and acidic or basic pH, followed by “renaturation” of the two subunits at neutral pH [21,[29], [30], [31], [32], [33], [34]]. In vitro ferritin reconstitution shows a clear preference for the formation of heteropolymers over homopolymers with a remarkably narrow distribution, suggesting the presence of preferential interactions between H and L chains [21,29,30,34]. This specific recognition is consistent with the fact that H and L homopolymers are poorly populated in mammalian tissues.
The 24-subunits of the highly conserved tertiary structure of classical ferritins are tightly packed together to create eight 3-fold, six 4-fold, and twelve 2-fold channels (Fig. 1), which are crucial to self-assembly and thought to provide permeation pathways for metal ions and a variety of small molecules [18,[21], [22], [23], [24],35,36]. Of particular interest are the 3-fold channels which are shown to be important for the transfer of iron ions through the protein shell to the di‑iron ferroxidase centers [[27], [28], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40], [41], [42]]. In theory, fewer ferritin ferroxidase centers decrease the rate of iron oxidation, and therefore, the rate of iron deposition and core formation and should lead to more crystalline iron cores. Differences in the rates of biomineral formation, biomineral order, degree of crystallinity, and iron turnover have been observed in natural ferritins and found to be partially associated with their subunit composition [[43], [44], [45]]. Whereas the structure and core crystallinity of ferritin iron minerals across species can be quite variable with varying degrees of crystallinity, disordered mineral cores in animal ferritins are mostly observed in L-rich heteropolymers having a large number of catalytically inactive L-subunits, as those found in livers and spleens. In contrast, a more ordered ferritin core is typically reported in H-rich heteropolymers having more catalytically active H-subunits, as those found in hearts and brains [21,46]. These differences between natural ferritins and recombinant homopolymer ferritins suggest that the morphology of the ferritin iron core depends on the ferritin subunits' composition and is affected by the number of nucleation and ferroxidase sites present on the protein shell [47]. Additionally, the crystallinity of the mineral core has been shown to be related to the phosphate content of the iron core, varying from amorphous in plants and microbial ferritins having Fe:P ratio of ∼1:1 to nanocrystalline in animal ferritins with Fe:P of ∼8:1 [18,[48], [49], [50], [51], [52]]. A recent study from our laboratory employing spectroscopy and scanning transmission electron microscopy (STEM) revealed striking differences in the iron oxidation and mobilization kinetics and the resulting morphologies of the iron core; recombinant L-rich human ferritin exhibits spherical iron core morphologies whereas recombinant H-rich human ferritin showed more irregular core morphologies with rod and crescent like features [53], suggesting that the structure of the iron mineral may have a profound impact on the iron core reactivity with important implication on ferritin iron management in vivo.
2.2 Thermostability
Owing to the unique architecture of the polymeric ferritin nanocage, ferritins exhibit remarkable resistance to physical and chemical denaturation. Native and recombinant mammalian ferritin have shown to withstand temperatures up to 100 °C [[54], [55], [56]]. Moreover, hyper-thermostability has been demonstrated in plant ferritin (soybean seed ferritin exhibited a melting point (Tm) of 106 °C) owing to an extra peptide (EP) domain at the N-terminal that is found in mature plant ferritin [55], in M. japonicus ferritin (Tm of 109 °C) [57], as well as in ferritin from hyperthermophilic archaeal anaerobe P. furiosus which can withstand incubation at 100°C for 1 day or autoclaving at 120°C for half an hour without loss to activity [58,59]. Furthermore, resistance to chemical denaturation by agents such as urea and guanidium chloride [54,60], and pH stability (pH 3–10) [61] has been reported for ferritin, making it an ideal candidate for nanobiotechnology applications. Using an engineered plasmid design that enables the synthesis of complex ferritin nanostructures with specific H to L subunit ratios (Fig. 2 ), more recent work from our laboratory showed that homopolymer L- and heteropolymer L-rich ferritins exhibit a remarkable hyperthermostability (Tm = 115 ± 1 °C) compared to H-rich ferritins [62]. The results revealed a significant linear correlation between protein thermal stability and the number of L subunits present on the ferritin shell. To our knowledge, this is the first report of recombinant human homo- and hetero-polymer ferritins that exhibit surprisingly high dissociation temperatures, the highest among all known ferritin species, including many known hyperthermophilic proteins and enzymes. This extraordinary thermostability may facilitate the use of human ferritin in a variety of applications, from a robust bio/nano template for the design of bio/nano materials, to the encapsulation and delivery of bioactive compounds and drugs.Fig. 2 (A) Computer generated models of human ferritin (homopolymers and heteropolymers) depicting an H24:L0 (homopolymer H-ferritin), H19L5, H12L12, and H7L17heteropolymers, and an H0:L24(homopolymer L-ferritin).
Fig. 2
2.3 Self-assembly
Structural studies have revealed that the monomers of ferritins self-assemble into a 24-meric cage with octahedral symmetry through a series of assembly intermediates. The overall assembly mechanism of horse spleen apoferritin was first investigated by Gerl et al. [63,64] who monitored the protein's reconstitution using intrinsic fluorescence, far-UV circular dichroism, and glutaraldehyde cross-linking experiments and proposed that the complete self-assembled 24-mer forms from monomers and dimers via tetramers and hexamers.
In a study employing time-resolved small-angle X-ray scattering (SAXS), Sato et al. demonstrated the time-dependent changes in the SAXS profiles of E. coli ferritin during its pH induced reassembly process and found that monomers and trimers are unlikely intermediates in the self-assembly process due to their unstable nature [65]. These differences may be attributed to differences in self-assembly between ferritins of varying subunit composition (i.e., heteropolymer of H and L in horse spleen ferritin vs. homopolymer H ferritin in E. coli ferritin), or limitations in the ability to identify all possible oligomer intermediates using SAXS data. Mohanty et al. tracked the kinetics of bullfrog M ferritin (structural and functional analogue of human H ferritin) self-assembly by laser light scattering and observed a biphasic profile during assembly kinetics consisting of a rapid folding phase of partially unstructured monomers/dimers into unidentified oligomers, followed by a slower reassembly/reorganization process to form the 24-mer within 10 min, the rates of which were accelerated by increasing protein concentration and ionic strength [66]. An interesting area of research particularly useful to the field of nanobiotechnology involves understanding ferritin's propensity to form heteropolymers made of different ratios of H-and L-chain subunits (in mammals), the driving forces that influence subunits assembly and their distribution/arrangement around the 24-mer ferritin molecule. Carmona et al. monitored the self-assembly process of human ferritin heteropolymers by fluorescence resonance energy transfer (FRET) technology using conjugate fluorophores bound to exposed residues on the subunits and found that the presence of L-chains displaced the formation of H—H dimers, thereby demonstrating preferential formation of H/L heterodimers over the homopolymers [34]. Additionally, it was found that the H-chain distribution on the heteropolymeric ferritin shell is not random, but instead occupies preferential sites at distant positions up until the number of H-subunits on the heteropolymer reaches 8, at which point they begin to co-localize [34]. This observation coincided with the ferroxidase activity increasing with the H-chain content until a plateau of 8H-chains per shell was reached, suggesting that the steric distribution of ferroxidase activity present on the H-chains is not random, and plays a role in the functionality of the protein [34]. To study ferritin self-assembly, mutational studies have been performed to assess the role of various residues and regions on the assembly and function of the ferritin nanocage and are elegantly reviewed by Zhang and Orner [67], as well as by Jin et al. [68]. Mutational studies showed that the C-terminus region has a major role in ferritin stability and assembly capacity but may be protein specific. For instance, deletion of the E helix from E. coli bacterioferritin resulted in a protein that existed solely as a dimer [69], demonstrating that the E helix is fundamental to the formation of higher order oligomers in bacteria, however, the E-helix was demonstrated to be non-essential for human ferritin H-homopolymer assembly [70,71]. Nonetheless, mutations around the 4-fold channel or in the DE loop (Fig. 1E) can elicit conformational changes. For instance, alteration of the hydrophobic profile of the 4-fold channel results in an incorrect “flop” conformation where the E-helix points outside the ferritin cavity [70]. On the other hand, mutations to the N-terminus region, which points outside of the ferritin cage, has been demonstrated to be less deleterious to proper self-assembly. For instance, deletion of the first thirteen residues at the N-terminus of human H-chain ferritin resulted in a fully assembled and functional protein [72]. Although the overall mechanism of ferritin self-assembly remains to be solved, numerous research groups have proposed and executed methods to control ferritin's self-assembly and structure-function relations to synthesize nanomaterials which will be discussed in the interface modification section of this review.
3 Preparation of ferritin nanoparticles
3.1 Production and purification of ferritin
Because the ferritin family of proteins is found in all kingdoms of life, there exists numerous methods, albeit of varying complexities, to isolate and purify the protein. Simple ferritin nanoparticle constructs are successfully produced in E. coli and then purified, but bacteria lack the ability to express glycosylated proteins and/or complex antigens. Thus, modified ferritin nanoparticles that require mammalian post-translational modifications must be produced in eukaryotic cells [73]. The simple recombinant ferritins derived from bacteria are typically synthesized by transforming the cells with vectors that express H and/or L subunits under a strong and inducible promotor [74]. Despite the discovery of recombinant human heteropolymer ferritins more than 8 decades ago, difficulties in cloning, expressing, and reconstitution of heteropolymer H/L ferritin have led to studies of ferritins being performed almost exclusively with homopolymer ferritins [74]. Our lab has engineered a novel plasmid design that enables the synthesis of ferritins of various H to L ratios (Fig. 2) depending on the concentrations of two inducers to mimic natural human heteropolymers [74]. Regardless of the H to L subunit composition, ferritin from bacterial cultures is purified through a series of steps in which the cells are disrupted through sonication, clarified by centrifugation to remove debris, then heat treated (70–75 °C) and centrifuged to denature and precipitate up to 80% of native E. coli proteins and leave only the thermostable ferritin in solution [74,75]. Alternatively, or in tandem with heat treatment, the supernatant can be subjected to protein precipitation through incubation with ammonium sulfate where native E. coli proteins precipitate and ferritin remains soluble [74,75]. Following precipitation and clarification steps, bacterial derived ferritin may be further purified through size-exclusion chromatography (SEC), differential centrifugation and/or ion-exchange chromatography (IEC) [75]. The precipitation steps may be skipped entirely to preserve the integrity of antigen/epitope whereby ferritin nanoparticles may be separated by (i) affinity chromatography using an inserted His-tag on the protein [76] or (ii) IEC followed by SEC [75]. On the other hand, purification of ferritin from animal cell cultures is rather straightforward, requiring simple centrifugation or filtration followed by further purification through chromatographic approaches [75]. Protein quantification may be achieved using established protocols including the BCA assay at A280nm, whereas overall structure and composition can be characterized through native and SDS PAGE, or methods such as mass spectrometry, capillary electrophoresis, dynamic light scattering (DLS), transmission electron microscopy (TEM), circular dichroism (CD), among others. Recombinant ferritin often contains a significant amount of iron within its mineral core upon purification and depending on the final application the iron core may be removed by reduction followed by chelation [[77], [78], [79]].
The formulation of ferritin-based vaccines, their design, production, and purification have been discussed elsewhere [75]. Several studies have used ferritin from different sources and different expression systems for ferritin-based vaccine production, including A. fulgidus, T. ni, and P. furiosus ferritins [75]. In other studies, recombinant ferritins have been expressed in different hosts (i.e. in animal cells like human embryonic kidney (HEK293) or Chinese hamster ovary (CHO)) with different levels of glycosylation. In some cases, un-glycosylated forms of ferritins (such as those produced in E. coli) or through point mutations of N-glycosylation sites such as in H. pylori have the benefits of preventing undesired interactions between the glycan and certain chemical groups on the antigens or the proteins of interest that are fused onto ferritin [75]. These mutations allow the antigen to acquire a native tertiary structure and potential immunogenicity.
4 Interface modification and encapsulation
Ferritin's cage architecture as well as its self-assembly and disassembly properties has been exploited for its ability to incorporate active molecules within the protein structure such as peptides, drugs, and imaging agents. While the ferritin protein has remarkable stability, it has been shown to disassemble into subunits under strongly acidic (pH < 2–3) or basic (pH 11–12) conditions and reassemble at neutral pH conditions [80]. Li et al. demonstrated that ferritin dimers maintain their secondary and tertiary structures at acidic pH, thereby substantiating that the dimer is the essential unit to ferritin's self-assembly [81]. This phenomenon has been taken advantage of through reassembling the protein by increasing the pH in the presence of highly concentrated cargo materials, in such a way that many molecules, statistically, become encapsulated within the ferritin cavity [[82], [83], [84]] (Fig. 3 ). However, this technique typically suffers from low protein yields (only 60% recovery), loss of the protein structure (hole defects), low encapsulation efficiency, and loss of cargo bioactivity associated with the harsh pH treatment required for loading the ferritin cage [80,[85], [86], [87]].Fig. 3 Schematic of direct cargo loading into ferritin (top row) or acid-denaturation/disassembly process in the presence of guest molecules (bottom row) followed by reassembly and refolding at neutral pH.
Fig. 3
Similarly to pH-dependent disassembly, high concentrations of chaotropic agents such as urea (8 M) could accomplish disassembly of the 24-mer through destroying its non-covalent forces [88]. Subsequent dialysis of the disassembled protein in stepwise gradients of urea from 8.0 M to 0 M in buffer reassembles the ferritin shell [88]. For this method, however, the formation of soluble aggregates and insoluble precipitates is common, leading to low ferritin recovery yields [89]. In recent years, researchers have sought to solve the issue of cargo and ferritin-shell integrity through developing milder approaches with higher efficiency to encapsulate guest molecules within ferritin nanocages. Chen et al. employed genetic engineering to delete the last 23 amino acids involved in the formation of the DE-turn and E-helix on HuHF, resulting in a fully intact protein shell which can be dissociated into subunits at pH 4.0 and reassembled at pH 7.5 [89]. On the other hand, while the deletion of these amino acids had very little effect on the assembly of ferritin, Chen et al. found that the thermal stability of HuHF decreased to 72.7 °C [90]. Similarly, Yang et al. engineered phytoferritin (plant-ferritin) capable of disassembly at pH 4.0 and reassembly at pH 6.7upon deletion of its EP domain, though the α-helix content decreased by 5.5% and the melting temperature decreased by ~4 °C [90]. Recently, Gu et al. introduced His6 motifs at the 4-fold channel interfaces of two different recombinant ferritins, rHuHF and recombinant shrimp (Marsupenaeus japonicus) ferritin (rSF) which enabled the ferritin nanocages to disassemble into tetramers at pH 7.5, and reversibly reassemble back into intact protein nanocages upon increasing the pH to 10.0 or incubation with transition metal ions, owing to histidine motifs bearing a pKa of 6.6 and their tendency to form a coordination bond with transition metal ions [91]. His-mediated ferritin nanocage self-assembly allowed for encapsulation of curcumin and doxorubicin under relatively mild conditions meanwhile keeping the protein shell completely intact, as confirmed by X-ray crystal structure, and demonstrating markedly higher loading efficiency compared to the reported values for the pH dependent disassembly/reassembly encapsulation method [91]. Other physical methods including atmospheric cold plasma (ACP), pulsed electric field (PEF), and manothermosonication (MTS), have been employed to destabilize treated ferritin, allowing for disassembly at milder pH values (pH 3–4) and reassembly into intact ferritin cages at neutral pH, thereby avoiding the harmful effects associated with harsh acidic treatment [[92], [93], [94]]. Apart from the property of reversible self-assembly, ferritin channels, which allow small molecules to enter and exit the protein, have been exploited as a method for encapsulation. As mentioned earlier, high concentrations of chaotropic agents are able to dissociate ferritin into its subunits, however, low concentrations appear to expand the four-fold channels of phytoferritin and allow encapsulation of cargo. For instance, Yang et al. was successful in using 20mM urea and 2.0 mM guanidine hydrochloride (GnHCl) to encapsulate molecules into phytoferritin at comparable efficiency to complete chaotropic disassembly without protein damage, although it is important to note that these methods may trap urea/GnHCl molecules within the ferritin cavity [[95], [96], [97]].
While ferritin is able to withstand temperatures over 80 °C, the channels are reported to be more sensitive to temperature [60,98,99] with initial iron release rates reaching a maximum value at 60 °C in phytoferritin [99], suggesting that cargo may be loaded into the ferritin cavity through the expansion of the ferritin channels caused by temperature changes. Through crystal structure and protein mutation analyses, Jiang et al. showed that ferritin's four-fold channels expand from 0.9 nm to 1.0 nm upon thermal treatment (60 °C) and recovered completely to the original upon decreasing the temperature to 20 °C [100]. Notably, the thermal expansion channel-based drug loading strategy achieves higher drug loading efficiency, better protein yield, and better stability compared to pH or chaotropic agent dependent disassembly, however, it is worth noting that many bioactive molecules are sensitive to temperature and may degrade during such thermal treatment. By modifying interactions at the subunit-subunit interface in ferritin we may not only control self-assembly to encapsulate cargoes of interest but also potentially regulate the release of the contents at acidic pH in a target cell environment. In addition to the pH-dependent disassembly/reassembly strategy in acidic compartments, other cargo delivery strategies include genetic or chemical conjugations and surface functionalization of ferritin using peptides, antibodies, and functional ligands, have been employed [101]. In addition to ferritin's natural function to oxidize and store iron, ferritin can bind several other metal ions, especially divalent cations [102]. Taking advantage of this phenomena, several metal-based nanoparticles and metal-containing compounds can be encapsulated within the cavity of ferritin through incubation of a metal-salt solution with apoferritin and a reducing agent, followed by dialysis to remove unincorporated molecules [102]. Another metal-based assembly strategy designed by Huard et al. involves modifying the C2 interface of ferritin via reverse metal-templated interface redesign (rMeTIR) to form a ferritin cage whose assembly is dependent on Cu(II) binding, with cage-assembly being irreversible after removal of copper ions [103]. Ferritin from the hyperthermophilic archaeon Archaeoglobus fulgidus (AfFtn) was demonstrated to disassemble in solutions of low ionic strength and reassemble in high ionic strength [104] seemingly due to ionic charges alleviating electrostatic repulsion between the negatively charged residues at subunit interfaces [105]. Additionally, AfFtn was demonstrated to be able to self-assemble around highly charged cargo such as gold nanoparticles without adjusting the ionic strength of the medium [[106], [107]] raising the prospect of encapsulating larger cargo without complete disassembly of the nanocage.
5 Interior modifications
The interior cavity of ferritin is commonly modified to encapsulate, and better accommodate various cargos including diagnostic or therapeutic agents. While the negatively charged interior of ferritin can readily coordinate metal ions, it has been demonstrated that mutating interior-facing surface residues to hold a net positive charge can facilitate electrostatic-mediated encapsulation such as supercharged fluorescent proteins and nucleic acids [[108], [109], [110]]. Similarly, the inner surface of the ferritin nanocage may be engineered by genetically fusing hydrophobic peptides to the C-terminal end of ferritin subunits to increase absorption of insoluble, hydrophobic cargo [111,112]. Introducing cysteine residues to the interior of the nanocage has been a widely used approach to facilitate covalent attachment of dye molecules, drugs, or other active compounds through disulfide bonds [113] or to enable specific chemical reactions such as copper-free click chemistry [114].
6 Exterior modifications
Perhaps the most exploited component of ferritin in the field of nanomedicine, and the most pertinent to the scope of this review is the external surface of the protein nanocage, which can be bioengineered to display specific molecules. The goal of such modifications is to present antigens to immune cells, deliver encapsulated molecules to specific targets, increase functionality, or even increase their circulatory half-life and decrease their immunogenicity. Similarly to interior modification methods, chemical conjugations, and genetic fusions are commonly used to modify the exterior surface of ferritin to attach antigens and trigger specific cellular responses. Below, we provide a brief overview of the most commonly employed surface modifications methods (Fig. 4 ).Fig. 4 An illustration of different strategies for the functionalization of ferritin nanocages. For greater details, please refer to the section “Exterior Modifications” of the text.
Fig. 4
6.1 Genetic fusion
A protein of interest (POI) may be conjugated to ferritin's surface through genetic fusion of the gene encoding the POI to the N-terminal end of ferritin subunits, which are exposed to the protein's outer surface, using established recombinant DNA technologies for subsequent production in a chosen expression system [115]. From the expression of simple peptides to complex trimers, genetic fusion of POI on ferritin's external surface has had wide applications in the field of vaccine development, including the search for vaccines against influenza [[116], [117], [118]] Epstein-Barr virus [119], hepatitis C [73,120,121], HIV-1 [73,122], Lyme [123], RSV [124], and SARS-CoV-2 [[125], [126], [127], [128], [129], [130], [131], [132], [133]] which will be discussed in a later section. A pitfall to expressing a protein of interest on ferritin's exterior through genetic fusion, however, is that care must be taken to recombinantly express antigens in a way that does not impair their stability or conformation, nor impair ferritin's inter-subunit interactions during the self-assembly process [75].
6.2 Chemical crosslinking
The separate production, or modular assembly, of the nanoparticle and POI followed by their conjugation, is often used to bypass the steric issues that may arise from surface functionalization by genetic fusion [75]. One approach to chemical crosslinking involves the chemical treatment of a reactive residue on either the nanoparticle or the POI to conjugate with a reactive amino acid on the other respective protein, often exploiting conjugation of surface exposed cysteine (-SH group) residues with primary amines (−NH2) found in lysine residues, or less often hydroxyl groups found in serine and threonine residues [75,134]. The main drawbacks to this technique, however, are the lack of control over the number of cross-linkers attached or their orientation which can lead to the formation of aggregates or uneven decoration of the antigen on the surface of the protein nanocage which may hinder efficient presentation to target cells [135].
6.3 Intermediate based conjugation
To increase specificity of POI attachment to ferritin, intermediate based conjugation strategies may be appropriate. Chemically inducible dimerization (CID), or the use of a small molecule as an intermediate to induce the binding of two proteins, has been shown to successfully conjugate AOIs (antigen of interest) or POIs, to ferritin [75,136]. One example is the FKBP/FRB/rapamycin system, in which the FKBP and FRB peptides are genetically fused to the scaffold protein and POI and upon addition rapamycin form a ternary FKBP-FRB-rapamycin complex, which brings the POI to the target site on the scaffold protein [137]. Unlike classical crosslinking, CID allows for greater specificity, however conjugation via this method is restricted to the N- or C-terminals of the conjugated proteins as in the case of genetic fusion. Similarly to CID, enzyme-mediated conjugation involves using an enzyme intermediate to bind two peptides through activating certain residues to enable binding or conjugation between genetically encoded motifs [138]. However, unlike CID, enzyme-mediated conjugation does not restrict conjugation to the N- or C-terminals [75].
6.4 Click chemistry
Click chemistry involves the incorporation of unnatural amino acids (uaas) to the protein nanoparticle and AOI or POI, thereby introducing functionalized side chains such as those bearing biorthogonal reactive groups such as azides, alkynes, alkenes, and tetrazines, followed by their conjugation often through azide-alkyne coupling [139]. A commonly used system for the introduction of uaas relies on the generation of orthogonal aminoacyl-tRNA synthetases and tRNAs that function independently from those endogenous to the chosen biological expression system (such as E. coli) [140]. Using this approach, Khoshnejad et al. inserted the 4-azidophenylalanine (4-AzF) uaa at residue 5 of L-chain ferritin which allowed for conjugation of Dibenzylcyclooctyne (DBCO)-functionalized IgG, enabling specific pulmonary targeting in mice [141]. While click chemistry enjoys the benefits of high specificity and yield, the main drawback is the associated costs which would confer a great disadvantage in large scale productions such as vaccine manufacturing [75].
6.5 Genetically encodable linkers
The most common modular assembly strategy for protein-protein conjugation involves incorporation of genetically encodable peptide or protein tags. Most notable is the SpyTag (peptide tag of 13 residues) and SpyCatcher (protein of 138 residues) based conjugation mediated by the SpyLigase enzyme leading to irreversible isopeptide bond formation under a wide range of pH and temperature conditions [75,142]. Using this system, the Tag and the Catcher are genetically fused to the N- or C-terminal of both proteins, and upon mixing spontaneously form a stable covalent bond, thereby attaching the protein nanoparticle to the POI [75,142]. Other notable linkers include His tags (interact with Ni-NTA columns), Halotags (interact with chloroalkane-linked moieties), SNAP-tags (interact with alkyl-guanine ligands), Clip-tag (interact with benzyl cytosine derivatives), and biotin acceptor peptides (conjugation of biotin-linked moieties) [138]. As with genetic fusion, care must be taken when optimizing linkers for ferritin conjugation purposes in order to enable the correct assembly of the scaffold and displayed protein and thus enhance biological response [75].
6.6 “Stealth” coatings
In addition to displaying molecules on the ferritin nanocage for the purpose of eliciting an immune response, surface modifications could be engineered to increase half-life or improve delivery. For instance, glycosylated ferritin has been shown to be cleared at a slower rate by hepatocytes, therefore introducing glycans to ferritin's exterior surface would prolong half-life [75]. Similarly, “stealth” coatings such as polyethylene glycol (PEG) can be conjugated to the protein's surface which would mask the nanoparticle from cell receptors that may sense it as foreign agents, thereby increasing target specificity and improving gene delivery [143,144]. However, although ferritin biocompatibility and safety for human medicine development have been demonstrated, additional challenges remain including immunogenicity caused by cargo loading or ferritin surface functionalization, homogeneity of the final assembled product, and/or the immunogenic response of multi-antigen designs. Whereas a single antigen has limited immunogenicity, a highly organized supramolecular particle like ferritin can yield an improved immune response by forming a highly organized nano structure that mimics the original pathogen. Furthermore, because of its uniform structure, good plasticity, and desirable thermal and chemical stabilities, ferritin can be engineered to carry different antigens and expose immunogens in a repetitive and well-organized manner. Nonetheless, several studies have demonstrated great promises for the development of ferritin-based vaccines and the potential of these constructs to elicit immune responses against a wide range of pathogens, and some ferritin-based vaccines have already moved to Phase 1 clinical trials, as discussed below.
7 Biomedical applications of ferritin
7.1 Nanoreactor
The unique size, optical, magnetic, and chemical properties of inorganic nanoparticles (NPs) have shown to be highly useful in nanomedicine. However, several obstacles exist to their implementation as diagnostic and therapeutic agent including toxicity, biodistribution, and bioaccumulation [145]. When inorganic nanoparticles are introduced to a biological medium, they inevitably face significant physiochemical changes due to protein corona (PC) formation at the nanoparticle surface [105]. The adsorption of proteins on the surface of the nanoparticle can influence its body distribution, bioactivity, and cytotoxicity [146], which may or may not be beneficial to the diagnostic or therapeutic purpose of these nanoparticles. On one hand, the presence of a PC may increase biocompatibility of NPs, but on the other hand they can mask functionalized molecules on the NP surface thereby inhibiting their interaction with specific receptors on target cells, causing aggregation of the NPs, inducing conformational changes in the proteins, or increasing NP size and therefore affecting their biodistribution [147]. Furthermore, the size of nanoparticles can greatly affect their circulation, biodistribution, and clearance. For instance, smaller nanoparticles (< 20 nm) are more easily transported to the lymph nodes [148] and are most capable of perturbing membranes [149,150], whereas intermediate-sized nanoparticles (20–100nm) are most capable of circulating in the blood for long periods of time and avoiding clearance [148]. Superparamagnetic iron oxide, platinum, gold, silver, quantum dots (composed of group IIB-VIA or group IIIA-VA elements on the periodic table), and other inorganic nanoparticles have been demonstrated to be useful to a wide range of biomedical applications including targeted delivery of drugs, biosensing, imaging, antimicrobial therapy, and photothermal therapy [105,[150], [151], [152], [153], [154], [155]]. Drawbacks to their clinical use, however, includes their toxicity, induced by harmful metal ions or uncontrolled protein corona formation, poor solubility, and propensity to aggregate into structures larger than their optimal nano-size [152,154,156,157]. Ferritin's ability to restrict the size and shape of nanoparticles synthesized within the nanocage, enhance their solubility, mitigate their toxicity, and avoid nonspecific interactions due to PC formation. These highly desirable characteristics have allowed for the application of ferritin as an “inorganic material” in medicine [[152], [153], [154]]. For instance, apoferritin has been successfully used as a biotemplate for synthesizing CdSe, CdS, ZnSe, ZnS, PbS, and other quantum dots within its protein shell, mimicking ferritin's natural iron biomineralization process, and allowing for enhanced biocompatibility of these materials [151].
7.2 Nanocarrier
While significant progress has been made in the diagnosis of cancer over the past few decades, drug therapy still suffers from serious drawbacks including nonspecific distribution, uncontrolled drug release, and toxicity. Protein nanoparticles have the potential to overcome these obstacles by improving biocompatibility and tumor targeting ability. Seaman et al. demonstrated that human heavy chain ferritin can be recognized by the transferrin receptor-1 (TfR1) that is overexpressed on tumor cells [158] from various types of cancer, including lung and breast cancer [159]. In addition, ferritin has been shown to bind to a few other receptors including Tim-1, Tim-2, Scara5, and CXCR4 [160]. Ferritin's intrinsic tumor-selective properties, as well as the ability to engineer additional tumor-targeting moieties on its surface has been the subject of many studies on ferritin-coated chemotherapeutic drugs, ferritin in photothermal therapy (PTT) and ferritin in photodynamic therapy (PDT), and in bioimaging and other biomedical uses [[86], [87], [88],105,151,[158], [159], [160], [161], [162]] (Fig. 5 ). Since many chemotherapy drugs are hydrophobic, encapsulation within ferritin has been shown to dramatically increase their solubility and thus their biocompatibility, as well as achieve significant anti-cancer effects in cell and animal models [147]. Using encapsulation processes discussed earlier, many anti-cancer drugs have been loaded inside ferritin nanocages including doxorubicin, carboplatin, and cisplatin, exhibiting improved circulation time, decreased toxicity, and greater accumulation at the tumor sites [87,147,161,162]. Ferritin has also been used to encapsulate gadolinium and superparamagnetic iron oxide nanoparticles for application in magnetic resonance imaging (MRI), resulting in reduced toxicity, higher relaxivity of the MRI contrast agent, and significant signal increase with greater specificity for cancer cells [87,115,151,161,162]. The use of ferritin as a delivery platform for photothermal agents and photosensitizers, which generate heat and reactive oxygen species, respectively, has been shown to reduce the side effects of these therapies in normal tissues [115,148], improve cellular uptake [115], and inhibit tumor growth [87,115]. For thorough reviews on the encapsulation of inorganic nanoparticles, the use of ferritin nanoparticles in cancer therapies and diagnoses, and their application to nanomedicine and biotechnology, we refer the reader to several excellent reviews by Sun et al. [87], Jutz et al. [115] Mohanty et al. [151], Truffi et al. [161], and He et al. [162].Fig. 5 Schematic of various cargo loaded-ferritin represented by (Image 1).
Fig. 5
8 Vaccine platform
A vast array of vaccination platforms has been employed to circumvent diseases caused by pathogens throughout history. To date, vaccines are developed from attenuated or killed whole organisms, and RNA/DNA/protein subunits of viral pathogens. Despite the advantages of each method, there exists several drawbacks to their safety and efficacy. For instance, while inactivated, live-attenuated, or recombinant viral vaccines generally produce strong and long term immune responses, they are associated with more frequent adverse effects such as existing levels of immunity, reducing effectivity, and risk of residual illness [163]. RNA/DNA vaccines that code for a virulent antigen, while very safe, well tolerated, and fast to produce, often suffer from lower immunogenicity, and extensive logistical challenges for distribution and administration (i.e. very low temperature storage for mRNA-based vaccines) and the need for a special delivery system [163]. Subunit vaccines, in which a protein antigen from the pathogen is utilized to elicit an immune response, are an attractive option for reasons of safety and stability. However, they are typically less immunogenic and often require an adjuvant and/or multiple booster shots to elicit long term immune responses [163]. One strategy to overcome the lower levels of protective immunity of modern vaccines is the design of multiple antigen presenting nanoparticles that integrate various antigen components to improve antibody response [102,164]. Ferritin's architecture, biocompatibility, capacity for self-assembly into symmetrical, monodisperse architectures, ease of production with relatively large yields, and potential for engineering at multiple interfaces makes the protein an attractive target for the development of vaccines. This is corroborated by a recent study demonstrating that a minimum of 20–25 epitopes spaced by 5–10 nm are required to elicit sufficient B-cell activation [164]. Other studies have demonstrated that epitope-protein nanoparticles elicit stronger immune responses compared to soluble antigens [73]. Furthermore, intramuscular injections of spherical protein nanoparticles have been shown to have the ideal shape and size (~ 20–150 nm in diameter) to be taken up by peripheral or lymph node dendritic cells [165]. Altogether, the highly symmetrical architectures of the ferritin nanocages offer promising platforms for vaccine development, particularly in light of our ability to engineer their external surfaces with multi-copy antigen displays.
8.1 Influenza virus
The first ferritin vaccine, developed against an H1N1 influenza virus by Kanekiyo et al., featured the influenza virus haemagglutinin (HA) fused to the surface of Helicobacter pylori ferritin at the interface of adjacent subunits, generating eight trimeric viral spikes on its surface [116]. Immunization of mice with this influenza ferritin vaccine elicited antibody titers over 10-fold higher compared to the trivalent inactivated influenza vaccine (TIV) (current commercial vaccine), with a single immunization inducing an immune response comparable to two immunizations of the TIV vaccine [116]. Remarkably, the influenza ferritin vaccine demonstrated cross-reactivity against two independent highly conserved epitopes of H1N1 [116], suggesting a powerful potential use for a ferritin nanoparticle-based antigen display platform for a universal influenza vaccine. In a more recent work, Yassine et al. demonstrated that a ferritin-based nanoparticle immunogen displaying only the highly conserved stem region of the H1 HA glycoprotein was capable of eliciting broadly cross-reactive antibodies in mice and ferrets and was successful in protecting the immunized animals from lethal doses of the H5N1 influenza virus [117]. These powerful results in non-human animal trials prompted three vaccines against influenza to be examined in human Phase 1 clinical trials. In the first vaccine, Houser et al. developed a Helicobacter pylori ferritin nanoparticle influenza vaccine (NCT03186781) against an H2N2 strain and demonstrated a broad neutralizing antibody response after a single dose against not only H2-naïve and H2-exposed populations, but also against seasonal H1 and avian H5 subtypes, due to their targeting of the highly conserved HA stem domain [118]. The results of the other two vaccine trials, NCT03814720 and NCT04579250, are still underway.
8.2 Epstein-Barr virus
The receptor binding domains (RBDs) are viral fragments that allow attachment host cell membranes. They are the target of major neutralizing antibodies and have been investigated as vaccine candidates against numerous viruses. Despite being sites of vulnerability in viruses, isolated RBDs are often weakly immunogenic due to steric hinderance from surrounding glycans, and protein misfolding/destabilization/conformational changes in a soluble form [119]. To improve immunogenicity and circumvent these issues, nanoparticle technology has been employed to design immunogens with modified configuration that would otherwise be impossible to apply to nonrecombinant vaccine platforms. For instance, Kanekiyo et al. designed a ferritin-based nanoparticle immunogen displaying the conserved receptor-binding domain of the gp350 antigen from Epstein-Barr virus, a major envelope glycoprotein which is the target of neutralizing antibodies in naturally infected individuals [119]. In mice and non-human primates, the nanoparticles conjugated to the gp350 RBD generated neutralizing antibody responses that significantly exceeded those obtained with the soluble gp350 protein, suggesting that gp350 is conformationally stabilized when conjugated to the ferritin nanoparticles, thereby focusing the immune response more efficiently [119]. In recent developments, the gp350-ferritin nanoparticle vaccine with a saponin-based Matrix-M adjuvant is being investigated in a phase 1 clinical trial (NCT04645147), although the trial is expected to last for a few years, and developments have yet to be published.
8.3 Hepatitis C
Conventional approaches such as attenuated or live viruses and purified or recombinant viral proteins, though successful against a great range of pathogens, falter against highly variable viruses such as HIV-1 and hepatitis C virus (HCV), which have evolved mechanisms to evade host immunity. As a result, a great deal of effort has been made to develop vaccines directed towards conserved regions which produce broadly neutralizing antibodies (bnAbs) against a wide spectrum of genotypes. Recently, two cross-neutralizing antigenic sites of HCV glycoproteins E1 (residues 314–324) and E2 (residues 412–423) have been identified as major targets for the antibody response [120]. He et al. presented in silico studies of multivalent E1 and E2 epitopes on ferritin nanoparticles and found that conjugation of the epitopes to the ferritin nanoparticles significantly increased antibody binding [120]. A more recent in vivo study demonstrated that a ferritin nanoparticle vaccine displaying the E2 antigen markedly enhanced the neutralizing antibody response against several clinically adaptive HCV strains compared to the unfused antigen in murine models [121].
8.4 HIV
Ferritin has also been used as an antigen display platform in the search for a safe and effective vaccine against HIV, particularly focusing on the viral gp120 glycoprotein, the major component of the viral envelope [73]. Zhou et al. demonstrated that grafting of a glycopeptide from the variable region 3 on gp120 onto ferritin nanoparticles resulted in successful recognition by neutralizing antibodies from three different donors whereas the free antigen failed to be recognized. These results suggest that the structural integrity provided by the ferritin scaffold was critical to elicit immunogenicity [122].
8.5 Lyme disease
Lyme disease, the most common tick-borne disease in the United States and Europe caused by Borrelia burgdorferi, poses extreme challenges to public health given the challenges in its diagnosis and treatment, and lack of a vaccine. In a recent study, OspA, a lipoprotein expressed on the outer membrane surface of B. burgdorferi and the main target of antibody response, was genetically fused to the N-terminus of H. pylori ferritin to produce a vaccine displaying 24 OspA proteins on the nanoparticle surface [123]. The OspA-ferritin nanoparticle vaccine elicited high antibody titers against seven major serotypes in mice and non-human primates, higher than those achieved by a previously licensed vaccine [123]. In addition, the high antibody titer was demonstrated to be maintained in rhesus macaques for at least 6 months following vaccination [123], suggesting this multivalent ferritin vaccine has the potential to offer broad, and long-lasting immune protection against Borrelia infection.
8.6 Respiratory syncytial virus (RSV)
Respiratory syncytial virus (RSV), a leading cause of respiratory illness in immunocompromised individuals, has also been a target of ferritin vaccine development. Swanson et al. engineered H. pylori ferritin to express eight prefusion RSV moieties on its surface, and upon immunization elicited strong pre-F specific neutralizing antibodies in mice, nonhuman primates, and in vitro human cells when combined with the squalene-based adjuvant AF03, especially compared to the antigen alone [124].
8.7 SARS-CoV-2
The unprecedented development and roll-out of hundreds of vaccines to mitigate the SARS-CoV-2 pandemic also involved the use of ferritin and other nanoparticles as platforms. In an early approach, 72 amino acids of the receptor binding motif of the Sars-cov2 spike were fused to the N-terminus of human H ferritin. The construct was expressed in E. coli as an insoluble protein that could be renatured and was able to bind the receptor ACE2 [166]. Most of the following studies were based on Helicobacter pylori ferritin to avoid the induction of antibodies for the human protein [167] added four glycosylation sites to 197 amino acids of the receptor binding domain (RBD) of the spike protein expressed in mammalian cells. It was covalently fused to H. pylori ferritin and the complex was shown to elicit more potent neutralizing antibody responses than the wild-type RBD. A similar approach of fusing the RBD directly to the N-terminus of H. pylori ferritin and expressing the protein in mammalian cells was used in other works [125,168,169]. These vaccines were tested in mice, ferrets or monkeys and always produced neutralizing antibodies at levels much higher than the RBD alone.
The SpyTag-SpyCatcher system was then used in various studies to produce RBD exposed to H. pylori ferritin. Zhang et al. [170] added at the N-terminus a signal peptide followed by a spy-tag, and then a glycosylation site was included to facilitate expression and secretion by mammalian cells. The product purified by the supernatant of the transfected cells was then bound to SARS-CoV-2 spike glycoprotein containing a spy-catcher. The complex was stable and considered for vaccine development. Other studies inserted SpyCatcher sequence at the N-terminus of H. pylori ferritin that was expressed in E. coli and bound it to RBD containing SpyTag and the products were shown to induce strong immune response in mice [[162], [163], [164], [165], [166], [167], [168], [169], [170], [171], [172], [173]]. In one study the RBD was added to a modified heptad repeat 2 (HR2) that increased stability and antibody titer [174]. The RBD was also coupled to the 12-mer ferritin-like Dps from hyperthermophilic Sulfolobus islandicus and it elicited neutralizing antibodies [175].
A more recent challenge to SARS-COV2 vaccines is the identification of variants of concern (VOCs), five of which have been defined by The World Health Organization since the spread of the original Wuhan Hu-1 strain of SARS-CoV-2, four of which have previously circulated; (i) Alpha (B.1.1.7), (ii) Beta (B.1.351), (iii) Gamma (P.1), and Delta (B.1.617.2), as well as one currently circulating; (iv) Omicron (B.1.1.529) [176,177]. Multiple studies have observed a substantial reduction in vaccine-elicited neutralization capacity across all variants of concern, especially the recent Omicron variant [155,[176], [177], [178], [179], [180], [181]]. Given the number of infections and re-infections, as well as the cost and supply-chain issues of booster programs with currently available vaccines requiring ultra-cold storage, there exists a great rationale for the development of a safe, cost effective, and thermostable SARS-CoV-2 vaccine that provides broad protection against future emerging variants, and possibly other sarbecoviruses. Given the success of the multimerization platform approach of presenting epitopes on ferritin nanoparticles in preclinical trials against viruses, there has been a great interest in the use of a ferritin platform as a second-generation vaccine against SARS-CoV-2. Powell et al. designed H. pylori ferritin nanoparticles fusing one of two SARS-CoV-2 spikes: (i) full length ectodomain (residues 1–1143) or (ii) a C-terminal 70 amino-acid deletion ectodomain, resulting in protein nanoparticles displaying eight copies of a trimeric antigen on the surface of the 3-fold axis [125]. Following a single immunization with either immunogen, mice exhibited neutralizing antibody titers 2-fold greater than those in convalescent plasma from COVID-19 patients, whereas immunization with unconjugated trimers elicited little to no neutralizing titers with the same dose [122]. In a similar study, researchers at the Walter Reed Army Institute of Research and their collaborators designed a spike-domain ferritin nanoparticle (SpFN) vaccine by genetically linking an H. pylori ferritin molecule to the C-terminal region of the spike protein ectodomain (residues 12–1158), generating a ferritin nanoparticle displaying eight trimeric spikes [126]. Following a single immunization in mice, SpFN elicited potent neutralizing antibody titers against both SARS-CoV-2 (infective dose ID50 > 10,000) and SARS-CoV-1(ID50 > 2000) which has 26% sequence divergence in the spike protein, thereby demonstrating broad cross protection against different coronaviruses [126,127]. Furthermore, the immune response elicited by these ferritin nanoparticle vaccines were tested in combination with the adjuvants ALFQ (Army Liposome Formulation containing QS-210) and Alhydrogel (AH) and were found to be consistently superior with the ALFQ adjuvant in mice [126]. In a companion study in non-human primates, Joyce et al. demonstrated that the SpFN vaccine combined with the ALFQ adjuvant elicited high titers of antibodies that neutralized SARS-CoV-2 and rapidly eliminated the virus in the upper and lower airways and lung parenchyma [129]. Remarkably, the adjuvanted SpFN vaccine elicited neutralization activity that was either equivalent to, or higher than the wild-type virus against four variants of concern [B.1.1.7 (alpha variant), B.1.351 (beta variant), P.1 (gamma variant), and B.1.617.2 (delta variant)], as well as against SARS-CoV-1 [129]. Similarly, Wuertz et al. demonstrated that SpFN-ALFQ generates a robust immune response against the RBS and spike proteins of the alpha and beta variants with a 2-dose regimen in hamsters [130]. In a comprehensive study of the cellular immune responses induced by the adjuvanted SpFN vaccine, Carmen et al. found that after immunization, mice showed a potent multifactorial immune response, particularly with increased frequency of polyfunctional spike-specific memory CD4+ T cells, and long-lived memory CD8+ T cells with effective cytolytic function and distribution to the lungs [131]. The effectivity and broad protection of the SpFN-ALFQ vaccine against multiple VOCs of SARS-CoV-2 in rodent and non-human primate models has led to its evaluation in human phase I clinical trial (NCT04784767), although no results have been posted as of November 2022.
In addition to the SpFN vaccine, Joyce and collaborators have developed a secondary vaccine candidate in which bullfrog-H. pylori chimeric ferritin was genetically linked to the C-terminal region of the receptor binding domain (RBD) on the S1 subunit (residues 331–527) [126]. In mice, two doses of the RBD-ferritin vaccine elicited comparable neutralizing antibody titers to just one dose of the SpFN vaccine, however the RBD-ferritin vaccine elicited higher SARS-CoV-1 neutralizing responses compared to SpFN, which is 36% aa sequence divergent from SARS-CoV-2 in the RBD [126,127]. In a companion study evaluating the RBD-ferritin vaccine in combination with the ALFQ adjuvant in macaques, King et al. found that a two-dose regimen resulted in a robust humoral and cellular immune response, as well as protection against viral replication and lung pathology following respiratory exposure to a high dose of SARS-CoV-2 virus [128]. Additionally, RBD-ferritin vaccination in macaques demonstrated cross reactivity to the alpha (B.1.1.7) and beta (B.1.351) VOCs as well as to SARS-CoV-1 [128]. While the SpFN-ALFQ vaccine had been chosen to continue into clinical testing due to the level of immune response observed after a single immunization, further investigation of the RBD-ferritin vaccine is warranted given its potent immunogenicity. Given that expression of a large and complex antigen takes a considerable amount of energy, one advantage to the RBD-ferritin vaccine is its smaller size compared to the SpFN vaccine, manifesting as greater production yields (> 20 mg/L compared to 5 mg/L, respectively) [126], which may be beneficial to large-scale manufacturing efforts.
8.8 Mosaic vaccines
As described above, protein nanocages engineered with multiple copies of a single antigen have been very successful in eliciting a robust immune response and broad protection against viral pathogens. However, the prospect of furbishing nanocages with multiple different proteins from different viral strains remains mostly unexplored, but has great potential in stimulating broader immune protection, especially against viruses with high mutation rates. Successful examples of vaccination with multiple antigenic types include four FDA licensed pneumococcal vaccines; Prevar13 ®, Vaxneuvance®, Prevnar20®, and Pneumovax23®, which contain purified preparations of pneumococcal bacteria capsular polysaccharides from 13, 15, 20, and 23 different serotypes, respectively [182], as well as the FDA licensed Gardasil-9 ® vaccine which contains purified proteins from 9 human papillomavirus (HPV) types [183]. While these multivalent vaccines have shown to be highly effective in humans, similar approaches may not be as successful against pathogens with high rates of mutation which results in substantial antigenic variation. A solution to this issue may be to engineer multivalent vaccines to present more conserved domains or subdomains of viral proteins to elicit response by cross-reactive immune cells. Kanekiyo et al. demonstrated that co-display of RBDs from hemagglutinin antigens from different influenza strains spanning over 90-years on bullfrog-H. pylori chimeric ferritin nanoparticles elicited superior antibody responses in mice than those induced by the antigenically homotypic immunogens, even when the separate homotypic RBD-np vaccines are mixed [12]. Similarly, a trivalent mosaic HIV-1 vaccine displaying envelope (Env) sequences provided 66% protection against heterologous challenges with simian immunodeficiency virus (SIV) in Rhesus monkeys [184] and is currently being evaluated in human clinical trials. This immunofocusing approach has also been used to construct mosaic nanoparticle vaccine co-displaying the RBD of SARS-CoV-2 along with RBDs from seven animal sarbecoviruses on a 60-mer protein nanoparticle [133]. This approach has been demonstrated to elicit broad binding and neutralizing responses against both human and bat viruses in mice and macaques [132,133]. Immunization of animals with the homotypic and the mosaic vaccine resulted in protection against SARS-CoV-2 challenge, despite the mosaic vaccine containing just 1/8th of the SARS-CoV-2 RBDs compared to the homotypic vaccine [132]. Moreover, only animals immunized with the mosaic vaccine demonstrated significant protection against SARS-CoV-1 and animal sarbecoviruses whose RBDs were not displayed on the nanoparticles [132]. Additionally, it has been demonstrated that a conserved internal nucleoprotein antigen peptide from influenza virus can be encapsulated within apoferritin, which when coupled with HA-surface conjugation mimicked the structure of the natural influenza virus. Such arrangement provided 100% protection against lethal doses of H1N1 strains in mice compared to just 60% protection in the vaccine containing only the HA protein on the outer surface [185]. Given the potential to display RBDs on the outer surface as well as encapsulate antigenic peptides within the protein cavity, ferritin can be used as a platform for development of a mosaic vaccine (Fig. 6 ) against SARS-CoV-2 to confer broad protection against viral variants [186].Fig. 6 Schematic illustration of surface engineering of human heteropolymer ferritin with 12 different and randomly placed RBDs to represent the prospect of a universal mosaic ferritin nanocage vaccine candidate that could offer broader neutralizing antibody capability. This design could carry up to 24 RBDs and an antigenic peptide inside the protein cavity to improve immune response.
Fig. 6
9 Concluding remarks and future prospects
The intracellular trafficking and in vivo behavior and fate of vaccines administered via various routes and delivery systems have been recently reviewed [187]. In general, because of limited accessibility and/or inefficient cell permeation, most vaccine molecules are not efficiently recognized by the immune system and thus are susceptible to degradation. However, effective vaccine delivery systems should encompass protection from enzymatic degradation, improvement of pharmacokinetic properties through surface engineering strategies such as PEGylation, and sophisticated vaccine targeting and delivery strategies, as discussed in detail elsewhere [187]. Ferritin nanoparticles are remarkably stable, biocompatible, and amenable to genetic fusion and chemical conjugation, making the protein nanocage a very attractive platform for a wide range of biomedical applications, especially as an antigen display platform in the development of vaccines. The ability to express and purify ferritin in bacterial and animal cultures using industrial processes already in place allows for cost-effective large-scale manufacturing. More importantly, given the ability to selectively engineer antigen display and fine-tune immune focusing, ferritin vaccines are one effective and practical approach that have shown good safety profiles in comparison to more traditional approaches such as whole pathogen vaccines. Indeed, the pre-clinical successes of ferritin vaccines eliciting immune responses against a wide range of pathogens has led to its investigation in three phase I clinical trials, including a SpFN-ALFQ vaccine against SARS-CoV-2 [126]. However, there remain challenges in the design and manufacturing of antigen conjugated nanoparticles vaccines due to steric and conformational constraints. In silico studies and computational models to optimize conjugation of antibodies, targeting efficiency, and improve immunogenicity, will be crucial to vaccine development and preclinical evaluation. While ferritin vaccines hold a great promise in terms of eliciting broad protection and circulation of neutralizing antibodies in the bloodstream against SARS-CoV-2 variants of concerns, mucosal vaccines are another potential avenue to block mild covid-related illnesses and prevent transmission. A nasal vaccine for instance could spur a broader and more lasting immune response, by targeting parts of the body (i.e. nose and throat) where SARS-CoV-2 prefers to lodge before spreading to the lungs and other organs. Thus, instead of focusing on a circulating, whole body immune response, a mucosal vaccine would activate immune cells in the mucosal tissue of the upper respiratory tract. Currently, there are several nasal vaccines with two leading efforts, one at the Coalition for Epidemic Preparedness Innovations in Norway, and the other at the U.S. National Institute of Allergy and Infectious Diseases. It is worth noting that a SARS-CoV-2 vaccine administered through the nose or mouth, developed by the Chinese company CanSino Biologics, has been recently approved for use as a booster in China [188]. If proven safe and effective, mucosal vaccines in tandem with a potentially universal ferritin nanoparticle vaccine may offer reliable solutions to cease viral transmission and boost global immunity against the current SARS-CoV-2 pandemic, and future zoonotic viruses. In addition to mosaic nanoparticles approach, there is about a dozen major universal or pan-coronavirus vaccines that are currently in development designed to induce immunity against SARS-CoV-2 and other related coronaviruses. The broad protective antibody response of these different vaccines, compared to a homotypic nanoparticle approach, demonstrate the usefulness of the 24-mer ferritin (12 nm diameter), and perhaps other ferritin-like architectures such as the 48-mer assembled ferritin (17 nm) [189] or the 60, 180or 240-mer encapsulin nanoparticles (20 to 42 nm diameter) [190], as highly promising and effective approaches for vaccine development.
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
This work is supported by the 10.13039/100000001 National Science Foundation , Division of Molecular and Cellular Biosciences (MCB) award 1934666 (F.B.-A.), the National Institute of Health grant R15GM104879 (F.B.-A.), and a Cottrell Instrumentation Supplements Award from the 10.13039/100001309 Research Corporation for Science Advancement award 27452 (F.B.-A.)
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| 36470367 | PMC9721431 | NO-CC CODE | 2022-12-07 23:19:08 | no | Biochim Biophys Acta Gen Subj. 2023 Mar 5; 1867(3):130288 | utf-8 | Biochim Biophys Acta Gen Subj | 2,022 | 10.1016/j.bbagen.2022.130288 | oa_other |
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Psychiatry Res
Psychiatry Res
Psychiatry Research
0165-1781
1872-7123
Elsevier B.V.
S0165-1781(22)00590-X
10.1016/j.psychres.2022.114999
114999
Letter to the Editor
The impact of COVID-19 on psychiatry research: A cross-sectional analysis of discontinued clinical trials for depressive disorders
Sajjadi Nicholas B. a⁎
Howard Conner a
Papa Colton a
Mashigian Evan b
Vassar Matt ab
Hartwell Micah ab
a Office of Medical Student Research, Oklahoma State University Center for Health Sciences, Tulsa, OK, United States
b Oklahoma State University Center for Health Sciences, Department of Psychiatry and Behavioral Sciences, Tulsa, Oklahoma, United States
⁎ Corresponding author at: 1111 W 17th St., Tulsa, OK 74107, United States
5 12 2022
1 2023
5 12 2022
319 114999114999
13 12 2021
1 12 2022
2 12 2022
© 2022 Elsevier B.V. All rights reserved.
2022
Elsevier B.V.
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pmcDear Editor,
The COVID-19 pandemic has directly impacted psychiatry practice and subsequent referrals to clinical trials (Galletly, 2020). Clinical trials are required to vet and validate the interventions used to treat psychiatric conditions such as depressive disorders. Delayed progress of clinical research for depressive disorders is particularly concerning, as the percentage of US adults reporting recent depressive symptoms increased significantly during the pandemic (Vahratian et al., 2021). Moreover, the increase in depressive symptoms was met with reduced access to care, widening the mental health care gap (Ettman et al., 2021). The pandemic's impact on psychiatric research is likely to have long-lasting effects (Türközer and Öngür, 2020). The effect of the pandemic on Autism Spectrum Disorder clinical research has been described (Neale et al., 2022), but it has yet to be described for the depressive disorders. Thus, the impact of the pandemic on registered clinical trials investigating depressive disorder interventions may be significant and warrants investigation.
We searched clinicaltrials.gov on August 2, 2021, for depressive disorders (as per the DSM-5) without specifiers to have more generalizable results. We set the “Last Update Posted” date range from January 1, 2020, to August 1, 2021 to capture all trials potentially affected by the pandemic at the time of our search. The search string is presented in the Fig. 1 . We downloaded all available data for the return and screened for relevance, excluding studies that did not pertain to psychiatry research. Using a pilot-tested Google form, authors CH and CP extracted the reason for discontinuation among Withdrawn, Suspended, and Terminated trials provided on clinicaltrials.gov. The authors coded whether studies mentioned the COVID-19 pandemic, other reasons, or did not provide a reason for discontinuation. We used Chi-square tests to look for differences in trial characteristics among studies citing the pandemic as a cause for discontinuation and studies that did not. We used a Mann-Whitney U test to assess for differences between these groups and the median trial enrollment.Fig. 1
Fig 1
The search returned 58 trials, 2 of which were excluded because the condition being studied was not a depressive disorder. Of the 56 included studies, 10 trials (17.86%) involving 297 participants explicitly stated the COVID-19 pandemic as the reason for discontinuation. Studies quoting the pandemic as a disruption were more likely to be Suspended (vs. Withdrawn or Terminated) than studies that were not explicitly disrupted due to the pandemic (X2=8.6459,P = 0.013). Studies disrupted by the pandemic were more likely to have funding from the Other category (vs. Industry or Government) (X2=7.8417,P = 0.020) (Table 1 ). The reasons for discontinuation were as follows: 7 Business Decision (12.5%), 8 Design-related (14.29%), 5 Funding (8.93%), 3 Not Provided (5.36%), 1 Other (1.79%), 4 PI-related (7.14%), 10 Recruitment (17.86%), 3 Safety and Efficacy (5.63%), 5 Sponsor-related (8.93%), 10 COVID-19 related (17.86%).Table 1
Table 1 Does not explicitly state COVID-19 related Explicitly states COVID-19 related Total
Status (n,% of total) (n,% of total)
X2 (DF=2)
Suspended 5 (8.93) 5 (8.93) 10 (17.86) 8.6459 P = 0.013
Terminated 30 (53.57) 4 (7.14) 34 (60.71)
Withdrawn 11 (19.64) 1 (1.79) 12 (21.43)
Intervention X2 (DF=3)
Device 3 (5.36) 1 (1.79) 4 (7.14) 5.4377 P = 0.142
Dietary 0 (0) 1 (1.79) 1 (1.79)
Drug 40 (71.43) 8 (14.29) 48 (85.71)
Other 3 (5.36) 0 (0) 3 (5.36)
Study Design X2 (DF=1)
Not Randomized 16 (28.57) 3 (5.36) 19 (33.93) 0.0838 P = 0.772
Randomized 30 (53.57) 7 (12.5) 37 (66.07)
Study Design X2 (DF=1)
Masked 28 (50) 7 (12.5) 35 (62.5) 0.2922 P = 0.589
Unmasked 18 (32.14) 3 (5.36) 21 (37.5)
Funded By X2 (DF=2)
Industry 20 (35.71) 0 (0) 20 (35.71) 7.8417 P = 0.020
NIH 6 (10.71) 1 (1.79) 7 (12.5)
Other 20 (35.71) 9 (16.07) 29 (51.79)
Location X2 (DF=1)
Not US 16 (28.57) 4 (7.14) 20 (35.71) 0.0974 P = 0.755
US 30 (53.57) 6 (10.71) 36 (64.29)
Enrollment (median, IQR) (median, IQR) Mann-Whitney U
Med, IQR 9 (1–175) 26.5 (3–40) 13.5 (1.5–124) 0.269, P = 0.7881
Range 0–1570 0–90 0–1570
Total 6712 297 7009
The COVID-19 pandemic and recruitment difficulties were equally likely to be reported as a reason for discontinuation, together accounting for over a third of trial discontinuations during the pandemic. The pandemic revealed the vulnerability of psychiatric clinical trials regarding recruitment and the need to adapt trial design for future psychiatric trials (Neale et al., 2022; Perlis, 2020). We recommend that the use of telemedicine for monitoring and treatment of depressive disorders continue to be used and explored, as this will likely limit the impact of future pandemics on clinical research. Indeed, our previous work (Greenough et al., 2022) demonstrated that ongoing medical trials for COVID-19 were more likely to report using telecommunication than discontinued trials, and that behavioral trials were the most likely to report using telecommunication. Some trials reported using distanced methods for drug administration, patient monitoring, and collecting lab values. We recommend that the psychiatric application of these distanced methods be explored further. The discontinuation of depressive disorder studies, while detrimental, may have been necessary, as patients with pre-existing mood disorders were more likely to be hospitalized or to die from COVID-19 compared to those without, especially when restricting analysis to depression (Ceban et al., 2021). Trials were more likely to be suspended than terminated, likely reflecting trialists’ intentions to continue studies after ensuring safer conditions and the lack of confounding from concurrent COVID-19 disease.
Our study is limited by the cross-sectional nature and cannot establish causality. The extent of the pandemic's influence on trials may have been underreported. We did not compare baseline discontinuation rates among depressive disorder trials and cannot assume the discontinuation rate during the pandemic differs from previously.
==== Refs
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Ettman C.K. Cohen G.H. Abdalla S.M. Sampson L. Trinquart L. Castrucci B.C. Bork R.H. Clark M.A. Wilson I. Vivier P.M. Galea S. Persistent depressive symptoms during COVID-19: a national, population-representative, longitudinal study of U.S. adults Lancet Reg. Health Am. 2021 100091 10.1016/j.lana.2021.100091
Galletly C. Psychiatry in the COVID-19 Era Aust. N. Z. J. Psychiatry. 2020 10.1177/0004867420920359
Greenough M.C. Sajjadi N.B. Rucker B. Vassar M. Hartwell M. The use of telecommunication and virtualization among ongoing and discontinued COVID-19 clinical trials: a cross-sectional analysis Contemp. Clin. Trials 114 2022 106681 10.1016/j.cct.2022.106681
Neale M. Landers E. Sajjadi N.B. Mazur-Mosiewicz A. Hartwell M. The impact of COVID -19 on autism research: a cross-sectional analysis of discontinued or suspended clinical trials Autism Research 2022 10.1002/aur.2764
Perlis R.H. Essential work: psychiatric clinical investigation in the midst of a pandemic Am. J. Psychiatry 177 2020 1117 1118 10.1176/appi.ajp.2020.20050722 33256455
Türközer H.B. Öngür D. A projection for psychiatry in the post-COVID-19 era: potential trends, challenges, and directions Mol. Psychiatry 25 2020 2214 2219 10.1038/s41380-020-0841-2 32681098
Vahratian A. Blumberg S.J. Terlizzi E.P. Schiller J.S. Symptoms of anxiety or depressive disorder and use of mental health care among adults during the COVID-19 pandemic — United States, August 2020–February 2021 MMWR. Morbidity and Mortality Weekly Rep. 2021 10.15585/mmwr.mm7013e2
| 36495618 | PMC9721472 | NO-CC CODE | 2022-12-07 23:22:08 | no | Psychiatry Res. 2023 Jan 5; 319:114999 | utf-8 | Psychiatry Res | 2,022 | 10.1016/j.psychres.2022.114999 | oa_other |
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MethodsX
MethodsX
MethodsX
2215-0161
Published by Elsevier B.V.
S2215-0161(22)00335-1
10.1016/j.mex.2022.101961
101961
Economics/Business
Exploring the Effect of Covid-19 on Herding in Asian Financial Markets
Vidya C.T. 1⁎
Ravichandran Rashika 1
Deorukhkar Aditya 2
1 Centre for Economic and Social studies (CESS), Begumpet, Hyderabad, India
2 Christ University, Bengaluru, India
⁎ Corresponding author
5 12 2022
5 12 2022
1019612 9 2022
2 12 2022
© 2022 Published by Elsevier B.V.
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.
We examine herding behavior before, during, and after the Covid-19 pandemic in eight prominent Asian stock markets. Daily stock returns for the period Jan- 2018 to July- 2022 in the markets were investigated using the models prescribed by Chang et al., (2000) and Chiang and Zheng (2010). The empirical results provide strong support to earlier studies by providing robust evidence of herding in Vietnam, Indonesia, India, South Korea, and Singapore when the market is bullish and Indonesia and Vietnam also exhibit herding when the market is bearish. Herding tendency is dominant for Vietnam, India, and Indonesia during the pandemic with the post-pandemic time being more potent for China and Vietnam. Notably, an anti-herding tendency is found in China, Hong Kong, and Singapore. As a policy measure, efficient information dissemination, deterrence of insider trading, and regulation of mispricing can be undertaken.
Graphical Abstract
Image, graphical abstract
Keywords
Covid-19
Herding
Investor behaviour
Asian economies
Market returns
Anti-herding
==== Body
pmcIntroduction
In February 2020, the outbreak of COVID-19 caused an unprecedented crisis in economies and these gyrations were being red in the stocks’ prices. The investors seemed to be on a roller-coaster ride with their expectations and anxiety taking the form of irrational decisions due to abnormality being present [1, 2, 3]. Financial market volatility drives investors to follow the crowd by manifesting into herding [4]. Herding is a situation in which investors ‘decisions influence the collective behaviour of the market and they act irrationally thereby turning the market inefficient. Documenting herding behaviour has reported mixed trends for the developed and developing markets [5, 6, 7]. The Asian financial markets are one of the largest in the world and Asians are known to suffer from cognitive and behavioral biases on a different level as compared to other cultures such as European and Latin American [8]. Thus, studying herding in Asia can give interesting results concerning herd propensity and the impact of exogenous informational cascades on investors' rationality. We also study herding during tranquil and turbulent times and conclusively prove that investors in developing markets can be motivated by greed due to their propensity to form herds during bullish market conditions.
In this paper, we study herding amongst individual investors in Asian countries - India, China, Vietnam, Indonesia, Malaysia, Singapore, Hong Kong, and South Korea. We use the model of Chiang and Zheng (2010) to investigate the effects of herding in normal market conditions and a model used in Chang et al., (2000) to study extreme market conditions while providing evidence regarding the prevalence of the “anti-herding” sentiment.
Our findings contribute to a growing literature on the financial market effect of COVID-19. The following studies examined how COVID-19 has influenced the financial market [9, 10, 11] on global trade [12, 13, 14] on the aggregate market sentiments and impact on stock markets specifically [15, 16, 17, 18, 19, 20]
Our study also contributes to the expanding body of research examining the pandemic's impact on investor behavior and challenging decisions while connecting it with market movements across various Asian markets. In addition, it reports findings of any asymmetries in the market movements across markets and proves if stock exchanges are resilient to global financial shocks.
Methods
In this section, we discuss the methodology we have adopted to investigate the presence of herding. Our sample consists of daily stock market data from Jan’2018 to July’2022. We use the following model Chiang by and Zheng (2010) to comprehend the presence of herd behavior in the markets.(1) CSADt=α+γ1Rm,t+γ2|Rm,t|+γ3(Rm,t)2+εt
Where γ1 is the linear co-efficient between market return and CSAD, γ2 is the non-linear coefficient between absolute market return and CSAD. γ3relates cross-sectional absolute deviation to squared market return and checks for nonlinear dynamics of herding and if herding is present then this coefficient would be negative and statistically significant.
To measure herding that occurs in extreme market conditions and its direction we use the following models outlined in Chang et al., (2000)(2) CSADtUP=α+γ1UP|Rm,tUP|+γ2UP(Rm,tUP)2+εt
(3) CSADtDOWN=α+γ1DOWN|Rm,tDOWN|+γ2DOWN(Rm,tDOWN)2+εt
Here, CSADt is the average absolute deviation of each stock compared to the return of the market (Rm,t) in period t. The negative and statistically significant estimated coefficients γ2UPand γ2DOWNrespectively, indicate presence of herding during bullish and bearish markets and if the magnitudes are significant then we can conclude that herding behaviour is asymmetric in both up and down markets.
To test for herding behavior during tranquil and turbulent times, we divide our dataset into pre-covid, during-covid, and post-covid subsets to examine the presence of herding using Eq. 1 in all the countries and to test for anti-herding sentiment we adopt the methodology proposed by Babalos and Stavroyiannis (2015) where they opined that if anti-herding is present then γ3>0 in Eq. 1.
Results
Table 1 reports the results of estimating the herding regression for all countries in Eq. 1, there is no presence of any herd formation in our overall data set in China, Singapore, Hong Kong, and India. However, there is significant herding present in Vietnam, Malaysia, South Korea, and Indonesia in pre-crisis period. During our subsample analysis, we find that investors were motivated to herd more strongly during pandemic time in Vietnam, India, and Indonesia while herding was prevalent before covid in only South Korea, Indonesia, and Malaysia. Vietnam was the only country to report post-crisis herding suggesting prolonged effects of the crisis on the investor behaviour. These findings seem to be in contrast with results obtained in the whole sample providing evidence that herding is a short-lived phenomenon.Table 1 Herding Estimates of the whole sample, pre-crisis, during crisis and post crisis
Table 1 Whole Period Pre-Crisis During Crisis Post Crisis
China
α 0.019** (0.100) 0.012* (34.800) 0.013* (31.220) 0.320** (2.500)
γ1 0.025 (-0.270) -0.015 (-0.930) 0.021*** (1.040) 0.039*** (1.380)
γ2 -0.099 (-0.270) 0.126 (0.013) 0.159* (2.860) -0.895 (-0.690)
γ3 1.110** (2.550) 4.355** (3.380) 0.687 (0.620) 1.918** (1.380)
Adj R2 0.081 0.265 0.164 0.077**
F-Stat 33.620 58.940 16.800 11.470
Vietnam
α 0.016** (22.170) 0.015** (10.080) 0.173** (30.740) 0.020** (21.890)
γ1 0.025** (0.970) 0.045** (0.690) 0.028*** (1.40) -0.003** (-0.130)
γ2 0.496* (5.950) 0.376*** (1.940) 0.378* (5.620) 0.424* (4.810)
γ3 -0.859 (-0.550) 3.194** (0.780) -0.281 (-0.220) -1.137 (-0.750)
Adj R2 0.139 0.062 0.446 0.238
F-Stat 62.600 11.950 67.970 41.500
South Korea
α 0.011* (43.400) 0.010* (30.780) 0.012* (23.900) 0.011* (18.750)
γ1 0.054* (4.410) 0.030*** (1.340) 0.047* (3.010) 0.053** (1.990)
γ2 0.327* (10.030) 0.325* (4.790) 0.183* (3.600) 0.316* (2.830)
γ3 0.064 (1.010) -3.525*** (-1.340) 2.395* (3.100) 5.314*** (1.340)
Adj R2 0.278 0.100 0.499 0.226
F-Stat 145.810 19.060 82.830 38.650
Singapore
α 0.007* (22.530) 0.007* (22.240) 0.010* (7.970) 0.006* (17.000)
γ1 0.007*** (1.190) 0.014** (2.030) 0.009 (0.640) -0.010** (-1.110)
γ2 0.817* (53.160) 0.793* (47.990) 0.812* (17.270) 0.816* (26.810)
γ3 0.894* (9.330) 0.958* (8.190) 0.861* (3.390) 1.078* (2.820)
Adj R2 0.945 0.962 0.933 0.947
F-Stat 6630.080 4270.0 1167.910 2120.310
Malaysia
α 0.0138* (8.720) 0.008*** (1.690) 0.016* (18.250) -0.016* (25.240)
γ1 0.254*** (1.830) 1.058* (2.210) 0.059*** (1.460) -0.016 (-0.390)
γ2 0.738* (3.680) 2.670** (1.860) 0.476* (3.900) 0.363** (1.990)
γ3 0.736* (3.380) -51.690 (-0.640) 2.162 (0.840) 4.753 (0.430)
Adj R2 0.748 0.018 0.285 0.102
F-Stat 1132.940 3.960 33.740 15.450
Indonesia
α 0.013* (43.95) 0.013* (20.730) 0.013* (20.720) 0.014* (30.160)
γ1 0.080* (4.780) 0.014 (0.360) 0.117* (4.880) 0.079* (2.860)
γ2 0.378* (9.200) 0.360** (2.570) 0.516* (7.820) 0.250* (2.710)
γ3 -0.401 (-0.520) -4.722 (-0.810) -2.318** (-2.320) 2.958 (0.870)
Adj R2 0.187* 0.036 0.412* 0.145
F-Stat 86.110 7.040 57.030 21.490
Hong Kong
α 0.015* (7.900) 0.012* (40.590) 0.014* (24.870) 0.020* (3.700)
γ1 0.048 (0.520) 0.051* (3.530) 0.068* (3.350) 0.055 (0.230)
γ2 0.159*** (1.120) 0.118** (2.290) -0.025 (-0.320) 0.024 (0.0700)
γ3 0.855*** (1.120) 3.632** (2.160) 6.496* (3.480) 0.991** (2.240)
Adj R2 0.311 0.179 0.235 0.312
F-Stat 169.970 36.740 26.210 57.670
India
α 0.010* (56.100) 0.011* (36.520) 0.011* (23.710) 0.011* (35.610)
γ1 0.043* (4.500) 0.014 (0.720) 0.058* (3.540) -0.005 (-0.370)
γ2 0.268* (12.180) 0.076*** (1.370) 0.343* (7.940) 0.056*** (1.120)
γ3 0.412*** (1.360) 8.663* (4.720) -0.467 (-0.950) 3.919** (2.560)
Adj R2 0.335 0.216 0.481 0.143
F-Stat 191.400 45.910 78.400 22.730
Note: This table reports the regression results of the equation (2)CSADt=α+γ1Rm,t+γ2|Rm,t|+γ3(Rm,t)2+εt in which CSADt is the cross-sectional standard absolute deviations of returns at time t, α is the intercept, γ1 is the linear co-efficient between Rm,t and CSAD, γ2 is the non-linear coefficient between Rm,t and CSAD. Rmt is the market return at time t and εt is the error term, γ3relates CSAD to (Rm,t)2. The asterisks *, ** and *** denote statistical significance at 1%, 5% and 10% levels. The values in parenthesis () denote t-statistics.
We also examine asymmetries in extreme market returns in Eq. 3 and Eq. 4. Vietnam and Indonesia report high asymmetries with herd formation being stronger in bullish markets. Negative coefficient γ2UP in India, Indonesia, South Korea, Singapore, and Vietnam indicates that investors have a healthy risk appetite during bullish markets. Negative γ2DOWNcoefficients in Indonesia and Vietnam suggest strong herd formation during bearish markets. The deviation between dispersions between γ2UP and γ2DOWN suggest the urge to resort to “flight to safety” strategy. In China, Malaysia, and Hong Kong γ2DOWN values are significantly greater than γ2UP implying the reliance of investors on available information.
We also test for “anti-herding” sentiment and find that a strong presence can be detected in China, Singapore, and Hong Kong in normal and extreme market conditions due to their highly developed and stable financial market system preventing any strong impact of negative news leading to herding.
Conclusion
We find conclusive evidence that herding is strongly present in emerging markets compared to developed markets in Asia. We also investigated asymmetries and found that investors tend to herd more during bullish market periods implying that the investors may tend to become “greedy” while harbouring a healthy propensity to take risks. Still, in times of economic distress, they could resort to a “flight to safety” strategy. The study finds that herding seems to be strong during and after Covid-19, which is in contrast with evidence of no strong herding being observed in the overall dataset, lending support to the earlier assertion made by researchers that herding is short-lived. These results suggest increased investor confidence and risk-taking appetite in the markets. Thus, in each Asian market, the herding behaviour has different implications and helps in policy decisions. In the Chinese market, investors behave rationally due to high information disclosure and the non-existence of informational cascades. Whereas, in Vietnam, the investors imitated the actions and thought processes of giant brokers or foreign institutional investors. In South Korea, the high degree of financial stability and the market operates relatively independently of government intervention leading to the absence of herding in South Korea. Herd formation can be said to be partially present in the Malaysian stock market during the pre-crisis period implying that the investors expected government intervention during the crisis. Notably, high sophistication of the financial system and complete transparency in disclosure elements, Singapore does exhibit a decisive “anti-herding” sentiment. However, in Indonesian financial markets, investors seem to be completely reliant on external sources of information due to the weak and opaque information disclosures by the required authorities coupled with the heavy government intervention in the stock markets. Similarly, in the case of Hong Kong, the investors are well informed and hence decisions are rational. Finally, the Indian financial market is faced with high informational cascades and asymmetries; thus, herd formation takes place during the crisis. Our results lend support to the earlier studies studying investor behaviour in Asia and are also helpful for understanding the creation of market bubbles due to herd formation while providing policy guidance for informational cascades and market transparency
Specification TableSubject area Economics and Finance
More specific subject area Behavioral Finance
Name of your method Cross-Sectional Absolute Deviation (CSAD)
Name and reference of original method • Chang, E. C., Cheng, J. W., & Khorana, A. (2000). An examination of herd behavior in equity. Journal of Banking & Finance, 24, 1651 - 1679.
• Chiang, T., & Zheng, D. (2010). An empirical analysis of herd behavior in global stock markets. Journal of Banking & Finance, 34(8), 1911-1921.
Resource availability • Data is included in this publication
• Results can be reproduced using any economic/statistical software like EViews, Stata, R, Python, etc.
Uncited References
[21, 22], Figure 1 , Table 2 Figure 1 Trends in Herding during Pre, During and Post Covid-19 Crisis
Note – The figure shows the dynamics of herding behaviour in India, Hong Kong, Indonesia, Malaysia, South Korea, Vietnam, China, and Singapore in pre-crisis, during and post-crisis subsamples where Singapore's volatility has been taken as a proxy for market wide volatility. During the earlier part of the pandemic, herding was not very prominent in any of the countries but with progression, herding became very pronounced during the pandemic and this trend continued in a reduced capacity even after the severity of the pandemic reduced.
Figure 1
Table 2 Herding estimates during extreme market returns
Table 2Panel A: Lower Market
Criteria 1% 5%
Country α Y1DOWN Y2DOWN Adj R2 α Y1DOWN Y2DOWN Adj R2
China 0.006* (25.417) 0.118* (4.279) 0.876* (31.393) 0.104 0.008* (36.640) 0.124* (5.246) 0.869* (36.521) 0.118
Hong Kong 0.006* (19.184) 0.187* (6.784) 0.787* (28.768) 0.177 0.007* (27.796) 0.196* (9.102) 0.777* (36.432) 0.168
India 0.005* (15.957) 0.130** (2.526) 0.568 (0.843) 0.050 0.010* (26.422) 0.152* (4.049) 0.087 (0.146) 0.059
Indonesia 0.008* (24.984) 0.218* (3.72) -0.212 (-0.165) 0.090 0.009* (26.995) 0.211* (3.124) -0.420 (-0.263) 0.082
Malaysia 0.006* (19.184) 0.187* (6.784) 0.787* (28.768) 0.177 0.007* (27.796) 0.196* (9.102) 0.777* (36.432) 0.169
Singapore 0.002* (5.466) 0.3717* (6.937) 2.448* (11.050) 0.370 0.003* (5.489) 0.531* (9.318) 1.784* (8.265) 0.479
South Korea 0.005* (12.512) 0.156*** (1.204) 0.300 (0.057) 0.082 0.007* (19.585) 0.195** (2.278) 0.002 (0.001) 0.093
Vietnam 0.008* (18.084) 0.383* (5.381) -1.156*** (-1.279) 0.200 0.009* (29.108) 0.511* (13.901) -2.464* (-4.951) 0.230
Panel B: Upper Market
Criteria 1% 5%
Country α Y1UP Y2UP Adj R2 α Y1UP Y2UP Adj R2
China 0.028* (20.173) 0.293* (6.395) 0.676* (14.834) 0.222 0.021* (32.337) 0.239* (3.136) 0.738* (9.619) 0.205
Hong Kong 0.023* (38.167) 0.584* (32.226) 0.373* (20.743) 0.393 0.019 (0.353) 0.34* (9.026) 0.621* (16.583) 0.352
India 0.020* (26.847) 0.638* (9.502) -3.506* (-7.166) 0.373 0.017* (24.576) 0.355** (2.024) 0.866 (0.232) 0.299
Indonesia 0.027* (12.468) 1.005* (4.524) -7.372* (-3.529) 0.165 0.019* (10.953) 0.619* (2.824) 1.183 (0.219) 0.1863
Malaysia 0.228* (38.167) 0.584* (32.226) 0.373* (20.743) 0.393 0.0194* (41.1962) 0.34* (9.026) 0.621 (0.037) 0.352
Singapore 0.020* (12.905) 1.535* (11.402) -1.563* (-2.929) 0.803 0.013* (22.911) 0.983* (12.837) 0.658** (2.270) 0.828
South Korea 0.021* (18.464) 1.670* (6.1627) 17.142* (-5.446) 0.275 0.0179* (25.403) 0.474* (3.853) 2.650*** (1.125) 0.217
Vietnam 0.034* (10.306) 1.496** (2.420) -9.317 (-1.080) 0.094 0.026* (18.585) 0.536* (3.732) -0.814 (-0.315) 0.169
Note: This table reports the regression results of the equations (2) and (3) respectively CSADtUP=α+γ1UP|Rm,tUP|+γ2UP(Rm,tUP)2+εtCSADtDOWN=α+γ1DOWN|Rm,tDOWN|+γ2DOWN(Rm,tDOWN)2+εt.
Up and down indicate rising (Rm,t > 0) and falling (Rm,t< 0) market conditions respectively. Rm,tis the market return at time t. CSAD is the cross-sectional absolute deviation and is computed as in equation (1)- CSADt=α+γ1Rm,t+γ2|Rm,t|+γ3(Rm,t)2+εt, *, **, and *** denotes significance at 1%, 5% and 10% level. The values in parenthesis () denote t-statistics.
Declaration of 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
Data will be made available on request.
Acknowledgments
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
==== Refs
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| 36507467 | PMC9721475 | NO-CC CODE | 2022-12-07 23:19:08 | no | MethodsX. 2023 Dec 5; 10:101961 | utf-8 | MethodsX | 2,022 | 10.1016/j.mex.2022.101961 | oa_other |
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Elsevier Inc.
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10.1016/j.ajem.2022.12.004
Article
Dysrhythmias associated with COVID-19: Review and management considerations
Alblaihed Leen MBBS, MHA a⁎
Brady William J. MD b
Al-Salamah Tareq MBBS, MPH c
Mattu Amal MD a
a Department of Emergency Medicine, University of Maryland School of Medicine, 110 S Paca Street, 6th Floor, Suite 200, Baltimore, MD 21201, United States of America
b Department of Emergency Medicine, University of Virginia School of Medicine, Charlottesville, VA 22908, United States of America
c Department of Emergency Medicine, College of Medicine, King Saud University, PO Box 7805, Riyadh 11472, Saudi Arabia
⁎ Corresponding author.
5 12 2022
5 12 2022
8 9 2022
21 11 2022
1 12 2022
© 2022 Elsevier Inc. All rights reserved.
2022
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Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), is known to affect the cardiovascular system. Cardiac manifestations in COVID-19 can be due to direct damage to the myocardium and conduction system as well as by the disease's effect on the various organ systems. These manifestations include acute coronary syndrome, ST- segment elevations, cardiomyopathy, and dysrhythmias. Some of these dysrhythmias can be detrimental to the patient. Therefore, it is important for the emergency physician to be aware of the different arrhythmias associated with COVID-19 and how to manage them. This narrative review discusses the pathophysiology underlying the various arrhythmias associated with COVID-19 and their management considerations.
Keywords
COVID-19
Prolonged QTc
Dysrhythmia
Atrial fibrillation
Atrioventricular block
Bradycardia
Ventricular tachycardia
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pmc1 Introduction
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), was first reported in Wuhan, Hubei Province, in China on December 31, 2019. [1] Although mainly thought of as a respiratory illness, COVID-19 soon proved to affect the cardiovascular system. [2,3] Most patients with myocardial injury do not have previously diagnosed cardiovascular disease and frequently present without chest pain. [2,3] Myocardial involvement in COVID-19 is associated with higher morbidity and mortality, [4] up to 60% mortality in some studies. [3,5] A study based in Germany showed that 60% of patients who had recovered from COVID-19 still had ongoing inflammatory cardiac changes visible on cardiac magnetic resonance imaging 2 to 3 months after recovery, regardless of disease severity and course of illness. [6] Many cardiac manifestations have been described and reported in the literature thus far:• Myocarditis [7,8]
• Acute coronary syndrome [9,10]
• Right ventricular dysfunction [11,12]
• ST-segment elevations [13]
• Stress-induced (takotsubo) cardiomyopathy [14]
• Dysrhythmias [[15], [16], [17]]
Some of these dysrhythmias can be detrimental to the patient. Therefore, it is important for the emergency physician to be aware of the different arrhythmias associated with COVID-19 and how to manage them. This narrative review discusses the pathophysiology underlying the various arrhythmias associated with COVID-19 and their management considerations.
2 Methods
The authors of this narrative review performed a literature search of PubMed and Google Scholar databases for articles up to August 2022. We searched for the keywords ‘COVID’ OR ‘SARS-CoV-2’ OR ‘coronavirus’ AND ‘arrhythmia’ OR ‘dysrhythmias’ OR ‘cardiac manifestations’ OR ‘myocarditis.’ We included case reports and series, retrospective and prospective studies, systematic reviews, meta-analyses, clinical guidelines, and narrative reviews. The literature search was restricted to studies published or translated into English. The initial search revealed over 220 articles, the majority of them case reports. All relevant articles were reviewed, and we focused on those relevant to emergency medicine. A total of 59 articles were selected for inclusion in this review.
3 Discussion
3.1 Pathophysiology
Dysrhythmias associated with COVID-19 are thought to be caused either directly as an effect of SARS-CoV-2 on the heart, or indirectly through its effect on other organ systems that ultimately affect the heart or cardiac conduction system, leading to dysrhythmias. Between 19.7% and 27.8% of patients with COVID-19 also sustain myocardial injury, defined by elevated cardiac troponin levels. [2,3] When myocardial injury is present, the incidence of arrhythmias increases substantially (1.5% in those without vs 17.3% in those with myocardial injury).
SARS-CoV-2 is a single-stranded RNA virus whose outer membrane spike protein binds with high affinity to the angiotensin-converting enzyme 2 (ACE2) receptors, resulting in receptor-mediated endocytosis. [18] ACE2 is expressed in a number of tissues including the heart, lungs, liver, kidney, gut, and immune cells. [19] When SARS-CoV-2 binds to ACE2 receptors, it leads to their downregulation, causing accumulation of angiotensin II. [18] Angiotensin II has proinflammatory, vasoconstrictive, and profibrotic effects which can promote dysrhythmias. [18]
3.1.1 The heart
SARS-CoV-2 invades the cardiac myocytes via ACE2 receptors. [20] Once inside the cell, the virus uses the cell's machinery to replicate, and the new viruses are released by exocytosis, destroying the host cell in the process and potentially triggering an immune response. [19] Furthermore, the angiotensin II that has accumulated as a result of ACE2 downregulation (due to viral attachment) causes further proinflammatory changes in the heart [21] and adverse myocardial remodeling by its action on angiotensin II type 1 receptors. [22] It is theorized that both these factors (direct viral invasion and inflammatory changes) play a role in myocardial injury and myocarditis [22].
Myocarditis is the most probable cause of myocardial injury and has been observed in 7.2%–27.8% of COVID-19 patients. [3] Myocarditis can cause arrhythmia in the acute stage of COVID-19 through [16]:• Direct cell damage (cytopathic effect) causing electrical imbalance
• Gap junction dysfunction
• Ion channel impairment
• Inflammatory channelopathies
Myocarditis is a leading cause for the development of ventricular dysrhythmias. [23] In the latent phase of myocarditis, post-inflammatory scarring can promote reentrant dysrhythmias. [24] Favorable outcomes have been demonstrated in those treated with glucocorticoids. [25]
Myocardial ischemia is another mechanism for cardiac dysrhythmias. Both myocardial infarction (MI) types 1 (atherosclerotic plaque rupture) and 2 (supply-demand imbalance) have been reported in COVID-19 patients. [13]
Right heart strain (as a result of pulmonary embolism, [26] pulmonary hypertension secondary to acute respiratory distress syndrome (ARDS), [27] sepsis, or heart failure) can predispose patients to atrial tachydysrhythmias [28] and complete heart block. [29]
3.1.2 The lungs
COVID-19 is known to cause acute respiratory failure leading to hypoxia. This causes an imbalance between myocardial oxygen supply and demand, leading to type 2 MI. [30] Other factors might also contribute to COVID-19 patients being particularly vulnerable for type 2 MI, such as having [18]:• fixed atherosclerosis limiting coronary circulation
• endothelial dysfunction within the coronary circulation
• systemic hypertension as a result of excess circulating angiotensin II causing vasoconstriction
Hypoxia can also activate anaerobic glycolysis, thus reducing intracellular pH which in turn increases intracellular calcium levels, facilitating early and late depolarizations and altering the action potential of the myocardial cells. Hypoxia also increases extracellular potassium levels; this decreases the threshold for depolarization, accelerating action potentials and leading to dysrhythmias. [31]
3.1.3 The immune system
The immune response to SARS-CoV-2 and cytokine storm play an integral role in how COVID-19 simultaneously affects multiple organ systems.
Myocardial inflammation (myocarditis) can occur due to migration of the proinflammatory cytokines, macrophages, and also through cell mediated cytotoxicity (CD8 and T-lymphocytes) leading to cardiomyocyte damage and dysrhythmias. [16,32]
Cytokines (interleukins 6 and 1, and tumor necrosis factor-α) can trigger dysrhythmias by:• modifying the expression and function of calcium and potassium channels (inflammatory channelopathies), prolonging the action potential, [33] which leads to prolonged QT intervals [34]
• over-activating the cardiac sympathetic system via the hypothalamus-mediated inflammatory reflex
• inhibiting cytochrome P450, increasing the bioavailability of drugs that prolong the QT interval [35]
3.1.4 The vascular system
Both arterial and venous endothelial cells express ACE2 receptors [36] to which SARS-CoV-2 attaches, leading to microvascular dysfunction (infection-mediated vasculitis) causing myocardial ischemia. The cytokine surge and inflammatory mediators may cause acute coronary syndrome by activating inflammatory cells within a preexisting plaque, as well as via vasoconstriction of the coronary arteries. [37]
3.1.5 The kidneys and gastrointestinal tract
Electrolyte disturbances have been reported in 7.2% of 416 hospitalized patients with COVID-19 infection. [2] These disturbances were thought to be a result of diarrhea and acute kidney injury related to SARS-CoV-2. Acute kidney injury has been reported in 27% of hospitalized patients with COVID-19 infection. [38] There are also multiple case reports of dysrhythmias in COVID-19 patients related to hypokalemia, hypomagnesemia, and hypophosphatemia. [39,40] Additionally, fluid disturbances caused by diarrhea, vomiting, or diuresis can contribute to dysrhythmias in COVID-19 patients.
3.1.6 Adverse effects of medication
Many of the antivirals and antibiotics used to treat COVID-19 are known to prolong the QT interval and increase the risk of torsades de pointes (TdP). [41] These medications inhibit the potassium channels, causing prolongation of the action potential; this prolongation along the unopposed inward sodium and calcium currents will trigger early afterdepolarization and can lead to TdP. [42]
3.2 Types of dysrhythmias and management considerations
Earlier in the pandemic, reports from Wuhan showed that 44% of patients admitted to the intensive care unit (ICU) with COVID-19 had dysrhythmias, raising concerns about a possible association between the 2 conditions. [43] Dysrhythmias were the leading complication (16.7%) after ARDS (19.6%) across all patients, ICU and non. [43] Dysrhythmias were reported in 7% of patients with COVID-19 not admitted to the ICU. [43] Another study, based in the United States, reported a 9.6% rate of dysrhythmias. [44]
3.2.1 Atrial dysrhythmias
Atrial dysrhythmias are the most common form of dysrhythmias reported in COVID-19 patients. In one study, atrial fibrillation (Afib) was the most commonly reported tachydysrrhythmia at 20.8%, with paroxysmal supraventricular tachycardia at 5.7%, atrial flutter (Aflutter) at 5.4%, and sustained atrial tachycardia at 3.5%. [45] Colon et al. reported that new-onset atrial dysrhythmias (Afib, Aflutter, and atrial tachycardia) were seen in 16.5% of patients admitted to the ICU. [46] During the initial peak of the pandemic, a group of authors affiliated with Columbia University in New York City reported that Afib was the most common reason for electrophysiology consultation in patients with COVID-19 (31%), with only 13% of those patients having a known history of Afib. [23] Interestingly, none of the patients with COVID-19 and new-onset Afib in the Columbia study had a history of cardiac surgery, ablation, cardioversion, or antidysrhythmic drug use. [23]] A study by Bertini et al. shows similar results with Afib/flutter occurring in 22% of patients, while only 9% have a prior history of Afib/flutter. [47] A more modest rate of new-onset atrial fibrillation at a rate of 5.6% is reported from the American Heart Association through their cardiovascular registry. [48] The exact mechanism of these atrial dysrhythmias is still unclear; however, proposed mechanisms include ACE2-related signaling pathways, inflammation, endothelial damage, and metabolic derangements. [49] Multiple earlier studies found Afib to be an independent predictor of illness severity, myocardial injury, poor outcomes, and mortality in patients with COVID-19. [50,51] However, results from the AHA COVID-19 Cardiovascular Registry does not suggest an increased risk of death from new-onset atrial fibrillation after adjusting for demographics, comorbidities, and severity of disease. [48]
In one case that illustrates these new-onset of atrial arrhythmias, we treated a healthy 28-year-old male who had recovered from COVID-19 3 weeks prior to presenting to our Emergency Department with palpitations. He had no known risk factors for Afib. We witnessed his rhythm on the monitor and electrocardiogram (Fig. 1a -d) change between supraventricular tachycardia (Fig. 1a), sinus tachycardia (Fig. 1b, Fig. 1c ), and Afib (Fig. 1d ) within an extremely short period of time (minutes to 2 h). During his hospital stay, his atrial dysrhythmia did not respond to multiple antidysrhythmics and he underwent an ablation. Unfortunately, he later had recurrence of Afib and SVT and required 2 more ablations within a span of 2 months.Fig. 1a Electrocardiograms of 28-year-old man with atrial dysrhythmias following recovery from COVID-19. a. Supraventricular tachycardia.
Fig. 1a
Fig. 1b Supraventricular tachycardia spontaneously converting to sinus tachycardia.
Fig. 1b
Fig. 1c Supraventricular tachycardia spontaneously converting to sinus tachycardia.
Fig. 1c
Fig. 1d Atrial fibrillation with occasional aberrantly conducted complexes.
Fig. 1d
In another case we cared for a 76-year-old woman with no prior history of lung disease or atrial dysrhythmias who presented with fevers, dyspnea, mild hypoxia, and a new diagnosis of COVID-19-related bilateral pneumonia. Her ECG demonstrated multifocal atrial tachycardia (Fig. 2 ). She later converted to sinus tachycardia a few hours after initiation of supplemental oxygen and intravenous fluids.Fig. 2 Electrocardiogram of 76-year-old woman with multifocal atrial tachycardia (rate ~ 155/min) during presentation and initial treatment of COVID-19-associatiated bilateral pneumonia.
Fig. 2
When managing atrial tachydysrhythmias, first identify and treat underlying causes such as hypoxia, metabolic derangements, pro-dysrhythmic side effects of drugs, or ischemia. [16] Typically, Afib is managed with rate or rhythm control and an anticoagulant in those who meet criteria and have no contraindications. [52] However, because of concerns that beta blockers might induce bronchospasm during the acute respiratory illness in patients with COVID-19, a calcium channel blocker (eg, diltiazem) is preferentially used. [53] Amiodarone has sodium-channel blocking effects and therefore should be used with caution in patients with a prolonged QT interval.
3.2.2 Ventricular dysrhythmias (ventricular tachycardia and ventricular fibrillation)
Ventricular dysrhythmias are reported much less commonly than atrial dysrhythmias in COVID-19 patients. Seven percent of electrophysiology consults in the Columbia study were for ventricular dysrhythmia (vs. 31% for atrial dysrhythmias). [23] In a different, survey-based study, 683 electrophysiology professionals who answered the question “What tachyarrhythmic manifestations of COVID-19 have you seen?” responded as follows [45]:• Premature monomorphic ventricular contractions (5.3%)
• Nonsustained ventricular tachycardia (VT) (6.3%)
• Sustained monomorphic VT (3.8%)
• Premature multiform ventricular contractions (3.5%)
• Polymorphic VT/TdP (3.5%)
• VT/ventricular fibrillation (VF) arrest (4.8%)
• Pulseless electrical activity (5.6%)
These dysrhythmias might be due to cardiac ischemia, metabolic derangements, myocarditis, or prolongation of the QT interval. [18,19,24,52] (Fig. 3 ). In general, monomorphic VT is the most frequent VT seen in COVID-19 patients. It occurs as a result of a structural disease of the heart (eg, MI, myocarditis), whereas polymorphic VT (including TdP) results from a functional heart disease (eg, medication toxicity, electrolyte abnormalities, prolonged QT-interval, acute cardiac ischemia). [53]Fig. 3 Bursts of non-sustained VT in patient with COVID-19. The patient was later diagnosed with myocarditis.
Fig. 3
Since it is still unknown why some COVID-19 patients develop ventricular dysrhythmias and others do not, the advisability of using antidysrhythmic medication as prophylaxis is unclear. [55]
When managing COVID-19 patients with ventricular dysrhythmias, follow standard care measures. Look for and treat underlying causes such as hypoxia, metabolic or electrolyte abnormalities, and side effects of drugs. In those with polymorphic VT/VF with prolonged QT interval, optimize potassium levels to a target over 4.5 mmol/L and magnesium levels to a target over 2.0 mmol/L; and when necessary, initiate an isoproterenol infusion, consider overdrive pacing, and discontinue any medications that prolong the QT- interval [16].
Care should be taken when administering antidysrhythmics. Amiodarone, for example, can increase the risk of TdP by prolonging the QT-interval. Cardiologists are still unclear whether implantable cardiac defibrillators are needed to treat dysrhythmias in patients with COVID-19. Defibrillators are usually placed in those with structural/conduction abnormalities. However, in those with COVID-19 related cardiac involvement there may actually be no evidence of structural heart disease, and the inflammation may be a reversible precipitant of the dysrhythmia. [56]
3.2.3 Atrioventricular block and bradycardia
Bradycardia and atrioventricular (AV) blocks account for almost 12% of arrhythmias seen in COVID-19 patients. [17] Both Amaratunga et al. and Chinitz et al. have suggested that the occurrence of high-grade AV blocks is a poor prognostic sign and a marker of impending cardiovascular collapse. [57,58] The exact mechanism that causes AV blocks to develop in patients with COVID-19 is still unclear. It is well known that heart blocks can be a manifestation of myocarditis. [59] However, in addition to Chinitz et al.'s study, there have been several case reports of COVID-19–related AV blocks in patients with preserved ventricular function and normal, or near normal, cardiac biomarkers. [58,60,61] Subclinical myocardial inflammation has been postulated as a possible cause of AV block. In one case report that supports that theory, cardiac magnetic resonance imaging demonstrated edema of the ventricular wall (a sign of myocarditis) despite absence of evidence of myocardial injury. [61]
AV block seen in the setting of acute infection can resolve spontaneously. [62,63] When managing patients with COVID-19 and AV blocks, one should treat reversible and underlying conditions first. It is important to avoid nodal blocking agents in all degrees of AV blocks. As in typical management of AV blocks in patients without COVID-19, first-degree AV blocks usually require no treatment. Second- and third-degree blocks, if unstable, might require atropine or transvenous pacing. [55] Permanent pacemakers are indicated in symptomatic patients with second-degree type II or third-degree AV blocks.
3.2.4 Prolonged QTc interval
Over 13% of COVID-19 patients have prolonged QTc intervals. Although medications—such as some antivirals, antibiotics, and antidysrhythmics—have this side effect, COVID-19 increases patients' risk for prolonged QTc interval through inflammation, renal dysfunction, cardiac involvement, and electrolyte imbalance. [64] Electrolyte disturbances that can be associated with a prolonged QTc interval include hypokalemia, hypomagnesemia, and possibly hypophosphatemia; and these can be caused by COVID-19-induced diarrhea, kaliuresis, and intravascular fluid imbalances. [16]
It is crucial to minimize the risk that patients with prolonged QTc intervals develop TdP by [16]:• Checking electrolyte levels (especially potassium and magnesium) of those at risk.
• Maintaining a potassium level at the higher range of normal (> 4.5 mmol/L).
• Cautionary use of medications known to cause QTc prolongation with a risk/benefit analysis
4 Conclusion
Dysrhythmogenicity in patients with COVID-19 can be caused by direct damage to the myocardium and conduction system as well as by the disease's effect on the various organ systems resulting in hypoxia, plaque rupture, electrolyte and fluid imbalance, catecholamine surge, inflammatory changes, and cytokine storm. The most common dysrhythmia is Afib. Bradycardias, AV blocks, ventricular dysrhythmias, and TdP have all been reported in patients with COVID-19. Care should be taken to minimize risk and to treat underlying changes that contribute to these dysrhythmias. Paying particular attention to the QTc interval is crucial since COVID-19 on its own may prolong the QTc interval. Knowledge of these dysrhythmias and their management considerations will be useful to the emergency clinician when treating patients with COVID-19.
Uncited reference
[54]
Declaration of Competing Interest
None. All authors were substantially involved in the conception, drafting, and revision of this manuscript.
Acknowledgement
Deborah M. Stein, ELS, provided language and technical editing of the manuscript.
==== Refs
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42 Mercuro N.J. Yen C.F. Shim D.J. Maher T.R. McCoy C.M. Zimetbaum P.J. Risk of QT interval prolongation associated with use of hydroxychloroquine with or without concomitant azithromycin among hospitalized patients testing positive for coronavirus disease 2019 (COVID-19) JAMA Cardiol 5 9 2020 1036 1041 10.1001/jamacardio.2020.1834 32936252
43 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 11 2020 1061 10.1001/jama.2020.1585 32031570
44 Gottlieb M. Sansom S. Frankenberger C. Ward E. Hota B. Clinical course and factors associated with hospitalization and critical illness among COVID-19 patients in Chicago Illinois Acad Emerg Med 27 10 2020 963 973 10.1111/acem.14104 32762106
45 Gopinathannair R. Merchant F.M. Lakkireddy D.R. Etheridge S.P. Feigofsky S. Han J.K. COVID-19 and cardiac arrhythmias: a global perspective on arrhythmia characteristics and management strategies J Interv Card Electrophysiol 59 2 2020 329 336 10.1007/s10840-020-00789-9 32494896
46 Colon C.M. Barrios J.G. Chiles J.W. McElwee S.K. Russell D.W. Maddox W.R. Atrial arrhythmias in COVID-19 patients JACC Clin Electrophysiol 6 9 2020 1189 1190 10.1016/j.jacep.2020.05.015 32972558
47 Bertini M. Ferrari R. Guardigli G. Electrocardiographic features of 431 consecutive, critically ill COVID-19 patients: an insight into the mechanisms of cardiac involvement Europace. 22 12 2020 1848 1854 10.1093/europace/euaa258 32944767
48 Rosenblatt A.G. Ayers C.R. Rao A. New-onset atrial fibrillation in patients hospitalized with COVID-19: results from the American Heart Association COVID-19 cardiovascular registry Circ Arrhythm Electrophysiol 15 5 2022 e010666 10.1161/CIRCEP.121.010666
49 Gawałko M. Kapłon-Cieślicka A. Hohl M. Dobrev D. Linz D. COVID-19 associated atrial fibrillation: incidence, putative mechanisms and potential clinical implications Int J Cardiol Heart Vasc 30 2020 100631 10.1016/j.ijcha.2020.100631
50 Wang Y. Chen L. Wang J. He X. Huang F. Chen J. Electrocardiogram analysis of patients with different types of COVID-19 Ann Noninvasive Electrocardiol 25 6 2020 e12806 10.1111/anec.12806
51 Mountantonakis S.E. Saleh M. Fishbein J. Atrial fibrillation is an independent predictor for in-hospital mortality in patients admitted with SARS-CoV-2 infection Heart Rhythm 18 4 2021 501 507 10.1016/j.hrthm.2021.01.018 33493650
52 January C.T. Wann L.S. Alpert J.S. Calkins H. Cigarroa J.E. Cleveland J.C. Jr. 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American heart association task force on practice guidelines and the Heart Rhythm Society J Am Coll Cardiol 64 21 2014 e1 76 10.1016/j.jacc.2014.03.022 24685669
53 Long B. Brady W.J. Bridwell R.E. Ramzy M. Montrief T. Singh M. Electrocardiographic manifestations of COVID-19 Am J Emerg Med 41 2021 96 103 10.1016/j.ajem.2020.12.060 33412365
54 Turagam M.K. Musikantow D. Goldman M.E. Bassily-Marcus A. Chu E. Shivamurthy P. Malignant arrhythmias in patients with COVID-19: incidence, mechanisms, and outcomes Circ Arrhythm Electrophysiol 13 11 2020 e008920 10.1161/CIRCEP.120.008920
55 Desai A.D. Boursiquot B.C. Melki L. Wan E.Y. Management of arrhythmias associated with COVID-19 Curr Cardiol Rep 23 1 2020 2 10.1007/s11886-020-01434-7 33231782
56 Abrams M.P. Coromilas E.J. Wan E.Y. Rubin G.A. Garan H. Dizon J.M. Malignant ventricular arrhythmias in patients with severe acute respiratory distress syndrome due to COVID-19 without significant structural heart disease HeartRhythm Case Rep 6 11 2020 858 862 10.1016/j.hrcr.2020.08.017 32864335
57 Amaratunga E.A. Corwin D.S. Moran L. Snyder R. Bradycardia in patients with COVID-19: a calm before the storm? Cureus. 12 6 2020 e8599 10.7759/cureus.8599
58 Chinitz J.S. Goyal R. Harding M. Bradyarrhythmias in patients with COVID-19: marker of poor prognosis? Pacing Clin Electrophysiol 43 10 2020 1199 1204 10.1111/pace.14042 32820823
59 Cooper L.T. Blauwet L.A. When should high-grade heart block trigger a search for a treatable cardiomyopathy? Circ Arrhythm Electrophysiol 4 3 2011 260 261 10.1161/CIRCEP.111.963249 21673024
60 Gupta M.D. Qamar A. Mp G. Safal S. Batra V. Basia D. Bradyarrhythmias in patients with COVID-19: a case series Indian Pacing Electrophysiol J 20 5 2020 211 212 10.1016/j.ipej.2020.08.004 32822746
61 Al-assaf O. Mirza M. Musa A. Atypical presentation of COVID-19 as subclinical myocarditis with persistent high-degree atrioventricular block treated with pacemaker implant HeartRhythm Case Rep 6 11 2020 884 887 10.1016/j.hrcr.2020.09.003 32953452
62 Kir D. Mohan C. Sancassani R. Heart brake: an unusual cardiac manifestation of COVID-19 JACC Case Rep 2 9 2020 1252 1255 10.1016/j.jaccas.2020.04.026 32368756
63 Eneizat Mahdawi T. Wang H. Haddadin F.I. Al-Qaysi D. Wylie J.V. Heart block in patients with coronavirus disease 2019: a case series of 3 patients infected with SARS-CoV-2 HeartRhythm Case Rep 6 9 2020 652 656 10.1016/j.hrcr.2020.06.014 32837907
64 Chen L. Feng Y. Tang J. Hu W. Zhao P. Guo X. Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated with cardiac involvement ESC Heart Fail 7 6 2020 4408 4415 10.1002/ehf2.12991 32898341
| 0 | PMC9721478 | NO-CC CODE | 2022-12-07 23:22:08 | no | Am J Emerg Med. 2022 Dec 5; doi: 10.1016/j.ajem.2022.12.004 | utf-8 | Am J Emerg Med | 2,022 | 10.1016/j.ajem.2022.12.004 | oa_other |
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Lancet Gastroenterol Hepatol
Lancet Gastroenterol Hepatol
The Lancet. Gastroenterology & Hepatology
2468-1253
Elsevier Ltd.
S2468-1253(22)00404-6
10.1016/S2468-1253(22)00404-6
Comment
Working towards a comprehensive appraisal of vaccine-induced immunity against SARS-CoV-2 in IBD
Otten Antonius T a
Bourgonje Arno R a
Visschedijk Marijn C a
a Department of Gastroenterology and Hepatology, University of Groningen, University Medical Centre Groningen, 9713 GZ Groningen, Netherlands
5 12 2022
5 12 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
pmcIn The Lancet Gastroenterology & Hepatology, Zhigang Liu and colleagues,1 on behalf of the CLARITY-IBD study investigators, report on the effect of different immunomodulating treatments commonly prescribed to patients with inflammatory bowel disease (IBD) on serological responses against the highly transmissible SARS-CoV-2 omicron (B.1.1.529) variants (BA.1 and BA.4 and BA.5 [hereafter BA.4/5]). In this prospective, multicentre, cohort study, functional neutralising antibody responses against SARS-CoV-2 wild-type and omicron BA.1 and BA.4/5 variants after three doses of SARS-CoV-2 vaccine were investigated in 1288 patients with IBD without previous SARS-CoV-2 infection, and who were treated with either infliximab (n=871) or vedolizumab (n=417) recruited from infusion units across the UK. The median age of patients was 46·1 years (IQR 33·6–58·2) and 612 (47·5%) of 1288 were female, 663 (51·5%) were male, 1209 (93·9%) were White, and 46 (3·6%) were Asian. The investigators found that patients treated with infliximab had significantly lower neutralising antibodies against all investigated SARS-CoV-2 variants than did those being treated with vedolizumab, irrespective of the primary vaccination schedule. Additionally, they found that breakthrough SARS-CoV-2 infections were associated with lower neutralising antibody titres against the BA.4/5 variant in patients being treated with infliximab and in those being treated with vedolizumab. These trends remained present after adjusting for potentially confounding patient characteristics (eg, age, concomitant use of immunomodulators and corticosteroids, and comorbidities) with inverse probability of treatment weighting, a statistical method used to correct for such characteristics by reducing the bias of potentially unweighted estimators.2 The findings of this study have important implications for patients with IBD, supporting the prioritisation of second-generation, bivalent booster vaccinations for patients who are treated with infliximab, who generally have lower neutralising antibody titres against the SARS-CoV-2 BA.1 and BA.4/5 omicron variants than those treated with vedolizumab.
Continued assessment of the effect of immunomodulating treatment on the antiviral immune response is essential, especially when considering the ongoing spread of novel dominant SARS-CoV-2 variants caused by viral mutation drift driving global infection rates. The study of Liu and colleagues1 highlights the consequences of infliximab and vedolizumab therapy for neutralising antibody responses against omicron BA.1 and BA.4/5 variants for patients with IBD; however, similar efforts for other immunosuppressive agents like methotrexate and JAK inhibitors are also important, because these treatments are likely to affect vaccine-induced immunity against SARS-CoV-2.3 More real-world evidence is required to inform vaccine prioritisation in the foreseeable future. Such information would aid in substantiating personalised vaccination strategies for specific subgroups of patients with IBD—eg, across different disease activity states, disease complications, and the presence of relevant comorbidities.
An important point of discussion relates to focusing on specific immunological domains when assessing the immune response after vaccination. Liu and colleagues1 focused on the humoral immune response through examination of functional neutralising antibody responses. Although this functional neutralising capacity reflects efficient protection against SARS-CoV-2 infection, it only partially informs on immunological protection.4, 5 T-cell-mediated immunity against SARS-CoV-2 is at least equally important in combating the infection when neutralising antibody concentrations decay. Further study of cellular immune responses could provide important additional insights. A flow cytometry approach could already be sufficient to enumerate and phenotypically characterise variant-specific T cells (eg, using IFN-γ release assays or ELISpot assays on cryopreserved peripheral blood mononuclear cells). Previously, the CLARITY-IBD study investigators reported no significant differences in quantities of anti-spike T-cell fractions or IFN-γ-producing T cells in patients with IBD treated with infliximab versus vedolizumab after one or two doses of SARS-CoV-2 vaccine.4 Similarly, the VIP study reported similar T-cell concentrations among patients with IBD treated with infliximab or vedolizumab,3 and some other studies have found augmented anti-SARS-CoV-2 T-cell fractions in patients with IBD treated with TNF antagonists.6, 7 By contrast, a study investigating anti-SARS-CoV-2 serological responses after a third vaccine dose in patients with IBD treated with biologics reported reduced T-cell-mediated IFN-γ concentrations in those treated with TNF antagonists compared with those not treated with such agents.8 Likewise, two other studies reported reduced IFN-γ secretion in vaccinated patients treated with TNF antagonists, whereas those not treated with TNF antagonists had T-cell responses similar to controls without IBD.9, 10 These observations could be explained by a potential reduction in T-cell functionality or specificity in the absence of quantitative alterations in relevant T-cell subsets. As such, a comprehensive appraisal of qualitative T-cell responses, neutralising antibody responses, and the risk of breakthrough infections deserves attention in future studies, assessing which domains are most relevant for guiding vaccination prioritisation in patients with IBD receiving immunomodulating treatment.
The efforts of Liu and colleagues in identifying patient subgroups at risk of reduced neutralising capacity against the dominating SARS-CoV-2 omicron variants are important and can only be applauded. However, more in-depth and mechanistic assessment of waning vaccine-induced immunity is warranted to secure improved vaccine immunogenicity for patients with IBD.
ATO and ARB contributed equally. ARB has received a research grant from Janssen Research & Development, unrelated to the topic of this Comment. MCV had served on the advisory board for Janssen-Cilag and received a speaker's fee from Galapagos, unrelated to the topic of this Comment. ATO declares no competing interests.
==== Refs
References
1 Liu Z Le K Zhou X Neutralising antibody potency against SARS-CoV-2 wildtype and omicron BA.1 and BA.4/5 variants in patients with inflammatory bowel disease treated with infliximab and vedolizumab after three doses of COVID-19 vaccine (CLARITY IBD): an analysis of a prospective multicentre cohort study Lancet Gastroenterol Hepatol 2022 published online Dec 5. 10.1016/S2468-1253(22)00389-2
2 Chesnaye NC Stel VS Tripepi G An introduction to inverse probability of treatment weighting in observational research Clin Kidney J 15 2021 14 20 35035932
3 Alexander JL Liu Z Muñoz Sandoval D COVID-19 vaccine-induced antibody and T-cell responses in immunosuppressed patients with inflammatory bowel disease after the third vaccine dose (VIP): a multicentre, prospective, case-control study Lancet Gastroenterol Hepatol 7 2022 1005 1015 36088954
4 Lin S Kennedy NA Saifuddin A Antibody decay, T cell immunity and breakthrough infections following two SARS-CoV-2 vaccine doses in inflammatory bowel disease patients treated with infliximab and vedolizumab Nat Commun 13 2022 1379 35296643
5 Geisen UM Rose R Neumann F The long term vaccine-induced anti-SARS-CoV-2 immune response is impaired in quantity and quality under TNFα blockade J Med Virol 94 2022 5780 5789 35945627
6 Caldera F Farraye FA Necela BM Higher cell-mediated immune responses in patients with inflammatory bowel disease on anti-TNF therapy after COVID-19 vaccination Inflamm Bowel Dis 2022 pubslihed online Sept 14. 10.1093/ibd/izac193
7 Li D Xu A Mengesha E The T-cell response to SARS-CoV-2 vaccination in inflammatory bowel disease is augmented with anti-TNF therapy Inflamm Bowel Dis 28 2022 1130 1133 35397000
8 Ramos L Hernández-Porto M Carrillo-Palau M Alonso-Abreu I Reygosa C Hernandez-Guerra M Impact of biologic agents on the immune response induced by the additional dose of SARS-CoV-2 vaccine in inflammatory bowel disease patients Inflamm Bowel Dis 2022 published online Nov 2. 10.1093/ibd/izac228
9 Cerna K Duricova D Hindos M Cellular and humoral immune responses to SARS-CoV-2 vaccination in inflammatory bowel disease patients J Crohn's Colitis 16 2022 1347 1353 35358307
10 Woelfel S Dütschler J König M Systemic and T cell-associated responses to SARS-CoV-2 immunisation in gut inflammation (STAR SIGN study): effects of biologics on vaccination efficacy of the third dose of mRNA vaccines against SARS-CoV-2 Aliment Pharmacol Ther 2022 published online Oct 28. 10.1111/apt.17264
| 36481042 | PMC9721761 | NO-CC CODE | 2022-12-07 23:19:09 | no | Lancet Gastroenterol Hepatol. 2022 Dec 5; doi: 10.1016/S2468-1253(22)00404-6 | utf-8 | Lancet Gastroenterol Hepatol | 2,022 | 10.1016/S2468-1253(22)00404-6 | oa_other |
==== Front
Lancet Infect Dis
Lancet Infect Dis
The Lancet. Infectious Diseases
1473-3099
1474-4457
Elsevier Ltd.
S1473-3099(22)00792-7
10.1016/S1473-3099(22)00792-7
Correspondence
Effect of hybrid immunity and bivalent booster vaccination on omicron sublineage neutralisation
Hoffmann Markus ab
Behrens Georg M N cde
Arora Prerna ab
Kempf Amy ab
Nehlmeier Inga a
Cossmann Anne c
Manthey Luis c
Dopfer-Jablonka Alexandra c
Pöhlmann Stefan ab
a Infection Biology Unit, German Primate Center – Leibniz Institute for Primate Research, Göttingen, Germany
b Faculty of Biology and Psychology, Georg-August-University Göttingen, Göttingen, Germany
c Department for Rheumatology and Immunology, Hannover Medical School, Hannover, Germany
d German Centre for Infection Research, partner site Hannover-Braunschweig, Hannover, Germany
e Centre for Individualized Infection Medicine, Hannover, Germany
5 12 2022
5 12 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 is the central strategy to control the COVID-19 pandemic. Vaccination-induced antibodies that target the viral spike (S) protein and neutralise SARS-CoV-2 are crucial for protection against infection and disease. However, most vaccines encode for the S protein of the virus that circulated early in the pandemic (eg, the B.1 lineage), and emerging SARS-CoV-2 variants have mutations in the S protein that reduce neutralisation sensitivity. In particular, the omicron variant (B.1.1.529 lineage and sublineages) is highly mutated and efficiently evades antibodies.1, 2, 3 Therefore, bivalent mRNA vaccines have been developed that include the genetic information for S proteins of the B.1 lineage and the currently dominating omicron BA.5 lineage. These vaccines have shown increased immunogenicity and protection in mice,4 but information on potential differences in the effectiveness of monovalent and bivalent vaccine boosters in humans is scarce.5, 6, 7
We compared neutralisation of BA.1, BA.4 and BA.5 (identical S proteins, BA.4-5), BA.4.6, and the emerging omicron sublineages BA.2.75.2 (circulating mainly in India), BJ.1 (parental lineage of the currently expanding XBB recombinant), and BQ.1.1 (the incidence of which is increasing in the USA and Europe). We tested neutralisation by antibodies that were induced upon triple vaccination, vaccination and breakthrough infection during the BA.1 and BA.2 wave or BA.5 wave in Germany, triple vaccination plus monovalent or bivalent mRNA booster vaccination, or triple vaccination plus breakthrough infection (BA.1 and BA.2 wave) and a bivalent mRNA booster vaccination. For this, we used S protein bearing pseudotypes, which adequately model antibody-mediated neutralisation of SARS-CoV-2.8 We found that neutralisation of particles pseudotyped with the B.1 S protein (B.1pp) was highest for all cohorts, followed by neutralisation of BA.1pp and BA.4-5pp, which is in line with expectations (figure ; appendix p 17).1, 2 Compared with BA.4-5pp, neutralisation of BA.4.6pp and BJ.1pp was moderately reduced (up to 2·2 times lower), whereas neutralisation of BA.2.75.2pp and BQ.1.1pp was strongly reduced (up to 15·5 times lower; figure; appendix p 8). These results suggest that omicron sublineages BA.2.75.2 and BQ.1.1 possess high potential to evade neutralising antibodies elicited upon diverse immunisation histories. We observed that BA.1 and BA.2 breakthrough infections and BA.5 breakthrough infections in individuals who had been triple vaccinated induced higher omicron sublineage neutralisation (on average 3·7–8·5 times higher compared with triple vaccinated individuals without breakthrough infection) than monovalent or bivalent booster vaccination (on average 1·9–2·2 times higher compared with triple vaccinated individuals without breakthrough infection; appendix p 17). Furthermore, the highest omicron sublineage neutralisation was obtained for individuals who were triple vaccinated and also had a BA.1 or BA.2 breakthrough infection plus a subsequent bivalent booster vaccination (on average 17·6 times higher compared with triple vaccinated individuals without breakthrough infection; appendix p 17). No notable differences were detected between the neutralisation activity induced upon monovalent or bivalent vaccine boosters (on average 2·0 times higher following monovalent vaccination and 2·1 times higher following bivalent vaccination compared with triple vaccinated individuals without breakthrough infection).Figure Omicron sublineage-specific neutralisation activity elicited upon triple vaccination, breakthrough infection, and monovalent or bivalent vaccine boosters.
(A) Neutralising activity in patient plasma. Plasma samples were analysed from individuals who were (i) triple vaccinated (n=16), (ii) triple vaccinated with a BTI during the BA.1 and BA.2 wave in Germany (n=17), (iii) triple vaccinated with a BTI during the BA.5 wave in Germany (n=27), (iv) triple vaccinated that received the monovalent BNT162b2 (Pfizer–BioNTech) vaccine booster (n=11), (v) triple vaccinated with a subsequent monovalent BNT162b2 vaccine booster and a BTI during the BA.5 wave in Germany (n=8), (vi) triple vaccinated individuals with a subsequent bivalent BNT162b2 original and omicron BA.4-5 vaccine booster (n=21), (vii) or triple vaccinated with a BTI during the BA.1 and BA.2 wave in Germany and a subsequent bivalent BNT162b2 original and omicron BA.4-5 vaccine booster (n=11). Information on the methods and statistical analysis are reported in the appendix (pp 10–12). (B) Individual analysis of vaccinated cohorts without BTI. Information on the methods and statistical analysis are reported in the appendix (pp 10–12). Dashed lines indicate the lowest plasma dilution tested. Of note, all samples yielding an NT50 value of less than 6·25 (starting dilution of 1:25) or 12·5 (starting dilution of 1:50) were considered negative and were assigned an NT50 value of 1. BTI=breakthrough infection. NT50=neutralising titre 50. Recipr. dilution factor=reciprocal dilution factor. V=vaccination.
Collectively, our results show that the emerging omicron sublineages BQ.1.1 and particularly BA.2.75.2 efficiently evade neutralisation independent of the immunisation history. Although monovalent and bivalent vaccine boosters both induce high neutralising activity and increase neutralisation breadth, BA.2.75.2-specific and BQ.1.1-specific neutralisation activity remained relatively low. This finding is in keeping with the concept of immune imprinting by initial immunisation with vaccines targeting the ancestral SARS-CoV-2 B.1 lineage.9, 10 Furthermore, the observation that neutralisation of BA.2.75.2pp and BQ.1.1pp was most efficient in the cohort that had a breakthrough infection during the BA.1 and BA.2 wave and later received a bivalent booster vaccination, but was still less efficient than neutralisation of B.1pp, implies that affinity maturation of antibodies and two-time stimulation with different omicron antigens might still not be sufficient to overcome immune imprinting. As a consequence, novel vaccination strategies have to be developed to overcome immune imprinting by ancestral SARS-CoV-2 antigen.
AK, IN, SP, and MH have done contract research (testing of vaccinee sera for neutralising activity against SARS-CoV-2) for Valneva unrelated to this work. GMNB served as advisor for Moderna. SP served as advisor for BioNTech, unrelated to this work. All other authors declare no competing interests. MH and GMNB are co-first authors of this study.
Supplementary Material
Supplementary appendix
==== Refs
References
1 Arora P Zhang L Rocha C Comparable neutralisation evasion of SARS-CoV-2 omicron subvariants BA.1, BA.2, and BA.3 Lancet Infect Dis 22 2022 766 767 35427493
2 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
3 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
4 Scheaffer SM Lee D Whitener B Bivalent SARS-CoV-2 mRNA vaccines increase breadth of neutralization and protect against the BA.5 omicron variant in mice Nat Med 2022 published online Oct 20. 10.1038/s41591-022-02092-8
5 Kurhade C Zou J Xia H Low neutralization of SARS-CoV-2 omicron BA.2.75.2, BQ.1.1, and XBB.1 by 4 doses of parental mRNA vaccine or a BA.5-bivalent booster bioRxiv 2022 published online Nov 2. 10.1101/2022.10.31.514580 (preprint).
6 Miller J Hachmann NP Collier A-r Y, et al. Substantial neutralization escape by the SARS-CoV-2 omicron variant BQ.1.1 bioRxiv 2022 published online Nov 2. 10.1101/2022.11.01.514722 (preprint).
7 Davis-Gardner ME Lai L Wali B mRNA bivalent booster enhances neutralization against BA.2.75.2 and BQ.1.1 bioRxiv 2022 published online Nov 1. 10.1101/2022.10.31.514636 (preprint).
8 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
9 Cao Y Yisimayi A Jian F BA.2.12.1, BA.4 and BA.5 escape antibodies elicited by Omicron infection Nature 608 2022 593 602 35714668
10 Park YJ Pinto D Walls AC Imprinted antibody responses against SARS-CoV-2 Omicron sublineages Science 378 2022 619 627 36264829
| 36480944 | PMC9721839 | NO-CC CODE | 2022-12-07 23:19:09 | no | Lancet Infect Dis. 2022 Dec 5; doi: 10.1016/S1473-3099(22)00792-7 | utf-8 | Lancet Infect Dis | 2,022 | 10.1016/S1473-3099(22)00792-7 | oa_other |
==== Front
J Soc Cardiovasc Angiogr Interv
J Soc Cardiovasc Angiogr Interv
Journal of the Society for Cardiovascular Angiography & Interventions
2772-9303
Published by Elsevier Inc. on behalf of the Society for Cardiovascular Angiography and Interventions Foundation.
S2772-9303(22)00591-9
10.1016/j.jscai.2022.100551
100551
Imaging and Case Report
“Spontaneous” Coronary Artery Dissection After SARS-CoV-2 Messenger RNA Vaccination
Mohammed Ahmed
Cortese Bernardo MD ∗
Fondazione Ricerca e Innovazione Cardiovascolare, Milano, Italy
∗ Corresponding author:
5 12 2022
5 12 2022
1005518 10 2022
4 11 2022
10 11 2022
© 2022 Published by Elsevier Inc. on behalf of the Society for Cardiovascular Angiography and Interventions Foundation.
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 vaccination
SCAD
vaccination complication
==== Body
pmcInfection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes significant systemic inflammation, which can potentially affect heart muscles and coronaries, leading to myocarditis, arrhythmias, or myocardial infarction.1 The exact mechanism of myocarditis is unknown; however, cardiac damage has been correlated with either direct viral injury or host immune response.
There are some reports that have suggested the occurrence of spontaneous coronary dissection (SCAD) with acute myocardial infarction after SARS-CoV-2 infection.2 SCAD is defined as nontraumatic and noniatrogenic separation of the coronary arterial wall.3 Hematomas formed in the false lumen can grow up to a point where they obstruct the flow in the coronary and potentially lead to myocardial infarction. So far, there has been no report regarding a possible association between SARS-CoV-2 vaccination and the occurrence of SCAD.
Case 1
A 58-year-old female patient was admitted to the emergency department on February 2022 with severe chest pain that had started 2 hours before presentation, occurring 2 days after SARS-CoV-2 messenger RNA (mRNA) vaccination (Comirnaty; Pfizer-BioNTech). A physical examination yielded unremarkable results. The patient had the following cardiovascular risk factors: arterial hypertension and dyslipidemia. Laboratory tests showed a troponin value of 3205 ng/mL (normal value [n.v.], <12 mg/mL) and yielded positive results for inflammatory markers such as C-reactive protein (CRP) (5.4 ng/mL; normal value, <0.5 ng/mL). Echocardiography showed preserved left ventricular ejection fraction with apical and septal hypokinesia, with no significant valvular defect. The patient was brought to the catheterization laboratory, where she was diagnosed with non–ST-elevation myocardial infarction. Proximal and middle left anterior descending artery showed a plaque determining 50% tandem stenosis, with impaired flow distally (Figure 1A), whereas the other coronary segments were normal. Because of the atypical angiographic pattern of such lesions, we decided to perform intravascular ultrasound imaging, which revealed a dissection of the left anterior descending artery into the ostioproximal segment, with an intramural hematoma (Figure 1B). Based on the SCAD European consensus document,3 because the vessel was well patent and the distal flow was maintained, the patient was treated conservatively with statins and a single antiplatelet agent. The patient was discharged 2 days later and was asymptomatic. After 3 months, no further events occurred.Figure 1 (A) Coronary angiography showing stenosis in the proximal aspect of the left anterior descending artery, with suspicion of spontaneous coronary dissection (SCAD). (B) Intravascular ultrasound imaging of the proximal aspect of the left anterior descending artery showing a dissection flap, with an intramural hematoma. (C) Coronary angiography showing stenosis in the middistal aspect of the circumflex artery, with high suspicion of SCAD. (D) Optical coherence tomography analysis confirming the dual-lumen appearance of SCAD, with no clear sign of vessel rupture. LAD, left anterior descending artery; LCX,; SCAD, spontaneous coronary dissection.
Case 2
A 48-year-old female patient was admitted to our catheterization laboratory in January 2022 for a 3-day-long chest pain that occurred 4 days after SARS-CoV-2 mRNA vaccination (Comirnaty; Pfizer-BioNTech). A laboratory analysis showed mild leukocytosis (11.500/mL) and positive CRP results (13.3 ng/mL; normal value, <0.5 ng/mL). Angiography showed a diffusely narrowed distal circumflex artery (Figure 1C), with no other significant coronary artery disease. Because of suspicion of SCAD, we performed an optical coherence tomographic analysis (Supplemental Video 1) and confirmed the diagnosis based on a clear 2-lumen vessel and the absence of atherosclerotic coronary artery disease (Figure 1D). Because of the patency of the vessel, conservative management was chosen. The patient was still asymptomatic 3 months later.
Discussion
No single case of the occurrence of SCAD after receiving SARS-CoV-2 mRNA vaccination has been reported so far. Previously, a strong relationship was well described between influenza epidemics and the occurrence of myocardial infarction,4 and it is well recognized that coronary arteries may be affected by systemic inflammation associated with acute viral infections.5 Vaccination has been associated with some complications, from common pain at the site of injection to fever, myalgia, headache, and rash; other more severe complications, such as myocarditis, are really rare but could lead to serious illnesses and death of the patient. An inflammatory response is common to most of the side effects of vaccination and is usually related to local production of cytokines and complement factors, which is sometimes followed by the release of cytokines, prostaglandins, and CRP in the blood. In some genetically predisposed individuals, the mRNA of the vaccine can trigger the exact immune response seen with the infection, resulting in systemic inflammation, thus also leading to myocarditis.6 In a series of US patients treated with vaccination in whom viral myocarditis occurred, this complication was correlated with an abnormal individual immunologic response.7
The ESC position article on SCAD has suggested that adventitial inflammation leads to disruption of vasa vasorum, resulting in hemorrhage into the tunica media. The exact mechanism by which this vaccine could damage the arterial wall remains unknown; however, it can be argued that the mechanism is immune mediated by cytokines produced in response to the vaccine. We observed 2 cases of resting chest pain occurring few days after coronavirus disease 2019 (COVID-19) mRNA vaccination, with a possible relationship between the immunologic milieu of vaccination and disruption of the tunica intima observed. Because of the limited number of observations, we cannot claim that there is a causal effect between mRNA vaccination and the occurrence of SCAD, and we do not know whether a similar risk is present with other types of COVID-19 vaccination. According to the ESC position article on SCAD,3 no specific intervention should be performed unless coronary flow is absent (vessel occlusion) or severely impaired; only in these cases, an interventional approach should be preferred over a conservative one. For further diagnostic purposes, intravascular imaging may be performed unless angiography shows clear signs of SCAD. In case of intravascular imaging, it should be well kept in mind that this further analysis may carry a risk of complications, often related to unclear wire positioning in the vessel lumen, and in the case of OCT, it may carry a risk of propagation of dissection because of the required power injection. In case 2, the operator decided to perform an OCT analysis despite clear angiographic signs of dissection in order to evaluate the real vessel dimension, which was not clear at the time of angiography, and because intravascular ultrasound was not available in the catheterization laboratory that day. Regarding the antiplatelet regimen, the same consensus has indicated treatment with a single antiplatelet agent as the first choice in case of conservative management. No specific workup has been suggested by current guidelines with regard to other associated vascular abnormalities after SCAD diagnosis. In summary, we believe that the focus and management of SCAD potentially related to vaccination should not be different from that reserved for other forms of SCAD.
Conclusions
These cases showed a potential correlation between SARS-CoV-2 mRNA vaccination and SCAD; however, these preliminary findings should be assessed in detail before claiming a potential causal effect.
Learning points
• To understand one of the potential complications of SARS-Cov-2 vaccination.
• To be prepared and act promptly in case of chest pain occurring just after COVID-19 vaccination, with fast diagnosis and prevention of myocardial infarction.
Supplementary material
Movie
OCT imaging of the SCAD, with clear signs of the double lumen.
Declaration of competing interest
The authors declared no potential conflicts of interest with respect to this research, authorship, and/or publication of this article
Funding sources
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Ethics statement and patient consent
The authors confirm that the research was performed in accordance with the appropriate ethical guidelines, and written consent for submission and publication of this case report, including images and associated text, has been obtained from the patients in line with COPE guidance.
To access the supplementary material accompanying this article, visit the online version of the Journal of the Society for Cardiovascular Angiography & Interventions at 10.1016/j.jscai.2022.100551.
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References
1 Madjid M. Safavi-Naeini P. Solomon S.D. Vardeny O. Potential effects of coronaviruses on the cardiovascular system: a review JAMA Cardiol 5 7 2020 831 840 32219363
2 Courand P.Y. Harbaoui B. Bonnet M. Lantelme P. Spontaneous coronary artery dissection in a patient with COVID-19 JACC Cardiovasc Interv 13 12 2020 e107 e108 32553344
3 Adlam D. Alfonso F. Maas A. Vrints C. European Society of Cardiology, acute cardiovascular care association, SCAD study group: a position paper on spontaneous coronary artery dissection Eur Heart J 39 36 2018 3353 3368 29481627
4 Madjid M. Miller C.C. Zarubaev V.V. Influenza epidemics and acute respiratory disease activity are associated with a surge in autopsy-confirmed coronary heart disease death: results from 8 years of autopsies in 34,892 subjects Eur Heart J 28 10 2007 1205 1210 17440221
5 Corrales-Medina V.F. Madjid M. Musher D.M. Role of acute infection in triggering acute coronary syndromes Lancet Infect Dis 10 2 2010 83 92 20113977
6 Caso F. Costa L. Ruscitti P. Could SARS-coronavirus-2 trigger autoimmune and/or autoinflammatory mechanisms in genetically predisposed subjects? Autoimmun Rev 19 5 2020 102524
7 Bozkurt B. Kamat I. Hotez P.J. Myocarditis with COVID-19 mRNA vaccines Circulation 144 6 2021 471 484 34281357
| 36504691 | PMC9722217 | NO-CC CODE | 2022-12-12 23:20:58 | no | J Soc Cardiovasc Angiogr Interv. 2022 Dec 5;:100551 | utf-8 | J Soc Cardiovasc Angiogr Interv | 2,022 | 10.1016/j.jscai.2022.100551 | oa_other |
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Teach Learn Nurs
Teach Learn Nurs
Teaching and Learning in Nursing
1557-2013
1557-3087
Organization for Associate Degree Nursing. Published by Elsevier Inc.
S1557-3087(22)00134-2
10.1016/j.teln.2022.11.002
Research
The synchronous group virtual simulation experience: Associate degree nursing students' perceptions
Penalo Laura M. PhD, RN, CNL 1⁎
Store Stephanie MS, FNP, RN, CLC 2
1 The City University of New York, Borough of Manhattan Community College, New York, NY, USA
2 The City University of New York, York College School of Health Sciences and Professional Programs, New York, NY, USA
⁎ Corresponding author. Tel.: 212-776-6413.
5 12 2022
5 12 2022
13 11 2022
© 2022 Organization for Associate Degree Nursing. Published by Elsevier Inc. All rights reserved.
2022
Organization for Associate Degree Nursing
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 use of virtual simulations exponentially increased as nursing schools experienced an urgent need to integrate online educational technologies during the COVID-19 pandemic. This qualitative descriptive study was conducted to explore associate degree nursing students’ perceptions about the comprehensive Synchronous Group Virtual Simulation educational strategy. This strategy was developed based on the National League for Nursing Jeffries Simulation Theory (JST) and the Healthcare Simulation Standards of Best PracticeTM, including structured synchronous prebriefing and debriefing stages. Content analysis revealed 12 recurrent themes, from which 10 positive themes strongly correlated with concepts of the JST. Our findings support the effectiveness of a high-quality synchronous virtual simulation—guided by an empirically supported simulation theoretical framework and evidence-based simulation best practice standards—can be realistic, experiential, interactive, collaborative, learner-centered, and promote simulation participant outcomes.
Keywords
Virtual simulation
Nursing education
Synchronous prebriefing
Synchronous debriefing
PEARLS debriefing
NLN jeffries simulation theory
Healthcare simulation standards of best practiceTM
Descriptive qualitative
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pmcBackground
The use of virtual simulations exponentially increased in 2020 with the COVID-19 global public health crisis as nursing schools experienced an urgent need to implement online educational technologies. However, despite the rapid growth in the virtual simulations literature supporting positive student (participant) outcomes, there was limited evidence to guide virtual simulation best practices and curriculum integration. A systematic review conducted by Foronda et al. (2020) demonstrated that virtual simulations can improve nursing students learning outcomes, including “learning (knowledge), skills/performance, critical thinking, self-confidence, and provide learner satisfaction” (p. 51). However, this review identified a literature gap concerning best practices in the methods of administering virtual simulation as well as debriefing practices. Another systematic review conducted by Tolarba (2021) also found that virtual simulations have a positive impact on nursing student learning outcomes in the cognitive, skills, and affective domains of learning. This review also identified many variations in the use of virtual simulation technologies in nursing education, which limits the conclusions that can be made concerning the effectiveness of a particular virtual simulation technology or delivery method. These findings were evident during the 2020–2021 global lockdown as nurse educators rapidly transitioned from facilitating in-person human patient simulations (HPS) to online virtual simulations—experiencing a lack of consistency in virtual simulation delivery methods as well as virtual simulation prebriefing and debriefing practices (Badowski & Wells-Beede, 2022; Luctkar-Flude et al., 2021). Now, as nurse educators return to the “new normal,” which may include the permanent use of virtual simulations in the nursing curriculum, they must take the time to evaluate the effectiveness of virtual simulations experiences delivered during the COVID-19 pandemic and incorporate them following appropriate theoretical frameworks and evidence-based standards of best practice in simulation-based teaching and learning.
The National League for Nursing (NLN) Jeffries (2016) Simulation Theory provides a comprehensive view of the simulation experience, including its primary components, key elements, relationships, and outcomes. This theory has seven conceptual components: simulation context, background, design, simulation experience, facilitator and educational strategies, participant, and outcomes. During the simulation experience, there is a dynamic interaction between the facilitator and the participants. Facilitatory attributes such as skill, educational techniques, and preparation can influence the simulation experience. The facilitator implements educational strategies such as providing appropriate guidance and feedback during the prebriefing and debriefing stages, which enhance the simulation experience (Jeffries, 2016, 2021).
The International Nursing Association for Clinical Simulation and Learning (INACSL) Standards of Best Practice: SimulationSM were announced initially in 2011 and revised in 2021 as the Healthcare Simulation Standards of Best PracticeTM. These standards provide guidelines to support the integration, use, and advancement of a simulation-based experience, including “virtual” and “online” learning (Watts et al., 2021). These standards provide detailed, evidence-based recommendations to guide the simulation design and all stages of the simulation experience, including the prebriefing and debriefing processes (Sittner et al., 2015; Watts et al., 2021). Prebriefing refers to not only briefing aspects but also the preparatory activities that occur before the simulation-based experience. Prebriefing activities are purposefully designed and may enhance learners’ success, the debriefing process, and the effectiveness of the simulation experience (McDermott et al., 2021). The debriefing process includes any activities of feedback or guided reflection in which participants have an opportunity to consider the consequences of their actions and assimilate knowledge, skills, attitudes, and behaviors. All simulation experiences are recommended to integrate a planned debriefing session, preceded by a structured prebriefing and guided by a skilled facilitator (Decker et al., 2021).
Methods
This qualitative descriptive single site study was conducted in the United States at a Northeastern urban public community college. This research design allowed for discovering and understanding associate degree nursing (ADN) students’ perceptions after participating in the Synchronous Group Virtual Simulation (SG-VS) educational strategy, which included structured prebriefing and debriefing strategies. The overarching question was: What are the perceptions of ADN students about their experience participating in the SG-VS educational strategy?
Educational Strategy: The Synchronous Group Virtual Simulation (SG-VS)
This educational strategy was designed based upon the NLN Jeffries Simulation Theory, the Healthcare Simulation Standards of Best PracticeTM, and the NLN vSim® for Nursing Curriculum Integration Guide for Faculty. The NLN Jeffries Simulation Theory is a widely used theory that provides the constructs needed to conduct a high-quality virtual simulations experience. The Healthcare Simulation Standards of Best PracticeTM align with this theory, provide a roadmap for nurse educators (Jeffries, 2021), and should also be applied to virtual simulations, just as it would to the other simulation modalities. In consultation with the nursing department chair and course instructors, the SG-VS educational strategy (described in Table 1 ) was developed and facilitated by the primary investigator, who is an assistant professor of nursing, certified Clinical Nurse Leader, holds a post-master's certificate in nursing education, and is experienced in simulation-based teaching and virtual simulation technologies.Table 1. The Synchronous Group Virtual Simulation (SG-VS) Educational Strategy
Table 1Virtual Simulation Stages Description
1) Students’ Preparatory Activities (Completed before the virtual simulation day)
Completion of assigned classroom materials and essential questions about the simulation topic before the virtual simulation day.
2) Synchronous Structured Prebriefing (45 Minutes)
Participation in the group prebriefing session, including the following:1. Introduction of simulation facilitator and participants
2. Review of fiction contract and confidentiality
3. Review of simulation learning objectives
4. Review of essential questions included in the preparatory activities
5. Review of scenario overview
6. Review of provider's orders
7. Explanation of participants’ roles
8. Review of the vSim® for Nursing tutorial, including orientation to the virtual clinical environment
9. Completion of the vSim® for Nursing pre-test
3) Synchronous Group Virtual Simulation (35 min)
Group completion of the vSim® for Nursing scenario, which was controlled by the facilitator who shared the screen with all participants, followed their recommendations for nursing actions and paused the scenario as the participants brainstormed and agreed on nursing interventions.
Completion of the vSim® for Nursing post-test.
4) Synchronous Structured Debriefing (45 Minutes)
Participation in the group debriefing session adopted from the Promoting Excellence and Reflective Learning in Simulation (PEARLS) debriefing framework (Cheng et al., 2016) including the following stages:1. Reactions
2. Description
3. Analysis
4. Application and Summary
5) Virtual Simulation Evaluation Completion of the Virtual Simulation Experience Questionnaire
As part of the course requirements, ADN students enrolled in Fundamentals of Nursing (first semester) and Medical-Surgical Nursing I (third semester) courses were expected to participate in the SG-VS experience in their assigned clinical sections (8–10 students). Students only had from “none” to “very little” prior experience with nursing virtual simulations. The SG-VS included students' participation in a 2-hour synchronous virtual simulation facilitated via the Zoom platform, which included a structured synchronous prebriefing, a group completion of one NLN/Laerdal vSim® Medical-Surgical Scenario, and a structured synchronous debriefing. The Promoting Excellence and Reflective Learning in Simulation (PEARLS) debriefing framework (Cheng et al., 2016) was used to guide the virtual simulation debriefing.
Data Collection
Due to the lack of validated measures to evaluate synchronous virtual simulation experiences, and the need to explore students’ perceptions about the SG-VS educational strategy, the researchers developed a qualitative Virtual Simulation Experience Questionnaire. This questionnaire consisted of the following open-ended questions:1. How do you feel about the synchronous virtual simulation experience?
2. How did this experience help you meet the course's clinical objectives?
3. How do you feel about the prebriefing session?
4. How did the prebriefing session help you meet the simulation objectives?
5. How do you feel about the debriefing session?
6. How did the debriefing session help you meet the simulation objectives?
7. What did you like the most about this synchronous virtual simulation experience, including the prebriefing and debriefing sessions facilitated by your clinical instructor?
8. Please share any challenges or anything that you did not like about this virtual simulation experience
9. Are there any recommendations you would like to make to improve this experience?
This questionnaire was administered online via Survey Monkey immediately after completion of the debriefing. A generic online access link was shared with the students at the end of the virtual simulations experience via Zoom chat. The completion of this questionnaire was voluntary and anonymous. No student identifiers were collected.
Data Analysis
The responses were analyzed and coded using a content analysis approach, which is used to systematically explore large amounts of textual information to determine trends, patterns, and relationships of the words used (Vaismoradi et al., 2013). Content analysis is well suited for studies where every participant is asked and responds to the same questions (Waltz et al., 2017). This analysis was conducted by two study investigators who: 1) defined the units and categories of analysis, 2) developed a set of rules for coding, 3) individually read responses several times to familiarize themselves with the information; 4) individually categorized ideas and generated initial codes; 5) individually searched for, reviewed, and defined themes; and 6) compared and combined individual codes and themes into final ones by consensus agreement. To ensure trustworthiness: 1) data were meticulously transferred and organized using excel tables, 2) codes and themes were first independently developed by each investigator, 3) multiple meetings and debriefing sessions were conducted to compare and develop mutual codes and themes, and 4) investigators used an audit trail including detailed notes to explain the coding process and rationale for what codes were clustered together to form the basis of the themes.
Ethical Considerations
Exempt institutional review board (IRB) approval was received from the college where the study was conducted. The researchers completed the required modules for human subjects protection through the Collaborative Institutional Training Initiative (CITI) and obtained permission from the nursing department chair and course faculty to collect questionnaire data. Although participation in the SG-VS educational strategy was a course requirement, completion of the online questionnaire was optional. The SG-VS was not a graded assignment. Online questionnaire data were password protected and only accessed by the study investigators. To protect participants’ anonymity, questionnaires did not include any student names or demographic data. However, students’ aggregate demographic data were obtained from the program administrator for all participating courses.
Results
Of 125 students who participated in the SG-VS educational strategy, 86 completed the Virtual Simulation Experience Questionnaire (69% response rate). The students were predominantly female (84%). Students from the following racial/ethnic groups were represented: White (34%), Black (28%), Asian (20%), Hispanic (16%), and Multiracial (2%). Most students were between 25 and 44 years of age (79%); other students were under 25 years of age (12%) and between 45 and 54 years of age (9%).
Content analysis revealed 12 recurrent themes, from which ten positive themes were consistent with concepts of the NLN Jeffries Simulation Theory. Therefore, themes were categorized using these theoretical constructs. This section will highlight the most relevant supporting quotes. Additional supporting quotes are presented in Appendix A.
Simulation Experience
Realistic
Students expressed that the SG-VS experience was “realistic” and “felt as if [they] were in an actual clinical setting.” Additional key quotes included: “It put me back into the clinical setting, it got my heart pumping and adrenaline going,” and “Just like with a real patient in the hospital, we were able to begin with a physical assessment and walk through what we would do as soon as we set foot in a patient's room.”
Experiential
Students expressed that the SG-VS experience was “hands-on” and allowed them to apply the nursing process. Key quotes included: “Loved to be virtually hands-on with the patient” and “I learned a lot about assessment and implementation of procedures.”
Interactive
Students expressed that the SG-VS experience was “very interactive.” Key quotes include: “I liked the interaction with my peers and the [facilitator]” and “Loved the interactivity.”
Collaborative
Students acknowledged teamwork, collaboration, and communication. Key quotes include: “I specifically enjoyed working as a team,” “I liked doing it as a group because we can brainstorm and learn from each other,” and “It also assisted us with learning better communication and delegation within a clinical setting.”
Facilitator and Educational Strategies
Facilitator
Students acknowledged the importance of having a virtual simulations facilitator. Key quotes include: “I think doing it alone is not helpful, the guidance from [the facilitator] is vital”; “[The facilitator] truly helped keep us focused and productive”; and “I enjoyed [the facilitator] the most, the simulation experience was great because she made me feel comfortable participating and kept us all on track and focused.”
Prebriefing
Students expressed their satisfaction with the prebriefing and acknowledged that it was helpful, set the tone, and promoted confidence. Key quotes include: “helpful,” “The prebriefing prepared me,” “The prebriefing helped me organize the steps I would take to deliver nursing care,” “The prebriefing helped me to develop confidence,” and “The prebriefing made us understand things better and, as a result, be more confident during the simulation.”
Debriefing
Students expressed that the debriefing was “great,” “excellent,” “wonderful,” “very good,” and acknowledged that it helped them express feelings and emotions, identify performance gaps, and get valuable feedback. Key quotes include: “Debriefing was a good way to decompress and talk about some of our emotions and feelings after going through the scenario”; “It was nice to discuss our feelings, our success, and what could've been done better”; and “The debriefing helps us understand our mistakes and how we can prevent future mistakes.”
Participant Outcomes
Reaction (Satisfaction and Confidence)
Students expressed satisfaction with the SG-VS experience and acknowledged that it positively affected their confidence. Key quotes include: “Great experience,” “Excellent experience,” “Really enjoyed it,” “Great! I learned a lot, and it increased my confidence in preparing for actual hospital practice.”
Learning (Changes in Knowledge, Skills, and Attitudes)
Students expressed that the SG-VS promoted learning and understanding of the nursing process, critical thinking, and clinical judgment. Key quotes include: “It helped me understand,” “This experience made me think critically while maintaining patient care and following protocols,” “It allowed us to put into practice nursing interventions and assessment while utilizing critical thinking skills,” “It wasn't just about getting the work done but understanding our decisions and what is best for the patient,” and “It allowed us to feel invested in the patient and understand the consequences of making decisions in a clinical setting.”
Behavior (Transfer of Learning to Clinical Practice)
Students acknowledged that the SG-VS experience helped them connect with real clinical experiences and apply nursing knowledge, skills, and attitudes (KSAs). Key quotes include: “We were able to connect the case scenario with clinical site situations,” “This experience will stick with me so I would be able to know what to do in similar situations in the future,” “This experience allowed me to apply the knowledge I received through lectures and readings to a real-life situation,” and “[…] just as how the hospital provides a sense of teamwork, you are also able to get that feeling from the group simulation.”
More Virtual Simulations in Smaller Groups
Despite the small number of negative responses, two critical themes arose: 1) more virtual simulations and 2) smaller virtual simulations groups. Participants recommended “virtual simulations experiences to be a part of every clinical semester” and “to incorporate more virtual simulations in the nursing program.” In addition, several students recommended conducting virtual simulations in “smaller groups.”
Discussion
Our findings are consistent with concepts of the NLN Jeffries Simulation Theory and support that a well-structured synchronous virtual simulation that integrates the Healthcare Simulation Standards of Best PracticeTM can be realistic, experiential, interactive, collaborative, and learner-centered. Participants' interactions occurring during a SG-VS may improve realism, psychological fidelity, students' connectedness, and virtual simulations performance. In addition, preparatory activities and prebriefing (McDermott et al., 2021) as well as debriefing on demand or post-scenario (Decker et al., 2021) facilitated by a competent instructor (Persico et al., 2021), may promote participants' satisfaction, confidence, learning of KSAs, critical thinking, clinical judgment, and future clinical behaviors. A well-structured prebriefing and debriefing also have the potential to improve students’ self-efficacy and future clinical performance (Penalo & Ozkara San, 2021)
One of the main strengths of this study was the comprehensive SG-VS educational strategy, which was conducted synchronously online, allowing for the simulation participants and the facilitator to have real-time interactions and discussions. Addressing a gap in the virtual simulations literature (Badowski & Wells-Beede, 2022; Luctkar-Flude et al., 2021), the SG-VS included structured prebriefing and debriefing strategies (Table 1), which were positively perceived by the students. Prebriefing promotes participants’ psychological safety and the achievement of simulation objectives. A structured prebriefing must be purposefully planned to promote student learning outcomes by clarifying simulation expectations, introducing learners to simulation objectives, and fostering a safe and collaborative learning environment (Decker et al., 2021; Jeffries, 2021; Leigh & Steuben, 2018; McDermott, 2016; McDermott et al., 2021; Persico et al., 2021). Although prebriefing does not get as much attention as other simulation elements (such as debriefing) in the simulation literature, it is a vital component of the virtual simulation experience that sets the stage for other simulation elements, including facilitation, scenario performance, and debriefing.
The SG-VS debriefing followed the PEARLS debriefing framework (Cheng et al., 2016), which includes phases of reaction, description, analysis, and summary. The PEARLS “blended approach to debriefing encourages educators to purposefully merge various debriefing strategies to tailor discussion to learner needs and learning context” (Cheng et al., 2016, p. 1). The PEARLS debriefing script is a valuable tool for debriefers because it does not only provide specific phases of debriefing, but also representative phrases are provided to guide possible wording choices, which can be easily adopted to a virtual simulation debriefing. Based on study findings, the virtual simulations literature (Goldsworthy & Verkuyl, 2021; Gordon, 2017; Gordon & McGonigle, 2018), and considerable experience implementing the PEARLS debriefing in nursing simulations, the authors believe that this is an effective framework to facilitate a synchronous virtual simulation debriefing.
Additional emerging themes were "smaller virtual simulations groups” and "more virtual simulations." We recommend further studies examining appropriate virtual simulations group size and the effect of the integration of virtual simulations in the undergraduate nursing curriculum (e.g., virtual simulations as an in-person clinical or HPS preparatory activity). Additional research priorities include studies evaluating the effect of different prebriefing and debriefing frameworks such as the PEARLS in virtual simulations, Debriefing for Meaningful Learning, and Debriefing with Good Judgment. In addition, an exploration of the effect of different virtual simulations prebriefing and debriefing delivery methods including face-to-face, synchronous, asynchronous, or self-debrief is needed.
The use of virtual simulations exponentially increased in prelicensure nursing education during the COVID-19 global pandemic. As a result, virtual simulation technologies became more easily accessible and many nursing programs integrated them to their curriculum. If properly designed and enhanced with evidence based education strategies, such as prebriefing and debriefing, virtual simulation technologies can be an important complementary clinical education tool for nursing schools that have limited access to high fidelity simulation laboratories; do not have sufficient educators able to deliver in-person simulation activities; aim to provide on-demand resources to prepare students for in-person clinical or simulation experiences; and are required to deliver on-line clinical experiences due to cancelations of in-person clinical activities due to public health concerns. However, nurse educators must consider this with caution and strive to collect outcome data to inform future decisions concerning the use of virtual simulations to replace a percentage of traditional in-person clinicals.
Lastly, preparing students for the Next Generation NCLEX® (NGN) examination is a priority and challenge for ADN program educators. High-quality virtual simulations have the potential to help students prepare for the expected behaviors that they need to know, perform, and become comfortable with for this examination, which is expected to be more interactive than the current NCLEX examination. Most importantly, virtual simulations that include evidence-based structured prebriefing and debriefing strategies can be an important tool in promoting students’ clinical judgement and clinical decision-making, which are vital skills for entry-level nurses and will be tested in the NGN examination (NCSBN, 2019).
Limitations
This study had several limitations. First, the use of a convenience sample of ADN students at an urban public community college. Since the participants in this study were all from the same college, the experiences of students in other regions or programs may not be reflected in the findings of this study. Another limitation was experienced in the prebriefing and debriefing processes. Although the structured virtual simulations prebriefing and debriefing were facilitated by the same instructor (researcher), following a specific set of guidelines, participants in different groups may have thought and reacted differently regarding student-facilitator questions and comments; therefore, the prebriefing and debriefing sessions were not identical between groups. In addition, variables outside the researcher's control—including technological barriers, lack of commitment to participate in synchronous activities, stressors associated with the COVID-19 pandemic, and external distractors—may have impacted students’ engagement in the SG-VS educational strategy. Lastly, this study included a researcher-developed online questionnaire, which included questions that were written in a way that may have not provoked neutral or open answers. Also, the primary investigator (simulation facilitator) is a full-time faculty person at the college where the study was conducted, which may have caused students to be less expressive of the negative aspects of the SG-VS educational strategy. In addition, online questionnaires are one-way conversations, since the researcher can't ask clarifying questions or probe for more information. Therefore, the authors recommend replicating this study using more rigorous qualitative methodology using interviews or focus groups. Despite these limitations, qualitative responses were substantial and represented important elements of the NLN Jeffries Simulation Theory and Healthcare Simulation Standards of Best PracticeTM.
Conclusion
Among the various types of programs that educate prelicensure nursing students, ADN programs prepare the largest number of undergraduate students within a short curriculum timeframe. Therefore, ADN programs may benefit from the use of high-quality virtual simulations to promote students’ readiness for clinical or HPS experiences, the NGN licensure examination, and future clinical practice. As nurse educators continue to integrate virtual simulations in the ADN curriculum, it is imperative to design, implement, and evaluate high-quality virtual simulations experiences that include theoretical frameworks and the Healthcare Simulation Standards of Best PracticeTM.
Declaration of Competing Interest
The authors declare no conflict of interest.
Appendix A
Synchronous Group Virtual Simulation Experience:
Recurrent Themes and Examples of Students’ Supporting QuotesConcepts of the NLN Jeffries Simulation Theory Recurrent Themes Examples of Students’ Supporting Quotes
Simulation Experience
(Synchronous Group Virtual Simulation) Realistic “Realistic” (n=12)
“It is the closest I've felt to actually being in a clinical setting.”
“I felt as if I was in an actual clinical setting.”
“It put me back into the clinical setting. It got my heart pumping and adrenaline going.”
“Just like with a real patient in the hospital, we were able to begin with a physical assessment and walk through what we would do as soon as we set foot in a patient's room.”
“It felt as close to having a real patient […] during a time when we aren't able to be in the hospital as students.”
“[…] during this time where we can't go on clinical rotations; it provided a somewhat real-life experience.”
“It pretty much seems like we are providing care for a real patient.”
“This experience makes us feel like we are in the hospital taking care of a real patient.”
“It puts you in a near real-life situation. You feel as though you are in a patient's room.
Experiential
1. “Hands-on”
2. Application of the nursing process. “Hands-on.” (n=9)
“Loved to be virtually hands-on with the patient.”
“This type of hands-on exposure really helps me learn.”
“Given the current distance learning, this was the most hands-on training we have gotten.”
“I learned a lot about assessment and implementation of procedures.”
“[…], we were able to begin with a physical assessment and walk through what we would do as soon as we set foot in a patient's room.”
“It goes through the nursing process effectively.”
“Going through the nursing process, especially during this time of remote learning.”
Interactive
3. Very interactive experience “Very interactive.” (n=7)
“I liked the interaction with my peers and the [facilitator].”
“Interesting, stimulating, interactive.”
“Loved the interactivity.”
Collaborative
4. Teamwork and collaboration
5. Communication “I specifically enjoyed working as a team during this online group virtual simulation.”
“This was a great opportunity to work as a team […].”
“I really liked the teamwork.”
“[…] doing it as a group—because we can brainstorm and learn from each other.”
“We learn a lot from each other's points of views and experiences.”
“We were able to evaluate ourselves as a team.”
“We (classmates) worked together as a group; learning from each other as a result.
“It also assisted us with learning better communication and delegation within a clinical setting.”
“[This experience] assisted us with learning better communication and delegation within a clinical setting.”
Facilitator and Educational Strategies
Facilitator
6. Promotes a Dynamic Interaction
7. Promotes an Environment of Trust and psychological safety
Note: This theme supports the following Healthcare Simulation Standards of Best PracticeTM: Facilitation
"I think doing it alone is not helpful. The guidance from [the facilitator] is vital.”
“[The facilitator's] skills on prebriefing and debriefing make everyone actively participate […]”
“[The facilitator's] was kind and instructive, prodding without being harsh or condescending, and truly helped keep us focused and productive.”
“I enjoyed [the facilitator] the most. The simulation experience was great because she made me feel comfortable participating and kept us all on track and focused.
“I loved how detail-oriented our [facilitator] was. She explained everything thoroughly and was great in leading and guiding us through the entire session.”
“The [facilitator] did not make it uncomfortable for students when a question was answered wrong. Rather, she provided a chance to correct the mistake through her non-judgmental guidance.
“[The facilitator] made it so enjoyable and made us feel at ease.’
Prebriefing
8. Helpful
9. Sets the tone
10. Promotes confidence
Note: This theme supports the following Healthcare Simulation Standards of Best PracticeTM: Prebriefing: Preparation and Briefing (McDermott et al., 2021). “[The prebriefing] was helpful […]” (n=52)
“[The prebriefing] prepared me […]” (n=12)
“The prebriefing conference helped me organize the steps I would take to deliver nursing care.”
“[In the prebriefing] the objectives were laid out very clearly, which enabled me to understand exactly what was expected of me […].”
“I think was a great way to get you thinking and refresh your knowledge before the simulation started.”
“I think [the prebriefing] was a good way to get our mindset ready. We started working as a team as soon as the prebriefing started.”
“I like [the prebriefing] a lot. It was a nice warm-up to the simulation; it got our head thinking in the right direction.”
“The [prebriefing] helped me to develop confidence.”
“[The prebriefing] made us understand things better and, as a result, more confidence during the simulation.”
Debriefing
11. Promotes expression of feeling and emotions
12. Helps identify performance gaps and achievements
13. Valuable Feedback
14. Promotes student satisfaction
Note: This theme supports the Healthcare Simulation Standards of Best PracticeTM: Debriefing (Decker et al., 2021).
The debriefing was: “great” (n=8); “excellent” (n=5); “wonderful” (n=3); “very good” (n=5).
“Debriefing is very beneficial as it allows the participants to share their thoughts and feelings regarding the stimulation.”
“A safe place to reflect on our interventions.”
“[Debriefing] was a good way to decompress and talk about some of our emotions and feelings after going thru the scenario.
“It was nice to discuss our feelings, our success, and what could've been done better.”
“The debriefing was great; it allowed us to see what we succeeded in and what we need to work on. It was great to voice our strengths and weaknesses.”
“The debriefing gave me insight into my weakness and showed me how to improve.”
“[The debriefing] helps us understand our mistakes and how we can prevent future mistakes.”
“It was well needed to discuss what I may have forgotten, what I could have done better, and what I did right.”
“I think it was a good way to recap everything that was done and talk about what was good and any mistakes made.”
“[The Debriefing] was valuable in terms of feedback, what went well, and what needs additional work.
“It allowed us to ask questions or get further feedback on areas we need to focus […].”
Outcomes: Participant Reaction
15. Satisfaction
16. Self-Confidence “Great experience.” (n=24)
“Excellent experience.” (n=12)
“Really enjoyed it.” (n=10)
“Wonderful” (n=3)
“Amazing” (n=3)
“I was pleasantly surprised by how much I enjoyed it. It was orderly and informative.”
“Great! I learned a lot, and it increased my confidence in preparing for actual hospital practice.”
“[This experience] was very stimulating to our minds. It brought to the forefront the concepts and knowledge we need to expand on, i.e., time management and confidence in using the knowledge we already know […].”
Learning
17. Understanding of nursing care concepts
18. Knowledge, skills, attitudes
19. Critical thinking
20. Clinical decision making “[This experience] helped me understand [nursing care concepts].” (n=36)
“This type of "hands-on exposure" really helps me learn.”
“[This experience] refreshes my knowledge on the symptoms and help me connect the points, think about nursing interventions, and the care plan.”
“I had a reflection on everything we've talked about in class by applying critical thinking in nursing.”
“This experience made me think critically while maintaining patient care and following protocols.”
“This [experience] allowed us to put into practice nursing interventions and assessment while utilizing critical thinking skills.”
“I really enjoyed the simulation itself and feeling like we had to think on our feet quickly.”
“[This experience] was useful to think, decide, and learn about the right assessments and interventions.”
“It wasn't just about getting the work done but understanding our decisions and what is best for the patient […]”
“[This experience] allowed us to feel invested in the patient and understand the consequences of making decisions in a clinical setting.”
Behavior
21. Cognitive connection with real clinical experiences
22. Application of nursing knowledge, skills, and attitudes
“This experience will stick with me so I would be able to know what to do in similar situations in the future.”
“We were able to connect the case scenario with clinical site situations.”
“In these uncertain times, group virtual simulations provided an opportunity for me to continue with the clinical portion of the class and merged all that I have learned in the lecture portion.”
“[This experience] gives us the chance to apply what we have learned from the book into a real situation […].”
“This experience allowed me to apply the knowledge I received through lectures and readings to a real-life situation.”
“It helped me meet the objectives by allowing me to be more hands-on with the scenario that was provided (even though virtual) you are able to see the patient and apply nursing care.”
“[…] just as how the hospital provides a sense of teamwork, you are also able to get that feeling from the group simulation.”
==== Refs
References
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Cheng A. Grant V. Robinson T. Catena H. Lachapelle K. Kim J. Adler M. Eppich W. The Promoting Excellence and Reflective Learning in Simulation (PEARLS) approach to health care debriefing: A faculty development guide Clinical Simulation in Nursing 12 10 2016 419 428 10.1016/j.ecns.2016.05.002
Decker S. Alinier G. Crawford S.B. Gordon R.M. Jenkins D. Wilson C. Healthcare Simulation Standards of Best PracticeTM the debriefing process Clinical Simulation in Nursing 58 2021 27 32 10.1016/j.ecns.2021.08.011
Foronda C.L. Fernandez-Burgos M. Nadeau C. Kelley C.N. Henry M.N. Virtual simulation in nursing education: A systematic review spanning 1996 to 2018 Simulation in Healthcare: The Journal of the Society for Simulation in Healthcare 15 1 2020 46 54 10.1097/SIH.0000000000000411
Goldsworthy S. Verkuyl M. Facilitated virtual synchronous debriefing: A practical approach Clinical Simulation in Nursing 59 2021 81 84 10.1016/j.ecns.2021.06.002
Gordon R.M. Debriefing virtual simulation using an online conferencing platform: Lessons learned Clinical Simulation in Nursing 13 12 2017 668 674 10.1016/j.ecns.2017.08.003
Gordon R.M. McGonigle D. Virtual simulation in nursing education 2018 Springer Publishing Company, LLC
Jeffries P.R. The NLN Jeffries simulation theory 2016 Wolters Kluwer
Jeffries P.R. Simulation in nursing education: From conceptualization to evaluation 3rd ed 2021 Wolters Kluwer
Leigh G. Steuben F. Setting learners up for success: Presimulation and prebriefing strategies Teaching and Learning in Nursing 13 3 2018 185 189 10.1016/j.teln.2018.03.004
Luctkar-Flude M. Tyerman J. Verkuyl M. Goldsworthy S. Harder N. Wilson-Keates B. Kruizinga J. Gumapac N. Effectiveness of debriefing methods for virtual simulation: A systematic review Clinical Simulation in Nursing 57 2021 18 30 10.1016/j.ecns.2021.04.009
McDermott D.S. The prebriefing concept: A Delphi study of CHSE experts Clinical Simulation in Nursing 12 6 2016 219 227 10.1016/j.ecns.2016.02.001
McDermott D.S. Ludlow J. Horsley E. Meakim C. Healthcare Simulation Standards of Best PracticeTM prebriefing: Preparation and briefing Clinical Simulation in Nursing 58 2021 9 13 10.1016/j.ecns.2021.08.008
NCSBN. (2019). Next generation NCLEX project. https://www.ncsbn.org/next-generation-nclex.htm
Penalo L.M. Ozkara San E. Potential influences of virtual simulation prebriefing and debriefing on learners’ self-efficacy Nurse Educator 46 4 2021 195 197 10.1097/NNE.0000000000000921 32941306
Persico L. Belle A. DiGregorio H. Wilson-Keates B. Shelton C. Healthcare Simulation Standards of Best PracticeTM facilitation Clinical Simulation in Nursing 58 2021 22 26 10.1016/j.ecns.2021.08.010
Sittner B.J. Aebersold M.L. Paige J.B. Graham L.L.M. Schram A.P. Decker S.I. Lioce L. INACSL standards of best practice for simulation: Past, present, and future Nursing Education Perspectives 36 5 2015 294 298 10.5480/15-1670 26521497
Tolarba J.E.L. Virtual simulation in nursing education: A systematic review International Journal of Nursing Education 13 3 2021 48 54
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| 36506704 | PMC9722218 | NO-CC CODE | 2022-12-07 23:19:09 | no | Teach Learn Nurs. 2022 Dec 5; doi: 10.1016/j.teln.2022.11.002 | utf-8 | Teach Learn Nurs | 2,022 | 10.1016/j.teln.2022.11.002 | oa_other |
==== Front
Biol Psychiatry
Biol Psychiatry
Biological Psychiatry
0006-3223
1873-2402
Society of Biological Psychiatry.
S0006-3223(22)01585-2
10.1016/j.biopsych.2022.09.007
Correspondence
Anti-SARS-CoV-2 and Autoantibody Profiling of a COVID-19 Patient With Subacute Psychosis Who Remitted After Treatment With Intravenous Immunoglobulin
McAlpine Lindsay S. a
Lifland Brooke b
Check Joseph R. b
Angarita Gustavo A. b
Ngo Thomas T. cd
Chen Peixi ce
Dandekar Ravi ce
Alvarenga Bonny D. ce
Browne Weston D. ce
Pleasure Samuel J. ce
Wilson Michael R. ce
Spudich Serena S. a
Farhadian Shelli F. f
Bartley Christopher M. cd∗
a Department of Neurology, Yale University School of Medicine, New Haven, Connecticut
b Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut
c Weill Institute for Neurosciences, University of California, San Francisco, California
d Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, California
e Department of Neurology, University of California, San Francisco, California
f Department of Internal Medicine, Section of Infectious Diseases, Yale School of Medicine, New Haven, Connecticut
∗ Address correspondence to Christopher M. Bartley, M.D., Ph.D.
5 12 2022
5 12 2022
6 9 2022
7 9 2022
© 2022 Society of Biological Psychiatry.
2022
Society of Biological Psychiatry
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
pmcTo the Editor:
Patients with COVID-19 are at increased risk for developing new or recurrent psychosis (1,2). Viral infections—including SARS-CoV-2 (3, 4, 5)—can cause psychosis in the context of autoimmune encephalitis (6). However, some individuals with parainfectious psychosis do not meet criteria for autoimmune encephalitis, yet they respond to immunotherapy (7,8). We identified anti-SARS-CoV-2 and candidate autoantibodies in the serum and cerebrospinal fluid (CSF) of a case of COVID-19–associated subacute psychosis that did not meet criteria for autoimmune or infectious encephalitis yet remitted after treatment with intravenous immunoglobulin (IVIg).
A 30-year-old man without medical, psychiatric, or substance use history developed fever and malaise. The following day, he developed a delusion that the rapture was imminent. On day 2, a nasopharyngeal swab was positive for SARS-CoV-2 by real-time reverse transcription–polymerase chain reaction. He began a 14-day isolation but maintained daily contact with family. He did not have anosmia, ageusia, or respiratory symptoms, nor did he receive treatment for COVID-19. He initially suffered from hypersomnia and slept 22 hours/day. He then developed insomnia, sleeping only 3 to 4 hours/day. During this time, he paced, rambled, and believed that he was dying and communicating with deceased relatives and God.
On day 22, he kicked through a door and pushed his mother, prompting an emergency department evaluation. In the emergency department, he falsely claimed to be a veteran, and worried about being experimented on with radiation. He did not have suicidal ideation, homicidal ideation, or hallucinations. Noncontrast head computed tomography was normal, and urine toxicology was negative. He was started on haloperidol 5 mg by mouth twice daily with significant improvement of his agitation and delusions. After 48 hours, he was discharged to outpatient follow-up. Outpatient magnetic resonance imaging of the brain with and without gadolinium was unremarkable.
After discharge, his restlessness, insomnia, and cognitive slowing recurred, as did his fears that he would be experimented on “like a guinea pig.” On day 34, he punched through a wall and was hospitalized and evaluated for autoimmune encephalitis. A detailed neurological exam was unremarkable. He had a flat affect, slowed speech, and akathisia, which resolved after decreasing haloperidol and starting benztropine and lorazepam. A 12-hour video electroencephalogram was normal. CSF studies, including a clinical autoimmune encephalitis autoantibody panel, were notable only for an elevated IgG of 4.8 mg/dL (reference 1.0–3.0 mg/dL) with a normal IgG index (see Table 1 ).Table 1 Clinical Studies
Source Test Result (Reference)
Nasopharyngeal Swab SARS-CoV-2 RNA PCR Day 2: positive
Day 34: negative
Urine 9-drug toxicology screen Negative
Serum Basic metabolic panel Within acceptable limits:Sodium 146 mmol/L (136–144 mmol/L)
Potassium 3.1 mmol/L (3.3–5.1 mmol/L)
Prothrombin time 11.5 seconds (9.6–12.3 seconds)
International normalized ratio 1.07
Complete blood count Day 24 WBC: 6.9 × 1000/μL (4.0–10.0 × 1000/μL)
Day 34 WBC: 5.4 × 1000/μL (4.0–10.0 × 1000/μL)
MPV 11.6 fL (6.0–11.0 fL)
Thyroid-stimulating hormone 2.520 μIU/mL (0.270–4.200 μIU/mL)
D-dimer 1.89 mg/L (≤0.50 mg/L)
Liver enzymes AST 156 U/L (<35 U/L)
ALT 372 U/L (<59 U/L)
C-reactive protein 1.7 mg/L (<1.0 mg/L)
Ferritin 1124 ng/mL (30–400 mg/mL)
Ammonia 27 μmol/L (11–35 μmol/L)
Albumin 4.2 g/dL (3.6–4.9 g/dL)
IgG 1230 mg/dL (700–1600 mg/dL)
CSF Cell count 0 nucleated cells
Protein 41.2 mg/dL (15–45 mg/dL)
Glucose 60 mg/dL (40–70 mg/dL)
Culture No growth
Oligoclonal banding None
Albumin 25.8 mg/dL (10–30 mg/dL)
IgG 4.8 mg/dL (1.0–3.0 mg/dL)
IgG index 0.67 (<0.7)
Autoimmune encephalopathy panel Negative for AMPA Ab, amphiphysin Ab, anti-glial nuclear Ab, neuronal nuclear Ab (types 1, 2, and 3), CASPR2, CRMP–5, DPPX, GABA-B receptor, GAD65, GFAP, IgLON5, LGI1-IgG, MGLUR1, NIF, NMDA receptor, Purkinje cell cytoplasmic Ab (types Tr, 1, and 2)
Imaging CT head without contrast No acute intracranial findings
MRI brain with contrast No acute intracranial abnormality or definitive structural abnormality identified; specifically, no imaging findings suggestive of encephalitis or acute demyelination
Electroencephalography Normal prolonged (>12 hours) awake and asleep inpatient video EEG
Ab, antibody; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CSF, cerebrospinal fluid; CT, computed tomography; EEG, electroencephalogram; MPV, mean platelet volume; MRI, magnetic resonance imaging; PCR, polymerase chain reaction.
Lacking focal neurologic symptoms, seizures, magnetic resonance imaging abnormalities, or CSF pleocytosis, his presentation did not meet consensus criteria for autoimmune encephalitis (8). Nevertheless, his subacute psychosis, cognitive slowing, and recent SARS-CoV-2 infection raised concern for autoimmune-mediated psychosis. Therefore, starting on day 35, he received a total of 2 g/kg of IVIg over 3 days. His cognitive slowing and psychotic symptoms remitted after the first day of treatment. His sleep cycle normalized, and he was discharged without scheduled antipsychotics. He returned to work immediately after discharge and remained symptom-free 3 months later.
Because his robust response to IVIg suggested an underlying neuroinflammatory process, we tested for anti-SARS-CoV-2 and anti-neural autoantibodies. Using a Luminex SARS-CoV-2 antigen panel (9,10), we detected anti-spike, anti-receptor binding domain, and anti-nucleocapsid protein antibodies in his serum and CSF (Figure 1A ) (9,10).Figure 1 Characterization of anti-neuronal antibody staining. (A) Case and control (n = 5, C1–C5) CSF and serum were screened in technical replicate for anti-SARS-CoV-2 antibodies by Luminex antigen assay. Both replicates are shown. BSA was used as a negative control. Horizontal bars = median of technical replicates. C1 through C5 refer to control individuals 1 through 5. (B) Mice were perfused with 4% paraformaldehyde. Twelve-micrometer frozen sagittal brain sections were immunostained with CSF at a 1:4 dilution and counterstained with an anti-human IgG secondary antibody (green) (Jackson #709-545-149 at 2 μg/mL). Neg control = secondary only staining. Nuclei were labeled with DAPI (blue). (C) Heatmap of log(fold change) PhIP-Seq human peptide enrichments relative to the mean of controls. Both technical replicates for case CSF and serum are plotted (r1 and r2), while the means of technical replicates are plotted for controls. Each row represents 1 peptide and peptides mapping to the same protein are grouped together according to the black-and-white vertical runner. (D) Dot plot of MCTP1 PhIP-Seq enrichment compared with a database of 4216 control samples. The y-axis is log(rpK) (rpK = sequencing reads per 100,000 reads). Total MCTP1 and the top MCTP1 peptide enrichments were tested for statistical significance by Kruskal-Wallis 1-way ANOVA followed by Dunn’s multiple comparisons testing using GraphPad Prism 9.4.1. Note, the top MCTP1 peptide represented 99.98% of all MCTP1 reads. ∗p < .05, ∗∗p < .01. PhIP-Seq and other data are available upon request. (E) Table of the most enriched MCTP1 and coronaphage peptides. Bolded regions represent minimal sequences present in 2 or more coronaphage peptides. Neither SARS-CoV-2 peptides nor epitopes mapped to any of the enriched PhIP-Seq peptides according to the blastp suite of the National Center for Biotechnology Information Basic Local Alignment Search Tool (BLAST). ANOVA, analysis of variance; AOB, accessory olfactory bulb; BSA, bovine serum albumin; CSF, cerebrospinal fluid; MFI, mean fluorescence intensity; Nuc., nucleocapsid protein; PhIP-Seq, peptidome phage display immunoprecipitation sequencing; RBD, receptor binding domain.
We then screened for anti-neural autoantibodies using anatomic mouse brain tissue staining (11), a validated and standard method performed by incubating rodent brain sections with CSF. At a 1:4 dilution, his CSF IgG produced prominent punctate immunostaining of the accessory olfactory bulb, cytoplasmic and neuropil staining in upper layers of the cortex and thalamus, and cytoplasmic staining of hilar and granule neurons in the hippocampus (Figure 1B).
We next used whole human peptidome phage display immunoprecipitation sequencing (PhIP-Seq) (12) to screen for candidate autoantigens. Similar to COVID-19 patients with neurological symptoms (13), the patient’s CSF enriched a diverse set of candidate autoantigens (n = 27), including multiple peptides mapping to MCTP1, a protein implicated in neurotransmitter release (Figure 1C) (14,15). The top PhIP-Seq–enriched peptide is encoded by 11 MCTP1 isoforms—but not the canonical isoform MCTP1L (National Center for Biotechnology Information RefSeq [https://www.ncbi.nlm.nih.gov/refseq/]). Surprisingly, MCTP1 autoantibodies did not validate by overexpression cell-based assay or immunoprecipitation using a representative isoform (isoform 3). However, an expanded PhIP-Seq comparison revealed that the patient enriched MCTP1 significantly more than a combined 3408 healthy CSF and sera and 808 negative control samples (Figure 1D).
Finally, we evaluated whether PhIP-Seq candidate antigen enrichment was due to sequence similarity with SARS-CoV-2. We mapped our patient’s anti-SARS-CoV-2 target epitopes by SARS-CoV-1/2 phage display (9) and compared viral epitopes with PhIP-Seq–identified candidate autoantigens using National Center for Biotechnology Information BlastP (https://blast.ncbi.nlm.nih.gov/Blast.cgi). Among the top 10 CSF- and serum-enriched SARS-CoV-2 peptides, we identified 15 unique peptides, none of which aligned to PhIP-Seq candidate autoantigens (Figure 1E).
In this correspondence, we have profiled the antibody response of a COVID-19 patient with antipsychotic-refractory subacute psychosis whose symptoms rapidly and completely remitted after treatment with IVIg. We identified and mapped the epitope specificity of anti-SARS-CoV-2 antibodies in the patient’s CSF and characterized autoantibodies by rodent brain tissue staining and PhIP-Seq. Although anti-neural autoantibodies have been described in neurologically impaired COVID-19 patients (16, 17, 18), autoantibody screening is rarely performed in COVID-19–associated psychosis (19, 20, 21, 22, 23, 24, 25, 26, 27, 28).
The need for autoantigen discovery in psychotic spectrum disorders is well recognized (29,30). By PhIP-Seq, our patient’s CSF and serum significantly enriched MCTP1. MCTP1 enrichment was not explained by sequence similarity with SARS-CoV-2 proteins, suggesting a distinct antibody response, rather than molecular mimicry. Although anti-MCTP1 autoantibodies did not validate by cell-based assay or immunoprecipitation, neither method is dispositive (11), and only 1 of 11 candidate MCTP1 isoforms was tested. Given the patient’s extreme PhIP-Seq enrichment of MCTP1, it remains a candidate autoantigen.
Importantly, early initiation of immunotherapy for autoimmune disorders of the central nervous system significantly improves outcomes (31). Although autoimmune encephalitis can be established on clinical grounds, the diagnosis requires neurologic, magnetic resonance imaging, and/or CSF abnormalities (8). To identify individuals with potentially immune-responsive acute psychosis without neurological impairment, Pollak et al. (32) proposed criteria for autoimmune psychosis. While “possible” autoimmune psychosis relies solely on clinical factors, “probable” and “definite” autoimmune psychosis require abnormal imaging or laboratory studies.
Our patient’s subacute psychosis and cognitive dysfunction qualified him for possible autoimmune psychosis. However, he had several red flags for autoimmune psychosis: infectious prodrome, rapid progression, and insufficient response to antipsychotics (32). Moreover, his mood dysregulation, cognitive slowing, and hypersomnia were evocative of the mixed symptomatology more typical of autoimmune encephalitis (33,34). Given his overall clinical picture, we administered IVIg with apparent clinical response. Only by relying on ancillary criteria were we able to justify immunotherapy for our patient, suggesting that re-evaluating the criteria for autoimmune psychosis may improve its sensitivity (35).
Even so, this case should be interpreted with caution. Psychotic disorders are protean by nature, mixed symptomatology does occur, and most psychotic presentations are unlikely to be immune mediated. However, given the scale of the COVID-19 pandemic, psychiatric practitioners should consider autoimmune psychosis in patients with COVID-19–associated psychosis.
Acknowledgments and Disclosures
This work was supported by 10.13039/100000025 National Institute of Mental Health Grant Nos. R01MH122471 (to SJP, MRW), R01MH125396 (to SSS), K23MH118999 (to SFF), and R21MH118109 (to SS); 10.13039/100000065 National Institute of Neurological Disorders and Stroke Grant No. R01NS118995-14S (to SJP); the 10.13039/100000882 Brain Research Foundation (to SJP); the National Intitute of Allergy and Infectious Diseases Grant No. R01AI157488 (to SFF); the Hanna H. Gray Fellowship of the Howard Hughes Medical Institute (to CMB); the President’s Postdoctoral Fellowship Program of the University of California (to CMB); the John A. Watson Scholar Program of the University of California, San Francisco (to CMB); and the Deeda Blair Research Initiative for Disorders of the Brain of the Foundation for the National Institutes of Health (to CMB). Sequencing was performed at the University of California, San Francisco (UCSF) Center for Advanced Technology, supported by UCSF Sandler Program for Breakthrough Biomedical Research, Research Resource Program Institutional Matching Instrumentation Awards, and National Institutes of Health (NIH Office of the Director) Grant Nos. 1S10OD028511-01.
We thank Trung Huynh and Anne Wapniarski for laboratory assistance. We thank Andrew Kung and Joseph Derisi for use of the PhIP-Seq database.
During the course of treatment, we obtained surrogate consent to use surplus cerebrospinal fluid for research. After regaining capacity, the patient provided written informed consent for this case report. This work has not previously been published in any form.
MRW received a research grant from Roche/Genentech. All other authors report no biomedical financial interests or potential conflicts of interest.
LSM and BL contributed equally to this work.
==== Refs
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16 Song E. Bartley C.M. Chow R.D. Ngo T. Jiang R. Zamecnik C.R. Exploratory neuroimmune profiling identifies CNS-specific alterations in COVID-19 patients with neurological involvement bioRxiv 2020 10.1101/2020.2009.2011.293464
17 Franke C. Ferse C. Kreye J. Momsen Reincke S. Sanchez-Sendin E. Rocco A. High frequency of cerebrospinal fluid autoantibodies in COVID-19 patients with neurological symptoms Brain Behav Immun 93 2021 415 419 33359380
18 Severance E.G. Dickerson F.B. Viscidi R.P. Bossis I. Stallings C.R. Origoni A.E. Coronavirus immunoreactivity in individuals with a recent onset of psychotic symptoms Schizophr Bull 37 2011 101 107 19491313
19 Parra A. Juanes A. Losada C.P. Álvarez-Sesmero S. Santana V.D. Martí I. Psychotic symptoms in COVID-19 patients. A retrospective descriptive study Psychiatry Res 291 2020 113254
20 Smith C.M. Komisar J.R. Mourad A. Kincaid B.R. COVID-19-associated brief psychotic disorder BMJ Case Rep 13 2020 e236940
21 Ferrando S.J. Klepacz L. Lynch S. Tavakkoli M. Dornbush R. Baharani R. COVID-19 psychosis: A potential new neuropsychiatric condition triggered by novel coronavirus infection and the inflammatory response? Psychosomatics 61 2020 551 555 32593479
22 DeLisi L.E. A commentary revisiting the viral hypothesis of schizophrenia: Onset of a schizophreniform disorder subsequent to SARS CoV-2 infection Psychiatry Res 295 2021 113573
23 Lanier C.G. Lewis S.A. Patel P.D. Ahmed A.M. Lewis P.O. An unusual case of COVID-19 presenting as acute psychosis J Pharm Pract 35 2022 488 491 33280502
24 Majadas S. Pérez J. Casado-Espada N.M. Zambrana A. Bullón A. Roncero C. Case with psychotic disorder as a clinical presentation of COVID-19 Psychiatry Clin Neurosci 74 2020 551 552 32639089
25 Clouden T.A. Persistent hallucinations in a 46-year-old woman after COVID-19 infection: A case report Cureus 12 2020 e11993
26 Chacko M. Job A. Caston F. 3rd George P. Yacoub A. Cáceda R. COVID-19-induced psychosis and suicidal behavior: Case report SN Compr Clin Med 2 2020 2391 2395 33015547
27 Gillett G. Jordan I. Severe psychiatric disturbance and attempted suicide in a patient with COVID-19 and no psychiatric history BMJ Case Rep 13 2020 e239191
28 Bartley C.M. Johns C. Ngo T.T. Dandekar R. Loudermilk R.L. Alvarenga B.D. Anti-SARS-CoV-2 and autoantibody profiles in the cerebrospinal fluid of 3 teenaged patients with COVID-19 and subacute neuropsychiatric symptoms JAMA Neurol 78 2021 1503 1509 34694339
29 Endres D. von Zedtwitz K. Matteit I. Bünger I. Foverskov-Rasmussen H. Runge K. Spectrum of novel anti-central nervous system autoantibodies in the cerebrospinal fluid of 119 patients with schizophreniform and affective disorders Biol Psychiatry 92 2022 261 274 35606187
30 Ehrenreich H. Gastaldi V.D. Wilke J.B.H. Quo vaditis anti-brain autoantibodies: Causes, consequences, or epiphenomena? Biol Psychiatry 92 2022 254 255 35902135
31 Nosadini M. Mohammad S.S. Ramanathan S. Brilot F. Dale R.C. Immune therapy in autoimmune encephalitis: A systematic review Expert Rev Neurother 15 2015 1391 1419 26559389
32 Pollak T.A. Lennox B.R. Müller S. Benros M.E. Prüss H. Tebartz van Elst L. Autoimmune psychosis: An international consensus on an approach to the diagnosis and management of psychosis of suspected autoimmune origin Lancet Psychiatry 7 2020 93 108 31669058
33 Muñoz-Lopetegi A. Graus F. Dalmau J. Santamaria J. Sleep disorders in autoimmune encephalitis Lancet Neurol 19 2020 1010 1022 33212053
34 Al-Diwani A. Handel A. Townsend L. Pollak T. Leite M.I. Harrison P.J. The psychopathology of NMDAR-antibody encephalitis in adults: A systematic review and phenotypic analysis of individual patient data Lancet Psychiatry 6 2019 235 246 30765329
35 Franke C. Prüss H. Letter to the Editor: Comment on Mulder J et al. (2021) Indirect Immunofluorescence for Detecting Anti-Neuronal Autoimmunity in CSF after COVID-19 - Possibilities and pitfalls Brain Behav Immun 94 2021 473 474 33631284
| 36481066 | PMC9722219 | NO-CC CODE | 2022-12-07 23:19:09 | no | Biol Psychiatry. 2022 Dec 5; doi: 10.1016/j.biopsych.2022.09.007 | utf-8 | Biol Psychiatry | 2,022 | 10.1016/j.biopsych.2022.09.007 | oa_other |
==== Front
Int Dent J
Int Dent J
International Dental Journal
0020-6539
1875-595X
The Authors. Published by Elsevier Inc. on behalf of FDI World Dental Federation.
S0020-6539(22)00262-3
10.1016/j.identj.2022.11.007
Letter to the Editor
Monkeypox in Dentistry: A New Opportunity for Research and Collaboration
Mayta-Tovalino Frank a⁎
a CHANGE Research Working Group, Universidad Científica de Sur, Lima, Peru
Barja-Ore John b
b Research Direction, Universidad Continental, Lima, Peru
Alvitez-Temoche Daniel c
c Postgraduate Department, Faculty of Dentistry, Universidad Nacional Federico Villarreal, Lima, Peru
⁎ Corresponding author. Postgraduate Department, Universidad Cientifica del Sur, Panamericana Sur Km 19, Villa, Lima, Peru.
5 12 2022
5 12 2022
© 2022 The Authors
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
pmcDear Editor:
Monkeypox virus (MPX) is a new outbreak of an old disease now manifesting in many regions of the world.1 Caused by a zoonotic virus, it may be transmitted through the airborne route, although direct contact with affected lesions or contagious materials are the usual modes of transmission.2 In the recent outbreak, many of the cases have been identified as being in men who have sex with men, with lesions mainly in the genital regions.3 During 2022, as of October 19, the World Health Organization has reported in all regions a total of 73,437 cases of monkeypox and 29 deaths.4 Infected patients usually present with headache, chills, fever, sore throat, fatigue, muscle discomfort, lymphadenopathy, and oral and skin lesions that evolve into ulcerative pustules.5, 6, 7 Early warning signs of the disease often appear as spots and ulcers on the oral mucosa before the typical skin lesions.6
Prevention and control of MPX transmission in dental practice necessitates maintaining the extant robust standard infection control measures applied with all patients.6 Current recommendations are to avoid dental treatment of patients with MPX who can still transmit the virus and, if essential, to provide such care in an isolated environment with the appropriate protective measures for the dentist and their team.7
We recently conducted a systematised Scopus search on the global scientific publications on MPX in dentistry. It was evident that a few publications on the latter topic were available in this emerging field. These were, notably, in 2 major scientific journals of high impact: the International Dental Journal (a single publication) and Oral Diseases (2 publications). Samaranayake, Lakshman Perera (h-index 97; Google Scholar) and Lo, Muzio Lorenzo (h-index 56) were the major contributing authors of these, and additionally, most of the authors were from Brazil and India. Figure 1 Citations by author on Monkeypox in dentistry.
Figure 1
As MPX is a relatively new subject beginning to be explored by the scientific community in dentistry, researchers have an opportunity now to contribute actively to the generation of new knowledge on MPX so that, over time, a better visualisation of the dynamics of publications and collaboration between authors and research institutions could be deciphered.
In conclusion, MPX appears to be a serious disease, and dentists should be aware of the premonitory signs of the disease in the oral cavity and how to treat patients with suspect lesions, as per the local guidelines. Oral manifestations of the disease in all who present with MPX should be recorded and published to generate a comprehensive account.
Conflict of interest
None disclosed.
==== Refs
REFERENCES
1 Velavan TP Meyer CG. Monkeypox 2022 outbreak: an update Trop Med Int Health 27 7 2022 604 605 35633308
2 Alakunle E Moens U Nchinda G Okeke MI. Monkeypox virus in Nigeria: infection biology, epidemiology, and evolution Viruses 12 11 2020 1257 33167496
3 Liu X Zhu Z He Y Monkeypox claims new victims: the outbreak in men who have sex with men Infect Dis Poverty 11 1 2022 84 35871003
4 World Health Organization. Multi-country outbreak of monkeypox, External situation report #6 - 19 October 2022. 2022. Available from: https://www.who.int/publications/m/item/multi-country-outbreak-of-monkeypox–external-situation-report–8—19-october-2022. Accessed 18 October 2022.
5 Guarner J del Rio C Malani PN. Monkeypox in 2022—what clinicians need to know JAMA 328 2 2022 139 140 35696257
6 Samaranayake L Anil S. The monkeypox outbreak and implications for dental practice Int Dent J 72 5 2022 589 596 35934521
7 Agency UHS. Principles for monkeypox control in the UK: 4 nations consensus statement. 2022. Available from:https://www.gov.uk/government/publications/principles-for-monkeypox-control-in-the-uk-4-nations-consensus-statement/principles-for-monkeypox-control-in-the-uk-4-nations-consensus-statement. Accessed 18 October 2022.
| 36481095 | PMC9722220 | NO-CC CODE | 2022-12-08 23:18:12 | no | Int Dent J. 2022 Dec 5; doi: 10.1016/j.identj.2022.11.007 | utf-8 | Int Dent J | 2,022 | 10.1016/j.identj.2022.11.007 | oa_other |
==== Front
Measur Sens
Measur Sens
Measurement. Sensors
2665-9174
The Authors. Published by Elsevier Ltd.
S2665-9174(22)00219-7
10.1016/j.measen.2022.100585
100585
Article
COVID-19 outbreak data analysis and prediction
Anandhan R. a
Nalini T. b∗
Chiwhane Shwetambari c
Shanmuganathan M. d
Radhakrishnan P. e
a Dept of C.S.E, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Pallavaram, Chennai, 600117, Tamil Nadu, India
b Dept of C.S.E, Dr.M.G.R.Educational and Research Institute, Maduravoyal, Chennai, 600095, Tamil Nadu, India
c Dept of C.S.E, Symbiosis Institute of Technology, Symbiosis International University, Lavale, Pune, India
d Dept of C.S.E, Panimalar Engineering College, Chennai, 600123, Tamil Nadu, India
e School of CS and AI, S.R.University, Warangal, Telangana, India
∗ Corresponding author.
5 12 2022
5 12 2022
1005855 10 2022
2 11 2022
20 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-19 is a novel pandemic disease with no potential vaccine treatment or medicine, the world is facing currently as of now. The death toll has increased to several lakhs and recovery rate is comparatively very less, was initially spotted in Wuhan (China). This spreads through close contact with people and socializing. The number of infected people varies with different parts of the world In our particular country India we are going through the lock down period which is the only vaccine to promote “social distancing” The hurdle arose due to the widespread of corona is major economy loss in combo with innocent lives. In this manuscript, we are visualizing the dataset which is publicly available to map, differentiate and separate the data in order to segregate the places that are most prone and perform basic regression to identify and predict the increasability of the counts from the dataset.
Keywords
Regression
Linear-regression
Covid-19
Data analysis
MERS
SARS
==== Body
pmc1 Introduction
Covid-19 which is also called the Corona Virus is a pandemic that the world is facing currently. It has spread globally since its first identification. The virus typically spreads among people with close contact and socializing. The first Coronavirus outbreak occurred in Wuhan, China around the time of December 2019. Later there were cases that appeared in Thailand [1]. Now, this has transmitted to more than 70 countries around the world. WHO confirmed 76000 cases of COVID-19 worldwide as of 30 January 2020 [2]. Coronavirus is a disease that ranges from normal symptoms like cold, cough, Middle East Respiratory Syndrome (MERS), Pneumonia to Severe Acute Respiratory Syndrome (SARS) though the symptoms are undetected until a few days [3]. If the symptoms get worse with the affected person losing immunity then this may lead to death [3]. Now the doctors say even if the coronavirus enters the human body one cannot find it out that easily because it is going symptom-less and one person can infect tons of people without even getting identified as corona positive. If the person is detected corona positive then it is isolated for fifteen days which is termed as incubation period and more until the person is completely cured, worldwide around 2,322,320 people are affected by this novel virus [4]. The death rates have been massively increasing throughout 210 countries. The demographics vary with different parts of the country and continents. There is no vaccination as of such in this current situation other than social distancing. The precautions and control measures are taken in such ways they are listed as follows. This social distancing in the world going on is in the form of lockdown in the countries where the colleges, universities, public sectors, private sectors, restaurants, hotels, and public gatherings everything is all closed the people themselves are locked down in their respective homes. Only hospitals are opened doctors and medical staff is allowed to work, the media persons but that too 1 m of distance to be maintained is the conditions applied while interviewing, the slogan of “stay home stay safe “is followed. The precautions taken are washing hands, using alcohol-based sanitizers, and not frequently touching the face. Basically, the negative impact of lockdown and coronavirus will certainly fall on people who are losing their lives and the economy of the world will find a huge fat loss, the victim of this virus is the poor, the lower middle class who tries to earn their daily bread by daily/monthly wages who is now left with no money to survive in the lockdown.
So, this kind of drastic situation should be analyzed and drastic effects should be predicted also the counts should be measured wherein the role of this manuscript takes place in the prediction of the active cases and the total number of cases. (see Table 1, Table 2, Table 3 , Fig. 1, Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6 )Table 1 Reported cases as of 27th March 2020.
Table 1S.No Name of State/UT Total Confirmed Cases (Indian National) Total Confirmed Cases (Foreign National) Cured Death
1. Andhra Pradesh 12 0 1 0
2. Chattisgarh 6 0 0 0
3. Delhi 38 1 6 1
4. Gujarat 43 0 0 3
5. Harayana 16 14 11 0
6. Himachal Pradesh 4 0 0 1
7. Karnataka 20 0 3 2
8. Kerala 131 7 11 0
9. Madhya Pradesh 23 0 0 1
10. Maharashtra 144 3 15 4
11. Odisha 3 0 0 0
12. Puducherry 1 0 0 0
13. Punjab 29 0 0 1
14. Rajasthan 41 2 3 0
15. Tamil Nadu 32 3 1 1
16. Telangana 34 11 1 0
17. Chandigarh 7 0 0 0
18. Jammu and Kashmir 18 0 1 1
19. Ladakh 13 0 0 0
20. Uttar Pradesh 42 1 11 0
21. Uttarkhand 4 0 0 0
22. West Bengal 11 0 0 1
23. Bihar 7 0 0 1
24. Mizoram 1 0 0 1
25. Goa 6 0 0 0
26. Manipur 1 0 0 0
Table 2 Reported cases as of 27th March 2020 with Total and Active Cases.
Table 2S.No Name of State/UT Total Confirmed Cases (Indian National) Total Confirmed Cases (Foreign National) Cured Death Total Cases Active Cases
1. Andhra Pradesh 12 0 1 0 12 11
2. Chattisgarh 6 0 0 0 6 6
3. Delhi 38 1 6 1 39 32
4. Gujarat 43 0 0 3 43 40
5. Harayana 16 14 11 0 30 19
6. Himachal Pradesh 4 0 0 1 4 3
7. Karnataka 20 0 3 2 20 15
8. Kerala 131 7 11 0 138 127
9. Madhya Pradesh 23 0 0 1 23 22
10. Maharashtra 144 3 15 4 147 128
11. Odisha 3 0 0 0 3 3
12. Puducherry 1 0 0 0 1 1
13. Punjab 29 0 0 1 29 28
14. Rajasthan 41 2 3 0 43 40
15. Tamil Nadu 32 3 1 1 35 33
16. Telangana 34 11 1 0 45 44
17. Chandigarh 7 0 0 0 7 7
18. Jammu and Kashmir 18 0 1 1 18 16
19. Ladakh 13 0 0 0 13 13
20. Uttar Pradesh 42 1 11 0 43 32
21. Uttarkhand 4 0 0 0 4 4
22. West Bengal 11 0 0 1 11 10
23. Bihar 7 0 0 1 7 6
24. Mizoram 1 0 0 1 1 1
25. Goa 6 0 0 0 6 6
26. Manipur 1 0 0 0 1 1
Table 3 Color Spectrum of Different Segments of Data.
Table 3
Fig. 1 Output of linear regression.
Fig. 1
Fig. 2 Output of predicted values of Y.
Fig. 2
Fig. 3 Output of R-square model.
Fig. 3
Fig. 4 Scatter plot.
Fig. 4
Fig. 5 Bar graph.
Fig. 5
Fig. 6 World map view of the affected regions.
Fig. 6
2 Literature review
The Arogya Setu App is proposed by the Indian government where in the self-health analysis is taken into consideration and the citizens of India, the users are given enough knowledge, tips and treatments besides the precautions to deal with coronavirus. The Britain government has proposed an application wherein the smartphone tracking system is used wherein a person whom you crossed can be detected whether he/she is corona positive or not and we can take safety measures on that analysis. The application called COVID-19 Global Case Tracker is an interactive map provides people with the most up-to-date information on the coronavirus pandemic to capture all confirmed COVID-19 cases, fatalities, affected zones, regions, and recoveries. Also, a web application called the Worldomater gives pandemic information from around the world.
3 Proposed system
This manuscript is basically based on the real-time scenario in which data science participates where in data analysis plays a major role it is described in the numerical form, represented graphically it helps to read the data and also prediction process takes place for the further queries in future which are done by time series analysis we can also predict the deaths so the corona cases will be detected and will be reported in the numeric format then will set out in the graphical format with active cases and total cases can be recognized.
For example, a state in India says Maharashtra has a certain number of cases of corona it will give the exact figure and also can compare with all other states, it will display the recoveries of patients, the upcoming statistics by moving the cursor across the map presented not only this but also will evaluate regions as such where the number of cases is relatively high and where it is low, it will also give the outline of the total number of cases and highlight the prospective areas, differentiate the hotspot high alert zone areas. This work can be very useful for the general public and also the government because it will give the ratio proportion and instead of manual/database records this project will be more valuable, it can keep the records transparent to the public and can be used by general public awareness method wherein we can spread awareness on the ground levels as well. In the jupyter notebook using python, Pandas library is used for playing with the data, Matplotlib is used for plotting graphs, the style used in the graph is ggplot, Plotly and Plotly Express is also used, folium assists us to use the map to explain in which zone the cases are appearing or reducing or about the recoveries and deaths, the data frames are set up. The linear regression supports the dataset by performing the activities and stating the relationships like giving the proper analysis about the variables involved such as the independent and dependent, it gives the statistics also explains the influence in the change and peculiar idea about the increment, decrement, deaths in the corona cases also the active cases and the total number of cases in the country.
4 Methodology
4.1 Collecting the dataset
In this project, we have collected the Dataset from GitHub which is an open source platform. The data is then converted into a CSV (Comma Separated Values) file which is imported into the program to process and visualize the data.
4.2 Linear regression
Basic regression analysis is performed on the dataset where we determine the relations amidst a dependent variable and one or more independent variables depend on the given aspects [5]. We are using a set of statistical processes, to estimate the records of the Number of people deceased, active cases, amount of death, Number of people cured, Location of the deceased.
We are using Linear Regression because it serves the purpose to find which factor is more influencing change.
The regression result shows whether the relationship is valid. Simple linear regression is a kind of regression analysis in which the number of in dependent variables is one and there may be a linear an accord among the independent(x) and dependent(y) variable.
From the dataset, Dependent variable(X) would be total number of Cases and the Independent variable(Y) would be Active Cases.
The red highlighted line in the above graph is represented as the best fit straight line. Based on the given data points, we try to plot a line that models the points the best.
From the above graph the shaded red part represents the predicted values of Y. Independent (active Cases/x) which could be one or more Dependent variable (Total cases/Y).
The steps to be followed for building a mathematical mod model are as follows:➢ Find the Independent variable (x) and Reliant variable(y) and denote it as x & y.
➢ Evaluate the median of x and y.
➢ Determine the linear Regression equation as Y = m x + c.
➢ Find the slope m from the above equation as m = ∑[x−x] [y− y] ∑ [x− x]2
➢ Find the constant c from the above equation.
➢ From the given values of m and c, determine the predicted values of y.
➢ Compare the distance between actual and predicted value.
➢ Find the goodness of fit using the R2 method [6].
4.3 R-squared(R2)
R-squared is a form of approach which is used to grade goodness-of-fit ranging for linear regression models. It is described as the proportion of deviation in the dependent or predicted variable (y) independent variable (x) using the best-fit line propagated by the regression analysis [7].
R-squared gives the proportion of the based dependent variable (y) variation that a linear model explains.R2 = Variance explained by the model / Total Variance
Residuals represent the distance amid the noticed value and fitted value.
R-squared is always between zero percent to a hundred percentage:➢ Zero percentage is denoted while the model does not explain any of the fluctuations in the return variable around its mean. The mean of the dependent variable predicts the dependent variable and the regression model.
➢ A hundred percent intends that a model that explains all of the variations in the return variable i.e., the response around its mean.
4.4 Heat map
A Heat map uses a wide color spectrum to denote different segments of data. The Darker the color spectrum the higher the cases and affected areas. The lighter color tones represent the average and less affected areas. The heat maps are highly used to visualize data, segregate into a more meaningful and understandable manner [11].
4.5 Scatter Plot
Scatter plots are typically used to visualize the dispersal of scattered dots or marks to the relationship of the variables which use the Cartesian coordinates to display the values i.e. the scatter gram.
The main purpose of a scatter chart is to show the type of relationship. Correlation, that exists between two sets of data or the variables [12].
4.6 Bar chart
Bar charts are generally used to visualize the comparisons between categories of data in the dataset. This could be displayed in two forms namely the vertical projection and the other is horizontal projection [14].
The World map view of the affected regions, wherein the darker tones i.e., the redness in the map defines more infected cases detected. Lighter tones represent commonly average or lower instances in that region.
5 Conclusion
This research presented current trends of COVID-19 outbreak till 27th March 2020. This process will be helpful in order to compare the situations based on the values. The graphical representations, plotting's give us a proper display of the affected areas, active proceedings and death rate. Further updates could be done to the system with UpToDate data additions which would follow the same analysis method. Tool used was jupyter notebook. Matplotlib is used for plotting graphs for visualization. Predicted the deaths of different states because of COVID-19 using Time Series Analysis. Majority of death rate was 2020 crude(per 1000 people), we used 2020 dataset.
Credit authorship contribution statement
R.Anandhan: Conceptualization, T. Nalini: Writing, Shwetambari Chiwhane, M. Shanmuganathan-review and editing, P. Radhakrishnan– editing and Supervision.
Uncited References
[8]; [9]; [10]; [13].
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
Data will be made available on request.
==== Refs
References
1 Hui The continuing 2019-nCoV epidemic threat of novel coronavirus to global health - the latest 2019 novel coronavirus outbreak in Wuhan, China Int. J. Infect. Dis. 91 2020 264 266 31953166
2 World Health Organization WHO Coronavirus Disease 2019 (COVID-19) Situation Report - 35 2020 WHO
3 WHO Advice for Public 2020 WHO Int. [Online]. Available: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public
4 World Health Organization Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19) 2020 World Health Organization
5 https://www.geeksforgeeks.org/ml-linear-regression/
6 Kumari K. Yadav S. Linear regression analysis study J Pract Cardiovasc Sci [serial online] 4 2018 33 36 [cited 2020 May 3]
7 https://statisticsbyjim.com/regression/interpret-r-squared-regression/
8 https://towardsdatascience.com/introduction-to-machine-learning-algorithms-linear-regression-14c4e325882a
9 https://medium.com/datadriveninvestor/machine-learning-algorithms-linear-regression-f89ab64ac490
10 https://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit
11 https://www.tutorialspoint.com/python_data_science/python_heat_maps.htm
12 https://towardsdatascience.com/data-visualization-for-machine-learning-and-data-science-a45178970be7
13 https://www.marsja.se/how-to-make-a-scatter-plot-in-python-using-seaborn/
14 https://medium.com/python-pandemonium/data-visualization-in-python-bar-graph-in-matplotlib-f1738602e9c4
| 36507366 | PMC9722230 | NO-CC CODE | 2022-12-15 23:15:07 | no | Measur Sens. 2022 Dec 5;:100585 | utf-8 | Measur Sens | 2,022 | 10.1016/j.measen.2022.100585 | oa_other |
==== Front
Midwifery
Midwifery
Midwifery
0266-6138
1532-3099
Elsevier Ltd.
S0266-6138(22)00319-9
10.1016/j.midw.2022.103571
103571
Article
Psychological profile and mood disturbance of women who gave birth during the COVID-19 pandemic in Romania
Pop-Tudose Melania Elena ac⁎
Popescu-Spineni Dana Maria cd
Manolescu Loredana Sabina Cornelia c
Radu Mihaela Corina c
Iancu Felicia Claudia c
Armean Sebastian Mihai b
a Buzau County Emergency Hospital, Department of Obstetrics, Victory Street, no.18, Buzau, Romania
b “Iuliu Hatieganu” University of Medicine and Pharmacy, Department of Pharmacology, Toxicology and Clinical Pharmacology, Victor Babeș Street, no. 8, 400000, Cluj-Napoca, Romania
c “Carol Davila” University of Medicine and Pharmacy, Faculty of Midwifery and Healthcare Assistance, Eroii Sanitari Boulevard, no. 8, Sector 5, 050474 Bucharest, Romania
d ”Francisc I. Rainer” Institute of Anthropology of the Romanian Academy, House of Academy Street, September 13 Boulevard., no 13, 3rd floor, 050725, Bucharest, Romania
⁎ Corresponding author.
5 12 2022
2 2023
5 12 2022
117 103571103571
10 2 2022
12 11 2022
4 12 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.
Objective
This study aimed to outline the emotional profile and the mood disturbance of women who gave birth during Emergency and Alert states in Covid-19 pandemic.
Methods
A cross-sectional study was carried out to investigate how the emergency and alert states due to Covid-19 affected the emotional profile and the mood disturbance of pregnant women who gave birth during these times. We included 244 postpartum women, divided into two groups: 124 women during the State of Emergency and another 120 women during the State of Alert. After expressing their informed consent, they completed an anonymous questionnaire that collected demographic data and the Profile of Mood States Questionnaire, as well as a follow-up survey. Data analysis was performed using the statistical program SPSS 24.0.
Results
Out of the 300 questionnaires distributed, we collected 244 valid questionnaires. 45.2% of State of Emergency group and 53.3% of State of Alert group experienced Anxiety, 16.9% of State of Emergency group, respectively 18.3% of State of Alert group, Depression, and 25% of State of Emergency group respectively 34.2% of State of Alert group, Distress. Compared to the ideal Iceberg profile, the emotional profile of both groups presented an inverted graph for Anxiety and Depression and much lower values for Vigor. Only 35.5% of State of Emergency group and 16.7% of State of Alert group received information concerning the virus, symptoms, and evolution of the disease from the specialists who monitored their pregnancy and 25.8% of State of Emergency group respectively 11.7% of State of Alert group received information about measures to prevent contamination and infection. Psycho-emotional and mood disturbance was more pronounced among State of Alert group.
Conclusions
There was a significant psycho-emotional alteration of surveyed women during the pandemic, worsened by the radical measures of the State of Emergency and associated with the major deficiency of care services in supplying valid information and counseling for pregnant women's safety in the State of Alert. There is a highlighted need to pay more attention to the psychological profile of pregnant women and to modernize the health services in this field and adapt them to pandemic situations with the use of modern virtual techniques. In addition, the Romanian health care system should round off the team responsible for the care of mother and child with midwives, internationally recognized very skilled in informing, monitoring, counseling, and support in this field.
Keyword
Covid-19 pandemic
Pregnant women
Anxiety
Depression
Stress
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pmcIntroduction
The COVID-19 pandemic has been and continues to be a major threat to the physical and mental health (Pfefferbaum and North, 2020). The aggressiveness and mutagenic potential of the new coronavirus has hampered global health systems, forcing governments to adopt extreme, unusual attitudes, that conflict with human and social freedoms, with a psychological impact on the entire population (Kringos et al, 2020).
On March 16th, 2020, Romania declared a State of Emergency until May 14th, which meant the prohibition of free movement and therefore the impossibility of pregnant women to physically receive help from prenatal consultations (Presidential Decree no. 195, 2020). In Romania, by law, primary perinatal healthcare services are provided on a contract with the National Health Insurance House by family physicians/general practitioners (Pop et al., 2020) and the specialized outpatient care by obstetricians (Law no. 95, 2006). Midwives, recognized worldwide as the most appropriate providers in primary perinatal care and the main psycho-emotional supporters of the childbearing women (Renfrew and Malata, 2021), were excluded from the maternal and child healthcare team by the system decentralization (WHO Regional Office for Europe, 2012). In this pandemic situation, a government decision regulated the provision of remote medical care by family physicians and specialists in the outpatient clinic using any means of communication, with a maximum of 8 consultations / hour (Guvern Decision no. 438, 2020). The absence of a telemedicine platform, an exercise for remote communication through modern technical means, panic and case ancestry associated with the pandemic took by surprise the Romanian health system, which led to overcrowding and blockage of primary care and implicit neglect of prenatal services. According to the Romanian Guvernment's Decision no. 394, 2020, the Emergency State was replaced by the Alert state and the Law no. 55, 2020, regarding certain measures set in place to prevent and combat the effects of the COVID-19 pandemic was published. Through the Minister's Order No. 828, 2020, the outpatient clinics and private offices were reopened, but with the mandatory stipulation that infection prevention and control measures must be followed. Due to the epidemiological situation caused by the SARS-CoV-2 virus, Romanian family physicians were overwhelmed by cases. This fact, along with the restrictions imposed, reduced their services potential in the antenatal field. On the other hand, pregnant women restricted themselves to monitoring visits to the family doctor or obstetricians, thinking that in this way they reduced their risk of becoming infected. Several studies suggested that the lockdown, the alert restrictions measures, fear of contracting the virus and its possible effects on pregnancy, inadequate antenatal care and misinformation could be responsible for anxiety and depression symptoms during the COVID-19 pandemic (Perez et al., 2022; Liu et al, 2021; Mayopoulos et al., 2021). Also, the restriction of social support surrounding childbirth and the separation of newborns from their mother were incriminated, but in Romania the separation of the two was already a regular practice and the presence of a partner or someone from the family wasn't allowed in the delivery room, even before this pandemic. Moreover, the quality of maternal and newborn care worldwide has been affected and this also could be considered an important factor in the imbalance of the psychological status of women who gave birth during the pandemic (Lazzerini et al., 2022). During the Covid-19 pandemic, researchers from all over the world paid more attention to maternal mental health, and most showed that it had a significant impact even though different types of psychological tools were used and were applied in different maternal stages and in different geographical, social, and cultural places (Perez et al., 2022; Liu et al, 2021; Mayopoulos et al., 2021).
A study conducted in Romania before the pandemic already showed a prevalence of 32%–38% of negative emotions symptoms among pregnant women (Wallis et al., 2012), higher, compared to the prevalence identified (30%) in other international studies (Ahmad and Vismara, 2021). However, although studies have shown an increase in the psycho-emotional vulnerability of pregnant women in potentially life-threatening situations, no action has been taken in this regard (Riyad Fatema et al., 2019). This study aimed to outline the emotional profile and the mood disturbance of women who gave birth during Emergency and Alert states in Covid-19 pandemic.
Materials and methods
We conducted an anonymous descriptive, cross-sectional study in Buzau County maternity, Romania. Out of 300 women at 2 days postpartum initially included, we collected 244 valid answers. We created two groups: 124 women during the State of Emergency, from April - May 2020, and 120 women during the State of Alert, from October - November 2020. As a psychometric tool we used a Shortened Version of the Profile of Mood States (DiLorenzo et al., 1999), accompanied by a 14-items questionnaire for collecting demographic data and monitoring pregnancy. The average time required of women surveyed to complete the questionnaire was 15 min.
POMS is a self-report questionnaire widely used for retrospective measuring mood states and levels of psychological distress. The original version formulated by McNair et al. (1971) consisted of 65 items and six subscales: Anxiety, Depression, Anger, Vigor, Fatigue and Confusion. POMS has been used for over 40 years, translated into 42 languages, and adapted to different needs (Boyle et al., 2015). The Shortened Version of the Profile of Mood States (POMS-SV) consists of 47 items and retains the six subscales of the longer version: For each item, women surveyed indicated on a 5-point scale (“0 = not at all” to “4 = very strong”) the degree to which an adjective describes feelings experienced during the past two week. The total psychological distress score is calculated by summing the negative emotions scores and the TMD score (Total Mood Disturbance) represents the degree of disturbance of the general mental state or the level of total distress and is obtained by subtracting the score obtained at subscale Vigor from the sum of the scores obtained at the five subscales that measure negative emotions. Previous studies have proved that POMS-SV can be used successfully to assess psychological distress in Romanian population (David et al., 2005; Marian, 2007). We performed a POMS-SV check on 20 childbearing women, and it showed a good internal consistency, with an Alpha Cronbach coefficient of .95.
Due to the institution restrictions related to Covid-19 (no free access between departments), the distribution of the questionnaires was carried out personally, by one of the authors and 3 other middle managers, midwives working in the postpartum department, previously trained by the author. An information form was attached to each questionnaire and the completion of the questionnaire required the informed consent. We asked mothers to concentrate only on the mentioned period. We did not collect information about their mental health status at the postpartum level. Two women with mental/cognitive or other disabilities who would have prevented them from understanding the nature and purpose of the study and/or providing the required information and five women whose newborns were in critical condition were excluded from the study. Also, due to the impossibility of obtaining the tutorial agreement in a pandemic context, twenty-nine minor mothers were excluded. Data processing was performed using the statistical program IBM SPSS 24.0 (IBM Corp., Armonk, NY, USA). The Kolmogorov-Smirnov test indicated that the variables do not follow a normal distribution. A descriptive, frequency, correlation (Spearman coefficient) and comparison (Mann-Whitney U and Kruskal Wallis tests) analysis was performed.
Results
The average age of the State of Emergency group was 25.39 (SD=5.96, min.18, max.41), and of the State of Alert group was 26.85 (SD=5.68, min.18, max 42). The profile of the two groups was almost similar in terms of demographic variables: almost the majority were ethnic Romanians, of Christian religion, more than three quarters came from rural areas and almost two thirds of them said they were unemployed, half of them had an average level of education and in terms of income per family almost half said they have an average income and a similar percent, minimum wage.
Half of the women from State of Emergency group stated that they kept in touch by phone with the physician who monitored their pregnancy and of these, up to 44 (35.5% of the total) received from them information about the virus, prevention measures, symptomatology, and evolution of the disease. Also, half of them declared that most of the information they had about the virus, the disease and the evolution of the pandemic came from television stations (TV) and the same source was mentioned by 40% of them for information on preventive measures.
Half of State of Alert group and almost three quarters of State of Emergency group stated that they monitored their pregnancy with an obstetrician, 40% of those belonging to the first group preferred state services and as many of the second group preferred private services.
One third of the women from State of Alert group declared that the main source of information regarding the Covid-19 virus and its effects was “family and friends”, for another third TV was the primary source, and for the rest, in equal percentages, specialists and the Internet. Also, well over a third of them declared that most of the information about prevention measures were obtained through TV shows and in only 11.7% of cases the information was obtained from physicians.
From the State of Alert group, 42 (47%) of women declared that they had 7-9 visits to the physician to monitor the pregnancy, 35 (29.2%) 4-6 visits, 29 (31.1%) 1-3 visits and 14 (11.7%) stated that they had not been to any antenatal medical consultation. Only 23.3% (N=28) of State of Alert group stated that the physicians who monitored their pregnancy also provided them with some information about the virus and the disease triggered by it and 16.7% (N=20) declared that they also received some information on measures to prevent contamination and infection with this virus.
Kruskal Wallis and U Mann-Whitney tests showed that between the two groups there are significant differences in terms of age group, so those from the State of Alert group tend to be of more mature ages (>25 years) H=4.726, p<0.05, and towards a higher parity (≥3 children) H=7.085, p<0.05 compared to those in the State of Emergency group. There are also significant differences in informing women about the Covid-19 virus, its effects and the measures needed to prevent contamination, so that if a third in the State of Emergency group benefited from information from specialists, most of the State of Alert group received information from unqualified sources H=11.767, p=0.001, H=12.288, p<0.001.
Highly significant differences were also identified in the communication and information of pregnant women from the physicians who monitored their pregnancy, the involvement of physicians who cared for women belonging to the State of Emergency group was higher than those responsible for monitoring women in the State of Alert group, both in terms of information about the Covid-19 virus and its pathogenic potential U=6092, N1=124, N2=120, p<0.01, and about the hygiene measures necessary to prevent disease U=6085, N1=124, N2=120, p<0.01.
The psychological and mood profile of the women surveyed (Fig. 1 ) compared to the Iceberg profile, considered the ideal profile, illustrates significant changes for Anxiety, Depression, and Vigor. The profiles of both groups showed an inverted graph for Anxiety and Depression and very low values for Vigor.Fig. 1 Psychological Profile and Mood Disturbance.
Fig 1
Out of the total number of women in the State of Emergency group, 45.2% (N=56) experienced Anxiety 16.9% (N=21) Depression, 51.6% (N=64) Anger, 33.9% (N=42) Fatigue and 40.3% (N=50) Confusion. Of the total women in the State of Alert group, 53.3% (N=64) experienced Anxiety, 18.3% (N=22) Depression, 56.7% (N=69) Anger, 41.7% (N=50) Fatigue, and 43.3% Confusion. Regarding distress, which represents the sum of the scores of negative affective dispositions, were identified in 25% (N=31) of the State of Emergency group and in 34.2% (N=41) from the State of Alert group.
Regarding the TMD score (Total Mood Disturbance) only three women from each group exceeded the cut-off value with the amendment that the State of Alert group recorded scores close to this value in a much higher percentage (37.5 %) compared to State of Emergency group (25%).
Moreover, it was observed that the profile of women from State of Alert group was more altered than that of those from State of Emergency group, as demonstrated by the comparative analysis performed with the (U) Mann-Whitney test which identified significant differences between the two samples regarding to Anxiety U=6360, z=-1,962, p=0.05, Anger U=5891.5, z=-2,815, p<0.05, Vigor U=5641.5, z=-3,268, p=0.001, Distress U=6274.5, z=-2,115, p<0.05, TMD U=6169, z=-2,309, p<0.05. For the variables Depression, Fatigue and Confusion, a significance threshold p>0.05 was obtained, which proves that the test is insignificant.
The POMS is a valid instrument for measuring the women's emotions and moods in the 2 weeks before giving birth. As shown in Table 4, this study used Spearman's correlation analysis to test the correlations between the six subscales of the POMS. The results showed significant correlations among the subscales, which indicated that the POMS had good content validity. No significant correlations were identified between emotional and socio-demographic parameters, but only a few weak correlations between emotional parameters and follow-up. Thus, in the State of Emergency group, it seems that Anxiety, Depression, Confusion, and Distress tended to increase among those who had had information from unreliable sources about prevention measures (r s=-.234, p<0.01, r s=-.240, p<0.01, r s=-.250, p<0.01, respectively r s=-.287, p=0.001). Also, the information received about Covid 19 tended to increase the degree of Confusion and Distress (r s=-.205, p<0.05, r =-.219, p<0.05). In the State of Alert group, only the level of Depression was slightly enhanced by the information received on Covid 19.Table 1 Demographic characteristics.
Table 1CATEGORY STATE OF EMERGENCY N=124 STATE OF ALERT N=120
N % N %
Age 18-24 70 56.5 50 41.7
25-34 47 37.9 62 51.7
35-41 7 5.6 8 6.7
Residence Urban 33 26.6 35 29.2
Rural 91 73.4 85 70.8
Ethnicity Romanian 103 83.1 107 89.2
Romma 21 16.9 13 10.8
Religion Christian 117 94.4 115 95.8
Other 7 5.6 5 4.2
Education level None 11 8.9 8 6.7
Low 34 27.4 39 32.5
Medium 64 51.6 63 52.5
High 15 12.1 10 8.3
Employed Yes 38 30.6 49 40.8
No 86 69.4 71 59.2
Income Social Help 12 9.7 9 7.5
Minim 53 42.7 50 41.7
Medium 46 37.1 51 42.5
>Medium 13 10.5 10 8.3
No. of children 1 65 52.4 44 36.6
2 36 29.0 38 31.7
≥3 23 18.6 38 31.7
Abortion/ miscarriage 0 80 64.5 67 55.8
1 25 20.2 36 30.0
≥2 19 15.3 17 14.2
Table 2 Data related to follow-up and information.
Table 2CATEGORY STARE OF EMERGENCY STATE OF ALERT
N % N %
Follow-up Nobody 12 9.7 16 13.3
Family Physician 24 19.4 27 22.5
Obstetrician 78 62.9 60 50.0
Both 10 8.1 17 14.2
Where State 53 42.7 35 29.2
Privat 32 25.8 51 42.5
Both 27 21.8 18 15.0
Information C19 Family/Friends 15 12.1 38 31.7
Internet 15 12.1 20 16.7
TV 63 50.8 39 32.5
Physicians 28 22.6 20 16.7
Other 3 2.4 3 2.5
Information measures Family/Friends 19 15.3 32 26.7
Internet 14 11.3 23 19.2
TV 56 45.2 49 40.8
Physicians 32 25.8 14 11.7
Others 3 2.4 2 1.7
Table 3 POMS-SV total mood disturbance (TMD), distress and subscales scores.
Table 3Subscales State of Emergency Group
Mean (SD, min.-max.) State of Alert Group
Mean (SD, min.-max.)
TMD 30.73 (32.2, -32-100) * 39.95 (27.0, -10-113) *
Distress 50.13 (28.9, 0-113) * 57.02 (23.4, 21-116) ⁎⁎
Anxiety 13.62 (7.5, 0-29) * 15.30 (6.2, 4-29) *
Depression 15.63 (11,2, 0-43) ⁎⁎ 17.63 (9.2,5-42) ⁎⁎
Anger 7.58 (5.3, 0-19) * 9.44 (4.6, 3-31) ⁎⁎
Vigor 19.56 (7.0-34) 16.83 (5.5, 3-31)
Fatigue 6.06 (3.5, 0-14) * 6.78 (3.1, 1-14) ⁎⁎
Confusion 6.73 (4.6, 0-18) * 7.81 (3.7, 2-18) ⁎⁎
⁎ p<.05.
⁎⁎ p<.001.
Table 4 Correlations among POMS factors scores.
Table 4Subscale State of Emergency (n=124) State of Alert (n=120)
1 2 3 4 5 1 2 3 4 5
1. Anxiety
2. Depression .781⁎⁎ .777⁎⁎
3. Anger .713⁎⁎ .759⁎⁎ .746⁎⁎ .782⁎⁎
4. Vigor -.355⁎⁎ -.407⁎⁎ -.296⁎⁎ -.396⁎⁎ -.536⁎⁎ -.484⁎⁎
5. Fatigue .690⁎⁎ .624⁎⁎ .657⁎⁎ -.217* .590⁎⁎ .470⁎⁎ .510⁎⁎ -.129
6. Confusion .775⁎⁎ .762⁎⁎ .680⁎⁎ -.397⁎⁎ .691⁎⁎ .643⁎⁎ .694⁎⁎ .684⁎⁎ -.450⁎⁎ .493⁎⁎
⁎ p <0.05.
⁎⁎ p<0.001.
Discussions
Although a normality, pregnancy is a time of increased psycho-emotional vulnerability, enhanced not only by the multiple adaptive morpho-functional transformations but also by the permanent uncertainties related to the safety and health of the child. During this stage of woman's life, emotional sensitivity reaches its peak, and any potential threat may cause anxiety (Nath et al., 2017). Scientifically and clinically, more attention has been paid to the psychological status of postpartum women and less to the psychological status of pregnant women, although it has been shown that the postnatal psychological disorders (depression, psychosis) may have correspondence in the antenatal period (Kinsella and Monk, 2009). It should also not be ignored that this stage overlaps with the neurological development of the future child and that there is a two-way mother-foetus connection both physically and psychologically (Kinsella and Monk, 2009, Tudose et al., 2017). The literature suggests that the foetus perceives the outside world through the feelings and experiences of the mother who carries it, so that her emotional disturbance will have the effect of emotional disturbance of the foetus with possible serious long-term effects (Iqbal et al., 2020, Goodman et al., 2014). Acute stress or the anxious state of the pregnant woman can negatively interfere with the neuropsychiatric development of the foetus, whose alteration may result in a future child with psycho-behavioral issues and reduced cognitive ability (Goodman et al., 2014, Pop-Tudose et al., 2019). Moreover, the maternal stress resulting from the pandemic extreme measures, especially the lockdown, could have a high mental response from the foetus, even affecting its brain development (Iqbal et al., 2020).
Previous research shows that the pandemic situations may affect people's emotional state and that misinformation or deficiencies of health systems in providing information and preparing the population to have appropriate attitudes may have a negative psychological impact (Cao et al., 2021).
Our findings are very similar to those of Tomfohr-Madsen et al. (2021), Sun et al. (2021), Fan et al. (2021), and indicate significantly elevated rates of antenatal anxiety during the COVID-19 pandemic, compared to historical norms. However, we found a higher prevalence of anxiety and a lower prevalence of depression compared to their results. Brik et al. (2021) have conducted research during lockdown and found a prevalence of pregnant women with anxiety symptoms of about 59% which was the closest to our results. By contrast, Zilver et al. (2021) found that COVID-19 had not increased anxiety and depression levels in Dutch pregnant women, maybe because in the Netherlands midwives are the main providers of prenatal care (Tikkanen et al., 2020; Wiegers, 2009).
Our study shows that access to private medical services increased during the alert-level state and the obstetrician was the most accessed specialist during both times, even though the antenatal care is provided by law through the family physicians’ services. However, the sources of information declared by the women surveyed on the virus, symptoms of the disease and the prevention measures came only to a very small extent from specialists, while the main source declared was the TV, similar to findings of the Romanian study conducted by Cigăran et al. (2021).
In both groups, we identified psychological and mood changes, and high values of anxiety and depression and less vigor, respectively, with the specification that both the values and the percentage of women of the State of Alert group were slightly higher compared to those of the State of Emergency group.
A Canadian study showed that the effects of the Covid-19 increased anxiety and depression, especially among women of childbearing potential and the uncertainty about the transmission of the disease to the foetus or the increased risk of death among childbearing women has led to higher levels of stress and other negative emotions in pregnancy (Robinson et al., 2021).
In our study, we also found that the changes in emotional and mood parameters seemed to be related to the sources of information, but a larger analysis is needed to support this hypothesis. However, it is clear that the health services did not consider the psychological aspects associated with such a situation, nor did they manage to prepare an information and support strategy in this regard during the state of alert, by the time of this study (6 months after declaring the state of emergency). And even if half of State of Emergency group declared that during the state of emergency, they communicated by phone with the physician who monitored their pregnancy by then, they had not been the main source of information and, they had not provided enough knowledge to approach the pandemic safely.
The immuno-psycho-emotional vulnerability of pregnancy, in the context of the Covid-19 pandemic may increase, facilitating both the physical viral attack on the mother-foetus dyads as well as the psychological one (Cao et al., 2021; Zanardo et al., 2020). Health systems should find solutions to provide additional psycho-emotional support to pregnant women during pandemics or any other unusual situations related to unpredictable natural disasters (Ahmad and Vismara, 2021; Zanardo et al., 2020). Also, they should use antenatal screenings to identify psychological pathology, with the provision of adapted cognitive therapies supported by specialists. In addition, the Romanian health system should reinstate the midwife into the community and facilitate women's access to their full scope of practice including their psycho-emotional support services (Radu et al., 2021).
Our study's limitations are mostly due to the pandemic situation. We had limited access and that restricted the number of potential subjects: only women in early postpartum, without access to pregnant women and minors. Because of the short time in terms of State of Emergency, we used the most accessible retrospective instrument to analyze the state of emotions and mood during the last 2 weeks of pregnancy of the women surveyed and we asked mothers to concentrate only on it. Moreover, we did not collect information about potential Covid-19 infections in their pregnancy, either theirs or of their partners or family members, that could have had an impact on their mental status. Also, the experience and feelings felt during and after the birth process and the women's postpartum mental health could have influenced their responses. Isolated, but possible, the responses related to the follow-up could have been biased, due to the unjustified fear that the physicians who monitored their pregnancy and were working in the hospital may see the questionnaires.
Conclusions
This study had showed a significant alteration of the psycho-emotional state of pregnant women against the background of the pandemic evolution, worsened by the radical measures of the State of Emergency and associated with the major deficiency of care services in supplying valid information and counseling for pregnant women's safety in the State of Alert.
The results highlighted the need to improve mental healthcare during pregnancy, especially in exceptional circumstances, such as the global pandemic situation or lockdown, as these can cause added stress and increased anxiety and depression symptoms, resulting in undesirable consequences for pregnancy and the future child. They also suggested the need to modernize health services and adapt them to the unique situations with the use of modern virtual techniques. In addition, the Romanian health care system should round off the team responsible for the care of mother and child with midwives, internationally recognized very skilled in informing, monitoring, counseling, and support in this field, and reinstate them into the community to ease the women's access to medical care and to more information and psycho-emotional support services. The health systems need to pay more attention to the psychological profile of pregnant women and should use screenings to identify pathological forms with the provision of adapted cognitive therapies supported by specialists.
Ethical approval
Prior to starting this study we obtained the approval from the chief of Obstetrics and Gynaecology Department of Buzau County Emergency Hospital, Buzau, Romania.
This study was approved by the Ethics Committee of Research “Carol Davila” University of Medicine and Pharmacy of Bucharest, Romania (no.11387/07.05.2021). It has unfolded under the aegis of anonymity. Every questionnaire was accompanied by an information sheet and filling in the questionnaire was considered a consent to participate. The Ethics Committee approved the consent procedures used.
Funding sources
Not applicable.
CRediT authorship contribution statement
Melania Elena Pop-Tudose: Conceptualization, Methodology, Investigation, Software, Writing – original draft, Writing – review & editing, Data curation, Validation. Dana Maria Popescu-Spineni: Methodology, Software, Data curation. Loredana Sabina Cornelia Manolescu: Methodology, Visualization, Validation. Mihaela Corina Radu: Data curation, Writing – review & editing. Felicia Claudia Iancu: Visualization, Writing – original draft. Sebastian Mihai Armean: Conceptualization, Supervision, Writing – review & editing, Validation.
Declaration of Competing Interest
None Declared.
Acknowledgments
N/A.
==== Refs
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| 0 | PMC9722231 | NO-CC CODE | 2022-12-14 23:53:36 | no | Midwifery. 2023 Feb 5; 117:103571 | utf-8 | Midwifery | 2,022 | 10.1016/j.midw.2022.103571 | oa_other |
==== Front
Health Policy
Health Policy
Health Policy (Amsterdam, Netherlands)
0168-8510
1872-6054
The Authors. Published by Elsevier B.V.
S0168-8510(22)00309-8
10.1016/j.healthpol.2022.12.001
Article
Uncovering the potential of innovation ecosystems in the healthcare sector after the COVID-19 crisis
Lepore Dominique a⁎
Frontoni Emanuele b1
Micozzi Alessandra c2
Moccia Sara d3
Romeo Luca e4
Spigarelli Francesca f
a Department of Law, University of Macerata, Piaggia dell'Università 2, 62100, Macerata MC, Italy
b Department of Political Sciences, Communication and International Relations, University of Macerata, Via Don Giovanni Minzoni, 22/A, 62100 Macerata MC, Italy
c Faculty of Economics, Universitas Mercatorum, University of the System of the Italian Chambers of Commerce, Rome, Italy
d The BioRobotics Institute and at the Department of Excellence in Robotics & AI, Sant'Anna School of Advanced Studies, Viale Rinaldo Piaggio, 34 56025, Pontedera, Italy
e Department of Economics and Law, University of Macerata, Piazza S. Vincenzo Maria Strambi, 1, 62100 Macerata MC, Italy
f Department of Law, University of Macerata, Piaggia dell'Università 2, 62100, Macerata, MC, Italy
⁎ Corresponding author: +39 0733 258 2698
1 +39 0733.258.2510
2 +39 3471268868
3 +39 050 883420
4 +39 073 32583280
5 12 2022
5 12 2022
5 1 2022
27 7 2022
4 12 2022
© 2022 The Authors. Published by Elsevier B.V.
2022
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Industry 4.0 technologies are expected to enhance healthcare quality at the minimum cost feasible by using innovative solutions based on a fruitful exchange of knowledge and resources among institutions, firms and academia. These collaborative mechanisms are likely to occur in an innovation ecosystem where different stakeholders and resources interact to provide ground-breaking solutions to the market.
The paper proposes a framework for studying the creation and development of innovation ecosystems in the healthcare sector by using a set of interrelated dimensions including, technology, value, and capabilities within a Triple-Helix model guided by focal actors. The model is applied to an exemplary Italian innovation ecosystem providing cloud and artificial intelligence-based solutions to general practitioners (GPs) under the focal role of the Italian association of GPs.
Primary and secondary data are examined starting from the innovation ecosystem's origins and continuing until the COVID-19 crisis. The findings show that the pandemic represented the turning point that altered the ecosystem's dimensions in order to find immediate solutions for monitoring health conditions and organizing the booking of swabs and vaccines. The data triangulation points out the technical, organizational, and administrative barriers hindering the widespread adoption of these solutions at the national and regional levels, revealing several implications for health policy.
Keywords
Artificial Intelligence
Healthcare
Innovation Ecosystem
Machine Learning
Pandemic
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pmc1 Introduction
Industry 4.0 (I4.0) technologies, such as artificial intelligence (AI), are addressing the unmet needs of the healthcare sector [26] by timely addressing patients’ health care conditions and performing accurate predictions by examining numerous variables [17].
Immediately after the COVID-19 outbreak, governments, researchers and firms acknowledged the urgency of adopting I4.0 technologies for containing the disease's spread and treating infected cases [1].
Solutions have been introduced by exploiting the capabilities of different stakeholders, including universities and innovative firms, based on the premise that outcomes can be effectively developed through an ecosystem approach (Arribas-Ibar et al., 2021). Integrating innovations into the health system is a complex endeavour that requires a well-designed planning process for stakeholders [15, 39]. Innovation ecosystems, described as the interrelation between actors and resources, could be an effective framework of reference (Granstrand and Holgersson, 2019). These ecosystems usually take place through a triple-helix model (THM), including government, academia, and firms. They may include focal actors, initiating and guiding their evolution [34].
Combining insights from the literature, this paper proposes a conceptual model for examining the origin and evolution of innovation ecosystems in healthcare. The research questions (RQs) we aim to answer are:• What are the key dimensions of an innovation ecosystem?
• How can a crisis change its dimensions?
To answer the RQs, the model is applied to an exemplary Italian innovation ecosystem that since 2014 has been providing cloud-based and AI solutions to general practitioners (GPs) under the guidance of the Italian association of GPs. This ecosystem has boosted its impact during the COVID-19 crisis by designing solutions for monitoring patients and organizing the booking of swabs and vaccines.
1.1 AI for healthcare
I4.0 enables the constant exchange of information on health conditions [2], improving the quality of services at the minimum cost [3]. Among I.40 technologies, there are high expectations of AI [23]. AI is the study of algorithms that give machines the ability to reason and perform cognitive functions [5]. Machine Learning (ML) is a branch of computation algorithms that emulates human intelligence by learning directly from the environment [14].
This technology is expanding its footprint in clinical systems ranging from databases to video analysis [23]. Even if Big Data is rich with variables, it is flawed in delivering appropriate clinical context for interpreting data. Instead, thanks to AI, physicians can evaluate predictions in meaningful ways [31] and improve the efficiency of care processes [19]. ML ensures interpretability in terms of being "clinically meaningful" and generalizes on new subjects and different clinical and social conditions. Robustness is guaranteed with a high number of predictors, natural imbalance of classes, and sparsity of predictors and labels.
Recent work has proposed ML methodologies for predicting type 2 diabetes (T2D) [9], risk conditions [8] and complications [37] by using EHR data collected from GPs. These cases prove that ML can represent the core of the clinical decision support system (CDSS). Scholars also proposed an integrated chronic care model based on ML and data sharing between GPs and cloud computing platforms [16]. The quality care evaluation in a clinical use-case scenario demonstrated how empowering GPs using the platform with economic incentives improves care processes [17].
However, advancements in digital health raised concerns about the reliability of AI tools (Seyhan and Carini, 2019). To respond to healthcare challenges, algorithms must be integrated with software environments that are already operative in the national scenario. Moreover, AI cannot function without high-quality data. AI models are often powered by clinical data that is generated in the medical system, for which the primary purpose is to support care rather than facilitate subsequent analysis. Many AI approaches use EHR data, which document healthcare delivery and operational needs to understand patient health [20]. However, these are frequently affected by non-uniformity and missing data [47], which may be caused by lack of time to deliver annotations, insufficient IT equipment [43] or lack of standardization in data entry, EHR data interoperability, and clinical and socio-economic variables [36].
The integration of clinical-medical information, dispersed in many databases, is far from being achieved despite the existence of interoperability protocols. Finally, there are privacy issues [29] since the information managed is identifying and sensitive and must be treated according to regulations on data protection. Thus, solid data frameworks must be proposed within and across countries [32]. In this field, the European General Data Protection Regulation (GDPR, Regulation (EU) 2016/679) established detailed requirements for companies and organisations regarding collecting, storing and managing personal data and recognises data concerning health as a special category. Data concerning health as well as genetic and biometric data are considered highly sensitive. ‘Sensitive data’ are assigned a more protective framework than that applicable to other types of personal data (art. 6, GDPR). In general, processing of all sensitive data is prohibited under GDPR, but a list of exceptions is specified (art 9.2, GDPR).
Indeed, digital transformation in healthcare does not only depend only on technical changes but requires an adaptive transformation in legal and financial frameworks (OECD, 2020). Even if technology played a vital role in COVID-19 control, state policies continue to be one of the driving factors (Flores et al., 2021) and COVID-19 will likely shape future health data policy [30]. Thus, different stakeholders should find ways to effectively integrate technological change [2].
1.2 Innovation ecosystems
Innovation ecosystems may address the challenges that hinder AI adoption. An innovation ecosystem is a combination of actors, activities, artefacts, institutions and relations that drives innovative performance [22]. These ecosystems specialize in complementary technologies and competencies [6] to turn research and development into profits [25].
Scholars agree that this framework is rooted in interactions among multiple stakeholders. In this context, managers can learn how to establish I4.0 strategies, while policymakers learn how to organize the evolution of ecosystems [7]. These ecosystems can manage regional development [34] and evolve according to needs and circumstances, including new policy initiatives [44].
Innovation ecosystems have been considered in healthcare, reporting positive effects on performance [42]. Within these ecosystems, it is economically beneficial to foster relationships between universities, industry and administrations, favouring a better allocation of budget items and boosting commitments [42]. Governments are acknowledging that for developing innovations it is necessary to establish partnerships with the private sector. Li and Garnsey [28] showed that an ecosystem endorsed by the government can help innovation in the private sector by filling financial gaps and addressing technology and public health issues. Similarly, Dixit et al. [13] discussed the need for interdisciplinary collaboration to foster research diversification, robust regulatory approaches and geographic growth.
Remarkably, professional hierarchies, conflicting interests and lack of awareness make the healthcare ecosystem different from others [35]. The competing interests push for a planned innovation orchestration. Finally, innovation ecosystems can shape firms’ business models [4] to face the occurring complexities, especially from a regulatory and financial standpoint.
Relationships in innovation ecosystems are often unstable and unclear (De Vasconcelos Gomes et al. 2018) and critical questions need to be addressed to capture their complexity [12]. Even if scholars acknowledged the importance of innovation ecosystems in healthcare, conceptual models analysing their origin and development are lacking. Thus, it is up to researchers to give meaning and usefulness to this concept [33].
Conceptualizing innovation ecosystems could support researchers in making comparisons and governments to stimulate their emergence. The urgency of the topic has been confirmed during the COVID-19 crisis, which exposed the need for collaboration to exploit technological development. Hence, innovation ecosystems should be based on cooperation between universities, industry and government in a THM [34].
Granstrand and Holgersson [22] specified the elements to be investigated including actors, activities, artefacts, institutions, and relations. Dedehayir et al. [12] discussed that scholars should capture the actions shaping the ecosystem, actors and their roles. Similarly, Oksanen and Hautamaki [34] conceptualized the elements for building an innovation ecosystem, including resources from the THM, organization, actors’ commitment and consensus, and strategic vision. Chen et al. [10] integrated these aspects by looking at the evolution of innovation ecosystems, through the dimensions of technology, value and capability.
Among the actors involved, there may be focal actors, who manage the mix of identities, which may create a faultline and have a negative impact on the outcome [41]. A faultline is a dividing line [41] that separates components of teams and generates subgroups that cannot communicate. Differences in scientific cultures among individuals could lead to difficulties in teamwork [24, 27].
Combining these studies, we provide a conceptual model (Figure 1 ), as described in Table 1 . This model is applied to an exemplary healthcare innovation ecosystem. The model is designed to capture the key variables shaping the creation of innovation ecosystems in healthcare (RQ1) as well as their development in response to a crisis (RQ2). The model looks into how these variables interact within a THM investigating the emergence and evolution of focal actors and relationships among governmental institutions, academia and firms.Figure 1 Conceptualizing innovation ecosystems
Figure 1
Table 1 innovation ecosystem dimensions
Table 1Variables Key aspects to address References
Actors • Presence of focal actors
• Actors involved in the ecosystem's introduction and/or development.
• Actors’ roles in building the ecosystem's dimensions.
• Reasons for involving the actors
Oksanen and Hautamäki [34]; Granstrand and Holgersson [22]; Dedehayir et al. [12]
Technology • Technologies addressed by the ecosystem
Granstrand and Holgersson [22]
Value • Tangible value: economic value and added value of the solutions offered
• Intangible value: credibility and diffusion of technologies among users
Chen et al. [10]; Granstrand and Holgersson [22];
Capability • Internal and external resources, skills and knowledge
Chen et al. [10]; Oksanen and Hautamaki [34]
Interactions among dimensions • Coordination of activities among actors and dimensions
Chen et al. [10]; Oksanen and Hautamaki [34]
2 Material and methods
The paper illustrates the case study [46] of an exemplary healthcare innovation ecosystem that acted responsively to the pandemic's challenges by broadening its activities and networks in a THM. The case study methodology is well suited to healthcare service research because it can track and examine complex relationships, contexts, and systems as they evolve [45].
The selected innovation ecosystem is guided by the Italian association of GPs, called FIMMG (Federazione Italiana Medici di Medicina Generale), which launched an innovative cloud-based platform called Net Medica Italia in 2014. The case is exemplary for the attention that FIMMG placed on improving their cloud-based platform with AI-based techniques and for the recognition gained by their solutions during the COVID-19 outbreak in the national and regional areas. This case is revelatory also for its context. Italy was the first European country dramatically hit by COVID-19 in early 2020. It is of interest to note that the Italian National Healthcare System (NHS) has been increasingly decentralized, with many powers devolved to regions. This has progressively translated the Italian NHS into uneven regional health services [18], potentially limiting a harmonized adoption of technical and structural standards. Data was gathered from several sources (Table 2 ). Semi-structured interviews were used to allow greater flexibility and provide a wider description of the phenomenon [40]. Interviews were addressed to the doctor responsible for the ICT activities of FIMMG and the ICT expect that followed the launch and development of the platform.Table 2 Data
Table 2Source Details
Primary • First round of interviews (2h)
• Second round of interviews (2h)
• Transcript of interviews (20 pages)
• E-mail interaction to integrate the findings (20 e-mails)
Secondary • Statistics on Net Medica Italia usage among doctors
• Guide on the web services offered by Net Medica Italia
• Videos of presentation of Net Medica Italia (tot. length 1h)
• Description of Net Medica Italia implementation in Campania
• N. 4 Scientific articles published on Net Medica Italia
• N. 15 Press articles on Net Medica Italia
The data collection consisted of two rounds of face-to-face interviews (January – March 2021). The interviews were audio-recorded and transcribed. Four interviews were conducted for a total time of 4h. Subsequently, the interview transcripts were sent back to the interviewees for confirmation before we continued developing the case study. E-mails were exchanged to compare and contrast findings. Additional data and documents were used to complement the transcripts through triangulation to support the reliability of our study [21]. Thus, we cross-checked interview data with publicly available information and internal documents. Moreover, three of the authors were directly involved in the platform's technical development. The triangulation of data aimed at uncovering the selected variables for describing both the emergence and evolution of the innovation ecosystem after the crisis within a THM guided by focal actors.
3 Results
The findings illustrate the evolution of Net Medica Italia before and after COVID-19 by presenting technological, value, and capability dimensions.
3.1 The technology dimension
Since its introduction, the Net Medica Italia platform has focused on replication in the cloud of EHR data, according to a standardized language. The platform enables GPs to access the database (DB) remotely, using the PC or mobile devices. The DB allows sharing of data, even between professionals who use different management systems. The application, which is a Web resource, ensures complete interoperability to the data shared. From the Net Medica Italia portal, GPs can access a store area where additional applications are available. This area includes:• Training – this section can be used to test and integrate the offered Web services.
• NMI Privacy - In line with GDPR, doctors can register their activities by answering a questionnaire available on the platform. The questionnaire examines the activities and procedures of which the doctor is in charge.
• NMI Cloud for sharing, analysing and monitoring clinical records. The platform provides key performance indicators (KPIs) measuring GPs’ performance. Based on the KPI, training activities can be organized.
• NMI e-HEALTH, from which the doctor can provide online consultancy to patients, who download the app “Ciao Dottore”.
• NMI CARE3, including data sharing among GPs for chronic care management. A set of indicators are included for monitoring and evaluating treatment responses. A pilot case on ML techniques was examined for the chronic case of diabetes to classify chronic care quality [17].
• My Net Medica Italia, from which it is possible to share medical records at the territorial level.
Patients can access a part of the GPs records in the Cloud through an authentication system that generates personal credentials. This area collects medical reports uploaded by the doctor and notes and health documents uploaded by patients.
The EHR data collected on the platform enables GPs to trace their patients’ conditions, including rare and chronic diseases, disabilities and medical prescriptions. The heterogeneity of EHR fields and the presence of longitudinal observations represent the basis for undertaking a detailed analysis of health conditions over time. AI-based techniques are integrated to analyse the multifactorial temporal data stored in the Cloud to discover complex patterns and set up advanced ML models that forecast clinical outcomes and interpret patterns that physicians sometimes miss. These ML models can help physicians to predict early-stage diseases but also detect the relevant clinical factors associated with a risk condition profile.
ML techniques have been used to extract information from a large amount of data and have proven helpful in improving the diagnosis, predictions and management of chronic diseases. Based on these models, Net Medica Italia identifies high-risk profiles such as the transition into another disease state for example, the progression from pre-diabetes to T2D.
Moreover, AI-based techniques integrated into the Net Medica Italia Cloud play a fundamental role in the development of learning healthcare systems. These systems may combine multi-source data with ML techniques, including data collected by territorial aggregations and national providers. The result consists of a continuous source of data-driven insights to optimize biomedical research, public health, and healthcare quality.
3.2 The value dimension
The value proposition is articulated in tangible and intangible layers. The tangible part includes the added value of the platform compared to existing solutions and its economic value. Instead, intangible aspects are linked to the GPs’ loyalty.
3.2.1 Tangible aspects
Family medicine was already the most computerized field among national health contexts. Since the 1980s, GPs were sending information and making prescriptions using databases. However, relying on these generic systems, GPs were not able to assign value to the information recorded.
Current management systems allow the treatment of patient data but do not enable data sharing from the perspective of integrated teamwork. Net Medica Italia wanted to exceed these limits by proposing a cloud computing system for GPs, with an interface available for interoperability with the rest of the healthcare system, including interactions with specialists. The idea was to bring together the DB of GPs in a cloud structure where the data could be stored in a standard way.
Differently from the state-of-the-art on EHR, the FIMMG Net Medica Italia Cloud Platform is based on the GPs’ daily activities. Moreover, the heterogeneity of the data collected, and the presence of longitudinal observations allow analysing of the evolution of the subjects in each community. Net Medica Italia, beyond the typical demographic and primary care attributes, contains information on allergies, intolerances, smoking, alcohol, and physical activity.
The timely observation of health status data in primary care, even in association with a prolonged lifestyle, is promising for predictive purposes. The multi-modal temporal information could lay the foundation for a CDSS.
Net Medica Italia emerged as a limited liability company for members, who pay a fee of 50 euros including VAT per year. However, this model is not sustainable in the long term, considering the high investments behind the platform's development. Before investing in a new initiative, FIMMG has to be very selective when asking for funds. Net Medica Italia will continue working on the intangible aspects by offering services for which GPs will be willing to pay, thereby recognizing the value of innovation.
3.2.2 Intangible aspects
Building loyalty among GPs by acting on their territorial aggregations is recognized as crucial by FIMMG. Net Medica Italia was established as a turning point for GPs. In 2012 a law (“Balduzzi Law”), required doctors to be organized in territorial aggregations with a number not below 20. In their aggregation, GPs have to ensure continuity of care to patients in that area. To start spreading awareness on the platform, Net Medica Italia emerged as a practical tool used to comply with the procedures required by GDPR. Using the Net Medica Italia platform, the doctor realized they had more time to focus on the clinical activity.
The doctors appreciated this outcome, also because dashboards were made available to assess their operational performances. The goal is to prove to doctors that it is possible to achieve improvements in time management and economic value by changing aspects of their activity. Net Medica Italia is the only platform that manages to combine different data to monitor KPIs, including administrative ones.
Another novelty is the possibility of having a complete web-based medical record, resulting in a cloud software for data as well as interfaces. The medical record can be reached from any device, including mobile. To date, this solution has been made available in the regions of Piedmont, Campania, Calabria, Basilicata, Marche, Lazio, Sicily and Puglia. Net Medica Italia showed a growth trend from its launch. Currently, FIMMG manages more than 5000 GPs and 11 million medical records.
However, its diffusion is taking place in a chaotic manner on the national territory even if Net Medica Italia could potentially cover most of the Italian territory, being compatible with many software systems. The first success was achieved in the Campania region. In this region, all of the GPs joined Net Medica Italia because of a regional agreement (Regional Law 9/2009), seeking to guarantee continuity of care for patients with diabetes. The region recognized that this was possible by implementing a chronic care model. Net Medica Italia ensured access to a database that contained data subject to the agreement, on which four million health cards were uploaded to be aggregated and shared with colleagues. Data can be viewed at the individual level but also at the territorial one to compare aggregations.
Nevertheless, Campania is an exception. To continue creating awareness of the added value of Net Medica Italia it is necessary to gain trust among other territorial aggregations. Coordinators have been appointed to create awareness, and GPs are becoming promotors among others.
3.3 The capability dimension
The value proposition of Net Medica Italia results from the successful integration of multidisciplinary capabilities. The integration of internal and external resources acted as the driver for its development. Collaboration was fostered from the beginning between doctors of FIMMG, ICT and legal experts, software companies, and researchers.
As for the internal resources, Net Medica Italia has 11 employees, including ICT experts, lawyers and different collaborators. Heterogeneous teams were organized to create a virtuous mechanism: “Doctors tried to understand the language of computer scientists and computer scientists tried to understand the needs of doctors” (doctor FIMMG).
In general, when the teams are heterogeneous, there is the risk that they will not create value due to inefficient communication. Net Medica Italia is an example where the faultline theory did not occur [41]: the medical skills integrated with the technical and managerial skills and contributed to the value dimension.
The ICT experts worked on enabling interoperability between the DBs of GPs, and then with other DBs and operators of the NHS to ensure continuity of care. Legal experts were needed to ensure that data was managed in compliance with regulations and laws. Legal experts collaborated in building a strong security system based on encrypted data. The data encryption algorithm was developed in collaboration with the Polytechnic University of Marche in Italy.
Moreover, there have been collaborations with software houses to share some additional operations for enabling doctors to use data stored on the Cloud and using other software. Net Medica Italia does not replace the software that doctors use and hopes that software houses will become more cooperative. Then, to work in connection for the continuity of care, sharing data between professional levels was ensured by a partnership with Federsanità, which is the institutional actor representing the Local Health Agencies in different municipalities. Through this partnership, the sharing of specific areas of the cloud was enabled for managing chronic diseases. Furthermore, Net Medica Italia has worked with a leading Italian company managing patients with diabetes mellitus, making it possible to share data between diabetic centres.
3.4 Dimensions after the pandemic
After the COVID-19 outbreak, Net Medica Italia upgraded its dimensions in a new stage of development that increased the borders of the healthcare innovation ecosystem, in which FIMMG continues to be the focal actor.
As for the technological dimension, it included NMI CARE COVID-19. This section offers telemedicine services to guarantee continuity of care for patients with COVID-19. Doctors use the platform to monitor patients in the disease course, even daily. The section includes an agenda of appointments and teleconsultation services. Bluetooth devices were introduced to remotely monitor vital parameters. Doctors can access their patients’ data in the Cloud and share the data with the emergency medical service, which is unaware of each patient's conditions.
In addition, a COVID-swab booking system was proposed for supporting the local health service and health companies in managing bookings and reporting the result to the doctor.
Net Medica Italia also proposed an algorithm that supports doctors in managing priorities for the COVID-19 vaccine [38], considering the characteristics of the patient in line with the national priorities.
Therefore, Net Medica Italia was able to react to the needs led by the pandemic generating value in the innovation ecosystem for doctors and the local community. COVID-19 boosted the solutions proposed. The new solutions created awareness of the services that Net Medica Italia offered at the local and national levels. The subscriptions on the platform increased and the experience recorded in Campania is repeating itself in other regions, such as Calabria and Puglia.
The new services made Net Medica Italia rethink its business model by recognizing that economic sustainability is essential. In 2021, Net Medica Italia became a benefit company, therefore, a company that maintains profit objectives with a mission in favour of societal goals.
External resources supported the new technological and value dimensions. Thanks to the collaboration with the Polytechnic University of Marche and experts from different universities, the algorithm was created for managing priorities in the vaccine booking [38]. The algorithm collects data from the GPs and establishes priorities based on the characteristics of a vulnerable person. The algorithm identifies profiles based on the data the GPs collected, which is not included in regional and national healthcare platforms. The algorithm could consider certain side effects deriving from the vaccine's administration based on the patient's health conditions.
The algorithm would be at the disposal of GPs and institutions for free, but the GPs databases need to be connected with regional and national healthcare platforms in order to work. This means that regional administrations must authorize the connection between platforms. Up until now, the algorithm has only been implemented in Puglia and Calabria. The FIMMG's involvement in the mass vaccination against COVID-19 was necessary, considering the widespread distribution of GPs in the territory and their knowledge about the clinical history of individual patients.
Moreover, to exploit the platform's new functionalities, collaborations with Local Health Centres increased. A new collaboration emerged with IBM, a leading ICT company, and Novartis, a global health company, to advance the platform's functionalities. In collaboration with IBM, Watson's query of AI was integrated for managing unstructured text.
4 Discussion
The conceptual model suggested and its validation through the exploratory case of Net Medica Italia contributes to the literature on innovation ecosystems by suggesting a set of variables to be explored in a THM. Indeed, the model enabled the uncovering of factors and critical relationships that determined the creation of an innovation ecosystem in healthcare (RQ1) as well as its development during a crisis (RQ2). The case shows that the capability dimension contributed to creating the technological dimension. The FIMMG, as the focal actor, gave rise to the innovation ecosystem by combining external capabilities with internal ones through partnerships with multiple actors.
The case confirms that innovation ecosystems are rooted in interactions between stakeholders. Indeed, agreeing with Oksanen and Hautamaki [34], a THM was built by including the institutional role of FIMMG, academia and firms. In addition, the case supports the use of multidisciplinary teams to encourage technological change as Dixit et al. [13] and Aceto et al. [2] both suggested.
Net Medica Italia integrates the findings of Vlaisavljevic et al. [42] by showing that fostering relationships in a THM is beneficial when developing initiatives, even if the same cannot be said for the economic aspect, which is still a big challenge.
Instead, the pandemic represented a turning point towards providing new technologies and value dimensions by leveraging new capabilities. The urgency of managing COVID-19 led to introducing telemedicine services that are extendable to all chronic cases. In this stage, partnerships within the health innovation ecosystem resulted crucial in providing a timely response. Moreover, thanks to its algorithm for managing vaccines, Net Medica Italia is expected to increase awareness in the territory and continue driving new partnerships that could open the way for its national diffusion.
However, the extension of Net Medica Italia's capabilities required a structural change. This stage of development consolidates the assumption of Banda et al. [4] since an innovation ecosystem influences firms’ business models. The business model of Net Medica Italia is meant to change based on AI and ML developments. While the platform and the functionalities offered were considered an accessory before COVID-19, they are now becoming central for local health agencies. “The objective is to become the centre of innovation in healthcare thanks to AI”(ICT expert). Even if technology plays an important role in COVID-19 control, it is not the only factor affecting the results, as state policies continue to be a driver in achieving a better outcome by filling financial resources (Flores et al., 2021).
5 Conclusions
The case of Net Medica Italia shows how an innovation ecosystem guided by trusted focal actors and built on multidisciplinary teams can provide advanced technological solutions that are tailored to the doctors’ needs, offering a new value proposition in the sector. The findings underlined that innovation ecosystems built on a strong capability dimension can create technological and value propositions only if they are guided by committed focal actors able to match different competences and interests. Theoretically, the paper contributes to the literature on innovation ecosystems by defining and applying a set of dimensions for analysing their creation and evolution. In this sense, a conceptual model is proposed to study key dimensions including, technology, value, and capabilities in a THM guided by focal actors.
As for managerial implications, the paper shows how AI can improve the quality of healthcare services in the territory and provide predictive capabilities that would benefit the sector in terms of efficiency and costs. Furthermore, this case indicates that technological developments and external factors, such as the pandemic, require a revision of the existing business models to balance new costs and users’ needs. In terms of policy implications, the case underlines the need for more substantial governmental support for ensuring the widespread adoption of the solution in the territory. Specifically, policy programs at the national and regional levels should provide conditions for overcoming administrative, financial and technical challenges in experimenting and adopting innovative solutions in healthcare. This case highlights the need to identify structural and technological standards which should be uniform in the territory. Furthermore, it shows that measuring GPs’ performances should encourage them to improve their performances through targeted training programmes. These aspects are crucial for implementing the Italian National Recovery and Resilience Plan (NRRP) whose mission dedicated to healthcare aims at modernizing the healthcare system, reducing territorial fragmentation and favouring research activity. Nevertheless, results cannot be generalized. Thus, studies should compare other experiences to understand how the dimensions evolve after a crisis. The proposed model may be applied in different sectors and countries to favour comparative analysis. It would be useful to investigate different types of focal actors, thereby understanding how they coordinated multiple actors and resources. Longitudinal cases are suggested for following up on the collaboration in the THM.
Source of funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Uncited References
[11]
Declaration of interest statement
The Authors declare there is no conflict of interest
Acknowledgements
We thank Dr Paolo Misericordia and Dr Rino Moraglia from Net Medica Italia and FIMMG for their support and collaboration in this work.
==== Refs
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| 36509555 | PMC9722232 | NO-CC CODE | 2022-12-09 23:15:17 | no | Health Policy. 2022 Dec 5; doi: 10.1016/j.healthpol.2022.12.001 | utf-8 | Health Policy | 2,022 | 10.1016/j.healthpol.2022.12.001 | oa_other |
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Kidney Med
Kidney Med
Kidney Medicine
2590-0595
Published by Elsevier Inc. on behalf of the National Kidney Foundation, Inc.
S2590-0595(22)00209-6
10.1016/j.xkme.2022.100576
100576
Letter to the Editor
Emergent Management of Severe Hyperkalemia and Volume Overload in a Maintenance Hemodialysis Patient With Castor Oil and Enemas in the Absence of Accessible Hemodialysis
Ramírez Marmolejo Roberto ∗
Ramírez Isaza Sofia
Asociación Colombiana de Nefrología e Hipertensión (RRM), Facultad de Salud, Universidad Javeriana Cali (SRI)
∗ Address for Correspondence: Roberto Ramírez Marmolejo,
6 12 2022
6 12 2022
1005769 8 2022
16 10 2022
© 2022 Published by Elsevier Inc. on behalf of the National Kidney Foundation, 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.
==== Body
pmcTo the Editor:
The COVID-19 pandemic resulted in substantial medical and social upheaval.1,2 In Colombia, a social crisis ensued when the Colombian government made the decision to close economic gaps by increasing taxes. In protest, counter-government groups took control of the main roads and intermunicipal highways, completely blocking traffic, including medical missions,3 resulting in an inability of patients, including those dependent on hemodialysis, to receive essential medical care.
An anuric hemodialysis dependent man in his late 60s presented with fluid overload and hyperkalemia to a community hospital where dialysis was unavailable four days after his last hemodialysis session. He usually received dialysis in the inaccessible city of Cali, Colombia. At the time of hospitalization, his serum potassium was 7.9 mEq/L (Table 1 ). Of note, most blood pressure medications and all potassium binders also were unavailable at this hospital.Table 1 Key laboratory and examination data
Time Weight (Kg) SpO2 K (mEq/L) Blood Pressure (mm Hg) EKG
Admission 82 92% 7.9 198/112 Peaked T waves in V1-V2
Day 1 of Enemas 83 76% 8.8 212/116
+12 hours 81 84% 186/110
+20 hours 80
+24 hours 86% 7.4 174/106
+30 hours 78 94% 6.4 168/94 Resolved
Following a phone consultation, we implemented the following interventions, recognizing that these were the only options available at the time. These included beta-agonists via nebulizer, a dihydropyridine calcium channel blocker and minoxidil. Despite this, hypertension and hypervolemia persisted with potassium increasing to 8.8 mEq/L and oxygen saturation dropping. In this setting, we initiated enemas every 15 minutes as well as oral castor oil, one ounce every 12 hours. With the enemas and oral castor oil, we were able to temporize him for 5 days, with marked improvement in blood pressure and volume status as well as potassium dropping to 6.4 mEq/L with a resolution of ECG changes after 30 hours of treatment initiation. Nine days after his last hemodialysis session, he was able to resume hemodialysis.
These measures, although representing a desperate measure, proved effective in allowing our patient sufficient time until he could receive hemodialysis.
Uncited reference
1., 2., 3., 4., 5..
Article Information
Financial Disclosure: The authors declare that they have no relevant financial interests.
Patient Protections: The authors declare that they have obtained consent from the patient reported in this article for publication of the information about him that appears within this Letter.
Peer Review: Received August 9, 2022. Direct editorial input from the Editor-in-Chief. Accepted in revised form October 16, 2022.
==== Refs
Bibliography
1 Ramírez Marmolejo R, Aristizábal Gómez LY, Gómez Franco LM, Dueñas Suarez EP, Ramírez Isaza S, Soto Carvajal MP. Advance voluntary directive versus distanasia in the care of the elderly with COVID-19 and kidney disease. Rev. Colomb. Nefrol. [Internet]. 2020 Oct. 20 [cited 2022 Oct. 3];7(2). Available from: https://revistanefrologia.org/index.php/rcn/article/view/519
2 Ramírez Marmolejo R, et al. Colombian consensus of experts on evidence-informed recommendations for the management of SARS-CoV-2 / COVID-19 infection in multimorbid older adults with chronic kidney disease. Rev. Colomb. Nefrol. [Internet]. 2021 May 5 [cited 2022 Oct. 3];8(2):e525. Available from: https://revistanefrologia.org/index.php/rcn/article/view/525
3 Soto, S. A. (19 de Mayo de 2021). Empresas del Valle del Cauca, las más afectadas por el Paro Nacional. La Republica, https://www.larepublica.co/especiales/los-costos-del-paro/empresas-del-valle-del-cauca-las-mas-afectadas-por-el-paro-nacional-y-los-bloqueos-3171829.
4 https://www.valledelcauca.gov.co/publicaciones/60137/mapas-y-territorios/
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| 36506247 | PMC9722233 | NO-CC CODE | 2022-12-08 23:16:26 | no | Kidney Med. 2022 Dec 6;:100576 | utf-8 | Kidney Med | 2,022 | 10.1016/j.xkme.2022.100576 | oa_other |
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J Clin Virol Plus
J Clin Virol Plus
Journal of Clinical Virology plus
2667-0380
The Author(s). Published by Elsevier Ltd.
S2667-0380(22)00067-9
10.1016/j.jcvp.2022.100128
100128
Article
Convalescent plasma for COVID-19 in oncohematological patients: a call for revision of the European Conference on Infections in Leukemia-9 (ECIL-9) guidelines
Focosi Daniele a⁎
Franchini Massimo b
Senefeld Jonathon W. c
Casadevall Arturo d
Joyner Michael J. c
a North-Western Tuscany Blood Bank, Pisa University Hospital, via Paradisa 2, Pisa 56124, Italy
b Division of Transfusion Medicine, Carlo Poma Hospital, 46100 Mantua, Italy
c Department of Anesthesiology, Perioperative Medicine, Mayo Clinic, Rochester, MN 55902, United States
d Department of Medicine, Johns Hopkins School of Public Health and School of Medicine, Baltimore, MD 21218, United States
⁎ Corresponding author.
6 12 2022
2 2023
6 12 2022
3 1 100128100128
31 5 2022
5 12 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.
Keywords
COVID19
SARS-CoV-2
Monoclonal antibodies
Convalescent plasma
Immunosuppression
Abbreviations
CCP, COVID-19 convalescent plasma
ECIL-9, European Conference on Infections in Leukemia
==== Body
pmcThe recommendations for the management of COVID-19 in patients with hematological malignancies or hematopoietic cell transplantation, from the 2021 European Conference on Infections in Leukaemia (ECIL 9) were published online in Leukemia on April 29, 2022 [1]. This conference was held virtually on September 2021 and we worry some of the recommendations are out of date. Keeping guidelines updated represents a formidable challenge in a rapidly evolving pandemic, but is nevertheless fundamental to avoid misuse of resources.
The prerequisite “when monoclonal antibodies are not available”, for recommending COVID19 convalescent plasma (CCP) use, and the sentence “sotrovimab is the only anti-S MAb to retain activity against the recent Omicron variant” suggest that the manuscript was drafted before the onset of the Omicron BA.2 and BA.5 variant waves, which dominated the landscape and are resistant to any monoclonal antibody (mAb) authorized by the EMA so far. In the USA, the FDA has withdrawn the usage of casirivimab+imdevimab (since December 2021), sotrovimab (since March 2022) [2], and bebtelovimab (since December 2022)
For prophylaxis, cilgavimab+tixagevimab is the only mAb approved by EMA, on the basis of randomized controlled trials (RCT) completed during the Delta variant wave: unfortunately the tixagevimab component of the cocktail was ineffective in vitro against BA.2 and BA.5, and real-world evidences suggest that there could be dosing failure [3] leading to a high incidence of breakthrough infections [4]. Overall, it has been clearly demonstrated that the risk of immune escape with each single mAb is very high, especially among immunosuppressed patients who harbor persistent infections with high viral load [5], [6]. Furthermore, BQ.1.1.* and XBB.* are fully resistant to cilgavimab+tixagevimab.
As stated by the authors, seronegativity (as opposed to earliness) is the key driver for the success of antibody-based treatments in COVID-19 in immunocompromised patients. As such, there is huge rationale for CCP in B-cell depleted patients, and we can't understand why in Table 2 CCP has been appropriately recommended by ECIL-9 until seronegativity persists in mild COVID-19 cases, but instead only within 72 hours from onset of symptoms in moderate COVID-19, since numerous reports document responses in patients even after weeks to months of disease [7]. For the same reasons, patients with critical COVID-19, manifest similar (minor) benefits from either casirivimab+imdevimab (now totally useless against Omicron) and CCP (which is instead not recommended at all in the Table). Of interest, there are now reports that CCP can halves the risk ratio for mortality in immunocompromised patients [8] and rescue the ones who have failed under mAbs [9], but the contrary situation has never been published, confirming the superior potential of polyclonal preparations . We note in vitro data showing CCP from donors who have been vaccinated and recovered from infection is extremely high titer and shows neutralizing activity against emerging variants of concern [10]
Concerns also currently exist with oral small-chemical antivirals (specifically, viral and clinical rebounds for nirmatrelvir [11] and low efficacy and mutagenicity for molnupiravir [12]), for which repeated or chronic exposure has not been formally investigated in immunocompromised patients yet.
Nowadays CCP can be easily collected by convalescent vaccinees that were already regular donors, who have far higher levels of neutralizing antibodies than CCP collected in the before-vaccine era. It works against BQ.1.1.* and XBB.1.* [13], is relatively safe, has fewer contraindications than small-chemical antivirals, and, in addition to polyclonal IgG, includes IgM and IgA (which retain much of the neutralization capability on respiratory mucosae). Furthermore, CCP could be the only antiviral affordable to low-and-middle income countries. Hence, we feel that evidences for usage should be ranked equal to or higher to other antivirals.
We hope that, on the basis of such evidence, ECIL-9 will soon revise its recommendations to remove treatments that are no longer effective (e.g., sotrovimab and Evusheld) and by increasing the level of supportive evidence for CCP. Furthermore, to date no head-to-head randomized controlled trial has been even run in immunocompetent COVID19 patients, but it could be time to launch it for immunosuppressed oncohematological patients in the era of Omicron and its likely follow up variants. Contemporary updates to the guidelines will continue to be important given the suboptimal antibody responses to COVID-19 vaccination and boosters among patients with hematological malignancies or hematopoietic stem cell transplant recipients.
Author contributions
D.F. wrote the first draft. A.C., M.J. and J.S. revised the manuscript.
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.
A.C. was an investigator in the CSSC-004 RCT of CCP. D.F. was an investigator in the TSUNAMI RCT of CCP. M.J. and J.S. were investigators in the US Expanded Access Program of CCP.
==== Refs
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| 36504695 | PMC9722234 | NO-CC CODE | 2022-12-14 23:54:20 | no | J Clin Virol Plus. 2023 Feb 6; 3(1):100128 | utf-8 | J Clin Virol Plus | 2,022 | 10.1016/j.jcvp.2022.100128 | oa_other |
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Asian J Psychiatr
Asian J Psychiatr
Asian Journal of Psychiatry
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Elsevier B.V.
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10.1016/j.ajp.2022.103388
103388
Article
Impact of incentivizing ASHAs on the outcome of persons with severe mental illness in a rural South Indian community amidst the COVID-19 pandemic
Sivakumar Thanapal a⁎
Basavarajappa Chethan a
Philip Mariamma b
Kumar C Naveen a
Thirthalli Jagadisha a
Parthasarathy Rajani c
a Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru – 560029
b Department of Biostatistics, National Institute of Mental Health and Neurosciences, Bengaluru – 560029
c Mental Health, Directorate of Health and Family Welfare, Bengaluru-560009
⁎ Correspondence to: Psychiatric Rehabilitation Services, Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru – 560029,
5 12 2022
5 12 2022
10338810 6 2022
3 9 2022
12 9 2022
© 2022 Elsevier B.V. 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.
Background & objectives
Task shifting has been recommended as a strategy to reach out to persons with mental illness and bridge the treatment gap. There is a need to explore task-shifting using existing health staff like Accredited Social Health Activists (ASHAs). We examined the impact of incentivizing ASHAs on the outcome of persons with severe mental illness (SMI) amidst the pandemic.
Methods
One hundred eighty-four adults with SMI from Jagaluru taluk were enrolled and followed up for a year. They were assessed for disability, work performance, internalized stigma, and illness severity at baseline, six months, and 12 months follow-up. ASHA workers were incentivized to ensure follow-up consultations, address concerns regarding illness/ medication side effects and monitor medication adherence.
Results
Out of the 184 recruited patients, 7 died (non-COVID-19 causes), 22 stopped treatment and did not report for follow-up consultations, 11 shifted to treatment from other centers, and in 1 case, there was a change in diagnosis. 143 (78%) patients with SMI completed the study amidst the COVID-19 pandemic. At one year follow-up, there was a significant reduction in disability, illness severity, self-stigma, and improved work performance.
Conclusion
Incentivization of ASHAs helped ensure continuity of care to persons with SMI despite lockdowns and COVID-19 exigencies. It is feasible to involve ASHAs in the treatment of persons with SMI.
Keywords
community-based rehabilitation
severe mental illness
rural
India
ASHA
task-shifting
==== Body
pmc1 Introduction
According to the National Mental health survey, 2015-16 of India, more than 70% of the Indian population lives in rural areas with limited awareness of mental illness and limited availability, accessibility, and affordability of mental health services (Gururaj et al., 2016). In India, schizophrenia and bipolar affective disorders (called severe mental illnesses; SMI) are leading causes of years lived with disability (YLDs) (Collins et al., 2011, Sagar et al., 2020). The treatment gap for schizophrenia, other psychotic disorders, and bipolar affective disorders is as high as 75.5% and 70.4%, respectively (Gururaj et al., 2016).
Major barriers to addressing the treatment gap are scarcity of trained human resources, inequitable and inefficient resource distribution and utilization, poverty, social deprivation, poor community awareness, and stigma associated with mental illness (Purgato et al., 2020). To address this, task shifting (also known as task sharing), defined as “delegating tasks to existing or new cadres with either less training or narrowly tailored training,” has been advised (Purgato et al., 2020, Ginneken et al., 2021, Kakuma et al., 2011). Task shifting may involve primary care health workers who have received general health training and community workers with no background health training (Purgato et al., 2020). Treatment from primary care health workers alone or in collaboration with mental health specialists improves day to day functioning of adults with SMI (Ginneken et al., 2021).
The 12th five-year plan of the district mental health program (DMHP) speaks about a cadre of ‘community mental health workers’ who are local residents with ten years of schooling and will be offered an honorarium for their services (Policy Group). This cadre is not functioning in any of the DMHPs to date. Task-shifting with the help of Accredited Social Health Activists (ASHAs) is a feasible and effective strategy to reach out to persons with severe mental illness (SMI) in the community and address the treatment gap (Sivakumar et al., 2022, Sivakumar et al., 2022). If adequately supported, ASHAs can fulfill the role of community mental health workers for persons with SMI with little additional burden on their existing duties (Sivakumar et al., 2022).
ASHAs are honorary, literate female primary health workers who offer multiple maternal and child health services for an honorarium (Sivakumar et al., 2022). ASHAs are not paid an honorarium for working with persons with mental illness (Sivakumar et al., 2022. Incentivizing ASHAs to offer services to persons with SMI is likely to improve the outcomes (Sivakumar et al., 2022).
We planned a randomized control trial examining the effect of incentivizing ASHAs to deliver community-based rehabilitation interventions to persons with SMI. PHCs in the Taluk were randomized to implement intervention through ASHAs who were incentivized or to function as usual – in the latter group, volunteers from an NGO working for persons with disability were expected to provide their services as usual. However, due to the COVID-19 pandemic, the trial could not be conducted as planned (Kakuma et al., 2011, Sivakumar et al., 2022). Instead, we incentivized all ASHAs to ensure follow-up consultations, address illness/ medication side effects concerns, and monitor medication adherence. This paper describes the impact of incentivizing ASHAs on the outcome for persons with SMI.
2 Methodology
2.1 Setting
The study was conducted in Jagaluru, a taluk (an administrative block) of Davangere District, Karnataka State, India. The taluk has 10 Primary Health Centres (PHCs) and 1 Taluk hospital. As part of ongoing CBR program, free mental health camps have been conducted fortnightly on Tuesdays at PHCs & the taluk hospital since August 2015 in partnership with the Government of Karnataka and NGOs. More details about Jagaluru taluk and the CBR program are described elsewhere (Sivakumar et al., 2020).
2.2 Sample
For the study, SMI was operationally defined as schizophrenia spectrum disorders (F20-29) and bipolar affective disorders (F31) as per the international classification of diseases (ICD-10). Adults with SMI residing in the taluk, availing of treatment from the Jagaluru CBR program at PHC/ taluk hospital, and consenting to participate in the trial were included in the study from November 2019 to September 2020. The duration of the intervention was a minimum of one year for each person with SMI.
2.3 Intervention
As part of the Jagaluru CBR program, we have trained ASHAs to identify persons with SMI from their villages and refer them for treatment. The ASHAs were trained for 90 minutes by a psychiatrist in the local language about symptoms, course, outcome, and treatment of SMI, followed by an interactive session where queries were clarified. ASHAs were encouraged to refer patients to nearby PHC or taluk hospital for consultation.
This was followed by on-the-job training. ASHAs accompanied patients for evaluation. Patients were interviewed, and treatment was initiated in the presence of ASHAs who were requested to supervise treatment and ensure follow-up. ASHAs would observe the psychiatrist assess and educate patients and their family members about their illness, explain the side-effects of medications (if any), and the need for continued care, including engaging in constructive activities as part of patients’ recovery. On-the-job training differed among ASHAs based on the number of patients referred. A social worker from the team was available to ASHAs for guidance over the phone.
As part of the present study, we incentivized the ASHAs by Rupees 150 [2 US Dollars (1 US Dollar= Rupees 75)] to ensure follow-up consultations are scheduled once in three months for persons with SMI from the respective villages. The ASHAs informed persons with SMI about scheduled consultations and accompanied them for consultations when possible. During home visits, ASHAs inquired about the patient’s clinical status from family, addressed concerns related to illness/ medication side effects, and advised the family about the importance of medication adherence.
The institutional ethics committee approved the protocol and was updated about protocol deviations due to the COVID-19 pandemic. The study was registered in Clinical Trials Registry India (CTRI) CTRI/2019/08/020585 dated 6th August 2019.
2.4 Assessment tools
1. Socio-demographic proforma: A semi-structured proforma was developed for the study to collect the socio-demographic information of the patient and family members.
2. Indian Disability Evaluation and Assessment Scale (IDEAS) (Ministry of Social Justice and Empowerment D of E of P with D, 2016): The IDEAS was originally developed for measuring and certifying disability due to mental illness in India. It assesses disability across four domains: self-care, interpersonal relationships, communication and understanding, and work. The total score is added with the duration of illness score to give a global score. IDEAS has good face validity, criterion validity, and internal consistency.
3. World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) (Üstün et al., 2010). WHODAS 2.0 was developed synchronously with the international classification of functioning (ICF) to measure health status and disability across different cultures and settings. It can measure disability across all diseases, including mental and substance use disorders. The instrument was developed through an international collaborative approach and had excellent psychometric properties. We used the 12-item interviewer-administered questionnaire translated into Kannada for this study.
4. CGI-SCH (Haro et al., 2003) and CGI-BP (Spearing et al., 1997). The clinical global impressions scale (CGI) was modified to assess global illness severity and change in schizophrenia and bipolar disorder patients. CGI-SCH rates the severity of illness over the last one week and degree of change compared to a previous evaluation in positive, negative, depressive, cognitive symptoms, and overall severity (Haro et al., 2003). It is a simple, valid, reliable instrument to evaluate severity and treatment response in schizophrenia (Haro et al., 2003). CGI-BP can be used for acute and prophylactic assessments of the severity of illness, change from the preceding phase, and change from the worst phase of illness (Spearing et al., 1997). The investigators have obtained permission from the authors for use in studies.
5. Internalized stigma of mental illness (ISMI) scale (Boyd et al., 2014). The ISMI contains 29 items producing five subscales (alienation, stereotype endorsement, discrimination experience, social withdrawal, stigma resistance) and a total score. The scale has good psychometric properties across various languages, cultures, conditions, and situations. The investigators have obtained permission from the authors for translation into Kannada and use them in studies.
The assessments were done at baseline, six months follow-up, and 12 months follow-up. The patient, caregiver, and ASHAs were interviewed for the assessments. The first author did the IDEAS, CGI-Schiz and CGI-BP assessments. The first author trained the project staff who did the WHO-DAS and ISMI assessments.
2.5 Statistical methods
Descriptive statistics were used to describe study participants’ socio-demographic and clinical variables. Friedman’s test compared variables across baseline, six-month, and 12-month follow-ups. SPSS version 28 was used for data analysis.
3 Results
Table 1 depicts the baseline characteristics of participants. The average duration of illness was 11 years. Most patients were married.Table 1 Socio-demographic variables of the participants.
Table 1Socio-demographic variables Total population (n=184) Schizophrenia spectrum disorders (N= 138) Bipolar Disorder (N=46)
Mean (S.D.)/ frequency (%) Mean (S.D.)/ frequency (%) Mean (S.D.)/frequency (%)
1. Age (years) 47.22 (13.38) 46.56 (13.26) 49 (13.67)
2.Gender (Male: Female) 78:106 (58% females) 52:86 (62% females) 26:20 (43% females)
3. Duration of Illness (in years) 11.21 (7.18) 10.4 (6.72) 13.6 (8.01)
4.Income per annum (in US Dollars)
($1 = Rs. 75) $ 834.92 (912.33) $ 818.13 (977.98) $ 882.01 (704.57)
5. Marital Status
a. Married
113 (61%) 80 (58%) 33 (72%)
b. Separated/Divorced
16 (8%) 14 (10%) 2 (4%)
c. Single 33 (18%) 26 (19%) 7 (15%)
d. Widowed 21 (11%) 17 (12%) 4 (9%)
6. Years of Education 4.64 (4.88) 4.7 (4.95) 4.22 (4.72)
143 (78%) patients with SMI followed up at the end of 12 months amidst the COVID-19 pandemic.
Table 2 depicts the statistically significant improvement in disability, illness severity, work functioning, and self-stigma from baseline to 12-month follow-ups.Table 2 Comparison of scores across time: Friedman’s Test.
Table 2Variables Timepoint Test statistic p-value
Baseline 1stfollow-up 2ndfollow-up
Median (inter-quartile range)
Mean ± SD
WHO-DAS
(n=143) 16 (Üstün et al., 2010; Haro et al., 2003; Spearing et al., 1997; Boyd et al., 2014; Kumar et al., 2017; James et al., 2019; 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 28.58 ± 26.15 13 [12–17] 20.08 ± 20.90 12 [12–15] 18.55 ± 18.18 107.14# 0.001
IDEAS (n=123) 5 [3–7] 5.86 ± 3.86 4 (Sagar et al., 2020, Purgato et al., 2020, Ginneken et al., 2021) 5.11 ± 3.33 4 (Sagar et al., 2020, Purgato et al., 2020, Ginneken et al., 2021) 4.85 ± 2.85 23.21# 0.001
ISMI
(n=136) 1.60 (1.24-2.27)
1.79 ± 0.66 1.48 (1.15-2.04)
1.65 ± 0.60 1.21 (1.07-1.56)
1.37 ± 0.44 115.42#@ 0.001
IDEAS-work
(n=123) 1 (0-2)
1.1 ± 1.24 0 (0-1)
0.93 ± 1.21 0 (0-1)
0.76 ± 1.12 27.56@ 0.001
CGI-schiz overall severity of illness
(n= 99) 1 (1-3)
1.82 ± 1.23 1 (1-2)
1.68 ± 1.09 1(1-2)
1.49 ± 0.94 11.18@ 0.001
CGI-BP overall severity of illness
(n=31) 1 (1-1)
1.39 ± 0.99 1 (1-1)
1.29 ± 1.04 1(1-1)
1.1 ± 0.7 7.118@ 0.028
# @ post-hoc comparisons.
# p<0.05 for comparison of baseline with the first and second follow-up assessment
@ p<0.05 for comparison of the first follow-up assessment with the second follow-up assessment
4 Discussion
The patients were chosen from the Jagaluru cohort, which we have followed up with since 2015. The cohort had a low level of illness severity as most of them were on regular medications and had mild disability. Most patients with schizophrenia spectrum disorder were on either risperidone, olanzapine, or injection fluphenazine depot. Three patients were on clozapine. Patients with bipolar affective disorder were on valproate, lithium, carbamazepine, or olanzapine as mood stabilizers.
Only one patient refused consent for the proposed intervention by ASHAs. This indicates a high level of acceptability of ASHA intervention. As the patient was an unmarried female, the family did not want ASHA or others in the locality to know about her illness as they felt it would affect her marital prospects. The family would come and avail treatment from a different PHC far from their residence.
78% (143/ 184) of persons with SMI recruited for the study completed the one-year follow-up. Out of the 184 recruited patients, 7 died (non-COVID19 causes), 22 stopped treatment, 11 shifted to treatment from other centers, and in 1 case, there was a change in diagnosis. Twenty-two persons with SMI had stopped medications as they were feeling fine and did not see the need to continue treatment. Usually, such patients consult us when the symptoms recur.
The study was conducted amidst the COVID-19 pandemic. Continuity of care was ensured with the help of telepsychiatry and ASHAs during the COVID-19 lockdowns (Sivakumar et al., 2022). Treatment with antipsychotics and psychoeducation alone has been shown to favorably influence the course of schizophrenia and reduce disability in a substantial proportion of patients from rural communities (Kumar et al., 2017). As ASHAs live in the same community, they could psychoeducate families, monitor medication adherence, identify early signs of relapse, contact treating psychiatrists and initiate remedial measures. They were also better positioned to cater to the health needs of persons with SMI through referrals to the government health system. During COVID-19 lockdowns, the patients could not travel for the consultations, and there was a risk of medication default leading to relapse. During such a situation, ASHAs took part in teleconsultations with the treating psychiatrist from respective PHCs. ASHAs updated the status of the patients, collected medications from the hospital, and gave it to patients at their homes (Sivakumar et al., 2022). These interventions likely had a cascading effect on the clinical stability of the person with SMI resulting in better functioning.
The ASHAs were receptive to mental health issues as they saw the change in the lives of families of persons with SMI in their community. They were also empowered and seen as ‘agents of change’ in the local community (James et al., 2019). As residents of the same community, their expertise will remain in the same community, continuing to benefit it. We believe that the local community also supported people with SMI helping in community reintegration and reducing internalized stigma (Sivakumar et al., 2022).
ASHAs helped ensure continuity of care for persons with SMI in Jagaluru amidst the COVID-19 pandemic. The rapport and collaboration with ASHAs built over the years in the CBR program ensured that care was available when needed. It is well known that continuity of mental health care, particularly for persons with SMI, was disrupted during the lockdowns and travel restrictions in the context of the pandemic. This was not the case in this community, thanks mainly to the service rendered by the ASHAs. We believe that the incentivization of ASHAs helped ensure continuity of care for persons with SMI. ASHAs can be involved in mental health service delivery, and methods of recognizing and incentivizing their services should be explored.
4.1 Limitations
Due to COVID-19 related work, ASHAs could not regularly accompany patients for consultations. Due to COVID-19 exigencies, the originally proposed randomized controlled trial could not be carried out; only medication adherence could be ensured, and other community-based rehabilitation interventions could not be implemented.
The same raters did the assessments over time.
4.2 Future directions
The cost-effectiveness of ASHAs delivered interventions for persons with SMI needs to be studied prospectively in a randomized controlled study.
5 Conclusion
Incentivization of ASHAs helped us ensure continuity of care to persons with SMI despite lockdowns and COVID-19 exigencies. It is feasible to involve ASHAs in the treatment of persons with SMI.
Financial disclosure
This work was supported by a research grant to Dr. Thanapal Sivakumar from the Indian Council of Medical Research.
Acknowledgment
The work was supported by the Indian Council Medical Research (ICMR) under Capacity Building Projects for National Mental Health Programme, ICMR-NMHP. We thank Dr. Soumya Swaminathan (then Secretary, Dept. of Health Research, DHR), Dr. Balram Bhargav, current Secretary DHR, Prof. V.L. Nimgaonkar, Prof. Smita N. Deshpande, Dr. Ravinder Singh, and Dr. Harpreet Singh. We thank the faculty of ‘Cross-Fertilized Research Training for New Investigators in India and Egypt’ (D43 TW009114, HMSC File No. Indo-Foreign/35/M/2012-NCD-1, funded by Fogarty International Centre, NIH). We also thank the National Coordinating Unit of ICMR for NMHP Projects for their constant support and guidance. We thank the Data Management Unit of ICMR for designing the database. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of NIH or ICMR. NIH and ICMR 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.
The authors would like to place on record their appreciation of the support provided by the Department of Psychiatry, NIMHANS (Bengaluru), National Health Mission (Bengaluru), District Health Officer (Davangere), District Leprosy officer (Davangere), District Mental health program (Davangere), Taluk Health Officer (Jagaluru) and all the health staff in Jagaluru taluk.
The work would not have been possible without the support of the Association of People with Disability: [Mr. Janardhana AL (Deputy Director: programs), Mr. Santosha S (Senior coordinator, Community mental health program, Davangere), Chittasanjeevini charitable trust, Mr. Dundappa Doddur (project staff), and officials/ key community members at Jagaluru.
Conflict of Interest
None
Author contribution
All authors have made substantial contributions to all of the following: (Gururaj et al., 2016) the conception and design of the study, or acquisition of data, or analysis and interpretation of data, (Collins et al., 2011) drafting the article or revising it critically for important intellectual content, (Sagar et al., 2020) final approval of the version to be submitted.
Statement on concurrent submissions
We declare that we have not submitted this paper to any other Journal.
==== Refs
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| 36495728 | PMC9722235 | NO-CC CODE | 2022-12-08 23:15:57 | no | Asian J Psychiatr. 2023 Feb 5; 80:103388 | utf-8 | Asian J Psychiatr | 2,022 | 10.1016/j.ajp.2022.103388 | oa_other |
==== Front
Soc Sci Med
Soc Sci Med
Social Science & Medicine (1982)
0277-9536
1873-5347
Published by Elsevier Ltd.
S0277-9536(22)00908-X
10.1016/j.socscimed.2022.115602
115602
Article
The efficacy of health experts’ communication in inducing support for COVID-19 measures and effect on trustworthiness: A survey in Hong Kong
Yuen Vera W.H.
Faculty of Business and Economics, University of Hong Kong, Hong Kong
5 12 2022
5 12 2022
11560231 5 2022
29 10 2022
4 12 2022
© 2022 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 control the spread of COVID-19, governments may implement freedoms-infringing health measures. Therefore, citizens' support for these measures is important. This study investigates: (1) whether health experts' communication induces support for COVID-19 measures, and (2) whether health experts' agreeing or disagreeing with government directives affects their trustworthiness. A cross-sectional online questionnaire was completed by 1072 adults in the Hong Kong general population between May 26 and June 3, 2021. Three COVID-19 measures were examined: contact-tracing mobile application, restriction-testing, and ban of public assembly. For each, participants were randomly assigned to three groups to view, respectively: vignettes with a neutral government announcement only; vignettes with a government announcement and a health expert's quote supporting the government's decision; and vignettes with a government announcement and a health expert's quote disagreeing with the government's decision. The result shows that positive health experts' communication increased the support for banning public assembly; no effects were found for the support for contact-tracing mobile applications and restriction testing. Participants who only viewed health experts disagreeing with the government had higher trust in health experts relative to participants who viewed health experts agreeing with the government at least once. The results render doubtful the strategy that health experts can be involved for garnering support for unpopular health measures without jeopardizing public trust in them.
Keywords
Trust
COVID-19
Pandemic
Experts
Health communication
Public health
Hong Kong
Science communication
==== Body
pmc1 Introduction
The COVID-19 pandemic, which started in 2020, continues to impact human life worldwide. Based on the recommendations of health experts, governments have imposed various public health measures such as social distancing, mandatory mask-wearing, and lockdown. Public support for government-imposed health measures is important to control the spread of contagious diseases. To increase compliance, health experts have assumed important roles—either invited to do so by their respective countries' governments, media, or out of personal civil responsibility—to communicate to the public the necessity of following COVID-19 restrictions. In general, scientists enjoy fairly high levels of public trust worldwide (Gallup, 2019; Pew Research Center, 2019). Research has revealed a “rallying effect” that boosted support for scientists and expertise during the pandemic (Battiston et al., 2020; Daniele et al., 2020). As an epistemological authority and a credible source of information, health experts are expected to command significant cooperation from society, but evidence in this regard is mixed at best and therefore warrant further research. Arceneaux et al. (2020) found evidence of greater support for COVID-19 restrictive measures endorsed by health experts as compared to measures endorsed by lawmakers only; Deslatte (2020) found less influence of health experts' endorsement than policymakers. Alsan and Eichmeyer (2021) found laypeople to be more effective than experts at promoting vaccination among low socio-economic classes. Does health experts' public communication have an effect on increasing support for governments’ COVID-19 measures? This is the first research question of this study.
In public decision-making, policymakers sometimes delegate strategies and decisions to experts. Alternatively, experts serve as consultants in committees to provide suggestions and advices, but ultimately the policymakers bear the responsibility of decision-making. In the latter case, directives of policymakers and opinions of health experts could diverge (Antoci et al., 2020). There are three reasons. First, while experts tend to make issue-specific recommendations based on their own expertise, policymakers have to consider diverse interests of the society (Moore and MacKenzie, 2020). For example, with the single goal of pandemic control, health experts can recommend stringent measures, but policymakers must consider economic, social, administrative, and fiscal aspects. Second, while experts presumably make recommendations in good faith, policymaking often involves political calculations. For example, Pulejo and Querubín (2020) found that incumbents who had to run for re-election implemented less stringent COVID-19 restrictions when elections were near. Third, opinions among health experts themselves could diverge. Making recommendations regarding COVID-19 is far from finding the best technical solution (Lavazza and Farina, 2020). Recommendations require application of scientific knowledge and making assumptions of the real world in case of uncertainty. Divergence also emanates from different values of individual experts and societies, such as the importance of freedom and priority of the right to live. Therefore, even though health experts were enlisted by governments to tackle the pandemic, diverse views among expert groups resulted in the co-existence of affirming and doubting opinions on government directives. This study focuses on whether health experts' agreements and disagreements with government directives affects experts’ trustworthiness—this is the second research question.
Experts’ trustworthiness was of interest because studies have reported lower trust in science and scientists after epidemics (Eichengreen et al., 2021; Feufel et al., 2010). In recent years, lower trust in experts has been observed in issues such as climate change (Anderegg et al., 2010) and Brexit (Bauer, 2017; Clarke and Newman, 2017) and become a subject of research interest (Gauchat, 2012) and public interest. The result also has implications on future risk management as the field literature suggests correlation between expert trust and compliance (Gilles et al., 2011; Siegrist and Zingg, 2014).
Many studies have investigated the factors associated with the acceptance of various COVID-19 measures that restrict freedom (Arceneaux et al., 2020; Bargain & Aminjonov, 2020; Guglielmi et al., 2020; Guillon and Kergall, 2020; Kuiper et al., 2020; Yu et al., 2020). It was believed that people living in democratic countries have lower tolerance for restrictive measures than those in authoritarian countries. In the context of Hong Kong, a major social movement had broken out in 2019 and made citizens sensitive about measures that restricted personal freedom. At the same time, trustworthiness of the government dropped to an all-time low in February 2020 when the pandemic began (Hong Kong Public Opinion Research Institute, 2022b). The existing tension made it difficult to support the government's freedom-infringing COVID-19 measures. Under such circumstance, health experts' recommendations—although based entirely on scientific grounds—might have been viewed with political skepticism.
The three COVID-19 measures tested in this study were the use of contact-tracing app, restriction-testing, and ban of public assembly. A contact-tracing app, “Leave Home Safe” was developed by the Hong Kong government for scanning and storing information of entry into premises. The use of this app was not mandatory but encouraged at the time of the survey conducted as part of this study. According to another survey, the use of digital contact-tracing was the most disagreed—approximately 60%—among the four measures surveyed in Hong Kong participants (Voo et al., 2021). Hong Kong people showed more resistance against digital contact-tracing than their counterparts in Malaysia, Singapore, the US, and South Korea (Huang et al., 2021; Voo et al., 2021).
Another controversial measure was the ban of public assembly. Under the Prevention and Control of Disease Regulation (Cap. 599 F) enacted because of the COVID-19 emergency, public gatherings of more than four people were prohibited during the survey period. Offenders could face a maximum fine of $25,000 and/or imprisonment for up to six months (Government of Hong Kong, 2021a). In Hong Kong, which has built its reputation as a “city of protest” (Dapiran, 2017) and “China's rebel city,” (South China Morning Post, 2020) public demonstrations and protests were banned because of the pandemic, which was in stark contrast to the fierce social movement of 2019. This measure was criticized by activists on grounds of “prevent (ing) the return of anti-government demonstrations, while largely allowing other groups to gather with impunity.” (Time, 2020).
“Restriction-testing” refers to a short-term lockdown of a small area consisting of a few buildings or a housing estate, and lasts from a half-day to about a week. During such lockdowns, residents within the “restricted areas” were mandated to undergo polymerase chain reaction testing of virus and not allowed to leave the areas. Between January 2021 and March 2022, restriction-testing operations were imposed approximately 250 times, affecting 350,000 people, or approximately 5% of the population (Ming, 2022). Restriction-testing operations were akin a lockdown, albeit at a small scale. It was not a common COVID-19 measure implemented in other countries and to the best of the author's knowledge, no study has examined support for such a measure.
2 Methods
2.1 Study design
An online survey was conducted by a survey company (Dynata) from May 26 to June 3, 2021 using quota sampling to mimic the general Hong Kong adult population by age and sex. Points to exchange for coupons were given by the survey company as an incentive. A total of 1105 samples were collected anonymously. After eliminating incomplete responses and machine-detected ballot-stuffing using built-in functions of Qualtrics, a survey management platform, 1072 valid samples were analyzed. Electronic consent was obtained from participants before the survey began. Participants could opt out any time during the survey. The collected data were retrieved from the online survey platform protected by passwords. Ethics approval was obtained from the corresponding author's affiliated institution.
Daily new confirmed COVID-19 cases ranged from zero to seven during the survey period with no new death case (Government of Hong Kong, 2022). The epidemic was stable at a low level. However, the pandemic control measure had been held at a high level despite the drop in number of cases (Fig. 1 ). Following the strict pandemic control policy of China, the stringency index of Hong Kong stood at 71, whereas the world average was 58 (Hale et al., 2021). Social distancing and mask-wearing requirements continued; vaccination requirements and limits on capacity and operating hours were in place for catering business and various premises. The government explained that it was to avoid a rebound (Government of Hong Kong, 2021b). According to Hong Kong Public Opinion Research Institute (2021)'s representative sample collected during the survey period, respondents estimated the risk of infection to be 12%; 54% of the respondents were dissatisfied with the government; an average of 94% respondents thought that the social gathering ban was too strict.Fig. 1 Daily new confirmed COVID-19 cases and stringency index in Hong Kong (2020–2021).
Source: Government of Hong Kong (2022); Hale et al. (2021).
Fig. 1
Fig. 2 shows the flow of the survey. Part 1 asked participants about their COVID-19 experiences and political attitudes. Part 2 presented vignettes (excerpts from news reports) of three different COVID-19 measures in a random order: the use of contact-tracing mobile application, restriction-testing, and the ban of public assembly. For each response, a participant viewed one vignette, which was randomly drawn from consent, dissent, or control groups. This implies that the number of respondents in each group is roughly one-third of the total sample, barring any variations in randomization. The consent group referred to health experts' agreement with the government measure. The dissent group referred to disagreement. The control group did not contain health experts’ communication. Immediately after viewing the vignette, the participant was asked for their extent of support for the measure stated in the vignette. After viewing all three vignettes, participants were asked about their trust level in the government and health experts. Finally, demographic information was collected. The opinion, preference, and attitude questions were asked before the vignettes because they were used as control variables in this study. Viewing the few vignettes might affect what respondents think about the seriousness of the epidemic, and hence affects, for example, how they weight between pandemic control and freedom; and whether they think the Hong Kong government is competent. Therefore, these questions were ordered before the vignettes. The variables aimed to be treated only included trustworthiness and compliance. Demographics were fixed for the respondents and would not be affected by the treatment, and therefore could be asked in rear.Fig. 2 Flow of the survey.
Fig. 2
Three broad categories of control variables were used: experience with COVID-19, political attitudes, and demographics. The controls for COVID-19 experience follow the crisis and trust literature and are relatively standard (Aassve et al., 2020; Aksoy et al., 2020; Albertson and Gadarian, 2015; Arceneaux et al., 2020; Citrin and Stoker, 2018; Gill, 2007; Goldfinch et al., 2021; Jensen and Naumann, 2016; Newton, 2020; Reinhardt, 2015; Sibley et al., 2020). Political attitudes have to be controlled because partisanship affects how people view issues (Bullock and Lenz 2019; Holmberg 2007), which is also true in the COVID-19 situation where compliance and trust were divided between party supporters (Goldstein and Wiedemann, 2021; Kerr et al., 2021; Ward et al., 2020). In Hong Kong, heated social movement and conflict between the government and the residents would require control of these variables. How much people had been exposed to the partisanship effect was gauged by the political attentiveness variable.
2.2 Health experts’ communication treatment groups
The vignettes viewed by the control group contained only a neutral government announcement. The consent group received the same government announcement along with a supportive communication from health experts, health expert groups, or public health organizations. The dissent group received the government announcement together with a disagreeing remark given by other similar health experts and groups. The principle for making the pairs of treatment vignettes was similarity. Variations could be due to the length of the vignettes, the persons or groups presented, organization affiliation of the persons cited, and their arguments. This study attempts to make the vignettes in the pairs as similar as possible in all these aspects. For each COVID-19 measure, the vignettes used in consent and dissent groups were made to similar length. All experts used in the vignettes were presented with titles related to healthcare. The adoption of a person was paired with another person with a similar title, whereas the use of a medical organization was matched with another organization. In making the vignettes, results that appeared higher in google search result were considered first because it implied that the news report was more popular among the public. The goal was to mimic real-world information dissemination as closely as possible. The vignettes of public assembly ban were presented as an example. Please refer to Section S1 of Supplementary materials for the full sets of vignette.
3 Control group
It has been reported that on a number of occasions since the No-gathering Order came into force, the police have issued notices of objection to public meetings and processions on grounds of epidemic prevention, and have issued to participants a fixed penalty for violating the Order.
3.1 Consent treatment group
It has been reported that on a number of occasions since the No-gathering Order came into force, the police have issued notices of objection to public meetings and processions on grounds of epidemic prevention, and have issued to participants fixed penalty for violating the Order. Professor Wong Tze Wai, from the School of Public Health and Primary Care in the Chinese University of Hong Kong, said that the virus is apparently spreading rapidly through high-efficiency person-to-person transmission. Therefore, the public is urged to avoid densely populated places, including public assembly.
3.2 Dissent treatment group
It has been reported that on a number of occasions since the No-gathering Order came into force, the police have issued notices of objection to public meetings and processions on grounds of epidemic prevention, and have issued to participants fixed penalty for violating the Order. Dr. Leung Chi Chiu, Chairman of Advisory Committee on Communicable Diseases of the Hong Kong Medical Association, said that if everyone wears a mask during an assembly, and stands 1-m apart, there is no big problem. The risk of virus spread is low in outdoor assembly and open spaces.
3.3 Support for COVID-19 measures
After viewing each vignette, participants answered the following question: “To what extent do you support [the COVID-19 measure]?” They were asked to rate the answer using an 11-point scale, from “0” (least support) to “10” (strongly support). A total of three responses were recorded.
3.4 Health–expert–government interaction
As discussed above, for each COVID-19 policy measure, a participant was assigned randomly to the control, consent treatment, or dissent treatment groups. As there are three COVID-19 measures, each participant was assigned to a total of three random vignettes. For each participant, dummy variables concerning interaction between health experts and the government were constructed as follows:
Discord: participants viewed only dissent and control vignettes.
Contradiction: participants viewed a combination of consent, dissent, and control vignettes.
Consensus: participants viewed only consent and control vignettes.
3.5 Health expert and government trustworthiness
Participants were posed two questions in random order: “To what extent do you find health experts trustworthy?” and “To what extent do you find the Hong Kong government trustworthy?” They provided answers on an 11-point scale, with 0 being very untrustworthy and 10 being very trustworthy. Two responses were recorded.
3.6 Experience with COVID-19
Participants’ COVID-19 experience, COVID-19 knowledge, perception of responsibility, and economic preferences were collected as control variables. COVID-19 experience was measured via yes/no answers to the two statements: “I have lost my job or main source of income since the (COVID-19) outbreak,” and “I have been in quarantine because of COVID-19.”
Participants’ knowledge of COVID-19 was tested using four questions. Sample statements included: “a person recovered from COVID-19 will not be infected the second time.” Participants were asked to indicate whether the item was correct or not by choosing from “true,” “false,” and “not sure.” One mark was awarded for each correct answer. Total score ranged from 0 to 4, with higher scores reflecting better knowledge.
Perception of responsibility was assessed using the question: “The spread of COVID-19 is natural, not human-induced”; it was rated on an 11-point scale, ranging from “0” (strongly disagree) to “10” (strongly agree).
Economic preference was measured by the extent to which they agreed that “economic recovery is more important than pandemic control.” They responded on an 11-point scale, ranging from 0, which indicated that pandemic control was more important, to 10, which favored economic recovery.
3.7 Political attitudes
Prior to treatment, political attitudes were measured in four dimensions. The first item was about expectations with the government; participants were asked to what extent they agreed that “The Hong Kong government is competent in tackling COVID-19.” The second item was about freedom preference: “Freedom is more important than pandemic control.” The third items measured political attentiveness: “In general, do you pay attention to politics?” The answers to these questions were rated on a scale of 0–10, with 0 (10) indicating the strongly disagree (agree) for the first two items and least (most) attention for the third item, respectively.
The participants were also asked to self-report their political stance. In Hong Kong, the major political cleavage is not between the left and right but between being pro-government and pro-democracy. The choices provided in the survey were “pro-establishment,” “moderate/center,” “pan-democratic,” “pan-localist,” “others,” and “don't know.” For statistical analysis, these responses were grouped into three categories: pro-government (pro-establishment), opposition (democrat and localist), and others (not included in the above categories).
4 Demographics
The demographic background of participants including sex, education, age, and income.
4.1 Statistical analysis
The Chi-squared test and one-way ANOVA were used to confirm that the independent variables across the treatment and control groups were not statistically different. Two sets of ordinary least square linear regression analyses were conducted. The first set tested the associations between health experts’ communication and support for COVID-19 measures. The second set tested the association between health-expert–government interaction and trust level. All statistical analyses were performed using Stata version 16.1 (StataCorp LLC, College Station, Texas, USA). Statistical significance was set at p < 0.05.
5 Results
5.1 Descriptive statistics
5.1.1 Demographics of participants
Among the 1072 participants, more than half were females (53.6%) and had university education (56.5%); the average age was 39.8 years. The majority (54.4%) had income between 20 and 55 percentiles of the general population (Table 1 ).Table 1 Descriptive statistics of responses.
Table 1 N % of full sample
Male 497 46.40%
University Education 606 56.50%
Age (years) 39.8 (12.2)
Income group: Bottom 20% 127 11.90%
Income group: Middle 20%–55% 583 54.40%
Income group: Top 45% 362 33.80%
Experience with COVID-19
Loss of income 204 19.00%
Quarantine experience 72 6.70%
Knowledge of COVID-19 (0–4) 2.7 (1.2)
COVID-19 is a natural occurrence (0–10) 4.0 (2.9)
Economic recovery over freedom (0–10) 2.9 (2.1)
Political attitude
Political stance: Pro-government 99 9.20%
Political stance: Opposition 301 28.10%
Political stance: Others 672 62.70%
Government's competence (0–10) 4.7 (2.8)
Freedom over pandemic control (0–10) 3.1 (2.4)
Political attentiveness (0–10) 6.1 (2.3)
Note: Mean and standard deviation in brackets are shown for discrete variables. Income groups are approximations of percentiles using 2021 Q2 data of the General Household Survey, Census and Statistics Department.
5.2 Experience with COVID-19
Approximately one-fifth of the participants (19%) declared loss of job or a major source of income since the outbreak of COVID-19. A small percentage (6.7%) were in quarantine because of COVID-19. The mean knowledge score was 2.7 out of 4 (Standard Deviation [SD]: 1.2). Their assessment of whether COVID-19 was a natural occurrence was overall 4/10 (SD: 2.9). Their economic preference over pandemic control was on average 2.9/10 (SD: 2.1) (Table 1).
5.3 Political attitude
The percentage of participants who declared themselves to be either pro-government or in opposition were 9.2% and 28.1%, respectively. Their expectation regarding government's competence to cope with COVID-19 was on average 4.7/10 (SD: 2.8). The average preference for freedom over pandemic control was 3.1/10 (SD: 2.4). The average political attentiveness was 6.1/10 (SD: 2.3) (Table 1).
5.4 Test for statistical difference in independent variables across treatment groups
Their distribution across the three treatment groups was 32% (consent), 32% (dissent), and 36% (control), respectively; it was about the same in all three COVID-19 measures. Using the Chi-squared test for categorical variables and ANOVA for other variables, we could not reject the null hypothesis that any of the characteristics listed are not systematically different across groups (all p-values >0.05) (Table 2 ). Thus, we were confident that the independent variables used in this sample were not statistically different across groups.Table 2 Statistical test result for difference in independent variables across treatment groups.
Table 2 Health experts' communication (consent/dissent/control) Expert–gov interaction
Contact-tracing Restrictive-testing Public assembly ban (discord/contradict-ion/consensus)
Sex 0.654 0.289 0.908 0.911
Education 0.418 0.609 0.552 0.751
Age 0.963 0.789 0.409 0.186
Income group 0.328 0.422 0.475 0.735
Experience with COVID-19
Loss of income 0.558 0.178 0.34 0.218
Quarantine experience 0.841 0.258 0.443 0.354
Knowledge of COVID-19 0.649 0.873 0.843 0.354
COVID-19 is a natural occurrence 0.57 0.908 0.555 0.648
Economic recovery over freedom 0.944 0.485 0.7 0.834
Political attitude
Political stance: Pro-government 0.725 0.892 0.745 0.401
Political stance: Opposition 0.251 0.239 0.8 0.957
Government's competence 0.474 0.604 0.546 0.764
Freedom over pandemic control 0.241 0.422 0.817 0.931
Political attentiveness 0.69 0.385 0.924 0.581
5.5 Unconditioned support for COVID-19 measures and trustworthiness
Table 3 shows the unconditional support for different COVID-19 measures across health experts' communication treatment groups. In all three measures, the consent group had the highest support rate. In contrast, only the dissent group of the use of contact-tracing app had the lowest support rate. In the other two measures, the control group had the lowest support. Health experts' trustworthiness is higher than government's trustworthiness by 2.42 points on a 0–10 point scale. The difference across treatment groups for restriction-testing is significant with p value equals to 0.044. No other result is statistically significant across groups.Table 3 Support for COVID-19 measures.
Table 3COVID-19 measures
Support rating (0–10) Dissent Control Consent Comparison across treatment groups (p-value) All
Contact-tracing app 4.8 5.32 5.35 0.237 5.16
Restriction-testing 5.84 5.69 6.00 0.044 5.84
Public assembly ban 5.57 5.45 5.83 0.264 5.61
Health-expert–government interaction Discord Contradiction Consensus
Health experts' trustworthiness (0–10) 6.65 6.26 6.37 0.338 6.46
Government's trustworthiness (0–10) 3.97 3.97 4.14 0.286 4.04
Regression for Support of COVID-19 Measures.
The regression results are shown in Table 4 (full table in Section S2 of Supplementary materials). In these regressions, the control group is used as the base to study whether being assigned dissent and consent groups affects the support for COVID-19 measures. No treatment effect of statistical significance was detected for health experts’ communication except for public assembly ban (Model 3), in which the consent group showed higher support compared to the control group (coefficient = 0.333, p = 0.045). Belief in the natural occurrence of COVID-19, being pro-government, having higher expectation from the government, and older age were found to be associated with higher support for all COVID-19 measures tested. Having an opposition political stance and higher freedom preference showed lower support. The models used here explained 48.6%–56.5% of the support for COVID-19 measures.Table 4 Linear regression analysis of COVID-19 measures.
Table 4Model 1 2 3
Contact-tracing Restrictive-testing operations Public assembly ban
Health experts' communication (base = control)
-Dissent −0.186 −0.183 0.191
(0.172) (0.158) (0.166)
-Consent 0.0729 −0.0913 0.333*
(0.171) (0.156) (0.166)
Control for COVID-19 experience Yes Yes Yes
Control for political attitudes Yes Yes Yes
Control for demographics Yes Yes Yes
Standard errors in parentheses; ***p < 0.001, **p < 0.01, *p < 0.05.
5.6 Regression for trustworthiness
In Table 5 (full table in Section S3 of Supplementary materials), health-expert–government interaction dummies were found to have effect on health experts' trustworthiness (Model 4). Here, the discord group is used as the base to study whether being assigned the contradiction and consensus groups affects trustworthiness. Participants in the contradiction (coefficient = −0.374, p = 0.019) and consensus groups (coefficient = −0.339, p = 0.010) found experts less trustworthy compared to those in the discord group. The same effect was not found in government's trustworthiness in Model 5. In Model 6, the interaction term between government's trustworthiness and the contradiction and consensus dummies were included as independent variables on health experts' trustworthiness. The higher the trust in government of the participants in the contradiction (coefficient = 0.180, p < 0.001) and consensus groups (coefficient = 0.252, p < 0.001), the higher trust they had in health experts. In Model 7, a new variable, “average support for COVID-19 measures” was introduced, averaging the support rating for the three measures asked. This variable and its interaction terms with the contradiction and consensus dummies were added as independent variables in the model. Higher support for COVID-19 measures correlated with health experts' trustworthiness with statistical significance (coefficient = 0.161, p < 0.001). In the consensus group, participants with higher support for COVID-19 measures showed higher trust in experts (coefficient = 0.145, p = 0.001) as opposed to the other two groups. Across all three models of health experts' trustworthiness, participants who had higher expectations with the government, lower freedom preference, and higher political attentiveness, had higher trust in health experts. Middle and upper income participants had lower trust in health experts compared to those whose income is in the bottom 20 percentile.Table 5 Linear regression analysis of health experts' and government's trustworthiness.
Table 5Model 4 5 6 7
Experts' trustworthiness Government's trustworthiness Experts' trustworthiness Experts' trustworthiness
Health-expert–government interaction (base = discord)
-Contradiction −0.374** −0.0639 −1.081*** −0.674**
(0.159) (0.159) (0.233) (0.342)
-Consensus −0.339*** 0.0136 −1.365*** −1.165***
(0.131) (0.130) (0.194) (0.281)
Gov. Trustworthiness X Contradiction 0.180***
(0.0442)
Gov. Trustworthiness X Consensus 0.252***
(0.0357)
Average support for COVID-19 measures 0.161***
(0.0404)
Average support for COVID-19 measures X Contradiction 0.0507
(0.0552)
Average support for COVID-19 measures X Consensus 0.145***
(0.0453)
Control for COVID-19 experience Yes Yes Yes Yes
Control for political attitudes Yes Yes Yes Yes
Control for demographics Yes Yes Yes Yes
Standard errors in parentheses; ***p < 0.001, **p < 0.01, *p < 0.05.
6 Discussion
During the COVID-19 pandemic, governments worldwide struggled with citizens' compliance with disease containment measures. Moreover, health experts served as the source of authority for governments to take appropriate actions and persuade the public to support COVID-19 measures. This study finds some evidence that health experts' communication helps increase support for ban of public assembly. However, if health experts’ opinions agree with government directives, their trustworthiness may be reduced.
The three measures examined impacted freedom and rights in different dimensions. In the case of contact-tracing, although at the time of survey, the mobility data were only stored in users’ phone as proclaimed by the government, the prolonged use of contact-tracing app—and the institution once set up—could be an intrusion of privacy (Chan and Saqib, 2021; Dowthwaite et al., 2021) — a reason widely cited for reluctance in adoption. Restriction-testing is essentially a limited-scale lockdown plus mandatory testing, which was an intrusion of personal freedom of movement. Ban of public assembly was in violation of civil rights.
This study examined the efficacy of health experts’ communication in garnering public support for COVID-19 measures, including the use of contact-tracing app, restriction-testing, and public assembly ban; overall, the evidence was weak. In the sample, the support for the use of contact-tracing app was the lowest at 5.16/10 among the three measures (Table 3). The low support for digital contact-tracing in Hong Kong was related to the lack of trust in data privacy and efficacy and low trust in the government (Huang et al., 2021; Voo et al., 2021). The support for restriction-testing was the highest at 5.84/10 out of the three measures. No known survey has opinion rating on this measure for comparison. At the time of survey, most restriction-testing were limited to a few buildings and the operations usually began at dawn and ended the next morning. The restriction on personal freedom was little compared to a large-scale lockdown. Additionally, not many people were affected, as it only targeted a small population of Hong Kong. Regarding the support of public assembly ban, the support rate recorded in this study was 5.61/10. A comparable measure compiled by the Hong Kong Public Opinion Research Institute (2022a) on May 31, 2021 showed that 98.1% participants found the gathering ban too strict.
We found that viewing a vignette of a health expert, with a professional title, citing reasons, and urging the public not to attend public assembly can increase support for the public assembly ban when compared with only a government announcement of such a rule and the penalty involved. This is coherent with the finding in the US and the UK that a COVID-19 policy supported by a health expert received greater public support as compared to a policy supported only by lawmakers (Arceneaux et al., 2020). In the study, health experts increased support for measures that help reduce infection, such as mandatory stay-home orders and use of a contact tracing app; however, their opinions on indefinitely postponing elections were ignored. The expert effect on increasing compliance is still doubtful, given mixed evidence in the field. Deslatte (2020) tested on the messenger effect of experts and found no significant difference from the control. In fact, expert actors weaken the effect of a pro-public-health message relative to a federal official. Another study found that experts’ communication can induce participants of certain political stances, but not all, to have higher vaccine intent (Vlasceanu and Coman, 2022).
Overall, this study found weak evidence that experts’ communication can induce support for measures that restrict freedom; a few reasons may explain it. First, as the participants only briefly read the vignettes, it might not induce an effect strong enough to be detected. Second, there might be pre-exposure effect. In the world of information overload, participants may have already read similar communications. Third, support for different measures could be context-specific, which differs across countries and time of survey.
The second part of this study examines whether experts agreeing and disagreeing with government measures affects experts' trustworthiness. Participants who read only experts agreeing with government's directives (consensus group), and who read experts sometimes agreeing and sometimes disagreeing (contradiction group), had lower trust in experts compared to those reading only disagreements (discord group); the effect was statistically significant. A similar effect of lowering trust was not found toward government's trustworthiness.
Despite recent distrust in experts and the refrain: “had enough of experts,” (Clarke and Newman, 2017) the uncertainty and high stakes around COVID-19 helped experts gain a central role in pandemic management (Radaelli, 1999). Experts were called upon to offer scientific advice, formulate strategies, and communicate with the public, and earned prominent roles based on two main reasons: their epistemic authority in a field that is based on scientific reasoning and empirical support; and their independence. They are trusted as virtuous and personally disinterested individuals, implying no personal stakes or desire for immediate rewards. Governments rely on experts because: (1) they are likely to provide efficacious solutions; and (2) help curb controversy (Lavazza and Farina, 2020). By involving them in finding solutions, or simply by consulting them, governments can delegate—or appear to delegate—power to experts, thereby reducing or negating social conflicts (Flinders and Dimova, 2020).
Possible explanations are provided for decrease in health experts' trustworthiness when they agreed with government's COVID-19 policy. First, health experts had agreed with a low-trustworthiness entity. Several studies in Hong Kong indicated that the Hong Kong government had low trust (Ho et al., 2021; Huang et al., 2021; Yuen et al., 2021). In this sample, it had an overall trustworthiness of 4.04/10, which was much lower than that of experts (6.46). Agreeing with a low-trust entity may have a spillover effect on experts themselves. The result of Model 6 is consistent with this argument. It shows that for participants with higher trust in the government, when they were presented with experts' views that sometimes agreed with the government's, they had higher trust in the experts; when presented with experts' views that unilaterally agreed with the government, the trust in experts was even higher. It is noteworthy that the signs of these effects are opposite to the unconditional (negative) effects of reading expert views that are sometimes or always in agreement with the government's—the main result of Model 4. A similar study in Hong Kong found that medical experts bearing a government title could not induce more trust in medical experts themselves; a source critical of the government could enhance the credibility of official government messages (Sheen et al., 2021).
The second explanation is that by agreeing with government directives, health experts shoulder the responsibility for these public decisions, and if support is low for these COVID-19 measures, health experts also take the blame. Normally, policymakers—that is, politicians and bureaucrats—bear the responsibility of consequences in public decision. Technocrats prevail at situations of high uncertainty and when information and ideas are of high cost, such as the COVID-19. High salience of COVID-19 also leads to politicization of the issue (Haas, 1992). Health experts who performed prominent roles in COVID-19 management have to bear responsibility for the decisions taken. The British case of preferring herd immunity and the US policy of excluding disabled people from medical care during COVID-19 are examples (Lavazza and Farina, 2020). In these cases, health experts were no longer providing technical solutions limited to their area of expertise. The public, therefore, held them at least partially responsible for the consequences of these measures. Even though in a few countries the final decision largely rested with government officials, the act of concurring with the government resulted in sharing the responsibility. This is coherent with the blame defection argument for bringing in health experts (Flinders and Dimova, 2020), —regardless of whether blame defection is intentional or not.
The third explanation is that the disagreement signals independence. Health experts can signal independence by publicly disagreeing with the government public health measures, but not publicly agreeing with public health measures, even if they could still be independent in the latter case. However, this independence explanation is not distinguishable from the first (trust spillover) explanation in this single-case study of Hong Kong. But they can be separated in a state in which the government has high trust, then agreeing with the government would induce higher expert trustworthiness according to the ‘trust spillover’ explanation, but lower expert trustworthiness according to the ‘independence’ explanation.
In this survey, participants exposed to vignettes of health experts agreeing with government directives might have prompted them to think that experts were endorsing or supporting the decision, and the experts, therefore, shared the responsibility. The average support rate for the three COVID-19 measures, and its interaction terms with the contradiction and consensus dummies, were added to Model 7. Support for COVID-19 policies was statistically significant and associated with experts’ trustworthiness. Participants in the consensus group showing stronger support had higher trust in experts as opposed to those in the same group showing weaker support. The same interaction effect with support for COVID-19 measures was not found in the discord and contradicting groups; in these groups, discord between health experts and government directives might have relieved the experts from sharing the responsibility.
This study has several limitations. First, the result could be affected by the use of real-world vignettes, which have the advantage of social–ecological validity to help inform potential solutions to real-world problems (McDonald, 2020; Reis et al., 2000). However, their use also prevents the isolation of the exact factor in a health expert that induces (or fails to induce) trust and the associated causal mechanism—we leave this question for future research. A related limitation is that using a real-world vignette might have pre-treatment exposure effect, which may affect the results. However, given the salience and time-span of the pandemic, participants would have likely read ample COVID-19 information and recommendations anyway. This study partially captured the pre-treatment exposure by including variables such as knowledge of COVID-19 and COVID-19 experiences.
Additionally, participants were randomly allocated to experiment groups. Thus, there is no compelling reason to believe that participants assigned to different groups had different pre-treatment effects. Further studies could examine whether the effect of lowering expert trustworthiness could be generalized to other countries and the causal link or reasons behind lower trust in experts for concurring with the government.
7 Conclusion
This study finds limited evidence of the efficacy of using health experts to induce support for COVID-19 measures. However, health experts agreeing with government's COVID-19 directives could reduce health experts' trustworthiness, compared to health experts disagreeing with the government.
Governments involved health experts in policymaking for pandemic management. As it became a moral mission for experts to motivate public compliance for public good, they publicly supported government directives. However, evidence regarding the efficacy of such communication strategy in inducing compliance is mixed. The worse is providing recommendations concurring with government's directives to encourage compliance as that could reduce health experts' trustworthiness.
The implication of the findings from this study is profound. While the expected effect on compliance is not substantiated, what the health experts did could hurt their trustworthiness. Many studies have reported lower trust in science and scientists after epidemics (Eichengreen et al., 2021; Feufel et al., 2010). The repeated use of health experts for dissemination for information could be part of the reason. Trust in health experts, medical organizations, and health authorities has been widely cited as an important factor that correlates with citizens’ compliance during epidemics (Gilles et al., 2011; Siegrist and Zingg, 2014). Low trust in health experts and scientists could undermine the effectiveness of risk communication in the future.
Funding
The work was supported by the Hong Kong Institute of Economics and Business Strategy, 10.13039/501100003803 University of Hong Kong .
Ethical issues
The study was approved by the Human Research Ethics Committee of the University of Hong Kong.
Author contribution
Vera Wing Han Yuen is the sole author of the manuscript.
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
The following is the Supplementary data to this article:Multimedia component 1
Multimedia component 1
Data availability
The data that has been used is confidential.
Acknowledgements
I would like to thank all the participants for their contributions and the anonymous reviewers for helpful feedback. I express my gratitude to the Hong Kong Institute of Economics and Business Strategy of the 10.13039/501100003803 University of Hong Kong for supporting the research.
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.socscimed.2022.115602.
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| 36502713 | PMC9722236 | NO-CC CODE | 2022-12-08 23:16:24 | no | Soc Sci Med. 2023 Jan 5; 317:115602 | utf-8 | Soc Sci Med | 2,022 | 10.1016/j.socscimed.2022.115602 | oa_other |
==== Front
Ann Vasc Surg
Ann Vasc Surg
Annals of Vascular Surgery
0890-5096
1615-5947
Elsevier Inc.
S0890-5096(22)00740-3
10.1016/j.avsg.2022.11.001
Article
A Comparative Analysis of Critical Limb Ischemia in the Intensive Care Unit since the COVID-19 Pandemic
Malkoc Aldin MD a∗
GnanaDev Raja MD a
Botea Lev MD b
Jeney Ashtin MD a
Glover Keith MD ab
Retamozo Milton MD a
GnanaDev Dev MD ab
Schwartz Samuel MD ab
a Arrowhead Regional Medical Center, 400 N Pepper Ave, Colton, CA, 92324, USA. Department of Surgery
b California University of Science and Medicine, 1501 Violet Street, Colton, CA, 92324, USA
∗ Corresponding Author: Aldin Malkoc, Arrowhead Regional Medical Center, 400 N Pepper Ave, Colton, CA, 92324, USA
5 12 2022
5 12 2022
8 8 2022
12 10 2022
4 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.
Objective
Emerging data and case reports have found coagulation abnormalities and thrombosis as sequelae of infection with SARS-CoV-2 (COVID-19). Case reports have reported thrombotic complications caused by COVID-19-related coagulopathy leading to limb loss. Alarmingly, many of these patients had no underlying vascular disease prior to being infected with COVID-19. Many of these case reports discuss patients developing gangrene in the intensive care unit (ICU). Our study compares the incidence of gangrene in the ICU in COVID-19 patients to baseline inpatient levels prior to the pandemic.
Methods
This retrospective analysis investigates two subsets of patients from a single institution. The first was from 2020 during the COVID-19 pandemic; the second subset was from 2019 before the pandemic. Demographic data and medication history were ascertained for both groups. Primary outcomes measures included extremity gangrene that developed in the ICU, mortality, and major amputation.
Results
There were 249 COVID-19 positive patients admitted to the ICU in 2020. In 2019, 1846 admissions to the ICU took place, of which 249 patients were randomized to chart review. There were 13 cases of gangrene that developed in the ICU, 12 of which took place in 2020. In hospital mortality was 11.6% in non-COVID patients in 2019 versus 41.4% in 2021 (p < 0.001). Only 16.7% of the COVID-19 gangrene patients had previously known arterial disease. Also, patients in the COVID-19 group with gangrene were four times more likely to be smokers (p=0.004). When the data was stratified to compare between gangrene development and no gangrene development, the combined total gangrene group had longer hospital stays, higher need for blood transfusions, required major amputations, and revascularization. A multivariate logistic regression from the total study same demonstrated COVID-19 infection is associated with an 18.23 times increased risk of gangrene.
Conclusion
COVID-19 has resulted in an incomprehensible societal impact that will linger for years to come. The last two years have reinforced that COVID-19 will be a part of our clinical practice indefinitely. This study emphasizes the importance of clinician awareness of COVID-19 induced critical limb ischemia in those without underlying arterial disease and few medical comorbidities. More research efforts toward preventing limb loss and COVID-19 coagulopathy must be performed expeditiously to achieve a better understanding.
Keywords
COVID-19
Gangrene
Acute Limb Ischemia
Intensive Care Unit
==== Body
pmcIntroduction
The SARS-CoV-2 (COVID-19) infection has led to rather unexpected complications. While many individuals do not develop significant complications, those with moderate to severe cases of COVID-19 infection have been known to suffer atypical acute respiratory distress syndrome with maintained compliance, multi-organ failure as a result of sepsis, acute kidney injury, and strokes as a result of coagulation abnormalities.1 Multiple case reports have shown serious coagulation abnormalities and thrombosis as a sequelae of severe infection COVID-19.2, 3, 4 Upwards of 55% of patients with COVID-19 can develop mild to severe coagulation abnormalities, with more severe abnormalities leading to a higher incidence of mortality.4 In many cases of COVID-19 coagulopathy, there is development of both arterial and venous thrombotic events.5 Currently, there is no consensus on the pathophysiology behind the COVID-19 coagulopathy, leaving variation in the management of these patients.6
Some have hypothesized that the pathophysiology is similar to coagulopathic mechanisms found in severe systemic inflammatory response and disseminated intravascular infections.2 , 5 , 7 Multiple case reports during the pandemic have also reported acute limb ischemic as a complication in this coagulopathy.8, 9, 10 Alarmingly, many of these patients had no underlying vascular disease prior to getting infected with COVID-19. Bellosta et al. reported an increased incidence of acute limb ischemic increased during the COVID-19 pandemic in a twenty patient cohort. Limb salvage is complicated by the inherent coagulopathy, but therapeutic intravenous heparin postoperatively improves outcomes.6 Incidentally, many of the case reports have illustrated patients developing gangrene during ICU admission early, within four to six weeks.11, 12, 13 The purpose of this study is to compare the incidence of gangrene in the ICU associated the limb loss – in COVID patients specifically – to baseline inpatient levels before the pandemic. Secondary outcomes report favorable and unfavorable predictors associated with ICU limb gangrene.
Methods
A retrospective observational case-control review of patients with COVID-19 admitted to intensive care over a 2-year period at a single community high-capacity Level 1 trauma center was performed to assess for gangrene development during their hospitalization. Arrowhead Regional Medical Center (ARMC) is a Level 1 trauma center, the county facility for San Bernardino County in California, and a teaching hospital seeing more than 120,000 patients in the emergency department annually. For this study, 498 patients were identified with 249 being COVID-19 positive patients and 249 COVID-19 negative patients. Patients in the year 2019 were considered as Pre-COVID-19 and all COVID-19 diagnosed patients admitted to the ICU are sampled from the year 2020. All patients with a diagnosis of COVID-19 were gathered and using a randomization generator a sample of 249 was considered. Similarly, we randomly sampled 249 patients from all ICU admissions in the year 2019 that were admitted to the ICU. All patients (aged 18 and above) admitted to the intensive care unit between January 2019 to December 2020 were included and screened for the development of gangrene. Data gathering and analysis was conducted according to the ARMC Institutional Review Board.
The retrospective chart review was performed for all patients’ records and included admission records, inpatient charts, and emergency department records. Infection with COVID-19 was confirmed in all patients with a nasal PCR analysis. The data collected included demographics, comorbidities, medication use, mortality, gangrene development in extremity digits, and therapeutic anticoagulation use. Medication use was focused on hematologic medications such as aspirin, clopidogrel, other antiplatelet agents, coumadin, direct oral anticoagulants, and statins. The rates of hospital acquired gangrene was gathered for ICU patients in 2019 (pre-COVID); this served as the control. Anticoagulation administration in the COVID-19 group consisted of an unfractured Heparin continues intravenous administration with a partial thromboplastin time goal of 90-120 seconds. Additional outcome measures such as major limb loss (below knee amputation or above knee amputation), revascularization in form of open surgical embolectomy and percutaneous thrombectomy and/or thrombolysis, and pre-hospital vascular status (e.g., whether the patient had underlying “peripheral arterial disease” to start) were investigated, and history of hypercoagulable state were both acquired and genetic.
The data was collected using Excel and analyzed using descriptive statistics and univariate analysis. Statistical analysis was performed using the Statistical Package for the Social Sciences (SPSS) version 27.0 (SPSS Inc, Chicago, IL) software. Univariate analyses were performed using Chi-Squared for categorical data. Continuous data was analyzed using non-parametric Mann-Whitney U tests. Continuous data were presented according to the medians with interquartile ranges (IQR), and categorical data are presented with percentages. A binary logistic regression analysis was performed to determine if the odds of gangrene are impacted by COVID-19 while adjusting for other risk factors. Unless otherwise indicated, a p-value less than 0.05 was considered to be statistically significant.
Results
249 patients were identified as having COVID-19 requiring intensive care admission at ARMC in 2020. In 2019, 249 patients were identified as controls pre COVID-19. From the COVID-19 group, 12 patients were found to have developed gangrene, and only 1 patient developed gangrene in the non-COVID-19 group (Figure 1 ).Figure 1 Consort Diagram detailing the patient selection in the COVID-19 positive group and COVID-19 negative group
In the study the median age Pre-COVID was 60 (IQR 48-73) and the median age of patients that were diagnosed with COVID-19 and admitted to the ICU was 60 (IQR 49-69) (P = 0.643). The percentage of male patients in the Pre-COVID group was 54.6% and the percentage of males in the COVID-19 group was 58.2% (P = 0.416). Compared to non-COVID-19 patients, patients with COVID-19 had multiple significant comorbidities. Notably, COVID-19 patients compared to Pre-COVID patients had a higher body mass index (30.8 (IQR 27, 36) vs 27.5 (IQR 23.5, 32.2), P <0.001), higher percentage of diabetes mellitus (56.6%, 36.9%, P = 0.001), lower percentage of hypertension (48.9%, 59%, P = 0.025), lower percentage of cerebrovascular disease (5.2%, 34.5%, P <0.001), higher percentage of arterial disease (20.8%, 5.2%, P<0.001), higher percentage of having a hypercoagulable state (16%, 2.8%), and lower percentage of having a smoking history (9.6%, 37.3%, P<0.001). The reminder of the patient demographics are outlined in Table 1 .Table 1 Patient Characteristics between pre-COVID ICU patients and COVID-19 ICU patients. Categorical variables are presented as total number with percentages; non-normally distributed continuous variables are expressed as mean with IQR. Significant differences are bolded.
Pre-Covid 2019 N = 249 Covid-19 N = 249 p-value
Demographics
aAge 60 (48, 73) 60 (49, 69) 0.643
Male (%) 136 (54.6%) 145 (58.2%) 0.416
aBody Mass Index 27.5 (23.5, 32.2) 30.8 (27, 36) <0.001
Ethnicity - - <0.001
White 73 14 -
African American 38 11 -
Hispanic 130 201 -
Asian 5 21 -
Native American 3 2 -
Comorbid Conditions
Diabetes Mellitus (%) 92 (36.9%) 141 (56.6%) <0.001
Hypertension (%) 147 (59%) 122 (48.9%) 0.025
Hyperlipidemia (%) 67 (26.9%) 75 (30.1%) 0.427
Chronic Kidney Disease (%) 32 (12.9%) 37 (14.9%) 0.517
End Stage Renal Disease (%) 18 (7.2%) 19 (7.6%) 0.864
Congestive Heart Failure (%) 26 (10.4%) 30 (12%) 0.570
Coronary Artery Disease (%) 41 (16.5%) 58 (23.3%) 0.056
Chronic Obstructive Pulmonary Disease (%) 16 (6.4%) 21 (8.4%) 0.568
Asthma (%) 10 (4%) 10 (4%) 1.000
Cerebrovascular Disease (%) 86 (34.5%) 13 (5.2%) <0.001
Cirrhosis (%) 9 (3.6%) 5 (2%) 0.278
Dementia (%) 9 (3.6%) 4 (1.6%) 0.160
Arterial Disease (%) 13 (5.2%) 52 (20.8%) <0.001
Venous Disease (%) 8 (3.2%) 4 (1.6%) 0.242
Hypercoagulable State (%) 7 (2.8%) 40 (16%) <0.001
Smoking (%) 93 (37.3%) 24 (9.6%) <0.001
Medication Use
Aspirin (%) 75 (30%) 71 (28.5%) 0.694
Clopidogrel (%) 31 (12.4%) 19 (7.6%) 0.074
Other Antiplatelet (%) 2 (0.8%) 0 0.156
Coumadin (%) 4 (1.6%) 8 (3.2%) 0.242
Direct Oral Anticoagulant (%) 17 (6.8%) 12 (4.8%) 0.339
Statin (%) 86 (34.5%) 62 (24.9%) 0.019
a analyzed with non-parametric Mann Whitney U Test. All other comparisons with Chi-square test.
Patient outcomes and hospital anticoagulation information between pre-COVID-19 patients and COVID-19 patients can be found in Table 2 . We can find that patients diagnosed with COVID-19 had a statistically significant longer (P<0.001) median hospitalization of 9 days (IQR 4,14) compared with Pre-COVID median hospitalization of 3 days (IQR 2,6). In the COVID-19 population there were 4 patients that developed extremity gangrene in the left lower extremity, 3 patients that developed gangrene in the right lower extremity, 5 patients that developed gangrene in bilateral lower extremities, and 2 patients that developed extremity digit gangrene in the upper extremity compared to 1 patient in the Pre-COVID population (P = 0.002). The percentage mortality in the COVID population was 41.4% compared to 11.6% in the Pre-COVID population (P<0.001). Overall, patients with COVID-19 had longer hospitalization stays, more frequently developed lower extremity gangrene, had increased mortality, and had an increased need for major amputations and revascularization (open surgical embolectomy, percutaneous thrombectomy and/or thrombolysis) surgeries.Table 2 Patient Outcomes between Non-COVID-19 ICU patients and COVID-19 ICU patients. Categorical variables are presented as total number with percentages; Non-normally distributed continuous variables are expressed as median with IQR. Significant differences are bolded.
Pre-Covid 2019 N = 249 Covid-19 N = 249 p-value
Patient Outcomes
aHospital Length of Stay 3 (2, 6) 9 (4,14) <0.001
Blood Transfusion (%) 40 (16%) 56 (22.5%) 0.069
Gangrene (%) 1 (0.4%) 12 (4.8%) 0.002
Gangrene Lower (%) - - 0.017
Gangrene Lower - None 248 237 -
Gangrene Lower – Left Lower Extremity 0 4 -
Gangrene Lower – Right Lower Extremity 1 3 -
Gangrene Lower – Bilateral Lower Extremities 0 5 -
Gangrene Upper (%) 0 2 0.249
Mortality (%) 29 (11.6%) 103 (41.4%) <0.001
Major Amputation (%) 0 4 (1.6%) 0.045
Minor Amputation (%) 0 0 0
Revascularization (%) 2 (0.8%) 5 (2%) 0.253
Hospital Anticoagulation
Timing of Anticoagulation - - 0.212
Not Started (%) 228 (91.6%) 227 (91.2%) -
Admission (%) 21 19 -
Hospital Day two and Onwards (%) 0 3 -
Contraindications to Anticoagulation (%) 8 (3.2%) 12 (4.8%) 0.361
a analyzed with non-parametric Mann Whitney U Test. All other comparisons with Chi-square test.
Table 3 demonstrates the difference in patients that developed gangrene in the COVID-19 only group and, separately, the combined control and COVID-19 group. Within the cohort, 485 patients did not develop gangrene and 13 developed lower extremity gangrene. Body mass index was higher in the gangrene group, as was the prevalence of COVID-19. There was a statistically significant increased number of individuals with diabetes mellitus, hyperlipidemia, chronic obstructive pulmonary disease, and dementia. Patients with gangrene, regardless of COVID 19 status, had longer hospital stays, increased need for blood transfusions, requirement for major amputations, and need for revascularization. Similarly, the COVID-19 cohort, without gangrene, was significant for more diabetes mellitus, hyperlipidemia, chronic obstructive pulmonary disease, dementia, and smoking. Additionally, there were longer hospital stays, a greater requirement for major amputations, and revascularization in the COVID-19 gangrene group.Table 3 Patient Development of Gangrene. Categorical variables are presented as total number with percentages; non-normally distributed continuous variables are expressed as median with IQR. Significant differences are bolded.
COVID-19 Total p-value (Covid-19) p-value (Total)
No-Gangrene N=237 Gangrene N=12 No-Gangrene N=485 Gangrene N=13
Demographics
Age 60 (19.5) 59.5 (14) 60 (23) 60 (15.5) 0.689 0.918
Male (%) 137 (57.8%) 8 (66.7%) 273 (54.8%) 8 (61.5%) 0.706
Body Mass Index 30 (9.15) 33 (10.48) 28.7 (8.7) 35 (8.95) 0.144 0.013
Ethnicity - - - - 0.722 0.585
White 13 1 85 2 - -
African American 11 0 49 0 - -
Hispanic 190 11 320 11 - -
Asian 21 0 26 0 - -
Native American 2 0 5 0
Comorbid Conditions
COVID-19 (%) - - 237 (48.9%) 12 (92.3%) - 0.002
Diabetes Mellitus (%) 130 (54.8%) 11 (91.7%) 221 (45.5%) 12 (92.3%) 0.012 <0.001
Hypertension (%) 114 (48%) 8 (66.7%) 260 (53.6%) 9 (69.2%) 0.209 0.265
Hyperlipidemia 67 (28.3%) 8 (66.7%) 133 (27.4%) 9 (69.2%) 0.005 <0.001
Chronic Kidney Disease 34 (14%) 3 (25%) 65 (13.4%) 4 (30.7%) 0.311 0.074
End Stage Renal 17 (7.2%) 2 (16.7%) 35 (7.2%) 2 (15.4%) 0.227 0.268
Disease
Congestive Heart 27 (11.4%) 3 (25%) 53 (11%) 3 (23%) 0.158 0.171
Failure
Coronary Artery 54 (24.1%) 4 (34%) 94 (19.4%) 5 (38%) 0.399 0.089
Disease
Chronic Obstructive 18 (7.6%) 3 (25%) 34 (7.8%) 3 (23%) 0.034 0.029
Pulmonary Disease
Asthma 9 (3.7) 1 (8.3%) 19 (3.9%) 1 (7.7%) 0.435 0.494
Cerebrovascular 11 (4.6%) 2 (16.7%) 96 (19.8%) 3 (23%) 0.068 0.770
Disease
Cirrhosis 5 (2.1%) 0 14 (2.9%) 0 0.611 0.534
Dementia 2 (0.8%) 2 (16.7%) 11 (2.3%) 2 (15%) <0.001 0.003
Arterial Disease 50 (21.1%) 2 (16.7%) 62 (12.8%) 3 (23%) 0.713 0.277
Venous Disease 4 (1.7%) 0 12 (2.5%) 0 0.650 0.566
Hypercoagulable State 39 (16.5%) 1 (8.3%) 46 (9.5%) 1 (7.7%) 0.455 0.827
Smoking 20 (8.4%) 4 (34%) 112 (23.1%) 5 (38.5%) 0.004 0.197
Medication Use
Aspirin 69 (29%) 2 (16.7%) 143 (29.5%) 3 (23.1%) 0.351 0.616
Clopidogrel 19 (8%) 0 50 (10.3%) 0 0.307 0.222
Other Antiplatelet - - 2 (0.4%) 0 - 0.817
Coumadin 8 (3.34%) 0 12 (2.5%) 0 0.518 0.566
Direct Oral 11 (4.6%) 1 (8.3%) 28 (5.8%) 1 (7.7%) 0.560 0.771
Anticoagulant
Statin 59 (25%) 3 (25%) 145 (30%) 3 (23.1%) 0.993 0.595
Patient Outcomes
Hospital Length of Stay 8 (9) 22.5 (27) 5 (82) 20 (65) <0.001 <0.001
Blood Transfusion (%) 51 (22%) 5 (42%) 90 (18.6%) 6 (46.2%) 0.103 0.013
Mortality (%) 98 (41.2%) 5 (42%) 127 (26.2%) 5 (38.5%) 0.983 0.322
Major Amputation (%) 0 4 (34%) 0 4 (31%) <0.001 <0.001
Minor Amputation (%) - - - - - -
Revascularization (%) 3 (1.2%) 2 (16.7%) 5 (1%) 2 (15.4%) <0.001 <0.001
Hospital Anticoagulation
Therapeutic 28 (12%) 7 (58%) 49 (10%) 7 (54%) <0.001 <0.001
Anticoagulation Started
(%)
Timing of - - - - <0.001 <0.001
Anticoagulation
Not Started 221 (93.2%) 6 (50%) 448 (92.3%) 7 (54%) -
Admission 16 (6.8%) 3 (25%) 37 (7.6%) 3 (23.1%) -
Hospital Day 2 0 3 (25%) 0 3 (23.1%) -
Contraindications (%) 7 (3%) 5 (42%) 15 (3.1%) 5 (38.5%) <0.001 <0.001
aanalyzed with non-parametric Mann Whitney U Test. All other comparisons with Chi-square test.
To determine whether COVID-19 infection affects the development of gangrene, we analyzed the data utilizing binary logistic regression. Logistic regression was performed for the total set of patients (N = 498) and the COVID-19 only patients (N=249). In the COVID-19 only group, patients with gangrene were more likely to smoke and experienced longer hospital stays. In the total patient population, those patients with gangrene were more likely to have COVID-19 infection, smoke, and experienced increased hospital length of stay. A multivariate logistic regression from the total patient population demonstrated that COVID-19 infection is associated with an 18.23 times increased risk of gangrene after adjusting for body mass index, diabetes mellitus, hyperlipidemia, smoking, length of hospital stay, therapeutic anticoagulation initiation, timing of anticoagulation, history of arterial disease, and history of hypercoagulable state (Table 4 )Table 4 Logistic Regression predicting patient development of gangrene for COVID-19 only patients and COVID-19 and non-COVID-19 patients. Significant values are bolded.
COVID-19 Only COVID-19 Only Total Patients Total Patients
Odds Ratio 95% Confidence Interval Pc Odds Ratio 95% Confidence Interval Pc
N = 249 N = 249 N = 249 N = 249
Body Mass 0.970 0.877-1.073 0.558 0.966 0.879-1.061 0.466
Index
COVID-19 - - - 18.237 1.187-280.2 0.037
Diabetes Mellitus (%) 13.3 0.677-260.993 0.088 13.214 0.752-232.27 0.078
Hyperlipidemia 3.775 0.488-29.21 0.203 6.088 0.863-42.97 0.070
Smoking 22.51 2.021-250.73 0.011 35.40 3.164-396.05 0.004
Hospital Length of Stay 0.897 0.847-0.950 <0.001 0.893 0.841-0.950 <0.001
Therapeutic Anticoagulation Started (%) 1.259 0.081-19.572 0.869 0.772 0.046-13.06 0.858
Timing of Anticoagulation 0.082 0.006-1.119 0.061 0.095 0.008-1.136 0.063
Arterial Disease 0.321 0.022-4.577 0.402 1.191 0.143-9.909 0.871
Hypercoagulable State 0.086 0.001-36.585 0.426 0.041 0.001-4.126 0.175
aTotal cases analyzed = 249. A Hosmer-Lemeshow test indicated that model fit was good (chi-squarewith 8 df = 6.077, p = 0.639).
aTotal cases analyzed = 489. A Hosmer-Lemeshow test indicated that model fit was good (chi-squarewith 8 df = 3.771, p = 0.877).
Discussion
COVID-19 infection produces a wide range of clinical symptoms from mild to severe cases. Mild symptoms can include fever, myalgia, cough, dyspnea, diarrhea, and nausea. More severe symptoms include multi organ dysfunction, acute respiratory distress syndrome, and thrombosis.14 Additionally, it has been noted that COVID-19 leads to more severe infection in patients with advanced age and multiple medical comorbidities.6 From our results in Table 1, we found that patients with COVID-19 admitted to the ICU had a statistically higher BMI and larger percentage of comorbidities such as diabetes mellitus, hypertension, cerebrovascular disease, arterial disease, hypercoagulable state, and smoking. This is mirrors results found in other published reports, which found similar significant medical comorbidities in patients infected with more severe cases of COVID-19.14 While there have been clinical case reports that have showed an association between COVID-19 and acute limb ischemia resulting in limb gangrene, the exact pathophysiology and specific cause has been difficult to determine.
The hypercoagulable state as a result of the cytokine storm with severe cases of infection has been debated.6 , 8 , 9 , 15 Regardless of the etiology, published data demonstrates increased thromboembolic events in patients, including both acute limb ischemia and venous thromboembolism.15 Tang et al determined that over 74% of patients who died with a diagnosis of COVID-19 also suffered a disseminated intravascular coagulopathy,16 Additionally, Cui et al showed in a study of 81 COVID-19 patients in the ICU that the incidence of venous thromboembolism was 25% and was related to a higher rate of mortality.17 A study by Klok et al also found that there was a large percentage of patients with venous thromboembolisms and, out of 184 analyzed patients, 3.7% had arterial thrombotic events that lead to limb gangrene,18 We found that there was a 4.8% incidence of gangrene from arterial thrombotic events in COVID-19 patients admitted to the intensive care unit. Table 3 highlights the difference in patients that developed gangrene in the COVID-19 group and, separately, the total combined control and COVID-19 group. In the total group, there were 485 patients that did not develop gangrene compared to 13 that did. Like other previously published studies, the combined total risk factors that predisposed patients to the development of gangrene included elevated BMI, diabetes mellitus, hyperlipemia, chronic obstructive pulmonary disease, smoking, and COVID-19 infection. Interestingly, we also noted that patients who developed gangrene in the COVID-19 or combined total group had a history of arterial, venous, or hypercoagulable pathologies was statistically similar and nonsignificant. This suggests that a history of vascular pathologies does not lead to extremity gangrene when infected with COVID-19. Acute limb ischemia resulting in gangrene can have drastic implications and result in severe disability in patients. While a prompt diagnosis is needed for successful treatment, determining potential causes of gangrene is similarly important.
When we analyzed our patient population for clinical outcomes, we noted several factors including longer hospital stay, need for major amputations, and revascularization. We found our median hospital stay in the group that developed gangrene to be 15 days longer. Additionally, the need for major amputation was 31% greater and revascularization was 14% greater. Unlike other studies, we noted that the start of anticoagulation prior to the development of gangrene was not protective6. Over 50% of patients that developed gangrene had started therapeutic anticoagulation. The American Society of Hematology has recommended that all patients with COVID-19 receive some form of pharmacologic thromboprophylaxis and, unless contraindicated, full therapeutic intensity anticoagulation,19 Our data and patient population suggest that starting anticoagulation and the timing of anticoagulation was not protective in patients that developed extremity gangrene. Patients on anticoagulation without a history of arterial and venous pathologies can develop acute limb ischemia and gangrene from the intense infectious burden of COVID-19.
When we addressed the relationship between causes of gangrene and COVID-19 infection, we demonstrated an 18.2 times increased risk of gangrene when adjusting for body mass index, diabetes mellitus, hyperlipidemia, smoking, length of hospital stay, therapeutic anticoagulation initiation, timing of anticoagulation, history of arterial disease, and history of hypercoagulable state. This suggests that regardless of medical and vascular comorbidities during a COVID-19 infection there is a strong associated risk of extremity gangrene. Additionally, it was noted that smoking also carried a 35.4 times increased risk of gangrene. A combination of COVID-19 and history of smoking can explain the increased disease burden and increased hospital length of stay requiring major amputations and revascularization of the femoropopliteal arterial level.
Our current study has several limitations. First, as a retrospective review of data from a single institution, it is possible that our findings may represent a cohort of patients unique to a particular region. We focused on outcomes such as gangrene and considered patients risk factors. Unlike other studies that measured patients d-dimmer levels and correlated it with COVID-19 disease severity, this study is unable to determine whether there were any secondary causes leading to extremity gangrene. Additionally, the hypercoagulable panel included acquired and genetic causes. Our study did not differentiate between the two causes, but this could be addressed in a follow up study. Additionally, we did not account for the degree of vasopressor requirement in these critically ill patients. Regardless, we feel that these limitations are relatively minor and likely do not change the overall findings presented in this study.
Conclusion
COVID-19 has resulted in an incomprehensible societal impact that will linger for years to come. Our study has shown that COVID-19 infection is associated with extremity gangrene development in the absence of arterial and venous medical history. We noted in patients with gangrene there was a strong association with BMI, diabetes mellitus, hyperlipidemia, chronic obstructive pulmonary disease, and smoking. It is imperative providers at every level are aware of the vascular complications associated with COVID-19 infection.
Declaration of Conflicting Interest
The authors declare there is no conflict of interest.
Funding
The research presented in this manuscript had no specific funding from any agency in the public, commercial or not-for-profit sectors.
Author Contributions
Aldin Malkoc assisted with drafting, editing, and data analysis of the manuscript. Raja GnanaDev assisted with data collection, review, and editing of the manuscript. Lev Botea assisted with data collection and writing of the initial manuscript. Ashtin Jeney assisted with data collection and writing of the initial manuscript. Keith Glover assisted with patient care, review and editing of the manuscript. Milton Retamozo assisted with patient care, review and editing of the manuscript. Dev GnanaDev assisted with patient care, review and editing of the manuscript. Samuel Schwartz assisted with patient care, review, data collection, writing, and editing of the manuscript.
Submission Type: Retrospective Review
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References
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2 Galanis N. Stavraka C. Agathangelidis F. Petsatodis E. Giankoulof C. Givissis P. Coagulopathy in COVID-19 infection: a case of acute upper limb ischemia. J Surg Case Rep Oxford University Press 2020 6 2020 rjaa204
3 Kipshidze N. Dangas G. White C.J. Kipshidze N. Siddiqui F. Lattimer C.R. Carter C.A. Fareed J. Viral coagulopathy in patients with COVID-19: treatment and care Clin Appl Thromb. SAGE Publications Sage CA: Los Angeles, CA 26 2020 1076029620936776
4 Lee S.G. Fralick M. Sholzberg M. Coagulopathy associated with COVID-19. CMAJ Can Med Assoc 192 21 2020 E583–E583
5 Oxley T.J. Mocco J. Majidi S. Kellner C.P. Shoirah H. Singh I.P. De Leacy R.A. Shigematsu T. Ladner T.R. Yaeger K.A. Large-vessel stroke as a presenting feature of Covid-19 in the young N Engl J Med. Mass Medical Soc 382 20 2020 e60
6 Bellosta R. Luzzani L. Natalini G. Pegorer M.A. Attisani L. Cossu L.G. Ferrandina C. Fossati A. Conti E. Bush R.L. Acute limb ischemia in patients with COVID-19 pneumonia J Vasc Surg 72 6 2020 1864 1872 Elsevier 32360679
7 Bikdeli B. Anticoagulation in COVID-19: Randomized trials should set the balance between excitement and evidence Thromb Res. Elsevier 196 2020 638 640
8 Ramachandran R. Pillai A.V. Raja S. Sailesh S. Axillary artery thrombosis resulting in upper limb amputation as a COVID-19 sequela. BMJ Case Rep CP BMJ Specialist Journals 14 1 2021 e240981
9 Hanif M. Ali M.J. Haider M.A. Naz S. Ahmad Z. Acute Upper Limb Ischemia Due To Arterial Thrombosis in a Mild COVID-19 Patient: A Case Report Cureus. Cureus Inc 12 9 2020
10 Bamgboje A. Hong J. Mushiyev S. Pekler G. A 61-Year-Old Man with SARS-CoV-2 Infection and Venous Thrombosis Presenting with Painful Swelling and Gangrene of the Lower Limb Consistent with Phlegmasia Cerulea Dolens Am J Case Rep. International Scientific Information, Inc 21 2020 e928342-1
11 Kasinathan G, Sathar J. Haematological manifestations, mechanisms of thrombosis and anti-coagulation in COVID-19 disease: A review. Ann Med Surg. Elsevier; 2020;
12 Sung J. Anjum S. Coronavirus Disease 2019 (COVID-19) infection associated with antiphospholipid antibodies and four-extremity deep vein thrombosis in a previously healthy female Cureus. Cureus Inc 12 6 2020
13 Chowdhury Y.S. Mitre C.A. Rotella V.E. Garg K. Lee D.K. Belligund P. Chen L. Madaj P. Aboushi H.A. Al-Ajam M.R. Extensive Peripheral Arterial Thrombosis in a Patient with SARS-CoV-2 Infection Am J Med Case Rep. NIH Public Access 8 12 2020 486
14 Guan W. Ni Z. Hu Y. Liang W. Ou C. He J. Liu L. Shan H. Lei C. Hui D.S. Clinical characteristics of coronavirus disease 2019 in China N Engl J Med. Mass Medical Soc 382 18 2020 1708 1720
15 Melissano G. Mascia D. Baccellieri D. Kahlberg A. Bertoglio L. Rinaldi E. Chiesa R. Pattern of vascular disease in Lombardy, Italy, during the first month of the COVID-19 outbreak J Vasc Surg. Elsevier 72 1 2020 4 5
16 Tang N. Li D. Wang X. Sun Z. Abnormal coagulation parameters are associated with poor prognosis in patients with novel coronavirus pneumonia J Thromb Haemost. Wiley Online Library 18 4 2020 844 847
17 Cui S. Chen S. Li X. Liu S. Wang F. Prevalence of venous thromboembolism in patients with severe novel coronavirus pneumonia J Thromb Haemost 18 6 2020 1421 1424 Wiley Online Library 32271988
18 Klok F. Kruip M. Van der Meer N. Arbous M. Gommers D. Kant K. Kaptein F. van Paassen J. Stals M. Huisman M. Incidence of thrombotic complications in critically ill ICU patients with COVID-19 Thromb Res. Elsevier 191 2020 145 147
19 Bensaid A. Melhaoui I. Oujidi Y. El Rhalete A. El Haddad I.A. Bkiyar H. Housni B. Acute limb ischemia in patients with COVID-19 pneumonia Ann Med Surg. Elsevier 69 2021 102747
| 36481674 | PMC9722237 | NO-CC CODE | 2022-12-07 23:19:09 | no | Ann Vasc Surg. 2022 Dec 5; doi: 10.1016/j.avsg.2022.11.001 | utf-8 | Ann Vasc Surg | 2,022 | 10.1016/j.avsg.2022.11.001 | oa_other |
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Prog Cardiovasc Dis
Prog Cardiovasc Dis
Progress in Cardiovascular Diseases
0033-0620
1873-1740
Elsevier Inc.
S0033-0620(22)00138-4
10.1016/j.pcad.2022.11.017
Article
The impact of the COVID-19 pandemic on cardiovascular health behaviors and risk factors: A new troubling normal that may be here to stay
Laddu Deepika R. ab⁎
Biggs Elisabeth b
Kaar Jill c
Khadanga Sherrie d
Alman Rocio b
Arena Ross ab
a Department of Physical Therapy, College of Applied Science, University of Illinois Chicago, Chicago, IL, United States of America
b Healthy Living for Pandemic Event Protection (HL – PIVOT) Network, Chicago, IL, United States of America
c Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America
d Department of Medicine, Division of Cardiology, Larner College of Medicine, University of Vermont, Burlington, VT, United States of America
⁎ Corresponding author at: Department of Physical Therapy, College of Applied Health Sciences, The University of Illinois at Chicago, 1919 W. Taylor St., M/C 898, Chicago, IL 60612, United States of America.
5 12 2022
5 12 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 March 2020, the Coronavirus disease 2019 (COVID-19) outbreak was officially declared a global pandemic, leading to closure of public facilities, enforced social distancing and stay-at-home mandates to limit exposures and reduce transmission rates. While the severity of this “lockdown” period varied by country, the disruptions of the pandemic on multiple facets of life (e.g., daily activities, education, the workplace) as well as the social, economic, and healthcare systems impacts were unprecedented. These disruptions and impacts are having a profound negative effect on multiple facets of behavioral health and psychosocial wellbeing that are inextricably linked to cardiometabolic health and associated with adverse outcomes of COVID-19. For example, adoption of various cardiometabolic risk behavior behaviors observed during the pandemic contributed to irretractable trends in weight gain and poor mental health, raising concerns on the possible long-term consequences of the pandemic on cardiometabolic disease risk, and vulnerabilities to future viral pandemics. The purpose of this review is to summarize the direct and indirect effects of the pandemic on cardiometabolic health risk behaviors, particularly related to poor diet quality, physical inactivity and sedentary behaviors, smoking, sleep patterns and mental health. Additional insights into how the pandemic has amplified cardiovascular risk behaviors, particularly in our most vulnerable populations, and the potential implications for the future if these modifiable risk behaviors do not become better controlled, are described.
Keywords
COVID-19
Cardiovascular disease
Health behaviors
Abbreviations
BMI, body mass index
COVID-19, Coronavirus disease 2019
CVD, cardiovascular disease
ENDS, electronic nicotine delivery systems
NCD, noncommunicable chronic diseases
PA, physical activity
SB, sedentary behavior
T2D, type 2 diabetes
==== Body
pmcThe Coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global health crisis that continues to impact nearly every health, economic and social aspect of life worldwide.1 During the initial surge of the pandemic, several countries and most state/local governments within the United States (US, 45 out of 50 states) issued temporary shelter in place orders to suppress community transmission of COVID-19 transmission, with an overarching goal of mitigating viral spread and protecting those most vulnerable to severe illness.2 Included under these public health mitigation strategies were closures of schools and varying levels of restrictions on outdoor recreation, social gatherings, and economic activities. Although these measures have helped saved lives, they have come with added sociodemographic and behavioral health costs, fueling a nexus for preventable lifestyle-related noncommunicable chronic diseases (NCDs), and potentially worsening unhealthy lifestyle, cardiometabolic risk factor and NCD pandemics that have been ongoing well before the emergence of COVID-19.3 , 4 Multiple studies consistently demonstrate that prevalence of certain common cardiometabolic conditions, such as obesity, type-2 diabetes (T2D), cardiovascular disease (CVD), and hypertension significantly heightens the risk and severity of COVID-19, including in-hospital mortality.2 , 5., 6., 7., 8. Whether presence of either of these conditions directly make individuals more susceptible to COVID-19 and its complications is unclear, as it is also plausible that certain behavioral risk factors physiologically make individuals more susceptible to infection.2 , 9 , 10 For example, extensive restrictions implemented during the peak lockdown period (March through May 2020) coincided with reduced step count11 and concurrent increases in snacking, energy-dense and healthy food consumption,12 sedentary behaviors (SB),13 and unfavorable sleep patterns14 that supported weight gain and poor mental health.12 , 15 Findings from a recently published comparative risk assessment study found that nearly 64% of the >900,000 COVID-19 hospitalizations occurring in the US through November 2020 were attributable primarily to obesity (30.2%), hypertension (26.2%), and T2D(20.5%), suggesting that the majority of hospitalizations could have been prevented if these conditions were not present.8 Individuals with obesity were found to have increased risk of COVID-19 hospitalization and severe illness similarly across the lifespan in adults2 , 8 as well as in children and adolescents16 — an observation that is not surprising given the role of obesity in inciting inflammation,17 and impairing the immune response as evidenced in prior disease-related pandemics (e.g., 2009 H1N1 Influenza A Virus).18 Of grave concern were the well documented higher cases of COVID-1919 among racially and ethnically minorized communities with whom already had unequal burden due to the higher prevalence of cardiometabolic comorbidities20 and are more likely to suffer worse outcomes,21., 22., 23. bringing to the forefront longstanding health inequities and further perpetuating health disparities.24
With current estimates of over 537 million confirmed cases of COVID-19 worldwide,25 a death toll in excess of 6.32 million, >1.01 million occurring in the US25 with most occurring in individuals diagnosed with or predisposed to NCDs,2 , 8 the robust impact of poor cardiometabolic health on risk of COVID-19 outcomes is gravely concerning. Prior to COVID-19, modifiable lifestyle-related NCDs were the leading causes of adverse physical and mental health outcomes and pre-mature mortality globally,26 including the US.27 In this context, it is critical to understand the impact of the current viral pandemic on underlying obesogenic behavioral and lifestyle factors driving cardiometabolic risk that could also predispose or exacerbate COVID-19 related outcomes. Equally important to consider is the collateral toll of the pandemic on mental and behavioral health, which has drastically impacted lifestyle behaviors and quality of life, especially among older, racially and ethnically minoritized, low-income populations.28 In this review, we aim to highlight the direct and indirect effects of the COVID-19 pandemic on cardiometabolic and obesogenic health behaviors, particularly related to poor diet quality, physical inactivity and sedentary behaviors, smoking, sleep patterns and mental health. We will further theorize the implications for the future if these modifiable risk behaviors do not become better controlled.
Effects of COVID-19 lockdown on diet quality and behaviors
The COVID-19 pandemic and stay-at-home orders across the world have had substantial consequences on the access to food and dietary behavior, contributing to population-wide shifts in food choices and eating behaviors worldwide. Despite limitations in food availability and accessibility during the peak lockdown period, the effects of the pandemic on dietary practices have been fairly inconsistent.12 Studies reporting on the deleterious dietary behavior adopted during the pandemic have been described with respect to concomitant trends in weight gain.12 , 15 , 29., 30., 31. Bhutani et al. examined weight gain and lifestyle habits of 727 adults in the US both during peak lockdown and at a five month follow-up. Results showed that 18.2% of respondents gained at least 1–4 pounds and 19% gained 5 or more pounds.30 Another study in a smaller cohort of US adults reported similar to larger gains in body weight of 5–10 pounds during the observed lockdown period.32 In European and Middle Eastern countries, a greater proportion (i.e., 35%–49%) of residents reported experiencing weight gain, though average gains were modest (i.e., 2–4 pounds).12 , 29 , 31 Nonetheless, poor dietary behaviors consistently associated with weight gain across studies included: 1) increased snacking, especially in the evening; 2) snacking on energy dense and nutrient poor foods; 3) increased intake of sweets and comfort foods; and 4) increase in quantity of food consumed regardless of nutritional value.12 , 29., 30., 31. Other changes in food choices that contributed to poorer diet quality, and resultingly, suboptimal dietary patterns during the pandemic included decreased fresh fruit, vegetable, and fish consumption, increased consumption of starchy vegetables, ultra-processed foods, and red and processed meats.12 , 29 , 30 Consequently, each of these behaviors have been shown to complicate cardiometabolic health and contribute to an increased risk of NCDs.33
The intensions of public-mandated social distancing and quarantine measures were to reduce transmission spread of the COVID-19 virus. However, home confinement and changes in cohabitation dynamics, increased unemployment, and uncertainties of food availability, accessibility and affordability brought on by the pandemic triggered a spiral of psychological problems that contributed further to dysfunctional eating habits.34., 35., 36. Psychological distress and worsening mental health, including depression and anxiety have specifically been linked to emotional overeating of ultra-processed foods, and increased weight gain during the peak pandemic period12 , 30 , 31 , 37 , 38 In a large study of 1865 participants surveyed in Italy, Lo Moro et al. showed that 57.6% of respondents reported depression while 49.3% reported emotional overeating of pastries and foods high in fat and sugar, and 35.2% reported both conditions during the lockdown.39 Prevalence of emotional eating appears to be higher in women, and in individuals with pre-existing overweight or obesity, the latter of which is not surprising considering evidence suggesting that the relationship between stress and emotional eating may be modulated by body mass index (BMI).36Other studies have shown that pandemic-related increases in emotional overeating and general overeating due to boredom are associated with increased screen time,12 , 30 , 40 an established risk factor of obesity.41 Surges in screen time due to working from home, on-line education, and increased social media usage are inevitable consequences of the pandemic, that may have contributed to worsening depression and more severe anxiety.42 , 43 While evidence is mixed regarding the influence of greater screen time on food intake and weight gain during pandemic,32 , 40 it is worth acknowledging that the inter-relationship of these behaviors may explain the increased incidence and overall burden of mental health problems resulting from pandemic.44
While the unfavorable effects of the pandemic on diet is more widely reported, some populations did in fact, demonstrate positive changes in diet patterns or eating behaviors during the pandemic.12 , 29 , 30 For example, in the French NutriNet-Santé study of 37,252 adults, 14.1% of respondents indicated their diet improved, and 74.4% reported no change.29 Favorable changes in diet behaviors included decreased consumption of sweets and increase in fresh fruit, vegetable, and fish intake along with avoidance of certain foods for weight management reasons, an interest in balancing their diets, and spending more time making home cooked meals. Further analyses indicated that participants who improved in their diet-risk behaviors were overweight or obese at baseline, experienced increased anxiety without depression, and had a poor diet quality prior to lockdown. Notably, however, those who reported improvements or no change in diet-related practices were more likely to occur in those with higher versus lower socioeconomic status, an observation that echoes socioeconomic inequalities in nutrition.45 In another study of 1140 adults in Spain, one third of participants increased fresh food intake and decreased packaged food intake. Accordingly, adoption of nutritionally-favored behaviors were most notable in younger adults aged 18–35 years who reported increased physical activity (PA), whereas those over 65 displayed no significant changes in dietary practices.46
The unprecedented COVID-19 pandemic has led to untoward changes in children and adolescents daily routines, including an increase in unfavorable diet and lifestyle behavioral practices, which are concordant with the striking increase in pediatric overweight and obesity.47., 48., 49. Results from a multi-country study of adolescents aged 10–19 years noted that while increases in fruit, vegetable, and legume consumption and decrease in fast food consumption correlated with improved dietary profiles, such improvements were offset by tandem increases in fried and sweet food consumption and screen time during meals.50 Moreover, greater consumption of fruit juice, pasta, fried potato intake, red meat, sweets, salty/total snacks, and breakfast foods, which are associated with increases in body weight in children and adolescents, has also been reported as result of the COVID-19 lockdown.48 , 51 , 52 Notably, adoption of riskier dietary practices clearly translated to excess weight gain, though these observations were disproportionately more evident in those with a pre-existing elevation in body weight. For example, Cena et al showed that children with obesity prior to the pandemic appear to be at a higher risk of negative lifestyle changes (e.g., poor diet, reduced PA) and weight gain during lockdown.52 Children with pre-existing obesity were also more likely to have lower states of wellbeing and psychophysical health, attributed largely to poor food choices, increased snacking between meals, and greater intake of comfort-foods.52 If poor dietary practices are sustained, as one small cohort study suggests,48 these findings could have far-reaching implications on long-term pediatric health that warrants both acknowledgement and urgent action to implement earlier behavioral screening and intervention strategies, especially in overweight/obese children at high risk of adverse health trajectories.
How the COVID-19 pandemic has affected PA and SB
Regardless of sex, age, race, or body composition, PA has been found to be beneficial in reducing stress, improving sleep, lowering CVD risk factors, obesity, and systemic inflammation as well as improving mood and cognitive function.53 Currently, PA guidelines recommend at least 150 min per week of moderate intensity aerobic PA or 75 min of vigorous aerobic PA. Despite these benefits, many struggle to adhere to these recommendations. According to federal monitoring data by the National Institute of Health, only 25% of healthy men and 19% of healthy women meet these guidelines for PA.54
Additionally, sedentary behavior (SB), which includes sitting or lying down beyond sleep, is a distinct entity from physical inactivity and has been found to be an independent risk factor for NCDs and all-cause mortality.55 To highlight the importance of this and help overcome the problem, the World Health Organization provided guidelines on SB and PA with a goal to reduce physical inactivity by 15% over the next 10 years.56
Unfortunately, due to the COVID-19 pandemic, efforts to increase PA and reduce SB have been even more challenging. In a recent meta-analysis, Runacres et al. examined the change in sedentary time during the pandemic and its overall effect on health for the general population (N = 282,202 participants), subcategorizing into children (<18 years), adults (18–64 years) and older adults (>65 years). Among 262,630 adults (93.1%; 36.5 ± 5.5 years), sedentary time increased by 126.9 ± 42.2 min/day, whereas older adults (1.2%; 60.6 ± 8.0 years) experienced smaller increases in sedentary time by 46.9 ± 22.0 min/day. Notably, children increased their sedentary time by 159.5 ± 142.6 min/day, which was the most for any subgroup. The large variations in reporting highlight how multifaceted factors may influence sedentary time and behaviors, including disparities in access to green spaces or outdoor playgrounds used to break-up prolonged sitting time, and pre-existing inequalities and social factors that are historically correlated with increased sedentary time.13 Furthermore, the increases in sedentary time negatively correlated with mental health and quality of life, regardless of age, highlighting the potentially detrimental implications of sedentary habits on mood disturbance, fatigue, and stress.13
A recent retrospective study examined effects of PA and SB among 387 older adults (aged 75 ± 6 years) during the pandemic.53 Before COVID-19, 67.9%, 61.9%, and 48.9% of older adults met the aerobic, muscle-strengthening, and both PA guidelines, respectively. During the first 3 months of the pandemic (March–May 2020) the percentage of older adults meeting aerobic, muscle-strengthening, or both PA guidelines significantly decreased to 54.8% (−13.1%), 46.1% (−15.8%), and 33.5% (−15.4%), respectively, with a concomitant increase in sedentary time by 7.4 ± 3.1 h/day. Although far from ideal prior to the COVID-19 pandemic, one year into the pandemic, overall sedentary time (6.1 ± 2.9 h/day) and the percentage of older adults meeting the PA guidelines were similar to pre-pandemic levels (71.1% aerobic, 61.3% muscle-strengthening, 50.5% both). Importantly, pre-pandemic BMI and PA levels influenced the likelihood of sedentary time and/or changes in PA patterns during the first 3 months of the pandemic. That is, overweight or obese individuals experienced greater increases in sitting time compared to their normal weight counterparts, and those individuals who met the PA guidelines prior to the pandemic had larger reductions in both aerobic and muscle-strengthening PA during the pandemic.53 While the exact source for the return of PA and sedentary time one-year after the pandemic is uncertain and likely multifactorial, these findings optimistically suggest that pandemic-related impacts on activity levels is not long-lasting and in fact reversible among populations who are highly prone to have severe COVID-19 outcomes.4 , 57 Additional efforts should therefore be placed to highlight the importance of PA and longitudinal studies are needed to examine the effects of physical inactivity and SB on health.
The impact of COVID-19 on smoking behaviors
Nicotine exposure, through use of tobacco products including smoked and smokeless forms, is the most modifiable behavioral risk factor and a leading cause of preventable NCDs, disability, and death in the US.58 , 59 Tobacco use causes >8 million deaths per year, globally,60 with approximately half a million deaths in the US.59 In recent years prior to the COVID-19 pandemic, considerable progress was made in reducing the number of adults, adolescents and young adults smoking tobacco products. From 2019 to 2020 (prior to the pandemic), the prevalence of adult cigarette smoking alone, the most commonly used product among adults, reached an all-time low of 12.5% (versus 14.0% in 2019).61 E-cigarette use, the most commonly used non-cigarette tobacco product, was also considerably lower prior to the pandemic (3.7% vs. 4.5% in 2019).61
The significant progress in preventing and reducing tobacco product use in 2019 quickly stalled as public health efforts transitioned towards the pandemic. In the US, an estimated increase of 0.34 pack per month per capita, corresponding to a 14.1% increase in cigarette sales were observed from March 2020 to June 2021.62 Although, increase in sales do not necessarily imply changes in tobacco use behavior, it is plausible to suggest that increases in cigarette sales are the result of consumer stockpiling as fears of product accessibility, or shortages and subsequent panic-buying in response to local mitigation strategies. Accordingly, existing research on the impact of COVID-19 on adult tobacco-related behaviors show variable changes in smoking behavior since the onset of COVID-19.63 Although at the start of the pandemic (February 2020) increases in cigarette smoking consumption (30%–33%)64 , 65 and e-cigarette use (23%)65 were reported in the US among some adult smokers, others (28%) reported decreases in the number of cigarettes they smoked daily.66 These inconsistencies in tobacco use behaviors have been reported in other countries as well.66., 67., 68. The pandemic not only had a disruptive influence for smoking cessation and tobacco dependence but also has been associated with a decrease in abstinence rates and a 15% increase in smoking rates among adults.65 , 66 , 69 Tips from Former Smokers, a national telephone-based tobacco cessation service, reported a 27% decrease in call volume in 2020 compared to 2019, indicating that COVID-19 may be influencing individual smoking cessation efforts.70
It is well-established that exposure to tobacco products, including smokeless forms and electronic nicotine delivery systems (ENDS) are associated with increased risks of CVD events.58 However, there is a need to better understand what factors associated with the pandemic influenced changes in tobacco use and consumption behaviors and their long-term effects on CVD and other preventable health-related outcomes. Several studies have linked the increase in tobacco product use with interpersonal factors, such as heightened stress, anxiety, loneliness and isolation, and reduced psychosocial well-being.66 , 67 , 71 , 72 Further, increases in individual-level stress and anxiety attributed to fears about the virus, financial insecurity and job instability, changes in household dynamics, and increased isolation have been described as primary drivers of increased tobacco product use during the pandemic.66 , 71
Impact of the pandemic on psychological well-being, mental health, and sleep behaviors
The nation continues to emerge from the COVID-19 pandemic lockdown period, a period which: 1) forced many individuals and families to isolate in their homes; 2) stop or decrease the frequency of seeing friends/family; 3) diminished the ability to participate in group sports and recreational activities; and 4) altered sleep schedules by blurring the boundaries between home, school, and work. Many of these behaviors remain in flux thus offering new opportunities for CVD prevention strategies. One such opportunity is to refocus efforts on the underlying, silent changes caused by the lockdowns, the worsening of mental health symptoms across all age groups and communities.
The constraints of psychosocial well-being associated with the COVD-19 pandemic has further exacerbated existing mental health issues and perceptions by US adults, with an alarming 55% disclosing harmful effects to their mental health and 71% expressing concerns about the overall mental health of Americans.73 The request for services to manage pandemic-related stress and distress parallels these statistics, especially early on in the pandemic, wherein the National Disaster Distress Helpline reported an 8-fold increase in calls to provide assistance to those experiencing substance abuse or mental health or other behavioral crisis.74 In 2021 numerous reports were published to document the increased rates of anxiety, depression, and insomnia affecting individuals because of the pandemic lockdown and ongoing fears of what the new normal would eventually be, specifically living with the constant threat of the virus.75., 76., 77. Among adult populations, as changes in PA, sleep, smoking, and alcohol intake declined, reported symptoms of depression, anxiety, and stress increased.78., 79., 80. There is a known bi-directional relationship between health behaviors and mental health disorders and if such mental health disorders are left untreated they have the potential to directly impact cardiovascular health by negatively impacting behaviors such as diet, PA, sleep, and tobacco use. The impacts on such changes have already started to become apparent as adults that gained five or more pounds during the peak of COVID-19 related lockdowns have either continued to gain weight or maintained the weight that was gained.30 Fortunately, among adult populations, recent data has started showing an upward trend in PA levels, improvement in mental well-being, and individuals reporting less feelings of loneliness.80
Unfortunately, the most impacted proportion of population from the pandemic may fall to the younger generations.81 The longer-term consequences of the stay-at-home orders on students as they stopped attending school and were taught virtually for three months has been estimated to be full years' worth of learning lost and significant delays in development, especially among low-income families that did not have access to the internet or computers.82 , 83 With students no longer going to school, many services provided to students also become inaccessible, including access to mental health counselors, free/reduced breakfast and lunch programs, and before and after care to assist working parents. For many racially and ethnically minoritized and low-income families that relied on such services, pre-existing mental health disparities in these communities widen as the number of children and adolescents reporting symptoms of depression and anxiety soared.84 During the last two years, studies have reported that children and adolescents are stressed, worry more often, have a higher sense of helplessness, and there are a higher number of reported social and risky behavioral problems (i.e., smoking, suicide, academic issues).85
With some resolution and perhaps adjustment to a new normal, post “peak” COVID-19, we are seeing children and adolescents return to school, and adults return to work. However, the burgeoning mental and physical effects of COVID-19, and now, long-COVID, suggest there is a long-recovery ahead. Public health strategies are needed to re-energize communities after these long, tireless years since COVID-19 became a part of every household. Such strategies need to start with the foundation of improving individuals' mental health. As new strategies are developed for CVD prevention, their focus needs to start with providing individuals with the tools needed to properly cope with stress and anxiety, recognize signs and symptoms when their solen mood may be a more serious condition, depression, and overall, how to reach out and access mental health resources when needed.
The heartache of COVID-19 pandemic and Where to go from here
The COVID-19 pandemic has unquestionably had a multifaceted impact on key cardiovascular health behaviors, including diet quality, PA, tobacco use, sleep, and mental health. However, few studies exist that document the aftermath of the pandemic on critical behavioral targets for CVD prevention.53 Thus, while most of the world has moved towards adopting a new normal in a post-COVID era, it remains uncertain what long-lasting effects of the COVID-19 pandemic will have on behavioral patterns, for better or for worse, and how this relationship may impact the growing global burden of CVD and related cardiometabolic disorders.30 , 86 Emerging studies have attempted to shed light on some promising signs on critical behavioral targets of CVD, such as in PA and SB, which among older adults appear to have returned back to the healthier pre-pandemic levels.53 Whereas, suboptimal diet behaviors that contributed to weight gain during the initial stages of the pandemic have shown to persist five months after lockdown mandates eased, with additional evidence indicating further weight gain in some populations.30
Of course, preventing the clear pattern of higher CVD risk and suboptimal health behaviors that have emerged or accelerated during the COVID-19 pandemic has broader clinical, population and public health implications. Large increases in the prevalence of CVD risk factors and CVD forecasted in the future are due partly by the direct effects of COVID-19 infection on the cardiovascular system, but also as a consequence of the limited access to quality preventative care and subsequently, reduced monitoring of cardiovascular behaviors during the pandemic.87 A continued trajectory of worsening health behaviors may exacerbate a long-term sequalae of COVID-19 on the cardiovascular system.88., 89., 90. This is particularly concerning among under-represented and medically marginalized populations who experience disproportionately higher rates of COVID-19 cases, severity and death,91 demonstrate lower adherence to cardiovascular health behaviors,58 and face greater social and structural barriers to behavior change.92
Reversing the deleterious trends in health behaviors is not for the faint of heart. If anything, the COVID-19 pandemic has exhausted healthcare systems worldwide and compromised the delivery of high-quality care, leaving much of the world unprepared for the next global health crisis. To mitigate further vulnerabilities to future viruses, like COVID-19, a coordinated, more aggressive response by health care providers and educators, national policy advisors, and public health officials that prioritize adoption and implementation of primary and secondary lifestyle-based prevention strategies and encourage equitable health promotion is desperately needed. This type of action is the only tool we have to improve health-outcomes and overall resiliency to future syndemic-level global health emergencies.
Declaration of Competing Interest
None.
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75. 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 Psychol Med 1-9 2021
76. Morin C.M. Bjorvatn B. Chung F. Insomnia, anxiety, and depression during the COVID-19 pandemic: an international collaborative study Sleep Med 87 2021 38 45 34508986
77. Shah S.M.A. Mohammad D. Qureshi M.F.H. Abbas M.Z. Aleem S. Prevalence, psychological responses and associated correlates of depression, anxiety and stress in a global population, during the coronavirus disease (COVID-19) pandemic Community Ment Health J 57 2021 101 110 33108569
78. Stanton R. To Q.G. Khalesi S. Depression, anxiety and stress during COVID-19: associations with changes in physical activity, sleep, tobacco and alcohol use in Australian adults Int J Environ Res Public Health 17 2020 4065 32517294
79. Sepúlveda-Loyola W. Rodríguez-Sánchez I. Pérez-Rodríguez P. Impact of social isolation due to COVID-19 on health in older people: mental and physical effects and recommendations J Nutr Health Aging 24 2020 938 947 33155618
80. Bhoyroo R. Chivers P. Millar L. Life in a time of COVID: a mixed method study of the changes in lifestyle, mental and psychosocial health during and after lockdown in Western Australians BMC Public Health 21 2021 1947 34702238
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| 36481209 | PMC9722238 | NO-CC CODE | 2022-12-07 23:19:09 | no | Prog Cardiovasc Dis. 2022 Dec 5; doi: 10.1016/j.pcad.2022.11.017 | utf-8 | Prog Cardiovasc Dis | 2,022 | 10.1016/j.pcad.2022.11.017 | oa_other |
==== Front
Prog Cardiovasc Dis
Prog Cardiovasc Dis
Progress in Cardiovascular Diseases
0033-0620
1873-1740
The Authors. Published by Elsevier Inc.
S0033-0620(22)00140-2
10.1016/j.pcad.2022.11.019
Article
The response to the COVID-19 pandemic: With hindsight what lessons can we learn?
Faghy Mark ac⁎
Arena Ross ac
Hills Andrew P. cd
Yates James ac
Vermeesch Amber L. ce
Franklin Barry A. cf
Popovic Dejana cgi
Strieter Lindsey bc
Lavie Carl J. ch
Smith Andy c
a School of Human Sciences, University of Derby, Derby, UK
b Department of Physical Therapy, College of Applied Sciences, University of Illinois at Chicago, Chicago, IL, USA
c Healthy Living for Pandemic Event Protection (HL – PIVOT) Network, Chicago, IL, USA
d School of Health Sciences, University of Tasmania, Tasmania, Australia
e Department of Family and Community Nursing, School of Nursing, University of North, Carolina Greensboro, Greensboro, NC, USA
f Preventive Cardiology and Cardiac Rehabilitation, Beaumont Health, Royal Oak, MI, USA
g University Clinical Center of Serbia, Clinic for Cardiology, Belgrade, Serbia
h Department of Cardiovascular Diseases, John Ochsner Heart and Vascular Institute, Ochsner Clinical School-University of Queensland School of Medicine, New Orleans, LA, USA
i Mayo Clinic, Rochester, MN, USA
⁎ Corresponding author at: Biomedical Research Theme, School of Human Sciences, University of Derby, England, UK.
6 12 2022
6 12 2022
© 2022 The Authors. 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.
The purpose of this paper is to put forward some evidence-based lessons that can be learned from how to respond to a Pandemic that relate to healthy living behaviours (HLB). A 4-step methodology was followed to conduct a narrative review of the literature and to present a professional practice vignette. The narrative review identified 8 lessons: 1) peer review; 2) historical perspectives; 3) investing in resilience and protection; 4) unintended consequences; 5) protecting physical activity; 6) school closures; 7) mental health; and 8) obesity. As in all probability there will be another Pandemic, it is important that the lessons learned over the last three years in relation to HLB are acted upon. Whilst there will not always be a consensus on what to emphasise, it is important that many evidence-based positions are presented. The authors of this paper recognise that this work is a starting point and that the lessons presented here will need to be revisited as new evidence becomes available.
Keywords
Physical activity
Unhealthy lifestyle
Social distancing
School-based interventions
Obesity
Wellbeing
Abbreviations
CRF, Cardiorespiratory fitness
COVID-19, Coronavirus disease 2019
HL, Healthy living
HLB, Healthy living behaviours
METs, Metabolic equivalents
NR, Narrative Review
PA, Physical activity
QoL, Quality of life
VO2, Oxygen consumption
UK, United Kingdom
US, United States
==== Body
pmcIntroduction
The purpose of this paper is to add to the emerging literature1 that reflects on what, with hindsight, healthcare professionals, policy makers, and researchers could have done better in response to the coronavirus disease 2019 (COVID-19) pandemic. This paper does not seek to apportion blame to those, who with the benefit of hindsight, could have done things differently. Rather, this work seeks to help learn lessons from the Pandemic in the hope that next time things will be done better and adds to the work of others who are attempting to do the same.2
There are many different types of lessons that could be learned from the Pandemic. They include lessons related to medicine, economics, sociology, politics, and international relations. Here our focus is on the lessons that can be learned that relate to healthy living (HL) behaviours (HLB), quality of life (QoL), wellbeing, and non-communicable diseases as studied and promoted through the Healthy Living for Pandemic Event Protection (HL- PIVOT) network.
Methods
As described in detail below, a 4-step methodology was followed to ensure the quality of the work and to triangulate the conclusions.
Step 1: An international and interdisciplinary authorship Team was recruited through the HL- PIVOT network.3 The overarching goal of this network is to promote human resilience and quality of life by increasing HLB. This step helped ensure the quality of the work as it brought together an interdisciplinary Team of authors with a range of experiences and professional insights. The international dimension of the Team helped triangulate the perspective reported here as each of the countries ‘represented’ had different responses to the Pandemic.
Step 2: A narrative review (NR) of the emerging literature, which is challenging and contesting the response to the Pandemic, was conducted by following authors: DP, LS, AH, RA and AS. A NR was used rather than a systematic review and/or meta-analysis because: i) the literature in this area has yet to mature; ii) we wanted to purposefully select from a range of qualitative and quantitative sources; and iii) this approach better suited our aim to present a short historical overview. By conducting this review, the authors were able to compare their own thinking with that of other scholars past and present.
Step 3: One of the authors (BF) drafted a reflective practice vignette based on his reading of the literature and experiences during the Pandemic.
Step 4: Through the project, drafts of the paper were circulated to all members of the Authorship Team for comment and to identify: i) points of consensus; and ii) any differences of emphases. AV, RA, MF, AS and JY contributed to the manuscript as a whole and had a particular responsibility for taking this step. This was done largely through emails and the use of a shared drive but culminated in all Authors reviewing and revising the final draft.
It is important to note that this methodology was designed to allow many evidence-based views to be heard rather than to reach a consensus on every point. That is why we have been clear about which authors drafted which section.
The narrative review
The method used to construct the NR created a bibliography that we considered relevant. Work was considered relevant if it addressed: i) lessons that could be learned from the Pandemic; and ii) was directly or indirectly linked to healthy living behaviours.
To ensure that a broad range of sources were reviewed, and whilst giving precedence to peer-reviewed journal papers, books were also included.
The literature was read, and selected papers are reported in three clusters. Cluster A addresses four ‘higher order lessons’ that are applicable to a range of settings and professions. Cluster B relates directly to lessons that can be learned about HLB [e.g., physical activity (PA), sedentary behaviour]. Cluster C relates to lessons that can be learned that relate to possible outcomes (e.g., obesity, depressive symptoms) that may have resulted from changes in HLB. These clusters are not discrete, and the lessons identified in each are interlinked. A theme that links many of them is the potential for damage to mental and physical health, education and general wellbeing by the measures put in place to control the Pandemic.
The strengths of the literature reviewed here are that they are international and multidisciplinary. Additionally, the literature is contemporaneous with the Pandemic and therefore, has an immediacy and sense of urgency.
The weaknesses of the existing literature reviewed here include a lack of interdisciplinary perspectives. Whilst the literature includes work from multiple disciplines, individual papers lack true interdisciplinary thinking and authorship teams, methodologies, and analysis. In addition, the work is by its very nature, post-hoc, as it reviews what happened after the event rather than being established at the start of the Pandemic with evaluation in mind. More contentiously, within the work, there is little evidence of authors reflecting on how the Pandemic changed their thinking or discussion about what they themselves might have done differently. Closely related to these observations, there is a danger that within the health-related professions, ‘group think’ may be occurring.
NR Cluster A: higher order lessons
NR Lesson 1 – Peer Review, Publish Quickly but with rigorous vetting of data and viewpoints and Provide Open Access: The Pandemic had an impact on the publication of scientific and medical papers. Three Pandemic impacts can be identified. First the sheer number of papers and books on COVID-19 published since January 2020 is staggering and at this point defies simple characterisation and summary. Secondly, the speed at which both empirical data was collected and published, and opinions formed and shared, was at an unprecedented pace. On the positive side, this speed enabled the rapid development of, for example, vaccines, and is to be commended. However, moving forward, care needs to be taken to ensure that haste does not undermine the opportunity for high-quality peer review and resulting literature. The substantial immediate benefit of disseminating results quickly does not have to be at the cost of long-term damage to the peer review process - if the risk is identified and mitigated appropriately. The importance of peer review is emphasised when it is reported, for example, that President Lukashenko of Belarus informed his population that COVID-19 could be defeated by ‘drinking vodka’.4 Suggestions such as this are alarming given the propensity of individuals with irrational beliefs to engage in pseudoscience practices, and have reduced vaccination intention and adherence to follow official COVID-19 guidelines.5 Thirdly, many Journals made all papers related to the Pandemic open access for which they should be applauded. This precedence should become a standard practice for global health crises in the future.
NR Lesson 2 – Learn from the Past: With great foresight in 2016, McMillen6 wrote “very often history is forgotten or rediscovered only when we confront contemporary epidemics and pandemics, and thus patterns from the past are repeated thoughtlessly” (page 1). As Fig. 1 illustrates, COVID-19 was not the first-time humankind has encountered a Pandemic. Despite these historical Pandemics, arguably, when COVID-19 struck, it appeared to arrive as a complete surprise. Why else would so many governments, health care systems and professions appear to have been so unprepared. The delayed implementation of control measures internationally manifested despite widely observed preceding inter-country spread of the virus.7 A key takeaway is in the knowledge that another Pandemic will occur, and we should urgently prepare for it by learning all possible lessons from COVID-19. This paper is part of that global endeavour which includes, for example, the Public Enquiry into the Pandemic being conducted by the United Kingdom (UK) government.Fig. 1 Previous Pandemics as Identified and Ordered by McMillen (2016).
Legend: HIV/AIDS, human immunodeficiency virus/acquired immunodeficiency syndrome; COVID-19, coronavirus disease 2019.
Fig. 1
As well as learning from previous Pandemics, other historical events provide an opportunity to learn. For example, during the German bombing of London during the Second World War (‘The Blitz’), the British Government introduced a night-time blackout to prevent the Luftwaffe using the City's lights to target the area. It is reported that this well-intentioned action led to a massive increase in road deaths with a suggestion in the British Medical Journal that it killed 600 people a month.2
NR Lesson 3 – Invest in Resilience, Wellbeing, and Protection: The Pandemic shone a bright and uncomfortable light on the lack of resilience, wellbeing, and unpreparedness of many countries' health systems, and their populations. National stockpiles of personal protective equipment might be part of the solution along with ensuring that countries have the necessary infrastructure to manufacture their own basic medical and health equipment. In addition, nations need to invest in improved methods of communication between government departments and the public. To tackle future Pandemics, societies need the capacity to adopt “whole of society” approaches which mobilise the contribution of all for the benefit of all. The challenges of coping with future Pandemics necessitates parallel disruptions to prevention and management, including novel behavioural insights to address poor behaviours, including panic buying and sedentary behaviours.8
NR Lesson 4 – Beware of Unintended Consequences: As well as producing the intended outcomes, any action tends to result in unintended consequences. Arguably the most significant one during the Pandemic has been the politicisation of public health.9 This was perhaps most evident in the United States (US) where mask wearing became, and may still be, a hotly contested political issue.
Another unintended consequence was how social distancing mandates negatively impacted initiatives to promote PA. The initial assumption being mandates that limited PA would decrease viral transmission, an assumption that following further analysis was proven to be false; “most traced contagions diagnosed after lockdown mitigation continued to happen inside closed communities [May 1–20: 49.5% nursing homes; 24.3% private houses; 7.2% hospitals—June 1–30: 35.1% nursing homes; 24.6% private houses/relatives; 6.6% hospitals (2020)]”.9
By their very nature, unintended consequences are unpredictable - the lesson here is to ensure that any action is well-thought-out and that group think is avoided. In addition, from time to time the inclination to act may need to be tempered by an acknowledgment that sometimes doing nothing may be the right course of action.
NR Cluster B lessons directly related to HLB and lifestyle
NR Lesson 5 – Protect PA Levels: The evidence that physical inactivity and living a sedentary lifestyle is a major cause of disease and lower QoL is well established.10 Before the Pandemic, many countries and organisations were taking steps to increase levels of PA, although many of these initiatives have not been as successful as hoped. In addition, exercise was being used in a range of clinical settings as a means of both pre-surgical preparation and rehabilitation from, for example, coronary heart disease. In many countries, sport and fitness are large parts of both the economy and cultural life of the nation. PA, exercise, sport, and fitness can, therefore, be seen as crucial elements of the health and wellbeing of a nation. Prior to early 2020, who could have imagined that across the world people would be confined to their homes, gyms and rehabilitation services would be closed, and outdoor public spaces shut? Not surprisingly, lockdowns resulted in an inevitable reduction in levels of PA and patients being unable to access clinical exercise rehabilitation programs.11 Any benefits that accrued from lockdowns in terms of reducing the rate of transmission of COVID-19, need to be offset by the cost of reducing levels of PA. Such a cost-benefit analysis has yet to be conducted and will be challenging to perform because of the different timescales on which COVID-19 disease burden and physical inactivity impact the health of both individuals and populations and should recognise that the cost-benefit may differ across groups. Infection by the COVID-19 virus results in illness and sometimes death within days, or results in long-lasting, yet-to-be determined deleterious health effects, whilst the results of low levels of PA and sedentary living may take years or decades to accrue. This time difference in negative effect may account for many governments acting in ways that resulted in a relatively quick reduction in the reproduction rate of the virus whilst potentially sabotaging the long-term health of many by reducing their levels of PA.
NR Lesson 6 – Keep Schools Open: At the start of the Pandemic, it was understandable, and necessary to prevent viral spread, that many countries closed their schools. However, our actions to continue shutdowns and remote schooling long after it was known that infections and death rates from COVID-19 among children were low12 was, with hindsight, perhaps not the ideal approach. Lewis et al13 stated “Children have least to gain and most to lose from school closures. This Pandemic has seen unprecedented intergenerational transfer of harm and costs from elderly socioeconomically privileged people to disadvantaged children”.
During the Pandemic in the US, overall leading causes of death for children ages 0–14 remained consistent (i.e., unintentional injury and cancers).14 Concurrently, the number of suicides and hospitalizations due to mental health issues increased. In 2020, the second leading cause of death, behind unintentional injury, for ages 5–9 and 10–18 was suicide.15 More and more, children are being hospitalized for mental health issues including depression and anxiety.16
In addition to the mental health effects, children's nutrition has suffered. In many countries, schools provide nutritious food to low-income families through various government initiatives. When schools shut down, so does access to these programs. In the US, food insecurity increased by 4% from March 2020 to July 2020.17 Most likely from fear of contracting COVID-19 from grocery stores, more and more families resorted to ordering food, resulting in an increase in the purchasing of packaged foods and consumption of less fresh fruits and vegetables.18
Remote schooling, remote programming, and shut-downs of after-school activities also contributed to a reduction in children's PA and fitness.19 With an increase in screen time came a decrease in PA. Other children lost access to physical education altogether. Sports and physical extracurriculars were also put on hold.
Goldfeld et al [ 20 ] made an important contribution to the issues explored here through their narrative review on the impact of the Pandemic on children's health. Taking Australia as its starting point, a country that had some of the strictest restrictions in place, they identified 11 potential negative impacts of these restrictions on young people. Negative impacts were grouped in the following clusters: i) child-level factors; ii) family-level factors; and iii) service-level factors. In stating that “these restrictions are having immediate and likely longer term adverse consequences on children's developmental potential” (page 364), Goldfeld et al. adds to the commentary of many researchers globally from different disciplines who have expressed concerns about the adverse impact the Pandemic is having on children and adolescents.21., 22., 23.
Individually, the impacts of the Pandemic and school closures on healthy living behaviours is a major cause for concern, when combined with childhood obesity, the full scale of the problem becomes worryingly apparent.24 To address the damage that has been done to children by the Pandemic and lockdowns calls have begun for recovery plans for children, see for example Gauvin et al.25 Of course, not just children have suffered from the closure of schools but parents, and particularly mothers, had to take on the responsibility of teachers which many found had a negative impact on their mental health.26 It would be remiss of researchers to not continue to monitor and assess the long term effects of this period on young people and their families following such an unprecedented compromise of public health.
NR Cluster C lessons that can be learned that relate to health outcomes
NR Lesson 7 – Protect Mental Health: Whilst the evidence on the impact of the Pandemic on mental health is still emerging, a negative impact is clearly apparent.27 , 28 Many people went through illness and spent days in lockdown feeling anxious and fearing for their own health and for that of loved ones. A meta-analysis showed that COVID-19 quarantine had varying impacts on psychological stress, regardless of country of origin.29 Every age category was hit: i) children sharply changed their environment, some were separated from their families, isolated and demonstrated increased mental health disruption30; ii) adolescents had a higher frequency of using alcohol and cannabis and particularly negative mental health impact was prominent among adolescents in one-parent, one-child, and low-income households; and iii) adults exhibited diverse mental health problems and the elderly were particularly under the risk due to self-integration, self-efficacy, and resilience issues.31 The Pandemic poisonous polypill included not only illness and fatigue, but physical inactivity, mental stress, loss of family members, financial and food insecurities, isolation, loneliness, reduced or no access to social networks, travel restrictions, lockdowns, work from home, and general uncertainty leading to diverse stress-related conditions.32 Limited or no access to mental health services further worsened the situation. There were no organized and planned healthy living programs to combat Pandemic related physical and mental health issues. Some countries recognized the problem and attempted to intervene to combat mental health issues, however it was often sporadic and insufficient.33 Many vulnerable population groups were forgotten including those with pre-existing mental health conditions and those with learning difficulties.34 If in response to future Pandemics lockdowns are imposed, it is important that compensatory measures are put in place to protect the mental health of the population. Besides psychosocial support, implementation of training programs, guidelines and system level protocols, the measures should include the implementation of pre-planned healthy living programs designed to protect mental health which include exercise interventions that have been shown to reduce psychosocial stress.
NR Lesson 8 – Factor in Obesity: Well before the COVID-19 Pandemic, obesity was, and will continue to be, a major global health crisis; it should be considered a Pandemic in its own right.35 Very early in the COVID – 19 Pandemic, the risk that lockdowns would make an already major health crisis such as obesity worse, was identified.36 Such a risk should not have been a surprise to anyone given the already well understood multi-factorial nature of obesity. As the prevalence of overweight and obesity continues to rise and impact individuals of all ages, this points to the fact that, as a global community, we have been summarily unsuccessful in preventing unhealthy weight gain. Prior to the COVID-19 pandemic, we had certainly failed to address the broader biopsychosocial nature of obesity, so with the overlay of an additional Pandemic, many individuals are further compromised, even if we simply consider primary drivers including reduced levels of PA, increases in sedentary behaviour and food insecurity. Evidence is now emerging that these fears were justified, and that obesity, and a range of other health indicators, have worsened over the COVID-19 period.37 For example, there is considerable evidence that in countries with higher levels of obesity, the impact of COVID – 19 has been associated with higher death rates and health complications.38., 39., 40., 41., 42., 43., 44. Unfortunately, COVID-19 has exposed and amplified existing inequalities in society. Marmot and Allen45 framed this notion in relation to the UK as “inequalities in COVID-19 and inequalities in the social conditions that lead to inequalities more generally”. An ongoing issue for societies will be to address inequalities in health and effectively respond to additional impacts; otherwise, we risk greater challenges over time for a larger proportion of the global population.
NR Conclusion
Of the lessons learned through this NR, the one we attach the most weight to is the need in future Pandemics (and other global emergencies), to keep schools open (i.e., lesson 6). Evidently, we cannot say based on the arguments reviewed here what would have happened if we had kept schools open. In forming our view that schools should be kept open, we were of the view that policy makers and others reduced the function of schools to teaching and learning – activities that arguably can be conducted online. This view disregards the essential role schools play in socialisation, cultural transition, and integration. Also, it ignores the importance of ‘hands on learning’ that is required in a range of subjects including physical education, music, and the arts, and underestimates the important role schools play in ensuring some of our most vulnerable children have access to food, psychological support, and safeguarding services.
In concluding this section of the paper, we are conscious of the fact that it is the nature of any exercise in ‘learning lessons’ that the focus is on what went wrong and negative outcomes. However, there is a possibility that for some individuals and things that there could be post traumatic growth. People who overcome great challenges can become stronger and societies who tackle common problems can change. Regarding the latter, arguably the Pandemic has put a greater focus on the importance of health and community cohesion. Nonetheless, any positive outcomes from the Pandemic will need nurturing if they are to have a lasting impact.
Vignette
Lessons Learned from the Lingering COVID-19 Pandemic: Need for Greater Self-Responsibility
This vignette and the lessons it contains was drafted by BF. As the Pandemic appears to have shifted into a new phase (e.g., less public lockdowns and social distancing mandates), we should reflect on what we have learned and apply those lessons should other viral mutations or an entirely new Pandemic strike in the future. Although the Pandemic has been particularly devastating in the US, not all citizens were affected equally. Without question, cultural and societal factors placed some demographic, racial, and ethnic groups at increased risk of contracting and dying from COVID-19.46
Population disparities
Hispanic, Black, and Native American Indians experienced disproportionately high rates of COVID-19 hospitalizations, with reported rate ratios of 3.0, 2.8, and 3.5, respectively, when compared with their White counterparts.47 After adjusting for age, these population subsets also experienced a much higher percentage of the COVID-19 mortality burden relative to their percent of the US population.48 This may be partially attributed to the social determinants of health, income and wealth disparities, inequalities in health care access and use, occupational vulnerability (i.e., more face-to-face interactions with the general public), public transportation transmission, and living in multigenerational or multifamily housing.49
Rural Americans were more than twice as likely to die from COVID-19 infections than their urban counterparts.50 This heightened mortality may be attributed to higher age, lower socioeconomic status, limited access to health care, and a higher incidence of chronic diseases such as obesity and type 2 diabetes mellitus. Further, because rural Americans tend to express less enthusiasm for vaccine safety and effectiveness, they have lower rates of vaccination against COVID-19.51
Additionally, the aforementioned demographic groups, as well as other vulnerable populations, including low-income and older Americans (i.e., ≥65 years of age), confronted the virus with a much higher prevalence of chronic diseases, such as known cardiovascular and pulmonary conditions.52 These underlying risk factors, especially in combination, increased the likelihood of getting severely ill from COVID-19.
Misguided governmental responses, medical misinformation and constitutional rights
The COVID-19 Pandemic was initially exacerbated by a delayed, dampened, downplayed governmental response. It provided unprecedented challenges to the medical community, which was further compounded by antiscientific meddling and mishandling of those regulatory agencies responsible for the country's countermeasures.53 To eliminate or reduce panic, Americans were irresponsibly told by some in leadership positions that the end of the virus was just around the corner or that it would magically vanish with the seasonal transition to summer.54
Uncertainty abounded. Hydroxychloroquine and Ivermectin were initially promoted as a potential treatment for COVID-19 – a claim that has been subsequently refuted.55 , 56 According to numerous studies, in spite of reported reactions, some serious adverse events, and breakthrough cases, COVID-19 vaccines are generally safe and highly effective in preventing COVID-19-related hospitalization and death.57 Despite considerable evidence that masks block virus transmission and save lives, some argued that they have a “constitutional right” not to wear them, or that wearing a mask exposes the wearer to dangerous levels of carbon dioxide; neither of these suppositions are valid.58 , 59
Pandemic of the unvaccinated
During a July 2021 White House briefing, Centers for Disease Control and Prevention (CDC) Director Dr. Rochelle Walensky said that a data review by her agency revealed that 99.5% of the people who died from COVID-19 over the past 6 months were unvaccinated.60 Similarly, US Surgeon General Dr. Vivek Murthy added that nearly every recent death due to COVID-19 could have been prevented. In an accompanying advisory, Murthy wrote: “Health misinformation is a serious threat to public health. It can cause confusion, sow mistrust, harm people's health, and undermine public health efforts. It's one of several reasons why people are not getting vaccinated.”
Health of the US before the pandemic?
In 2018, the US spent approximately $3.6 trillion on health care – a level of spending that makes the American health care system, by far, the most expensive in the world.61 Yet, a Commonwealth Fund brief reported that the US had worse health outcomes compared to other high-income countries.
A widely cited study noted that Americans, prior to the pandemic, already had a high prevalence of unhealthy lifestyles, risk factors, and underlying chronic disease, as well as a shorter life expectancy compared with residents of all other high-income countries. Researchers analyzed data from 2 major ongoing cohort studies, the Nurses' Health Study (n = 78,865) and the Health Professionals Follow-up Study (n = 44,354) which, when combined with National Health and Nutrition Examination Survey data as well as mortality data from the CDC, were used to estimate life expectancy in the US population. Five low-risk lifestyle factors were considered: not smoking; body mass index 18.5 to 24.0 kg/m2; ≥30 min/day of moderate-to-vigorous PA; moderate alcohol intake; and a healthy diet score. During up to 34 years of follow-up, adherence to all 5 lifestyle-related factors significantly increased life expectancy at age 50 years for both men and women, 12.2 and 14.0 years, respectively. The most physically active cohorts of men and women demonstrated 7- to 8-year gains in life expectancy!62 The investigators concluded that Americans could narrow the life-expectancy gap between the US and other industrialized countries by adopting a healthier lifestyle, and that prevention should be a top priority for national health policy.
Need for greater self-responsibility
The cost of health care today in the US will soon approach 20% of the gross domestic product ($1 out of every $5 spent). It's simply not sustainable! We need to move from what has been referred to as a “reactive sick care system,” to a more proactive health care model.63 Consequently, self-responsibility (e.g., regular physical exams, completing health habit surveys and/or serial risk factor profiles, attaining certain risk factor goals) has become a greater priority in the contemporary health care environment.64 Both the COVID-19 Pandemic and our poor health as a nation can be improved with a traditional American value: self-responsibility.
Failure to take responsibility for our own health unequivocally increases the risks of COVID-19 and leads to many of the underlying chronic diseases that worsen the impact of the virus, such as obesity, type 2 diabetes, and coronary artery disease.65 Small, positive changes in what we eat, how often and vigorously we exercise, and avoiding cigarette smoking can have a profound and favorable effect on preventing and treating illness and disease – and that includes COVID-19. A healthcare model that supports individuals in making positive changes to their health would reduce the impact of chronic and acute conditions while reducing costs to other aspects of the healthcare system.
Exercise, PA and cardiorespiratory fitness (CRF)
Increased levels of PA and structured exercise have positive protective effects that are realized within only days of initiation.66 Consistently meeting contemporary PA guidelines (≥150 min of moderate-intensity or 75 min of vigorous PA per week, or combinations thereof), is associated with a reduced likelihood for hospitalization, intensive care unit admission, and death among patients with COVID-19.67 Higher levels of CRF, expressed as peak oxygen consumption (VO2) in mLO2·kg−1·min−1 or metabolic equivalents (METs; 1 MET = 3.5 mLO2·kg−1·min−1), also appear to confer more favorable COVID-19 outcomes.68 , 69 An initial “goal” exercise training intensity ≥3 METs provides the greatest relative reduction in mortality and improvement in survival.70 Using the treadmill exercise mode as an example, this corresponds to walking at 2.0 mph, 3.5% grade, or on the level (0% grade) at 3.0 mph. Thereafter, exercise workloads, expressed as METs, should be progressively increased by using age-, sex-, and fitness adjusted target intensities for training.71
Obesity and dietary intake
Obesity is a major risk factor for COVID-19 complications due to its adverse effects on type 2 diabetes, immune function, lung capacity and cardiovascular health.72 Nevertheless, among overweight or obese individuals, the health benefits of losing even 5% of body weight have been reported.73 As an example of a dietary approach with a scientifically supported positive impact, the Mediterranean diet, for example, has been shown to elicit significant reductions in body weight and fat stores, systolic blood pressure, and inflammatory markers such as C-reactive protein.74 In aggregate, these adaptations may help decrease COVID-19 incidence, severity, hospitalization, and mortality. Foods that increase levels of nitric oxide, such as beets, leafy greens, and lean meats such as bison, have also been shown to improve vascular health.75 However, these dietary recommendations may be problematic in some underserved, low socioeconomic communities that are disproportionately impacted by the added costs and limited access to healthier foods.
Smoking and secondhand smoke
Because COVID-19 targets the lungs, quitting smoking and avoiding secondhand smoke are imperative. Cigarette smoking is responsible for 540,000 to 600,000 deaths each year. Moreover, on average, life expectancy is shortened by 10 to 12 years among lifelong smokers, as compared with those who never smoked.76 Chronic exposure to secondhand smoke also increases the risk of heart disease by ~30%, after adjusting for confounding variables.77 Fortunately, city- and country-wide smoking bans have invariably resulted in reduced population rates of cardiovascular events, especially among nonsmokers.78
Vignette conclusion
Perhaps the late General Norman Schwarzkopf summed it up best when asked how he would respond to an enemy attack. “Counterattack,” he replied. When the enemy is COVID-19 or other viral mutations, the strategy is no different. Adopt a HL, favorably modify your risk factor profile, exercise regularly, and adhere to public health guidelines, as summarized by the 3-W's: 1) Wait - don't go out if you have symptoms or had close contact with someone who has COVID19; 2) Wash your hands; and 3) Wear a face covering. Perhaps a fourth W can be added - Walk more. Most importantly, get vaccinated and stay up-to-date with booster shots.
The COVID-19 Pandemic exposed significant disparities within certain population subsets that manifested through greater disease burden and worse outcomes. It became increasingly apparent that barriers to prevention and treatment are often fundamentally embedded within the social determinants of health. Accordingly, we need to equitably address these critical health modulators, including attitudes toward and enthusiasm for vaccine safety and effectiveness, as well as decreasing the incidence/prevalence of unhealthy lifestyles, risk factors, and underlying chronic disease.63
Concluding reflections
This paper set out to learn the lessons from the Pandemic related to healthy living behaviours, quality of life and non-communicable diseases. It did so by conducting a narrative review and through the vignette which was embedded in the literature that was specific to the case in question.
Given the diversity of the authorship team in terms of their nationality, profession, and experiences, naturally individuals placed different emphases on the lessons presented here. For example, whilst all agreed that there needs to be a great focus on both tackling structural inequalities in health and enhancing personal responsibility different authors focused more on one then the other. However, there was agreement that all the lessons presented here need to be heard and reflected on.
Readers are encouraged to work with their colleagues to formally review the lessons that can be learned from their own experiences and thereby to improve our preparedness for the next Pandemic. The authors of this paper recognise that this work is a starting point and that the lesson which are presented here will need to be revisited as and when new evidence is published.
Declaration of Competing Interest
None.
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| 36481211 | PMC9722239 | NO-CC CODE | 2022-12-07 23:20:08 | no | Prog Cardiovasc Dis. 2022 Dec 6; doi: 10.1016/j.pcad.2022.11.019 | utf-8 | Prog Cardiovasc Dis | 2,022 | 10.1016/j.pcad.2022.11.019 | oa_other |
==== Front
Comput Ind Eng
Comput Ind Eng
Computers & Industrial Engineering
0360-8352
1879-0550
Published by Elsevier Ltd.
S0360-8352(22)00873-7
10.1016/j.cie.2022.108885
108885
Article
Vaccine supply chain coordination using blockchain and artificial intelligence technologies
Gao Ye a
Gao Hongwei b⁎
Xiao Han a
Yao Fanjun a
a School of Business, Qingdao University, Qingdao, 266071, PR China
b School of Mathematics and Statistics, Qingdao University, Qingdao, 266071, PR China
⁎ Corresponding author.
5 12 2022
5 12 2022
1088857 4 2022
24 10 2022
2 12 2022
© 2022 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.
Currently, the global spread of COVID-19 is taking a heavy toll on the lives of the global population. There is an urgent need to improve and strengthen the coordination of vaccine supply chains in response to this severe pandemic. In this study, we consider a closed-loop vaccine supply chain based on a combination of artificial intelligence and blockchain technologies and model the supply chain as a two-player dynamic game with inventory level as the dynamic equation of the system. The study focuses on the applicability and effectiveness of the two technologies in the vaccine supply chain and provides management insights. The impact of the application of the technologies on environmental performance is also considered in the model. We also examine factors such as the number of people vaccinated, positive and side effects of vaccines, vaccine decay rate, revenue-sharing/cost-sharing ratio, and commission ratio. The results are as follows: the correlation between the difficulty in obtaining certified vaccines and the profit of a vaccine manufacturer is not monotonous; the vaccine manufacturer is more sensitive to changes in the vaccine attenuation rate. The study’s major conclusions are as follows: First, the vaccine supply chain should estimate the level of consumers’ difficulty in obtaining a certified vaccine source and the magnitude of the production planning and demand forecasting error terms before adopting the two technologies. Second, the application of artificial intelligence (AI) technology is meaningful in the vaccine supply chain when the error terms satisfy a particular interval condition.
Keywords
Supply chain management
Differential game
Vaccine supply chain
Blockchain technology
AI technology
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pmc1 Introduction
1.1 Background and motivation
According to World Health Organization statistics, from the first outbreak at the end of 2019 to December 2021, the COVID-19 virus infected 464,809,377 people and caused 6,062,536 deaths. The virus continues to wreak havoc worldwide (World Health Organization, 2022). A study published in Science showed that the coronavirus is responsible for a monthly average of hundreds of thousands of deaths, reducing the global gross domestic product by hundreds of billions of dollars per month and causing ongoing and cumulative damage to human health and education (Castillo et al., 2021). People dread the virus, and effective solutions are urgently needed to drastically improve the situation.
Research published in Science by Knipe et al. (2020) showed that vaccination is one of the most successful medical and public health measures implemented to date. Vaccination is now recognized as a cost-effective method to suppress or even eradicate infectious diseases, and vaccination has effectively controlled many infectious diseases (Yong et al., 2020). Vaccination is a long-term solution for the global response to the COVID-19 pandemic (Schaffer et al., 2020). However, Dai and Song (2021) show that as of mid-2021, several countries, including Canada, Israel, the United Kingdom, and the United States (US), have vaccinated over 40% of their populations; however, there are still 90 countries with fewer than 10 vaccine doses per 100 people. The global average vaccination coverage has remained as low as 10.91% as of June 1, 2021, especially in low- and middle-income countries (Johns Hopkins Coronavirus Resource Center, 2021).
However, the endgame of the COVID-19 pandemic is not the successful development of a vaccine but its sustained availability and effective vaccination (Dai & Song, 2021). The COVID-19 vaccine scandal in Peru in August 2020 reinforced the need to address the shortage of vaccine resources and provide timely and effective vaccination in the face of outbreaks worldwide (Estivariz et al., 2017).
A survey by Edwards and Hackell (2016) noted that the issue of vaccine reliability and accessibility is the most critical concern about vaccination, reflecting concerns about future vaccine supply chain issues. Historically, vaccine safety and supply incidents have had severe negative consequences. In 1955, two batches of polio vaccines from Cutter Laboratories were contaminated with live poliovirus because of supply chain mismanagement, resulting in 51 cases of permanent paralysis, 5 deaths, and transmission to families and community members (Nathanson & Langmuir, 1995). Estivariz et al. (2017) investigated the shortage of inactivated polio vaccines in Bangladesh between August and October 2015 and showed that it could be attributed to the vaccine supply–demand mismatch, which caused massive localized wastage, resulting in an overall vaccine shortage and a severe governmental breach of trust.
Since the publication of the genetic sequence of SARS-CoV-2 of COVID-19 on January 11, 2020, several vaccines have been developed by national research teams, with 115 vaccine candidates worldwide as of April 2020 (Thanh Le et al., 2020). As of June 1, 2021, the WHO has approved 8 vaccines for full use, 7 for restricted use, 30 for phase III trials, 34 for phase II trials, and 51 for phase I trials (Zimmer et al., 2022). Requirements for vaccine storage temperature and vaccination timeliness are stringent, posing new challenges to the vaccine supply chain. Let us consider the Pfizer-BioNTech vaccine as an example. Its long-term storage temperature must be between −80 °C and −60 °C. Tham (2022) showed that as of December 2020, the Pfizer-BioNTech vaccine is only usable for five days given standard refrigerator temperatures of 2 °C to 8 °C (i.e., 35.6°F and 46.4 °F) and that the interval between vaccinations is four weeks. Schifflfling and Breen (2022) showed that vaccine waste rates may be as high as 30% in some developed countries owing to poor vaccine cold chain logistics or vaccine inventory management.
In summary, strengthening vaccine supply chain management and ensuring its reliability and accessibility is urgent.
1.2 Literature review
Since the birth of blockchain technology in 2008, it has received much attention from business, academia, and the government, with increasingly widespread applications. Blockchain technology is an emerging technology platform that tracks and manages shipment activities in the supply chain using peer-to-peer, secure, and distributed ledgers without the involvement of intermediaries or trusted third parties (Hasan et al., 2019). Blockchain technology has emerged as an effective solution for drug supply chain management and related security issues, including vaccines (Haq & Muselemu, 2018). In March 2017, the US Food and Drug Administration began to apply blockchain technology to trace and track prescription drug use. In July 2018, China’s Henan Ziyun Cloud Computing Co. officially released a vaccine traceability platform based on blockchain technology. When used for vaccines, the technology ensures that the vaccine information in circulation is valid and not tampered with, thus maximizing the traceability and reliability of vaccines. From the consumers’ perspective, a vaccine information traceability platform built on blockchain technology can make it easier to find certified vaccine sources and increase the credibility of vaccine information, thus increasing vaccine reliability. Several scholars have investigated vaccine supply chains based on blockchain technology. Yong et al. (2020) developed a “blockchain vaccine” system based on blockchain and machine learning technology to support vaccine traceability and smart contract functionality and address vaccine expiration and vaccine record fraud. Chauhan et al. (2021) proposed a blockchain-based solution to improve the security and transparency of COVID-19 vaccine traceability and monitor vaccine supply and distribution through intelligent contracts. Shah et al. (2021) provided a comprehensive review of blockchain technology application in the COVID-19 pandemic, presented a case study of a digital vaccine passport based on this technology, and analyzed its complexity. Bamakan et al. (2021) proposed a variety of solutions for blockchain technology in the medical cold chain.
Artificial intelligence (AI) is the core technology of Industry 4.0. It creates new economic and business value by analyzing large amounts of data to identify relevant content, improve operations, and provide actionable recommendations (Richardson, 2021). Several scholars have studied AI-based vaccine supply chains. Arora et al. (2021) focused on the potential application of AI technologies to COVID-19 surveillance, diagnosis, outcome prediction, drug discovery, and vaccine development, discussing the clinical utility of AI-based models and the limitations and challenges faced by AI systems. Golan et al. (2021) proposed that AI technology helps supply chain managers better quantify efficiency/resilience trade-offs across all associated networks/domains and support optimal system performance post disruption.
The vaccine supply chain is a particular class of physical supply chains for perishable products, and dynamic changes in vaccine inventory level will seriously affect the coordination of the vaccine supply chain. Physical supply chains based on AI or Vendor Managed Inventory (VMI) model have been amply researched. Naz et al. (2021) proposed a framework and urgent recommendations for improving supply chain resilience based on AI technology in response to frequent supply chain disruptions caused by outbreaks, which can help researchers and practitioners improve supply chain management. De Giovanni (2021) modeled a dynamic game of a physical supply chain with VMI model to assess the benefits of utilizing AI technology to improve supply chain coordination. Riewpaiboon et al. (2015) compared and performed economic analyses of the traditional and VMI models in the Thai vaccine supply chain system, concluding that the VMI model would improve vaccine supply chain efficiency. VMI model means the vaccine manufacturer located upstream directly controls the downstream vaccination unit’s inventory to ensure better vaccine supply. In VMI model, the reliability and accessibility of the vaccine supply chain is enhanced because the vaccine manufacturer directly manages the inventory. In addition, VMI model has strong applicability to the vaccine supply chain for the following reasons.
• Since the vaccine manufacturer directly manages the inventory of the vaccination unit in the VMI model, the vaccine manufacturer has the quickest access to all information about the inventory of the vaccination unit, which not only creates the conditions for the overall vaccine supply chain synergy, but also makes the necessary preparations for the addition of new technologies.
• VMI is a typical physical supply chain management model, and the physical attributes of the vaccine supply chain confirm its applicability.
• As the shelf life of vaccines is minimal, the VMI model can enable vaccines to reach vaccinators faster and in time, thereby reducing vaccine losses.
• The VMI model can effectively avoid duplicate inventory, reduce inventory costs, lower inventory risks, ensure vaccine inventory accuracy, and enhance vaccine supply chain coordination.
Since the pandemic, many studies have been done on vaccine supply chain management. Niu et al. (2020) established a static game model to explain the preference of overseas vaccine suppliers for exclusive and Competitive Retailing strategies, aiming at the profit and social responsibility of overseas vaccine suppliers. Liu et al. (2021) applied blockchain technology to vaccine supply chain management and constructed a static game model to reveal the impact of blockchain on the vaccine supply chain. Sun et al. (2021) analyzed the risk of vaccine production management and established the integration mode and outsourcing mode of vaccine supply chain by using the static game model. Xie et al. (2021) analyzed subsidy selection in a vaccine supply chain with risk-averse buyers using evolutionary game methods. Shamsi et al. (2018) used optimal control theory, Stackelberg game model, and a nonlinear programming method to construct SIR epidemic model to minimize procurement and social costs in the vaccine supply chain. Pan et al. (2022) discussed the impact of public and private hospital supply on the vaccine supply chain.
1.3 Research gap and objective
At present, the studies of scholars do not involve the temporal impact of industry 4.0 technology on the dynamic game results of all parties in modern vaccine supply chain management, the details are shown in Table 1.
Based on the robust results of existing research, this study considers a closed-loop vaccine supply chain based on a combination of AI and blockchain technologies and models the supply chain as a two-player dynamic game with inventory level as the dynamic equation of the system. The application of blockchain technology is likely to improve the security of the vaccine supply chain and the accessibility of vaccine consumers. The application of AI technology is expected to enhance the resilience of the vaccine supply chain and build a flexible vaccine supply chain to coordinate the production plans and demand forecasts of vaccines and avoid redundancies and shortages. As far as we know, this paper is the only one that uses the differential game method to build a dynamic game model and considers the combination of AI technology and blockchain technology in the model to improve vaccine supply chain coordination.Table 1 Summary of different existing content/model in previous literature.
Literature category Authors Dynamic game model Vaccine supply chain AI/Blockchain technology
Yong et al. (2020) – ✓ AI and Blockchain
De Giovanni (2021) ✓ – AI
Liu et al. (2021) – ✓ Blockchain
Chauhan et al. (2021) – ✓ Blockchain
New technology Shah et al. (2021) – ✓ Blockchain
Bamakan et al. (2021) – ✓ Blockchain
Arora et al. (2021) – ✓ AI
Naz et al. (2021) – ✓ AI
Golan et al. (2021) – ✓ AI
Niu et al. (2021) – ✓ –
Sun et al. (2021) – ✓ –
Supply chain management Xie et al. (2021) ✓ ✓ –
Shamsi et al. (2021) ✓ ✓ –
Pan et al. (2021) – ✓ –
This paper ✓ ✓ AI and Blockchain
The rest of the paper is organized as follows: Section 2 introduces the two dynamic game models proposed in this study; Section 3 presents the equilibrium results of the models; Section 4 compares these equilibrium results and offers management insights; and Section 5 presents the study’s conclusions and outlook.
2 Model
2.1 The basic assumptions
According to Davido et al. (2021), Given that antibody levels are declining over time and that the danger of contracting the infection often reappears after a few months, Covid booster shots should be suggested to and advised to all fully immunized individuals. This category of vaccine supply chain has the following characteristics:
• The demand for a continuous supply of vaccines.
• The wide area covered by vaccines.
• The huge business volume.
• The high vaccine penetration rate (especially under government leadership).
• The short response time required.
In this study, we assumed that the supply of vaccine is a continuous process in an infinite time interval. The vaccine supply chain in this paper represents a vaccine supply chain with a continuous supply of vaccines. We assume a two-player supply chain consisting of a vaccine manufacturer (the leader M, A vaccine manufacturer, such as Pfizer.) and a vaccination unit (the follower U, A facility that provides vaccination services, such as a hospital or a government-run vaccination site (Colombo et al., 2006).) and model this as a dynamic game in a time interval t∈0,∞. M is located upstream of the vaccine supply chain and decides the optimal production efficiency strategy ut. U is located downstream of the vaccine supply chain and decides the optimal sales price strategy pt. M needs to pay the production cost c per vaccine. The players share revenues according to the exogenous revenue-sharing/cost-sharing ratio ϕ (ϕ∈0,1) agreed upon in the sharing contract signed before the collaboration. It is assumed that the state variable is the inventory level Yt. The two players develop their respective strategies based on the current inventory level Yt. This approach to the inventory level as the state variable is necessary and it sets the stage for the important role of blockchain technology to be reflected later. This setup method is similar to De Giovanni (2021) and He et al. (2020). Therefore, strategies ut and pt, on both sides, are functions of the state variable.
M and U partner strategically with two technology service providers, BVP and G. BVP provides blockchain technology services to the vaccine supply chain to enhance the traceability of vaccines and increase the transparency of vaccine products in circulation, thereby reducing the consumers’ effort in locating certified vaccine sources. G provides AI technology services to the vaccine supply chain. When M implements a production plan, it generates a vaccine production error term ϵU, which is the difference in quantity between the actual and planned production. Similarly, when U forecasts the market for the vaccine, it generates a vaccine demand error term ϵD, the quantitative difference between the actual and projected vaccine demand. G eliminates these errors (ϵU,ϵD) by building an AI system for the vaccine supply chain to accurately match production efficiency and demand forecasts. We use superscripts B and AI to identify the basic dynamic game model that applies only blockchain technology and the AI dynamic game model that applies both blockchain technology and AI technology, respectively. A similar assumption approach can be seen in De Giovanni (2021).
2.2 Basic dynamic game model with blockchain technology application only (B-model)
First, we assume that the vaccine demand is DBt. The vaccine price is pBt (pBt>0), which is negatively correlated with the utility of vaccination Z, i.e., the utility of vaccination decreases as the price of vaccine pBt increases, and its utility conversion coefficient is β (β>0). The inventory level is YBt (YBt>0), which is positively correlated with the utility of vaccination Z, that is, the utility of vaccination Z increases as the inventory level YBt increases. The more inventory at a vaccination unit, the more consumers are willing to go to that unit for vaccination; its utility conversion coefficient is α (α>0). Under the same conditions, consumers are more likely to spend in shopping places that offer a greater quantity of goods and better quality and service. This setup method is similar to Liu et al. (2021).
Second, we assume that the number of potential consumers is n. The perceived value of the vaccine to the consumers is v, and its distribution function fv follows a uniform distribution on the interval 0,1. The positive effect of vaccination on the consumers is s (s>0), the consumers’ difficulty in obtaining a certified vaccine source (the acquisition effort) before vaccination is W (W>0), and the increase in difficulty has a negative utility on the vaccination utility of the vaccine. The negative utility conversion coefficient is γ (γ>0), and the side effects θ of vaccination, such as discomfort and other adverse effects, also have a negative utility of vaccination Z. The assumptions here are referenced to Liu et al. (2021). Therefore, we express the utility of vaccination by the following equation: (1) Z=v+αYBt−βpBt−γW−θ+s,
when Z>0, consumers receive the vaccine. Therefore, the number of people who eventually choose to be vaccinated, that is, the demand for vaccines, is (2) DBt=∫−αYBt+βpBt+γW+θ−s1fvdv=n1+αYBt−βpBt−γW−θ+s.
YBt (YB≥0) represents the inventory accumulated over time according to the following dynamic equation: (3) Y˙B(t)=uB(t)1+ϵU−DB(t)1+ϵD.
This dynamic equation expresses inventory accumulation per unit time as the difference between vaccine production per unit time (productivity uBt) and vaccine demand per unit time (DBt), where ϵU,ϵD are the production efficiency and demand forecast error terms, respectively. Notably, the state variable Yt represents the change in the physical vaccine inventory, which is a physical quantity change. Yt differs from state variables like brand value and goodwill, whose error terms can continuously and systematically affect the inventory state variable. Unlike the traditional error term, the error term in this paper is independent of time, state and policy, which is the same as the second method in Sethi (1983).
Based on the current extensive application and practice of AI technology in industry 4.0 manufacturing and supply systems, Section 2.3 will consider eliminating these error terms from the model, which will become feasible with the help of modern AI technology. Tao et al. (2018) pointed out that AI technology has the characteristic of proactively adapting to productivity amplitude, thus helping M eliminate random errors in production efficiency. Frank et al. (2019) pointed out that artificial intelligence technology, by its big data analysis ability, could realize amplitude instability and the last kilometer problem of long-term demand prediction, thus helping U eliminate demand prediction error. Gilchrist (2016) described and discussed the application and practice of artificial intelligence technology in modern industry in detail, which further supported our view.
In the B-model, M and U enter into a strategic partnership through a contract. Both players’ revenues as per the contract are distributed according to a revenue-sharing/cost-sharing ratio ϕ. The marginal profit of M is πMB=pBtϕ−c and the marginal profit of U is πUB=pBt1−ϕ, where c is the vaccine’s marginal cost of production.
Referring to Erickson (2011), let the vaccine production cost CuB be (4) CuBuB(t),t=h2uB(t)2,
where h is the production cost conversion factor.
Referring to Ben-Daya et al. (2013), let the inventory cost ChB of U located downstream be: (5) ChB=chYB(t)2,
where ch denotes the inventory cost conversion factor. After negotiations between M and U, both players decide to share the inventory cost using the revenue-sharing/cost-sharing ratio ϕ. The players’ shares can be expressed as ϕchYB(t)2 and (1−ϕ)chYB(t)2, respectively.
In addition, we also consider the environmental and profit impacts of vaccine production emissions. Assume that ω is the emissions per vaccine, and e¯ is the upper limit of emissions set by the local government. Then, the profit of M affected by emissions EB is (6) EB=ceωuBt−e¯.
When ωuBt>e¯, M pays a penalty to the government; when ωuBt<e¯, the government pays a subsidy to M. The conversion factor used is ce.
Assume that the number of vaccine losses because of mishandling in vaccine transportation, storage, warehousing, and network operations, as a percentage of vaccine demand (DBt), is λ (λ>0); the total vaccine loss is λDBt, and the vaccine loss causes lowers profits for U, which is expressed as (7) LB=λDBtπUB=λDBtpB(t)(1−ϕ).
After negotiations between M and U, both players decide to share the decrease in profit LB using the contracted revenue-sharing/cost-sharing ratio ϕ. The two players’ shares can be expressed as λDBtpB(t)(1−ϕ)ϕ and λDBtpB(t)(1−ϕ)2, respectively.
BVP is committed to applying blockchain technology to build a product traceability service platform, improve product information transparency and the difficulty of information tampering, and develop blockchain applications to control consumers’ difficulty in obtaining certified vaccine sources. After negotiations, BVP’s fees are paid from the total revenues of both M and U, at a commission ratio η. The blockchain fee to be paid by M is (8) FMB=ηDBt1+λpB(t)ϕ−c.
The blockchain fees to be paid by U is (9) FUB=ηDBtpB(t)(1−ϕ).
The BVP company removes its own costs after collecting the blockchain service fees paid by M and U, which is the profit of BVP. According to the results of Eqs. (8), (9), BVP’s profit is obtained as (10) ΠBVPB=FMB+FUB−cBVPDBt1+λ,
where cBVP (cBVP>0) is the retrospective marginal cost per vaccine borne by the vaccine traceability service platform.
Finally, considering the above components affecting the expected profits and benefits, M’s total profit ΠMB is (11) ΠMB=maxuB(t)∫0+∞e−ρtDBt1+λpB(t)ϕ−c−FMB−ϕLB−ϕChB−CuB−EBdt
U’s total profit ΠUB is (12) ΠUB=maxpB(t)∫0+∞e−ρtDBtpB(t)(1−ϕ)−FUB−LB(1−ϕ)−(1−ϕ)ChBdt
M and U have the same discount factor ρ (ρ>0).
Referring to Liu et al. (2021), this study uses consumer surplus (CS) to denote the impact on the utility of consumers and social welfare (SW) to denote the impact on the sum of the utilities of all vaccine supply chain participants. In the B-model, the CSB is (13) CSB=n∫−αYBt+βpBt+γW+θ−s1v+αYBt−βpBt−γW−θ+sf(v)dv=n2(1+αYBt−βpBt−γW−θ+s)2.
The SWB is (14) SWB=ΠMB+ΠUB+ΠBVPB+CSB.
2.3 Dynamic game model applying AI technology and blockchain technology (AI-model)
In the AI-model, M and U decide to adopt AI technology to aid vaccine supply chain collaboration and approach G, which specializes in AI technology solutions for manufacturing companies. Owing to the adoption of AI technology, the marginal manufacturing profit of M is πMAI=pAItϕ−c−cAI, where cAI represents the impact of AI technology adoption on M’s marginal cost, and U’s marginal revenue is πUAI=pAIt1−ϕ.
The benefit of using AI technology is the elimination of the errors ϵD,ϵU that appear in Eq. (3) in the B-model and their negative impact on the operational decisions of the vaccine supply chain, thereby making the dynamic supply–demand coordination more intelligent. The AI-model inventory-level dynamic equation is as follows: (15) Y˙AI(t)=uAI(t)−DAI(t),
where uAIt,DAIt represent the production efficiency and demand, respectively. The functional forms of the demand (DAIt), production cost (CuAI), and inventory cost (ChAI) in this model are the same as those in Eqs. (2), (4), (5) in the B-model, respectively. The total cost paid by the vaccine supply chain for G’s AI services is: (16) ΠGAI=AA+δϵu2+ϵD2+kYAI(t),
where A is the cost to G to allow the vaccine supply chain access to the “AI Cloud” (Intelligent Cloud Service); δ is the conversion factor for the fee charged by G based on the magnitude of the error term eliminated from the vaccine supply chain; and k is the conversion factor for the fee charged by G based on the vaccine supply chain’s inventory level. According to the revenue-sharing/cost-sharing ratio ϕ, the AI service costs must be shared by M and U. Therefore, the shares paid by M and U are ϕΠGAI and 1−ϕΠGAI, respectively.
In addition, the adoption of AI technology changes M’s emissions. In the AI-model, M’s profit, affected by emissions EAI, is (17) EAI=ceω+ωAIuAIt−e¯,
where ωAI is the increase in emissions per vaccine owing to AI technology adoption. This setting enables the vaccine supply chain to make the right decision regarding AI technology adoption.
In this model, the functional forms of the lost profit because of vaccine loss LAI, the blockchain cost to be paid by M FMAI, the blockchain cost to be paid by U FUAI, and BVP’s profit ΠBVPAI are consistent with the functional forms in Eqs. (7), (8), (9), and (10) in the B-model, respectively.
Finally, considering the above components affecting the expected profits and benefits, M’s total profit ΠMAI in the AI-model is: (18) ΠMAI=maxuAIt∫0+∞e−ρtDAIt1+λpAItϕ−c−cAI−FMAI−ϕLAI−CuAI−EAI−ϕChAI−ϕΠGAIdt
U’s total profit ΠUAI is (19) ΠUAI=maxpAI(t)∫0+∞e−ρtDAItpAIt1−ϕ−FUAI−1−ϕLAI−1−ϕChAI−1−ϕΠGAIdt
M and U have the same discount factor ρ (ρ>0). In the AI-model, the CSAI is (20) CSAI=n∫−αYAIt+βpAIt+γW+θ−s1v+αYAIt−βpAIt−γW−θ+sf(v)dv=n2(1+αYAIt−βpAIt−γW−θ+s)2.
The SWAI is (21) SWAI=ΠMAI+ΠUAI+ΠBVPAI+ΠGAI+CSAI.
All notations and definitions have been listed in Appendix C, as shown in Table C.5.
3 Equilibria
Drawing on Fershtman and Kamien (1987), this section analyzes the results of Nash equilibrium calculations for the B-model and the AI-model. We propose the perfect closed-loop Nash equilibrium strategy for the subgame in both models uSS,pSS. Furthermore, we derive the equilibrium trajectory Yt of the inventory level and specify its stability condition and calculate the equilibrium solutions for the other indicators (denoted by the subscript SS). For brevity, the time variable t is omitted in the following calculations.
3.1 B-model calculation results
In the B-model, ϵU,ϵD are not effectively eliminated because of a lack of AI technology, resulting in a mismatch between production planning and actual demand that makes supply chain collaboration challenging and highly detrimental to consumers.
3.1.1 Proposition 1
Assuming an interior solution, the equilibrium productivity and price strategies in the B-model are given by: (22) uSSB=YSSBM1B+M2B1+ϵU−ωceh,
(23) pSSB=121+s+YSSBα−γW−θβ+YSSBU1B+U2B1+ϵD−1+ϕ−1+η+λ−λϕ.
The equilibrium profits of M and U are (24) ΠMSSB=M1B2(YSSB)2+M2BYSSB+M3B,ΠUSSB=U1B2(YSSB)2+U2BYSSB+U3B,
where MiB,UiB (i=1...3) are the Riccati coefficients. The equilibrium trajectory of the inventory level is: (25) YBt=1−et(1+ϵD)(βU1B(1+ϵD)−α(1−ϕ)(1−η−λ1−ϕ))+2(1−ϕ)(1−η−λ1−ϕ)M1B(1+ϵU)2YSSB+et(1+ϵD)(βU1B(1+ϵD)−α(1−ϕ)(1−η−λ1−ϕ))+2(1−ϕ)(1−η−λ1−ϕ)M1B(1+ϵU)2Y0B,
where (26) YSSB=−hn(1+ϵD)(−(1+s−γW−θ)(−1+ϕ)(−1+η+λ−λϕ)+βU2B(1+ϵD))−2(−1+ϕ)(−1+η+λ−λϕ)ωce(1+ϵU)+2(−1+ϕ)(−1+η+λ−λϕ)M2B(1+ϵU)2hn(1+ϵD)(−α(−1+ϕ)(−1+η+λ−λϕ)+βU1B(1+ϵD))+2(−1+ϕ)(−1+η+λ−λϕ)M1B(1+ϵU)2,
is the equilibrium inventory level. This equilibrium is globally asymptotically stable if and only if: hn(1+ϵD)(α(1−ϕ)(1−η−λ1−ϕ)−βU1B(1+ϵD))−2(1−ϕ)(1−η−λ1−ϕ)M1B(1+ϵU)2<0.
The equilibrium demand can be calculated as (27) DSSB=12n(1+s+YSSBα−γW−θ−β(YSSBU1B+U2B)(1+ϵD)(−1+ϕ)(−1+η+λ−λϕ)),
The equilibrium BVP profit is (28) ΠBVPSSB=ηDSSB(1+λ)(pSSBϕ−c)+ηDSSBpSSB(1−ϕ)−cBVPDSSB(1+λ).
The equilibrium CS is (29) CSSSB=18n−1−s−YSSBα+γW+θ+β(YSSBU1B+U2B)(1+ϵD)(−1+ϕ)(−1+η+λ−λϕ)2.
The equilibrium SW is (30) SWSSB=DSSBpSSBη(1−ϕ)+DSSBη(1+λ)(−c+pSSBϕ)−DSSB(1+λ)cBVP+12(YSSB)2M1B+YSSBM2B+M3B+12(YSSB)2U1B+YSSBU2B+U3B+18n(−1−s−YSSBα+Wγ+θ+β(YSSBU1B+U2B)(1+ϵD)(−1+ϕ)(−1+η+λ−λϕ))2.
From Proposition 1, it follows that uSSB,pSSB,DSSB,CSSSB,SWSSB,ΠMSSB,ΠUSSB depend on the state of the inventory (YSSB) and are related to the value of MiB,UiB (i=1...3). As the inventory-level dynamic equation is a linear quadratic equation with fully coupled coefficients M1B,U1B, the benchmark parameter values must be determined to solve the Riccati system before analyzing the individual parameters and obtaining management insights. Referring to El Ouardighi et al. (2008) and Prasad and Sethi (2004), the benchmark parameter values are set as listed in Table C.6.
The values of ϵD and ϵU in Table C.6 are fixed and set assuming the following: On the one hand, ϵD and ϵU constantly and systematically affect the entire vaccine supply chain and lead to a logical solution; on the other hand, these can help enterprises better determine whether the error term lies within the valid region, providing a basis for deciding upon AI technology adoption. The case for non-fixed values of ϵD and ϵU is discussed in Section 4.
To determine the value of MiB,UiB (i=1...3), we substitute the values in Table C.6 into the Riccati system to obtain two sets of real roots. Please refer to Appendix C for details.
3.1.2 Corollary 1
• The trajectory of the equilibrium inventory level YSSB is monotonic. When Y0B<YSSB, YB∈Y0B,YSSB; when Y0B>YSSB, YB∈YSSB,Y0B.
• The trajectory of uSSBYSSB,pSSBYSSB,DSSBYSSB,ΠMSSBYSSB,ΠUSSBYSSB decreases monotonically.
(Proof: omitted)
3.2 The AI-model calculation results
In the AI-model, the production planning error ϵU and demand forecasting error ϵD in the production and supply processes of the vaccine supply chain, respectively, are effectively eliminated using AI technology. This helps in coordinating production planning and actual demand.
The vaccine supply chain pays G to access AI cloud services, eliminate errors, and effectively control inventory levels. M and U share costs according to the revenue-sharing/cost-sharing ratio ϕ.
3.2.1 Proposition 2
Assuming an interior solution, the equilibrium productivity and price strategies in the AI-model are given by: (31) uSSAI=(YSSAIM1AI+M2AI)−ce(ω+ωAI)h,
(32) pSSAI=12(1+s+YSSAIα−Wγ−θβ+YSSAIU1AI+U2AI(−1+ϕ)(−1+η+λ−λϕ)).
M and U’s equilibrium profits are (33) ΠMSSAI=M1AI2(YSSAI)2+M2AIYSSAI+M3AI,ΠUSSAI=U1AI2(YSSAI)2+U2AIYSSAI+U3AI.
where MiAI,UiAI (i=1...3) are the Riccati coefficients. The equilibrium trajectory of the inventory level is: (34) YAIt=1−et2M1AI−hnα(1−ϕ)(1−η−λ1−ϕ)+hnβU1AIYSSAI+et2M1AI−hnα(1−ϕ)(1−η−λ1−ϕ)+hnβU1AIY0AI,
where (35) YSSAI=−(−1+ϕ)(−1+η+λ−λϕ)(hn(1+s−Wγ−θ)−2M2AI)+hnβU2AI−2(−1+ϕ)(−1+η+λ−λϕ)ce(ω+ωAI)(−1+ϕ)(−1+η+λ−λϕ)(hnα−2M1AI)−hnβU1AI,
is the equilibrium inventory level. This equilibrium is globally asymptotically stable if and only if: hnα−2M1AI(1−ϕ)(1−η−λ1−ϕ)−hnβU1AI<0
The equilibrium demand can be calculated as (36) DSSAI=12n(1+s+YSSAIα−γW−θ−β(YSSAIU1AI+U2AI)(−1+ϕ)(−1+η+λ−λϕ)),
The equilibrium BVP profit is (37) ΠBVPSSAI=ηDSSAI(1+λ)(pSSAIϕ−c)+ηDSSAIpSSAI(1−ϕ)−cBVPDSSAI(1+λ).
The equilibrium G profit is (38) ΠGSSAI=A(A+δ(ϵU2+ϵD2)+kYSSAI).
The equilibrium CS is (39) CSSSAI=18n−1−s−YSSAIα+γW+θ+β(YSSAIU1AI+U2AI)(−1+ϕ)(−1+η+λ−λϕ)2.
The equilibrium SW is (40) SWSSAI=−DSSAIpSSAIη(−1+ϕ)−DSSAIη(1+λ)(c−pSSAIϕ+cAI)−DSSAI(1+λ)cBVP+(YSSAI)2M1AI2+YSSAIM2AI+M3AI+(YSSAI)2U1AI2+YSSAIU2AI+18n−1−s−YSSAIα+γW+θ+β(YSSAIU1AI+U2AI)(−1+ϕ)(−1+η+λ−λϕ)2+U3AI+A(A+kYSSAI+δ(ϵD2+ϵU2))
(Proof: see Appendix A)
From Proposition 2, it follows that the equilibrium strategy (uSSAI,pSSAI), demand (DSSAI), CS (CSSSAI), SW (SWSSAI), and profit (ΠMSSAI,ΠUSSAI) depend on the state of inventory (YSSAI) and are related to the value of MiAI,UiAI (i=1...3).
To determine the value of MiAI,UiAI (i=1...3), we substitute the values in Table C.6 into the Riccati system to obtain two sets of real roots. Please refer to Appendix C for details.
3.2.2 Corollary 2
• The trajectory of the equilibrium inventory level YSSAI is monotonic. When Y0AI<YSSAI, YAI∈Y0AI,YSSAI; when Y0AI>YSSAI, YAI∈YSSAI,Y0AI.
• The trajectory of uSSAIYSSAI,pSSAIYSSAI,DSSAIYSSAI,ΠMSSAIYSSAI,ΠUSSAIYSSAI decreases monotonically.
(Proof: omitted)
3.2.3 Lemma 1
Table C.1 shows the sensitivity analysis of each exogenous variable in the B-model to MiB,UiB (i=1...3) and Table C.2 shows the sensitivity analysis of each exogenous variable in the B-model for each performance indicator. Please refer to Appendix C for details. For details on the data in Table C.2, please refer to Table B.2 in Appendix B. The results in Table C.1, Table C.2 can be used to analyze the focus covariate for our research.
From Table C.1, we get ωAI, e¯, cAI, A, k, δ, cBVP have almost no effect on MiB,UiBi=1...3, Therefore, they cannot be the focus variable of our analysicc. c, ce, ω, γ, W, θ, s have no effect on the linear quadratic coefficient M1B, U1B, but γ, W, θ, s are effect on the linear primary term coefficients and the constant terms, this can be verified by their impact on the performance indicator in Table C.2. Combined with the theme of this paper, we finally selected ϵU,ϵD, W,λ,θ,s,n, ϕ,η as the focus covariate for our research.
Similar analytical results we can derive from Table C.3, Table C.4, but note that since the AI model eliminates ϵD and ϵU, ϵD and ϵU in Table C.4 have no effect on performance indicators and MiB,UiBi=1....3 has no effect, but its research value cannot be negated. ϵD and ϵU are the key variables for our study of the application of AI techniques.
4 Comparison of results and discussion
This section analyzes the equilibrium results of the B-model and AI-model to investigate the necessity of adopting AI technology. Propositions 1 and 2 present the numerical results of the B-model and AI-model equilibrium strategies and performance indicators when in equilibrium, respectively, as shown in Table ??.
Table ?? shows that ΠMSS,ΠUSS,ΠBVPSS,ΠGSS,CSSS,SWSS significantly improve with AI technology adoption. Notably, the increase in inventory levels YSS and the resulting price increase pSS are potentially beneficial for vaccine supply chain enterprises. The increase in vaccine inventory levels YSS increases the consumers’ willingness to go to U. Furthermore, the increase in vaccine inventory levels YSS reduces the risk of vaccine stock-outs and increases the consumers’ willingness to go to U even if the vaccine price pSS increases.
Referring to the sensitivity analysis results, this section analyzes the effects of the three sets of critical variables on the vaccine supply chain system equilibrium. First, we investigate the applicability of AI technology to the system by analyzing the impact of ϵU,ϵD on the system equilibrium. Second, we examine the applicability of blockchain technology to the system by analyzing the impact of W,λ,θ,s,n variables of the two models on the system equilibrium and the impact of the vaccine product characteristics on the vaccine supply chain. Finally, we study the impact of the revenue-sharing/cost-sharing ratio and the commission ratio on the system by analyzing the impact of the variables ϕ,η on the system equilibrium.
4.1 Effect of ϵD and ϵU on the system equilibrium
In Lemmas 1 and 2, the effects of ϵD and ϵU on the Riccati system in two different models are analyzed, showing that the vaccine supply chain, as a physical supply chain, has a change in ϵU and ϵD, which affects the system equilibrium.
4.1.1 Corollary 3
In the space corresponding to the couple ϵD,ϵU and Y, a subspace Ω includes all realizations of the couple ϵD,ϵU, such that YB>YAI.
Proof: From Lemmas 2 and 4, we obtain the results of the sensitivity analysis of ϵD,ϵU with respect to the inventory level Y in the two models, that is, ∂YSSB∂ϵD>0,∂YSSB∂ϵU<0,∂YSSAI∂ϵD=0,∂YSSAI∂ϵU=0. Therefore, there exists a subspace Ω that includes all realizations of the couple ϵD,ϵU, such that YB>YAI.
In the vaccine supply chain, when ϵD>0 and ϵU<0, supply and demand are always satisfied at equilibrium, and M and U can control the supply chain by adjusting their strategies; AI technology adoption is thus not necessary to eliminate errors. Instead, when using ϵD<0 or ϵU>0, M and U should apply AI technology to eliminate the errors.
The effects of ϵD and ϵU on the equilibrium inventory level are shown in Fig. 1. The B-model and AI-model are presented in orange and blue, respectively.
Fig. 1 shows the expected results of applying AI technology to the vaccine supply chain when ϵD<0 or ϵU>0. An increase in inventory level Y guarantees the tapping of higher levels of market potential and improves each party’s profits. There is an increase in uSS,pSS,DSS,ΠMSS,ΠUSS,ΠBVPSS,ΠGSS,CSSS,SWSS as well.Fig. 1 Effect of ϵD and ϵU on the system equilibrium.
4.2 Effect of W,λ,θ,s,n on the system equilibrium
This section focuses on the effect of the variables W,λ,θ,s,n on the equilibrium indicator YSS,uSS,pSS,DSS,ΠMSS,ΠUSS,ΠBVPSS,ΠGSS,CSSS,SWSS. Fig. 2 shows the effect of a consumer’s effort to obtain a certified vaccine W on the system equilibrium.
As shown in Fig. 2, all equilibrium performance indicators in the AI-model are generally higher than those in the B-model. The valid interval for M as the supply chain leader W is 1.5,7.1, as shown in Fig. 2(e).Fig. 2 Effect of W on the system equilibrium.
When W∈1.5,5.5, AI technology application significantly affects the improvement in M’s profit, which peaks in each of the two models. This is because, when the value of W is small, consumers can obtain certified vaccines more efficiently, thereby lowering the vaccine inventory level Y, the demand for vaccines, and M’s profit. Similarly, when the value of W is higher, the stock of vaccines and M’s inventory cost increases, thus making M less profitable.
However, when W∈5.5,7.1 is used, M’s profit is lower in the AI-model than that in the B-model. This is because an increase in W increases the inventory level and reduces M’s profit, as the AI service fee charged by G is positively correlated with the inventory level.
As shown in Fig. 2(e)–(h), the values of W corresponding to M and U’s optimal profits, optimal SW, and optimal CS are different; therefore, the vaccine supply chain should reasonably coordinate all parties’ profit shares when applying blockchain technology. When the value of W is larger, it means that the vaccine supply chain is not cooperating with BVP at this time, which we observe leads to a decrease in M’s profitability, which is worse in the B model. Currently, most of the pharmaceutical sector has covered blockchain technology, especially in the vaccine sector, such as he US Food and Drug Administration and China’s Henan Ziyun Cloud Computing Co. The above results also illustrate this trend.
Fig. 3 shows the effect of the decay rate λ during vaccine transportation or storage on the system equilibrium.
As shown in Fig. 3, the value of each equilibrium performance indicator decreases as the decay rate λ during vaccine transportation or storage increases, with each indicator generally higher for the AI-model than for the B-model. Notably, as shown in Fig. 3(e) and (f), M is more sensitive to the increase in λ. There is a critical decay rate λ∗ above which vaccine suppliers are more inclined to discontinue using AI technology; this critical decay rate is calculated to be λ∗=0.034.Fig. 3 The effect of λ on the system equilibrium.
Fig. 4 shows the effect of the vaccine’s side effect θ on the system equilibrium.
As shown in Fig. 4, the indicators of the side effects θ are generally higher in the AI-model than in the B-model within a specific interval. As shown in Fig. 4(f)–(j), the increase in the vaccine’s side effects θ in the AI-model partially lowers U’s profit, CS, SW, and BVP and G’s profits.Fig. 4 The effect of θ on the system equilibrium.
Fig. D1 shows the vaccine’s positive effect s on the system equilibrium. As shown in Fig. 5, the vaccine’s positive effect s is generally higher in the AI-model than in the B-model for a specific interval. As shown in Fig. D1(f)–(j), an increase in the vaccine’s positive effect s in the AI-model partially lowers U’s profit, CS, SW, and BVP and G’s profits.
Fig. 5 shows the effect of the number of vaccinators n on the system equilibrium.
As shown in Fig. 5(e), as the number of vaccinators in the distribution n increases, the indicators are generally higher in the AI- model than in the B-model. When n∈0,1, a smaller number of vaccinators nevertheless increases M and U’s profits. This is because supply and demand are satisfied at the system equilibrium, which raises the vaccine price and profits. However, the positive utility of vaccinators with higher stockpiles and fewer vaccinations offsets the negative utility of higher prices.Fig. 5 The effect of n on the system equilibrium.
4.3 Effect of ϕ,η on the system equilibrium
This section focuses on the effect of the variables ϕ,η on YSS,uSS,pSS,DSS,ΠMSS,ΠUSS,ΠBVPSS,ΠGSS,CSSS,SWSS in the equilibrium. Fig. 6 shows the effect of the revenue-sharing/cost-sharing ratio on the system equilibrium.
The revenue-sharing/cost-sharing ratio ϕ in the sharing contract between M and U is the primary parameter variable for revenue allocation between them and the basis for determining cost sharing for inventory, vaccine decay, and AI technology between the two players. Its value affects the system equilibrium. The rationale for setting the revenue-sharing ratio is the same as that for setting the cost-sharing ratio: In a realistic supply chain sharing contract, when either player’s revenue-sharing ratio is lower than their cost-sharing ratio, they are prone to opportunism by exchanging a lower level of effort for a higher revenue. As shown in Fig. 6(e), M’s profit reaches the maximum value when ϕ∈0.4,0.5 in the AI-model, which results from setting ϕ as the revenue-sharing/cost-sharing ratio. A reasonable value of ϕ should be ensured before the players sign the cooperation contract.Fig. 6 The effect of ϕ on the system equilibrium.
Fig. D2 shows the effect of the commission ratio η on the system equilibrium. As shown in Fig. D2, the indicators in the AI-model are generally higher than those in the B-model when the commission ratio η varies within a specific range; BVP, M, and U prefer to enter into cooperation contracts with a commission ratio of η in the interval η∈0.25,0.3 in the AI- model.
4.4 Summary of the managerial insights
This section analyze the equilibrium results of the B-model and AI-model, and the management insights obtained are as follows:
• The increase in vaccine inventory levels YSS reduces the risk of vaccine stock-outs and increases the consumers’ willingness to go to U even if the vaccine price pSS increases. The benefits of increased inventory level outweigh the drawbacks for the vaccine supply chain as a whole. Therefore, companies should pay more attention to avoiding the risk of out-of-stock as opposed to the increase in inventory costs.
• AI technology adoption is thus not necessary to eliminate errors. The companies in the vaccine supply chain should consider the application of AI technology when the production schedule for vaccines is lower than expected and the demand for vaccines is higher than expected.
• The vaccine manufacturer and the vaccination unit need to be concerned about the peak of W. Excessive demands on the blockchain service company to raise W will result in lower profits.
• There is no need to consider the application of AI technology when the vaccine loss rate is too high and has a greater impact on the vaccine manufacturer.
• In a realistic supply chain sharing contract, when either player’s revenue-sharing ratio is lower than their cost-sharing ratio, they are prone to opportunism by exchanging a lower level of effort for a higher revenue.
• M’s profit reaches the maximum value when the revenue-sharing/cost-sharing ratio ϕ∈0.4,0.5 in the AI-model.
• M, and U prefer to enter into cooperation contracts with a commission ratio of η in the interval η∈0.25,0.3 in the AI- model.
5 Conclusions
Against the background of the highly developed modern Industry 4.0, this study focuses on the impact of two widely-used modern digital industrial technologies on collaborative vaccine supply chain management: The first, blockchain technology, enhances transparency in vaccine regulation by recording each vaccine product’s unique label on the blockchain, reduces the possibility of tampering with label information through various blockchain nodes, and improves the traceability of vaccines, making it more convenient for consumers to obtain certified vaccine sources and enhancing the need for vaccinators; The second, AI technology, obtains the datasets of vaccine supply chain node enterprises through numerous data interactions among vaccine supply chain enterprises using machine learning technology to eliminate the production planning and demand forecasting errors attributable to unexpected epidemic events. This helps match production planning and demand forecast in the vaccine supply chain and enables accurate collaborations, thus reducing the stock-out risk and inventory risk caused by overcapacity.
To better analyze the benefits of the two technologies for the vaccine supply chain and lay the foundation for their application, we first introduce the VMI model, controlled by a dynamic inventory-level equation. Cooperation between two companies with a shared contract allows better coordination of profitability and revenue/cost-sharing between the two companies while improving the availability of vaccines. In this study, we use blockchain technology to establish the demand function and apply the idea of AI technology to eliminate the errors and establish a dynamic equation. Based on the two-player dynamic game modeled, we evaluate the improvements in the vaccine supply chain because of the application of the two technologies.
The results show that the application of blockchain and AI technologies enhances vaccine supply chain efficiency and improves vaccine supply chain coordination. However, while the application of blockchain technology depends on the level of W, that is, consumers’ difficulty in obtaining a certified vaccine source, the application of AI technology depends on the magnitude of the error terms ϵD,ϵU. Therefore, the vaccine supply chain should estimate the level of W and magnitude of the error terms ϵD,ϵU before adopting the two technologies. In addition to analyzing the above critical variables, this study also analyzes the impact of other critical variables on the system, such as the vaccine’s positive effects, vaccine’s side effects, revenue-sharing/cost-sharing ratio, and commission percentage.
The sensitivity analysis section allows us to better examine the impact of these variables on system equilibrium in the B-model and the AI-model, respectively, and the comparative analysis section indicates that M’s performance indicators are more sensitive to changes in the critical variables that make them more willing to abandon AI technology adoption. AI technology costs, production costs, and emissions have a more significant impact on M than on U and are more closely related to M.
Owing to the gradual normalization of the COVID-19 pandemic, collaborative research on the direction of the normalization of the pandemic and the environment for an emergency response can be performed in the future.
CRediT authorship contribution statement
Ye Gao: Writing – original draft, Formal analysis, Methodology, Software, Validation, Writing – review & editing. Hongwei Gao: Conceptualization, Funding acquisition, Project administration, Supervision, Writing – review & editing. Han Xiao: Validation, Writing – review & editing. Fanjun Yao: Software, 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.
Appendix A Proof of Proposition 1
According to the Bellman continuum dynamic planning theory, for any state variable YB, there exists a continuously differentiable optimal value function ΠMSSBYSSB and ΠUSSBYSSB, which satisfies the following Hamilton–Jacobi-Bellman (hereafter abbreviated as the HJB) equation: (A.1) ρ(ΠMSSB)=1−ηDBt1+λpB(t)ϕ−c−λDBtpB(t)(1−ϕ)ϕ−h2uB(t)2−ceωuBt−e¯−ϕchYB2+ΠMSSB′(uB(t)1+ϵu−DB(t)1+ϵD),
(A.2) ρ(ΠUSSB)=1−ηDBtpB(t)(1−ϕ)−λDBtpB(t)(1−ϕ)2−ch(1−ϕ)YB2+ΠUSSB′(uB(t)1+ϵu−DB(t)1+ϵD).
ΠMSSB′ and ΠUSSB′ are the first-order partial derivatives of the optimal value function with respect to the inventory level state variable YB, respectively, and are the marginal contributions of the inventory level to the value function. According to the structure of Eqs. (A.1), (A.2), let ΠMSSB,ΠUSSB,ΠMSSB′,ΠUSSB′ be. (A.3) ΠMSSB=M1B2(YSSB)2+M2BYSSB+M3B,ΠUSSB=U1B2(YSSB)2+U2BYSSB+U3B,
(A.4) ΠMSSB′=YSSBM1B+M2B,ΠUSSB′=YSSBU1B+U2B,
where M1B,M2B,M3B,U1B,U2B,U3B are constants. Substituting Eqs. (A.3), (A.4) into Eqs. (A.1), (A.2) respectively gives (A.5) ρ(YSSB)2M1B2+YSSBM2B+M3B=1−ηDBt1+λpB(t)ϕ−c−λDBtpB(t)(1−ϕ)ϕ−h2uB(t)2−ceωuBt−e¯−ϕchYB2+(YSSBM1B+M2B)(uB(t)1+ϵu−DB(t)1+ϵD),
(A.6) ρ(YSSB)2U1B2+YSSBU2B+U3B=1−ηDBtpB(t)(1−ϕ)−λDBtpB(t)(1−ϕ)2−ch(1−ϕ)YB2+(YSSBU1B+U2B)(uB(t)1+ϵu−DB(t)1+ϵD).
In a dynamic decision-making environment, the vaccine supply chain need to consider the impact of changes in inventory levels on future profits and make optimal decisions to maximize long-term profits. According to the first-order optimization conditions, it is obtained that (A.7) uSSB=(YSSBM1B+M2B)(1+ϵU)h,
(A.8) pSSB=12(1+s+YSSBα−Wγ−θβ+(YSSBU1B+U2B)(1+ϵD)(−1+ϕ)(−1+η+λ−λϕ)).
Substituting Eqs. (A.5), (A.6), the Riccati system is obtained after collation as follows. 14(nα2ϕ(1−η(1+λ)+λϕ)β−4ϕch+nβϕ(−1+η+ηλ−λϕ)(U1B)2(1+ϵD)2(−1+ϕ)2(−1+η+λ−λϕ)2+2M1B(−nα−ρ−nαϵD+nβU1B(1+ϵD)2(−1+ϕ)(−1+η+λ−λϕ))+2(M1B)2(1+ϵU)2h)=0,
12nα(cβ(−1+η)(1+λ)+(1+s−Wγ−θ)ϕ(1−η(1+λ)+λϕ))β+(nβU1B(1+ϵD)(−c(−1+η)(1+λ)(−1+ϕ)(−1+η+λ−λϕ)+ϕ(−1+η+ηλ−λϕ)U2B(1+ϵD))((−1+ϕ)2(−1+η+λ−λϕ)2)+M2B(−nα−2ρ−nαϵD+nβU1B(1+ϵD)2(−1+ϕ)(−1+η+λ−λϕ))+M1B(n(1+ϵD)(−1−s+Wγ+θ+βU2B(1+ϵD)(−1+ϕ)(−1+η+λ−λϕ))+2(1+ϵU)(−ωce+M2B(1+ϵU))h))=0,
14(2ω2ce2h−4ρM3B+2nM2B(1+ϵD)−1−s+Wγ+θ+βU2B(1+ϵD)(−1+ϕ)(−1+η+λ−λϕ))+(n(−(1+s−Tγ−θ)(−1+ϕ)(−1+η+λ−λϕ)+βU2B(1+ϵD))(−(−1+ϕ)(−1+η+λ−λϕ)(2cβ(−1+η)(1+λ)+(1+s−Wγ−θ)ϕ(1−η(1+λ)+λϕ))+βϕ(−1+η+ηλ−λϕ)U2B(1+ϵD))(β(−1+ϕ)2(−1+η+λ−λϕ)2)+2(M2B)2(1+ϵU)2h+4ce(eh−ωM2B(1+ϵU))h)=0,
14(nα2(−1+ϕ)(−1+η+λ−λϕ)β+4(−1+ϕ)ch+U1B(nβU1B(1+ϵD)2(−1+ϕ)(−1+η+λ−λϕ)−2(nα+ρ+nαϵD)+4M1B(1+ϵU)2h))=0,
12(nα(1+s−Wγ−θ)(−1+ϕ)(−1+η+λ−λϕ)β−U2B(nα+2ρ+nαϵD)+2M1BU2B(1+ϵU)2h+U1B(n(1+ϵD)(−(1+s−Wγ−θ)(−1+ϕ)(−1+η+λ−λϕ)+βU2B(1+ϵD))(−1+ϕ)(−1+η+λ−λϕ)+2(1+ϵU)(−ωce+M2B(1+ϵU))h))=0,
14(n(−1−s+Wγ+θ)2(−1+ϕ)(−1+η+λ−λϕ)β−4ρU3B+U2B(n(1+ϵD)(2(−1−s+Wγ+θ)+βU2B(1+ϵD)(−1+ϕ)(−1+η+λ−λϕ))+4(1+ϵU)(−ωce+M2B(1+ϵU))h))=0.
Proof of Proposition 2
According to the Bellman continuum dynamic planning theory, for any state variable YAI, there exists a continuously differentiable optimal value function ΠMSSAIYSSAI and ΠUSSAIYSSAI, which satisfies the following Hamilton–Jacobi-Bellman (hereafter abbreviated as the HJB) equation: (A.9) ρ(ΠMSSAI)=1−ηDAIt1+λpAItϕ−c−cAI−λDAItpAIt1−ϕϕ−h2uAIt2−ceω+ωAIuAIt−e¯−chϕYAIt2−ϕΠGAI+(ΠMSSAI′)(uAI(t)−DAI(t)),
(A.10) ρ(ΠUSSAI)=1−ηDAItpAIt1−ϕ−λDAItpAIt1−ϕ2−ch1−ϕYAIt2−1−ϕΠGAI+(ΠUSSAI′)(uAI(t)−DAI(t)).
ΠMSSAI′ and ΠUSSAI′ are the first-order partial derivatives of the optimal value function with respect to the inventory level state variable YAI, respectively, and are the marginal contributions of the inventory level to the value function. According to the structure of Eqs. (A.9), (A.10), let ΠMSSAI,ΠUSSAI,ΠMSSAI′,ΠUSSAI′ be. (A.11) ΠMSSAI=M1AI2(YSSAI)2+M2AIYSSAI+M3AI,ΠUSSAI=U1AI2(YSSAI)2+U2AIYSSAI+U3AI,
(A.12) ΠMSSAI′=YSSAIM1AI+M2AI,ΠUSSAI′=YSSAIU1AI+U2AI.
where M1AI,M2AI,M3AI,U1AI,U2AI,U3AI are constants. Substituting Eqs. (A.11), (A.12) into Eqs. (A.9), (A.10) respectively give (A.13) ρ(YSSAI)2M1AI2+YSSAIM2AI+M3AI=1−ηDAIt1+λpAItϕ−c−cAI−λDAItpAIt1−ϕϕ−h2uAIt2−ceω+ωAIuAIt−e¯−chϕYAIt2−ϕΠGAI+(YSSAIM1AI+M2AI)(uAI(t)−DAI(t)),
(A.14) ρ(YSSAI)2U1AI2+YSSAIU2AI+U3AI=1−ηDAItpAIt1−ϕ−λDAItpAIt1−ϕ2−ch1−ϕYAIt2−1−ϕΠGAI+(YSSAIU1AI+U2AI)(uAI(t)−DAI(t)).
In a dynamic decision-making environment, the vaccine supply chain need to consider the impact of changes in inventory levels on future profits and make optimal decisions to maximize long-term profits. According to the first-order optimization conditions, it is obtained that (A.15) uSSAI=(YSSAIM1AI+M2AI)−ce(ω+ωAI)h,
(A.16) pSSAI=12(1+s+YSSAIα−Wγ−θβ+YSSAIU1AI+U2AI(−1+ϕ)(−1+η+λ−λϕ)).
Substituting Eqs. (A.13), (A.16), the Riccati system is obtained after collation as follows. 14(−4ϕch+2(M1AI)2h+M1AI(−2(nα+ρ)+2nβU1AI(−1+ϕ)(−1+η+λ−λϕ))+nϕ(1−η(1+λ)+λϕ)(α2−β2(U1AI)2(−1+ϕ)2(−1+η+λ−λϕ)2)β)=0,
12(cnα(−1+η)(1+λ)−2Akϕ+nα(1+s−Wγ−θ)ϕ(1−η(1+λ)+λϕ)β+n(−1+η)(1+λ)cAI(α−βU1AI(−1+ϕ)(−1+η+λ−λϕ))+(−(1−η+λ(−1+ϕ))(−1+ϕ)(−(nα+2ρ)(−1+ϕ)(−1+η+λ−λϕ)M2AI+nβ(−c(−1+η)(1+λ)+M2AI)U1AI)+nβϕ(−1+η+ηλ−λϕ)U1AIU2AI)/((−1+ϕ)2(−1+η+λ−λϕ)2)+M1AI(n(−1−s+Wγ+θ+βU2AI(−1+ϕ)(−1+η+λ−λϕ))+2M2AI−2ce(ω+ωAI)h))=0,
14(2cn(−1+η)(1+s−Wγ−θ)(1+λ)−4A2ϕ+n(−1−s+Wγ+θ)2ϕ(1−η(1+λ)+λϕ)β+4ece+2ω2ce2h+2(M2AI)2h−4ρM3AI−2cnβ(−1+η)(1+λ)U2AI(−1+ϕ)(−1+η+λ−λϕ)+nβϕ(−1+η+ηλ−λϕ)(U2AI)2(−1+ϕ)2(−1+η+λ−λϕ)2+2n(−1+η)(1+λ)cAI(1+s−Wγ−θ−βU2AI(−1+ϕ)(−1+η+λ−λϕ))−4Aδϕ(ϵD2+ϵU2)+2ce2ωAI(2ω+ωAI)h+2M2AI(n(−1−s+Wγ+θ+βU2AI(−1+ϕ)(−1+η+λ−λϕ))−2ce(ω+ωAI)h))=0,
14(nα2(−1+ϕ)(−1+η+λ−λϕ)β+4(−1+ϕ)ch+U1AI(−2(nα+ρ)+4M1AIh+nβU1AI(−1+ϕ)(−1+η+λ−λϕ)))=0,
12((−1+ϕ)(2Akβ+nα(1+s−Wγ−θ)(−1+η+λ−λϕ))β+(−nα−2ρ+2M1AIh)U2AI+U1AI(n(−1−s+Wγ+θ+βU2AI(−1+ϕ)(−1+η+λ−λϕ))+2M2AI−2ce(ω+ωAI)h))=0,
14(−4ρU3AI+(−1+ϕ)(4A2β+n(−1−s+Wγ+θ)2(−1+η+λ−λϕ)+4Aβδ(ϵD2+ϵU2))β+U2AI(n(2(−1−s+Wγ+θ)+βU2AI(−1+ϕ)(−1+η+λ−λϕ))+4M2AI−4ce(ω+ωAI)h))=0.
Appendix B In Appendix B, we will analyze the sensitivity of each performance indicator in the model to each variable to test the robustness of the results obtained in this paper. The baseline values of the variables for the sensitivity analysis are α=1,β=0.8,c=0.2,ϕ=0.8,h=1,ch=0.11,ρ=0.1,ce=0.2,ω=1.5,ωAI=0.1,e¯=0.3,cAI=0.1,A=0.4,k=0.2,δ=0.1,ϵD=−0.1,ϵU=0.1,λ=0.02,n=1.5,γ=0.5,W=4,θ=0.01,s=1,cBV P=0.01,η=0.05.
The results of the benchmark tests for each performance indicator derived from the baseline values of the variables are YSSB=2.444,uSSB=0.302,pSSB=2.735,DSSB=0.369,CSSSB=0.0454,SWSSB=1.747,ΠMSSB=1.061,ΠUSSB=0.596,ΠBVPSSB=0.0438,ΠGSSB=0;
YSSAI=5.539,uSSAI=0.883,pSSAI=6.175,DSSAI=0.883,CSSSAI=0.260,SWSSAI=5.0437,ΠMSSAI=1.569,ΠUSSAI=2.357,ΠBVPSSAI=0.259,ΠGSSAI=0.604.
The MiB,UiB,MiAI,UiAI (i=1...3) benchmark test results derived from the baseline values of the variables are M1B=0.115,U1B=0.225,M2B=0.265,U2B=−0.039,M3B=0.068,U3B=0.021;M1AI=0.149,U1AI=0.201,M2AI=0.375,U2AI=−0.082,M3AI=−2.800,U3AI=−0.265.
Based on the baseline test results, we varied the values of each variable within a specific range and observed the consequent changes in each performance indicator and MiB,UiB,MiAI,UiAI (i=1...3). The results of the performance indicator tests are detailed in Table B.1, Table B.2. The results of the MiB,UiB,MiAI,UiAI (i=1...3) tests are detailed in Table B.3, Table B.4.
Table B.1 Results of sensitivity analysis of B-Model performance.
Variables Value YSSB uSSB pSSB DSSB CSSSB SWSSB ΠMSSB ΠUSSB ΠBVPSSB ΠGSSB
0.9 3.66395 0.490596 3.60976 0.599617 0.119847 3.01826 1.6589 1.14179 0.097723 0
α 0.95 2.93653 0.377055 3.0906 0.460845 0.0707927 2.20329 1.27191 0.797638 0.0629525 0
1 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.7 1.71254 0.193138 2.20738 0.236057 0.0185744 1.19927 0.818393 0.340649 0.0216547 0
β 0.75 2.04222 0.241314 2.44745 0.294939 0.0289963 1.42366 0.915815 0.448197 0.0306532 0
0.8 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.2 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
c 0.25 2.61397 0.32306 2.92592 0.394851 0.0519691 1.8427 1.05849 0.682607 0.0496276 0
0.3 2.78368 0.344009 3.11672 0.420456 0.0589277 1.94099 1.05161 0.774607 0.0558489 0
0.8 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
ϕ 0.85 2.22696 0.275065 2.49104 0.33619 0.0376746 1.42911 0.984389 0.371324 0.0357267 0
0.9 2.04437 0.252343 2.28594 0.30842 0.0317076 1.19503 0.925097 0.20863 0.0295943 0
1 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
h 1.05 2.50924 0.310147 2.80816 0.379069 0.0478978 1.83008 1.10702 0.628824 0.046343 0
1.1 2.57736 0.318573 2.88473 0.389367 0.0505356 1.92046 1.15709 0.663717 0.0491164 0
0.11 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
ch 0.115 2.60741 0.332692 2.90791 0.406623 0.0551142 1.91444 1.13408 0.673471 0.0517721 0
0.12 2.78535 0.366606 3.09579 0.448074 0.0669234 2.11148 1.22072 0.762514 0.0613262 0
0.1 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
ρ 0.105 2.39378 0.300462 2.6737 0.367231 0.044953 1.67784 1.02188 0.568619 0.0423873 0
0.11 2.34926 0.299344 2.61919 0.365864 0.0446189 1.6175 0.987252 0.544416 0.0412164 0
0.2 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
ce 0.25 2.87973 0.355867 3.22471 0.434948 0.06306 2.42427 1.46957 0.829254 0.0623782 0
0.3 3.31521 0.409623 3.7143 0.50065 0.0835502 3.19977 1.9316 1.10037 0.0842527 0
1.5 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
ω 1.55 2.50232 0.309278 2.8004 0.378006 0.0476296 1.81127 1.09232 0.625255 0.046064 0
1.6 2.56039 0.316445 2.86568 0.386767 0.0498628 1.87772 1.12467 0.654768 0.0484141 0
0.1 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
ωAI 0.15 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.2 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.3 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
e¯ 0.35 2.44426 0.30211 2.73512 0.369246 0.0454476 1.84656 1.16091 0.596422 0.043772 0
0.4 2.44426 0.30211 2.73512 0.369246 0.0454476 1.94656 1.26091 0.596422 0.043772 0
0.1 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
cAI 0.15 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.2 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.4 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
A 0.45 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.5 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.2 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
k 0.25 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.3 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.1 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
δ 0.15 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.2 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
−0.15 2.16676 0.257552 2.4182 0.333303 0.0370303 1.50377 0.940527 0.49207 0.0341451 0
ϵD −0.1 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
−0.05 2.7651 0.354806 3.10152 0.410827 0.0562597 2.05972 1.21842 0.728695 0.0563479 0
0.05 2.66107 0.34457 2.97884 0.401998 0.0538674 2.0209 1.20664 0.70776 0.0526317 0
ϵU 0.1 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.15 2.26771 0.268108 2.53665 0.342583 0.039121 1.54482 0.955717 0.512822 0.0371571 0
0.02 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
λ 0.025 2.3926 0.295941 2.67683 0.361706 0.0436104 1.69212 1.036 0.570548 0.0419645 0
0.03 2.34282 0.289998 2.62066 0.354442 0.0418764 1.64064 1.01233 0.546171 0.0402568 0
1.5 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
n 1.55 2.40942 0.297617 2.70593 0.363754 0.0426828 1.71974 1.04937 0.585109 0.0425815 0
1.6 2.37766 0.293522 2.6793 0.358749 0.040219 1.69556 1.03897 0.574866 0.0415102 0
0.49 2.35013 0.28896 2.68085 0.353173 0.0415771 1.73892 1.08018 0.57627 0.040893 0
γ 0.495 2.3972 0.295535 2.70799 0.36121 0.0434908 1.74326 1.07103 0.586421 0.0423214 0
0.5 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
3.9 2.3266 0.285672 2.66729 0.349155 0.0406364 1.73635 1.08439 0.571139 0.0401871 0
W 3.95 2.38543 0.293891 2.7012 0.359201 0.0430083 1.74227 1.07341 0.583897 0.0419622 0
4 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
0.01 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
θ 0.015 2.45603 0.303754 2.74191 0.371255 0.0459435 1.74722 1.05824 0.598899 0.0441381 0
0.02 2.46779 0.305398 2.74869 0.373264 0.0464421 1.74781 1.0555 0.601367 0.0445056 0
1 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
s 1.05 2.3266 0.285672 2.66729 0.349155 0.0406364 1.73635 1.08439 0.571139 0.0401871 0
1.1 2.20894 0.269234 2.59945 0.329064 0.0360944 1.7196 1.10184 0.544921 0.0367407 0
0.01 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
cBVP 0.015 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74467 1.06091 0.596422 0.0418888 0
0.02 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74279 1.06091 0.596422 0.0400057 0
0.05 2.44426 0.30211 2.73512 0.369246 0.0454476 1.74656 1.06091 0.596422 0.043772 0
η 0.055 2.47886 0.30754 2.77284 0.375882 0.0470957 1.78317 1.0762 0.609689 0.0501901 0
0.06 2.51446 0.313139 2.81164 0.382726 0.0488263 1.82147 1.09215 0.623481 0.0570098 0
Table B.2 Results of sensitivity analysis of AI-Model performance.
Variables Value YSSAI uSSAI pSSAI DSSAI CSSSAI SWSSAI ΠMSSAI ΠUSSAI ΠBVPSSAI ΠGSSAI
0.9 10.2086 1.70716 10.0495 1.70716 0.971463 18.8726 8.51854 7.5771 0.836705 0.977486
α 0.95 7.22312 1.177 7.58412 1.177 0.461779 9.13376 3.57634 3.93354 0.429457 0.73865
1 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
0.7 3.44007 0.51984 4.40501 0.51984 0.0900779 1.33633 −0.14981 0.856988 0.105722 0.436005
β 0.75 4.33094 0.67198 5.16394 0.67198 0.150519 2.70413 0.462928 1.42426 0.162571 0.507275
0.8 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
0.2 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
c 0.25 5.78792 0.919178 6.45642 0.919178 0.281629 5.45294 1.6612 2.61059 0.280383 0.623834
0.3 6.03713 0.955612 6.73757 0.955612 0.304398 5.87569 1.7539 2.87578 0.302708 0.64377
0.8 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
ϕ 0.85 4.92487 0.792248 5.48338 0.792248 0.209219 3.20164 0.900145 1.33678 0.204741 0.55479
0.9 4.43136 0.7195 4.92711 0.7195 0.17256 1.92722 0.39191 0.685343 0.165766 0.515308
1 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
h 1.05 5.8551 0.929113 6.53211 0.929113 0.28775 5.92882 2.04507 2.68218 0.289355 0.629208
1.1 6.20135 0.979874 6.92262 0.979874 0.320051 6.96123 2.60496 3.05971 0.324602 0.656908
0.11 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
ch 0.115 6.08221 0.992541 6.76315 0.992541 0.328379 6.41073 2.22206 2.89722 0.320758 0.647377
0.12 6.71112 1.12127 7.442 1.12127 0.419085 8.16194 3.06678 3.58308 0.401028 0.697689
0.1 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
ρ 0.105 5.4237 0.877351 6.03601 0.877351 0.256581 4.89822 1.55418 2.24611 0.251123 0.594696
0.11 5.32413 0.873709 5.91457 0.873709 0.254456 4.78164 1.54797 2.15224 0.244691 0.58673
0.2 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
ce 0.25 6.31261 0.995888 7.04835 0.995888 0.330598 7.21823 2.70838 3.18225 0.336268 0.665808
0.3 7.08649 1.10903 7.92142 1.10903 0.409985 9.68376 4.01009 4.11796 0.423659 0.727719
1.5 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
ω 1.55 5.63546 0.896887 6.28442 0.896887 0.268135 5.28081 1.68361 2.45397 0.268033 0.611637
1.6 5.73219 0.91103 6.39355 0.91103 0.276658 5.52252 1.80098 2.55284 0.277311 0.619375
0.1 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
ωAI 0.15 5.63546 0.896887 6.28442 0.896887 0.268135 5.28081 1.68361 2.45397 0.268033 0.611637
0.2 5.73219 0.91103 6.39355 0.91103 0.276658 5.52252 1.80098 2.55284 0.277311 0.619375
0.3 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
e¯ 0.35 5.53872 0.882744 6.17528 0.882744 0.259746 5.14365 1.66877 2.35682 0.258913 0.603898
0.4 5.53872 0.882744 6.17528 0.882744 0.259746 5.24365 1.76877 2.35682 0.258913 0.603898
0.1 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
cAI 0.15 5.78792 0.919178 6.45642 0.919178 0.281629 5.45294 1.6612 2.61059 0.282727 0.623834
0.2 6.03713 0.955612 6.73757 0.955612 0.304398 5.87569 1.7539 2.87578 0.307581 0.64377
0.4 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
A 0.45 5.65283 0.908481 6.29647 0.908481 0.275113 4.89121 1.26815 2.36837 0.272054 0.712155
0.5 5.76694 0.934218 6.41766 0.934218 0.290921 4.69867 0.927892 2.37142 0.285513 0.827694
0.2 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
k 0.25 5.76694 0.934218 6.41766 0.934218 0.290921 5.51047 1.64949 2.55182 0.285513 0.737494
0.3 5.99516 0.985693 6.66004 0.985693 0.323863 5.99693 1.73165 2.75285 0.313381 0.880219
0.1 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
δ 0.15 5.53872 0.882744 6.17528 0.882744 0.259746 5.04005 1.56557 2.35602 0.258913 0.604298
0.2 5.53872 0.882744 6.17528 0.882744 0.259746 5.03645 1.56237 2.35522 0.258913 0.604698
−0.15 5.53872 0.882744 6.17528 0.882744 0.259746 5.03915 1.56477 2.35582 0.258913 0.604398
ϵD −0.1 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
−0.05 5.53872 0.882744 6.17528 0.882744 0.259746 5.04635 1.57117 2.35742 0.258913 0.603598
0.05 5.53872 0.882744 6.17528 0.882744 0.259746 5.04635 1.57117 2.35742 0.258913 0.603598
ϵU 0.1 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
0.15 5.53872 0.882744 6.17528 0.882744 0.259746 5.03915 1.56477 2.35582 0.258913 0.604398
0.02 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
λ 0.025 5.37053 0.858782 5.98501 0.858782 0.245835 4.64863 1.38423 2.188 0.244525 0.590442
0.03 5.21044 0.83598 5.80391 0.83598 0.232954 4.28268 1.21272 2.03248 0.231199 0.577636
1.5 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
n 1.55 5.42689 0.865507 6.07312 0.865507 0.241646 4.82847 1.4711 2.27583 0.249365 0.594951
1.6 5.32576 0.849928 5.9807 0.849928 0.225743 4.63738 1.38463 2.2036 0.240886 0.586861
0.49 5.39831 0.860095 6.06864 0.860095 0.246588 4.91937 1.55791 2.27899 0.24761 0.592665
γ 0.495 5.46851 0.871419 6.12196 0.871419 0.253124 4.98169 1.56366 2.31785 0.253231 0.598281
0.5 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
3.9 5.3632 0.854433 6.04198 0.854433 0.243352 4.88806 1.55479 2.2596 0.244823 0.589856
W 3.95 5.45096 0.868588 6.10863 0.868588 0.251482 4.96615 1.56228 2.30812 0.25182 0.596877
4 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
0.01 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
θ 0.015 5.55627 0.885575 6.18861 0.885575 0.261414 5.05908 1.56995 2.36659 0.260343 0.605302
0.02 5.57383 0.888406 6.20194 0.888406 0.263088 5.07449 1.57109 2.37635 0.261777 0.606706
1 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
s 1.05 5.3632 0.854433 6.04198 0.854433 0.243352 4.88806 1.55479 2.2596 0.244823 0.589856
1.1 5.18768 0.826122 5.90867 0.826122 0.227492 4.73016 1.53685 2.1631 0.231116 0.575815
0.01 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
cBVP 0.015 5.53872 0.882744 6.17528 0.882744 0.259746 5.03915 1.56877 2.35682 0.254411 0.603898
0.02 5.53872 0.882744 6.17528 0.882744 0.259746 5.03465 1.56877 2.35682 0.249909 0.603898
0.05 5.53872 0.882744 6.17528 0.882744 0.259746 5.04365 1.56877 2.35682 0.258913 0.603898
η 0.055 5.65308 0.903148 6.30122 0.903148 0.271892 5.32715 1.69497 2.45364 0.298664 0.613046
0.06 5.77212 0.924418 6.4323 0.924418 0.284849 5.62894 1.82924 2.5562 0.341732 0.622569
Table B.3 B-model MiB,UiBi=1...3 sensitivity analysis test results.
Variables Value M1B U1B M2B U2B M3B U3B
0.9 0.125098 0.192497 0.260371 −0.0514928 −0.134775 0.0383666
α 0.95 0.119926 0.208927 0.263337 −0.0445435 −0.0184649 0.0276265
1 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.7 0.105545 0.263462 0.267557 −0.0344089 0.205421 0.0132369
β 0.75 0.110447 0.242918 0.266548 −0.0366957 0.14115 0.0165753
0.8 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.2 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
c 0.25 0.115393 0.224834 0.264784 −0.041788 −0.0278781 0.0237123
0.3 0.115393 0.224834 0.264246 −0.0443198 −0.131051 0.0268734
0.8 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
ϕ 0.85 0.115316 0.16903 0.265982 −0.0274728 0.106112 0.0133657
0.9 0.115238 0.11294 0.266542 −0.0171778 0.139372 0.00773423
1 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
h 1.05 0.121156 0.224615 0.264768 −0.0396823 0.0612326 0.0212746
1.1 0.126917 0.224392 0.264189 −0.0401173 0.0546416 0.021819
0.11 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
ch 0.115 0.119026 0.222864 0.264826 −0.041024 0.0389674 0.0228571
0.12 0.122685 0.220862 0.264286 −0.0428705 0.00868454 0.0251787
0.1 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
ρ 0.105 0.117455 0.224684 0.264714 −0.0401077 0.0516979 0.0208866
0.11 0.119516 0.224531 0.264083 −0.0409653 0.0370447 0.021056
0.2 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
ce 0.25 0.115393 0.224834 0.332123 −0.0457528 0.0346778 0.0287501
0.3 0.115393 0.224834 0.398924 −0.0522494 −0.0250345 0.0380527
1.5 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
ω 1.55 0.115393 0.224834 0.274229 −0.0401224 0.0448346 0.0217405
1.6 0.115393 0.224834 0.283136 −0.0409886 0.0215002 0.0227552
0.1 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
ωAI 0.15 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.2 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.3 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
e¯ 0.35 0.115393 0.224834 0.265322 −0.0392562 0.167694 0.0207488
0.4 0.115393 0.224834 0.265322 −0.0392562 0.267694 0.0207488
0.1 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
cAI 0.15 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.2 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.4 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
A 0.45 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.5 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.2 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
k 0.25 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.3 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.1 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
δ 0.15 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.2 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
−0.15 0.111286 0.238387 0.265735 −0.0400885 0.103505 0.0193357
ϵD −0.1 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
−0.05 0.119497 0.212689 0.264855 −0.0385961 0.0292438 0.0223324
0.05 0.12663 0.224403 0.276904 −0.0413591 0.0214209 0.023284
ϵU 0.1 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.15 0.105586 0.225199 0.254569 −0.0374415 0.106942 0.018686
0.02 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
λ 0.025 0.115484 0.224778 0.265459 −0.0389682 0.0703198 0.0204092
0.03 0.115574 0.22472 0.265593 −0.0386836 0.0729148 0.0200769
1.5 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
n 1.55 0.115213 0.226172 0.26569 −0.0377668 0.0747802 0.0196031
1.6 0.115046 0.227419 0.266024 −0.0363948 0.0812611 0.0185725
0.49 0.115393 0.224834 0.264229 −0.0266853 0.140539 0.0180919
γ 0.495 0.115393 0.224834 0.264776 −0.0329708 0.104752 0.0194483
0.5 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
3.9 0.115393 0.224834 0.263956 −0.0235426 0.157955 0.0173927
W 3.95 0.115393 0.224834 0.264639 −0.0313994 0.113818 0.0191144
4 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.01 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
θ 0.015 0.115393 0.224834 0.265459 −0.0408276 0.0582314 0.0210652
0.02 0.115393 0.224834 0.265596 −0.042399 0.0486889 0.0213781
1 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
s 1.05 0.115393 0.224834 0.263956 −0.0235426 0.157955 0.0173927
1.1 0.115393 0.224834 0.26259 −0.00782893 0.240272 0.0136873
0.01 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
cBVP 0.015 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.02 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
0.05 0.115393 0.224834 0.265322 −0.0392562 0.0676944 0.0207488
η 0.055 0.115816 0.223395 0.265218 −0.0394339 0.0629324 0.0210877
0.06 0.116243 0.221955 0.265111 −0.0396154 0.0580684 0.0214363
Table B.4 AI-model MiAI,UiAIi=1...3 sensitivity analysis test results.
Variables Value M1AI U1AI M2AI U2AI M3AI U3AI
0.9 0.162375 0.170674 0.369542 −0.110132 −3.71491 −0.192013
α 0.95 0.155529 0.186002 0.373601 −0.0941706 −3.17946 −0.238436
1 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
0.7 0.136306 0.235902 0.370939 −0.0717604 −2.23239 −0.291989
β 0.75 0.142874 0.21721 0.373203 −0.0767181 −2.49334 −0.280584
0.8 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
0.2 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
c 0.25 0.149491 0.200696 0.373938 −0.0849842 −3.0071 −0.259205
0.3 0.149491 0.200696 0.373119 −0.0875508 −3.2229 −0.253043
0.8 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
ϕ 0.85 0.149402 0.15099 0.37646 −0.0584755 −2.76571 −0.206319
0.9 0.149309 0.100947 0.377857 −0.0370823 −2.7485 −0.141476
1 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
h 1.05 0.156941 0.20038 0.376663 −0.0838597 −2.85047 −0.261547
1.1 0.164387 0.200056 0.378444 −0.0853501 −2.90278 −0.257753
0.11 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
ch 0.115 0.154355 0.19876 0.373721 −0.0858041 −2.90604 −0.257303
0.12 0.159251 0.196782 0.372522 −0.0893884 −3.01952 −0.248468
0.1 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
ρ 0.105 0.151977 0.200502 0.373074 −0.0837783 −2.70458 −0.248541
0.11 0.154462 0.200302 0.371336 −0.0851527 −2.61827 −0.233307
0.2 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
ce 0.25 0.149491 0.200696 0.452213 −0.090388 −3.12479 −0.24594
0.3 0.149491 0.200696 0.52967 −0.0983585 −3.49699 −0.224343
1.5 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
ω 1.55 0.149491 0.200696 0.384439 −0.0834139 −2.85667 −0.262851
1.6 0.149491 0.200696 0.394121 −0.0844102 −2.91418 −0.260549
0.1 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
ωAI 0.15 0.149491 0.200696 0.384439 −0.0834139 −2.85667 −0.262851
0.2 0.149491 0.200696 0.394121 −0.0844102 −2.91418 −0.260549
0.3 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
e¯ 0.35 0.149491 0.200696 0.374757 −0.0824176 −2.6999 −0.265116
0.4 0.149491 0.200696 0.374757 −0.0824176 −2.5999 −0.265116
0.1 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
cAI 0.15 0.149491 0.200696 0.373938 −0.0849842 −3.0071 −0.259205
0.2 0.149491 0.200696 0.373119 −0.0875508 −3.2229 −0.253043
0.4 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
A 0.45 0.149491 0.200696 0.383436 −0.0864479 −3.28779 −0.349537
0.5 0.149491 0.200696 0.392115 −0.0904782 −3.81926 −0.444142
0.2 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
k 0.25 0.149491 0.200696 0.392115 −0.0904782 −3.09766 −0.263742
0.3 0.149491 0.200696 0.409473 −0.0985388 −3.40969 −0.263105
0.1 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
δ 0.15 0.149491 0.200696 0.374757 −0.0824176 −2.8031 −0.265916
0.2 0.149491 0.200696 0.374757 −0.0824176 −2.8063 −0.266716
−0.15 0.149491 0.200696 0.374757 −0.0824176 −2.8039 −0.266116
ϵD −0.1 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
−0.05 0.149491 0.200696 0.374757 −0.0824176 −2.7975 −0.264516
0.05 0.149491 0.200696 0.374757 −0.0824176 −2.7975 −0.264516
ϵU 0.1 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
0.15 0.149491 0.200696 0.374757 −0.0824176 −2.8039 −0.266116
0.02 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
λ 0.025 0.149619 0.200668 0.375249 −0.081783 −2.78875 −0.266663
0.03 0.149747 0.200637 0.375732 −0.081156 −2.77773 −0.268172
1.5 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
n 1.55 0.149255 0.201974 0.375518 −0.079115 −2.76466 −0.269008
1.6 0.149035 0.203163 0.376201 −0.0760775 −2.73253 −0.272457
0.49 0.149491 0.200696 0.373099 −0.070843 −2.6344 −0.262901
γ 0.495 0.149491 0.200696 0.373928 −0.0766303 −2.71641 −0.263978
0.5 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
3.9 0.149491 0.200696 0.372685 −0.0679494 −2.59396 −0.262385
W 3.95 0.149491 0.200696 0.373721 −0.0751835 −2.69577 −0.263703
4 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
0.01 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
θ 0.015 0.149491 0.200696 0.374964 −0.0838644 −2.821 −0.26541
0.02 0.149491 0.200696 0.375172 −0.0853112 −2.8422 −0.265708
1 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
s 1.05 0.149491 0.200696 0.372685 −0.0679494 −2.59396 −0.262385
1.1 0.149491 0.200696 0.370612 −0.0534813 −2.39732 −0.260032
0.01 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
cBVP 0.015 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
0.02 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
0.05 0.149491 0.200696 0.374757 −0.0824176 −2.7999 −0.265116
η 0.055 0.150055 0.199379 0.374874 −0.0828592 −2.8219 −0.263753
0.06 0.150626 0.198061 0.374986 −0.0833115 −2.84446 −0.26234
Appendix C To determine the value of MiB,UiB (i=1...3), we substitute the values in Table C.6 into the Riccati system to obtain two sets of real roots. root1:M1B=1.2960704908814287,U1B=−0.09196658004726502,M2B=0.5922616130080464,U2B=0.06813554569491495,M3B=2.4427568427601654,U3B=0.3277503414865632;root2:M1B=0.11539303236839006,U1B=0.22483403189608073,M2B=0.2653223752286094,U2B=−0.039256229180258374,M3B=0.06769443130977659,U3B=0.02074879624526238.
Substituting the above two sets of roots and the values in Table C.6 into Eq. (26), we obtain YSS1B=−0.8651424149046247,YSS2B=2.4442615495361406,
where YSS2B satisfies the assumption of a positive inventory level. Substituting YSSB=2.4442615495361406 into Eqs. (22)–(24), we obtain uSSB=0.3021104400641489,pSSB=2.735121859077061,DSSB=0.36924609341173775,CSSSB=0.04544755916660992,SWSSB=1.746556311228034,ΠMSSB=1.0609146155035325,ΠUSSB=0.5964221573408529,ΠBVPSSB=0.043771979217038674,
Table C.1 The sensitivity analysis of each exogenous variable in the B-model to MiB,UiB (i=1...3).
M1B U1B M2B U2B M3B U3B
α ↓ ↑ ↑ ↑ ↑ ↓
β ↑ ↓ ↓ ↓ ↓ ↑
c – – ↓ ↓ ↓ ↑
ϕ ↓ ↓ ↑ ↑ ↑ ↓
h ↑ ↓ ↓ ↓ ↓ ↑
ch ↑ ↓ ↓ ↓ ↓ ↑
ρ ↑ ↓ ↓ ↓ ↓ ↑
ce – – ↑ ↓ ↓ ↑
ω – – ↑ ↓ ↓ ↑
ωAI – – – – – –
e¯ – – – – ↑ –
cAI – – – – – –
A – – – – – –
k – – – – – –
δ – – – – – –
ϵD ↑ ↓ ↓ ↑ ↓ ↑
ϵU ↓ ↑ ↓ ↑ ↑ ↓
λ ↑ ↓ ↑ ↑ ↑ ↓
n ↓ ↑ ↑ ↑ ↑ ↓
γ – – ↑ ↓ ↓ ↑
W – – ↑ ↓ ↓ ↑
θ – – ↑ ↓ ↓ ↑
s – – ↓ ↑ ↑ ↓
cBVP – – – – – –
η ↑ ↓ ↓ ↓ ↓ ↑
“↑”, “↓” and “–” indicate that the performance indicators are positively correlated, negatively correlated, and not correlated with the exogenous variables, respectively.
Table C.2 The sensitivity analysis of each exogenous variable in B-model for each performance indicator.
YSSB uSSB pSSB DSSB CSSSB SWSSB ΠMSSB ΠUSSB ΠBVPSSB ΠGSSB
α ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ –
β ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ –
c ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↑ ↑ –
ϕ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ –
h ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ –
ch ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ –
ρ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ –
ce ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ –
ω ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ –
ωAI – – – – – – – – – –
e¯ – – – – – – – – – –
cAI – – – – – – – – – –
A – – – – – – – – – –
k – – – – – – – – – –
δ – – – – – – – – – –
ϵD ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ –
ϵU ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ –
λ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ –
n ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ –
γ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↑ ↑ –
W ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↑ ↑ –
θ ↑ ↑ ↑ ↑ ↑ ↑ ↓ ↑ ↑ –
s ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↓ ↓ –
cBVP – – – – – ↓ – – ↓ –
η ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ –
“↑”, “↓” and “–” indicate that the performance indicators are positively correlated, negatively correlated, and not correlated with the exogenous variables, respectively.
Table C.3 The sensitivity analysis of each exogenous variable in the AI-model for MiAI,UiAI (i=1...3).
M1AI U1AI M2AI U2AI M3AI U3AI
α ↓ ↑ ↑ ↑ ↑ ↓
β ↑ ↓ ↑ ↓ ↓ ↑
c – – ↓ ↓ ↓ ↑
ϕ ↓ ↓ ↑ ↑ ↑ ↑
h ↑ ↓ ↑ ↓ ↓ ↑
ch ↑ ↓ ↓ ↓ ↓ ↑
ρ ↑ ↓ ↓ ↓ ↑ ↑
ce – – ↑ ↓ ↓ ↑
ω – – ↑ ↑ ↓ ↑
ωAI – – ↑ ↑ ↓ ↑
e¯ – – – – ↑ –
cAI – – ↓ ↓ ↓ ↑
A – – ↑ ↓ ↓ ↓
k – – ↑ ↓ ↓ ↑
δ – – – – ↓ ↓
ϵD – – – – ↑ ↑
ϵU – – – – ↓ ↓
λ ↑ ↓ ↑ ↑ ↑ ↓
n ↓ ↑ ↑ ↑ ↑ ↓
γ – – ↑ ↓ ↓ ↓
W – – ↑ ↓ ↓ ↓
θ – – ↑ ↓ ↓ ↓
s – – ↓ ↑ ↑ ↑
cBVP – – – – – –
η ↑ ↓ ↑ ↓ ↓ ↑
“↑”, “↓” and “–” indicate that the performance indicators are positively correlated, negatively correlated, and not correlated with the exogenous variables, respectively.
Table C.4 The sensitivity analysis of each exogenous variable in AI-model for each performance indicator.
YSSAI uSSAI pSSAI DSSAI CSSSAI SWSSAI ΠMSSAI ΠUSSAI ΠBVPSSAI ΠGSSAI
α ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓
β ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
c ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
ϕ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓
h ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
ch ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
ρ ↓ ↓ ↓ ↓ ↓ ↓ ↑ ↓ ↓ ↓
ce ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
ω ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
ωAI ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
e¯ – – – – – ↑ ↑ – – –
cAI ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
A ↑ ↑ ↑ ↑ ↑ ↓ ↓ ↑ ↑ ↑
k ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
δ – – – – – ↓ ↓ ↓ – ↑
ϵD – – – – – ↑ ↑ ↑ – ↓
ϵU – – – – – ↓ ↓ ↓ – ↑
λ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓
n ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓
γ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
W ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
θ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
s ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓
cBVP – – – – – ↓ – – ↓ –
η ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑
“↑”, “↓” and “–” indicate that the performance indicators are positively correlated, negatively correlated, and not correlated with the exogenous variables, respectively.
Table C.5 Notations and definitions.
Notations Definitions Unit
α Coefficient of influence of the inventory level on demand –
β Coefficient of influence of the price on demand –
c The production cost per vaccine Dollar
ϕ The revenue-sharing/cost-sharing ratio –
h Coefficient of influence of the production efficiency on the costs of vaccine producers –
ch Coefficient of the impact of the inventory level on vaccine production cost Dollar
ρ Discount rate –
ce Coefficient of the impact of emissions on producer costs Dollar
ω Emissions per vaccine Cubic meter
ωAI The increase in emissions per vaccine because of AI technology adoption Cubic meter
e¯ The upper limit of total emissions Cubic meter
cAI Increase in marginal production costs from AI technology adoption Dollar
A Cost of accessing cloud services in G’s profit Dollar
k Coefficient of influence of the controlled inventory level on G’s profit –
δ The marginal cost of eliminating the error from G’s profit Dollar
ϵD The demand forecasting error –
ϵU The production planning error –
λ Proportional coefficient of vaccine loss during transportation or preservation –
n Number of vaccinees under each vaccination facility Person
γ Coefficient of influence of the difficulty in obtaining certified vaccines on demand for –
W The consumers’ difficulty in obtaining certified vaccine sources –
θ Coefficient of the impact of the vaccine’s side effects on demand –
s Coefficient of the impact of the vaccine’s positive effects on demand –
cBVP Marginal retrospective cost per vaccine borne by BVP Dollar
η BVP’s commission as a percentage of revenue –
M A vaccine manufacturer, such as Pfizer –
U A facility that provides vaccination services, such as a hospital or a government-run vaccination site –
ut The optimal production efficiency strategy picec
pt The optimal sales price strategy Dollar
Yt The inventory level at time t picec
BVP The company that provides blockchain services to the vaccine supply chain –
G The company that provides AI technology services to the vaccine supply chain –
Dt The vaccine demand 10 000 Person
Z The utility of vaccination –
v The perceived value of the vaccine to the consumers –
fv The distribution function of the perceived value v –
πM The marginal profit of M Dollar
πU The marginal profit of U Dollar
Cu The vaccine production cost Dollar
Ch The vaccine inventory cost Dollar
E The profit of M affected by emissions Dollar
L The vaccine loss causes lowers profits for U Dollar
FM The blockchain fee to be paid by M 10 000 Dollar
FU The blockchain fee to be paid by U 10 000 Dollar
ΠBVP The BVP’s profit 10 000 Dollar
ΠM M’s total profit 10 000 Dollar
ΠU U’s total profit 10 000 Dollar
CS The consumer surplus –
SW The social welfare –
B The superscript of B-model –
AI The superscript of AI-model –
ΠGAI The total cost paid by the vaccine supply chain for G’s AI services 10 000 Dollar
SS The subscript of equilibrium solutions –
Mi,Ui (i=1...3) The Riccati coefficients –
Table C.6 Symbols, meanings, and the benchmark parameter values.
Symbols Meaning Values
α Coefficient of influence of the inventory level on demand 1
β Coefficient of influence of the price on demand 0.8
c The production cost per vaccine 0.2
ϕ The revenue-sharing/cost-sharing ratio 0.8
h Coefficient of influence of the production efficiency on the costs of vaccine producers 1
ch Coefficient of the impact of the inventory level on vaccine production cost 0.11
ρ Discount rate 0.1
ce Coefficient of the impact of emissions on producer costs 0.2
ω Emissions per vaccine 1.5
ωAI The increase in emissions per vaccine because of AI technology adoption 0.1
e¯ The upper limit of total emissions 0.3
cAI Increase in marginal production costs from AI technology adoption 0.1
A Cost of accessing cloud services in G’s profit 0.4
k Coefficient of influence of the controlled inventory level on G’s profit 0.2
δ The marginal cost of eliminating the error from G’s profit 0.1
ϵD The demand forecasting error −0.1
ϵU The production planning error 0.1
λ Proportional coefficient of vaccine loss during transportation or preservation 0.02
n Number of vaccinees under each vaccination facility 1.5
γ Coefficient of influence of the difficulty in obtaining certified vaccines on demand for 0.5
W The consumers’ difficulty in obtaining certified vaccine sources 4
θ Coefficient of the impact of the vaccine’s side effects on demand 0.01
s Coefficient of the impact of the vaccine’s positive effects on demand 1
cBVP Marginal retrospective cost per vaccine borne by BVP 0.1
η BVP’s commission as a percentage of revenue 0.05
To determine the value of MiAI,UiAI (i=1...3), we substitute the values in Table C.6 into the Riccati system to obtain two sets of real roots, as follows: root1:M1AI=1.8112093987987976,U1AI=−0.07469752136786388,M2AI=0.6889018498476401,U2AI=0.06335557940748664,M3AI=1.776357377333373,U3AI=−0.01939392395107195;root2:M1AI=0.14949053965698567,U1AI=0.2006964431004427,M2AI=0.37475715621200184,U2AI=−0.08241756711145327,M3AI=−2.799895848958785,U3AI=−0.2651161940697662.
Substituting the above two sets of roots and the values in Table C.6 into Eq. (35), we obtain YSS1AI=−0.7003524174197305,YSS2AI=5.538722258883102
where YSS2AI satisfies the assumption of a positive inventory level. Substituting YSSAI=5.538722258883102 into Eqs. (31)–(33), we obtain: uSSAI=0.8827437357025953,pSSAI=6.1752830438517154,DSSAI=0.8827437357025962,CSSSAI=0.25974550097405835,SWSSAI=5.043650460038378,ΠMSSAI=1.5687738027688787,ΠUSSAI=2.35682276574916,ΠBVPSSAI=0.25891260288771567,ΠGSSAI=0.6038977807106483
Appendix D The effect of s on the system equilibrium and the effect of η on the system equilibrium are in Fig. D1, Fig. D2.
Fig. D1 The effect of s on the system equilibrium.
Fig. D2 The effect of η on the system equilibrium.
Data availability
Data will be made available on request.
Acknowledgments
This work was supported by the National Natural Science Foundation of China [grant number 72171126].
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| 36505091 | PMC9722244 | NO-CC CODE | 2022-12-12 23:20:28 | no | Comput Ind Eng. 2023 Jan 5; 175:108885 | utf-8 | Comput Ind Eng | 2,022 | 10.1016/j.cie.2022.108885 | oa_other |
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J Appl Physiol (1985)
J Appl Physiol (1985)
J Appl Physiol (1985)
JAPPL
Journal of Applied Physiology
8750-7587
1522-1601
American Physiological Society Rockville, MD
36269576
JAPPL-00458-2022
JAPPL-00458-2022
10.1152/japplphysiol.00458.2022
Rapid Report
Long-Term Recovery from SARS-CoV-2 (COVID-19)CT-derived measurements of pulmonary blood volume in small vessels and the need for supplemental oxygen in COVID-19 patients
PULMONARY BLOOD VOLUME IN SMALL VESSELS
https://orcid.org/0000-0002-1418-8695
Dierckx Wendel 1 3 10
https://orcid.org/0000-0002-6599-8276
De Backer Wilfried 1 10
Lins Muriel 2
De Meyer Yinka 3 10
https://orcid.org/0000-0003-4865-0507
Ides Kris 1 8 10
Vandevenne Jan 4 5
De Backer Jan 3
Franck Erik 9
Lavon Ben R. 3
Lanclus Maarten 3
Thillai Muhunthan 6 7
1Faculty of Medicine, University of Antwerp, Antwerp, Belgium
2General Hospital Sint-Maarten, Mechelen, Belgium
3Fluidda NV, Kontich, Belgium
4Department of Radiology, Ziekenhuis Oost-Limburg, Genk, Belgium
5Faculty of Medicine, University of Hasselt, Diepenbeek, Belgium
6Interstitial Lung Disease Unit, Royal Papworth Hospital NHS Foundation Trust, Cambridge, United Kingdom
7Department of Medicine, University of Cambridge, Cambridge, United Kingdom
8Department of Engineering, Cosys Labs, University of Antwerp, Antwerp, Belgium
9Centre for Research and Innovation in Care, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
10Multidisciplinary praxis Medimprove, Kontich, Belgium
Correspondence: K. Ides ([email protected]); W. Dierckx ([email protected]).
1 12 2022
21 10 2022
21 10 2022
133 6 12951299
3 8 2022
14 10 2022
14 10 2022
Copyright © 2022 the American Physiological Society.
2022
American Physiological Society
Throughout the COVID-19 pandemic, a portion of those affected have evolved toward acute hypoxic respiratory failure. Initially, this was hypothesized to result from acute lung injury leading to acute respiratory distress syndrome (ARDS). In previous research, a novel quantitative CT post-processing technique was described to quantify the volume of blood contained within pulmonary blood vessels of a given size. We hypothesized that patients with lower BV5 blood flow would have higher supplemental oxygen needs and less favorable arterial blood gas profiles. From the initial data analysis, 111 hospitalized COVID-19 patients were retrospectively selected based on the availability of CT scans of the lungs with a slice thickness of 1.5 mm or less, as well as PCR-confirmed SARS-CoV2 infection. Three-dimensional (3-D) reconstructions of the lungs and pulmonary vasculature were created. Further analysis was performed on 50 patients. Patients were divided into groups based on their need for oxygen at the time of CT scan acquisition. Eighteen out of 50 patients needed >2 L/min supplemental oxygen and this group demonstrated a significantly lower median percentage of total blood flow in the BV5 vessels compared with the 32 patients who needed <2 L/min supplemental oxygen (41.61% vs. 46.89%, P = 0.023). Both groups had significantly less blood as a proportion in BV5 vessels compared with healthy volunteers. These data are consistent with the hypothesis that reduced blood volume within small (BV5) pulmonary vessels is associated with higher needs for supplemental oxygen and more severe gas exchange anomalies in COVID-19 infections.
NEW & NOTEWORTHY This research provides, by using new imaging analysis on CT imaging, an insight into the pathophysiology of patients with COVID-19 infection. By visualizing and quantifying the blood in small vessels in the lung, we can link these results to the clinical need for oxygen in patients with COVID-19 infection.
BV5
; COVID-19
; functional imaging
; pulmonary blood vessels
Call for PapersTrue
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pmcINTRODUCTION
Throughout the COVID-19 pandemic, a portion of those affected have evolved toward acute hypoxic respiratory failure (1). Initially, this was hypothesized to result from acute lung injury leading to acute respiratory distress syndrome (ARDS), with many patients meeting the imaging requirements of the Berlin criteria for ARDS (2, 3). Treatment protocols for these patients were therefore designed based on the current guidelines for ARDS management, including mechanical ventilation with high levels of positive end-expiratory pressure (PEEP) (3). A growing number of reports suggest that some patients with COVID-19 present with relatively normal lung compliance but severe and refractory hypoxemia, which is inconsistent with the conventional understanding of ARDS, indicating that an alternate pathophysiology may be involved (4–6).
In previous research, a novel quantitative CT postprocessing technique was described to quantify the volume of blood contained within pulmonary blood vessels of a given size (7). In comparing scans of 103 patients with PCR-confirmed COVID-19 to 108 healthy volunteers, it was observed that patients with COVID-19 have significantly less blood (as a portion of total pulmonary blood volume) contained in pulmonary vessels below 5 mm2 in cross-sectional area (“BV5”), and significantly more in vessels greater than 10 mm2 in cross-sectional area (“BV10”) (Fig. 1A). These changes are equivalent to a redistribution of pulmonary blood away from the small pulmonary vessels and into larger vessels, consistent with increased pulmonary vascular resistance downstream. This may be due to dysregulated vasoconstriction, thrombotic events in the microvasculature, or both. The endothelial inflammatory pathways may play an important role in this dysregulation (8). Any of these could contribute to the observed refractory hypoxemia by impairing gas exchange across the alveolar-capillary membrane. These data suggest a possible pathophysiological explanation for the impression of severe dead space ventilation reported by intensivists (6).
Figure 1. A: proportion of pulmonary blood within different caliber levels in COVID-19 vs. healthy volunteers. B: proportion of blood within BV5 (blood vessels of <5 mm2 cross-sectional area) in patients with COVID-19 with different levels of inspired oxygen. C: proportion of blood through BV5 levels and correlation with alveolar-arterial oxygen gradient (AaDO2). D: proportion of blood through BV5 levels and correlation with PaO2.
Considering these previous data, the relationship (or predictive value) between CT-derived measures of small blood vessel loss and the degree of gas exchange anomaly experienced was investigated. Paired clinical data were analyzed from 43 of the 103 patients previously analyzed (7), as well as eight additional patients included after the previous research concluded. We hypothesized that patients with lower BV5 blood flow would have higher supplemental oxygen needs and less favorable arterial blood gas profiles.
METHODS
From the initial data analysis, 111 hospitalized COVID-19 patients were retrospectively selected based on the availability of CT scans of the lungs with slice thickness of 1.5 mm or less, as well as PCR-confirmed SARS-CoV2 infection. Thin-sliced CT scans were provided by the respective hospitals where they were acquired. Institutional review board approval was granted by the respective local committees. Written informed consent was obtained from the patients, volunteers, and/or institutions included in the article. Ninety-one scans came from Belgium [51 from AZ Sint-Maarten (Mechelen), 40 from Ziekenhuis Oost-Limburg (Genk)], 10 from the United Kingdom (Royal Papworth Hospital), and 10 from China (Wenzhou Medical University). It should be noted that the previous work included only 43 patients from AZ Sint-Maarten; the additional eight were provided after that research concluded. One hundred eight inspiratory scans from healthy patients were acquired from the COPDGene cohort (NCT00608764) and were used as reference data. No statistical comparison between these data and COVID-19 data set was performed (9, 10). Because scans were acquired without a standardized protocol, slice thickness varied between 0.6 mm and 1.5 mm. One patient had highly anomalous arterial blood gas readings (assumed to be a mixed venous sample) and was therefore excluded from the analysis. The measurements were performed on the TLC level without gaiting as this was not possible in this clinical setting. All patients, however, were instructed and encouraged during the scanning procedure to inhale to their maximal inspiration.
3-D reconstructions of the lungs and pulmonary vasculature were created (Fig. 2). An automated blood vessel segmentation algorithm performed an eigenvalue analysis of the Hessian matrix to enhance and identify tubular structures, by returning the probability of each voxel belonging to tubular structure based on shape analysis (11). Hounsfield unit (HU) thresholds, based on the vessels size defined by an automated adaptive iterative threshold method, were used to limit the vessels. During preprocessing, a gradient anisotropic diffusion filter was applied, and a region of interest was defined to remove false positives. Subsequently, the smaller nonconnected parts were removed. To account for the effects of slice thickness on results, sensitivity analysis was performed. Volumes were computed from the cross-sectional area of each vessel. Denoting these measurements “BVX”, where “X” indicates a range of vessel sizes in mm2 (BV5 is the volume of blood contained in vessels between 1.25 and 5 mm2 cross-sectional area, BV5-10 between 5 and 10 mm2, and BV10 > 10 mm2).
Figure 2. Example of blood vessel segmentation in healthy volunteers (A) and patients with COVID-19 (B).
To account for variation in lung volume, BVX was normalized to total pulmonary blood volume, allowing for the computation of a “BV spectrum,” a curve representing the percent of total pulmonary blood volume contained within vessels of a given caliber as a function of cross-sectional area. This method was previously described and was correlated to the clinical symptoms of patients as validation of the technique.
Further analysis was performed on 50 patients (30 male, 20 female, median age 61.98) included from AZ Sint-Maarten, as these were the patients for whom the most clinical data was available. These patients were divided into groups based on their need for oxygen at the time of CT scan acquisition, with a threshold of 2 L/min chosen a priori (<2 L/min O2 or >2 L/min O2). This threshold was chosen as patients in need of more than 2 L/min are oxygen dependent. Those below 2 L/min have a more intermittent need for oxygen. The oxygen needs of patients with more than 2 L vary over time. We chose to pool patients in this study, rather than analyze the amount of oxygen needed as a continuous variable. BV5 values were compared between cohorts. Nonparametrical spearman correlations were computed between BV5 and PaO2 AaDO2. Four patients did not have arterial blood gas data available and were therefore excluded from this portion of the analysis. Two-sample t tests were used to assess significance (P < 0.05). All analyses were performed using the open-source statistical environment R v. 3.2.5 or higher (The R Foundation for Statistical Computing, Vienna, Austria).
RESULTS
Eighteen out of 50 patients needed >2 L/min supplemental oxygen and this group demonstrated a significantly lower median percentage of total blood flow in the BV5 vessels compared with the 32 patients who needed <2 L/min supplemental oxygen (41.61% vs. 46.89%, P = 0.023). Both groups had significantly less blood as a proportion in BV5 vessels compared with healthy volunteers (Fig. 1B). The 46 patients for whom arterial blood gas data were available showed that decreased percentage of blood distribution through BV5 vessels correlated with both increased alveolar-arterial oxygen gradient (AaDO2) (Fig. 1C) and decreased PaO2 (Fig. 1D).
Conclusions
These data are consistent with the hypothesis that reduced blood volume within small (BV5) pulmonary vessels is associated with higher needs for supplemental oxygen and more severe gas exchange anomalies in COVID-19 infections. This supports the view that these patients develop acute hypoxemic respiratory failure due, at least in part, to pathologic alterations in the pulmonary microvasculature, which results in elevated pulmonary pressures and attendant impaired alveolar gas exchange. These changes may include occlusion of pulmonary blood vessels by thrombi, unusual vasoconstriction, and direct damage to the membranes across which gas exchange occurs (2, 6, 12). In a previous study, BV5% from patients with COVID-19 was significantly lower than BV5% from a heterogenous cohort of patients without COVID-19. This difference was driven mainly by patients with CT findings, in a multivariate model that did not account for lung opacification. A BV5% threshold below 25% was associated with an odds ratio (OR) of 5.58 for mortality, OR 3.20 for intubation, and OR 2.54 for the composite of mortality or intubation was found. After including the severity of lung opacification in the multivariate analysis, a BV5% threshold of 25% remained significantly associated with mortality, with OR 4.27. The current study shows that smaller changes in BV5% also result in a different need for oxygen, in fact indicating that BV5 changes and related blood redistribution contribute to oxygen need (9). Early signs of treatment efficacy in pilot studies using inhaled nitric oxide and heparin further support this perspective and underscore the potential utility of vasodilators and antithrombotic interventions in preventing dead space ventilation (13, 14). Recruitment of distal airways with mechanical ventilation (with low levels of PEEP and high FIO2) may help to facilitate the transport of the inhaled oxygen toward the alveoli (5). Those conducting clinical trials to assess the efficacy of these interventions should consider the use of small pulmonary blood vessel volumes as an end point.
Further study is needed to understand the role of these CT-derived metrics in monitoring disease progression. It remains to be seen whether longitudinal changes in pulmonary blood volume distribution are related to clinical state, and whether imaging changes can predict clinical outcomes, which could be a useful tool in the context of strained healthcare resources.
DISCLOSURES
W.D., Y.D.M., J.D.B., B.L., and M.Lanclus are employees of FLUIDDA, a company that develops and markets part of the technology described in this article. The other authors have no financial relationships with any organization or company that might have an interest in the submitted work and received no direct funding from FLUIDDA. None of the other authors has any conflicts of interest, financial or otherwise, to disclose.
AUTHOR CONTRIBUTIONS
W.D., W.D.B., M.Lins, J.D.B., E.F., and B.R.L. conceived and designed research; W.D., M.Lins, K.I., J.V., J.D.B., E.F., B.R.L., M.Lanclus, and M.T. performed experiments; W.D., M.Lins, K.I., J.D.B., E.F., B.R.L., M.Lanclus, and M.T. analyzed data; W.D., W.D.B, M.Lins, K.I., J.D.B., E.F., B.R.L., M.Lanclus, and M.T. interpreted results of experiments; W.D., W.D.B., J.D.B., and B.R.L. prepared figures; W.D., W.D.B., J.V., J.D.B., and B.R.L. drafted manuscript; W.D., W.D.B., Y.D.M., K.I., J.V., J.D.B., E.F., B.R.L., M.Lanclus, and M.T. edited and revised manuscript; W.D., W.D.B., M.Lins, Y.D.M, K.I., J.D.B., E.F., B.R.L., M.Lanclus, and M.T. approved final version of manuscript.
ACKNOWLEDGMENTS
The authors declare that this report does not contain any personal information that could lead to the identification of the patient(s). The authors declare that they obtained a written informed consent from the patients, volunteers, and/or institutions included in the article. The authors also confirm that the personal details of the patients and/or volunteers have been removed.
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| 36269576 | PMC9722246 | NO-CC CODE | 2022-12-16 23:23:44 | no | J Appl Physiol (1985). 2022 Dec 1; 133(6):1295-1299 | utf-8 | J Appl Physiol (1985) | 2,022 | 10.1152/japplphysiol.00458.2022 | oa_other |
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Commentaries
The Shape of Things to Come: COVID's Organizational Impact
Valdiserri Ronald O. MD, MPH [email protected]
Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Correspondence: Ronald O. Valdiserri, MD, MPH ([email protected]).
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pmcIt's no great secret that epidemics have an impact on society. In addition to their immediate toll of death and suffering, history records multiple examples of their ability to alter our patterns of behavior, to affect local and national economies, to influence the laws that we pass, and to bring about changes in the way that organizations operate, among other effects. The ongoing epidemic of SARS-CoV-2 is no exception to this observation.1–3 And while COVID-19's immense death toll4 may be its most obvious and immediately relevant impact, public health managers and practitioners should also pay close attention to the pandemic's influence on organizations within our public health system and the ways in which it has and will continue to influence the delivery of public health services.
Recognizing the variety of organizations that constitute America's public health system,5 and understanding how these organizations function and interact with one another, is critical to achieving many of the stated core competencies of public health professionals.6 And this understanding must encompass much more than an awareness of lines of authority or a detailing of who performs what function and when. Why? Because increasingly, theories explaining how organizations are structured and the contextual factors that influence their performance and interactions with one another are being used to better understand the factors that can affect the successful delivery of clinical and community health services.7,8
Traditional models of health care delivery, including the delivery of population health services, envision a linear “pipeline” extending from the researchers who develop and test new methods and technologies through to the practitioners who implement these new interventions in clinical or public health settings. This “build it and they will come” philosophy assumes that advances in curative and preventive health technology will be readily adopted, resulting in improved health outcomes, and tends to minimize the many internal and external contextual factors—political, cultural, and economic—that can influence the implementation and sustainability of scientifically proven interventions.9 However, more and more, complex system models are being advanced as a framework to better understand population health challenges, including the stubborn persistence of many health disparities, even in the face of effective interventions.10,11
Like other complex systems, the US public health system comprises numerous, interdependent components that interact in ways that are highly dynamic and often not predictable.12 For example, when public health leaders attempt to address the growing prevalence of obesity, they must consider many interrelated factors that contribute to obesity including industry's role in promoting fast food, the increased availability of high calorie foods, reductions in leisure-time physical activity, and barriers to walking and other physical activities posed by the built environment.13 Given this complex, interactive array of variables, it is unlikely that any one single intervention will be successful in reducing the prevalence of obesity. Furthermore, complex systems are characterized by the presence of feedback loops that are responsible for either amplifying (positive feedback) or dampening (negative feedback) variables in the system.14 Feedback loops can arise from a number of sources, including laws and policies, economic pressures, community norms, public perceptions, or environmental circumstances. Consider the issue of vaccine acceptance, highly relevant to our efforts to end the COVID-19 pandemic and prevent other life-threatening infectious diseases. Research reveals that the attitudes and practices of one's social circle can serve to either promote (positive feedback) or impede (negative feedback) vaccine uptake.15 This finding underscores the fact that tackling vaccine promotion from a complex system perspective requires not only an adequate supply of vaccine and an effective delivery system but also proactive efforts to build vaccine acceptance through substantial community engagement.16
Complex systems are also adaptive, that is, they can change in response to system pressures.12 Two recent examples confirm the power of exigent circumstances (in this instance, America's SARS-CoV-2 epidemic) to catalyze changes in the way that organizations within a system are structured and how they interact with one another. On August 17, 2022, the Director of the US Centers for Disease Control and Prevention (CDC) announced efforts to restructure the agency in response to “structural and systemic operational challenges which were exacerbated during the COVID-19 pandemic.”17 Several weeks earlier, The Commonwealth Fund released a report criticizing the current, “haphazard” approach to public health in the United States and cited the “tragedy” of the coronavirus pandemic as a “starting point” for reforming and strengthening America's public health system.18
Given widespread criticism that a lack of timely, comprehensive data blunted the US response to the COVID-19 pandemic,19 it's not surprising that both the CDC plan and the Commonwealth report call for changes that would promote the rapid collection, analysis, and dissemination of public health data. This is a good thing. Whether we're talking about SARS-CoV-2, hepatitis C virus infections, drug overdose deaths, or newly diagnosed cases of diabetes, surveillance is the starting point of public health action—providing the “when” and the “where” necessary to help guide intervention efforts. Unfortunately, surveillance does not always provide the “why” of underlying factors contributing to disease exacerbations or necessarily point to the “how” of what combination of strategies will be most effective in returning the balance of health. That's why it's critical that we avoid relying solely on linear constructs of action to address population health challenges. Even when we have effective vaccines to prevent serious illnesses such as COVID-19 or treatments to cure chronic infections such as hepatitis C, we cannot assume that these “silver bullets” will bring about an epidemic's end. Why? Because there are many interacting factors and diverse stakeholder communities that can influence the valuing and uptake of interventions, even if said interventions have been proven by rigorous scientific research to be highly effective.
What does this reality mean for public health organizations such as the CDC and the broader public health system, and how should it inform any proposed restructuring or reorganization? First, that efforts to improve the effectiveness of a public health organization must be simultaneously inward and outward looking. By all means, address internal operational shortcomings but don't ignore the policies, processes, and procedures that affect interaction with other entities within the public health system, be they governmental, public, or private. Stated another way, a reinvigorated CDC may still fall short of its goals in the context of a weak public health system.20 Second, invest in strengthening connections, both formal and informal, with the wide array of entities that compose the public health system. The CDC plan calls for strengthened collaboration with “its customers,” and the Commonwealth report challenges the Department of Health and Human Services (HHS) to commit to community engagement and to promote stronger multisector partnerships. Without question, these are positive statements, but they must be followed up with tangible and sustainable actions.
Although cross-sector collaborations and partnerships are now widely recognized as critical steps to ensuring population health,21 gaps in workforce capacity and shortfalls in funding are frequent barriers to initiating and sustaining these collaborations.22 An analysis of data from the 2016 National Profile of Local Health Departments found that “information exchange” was the most frequent way in which local health departments collaborated with public, private, and governmental partners; other, more intensive forms of collaboration were far less frequent.23 This situation has not changed much in recent times. The 2019 National Profile of Local Health Departments also found that local health departments were “less likely to collaborate in ways beyond exchanging information.”24 While information sharing is useful, responding effectively to population health challenges requires more intensive and sustainable collaborations in which planning, implementation, and evaluation are collective efforts, touching upon all of the varied system elements that can affect health outcomes.
A recent, COVID-19–specific example involving 44 organizations in North Carolina across 9 different sectors (public health, health care, education, business, nonprofit organizations, religious organizations, transportation, county government, and public safety) substantiates the importance of collaboration between organizations and stakeholders when responding to public health challenges.25 Leaders who were interviewed for this study confirmed that robust collaboration extending beyond information sharing helped to prevent “blind spots” in pandemic decision making, to translate public health guidance so as to make it locally relevant, and to identify and plan for social service needs resulting from “downstream” pandemic impacts. Furthermore, after conducting a network analysis, these researchers found that local health departments had the most direct and indirect connections to other organizations in the network map—providing credence to the recommendation that local public health leaders work in collaboration with all relevant partners to bring about improvements in community health.26
There is little doubt that the COVID-19 pandemic will bring about changes in our public health system. It's up to public health leaders and practitioners to make sure that these changes are meaningful and long-lasting, not superficial and transitory. What, then, is the take-home message when it comes to understanding COVID-19's potential impact on the US public health system? Simply this: Investment in building a stronger public health system in the United States must nurture all 10 of the essential public health services,27 understanding that, like the organ systems of the human body, the whole will not function effectively if even one of the systems is deficient. Nor can our focus be limited solely to improvements at the federal level. Public health capacity at the community level must be every bit as robust as it is at the federal level. Failure to invest in those elements of the system that are in closest reach of the populations we serve disregards everything we now understand about the workings of complex systems.
The author declares no conflicts of interest.
==== Refs
References
1. Krings VC Steeden B Abrams D Hogg MA . Social attitudes and behavior in the COVID-19 pandemic: evidence and prospects from research on group processes and intergroup relations. Group Processes Intergroup Relat. 2021;24 (2 ):195–200.
2. Goldstein I Koijen RS Mueller HM . COVID-19 and its impact on financial markets and the real economy. Rev Financ Stud. 2021;34 (11 ):5135–5148.
3. Burris S deGuia S Gable L The legal response to COVID-19: legal pathways to a more effective and equitable response. J Public Health Manag Pract. 2021;27 (1 )(suppl ):S72–S79.33239567
4. Donovan D . US Officially Surpasses 1 Million COVID-19 Deaths. Baltimore, MD: Coronavirus Resource Center, Johns Hopkins University; 2022.
5. National Association of County and City Health Officials. What is the public health system? https://www.naccho.org. Accessed September 13, 2022.
6. Council on Linkages between Academia and Public Health Practice. Core competencies for public health professionals. https://www.phf.org/competencies. Published October 2021. Accessed September 13, 2022.
7. Leeman J Baquero B Bender M Advancing the use of organization theory in implementation science. Prev Med. 2019;129S :105832.31521385
8. Weiner BJ Lewis MA Linnan LA . Using organization theory to understand the determinants of effective implementation of worksite health promotion programs. Health Educ Res. 2009;24 (2 ):292–305.18469319
9. Shelton RC Cooper BR Stirman SW . The sustainability of evidence-based interventions and practices in public health and health care. Annu Rev Public Health. 2018;39 :55–76.29328872
10. Rutter H Savona N Glonti K The need for complex systems model of evidence for public health. Lancet. 2017;390 (10112 ):2602–2604.28622953
11. Diez Roux AV . Complex health systems thinking and current impasses in health disparities research. Am J Public Health. 2011;101 (9 ):1627–1634.21778505
12. Khan S Vandermorris A Shepherd J Embracing uncertainty, managing complexity: applying complexity thinking principles to transformation efforts in healthcare systems. BMC Health Serv Res. 2018;18 (1 ):192.29562898
13. Wang Y Xue H Esposito L Applications of complex systems science in obesity and noncommunicable chronic disease research. Adv Nutr. 2014;5 :574–577.25469401
14. Braithwaite J Churruca K Long JC Ellis LA Herkes J . When complexity science meets implementation science: a theoretical and empirical analysis of systems change. BMC Med. 2018;16 (1 ):63.29706132
15. de Bruin WB Parker AM Galesic M Vardavas R . Reports of social circles' and own vaccination behavior. A national longitudinal survey. Health Psychol. 2019;38 (11 ):975–983.31259597
16. American Psychological Association. Building vaccine confidence through community engagement. https://www.apa.org/topics/covid-19/equity-resources/building-vaccine-confidence.pdf. Published 2020. Accessed September 19, 2022.
17. Centers for Disease Control and Prevention. CDC moving forward summary report. https://www.cdc.gov/about/organization/cdc-moving-forward-summary-report.html. Published September 1, 2022. Accessed September 6, 2022.
18. Hamburg MA Cohen M DeSalvo K Meeting America's Public Health Challenge: Recommendations for Building a National Public Health System That Addresses Ongoing and Future Health Crises, Advances Equity and Earns Trust. New York, NY: The Commonwealth Fund; 2022. https://www.commonwealthfund.org/publications/fund-reports/2022/jun/meeting-americas-public-health-challenge. Accessed September 7, 2022.
19. LaFraniere S . Very harmful lack of data blunts U.S. response to outbreaks. The New York Times. September 20, 2022. https://www.nytimes.com/2022/09/20/us/politics/covid-data-outbreaks.html. Accessed September 22, 2022.
20. Valdiserri RO . Fix the CDC but don't ignore the rest of our public health system. Health Affairs Forefront. July 18, 2022. https://www.healthaffairs.org/do/10.1377/forefront.20220714.712203. Accessed September 22, 2022.
21. Towe VL Leviton L Chandra A Sloan JC Tait M Orleans T . Cross-sector collaborations and partnerships: essential ingredients to help shape health and well-being. Health Aff (Millwood). 2016;35 (11 ):1964–1969.27834234
22. Carlin M Peterman E . Infrastructure for cross-sector collaboration: the state health leader perspective. J Public Health Manag Pract. 2019;25 (4 ):405–407.31136516
23. Grant AK . Patterns of cross-sector collaboration in local health departments: a cluster analysis. Health Prom Pract. 2022;23 (1 ):128–136.
24. National Association of County and City Health Officials. 2019 National Profile of Local Health Departments. https://naccho.org/uploads/downloadable-resources/Programs/Public-Health-Infrastructure/NACCHO_2019_Profile_final.pdf. Accessed September 25, 2022.
25. Biddell CB Johnson KT Patel MD Cross-sector decision landscape in response to COVID-19: a qualitative network mapping analysis of North Carolina decision-makers. Front Public Health. 2022;10 :906602 36052008
26. DeSalvo KB Wang YC Harris A Public Health 3.0: a call to action for public health to meet the challenges of the 21st century. Prev Chronic Dis. 2017;14 :E78.28880837
27. Centers for Disease Control and Prevention. 10 essential public health services. https://www.cdc.gov/publichealthgateway/publichealthservices/essentialhealthservices.html. Published 2020. Accessed September 25, 2022.
| 36398934 | PMC9722322 | NO-CC CODE | 2022-12-07 23:19:09 | no | J Public Health Manag Pract. 2023 Jan 25; 29(1):5-7 | utf-8 | J Public Health Manag Pract | 2,022 | 10.1097/PHH.0000000000001665 | oa_other |
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Pancreas
Pancreas
PANCREAS
Pancreas
0885-3177
1536-4828
Lippincott Williams & Wilkins
PANCREAS_220359
10.1097/MPA.0000000000002082
00023
3
Letters to the Editor
Association of Serum Amylase Levels With Mortality in Critically Ill Patients With Coronavirus Disease 2019
Kowalczyk Mark MD
Department of Medicine, University of California San Diego, La Jolla, CA [email protected]
Gabriel Rodney A. MD [email protected]
Department of Anesthesiology, University of California San Diego, La Jolla, CA.
Division of Biomedical Informatics, University of California San Diego, La Jolla, CA.
Malhotra Atul MD [email protected]
Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego, La Jolla, CA.
Kistler Erik B. MD, PhD [email protected]
Division of Critical Care Department of Anesthesiology University of California San Diego La Jolla, CA VA San Diego Healthcare System San Diego, CA
8 2022
9 11 2022
9 11 2022
51 7 e97e99
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
2022
Wolters Kluwer Health, Inc. All rights reserved.
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.
STATUSONLINE-ONLY
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pmcTo the Editor:
Progress in the understanding of coronavirus 2019 (COVID-19) has been substantial but incomplete. Infection of the gastrointestinal system with the SARS-CoV-2 virus may result in systemic inflammation and increased bowel permeability. Serum amylase released from the pancreas into the gut may exit the bowel and enter the systemic circulation in critical illness.1 Increases in serum amylase may thus serve as a surrogate marker for impaired bowel integrity and severity of illness in COVID-19.2,3 We sought to test the hypothesis that pancreatic inflammation as assessed by serum amylase is associated with poor outcomes in critically ill patients with COVID-19.
MATERIAL AND METHODS
The University of California, San Diego institutional review board approved waiver of consent for this prospective observational cohort study. All adult patients with COVID-19 admitted to the intensive care unit (ICU) for more than 1 day between May 1, 2020, and October 31, 2020, were enrolled. A random sample of COVID-19 “rule-out” patients admitted to the ICU and subsequently found to be COVID-19 negative were enrolled as comparators.
Serum amylase levels were recorded on all patients as part of usual care on ICU days 1, 4, 7, 10, and 20 as available. Maximum amylase levels in survivor and nonsurvivor cohorts during ICU stay were recorded as the primary outcome, with amylase 150 U/L or greater defined as “elevated.” Demographics, laboratory values, and outcomes were gathered as part of routine (Table 1).
TABLE 1 Characteristics of the Amylase Cohorts
Covariate Maximum Amylase ≤150 U/L, n (%) Maximum Amylase >150 U/L, n (%) P
Total, n 108 29
Inpatient mortality 25 (23.1) 13 (44.8) 0.037
Age, mean (SD), y 58.9 (15.05) 57.0 (14.3) 0.56
Sex, male, n 70 12 0.04
Ethnicity 0.99
Non-Hispanic 28 (25.9) 5 (17.2)
Hispanic 79 (73.1) 24 (82.8)
Unknown 1 (0.9) 0
Required endotracheal intubation 74 (68.5) 25 (86.2) 0.098
ECMO 16 (14.8) 4 (13.8) 0.99
CRRT 15 (13.9) 10 (34.5) 0.02
Required pressors 54 (50.0) 19 (65.5) 0.2
Had another infection 47 (43.5) 20 (69.0) 0.03
Received tracheostomy 20 (18.5) 7 (24.1) 0.68
Comorbidities
COPD 6 (5.6) 0 0.43
Chronic kidney disease 13 (12.0) 2 (6.9) 0.65
End-stage renal disease 4 (3.7) 0 0.67
Coronary artery disease 10 (9.3) 2 (6.9) 0.98
Hypertension 58 (53.7) 11 (37.9) 0.19
Congestive heart failure 11 (10.2) 2 (6.9) 0.86
Cardiac arrhythmia 7 (6.5) 2 (6.9) 0.99
Diabetes mellitus 44 (40.7) 10 (34.5) 0.69
Obesity 21 (19.4) 7 (24.1) 0.77
Liver cirrhosis 3 (2.8) 0 0.85
Maximum laboratory values, mean (SD)
White blood count, 109/L 16.4 (8.7) 19.7 (10.8) 0.14
International normalized ratio 1.5 (0.58) 1.3 (0.19) 0.03
D-dimer, ng/mL 6812 (12,370) 4407 (8766) 0.35
Fibrinogen, mg/dL 656.3 (241.3) 645.5 (237.4) 0.88
Ferritin, mcg/L 3052.1 (6702.8) 4784 (14,784.6) 0.66
C-reactive protein, mg/L 20.4 (13.4) 17.3 (12.6) 0.37
Lactate dehydrogenase, U/L 584.5 (353.1) 599.9 (515.8) 0.91
Procalcitonin, ng/mL 6.5 (33.3) 4.0 (11.0) 0.56
Lactate, mmol/L 3.7 (5.0) 4.4 (4.6) 0.49
P was calculated by Welch 2-sample t test and Pearson χ2 test for continuous and categorical variables, respectively.
COPD indicates chronic obstructive pulmonary disease; ECMO, extracorporeal membrane oxygenation; CRRT, continuous renal replacement therapy; SD, standard deviation.
Amylase values, demographics, interventions, laboratory values, and comorbidities between survivor and nonsurvivor cohorts were compared using Welch 2-sample t test and Pearson χ2 test for continuous and categorical variables, respectively. We performed univariate logistic regression for each covariate to determine its association with mortality. Covariates with P < 0.2 on univariate logistic regression were included in the initial model for the multivariable logistic regression. The final model was built via a combination of forward selection and backward elimination based on the Aikake information criterion. From the final model, the association of elevated amylase to mortality was reported. Odds ratios (OR) and their 95% confidence intervals (CI) were reported for covariates. P < 0.05 was considered to be significant.
We assessed discrimination of a predictive model using variables available upon ICU admission and reported the area under the receiver operating characteristics curve (AUC). The model was tested via 10-fold cross validation, and the mean AUC with 95% CI was reported.
RESULTS
Of the 137 patients with COVID-19 enrolled, there were 29 (21.2%) patients with a maximum amylase level during their ICU stay of 150 U/L or greater. Overall, 38 of the 137 patients with COVID-19 (27.8%) died. There was a statistically significantly greater proportion of patients who died in the elevated amylase cohort (44.8%) compared with the nonelevated amylase cohort (23.1%, P = 0.037) (Table 1). In contrast, mortality in COVID-19 rule-out patients (n = 23) was significantly less (17% vs 27.8%, P = 0.015) than for COVID-19–positive patients.
On univariate logistic regression analysis modeling covariates to mortality, maximum amylase 150 U/L or greater (OR, 2.70; 95% CI, 1.14–6.36; P = 0.02), endotracheal intubation (OR, 6.38; 95% CI, 1.83–22.24; P = 0.004), use of extracorporeal membrane oxygenation (OR, 3.18; 95% CI, 1.20–8.42; P = 0.02), use of continuous renal replacement therapy (OR, 15.5; 95% CI, 5.47–43.94; P < 0.0001), pressor requirement (OR, 9.74; 95% CI, 3.50–27.1; P < 0.0001), and presence of a concomitant infection (OR, 2.61; 95% CI, 1.20–5.69; P = 0.02) were associated with increased mortality.
We performed multivariable logistic regression to determine the association of maximum amylase values during ICU stay with mortality. The final model included maximum amylase, age, extracorporeal membrane oxygenation, continuous renal replacement therapy, need for pressors, tracheostomy, elevated white blood cell count, elevated D-dimer, and elevated C-reactive protein. When controlling for confounders, patients with COVID-19 with elevated maximum amylase levels had a significantly increased odds of mortality (OR, 4.64; 95% CI, 1.07–20.23; P = 0.04) (Fig. 1).
FIGURE 1 Forest plot of association of variables with mortality while controlling for various confounders. Results of the multivariable logistic regression modeling the association of maximum amylase during intensive care unit stay with mortality in patients with COVID-19. The final logistic regression model was built via backward elimination and forward selection. All covariates listed in the figure were included in the final multivariable model. CRP, C-reactive protein; CRRT, continuous renal replacement therapy; ECMO, extracorporeal membrane oxygenation; OR, adjusted odds ratio; WBC, white blood cell count.
From our predictive model for mortality using only covariates known at time of ICU admission, an initial amylase of 150 U/L or greater was predictive of mortality (OR, 6.17; 95% CI, 1.61–23.63; P = 0.008). The mean AUC was 0.769 (95% CI, 0.676–0.854) calculated on 10-fold cross-validation.
DISCUSSION
Results from this study demonstrate a strong association between elevated serum amylase and mortality in ICU patients with COVID-19. This finding is consistent with the notion that bowel permeability may be causally important in the pathophysiology of COVID-19. In theory, amylase elevations in ICU patients with COVID-19 reflect pancreatic inflammation (ie, amylase production) rather than changes in small bowel permeability, and elevated amylase has not consistently been associated with worsened outcomes in retrospective analyses of general (non–critically ill) hospitalized populations.4–6 Nonetheless, results from this study implicate bowel and perhaps pancreatic dysfunction in the propagation of severe disease in patients with COVID-19. Our design does not allow definitive mechanistic conclusions, and we welcome further data to corroborate or refute our findings. We believe our findings may be clinically important and hope that they stimulate further research.
Mark Kowalczyk, MD
Department of Medicine
University of California San Diego
La Jolla, CA
[email protected]
Rodney A. Gabriel, MD
Department of Anesthesiology
University of California San Diego
La Jolla, CA
Division of Biomedical Informatics
University of California San Diego
La Jolla, CA
Atul Malhotra, MD
Department of Medicine
Division of Pulmonary, Critical Care
and Sleep Medicine
University of California San Diego
La Jolla, CA
Erik B. Kistler, MD, PhD
Division of Critical Care
Department of Anesthesiology
University of California San Diego
La Jolla, CA
VA San Diego Healthcare System
San Diego, CA This study was supported by the California Breast Cancer Research Program of the University of California, RGPO Grant R01RG3766 (R00RG2541) (to E.B.K.), and USARMY MEDICAL RESEARCH ACQUISITION ACTIVITY (USAMRAA) Award #W81XWH-17-2-0047 (to E.B.K.).
The authors declare no conflict of interest.
The views and results presented here are entirely those of the authors and do not necessarily represent those of the Department of Defense or its components.
Ethics approval and consent to participate—included in text. The University of California, San Diego Institutional Review Board (IRB) provided approval of the study conduct (IRB approval #200722X); subject informed consent was waived by the IRB because of the observational nature of the study and deidentified use of the data.
The datasets used and/or analyzed during this study are available from the corresponding author on reasonable request.
M.K. helped with data collection and analysis, manuscript review, and approval; R.A.G. helped with statistical analysis and interpretation, manuscript preparation, review, and approval; A.M. helped to prepare, review, and approve the manuscript; and E.B.K. helped to design, prepare, review, and approve the manuscript.
==== Refs
REFERENCES
1 Mayer AD Airey M Hodgson J , . Enzyme transfer from pancreas to plasma during acute pancreatitis. The contribution of ascitic fluid and lymphatic drainage of the pancreas. Gut. 1985;26 :876–881.2411638
2 Zhang J Liu P Wang M , . The clinical data from 19 critically ill patients with coronavirus disease 2019: a single-centered, retrospective, observational study. Z Gesundh Wiss. 2022;30 :361–364.32318325
3 Alsaigh T Chang M Richter M , . In vivo analysis of intestinal permeability following hemorrhagic shock. World J Crit Care Med. 2015;4 :287–295.26557479
4 Pribadi RR Simadibrata M . Increased serum amylase and/or lipase in coronavirus disease 2019 (COVID-19) patients: is it really pancreatic injury? JGH Open. 2020;5 :190–192.33553654
5 Akarsu C Karabulut M Aydin H , . Association between acute pancreatitis and COVID-19: could pancreatitis be the missing piece of the puzzle about increased mortality rates? J Invest Surg. 2022;35 :119–125.33138658
6 Stephens JR Wong JLC Broomhead R , . Raised serum amylase in patients with COVID-19 may not be associated with pancreatitis. Br J Surg. 2021;108 :e152–e153.33793756
| 36395414 | PMC9722323 | NO-CC CODE | 2022-12-07 23:19:09 | no | Pancreas. 2022 Aug 9; 51(7):e97-e99 | utf-8 | Pancreas | 2,022 | 10.1097/MPA.0000000000002082 | oa_other |
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J Occup Environ Med
J Occup Environ Med
JOEM
Journal of Occupational and Environmental Medicine
1076-2752
1536-5948
Lippincott Williams & Wilkins
35901216
JOEM_220134
10.1097/JOM.0000000000002649
00012
3
Original Articles
Relationships Among Musculoskeletal Symptoms, Self-Rated Health, and Work Locations in Studies of Computer Work or Coronavirus Diagnosis
Dannecker Erin PhD, ATC
Clements Sandra PhD [email protected]
Schultz Eric PT, DPT, CEES [email protected]
Derrick Bret PT, DPT, OCS, CEES [email protected]
Keleh Shady Adib MD [email protected]
Golzy Mojgan PhD [email protected]
From the Department of Physical Therapy, University of Missouri, Columbia, Missouri (Dr Dannecker); Leaping Catch, LLC, Gainesville, Florida (Dr Clements); Broadway Ergonomics, LLC, Columbia, Missouri (Dr Schultz, Dr Derrick); Department of Anesthesiology and Perioperative Medicine University of Missouri, Columbia, Missouri (Dr Keleh); Family and Community Medicine University of Missouri, Columbia, Missouri (Dr Golzy).
Address correspondence to: Erin A. Dannecker, PhD, ATC, Department of Physical Therapy, University of Missouri-Columbia, 801 Clark Hall 4250, Columbia, MO 65211-4250 ([email protected]).
12 2022
21 7 2022
21 7 2022
64 12 10591066
Copyright © 2022 American College of Occupational and Environmental Medicine
2022
Wolters Kluwer Health, Inc. All rights reserved.
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.
About a third of participants with increased pain from computer work sought treatment specifically for that pain. When demographic differences were considered, work location was associated with self-rated health in a nationally representative sample. Work location was not associated with musculoskeletal symptoms regardless of how the symptoms were measured.
Objective
To clarify work location's association with musculoskeletal symptoms.
Methods
Study 1 surveyed 246 working adults who usually felt pain, in general, and increased pain from computer work. Study 2 surveyed a nationally representative sample of 1084 working adults.
Results
In study 1, 32.5% of the participants sought treatment for their increased pain from computer work. Education differed by work location. When education was considered, there were no significant work location differences in pain intensity, pain interference, or self-rated health. In study 2, COVID-19 diagnoses, education, and gender differed by work location. Age and work location explained self-rated health. Self-rated health was associated with musculoskeletal ache. Work location did not significantly predict musculoskeletal ache.
Conclusion
Working at home was associated with fewer COVID-19 diagnoses and higher self-rated health than working at employers' locations.
Keywords
musculoskeletal
symptoms
employment
setting
==== Body
pmcMusculoskeletal pain is prevalent among workers and working-age adults.1–3 It is also a leading reason for worker compensation4 and disability claims.5,6 Thus, musculoskeletal pain has been insufficiently prevented and managed.7
Many factors are known to influence people's musculoskeletal symptoms. However, the impact of work location (eg, collocated, home, or hybrid) on musculoskeletal symptoms is uncertain. A study by Giménez-Nadal et al.8 found that people who commuted to work reported more pain than those who did not commute to work. In contrast, a study by Song and Gao9 indicated no significant differences in pain between people who worked at their homes or at employers' locations. Additional studies have concluded that people recalled more musculoskeletal symptoms when working at their homes during the coronavirus (COVID-19) pandemic than when previously working at their employers' locations.10,11 However, these recalled measures were potentially confounded by the many factors that changed (eg, stress) because of the pandemic.12,13
A few studies have compared the musculoskeletal symptoms of people working at their homes or their employers' locations during the pandemic. Two such studies indicated that people working at their homes during the pandemic reported more symptoms than people working at their employers' locations.14,15 The study by Donalonso Siqueira et al.14 also found that people working at their homes during the pandemic recalled more symptoms than before the pandemic, whereas people working at employers' locations did not recall any difference in symptoms before and during the pandemic.
These studies have measured musculoskeletal symptoms differently. For example, Donalonso Siqueira et al.14 measured the presence or absence of pain. Toprak Celenay et al.15 measured the presence or absence of pain, ache, or discomfort, which may have included fatigue or stiffness. Neither study controlled for demographic differences between people who were working at their homes or employers’ locations. However, there are reports of differences in demographic variables, such as education levels between those who can and cannot work from home.16,17 Additional studies about the health impacts of work locations are needed because the percentage of people completing some or all of their work from home increased from 19% in 2003 up to 48% in 2020.18,19
We conducted two cross sectional studies that advance the literature on work locations’ health impacts by considering demographic differences between people who were working at their homes or at employers' locations during the COVID-19 pandemic. Study 1 innovatively measured pain intensity and pain interference with daily activities. We collected data from a convenience sample without measurement of COVID-19 diagnoses. In study 2, we conducted secondary analyses of musculoskeletal ache and COVID-19 diagnoses within a nationally representative sample.
In these studies, we examined both musculoskeletal symptoms and self-rated health. Self-rated health has been associated with musculoskeletal pain.20,21 Self-rated health is commonly measured in a more standardized way22 than musculoskeletal symptoms.23 The relationship between work location and self-rated health is unknown.
Because people working at home have higher education levels than those working at employers' locations16,17 and education level is positively associated with self-rated health,24 we hypothesized that better self-rated health and less musculoskeletal symptoms would be reported by people working at homes than at employers’ locations. We additionally hypothesized, in study 2, that COVID-19 diagnoses would be less frequent within people who worked at their homes than their employers' locations because physical distancing has been recommended to avoid COVID-19 transmission.25
STUDY 1
Materials and Methods
Participants
We recruited a convenience sample of participants by distributing an advertisement via university mass emails, a publicly accessible website, and community bulletin boards. Our inclusion criteria were the following: (1) 18 years or older; (2) employed full- or part-time or self-employed; (3) used a computer for a current job for the past 6 months; (4) computer work had not significantly changed for the past 6 months; (5) completed at least 4 hours of computer work on a usual weekday during the past week; (6) worked consistently from either home or an employer's location for the past week; (7) felt musculoskeletal pain during the past week; (8) felt increased pain from computer work during the past week; and (9) usually felt a minimum pain intensity of “3” or higher on a “0, no pain” to “10, worst pain you can think of” scale during the past week. After participants completed the survey, they were given the option to request compensation.
Procedures
We programmed an online survey using the Research Electronic Data Capture (REDCap) software. With institutional review board approval, the survey began with a consent page and eligibility screening questions. If participants were eligible to participate, conditional logic programming enabled them to advance to the remainder of the survey. The survey asked closed questions about demographics, health and employment history, and recent work experiences with nominal and ordinal response scales. Pain intensity during the past week was measured with a “0, no pain” to “10, worst pain you can think of” rating scale; we chose the scale's maximal anchor from the Patient-Reported Outcome Measurement Information System (PROMIS).26 Increased pain intensity during the past week’s computer work was measured with a “0, no increase in pain” to “10, extreme increase in pain” rating scale. Pain interference (“During the past 7 days, how much has the pain interfered with your daily activities?”) and self-rated health (“How is your health in general?”) were measured with five-point ordinal response scales. These ordinal scales were also selected from PROMIS.26 In addition, “usual” pain location/s was measured with “yes” or “no” response options for 20 body areas (eg, “head”). Questions also asked if participants had asked an expert to evaluate their body’s positions during computer work and asked a health care provider to treat increased pain from computer work. Furthermore, participants were asked for their experience with and expectations of wearable products for improving posture, but their responses to these questions are not reported here.
The survey questions were asked in the same order for all participants. A warning message appeared if participants skipped a question. However, the participants could skip questions and freely move backward and forward within the survey. They also could save their survey answers and return to the survey later.
We pilot tested the survey’s content, usability, and functionality by administering it to two patient advisors, who had experience with musculoskeletal pain and evaluating surveys, and three additional research team members. Based on the feedback received, we completed several revisions of the survey instructions and appearance. Once the survey was finalized, we distributed a study advertisement that contained a uniform resource locator (URL) for the survey.
Data Analyses
Chi-squared tests, Fischer's exact tests, Mann-Whitney U tests, and independent-samples t-tests compared the demographic variables of the eligible participants who did and did not complete the survey. We defined “survey completion” as answering 98% of the survey questions. Subsequent analyses were run on only the data from the participants who completed the survey. The education variable had fewer than five responses per response category so we recoded it into just three categories—no bachelor's degree, bachelor's degree, some graduate school.
Chi-squared tests, Mann-Whitney U tests, and independent-samples t tests compared the demographic variables of the participants who worked at their homes (ie, HOMES cohort) and at employers' locations (ie, EMPLOYERS' cohort). The cohorts only differed significantly on education level (see the results below).
Because pain interference and self-rated health were measured with ordinal scales, Spearman correlations examined their relationships with each other and the pain intensity ratings. Mann-Whitney U tests compared the work location cohorts' pain interference and self-rated health at each education level. Analyses of covariance examined cohort differences in pain intensity and increased pain intensity from computer work with education level entered as a covariate. Data were analyzed using IBM SPSS (Version 27). All P values were two-sided, and P values less than 0.05 were considered indicators of statistical significance.
RESULTS
Of the 260 respondents who were eligible to participate, most were women (81.5%). More of these eligible women (96.2%) completed the survey than the eligible men (86.4%; Fischer's exact test = 6.15, P = 0.04, Phi = 0.17). No other demographic variables were significantly different between the eligible participants who did and did not complete the survey.
The survey completion rate for eligible respondents was 94.6%. The final sample included 246 participants, most of whom were women (82.9%). The participants' average age was 36.35 years (SD = 13.94) and the majority were white (87.4%). There were about as many participants who had not completed a bachelor's degree (38.6%) as had completed some graduate coursework (38.6%) (see Table 1).
TABLE 1 Descriptive Statistics for Study 1's Demographic Variables
HOMES Cohort (n = 131) EMPLOYERS' Cohort (n = 115) Total (N = 246)
Gender 109 Women (83.2%) 95 Women (82.6%) 204 Women (82.9%)
Age (yr) 37.34 ± 14.46 35.21 ± 13.30 36.35 ± 13.94
Race 113 White (86.3%) 102 White (88.7%) 215 White (87.4%)
Education 39 without Bachelor's (29.8%)* 56 without Bachelor's (48.7%)* 95 without Bachelor's (38.6%)
Pain intensity (0 “no pain” to 10 “worst pain you can think of” scale) 4.68 ± 1.55 4.70 ± 1.50 4.69 ± 1.52
Increased pain intensity from computer work (0 “no increase in pain” to 10 “extreme increase in pain” scale) 4.98 ± 1.87 4.97 ± 1.83 4.98 ± 1.84
Pain interference (5-pt scale) 2.67 ± 0.73 2.61 ± 0.85 2.64 ± 0.78
Self-rated health (5-pt scale) 2.47 ± 0.81 2.55 ± 0.72 2.51 ± 0.77
M ± SD are displayed for age, pain intensity, increased pain from computer work, pain interference, and self-rated health.
*Cohorts were statistically different (P < 0.05).
Pain and Self-Rated Health of Total Sample
The participants tended to rate their usual pain intensity, increased pain intensity from computer work, pain interference, and self-rated health near the midpoints of the different response scales (see Table 1). Their most frequent locations of usual pain were neck (80.1%), lower spine (68.7%), and shoulder (52%). Their most frequent locations of increased pain from computer work were neck (76.8%), lower spine (58.1%), and upper spine (41.5%). To manage their increased pain from computer work, 17.1% of the participants had an expert evaluate their body's positions during computer work. A larger percentage of the participants (32.5%) had asked a health care provider to treat their increased pain from computer work.
Cohort Differences in Demographic Variables
There were no significant differences between the work location cohorts in gender, age, or race. However, the HOMES cohort had completed higher levels of education than the EMPLOYERS’ cohort (Mann-Whitney U = 9118, P < 0.01, r = 0.19). For example, 29.8% of the HOMES cohort and 48.7% of the EMPLOYERS' cohort had not completed a bachelor's degree. Thus, subsequent cohort comparisons considered participants' education level (see Table 1).
Relationships Among Pain, Self-Rated Health, and Work Location
None of the pain measures were significantly correlated with self-rated health (r's = 0.02–0.08, P's > 0.05). There were no significant work location cohort differences in pain interference across education levels (Mann-Whitney U's = 347.50–1041.00, P's > 0.05, r's < 0.02). There were also no significant cohort differences in self-reported health across education levels (Mann-Whitney U's = 374.00–1051.00, P's > 0.05, r's < 0.01) (see Table 2). In addition, there were no significant cohort differences in pain intensity (F1,243 = 0.04, P > 0.05, η2 < 0.01) or increased pain intensity from computer work (F1,243 = 0.01, P > 0.05, η2 < 0.01) with educational level as a covariate (see Table 3).
TABLE 2 Ranks and Mann-Whitney U Results per Education Level for Study 1's Pain Interference and Self-Rated Health
N Mean Rank Mann-Whitney U P r
No bachelor's degree
Pain interference HOMES cohort 39 46.69
EMPLOYERS' cohort 56 48.91
Total 95 1041.00 0.68 0.04
Self-rated health HOMES cohort 39 49.04
EMPLOYERS' cohort 56 47.28
Total 95 1051.00 0.74 0.03
Bachelor's degree
Pain interference HOMES cohort 32 29.64
EMPLOYERS' cohort 24 26.98
Total 56 347.50 0.51 0.09
Self-rated health HOMES cohort 32 28.29
EMPLOYERS' cohort 24 28.92
Total 56 374.00 0.86 0.02
Some graduate coursework
Pain interference HOMES cohort 60 50.38
EMPLOYERS' cohort 35 43.91
Total 95 907.00 0.23 0.12
Self-rated health HOMES cohort 60 47.08
EMPLOYERS' cohort 35 49.57
Total 95 995.00 0.64 0.05
TABLE 3 ANCOVA Results for Study 1's Pain Intensity and Increased Pain Intensity From Computer Work
Dependent Variables Independent Variables Df MS F P η2
Pain intensity Education 1 6.50 2.82 0.10 0.01
Work location 1 0.09 0.40 0.84 <0.01
Error 243 2.31
Increased pain intensity from computer work Education 1 0.15 0.04 0.84 <0.01
Work location 1 0.03 0.01 0.93 <0.01
Error 243 3.43
ANCOVA, analysis of covariance.
DISCUSSION
A methodological strength of Study 1 was that it innovatively measured pain intensity from computer work and pain interference per work location. It did not detect significant differences in pain intensity or pain interference between participants working at their homes or employers’ locations. These results are similar to Song and Gao's9 pre-pandemic findings, but are different from two reports of more pain in people working at their homes than at employers’ locations during the pandemic.14,15
Different pain measurement methodologies may partially explain the discordant results. Song and Gao9 measured pain level while Donalonso Siqueira et al.14 and Toprak Celenay et al.15 measured the presence or absence of musculoskeletal symptoms. Dichotomous choice questions about pain have not related strongly to pain intensity ratings such as the ratings we collected in our study.27 In addition, we specifically sampled people who usually felt pain, in general, and increased pain from computer work while the prior studies had less restrictive inclusion criteria (eg, working adults).
Many studies of work locations’ impacts have measured pain or discomfort felt during computer work instead of pain that increased during computer work.14,15,28–31 In our study, the participants rated their increased pain from computer work near the midpoint of the “no increase in pain” to “extreme increase in pain” response scale. Almost one third of the participants asked a health care provider to treat their increased pain from computer work. Thus, among our participants, pain from computer work was insufficiently prevented and managed regardless of work location.
In contrast to prior studies,20 none of the pain measures in Study 1 were significantly correlated with self-rated health. It is possible that the association was impaired by the sample’s size and/or characteristics. For example, our convenience sample of 246 participants was mostly composed of college-educated participants (61.4%) and female participants (82.9%). The prior study by Evangelos et al.20 sampled 486 men and 514 women, of whom only 29.3% and 19.5%, respectively, had any college-level education. Our sample was also offered compensation for participation, which may have increased selection bias.
COVID-19 diagnoses within the participants’ households were not measured in Study 1. It is possible that COVID-19 diagnoses for participants and/or a household member were less common among the participants working at their homes than their employers’ locations because of physical distancing recommendations.25 Such a difference could have differentially impacted the musculoskeletal pain reports by work locations because musculoskeletal pain is a common symptom of COVID-19.32–36 To explore this possibility, a second study with a nationally representative sample and measures of COVID-19 diagnoses was needed.
STUDY 2
Materials and Methods
Participants
We filtered the COVID-19 Impact Survey’s37 third wave data so that all of the participants for Study 2 were (1) 18 years old or older and (2) employed full- or part-time or self-employed. The COVID-19 Impact Survey sought to measure physical health, mental health, economic security, and social dynamics during the COVID-19 pandemic. The participants came from the National Opinion Research Center's (NORC's) AmeriSpeak Panel®. NORC identifies as an independent research organization that is governed by a Board of Trustees. The AmeriSpeak panel included one member of randomly selected households from within the NORC’s National Sample Frame. This sample frame was created using stratified and systematic sampling methods that were statistically representative of 97% of the US household population. NORC personnel contacted the households by mail, email, telephone, and/or field interviews. NORC offered participants compensation for their participation.
Procedures
Recruitment materials invited household members to complete the COVID-19 Impact Survey in English or Spanish. These materials provided (1) a URL and unique personal identification number for completing the survey via the Internet and (2) a toll-free telephone number for completing the survey via telephone with NORC personnel. The survey asked closed questions with nominal and ordinal response scales about demographics and health and employment history. Work location (“Are you working from home in response to the coronavirus?”) and the presence of musculoskeletal ache (“Have you experienced muscle or body aches in the past 7 days, or not?”) were measured with a nominal response scale. Self-rated health was measured in the same manner as in Study 1.
Participants could refuse to answer and skip questions. However, participants’ data were removed from the final data set if they completed less than half of the survey questions, responded in a pattern, or finished the survey in less than one-third of the median single-session web interview length.38 The COVID-19 Impact Survey’s publicly accessible information did not describe its process for pilot testing the survey.37
Data Analyses
The COVID-19 Impact Survey offered “not sure” and “refused” response options for all of our variables of interest except work location. We recoded these response options as “missing,” which removed them from subsequent analyses. We also recoded one of the COVID-19 Impact Survey’s education variables (“EDUCATION”) to create a new education variable with categories that matched the education categories in Study 1.
We completed chi-squared tests to compare the COVID-19 diagnosis variables of the HOMES and EMPLOYERS’ cohorts. Next, we filtered out all the respondents with a self or household member COVID-19 diagnosis and completed chi-Squared tests to compare the demographic variables of the work location cohorts. We subsequently examined the association between the presence of musculoskeletal ache and self-rated health with a chi-squared test. Fisher’s exact tests were used whenever more than 20% of the expected counts were less than 5.
We applied the COVID-19 Impact Survey’s national weighting variable for subsequent analyses. This variable was calculated using an iterative raking process after data collection was completed. The raking variables were age, gender, race/ethnicity, education, and county.39 A logistic regression model with gender, race/ethnicity, education, and age variables was fitted to assess the association between musculoskeletal ache and self-rated health. Then, we fitted a multinomial logistic regression model to assess the effect of work location on self-rated health when controlling for possible confounding variables (eg, gender, race/ethnicity, education, and age). Finally, we fitted a binary logistic regression model to assess the effect of work location on musculoskeletal ache when controlling for possible confounding variables. Age was treated as a continuous variable for the regression analyses. Wald chi-squared tests were used to evaluate the overall models and individual contributions of predictor variables. Data were analyzed using IBM SPSS (Version 27) and SAS® (Version 9.4). All P values were two-sided and P values of less than 0.05 were considered indicators of statistical significance.
RESULTS
The COVID-19 Impact Survey reported an overall survey completion rate of 19.7%. However, it did not calculate the eligible participants’ survey completion rate. The third wave dataset included 7505 participants. Of these participants, 3831 were employed and 1084 of them were nationally weighted cases. Most of these participants completed the survey online (97.9%) and a small majority were men (52.9%). The most frequently selected age category was 25 to 35 years old (28.6%). The majority of the participants were White/Non-Hispanic (59.0%) and had not completed a bachelor’s degree (58.6%). About half of the sample worked at home (50.1%) (see Table 4).
TABLE 4 Unweighted Descriptive Statistics for Study 2's Demographic Variables
HOMES Cohort With a COVID-19 Diagnosis (n = 6) EMPLOYERS' Cohort With a COVID-19 Diagnosis (n = 19) HOMES Cohort Without a COVID-19 Diagnosis (n = 532) EMPLOYERS' Cohort Without a COVID-19 Diagnosis (n = 509) Total (N = 1084)
Gender 4 women (66.7%) 11 women (57.9%) 267 women (50.2%)* 221 women (43.4%)* 511 women (47.1%)
Age 3; 25–34 yr (50.0%) 5; 25–34 yr (62.5%) 150; 25–34 yr (28.2%) 144; 25–34 yr (28.3%) 310; 25–34 yr (28.6%)
Race/ethnicity† 3 White/non-Hispanic (50.0%) 7 White/non-Hispanic (36.8%) 329 White/non-Hispanic (61.8%) 296 White/non-Hispanic (58.2%) 640 White/non-Hispanic (59.0%)
Education 2 Without bachelor's (33.3%) 14 Without bachelor's (73.7%) 226 Without bachelor's (42.5%)* 381 Without bachelor's (74.9%)* 635 Without bachelor's (58.6%)
Eighteen participants (1.7%) had missing responses to the questions about COVID-19 diagnoses.
*Within the participants without a COVID-19 diagnosis, these cohorts were statistically different (P < 0.05).
†Disclosure risk analysis led to the removal of the race/ethnicity variable for 23 (2.2%) of the participants without a COVID-19 diagnosis.
Cohort Differences in COVID-19 Diagnosis and Demographic Variables
Of the 1084 participants whose responses were nationally weighted, 6 reported self and household member COVID-19 diagnoses and 19 reported a self or a household member COVID-19 diagnosis. Thus, 25 total participants (2.3%) reported a COVID diagnosis in their household. The work location cohorts differed in the number of participants with household COVID-19 diagnoses (chi-squared = 6.97, P < 0.01, Cramer’s V = 0.08). For example, of the participants with COVID-19 diagnoses in their households, 24.0% worked at their homes while 76.0% worked at their employers’ locations.
Among the participants without COVID-19 diagnoses (n = 1041), the HOMES cohort had completed higher levels of education than the EMPLOYERS’ cohort (Mann-Whitney U = 181,782, P < 0.01, r = 0.34). For example, 42.5% of the HOMES cohort and 74.9% of the EMPLOYERS’ cohort had not completed a bachelor’s degree. In addition, the cohorts differed by gender (chi-squared = 4.79, P = 0.03, Phi = 0.07). The HOMES cohort was 50.2% women while the EMPLOYERS’ cohort was 43.4% women. Thus, education level and gender were considered in subsequent analyses. There were no significant differences between the cohorts in age or race/ethnicity (see Table 4).
Relationships Among Musculoskeletal Ache, Self-Rated Health, and Work Location
A chi-squared analysis found the presence of musculoskeletal ache was significantly correlated with self-rated health (chi-squared = 21.02, P < 0.01, Cramer’s V = 0.15). Similarly, a logistic regression model indicated that self-rated health significantly predicted the presence of musculoskeletal ache (Wald chi-squared = 17.40, df = 4, P < 0.01) without unique contributions from gender, race/ethnicity, education, or age. When keeping all other predictors constant, the odds of musculoskeletal ache being present were 86 [OR = 0.14] times less likely when self-rated health was “excellent” (see Table 5).
TABLE 5 Summary of Logistic Regression Analysis for Predicting Study 2's Musculoskeletal Ache
Parameter Musculoskeletal Ache Estimate SE Chi-Squared Pr > Chi-Squared OR 95% CI OR
Intercept (1) Yes −0.96 0.66 2.09 0.14
Age (1) Yes 0.09 0.07 1.46 0.23 1.09 0.95 1.25
Gender (1) Male (1) Yes 0.06 0.10 0.37 0.54 1.13 0.76 1.70
Race/ethnicity (1) White/non-Hispanic (1) Yes 0.13 0.21 0.40 0.53 0.80 0.19 3.41
Race/ethnicity (2) Black/non-Hispanic (1) Yes −0.50 0.35 2.03 0.15 0.43 0.09 2.14
Race/ethnicity (3) Hispanic (1) Yes −0.14 0.28 0.27 0.61 0.61 0.13 2.77
Race/ethnicity (4) Other/non-Hispanic (1) Yes 0.15 0.33 0.21 0.65 0.82 0.17 3.98
Education (1) No bachelor's degree (1) Yes 0.28 0.15 3.45 0.06 2.18 1.12 4.25
Education (2) Bachelor's degree (1) Yes 0.21 0.17 1.48 0.22 2.03 0.98 4.18
Self-rated health (1) Excellent (1) Yes −1.96 0.68 8.42 <0.01 0.14 0.04 0.53
Self-rated health (2) Very good (1) Yes −1.87 0.62 9.08 <0.01 0.15 0.05 0.52
Self-rated health (3) Good (1) Yes −1.17 0.62 3.60 0.06 0.31 0.09 1.04
Self-rated health (4) Fair (1) Yes −1.44 0.67 4.61 0.03 0.24 0.06 0.88
Variables' reference categories were the following: musculoskeletal ache (2), no; gender (2), female; race/ethnicity (88), removed for disclosure risk; education (3), some graduate coursework; self-rated health (5), poor. The sample size for this analysis was 1036.
In addition, a multinomial regression model significantly predicted the participants’ self-rated health. The overall model, which contained the demographic variables of age, gender, education, race, and work location, was significant (Global Wald chi-squared = 54.72, df = 36, P = 0.02). Age and work location uniquely explained variance in self-rated health (Wald chi-squared = 12.71, df = 4, P = 0.01 and Wald chi-squared = 12.20, df = 4, P = 0.02, respectively). The odds of “excellent” self-rated health were slightly higher [odds ratio (OR) = 1.01] for every 10-year increase in age. In addition, the odds of “excellent” self-rated health were 43 [OR = 0.56] times less likely when working from employers’ location than working from home (see Table 6). However, a logistic regression model, which contained gender, race/ethnicity, education, age, and work location variables, did not significantly predict the presence of musculoskeletal ache (Wald χ2 = 0.02, P = 0.88) (see Table 7).
TABLE 6 Summary of Multinomial Regression Analysis for Predicting Study 2's Self-Rated Health
Parameter Self-Rated Health Estimate SE Chi-Squared Pr > ChiSq OR 95% CI OR
Intercept (1) Excellent 5.15 66.34 0.01 0.94
Age (1) Excellent 0.01 0.21 0.00 0.97 1.01 0.66 1.53
Gender (1) Male (1) Excellent −0.15 0.31 0.22 0.64 0.75 0.22 2.52
Race/Ethnicity (1) White/non-Hispanic (1) Excellent −2.22 66.33 0.00 0.97 0.00 0.00 999.99
Race/Ethnicity (2) Black/non-Hispanic (1) Excellent −0.41 66.35 0.00 0.99 0.00 0.00 999.99
Race/Ethnicity (3) Hispanic (1) Excellent −2.79 66.33 0.00 0.97 0.00 0.00 999.99
Race/Ethnicity (4) Other/non-Hispanic (1) Excellent −2.59 66.34 0.00 0.97 0.00 0.00 999.99
Education (1) No bachelor’s degree (1) Excellent −0.63 0.58 1.18 0.28 0.24 0.02 3.62
Education (2) Bachelor's degree (1) Excellent −0.16 0.66 0.06 0.81 0.39 0.02 7.18
Work at home (0) No (1) Excellent −0.29 0.34 0.72 0.40 0.56 0.15 2.12
Intercept (2) Very good 6.03 66.35 0.01 0.93
Age (2) Very good 0.08 0.21 0.17 0.68 1.09 0.73 1.63
Gender (1) Male (2) Very good −0.10 0.30 0.10 0.75 0.83 0.25 2.71
Race/Ethnicity (1) White/non-Hispanic (2) Very good −2.24 66.33 0.00 0.97 0.00 0.00 999.99
Race/Ethnicity (2) Black/non-Hispanic (2) Very good −0.82 66.35 0.00 0.99 0.00 0.00 999.99
Race/Ethnicity (3) Hispanic (2) Very good −2.81 66.33 0.00 0.97 0.00 0.00 999.99
Race/Ethnicity (4) Other/non-Hispanic (2) Very good −2.38 66.34 0.00 0.97 0.00 0.00 999.99
Education (1) No bachelor's degree (2) Very good −0.67 0.57 1.38 0.24 0.30 0.02 4.33
Education (2) Bachelor's degree (2) Very good 0.13 0.65 0.04 0.84 0.66 0.37 11.83
Work at home (0) No (2) Very good −0.41 0.33 1.54 0.21 0.44 0.12 1.61
Intercept (3) Good 5.0.30 66.34 0.01 0.94
Age (3) Good 0.16 0.21 0.58 0.45 1.17 0.78 1.76
Gender (1) Male (3) Good −0.13 0.30 0.18 0.67 0.77 0.23 2.54
Race/Ethnicity (1) White/non-Hispanic (3) Good −2.21 66.33 0.00 0.97 0.00 0.00 999.99
Race/Ethnicity (2) Black/non-Hispanic (3) Good −0.50 66.35 0.00 0.99 0.00 0.00 999.99
Race/Ethnicity (3) Hispanic (3) Good −2.79 66.33 0.00 0.97 0.00 0.00 999.99
Race/Ethnicity (4) Other/non-Hispanic (3) Good −2.09 66.34 0.00 0.97 0.00 0.00 999.99
Education (1) No bachelor's degree (3) Good −0.47 0.57 0.67 0.41 0.33 0.02 4.84
Education (2) Bachelor's degree (3) Good −0.17 0.66 0.07 0.79 0.44 0.03 8.04
Work at home (0) No (3) Good −0.21 0.33 0.42 0.52 0.65 0.18 2.40
Intercept (4) Fair 3.72 66.34 0.00 0.96
Age (4) Fair 0.31 0.22 2.10 0.15 1.37 0.90 2.09
Gender (1) Male (4) Fair 0.08 0.32 0.06 0.81 1.16 0.34 4.03
Race/Ethnicity (1) White/non-Hispanic (4) Fair −2.49 66.33 0.00 0.97 0.00 0.00 999.99
Race/Ethnicity (2) Black/non-Hispanic (4) Fair −0.36 66.35 0.00 0.99 0.00 0.00 999.99
Race/Ethnicity (3) Hispanic (4) Fair −2.41 66.33 0.00 0.97 0.00 0.00 999.99
Race/Ethnicity (4) Other/non-Hispanic (4) Fair −2.22 66.34 0.00 0.97 0.00 0.00 999.99
Education (1) No bachelor's degree (4) Fair −0.64 0.59 1.20 0.27 0.24 0.02 3.61
Education (2) Bachelor's degree (4) Fair −0.16 0.67 0.05 0.82 0.39 0.02 7.27
Work at home (0) No (4) Fair −0.05 0.34 0.02 0.88 0.91 0.24 3.49
Variables' reference categories were the following: self-rated health (5), poor; gender (2), female; race/ethnicity (88), removed for disclosure risk; education (3), some graduate coursework; work at home (1), yes. We treated the survey's age variable, which contained seven response categories, as a continuous variable for ease of interpretation. The sample size for this analysis was 1036.
TABLE 7 Summary of Logistic Regression Analysis for Predicting Study 2's Musculoskeletal Ache
Parameters Musculoskeletal Ache Estimate SE Chi-Squared Pr > Chi-Squared OR 95% CI OR
Intercept (1) Yes −2.59 0.32 67.20 <0.01
Age (1) Yes 0.10 0.07 1.94 0.16 1.10 0.96 1.26
Gender (1) Male (1) Yes 0.06 0.10 0.39 0.53 1.14 0.76 1.69
Race/ethnicity (1) White/non-Hispanic (1) Yes 0.14 0.21 0.47 0.49 0.91 0.22 3.83
Race/ethnicity (2) Black/non-Hispanic (1) Yes −0.48 0.35 1.96 0.16 0.49 0.10 2.40
Race/ethnicity (3) Hispanic (1) Yes −0.10 0.27 0.12 0.73 0.72 0.16 3.23
Race/ethnicity (4) Other/non-Hispanic (1) Yes 0.20 0.33 0.37 0.54 0.96 0.20 4.64
Education (1) No bachelor's degree (1) Yes 0.34 0.16 4.54 0.03 2.32 1.17 4.58
Education (2) Bachelor's degree (1) Yes 0.17 0.17 0.94 0.33 1.96 0.95 4.02
Work at home (0) No (1) Yes 0.02 0.11 0.02 0.88 1.03 0.68 1.56
Variables' reference categories were the following: musculoskeletal ache (2), no; gender (2), female; race/ethnicity (88), removed for disclosure risk; education (3), some graduate coursework; work at home (1), yes.
DISCUSSION
A methodological strength of study 2 was its inclusion of a nationally representative sample of working adults. Such a sample facilitates generalizing the research results to our population of interest.40 Another strength of study 2 was that it measured COVID-19 diagnoses within participants and their households. COVID-19 is a contagious disease and a common symptom of COVID-19 is musculoskeletal pain.32–36 Thus, COVID-19 diagnoses within participants’ households could obscure work locations' influence on musculoskeletal symptoms during the pandemic. In fact, study 2 detected that significantly fewer of the participants working at home had COVID-19 diagnoses within their households compared with those working at employer locations. This finding is consistent with reports of COVID-19 transmissions at employers' locations41,42 and increased absenteeism among workers who could not work from home during the COVID-19 pandemic.43 These results suggest that working at home may be beneficial for reducing COVID-19 diagnoses.
Study 2 measured the presence of musculoskeletal ache. It did not detect significant differences in musculoskeletal ache between participants working at their homes and at employers' locations. These results differ from two reports of more pain in people working at their homes than at employers' locations during the pandemic.14,15 Donalonso Siqueira et al.14 measured the presence or absence of pain. Toprak Celenay et al.15 measured the presence or absence of pain, ache, or discomfort, which may have included fatigue or stiffness. Specific measures of ache may be less sensitive than other measures of musculoskeletal symptoms. However, in study 2, musculoskeletal ache was significantly correlated with self-rated health.
Study 2 had several limitations. NORC provided limited information about the methods of participant recruitment and survey development and administration. In addition, the COVID Impact Survey’s purpose was to measure physical health, mental health, economic security, and social dynamics during the COVID-19 pandemic. It did not include questions specifically about the participants’ computer work or musculoskeletal symptoms from computer work as was done in study 1.
GENERAL DISCUSSION
Both studies 1 and 2 found significant demographic differences between the participants working at their homes and at employers' locations. More educated participants and more women worked at their homes than at employers’ locations during the pandemic. These differences align with the findings of other studies.16,17 Thus, future investigations of work locations’ impacts on health need to consider demographic differences as our studies did.
Study 1 primarily sampled college-educated, white women. It found pain ratings were not significantly correlated with self-rated health. However, in study 2, which had a larger and more diverse sample, the presence of musculoskeletal ache was significantly correlated with worse self-rated health regardless of gender, race/ethnicity, education, and age. Other studies with large samples of more diverse people with chronic musculoskeletal pain have also found significant relationships between pain and self-rated health.20,21
Neither study found significant differences in musculoskeletal pain or ache between participants working at their homes or at employers' locations when demographic variables were considered. Thus, the sample of primarily college-educated, white women with usual pain, in general, and increased pain from computer work exhibited nonsignificant differences in a similar manner as the nationally representative sample of adult workers. These results may be interpreted as good news for employers and employees who want to offer and/or work at collocated and/or home locations. However, about one third of the participants in Study 1 reported asking a health care provider to treat their increased pain from computer work. Thus, more efforts to improve musculoskeletal symptoms, regardless of work location, are needed. Other studies have reported that the musculoskeletal symptoms of both collocated workers28,30 and home workers44,45 have related to characteristics of their computer workstations.
Study 1 found no significant difference in self-rated health between work locations. However, in study 2, higher self-rated health was more probable for participants working at their homes than at employers' locations. In addition, fewer of the study 2 participants who worked at home had COVID-19 diagnoses for themselves and their household members than the participants working at employers' locations. Thus, working at home instead of employers' locations may offer other health benefits than decreasing musculoskeletal symptoms.
The results of studies 1 and 2 support the need for a nationally representative survey of work locations' impacts on musculoskeletal symptoms and self-rated health. Ideally, the survey should collect detailed information about health history (eg, COVID-19 diagnoses), work and workstation characteristics (eg, duration of computer work and characteristics of commonly used chairs), reasons for current work location/s (eg, physical distancing, quarantine, or isolation), and multidimensional measures of pain (eg, location, intensity, and interference). Future samples should additionally include adults who are working at both their homes and employers’ locations in a hybrid manner. Employers and employees need such evidence for their decisions about collocated, home, and hybrid work locations.
ACKNOWLEDGMENTS
We would like to thank Jackie Griffen and Mary Hodson for their contributions to the research. In addition, we would like to thank all of our research participants for their thoughtful and sincere responses to our survey.
Conflicts of interest: None declared.
Ethical approval: This project was approved by an institutional review board.
Data availability statement: The data that support the findings of this study are available from the corresponding author,(E.A.D.), upon reasonable request.
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| 35901216 | PMC9722324 | NO-CC CODE | 2022-12-07 23:19:09 | no | J Occup Environ Med. 2022 Dec 21; 64(12):1059-1066 | utf-8 | J Occup Environ Med | 2,022 | 10.1097/JOM.0000000000002649 | oa_other |
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J Christ Nurs
J Christ Nurs
JCHN
Journal of Christian Nursing
0743-2550
Wolters Kluwer Health, Inc.
36469875
10.1097/CNJ.0000000000001017
00013
3
Feature: NCPD Connection: faith community nursing/research
COVID-19 Caregiving Strategies, Quality of Life, and Stress Among Faith Community Nurses and Faith Leaders in Appalachia
Smothers Angel
Morrissey Elizabeth
Melnick Helen
Beaver Molly
Wang Kesheng
Piamjariyakul Ubolrat
Angel Smothers, DNP, APRN, FNP-BC, is a clinical associate professor at West Virginia University School of Nursing in Morgantown, WV. She works as a Faith Community Nurse in the community setting.
Elizabeth Morrissey, BSN, RN, is a senior-level master's in nursing (MSN) student at West Virginia University School of Nursing. She works as a graduate assistant and helps with community-based research and practice.
Helen Melnick, BSN, RN, is an MSN student at West Virginia University School of Nursing. She is a part-time student research assistant and helps with community-based research and practice.
Molly Beaver, BSN, RN, is an MSN student at West Virginia University School of Nursing. She is a part-time student research assistant and helps with community-based research and practice.
Kesheng Wang, PhD, MA, BS, is an associate professor at the West Virginia University School of Nursing. He has experience as a statistician and works with both graduate and undergraduate faculty and students. Contributed to data analyses and data interpretation.
Ubolrat Piamjariyakul, PhD, RN, is the Associate Dean for Nursing Research at West Virginia University School of Nursing. Contributed to the design and interpretation of the results.
The authors declare no conflict of interest.
Accepted by peer review 6/2/2022.
For more than 47 additional nursing continuing professional development activities related to faith community nursing topics, go to NursingCenter.com/ce.
Jan-Mar 2023
5 12 2022
5 12 2022
40 1 3641
InterVarsity Christian Fellowship
2023
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.
ABSTRACT:
Faith community nurses (FCNs), pastors, and priests faced many challenges from the COVID-19 pandemic, serving as frontline sources of support for congregants. The aim of this study was to identify the most common care strategies used during the COVID-19 pandemic and examine professional quality of life, perceived stress, and associated factors in faith leaders and FCNs in rural Appalachia. Using a cross-sectional, descriptive survey design, high compassion satisfaction was reported along with compassion fatigue as caregiving moved to virtual platforms, suggesting the need for greater support.
KEY WORDS:
Appalachia
burnout
compassion satisfaction
COVID-19
faith community nurses
nursing
Perceived Stress Scale
Professional Quality of Life Scale (ProQOL)
CMECE
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pmcFigure No caption available.
The COVID-19 pandemic, an unprecedented health crisis, severely disrupted life. In the United States, the Centers for Disease Control and Prevention (2020) launched social distancing guidelines to safeguard healthcare providers and the public. Following the initial World Health Organization (WHO, 2020a) recommendations to minimize nonurgent hospital-based care and appointments, many preventive programs and noncritical care were canceled due to shortages of medications, providers, and essential medical supplies (i.e., personal protection equipment). According to a survey across 155 countries, 53% disrupted services for hypertension treatment, 49% for the treatment of diabetes and its complications, 42% for cancer treatment, and 31% for cardiovascular care (WHO, 2020b). During these unprecedented times of COVID-19, the United States and other countries adopted telemedicine (remote treatment via telecommunication) to replace in-person clinic visits. Telecommunication helped support ongoing visitations through phone, Facebook, FaceTime, online video conferencing (Zoom, Skype), and other sources (Lee et al., 2020).
COVID-19 AND RURAL COMMUNITIES
Rural communities across America were deeply impacted by the COVID-19 pandemic and restrictions. The Appalachian region, which runs from the state of New York through West Virginia and as far south as Alabama, has both urban and rural communities. With mountainous geographic isolation and lack of trust in the healthcare system, rural Appalachians in West Virginia were already suffering extreme inequities in health and economic resources (Feltner et al., 2017). Diminished resources in rural parts of West Virginia further aggravated the disruption of health services caused by COVID-19 and social distancing (WHO, 2020a). However, strong positive cultural traditions of religiosity and a preference for social support from family members, neighbors, and church members assisted those in rural areas.
Faith community leaders, such as pastors and priests, and faith community nurses (FCNs) play significant roles in healthcare support in rural communities. Faith community nurses work to provide for the health-related needs and education of the congregation they serve. Support from FCNs might include a home visit, hospital visit, nursing home visit, or education and screening at faith community meeting locations. Researchers report that the spiritual care provided by FCNs helps alleviate family anxiety during life and death transitions as well as support community members' decision-making for medical treatment (American Association of Critical-Care Nurses, 2017; Anaebere & DeLilly, 2012; Yeaworth & Sailors, 2014). Faith community nurses have a close alliance with members of the congregation and often are requested to support families managing serious chronic illnesses (Lentz, 2018; Ziebarth, 2014).
Within their roles, faith community leaders (leaders) and FCNs had the overwhelming task of assessing community members' physical and emotional demands during the social distancing and stay-at-home orders. This created a stressful experience for the leaders and FCNs as they reviewed the traumatic effects of the COVID-19 pandemic on their communities. Stressful life situations impacted professional quality of life and can lead to burnout (Powell, 2020).
EXPLORING CARE & COVID IMPACT ON FAITH LEADERSHIP
Study Purpose
The purpose of this study was to (a) identify the most common care strategies faith leaders and FCNs used during the beginning of the COVID-19 pandemic (March 2020–August 2020) and (b) examine the relationships between the professional quality of life, perceived stress, and associated factors in the leaders and FCNs. Reed's Theory of Self-Transcendence was used as a framework to guide development of the study (Abu Khait et al., 2020; Reed, 2018). The theory facilitates turning difficulties and struggles in life into meaningful systems of support. In the theory, commitment to care is defined as assuming a burden to create a place of refuge and occurs when a person assumes the caregiver's role to create a place of comfort and safety for a patient. Framing caregiver practices as purposeful interventions leading to intended outcomes, such as promoting well-being, provides support for the client. The work of faith leaders and FCNs aligns with this framework (Fiske, 2019).
Design, Settings, Sample, and Instruments
A cross-sectional, descriptive survey design was used for this study. The study was approved by the West Virginia University (WVU) Institutional Review Board. The survey was administered by clinical and research nursing faculty at WVU in May 2020. Participants were recruited using a convenience sampling method. Facebook groups designated for faith community leaders and FCNs were used to provide information about the study and encourage interested leaders and FCNs to complete the survey. Participants who volunteered completed an anonymous electronic survey via a secured Health Science Center URL link.
The study survey consisted of demographic questions, the Professional Quality of Life (ProQOL) Scale, the Perceived Stress Scale (PSS), and two open-ended questions. The survey took about 20 minutes for participants to complete.
The ProQOL is a 30-item, 5-point Likert-type scale (Stamm, 2010) with two subscales: Compassion Satisfaction and Compassion Fatigue. The Compassion Satisfaction subscale measures satisfaction derived from being able to do a job well. A higher score indicates better professional satisfaction from the job.
The Compassion Fatigue subscale has two components: Burnout and Traumatic Stress. The Burnout subscale measures feelings of hopelessness and difficulties in dealing with work or in doing the job effectively. Higher scores indicate more burnout on the job. The Traumatic Stress subscale measures work-related secondary exposure to extremely stressful events. A higher score indicates more stress. Each subscale has a standardized mean score of 50 (Stamm, 2010).
The PSS is a unidimensional 14-item, 5-point Likert-type scale (Cohen et al., 1983; Cohen & Williamson, 1988). The scale measures the degree to which situations in a person's life were perceived as stressful in the last month, ranging from 0 (never) to 4 (very often). The total score ranges from 0 to 56, where a higher score indicates higher perceived stress. The Center for Health Discovery and Well-Being (2018) categorized the scores into three levels: low stress (scores 0-18), moderate stress (scores 19-37), and high stress (scores 38-56).
Data Analysis
Data from the electronic survey were downloaded to IBM SPSS Statistics Version 26 (IBM, 2019) for analysis. A bivariate linear regression analysis was performed for each of the three ProQOL subscales to assess the association of a single independent variable with each subscale. Variables with p values significant at 0.20 in bivariate analyses were included in the final multiple linear regression models (Kutner et al., 2004). G∗Power v3.9.1.4 was used to compute the statistical power for multiple linear regression analysis (Faul et al., 2009). Based on the sample size (N = 49), assuming an α level (α = 0.05) and effect size = 0.15 (moderate effect), with five predictors, power could reach 77%. With two predictors, the power could reach 84%.
STUDY RESULTS
Demographic and Work-Related Characteristics
Out of approximately 100 leaders and FCNs, 49 (N = 49) from 10 West Virginia counties completed the survey; 12 (24.5%) were FCNs and 37 (75.5%) were faith leaders. Demographic data included age, gender, type of faith community, years of experience in the faith community, rural or urban location, previous online experience, previous experience with distance technology and distance-based interaction, and sizes of the faith communities served (see Table 1). Among the faith leader participants, 26% held a Master of Divinity degree, 20% were ordained pastors, and 47% had been working as a faith leader for more than 20 years. Educational status of the FCNs was not assessed. Among the 12 FCN participants, nine (75%) had been working as a nurse for more than 20 years.
Table 1. Demographic and Work-Related Characteristics (N = 49)
Variable Number (%)
Age (years)
<50 15 (30.6)
≥50 34 (69.4)
Gender
Male 24 (49.0)
Female 25 (51.0)
Employment/Job Status
Full-time 32 (65.3)
Other 17 (34.7)
FCN or Faith Leader
FCN (3 FCNs were also faith leaders) 12 (23.1)
Pastors/Faith Leaders 40 (76.9)
Having experience in distance technology prior to COVID-19
Yes 42 (85.7)
No 7 (14.3)
Distance-based interaction during COVID-19 (missing one response)
Increased 29 (60.4)
Other 19 (39.6)
Faith leader years of experience (Pastor or Priest)
<10 9 (22.5)
≥10 31 (77.5)
Type of Faith Community
Protestant 26 (53.1)
Catholic 19 (38.8)
Other 4 (8.1)
Urban or Rural Status (missing 5 responses)
Rural 16 (36.4)
Urban 28 (63.6)
Faith Community Size (missing 3 responses)
<100 22 (47.8)
101-300 11 (23.9)
>300 13 (28.3)
For both the leaders and FCNs, 65% worked full-time within the faith community setting and 35% part-time. Faith leaders tended to be in paid positions and the FCNs tended to be in unpaid positions. Participants reported performing various types of services for congregants prior to the pandemic, including home visits (20%), education (16%), nursing home visits (13%), hospital visits (19%), and other support upon request. During the pandemic, 60.4% (n = 29) reported increased distance-based interactions (e.g., consulting by telephone, answering email).
Multiple Regression Analysis
Compassion Satisfaction
The average (mean) Compassion Satisfaction subscale score was 39.27, suggesting that participants perceived a moderate sense of compassion satisfaction (Table 2). The Cronbach's alpha reliability coefficient (α) for the Compassion Satisfaction subscale in this study was .92. Five predicting variables with p < .20 in the bivariate analysis were selected and entered into the multiple regression model: gender, having experience in distance technology, urban or rural location, community size, and perceived stress. Three of these variables were associated with compassion satisfaction in the model at significant levels: having experience in distance technology (p = .010), urban location (p = .044), and low levels of stress (p = .002; Table 3). Forty percent (40%) of the variance in compassion satisfaction could be explained by these three variables.
Table 2. Mean, Standard Deviation, and Proportions on Professional Quality of Life Subscales (ProQOL) and Perceived Stress Among Participants (N = 49)∗
n (%)
Scale or Subscale M (SD) Range Low Moderate High
Compassion Satisfaction (10 items)a 39.27 (6.69) 22-50 1 (2.1%) 26 (54.2%) 21 (43.7%)
Burnout (10 items)a 21.17 (5.91) 10-36 28 (58.3%) 20 (41.7%) 0
Traumatic Stress (10 items)a 21.69 (7.71) 11-43 30 (62.5%) 16 (33.3%) 2 (4.2%)
Perceived Stress (14 items)b 19.61 (8.05) 5-35 23 (46.9%) 26 (53.1%) 0
∗ One out of the 49 participants did not answer three of the four questionnaires
a Professional Quality of Life scale and subscale scores: low quality of life (≤22), moderate quality of life (23-41), and high quality of life (≥42).
b Perceived Stress Scale scores: low stress (0-18), moderate stress (19-37), and high stress (scores 38-56).
Table 3. Multiple Regression Model Summary for ProQOL Subscales and Predicting Variables
QOL domain (predicting variables) R R 2 Adjusted R2 F ratio p
Compassion Satisfaction (distance technology experience, urban or rural location, and perceived stress) .63 .40 .31 4.53 <.001
Burnout (employment status, perceived stress) .83 .69 .65 14.87 <.001
Compassion Fatigue (perceived stress) .50 .25 .21 6.70 <.001
Note. QOL = quality of life. R refers to correlation coefficient; R2 = coefficient of determination and percent variance explained by each ProQOL subscale's independent variables.
Burnout
The average Burnout score was 21.17, suggesting a lower sense of burnout among participants (Table 2). In this study, Cronbach's α was .79 for the Burnout subscale. Five predicting variables with p < .20 in the bivariate analysis (age, having full-time job, Catholic faith background, faith leader year of experience, and perceived stress) were selected and entered into the multiple regression model (Table 3). Having a full-time job (p = .003) and high level of perceived stress (p < .001) were the significant variables associated with burnout, where 69% of variance in burnout could be explained by these two variables.
Traumatic Stress
The average score on the Traumatic Stress subscale was 21.69, suggesting a moderate sense of traumatic stress (Table 2). Cronbach's α was .91 for the Traumatic Stress subscale in this study. Perceived stress as measured by the PSS was moderate with a mean score of 19.61. The Cronbach's alpha coefficient for the PSS in this study was .89. Two predicting variables (Catholic faith background and perceived stress) with p < .20 in the bivariate analysis were selected and entered into the multiple regression model. Perceived stress was the only significant variable associated with traumatic stress (p < .001) in the model (Table 3), where 25% of variance in traumatic stress could be explained by perceived stress.
DISCUSSION AND RECOMMENDATIONS
Care Provided During the Pandemic
Faith leaders and FCNs play a vital role in healthcare provision in rural communities and have been frontline sources of support for congregants since the beginning of the COVID-19 pandemic. Based on the findings from this study, both the leaders and FCNs provided multiple virtual services during the COVID-19 pandemic.
Home visitations were completed during the pandemic using virtual methods including video chatting on cell phones or computers via FaceTime and other video conferencing, phone calls, texts, and email. Choosing the virtual method that was most accommodating for the congregant supported successful caregiving. Nursing home visitations were halted for faith leaders who were not direct employees of the nursing home. Leaders and FCNs from outside of the nursing home setting were not permitted inside, but phone calls, texts, video conferencing, and FaceTime were used to remain connected with congregants within the nursing home setting. Nursing home staff helped facilitate these encounters.
Hospital visits were halted during the pandemic. Families struggled with the care needs of their loved ones within the confines of the acute care setting. Families and patients connected with faith community care providers using phone, virtual conferencing, and FaceTime methods. The leaders and FCNs utilized the support of family members outside of the acute care setting as well as the support of the hospitalized patients to communicate with and care for congregants during the early days of the pandemic and extending into the winter months of 2020-2021.
Educational support was provided using Zoom virtual conferencing for meetings and recordings; some educational sessions were shared using YouTube and other platforms. Education centered on infection prevention in the early days of the pandemic and progressed to include education on vaccine safety and efficacy.
Most of the leaders and FCNs (60.4%) reported increased demand for distance-based interactions (e.g., consulting by telephone, answering email) during the COVID-19 pandemic. Leaders and FCNs were spending an increased number of hours reaching out virtually to provide support to individuals they would have normally seen at weekly worship gatherings. The elderly and most vulnerable congregation members required ongoing support, and additional congregants were suffering from the effects of isolation and needed virtual care support.
The FCNs played an integral part in their communities by providing congregants with necessary resources and linking them to additional care resources when needed, especially for low-income families who had limited access to services and information. The COVID-19 pandemic and shift of hospital care to the community added to the multiple roles played by faith leaders and FCNs within faith communities. The increasing demand during this time was stressful.
Impact of Pandemic on FCNs/Leaders
Most participants expressed that they were satisfied with their jobs and had moderate-to-high scores on the Compassion Satisfaction subscale. However, among those reporting high compassion satisfaction, over 40% reported moderate burnout, 38% reported moderate-to-high compassion fatigue, and over 50% reported moderately high perceived stress. Based on the multiple regression model, results confirmed that perceived stress was a key predictor for quality of life as measured by the ProQOL. When participants perceived low stress, they were likely to have better compassion satisfaction in their jobs. When perceived stress was high, they were more likely to have compassion fatigue and burnout. This finding is consistent with what other researchers have reported (Hotchkiss & Lesher, 2018; Jacobson et al., 2013; Visker et al., 2017).
Compassion fatigue was associated with experience in distance technology and working in an urban community. This may be due to the larger number of congregants who lived farther from family and friends, making them more dependent on faith community support. Although 85% of participants had experience in distance technology, they had to adapt to the rapid shift and demand, and the increase in online distance work could have contributed to higher stress levels. A shift from twice-weekly in-person worship services, where faith leaders and FCNs could engage with congregants, to all online worship services could have led to increased need for additional virtual interactions with the congregants. In rural settings, there often is limited high-speed Internet or cellular services. Without Internet and cellular services, the use of virtual tools such as video conferencing and texting is limited. Thus, the rural leaders and FCNs depended on the use of landline phones to reach their congregants. This is similar to what other researchers have reported (McNeely et al., 2020; Templeton et al., 2020).
Despite high levels of compassion satisfaction, the transition to alternative modalities of virtual care appeared to have created increased compassion fatigue and decreased perceived quality of life for participants. It is important to recognize compassion fatigue within those serving the faith community so they can initiate self-care methods. This might include using a team-based approach so that no individual faith leader or FCN is carrying a heavier load and working as a team so that duplicate support services are not provided, especially during times of higher workload and stress. Team meetings for all faith leaders and FCNs with sharing, prayer, and support could be an important self-care tool. Finally, utilizing virtual conferencing that takes place in urban settings to provide education and support for congregants across rural settings could help offset some of the workload burden for rural leaders and FCNs (Keener et al., 2021a; 2021b).
Limitations
Limitations of this study included data collected in only one highly rural state and at only one point in time. Additionally, survey data were collected in May 2020 during the height of the stay-at-home order in West Virginia. The circumstances of these communities may have progressed over the remainder of 2020 and not been reflected in study data. Moreover, word of mouth and Facebook were the primary sources of participant recruitment. These recruitment strategies could produce limited data regarding the experiences of rural community members. Limitations in the questionnaire included the lack of distinction between time as a nurse versus time as an FCN. Education level of the FCNs was not assessed, which could have impacted care and response to the job. Another limitation is the lack of breakdown between the FCNs and faith leaders regarding tasks and full-time work settings. If completed again, the questionnaire should include a clear distinction between the two.
CONCLUSION
Faith community leaders, including pastors, priests, and FCNs, have played an important role during the COVID-19 pandemic in providing supportive care for congregants. Although this type of support has been part of the roles of FCNs and pastors/priests in the past, circumstances surrounding the pandemic led to a need for change to the use of virtual encounters in place of in-person encounters. In addition to the traditional needs within a faith community, new needs associated with social isolation and environmental threats emerged. More congregants required psychosocial support than usual during the COVID-19 pandemic. Though the study findings revealed that high compassion satisfaction was reported among the leaders and FCNs, compassion fatigue also was experienced by many of them. Future research is needed to determine if this fatigue has continued beyond the first year of the pandemic. A need exists to determine strategies that support faith leaders and FCNs during stressful times as well as support emerging roles and caregiving to prepare them to work in stressful public health threats.
Web Resources
Spiritual Care Association
https://www.spiritualcareassociation.org
Compassion Fatigue
https://compassionfatigue.org/index.html
Perceived Stress Scale
https://www.das.nh.gov/wellness/docs/percieved%20stress%20scale.pdf
ProQOL
https://proqol.org
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| 36469875 | PMC9722325 | NO-CC CODE | 2022-12-07 23:19:09 | no | J Christ Nurs. 2023 Dec 5 Jan-Mar; 40(1):36-41 | utf-8 | J Christ Nurs | 2,022 | 10.1097/CNJ.0000000000001017 | oa_other |
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J Clin Rheumatol
J Clin Rheumatol
JCR
Journal of Clinical Rheumatology
1076-1608
1536-7355
Lippincott Williams & Wilkins
35696998
JCR_220173
10.1097/RHU.0000000000001876
00008
3
Concise Reports
Low Positivity Rate of Anti–SARS-CoV-2 IgG in Unvaccinated Patients With Rheumatic Diseases Treated With Rituximab
García-Fernández Antía MD
Morán-Álvarez Patricia MD [email protected]
Bachiller-Corral Javier MD [email protected]
Vázquez-Díaz Mónica MD [email protected]
From the Rheumatology Unit, Hospital Universitario Ramón y Cajal, Madrid, Spain.
Correspondence: Antía García-Fernández, MD, Rheumatology Unit, Hospital Universitario Ramón y Cajal Carretera de Colmenar Viejo, 9.1 km 28034 Madrid, Spain. E-mail: [email protected].
12 2022
12 6 2022
12 6 2022
28 8 424428
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
2022
Wolters Kluwer Health, Inc. All rights reserved.
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.
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pmcRituximab is a monoclonal antibody that targets CD20+ B cells. After rituximab infusion, B-cell depletion usually ensues, mainly of B memory cells, with a subsequent reduction of IgG production and of B effector cells, resulting in a lower production of IgM. Furthermore, a secondary effect on T helper cell response has also been described.1 Rituximab is widely used for treating rheumatic diseases (RMDs) such as systemic sclerosis, systemic lupus erythematosus, Sjögren syndrome, and idiopathic inflammatory myopathies, although it has only been approved for treating rheumatoid arthritis (RA), microscopic polyangiitis, and granulomatosis with polyangiitis.
The effect of rituximab on B-cell response has been studied, focusing on the possible associated higher rates of infection and the lower seroconversion rates after vaccination in treated patients. A lower IgG response has been previously described after influenza and pneumococcal vaccination in patients with RA.2 Retrospective studies have reported a higher infection rate in RA patients treated with rituximab compared with other biologics such as abatacept or tocilizumab.3
Whether patients with RMD have a higher risk for severe COVID-19, especially those receiving rituximab treatment, has been a matter of debate since the start of the pandemic. Initial case reports and small cohorts reported a possible increase in the risk of severe SARS-CoV-2 infection in patients treated with rituximab.4 This finding has not been consistently observed in larger cohorts.5 Only the COVID-19 Global Rheumatology Alliance registry, which evaluated factors associated with COVID-19–related deaths, found rituximab exposure to be an independent risk factor.6
A recent case report highlighted the lack of seroconversion and possibility of reinfection in patients treated with rituximab.7 After the first COVID-19 wave (polymerase chain reaction [PCR] detection was not widely available initially), Madrid was a registered high impact area with official data showing more than 390,242 infected people and 19,291 infection-related deaths until December 22, 2020.8
Seroconversion after possible SARS-CoV-2 infection, in a cohort of unvaccinated RMD patients treated with rituximab in a high impact area, has not previously been described.
METHODS
Study Design
Medical records review study of a cohort of patients with an RMD followed up at the Rheumatology Department of the Ramón y Cajal University Hospital (Madrid, Spain), who had undergone a serological test for anti–SARS-CoV-2 IgG between April 15, 2020 and December 22, 2020. Positivity rate for anti–SARS-CoV-2 IgG and predictors of a positive serological result were analyzed in rituximab-treated patients and compared with those not treated with rituximab.
The current study was a subanalysis of a larger study of SARS-CoV-2 infection in RMD patients (study number 136/20), approved by the local ethics committee (Comité de Ética de Investigación con Medicamentos del Hospital Universitario Ramón y Cajal) on May 5, 2020. All patients provided informed consent to participate and for publication of data before their inclusion. The research was conducted in compliance with the Helsinki Declaration.
Patients
Patients aged >16 years, regardless of previous COVID-19 history, were included. Patients with an RMD treated with targeted synthetic disease-modifying antirheumatic drugs (tsDMARDs) or biological tsDMARDS (bDMARDs), rather than rituximab or TNF inhibitor were excluded (Figure). Patients were classified into 2 groups according to rituximab treatment in the previous year (RTX group and n-RTX group). History of confirmed/suspected COVID-19 and serological test result for anti–SARS-CoV-2 were recorded. Patients treated with corticosteroids, conventional DMARDs (cDMARDs), and/or TNF inhibitor constituted the control group (n-RTX group).
FIGURE Patient selection. RTX group, patients treated with rituximab; n-RTX group, patients treated with TNF inhibitors, cDMARDs, and/or corticosteroids; *Other bDMARDs rather than rituximab or TNF inhibitor.
Rheumatic disease diagnosis, age at diagnosis, age at time of serological test for SARS-CoV-2, comorbidities, interstitial lung involvement, previous suspected/confirmed COVID-19, and previous PCR determination were documented at inclusion.
Rheumatic disease diagnosis was divided into 2 subgroups for the purpose of data analysis: arthropathies and connective tissue diseases. Arthropathies included RA (and secondary Sjögren syndrome), psoriatic arthritis, juvenile idiopathic arthritis, spondyloarthropathies, gout, and polymyalgia rheumatica. Connective tissue diseases included systemic sclerosis, inflammatory myopathies, systemic lupus erythematosus, vasculitis, and primary Sjögren syndrome.
Variables and Operative Definitions
Rate of anti–SARS-CoV-2 IgG positivity was considered the primary end point of the study, defined as the percentage of patients having a positive serological result in each group (also calculated according to previous history of confirmed or suspected COVID-19). Confirmed COVID-19 was considered in patients with at least 2 symptoms: a positive PCR for SARS-CoV-2 and/or a compatible chest x-ray. Suspected COVID-19 was diagnosed in patients presenting with at least 2 symptoms suggestive of SARS-CoV-2 infection.
Serological Test
One SARS-CoV-2 antibody assay was available in the hospital’s routine laboratory and was used during the study. A chemiluminescent microparticle immunoassay for SARS-CoV-2 IgG was used (SARS-CoV-2 IgG for use with ARCHITECT; Abbott Laboratories, Abbott Park, IL; reference 06R8620). This is a qualitative assay for the detection of IgG antibodies against the SARS-CoV-2 nucleocapsid protein (N-IgG) in human serum and plasma. Positivity of anti N-IgG is defined by an index >1.40.
Statistical Analysis
Categorical variables were reported as proportions and/or percentages, whereas continuous variables were expressed as the mean and standard deviation (SD) or median values and interquartile ranges (IQRs), for normally or nonnormally distributed variables, respectively. The Mann-Whitney U test, Student t test, and χ2 test were used to compare data (RTX and n-RTX groups), when appropriate. A multivariate logistic regression model was plotted to identify the association of rituximab treatment and a positive anti–SARS-CoV-2 IgG result. Odds ratios (ORs) were calculated with 95% confidence interval and adjusting for potential confounding factors. Variables were selected if they modified the crude OR by more than 10%. Statistical significance was assumed at a p value <0.05. Independent variables were selected for the multivariate model based on clinical judgment or if the p value was <0.20 in the bivariate analysis. Multicollinearity among independent variables was also explored, using Pearson and Spearman correlations to build the model. All the analyses were performed using the SPSS 25.0 statistical program.
RESULTS
One-hundred fifty-two patients were included, 48 of whom were on rituximab treatment. The demographic and clinical characteristics of patients included in the study and the bivariate analysis comparing the RTX and n-RTX groups are summarized in Table 1.
TABLE 1 Demographic and Clinical Characteristics
Rituximab (RTX = 48) No Rituximab (n-RTX = 104) Total Cohort (n = 152) p value
Patients, n (%) 48/152 (31.6) 104/152 (68.4) 152/152 (100) —
Age at inclusion, mean (SD), y 62.3 (14.9) 58.4 (17.5) 59.6 (16.8) p = 0.190
Female, n (%) 38 (79.2) 74 (71.2) 112/152 (73.7) p = 0.297
Diagnosis, n (%)
Arthropathies 25 (52.1) 58 (55.8) 83/152 (54.6) p = 0.727
Connective tissue diseases 23 (47.9) 46 (44.2) 69/152 (45.4)
Comorbidities, n (%)
Hypertension 18 (37.5) 34 (32.7) 52/152 (34.2) p = 0.561
Diabetes 5 (10.4) 10 (9.6) 15/152 (9.9) p = 0.878
Dyslipidemia 18 (37.5) 30 (28.8) 48/152 (31.6) p = 0.286
COPD/asthma 6 (12.5) 4 (3.8) 10/152 (6.6) p = 0.045*
CVD 11 (22.9) 25 (24) 36/152 (23.7) p = 0.831
ILD, n (%) 17 (35.4) 8 (7.7) 25/152 (16.4) p < 0.0001*
CCs, n (%) 26 (54.2) 33 (31.7) 59/152 (38.8) p = 0.008*
CCs, median (IQR), mg/d 5 (5–10) 5 (3.8–8.8) 5 (5–10) p = 0.217
cDMARDs, n (%) 27 (56.3) 73 (70.2) 100/152 (65.8) p = 0.092
bDMARDs, n (%)
None 0 (0) 82 (78.8) 82/152 (53.9) p < 0.0001*
TNF inhibitor 0 (0) 22 (21.2) 22/152 (14.5)
Rituximab 48 (100) 0 (0) 48/152 (31.6)
Previous PCR, n (%)
No previous PCR 29 (60.4) 72 (69.2) 101/152 (66.4) p = 0.191
Negative 11 (22.9) 12 (11.5) 23/152 (15.1)
Positive 8 (16.7) 20 (19.2) 28/152 (18.4)
Time from positive PCR to serological test, median (IQR), d 43.5 (10.5–89.3) 62.5 (18.3–88-3) 63 (19–90.5) p = 0.574
Previous symptoms, n (%) 13 (27.1) 36 (34.6) 49/152 (32.2) p = 0.356
Time from symptoms to serological test, mean (SD), d 107 (44–238-5) 80.5 (32.8–124.3) 92 (35.5–155) p = 0.181
COVID-19, n (%)
No suspected 35 (72.9) 66 (63.5) 101/152 (66.4) p = 0.183
Suspected 3 (6.3) 18 (17.3) 21/152 (13.8)
Confirmed 10 (20.8)a 20 (19.2) 30/152 (19.7)
aTwo patients had a negative PCR but compatible chest x-ray and symptoms requiring hospitalization.
cDMARDs, conventional disease-modifying anti-rheumatic drugs; CCs, corticosteroids; CI, confidence interval; COPD, chronic obstructive pulmonary disease; CVD, cardiovascular disease;IQR, interquartile ranges; ILD, interstitial lung disease; PCR, Polymerase chain reaction; SD, stardard deviation.
Median age at inclusion was similar in the groups, 73.7% of patients were female. Regarding diagnosis, this was equally distributed, almost half of the patients were diagnosed with a connective tissue disease in both groups, but a higher rate of interstitial lung disease (ILD) was reported in the RTX group (RTX, 35.4%; n-RTX, 7.7%; p < 0.0001).
More than half of the patients in the RTX group were treated with corticosteroids, but median dose was not different between groups. Conventional DMARDs were more frequently prescribed to n-RTX patients, and 53.9% were neither treated with rituximab nor TNF inhibitor.
Only 33.5% of the cohort had a history of suspected and confirmed COVID-19. Rates of confirmed COVID-19 were similar between groups; however, a higher percentage of n-RTX patients had a history of suspected disease (n-RTX, 17.3%; RTX, 6.3%; p = 0.079).
Positivity Rate
Overall, seropositivity rate for anti–SARS-CoV-2 IgG was 25.7%. Among RTX and n-RTX groups, 8.3% (4/48) and 33.7% (35/104) (p = 0.01) had a positive anti–SARS-CoV-2 IgG, respectively. Four of 104 (3.8%) n-RTX patients tested positive without previous symptoms. No asymptomatic infections were diagnosed in the RTX group.
Univariable analysis showed a lower rate of positive anti–SARS-CoV-2 IgG in the RTX group with both confirmed (40%) and suspected (0%) infection compared with the n-RTX group, 80% and 83.3%, respectively (p = 0.045 and p = 0.015).
Multivariate Regression Model
A multivariate analysis was plotted to identify the effect of rituximab treatment on a negative anti–SARS-CoV-2 IgG result (Table 2). Rituximab treatment was the main factor associated with a negative IgG result, followed by older age at inclusion. Male sex and a previous positive SARS-CoV-2 PCR were identified as independent factors associated with a positive anti–SARS-CoV-2 IgG. The presence of ILD and cDMARDs use was retained in the model as confounding factors, whereas corticosteroids and previous chronic obstructive pulmonary disease were not included as they did not influence the main variable (RTX group).
TABLE 2 Multivariate Analysis
Total Cohort (n = 152)
OR (95% CI) p value
Sex (ref female) 4.23 (1.55–11.50) 0.005
Age at inclusion, y 0.97 (0.94–0.99) 0.026
ILD (ref no ILD) 0.35 (0.08–1.47) 0.151
cDMARD (ref no cDMARD) 1.61 (0.58–4.44) 0.359
Previous PCR (ref no previous PCR)
Negative 1.98 (0.51–7.70) 0.325
Positive 18.72 (5.20–67.43) <0.0001
RTX group (ref n-RTX) 0.08 (0.02–0.37) 0.001
cDMARDs, conventional disease-modifying anti-rheumatic drugs; CI, confidence interval; ILD, interstitial lung disease; OR, Odds Ratio; PCR, polymerase chain reaction; Ref, Reference; RTX, rituximab.
DISCUSSION
The current study found a lower seroconversion rate in the RTX group, regardless of previous COVID-19 history. To the authors' knowledge, this is the first study studying the anti–SARS-CoV-2 IgG rate in unvaccinated patients with RMD.
Overall seroprevalence was 25.7%, higher than the prevalence reported by the Spanish ENE-COVID study. In this population-based study that included 61,075 participants up to the May 11, 2020, seroprevalence in Madrid was 11.5%.9 No data have been published after that date; however, RMD patients in the current study, in contact with hospital care, could be at higher risk of infection. In this study, 3.8% asymptomatic infections were reported in the n-RTX group. A higher proportion was reported in the ENE-COVID study, with up to 35.8% of asymptomatic individuals.9
n-RTX patients, when previous COVID-19 was suspected or confirmed, had high seroconversion rates (80%–83%), similar to those reported in other studies.10 However, time from previous symptoms or positive PCR to serological determination was shorter than in the study by Noh et al,10 and positive anti–SARS-COV2 IgG rate could decline overtime and be lower at 6 months for the current cohort.
Few studies address seroconversion after COVID-19. The prospective COVISEP registry evaluated postinfection immune response in patients with multiple sclerosis and neuromyelitis optica spectrum disorders with 3 different tests (anti–S IgG, anti–S IgA, and anti–N IgG).11 Overall seroconversion rate after COVID-19 was similar to the current study, and anti-CD20 treatment was associated with a decreased odds of positive serology.11 Of note, time from the last anti-CD20 infusion was the only different variable in patients with positive and negative anti–SARS-CoV-2 IgG. No differences were found in the seroconversion rate between the tests.11 Louapre et al11 findings support the validity of our results, despite the fact that only the anti–N IgG test was available.
In the current study, older age at inclusion was independently associated with a negative serological result. A prospective study found older age was associated with persistently positive serological results, but no concise information about previous treatments and comorbidities were described.10
Male sex was associated with a positive serological result. In previous studies, sex was not found to be associated with different rates of seroconversion.10,11 In the current cohort, the subgroup of patients treated with TNF inhibitors was mostly represented by young males (57.1% of males; median age, 53 years; IQR, 39.5–66) who could have a preserved serological response.
Seroconversion in patients with RMD after vaccination, which was not available when the study was performed, has been extensively investigated. An impaired humoral response has been reported in several cases and in a retrospective study of patients treated with rituximab.12,13 A prospective Dutch study of patients with RMD found a delayed rather than impaired humoral response in RMD patients overall, but lower seroconversion rates in patients treated with anti-CD20.14 Thus, it is still unclear if T-cell responses, which could be preserved in patients with B-cell depletion, are representative of vaccine efficacy.15
Although a large cohort of patients with RMD treated with rituximab was included in this study, there was a low prevalence of confirmed COVID-19, hindering the identification of possible risk factors for negative or positive serological results. Not all the available bDMARDs were included, limiting the possibility of identification of other bDMARDs associated with an impaired serological response. Also, due to the study design, no factors associated with the possible source of infection were assessed, limiting the accurate evaluation of the prevalence of the disease and the differences with the overall population. However, the uniqueness of these results should be highlighted, the study was carried out after the first COVID-19 wave, where there was an extreme overload of inpatients and outpatients, systematic PCR detection was not possible, and only a qualitative serological antibody assay was available. Because of the high vaccination rates in Spain, there is a limited possibility of conducting similar studies in future.
CONCLUSIONS
In this cohort, rituximab treatment was the main factor associated with a negative anti–SARS-CoV-2 IgG. A lower positivity rate of anti–SARS-CoV-2 IgG was found in the RTX group, regardless of previous COVID-19 history. No asymptomatic infections were diagnosed, and no suspected COVID-19 cases were confirmed in the RTX group. Therefore, seroconversion should be assessed after COVID-19, and vaccination strategies should be reviewed in patients treated with rituximab.
ACKNOWLEDGMENTS
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by all authors. The first draft of the manuscript was written by A.G.-F. and P.M.-A., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. The authors wish to thank M.A. Martín-Martínez for her outstanding help in conducting the statistical design and analysis.
ORCID ID: https://orcid.org/0000-0003-3670-6796
The authors declare no conflict of interest.
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REFERENCES
1 Pateinakis P Pyrpasopoulou A . CD20+ B cell depletion in systemic autoimmune diseases: common mechanism of inhibition or disease-specific effect on humoral immunity? Biomed Res Int. 2014;2014 :1–5.
2 van Assen S Holvast A Benne CA , . Humoral responses after influenza vaccination are severely reduced in patients with rheumatoid arthritis treated with rituximab. Arthritis Rheum. 2010;62 :75–81.20039396
3 Grøn KL Arkema EV Glintborg B , . Risk of serious infections in patients with rheumatoid arthritis treated in routine care with abatacept, rituximab and tocilizumab in Denmark and Sweden. Ann Rheum Dis. 2019;78 :320–327.30612115
4 Loarce-Martos J García-Fernández A Lopez-Gutierrez F , . High rates of severe disease and death due to SARS-CoV-2 infection in rheumatic disease patients treated with rituximab: a descriptive study. Rheumatol Int. 2020;40 :2015–2021. doi:10.1007/s00296-020-04699-x.32945944
5 Gianfrancesco M Hyrich KL Al-Adely S , . Characteristics associated with hospitalisation for COVID-19 in people with rheumatic disease: data from the COVID-19 Global Rheumatology Alliance Physician-Reported Registry. Ann Rheum Dis. 2020;79 :859–866.32471903
6 Strangfeld A Schäfer M Gianfrancesco MA , . Factors associated with COVID-19-related death in people with rheumatic diseases: results from the COVID-19 Global Rheumatology Alliance Physician-Reported Registry. Ann Rheum Dis. 2021;80 :930–942.33504483
7 Friedman MA Winthrop KL . Second COVID-19 infection in a patient with granulomatosis with polyangiitis on rituximab. Ann Rheum Dis. 2021;80 :1102–1104.33674264
8 Comunidad de Madrid. Consejeria de Sanidad. Salud Madrid. Available at: https://www.comunidad.madrid/sites/default/files/doc/sanidad/201222_cam_covid19.pdf. Accessed March 20, 2022.
9 Pollán M Pérez-Gómez B Pastor-Barriuso R , . Prevalence of SARS-CoV-2 in Spain (ENE-COVID): a nationwide, population-based seroepidemiological study. Lancet. 2020;396 :535–544.32645347
10 Noh JY Kwak JE Yang JS , . Longitudinal assessment of antisevere acute respiratory syndrome coronavirus 2 immune responses for six months based on the clinical severity of coronavirus disease 2019. J Infect Dis. 2021;224 :754–763.34467985
11 Louapre C Ibrahim M Maillart E , . Anti-CD20 therapies decrease humoral immune response to SARS-CoV-2 in patients with multiple sclerosis or neuromyelitis optica spectrum disorders. J Neurol Neurosurg Psychiatry. 2022;93 :24–31.34341142
12 Spiera R Jinich S Jannat-Khah D . Rituximab, but not other antirheumatic therapies, is associated with impaired serological response to SARS-CoV-2 vaccination in patients with rheumatic diseases. Ann Rheum Dis. 2021;80 :1357–1359.33975857
13 Connolly CM Boyarsky BJ Ruddy JA , . Absence of humoral response after two-dose SARS-CoV-2 messenger RNA vaccination in patients with rheumatic and musculoskeletal diseases: a case series. Ann Intern Med. 2021;174 :1332–1334.34029488
14 Boekel L Steenhuis M Hooijberg F , . Antibody development after COVID-19 vaccination in patients with autoimmune diseases in the Netherlands: a substudy of data from two prospective cohort studies. Lancet Rheumatol. 2021;3 :e778–e788.34396154
15 Benucci M Damiani A Infantino M , . Presence of specific T cell response after SARS-CoV-2 vaccination in rheumatoid arthritis patients receiving rituximab. Immunol Res. 2021;69 :309–311.34324159
| 35696998 | PMC9722326 | NO-CC CODE | 2022-12-07 23:19:09 | no | J Clin Rheumatol. 2022 Dec 12; 28(8):424-428 | utf-8 | J Clin Rheumatol | 2,022 | 10.1097/RHU.0000000000001876 | oa_other |
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J Occup Environ Med
J Occup Environ Med
JOEM
Journal of Occupational and Environmental Medicine
1076-2752
1536-5948
Lippincott Williams & Wilkins
36346982
JOEM_220109
10.1097/JOM.0000000000002626
00005
3
Original Articles
Experiences and Perspectives on Stressors and Organizational Strategies to Bolster Resiliency During the COVID-19 Pandemic
A Qualitative Study of Health Care Workers at a Tertiary Medical Center
Khalil Carine PhD [email protected]
Berdahl Carl T. MD, MS [email protected]
Simon Kevin MD [email protected]
Uphold Heatherlun PhD [email protected]
Ghandehari Sara MD [email protected]
Durra Omar MD [email protected]
Glenn Clarence III MD, MBA [email protected]
Kim Linda PhD, MSN, RN, PHN [email protected]
Yumul Roya MD, PhD, CHSE [email protected]
Milam Adam Jeraldo MD, PhD
From the Cedars Sinai Medical Center, Los Angeles, California (Drs Khalil, Berdahl, Ghandehari, Durra, Kim, Yumul, Milam); Université Paris Descartes, Paris, France (Dr Khalil); Boston Children's Hospital, Harvard Medical School, and Adolescent Substance Use and Addiction Program, Division of Developmental Medicine, Department of Pediatrics, Boston Children’s Hospital, Boston, Massachusetts (Dr Simon); Department of Psychiatry, Charles R. Dew University of Medicine and Science, Los Angeles, California (Dr Glenn); Division of Public Health, College of Human Medicine, Michigan State University, Flint, Michigan (Dr Uphold); Department of Anesthesiology and Perioperative Medicine, Mayo Clinic Arizona, Phoenix, Arizona (Dr Milam); and Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (Dr Milam).
Address correspondence to: Adam Jeraldo Milam, MD, PhD, Department of Anesthesiology and Perioperative Medicine, Mayo Clinic Arizona, Phoenix, AZ 85054 ([email protected]).
12 2022
2 12 2022
2 12 2022
64 12 10131017
Copyright © 2022 American College of Occupational and Environmental Medicine
2022
Wolters Kluwer Health, Inc. All rights reserved.
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 highlighted the stress and challenges associated with exposure to SARS-CoV-2, the importance for healthcare workers practicing self-care, and working in an environment where they feel safe, supported, and rewarded. The study findings can help inform interventions to support HCWs during pandemics and other crises.
Objective:
This qualitative study included a sample of health care workers (HCWs) at a tertiary care center providing direct care to patients with COVID-19 to explore experiences and perceptions regarding care delivery during the COVID-19 pandemic as well as factors that helped HCWs cope with the challenges of the pandemic. Methods: Grounded theory methodology was used to conduct virtual focus groups with a semistructured interview guide May to June 2020. Results: We identified major themes related to (1) HCWs' emotions during the pandemic, (2) the perceived triggers of these feelings, (3) organizational factors that made HCWs feel more supported and appreciated, and (4) personal factors that helped HCWs cope with the pandemic. Conclusion: Results highlighted the stress and challenges associated with exposure to SARS-CoV-2. The findings can help inform interventions to support HCWs during pandemics and other crises.
Keywords
COVID-19
SARS-CoV-2
pandemic
health care workers
resilience
SDCT
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pmcThe novel coronavirus (SARS-CoV-2), causing COVID-19, has caused an unprecedented global health crisis, leading to an increase in the number of related hospitalizations and deaths. This outbreak is having a crucial impact on health care workers (HCWs) on the frontlines managing patients with COVID-19. Many HCWs working in potentially contaminated environments1,2 during pandemics have experienced social stigmatization3,4 and mental health problems such as anxiety, burnout, depression, insomnia, and stress-related disorders including posttraumatic stress disorders.5–7
The physical and emotional stress experienced by frontline HCWs impacts their well-being and ability to effectively care for others, specifically their patients.8,9 Recent studies on the impact of the novel coronavirus highlighted the psychological burden of this infectious disease on frontline HCWs.1,10–13 Additional evidence suggests a high prevalence of mental health–related challenges among HCWs including but not limited to distress (72%), depression (50%), anxiety (45%), burnout (45%), and insomnia (34%). Risk factors associated with poorer psychological outcomes included younger age, female sex, identified role as a nurse, and working directly with patients experiencing COVID-19.7 In a study of HCWs throughout China, nearly one third of frontline workers experienced posttraumatic stress disorders.14 During an outbreak, HCWs tend to work long hours under pressure, and they do not always have the adequate resources to protect themselves, which can increase their risk of exposure and cause more discomfort.7,15
In addition to individual characteristics that may increase the risk of poorer physiological outcomes, extrinsic organizational characteristics may increase the risk of mental health symptoms among HCWs. Some extrinsic organizational characteristics related to workplace stress and mental health symptoms among HCWs include increased work demands, little control over their work environment, adequacy of training, deployment to COVID-19 units, and lack of perceived organizational support.7,16,17 Other studies, conducted in countries such as Oman,10 United Kingdom,13 India,18 Singapore,19 and Iran,20 have already portrayed the experiences of frontline HCWs during the COVID-19 crisis and highlighted the challenges HCWs have encountered. Although many of these studies examined the psychological impacts of COVID-19, they rarely focused on the role of health care organizations during the COVID-19 pandemic in improving well-being and enhancing resiliency. There is also a dearth of literature examining how HCWs perceive the role of health care institutions in providing mental health support and addressing challenges during a pandemic. A better understanding of organizational strategies may inform interventions to improve HCWs' well-being and prevent psychological distress during a pandemic or any other crisis. Accordingly, this study sought to explore the experiences and perceptions of HCWs regarding care delivery during the COVID-19 pandemic in the United States and to examine factors that helped HCWs cope with the challenges of the pandemic.
METHODS
Recruitment and Sampling
This study used grounded theory methodology21,22; this methodology is especially useful for preliminary studies. Through purposeful and iterative recruitment, our sample included personnel providing direct care to patients with COVID-19, including nurses, physicians, respiratory therapists, and social workers from the departments of Anesthesiology, Emergency Medicine, Medicine, and Psychiatry. To be eligible, participants had to be full-time employees at Cedars-Sinai Medical Center starting on or before September 10, 2019 (starting at least 6 months before the pandemic). One of the coauthors (A.M.) sent an e-mail invitation to a list-serv of potential participants. An informational letter was attached to this e-mail, explaining the study's purpose, goals, risks, and benefits. Participants that consented to participate in the study were contacted and scheduled for a virtual focus group. Participant remuneration included a $50 gift card. All participants received confidentiality assurances; we used numbers instead of names (for instance, nurse N1, N2…; physician P1, P2…; social worker SW1, SW2…) and removed any identifying information from the transcripts. The institutional review board approved this current study. There are no known conflicts of interest to report. All authors certify responsibility.
Data Collection
Data collection occurred iteratively based on grounded theory methodology22 between May 2020 and June 2020. There were six virtual focus groups with 37 participants (13 physicians, 19 nurses [registered nurses and nurse practitioners], 1 respiratory therapist, and 4 social workers). Early data analysis helped us identify the direction for further data gathering. The focus groups sought to gain insights into HCWs' thoughts, emotions, needs, and concerns when caring for patients with COVID-19. A semistructured guide with open-ended questions was developed for the focus groups (see Appendix 1, http://links.lww.com/JOM/B147). Focus groups lasted between 60 and 90 minutes; they were performed virtually by a qualitative researcher (C.K.). One of the coauthors, a clinician with experience in qualitative research (A.M.), also attended the focus groups, explained the purpose of the focus group to the participants, and took notes. The focus groups included questions such as “How do you describe the current situation that the hospital is facing?” “What type of fear and threat did COVID-19 create?” “What helps you cope with the different level of challenges faced today?” “How do contextual factors, such as the organizational culture, resources, etc, affect your way of dealing with the current situation?” Follow up questions, such as “Can you please tell me more about that” and “What did you mean by…?” were used to deepen the discussion. Focus groups were audio-recorded with the consent of all participants. Data about demographics were collected using a short online survey.
Data Analysis
The audio-recordings from each focus group were transcribed. Data analysis was performed by an experienced qualitative researcher (C.K.). We used an inductive approach to analyze the data.22 The analysis started as soon as we collected the data from the first focus group as concurrent data collection and analysis is central to grounded theory research.23 This iterative process of selecting, collecting, and analyzing the data allowed us to constantly compare the generated themes. It also helped us define the different samples of participants. After four focus groups, thematic saturation had been achieved. This was confirmed after we held two additional focus groups in which participants shared additional stories, but no new themes emerged.
The qualitative data were carefully read multiple times for a total immersion in the discussions. Throughout the reading, sentences and/or paragraphs were coded, and important sections of texts were highlighted and labeled. Key labels were inductively identified in the unstructured data. After sorting and combining the identified labels, a set of inductive themes and subthemes were subsequently defined and justified with verbatim quotes.24 These themes were summarized in a conceptual framework. Table 1 illustrates examples from the coding process. To ensure validity,25 findings were discussed during peer debriefing in the presence of the three coauthors (C.K., A.M., and C.B.). In addition, the presence of the two coauthors (C.K. and A.M.) in the focus groups allowed note taking and helped with data familiarization and interpretation. Data summaries were presented to the three coauthors to share perspectives on the insights obtained. Performing six focus groups was an opportunity to prolong our engagement in the study and gain an in-depth understanding of the phenomenon of interest.26 Transferability was achieved by involving different participant profiles, providing thick data description, and quoting participants.26
TABLE 1 Extracts From the Coding Process
Quotes Themes
“I definitely was fearful and scared of what I could bring home and how I could affect my family.” Fear from infecting their families
“You should really take care of your own health and invest in your own well-being, mentally and physically.” Perceived importance of self-care
“Peer support helped reinforce the bonds of we are all in this together.” Perceived importance of peer support
RESULTS
A total of six focus groups were conducted with 37 participants in total. Table 2 displays the demographic characteristics of focus group participants. Approximately 14% of the participants were in residency training; 22% had been in practice for fewer than 5 years. The majority of participants were female (66.6%), and ages ranged between 25 and 74 years (mean age, 39.1; SD, 11.1). About half of the participants identified as White, and 38% identified as Asian; 92% of participants identified as not Hispanic or Latino.
TABLE 2 Demographic Characteristics of Focus Group Participants
n (%)
Position
Nurse 19 (51.4)
Physician 13 (35.1)
Respiratory therapist 1 (2.7)
Social worker 4 (10.8)
Female 24 (66.6)
Race/ethnicity
Asian 14 (37.8)
Black 2 (5.6)
Hispanic 3 (8.3)
White 17 (47.2)
Mean age (SD) 39.1 (11.1)
Years in practice
Resident/training 5 (13.5)
1–5 y 8 (21.6)
5–9 y 9 (24.3)
10–20 y 11 (29.7)
>20 y 3 (8.1)
Four key themes and several subthemes were identified in the data analysis describing the experiences of HCWs during the COVID-19 pandemic and coping strategies. The major themes we identified were related to (1) HCWs' emotions during the pandemic (eg, feeling stressed, anxious, confused, scared, social isolated, stigmatized, and emotionally distressed); (2) the perceived triggers of these feelings (eg, the unknown/uncertainty, rapid changing guidelines, overload of nonvalidated information, lack of information, other people's fear and anxiety, indirect pressure from families and relatives); (3) organizational factors that made HCWs feel more supported and appreciated (eg, backup assistance, communication, perceived support from the hospital, supportive messages from the president of the hospital, peer collaboration and support, resource availability, remote work when possible, adequate training, and room for relaxation); and (4) personal factors that helped HCWs cope with the pandemic (eg, perceived importance of self-care, family and friends' support). Figure 1 shows an analytical framework that describes the themes and subthemes identified in the data analysis.
FIGURE 1 Analytic framework of the identified themes and subthemes. The themes identified include 1) HCWs' emotions during the pandemic, 2) the perceived triggers of these feelings, 3) organizational factors that made HCWs feel more supported and appreciated, and 4) personal factors that helped HCWs cope with the pandemic.
Health Care Workers Emotions During the Pandemic (Theme 1) and the Perceived Triggers (Theme 2)
Regardless of their experience at work and expertise with infectious diseases, participants unanimously felt stressed and anxious about the pandemic: “Honestly, it's been a little stressful here. I feel it's normal to be anxious about this situation.” According to them, the feeling of being stressed and anxious was triggered by the unknown, “This is something we haven't seen before in our lifetime,” and the uncertainty around the outbreak, “We were thrown into a situation that we knew very little about; it didn't seem like we had a clear plan for this.” The rapidly changing guidelines, “The guidelines were literally changing almost daily,” the overload of nonvalidated information, “Initially too much information was going on without being validated,” and the lack of information about the newly discovered coronavirus, “Everybody was concerned and scared of what this was…we didn't have a lot of information,” and the preventive measures, “There was a lot we didn't know…we definitely weren't taking the right precautions” “It has been really stressful making sure that we put the personal protective equipment (PPE) in a certain way,” amplified HCWs' confusion, fear, and stress with the situation. In addition, HCWs were afraid of contracting the virus through workplace exposure, “Initially people were terrified” “Having staff getting sick and the fears associated with coming to work with this patient population,” and from bringing the virus home and transmitting it to their families, “I definitely was fearful and scared of what I could bring home and how I could affect my family.” The HCWs described how they had to stay away from their families for a long time, which was “very, very difficult.” In addition, they were getting indirect pressure from their families and relatives, “They [my family] were pretty anxious about me infecting them” “Initially it was stressful for my wife and my parents…they didn't come visit”; they felt socially isolated as “people were being scared to come around and see [them] in person,” and stigmatized, “Getting text messages from the neighbors that we are all infected….That stigma is still there.” Moreover, frontline HCWs expressed their emotional distress “when watching these people in hospital beds, with the tear in their eyes.” For some of them, especially social workers, it was “emotionally difficult” and “frustrating to figure out how to authentically support patients over the phone or FaceTime.”
Organizational Factors that Improved HCWs' Experiences During the Pandemic (Theme 3)
Participants pointed out a few factors that supported them at work and helped them handle this unprecedented situation. They emphasized the importance of having backup assistance when there is too much to do, “Knowing that there is always a backup assistance relieved the anxiety,” and transparent communication when managing patients with COVID-19, “We communicate very well the updates and that's been extremely helpful” “I found really helpful to be heard about some questions.” In fact, the hospital deployed educators to support HCWs, especially regarding the guidelines for management of patients with COVID-19. This was highly appreciated by participants, “One thing that helped the continuity of care is having the educators on each floor, sometimes hourly” “The hospital has been doing its best to support and provide resources, especially, our educators, making themselves available 24 hours a day.” In addition, HCWs reported the positive impact of the support they were getting from the hospital in general, “The administrative leave pay was hugely helpful to calm people” “The possibility to stay at a nearby hotel in order to rest between shifts.” Health care workers also appreciated the implementation of the emotional support line available 24/7. They also valued the supportive messages from the president of the hospital. According to them, it is crucial to feel recognized in such context, “I think it is really nice to recognize that we are here, doing what we are doing….” Furthermore, peer collaboration was perceived as essential in such an ambiguous context where HCWs have to collectively overcome challenges without specific protocols, “There were no protocols, and it was all innovation” “We were figuring out how to set up the unit, how to set up the clean and dirty areas.” In addition, peer support and solidarity kept them highly motivated to come to work and deal with this continuously challenging situation, “Peer support helped reinforce the bonds of we are all in this together” “I work in a great unit where we look out for each other” “It's a big support system…you have people you can at least talk to…they understand what you are going through.” Last but not least, HCWs were thankful to be supported with redeployed staff,1 “We were really well supported with staff and equipment early on…this helped us adapt to the changes we needed to make,” and have access to personal protective equipment. This made them feel safe at work, which is very valuable in such context, “The rate of safety is really much higher at the hospital versus going out or pumping up gas.” They also valued the option of working remotely when it is possible, “Having that as an option [working remotely], once per pay period, or even once per week, is a huge help,” and found it helpful to have adequate training to deal with similar infectious diseases, “Did have some extra training for dealing with SARS and MERS in case we did have outbreak, so that did help.” In addition, HCWs expressed the need for creating a quiet isolated room for relaxation at work, “It is important to have a room where you can decompress. This would be really great.”
Personal Factors That Help HCWs Cope With the Pandemic (Theme 4)
In addition to being supported by their organization and colleagues at work, participants emphasized the need for self-care. According to them, it is primordial to take care of their physical and mental health to leverage stress and improve their overall well-being, “You should really take care of your own health and invest in your own well-being, mentally and physically.” The participants highlighted the importance of exercise, “I'm also doing mindfulness exercises to help me stay sane with all this,” and relaxation at home, “Trying to leave work at work and focus on other things at home.” Health care workers also mentioned how important is to stay in contact with friends and family, “Reaching out to friends and family virtually is very important” “One of the things that actually helped out is that everyone seemed to be more open on communication because everyone is at home.”
DISCUSSION
This study sought to gain an in-depth understanding of HCWs' perceptions of working during the COVID-19 pandemic using methods from the grounded theory. A series of six focus groups were performed with various HCWs (eg, registered nurses, nurse practitioners, physicians, and social workers) to gain insights into their feelings, needs, and perceptions of support when delivering care during the COVID-19 pandemic. The focus groups highlighted the stress and challenges associated with exposure to SARS-CoV-2, the importance of HCWs practicing self-care, and working in an environment where they felt safe, supported, and rewarded by their community and leadership. This study generated an analytical framework grounded in evidence.22 The study also uncovered the impact of individual factors and organizational factors on HCWs' well-being. These factors should be taken into consideration when developing interventions for the mental well-being of frontline health workers. These findings are timely as there is a lack of evidence from studies conducted during and after pandemics that can inform the selection of such interventions.27
Consistent with prior qualitative studies,13,28,29 the COVID-19 pandemic has substantially impacted the physical and mental health of HCWs at our institution. The participants were frustrated, anxious, and stressed about delivering care for sick patients, confused with the changing guidelines, and afraid of getting infected and infecting others. They highlighted the importance of having access to the correct PPE to feel safe, being supported by their management, and having assigned staff that assisted and helped them meet patients' needs. Our participants mentioned fear of PPE shortages in the beginning of the pandemic, although this did not come to fruition. The HCWs did not describe burnout at the time of the study, which was conducted between May and June 2020, just several months into the pandemic. According to them, they had continuous backup assistance and were supported by educators, redeployed staff, and their leadership; they even felt appreciative for being valued and recognized by the community (eg, people bringing them food) and the hospital in general. Notably, for some participants, just being a part of these focus groups was therapeutic as they felt heard and could share their feelings, thoughts, fears, and concerns; it may prove beneficial to incorporate group sessions in the workplace as a strategy to support HCWs.
Participants also reported feeling stigmatized and socially isolated by their relatives, friends, and neighbors who tended to avoid them; this finding is consistent with prior studies.30,31 Interestingly, the stigmatization enhanced solidarity at work and made HCWs feel closer to their colleagues as they were all dealing with the same social stigma outside work. Consistent with prior work,13,31,32 this study also highlights the importance of collaboration during the pandemic. According to our participants, the lack of guidelines, especially at the beginning of the pandemic, encouraged them to be “creative” and “figure out collectively” how to manage patients with COVID-19 while maintaining personal safety. This shows how HCWs faced with unknown situations were able to collectively create a shared understanding of their experiences through communication and socially constructed meaning.33 Hence, approaching this topic through the lens of sensemaking33 can be a step forward for future research to understand how HCWs, in crises, make sense of their experiences and collectively develop plausible solutions. In this regard, HCWs' shared experience, knowledge, and social interactions with each other.
For this study, several limitations warrant discussion. First, although our focus group sample was diverse in terms of sex, age, and job positions, all focus group participants were all recruited from a single medical center, which is a limitation of qualitative research. However, the findings can be transferable to other contexts and hospitals dealing with a similar situation. Second, the sample size was limited by budgetary constraints, and thus, we may not have captured a comprehensive list of coping strategies and experiences, and we were unable to stratify by demographic characteristics and workplace roles. Third, the study was conducted early in the COVID-19 pandemic and may not offer a representative picture of circumstances later in the pandemic’s evolution, such as after distribution of vaccines and dealing with staff shortages. Although the study was conducted in a large metropolitan area that was impacted by the pandemic relatively early, our institution had adequate staffing, and we were able to maintain our nurse/patient ratio during the time of this study. Future follow-up studies should explore HCWs' experiences over time (eg, exhaustion, burnout, lack of training opportunities, backup work that has been cumulated) and how job experience and demographic characteristics are associated with HCWs' experiences while managing infectious patients.
This qualitative research study extends recent studies examining HCWs' experiences during the COVID-19 pandemic. In addition to exploring HCWs perceptions of care during the pandemic, our study also focused on the organization's role in helping HCWs deal with this challenging situation through constant support and recognition. Future studies should continue to examine organizational characteristics and strategies to support HCWs. This can be useful in the future during similarly stressful pandemics. In addition, this study sheds light on the role of sensemaking in crises, where HCWs without adapted protocols and guidelines need to structure the unknown to provide care for their patients.
1 In the beginning of the pandemic, 500 employees were redeployed from their traditional jobs to the areas of greatest need in the Medical Center and the Medical Network.
Funding: Institutional funding (Department of Anesthesiology, Cedars-Sinai Medical Center).
Declarations of Interest: There are no conflicts of interest to report.
Dr Simon was supported by the National Institute on Drug Abuse (NIDA)/American Academy of Child and Adolescent Psychiatry (AACAP) Physician-Scientist Career Development Award (K12DA000357).
Author Contributions: All authors contributed to the preparation of the manuscript and approved the final version.
Data Availability: The datasets generated during and/or analyzed during the current study are not publicly available due to concerns regarding confidentiality (and assurances made to participants) but are available from the corresponding author on reasonable request.
Compliance With Ethical Standards: Research Involving Human Subjects and Informed Consent—Participants consented to participate in the study. All participants received confidentiality assurances; we used numbers instead of names (for instance, nurse N1, N2…; physician P1, P2…; social worker SW1, SW2…) and removed any identifying information from the transcripts. The Institutional Review Board approved this current study.
Supplemental digital contents are available for this article. Direct URL citation appears in the printed text and is provided in the HTML and PDF versions of this article on the journal’s Web site (www.joem.org).
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5 Kessler RC Bromet EJ . The epidemiology of depression across cultures. Annu Rev Public Health. 2013;34 :119–138.23514317
6 Maunder R . The experience of the 2003 SARS outbreak as a traumatic stress among frontline healthcare workers in Toronto: lessons learned. Philos Trans R Soc Lond B Biol Sci. 2004;359 :1117–1125.15306398
7 Preti E Di Mattei V Perego G , . The psychological impact of epidemic and pandemic outbreaks on healthcare workers: rapid review of the evidence. Curr Psychiatry Rep. 2020;22 :43–43.32651717
8 Barnett JE Baker EK Elman NS . In pursuit of wellness: the self-care imperative. Prof Psychol Res Pr. 2007;38 :603–612.
9 Van der Colff JJ Rothmann S . Occupational stress, sense of coherence, coping, burnout and work engagement of registered nurses in South Africa. SA J Ind Psychol. 2009;35 :1–10.
10 Al Ghafri T Al Ajmi F Anwar H , . The experiences and perceptions of health-care workers during the COVID-19 pandemic in Muscat, Oman: a qualitative study. J Prim Care Community Health. 2020;11 :2150132720967514.33089729
11 Bennett P Noble S Johnston S , . COVID-19 confessions: a qualitative exploration of healthcare workers experiences of working with COVID-19. BMJ Open. 2020;10 :e043949.
12 Spoorthy MS Pratapa SK Mahant S . Mental health problems faced by healthcare workers due to the COVID-19 pandemic—a review. Asian J Psychiatr. 2020;51 :102119.32339895
13 Vindrola-Padros C Andrews L Dowrick A , . Perceptions and experiences of healthcare workers during the COVID-19 pandemic in the UK. BMJ Open. 2020;10 :e040503.
14 Lai J Ma S Wang Y , . Factors associated with mental health outcomes among health care workers exposed to coronavirus disease 2019. JAMA Netw Open. 2020;3 :e203976.32202646
15 Wu PE Styra R Gold WL . Mitigating the psychological effects of COVID-19 on health care workers. CMAJ. 2020;192 :E459–E460.32295761
16 Van Bogaert P Adriaenssens J Dilles T , . Impact of role-, job- and organizational characteristics on nursing unit managers' work related stress and well-being. J Adv Nurs. 2014;70 :2622–2633.24842679
17 Revicki DA Whitley TW Gallery ME . Organizational characteristics, perceived work stress, and depression in emergency medicine residents. Behav Med. 1993;19 :74–81.8280965
18 Raj R Koyalada S Kumar A , . Psychological impact of the COVID-19 pandemic on healthcare workers in India: an observational study. J Family Med Prim Care. 2020;9 :5921–5926.33681020
19 Tan BYQ Chew NWS Lee GKH , . Psychological impact of the COVID-19 pandemic on health care workers in Singapore. Ann Intern Med. 2020;173 :317–320.32251513
20 Alizadeh A Khankeh HR Barati M , . Psychological distress among Iranian health-care providers exposed to coronavirus disease 2019 (COVID-19): a qualitative study. BMC Psychiatry. 2020;20 :494.33028290
21 Bryant A Charmaz K . The SAGE Handbook of Grounded Theory. London: Sage Publications; 2007.
22 Strauss A Corbin J . Basics of Qualitative Research: Techniques and Procedures for Developing Grounded Theory. London: Sage Publications; 2014:2014.
23 Chun Tie Y Birks M Francis K . Grounded theory research: a design framework for novice researchers. SAGE Open Med. 2019;7 :2050312118822927.30637106
24 Miles MB Huberman AM . Qualitative Data Analysis: An Expanded Sourcebook, (3). London: Sage Publications; 1994.
25 Guba EG Lincoln YS . Competing paradigms in qualitative research. In: Denzin NK Lincoln YS , eds. Handbook of Qualitative Research. London: Sage Publications; 1994:105–117.
26 Lincoln YS Guba EG . Establishing trustworthiness. Naturalistic inq. 1985;289 :289–327.
27 Pollock A Campbell P Cheyne J , . Interventions to support the resilience and mental health of frontline health and social care professionals during and after a disease outbreak, epidemic or pandemic: a mixed methods systematic review. Cochrane Database Syst Rev. 2020;11 :CD013779.33150970
28 Salazar de Pablo G Vaquerizo-Serrano J Catalan A , . Impact of coronavirus syndromes on physical and mental health of health care workers: systematic review and meta-analysis. J Affect Disord. 2020;275 :48–57.32658823
29 Missel M Bernild C Dagyaran I , . A stoic and altruistic orientation towards their work: a qualitative study of healthcare professionals’ experiences of awaiting a COVID-19 test result. BMC Health Serv Res. 2020;20 :1031.33176771
30 De Brier N Stroobants S Vandekerckhove P , . Factors affecting mental health of health care workers during coronavirus disease outbreaks (SARS, MERS & COVID-19): a rapid systematic review. PloS One. 2020;15 :e0244052.33320910
31 Koh D Lim MK Chia SE , . Risk perception and impact of severe acute respiratory syndrome (SARS) on work and personal lives of healthcare workers in Singapore What can we Learn? Med Care. 2005;43 :676–682.15970782
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| 36346982 | PMC9722327 | NO-CC CODE | 2022-12-07 23:19:10 | no | J Occup Environ Med. 2022 Dec 2; 64(12):1013-1017 | utf-8 | J Occup Environ Med | 2,022 | 10.1097/JOM.0000000000002626 | oa_other |
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J Occup Environ Med
J Occup Environ Med
JOEM
Journal of Occupational and Environmental Medicine
1076-2752
1536-5948
Lippincott Williams & Wilkins
36190909
JOEM_220202
10.1097/JOM.0000000000002717
00020
3
Online-Only: Original Articles
Forgotten Heroes
Experiences of Health Care Support Workers Regarding Burnout and Resilience During Pandemic, A Qualitative Approach
Valera-Hernández María Fernanda MD
Arenas-Pérez Luisa MD [email protected]
Fernandez-Capriles Isabella MD [email protected]
Omaña-Paipilla Felipe MD [email protected]
Palencia-Sánchez Francisco MD, MSc, PhD [email protected]
Cadena-Camargo Yazmin MD, MPH, PhD [email protected]
From the Department of Preventive and Social Medicine Faculty of Medicine, Pontificia Universidad Javeriana Bogotá, Colombia.
Address correspondence to: María Fernanda Valera Hernández, MD, Ak. 7 No. 40-62, Bogotá ([email protected]).
12 2022
3 10 2022
3 10 2022
64 12 e839e844
Copyright © 2022 American College of Occupational and Environmental Medicine
2022
Wolters Kluwer Health, Inc. All rights reserved.
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.
Given that the scientific literature about burnout and resilience in medical personnel during the COVID-19 pandemic is regarding physicians and nurses, there is a lack of information regarding the experiences of healthcare support workers. Our study contributes to filling a void in knowledge concerning mental health in healthcare support workers.
Objectives
Health care support workers have been facing several challenges due to the stressful environment in COVID-19 pandemic. Because of the gap in literature, it is mandatory to explore their experiences to identify burnout, predisposing factors, and possible interventions.
Methods
We conducted qualitative research with a hermeneutic phenomenological method. Participants belonged to cleaning services, security, and hospital administration areas at a hospital in Bogotá. We used semistructured individual interviews. The analysis approaches were deductive and inductive.
Results
As main concepts, we found the following: fear of infection and coping mechanisms, dealing with COVID-19 and being part of the health care support system, overwhelming workload and motivation to keep going and socioeconomic conditions.
Conclusions
We found burnout predisposing factors; however, the participants did not express symptoms of burnout syndrome. We believe protective factors such as resilience are influential concerning this outcome.
Keywords
burnout
pandemic
health care support workers
COVID-19
resilience
STATUSONLINE-ONLY
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pmcCOVID-19 global pandemic has generated a stressful environment for medical staff members around the world due to multiple factors such as the fear of getting infected and being a vector of transmission to family and friends, discrimination and stigma among the community, limited resources of hospitals, availability of personal protective equipment, longer shifts, augmented demand from patients, the indifference of personal and family needs with an increased workload, disruption in work-life balance, and lack of sufficient communication and updated information.1–4 Consequently, many authors like Chen et al,1 Giorgi et al,2 Adams and Walls,3 and Raudenská et al5 have observed implications with the development of burnout on this special population.1–4 The concept of burnout has been widely used to describe this relation between workplace and mental health, it was first coined in the 1970s by Maslach,6 and it can be defined as an excessive reaction to stress caused by one's environment that may be characterized by feelings of emotional and physical exhaustion, coupled with a sense of frustration and failure.
Since Maslach6 described burnout in 1970, studies have been conducted, demonstrating burnout as a factor related to poor outcomes on work, personal life, and health. At work, burnout has been related to job dissatisfaction, absenteeism, intention to leave the job and poor job performance. In personal life, workers experiencing burnout have higher levels of unsatisfactory marriage, and have reported work as having a negative impact on their family.7 Poor health outcomes are related to symptoms of prolonged stress, such as headaches, physical exhaustion, gastrointestinal problems, back pain, hypertension, colds and flu, and sleep disturbances.6,8
On the other hand, other authors, such as Heath et al,9 Rangachari and Woods,10 studied the concept of resilience as a response mechanism toward these distressing events faced by medical staff members during the COVID-19 pandemic. These studies have been conducted mainly on medical professionals (MPs) defined as physicians, nurses, and therapists. However, health care support workers (HSWs) defined as clinical assistants, patient services assistants, volunteers, security personnel, and ward clerks, are key to keeping hospitals working and have been facing similar challenges during the pandemic.11 Nevertheless, there is a lack of knowledge about their experiences, and they have not been reported previously in the literature. For this reason, it is important to explore how they have been experiencing the pandemic through the burnout approach and identify possible mechanisms of resilience in this specific population.
World Pandemic Context
In December of 2019, an outbreak of a novel coronavirus pneumonia occurred in Wuhan, China, captivating the whole world's attention.1 The World Health Organization declared it a Public Health Emergency of International Concern on January 30, 2020, and a global pandemic on March 11, 2020. This caused worldwide concern and has forced all countries to take action to contain the virus.12 To date (June 15, 2021), there have been 176,303,596 confirmed cases of COVID-19, including 3,820,026 deaths.13
Colombian Pandemic Context
Colombia had the first confirmed case on March 6, 2020. To date (February 15, 2022), there have been 6,023,257 cases, including 137,301 deaths. The most affected city is the capital, Bogota, with 1,104,052 cases and 21,123 deaths. During this period, the government has imposed many measures to decrease the number of infected people, and therefore, hospitalizations and deaths. Nevertheless, to the month of June 2021, the country and the capital were facing the toughest moment of the pandemic, having their worst numbers.14
In Colombia, the COVID-19 crisis and lockdown have severely hit those employed in the informal sector who constitute half of the labor force, and it is estimated that 3.5 million people have fallen into poverty because of the COVID-19 pandemic, bringing the number of those living in poverty from 17.5 million in 2019 to 21 million (42.5% of the population) in 2020.15,16 We consider that the socioeconomic situation that Colombia is going through is a factor to consider when talking about the implications of mental health in all workers including HSWs.
Burnout and Resilience in HSWs During the COVID-19 Pandemic
During the pandemic, the social media and the scientific community have given special attention to the well-being of MP, taking into account additional stress factors they have to face that are intrinsic to their workplace such as the fear of getting infected and being a vector of transmission to family and friends, discrimination and stigma among the community, limited resources of hospitals, availability of personal protective equipment, longer shifts, augmented demand from patients, the indifference of personal and family needs with an increased workload, disruption in work-life balance and lack of sufficient communication and updated information.2–4 It is believed or it seems that these factors contribute to creating a poor working environment that may cause negative outcomes such as burnout, among MP and HSW.3,4
Studies conducted in China and Italy have shown that during the coronavirus outbreak the rate of burnout has increased among MP, and symptoms of burnout such as anxiety and depression were more common among those working directly with COVID-19 patients or in areas with major burden of disease like the intensive care units.17–19
Wood et al20 described the relationship between burnout and four potential protective factors: grit, social support, psychological flexibility, and resilience. They found that grit had the strongest protective relationship to burnout, attached to social support and psychological flexibility while resilience was not included as a protective factor. Grit is a term introduced by Duckworth and colleagues21 in 2007, they defined it as perseverance and passion for long-term goals and addresses working strenuously toward challenges, maintaining effort and interest over years despite failure, adversities, and plateaus in progress. Wood et al22 also suggests a harmful effect on “gritty” persons based on their qualitative data, getting to the conclusion that participants with these characteristics can sacrifice their well-being for long-term goals as well. However, during the pandemic, studies of the protective factors of burnout in MP were carried out, and it was concluded that the model proposed by Wood and colleagues needed to be re-evaluated since it would probably increase the importance of protective factors such as resilience and social support.
In this context, it is important to talk about the concept of resilience, which has been defined as the ability to resist disruption of normal functioning in the face of a distressing event (in this case, the COVID-19 pandemic), by anticipation and preparation.9 Even though every person has different experiences and perspectives, it is very important to provide a variety of strategies to promote overall well-being in health care workers. Within these measures it is shown that, providing adequate amounts of personal protection equipment, sufficient rest, nutrition, and hydration, limiting overworking (e.g., shifts >16 hours), allowing MP to seek help if needed without stigma or repercussion, offering individualized emotional support plans, support groups (e.g., peers) and reading materials, were identified to be useful to reduce the burnout syndrome and other mental health problems in support health care workers.4,10,23
Rangachari and Woods10 described the components of a resilient organization in three interconnected levels in which resilience can be present: Individual, team and organizational level. All of them have three key elements that allow and strengthen the process of resilience, these being: Foresight (ability to predict something bad could happen), coping (ability to prevent something bad from becoming worse) and recovery (ability to recover from a bad occurrence). Other authors, like Wreathall, describe seven different themes characteristic of a highly resilient organization: top-level commitment (recognition of human performance concerns and the capability of addressing them), culture (reporting of issues up through the organization, yet not tolerating culpable behaviors), learning culture (ability to respond to events with denial vs repair or true reform), awareness (data gathering that provides information about people's performance, the grade of a problem, and the state of the solutions), preparedness (being aware of the problems, anticipating and being prepared for them), flexibility (adapting to new situations, maximizing the ability to solve, preserving the overall functions), and finally opacity (knowing the limits of the barriers and the defenses).24
As previously mentioned, resilience is an essential competence that support health care workers must develop to provide the best attention to their patients, so it is very important that employers and health centers offer the appropriate tools for the enhancement of this skill.10
Because multiple studies, Adams and Walls,3 Raudenská et al5, Trumello et al,17 Liu et al,18 and Kok et al,19 have shown the relationship between burn-out and essential workers in the pandemic, as well as the resilience capacity among them, we aim to explore and describe this phenomenon in HSW since they are not included in the scientific literature, in addition, this population was a crucial part of the first line of attention for the patients during the pandemic. Even though it has been widely described in the media to the point of calling them “forgotten heroes,”25,26 it is the reason why our study has the imperative to identify and describe how the HSW have experienced the pandemic and based on the results recognize improvement opportunities in the Colombian context.
Qualitative Research and Its Relevance in This Study
Qualitative research entails an interpretative and naturalistic approach to the world, which allows the researchers to interpret different events by observing, describing, interpreting, and analyzing the way that people experience, act on, or think about themselves and the world around them.27 The aim of a qualitative study is to get a complex and detailed understanding of the issue through direct conversations with people, visits to their homes or places of work, and allowing them to tell their stories. The collection of this data is achieved by empowering individuals to share their stories, hearing their voices, and minimizing the power relationships that exist between the researcher and the participants. All above for the purpose of avoiding bias from what the researchers expect to find or what is in the literature.28 We used qualitative research on this study because we wanted to make an interpretation of the meaning of the lived experiences of this workers to guide organizations in the creation of policies for the benefit of their workers and collaborators through the analysis of their subjective experiences.
METHODS
This is a qualitative study that uses a hermeneutic phenomenological analysis, which aims to identify the “essence” of a phenomenon through an individual's experience of that phenomenon28; in addition, the objective is to explore an under-researched topic with the purpose to acquire preliminary insights into key problems regarding resilience and burnout in HSW to aid future research.29 Even though HSW includes a variety of workers,11 in this study, we will focus on the administrative, cleaning, and security personnel.
Setting and Participants
We conducted qualitative research in a health care facility in a main Hospital in Bogota Colombia. Participants belong to the areas of cleaning services, security, and hospital administration and the majority were women of working age. We used as inclusion criterion, people older than 18 years working on the previously mentioned jobs at the Hospital Universitario San Ignacio, at least since the beginning of the pandemic, and we did not have exclusion criteria.
Recruitment, Data Collection, Data Analysis
We invited participants to be part of the research using a snowball sample, starting with key informants of the personnel who work at a University Hospital. Participants were part of the research as volunteers. We conducted eight interviews with a duration of about half an hour and nonparticipant observation during an estimated period of 4 months, from this moment we reached our saturation point, defined as “when no new information was added to coding categories.”30 We explored their experience working in a hospital during the pandemic and the changes compared with the past, their feelings (sadness, tiredness, overburdened), the economic and family dynamics changes, and interventions they considered appropriate to ease problems observed at the job. As gathering tools, we used semistructured individual interviews in Spanish, where we enquired about their experiences and the changes working in the hospital during the pandemic, their feelings, their families, the socioeconomic factors involved, and some interventions that could help them during this time. For the purpose of the article, quotes were translated into English. The interviews were recorded, and then, they were transcribed verbatim, with a hidden encoding procedure that consisted in changing the names of the participants.
In addition to this, we used nonparticipant observation to perceive the different behaviors exposed by the participants in their daily routine in a hospital setting to subsequently carry out a triangulation of these different methods. The information from the nonparticipant observation was collected by the investigators in field journals. Finally, we analyzed the information with an abductive approach, finding these preset categories: fear of getting infected, experiences with COVID-19, overwhelming workload, and economic issues. Also, these emergent categories: support from the institution, motivation to keep going, and the power of accurate information.
Ethical Framing
Research and ethical approval were granted by the Ethics Committee of the School of Medicine of the Pontificia Universidad Javeriana, Bogotá (FM-CIE-03359-20, Act 09/2020). In the same way, research protocol and methods were consistent with Colombian Law. The participants were asked to provide and sign informed consent. We took care that the participants were fully informed about all aspects of the project and were aware that they could withdraw at any moment without providing reasons, and that eventual withdrawal would not affect their jobs in any way. We destroyed the recordings of the interviews, and we will store the transcripts and notes in a protected location at Pontificia Universidad Javeriana for 10 years.
RESULTS
In this section, we will present the analysis of the experiences of eight HSWs in a University Hospital in Bogota, Colombia. For the purposes of the study, the participants' names were changed to preserve their privacy. Participants belong to the areas of cleaning services, security, and hospital administration and the majority were women of working age (six women and two men). These results show how they have lived through the pandemic and how they have been affected, as well as the tools they have used to get through the difficult moments. We found these preset categories: fear of getting infected, experiences with COVID-19, overwhelming workload, and economic issues. Also, these emergent categories: Support from the institution, motivation to keep going, and the power of accurate information.
1. Fear of infection and coping mechanisms, including institutional support.
For most of our participants getting infected or possibly bringing COVID-19 home generated fear, however, all of them mentioned a diversity of personal and organizational strategies that helped them cope with this.
Most of the feelings related to a possible COVID-19 infection that they expressed, and that we perceived in non-participant observation included fear, nervousness, stress, desperation, and anxiousness. Cristina, a security guard, described the virus as a mortal entity to which she was completely vulnerable, making her consider quitting her job.
“No, I'm going to quit, there's too much covid here, a lot of illness here, I don't want to get infected, I don't want to be sick…... I can't be here, I don't tolerate this, I'm not capable of doing this...I'm not staying here because I have children at home.” She even said that for some of them, it was their first day at work. (Cristina, 51 years old).
In addition to what was expressed by Cristina in the interview the non-participant observation unveiled how cleaning service personnel were feeling about their work in a hospital during the pandemic.
Researcher 3 (Field journal): “During lunch, I overheard a conversation between the participants in which they talked about their fear about cleaning the COVID rooms ICU.”
In addition to the risk of personal infection, the fear of infecting family members or people living with them was always present. Martha, a customer service counselor said it was very hard to be away from her family members to prevent their infection.
“It is your close family and having to get away from them for fear that something will happen to them is hard. You know that you also do it for their good, but it affects a lot not seeing them, not having them around or not being able to give them a hug” (Martha, 29 years old).
Despite what Cristina and Martha said, Julia, who belongs to the cleaning staff of the hospital, mentioned some individual characteristics that reduce the fear of getting infected, like following all the instructions of self-care and having a healthy lifestyle (exercising and healthy nutrition).
“I have always been calm in this issue of covid because more than anything we have to continue, put on the mask, wash our hands, take precautions [...] I consider myself a healthy person; from what I have seen this virus attacks obese people, who do not eat well and do not exercise. And well, I try to stay healthy, that is, eat well, exercise every day and be very careful with that virus” (Julia, 25 years old).
All our participants agreed that receiving training on how to use personal protective equipment and following personal biosecurity measures, as well as knowing that their contractor would always provide the equipment needed made them feel protected and dramatically reduced levels of fear. Juan who works in the hospital's cleaning route explained:
“Let's not talk about terror, stress, fear, or anguish. It is about precaution, about taking biosecurity measures and making sure they are followed at home and at the workplace. Here at the hospital, we have had virtual meetings on different topics like what are the biosecurity measures we should follow and how to ask for help [...] that helps because it makes us feel calm and like one is being taken care of” (Juan, 41 years old).
Researcher 1 (Field journal): “I assisted to a biosecurity meeting in which the lecturer explained to the staff where to find the personal protective equipment and how to use it properly. At the end of the meeting, the lecturer asked the attendees for feedback on the meeting, which was very positive because most of the attendees thanked the hospital for this learning space.”
In addition to what was mentioned about personal protective equipment, transportation was a common concern in these participants, considering that going on a public bus could be a source of infection. For example, Inés, a security guard, highlighted that the support with transportation provided by the hospital was a very significant action taken by the hospital.
“It seems to me a very, very good idea what the hospital did to place us routes to get to our houses because the transmilenio does not work well [...] you are less exposed, you know that you are going with the workers who are people that you always see with their protective elements at all times, so at least it gives me more confidence than leaving with a lot of people that I don't know, who go with their masks down, talking on the phone, eating, the vendors” (Ines, 27 years old).
Even with the support of the hospital, various participants expressed that the actual public order situation going on in the country, made it difficult to arrive safely to their homes even using the exclusive hospital buses.
2. Dealing with COVID-19 infection and being part of the health care support system
Most of our participants either got infected or had a family member who got sick from COVID-19. When asked about how they experienced this, some positive factors of being a hospital staff member came up.
In the case of Cristina, a security guard, her cousin died as a result of getting infected by COVID-19, but the fact of being surrounded by health care professionals allowed her to ask her doubts about the disease, getting trustworthy information. Also, we observed that having peers constantly asking her about her mood and her feelings, made her feel better. These became very important elements to live her grief process. Here, we have some of the experience she shared:
“There were very tough moments here at work…I tried to get over that, that I talk too much with the doctors, I ask them, so ask and avoid being quiet, makes you get off your chest a lot of things. That allows you to continue with your life and prevents marks... It's very useful...the first days the world came over me” (Cristina, 51 years old).
Another factor expressed by many of the participants that made them feel capable of dealing with sickness was the support from the hospital. Participants explained how they received the days off or the time they needed to deal with sick family members. The hospital also included among their online meetings, a space to talk about grief, loss, and managing emotions during tough times. The participants from the hospital's administration perceived a sense of relief and solidarity, Juan explained:
“The hospital also offers meetings regarding duel and psychological support to its workers, and when I was sick, they called me every day to see how I was doing [...] in the emotional part the hospital has being very supportive” (Juan, 41 years old).
3. Overwhelming workload and motivation to keep going
The participants also said that the workload increased during the pandemic, a result of the increase of infections and patients who require critical care units or medical management. Julia mentioned that not only doctors and nurses have had more workload due to the pandemic, but the cleaning staff has also had more decontaminations and at the same time, these must be deeper and take more time.
“More sick people have entered and there has been more work in this regard. In other words, before covid there were three cleanings, now there are ten, twelve [...] We have had to be more careful in doing the cleanings, making them deeper, as well as being more careful with the patients” (Julia, 25 years old).
Researcher 2 (Field journal): “Today in the emergency room I heard three calls at the same time to the cleaning services of the hospital to do decontaminations.”
Altruism was found as a key factor to keep going during the pandemic in some of our participants. One of the examples, Martha, mentioned that despite the increase in workload they have always felt good because of the impression of helping other people and their country in this difficult moment.
“It makes me very happy when I help these people because it makes me feel very good to help the families, especially when I help them to achieve their objectives. In general, it makes me feel very satisfied.” (Martha, 29 years old).
Related to altruism, some of the participants said that religious beliefs were always present to guide their actions, and therefore, it became a motivation to continue doing their job everyday. It is remarkable the expressions that people use when they talk about this topic, it seems to be a very strong conviction in their lives. Cristina is one example of this previous:
“My motivation to go to work, I am given to God's hands, I never let his hands go, and before anything else, I asked him for help for everybody. And that is my job, you know that if you don't work you don't survive. Thank God everything has been good for us” (Cristina, 51 years old).
4. Socio-economic conditions
Lastly, many participants expressed that the economic stability provided by the institution was a very important factor in the well-being during the pandemic, even Alejandro, a billing and portfolio assistant, expressed that the hospital was a good employer and has always fulfilled its legal obligations.
“The hospital is a good employer, it has paid us all that is mandatory by law, so really I haven't been affected by that.” (Alejandro, 46 years old).
Other employees mentioned that in different establishments, payments were retarded or even some of them were fired at the beginning of the pandemic.
“Well, at first a little bit because I was working elsewhere, and they fired all the women who were there. I went on vacation and spent about two months at home, but then the company called me to work here in the hospital about 8 months ago and since then I have had no problems.” (Ines, 27 years old).
On the other hand, participants said that the quarantine and measures taken by the government to prevent new cases, have affected many small businesses, and as Julia said, making it very difficult to create new ways to generate money. In addition to this, the actual socio-political situation occurring in Colombia has affected their economic situation by making food more expensive and increasing the crime rates.
“It has fostered a lot of crime and intolerance. Now it is up to you how to take care of yourself more. That little you have; you have to save it there in the meantime. That one cannot look at another form of progress yet because with this virus one does not know if they will close businesses again or those things. You think about starting a business or something like that to change your job, but then you can't do it yet. You continue with the uncertainty that there is no progress for something better.” (Julia, 25 years old).
Feedback From the Participants
Once the analysis of the information was finished, we communicated with the participants by telephone to receive feedback from them on the results. All participants agreed with the information we found and the conclusion about how they coped with the challenges of the pandemic. Julia remarked on the relevance of actions taken by the hospital aiming to help the HSW, and Cristina considered that the information found in this study could guide the hospital in particularly the Occupational Health and Environmental office and other organizations on the making of politics related to this population.
DISCUSSION
It is worth mentioning that there is a lack of information concerning the HSW. In this article, we explore experiences during the pandemic of this population.
The analysis of the behaviors identified in the non-participant observation combined with the experiences expressed by our participants showed that our participants identified stress factors that were previously mentioned in the literature. Among the most frequent, we found increased workload, family needs, and lack of sufficient communication or updated information. However, negative mental health outcomes or symptoms of burnout associated with these alterations in the working environment that have been previously described in the literature by Giorgi et al,2 Adams and Walls,3 and Raudenská et al5 were not suggested by our participant's experiences which turned out to be a new insight of this study.
When asked about their experience working in a hospital during the COVID-19 pandemic, HSWs mentioned that although having an increased workload, they were not in direct contact with COVID-19 patients, and they related this to be less emotionally affected. We can relate these results to what was found by Trumello et al,17 who describes how the professionals working with COVID-19 patients and health professionals working in the most affected areas are at higher risk of burnout than those who are not, which was an expected finding.
When discussing protective factors of burnout, as Ellinas and Ellinas22 described, these may have changed due to the context of the pandemic, thus changing the model proposed by Wood et al.20 We found in our study that most of the participants expressed resilience as a protective factor, followed by the other factors that they describe as social support, psychological flexibility, and grit, so we consider that it is worth delving into these factors to propose models where it is considered.
Resilience was one of the main findings in all the participants, who expressed that the hospital was a big source of help in the context of this pandemic, even some of them said that they were feeling “excellent,” and that the hospital was an “excellent employer”; however, this could be a socially accepted answer since the hospital is their direct employer and the interviewers are hospital employees. As Raudenská et al5 mentioned, different strategies can be useful in preventing burnout syndrome, depending on every person's perspective and experience, which is reflected in the variety of experiences we obtained from the interviewees. Factors, such as having adequate amounts of personal protection equipment and the easy access to seek help, were mentioned by these authors and were also found in the interviews registered in this article. Both of these previously mentioned factors are part of what Rangachari and Woods call organizational resilience. Moreover, our participants also mentioned the ease of access to secure conveyance and having a stable economic income as essential components of the contingency measures taken by the hospital, which were new findings of our study and that can relate to the actual public order situation in Colombia.8,18 The participants mentioned that they had difficulty getting to their homes due to blockades and riots, which worsened their transport conditions. Furthermore, the cost of life is getting more expensive because of the increase in prices and the impossibility to create new ways of earning money.
Organizational resilience measures were mentioned in the previous paragraph, nevertheless, individual resilience is also a very important component as Rangachari and Woods10 described, which is reflected in this study in situations like altruism and religious beliefs, elements that were not specified in the literature. Lastly, team resilience was evidenced in the experiences mentioning the importance of support groups composed of health care professionals and peers with the aim to express their emotions or share trustworthy and updated information to solve doubts in all the staff.
According to the researchers' concept and that the literature mentioned that burnout rates have increased during COVID-19,23,25 it is surprising that all the participants appeared to have plenty of tools individually and organizationally, allowing them to develop a successful resilience process that ends up being an advantage for patient safety and in general, for the hospital functioning, as Rangachari said.10
Limitations
We found as limitations in this research the fact that as researchers we are part of the Hospital community, in which the interview participants operate, which can cause some fear when answering some of the questions posed, however, by ensuring the anonymity of the participants and establishing rapport through an atmosphere of psychological safety, we try to minimize this to the maximum.
CONCLUSION
The information found in scientific literature about burnout and resilience in medical personnel is regarding physicians and nurses. Therefore, our study contributes to fill a void in knowledge concerning mental health in HSW.
In the experiences of our participants, we identified burnout predisposing factors, however the participants did not express explicitly symptoms of burnout syndrome. We believe protective factors as resilience, in all its levels (individual, team and organizational), are clearly influential concerning this outcome and that as Ellinas et al suggested, burnout protective factor models during the COVID 19 pandemic should include the concept of resilience.
ACKNOWLEDGMENTS
The authors would like to offer our special thanks to the workers of the Health Care Institution who shared their experiences during the investigation.
The authors would also like to thank Doctor Jose Antonio Garciandia for being part of the creation of the article and its review, and Doctors Luis Fernando Gomez, Nora Badoui, Gilma Mantilla and William Robles, who during the public health rotation gave us the tools to address this issue in the most comprehensive way.
The assistance provided by Dr. Klasien Horstman was greatly appreciated.
Funding sources: None.
Conflict of interest: None declared.
Ethical Considerations & Disclosure(s): The participants were asked to provide and to sign informed consent. We took care that the participants were fully informed about all aspects of the project and were aware that they could withdraw at any moment without providing reasons, and that eventual withdrawal would not affect their jobs in any way. We destroyed the recordings of the interviews, and we will store the transcripts in a protected location at Pontificia Universidad Javeriana for 10 years. Research and ethical approval was granted by the Ethic Committee of the School of Medicine of the Pontificia Universidad Javeriana, Bogotá (FM-CIE-03359-20, Act 09/2020). The research protocol and methods were consistent with Colombian Law.
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2 Giorgi G Lecca LI Alessio F , . COVID-19-related mental health effects in the workplace: A narrative review. Int J Environ Res Public Health [Internet]. 2020;17 :7857.33120930
3 Adams JG Walls RM . Supporting the health care workforce during the COVID-19 global epidemic. JAMA [Internet]. 2020;323 :1439–1440.32163102
4 Luceño-Moreno L Talavera-Velasco B García-Albuerne Y Martín-García J . Symptoms of posttraumatic stress, anxiety, depression, levels of resilience and burnout in Spanish health personnel during the COVID-19 pandemic. Int J Environ Res Public Health [Internet]. 2020;17 :5514.32751624
5 Raudenská J Steinerová V Javůrková A , . Occupational burnout syndrome and post-traumatic stress among healthcare professionals during the novel coronavirus disease 2019 (COVID-19) pandemic. Best Pract Res Clin Anaesthesiol [Internet]. 2020;34 :553–560.33004166
6 Maslach C Leiter MP . Understanding burnout: New models. In: The Handbook of Stress and Health. Chichester, UK: John Wiley & Sons, Ltd; 2017:36–56.
7 Jackson SE Maslach C . After-effects of job-related stress: families as victims. J Organ Behav [Internet]. 1982;3 :63–77.
8 Kahill S . Symptoms of professional burnout: a review of the empirical evidence. Can Psychol [Internet]. 1988;29 :284–297.
9 Heath C Sommerfield A von Ungern-Sternberg BS . Resilience strategies to manage psychological distress among healthcare workers during the COVID-19 pandemic: a narrative review. Anaesthesia [Internet]. 2020;75 :1364–1371.32534465
10 Rangachari P L Woods J . Preserving organizational resilience, patient safety, and staff retention during COVID-19 requires a holistic consideration of the psychological safety of healthcare workers. Int J Environ Res Public Health [Internet]. 2020;17 :4267.32549273
11 Hospital staff roles [Internet]. Gov.au. [cited 2021 Jun 16]. Available at: https://www.betterhealth.vic.gov.au/health/servicesandsupport/hospital-staff-roles. Accessed June 16, 2021.
12 Cucinotta D Vanelli M . WHO declares COVID-19 a pandemic. Acta Biomed [Internet]. 2020;91 :157–160.32191675
13 WHO Coronavirus (COVID-19) dashboard [Internet]. Who.int. [cited 2021 Jun 17]. Available at: https://covid19.who.int. Accessed June 17, 2021.
14 Coronavirus Colombia [Internet]. Gov.co. [cited 2021 Jun 17]. Available at: https://www.ins.gov.co/Noticias/Paginas/Coronavirus.aspx. Accessed June 17, 2021.
15 Pabón FAD Palacio MG . The Colombian protests reflect a deep legitimacy crisis [Internet]. Al Jazeera 2021 [cited 2021 Jun 21]. Available at: https://www.aljazeera.com/opinions/2021/5/22/the-colombian-protests-reflect-a-deep-legitimacy-crisis. Accessed June 17, 2021.
16 Espectador E . Duque responsabiliza al paro por incremento de muertes por COVID-19. El Espectador [Internet]. 2021 Jun 21 [cited 2021 Jun 21]; Available at: https://www.elespectador.com/politica/duque-responsabiliza-al-paro-por-incremento-de-muertes-por-covid-19/. Accessed June 21, 2021.
17 Trumello C Bramanti SM Ballarotto G , . Psychological adjustment of healthcare workers in Italy during the COVID-19 pandemic: differences in stress, anxiety, depression, burnout, secondary trauma, and compassion satisfaction between frontline and non-frontline professionals. Int J Environ Res Public Health [Internet]. 2020;17 :8358.33198084
18 Liu X Chen J Wang D , . COVID-19 outbreak can change the job burnout in health care professionals. Front Psychiatry [Internet]. 2020;11 :563781.33363480
19 Kok N van Gurp J Teerenstra S , . Coronavirus Disease 2019 Immediately Increases Burnout Symptoms in ICU Professionals: a longitudinal cohort study. Crit Care Med [Internet]. 2021;49 :419–427.33555778
20 Wood EA Egan SC Ange B Garduno H Williams DR Wyatt TR . Association of self-reported burnout and protective factors in single institution resident physicians. J Grad Med Educ [Internet]. 2020;12 :284–290.32595847
21 Duckworth AL Peterson C Matthews MD Kelly DR . Grit: perseverance and passion for long-term goals. J Pers Soc Psychol [Internet]. 2007;92 :1087–1101.17547490
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24 Woods DD . In: Hollnagel E Leveson NG , eds. Resilience Engineering: Concepts and Precepts. London, England: Ashgate Publishing; 2006.
25 Becatoros PE . Personal de limpieza de UCIs, héroes anónimos de la pandemia. The Los Angeles times [Internet]. 2021 Jan 29 [cited 2021 Jun 16]; Available at: https://www.latimes.com/espanol/internacional/articulo/2021-01-29/personal-de-limpieza-de-ucis-heroes-anonimos-de-la-pandemia. Accessed June 16, 2021.
26 Brady M . Coronavirus: Are hospital cleaners forgotten heroes in this crisis? BBC [Internet] 2020 Apr 20 [cited 2021 Jun 16]; Available at: https://www.bbc.com/news/world-us-canada-52359101. Accessed June 16, 2021.
27 Bazeley P . Qualitative data analysis: Practical strategies. London, England: SAGE Publications; 2013.
28 Creswell JW Poth CN . Qualitative inquiry and research design: Choosing among five approaches. 4th ed. Thousand Oaks, CA: SAGE Publications; 2017.
29 DSouza MJ . The practice of qualitative research. Qual Res Organ Manag Int J [Internet]. 2017;12 :247–248.
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| 36190909 | PMC9722328 | NO-CC CODE | 2022-12-07 23:19:10 | no | J Occup Environ Med. 2022 Dec 3; 64(12):e839-e844 | utf-8 | J Occup Environ Med | 2,022 | 10.1097/JOM.0000000000002717 | oa_other |
==== Front
Pediatr Emerg Care
Pediatr Emerg Care
PCARE
Pediatric Emergency Care
0749-5161
1535-1815
Lippincott Williams & Wilkins
35319855
PCARE_220341
10.1097/PEC.0000000000002671
00015
3
Original Articles
Seasonality of Pediatric Mental Health Emergency Department Visits, School, and COVID-19
Copeland John Nathan MD [email protected]
Babyak Michael PhD [email protected]
Inscoe Adrienne Banny PhD [email protected]
Maslow Gary R. MD [email protected]
From the Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC.
Reprints: John Nathan Copeland, MD, Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, 2608 Erwin Rd, Durham, NC 27705 (e-mail: [email protected]).
12 2022
23 2 2022
23 2 2022
38 12 e1673e1677
Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.
2022
Wolters Kluwer Health, Inc. All rights reserved.
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.
Objectives
The aim of this study was to explore how the academic calendar, and by extension school-year stressors, contributes to the seasonality of pediatric mental health emergency department (ED) visits.
Methods
The authors reviewed all pediatric mental health ED visits at a large urban medical center from 2014 to 2019. Patients who were younger than 18 years at time of presentation, were Durham residents, and had a primary payer of Medicaid were included in the sample population, and the dates of ED visits of the sample population were compared against dates of academic semesters and summer/winter breaks of a relevant school calendar. Of patients with multiple ED visits, only the first ED presentation was included, and descriptive statistics and a rate ratio were used to describe the study group and identify the rate of ED visits during semesters compared with breaks.
Results
Among the sample population from 2014 to 2019, there were 1004 first pediatric mental health ED visits. Of these ED visits, the average number of visits per week during summer/winter breaks was 2.2, and the average number of visits per week during academic semester dates was 3.4. The rate of ED visits was significantly greater during academic semesters compared with breaks (Rate Ratio, 1.6; 95% confidence interval, 1.4–2.0; P < 0.001).
Conclusions
Children may be at greater risk of behavioral health crises or having increased mental needs when school is in session. As many children's mental health has worsened during the COVID-19 (coronavirus disease 2019) pandemic, these findings highlight the need for increased mental health services in the school setting as children return to in-person learning. In addition, it may benefit health systems to plan behavioral health staffing around academic calendars.
Key Words
COVID-19
mental health
mental health access
school
seasonality
STATUSONLINE-ONLY
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pmcPediatric mental illness occurs with severe impairment, substantially interfering with one or more major life activities, in approximately 20% of children and adolescents1 and leads to significant family burden.2 In addition, of the 10 leading causes of death among those aged 10 to 24 years, more than 20% are suicide deaths.3 Nationwide, from 2007 to 2016, there was a 60% increase in emergency department (ED) visits for mental health disorders and a 329% increase in visits for self-harm.4 Consequently, identifying pediatric mental health risk factors is imperative for the health of children and health systems.
Notably, pediatric mental health visits have a seasonality, increasing in the fall and spring and decreasing in the summer.5–8 It has been hypothesized that this observed pattern is partly due to unique stressors of the school year and the impact of these stressors on children's mental health.8 However, demonstrating this association is difficult because school and health system data are not linked, thereby making individual-level data analysis difficult, and school calendars frequently differ within and between school districts, making it inappropriate to apply one school calendar to a heterogeneous group of patients.
In order to account for these statistical challenges and to better understand the impact of school year stressors on mental health ED visits, this study uses a defined patient population, only the first ED visit for patients with multiple ED visits, and exact dates of a relevant school calendar. This study has particular relevance for ED staffing needs, school mental health services, and for anticipating pediatric mental health needs as children return to in-person learning during and after the coronavirus disease 2019 (COVID-19) pandemic.
METHODS
From 2014 to 2019, monthly reports of all pediatric mental health ED visits at Duke University Health System were generated. These reports were reviewed for accuracy and to ensure visits were for mental health reasons. This monthly data included demographic and visit-related variables such as age, sex, race, ethnicity, home city, insurance payer, and ED date of arrival.
From these reports, the sample population was defined as patients who were younger than 18 years at time of presentation, a resident of Durham, and having a primary payer of Medicaid. Because most children who are likely to have Medicaid are in the public school system,9 and most school-age children in Durham public schools are on a traditional calendar,10,11 we compared the ED dates of arrival of the sample population to Durham Public School traditional calendar dates for academic semesters and summer/winter breaks. Spring break was not included in the comparison because it is a shorter break, does not occur consistently around the same date, and is during the middle of a semester rather than representing the end of a semester.
Because the available data did not allow a determination of precisely when or for how long a given child met the inclusion criteria for our defined sample population, we had to assume that each child met the inclusion criteria for the entire study period. In addition, because of the uncertainty of when or for how long a given child met the inclusion criteria during the study period and because some children had multiple ED visits, it may have been possible for some children to have multiple visits that were not included in the data (eg, they may have moved out of the district). Consequently, we limited the analysis set to include only the first ED visit for a given child who met the inclusion criteria.
The rate ratio comparing ED visits during semesters versus breaks was tested using PROC GENMOD in SAS 9.4 (SAS Institute, Cary, NC) specifying the Poisson distribution with log link and the natural logarithm of the exposure as an offset term. When a child had multiple ED visits for mental health needs, the rate ratio estimate was tested using only the child's first ED visit.
The Duke University Health System institutional review board approved this study.
RESULTS
From 2014 to 2019, there were 1004 ED visits among the sample population when using only the first ED visit for children with multiple ED visits. Of these visits, 63.3% were by Black youth, 80.2% were non-Hispanic/Latino, 55.1% were female, and 90.8% were older than 9 years (Table 1). Comparing the average monthly number of visits nadir, which occurred during the 2-week intervals of June 18 to July 1 and July 2 to July 15, there was a 139% visit increase from October 8 to October 21 and a 183% increase from May 7 to May 20 (Fig. 1). Depending on the year, the October 8 to October 21 interval was approximately 45 days after summer break, and the May 7 to May 20 interval was approximately 125 days after winter break and between approximately 20 to 40 days after spring break (Table 2).
TABLE 1 Demographics of First Pediatric Mental Health ED Visits for Durham Children With Medicaid From 2014 to 2019
2014 2015 2016 2017 2018 2019 Total
n 195 165 180 169 162 133 1004
Age, y
0–4 1 0 1 3 0 0 5
5–9 11 14 20 22 8 11 87
10–14 96 80 77 66 81 66 466
15–17 87 71 82 78 73 55 446
Sex
Female 104 85 106 92 89 77 553
Male 91 80 74 77 73 56 451
Race
Black or African American 130 112 115 102 102 75 636
White 34 20 24 33 27 23 161
Asian 0 2 2 1 2 1 8
American Indian or Alaskan Native 0 0 0 1 0 1 2
Native Hawaiian or Other Pacific Islander 0 0 1 1 0 1 3
Not reported/other 31 31 38 31 31 32 194
Ethnicity
Not Hispanic or Latino 163 133 142 135 132 101 806
Hispanic or Latino 25 24 34 32 23 27 165
Not reported/declined 7 8 4 2 7 5 33
FIGURE 1 Total pediatric mental health ED visits by 2-week intervals. Visits are for Durham children with Medicaid from 2014 to 2019 who arrived at Duke University Health System. Only the first ED visit was used for children with multiple ED visits. Average is the mean number of visits per 2-week interval from all years. Because 2016 was a leap year, the February 26 to March 11 interval in 2016 included 15 days, but no children from the sample presented on February 29. For reference, fall semesters typically started at the beginning of the August 27 to September 9 interval and ended at the beginning of the December 17 to December 31 interval, and spring semesters typically began a few days into the January 1 to January 14 interval and ended a few days into the June 4 to June 17 interval. Winter and summer breaks included all days between academic semesters. Spring break typically occurred during the March 26 to April 8 or April 9 to April 22 intervals.
TABLE 2 Durham Public School Traditional Calendar Semesters and Breaks for Years 2014–2019
Academic Year Semester and Breaks Dates
2013–2014
Winter break 1/1/2014–1/5/2014
Spring semester 1/6/2014–6/10/2014
Spring break 4/12/2014–4/20/2014
Summer break 6/11/2014–8/24/2014
2014–2015
Fall semester 8/25/2014–12/19/2014
Winter break 12/20/2014–1/4/15
Spring semester 1/5/2015–6/9/2015
Spring break 3/27/2015–4/5/2015
Summer break 6/10/2015–8/23/2015
2015–2016
Fall semester 8/24/2015–12/18/2015
Winter break 12/19/2015–1/3/2016
Spring semester 1/4/2016–6/9/2016
Spring break 3/25/2016–4/3/2016
Summer break 6/10/2016–8/28/2016
2016–2017
Fall semester 8/29/2016–12/22/2016
Winter break 12/23/2016–1/2/2017
Spring semester 1/3/2017–6/9/2017
Spring break 4/8/2017–4/17/2017
Summer break 6/10/2017–8/27/2017
2017–2018
Fall semester 8/28/2017–12/21/2017
Winter break 12/22/2017–1/12/2018
Spring semester 1/2/2018–6/7/2018
Spring break 3/30/2018–4/8/2018
Summer break 6/8/2018–8/26/2018
2018–2019
Fall semester 8/27/2018–12/21/2018
Winter break 12/22/2018–1/3/2019
Spring semester 1/4/2019–6/11/2019
Spring break 3/23/2019–3/31/2019
Summer break 6/12/2019–8/25/2019
2019–2020
Fall semester 8/26/2019–12/20/2019
Winter break 12/21/2019–12/31/2019
Of 1004 first ED visits from 2014 to 2019, there was an average of 2.2 (SD, 0.8) ED visits per week during summer/winter break and 3.4 (SD, 0.6) during semesters. The rate of ED visits was significantly greater during academic semesters compared with summer/winter break (Rate Ratio, 1.6; 95% confidence interval, 1.4–2.0; P < 0.001) (Fig. 2).
FIGURE 2 Pediatric mental health emergency department visits during traditional school semesters and summer/winter breaks. Graph displays mean visits per week for Durham children with Medicaid using only the first mental health ED visit per child from 2014 to 2019 at Duke University Health System. Semesters and breaks were defined for respective years using the exact dates of Durham Public School traditional calendars. The rate of ED visits was significantly greater during academic semesters compared with breaks (Rate Ratio, 1.6; 95% confidence interval, 1.4–2.0; P < 0.001).
DISCUSSION
Although preliminary and regional, this study suggests children may be at greater risk of mental health crises and increased mental health needs when school is in session. This is consistent with recent data from the United Kingdom demonstrating that school-level variables contributed to variation in the psychopathology of children.12
Although some children with mental health needs are likely identified in school settings and subsequently referred to EDs, this seems to be an incomplete explanation for increased ED visits during semesters, given that the bimodal peaks of ED visits occur many weeks to months after the academic semester begins (Fig. 1, Table 2). If this pattern of ED visits was entirely explained by school identification of student mental health needs and subsequent ED referral, we would likely see a bolus of ED referrals at the beginning of the academic year. Instead, it is possible that unique stressors of the school year accumulate during the semester and lead to increased behavioral health crises and mental health needs.
Despite school being a source of support and development for many children,13 for some, this setting is associated with unique stressors. Academic pressure and performance have been reported to be the greatest source of stress for children and adolescents.14,15 In addition, bullying and peer victimization, which are associated with adverse mental health outcomes (eg, anxiety, depression), frequently occur in the academic setting.16,17 Also, school may be particularly challenging for children with a social anxiety disorder, leading to school avoidance and worsened mental health,18 and school climate has been shown to impact childhood mental health.19 It is possible that the culmination of these and other stressors may lead to mental health crises and subsequent presentation to the ED.
However, school can also be an important place for improving pediatric mental health. Indeed, offering mental health services in the school setting is effective, captures nearly all children, brings care to students, improves access, and decreases mental health stigma.20–22 In addition, improving school climate has been identified as a universal intervention to reduce pediatric mental illness.12,19
During the COVID-19 pandemic, it has been documented that some children's mental health has worsened,23–26 and as children return to in-person learning, it will be important for schools to monitor and manage the mental health needs of children. In addition, based on this research, the confluence of worsened mental health of some children during the COVID-19 pandemic and the start of a new academic semester may lead to increased behavioral health crises and needs compared with previous years. It is this combination of factors that has led some to call for an increased focus on behavioral health interventions in the educational system.27
Fortunately, with the American Rescue Plan Act including $80 million for mental health care access26 and other federal legislation directly proposing increased mental health access in schools,28 the value of pediatric mental health services, particularly in the school setting, is increasingly being recognized. However, it will be crucial for these dollars to go to evidence-based services and to provide increased resources in the school setting to manage mental health concerns.
Finally, based on these data, it may be helpful for health systems and EDs to staff accordingly around academic semesters and breaks and to be aware that pediatric mental health staffing needs may increase as children return to school during and after the COVID-19 pandemic.
Important limitations to this study include its focus on a single geographic area, use of a single school district calendar, capturing only the first mental health ED visit of a given child, and not knowing the total population of students at any given time. In addition, because of necessary student privacy laws, there is limited access to individual student-level data or data on children who did not have an ED visit. An electronic link between school and health system data, which has been developed in other countries,29 would be an important innovation in the United States to accurately describe and identify the risks of school-related variables on pediatric mental health, and the development and discoveries of this link could guide future research and interventions.
Disclosure: The authors declare no conflict of interest.
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29 Downs J Gilbert R Hayes RD , . Linking health and education data to plan and evaluate services for children. Arch Dis Child. 2017;102 :599–602.28130218
| 35319855 | PMC9722329 | NO-CC CODE | 2022-12-07 23:19:10 | no | Pediatr Emerg Care. 2022 Dec 23; 38(12):e1673-e1677 | utf-8 | Pediatr Emerg Care | 2,022 | 10.1097/PEC.0000000000002671 | oa_other |
==== Front
J Occup Environ Med
J Occup Environ Med
JOEM
Journal of Occupational and Environmental Medicine
1076-2752
1536-5948
Lippincott Williams & Wilkins
36084630
JOEM_220187
10.1097/JOM.0000000000002704
00016
3
Online-Only: Original Articles
Telework Conditions, Ergonomic and Psychosocial Risks, and Musculoskeletal Problems in the COVID-19 Pandemic
El Kadri Filho Fauzi MSc
de Lucca Sérgio Roberto PhD [email protected]
From the School of Medical Sciences of the University of Campinas (Unicamp).
Address correspondence to: Fauzi El Kadri Filho, MSc, School of Medical Sciences, University of Campinas (Unicamp), 126 Tessália Vieira de Camargo St, Cidade Universitária “Zeferino Vaz,” Campinas, SP 13083-887, Brazil ([email protected]).
12 2022
12 9 2022
12 9 2022
64 12 e811e817
Copyright © 2022 American College of Occupational and Environmental Medicine
2022
Wolters Kluwer Health, Inc. All rights reserved.
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.
Employees who teleworked voluntarily since before the pandemic and those who had a dedicated place to work at home presented lower ergonomic and psychosocial risks and lower occurrence of musculoskeletal problems. Our results suggest that companies should be aware of the conditions under which their employees work from home.
Objective
To evaluate the association of telework conditions with ergonomic and psychosocial risks and with the occurrence of musculoskeletal problems among employees of the Brazilian Labor Judiciary during the COVID-19 pandemic.
Methods
A cross-sectional study was conducted with 934 workers from August to October 2021. The data were collected via Web using a self-administrated questionnaire survey. Nonparametric tests and generalized linear regression analysis were used.
Results
Previous experience in telework was associated with a better evaluation of the home workstation, a lower increase in workload, a greater increase in productivity, and greater preference to continue teleworking after the pandemic. The lack of a place dedicated to telework was specially related to greater ergonomic and psychosocial risks and to the greater occurrence of musculoskeletal problems.
Conclusions
Companies should monitor telework conditions to reduce health risks among their employees.
Keywords
ergonomics
musculoskeletal pain
occupational health
risk factors
teleworking
workplace
STATUSONLINE-ONLY
==== Body
pmcThe first regulations on telework among employees of the Labor Judiciary in Brazil emerged in 2012.1 After 3 years of experience, this work modality was considered successful based on productivity data and was permanently incorporated into the institutional practices so that up to 30% of the staff could perform telework.2 From the COVID-19 pandemic outbreak in March 2020, face-to-face work was suspended and telework was provisionally extended to all approximately 40,000 employees of the Brazilian Labor Judiciary.3
Until the COVID-19 pandemic, regulations established telework as a voluntary modality, dependent on leadership indication and predicting a productivity increase compared with face-to-face work. It also required that the employees would undergo a health assessment and attend a course on the health risks related to telework, including ergonomic aspects. From the COVID-19 pandemic onwards, the other employees were subjected to compulsory, unexpected, and indefinite telework due to the orientation toward social isolation. This emergency situation during which telework was imposed, especially in the initial phase of the pandemic, admittedly affected the employees' and employers' ability to respectively adapt and supervise the home working conditions.4
Some of the studies that evaluated the working conditions during the COVID-19 pandemic among workers who had to adopt telework in this period pointed out that lack of a dedicated place at the homes to work and inadequacy of the furniture and the workplace were among the main problems faced by the participants.5–9 For most of the workers who started teleworking as a result of the pandemic, the ergonomic conditions of the home workstation were significantly worse to those found in the company's premises,10 even with repercussions on the increase in musculoskeletal pain among the workers.11
A number of studies that evaluated the ergonomic conditions of the workstation pointed out that most of the workers surprised by the imposition of telework only had a laptop to work at their homes and mostly used inappropriate chairs and tables, even many months after the beginning of the pandemic.12–14 Many workers used inappropriate chairs, without any possibility of adjusting the seat or backrest, and pointed to lack of space on the work table to provide adequate support for the forearms and to the absence of adequate support for the feet.15 A number of studies that carried out a quantitative assessment of telework workstations during this period showed that approximately half of the evaluations performed pointed to ergonomic inadequacies in which immediate intervention in the workstation was suggested.16–18
In most of the studies, it was noticed that there was an increase in the prevalence of musculoskeletal problems after adopting telework as a result of the COVID-19 pandemic, although without establishing a direct relationship with the participants' working conditions,6,7,13,19–23 whereas some others pointed out that this increase was related to environmental and ergonomics conditions in telework, such as lack of a dedicated place where the workers could concentrate for work.10,24–26 Ergonomic and environmental suitability of the workplace at home can also be related to a positive view and to the perception of the advantages of telework, as well as to productivity.27 In the same sense, the positive evaluation of the ergonomic suitability of the furniture and the computer equipment used to work at home, as well as the company's support regarding supply of these materials, can be related to higher levels of productivity and satisfaction with telework.9
Traditionally, telework has been related to an increase in workload, greater autonomy, and a reduction in social support due to the difficulties communicating with colleagues and with the leaders.28–30 In the transition to telework, there are changes in the boundaries between personal and professional life, not only in terms of physical structure shared between home and workplace but also in terms of working time, which is no longer limited by the time spent at the company's premises, and regarding the psychological aspects related to the social roles that have to be fulfilled at home and at work. As telework reduces the boundaries between home and workplace, it tends to increase the working hours as a way of compensating for the time that would be spent commuting, and it can create communication barriers with the colleagues.31–33 Specifically during the COVID-19 pandemic, previous studies pointed to an increase in workload when compared with face-to-face work carried out before the pandemic,6,7,34 whereas the participants mentioned an increase in productivity and satisfaction with telework, with a large percentage of workers expressing an interest in maintaining partial or full telework after the pandemic.11,23,24,35,36
Telework, which has already shown growth in recent years around the world,37 was widely adopted during the COVID-19 pandemic, and there is a tendency for many workers to remain working from home after the need for social isolation.38 Thus, it is essential that the conditions in which telework is carried out and its relationships with workers' health are taken into consideration. The objectives of this study were to evaluate the conditions in which telework was carried out during the COVID-19 pandemic among employees of a Brazilian Labor Judiciary Court and to compare the ergonomic and psychosocial risks and the occurrence of musculoskeletal symptoms, according to some of those telework conditions.
METHODS
Procedure and Participants
A cross-sectional study with a quantitative approach was conducted among the employees from a Brazilian Regional Labor Court (Tribunal Regional do Trabalho). A total of 2849 employees from Tribunal Regional do Trabalho judicial and administrative units whose work activity had not been significantly modified in telework when compared with face-to-face work were invited to participate in this research. Employees who had worked less than 1 year at the institution and those who were on vacation or on sick leave during the data collection period were oriented not to join the study. Data from participants who did not complete the instruments in full were excluded from the analysis.
The invitations to participate in this study were sent to the employees' institutional e-mail address with basic information on the research, its objectives, and form of participation. Data collection was carried out between August and October 2021. Questionnaires were made available via the Internet through the SurveyMonkey® online questionnaire and survey platform.
This study was approved by the Research Ethics Committee (CEP) of the University of Campinas (Unicamp) under opinion 4,862,756/2021. Participants' access to the questionnaires was only possible by agreeing to the informed consent form presented on the survey initial page.
Measures
Diverse information was collected from the participants, referring to the following: age, sex, family status, people under care living in the same household (cohabitants in care), regular physical activity, weight, height, position, work unit, working time in the institution, effective weekly working hours, and number of working days per week.
The following questions were used to collect information specifically related to telework conditions:
− “Have you been teleworking before the onset of the pandemic (Y/N)?”
− “Do you have a dedicated space or a room intended exclusively for work in your residence (Y/N)?”
− “Have you purchased furniture and/or equipment or made any adjustments to your workplace at home for comfort and pain prevention (Y/N)?”
− “How do you rate the workplace provided by the Court for face-to-face work for comfort and ergonomics (very bad/bad/regular/good/very good)?”
− “How do you rate your workplace at home for comfort and ergonomics (very bad/bad/regular/good/very good)?”
− “With regard to face-to-face work, do you consider that your workload in telework has increased, decreased, or remained the same?”
− “With regard to face-to-face work, do you consider that your productivity in telework has increased, decreased, or remained the same?”
− “If it were solely up to you, would you choose which type of work you can choose from the possibility of returning to face-to-face work: face-to-face, telework, or hybrid?”
The assessment of ergonomic and psychosocial risks among teleworkers was performed by means of the Brazilian version of the Maastricht Upper Extremity Questionnaire.39 This is an instrument that assesses the risk factors related to musculoskeletal problems in work with intensive use of computers. Higher risks are associated to inadequate workstation and body posture at work, low control over work, excessive work demands, insufficient rest breaks, and reduced social support.40 The workstation domain (six questions) assesses the suitability of the desk, chair, and computer equipment. The body posture during work domain (six questions) assesses whether the worker maintains awkward postures during work activities. The control domain (nine questions) assesses the worker's autonomy in conducting the work tasks in relation to their skills. The demands domain (seven questions) addresses the workload, pressure, and difficulty of completing tasks during the working day. The breaks domain (six questions) addresses the worker's perception of the conditions to take rest breaks and vary work tasks. The social support domain (seven questions) addresses the worker's perception about the relationship with colleagues and supervisors.39,40
The occurrence of musculoskeletal problems in the last 12 months and in the last 7 days was assessed by the Brazilian version of the Nordic Musculoskeletal Questionnaire added with a numerical rating scale from 0 (absence of pain) to 10 (worst possible pain) for each body region.41 The Nordic Musculoskeletal Questionnaire standardizes the assessment of musculoskeletal symptoms in an occupational context by dividing the body into a diagram with nine regions: neck, shoulders, upper back, elbows, wrists/hands, lower back, hips/thighs, knees, and ankles/feet.41,42 This approach with a quantitative evaluation of the complaints in each body region (numerical rating scale) made it possible to evaluate musculoskeletal problems in relation to the number of body regions with complaints for each employee and the intensity of the complaints in each body region.
Statistical Analysis
Descriptive analyses present absolute and percentage values, position, and dispersion measures. Chi-squared and Fisher exact tests were performed to evaluate the associations between previous experience with telework and other telework conditions. Mann-Whitney U test was used for the comparison between the employees with previous experience in telework (group 1) and those who started telework during the pandemic (group 2) and for the comparison between the employees with and without a place dedicated to telework at home. A generalized linear regression analysis was conducted to assess the relationship between previous experience in telework and the existence of a place dedicated to telework in the house (independent variables selected using the stepwise backward method) with ergonomic and psychosocial risks and the occurrence of musculoskeletal problems (dependent variables). P values < 0.05 were considered statistically significant, and SAS, version 9.4, was used for the analyses.
RESULTS
Among the workers eligible to join the study, 1014 accessed the link sent by e-mail and answered the free and informed consent form, and nine did not accept to participate. Of all 1005 employees who agreed to participate in the study, 71 were excluded because of incomplete filling out of the data collection instruments. The final sample consisted of 934 employees, resulting in a 32.78% response rate.
Among the study participants, 27.62% stated having already teleworked before the beginning of the COVID-19 pandemic (group 1), whereas 72.38% had no previous experience in telework (group 2). In both groups, most of the participants were female and married, lived with one to three people, and practiced regular physical activities. The participants from group 1 were younger, had less time working at the institution, and, in greater proportion, held the position of judicial analyst and worked in the Court's second instance (Table 1).
TABLE 1 Descriptive Analysis of Sociodemographic and Occupational Characteristics According to Previous Experience in Telework (N = 934)
Characteristics Group 1 (n = 258)
n (%) or Mean (SD) Group 2 (n = 676)
n (%) or Mean (SD)
Age (years) 43.38 (7.14) 46.92 (8.61)
Sex
Male 97 (37.60) 267 (39.50)
Female 161 (62.40) 409 (60.50)
Marital status
Single 41 (15.89) 124 (18.34)
Married 191 (74.03) 456 (67.46)
Divorced 25 (9.69) 85 (12.57)
Widower 1 (0.39) 11 (1.63)
Number of cohabitants
0 25 (9.69) 86 (12.72)
1 67 (25.97) 174 (25.74)
2 83 (32.17) 171 (25.30)
3 62 (24.03) 187 (27.66)
More than 3 21 (8.14) 58 (8.58)
Cohabitants in care
No 137 (53.10) 442 (65.38)
Yes 121 (46.90) 234 (34.62)
Regular physical activity
No 90 (34.88) 272 (40.24)
Yes 168 (65.12) 404 (59.76)
BMI 25.60 (3.94) 26.03 (4.51)
Role
Judicial technician 158 (61.24) 470 (69.53)
Judicial analyst 95 (36.82) 164 (24.26)
Executor 5 (1.94) 42 (6.21)
Sector
1st instance 139 (53.88) 378 (55.92)
2nd instance 101 (39.15) 97 (14.35)
Administrative area 18 (6.98) 201 (29.73)
Working time (years) 13.65 (7.57) 16.46 (9.45)
Weekly workload (hours) 39.98 (5.09) 39.79 (5.95)
Working days per week 5.15 (0.42) 5.09 (0.34)
BMI, body mass index.
The participants from group 1 cited a dedicated place at their homes to work and made changes to their workplace aiming at comfort and ergonomics in a significantly greater proportion than the other participants. Group 1 also rated their workstation at home better and reported a lower increase in the working hours when migrating to telework (P < 0.001) and a greater increase in productivity in telework in relation to face-to-face work when compared with group 2 (P = 0.003). Both groups showed certain preference for telework after the pandemic; however, among the participants from group 2, there was a preference for the hybrid modality (partial telework) and for face-to-face work in a significantly greater proportion (Table 2).
TABLE 2 Telework Conditions According to Previous Experience in Telework (N = 934)
Variable Group 1 (n = 258)
n (%) Group 2 (n = 676)
n (%) P
Dedicated place
No 28 (10.85) 245 (36.24) <0.001a
Yes 230 (89.15) 431 (63.76)
Workplace changes
No 47 (18.22) 251 (37.13) <0.001a
Yes 211 (81.78) 425 (62.87)
Face-to-face workstation
Very bad or bad 13 (5.04) 26 (3.85) 0.270a
Regular 49 (18.99) 98 (14.50)
Good 129 (50.00) 355 (52.51)
Very good 67 (25.97) 197 (29.14)
Telework workstation
Very bad or bad 1 (0.39) 99 (14.64) <0.001a
Regular 58 (22.48) 245 (36.24)
Good 116 (44.96) 228 (33.73)
Very good 74 (28.68) 104 (15.38)
Workload in telework
Decreased 13 (5.04) 36 (5.33) <0.001a
Remained the same 141 (54.65) 278 (41.12)
Increased 104 (40.31) 362 (53.55)
Productivity in telework
Decreased 12 (4.65) 81 (11.98) 0.003a
Remained the same 61 (23.64) 157 (23.22)
Increased 185 (71.71) 438 (64.79)
Current preference
Face-to-face work 2 (0.78) 67 (9.91) <0.001b
Hybrid 69 (26.74) 294 (43.49)
Telework 187 (72.48) 315 (46.60)
aChi-squared test.
bFisher exact test.
When compared according to previous experience in telework and the existence of a dedicated place to work, the participants who started teleworking during the pandemic and those with no dedicated place presented significantly higher ergonomic and psychosocial risks (except for job control in previous telework) and greater occurrence of musculoskeletal problems (Tables 3 and 4).
TABLE 3 Comparison Between Ergonomic and Psychosocial Risks and Musculoskeletal Problems According to Previous Experience in Telework (n = 934)
Variable Previous Telework n Median (IQR) Min-Max P a
MUEQ-Br revised
Workstation No 676 1.00 (2.00) 0.00–6.00 <0.001
Yes 258 0.00 (0.00) 0.00–6.00
Posture No 676 6.00 (4.00) 0.00–17.00 0.005
Yes 258 5.00 (4.00) 0.00–15.00
Job control No 676 4.00 (5.00) 0.00–22.00 0.081
Yes 258 4.00 (4.00) 0.00–16.00
Job demands No 676 7.00 (7.00) 0.00–21.00 0.016
Yes 258 6.00 (7.00) 0.00–20.00
Break time No 676 4.00 (4.00) 0.00–18.00 <0.001
Yes 258 3.00 (3.00) 0.00–15.00
Social support No 676 1.00 (2.00) 0.00–18.00 0.002
Yes 258 0.00 (2.00) 0.00–14.00
Total score No 676 24.00 (17.00) 2.00–74.00 <0.001
Yes 258 20.00 (15.00) 1.00–61.00
Musculoskeletal problems
Total intensity in the last 12 months No 676 23.00 (29.00) 0.00–84.00 0.003
Yes 258 19.00 (25.00) 0.00–76.00
Regions with complaints in the last 12 months No 676 6.00 (5.00) 0.00–9.00 0.002
Yes 258 5.00 (4.00) 0.00–9.00
Total intensity in the last 7 days No 676 13.50 (26.00) 0.00–82.00 0.006
Yes 258 9.00 (22.00) 0.00–72.00
Regions with complaints in the last 7 days No 676 3.00 (4.00) 0.00–9.00 0.002
Yes 258 3.00 (4.00) 0.00–9.00
IQR, interquartile range; Max, maximum; Min, minimum; MUEQ-Br, Brazilian version of the Maastricht Upper Extremity Questionnaire.
aMann-Whitney U test.
TABLE 4 Comparison Between Ergonomic and Psychosocial Risks and Musculoskeletal Problems According to the Existence of a Dedicated Place to Work at Home (N = 934)
Variable Dedicated Place n Median (IQR) Min-Max P a
MUEQ-Br revised
Workstation No 661 2.00 (2.00) 0.00–6.00 <0.001
Yes 273 0.00 (1.00) 0.00–5.00
Posture No 661 7.00 (5.00) 0.00–17.00 <0.001
Yes 273 5.00 (3.00) 0.00–15.00
Job control No 661 5.00 (6.00) 0.00–19.00 <0.001
Yes 273 4.00 (4.00) 0.00–22.00
Job demands No 661 8.00 (8.00) 0.00–21.00 0.014
Yes 273 7.00 (7.00) 0.00–21.00
Break time No 661 4.00 (4.00) 0.00–18.00 <0.001
Yes 273 4.00 (4.00) 0.00–18.00
Social support No 661 1.00 (2.00) 0.00–18.00 <0.001
Yes 273 1.00 (2.00) 0.00–15.00
Total score No 661 27.00 (17.00) 4.00–74.00 <0.001
Yes 273 22.00 (14.00) 1.00–72.00
Musculoskeletal problems
Total intensity in the last 12 months No 661 26.00 (29.00) 0.00–84.00 <0.001
Yes 273 20.00 (28.00) 0.00–82.00
Regions with complaints in the last 12 months No 661 6.00 (4.00) 0.00–9.00 <0.001
Yes 273 5.00 (4.00) 0.00–9.00
Total intensity in the last 7 days No 661 16.00 (28.00) 0.00–82.00 <0.001
Yes 273 10.00 (22.00) 0.00–82.00
Regions with complaints in the last 7 days No 661 4.00 (5.00) 0.00–9.00 <0.001
Yes 273 3.00 (4.00) 0.00–9.00
IQR, interquartile range; Max, maximum; Min, minimum; MUEQ-Br, Brazilian version of the Maastricht Upper Extremity Questionnaire.
aMann-Whitney test.
The generalized linear regression analysis pointed that previous experience in telework was significantly related to lower risks in workstation, job demands, and break time domains. This analysis also showed that group 1 had a significantly lower occurrence of musculoskeletal problems in the last 12 months (intensity and body regions with complaints) and in the last 7 days (body regions with complaints). The lack of a place dedicated to telework in the house was significantly related to greater ergonomic and psychosocial risks (except job demands) and to the greater occurrence of musculoskeletal problems in both periods, regardless of previous experience in telework (Table 5).
TABLE 5 Generalized Linear Regression Analysis Between Previous Experience in Telework and Dedicated Place to Work at Home (Independent Variables) With Ergonomic and Psychosocial Risks and Musculoskeletal Problems (Dependent Variables) (n = 934)
Variable Previous Telework (Yes) Dedicated Place (Yes)
β P β P
MUEQ-Br revised
Workstation −0.308 <0.001 −1.078 <0.001
Posture – – −1.071 <0.001
Job control – – −1.285 <0.001
Job demands −0.861 0.019 – –
Break time −0.821 <0.001 −0.791 <0.001
Social support – – −0.510 <0.001
Total score −2.975 <0.001 −5.129 <0.001
Musculoskeletal problems
Total intensity in the last 12 months −3.133 0.027 −5.224 <0.001
Regions with complaints in the last 12 months −0.419 0.039 −0.770 <0.001
Total intensity in the last 7 days – – −5.862 <0.001
Regions with complaints in the last 7 days −0.448 0.034 −0.879 <0.001
MUEQ-Br, Brazilian version of the Maastricht Upper Extremity Questionnaire.
DISCUSSION
This study verified that the participants with previous experience in voluntary telework (group 1) presented better working conditions at their homes and a greater preference for staying in telework when compared with those who started teleworking compulsorily as a result of the COVID-19 pandemic (group 2). The participants from group 1 also presented reduced ergonomic and psychosocial risks and lower occurrence of musculoskeletal problems when compared with those from the second group. The existence of a dedicated place in the house to work was especially related to the reduction of these risks and to the lower occurrence of musculoskeletal problems, regardless of previous experience in telework.
Among the participants from group 1, 89.15% had a dedicated place to work at their homes, 81.78% had already made changes to their home workplace aiming at comfort and pain prevention, and 73.64% rated their workstations at home as good or very good. These results were significantly better in relation to those obtained by the participants from group 2. The participants from group 1 also showed a lower increase in the working hours, as well as a greater increase in productivity in telework, when compared with those from group 2. Whereas 72.48% of the participants with previous experience mentioned certain preference for continuing to telework full time after the pandemic, only 46.60% indicated this preference among participants who compulsorily teleworked. Previous studies verified lower percentages of participants with a dedicated place at their homes specifically intended for teleworking (between 23% and 45%), even in relation to group 2 in our study, and observed smaller proportions of employees interested in continuing teleworking.5,6,24,25
Considering the total sample, although the participants in our study rated workstations at their homes as worse in relation to face-to-face work, as was observed in other studies,10–16 we were able to notice that this trend was not verified in group 1. Among the participants from this group, 5.04% rated the workstations in the company's premises as bad or very bad and only 0.39% rated the home workstation as such. Among the participants from group 2, whereas 3.85% rated the workstation in the company's premises as bad or very bad, 14.64% rated the home workstation as such.
The higher proportion of judicial analysts and employees who worked in the second instance in group 1, whose tasks of procedural analysis and sentence writing are more individualized and less dependent on the collaboration of colleagues, can indicate that the participants from this group had a more appropriate profile for telework and should be better prepared regarding adequacy of their workplace and the organization of work at their homes.
The participants from group 2 mentioned an increase in the working hours that was proportionally higher in relation to face-to-face work in our study. This may have been due to a greater effort to adapt to telework without proper preparation and to the lack of a dedicated place to work at their homes, with more frequent interruptions and greater difficulty concentrating for work. In telework, greater autonomy and flexibility in terms of timetables and activities can favor rest breaks and better distributed working hours during the day, although they can also favor longer workdays when compared with face-to-face work.43 In the studies carried out during the COVID-19 pandemic, the workers commonly reported longer workdays, with longer meetings and a reduction in the number of rest breaks.6,7 On the other hand, positive experiences with telework during the pandemic were also related to greater flexibility in timetables and higher productivity due to fewer interruptions while working at home.36 A study conducted with researchers from a Brazilian public company pointed out that the participants' perceptions regarding telework were highly positive, with increased autonomy and productivity. Most of the participants revealed their expectation that the teleworking regime would be maintained after the pandemic, especially in the hybrid modality.35
Considering that, before the pandemic, the indication for telework was considered a form of recognition by the leadership regarding the responsibility and work performed by the employee, it is expected that the participants from group 1 had a relationship of trust and a good history of productivity to keep working remotely. Some studies indicate that a good relationship with the leaders is positively related to job satisfaction and that reduced communication with coworkers is related to increased productivity, reduced stress, and a lower number of unwanted interruptions among teleworkers, in contrast to the effects of social isolation.27,44 Another previous study observed that the employees who preferred to telework experienced less psychological distress with increasing telework frequency, whereas those who preferred not to telework suffered more psychological distress with increasing telework frequency.45
Other studies carried out during this period pointed out that, even among employees who started telework as a result of the pandemic, most of the participants would like to continue doing so even after this period.24,34 Especially those with previous experience in telework and those without any health problems showed greater satisfaction with teleworking during this period.23 The increase in productivity in telework can be positively related to the workers' physical and mental health and to the existence of a dedicated place to work at their homes.34 In another study, this condition was also related to a better assessment of the suitability of the workplace in relation to those without a dedicated place to work at their homes, and the ergonomic and environmental suitability of the home workplace was linked to a positive view and a better perception of the advantages of teleworking and to greater productivity.5,46 Another previous study showed that having an exclusive room for work, an ergonomically correct workstation, and knowledge of how to adjust the workstation were associated with lower chances of experiencing new health problems.47
Most of the studies that evaluated the teleworking conditions during the COVID-19 pandemic pointed to worse ergonomic conditions of the home workstation in relation to face-to-face work and greater occurrence of musculoskeletal problems, but some only evaluated participants who started teleworking specifically as a result of the pandemic and did not assess the relationship between the telework conditions and the occurrence of these problems.7,13,19,20 Although our study was carried out approximately 1 year and 6 months after the beginning of the pandemic, the conditions of the home workstation were evaluated as worse to the face-to-face working conditions among those in group 2. A previous study carried out approximately 9 months after the beginning of the pandemic showed that ergonomic working conditions had not changed among the participants in relation to the first months of the pandemic, which can indicate lack of investment on the part of companies and workers facing the permanent perspective of returning to face-to-face work.14
A previous study noticed a reduction in musculoskeletal symptoms during telework in the pandemic period when compared with the previous face-to-face period, although without evaluating how this reduction could be related to the telework conditions or to ergonomic aspects.48 Another study showed that the mere migration to telework at the beginning of the COVID-19 pandemic exerted no effect on the intensity of neck pain among the participants but that the greater intensity of this pain was related to worse ergonomic conditions of the home workstation, the number of hours working in the computer, and fewer breaks during the workday.10 In the same sense, other studies pointed out that the musculoskeletal complaints among teleworkers were mediated by inadequate ergonomic conditions and were related to the intensity of telework (days per week of telework) only when the telework conditions were deficient, such as lack of a home workplace where the employees could concentrate for work.22,24–26
Although our study may provide important information to assist companies in the implementation or expansion of telework among their employees, some limitations must be pointed out. Data collection for this research was conducted approximately 1 year and 6 months after the beginning of the pandemic, which may have favored a better adaptation of the participants from group 2 and reduced the difference in terms of the risks and occurrence of musculoskeletal problems in relation to group 1. Our sample consisted of employees from the Brazilian Federal Judiciary, who are well-paid workers with good housing conditions, which must have favored a higher proportion of participants with a dedicated place at their homes to work and with the ability to make changes aiming at comfort and pain prevention in their home workplaces. Consequently, these results cannot be generalized to other professional categories. Finally, another significant limitation is that the cross-sectional design of the study does not allow establishing cause-and-effect relationships between the variables. For this purpose, we suggest that longitudinal studies be conducted.
CONCLUSION
The results of our study indicate that the comparative analysis involving ergonomic and psychosocial risks and the occurrence of musculoskeletal problems among telework employees in relation to face-to-face work must take into account the conditions in which telework is being performed. In our study, the participants who voluntarily teleworked before the COVID-19 pandemic, and especially those who reported having a dedicated place to work at their homes, presented reduced ergonomic and psychosocial risks and lower occurrence of musculoskeletal problems. These results suggest that companies consider the conditions under which their employees are working from their homes to reduce the ergonomic and psychosocial risks and minimize the occurrence of musculoskeletal problems in telework.
ACKNOWLEDGMENTS
We would like to thank our study participants for their contributions to this study.
Ethical considerations: This study was approved by the Research Ethics Committee of the University of Campinas under opinion 4,862,756/2021. Informed consent was obtained from all the participants in the form of the Web site.
Funding sources: No funding.
Conflict of interest: None to declare.
==== Refs
REFERENCES
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| 36084630 | PMC9722330 | NO-CC CODE | 2022-12-07 23:19:10 | no | J Occup Environ Med. 2022 Dec 12; 64(12):e811-e817 | utf-8 | J Occup Environ Med | 2,022 | 10.1097/JOM.0000000000002704 | oa_other |
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J Occup Environ Med
J Occup Environ Med
JOEM
Journal of Occupational and Environmental Medicine
1076-2752
1536-5948
Lippincott Williams & Wilkins
JOEM_220113
10.1097/JOM.0000000000002630
00007
3
Original Articles
Prepandemic Mental Health and Well-being
Differences Within the Health Care Workforce and the Need for Targeted Resources
Silver Sharon R. MS
Li Jia MS [email protected]
Marsh Suzanne M. MPA [email protected]
Carbone Eric G. PhD [email protected]
From the Health Informatics Branch, Division of Field Studies and Engineering, National Institute for Occupational Safety and Health, Cincinnati, Ohio (Silver, Li); and Surveillance and Field Investigations Branch, Division of Safety Research, National Institute for Occupational Safety and Health, Morgantown, West Virginia (Marsh, Carbone).
Address correspondence to: Sharon R. Silver, MS, Health Informatics Branch, Division of Field Studies and Engineering, National Institute for Occupational Safety and Health, 1190 Tusculum, MS R-17, Cincinnati, OH 45226 ([email protected]).
12 2022
2 12 2022
2 12 2022
64 12 10251035
Copyright © 2022 American College of Occupational and Environmental Medicine
2022
Wolters Kluwer Health, Inc. All rights reserved.
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.
Adverse mental health prevalences among healthcare workers differ by occupation and industry. Workforces with many low income, female, and non-Hispanic African American workers are disproportionately affected. Clinical resources and prevention, mitigation, and intervention strategies to address organizational and personal stressors should be affordable, accessible, and tailored to specific healthcare workforces.
Background
Occupational stress and diminished well-being among health care workers were concerning even before the coronavirus disease 2019 pandemic exacerbated existing stressors and created new challenges for this workforce. Research on the mental health of health care workers has focused on physicians and nurses, with less attention to other occupations.
Methods
To assess pre–coronavirus disease mental health and well-being among workers in multiple health care occupations, we used 2017 to 2019 data from the Behavioral Risk Factor Surveillance System.
Results
Across the health care workforce, insufficient sleep (41.0%) and diagnosed depression (18.9%) were the most common conditions reported. Counselors had the highest prevalence of diagnosed depression. Health care support workers had elevated prevalences for most adverse health conditions.
Conclusions
Ensuring a robust health care workforce necessitates identifying and implementing effective occupation-specific prevention, intervention, and mitigation strategies that address organizational and personal conditions adversely affecting mental health.
Keywords
mental health
well-being
health care workforce
health care support workers
counselors
depression
insufficient sleep
occupation
industry
==== Body
pmcOccupational stress and diminished levels of overall well-being among health care workers were issues of concern even before the coronavirus disease 2019 (COVID-19) pandemic, which exacerbated existing stressors and introduced new challenges to this workforce.1,2 Mental health concerns that have been the focus of attention in this workforce include depression, anxiety, substance use disorders, posttraumatic stress disorder (PTSD), burnout, compassion fatigue, and suicide.
Health care workers have long faced a convergence of stressors that are less common in other types of work. These stressors include the emotional burden of dealing with individuals who are seriously ill or dying; witnessing traumatic events, which is associated with PTSD, particularly among first responders3,4; secondary traumatic stress after exposure to traumatized patients, particularly in emergency departments5,6; witnessing or being the target of workplace violence,7 which can result in adverse physical, psychological, social, and emotional effects7–10; and workplace bullying.10–12 The prevalence of these problems has been reported to be particularly pronounced in emergency and psychiatry departments within hospitals, in the home health setting, and in nursing care facilities.7,9,13 Among the additional stressors for many health care workers are poor job design, management challenges, suboptimal safety climate/safety culture, high caseloads, the effects of shift work and long work hours, and exposure to pathogens.
Most research on mental health in the health care workforce has focused on physicians (including physicians in training) and nurses. Problems noted among physicians include depression, anxiety, substance use disorders, burnout, and suicide.13–17 As with the general public, estimated prevalences of depression among health care workers vary depending on the case definition and characteristics of the measurement instrument (eg, criteria met for depressive disorder versus presence of subclinical depressive symptoms, self-reported symptoms versus provider-diagnosed depression, current depressive symptomatology versus incidence in the last 12 months), as well as demographic characteristics of the respondents (eg, variation by age).14–16,18,19 Depression has also been reported among physicians in training; a systematic review noted estimates of the prevalence of depression or depressive symptoms among residents ranging from 21% to 43%, depending on the case ascertainment variables listed previously,16 whereas a prospective study found an increase in depression based on Patient Health Questionnaire-9 (PHQ-9) scores from 3.9% before the internship year to more than 20% during each quarter of the internship.20 As with depression, suicide risk appears to accelerate during the physician training period.19 Beyond the training period, female physicians have been found to have higher rates of completed suicide, at 1.4 to 2.3 times the rate in the general population,18 although a recently published analysis suggests that the overall suicide risk among physicians is not significantly different from that of the general population.21 Identified risk factors for mental health issues among physicians are both individual and occupational, with the latter including the stress of patient interactions and expectations, easy access to medication, heavy workload, adverse work schedules, and problematic or limited social interaction in the workplace.13
A high prevalence of depression has been noted among registered nurses (RNs).22 A survey of nurses employed by hospitals reported rates of depressive symptomatology of 18%, approximately twice that of the US population, with job satisfaction, body mass index, number of health problems, mental well-being, and health-related productivity significantly associated with depression scores.23 The prevalence of depression among RNs is reported to be highest among those who are young, female, or working in intensive care or psychiatric units.22 Nurses appear to be at higher risk for suicide than both physicians and the general public.24,25
Mental health concerns in the health care workforce are not restricted to physicians and nurses. However, information about the prevalence of mental health problems among other health care occupations is more limited. Rates of depression, stress, and PTSD have been reported for emergency services personnel, but estimates vary widely.26 The scant literature on health care support workers (eg, patient care aides; occupational, physical, and dental aides; phlebotomists) has found that care and support workers have worse mental health than the general working population27 and that patient care aides are more likely to report depression than nurses.28 Female health care support workers have also been found to have elevated rates of suicide.25 Janitors across all industries have a higher prevalence of depression than other workers.29 Although ancillary health care workers such as housekeeping staff do not have direct patient care responsibilities, they frequently work in patient care areas.
Mental health is also a concern for social workers, counselors, psychologists, and others who are tasked with directly addressing the mental health needs of others.30,31 Male human service workers have been found to have higher levels of antidepressant use than other workers at the same skill level.32 Elevated suicide rates have been reported among male welfare support workers, social workers, and female welfare support workers.33
Although the pandemic has led to new mental health challenges for workers globally, health care workers have been particularly at risk because of increased emotional, physical, and organizational demands, as well as increased risk of infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19. However, fully addressing the long-term mental health needs of this workforce also requires understanding the levels of baseline, prepandemic mental health issues. To assess baseline mental health and well-being among health care workers in different occupations, we examined 2017 to 2019 data from the Behavioral Risk Factor Surveillance System (BRFSS). Although BRFSS does not include prevalence data on the full range of mental health conditions and is limited to self-reported conditions and diagnoses, it does sample from a wide range of health care (and other) industries and occupations.
To our knowledge, this is the first study to evaluate prepandemic mental health and well-being of the health care workforce using a broad definition of this workforce (health care industry workers who have patient care responsibilities or who work in patient care areas) and including low-wage health care workers. The purpose of this study was to identify segments of the health care workforce that had the highest prepandemic prevalences of selected adverse conditions related to mental health and well-being, as they might require additional services during and after the pandemic.
METHODS
Study Population
The BRFSS is a national survey of the noninstitutionalized US adult population (18 years or older) administered by state and jurisdictional health departments.34 Respondents are selected for the survey using random digit dialing techniques on both cellular phones and landlines. Overall response rates for this survey for landlines and cellphones, respectively, by year were 45.3% and 44.5% (2017); 53.3% and 43.4% (2018); and 53.5% and 45.9% (2019). Response rates overall and by state can be found at https://www.cdc.gov/brfss/data_documentation/index.htm.
In addition to a core survey, the BRFSS includes modules that states can opt to include. One of these modules is sponsored by the National Institute for Occupational Safety and Health and collects the industry and occupation of respondents who are employed for wages, out of work for less than 1 year, or self-employed. Occupation and industry are collected through open-ended questions: “What kind of work do you do?” followed by “What kind of business or industry do you work in?” This module is not implemented by the same states each year. A total of 33 states included this module during at least 1 year between 2017 and 2019 (22 states in 2017, 30 in 2018, and 25 in 2019, with 17 states participating all 3 years). We used the 3 most recent years of prepandemic data to enhance reportability for smaller health care occupations.
During BRFSS survey years 2017 to 2019, 314,078 respondents reported that they were employed or self-employed. A total of 51,895 respondents (16.5%) were excluded because of missing or uncodable industry or occupation, active-duty military status, or conflicting employment status information (respondents who reported being employed but whose responses to the industry or occupation question indicated they were unpaid workers, disabled, or retired). Industry and occupation free-text responses were autocoded to 2010 US Census Bureau industry and occupation codes by the National Institute for Occupational Safety and Health Industry and Occupation Computerized Coding System or, for items that could not be coded automatically, by human coders using computer-assisted coding.35
Although we provide results for all health care industry workers combined (all organizationally and self-employed workers with census industry codes 7970 to 8270), the focus of this study was on workers who interact directly with patients, as well as those who work in patient care areas as part of their duties, such as janitors and maids. Non–health care workers (employed outside both health care industries and health care occupations) comprised the comparison group for this work. A third, smaller set of workers are employed in health care occupations but outside the health care industries (eg school nurses, dieticians employed in the sports industry); we excluded them from reporting. Within the health care industry, we present results for occupational groups that had reportable results (denominator size ≥50 and relative standard error for prevalence estimates ≤30%) for at least 3 of the 6 conditions of interest.
Measures
We calculated distributions of demographic characteristics for each health care occupation. We also calculated unadjusted and adjusted prevalences of 6 health conditions elicited in the survey (Table 1). Because well-being and physical and mental health are not independent,36 in addition to conditions that explicitly concern mental health, we assessed prevalences of self-rated overall health, frequent physical distress, and insufficient sleep. Conditions evaluated were self-rated health (fair or poor general health), frequent physical distress (physical health not good at least 14 of past 30 days), frequent mental distress (mental health not good at least 14 of past 30 days), activity limitations (poor physical or mental health preventing usual activities for at least 14 of past 30 days), diagnosed depression, and insufficient sleep (<7 hours average sleep per 24-hour period; elicited only in 2018 BRFSS survey). Because responses for the items reported as number of days cluster at 0 and at multiples of 5 and 7, we did not treat them as continuous variables, instead dichotomizing them.
TABLE 1 BRFSS Survey Questions Related to Mental Health and Well-being, 2017–2019
Metric Title BRFSS Question Cut Point
Poor self-rated health Would you say that in general your health is: excellent, very good, good, fair or poor? Fair or poor = poor self-rated health
Frequent physical distress Now thinking about your physical health, which includes physical illness and injury, for how many days during the past 30 days was your physical health not good? ≥14 d = yes
Frequent mental distress Now thinking about your mental health, which includes stress, depression, and problems with emotions, for how many days during the past 30 days was your mental health not good? ≥14 d = yes
Activity limitations During the past 30 days, for about how manydays did poor physical or mental health keep
you from doing your usual activities, such as self-care, work, or recreation? ≥14 d = yes
Diagnosed depression Has a doctor, nurse, or other health professional ever told you that you had a depressive
disorder (including depression, major depression, dysthymia, or minor depression)? N/A
Insufficient sleep* On average, how many hours of sleep do you get in a 24-hour period? < 7 h average per 24-h period
*Elicited only in 2018.
Analysis
To account for the complex survey design and incorporate respondent sampling weights in BRFSS, we used SAS version 9.4 (SAS Institute Inc, Cary, NC) and SAS-callable SUDAAN version 11.0 (RTI International, Research Triangle Park, NC). To estimate population counts and weighted unadjusted prevalences for all variables, we used the SURVEYFREQ procedure. We identified differences in health conditions by health care worker occupations or industries using the RLOGISTIC procedure. We compared health care workers to non–health care workers by performing logistic regressions and estimating adjusted prevalence ratios (aPRs) and their 95% confidence intervals (CIs). Non–health care workers served as the comparison group for the full group of health care workers, as well as for specific subgroups of health care workers. We considered CIs for aPRs that do not span the null to be statistically significant.
Adjustment variables in the primary regression models were sex, race/ethnicity combined (classified as white non-Hispanic, black non-Hispanic, other non-Hispanic, Hispanic), age in years (18–34, 35–54, ≥55), and marital status (collapsed to married or part of an unmarried couple [as a proxy for level of social support] vs all other). All estimates in this report were weighted. Because of the complex relations between income and demographics, occupation/industry, and health outcomes,37,38 we did not adjust for household income.
RESULTS
The 37,685 BRFSS respondents who worked in health care industries were the focus of the study (Table 2). Another 4627 health care workers were employed in non–health care industries; results for this group are not further reported. The 219,871 non–health care workers comprised the comparison population. The largest subset of the 37,685 health care respondents were from the hospital industry (47%), followed by ambulatory care (29%), nursing care facilities (10%), home health (8%), dental offices (4%), and other health care industries (2%).
TABLE 2 Distribution of Workers From Health Care Occupations Across Health Care Industries, BRFSS 2017–2019
2010 Census Occupation Codes Sample
Size Weighted n (times 1000) Health Care Industry
(U.S. Census Industry Codes) Other
(Census 8180)
(%*)
Dental Office
(Census 7980)
(%*) Home Health
(Census 8170)
(%*) Ambulatory Care
(Census 7970, 7990, 8070–8090)
(%*) Hospitals
(Census 8190)
(%*) Nursing Care Facilities
(Census 8270)
(%*)
Non–health care workers† 219,871 74,862
Health care workers in non–health care industries‡ 4627 1330
Health care industry workers§ 37,685 12,051 3.7 8.4 29.4 46.5 10.1 1.9
Health care occupation grouping∥
Community and social service occupations 2000–2060 1288 317 NR 2.8 55.6 34.5 5.9 NR
Counselors 2000 516 142 0.0 NR 77.9 17.7 NR NR
Social workers 2010 554 125 0.0 4.1 46.3 40.5 8.9 NR
Health care practitioners and technical occupations 3000–3540 18,598 5646 3.5 4.5 26.6 55.8 6.9 2.8
Health diagnosing practitioners ¶ 4100 1235 6.9 1.1 52.2 37.9 1.5 NR
Physicians and surgeons 3060 2445 758 NR NR 58.1 40.6 NR NR
Health treating practitioners # 10,737 3126 NR 6.5 20.4 63.7 9.2 NR
Physical therapists 3160 529 152 0.0 10.5 52.7 26.8 10.0 0.0
Registered nurses 3255 8959 2626 NR 6.8 17.1 66.6 9.4 NR
Miscellaneous health technologists and technicians 3300–3535 3679 1257 8.6 2.9 16.2 53.7 6.7 11.9
Clinical laboratory technologists and technicians 3300 600 192 0.0 NR 8.4 68.0 NR 18.7
Health practitioner support technologists and technicians 3420 325 119 NR NR 11.1 86.7 NR NR
Licensed practical and licensed vocational nurses 3500 788 251 0.0 13.6 23.0 36.0 27.5 0.0
Miscellaneous health technologists and technicians, other 3535 403 132 NR NR 36.2 59.7 NR NR
Health care support occupations 3600–3655 5313 1931 7.2 20.7 22.2 24.2 25.0 0.7
Nursing, psychiatric, and home health aides 3600 3436 1223 0.0 31.6 7.5 24.3 36.6 NR
Dental assistants 3640 385 144 95.8 0.0 NR NR 0.0 0.0
Medical assistants 3645 823 356 NR NR 74.8 23.2 NR NR
Phlebotomists 3649 154 54 0.0 0.0 NR 63.4 0.0 19.5
Food preparation and serving 4000–4160 470 117 0.0 NR 6.6 50.7 41.4 0.0
Building and grounds cleaning and maintenance occupations 4200–4250 777 262 NR NR 16.3 60.3 21.1 NR
Janitors and building cleaners 4220 393 143 NR NR 26.1 58.8 NR NR
Maids and housekeeping cleaners 4230 338 109 0.0 NR NR 62.6 31.3 0.0
Personal care and service occupations 4300–4650 1336 476 NR 58.5 12.0 14.4 14.9 NR
Personal care aides 4610 1156 432 NR 63.6 11.0 13.7 11.5 NR
Office and administrative support occupations 5000–5940 3731 1280 5.5 2.1 43.4 44.1 3.7 1.2
Trades** 6200–9750 679 225 NR NR 24.2 50.5 10.1 7.3
Heavier shading indicates broader occupational grouping.
NR, not reported because relative standard error of estimates is >30%.
*Weighted.
†Respondents with census industry codes (0170-7890 or 8290-9500) and census occupation not in (3000–3655).
‡Respondents with census industry codes (0170-7890 or 8290-9550) and census occupation in (3000–3655).
§Respondents with census industry codes 7970 to 8270.
∥Within health care industry, includes health care occupational groups with at least 3 reportable mental health related outcomes (see Table 3).
¶3000, 3010, 3040, 3050, 3060, 3110, 3120, 3140, 3230, 3250, and 3258.
#3030, 3150, 3160, 3200, 3210, 3220, 3235, 3245, 3255, 3256, 3257, and 3260.
**Construction, extraction, maintenance, production, and transportation and materials moving industries.
Demographics of Health Care Workers by Occupation
Demographic characteristics of respondents differed markedly by health care occupation. Although approximately 65% of health care diagnosing and treating practitioners were White, most health care support workers (55%) were non-White, primarily non-Hispanic African American or Hispanic (Table 3). Age distributions also varied by occupation. Educational attainment generally tracked with educational requirements for the occupation: 90% of health diagnosing practitioners and 64% of health treating practitioners had completed college, but only 28% of health technicians and technologists and 14% of health care support workers had done so. Income distribution and home ownership levels followed patterns similar to those observed for education.
TABLE 3 Demographic Characteristics (Percentages) of Health Care Workers by Occupation, BRFSS 2017–2019
Age, %* Sex, %* Race/Ethnicity, %* Educational Attainment, %* Household Income, %*
18–34 y 35–54 y ≥55 y Male Female Non-Hispanic White Non-Hispanic African American Non-Hispanic Other Hispanic High School or Less Some College/Technical School College Graduate or More <$35 k $35–<$50 k $50–<$75 k ≥$75 k
Non–health care industries† 32.3 43.3 24.4 59.5 40.5 60.8 10.8 8.4 19.9 38.6 29.8 1431.6 26.6 12.4 15.8 45.2
Health care industry workers‡ 29.0 45.8 25.2 25.0 75.0 60.3 16.2 11.6 11.9 19.3 34.9 45.8 20.1 11.0 16.8 52.1
Health care occupation grouping§
Community and social service occupations 26.2 50.0 23.8 24.8 75.2 63.4 25.8 4.0 6.8 5.9 17.6 76.5 14.0 12.9 19.7 53.4
Counselors 24.8 53.1 22.1 31.3 68.7 58.8 29.9 NR NR 5.1 19.9 75.0 18.5 14.5 21.6 45.4
Social workers 29.7 45.6 24.6 14.7 85.3 69.5 21.1 NR 4.7 NR 10.2 83.8 8.5 11.7 15.5 64.3
Health care practitioners and technical occupations 26.2 49.1 24.7 24.5 75.5 66.2 12.4 12.9 8.4 6.5 32.0 61.6 7.4 8.2 16.8 67.6
Health diagnosing practitioners 17.6 52.8 29.6 47.3 52.7 64.1 6.8 21.6 7.5 3.4 6.3 90.3 3.2 3.3 5.2 88.3
Physicians and surgeons 14.6 52.9 32.4 56.2 43.8 62.4 6.0 25.1 6.4 3.2 6.2 90.6 3.9 2.8 4.0 89.3
Health treating practitioners 26.8 48.8 24.4 13.0 87.0 66.6 14.0 10.8 8.7 4.2 32.1 63.7 5.8 7.5 18.6 68.1
Physical therapists 25.4 54.0 20.6 23.8 76.2 77.8 NR NR NR NR 12.7 86.2 NR NR 12.5 83.6
Registered nurses 26.8 48.2 25.0 11.6 88.4 65.9 14.9 10.6 8.6 4.5 34.2 61.2 5.6 7.7 18.6 68.1
Miscellaneous health technologists and technicians 32.7 46.6 20.8 30.2 69.8 67.3 14.0 9.8 8.8 15.2 56.9 28.0 14.9 14.7 23.8 46.5
Clinical laboratory technologists and technicians 40.3 36.4 23.3 28.4 71.6 71.5 12.1 9.9 NR NR 43.7 40.1 NR 12.1 26.0 39.1
Health practitioner support technologists and technicians 42.0 43.1 NR 35.9 64.1 60.1 25.4 NR NR 17.4 61.9 20.7 15.9 22.1 31.4 30.5
Licensed practical nurses/LVNs 25.3 53.7 20.9 11.3 88.7 55.6 25.3 NR NR NR 66.9 20.5 15.3 23.6 28.6 32.6
Miscellaneous health technologists and technicians, other 27.9 50.8 21.3 41.1 58.9 56.1 14.4 NR 6.1 20.7 41.7 37.6 19.0 15.4 22.4 43.2
Health care support occupations 41.8 37.7 20.5 12.0 88.0 44.9 27.1 10.7 17.3 40.2 45.9 13.9 51.8 16.8 15.0 16.4
Nursing, psychiatric, and home health aides 39.4 36.1 24.4 11.8 88.2 39.5 33.8 11.7 14.9 51.3 38.0 10.7 60.1 16.1 12.1 11.6
Dental assistants 47.1 38.9 14.1 NR 97.1 54.3 NR NR 32.1 31.1 57.4 11.5 32.2 12.7 21.8 33.4
Medical assistants 47.0 44.1 9.0 8.9 91.1 48.4 19.4 9.2 23.0 16.3 63.2 20.5 42.4 19.8 17.9 19.8
Phlebotomists 51.0 35.7 13.3 NR 83.4 48.7 26.7 NR 19.4 20.8 59.3 19.9 43.5 28.2 NR NR
Food preparation and serving 31.9 40.6 27.5 36.6 63.4 48.0 27.7 3.5 20.8 67.4 27.3 5.3 56.5 14.8 15.7 NR
Building and grounds cleaning and maintenance occupations 31.7 42.9 25.4 46.1 53.9 45.2 24.3 NR 20.2 74.0 20.4 5.6 60.8 14.3 14.0 NR
Janitors and building cleaners 33.4 43.1 23.5 63.7 36.3 54.9 20.7 NR 19.6 66.4 27.0 NR 49.8 19.5 NR NR
Maids and housekeeping cleaners 31.3 42.8 25.9 22.2 77.8 31.5 28.5 NR 22.1 84.6 11.8 NR 78.4 NR NR NR
Personal care and service occupations 33.2 41.7 25.2 18.4 81.6 45.4 20.6 11.5 22.6 47.2 38.3 14.5 63.4 15.0 11.5 10.1
Personal care aides 34.4 42.0 23.6 18.3 81.7 43.3 20.8 12.1 23.8 49.4 37.1 13.6 66.8 13.1 11.6 8.5
Office and administrative support occupations 31.2 41.9 26.9 13.2 86.8 59.6 16.3 8.4 15.7 25.5 51.2 23.3 24.4 16.3 23.8 35.5
Trades∥ 32.3 33.9 33.8 75.4 24.6 58.7 11.0 NR 24.6 49.3 33.0 17.7 26.2 17.9 20.9 35.0
Heavier shading indicates broader occupational grouping.
LVN, licensed vocational nurse; NR, not reported because relative standard error of estimates is >30%.
*Weighted.
†Respondents with census industry codes (0170-7890 or 8290-9500) and census occupation not in (3000–3655).
‡Respondents with census industry codes 7970 to 8270.
§Within health care industry, includes health care occupational groups with at least 3 reportable mental health related outcomes.
∥Construction, extraction, maintenance, production, and transportation and materials moving industries.
Prevalence of Adverse Health Conditions by Health Care Occupation
Across the health care workforce, insufficient sleep and diagnosed depression were the most commonly reported issues, with prevalences of 41.0% and 18.9%, respectively (Table 4). For both conditions, health care workers had statistically significant elevated aPRs compared with the non–health care workers; prevalences among the latter were 36.5% for insufficient sleep and 14.2% for depression. Although health care workers had a higher prevalence of depression than non–health care workers (aPR, 1.11; 95% CI, 1.06–1.19), health care workers were slightly less likely to report frequent mental distress (aPR, 0.88; 95% CI, 0.81–0.94). Health care workers were also significantly less likely than non–health care workers to report poor self-rated health (aPR, 0.76; 95% CI, 0.70–0.83) and marginally less likely to report frequent physical distress (aPR, 0.90; 95% CI, 0.81–1.00).
TABLE 4 Health-Related Metrics by Health Care Occupation: Unadjusted Prevalences and aPRs*, BRFSS 2017–2019
Fair or Poor Self-rated Health
% (95% CI)
aPR (95% CI) Frequent Physical Distress
% (95% CI)
aPR (95% CI) Frequent Mental Distress
% (95% CI)
aPR (95% CI) Activity Limitations
% (95% CI)
aPR (95% CI) Diagnosed
Depression
% (95% CI)
aPR (95% CI) Insufficient Sleep†
% (95% CI)
aPR (95% CI)
Non–health care workers‡ 11.9 (11.6–12.3)
Ref 6.7 (6.5–6.9)
Ref 10.1 (9.8–10.4)
Ref 3.8 (3.6–4.0)
Ref 14.2 (13.9–14.6)
Ref 36.5 (35.7–37.3)
Ref
Health care industry workers§ 8.6 (8.0–9.3) 6.3 (5.8–7.0) 9.8 (9.1–10.4) 3.7 (3.2–4.2) 18.9 (18.0–19.8) 41.0 (39.0–43.1)
Health care occupation grouping∥ 0.76 (0.70–0.83) 0.90 (0.81–1.00) 0.88 (0.81–0.94) 0.91 (0.79–1.06) 1.11 (1.06–1.18) 1.13 (1.07–1.19)
Community and social service occupations 7.4 (5.3–10.0)
0.70 (0.52–0.95) 5.7 (3.8–8.2)
0.84 (0.58–1.22) 10.1 (6.6–14.5)
0.93 (0.63–1.37) 4.8 (2.6–8.1)
1.27(0.74–2.16) 27.3 (21.9–33.1)
1.65 (1.32–2.05) 36.5 (27.7–46.0)
1.03 (0.81–1.30)
Counselors 9.1 (5.3–14.2)
0.83 (0.53–1.31) 7.0 (3.7–11.9)
1.04 (0.60–1.80) NR NR 34.7 (25.3–45.1)
2.21 (1.62–3.02) 42.7 (27.3–59.1)
1.18 (0.83–1.68)
Social workers 6.1 (3.7–9.5)
0.62 (0.40–0.96) 5.1 (2.6–8.9)
0.77 (0.43–1.36) 9.1 (5.4–14.2)
0.81 (0.51–1.30) NR 20.5 (15.6–26.2)
1.14 (0.87–1.50) 32.0 (19.9–46.1)
0.94 (0.65–1.37)
Health care practitioners and technical occupations 5.1 (4.5–5.8)
0.49 (0.43–0.56) 5.1 (4.3–6.1)
0.75 (0.64–0.89) 7.7 (6.9–8.6)
0.72 (0.64–0.81) 2.5 (2.0–3.1)
0.64 (0.51–0.80) 17.9 (16.6–19.2)
1.05 (0.98–1.14) 41.5 (38.4–44.7)
1.16 (1.07–1.25)
Health diagnosing practitioners 3.3 (2.3–4.5)
0.33 (0.24–0.45) 2.5 (1.7–3.5)
0.38 (0.27–0.54) 5.0 (3.8–6.5)
0.57 (0.44–0.75) 1.4 (0.8–2.4)
0.40 (0.24–0.68) 12.6 (10.7–14.6)
0.91 (0.79–1.05) 35.1 (29.0–41.7)
0.99 (0.83–1.17)
Physicians and surgeons 3.5 (2.4–5.0)
0.35 (0.25–0.50) 2.5 (1.7–3.5)
0.38 (0.22–0.54) 5.0 (3.4–7.0)
0.61 (0.43–0.86) 1.5 (0.8–2.7)
0.45 (0.26–0.79) 13.6 (10.9–16.5)
1.07 (0.89–1.29) 32.5 (25.5–40.1)
0.92 (0.74–1.14)
Health treating practitioners 5.0 (4.1–5.9)
0.48 (0.40–0.57) 5.2 (4.1–6.5)
0.74 (0.59–0.94) 8.1 (6.9–9.4)
0.72 (0.61–0.84) 2.8 (2.0–3.8)
0.71 (0.52–0.98) 18.6 (17.0–20.4)
1.03 (0.93–1.13) 44.5 (40.4–48.7)
1.25 (1.14–1.37)
Physical therapists NR NR 2.6 (1.4–4.4)
0.24 (0.14–0.41) NR 12.0 (6.8–19.2)
0.68 (0.42–1.10) 24.3 (14.6–36.4)
0.72 (0.47–1.10)
Registered nurses 5.1 (4.2–6.1)
0.49 (0.41–0.59) 5.3 (4.1–6.7)
0.75 (0.59–0.97) 8.4 (7.0–10.0)
0.75 (0.63–0.89) 3.1 (2.2–4.3)
0.78 (0.56–1.09) 18.6 (16.9–20.4)
1.02 (0.93–1.13) 46.6 (42.0–51.2)
1.31 (1.19–1.44)
Miscellaneous health technologists and technicians 7.3 (5.7–9.1)
0.70 (0.56–0.87) 7.7 (5.6–10.3)
1.16 (0.86–1.55) 9.5 (7.8–11.4)
0.84 (0.69–1.02) 2.7 (1.8–3.8)
0.67 (0.47–0.97) 21.3 (17.9–25.0)
1.24 (1.04–1.48) 41.8 (34.1–49.7)
1.15 (0.96–1.38)
Clinical laboratory technologists and technicians 9.8 (5.6–15.8)
0.81 (0.48–1.37) 16.7 (8.9–27.5)
0.90 (0.51–1.59) 48.9 (21.8–76.4)
1.34 (0.76–2.35)
Health practitioner support technologists and technicians NR NR 14.5 (8.4–22.8)
1.20 (0.76–1.90) NR 29.7 (18.5–42.9)
1.77 (1.14–2.76) 51.5 (30.7–71.9)
1.40 (0.95–2.07)
Licensed practical and licensed vocational nurses 7.5 (4.1–12.3)
0.67 (0.40–1.12) NR 9.0 (5.7–13.3)
0.76 (0.50–1.15) NR 23.6 (17.5–30.6)
1.32 (1.01–1.71) 43.3 (30.8–56.4)
1.15 (0.86–1.55)
Miscellaneous health technologists and technicians (other) 9.4 (5.2–15.2)
0.92 (0.51–1.47) 12.9 (6.6–22.0)
2.01 (1.17–3.44) 14.1 (7.9–22.5)
1.44 (0.89–2.34) NR 18.9 (9.5–32.0)
1.29 (0.73–2.28) 34.5 (19.1–52.8)
0.93 (0.57–1.51)
Health care support occupations 14.5 (12.6–16.5)
1.17 (1.02–1.34) 8.3 (6.8–9.9)
1.14 (0.94–1.38) 15.6 (13.6–17.7)
1.18 (1.02–1.35) 6.5 (5.1–8.2)
1.46 (1.14–1.86) 21.5 (19.3–23.8)
1.16 (1.04–1.29) 46.5 (42.0–51.0)
1.22 (1.10–1.35)
Nursing, psychiatric, and home health aides 16.8 (14.3–19.5)
1.31 (1.12–1.54) 9.4 (7.4–11.7)
1.26 (1.00–1.59) 16.8 (14.1–19.8)
1.29 (1.09–1.53) 7.3 (5.4–9.7)
1.62 (1.20–2.18) 22.0 (19.1–25.1)
1.22 (1.06–1.40) 47.7 (41.8–53.6)
1.22 (1.07–1.40)
Dental assistants NR NR 13.3 (7.7–20.9)
0.97 (0.59–1.59) NR 20.5 (13.7–28.8)
0.99 (0.68–1.43) 33.9 (18.3–52.6)
0.97 (0.61–1.54)
Medical assistants 10.8 (7.2–15.4)
0.90 (0.63–1.30) 6.7 (4.2–10.1)
0.98 (0.65–1.47) 14.9 (11.2–19.2)
1.08 (0.82–1.44) 5.3 (3.0–8.6)
1.21 (0.73–2.01) 21.1 (16.7–26.2)
1.08 (0.86–1.36) 51.1 (41.0–61.2)
1.40 (1.15–1.71)
Phlebotomists 18.1 (9.1–30.6)
1.52 (0.86–2.70) NR 10.3 (5.3–17.6)
0.73 (0.42–1.29) NR 26.9 (16.5–39.5)
1.44 (0.93–2.23) 56.2 (37.8–73.4)
1.44 (1.02–2.04)
Food preparation and serving 18.8 (12.4–26.7)
1.43 (0.97–2.11) 12.7 (7.7–19.4)
1.72 (1.11–2.66) 11.7 (7.6–17.0)
0.99 (0.67–1.47) NR 16.2 (11.3–22.1)
0.98 (0.69–1.39) 53.4 (39.0–67.4)
1.37 (1.03–1.82)
Building and grounds cleaning and maintenance occupations 22.1 (14.4–31.6)
1.70 (1.13–2.55) 9.9 (5.4–16.2)
1.38 (0.82–2.31) 10.1 (6.2–15.3)
0.94 (0.62–1.44) NR 14.3 (9.7–20.1)
0.99 (0.69–1.39) 36.5 (23.8–50.6)
0.96 (0.67–1.38)
Janitors and building cleaners 23.5 (11.5–39.7)
1.84 (0.99–3.41) NR NR NR 11.1 (5.8–18.7)
0.80 (0.48–1.34) 29.2 (14.1–48.8)
0.75 (0.43–1.33)
Maids and housekeeping cleaners 20.8 (12.6–31.1)
1.55 (0.98–2.46) NR 10.6 (5.9–17.3)
0.96 (0.58–1.59) NR 18.5 (10.7–28.8)
1.21 (0.75–1.93) 47.5 (29.7–65.8)
1.28 (0.87–1.88)
Personal care and service occupations 22.7 (16.5–30.0)
1.53 (1.22–1.98) 10.0 (6.1–15.4)
1.35 (0.88–2.07) 18.2 (13.8–23.3)
1.57 (1.23–2.01) 7.5 (4.3–12.1)
1.73 (1.06–2.82) 30.8 (24.9–37.2)
1.87 (1.55–2.25) 47.7 (37.0–58.7)
1.35 (1.11–1.65)
Personal care aides 22.6 (16.0–30.5)
1.48 (1.16–1.89) 10.0 (5.8–15.9)
1.35 (0.85–2.15) 18.6 (13.8–24.3)
1.60 (1.23–2.08) 7.8 (4.3–12.9)
1.80 (1.08–3.00) 32.3 (25.8–39.3)
1.96 (1.62–2.38) 48.6 (37.2–60.1)
1.39 (1.13–1.70)
Office and administrative support occupations 9.2 (7.2–11.5)
0.76 (0.61–0.94) 5.5 (3.9–7.4)
0.69 (0.51–0.93) 9.9 (7.9–12.2)
0.84 (0.68–1.04) 3.6 (2.2–5.4)
0.74 (0.49–1.12) 20.9 (17.9–24.2)
1.13 (0.97–1.33) 31.6 (26.0–37.6)
0.87 (0.72–1.05)
Trades¶ 15.8 (10.0–23.3)
1.24 (0.86–1.79) 14.3 (8.8–21.5)
2.00 (1.30–3.05) 9.7 (5.3–15.9)
1.05 (0.63–1.74) 5.2 (2.9–8.6)
1.43 (0.87–2.35) 10.8 (6.6–16.4)
0.89 (0.58–1.35) 51.1 (38.2–63.9)
1.40 (1.10–1.78)
Italics indicate statistically significantly elevated aPR. Heavier shading indicates broader occupational grouping.
aPR, adjusted prevalence ratio; CI, confidence interval; NR, not reported because relative standard error of estimates is >30%; Ref, reference group.
*Adjusted prevalences given for non–health care workers. For each health care occupation, the unadjusted prevalence is given for each condition, followed by the aPR comparing adjusted prevalence in that occupation to the adjusted prevalence for non–health care workers. Results are adjusted for age (18–34, 35–54, ≥55 years), sex (male, female), and race/ethnicity (non-Hispanic White, non-Hispanic African American, non-Hispanic other, Hispanic).
†Elicited only in 2018 BRFSS questionnaire.
‡Respondents with census industry codes (0170-7890 or 8290-9500) and census occupation not in (3000–3655).
§Respondents with census industry codes 7970 to 8270.
∥Within health care industry, includes health care occupational groups with at least three reportable mental health related outcomes.
¶Construction, extraction, maintenance, production, and transportation and materials moving industries.
Workers in community and social service occupations had an elevated prevalence of diagnosed depression (compared with the prevalence observed in non–health care workers, the reference group for all comparisons), with an aPR of 1.65 (95% CI, 1.32–2.05). This result was driven largely by counselors in the health care industry; this group had the highest unadjusted prevalence estimate (34.7%) for depression of all health care occupation groups reported, as well as an aPR above 2. Social workers had a small elevation for depression that did not attain statistical significance (aPR, 1.14; 95% CI, 0.87–1.50). Community and social service occupations workers were significantly less likely than non–health care workers to report poor self-rated health (aPR, 0.70; 95% CI, 0.52–0.95).
In the broad grouping of health care practitioners and technical occupations, only the prevalence of insufficient sleep was significantly elevated (aPR, 1.16; 95% CI, 1.07–1.25). Health diagnosing practitioners had lower prevalences than non–health care workers for every condition except insufficient sleep, and the only significantly elevated prevalence was for insufficient sleep among nurse practitioners (prevalence of insufficient sleep, 58.7 [95% CI, 36.4–78.7]; aPR, 1.58 [95% CI, 1.11–2.26]; results for other groups of health diagnosing practitioners not shown). Results for health treating practitioners were similar, with a significant elevation only for insufficient sleep (primarily among RNs). The prevalence of diagnosed depression among treating practitioners was higher than for diagnosing practitioners but was not significantly elevated compared with the prevalence among non–health care workers. Health technologists and technicians (and particularly licensed practical nurses/licensed vocational nurses), a group with somewhat lower wages than health diagnosing and treating practitioners, had significant elevations of diagnosed depression.
Adverse health conditions were most common among the lowest-wage health care workers with patient care responsibilities. The health care support occupations grouping had statistically significant elevations of every condition except frequent physical distress. With the exception of insufficient sleep (which was most common among phlebotomists), these results are driven by the nursing, psychiatric, and home care aide occupation. The duties of nursing, psychiatric, and home health aides substantially overlap those of an occupation outside health support: the personal care aides and service occupation. Like health care aides, personal care aides had statistically significant elevations of almost every outcome: poor self-rated health, frequent mental distress, activity limitations, diagnosed depression, and insufficient sleep. For most of these outcomes, point estimates were higher than those for patient care aides. In addition, personal care aides had the highest unadjusted prevalence estimates among of frequent mental distress and activity limitations of any group assessed.
Ancillary support occupations (food preparation and serving, janitors, maids and housekeepers, trades) had elevated prevalences for some outcomes. Among these groups, the prevalence of poor self-rated health was at least 4 times the prevalence among health care diagnosing and treating practitioners. Both food preparation and serving workers and trades workers had statistically significant elevations of frequent physical distress and insufficient sleep.
Health Conditions and Behaviors by Health Care Industry
Prevalences of adverse health conditions also differed by industry (Table 5). Workers in the home health industry had the highest prevalences of most adverse health conditions: poor self-reported health, frequent physical distress, activity limitations, and diagnosed depression. Moreover, aPRs comparing prevalences of these conditions among home health workers to prevalences in non–health care workers were statistically significant. Home health and nursing care facility workers had the highest prevalences of frequent mental distress. Hospital workers had significantly lower prevalences of poor self-rated health (aPR, 0.62; 95% CI, 0.54–0.71) and frequent physical (aPR, 0.77; 95% CI, 0.66–0.90) and mental distress (aPR, 0.75; 95% CI, 0.68–0.84) than non–health care workers but did have a statistically significant elevation for insufficient sleep (aPR, 1.16; 95% CI, 1.07–1.25).
TABLE 5 Health-Related Metrics for Health Care Workers by Industry: Prevalences and aPRs*, BRFSS 2017–2019
Fair or Poor Self-rated Health
% (95% CI)
aPR (95% CI) Frequent Physical Distress
% (95% CI)
aPR (95% CI) Frequent Mental Distress
% (95% CI)
aPR (95% CI) Activity Limitations
% (95% CI)
aPR (95% CI) Diagnosed
Depression
% (95% CI)
aPR (95% CI) Insufficient Sleep†
% (95% CI)
aPR (95% CI)
Non–health care workers‡ 11.9 (11.6–12.3)
Ref 6.7 (6.5–6.9)
Ref 10.1 (9.8–10.4)
Ref 3.8 (3.6–4.0)
Ref 14.2 (13.9–14.6)
Ref 36.5 (35.7–37.3)
Ref
Health care industry workers§ 8.6 (8.0–9.3)
0.76 (0.70–0.83) 6.3 (5.8–7.0)
0.90 (0.81–1.00) 9.8 (9.1–10.4)
0.88 (0.81–0.94) 3.7 (3.2–4.2)
0.91 (0.79–1.06) 18.9 (18.0–19.8)
1.11 (1.06–1.18) 41.0 (39.0–43.1)
1.13 (1.07–1.19)
Industry group (US Census industry codes)
Dental office (7980) 5.5 (3.4–8.4)
0.5 (0.34–0.78) NR 8.5 (6.0–11.7)
0.74 (0.53–1.03) 2.9 (1.5–5.1)
0.75 (0.42–1.32) 12.5 (9.7–15.9)
0.67 (0.53–0.86) 28.6 (20.9–37.3)
0.84 (0.64–1.09)
Home health (8170) 20.2 (16.6–24.2)
1.45 (1.23–1.72) 12.5 (9.6–15.8)
1.62 (1.27–2.07) 15.9 (13.0–19.1)
1.38 (1.15–1.65) 7.9 (5.5–10.8)
1.80 (1.29–2.51) 26.3 (22.9–30.0)
1.56 (1.36–1.78) 42.1 (35.2–49.2)
1.15 (0.98–1.35)
Other ambulatory health care settings (7970, 7990, 8070–8090) 6.7 (5.7–7.8)
0.62 (0.53–0.72) 5.9 (4.9–7.0)
0.84 (0.70–1.00) 9.0 (7.9–10.3)
0.85 (0.74–0.97) 3.3 (2.5–4.2)
0.81 (0.62–1.05) 19.6 (18.0–21.3)
1.17 (1.07–1.28) 38.4 (34.8–42.0)
1.07 (0.98–1.18)
Hospital (8190) 6.8 (5.9–7.7)
0.62 (0.54–0.71) 5.3 (4.6–6.2)
0.77 (0.66–0.90) 8.3 (7.5–9.2)
0.75 (0.68–0.84) 3.2 (2.5–4.0)
0.80 (0.64–1.01) 17.0 (15.7–18.3)
1.01 (0.93–1.09) 42.0 (38.9–45.3)
1.16 (1.07–1.25)
Nursing care facilities (8270) 14.7 (12.4–17.2)
1.23 (1.05–1.45) 7.2 (5.7–9.0)
0.99 (0.78–1.24) 14.4 (12.2–16.9)
1.17 (0.99–1.37) 4.6 (3.3–6.2)
1.05 (0.77–1.42) 21.8 (19.3–24.4)
1.22 (1.08–1.37) 46.2 (40.8–51.7)
1.23 (1.09–1.39)
Italics indicate statistically significantly elevated aPR.
aPR, adjusted prevalence ratio; CI, confidence interval; NR, not reported because relative standard error of estimates is >30%; Ref, reference group.
*Adjusted prevalences given for non–health care workers. For each health care occupation, the unadjusted prevalence is given for each condition, followed by the aPR comparing adjusted prevalence in that occupation to the adjusted prevalence for non–health care workers. Results are adjusted for age (18–34, 35–54, ≥55 years), sex (male, female), and race/ethnicity (non-Hispanic White, non-Hispanic African American, non-Hispanic other, Hispanic).
†Elicited only in 2018 BRFSS questionnaire.
‡Respondents with census industry codes (0170-7980 or 8290-9500) and census occupation not in (3000-3655).
§Respondents with census industry codes 7970 to 8270.
DISCUSSION
Although much of the research on adverse mental health conditions among health care workers has focused on physicians and nurses, our study assessed mental health and well-being among multiple health care industry workforces and found that health care support workers bore the greatest burden of these conditions before the COVID-19 pandemic. Among low-wage workers, patient and personal care aides were particularly at risk, with higher prevalences of adverse mental health conditions and poorer well-being compared with both other health care workers and the non–health care workforce. These findings by occupation were reflected in health care industry results: workers in the home health and nursing care facility industries, where the majority of patient and personal care aides work, had higher prevalences of adverse health conditions than their counterparts in hospitals and ambulatory care settings.
Workforces with either a very high prevalence of a single condition or many conditions with significantly elevated aPRs can be considered to have high mental health burdens. By these metrics, the occupations with the highest burdens have workforces that are largely female (nursing, psychiatric, and home health aides; counselors; personal care aides), have relatively high percentages of non-Hispanic African American workers (nursing, psychiatric, and home health aides; counselors; food preparation and serving), have low educational attainment (nursing, psychiatric, and home health aides; patient and personal care aides; janitors; food preparation and serving; trades), and/or have low household incomes (nursing, psychiatric, and home health aides; patient and personal care aides; janitors; food preparation and serving). Many workforces with these demographic characteristics are disproportionately subject to multiple stressors, including discrimination and restricted occupational options.39,40
Previous literature has noted that self-reported health and the prevalence of mental health issues differ across demographic characteristics. Women report more mental health symptoms, both in general and in the workplace context,18 and they report more physical health repercussions from burnout.31 Socioeconomic status and race/ethnicity have been reported to affect self-rating of health in the general population, perhaps reflecting differing expectations41 or experiences.42 In an older study of mental health workers, non-White respondents reported lower levels of both emotional exhaustion and personal accomplishment, whereas higher education and salary were positively associated with both outcomes.30 This observation may stem from different configurations of job demand and control, as well as other occupational and nonoccupational stressors. Within the physician occupation, a recent study found no significant differences in prevalence of depressive symptoms by race/ethnicity.43 Of interest is that our study did not observe significantly increased adverse health conditions for occupations with the highest prevalences of Hispanic workers. Although Hispanic workers are disproportionately found in several low-wage health care occupations, they have a substantial presence in professional occupations (eg comprising 20% of dentists). Notably, the prevalence of depression in the general population is inversely related to income, with nearly 16% of adults with family income below the federal poverty limit reporting depression during the past 2 weeks, compared with 3.5% of adults from families with incomes at least 4 times the federal poverty limit.44 All of the demographic and occupational findings in our study of mental health and well-being should be considered within the context of complex relations between discrimination, income, educational opportunities, and occupational opportunities, as well as reporting differences. Although presentation of separate results by demographic group was beyond the scope of this scan of mental health outcomes by health care occupation, further research into demographic differences within specific occupations is warranted.
Mental health conditions among health care workers not only adversely affect the workers themselves and their families but also can also impact patient care.45 A systematic review found that common mental disorders in nurses were strongly associated with multiple adverse work themes: general errors, medication errors, near misses, and decreased patient safety and satisfaction.46 Self-reported exhaustion due to long-term stress has been associated with poor job performance and absence due to illness among health care and social insurance workers.47 Depression among physicians is also associated with lower quality medical care48; although research on the effects of depression on care quality among low-wage health care workers is lacking, there is little reason to believe that results would differ. The elevated burden of adverse health conditions observed among home health and nursing care facility industries in the current study may be linked to observed high staff turnover in these industries.
Multiple groups of health care workers reported insufficient sleep. The prevalence of insufficient sleep was elevated in the health care industry as a whole and specifically in the hospital and nursing care facility home industries and among workers in specific health care occupations: RNs, patient care aides, personal care aides, medical assistants, phlebotomists, food preparation and serving workers, and workers in the trades. Whether insufficient sleep primarily reflects long working hours and shift work, or is a function of insomnia (from physical or mental health conditions) or mental health issues or conditions (eg, anxiety, depression) likely varies by individual, as well as industry and occupation, and could not be determined in this cross-sectional study. Among all industries, shift work has been associated with increased risk of adverse mental health outcomes, with results varying by sex and shift type.49 Shift work is associated with insufficient sleep,50 which in turn has been associated with increased odds of poor self-rated health,51 burnout,52 and depressive symptoms.53 The mechanisms of relations between insufficient sleep and some adverse effects may be complex: one study found that, although long working hours appear to be linked to depression in physicians, the association disappeared after stratification for an occupational stress metric.54 However, the high prevalence of insufficient sleep across the health care workforce is concerning.
This study has a number of limitations. Foremost is that BRFSS questions related to mental health are not comprehensive. Although depression and “poor mental health days” are included, the survey does not specifically assess other common conditions such as anxiety. In addition, the “diagnosed depression” variable provides no specific information on severity or duration and is a single summary metric, with none of the detail included in survey instruments designed to ascertain symptoms or severity of depression. All information in BRFSS is self-reported and subject to social desirability and recall bias, with the former likely to lead to underestimated prevalences of adverse health conditions. Of the basic demographic characteristics, income was omitted most frequently (11%) for our study population. The results for several of the adverse health conditions we evaluated could also be affected by the stigma associated with mental health issues (resulting in underreporting); the level of stigma may differ by demographic and occupational group. Finally, as the BRFSS industry and occupation module is optional and is not administered by every locality, these results are not nationally representative. Despite these limitations, the current findings are useful for identifying groups within the health care workforce in most need of resources and interventions to address adverse mental health issues.
Prevention of the upstream (including organizational and structural) factors leading to mental health issues among health care workers, along with subsequent assessment, intervention, and treatment, is key. However, research on the efficacy of workplace mental health and well-being programs, practices, and policies (including those that are individually, group, and organizationally focused) has been characterized as sparse, methodologically weak, or failing to account adequately for differences in demographic or occupational groups.30,31,55,56 The results of the current study highlight the need for understanding and improving working conditions that may impact health care workers' mental health and well-being. Research on interventions among health care support staff and other low-wage health care workers, groups with the highest prevalences of adverse outcomes in this study, has been particularly limited. Such research is particularly important in light of the finding in previous research that the suicide rate in female health care support workers is significantly higher than that of all female workers.25
Other barriers to addressing mental health issues include stigmatization of acknowledging and seeking help for mental health issues, as well as access to affordable care. Stigma has been noted particularly for physicians, who have concerns about the professional implications of accessing mental health care.13 The need for specialized service providers who are aware of these concerns has been noted.13,18 Incorporating education about mental illness into medical training is recommended.48 Another barrier, access to affordable care, is most salient for lower-wage workers, such as patient and personal care aides; low-wage workers are more likely to lack health insurance and to be unable to afford health care visits.27
One potential approach to circumventing the stigma of seeking mental health assistance might be to focus on addressing burnout, a construct that is sometimes conflated with mental health concerns such as depression and anxiety. The nature of the relationship between burnout and mental health concerns is contested, with some research finding them indistinguishable57 and other analyses suggesting that depression and anxiety are distinct from burnout58 or that only specific characteristics of burnout are linked to depression59 or anxiety.60 Investigators have expressed concern that burnout is taken less seriously than the overlapping or coextensive diagnosis of depression.57 However, the possibility that burnout may be less stigmatized and may thus present a more acceptable reason for seeking treatment should be explored. Unfortunately, interventions around burnout have limitations similar to those described for other mental health and well-being concerns. Public health and health delivery systems should strive to implement evidence-based programs that (1) meet the needs of specific workforces to support employee mental health and well-being and (2) simultaneously address organizational impediments to the success of these programs through measures such as ensuring easy and affordable access, employee privacy, and supportive work cultures.
CONCLUSIONS
In these prepandemic survey data, elevated prevalences of the broadest range of mental health-related concerns were seen among low-wage health care workers. More recent work has documented the effects of both occupational and personal stressors associated with COVID-19 on a range of health care workers.1,2,61,62 Among the general public, the prevalence of depression has increased markedly from prepandemic levels, particularly for more severe depression,63 although whether this increase will be sustained is unclear. Regardless, the current emotional support needs of the health care workforce are likely greater than those indicated by this study. At the same time, mental health treatment resources have been heavily strained by the pandemic and its repercussions and are not available to all who would benefit from them. Moreover, many lower-income health care workers, such as the health care support group observed in this study to have high prevalences of multiple adverse health conditions, may not have access to affordable mental health treatment. A concerted effort to develop, implement, and evaluate occupation- and industry-specific, culturally competent prevention, intervention, and mitigation strategies addressing both organizational and personal conditions that lead to mental health issues is critical to ensuring a robust health care workforce.
ACKNOWLEDGMENTS
The authors wish to thank the following: Thomas Cunningham, Marie Haring Sweeney, Jennifer Cornell, CDC; Pamela Schumacher-Young (work performed under General Informatics, now retired), Susan Burton, Synergy; Katrina Bicknaver, Matt Hirst, Rebecca Purdin, Elizabeth Smith, Surprese Watts (work performed under General Informatics, now employed by CeTechs); Jeff Purdin, Maximus (formerly ATTAIN); and state BRFSS coordinators.
Ethical Considerations and Disclosures: Behavioral Risk Factor Surveillance System was reviewed by the Human Research Protection Office of the Centers for Disease Control and Prevention and determined to be exempt research. Survey information was collected under OMB control number 0920-1061.
The findings and conclusions presented in this article are those of the authors and do not necessarily represent the views of the National Institute for Occupational Safety and Health/Centers for Disease Control and Prevention.
Funding sources: None to disclose.
Conflict of interest: None declared.
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| 36472564 | PMC9722331 | NO-CC CODE | 2022-12-07 23:19:10 | no | J Occup Environ Med. 2022 Dec 2; 64(12):1025-1035 | utf-8 | J Occup Environ Med | 2,022 | 10.1097/JOM.0000000000002630 | oa_other |
==== Front
Transp Res E Logist Transp Rev
Transp Res E Logist Transp Rev
Transportation Research. Part E, Logistics and Transportation Review
1366-5545
1878-5794
Elsevier Ltd.
S1366-5545(22)00352-0
10.1016/j.tre.2022.102975
102975
Article
An innovative tool for cost control under fragmented scenarios: The container freight index microinsurance
Yu Fangping a
Xiang Zhiyuan a
Wang Xuanhe b⁎
Yang Mo b
Kuang Haibo a
a Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian, Liaoning 116026, China
b School of Finance, Dongbei University of Finance and Economics, Dalian, Liaoning 116025, China
⁎ Corresponding author.
6 12 2022
1 2023
6 12 2022
169 102975102975
25 4 2022
10 11 2022
21 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.
With the impact of the COVID-19 pandemic, global container freights have increased dramatically since the second half of 2020, which has significantly hampered the booking activities of fragmented transportation space for small and medium-sized import and export enterprises (SMIEEs). To provide SMIEEs with an effective tool for controlling shipping costs, we propose the design principles of index microinsurance under fragmented scenarios and design the container freight index microinsurance (CFIM) based on a comprehensive analysis of the term, compensation and share structures. We further establish the pricing model for the CFIM and selection procedure for product optimization, and illustrate the framework with a case study based on the data of the China Containerized Freight Index Europe Service, which demonstrates the good performance of the designed product even under extreme market conditions. The design principles proposed can shed light on the innovation of index microinsurance product that meets fragmented needs and the newly designed CFIM, along with the pricing and optimization procedure, provides practitioners with useful tools for cost control.
Keywords
Container freight index microinsurance
Small and medium-sized import and export enterprises
Fragment insurance scenario
Cost control
Forward-starting arithmetic average Asian call option
China Containerized Freight Index Europe Service
==== Body
pmc1 Introduction
Container maritime transportation plays an irreplaceable role in the worldwide trading system (Stopford, 2008). In recent years, with the rise of global cross-border e-commerce, a large number of small and medium-sized import and export enterprises (SMIEEs) have joined the global maritime supply chain system, and a new trend of international logistics characterized by high-frequency dispersed replenishment and short-term spot freight contract has been formed. According to Southern Africa’s freight news, the fragmented less container load (LCL) business accounted for nearly 20% of the total global container throughput in 2021. The increase in economic uncertainties and decrease in global trade orders have brought great challenges to SMIEEs. Large fluctuations in container freight can cause increases in logistics costs, and hence reduce meager profits for SMIEEs.
Due to the impact of the recent COVID-19 pandemic, the global container liner shipping market has been experiencing steep surges in freight costs since the second half of 2020, and the freights of some liner routes such as Asia-US East Coast have even increased by 400%. The China Containerized Freight Index (CCFI) reached a historic peak of 3047.32 on Aug. 20, 2021, with an increase of 215% from last year. The increase in logistic costs has undoubtedly pushed SMIEEs further toward plight. Consequently, a large number of SMIEEs, which are in a weak position in the procurement of short-term spot freight contracts, urgently need a tool for freight risk management and cost control.
However, there are still no effective market-oriented tools for managing the risk of container freight fluctuations faced by SMIEEs. On the one hand, traditional trading tools including Free on Board (FOB), Cost and Freight (CFR), Cost, Insurance and Freight (CIF), and Long Term Contract (LTC), are only limited to the allocation between two trading parties, and cannot effectively reduce the cost through risk transitions. On the other hand, there is no effective financial derivative for container freights, which makes SMIEEs helpless towards fluctuations of container freights. Previous studies indicate that financial derivatives can be used to significantly reduce business costs and financial risks for non-financial enterprises (Gay et al., 2011, Ahmed et al., 2018) and reduce logistic costs and increase firm value in the shipping industry (Kavussanos and Visvikis, 2006, Tsai et al., 2009). From a practical perspective, the development of global container freight derivatives is far behind the development of dry bulk cargo and tanker freight derivatives, which is unfavorable to controlling the additional costs caused by the fluctuation of freights. The Shanghai Shipping Exchange launched container freight index futures in 2011, but the daily trading volume is still quite small, only several hundred contracts, because of the regulatory constraints of the China Securities Regulatory Commission. Actually, it is difficult for SMIEEs to participate in the trading of container freight derivatives because of the requirement of large face value, trading capital and advanced skills.
In this paper, we propose a new financial tool, i.e., the container freight index microinsurance (CFIM) to help SMIEEs manage the freight rate risk and control costs. To ensure the sustainability of the product business model, our design of the CFIM is both market-oriented and inclusive, which means that the CFIM meets both the demand of SMIEEs and the operationality of insurers. Index microinsurance is a new application of the existing index insurance under fragmented scenarios, which requires new design principles to reflect the new features. The existing index insurance products are mainly designed for the benefit of insurers, thus leading to excessively high rates and harsh underwriting conditions. In addition, most of the existing index insurance products overlook the demands of fragmented insurance scenarios such as small-amount and high-frequency coverage. To address this problem, in the context of the development of small-amount and high-frequency fragmented insurance scenarios, we propose the design principles of index microinsurance for the fragmented scenario from the interest of both insurers and policyholders. Based on the principles, we design the CFIM to provide a financial tool for SMIEEs to manage the risk of container freight cost fluctuations at reasonable prices.
The main contributions of this paper are as follows: To the best of our knowledge, we are the first to propose the design principles of index microinsurance in fragmented insurance scenarios, and design the CFIM for SMIEEs based on the proposed principles. The CFIM designed has unique term, compensation and share structures, meeting the risk management needs of both SMIEEs and insurers. We also derive the pricing model and establish the selection procedure for optimal product designs. The whole framework proposed not only fills the gaps in the existing literature but also provides practitioners with useful tools for risk management.
The rest of the paper is structured as follows: Section 2 reviews the relevant literature. Section 3 describes the design principle of CFIM. Section 4 presents the details of the design procedure including pricing. Section 5 illustrates the proposed framework with a case study, and Section 6 concludes the paper.
2 Literature review
Due to the distinctive characteristics of extremely dramatic fluctuations in maritime freights, how to reduce the risk of freight fluctuations by means of freight futures, freight options and other shipping derivatives has drawn widespread attention. The container freight index insurance is a contingent right in the future, essentially the same as an option. Therefore, the hedging strategy and pricing model of freight options can serve as the theoretical base of our study.
One branch of the literature mainly focuses on the strategies and efficiency of hedging freight fluctuations with the forward freight agreement (FFA) and freight futures in the dry bulk cargo and tanker marine sub-market. Many of these studies examine the optimization of the hedging strategy of freights. For example, Tezuka et al. (2012) derived the equilibrium spot price and forward/futures curve formulae for freight shipping markets by generalizing the Bessembinder and Lemmon (2002) model and discussed the properties of the forward risk premium and the optimal hedge ratio that corresponds to the reduced spot freight risk. Prokopczuk (2011) empirically compared the pricing and hedging accuracy of a variety of continuous-time futures pricing models with single-route dry bulk freight futures contracts and showed that the inclusion of a second stochastic factor significantly improved the pricing and hedging accuracy. Nomikos and Doctor (2013) used the Sharpe ratio to study the dynamic hedging strategies for different FFA contracts and different maturities. Hsu et al. (2015) developed tanker freights FFA hedging strategies with the bivariate asymmetric non-linear smooth-transition GARCH method. Adland and Alizadeh (2018) claimed that when hedging a vessel's earnings, the hedging ratio of FFAs could be adjusted according to the related factors of the vessel and the contract. Gu et al. (2020) evaluated the quantile hedge ratios of the FFAs, and found that the hedging performance tends to be different from the approach of minimum variance. Sun et al. (2018) proposed an optimal combination of hedging strategies for trading the derivatives of crude oil futures and dry bulk FFA simultaneously with the cross-market dynamic relationship. There are also studies about the hedging efficiency of freight futures. For example, Goulas and Skiadopoulos (2012) investigated whether the dry and wet freight futures market was efficient over the daily and weekly horizons, and found that those were not efficient over the shorter daily horizon. Adland et al. (2020) investigated the corresponding hedging efficiency when using a portfolio of FFA prices to hedge the ship price risk of both static and dynamic hedge ratios, indicating that the hedging efficiency was greater for newer vessels than older vessels and that the static ratio outperformed the dynamic ratio. Bai et al. (2022) investigated the effectiveness of financial hedging strategies of tramp shipping companies and concluded that financial hedging can effectively reduce bunker fuel prices but not freights. In contrast, few studies have explored the hedging strategy or efficiency of container freight futures.
Another branch of the literature mainly concentrates on improving the accuracy of freight options pricing for hedging the freight fluctuation with freight options. Since most freight options belong to path-dependent Asian options, many scholars have studied pricing models and accuracy. In the early days, the pricing model of freight option is constructed assuming that the price of the underlying freight follows a lognormal distribution. For instance, Koekebakker et al. (2007) constructed a freight fitting model based on lognormal distribution, and deduced the closed pricing formula of Asian call and put options with fixed execution prices, which significantly improved the pricing and hedging accuracy. Based on Monte Carlo simulation, Wang et al. (2009) constructed the Asian option pricing model with the lognormal distribution and fixed strike price. Haug (2021) gave the Turnbull and Wakeman (1991) adjustment closed formula to calculate the settlement price of freight options on the European Energy Exchange. Previous studies found that the invalidity of the lognormal distribution assumption will distort the freight option price. Therefore, it is necessary to further improve the underlying freight distribution assumption in order to get a more accurate pricing model. Nomikos et al. (2013) built a valuation approach for options on the average spot freight by extending the traditional lognormal representation for the risk-neutral spot freight dynamics to a diffusion model overlaid with jumps. Kyriakou et al. (2017) used the exponential mean-reverting diffusion model with a decay factor to price the fixed strike price of the freight option, and calculated the price of the freight option and the dynamic changes under different diffusion levels, which demonstrates improvement in reducing pricing bias. Gómez-Valle et al. (2020) proposed a freight option pricing model based on stochastic delay partial differential equations and gave the cap and floor limits of the option price. Besides, there are also studies discussing the characteristics of freight options. Alexandridis et al. (2017) analyzed the relationship of economic spillovers among spots, futures, and options. Lim et al. (2019) analyzed the drivers of freight market volatility and examined their impact factors on the term structure of freight options implied volatilities. It can be seen that due to the complexity of freight, the pricing accuracy of freight options largely depends on the validity of the assumptions. In addition, the pricing of more complex freight option portfolios remains unexplored.
The customers of the dry bulk and tanker market are mainly large import and export enterprises, while SMIEEs are taking up more shares in the container market. The existing literature about maritime freight derivatives has limitations as follows. The small-amount and high-frequency characteristics of SMIEEs’ demands for freight risk management require specialized product design with unconventional term, compensation and share structures, which has not been addressed by previous studies. Also, the complicated product design requires more advanced pricing modeling and optimization procedure, which has not been fully covered in the literature.
3 Design principles of container freight index microinsurance
This is a true story experienced by many SMIEEs. On Oct.16, 2020, YR, a small toy manufacturer in Wenzhou, China, obtained an order for children's toys from a European customer due in 3 months. The contract value is $10,000, and YR beard the cost of linear freight. YR's estimated net profit rate was 3%. As production and transportation each take about 1 month, YR had about 1 month for booking the liner shipping space. On Oct.17, YR inquired about two pieces of information: the container freight from China to Europe was $1082 /TEU, and the pallets needed was 0.5 TEU with a cost of $550. The shipping cost accounted for 5.5% of the contract transaction amount. When YR completed production and started ordering container space, the container shipping market underwent a dramatic change. The freight quoted the next day reached $1324 /TEU, and the pallet container freight increased to $700 with a rising trend. Due to the significant increase in container freight, the actual profit rate became 3%−(700/550−1)*5.5% = 1.5%. After investigation, we find that none of the container transportation, futures and insurance markets have provided effective risk management tools to help SMIFEs cope with the increased financial pressure caused by rising freight costs. The impact of COVID-19 further increases the need of such tools.
Indeed, with the in-depth development of digitalization and technology in various industries in recent years, fragmented insurance scenarios featured with small-amount and high-frequency transactions have an increasingly frequent presence, which is particularly the case in China. Such fragmented insurance scenarios emphasize affordable or inclusive insurance and the interests of insurance policyholders. In contrast, traditional index insurance is basically designed for risk management from the perspective of insurers (Goodrich et al., 2020, Assa and Wang, 2021), which may have adverse effects on potential policyholders, such as high premiums, low leverage and harsh underwriting conditions, and hence filter out the consumers who have actual needs.
Our proposed index microinsurance highlights market orientation and intrinsic inclusiveness. This requires that the design principles should reflect the need of both the supply side and the demand side. Consequently, we propose the four principles of index microinsurance: reliable index, adaptive structure, affordable price, and controllable risk, hereinafter referred to as the RAAC principles.
As the CFIM is designed for risk management in fragmented insurance scenarios, our proposed principles should be followed as elaborated below.
Principle 1, reliable index. The underlying index of an index insurance or index derivative should be transparent and objective. If the index information is not transparent and publicly accessible, it can be easily manipulated. When index insurance is based on an opaque index, the moral hazard will be very large (Goodrich et al., 2020). To ensure higher risk transfer efficiency and lower replication cost, the compilation of the index should aim at capturing the true trajectory of the target and reducing the underlying basis risk (Li et al., 2021). The wide application of weather index insurance highlights the need for transparency and objectivity for the underlying index (Hohl et al., 2020). Therefore, the CFIM should be built on a reliable container freight index.
Principle 2, adaptive structure. In practice, the index insurance should be designed to match the needs of potential customers for risk management including the period and exposure to be covered. Also, the term, compensation and share structures should be adaptive to the actual needs of consumers. Products tailored to customers' needs are more easily accepted by the market (Jensen et al., 2019). As for the CFIM, the structure should meet the cost transfer requirements of SMIEEs for container booking activities. More specifically, the term structure should match the most important and common term structure in the booking activities of SMIEEs. Adaptive compensation mechanisms such as hierarchical compensation and limit compensation should be designed according to whether the index is specifically triggered or not at specific frequency and severity levels. Considering the fragmentation characteristics of SMIEEs to book container space, the basic share or minimum share in a container should be set.
Principle 3, affordable price. Most index insurances belong to inclusive insurance (Institute of International Finance, 2018). Different from exchange-traded financial derivatives, there are no entrance barriers for purchasing index insurances, which makes them accessible to all kinds of consumers including SMIEEs (Assa and Wang, 2021). Fragmented risk management requires the price should be affordable, enabling potential customers to obtain risk protection or cost transfer without significantly increasing their economic burden. Therefore, it is important that the CFIM is designed in line with the current development trend of inclusiveness and minimalist of financial derivatives, and is featured with lower insurance premium rates and higher financial leverage to benefit many SMIEEs.
Principle 4, controllable risk. Index insurance is essentially a claim game around the specific frequency and severity of the fluctuation of the underlying index, which requires the insurance product to be designed under sufficient considerations of risk controls and sustainable operation (Taib and Benth, 2012, Conradt et al., 2015, Arandara et al., 2019, Leblois et al., 2020, Ceballos and Robles, 2020). The design of the CFIM should also follow this principle. For example, the key parameters used in insurance pricing, such as the volatility and triggering target price, should be set prudently. When adjusting the underwriting scale should be adjusted dynamically according to the market volatility. The safety buffer of the insurer should be sufficient to deal with extreme fluctuations in the container freight index. In brief, a well-designed insurance product can not only help SMIEEs transfer risks under different conditions but also help insurers achieve reasonable profits and commercial sustainability.
The RAAC principles of the index microinsurance design are mutually complementary and indispensable: reliable index guarantees the objectiveness of the product; adaptive structure reflects the specialized design; affordable price affirms the product inclusiveness, and controllable risk meets the risk control requirements of the insurer.
4 Design of container freight index microinsurance
In this section, we will introduce the basic framework of CFIM design and discuss about the key procedures including index selection, microinsurance structure, pricing model, and scheme selection criteria.
4.1 The basic framework
In this paper, we propose a design of the CFIM based on the RAAC principles, taking into account the needs and interests of both the insurer and SMIEEs. The design consists of the following steps. First, we select the container freight index to ensure the reliability of the developed products. Second, we design the compensation structure, term structure (including without compensation cap and with compensation cap) and basic share structure of CFIM, and the scheme set of initial microinsurance products under the combination of various parameters such as the target point of compensation and insurance amount. Third, inspired by the forward-starting arithmetic average Asian call option pricing model of Turnbull and Wakeman (1991), we set up two microinsurance pricing models of container freight index compensation without and with a cap to determine the premium rate of product schemes. Last, we construct criteria such as the average net premium rate (ANPR) per basic share and cumulative underwriting profit ratio (CUPR) to select the optimal scheme of CFIM products.
4.2 Index selection
According to the principle of reliable index, the container freight index with strong reliability should be adopted. The China Containerized Freight Index (CCFI) and Shanghai Containerized Freight Index (SCFI) compiled by the Shanghai Shipping Exchange, the Freightos Baltic Container Index (FBX) compiled by the Baltic Exchange, the World Container Freight Index (WCI) compiled by the Drewry, and the Platts Container Freight Index system of Reuters are the prevailing indices in the global maritime container transportation market. Reflecting the huge volume of container freight in the world's leading trading economy, the CCFI has become an important reference for daily operations for increasingly more participants in the global shipping market (Hsiao et al., 2014, Kim et al., 2016). The CCFI is a weekly index mainly compiled from the average price information of spot market booking for general shippers provided by 22 shipping companies with outstanding reputations and large market shares. The index covers 10 ports of departure in China including Dalian, Tianjin, Qingdao, and Shanghai, and 12 line services from Europe, the United States and Japan. The index-linked agreement and index derivatives trading based on the CCFI have led to innovative pricing and trading mode in the shipping industry. It is clear that the CCFI meets the principle of reliable index, and hence is selected as the index for the CFIM.
4.3 Microinsurance structure
The story of YR introduced above vividly illustrates the traditional and typical trading process of SMIFEs. As the forward-starting booking is conventionally adopted, SMIEEs follow the three-stage procedure: (1) Space inquiry. After taking trading orders, SMIEEs make inquiries to third-party shipping agents and container shipping companies for the liner shipping space of the transport service. (2) Booking and payment. Booking orders are used to lock the space in the container before the departure time of the order goods. Noteworthily, most of the trade orders of SMIEEs are less container load (LCL) rather than full container load (FCL). For example, the pallet of YR is only 0.5 TEU, so it belongs to LCL. (3) Freight departure. The shipment will be launched as scheduled. During the process, SMIEEs will bear the burden of additional contingent costs caused by the increase in container freights. The newly proposed CFIM is designed to solve this problem. It works as follows: if the settlement point of a container freight index in a time window before expiration is higher than the target point of the freight index agreed between the insurer and SMIEEs, the insurer will cover the related costs; otherwise, the insurer pays nothing. The term, compensation and share structures of the CFIM are elaborated below.
Following the principle of adaptive structure, we further specify the structures of the insurance term, compensation and basic share as below.
4.3.1 Term structure
The term structure of microinsurance is designed as the forward-starting mode following a conventional practice. The term of the insurance contract, denoted as T, is set to cover the period between space inquiry and freight departure, and the insurance contract officially takes into effect with the beginning of the shipping space inquiry. The insurance term can be divided into the lock-up and valuation periods: The lock-up period covers the time period before booking, during which SMIEEs mainly consult about booking shipping space and follow up on the shipping price and space of container liners without actually making the booking. The valuation period is the key period during which SMIEEs will issue booking orders and determine the actual shipping cost (see Fig. 1 ).Fig. 1 The CFIM term structure.
4.3.2 Compensation structure
For the CFIM, we consider two types of compensation structures, i.e., with and without a compensation cap.
CFIM without compensation cap. At the expiration of the microinsurance, if the settlement point of the container freight index, denoted as J, is larger than the trigger target index point agreed in advance, denoted as X, the insurer will pay the amount θ(J-X) to the SMIEEs, where θ denotes the currency conversion coefficient of the freight index. Assuming that it is equally likely for SMIEEs to determine the freights at any time in the valuation period, the arithmetic average of the freight index in the valuation period is taken as the settlement point. If the settlement point is no larger than the target point, the insurer pays nothing. The risk exposure under CFIM without a cap is boundless for the insurer.
CFIM with compensation cap. The compensation structure is the same as that of the CFIM without a compensation cap except that, when the settlement point of the container freight index is larger than the trigger target index point, the insurer shall compensate the SMIEEs for the insured amount G negotiated in advance by both parties. Under this compensation structure, the insurer is only subject to limited risk exposure.
As shown in Fig. 2 , under the compensation structure without a cap, SMIEEs can effectively transfer all additional logistics costs caused by the rise of freights to insurers, while the risk exposure of insurers is infinite. Under the compensation structure with a cap, the risk exposure of the insurer is bounded, though SMIEEs can only partially transfer the additional logistics costs.Fig. 2 Both types of compensation structures when the CFIM contract expires and settles.
4.3.3 Share structure
Standard container booking is mainly FCL or LCL. With the rapid development of cross-border e-commerce, it is more conventional for SMIEEs to adopt LCL. To meet the need of controlling transportation costs of small pallets of SMIEEs, we design the CMIF to make better use of the fragmented transport space. In practice, freight ton, the lowest charge standard, is often used for LCL. Freight ton consists of weight ton and volume ton: Weight ton is based on the gross weight of goods, with 1 metric ton as 1 freight ton, and volume ton is based on the gross volume of goods, with 1 cubic meter as 1 freight ton. If the cargo is less than 1 freight ton, it will be charged at 1 freight ton. By dividing a standard container into Υ shares (Υ = one standard container/freight ton), we decompose the FCL CFIM into the LCL CFIM.
In brief, the proposed CFIM meets the actual needs of SMIEEs. The design of the forward-starting term structure is consistent with the practice of SMIEEs, and the layered compensation structure satisfies the demand for cost transfer. In addition, the subdivision of containers suits the fragmentation characteristics of SMIEEs.
4.4 Pricing model
Based on the term, compensation and share structures, we derive the pricing model of the CFIM with and without compensation a cap. As index insurances have efficient purchase and claim settlement and very low management and transaction costs (Arandara et al., 2019, Assa and Wang, 2021), we focus on calculating the net premium per basic share of CFIM and ignore costs including sale and management expenses, shareholder returns and tax costs.
4.4.1 Net premium of CFIM without compensation cap
The net premium per basic share of the CFIM without cap, denoted Ca(X), can be expressed as follows:(1) Ca(X)=Υe-rTmax{θ(J-X),0}[0,T]=EΥθe-rTJ-X,ifJ⩾X0,ifJ⩽X[0,T],
where the settlement point of the CFIM contract is the arithmetic average of the container freight index during the valuation period, i.e., J=∑t=t1TSt/T-t1 with St being the point of the container freight index at time t and r being the pricing interest rate; X denotes the trigger target index point; θ denotes the currency conversion coefficient of the freight index.
In terms of the specific pricing model of the CFIM, we choose the derivative pricing method. Actuarial pricing and derivative pricing are the two main methods for index insurance pricing, and the choice is normally based on considerations of the subject law, insurance structure, risk control requirements, and regulatory standards. For example, actuarial methods such as the classical Burn model are often used for the pricing of weather index insurance with typical periodicity (Han, et al., 2019). For the pricing of the CFIM, we adopt the derivative pricing for the following reasons: The stable matching between supply and demand of the global container shipping market and the asymmetry of shipping information leads to large and unpredictable volatilities of the container freight index (Jeon, et al., 2021). Therefore, the derivative pricing based on second moments can be more suitable for quantifying the risk under the insurance scenario. Also, the complex product structure is more consistent with the existing exotic derivatives structure. More specifically, under the forward-starting term structure, layered compensation structure and arithmetic mean settlement point, the CFIM is precisely the forward-starting arithmetic average Asian call option.
Following the approximate analytical formula of forward-starting arithmetic average Asian call option pricing proposed by Turnbull and Wakeman (1991), the pricing model of the CFIM without a compensation cap can be rewritten as follows:(2) Ca(X)≈ΥθS0e-rTN(d1)-Xe-rTN(d2),
(3) d1=ln(S0/X)+σA2T/2σAT,d2=d1-σAT,
(4) σA=ln(M1)T,
(5) M1=2eσ2T-2eσ2t11+σ2(T-t1)σ4(T-t1),
where σ and σA are the volatility and adjusted and annualized volatility of the container freight index, and M1 is the adjusted and annualized volatility of the container freight index. S0 is the initial point of the container freight index at the time t=0; N(d1) and N(d2) denote the cumulative distribution function of the standard normal distribution valued at d1 and d2, respectively.
It is worth noting that the volatility parameter plays a key role in pricing. Setting larger pricing volatilities can lead to higher net premiums, which is beneficial for insurers but less attractive for SMIEEs. On the other hand, lower net premiums due to setting smaller volatilities can attract more SMIEEs but will be less welcomed by insurers. Therefore, sufficient consideration should be given to the choice the volatility parameter.
4.4.2 Net premium of CFIM with compensation cap
The basic share of the CFIM with compensation cap, denoted Cb(ΥG+X), belongs to the interval compensation insurance, and the pricing model can be expressed as(6) Cb(ΥG+X)=EΥθe-rTG,ifJ⩾ΥG+XJ-X,ifΥG+X⩾J⩾X0,ifJ⩽X[0,T],
where G is the insured amount and is the ceiling of indemnity, and other parameters are defined above. Following the approximation proposed by Turnbull and Wakeman (1991), Eq. (6) can be further derived as follows:(7) Cb(ΥG+X)=EΥθe-rTG,ifJ⩾ΥG+XJ-X,ifΥG+X⩾J⩾X0,ifJ<X[0,T]=EΥθe-rTJ-X,ifJ⩾X0,ifJ<X[0,T]-EΥθe-rTJ-(ΥG+X),ifJ⩾ΥG+X0,ifJ<ΥG+X[0,T]=Ca(X)-Ca(ΥG+X)=ΥθS0e-rTN(d1)-Xe-rTN(d2)-S0e-rTN(d1∗)-Xe-rTN(d2∗).
with d1∗ and d2∗ defined as(8) d1*=ln(S0/(ΥG+X))+σA2T/2σAT,d2*=d1*-σAT.
Notably, Eq. (7) becomes Eq. (2) when the insured indemnity ceiling tends to infinity, which implies that the CFIM without a cap can be viewed as a limiting case of the CFIM with a cap.
4.5 Scheme selection
For the principles of reliable index and adaptive structure, we have fully explained their roles in determining the underlying index and specific structure of CFIM. In the following, we further use the principles of affordable price and controllable risk to design relevant selection criteria so as to screen out the optimization scheme of the CFIM products.
The main challenge in designing the CFIM is to follow the principles of affordable price and controllable risk, which consider the interests of both insurers and SMIEEs. To achieve this target, we propose a set of selection criteria to choose the optimal scheme from the initial set of product schemes under various target points, extensions of pricing periods and insurance amounts.
More specifically, we use {X(i) + F(j) + G(l)} to denote the initial set of product schemes, where X(i) is the target point triggering compensation of the freight index in grade i, F(j) is the settlement point for determining whether to settle claims or not when the valuation period forward-starting is j, and G(l) is the insured indemnity ceiling in grade l. For example, in the case of insurance term T=8, F(0) is equivalent to no extension, and only the freight index point of the eighth period of the pricing period is used as the settlement point. In contrast, F(2) is equivalent to forward-starting j=2, and the arithmetic average of the freight index of the 6th, 7th and 8th valuation periods is taken as the claim settlement point. Without compensation cap, denoted G(U), can be seen as a special case of the compensation cap. X(i) and G(l) should be set by insurers and SMIEEs through negotiations according to the fluctuation range and probability of historical data of the anchored container freight index.
4.5.1 Criteria for affordable price
Many criteria have been proposed as the measure of affordable prices, such as the basic share, insurance premium rate, and the number of covered groups (Institute of International Finance, 2018), which can be used for quantifying the affordability of the CFIM from different dimensions. As the basic share is already reflected in the product structure and the number of covered groups measures business results rather than product design, the insurance premium rate best reflects affordability. In practice, insurers generally set an upper bound for index or price rates, such as 10%, above which the premium rate will be so high that the leverage effect will be greatly reduced and the product will not be welcomed by SMIEEs.
We adopt the ANPR per basic share in the historical period as follows. Assuming that the historical period can be divided into m periods, the average net premium (ANP), Pm′, and ANPR, PRm′, for the basic share of the CFIM shall meet the following conditions:(9) Pm′=∑t=0mCb,t(ΥG+X)/(m+1),
(10) PRm′=Pm′/G⩽ϕ,
where Cb,t is the net premium per basic share for week t of the historical period, and ϕ is the highest acceptable rate level. For CFIM without a compensation cap, the condition is obviously satisfied since G tends to infinity.
4.5.2 Criteria for controllable risk
The principle of controllable risk is crucial for insurers because it closely relates to the profitability and sustainability of the insurance business. In the practice of risk management, criteria including risk exposure, underwriting profit rate, comprehensive loss rate, and comprehensive cost rate are conventionally adopted. Risk exposure is mainly used for assessing the business progress rather than product design. Underwriting profit rate, comprehensive loss ratio and comprehensive cost ratio are measures of financial positions, among which underwriting profit rate is more representative. Therefore, we propose the measure of CUPR as elaborated below.
In practice, insurers set upper bounds on the total number of basic insured shares in a certain period to control the total insured amount. If the upper bound is reached, the insurer will stop selling. Continuous increase in container freights will motivate expectations for further increases and hence stimulate insurance demands; on the contrary, if the container freight continues to fall, it will lead to a reduced insurance amount. Therefore, we assume that the ratio Dt of the underwriting amount of basic shares in period t to the upper bound of the total amount of basic shares in each period is consistent with the change in the container freight index, which satisfies the condition:(11) Dt=St-minStΩmaxStΩ-minStΩ,
where {St}Ω is the data set of the container freight index during the retrospective period. For any of the X(i) + F(j) + G(l) product schemes, the actual underwriting profit, denoted Rb,t′, and CUPR, denoted Pfm′, in the retrospective period time t are calculated as follows:(12) Rb,t′=DtCb,t-DtLb,t,
(13) Pfm′=∑t=0mRb,t′/∑t=0m(DtCb,t),
wherein Lb,t is the actual loss per basic share of CFIM with the period determined by Eqs. (2), (7). The CUPR shall meet the following condition(14) 0⩽Pfm′⩽υ,
where, υ is the upper bound of the CUPR in the retrospective period. The condition indicates that the CUPR cannot be less than 0 or higher than its upper bound in the retrospective period. It provides insurers with profit protection against extreme fluctuations in the container freight market.
5 Case study
With extraordinary capacities of liner containers, the CCFI Europe Service (CCFIES) is one of the busiest container shipping lines in the world, accounting for nearly €600 billion of annual merchandise trade. Therefore, we illustrate the design and scheme optimization process of the CFIM with the CCFIES index.
5.1 Data
The data covers the period from Mar. 14, 2003, to Aug. 13, 2021, consisting of 935 weeks, as shown in Fig. 3 . Data were collected from the iFinD database provided by Tong Hua Shun, a major financial data service company in China. The time interval from Mar. 14, 2003, to Dec. 25, 2020, (902 weeks) is taken as the retrospective period, and the time interval from Jan. 1, 2021, to Aug. 13, 2021, (33 weeks) is used to find the optimal schemes (shown as the pink area in Fig. 3). Due to the impact of the COVID-19 pandemic, the CCFIES index rose from 2318.13 on Jan. 1 to 5165.62 on Aug. 13, 2021, the period of which is very suitable for illustrating the optimization procedure under extreme scenarios. We chose the data for two reasons: First, the CCFI and its sub-line index were compiled by Shanghai Shipping Exchange in 1998. After the implementation of the new and improved data collection rules on Mar. 14, 2003, the index was gradually accepted by the global container shipping market and became the benchmark and authoritative container freight index in the world, which satisfies the reliable index principle of our product design. Second, product design and optimization scheme screening can effectively demonstrate their robustness, after a long period of inspection of the complete shipping cycle of 5–15 years (Stopford, 2008). In particular, the robustness of the product design and the selection procedure for the optimal product schemes can be tested in the scenario of severe fluctuation of freights in 2020.Fig. 3 The CCFIES index.
Table 1 exhibits the descriptive statistics. As can be seen, the weekly volatility of the CCFIES index is 2.98%, and the corresponding annualized volatility is 21.49% (2.98%×52), which indicates that the CCFIES index is quite volatile.Table 1 Descriptive statistics of the CCFIES index (from Mar. 14, 2003, to Aug. 13, 2021).
Max. Min. Mean Std. Dev. Skewness Kurtosis
CCFIES Index 5230.11 625.12 1365.34 536.53 3.41 17.15
Return 22.19% −9.39% 0.20% 2.98% 1.58 7.77
Note: Std. Dev. stand for Standard Deviation.
5.2 Parameters setting
The initial product scheme set of the basic share CCFIES index microinsurance, { X(i) + F(j) + G(l)}, is designed as follows:(1) Set the insurance term to 8 weeks. In the CCFIES, the term of the container booking activities for SMIEEs is generally 8 weeks or 2 months.
(2) Set the settlement point to 8 levels. The forward-starting term j of the valuation period is divided into 8 weeks, which corresponds to the last (j+1) week for j=0,1,⋯,7, respectively. The corresponding claim settlement points are the arithmetic mean of the weekly index points of the last (j+1), denoted as F(j).
(3) Set the target index point triggering compensation into 5 grades, i.e.,X(i) (i= 0, 50, 100, 150, 200), based on the CCFIES of the current week. More specifically, X(0) is the trigger point of the compensation, which is the CCFIES index point of the current week, X(50) is the trigger point of the CCFIES index of the current week plus 50 points, and X(i) (i= 100, 150, 200) are defined analogously. The grades are determined according to the following procedure: Firstly, the CCFIES index settlement point minus the target points from the settlement points between Mar. 14, 2003, and Dec. 25, 2020, to obtain the corresponding weekly change points ΔF(j), i.e., F(j)-X(0)=ΔF(j). Secondly, the ΔF(j) sequence is sorted from small to large, and the points of change are taken according to the percentile values of 50%, 75%, 85%, 90%, and 95%. Thirdly, the arithmetic average of ΔF(j) is calculated. Taking 50 points as the basic unit, the trigger point X(i) in 5 grades is determined following the nearest neighbor principle. It can be seen from Table 2 that the 200-point target has covered 95% of the historical uptrend, which indicates that the setting of 5 grades is appropriate.Table 2 Summary of the changes in target points.
Percentile ΔF(0) ΔF(1) ΔF(2) ΔF(3) ΔF(4) ΔF(5) ΔF(6) ΔF(7) Mean ΔF(j) Target point
50% 2 2 2 2 1 1 −1 1 1 0
75% 57 54 50 49 45 41 38 36 46 50
85% 106 101 95 91 82 77 70 65 86 100
90% 154 147 142 136 121 114 104 95 127 150
95% 253 234 212 207 189 182 150 153 197 200
96% 307 288 261 241 219 197 188 166 233 250
97% 364 319 311 289 272 254 215 197 278 300
98% 398 381 382 353 318 293 267 250 330 350
(4) Set the basic share Υ=10. The CCFIES line mainly takes the TEU, which can be divided into 20 freight tons. Following the industrial rule that the minimum LCL booking is 2 freight tons, we take 10 as the basic share.
(5) Set the insured amount per basic share G(l). The G(l) with compensation cap is set through the following two steps. First, we obtain the arithmetic average of the weekly change point values ΔF(j), which corresponds to the 96%, 97%, and 98% percentiles of the historical period following the method of setting the trigger compensation, which leads to the gear point 250, 300, and 350 as shown in Table 2. Then, we set the insured amount by quantifying the risk of the freight index settlement point breaking through the trigger compensation X(i) above. To be more precise, for example, the highest target point triggering compensation X(200), continues to move 250–200 = 50, 300–200 = 100 and 350–200 = 150 points higher, again covering 96%, 97% and 98% of historical weekly change point values ΔF(j), respectively. Correspondingly, the insured amount per basic share can be set into 3 levels of 50/10 = 5, 100/10 = 10 and 150/10 = 15 points, namely, G(l) (l= 5, 10, 15). Similarly, for X(i) (i= 50, 100, 150), G(l) (l= 5, 10, 15) ensures that 95% or more of the historical uptrend is covered. Specially, for X(0), we add the gear point 200, ensuring that it covered 95% of the historical uptrend. Consequently, the insured amount at the trigger point of the compensation is set into 4 grades, i.e.,G(l) (l= 5, 10, 15, 20). In addition, the insured amount without compensation cap is recorded as G(U).
(6) Set the point value currency conversion coefficient to $1 per point, i.e., θ=1.
Table 3 shows the details of the initial product scheme set of the CCFIES index microinsurance, with a total of 176 product schemes.Table 3 The initial product scheme set of the CCFIES index microinsurance.
Component Setting
Underlying index The CCFIES index
Insurance period T 8 weeks (2 months)
Target point X(i) 5 levels, i=0,50,100,150,200 points up, based on the CCFIES for the week
Settlement point F(j) The arithmetic average value of the weekly index points of last (j+1) week, for j=0,1,⋯,7
Point value currency conversion coefficient θ $1 per point or equivalently θ=1
Insured amount G(l) For X(0), G(l) is divided into 4 grades, with 5, 10, 15, and 20 points; for X(i) (i= 50, 100, 150, 200) are divided into 3 grades, with 5, 10 and 15 points. Without compensation cap, G(U).
The settings of the other relevant parameters are described as follows:(1) Volatility. The pricing volatility, σ, is set based on the CCFIES index in the retrospective period. First, the initial and annualized volatility series are calculated based on historical volatility with a window of 8 weeks, which is consistent with the insurance term of the CFIM. Second, the quantiles at the level of 95% and 99% are taken as the pricing volatility σ. Third, the annualized volatility series σA of the different valuation periods can be obtained via Eq. (4). Fig. 4 shows the volatility trend at the 99% level. The volatility trend at the 95% level presents a similar pattern.Fig. 4 8-weekly CCFIES index adjusted annual volatility at the level of 99%.
(2) Pricing interest rate. Since the insurance term is very short, we assume a fixed annualized interest rate of r=3%.
(3) Premium rate cap. Following practice, the upper bound of the ANPR for all product schemes is set to be 10%, leading to an insurance leverage ratio of 1/10% = 10. This guarantees that the premium rates are not too high to be acceptable for SMIEEs, while insurers have sufficient risk control for sustainable operation.
(4) Underwriting profit ratio cap. The retrospective period covers a span of nearly 18 years. Considering the current property insurance market in China, it seems reasonable to limit the average annual premium underwriting profit ratio of the CFIM to 2%. Then, by Eq. (14), the CUPR is capped at 36% while satisfying Pfm′>0. Otherwise, the insurer’s long-term loss will not be in accordance with commercial insurance essentials.
5.3 Premium calculation
The net premium per basic share of the CCFIES index microinsurance is calculated via Eqs. (2), (7). Table 4, Table 5 show the net premium per basic share with the descriptive statistics for all product schemes under the volatility levels of 99% and 95%, respectively. As can be seen from Table 4, Table 5, more conservative pricing volatility leads to higher premiums regardless of the setting of the compensation cap. Also, under the same conditions, the product scheme with a compensation cap has a lower premium than the product scheme without a compensation cap. Fig. 5 shows the net premiums per basic share for the product schemes of {X(50) + F(0, 1, 2, 3, 4, 5, 6, 7) + G(10, 15)} at volatility level 99%. As can be seen, the premium has large variations between $0 and $2.50.Table 4 Weekly ANP per basic share at a volatility level of 99%.
Schemes ANP Max. Min. Std. Dev. Schemes ANP Max. Min. Std. Dev. Schemes ANP Max. Min. Std. Dev.
X(0) + F(0) + G(U) 3.45 57.50 16.80 7.95 X(0) + F(0) + G(5) 1.85 20.90 14.00 1.32 X(50) + F(0) + G(10) 1.35 24.20 2.79 4.58
X(0) + F(1) + G(U) 3.31 55.10 16.10 7.61 X(0) + F(1) + G(5) 1.83 20.70 13.70 1.36 X(50) + F(1) + G(10) 1.27 23.40 2.42 4.50
X(0) + F(2) + G(U) 3.15 52.50 15.40 7.25 X(0) + F(2) + G(5) 1.80 20.60 13.30 1.41 X(50) + F(2) + G(10) 1.18 22.50 2.05 4.38
X(0) + F(3) + G(U) 2.99 49.80 14.60 6.88 X(0) + F(3) + G(5) 1.78 20.40 12.90 1.47 X(50) + F(3) + G(10) 1.08 21.40 1.69 4.24
X(0) + F(4) + G(U) 2.82 47.00 13.70 6.49 X(0) + F(4) + G(5) 1.74 20.20 12.40 1.53 X(50) + F(4) + G(10) 0.98 20.20 1.34 4.05
X(0) + F(5) + G(U) 2.64 43.90 12.90 6.07 X(0) + F(5) + G(5) 1.71 20.00 11.80 1.60 X(50) + F(5) + G(10) 0.86 18.90 1.01 3.81
X(0) + F(6) + G(U) 2.44 40.70 11.90 5.62 X(0) + F(6) + G(5) 1.66 19.70 11.20 1.68 X(50) + F(6) + G(10) 0.74 17.20 0.71 3.51
X(0) + F(7) + G(U) 2.23 37.10 10.90 5.13 X(0) + F(7) + G(5) 1.60 19.30 10.40 1.77 X(50) + F(7) + G(10) 0.61 15.30 0.45 3.12
X(50) + F(0) + G(U) 1.61 36.70 2.80 6.67 X(0) + F(0) + G(10) 2.79 35.50 16.60 3.84 X(50) + F(0) + G(15) 1.51 30.10 2.80 5.76
X(50) + F(1) + G(U) 1.48 34.30 2.42 6.29 X(0) + F(1) + G(10) 2.72 35.00 15.90 3.89 X(50) + F(1) + G(15) 1.41 28.80 2.42 5.56
X(50) + F(2) + G(U) 1.35 31.90 2.05 5.89 X(0) + F(2) + G(10) 2.65 34.50 15.20 3.94 X(50) + F(2) + G(15) 1.29 27.30 2.05 5.31
X(50) + F(3) + G(U) 1.21 29.40 1.69 5.46 X(0) + F(3) + G(10) 2.57 33.90 14.50 3.98 X(50) + F(3) + G(15) 1.18 25.70 1.69 5.03
X(50) + F(4) + G(U) 1.08 26.70 1.34 5.01 X(0) + F(4) + G(10) 2.47 33.10 13.70 4.01 X(50) + F(4) + G(15) 1.05 24.00 1.34 4.71
X(50) + F(5) + G(U) 0.93 23.90 1.01 4.52 X(0) + F(5) + G(10) 2.36 32.30 12.80 4.03 X(50) + F(5) + G(15) 0.92 22.00 1.01 4.32
X(50) + F(6) + G(U) 0.79 21.00 0.71 3.99 X(0) + F(6) + G(10) 2.24 31.20 11.90 4.02 X(50) + F(6) + G(15) 0.78 19.70 0.71 3.88
X(50) + F(7) + G(U) 0.63 17.80 0.45 3.41 X(0) + F(7) + G(10) 2.09 29.90 10.90 3.96 X(50) + F(7) + G(15) 0.63 17.10 0.45 3.36
X(100) + F(0) + G(U) 0.67 22.00 0.23 4.20 X(0) + F(0) + G(15) 3.20 45.10 16.80 5.89 X(100) + F(0) + G(5) 0.41 9.58 0.22 2.07
X(100) + F(1) + G(U) 0.59 20.00 0.17 3.81 X(0) + F(1) + G(15) 3.09 44.20 16.10 5.84 X(100) + F(1) + G(5) 0.37 9.11 0.16 1.97
X(100) + F(2) + G(U) 0.51 18.00 0.11 3.41 X(0) + F(2) + G(15) 2.98 43.10 15.40 5.78 X(100) + F(2) + G(5) 0.33 8.59 0.11 1.86
X(100) + F(3) + G(U) 0.43 15.90 0.07 2.99 X(0) + F(3) + G(15) 2.86 41.90 14.60 5.68 X(100) + F(3) + G(5) 0.29 8.00 0.07 1.73
X(100) + F(4) + G(U) 0.35 13.80 0.04 2.57 X(0) + F(4) + G(15) 2.72 40.50 13.70 5.56 X(100) + F(4) + G(5) 0.25 7.34 0.04 1.58
X(100) + F(5) + G(U) 0.27 11.70 0.02 2.13 X(0) + F(5) + G(15) 2.57 38.90 12.90 5.38 X(100) + F(5) + G(5) 0.20 6.59 0.02 1.39
X(100) + F(6) + G(U) 0.20 9.48 0.01 1.69 X(0) + F(6) + G(15) 2.40 36.90 11.90 5.16 X(100) + F(6) + G(5) 0.16 5.74 0.01 1.18
X(100) + F(7) + G(U) 0.14 7.28 0.00 1.24 X(0) + F(7) + G(15) 2.20 34.60 10.90 4.85 X(100) + F(7) + G(5) 0.11 4.76 0.00 0.93
X(150) + F(0) + G(U) 0.26 12.40 0.01 2.17 X(0) + F(0) + G(20) 3.36 50.90 16.80 7.05 X(100) + F(0) + G(10) 0.58 15.40 0.23 3.26
X(150) + F(1) + G(U) 0.21 10.90 0.01 1.87 X(0) + F(1) + G(20) 3.24 49.50 16.10 6.89 X(100) + F(1) + G(10) 0.51 14.50 0.17 3.05
X(150) + F(2) + G(U) 0.17 9.41 0.00 1.58 X(0) + F(2) + G(20) 3.10 47.90 15.40 6.69 X(100) + F(2) + G(10) 0.45 13.40 0.11 2.81
X(150) + F(3) + G(U) 0.13 7.93 0.00 1.29 X(0) + F(3) + G(20) 2.95 46.20 14.60 6.47 X(100) + F(3) + G(10) 0.39 12.30 0.07 2.55
X(150) + F(4) + G(U) 0.10 6.48 0.00 1.02 X(0) + F(4) + G(20) 2.79 44.20 13.70 6.20 X(100) + F(4) + G(10) 0.32 11.10 0.04 2.25
X(150) + F(5) + G(U) 0.07 5.08 0.00 0.76 X(0) + F(5) + G(20) 2.62 42.00 12.90 5.88 X(100) + F(5) + G(10) 0.26 9.70 0.02 1.92
X(150) + F(6) + G(U) 0.04 3.74 0.00 0.52 X(0) + F(6) + G(20) 2.43 39.40 11.90 5.51 X(100) + F(6) + G(10) 0.20 8.19 0.01 1.56
X(150) + F(7) + G(U) 0.03 2.52 0.00 0.32 X(0) + F(7) + G(20) 2.23 36.40 10.90 5.08 X(100) + F(7) + G(10) 0.14 6.54 0.00 1.18
X(200) + F(0) + G(U) 0.09 6.58 0.00 0.98 X(50) + F(0) + G(5) 0.94 14.70 2.57 2.53 X(150) + F(0) + G(5) 0.16 5.83 0.01 1.21
X(200) + F(1) + G(U) 0.07 5.55 0.00 0.80 X(50) + F(1) + G(5) 0.89 14.30 2.26 2.54 X(150) + F(1) + G(5) 0.14 5.36 0.01 1.09
X(200) + F(2) + G(U) 0.05 4.56 0.00 0.63 X(50) + F(2) + G(5) 0.84 13.90 1.94 2.54 X(150) + F(2) + G(5) 0.12 4.85 0.00 0.96
X(200) + F(3) + G(U) 0.04 3.62 0.00 0.48 X(50) + F(3) + G(5) 0.79 13.40 1.62 2.52 X(150) + F(3) + G(5) 0.10 4.31 0.00 0.83
X(200) + F(4) + G(U) 0.03 2.75 0.00 0.34 X(50) + F(4) + G(5) 0.73 12.90 1.30 2.49 X(150) + F(4) + G(5) 0.07 3.73 0.00 0.68
X(200) + F(5) + G(U) 0.02 1.97 0.00 0.22 X(50) + F(5) + G(5) 0.66 12.30 0.99 2.44 X(150) + F(5) + G(5) 0.05 3.11 0.00 0.54
X(200) + F(6) + G(U) 0.01 1.29 0.00 0.13 X(50) + F(6) + G(5) 0.58 11.50 0.70 2.35 X(150) + F(6) + G(5) 0.04 2.45 0.00 0.39
X(200) + F(7) + G(U) 0.00 0.73 0.00 0.07 X(50) + F(7) + G(5) 0.49 10.50 0.45 2.21 X(150) + F(7) + G(5) 0.02 1.78 0.00 0.26
Table 5 Weekly ANP per basic share at volatility level of 95%.
Schemes ANP Max. Min. Std. Dev. Schemes ANP Max. Min. Std. Dev. Schemes ANP Max. Min. Std. Dev.
X(0) + F(0) + G(U) 2.65 44.10 12.90 6.10 X(0) + F(0) + G(5) 1.71 20.00 11.90 1.60 X(50) + F(0) + G(10) 0.87 19.00 1.03 3.83
X(0) + F(1) + G(U) 2.54 42.30 12.40 5.84 X(0) + F(1) + G(5) 1.68 19.90 11.50 1.64 X(50) + F(1) + G(10) 0.80 18.00 0.85 3.66
X(0) + F(2) + G(U) 2.42 40.30 11.80 5.57 X(0) + F(2) + G(5) 1.65 19.70 11.10 1.69 X(50) + F(2) + G(10) 0.73 17.00 0.68 3.47
X(0) + F(3) + G(U) 2.30 38.20 11.20 5.28 X(0) + F(3) + G(5) 1.62 19.50 10.70 1.74 X(50) + F(3) + G(10) 0.65 15.90 0.52 3.25
X(0) + F(4) + G(U) 2.17 36.00 10.50 4.98 X(0) + F(4) + G(5) 1.58 19.20 10.20 1.80 X(50) + F(4) + G(10) 0.57 14.70 0.38 2.99
X(0) + F(5) + G(U) 2.03 33.70 9.86 4.66 X(0) + F(5) + G(5) 1.53 18.90 9.60 1.86 X(50) + F(5) + G(10) 0.48 13.30 0.26 2.70
X(0) + F(6) + G(U) 1.88 31.20 9.13 4.31 X(0) + F(6) + G(5) 1.47 18.50 8.97 1.92 X(50) + F(6) + G(10) 0.40 11.70 0.16 2.36
X(0) + F(7) + G(U) 1.71 28.50 8.33 3.94 X(0) + F(7) + G(5) 1.40 18.00 8.25 1.98 X(50) + F(7) + G(10) 0.31 9.93 0.08 1.97
X(50) + F(0) + G(U) 0.94 24.10 1.03 4.55 X(0) + F(0) + G(10) 2.37 32.30 12.90 4.03 X(50) + F(0) + G(15) 0.93 22.10 1.03 4.35
X(50) + F(1) + G(U) 0.86 22.40 0.85 4.25 X(0) + F(1) + G(10) 2.30 31.70 12.40 4.03 X(50) + F(1) + G(15) 0.85 20.80 0.85 4.10
X(50) + F(2) + G(U) 0.77 20.60 0.68 3.93 X(0) + F(2) + G(10) 2.22 31.10 11.80 4.01 X(50) + F(2) + G(15) 0.76 19.40 0.68 3.82
X(50) + F(3) + G(U) 0.68 18.80 0.52 3.59 X(0) + F(3) + G(10) 2.14 30.30 11.20 3.98 X(50) + F(3) + G(15) 0.67 17.90 0.52 3.52
X(50) + F(4) + G(U) 0.59 16.90 0.38 3.23 X(0) + F(4) + G(10) 2.04 29.40 10.50 3.93 X(50) + F(4) + G(15) 0.59 16.30 0.38 3.19
X(50) + F(5) + G(U) 0.50 14.90 0.26 2.85 X(0) + F(5) + G(10) 1.94 28.40 9.86 3.85 X(50) + F(5) + G(15) 0.49 14.50 0.26 2.83
X(50) + F(6) + G(U) 0.40 12.70 0.16 2.45 X(0) + F(6) + G(10) 1.81 27.10 9.13 3.74 X(50) + F(6) + G(15) 0.40 12.50 0.16 2.44
X(50) + F(7) + G(U) 0.31 10.50 0.08 2.01 X(0) + F(7) + G(10) 1.67 25.60 8.33 3.56 X(50) + F(7) + G(15) 0.31 10.40 0.08 2.01
X(100) + F(0) + G(U) 0.28 11.80 0.02 2.16 X(0) + F(0) + G(15) 2.58 39.00 12.90 5.40 X(100) + F(0) + G(5) 0.21 6.65 0.02 1.41
X(100) + F(1) + G(U) 0.24 10.50 0.01 1.90 X(0) + F(1) + G(15) 2.48 37.90 12.40 5.27 X(100) + F(1) + G(5) 0.18 6.16 0.01 1.29
X(100) + F(2) + G(U) 0.20 9.24 0.01 1.64 X(0) + F(2) + G(15) 2.38 36.70 11.80 5.13 X(100) + F(2) + G(5) 0.15 5.64 0.01 1.15
X(100) + F(3) + G(U) 0.16 7.94 0.00 1.38 X(0) + F(3) + G(15) 2.27 35.40 11.20 4.95 X(100) + F(3) + G(5) 0.13 5.07 0.00 1.01
X(100) + F(4) + G(U) 0.12 6.65 0.00 1.12 X(0) + F(4) + G(15) 2.14 33.90 10.50 4.75 X(100) + F(4) + G(5) 0.10 4.46 0.00 0.86
X(100) + F(5) + G(U) 0.09 5.36 0.00 0.87 X(0) + F(5) + G(15) 2.01 32.10 9.86 4.51 X(100) + F(5) + G(5) 0.08 3.79 0.00 0.70
X(100) + F(6) + G(U) 0.06 4.11 0.00 0.63 X(0) + F(6) + G(15) 1.87 30.20 9.13 4.23 X(100) + F(6) + G(5) 0.05 3.08 0.00 0.53
X(100) + F(7) + G(U) 0.04 2.92 0.00 0.42 X(0) + F(7) + G(15) 1.71 27.90 8.33 3.90 X(100) + F(7) + G(5) 0.03 2.32 0.00 0.37
X(150) + F(0) + G(U) 0.07 5.17 0.00 0.77 X(0) + F(0) + G(20) 2.63 42.10 12.90 5.91 X(100) + F(0) + G(10) 0.26 9.80 0.02 1.95
X(150) + F(1) + G(U) 0.06 4.37 0.00 0.63 X(0) + F(1) + G(20) 2.53 40.70 12.40 5.70 X(100) + F(1) + G(10) 0.23 8.93 0.01 1.74
X(150) + F(2) + G(U) 0.04 3.60 0.00 0.50 X(0) + F(2) + G(20) 2.41 39.10 11.80 5.47 X(100) + F(2) + G(10) 0.19 8.02 0.01 1.52
X(150) + F(3) + G(U) 0.03 2.87 0.00 0.38 X(0) + F(3) + G(20) 2.29 37.30 11.20 5.22 X(100) + F(3) + G(10) 0.15 7.06 0.00 1.30
X(150) + F(4) + G(U) 0.02 2.19 0.00 0.27 X(0) + F(4) + G(20) 2.16 35.40 10.50 4.94 X(100) + F(4) + G(10) 0.12 6.04 0.00 1.07
X(150) + F(5) + G(U) 0.01 1.57 0.00 0.18 X(0) + F(5) + G(20) 2.02 33.30 9.86 4.64 X(100) + F(5) + G(10) 0.09 4.99 0.00 0.84
X(150) + F(6) + G(U) 0.01 1.03 0.00 0.11 X(0) + F(6) + G(20) 1.87 31.00 9.13 4.30 X(100) + F(6) + G(10) 0.06 3.91 0.00 0.62
X(150) + F(7) + G(U) 0.00 0.60 0.00 0.05 X(0) + F(7) + G(20) 1.71 28.40 8.33 3.93 X(100) + F(7) + G(10) 0.04 2.83 0.00 0.41
X(200) + F(0) + G(U) 0.02 2.02 0.00 0.23 X(50) + F(0) + G(5) 0.66 12.30 1.01 2.44 X(150) + F(0) + G(5) 0.06 3.15 0.00 0.55
X(200) + F(1) + G(U) 0.01 1.60 0.00 0.17 X(50) + F(1) + G(5) 0.62 11.90 0.84 2.40 X(150) + F(1) + G(5) 0.04 2.77 0.00 0.46
X(200) + F(2) + G(U) 0.01 1.22 0.00 0.12 X(50) + F(2) + G(5) 0.57 11.40 0.67 2.34 X(150) + F(2) + G(5) 0.03 2.38 0.00 0.38
X(200) + F(3) + G(U) 0.01 0.89 0.00 0.08 X(50) + F(3) + G(5) 0.52 10.80 0.52 2.26 X(150) + F(3) + G(5) 0.03 1.99 0.00 0.30
X(200) + F(4) + G(U) 0.00 0.60 0.00 0.05 X(50) + F(4) + G(5) 0.47 10.20 0.38 2.15 X(150) + F(4) + G(5) 0.02 1.59 0.00 0.22
X(200) + F(5) + G(U) 0.00 0.37 0.00 0.03 X(50) + F(5) + G(5) 0.41 9.49 0.26 2.02 X(150) + F(5) + G(5) 0.01 1.20 0.00 0.15
X(200) + F(6) + G(U) 0.00 0.20 0.00 0.01 X(50) + F(6) + G(5) 0.34 8.63 0.16 1.84 X(150) + F(6) + G(5) 0.01 0.83 0.00 0.09
X(200) + F(7) + G(U) 0.00 0.09 0.00 0.01 X(50) + F(7) + G(5) 0.27 7.61 0.08 1.62 X(150) + F(7) + G(5) 0.00 0.51 0.00 0.05
Fig. 5 Weekly net premium per basic share of CCFIES index microinsurance at a volatility level of 99%.
5.4 Scheme selection
We adopt the ANPR and CUPR to find the optimal product scheme. The period from Mar. 14, 2003, to Dec. 25, 2020, is still selected. Fig. 6 shows the distribution curve of the total number of weekly underwriting basic shares of the CCFIES index microinsurance calculated via Eq. (11) for the retrospective period. It can be seen that the weekly underwriting volumes are in line with the changing trend of the CCFIES index.Fig. 6 Weekly coverage distribution of CCFIES index microinsurance during the retrospective period.
Fig. 7 shows the weekly underwriting profits of the CCFIES index microinsurance over the retrospective period calculated via Eq. (12) at the volatility level of 99%. As can be seen, the profit series presents typical patterns of large aggregate loss, which is particularly observable in the years 2007, 2009 and 2012. This means that the CCFIES index microinsurance requires earnings from other periods to cover the losses, which also highlights the importance of weekly underwriting caps for insurers.Fig. 7 Weekly underwriting profit of CCFIES index microinsurance at the volatility level of 99%.
Fig. 8, Fig. 9 show the ANP, ANPR and CUPR at the volatility level of 99% under the conditions of with and without a compensation cap. We can see that all product schemes without compensation cap suffer from loss and hence are dropped from the selection. In contrast, the product schemes with a compensation cap of {X(50) + F(0, 1, 2, 3, 4, 5, 6, 7) + G(5, 10, 15)} and {X(100) + F(0, 1) + G(5)} meet the requirement of all three criteria. Taking X(50) + F(6) + G(15) as an example, the ANP is 0.78, the ANPR is 5.16%, which is 10% below the upper bound, and the CUPR is 19.72%, which is in the 0–36% range.Fig. 8 ANP and CUPR without compensation cap at volatility level of 99%.
Fig. 9 ANP, ANPR and CUPR with compensation cap at volatility level of 99%.
Analogously, we also calculate the criteria for all product schemes at the volatility level of 95%, as shown in Fig. 10, Fig. 11 . Again, the product schemes without a compensation cap all suffer loss. On the other hand, only the CUPRs of the product schemes of {X(0) + F(0, 1, 2, 3, 4, 5, 6, 7) + G(5)} and {X(0) + F(0, 1, 2, 3, 4) + G(10)} are greater than zero. Meanwhile, the ANPRs of these product schemes are more than 20%, which is obviously unattractive for SMIEEs. In brief, SMIEEs and insurers have more options under the setting of higher volatility levels.Fig. 10 ANP and CUPR without compensation cap at volatility level of 95%.
Fig. 11 ANP, ANPR and CUPR with compensation cap at volatility level of 95 %.
Based on the criterion tests for the retrospective period, we select 12 product schemes at a volatility level of 99%, i.e.,{X(50) + F(4, 5, 6, 7) + G(10), X(50) + F(0, 1, 2, 3, 4, 5, 6, 7) + G(15)}, as summarized in Table 6 . These product schemes have good performance even in extreme scenarios, especially during the booms of the global container shipping market in 2007, 2009 and 2012. The selected product schemes are featured with relatively low ANPR, and the ANPR is no more than 10%, providing high insurance leverage for SMIEEs. Meanwhile, the CUPRs of these product schemes are more than 0 and less than 36%, which means insurers can effectively make cross-term and cross-enterprise risk diversifications.Table 6 The performance of selected product schemes in the retrospective period.
Schemes Proposed model Burn model
ANP ANPR CUPR ANP ANPR CUPR
X(50) + F(4) + G(10) 0.98 9.73% 34.23% 1.25 12.5 % 29.43%
X(50) + F(5) + G(10) 0.86 8.60% 32.20% 1.15 11.52% 29.53%
X(50) + F(6) + G(10) 0.74 7.38% 29.82% 1.00 10.04% 30.88%
X(50) + F(7) + G(10) 0.61 6.05% 25.65% 0.93 9.30% 30.04%
X(50) + F(0) + G(15) 1.51 10.00% 39.84% 1.95 12.99% 30.13%
X(50) + F(1) + G(15) 1.41 9.35% 38.54% 1.85 12.36% 30.29%
X(50) + F(2) + G(15) 1.29 8.61% 36.38% 1.75 11.68% 30.20%
X(50) + F(3) + G(15) 1.18 7.82% 33.37% 1.65 10.97% 30.11%
X(50) + F(4) + G(15) 1.05 6.98% 30.21% 1.53 10.18% 30.13%
X(50) + F(5) + G(15) 0.92 6.09% 25.25% 1.40 9.35% 30.13%
X(50) + F(6) + G(15) 0.78 5.16% 19.72% 1.21 8.04% 32.05%
X(50) + F(7) + G(15) 0.63 4.18% 9.79% 1.11 7.43% 30.59%
We revisit the YR order to see how insurance can make a difference. If YR purchased a 2-month term insurance and selected the X(50) + F(3) + G(15) product scheme On Oct.17, 2020. The ANP on the corresponding day was $0.31. So YR needed to pay a premium of 5 * 0.31 = $1.55. When the CFIM product was matured, YR would receive an insurance payout of $63.43. This reduced the loss of YR caused by freight increase.
5.5 Discussions
We use the classical Burn model (Han, et al., 2019) to analyze the selected product scheme of the CFIM. The Burn model assumes that the probability distribution of future losses is consistent with historical experience and takes the expectation based on historical data as the optimal estimate of the net premium. The pricing of the CFIM under the Burn model is as follows: We set the rolling window of periods to be 1 year (48 weeks). Then we obtain the new sequence of the change points, i.e., the difference between the settlement point and the target points, based on the CCFIES index for each time window. Finally, we calculate the average of the new sequence points that are larger than 0, which is the weekly net premium.
We use the Burn model to calculate the ANP, ANPR and CUPR per basic share for the selected product schemes, i.e.,{X(50) + F(4, 5, 6, 7) + G(10), X(50) + F(0, 1, 2, 3, 4, 5, 6, 7) + G(15)}, as shown in Table 6. As can be seen, under the proposed model, the insurance leverage of the proposed model is significantly higher. Furthermore, the ANPRs calculated by the proposed model are less than 10%, while those of the Burn model can be above 10%. Also, the CUPR decreases with the increase in the valuation period, while the change under the Burn model is not relatively small, within a level of approximately 30%.
We further test the robustness of the selected product schemes during the boom of the CCFIES index between Jan. 1 and Aug. 13, 2021. For consistency, we assume that the weekly insurance volume is based on the maximum of the total number of basic shares. The test results are shown in Table 7 . It can be seen that, during the testing period, the CUPRs of the selected product schemes drop by nearly 10% compared with the backdating historical period. In response to the rapid increase in the CCFIES index during the testing period, the calculation in our model shows that the ANPR and net premium also increase adaptively. In contrast, as shown in Table 7, although the ANPRs and ANPs under the Burn model also increase accordingly, the CUPR drops by more than 20%, which can be unacceptable for insurers.Table 7 The Performance of selected schemes in the testing period from Mar. 14, 2003, to Aug. 13, 2021.
Schemes Proposed model Burn model
ANP ANPR CUPR ANP ANPR CUPR
X(50) + F(4) + G(10) 1.05 10.47% 24.95% 1.36 13.59% 5.00%
X(50) + F(5) + G(10) 0.93 9.34% 21.55% 1.26 12.55% 2.95%
X(50) + F(6) + G(10) 0.81 8.11% 17.03% 1.11 11.12% 1.19%
X(50) + F(7) + G(10) 0.68 6.77% 7.23% 1.03 10.29% −2.10%
X(50) + F(0) + G(15) 1.62 10.77% 39.99% 2.12 14.12% 8.19%
X(50) + F(1) + G(15) 1.51 10.06% 38.07% 2.03 13.50% 6.22%
X(50) + F(2) + G(15) 1.40 9.31% 35.24% 1.92 12.80% 4.60%
X(50) + F(3) + G(15) 1.28 8.52% 29.94% 1.81 12.06% 2.81%
X(50) + F(4) + G(15) 1.15 7.67% 26.07% 1.69 11.24% 0.81%
X(50) + F(5) + G(15) 1.02 6.77% 17.48% 1.56 10.38% −1.52%
X(50) + F(6) + G(15) 0.87 5.82% 10.32% 1.37 9.14% −3.37%
X(50) + F(7) + G(15) 0.72 4.80% −3.16% 1.26 8.42% −7.23%
To sum up, it is found that the forward-starting insurance with the target point, limited payouts and conservative pricing meets the needs of the CFIM. For example, under extreme conditions, the selected product scheme X(50) + F(6) + G(15), maintains reasonable ANPR (5.82%) and CUPR (10.32%).
The screened product solutions have important implications for relevant stakeholders. SMIEEs can make full use of the CFIM to transfer the cost of freight increase, especially under the scenario of rapid increases in container freight. In this process, SMIEEs need to determine the insurance period, basic share quantity and acceptable premium according to the trade order period, freight volume and their financial capacity. Then, a specific product will be selected from the specifically selected product scheme for insurance to lock in the additional or increased freight cost. Insurers also need to dynamically adjust these optimized product plans to ensure that the attractiveness of products and risks are controllable. In addition, CFIM is a kind of green box policy financial instrument to support import and export trade. The government departments of foreign trade or commerce could provide certain financial premium subsidies and other policy support to continuously expand the coverage of CFIM.
6 Conclusions
SMIEEs have long faced the challenge of additional costs caused by container freight fluctuations under fragmented transportation. The impact of COVID-19 has further added to the costs of fragmented space booking activities. In this study, we propose a design of the CFIM and illustrate the framework with the application to the CCFIES index. First, according to the market demand and inclusion, we put forward four basic principles framework for the design of index microinsurance, which is used as the benchmark for CFIM designs. Second, considering the characteristics of actual booking activities, we analyze the term, compensation and share structures of the insurance product, and design two basic share products of the CFIM without and with the compensation cap. Third, with reference to the forward-starting arithmetic average Asian call option pricing model, we establish the dynamic pricing models for the product under various settings, which allows dynamic calculation and adjustment of the net premium. Fourth, we further propose the selection procedure for the product schemes based on criteria including the ANPR and the CUPR to ensure the designed product meets the interests of both SMIEEs and insurers. Last, we conduct a case study of the CCFIES index microinsurance using 18-year retrospective period weekly data. The empirical results indicate that the CFIM can effectively meet SMIEEs’ needs for risk management and cost control.
This study is of potential interest to both academics and practitioners. The proposed RAAC principles (reliable index, adaptive structure, affordable price, and controllable risk) can shed light on the design of other index microinsurance. The dynamic pricing model derived for the index microinsurance with the compensation cap may also be useful for the pricing of freight options bilaterally. The CFIM designed in this paper can effectively help SMIEEs control the risk of additional costs caused by container freight fluctuations, especially under extreme market conditions such as boomed freights.
The Nymex Exchange of the CME Group launched the FBX container index futures on Feb. 28, 2022, China also has been preparing to re-launch the container freight index futures. The CFIM designed in this paper can be used for hedging with the aid of the container freight index futures, which can not only help insurers to increase underwriting capability but also provide SMIEEs with strong support for risk management. In this context, it is of interest to further explore the hedging strategies for the underwriting risk of CFIMs using the container freight index futures and the financial subsidies to CFIM premiums, so as to promote the wider application of the CFIM and the stable development of the global maritime supply chain.
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
The following are the Supplementary data to this article:Supplementary Data 1
Data availability
Data provided in the supplementary material.
Acknowledgement
This work is supported by National Natural Science Foundation of China (Grant No. 72072018, 71831002, 71601037), National Social Science Foundation of China (Grant No. 20BJY262), Postdoctoral Research Foundation of China (Grant No. 2019M651101, 2021T140081), and Humanities and Social Science Project of Ministry of Education of China (Grant No. 19YJC790171).
Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.tre.2022.102975.
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| 36506938 | PMC9722377 | NO-CC CODE | 2022-12-07 23:19:10 | no | Transp Res E Logist Transp Rev. 2023 Jan 6; 169:102975 | utf-8 | Transp Res E Logist Transp Rev | 2,022 | 10.1016/j.tre.2022.102975 | oa_other |
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Heliyon
Heliyon
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e12024
Corrigendum
Corrigendum to “Anxiety among the Sudanese university students during the initial stage of COVID-19 pandemic” [Heliyon 7 (3), (March 2021) Article e06300]
Yassin Abas Isra Mohamed a∗
Mohmmed Mohmmedalageel Isra Isameldeen a
Ali Suad Mohamed b
a Faculty of Medicine, University of Khartoum, ElQasr Avenue, 11111, Khartoum, Sudan
b Consultant Community Medicine Assistant Professor, Community Medicine Department, University of Khartoum, ElQasr Avenue, 11111, Khartoum, Sudan
∗ Corresponding author.
6 12 2022
6 12 2022
e1202424 11 2022
24 11 2022
© 2021 The Author(s)
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pmcIn the original published version of this article, co-author Isra Isameldeen Mohmmed Mohmmedalageel’s name was spelled incorrectly. The spelling has now been corrected. The authors apologize for the error. Both the HTML and PDF versions of the article have been updated to correct the error.
Declaration of interests statement
The authors declare no conflict of interest.
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10.1016/j.burns.2022.11.009
Article
Adult kitchen-related burn injuries: The impact of COVID-19
Chawla Sahil a
Papp Anthony b⁎
a Faculty of Medicine, University of British Columbia, Vancouver, British Columbia, Canada
b Division of Plastic Surgery, Department of Surgery, University of British Columbia, Vancouver, British Columbia, Canada
⁎ Corresponding author at: UBC Division of Plastic Surgery, Department of Surgery, University of British Columbia, 899 12th Avenue, Vancouver, British Columbia, V5Z1M9, Canada.
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© 2022 Elsevier Ltd and ISBI. 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.
Introduction
Kitchen-related burn injuries are common and preventable. To limit the spread of COVID-19, public health orders encouraged the public to stay at home which may have led to an increase in kitchen-related burn injuries.
Objective
To assess adult kitchen-related burns treated in an outpatient setting in general, and especially looking at the impact of the COVID-19 pandemic on the incidence and epidemiology of these burns.
Methods
For this retrospective, population-based study, data were obtained for adult patients who suffered burn injuries resulting in a visit to a Canadian tertiary Burn Clinic between April 2016 and March 2021 specifically looking at demographics, burn etiology, severity and anatomical location and the need for surgery. Separately, we compared the patients before and after the beginning of the COVID-19 pandemic (April 1, 2020).
Results
A total of 1380 burn patients were identified. Of these, 38 % (N = 521) sustained a kitchen-related burn. The median patient age was 40 years (range 18–95) and 282 (54.1 %) were female. The most common etiology and location were scald (76.8 %) and anterior arm (28.5 %), respectively. Thirty-two (6.1 %) patients required admission to the Burn Unit and 26 of these had surgeries. Additionally, 72 (13.8 %) patients had surgery as an outpatient. During the COVID-19 pandemic, East Asian patients saw a significant increase in kitchen burns (p < 0.01).
Conclusion
Over 1/3 of burns at the outpatient burn clinic were kitchen-related. About 94 % of these were treated as outpatient only. The incidence of kitchen-related burns did not change during the COVID-19 pandemic, but there were significant differences in ethnic distribution. These results provide a unique opportunity to focus on communication and education and set up preventative measures.
Keywords
Kitchen burn
Cooking burn
Coronavirus
COVID-19
Pandemic
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pmc1 Introduction
Burns are devastating injuries that can cause significant morbidity and mortality, as well as a substantial burden on the health care system in Canada [1], [2], [3]. Each year, there are nearly 46,000 burn injuries in Canada, costing the healthcare system $366 million annually [3]. The majority of burn injuries are both predictable and preventable [4]. However, to ensure that appropriate prevention programs are set up, it is important to study the etiology and circumstances of these burn injuries.
There is a paucity of data on kitchen-related burn injuries in developed and Western populations. The epidemiology and outcomes of kitchen burns in a given geographical area are important to evaluate the severity and current management of the problem. The 2017 report from the American Burn Association’s National Burn Repository (NBR) included only 4 Canadian burn centres, which is a relatively small sample size in comparison to the 101 United States burn centres [5], [6]. The NBR report did not include kitchen burns as a separate etiology, yet reported that flame burns and scalds collectively comprised 78 % of all reported burns. Amongst these, it was found that the majority of events were accidental and took place at home [5], implying that kitchen-related burn injuries might form a major preventable health care burden.
In light of the COVID-19 pandemic, British Columbia health officials declared a public health emergency, resulting in various restrictions and recommendations to maintain physical distancing with anyone who is not a part of their household [7], [8]. With gradually tightening restrictions, academic institutions, restaurants and workplaces encouraged students and employees to work from home. All of this led to greater time being spent at home [9], which may increase the likelihood of cooking at home instead of eating out and hence cooking-related burn injuries.
While there are many publications on the epidemiology and risk factors for burn injuries [1], [10], [11], [12], [13], literature on kitchen-related burn injuries in Canada remains scarce. It is important to evaluate epidemiology and trends for kitchen-related burn injuries so that a specialized focus on communication and education can occur for at-risk populations.
The objective of this study was to assess adult kitchen-related burn injuries in the Vancouver Coastal Health (VCH) region from 2016 to 2021, with a special emphasis on looking at the impact of the COVID-19 pandemic on the incidence and epidemiology of these burns. Our hypothesis was that adult kitchen-related burn injuries would comprise a quarter of all burn injuries presented at the outpatient burn clinic. We also hypothesized that the incidence of kitchen injuries would increase significantly during the pandemic.
2 Methods
2.1 Study area and setting
The VCH region is the largest publicly funded regional health authority care system within the province of British Columbia (BC), Canada [14]. VCH serves ethnically and socioeconomically diverse subpopulations, consisting of more than 1 million residents [15]. Within the VCH, the Vancouver General Hospital (VGH) is the only adult Level 1 Burn and Trauma Center [14]. The Intensive Care Unit (ICU), Burns, Trauma, and High Acuity Unit (BTHAU), and Burn Clinic are fully equipped to provide acute and reconstructive care for adult patients with severe burns and wounds. The VGH Burn Clinic provides provincial burn wound consultations and management, scar management and follow-up care to adults with burn injuries. Patient management involves a multidisciplinary team of plastic surgeons, nurses, occupational therapists and physiotherapists [16].
2.2 Patient data
This retrospective, population-based study was approved by the University of British Columbia Clinical Research Ethics Board (REB#: H21–00598). Data were derived entirely from the VGH Burn Clinic database to identify all adult patients (age ≥ 18 years) treated as outpatients for kitchen-related burn injuries (classified using the International Statistical Classification of Diseases [ICD] code T20–32, 10th revision) between April 2016 and March 2021. Exclusion criteria included any patients under 18 years of age at the time of injury, patients who first required admission to the hospital for burn management, and patients not satisfying a threshold for reliable ethnicity identification. Separately, we compared the patients before and during the COVID-19 pandemic (April 2020 – March 2021). Data from this period was compared to the pre-COVID-19 period consisting of the past 4 years.
Key supporting variables in the analysis included patient demographics, burn etiology, severity, Baux Score, and anatomical location. None of our patients had inhalation injuries as they were all outpatients and hence revised Baux Score was not used. Circumstances of injury, the necessity for surgery or admission, and the number of required visits to the Burn Clinic were also assessed. Ethnicity was derived from patient charts and categorized as Caucasian, South Asian, East Asian, Middle Eastern, Latino/Hispanic and Black. Ethnicity was defined based on multiple details including self-identification on chart records, surname analysis, and the photographic record in chart reviews.”
All data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools [17], [18].
2.3 Statistical analysis
Kolmogorov-Smirnov test demonstrated that our continuous variables were non-Gaussian. Continuous variables were reported as medians and ranges and examined using a Mann–Whitney U tests, and categorical variables were examined using Pearson’s chi-square tests. Odds ratios were applied as appropriate to compare patient demographics with burn incidence. Two-sided p-values were used for hypothesis testing, and statistical significance was set at p < 0.05. All statistical analyses were performed using the SPSS software package version 27.0 (SPSS Inc., Chicago, IL).
3 Results
3.1 Trends and patient characteristics
Between April 2016 and March 2021, a total of 1380 patients presented to the VGH Burn Clinic and of these, 38 % (N = 521) of patients presented with kitchen-related burn injuries ( Table 1). The median age was 40 years (range 18–95), consisting of 239 males (45.9 %) and 282 females (54.1 %). The median TBSA was 1.0 % and the median Baux score was 42.5. The largest age group was patients 45–64 years old (Fig. 2). There was no significant difference in age groups during the studied period. Five patients fit our exclusion criteria for ethnicity not satisfying a threshold for reliable identification. Patient ethnicity did not reflect provincial demographics19 and showed a majority of kitchen burn patients identified as Caucasian (50.7 %), East Asian (22.8 %), South Asian (12.1 %), Latino/Hispanic (7.7 %), Middle Eastern (5.4 %), Black (1.2 %). Meanwhile, provincial demographics consist of Caucasian (68.0 %), East Asian (15.3 %), South Asian (6.5 %), Latino/Hispanic (1.2 %), Middle Eastern (1.8 %), Black (1.1 %) and other (6.1 %). When ethnicity was segmented by gender, no significant difference was found between patient demographics and provincial demographics (Table 3).19 The majority had burn injuries to their upper extremities (52.0 %) and lower extremities (30.2 %). Scald injuries were most common (76.8 %), followed by contact injuries (13.4 %), flame injuries (9.0 %), chemical injuries (0.6 %) and electrical injuries (0.2 %). These injuries most commonly took place at home (83.7 %) and rarely required admission (6.1 %) or surgery (15.2 %). The majority of kitchen-related burn injuries were treated as outpatient entirely (93.8 %) The median number of visits required to the Burn Clinic before discharge was 2 (range, 1–35).Table 1 Characteristics of kitchen-related burn injuries, 2016–2021.
Table 1 All burn patients (n = 521)
Mean age (years) 43.2
Mean number of visits 3.2
TBSA 2.3
Baux Score 45.4
Sex
Female 282 54.1 %
Male 239 45.9 %
Ethnicity
White 264 50.7 %
East Asian 119 22.8 %
South Asian 63 12.1 %
Latino/Hispanic 40 7.7 %
Middle Eastern 28 5.4 %
Black 6 1.2 %
Body Part Burned
Anterior Arm 263 28.5 %
Posterior Arm 217 23.5 %
Anterior Leg 167 18.1 %
Posterior Leg 112 12.1 %
Face/Scalp 63 6.8 %
Abdomen 44 4.8 %
Anterior Chest 35 3.8 %
Back 11 1.2 %
Buttocks 9 1.0 %
Back of head 2 0.2 %
Burn Type
Scald 418 76.8 %
Contact 73 13.4 %
Flame 49 9.0 %
Chemical 3 0.6 %
Electrical 1 0.2 %
Circumstance of Injury
Home 436 83.7 %
Work 85 16.3 %
Need For Admission
Yes 32 6.1 %
No 489 93.9 %
Need For Surgery
Yes 79 15.2 %
No 442 84.8 %
3.2 Impact of COVID-19 pandemic
During the COVID-19 pandemic, a total of 265 presented to the VGH Burn clinic and of these, 45.3 % (N = 120) presented with kitchen-related burn injuries ( Table 2). However, the increase from 36.0 % to 45.3 % (9.3 % increase) did not reach statistical significance (p = 0.06). On the other hand, there was an increase in the frequency of burn injuries at home from 81.3 % to 91.7 % (p = 0.007). There was no significant difference seen during the two periods in regard to patient age, sex, and the median number of visits (p > 0.05).Table 2 Comparing characteristics of kitchen-related burn injuries between COVID-19 period and the four years prior to the pandemic.
Table 2Variable Pre-Pandemic Years COVID-19 Period p-value
Total Patients with burns 1115 265
Patients with kitchen-related burns 401 36.0 % 120 45.3 % 0.06
Median age, years 40 442 0.67
Median number of visits 2 2 0.20
TBSA, % 1 1 0.76
Baux Score 41.5 43.5 0.96
Sex 0.53
Female 221 55.1 % 61 50.8 %
Male 180 44.9 % 59 49.2 %
Ethnicity 0.02
White 218 54.4 % 46 38.7 %
East Asian 80 20.0 % 39 32.8 %
South Asian 50 12.5 % 13 10.9 %
Latino/Hispanic 27 6.7 % 13 10.9 %
Other 26 6.5 % 8 6.7 %
Body Part Burned 0.01
Anterior Arm 202 27.9 % 61 30.5 %
Anterior Leg 111 15.4 % 56 28.0 %
Posterior Arm 181 25.0 % 36 18.0 %
Posterior Leg 96 13.3 % 16 8.0 %
Face/Scalp 51 7.1 % 12 6.0 %
Abdomen 35 4.8 % 9 4.5 %
Chest 28 3.9 % 7 3.5 %
Other 19 2.6 % 3 1.5 %
Burn Type 0.33
Scald 318 75.5 % 100 81.3 %
Contact 56 13.3 % 17 13.8 %
Other 47 11.1 % 6 4.9 %
Circumstance of Injury 0.01
Home 326 81.3 % 110 91.7 %
Work 75 18.7% 10 8.3 %
Need for Admission 0.06
Yes 29 7.2 % 3 2.5 %
No 372 92.8 % 117 97.5 %
Need for Surgery 0.35
Yes 64 16.0 % 15 12.5 %
No 337 84.0 % 105 87.5 %
Table 3 Comparing ethnicity and gender demographics of total kitchen-related burn and British Columbia (BC) population19.
Table 3Ethnicity by Gender Total BC Population p-value
White 0.265
Male 121 23.3 % 1881,215 35.8 %
Female 142 27.4 % 1923,570 36.6 %
South Asian 0.244
Male 27 5.2 % 182,650 3.5 %
Female 36 6.9 % 181,235 3.4 %
Middle Eastern 0.870
Male 14 2.7% 51,315 1.0 %
Female 14 2.7 % 48,245 0.9 %
East Asian 0.817
Male 56 10.8 % 393,475 7.5 %
Female 63 12.1 % 461,875 8.8 %
Latino/Hispanic 0.484
Male 17 3.3 % 33,580 0.6 %
Female 23 4.4 % 36,335 0.7 %
Black 0.092
Male 1 0.2 % 30,635 0.6 %
Female 5 1.0 % 29,345 0.6 %
The distribution of patients according to ethnic groups differed significantly between the pre-pandemic and COVID-19 pandemic (p = 0.02) ( Fig. 1). There was a decrease in incidence among Caucasians from 54 % to 39 %, and an increase in incidence from 20 % to 33 % and 7–11 % among East Asian and Latino/Hispanics, respectively.Fig. 1 Patients with Kitchen-Related Burn Injuries by Age and Sex, 2016–2021.
Fig. 1
Fig. 2 Impact of COVID-19 on Ethnic Distribution of Kitchen-Related Burn Injuries.
Fig. 2
Kitchen burn injuries by sex showed a ratio of practically 1:1 male to female (1.2:1 and 1.03:1), a finding that was stable over the studied period. Subgroup analysis by sex and ethnicity demonstrated that Caucasian males were 2 times more likely than females to get kitchen burns (OR=2.11) (p < 0.05) during the pandemic. On the contrary, East Asian females were 2 times more likely than males to get kitchen burns during the pandemic (OR=2.04) (p < 0.05). Other ethnicities did not have any significant male-to-female odds ratios (p > 0.05).
The median TBSA burned was not significantly different before and during the pandemic (1 % vs 1 %, p = 0.76). Additionally, the fraction of patients requiring admission to the hospital before and during the pandemic (7.2 % and 2.5 %) or surgery (16 % and 12.5 %), respectively did not significantly differ during the pandemic either (p > 0.05). Burn etiology of scald injuries was the most common before and during the pandemic (75.5 % vs 81.3 %, p = 0.33).
4 Discussion
This study provides insights into the incidence and epidemiology of kitchen-related burn injuries in the VCH region over the last five years, while also evaluating the impact of the COVID-19 pandemic on kitchen-related burns. The analyses revealed several major findings. Specifically, over 1/3 of burns at the outpatient burn clinic were kitchen-related with the number being as high as 45.3 % during the pandemic, and the vast majority of these were treated as outpatient only. The incidence of kitchen-related burn injuries has been steadily increasing over the past five years however, there was no statistically significant change during the COVID-19 pandemic (p = 0.06). There were significant differences in the burn injury population when segmented by ethnicity and sex.
Our study supports that VCH has seen a steady increase in the incidence of kitchen-related burn injuries over the past five years. However, there was no statistically significant change between the average of pre-pandemic years and the COVID-19 period (p = 0.06). The median age of patients experiencing kitchen burns was similar to those experiencing burn trauma and requiring in-patient treatment at VGH [19]. Even though the incidence of kitchen-related burn injuries during the COVID-19 pandemic increased from 36 % to 45.3 %, this did not reach statistical significance most likely due to the overall small number of patients. This finding may suggest that although the pandemic was a sensitive time that presumably deterred many from going to the hospital out of fear of COVID-19 exposure, most patients continued to seek medical care. However, as expected, more kitchen-related burn injuries were sustained during the pandemic at home than at work. This is in keeping with prior studies as the kitchen is a common location for burn injuries at home [20].
Overall, 84 % of kitchen-related burn injuries took place at home, with the rest occurring at work. It is important to note that work kitchens pose different risk factors than home kitchen environments. We found no statistically significant difference between the two groups regarding patient demographics, burn etiology, severity, and anatomical location.
While various studies have reported the reduction of burn injuries during COVID-19 lockdown periods [21], [22], [23], kitchen burn epidemiologic studies in the context of COVID-19, are scarce. In general, there has been an increase in scald and contact burns to the upper extremities sustained at home [24]; the results of our study support this finding. Kruchevsky et al. described increased levels of burn injuries sustained by female patients during the lockdown period [23]. Our findings do not reflect this, which may be explained by various reasons. Notably, the differences in a country’s socioeconomic status are known to affect the epidemiology of burn injuries [25]. Another reason could be that developing countries generally have poor kitchen safety measures and a decreased focus on kitchen safety, compared to developed and Western countries [26].
Kitchen-related burn injuries presented with considerable diversity and were not consistent with provincial statistics [27]. Almost 50 % of kitchen burns treated at the outpatient Burn Clinic, were sustained by patients from an ethnicity other than Caucasian. Despite this, provincial statistics only identify about one-quarter of the province’s population as a visible minority [27]. This may provide a unique opportunity to focus on communication and education for targeted ethnic populations.
The incidence of kitchen burns had strong ethnic differences appear during the pandemic. In particular, two ethnic groups emerged in the analysis: Caucasian and East Asian. Though other ethnicities did have differences in demographics, burn severity and circumstance of injury, between the two periods, it was not statistically significant. This may be due to the relatively small sample size, resulting in decreased statistical power.
During the five-year study period, Caucasians were the largest ethnic group, followed by East Asian patients, who encompass roughly 1/5 of kitchen burns. During the pandemic, they saw the largest increase in burn injuries and were the only ethnic group in which females were two times more likely to sustain kitchen burns than males. In contrast, Caucasians were the only ethnic group during the pandemic where males were two times more likely to sustain kitchen burns than females. Other ethnic groups had no significant gender disparity in injury incidence. Prior studies have shown that males are almost twice as more likely to sustain general burn trauma as their female counterparts [20]. This likely stems from a combination of environmental, psychosocial, and socioeconomic factors [28]. This trend was only observed in Caucasian groups and was not observed for non-Caucasian ethnic groups. Possible reasons for this may be that kitchen-related burn injuries are unique burn injuries and may not follow the general trend for burn trauma. This may also be partly explained by the pandemic restrictions, which may have promoted more cooking activity at home. Prior studies have shown that traditional housework roles still prevail and females are more frequently engaged in cooking [29], [30].
Caucasians also saw the largest decline in kitchen burns during the pandemic, followed by South Asians. Possible reasons for this may be due to greater caution practiced when in the kitchen, or relatively minor burn injuries requiring no significant medical attention. Another possibility may be that these groups were presumably deterred from going to seek healthcare out of fear of COVID-19 exposure.
Kitchen-related burn advocacy is necessary to prevent burns, increase awareness and ensure a safe environment. In addition to current advocacy campaigns such as National Burn Awareness Week and National Scald Prevention Campaign, more specialized initiatives may be necessary to encourage patient education and participation. This may include educational programs targeting populations via language-specific literature and communication strategies, those in low socioeconomic regions, or rural locations. These efforts may benefit from taking various forms in the delivery of their messages and leveraging ethnic radio stations, TV channels, social media, or organizing tabling sessions outside local ethnic grocery stores. Efforts should also be made to better understand how these high-risk population groups commonly consume information and tailor prevention campaigns by these means. Given that East Asian females were two times more likely than males to get kitchen burns during the pandemic, an increased focus on female patients is required.
Although cooking burns can affect anyone, prevention campaigns may benefit from providing groups with cultural and age-tailored tips. For example, loose, billowing clothing such as a Japanese Kimono or East Indian saree can easily catch fire and should be worn with extra caution when in the kitchen. Although no significant difference in incidence was seen among different age groups, it is suspected that the etiology of these burns may differ among age groups. Age-tailored education and prevention campaigns may allow for a unique focus. For example, in educating young adults from refraining to participate in dangerous, popular TikTok trends [31], may require a different approach than elderly patients who may be more prone to burn injuries due to impaired vision, lower mobility and age-related deterioration in judgement [32].
Future studies may wish to identify and quantify other factors, like social and economic status, which may be influencing the incidence and reporting of kitchen burns in certain patient populations. There are several limitations to this study. First, our study looks at kitchen-related burn injuries treated at an outpatient clinic and thus, may not capture kitchen-related burn injuries requiring inpatient treatment, excessive resuscitation or those leading to death before healthcare intervention. Second, patients with relatively minor burn injuries who did not seek medical attention likely were not captured in our data. Third, we recognize that race is a social construct without a biological basis, and while a patient’s skin colour may dictate their lived experiences, it should not be confounded with ethnicity. Patients not satisfying a threshold for reliable identification were omitted from the study. This may have resulted in a selection bias for certain patient ethnicities.
5 Conclusion
Given that over one-third of burns at the outpatient burn clinic were kitchen-related, there is an increasing need for burn advocacy. The incidence of kitchen-related burn injuries did not change during the COVID-19 pandemic, but there were significant differences in the burn injury population when segmented by ethnicity. This study’s findings offer important insight into kitchen-related burn injury patterns and recommendations regarding opportunities for prevention. These results provide a unique opportunity to focus on communication and education and set up preventative measures for future lockdowns.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.
Conflict of interest statement
The authors declare no potential conflict of interest.
==== Refs
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| 0 | PMC9722612 | NO-CC CODE | 2022-12-07 23:22:08 | no | Burns. 2022 Dec 6; doi: 10.1016/j.burns.2022.11.009 | utf-8 | Burns | 2,022 | 10.1016/j.burns.2022.11.009 | oa_other |
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iScience
iScience
iScience
2589-0042
The Author(s).
S2589-0042(22)02021-1
10.1016/j.isci.2022.105748
105748
Article
Early alveolar epithelial cell necrosis is a potential driver of COVID-19-induced acute respiratory distress syndrome
Tojo Kentaro 15∗
Yamamoto Natsuhiro 1
Tamada Nao 12
Mihara Takahiro 3
Abe Miyo 1
Nishii Mototsugu 4
Takeuchi Ichiro 4
Goto Takahisa 1
1 Department of Anesthesiology and Critical Care Medicine, Yokohama City University School of Medicine, Yokohama, Kanagawa, Japan
2 Department of Paramedic, Kyorin University Faculty of Health Sciences, Mitaka, Tokyo, Japan
3 Department of Health Data Science, Yokohama City University Graduate School of Data Science, Yokohama, Kanagawa, Japan
4 Department of Emergency Medicine, Yokohama City University School of Medicine, Yokohama, Kanagawa, Japan
∗ Corresponding author
5 Lead Contact
6 12 2022
6 12 2022
1057482 6 2022
30 10 2022
2 12 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.
Acute respiratory distress syndrome (ARDS) with COVID-19 is aggravated by hyperinflammatory responses even after the peak of viral load has passed; however, its underlying mechanisms remain unclear. In the present study, analysis of the alveolar tissue injury markers and epithelial cell death markers in patients with COVID-19 revealed that COVID-19-induced ARDS was characterized by alveolar epithelial necrosis at an early disease stage. Serum levels of HMGB-1, one of DAMPs released from necrotic cells, were also significantly elevated in these patients. Further analysis using mouse model mimicking COVID-19-induced ARDS showed that the alveolar epithelial cell necrosis involved two forms of programmed necrosis, namely necroptosis and pyroptosis. Finally, the neutralization of HMGB-1 attenuated alveolar tissue injury in the mouse model. Collectively, necrosis, including necroptosis and pyroptosis, is the predominant form of alveolar epithelial cell death at an early disease stage and subsequent release of DAMPs is a potential driver of COVID-19-induced ARDS.
Graphical abstract
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pmc
| 36507222 | PMC9722615 | NO-CC CODE | 2022-12-07 23:19:12 | no | iScience. 2022 Dec 6;:105748 | utf-8 | iScience | 2,022 | 10.1016/j.isci.2022.105748 | oa_other |
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J Hosp Infect
J Hosp Infect
The Journal of Hospital Infection
0195-6701
1532-2939
The Healthcare Infection Society. Published by Elsevier Ltd.
S0195-6701(22)00368-1
10.1016/j.jhin.2022.11.014
Letter to the Editor
SARS-CoV-2 viability and viral RNA persistence on microbiological agar plates
Farfour E. a∗
Lebourgeois S. b
Chenane H. Redha b
Charpentier C. bc
Pascreau T. ad
Jolly E. a
Descamps D. bc
Vasse M. ad
Visseaux B. bc
a Service de Biologie Clinique, Hôpital Foch, Suresnes, France
b Université de Paris Cité, IAME, INSERM, F-75018 Paris, France
c AP-HP, University Hospital Bichat-Claude Bernard, Virology, Paris, France
d UMRS 1176, le Kremlin-Bicêtre, Paris-Saclay, France
∗ Corresponding author. Service de Biologie Clinique, Hôpital Foch, 40 rue Worth, 92150 Suresnes, France. Tel.: +33 1 46 25 75 51; fax: +33 1 46 25 24 22. ; .
6 12 2022
6 12 2022
17 11 2022
24 11 2022
25 11 2022
© 2022 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.
2022
The Healthcare Infection Society
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
pmcSir,
COVID-19 is an airborne disease due to SARS-CoV-2 that could be transmitted by infectious droplets that are inhaled, that land directly on mucous membranes, or that are inoculated via contaminated hands. Transmission through surfaces or fomites contaminated by deposition of larger particles could potentially occur but seems less common. SARS-CoV-2 viral RNA has been recovered from various surfaces [[1], [2], [3]]. Infectious samples are handled in microbiological laboratories, and agar plates could potentially be contaminated with viable virus when inoculated with virus-containing clinical samples. To our knowledge, recovery of SARS-CoV-2 RNA and viable virus from agar plates has not been investigated. In the present study, we assess SARS-CoV-2 viability and viral RNA persistence on the three agar-plate types that are the most frequently used for bacterial culture.
Three types of agar plate were tested: Columbia supplemented with 5% sheep blood, chocolate, and BBL™ CHROMagar™ MRSA II (MRSA II) (Becton Dickinson, Franklin Lakes, NJ, USA). A SARS-CoV-2-positive sample was diluted in order to reach a C T-value <20 and obtain a sufficient amount of suspension. One hundred microlitres of the suspension were then plated on 19 agar plates of each medium: nine were incubated at room temperature and nine others at 35°C for 1 to 42 days. The plates were wrapped in aluminium foil before starting incubation. The remaining plate was processed 30 min after plating to provide a baseline. For detection of SARS-CoV-2 RNA and viable virus, the surface of the agar plate was swabbed for 30 s. The swab was then placed in 3 mL of Universal Transport Medium and vortexed for 10s. SARS-CoV-2 reverse transcription–polymerase chain reaction (RT–PCR) was performed with the RESP-4-Plex assay on an Alinity M instrument (Abbott Molecular, Desplaines, IL, USA) according to the manufacturer’s instruction. Viral culture was performed on Vero E6 cells (ATCC, reference R CRL-1586) (LGC standards SARL, Illkirch, France) as previously described [4].
The C T-value of the baseline agar plate ranged from 23.4 to 24.1. On blood and chocolate agars the C T-value of the SARS-CoV-2 RT-PCR remained highly stable after 42 days, C T <25.0, at both incubation temperatures (Figure 1 ). Conversely, the C T-value tended to increase for MRSA II agar, indicating viral load decay. This increase in C T-values was more gradual at room temperature compared with 35°C. A C T-value of 30 was reached on days 5 and 1 at room temperature and 35°C, respectively. Thereafter, SARS-CoV-2 RNA remained detectable at all incubation times, the C T-values reaching a plateau. On day 42, the C T-values were 31.6 and 32.8 at room temperature and 35°C, respectively. SARS-CoV-2 cultures were positive for the baseline Blood Colombia and chocolate plates but not on the MRSA II plate. On day 1, SARS-CoV-2 remained cultivable on the chocolate agar plate incubated at room temperature only. No viable virus was detected in any of the other port-30 min incubation samples.Figure 1 Recovery of SARS-CoV-2 RNA and viable virus on three types of agar plate incubated at room temperature or 35°C. Types of agar plate and incubation temperatures were as follows: chocolate agar at room temperature (a) and 35°C (d); blood agar at room temperature (b) and 35°C (e); MRSA II agar at room temperature (c) and 35°C (f). Positive viral cultures are indicated by the black arrows.
Figure 1
Our results show that viral RNA could be detected for up to 42 days on widely used agar plates. However, SARS-CoV-2 remained cultivable for only 24 h, and only one chocolate agar. Laboratory workers are at risk of infections when processing clinical samples or handling bacterial cultures, and laboratory-acquired infections with a wide range of pathogens have previously been reported [5,6]. Our data suggest that there is little or no risk to laboratory staff of contracting SARS-CoV-2 from handling bacterial culture plates inoculated with samples from infected patients. However, viral culture is less sensitive for samples with C T-values >25 or >30; thus it is still possible that viable virus might be present on agar plates incubated for ≥24 h 35°C.
In conclusion, whereas SARS-CoV-2 RNA could be detected up to 42 days on agar media, the likelihood that incubated culture plates may contain viable virus is low. Additional studies are required to assess the risk of contamination of the microbiology laboratory environment with viral RNA, but at this stage we suggest that care is needed in the handling of bacterial cultures from patients with SARS-CoV-2 to minimize the risk of environmental contamination with viral RNA.
Author contributions
E.F. conceived the study. E.F., S.L., and B.V. conducted the searches and collected data. E.F., S.L., and B.V. analysed and interpreted the datasets. E.F. and S.L. drafted and edited the manuscript. All authors commented on or edited drafts and approved the final version of the manuscript.
Funding sources
This study was supported in part by the ANRS I MIE (Agence Nationale de la Recherche sur le SIDA et les hépatites virales).
Declaration of Competing Interest
None declared.
Acknowledgements
This study has been funded in part by AC43 of the French ANRS (Agence Nationale de Recherche sur le SIDA et les hépatites virales). We wish to thank the team of the National Reference Center (CNR) of Mycobacteria for their valuable help in this work.
==== Refs
References
1 Sun Z.P. Yang S.Y. Cai X. Han W.D. Hu G.W. Qian Y. Survival of SARS-CoV-2 in artificial seawater and on the surface of inanimate materials J Med Virol 94 2022 3982 3987 10.1002/JMV.27807 35474579
2 Mody L. Gibson K.E. Mantey J. Bautista L. Montoya A. Neeb K. Environmental contamination with SARS-CoV-2 in nursing homes J Am Geriatr Soc 70 2022 29 39 10.1111/JGS.17531 34674220
3 Zhou J. Otter J.A. Price J.R. Cimpeanu C. Meno Garcia D. Kinross J. Investigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) surface and air contamination in an acute healthcare setting during the peak of the coronavirus disease 2019 (COVID-19) pandemic in London Clin Infect Dis 73 2021 e1870 10.1093/CID/CIAA905 –7
4 Lebourgeois S. Chenane H.R. Houhou-Fidouh N. Menidjel R. Ferré V.M. Collin G. Earlier in vitro viral production with SARS-CoV-2 alpha than with beta, gamma, B, or A.27 variants Front Cell Infect Microbiol 11 2021 1247 10.3389/FCIMB.2021.792202/BIBTEX
5 Cornish N.E. Anderson N.L. Arambula D.G. Arduino M.J. Bryan A. Burton N.C. Clinical laboratory biosafety gaps: lessons learned from past outbreaks reveal a path to a safer future Clin Microbiol Rev 34 2021 e0012618 10.1128/CMR.00126-18
6 Singh K. Laboratory-acquired infections Clin Infect Dis 49 2009 142 147 10.1086/599104 19480580
| 36493967 | PMC9722616 | NO-CC CODE | 2022-12-07 23:19:12 | no | J Hosp Infect. 2022 Dec 6; doi: 10.1016/j.jhin.2022.11.014 | utf-8 | J Hosp Infect | 2,022 | 10.1016/j.jhin.2022.11.014 | oa_other |
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Anaesth Crit Care Pain Med
Anaesth Crit Care Pain Med
Anaesthesia, Critical Care & Pain Medicine
2352-5568
Société française d'anesthésie et de réanimation (Sfar). Published by Elsevier Masson SAS.
S2352-5568(22)00162-X
10.1016/j.accpm.2022.101181
101181
Letter to the Editor
COVID-19: Brief overview of therapeutic strategies
Beaumont Anne-Lise
Infectious Diseases Department, Bichat-Claude Bernard Hospital, Assistance-Publique Hôpitaux de Paris, Paris, France
Rozencwajg Sacha ab
a Infectious Diseases Department, Bichat-Claude Bernard Hospital, Assistance-Publique Hôpitaux de Paris, Paris, France
b Université Paris Cité, Paris, France
Peiffer-Smadja Nathan abcd⁎
a Infectious Diseases Department, Bichat-Claude Bernard Hospital, Assistance-Publique Hôpitaux de Paris, Paris, France
b Université Paris Cité, INSERM, IAME, F-75018 Paris, France
c National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London, London, UK
d Université Paris Cité, Paris, France
Montravers Philippe abc
a Department of Anesthesiology and Critical Care Medicine, Bichat-Claude Bernard Hospital, Assistance-Publique Hôpitaux de Paris, Paris, France
b INSERM UMRS1152 - PHERE, Paris, France
c Université Paris Cité, Paris, France
⁎ Corresponding author at: Infectious Diseases Department, Bichat-Claude Bernard Hospital, Assistance-Publique Hôpitaux de Paris, 46 rue Henri Huchard, B.P. 416, 75870 Paris Cedex 18, France.
6 12 2022
6 12 2022
101181© 2022 Société française d'anesthésie et de réanimation (Sfar). Published by Elsevier Masson SAS. 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.
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pmcIntroduction
In less than 3 years, more than 6 million people died from COVID-19. If no panacea has been discovered yet, the joint effort of the worldwide scientific community has enabled the development of solid evidence-based treatment strategies. An unprecedented number of studies have been published in a very limited amount of time. If this is remarkable from a scientific point of view, physicians do not have time to absorb so much data and thus are often unable to implement new knowledge in day-to-day practice. Here, we aim to draw a brief operational review of state-of-the-art COVID-19 treatment.
Severe SARS-CoV-2 infection is often divided into two phases. First, when the disease seems mostly driven by viral replication, the clinical expression is often minor resulting in flu-like symptoms. The majority of patients are cured after this first step and do not develop severe disease. Risk factors for progressing to severe COVID-19 are mainly age (>80 years or >60 if the vaccinal schedule is incomplete), immunodeficiency (stem cell or organ transplant, chemotherapy, immunosuppressive agents including corticosteroids or rituximab, complex immunocompromised status including auto-immune diseases) and some underlying medical conditions (organ failure, diabetes, obesity, hypertension or cardiovascular disease, etc...). Second, later in the clinical course (5 to 10 days after symptom onset), an exacerbated inflammatory response to SARS-CoV-2 may develop, leading to tissue damage and acute respiratory failure. Symptomatic patients should be monitored with SpO2 pulsometer after 5 days of symptoms to detect oxygen requirement that reflects the severity of the disease.
Therapies that directly target SARS-CoV-2 are anticipated to be more beneficial early in the course of the disease, while anti-inflammatory therapies are likely to be more effective in the later stages of COVID-19.
COVID-19 patients without oxygen requirement
In most cases, this situation concerns outpatients, but it can also apply to nosocomial cases or patients hospitalized for another reason than COVID-19. All patients should be offered symptomatic management (analgesics, antipyretics), advice to respect isolation, and, for outpatients, education on when to contact a physician. Then, the risk of progressing to severe COVID-19 needs to be assessed by the clinician. No treatment is recommended if no risk factor is identified. If at least one risk factor is present, ritonavir-boosted nirmatrelvir (100 mg-300 mg twice daily for 5 days) (RBN, Paxlovid®) should first be considered (Fig. 1 ). This treatment has to be started within the first 5 days of symptoms and clinicians should carefully check concomitant medications and evaluate potential drug-drug interactions that are frequent (anticoagulants, immunosuppressive therapies, etc.). The Liverpool interaction checker might be useful for clinicians willing to evaluate the risk of interaction [1]. If no interaction is detected, treatment should be initiated as soon as possible. If RBN is contra-indicated, the second-line treatment is remdesivir. Remdesivir is contra-indicated when eGFR is <30 mL min−1 and can be nephrotoxic. Administration of remdesivir requires 3 days of intra-veinous infusion (200 mg on day 1, then 100 mg daily), which makes it hardly available for outpatients. Both RBN and remdesivir retain a therapeutic activity against variants, contrary to monoclonal antibodies [2].Fig. 1 COVID-19 treatment algorithm.
SOT: Solid Organ Transplant. BMT: Bone Marrow Transplant. eGFR: estimated Glomerular Filtration Rate. MV: Mechanical Ventilation.
Fig. 1
The status of monoclonal antibodies in therapeutic guidelines is consistently changing because their efficiency is highly dependent on the predominantly circulating viral variant. Because the Omicron variant of concern and its subvariants have become the dominant SARS-CoV-2 variants worldwide, bamlanivimab plus etesevimab, casirivimab plus imdevimab, or sotrovimab are no longer recommended for the treatment of COVID-19. Bebtelovimab is an alternative therapy when both RBN and remdesivir are not available, feasible to use, or clinically appropriate. However, it is currently only available in the United States and recent data shows that it is not effective on BQ.1.1.
There is insufficient data to establish a recommendation for COVID-19 convalescent plasma prescription [3]. However, it can be discussed on a case-by-case basis for an immunocompromised patient with contra-indications to paxlovid and remdesivir.
COVID-19 patients who require supplemental oxygen (hospitalized patients)
Among hospitalized patients who require oxygen therapy, conventional management includes systematic anticoagulant therapy and supportive care. While some patients will rapidly get better, others will develop a hyper-inflammatory state and progress toward acute respiratory failure.
The therapeutic strategy depends on the level of oxygen:• Conventional oxygen: the association of dexamethasone (6 mg per day for up to 10 days, or until hospital discharge, whichever comes first) and remdesivir [4] is recommended. The antiviral treatment should be started as soon as possible, as its benefit is greatest when administered in the first stage of the disease (e.g., within 10 days of symptom onset). The duration of remdesivir administration is 5 days (or until hospital discharge). If dexamethasone is not available, an equivalent dose of another steroid (e.g., prednisone, methylprednisolone, or hydrocortisone) may be used instead. For patients who have rapidly increasing oxygen needs and systemic inflammation (e.g., CRP ≥ 75 mg L−1) despite steroids, an immunomodulator drug can be added, such as tocilizumab [5], an IL-6 receptor inhibitor or baricitinib, a JAK inhibitor.
• High flow oxygen or mechanical ventilation: there is no evidence supporting the benefit of any antiviral treatment. Large randomized controlled trials have demonstrated that these patients benefit from combining dexamethasone with an additional immunomodulator from the outset, such as IV tocilizumab [5] or oral baricitinib.
This overview has been written in accordance with the epidemiological setting up to fall 2022. Every piece of information can quickly become obsolete depending on the onset of a new variant, or the publication of new data. The paramount importance of prevention measures and vaccination also needs to be underlined as a new vaccinal campaign with bivalent vaccines is currently being deployed in numerous countries.
Declaration of interests
None.
==== Refs
References
1 Liverpool drug interactions group. (Last access: October 12, 2022). Available in: https://www.covid19-druginteractions.org/.
2 Takashita E. Yamayoshi S. Simon V. van Bakel H. Sordillo E.M. Pekosz A. Efficacy of antibodies and antiviral drugs against omicron ba.2.12.1, ba.4, and ba.5 subvariants N Engl J Med 387 2022 468 470 10.1056/NEJMc2207519 35857646
3 Piechotta V. Iannizzi C. Chai K.L. Valk S.J. Kimber C. Dorando E Convalescent plasma or hyperimmune immunoglobulin for people with COVID‐19: a living systematic review Cochrane Database Syst Rev 5 2021 CD013600 10.1002/14651858.CD013600.pub4
4 Lee T.C. Murthy S. Del Corpo O. Senécal J. Butler-Laporte G. Sohani Z.N. Remdesivir for the treatment of COVID-19: a systematic review and meta-analysis Clin Microbiol Infect 28 2022 1203 1210 10.1016/j.cmi.2022.04.018 35598856
5 Recovery collaborative group Tocilizumab in patients admitted to hospital with COVID-19 (RECOVERY): a randomised, controlled, open-label, platform trial Lancet 397 2021 1637 1645 10.1016/S0140-6736(21)00676-0 33933206
| 36494003 | PMC9722617 | NO-CC CODE | 2022-12-07 23:19:12 | no | Anaesth Crit Care Pain Med. 2022 Dec 6;:101181 | utf-8 | Anaesth Crit Care Pain Med | 2,022 | 10.1016/j.accpm.2022.101181 | oa_other |
==== Front
Prev Med
Prev Med
Preventive Medicine
0091-7435
1096-0260
The Authors. Published by Elsevier Inc.
S0091-7435(22)00441-8
10.1016/j.ypmed.2022.107376
107376
Article
Long-term effects of the interruption of the Dutch breast cancer screening program due to COVID-19: A modelling study
Poelhekken Keris ab⁎
Greuter Marcel J.W. b
de Munck Linda c
Siesling Sabine cd
Brokken Frank B. e
de Bock Geertruida H. a
a University of Groningen, University Medical Center Groningen, Groningen, Department of Epidemiology, P.O. Box 30 001, FA40, Groningen 9700 RB, the Netherlands
b University of Groningen, University Medical Center Groningen, Groningen, Department of Radiology, PO Box 30.001, EB44, Groningen 9700 RB, the Netherlands
c Department of Research, Netherlands Comprehensive Cancer Organisation, Godebaldkwartier 419, Utrecht 3511 DT, the Netherlands
d University of Twente, Technical Medical Centre, Department of Health Technology and Services Research, Drienerlolaan 5, Enschede 7522NB, the Netherlands
e University of Groningen, Department of Computing Science, Postbus 72, Groningen 9700AB, the Netherlands
⁎ Corresponding author at: University of Groningen, University Medical Center Groningen, Groningen, Department of Epidemiology, P.O. Box 30 001, FA40, Groningen 9700 RB, the Netherlands.
6 12 2022
6 12 2022
10737613 5 2022
22 11 2022
30 11 2022
© 2022 The Authors. 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.
Due to COVID-19, the Dutch breast cancer screening program was interrupted for three months with uncertain long-term effects. The aim of this study was to estimate the long-term impact of this interruption on delay in detection, tumour size of screen-detected breast cancers, and interval cancer rate. After validation, the micro-simulation model SiMRiSc was used to calculate the effects of interruption of the breast cancer screening program for three months and for hypothetical interruptions of six and twelve months. A scenario without interruption was used as reference. Outcomes considered were tumour size of screen-detected breast cancers and interval cancer rate. Women of 55–59 and 60–64 years old at time of interruption were considered. Uncertainties were estimated using a sensitivity analysis. The three-month interruption had no clinically relevant long-term effect on the tumour size of screen-detected breast cancers. A 19% increase in interval cancer rate was found between last screening before and first screening after interruption compared to no interruption. Hypothetical interruptions of six and twelve months resulted in larger increases in interval cancer rate of 38% and 78% between last screening before and first screening after interruption, respectively, and an increase in middle-sized tumours in first screening after interruption of 26% and 47%, respectively. In conclusion, the interruption of the Dutch screening program is not expected to result in a long-term delay in detection or clinically relevant change in tumour size of screen-detected cancers, but only affects the interval cancer rate between last screening before and first screening after interruption.
Keywords
Breast neoplasms
COVID-19
Computational modelling
Mass screening
Mammography
Incidence rate
Netherlands
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pmcData availability
For this study, only publically available data was used. The code of the model is also publically available.
| 36493865 | PMC9722618 | NO-CC CODE | 2022-12-09 23:15:04 | no | Prev Med. 2023 Jan 6; 166:107376 | utf-8 | Prev Med | 2,022 | 10.1016/j.ypmed.2022.107376 | oa_other |
==== Front
J Infect Chemother
J Infect Chemother
Journal of Infection and Chemotherapy
1341-321X
1437-7780
Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd.
S1341-321X(22)00321-X
10.1016/j.jiac.2022.11.014
Original Article
Clinical outcomes of COVID-19 caused by the Alpha variant compared with one by wild type in Kobe, Japan. A multi-center nested case-control study
Doi Asako a∗
Iwata Kentaro b
Nakamura Tadahiro c
Oh Koji d
Isome Kenichi e
Hasegawa Kohei a
Kuroda Hirokazu a
Hasuike Toshikazu a
Seo Ryutaro f
Kosai Hisato g
Nakanishi Noriko h
Nomoto Ryohei h
Fujiyama Riyo g
Kusunoki Nobuya g
Iwamoto Tomotada h
Nishioka Hiroaki a
Tomii Keisuke i
Kihara Yasuki j
a Department of Infectious Diseases, Kobe City Medical Center General Hospital, 2-1-1, Minamimachi, Minatojima, Chuoku, Kobe, Hyogo, 650-0047, Japan
b Division of Infectious Diseases, Kobe University Hospital, 7-5-2 Kusunokicho, Chuoku, Kobe, Hyogo, 650-0017, Japan
c Department of Infectious Diseases, National Hospital Organization Kobe Medical Center, 3-1-1, Nishi-Ochiai, Sumaku, Kobe, Hyogo, 654-0155, Japan
d Department of General Internal Medicine, Kobe City Medical Center West Hospital, 2-4 Ichiban-cho, Nagataku, Kobe, Hyogo, 653-0013, Japan
e Department of Pediatrics, Kobe City Nishi-Kobe Medical Center, 5-7-1, Kojidai, Nishiku, Kobe, Hyogo, 651-2273, Japan
f Emergency Department, Kobe City Medical Center General Hospital, 2-1-1, Minamimachi, Minatojima, Chuoku, Kobe, Hyogo, 650-0047, Japan
g Kobe City Public Health Management Center, 6-5-1, Kanocho, Chuoku, Kobe, Hyogo, 650-8570, Japan
h Kobe Institute of Health, 6-5, Nakamachi, Minatojima, Chuoku, Kobe, Hyogo, 650-0046, Japan
i Department of Respiratory Medicine, Kobe City Medical Center General Hospital, 2-1-1, Minamimachi, Minatojima, Chuoku, Kobe, Hyogo, 650-0047, Japan
j Kobe City Medical Center General Hospital, 2-1-1, Minamimachi, Minatojima, Chuoku, Kobe, Hyogo, 650-0047, Japan
∗ Corresponding author. 2-1-1, minamimachi, Minatojima, Chuoku, Kobe, Hyogo, 650-0047, Japan.
6 12 2022
6 12 2022
27 8 2022
2 11 2022
30 11 2022
© 2022 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases. Published by Elsevier Ltd.
2022
Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases
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
The emergence of the Alpha variant of novel coronavirus 2019 (SARS-CoV-2) is a concerning issue but their clinical implications have not been investigated fully.
Methods
We conducted a nested case-control study to compare severity and mortality caused by the Alpha variant (B.1.1.7) with the one caused by the wild type as a control from December 2020 to March 2021, using whole-genome sequencing. 28-day mortality and other clinically important outcomes were evaluated.
Results
Infections caused by the Alpha variant were associated with an increase in the use of oxygen (43.4% vs 26.3%. p = 0.017), high flow nasal cannula (21.2% vs 4.0%, p = 0.0007), mechanical ventilation (16.2% vs 6.1%, p = 0.049), ICU care (30.3% vs 14.1%, p = 0.01) and the length of hospital stay (17 vs 10 days, p = 0.031). More patients with the Alpha variant received medications such as dexamethasone. However, the duration of each modality did not differ between the 2 groups. Likewise, there was no difference in 28-day mortality between the 2 groups (12% vs 8%, p = 0.48), even after multiple sensitivity analyses, including propensity score analysis.
Conclusion
The Alpha variant was associated with a severe form of COVID-19, compared with the non-Alpha wild type, but might not be associated with higher mortality.
Keywords
COVID-19
Alpha variant
Clinical outcomes
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pmc1 Background
So-called “the third wave” of COVID-19 started at the end of November 2020 [1]. The Alpha variant (B.1.1.7) was first identified and commenced a surge in England in the autumn, 2020, and it soon spread to other countries, including Japan, where the variant was identified first at the end of January 2021. It became the dominant strain in Japan by the beginning of 2021. The Alpha strain is reported to be more transmissible [2] and could potentially be associated with a more severe form of the illness [3] and even higher mortality [[4], [5], [6]], according to cohort studies in England, but another hospital-based cohort study did not find the association with either severity or mortality [7]. There are little data, however, regarding the clinical implication of the Alpha variant among Asian cohorts such as one in Japan.
Kobe City has had an epidemiological surveillance system for COVID-19 since March 2020. It investigates the variant strains using whole-genome sequencing.
The current study aims to better understand the clinical characteristics, particularly regarding the severity and the mortality due to COVID-19 caused by the Alpha variant compared with the non-alpha wild type (B.1.1.214) in Japan.
2 Patients and methods
2.1 Study design and population
We conducted a nested case-control study to compare COVID-19 caused by the Alpha variant and the one caused by the wild type (B.1.1.214) as a control. The study period was between December 2020 to March 2021. This is the period when so-called the third wave occurred in Japan. This was also a period in which the capacity for hospitalization was not impaired in Kobe City, unlike the surge that occurred later to jeopardize the healthcare function in Japan [8]. This allows us to avoid bias caused by the availability of healthcare access, which was frequently impaired during the surge of COVID-19 in Japan. In addition, the influence of COVID-19 vaccination did not have to be taken into consideration in the current study, because the vaccination program in the general population has not started during this study period in Japan, and only healthcare workers have received the vaccines.
Five major hospitals in Kobe City, which were designated to take care of COVID-19 patients, participated in the current study. Only 2 among 5 these hospitals did have a capability to take care of severe COVID-19 cases (Kobe City Medical Center General Hospital, and Kobe University Hospital), with the availability of high flow nasal cannula (HFNC), mechanical ventilation, or extracorporeal membrane oxygenation (ECMO), as well as the intensive care unit with certified intensivists. The remaining 3 hospitals took care of mild to moderate COVID-19 patients who did not require such measures described above (Kobe City Nishi-Kobe Medical Center, Kobe City Hospital Organization Kobe City Medical Center West Hospital, and National Hospital Organization Kobe Medical Center).
We included hospitalized patients with COVID-19 during the study period, confirmed by polymerase chain reaction (PCR) testing, with available whole-genome sequencing conducted by a reference laboratory of Kobe City, The Kobe Institute of Health, which performed whole-genome sequencing to distinguish between the Alpha variant and other wild types and provided us with the results of the above patients.
The case was defined as hospitalized COVID-19 patients caused by the Alpha variant during the study period, and the control was defined as those caused by the wild type. The cases and the control were matched by age, sex, and body mass index (BMI) with a ratio of 1:1.
We extracted the clinical data of the patients from electric charts at each hospital. The patient's characteristics were documented, such as age, sex, body mass index (BMI), comorbidities such as obesity, cerebral vascular accident (CVA), chronic kidney disease (CKD), asthma, chronic obstructive pulmonary disease (COPD), diabetes mellitus, hypertension, malignancies, immunosuppression which means the use of glucocorticoids or immunosuppressive agents otherwise, infection with human immunodeficiency virus (HIV), and smoking status (past or present). Clinical data also included respiratory rate, consciousness level (Glasgow coma scale less than 15 or not), oxygen therapy requirement on admission, laboratory data such as C-reactive protein level (CRP) on admission, and the requirement of other treatment modalities, such as the use of dexamethasone, remdesivir, tocilizumab, ivermectin, and favipiravir. Indication of dexamethasone was standardized in Kobe city. It is given to patients when oxygen therapy was started. Remdesivir was usually given with a dosage of 200 mg once and 100 mg thereafter once a day for 5 days, and tocilizumab was given once or twice (8 mg/kg/dose). Re-administration of steroids occurred when there was no clinical improvement of the severe COVID-19 cases, at the discretion of treating physicians.
The primary outcome is the 28-day mortality since the onset. Secondary outcomes included the oxygen requirement on admission, duration of the oxygen therapy, HFNC requirement and its duration, mechanical ventilation requirement and its duration, ECMO requirement and its duration, the need for intensive care unit (ICU) treatment, and the length of ICU stay, and tracheostomy requirement.
Categorical variables were analyzed using a chi-square test or Fisher's exact test when appropriate. Continuous variables were analyzed using the Student t-test. Two-sided tests with 95% confidence intervals were determined, and a P-value of 0.05 or less was considered to be statistically significant.
As a sensitivity analysis, we constructed a Bayesian model for the primary outcome as follows: we assumed a Bernoulli distribution for the mortality. We assumed a non-informative distribution for the parameter. The posterior distributions of parameters were obtained by the Markov chain Monte Carlo (MCMC) method. We set 4 separate sampling sequences, each consisting of 1000 random samples (including 500 samples discarded for convergence). Sampling convergence was evaluated by Gelman-Rubin statistics and by visually inspecting a trace plot. The region of practical equivalence (ROPE) was arbitrarily determined from −0.2 to 0.2. The null value is declared to be rejected if the 95% highest density interval (HDI) falls completely outside the ROPE, and the null value is declared to be accepted if the 95% HDI falls completely inside the ROPE.
We also conducted a propensity score analysis as another sensitivity analysis for the primary outcome, and estimated OR with inverse probability weighting (IPW) methods, using variables likely to influence the outcomes was measured.
We used the R software program, version 4.1.1 (R Foundation for Statistical Computing, Vienna, Austria) for all the analyses.
This study was approved by the ethics committee of Kobe City Medical Center General Hospital, Kobe City Nishi-Kobe Medical Center, Kobe City Hospital Organization Kobe City Medical Center West Hospital, National Hospital Organization Kobe Medical Center, Kobe University Graduate School of Medicine, and Kobe City Public Health Management Center (PHMC).
2.2 Whole-genome sequencing
Whole-genome sequences of SARS-CoV-2 were obtained using the primalSeq protocol based on a modified ARTIC Network protocol [9]. The PCR products of pools 1 and 2 from the same clinical samples were pooled and purified for Illumina library construction using the QIAseq FX DNA library kit (Qiagen, Hilden, Germany). The Miseq platform (Illumina, San Diego, CA) was used to sequence the indexed libraries. The next-generation sequencing (NGS) reads were mapped to the SARS-CoV-2 Wuhan-Hu-1 reference genome sequence (29.9-kb single-stranded RNA [ssRNA] [GenBank accession no. MN908947]) and were assembled using A5-miseq v.20140604 [10] to determine the full genome sequence. The obtained sequences were assigned according to the PANGO lineage definition (2021/04/28 version) [11].
3 Results
A total of 271 patients were screened from 5 medical centers in Kobe city. After removing duplicates and wrongly screened patients, 246 patients remained on the list. We then screened for the availability of data regarding age, sex, and body mass index (BMI), and subsequently, 240 patients were matched by age, sex, and BMI with a ratio of 1:1, and 198 patients were matched for the analysis (99 patients each).
The characteristics of the patients are shown in Table 1 . Overall, there were no statistical differences between the 2 groups, including the number of risk factors or time from the onset to the admission. The time when the patients with wild-type infections were admitted to the hospitals ranged from December 12, 2020, to April 4, 2021, whereas the patients with the variant infections were hospitalized later, from February 2 to April 9, 2021. As per the routes to admission, the patients were admitted from the sites as follows; 1. Transfer from other healthcare facilities, 2. Transfer from nursing homes, 3. From home, 4. From the hotels designated for mildly ill patients and 5. Others. There were no significant differences in regard to the routes of the hospitalizations.Table 1 Patient Characteristics on admission. Abbreviations: BMI, body mass index. CVA, cerebrovascular accident. CKD, chronic kidney disease. COPD, chronic obstructive pulmonary disease.
Table 1Characteristics n, (%) Alpha variant Wild type P-value
Number of the patients 99 99
Age (mean, range) 69.0 (22–97) 69.9 (22–103)
Female sex 45 (45.5) 43 (43.4)
BMI (mean, range) 23.1 (13.1–35.9) 23.1 (13.0–37.2)
Risk factors
Obesity 4 (4.0) 6 (6.1) 0.75
CVA 2 (2.0) 4 (4.0) 0.68
CKD 3 (3.0) 7 (7.1) 0.21
Asthma 7 (7.1) 4 (5.0) 0.54
COPD 8 (8.1) 2 (2.0) 0.10
DM 16 (16.2) 15 (15.2) 1.0
Dyslipidemia 15 (15.2) 27 (27.3) 0.17
Hypertension 47 (47.5) 49 (49.5) 0.89
Malignancies 9 (9.1) 6 (6.1) 0.59
Immunosuppression 3 (3.0) 3 (3.0) 1.0
HIV/AIDS 0 (0) 0 (0) 1.0
Current smoker 17 (17.2) 15 (15.2) 0.88a
Previous smoker 22 (22.2) 24 (24.2) 1.0a
Number of risk factors (median, range) 1 (0–4) 1 (0–4) 0.41
Mean time from the onset to admission (days, range) 6.8 (1–34) 6.4 (1–19) 0.32
Median date of admission (date, range) March 18, 2021 (February 2-April 9) January 21, 2021 (December 12, 2020–April 4, 2021)
DM, diabetes mellitus. HIV, human immunodeficiency virus. AIDS, acquired immune deficiency syndrome.
a Compared with those who never smoked.
The clinical features of both groups upon and after admission are shown in Table 2 . Despite similar baseline patient characteristics, the variant group tended to have a more severe form of infection. Although there was no statistical difference in respiratory rate on admission, and more patients in the variant group required oxygen therapy (43.4% vs 26.3%. P = 0.017). There were no statistical differences between the 2 groups regarding the level of consciousness and C-reactive protein level.Table 2 Clinical features of COVID-19 on both the variant group and those with wild-type.
Table 2Clinical features n, (%) Alpha variant Wild type P-value
Respiratory rate on admission (per minute, median, range) 22.0 (13–45) 20 (14–30) 0.11
Oxygen therapy needed upon admission 43 (43.4) 26 (26.3) 0.017
Impaired level of consciousnessa 13 (13.1) 18 (18.2) 0.41
C-reactive protein level (mg/dL, mean, range) 5.33 (0.1–25.85) 5.00 (0.03–38.80) 0.69
Treatment outcome
Duration of oxygen therapy (days, median, range) 11 (1–72) 10 (1–65) 0.29
HFNC required 21 (21.2) 4 (4.0) 0.0007
HFNC duration (days, median, range) 5 (2–20) 5 (2–8) 0.60
Mechanical ventilation required 16 (16.2) 6 (6.1) 0.049
Mechanical ventilation duration (days, median, range) 14.5 (3–69) 24 (4–46) 0.89
ECMO required 1 (1.0) 0 (0) 1
ECMO duration (days) 50 NA
ICU care required 30 (30.3) 14 (14.1) 0.01
Duration of ICU stay (days, median, range) 8 (1–66) 4 (1–51) 0.67
Tracheostomy required 5 (5.1) 3 (3.0) 0.29
Duration of hospital stay (days, median, range) 17 (3–74) 10 (2–99) 0.031
Dexamethasone use 82 (82.9) 49 (49.5) <0.0001
Remdesivir use 35 (35.4) 11 (11.1) 0.0001
Tocilizumab use 26 (26.3) 10 (10.1) 0.0057
Ivermectin use 10 (10.1) 1 (1.0) 0.013
Favipiravir use 7 (7.1) 6 (6.1) 1.0
Additional corticosteroid required 16 (16.2) 5 (5.1) 0.021
28-day mortality 12 (12.1) 8 (8.1) 0.48
Abbreviations, HFNC, high flow nasal canula. ECMO, extracorporeal membrane oxygenation.
ICU, intensive care unit.
a Impaired level of consciousness is defined as Glasgow coma scale less than 15 on admission.
Even though more patients on the variant group required oxygen therapy, the duration of oxygen therapy was not different between the two groups (median 11 days vs 10 days, p = 0.29). Likewise, more patients on the variant group required high-flow nasal cannula (HFNC) therapy, (21.2% vs 4.0%, p = 0.0007) and mechanical ventilation (16.2% vs 6.1%, p = 0.049), and more patients on the variant group required intensive care unit (ICU) stay, (30.3% vs 14.1%, p = 0.01). The duration of HFNC, mechanical ventilation, and ICU stay were not different between the two groups (median of 5 days vs 5 days, p = 0.60, 14.5 days vs 24 days, p = 0.89, and 8 days and 4 days, p = 0.67, respectively). Duration of hospitalization was longer on the variant group (median of 17 vs 10 days, p = 0.031). The variant group also required more drug therapies such as dexamethasone, remdesivir, tocilizumab, and ivermectin (82.9% vs 49.5%, p < 0.0001, 35.4% vs 11.1%, p = 0.0001, 26.3% vs 10.1%, p = 0.0057, and 10.1 vs1.0%, p = 0.013, respectively). They also required the addition of corticosteroid therapy on top of initial dexamethasone therapy (16.2% vs 5.1%, p = 0.021).
However, the 28-day mortality was not statistically different between the 2 groups (12% vs 8%, p = 0.48).
For the Bayesian model as a sensitivity analysis for the primary outcome, the mean posterior probability of the mortality on the Alpha variant group was 13% (95% CrI: 7–19%), compared with the control group of 9% (95% CrI: 4–15%) (Fig. 1 ). Regarding HDI, 93.0% fell inside the ROPE. A sensitivity analysis with ROPE ranging from −0.2 to 0.2 was 100%. Convergence was confirmed, with all values of Gelman-Rubin statistics being less than 1.1, and all trace plots also indicated convergence across four chains.Fig. 1 Posterior probability density function (PDF) of 28-day mortality for both the Alpha variant group and wild type group.
Fig. 1
Another sensitivity analysis using propensity score analysis with IPW, using the use of dexamethasone, remdesivir, tocilizumab, ivermectin, and use of additional steroid on top of dexamethasone as co-variates, showed an average treatment effect (ATE) of 0.0058 (standard error 0.042, p = 0.89), again did not show the difference between the Alpha variant and the control group.
4 Discussion
We found that the infection caused by the Alpha variant was associated with a more severe form of the illness than the one caused by the wild type, with higher oxygen therapy requirement, and more patients required aggressive measures such as HFNC and mechanical ventilation. The duration of the hospital stay was also longer in the variant group. The Alpha variant was also associated with more ICU requirements. However, there was no difference in 28-day mortality between the 2 groups, even after multiple sensitivity analyses, such as Bayesian analysis and propensity score analysis using IPW. Likewise, the duration of oxygen therapy, HFNC, mechanical ventilation, the length of ICU stay, and tracheostomy requirement were not different between the two groups. These findings suggest that the Alpha variant is more likely to cause a severe form of infection, but the clinical outcome was not significantly altered compared with those severe diseases caused by the wild type. One can argue that these outcomes are associated with the judgment of treating physicians, and they could be confounded by their bias. However, the treating physicians didn't have information about what kind of virus they are dealing with and even if they could know how much the variant had replaced the wild type from the announcement from the local public health management center, the clinical management didn't alter based on whether the virus was a variant or not. More patients with the Alpha variant group required oxygen therapy on admission, and this reflects that the patient had hypoxia, dyspnea, or both. Therefore, we consider that the Alpha group indeed tended to cause a more severe form of infection than the wild type.
In a large cohort study conducted in England, using S-gene target failure (SGTF) was used as a proxy as B.1.1.7, the Alpha variant was associated with an increased risk of both hospitalization and mortality than the wild-type virus [3,4,6,11]. However, another cohort study using whole-genome sequence for hospitalized patients found no evidence of higher severity and mortality [7]. The former studies had a limitation since the initial outbreaks of the Alpha variant were local to southeast England and neighboring regions, with subsequent high local pressure on the healthcare system might have led to higher mortality [3]. Likewise, findings by the latter could be potentially confounded by not sequencing all patients diagnosed with COVID-19 [3]. We are not sure whether these differences affected the differences in the outcomes of all studies, but we were able to overcome these potential issues in our current study because in the study period, a shortage of beds was not present and patients enrolled were whom whose sequencing was implemented. It could also be explained by other factors, such as ethnicity, weather, or the capacity of healthcare systems.
Because the Alpha variant was associated with a more severe form of illness in our cohort, they tended to receive medications such as dexamethasone, remdesivir, and tocilizumab. Tocilizumab is not approved to use for the treatment of COVID-19 in Japan, but some centers used it on an off-label basis given the available evidence during the study period [1,12]. It is not surprising to see that the more patient in the variant group received a variety of medications, such as dexamethasone, remdesivir, tocilizumab, and others since they were sicker than the control group on admission [[13], [14], [15], [16], [17]]. For the patients whose pneumonia deteriorated after the administration of dexamethasone and other medications, additional steroids were given, usually methylprednisolone of 250 mg for three days followed by a tapered dose, although this regimen has not been evaluated in the clinical study [18,19]. Complications and influence of a longer period of steroids were not evaluated in this study. Regardless of these, our propensity score analysis including these differences into account did not show a 28-day mortality difference between the two groups, so were the Bayesian analysis conducted as a sensitivity analysis, again suggested that the Alpha variant might be associated with severity with lengthy hospitalization and aggressive medical care, but not with mortality.
Why does the Alpha variant cause severe disease, while not the higher death? We are not able to derive the reason from our retrospective study. Since COVID-19 tends to damage the lungs more specifically than other organs, compared to other severe infections by other pathogens, which are often associated with sepsis with multi-organ damage [20]. Modern respiratory support such as mechanical ventilation and ECMO could have saved the lives of these patients, although they cannot prevent severe diseases to occur. Further studies need to be conducted to better elucidate the biological understanding of SARS-CoV-2 causing deaths to the patients.
There are some strengths in our study. First, the genetic information derived from whole-genome sequencing of all patients was included in the case-control study. Second, this is a multi-center retrospective study, and data were from a so-called real-world setting, the results are likely to be more generalizable rather than the prospective controlled studies and those derived from single-center studies. Third, the period of this study, between December 2020 and March 2021, was the best time for clinical comparison of alpha and wild strains because it was just before the beginning of mass vaccination, and we were able to avoid vaccinations affecting our findings. In addition, we could avoid being influenced by the tightness of capacity of hospitalization, since we did not suffer from a shortage of hospital beds during the study period, unlike the other time at the later “wave".
Our study also has several inherent limitations. First, the number of patients enrolled is relatively small, and all patients were Japanese of ethnic origin. Therefore, the results could be generalizable to Japanese people, but not necessarily to other ethnicities. The small sample size might make our finding of the primary outcome not conclusive, and the Alpha variant might show higher mortality if evaluated by the larger studies. However, our sensitivity analysis using Bayesian inference, which does not require as many patients as the conventional frequentist statistic approach, also showed little difference between the two groups, and multiple sets of ROPE were very high. Therefore, our findings were not to conclusively state that the Alpha is associated with higher mortality than the wild type, but they suggest that it might be. Second, because of the retrospective nature of our analysis, potential biases not measured in all analyses we conducted could exist and it might have rendered our findings. In addition, some variables such as the socioeconomic status of the patients were not available in our cohort and we were unable to use these variables, which could have rendered our findings [3]. Third, our study is limited to hospitalized patients, and we were not able to investigate the infectiousness of the Alpha variant in the community. Fourth, the characteristics of the patients of our cohort might not be the same as the patients admitted to other hospitals in Kobe City during the study period. Fifth, we were not able to measure virological data, such as viral loads of SARS-CoV-2 from each given patient, with the nature of the retrospective study. Therefore, our findings are entirely clinical and did not investigate the differences regarding viral replication between the Alpha variant and the wild type, unlike the previous studies.
In conclusion, the Alpha variant was associated with a severe form of COVID-19, compared with the non-Alpha wild type, but might not be associated with higher mortality.
5 Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Funding
This work was partially supported by a grant-in-aid from the 10.13039/100009619 Japan Agency for Medical Research and Development (AMED) Research Program on Emerging and Re-emerging Infectious Diseases (grant no. JP21fk0108103)
Contributions
Conceptualization was made by A.D., K.I.; Methodology was developed by A.D, K.I.; Whole-genome sequencing was conducted by N.N, R.N, T.I, data extract in Kobe City Public Health Management Center was conducted by H.K, R.F, N.K, Data extraction from electric charts at each facility was conducted by A.D, K.I, K.O, K.I, T.N, Statistical analyses were mainly conducted by K.I.; Study validation was given by K.I.; Writing–the original draft was prepared by A.D, K.I.; Writing—Review and Editing were done by All authors. All authors have read and agreed to the published version of the manuscript.
Ethical approval and consent to participate
This study was approved by the ethics committee of Kobe City Medical Center General Hospital, Kobe City Nishi-Kobe Medical Center, Kobe City Medical Center West Hospital, National Hospital Organization Kobe Medical Center, Kobe University Graduate School of Medicine, Kobe City Public Health Management Center. The patients’ informed consents were not required given the retrospective nature of this study.
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Declaration of competing interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be considered a potential conflict of interest.
Abbreviations
COVID-19 novel coronavirus 2019
HFNC high flow nasal cannula
ECMO extracorporeal membrane oxygenation
PCR polymerase chain reaction
BMI body mass index
CRP C-reactive protein
ICU intensive care unit
ROPE region of practical equivalence
HDI highest density interval
IPW inverse probability weighting
Acknowledgments
Not applicable.
==== Refs
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| 36494058 | PMC9722619 | NO-CC CODE | 2022-12-15 23:15:09 | no | J Infect Chemother. 2022 Dec 6; doi: 10.1016/j.jiac.2022.11.014 | utf-8 | J Infect Chemother | 2,022 | 10.1016/j.jiac.2022.11.014 | oa_other |
==== Front
Clin Nutr ESPEN
Clin Nutr ESPEN
Clinical Nutrition Espen
2405-4577
European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd.
S2405-4577(22)01415-2
10.1016/j.clnesp.2022.12.005
Original Article
Nutritional Biomarkers as Predictors of Clinical Outcomes between COVID-19 Severity Groups in a Tertiary Government Hospital
Anzo Ferdinand M. MD a∗
Mayo Maribeth B. MD, DPCP, FPSHBT, FPCHTM b
a Department of Internal Medicine
b Batangas Medical Center, Batangas City, Philippines
∗ Corresponding author.
6 12 2022
6 12 2022
27 9 2022
19 11 2022
2 12 2022
© 2022 European Society for Clinical Nutrition and Metabolism. Published by Elsevier Ltd. All rights reserved.
2022
European Society for Clinical Nutrition and Metabolism
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
Malnutrition, as defined by the World Health Organization (WHO), includes undernutrition. In the Philippines, malnutrition is common due to several factors. The nutritional biomarkers can be used as an alternative indicator of dietary intake and nutritional status that can detect deficiencies in support to clinical management of COVID-19 patients. Apart from that, biomarkers are potentially useful for screening, clinical management, and prevention of serious complications of COVID-19 patients. Serum albumin, c-reactive protein (CRP), leukocyte count, lymphocyte count, blood urea nitrogen to compute/the nutritional prognostic indices (Prognostic nutritional index (PNI) score, Blood Urea Nitrogen/Albumin ratio (BAR) and C-reactive protein/Albumin ratio (CAR).
Objectives
To compare the nutritional biomarkers of patients with COVID-19 based on case severity and determine the nutrition prognostic indices and associate to patients’ clinical outcome during hospital stay.
Methods
A single center, cross-sectional study was performed between June 2021 to August 2021 in a COVID-19 designated referral center in CALABARZON which comprised of 167 patients as part of the study. Clinicodemographic profile including patients’ age, sex, co-morbidities, weight, height, laboratory, and serum biomarkers during the first 48 hours of admission (serum albumin, leukocyte count, lymphocytes count, CRP, and BUN) were collated wherein the nutritional prognostic indices were computed and analyzed. Clinical outcomes of the patients were based on the patients’ final diagnoses (recovered, length of hospital stay, progression of severity and mortality).
Results
167 non-critically ill COVID-19 patients were included in the analysis, of which 52.7% are admitted under the COVID-19 severe group and 47.3% for COVID-19 Mild/Moderate. Mostly are male (53.3%) with an average BMI of 24.26 (SD=3.52) and have hypertension (55.1%) and diabetes (42.5%). Among the nutritional biomarker, albumin (p=0.028; p=0.004), TLC (p=0.013; p=0.005) and BUN (p=0.001; p=<0.001) were shown to be significantly associated with progression of severity and mortality. Univariate logistic regression analysis showed the following nutritional prognostic score were correlated: (1) progression of COVID-19 severity: PNI score (OR 0.928, 95% CI 0.886, 0.971, p=<0.001), and BAR value (OR 1.130, 95% CI 1.027, 1.242, p=0.012); (2) Mortality: PNI score (OR 0.926, 95% CI 0.878, 0.977, p=0.005), CAR (OR 1.809, 95% CI 1.243, 2.632, p=0.002), and BAR (OR 1.180, 95% CI 1.077, 1.292, p=<0.001). The average length of stay of COVID-19 patients was 12 days (SD=7.72). However, it does not show any significant correlation between any nutritional biomarker, prognostic indices and length of hospital stay.
Conclusion
This study demonstrated that deranged level of nutritional biomarkers can affect patient’s COVID-19 severity and associated with patient’s clinical outcome. Low albumin (<2.5g/dL), low level of TLC (<1500 cells/mm3), elevated BUN (>7.1 mmol/L) are associated with patient’s case severity progression and mortality while low PNI score (<42.49), high BAR value (>2.8) and CAR value (>2.04) provided an important nutritional prognostic information and could predict mortality which can be a useful parameter in admission, hence it is recommended to screen all COVID-19 patients to reduce mortality.
Keywords
COVID-19
non-critically ill
malnutrition
nutritional biomarker
prognostic nutritional score
C-Reactive protein and Albumin ratio
blood urea nitrogen and albumin ratio
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pmc
| 0 | PMC9722622 | NO-CC CODE | 2022-12-07 23:19:12 | no | Clin Nutr ESPEN. 2022 Dec 6; doi: 10.1016/j.clnesp.2022.12.005 | utf-8 | Clin Nutr ESPEN | 2,022 | 10.1016/j.clnesp.2022.12.005 | oa_other |
==== Front
Prev Med Rep
Prev Med Rep
Preventive Medicine Reports
2211-3355
Elsevier
S2211-3355(22)00394-1
10.1016/j.pmedr.2022.102087
102087
Article
Psychometric development of the COVID-19 vaccine misinformation scale and effects on vaccine hesitancy
Bok Stephen a⁎
Martin Daniel b
Acosta Erik c
Shum James d
Harvie Jason c
Lee Maria e
a Department of Marketing, College of Business and Economics, California State University, East Bay, Hayward, CA, USA
b Department of Management, College of Business and Economics, California State University, East Bay, Hayward, CA, USA
c California State University, East Bay, Hayward, CA, USA
d Golden Gate University, San Francisco, CA, USA
e Nutritional Therapist, NTP, CMT, University of California, Irvine, Irvine, CA, USA
⁎ Corresponding author.
6 12 2022
6 12 2022
10208712 6 2022
31 10 2022
1 12 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 help inform post-COVID-19 pandemic practical health policies, the researchers created the COVID-19 vaccine misinformation scale (CVMS). During the COVID-19 pandemic, falsehoods spread online which casted doubt and concerns about the vaccine. Example misconceptions included vaccination leads to greater vulnerability to other illness and would alter someone’s DNA. The researchers performed two large surveys with U.S. participants. The researchers reviewed debunked COVID-19 vaccine falsehoods online. Construction of the CVMS followed standard psychometric scale development steps. Statistical analysis provided support for the 10-item CVMS with satisfactory reliability, discriminant validity, and convergent validity. Predictive validity regression analysis demonstrated the CVMS associated with higher vaccine hesitancy. The prevalence of vaccine misbeliefs broadened pandemic healthcare challenges. On top of existing duties, healthcare workers had to explain vaccine efficacy and safety to dispel fallacies. The researchers discuss implications for the CVMS within the context of motivated reasoning theory.
Keywords
SARS-CoV-2 vaccine misinformation beliefs
Preventative health care
Psychometric validation
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pmc1 Introduction
1.1 COVID-19 vaccine misinformation
The COVID-19 pandemic altered the world economy and impacted countless of lives through infections. Across the globe, misinformation negatively impacted COVID-19 vaccine perceptions (Feleszko et al., 2021, Lazarus et al., 2021). In India, health and allergic reactions to COVID-19 vaccines was a major concern discussed on social media (Praveen et al., 2021). In the U.K., qualitative research found negative stories, personal knowledge, and safety concerns generated confusion and mistrust towards COVID-19 vaccines (Lockyer et al., 2021). In the Democratic Republic of Congo, 24.1% of research participants (996/4,131) denied the existence of COVID-19 which associated with a lower likelihood to accept legitimacy of the COVID-19 vaccine (Ditekemena et al., 2021). Ditekemena et al. (2021) found health falsehoods (e.g., will cause death and sterilization) as reasons against the COVID-19 vaccine. The development of the COVID-19 vaccine misinformation scale (CVMS) equips researchers with a psychometric measure to study individuals with these misbeliefs. It becomes possible to assess individual differences and develop preventative health marketing materials to influence the decision-making process.
1.2 Theory of motivated reasoning and COVID-19 vaccine misinformation
The theory of motivated reasoning explains individuals have pre-determined goals and evaluate new (mis)information to serve these goals (Leeper and Slothuus, 2014, van der Linden, 2022). Individuals will interpret or seek information that supports preexisting opinions. Individuals are likely to arrive at their preconceived conclusions when bits of information are present to substantiate outcomes (Kunda, 1990). Misinformation propagated during the COVID-19 pandemic provided the false justifications to cast doubts about COVID-19 vaccines and the institutions advocating for them (Bruns et al., 2022, Jennings et al., 2021). Vaccine conspiracies shared as social media posts gave individuals what they needed to believe false claims. For instance, Nicki Minaj’s tweet to her over 22 million followers of her cousin’s story stating, “… his friend got it [the COVID-19 vaccine] & became impotent. His testicles became swollen….” was shared around the world (Hall Jamieson, 2021, Minaj, 2021). This provided faulty support to existing concerns that vaccines harm someone’s reproductive system. Health institutions publicly spoke out to counter these claims (Hall Jamieson, 2021). Unfortunately, lower trust in institutions dealing with the COVID-19 pandemic was found to associate with lower vaccine intent (Dal and Tokdemir, 2022). Similarly, lower trust in government and higher conspiracy beliefs was found to associate with lower COVID-19 vaccine intent (Van Oost et al., 2022). This posed a problem to combat COVID-19 because mass vaccinations was a major public health policy to reach safe levels of herd immunity (Randolph and Barreiro, 2020).
Moreover, motivated reasoning attributed to the spread of online COVID-19 vaccine misinformation (Pennycook et al., 2022, Sylvester, 2021). These goals are often politically motivated based on the set of established beliefs (Rousseau and Tijoriwala, 1999). Conservative news outlets contributed to the spread of doubt and misinformation about the COVID-19 vaccine in the U.S. (Sylvester, 2021). Sylvester (2021) found lower COVID-19 knowledge and conservative ideology both independently associated with lower vaccine acceptance. Stricken, Taber, and Lodge (2011) argued those more knowledgeable about an issue and politically engaged are more determined to defend their beliefs. The mechanisms in operation include prior beliefs, confirmation bias, and disconfirmation bias (Strickland et al., 2011). The politicization of COVID-19 vaccines in the U.S. contributed to online misinformation campaigns that dampened public vaccine acceptance (Bolsen and Palm, 2022). This politization made COVID-19 vaccine misinformation widely shared, viewed, and unnecessarily contemplated by the general U.S. population.
Users have the flexibility and freedom to share (mis)information on social media platforms (like Facebook) without substantial third party filtering (Allcott and Gentzkow, 2017). Active users, known as ‘supersharers’ and ‘superconsumers’ are exposed to greater amounts of online content (Grinberg et al., 2019). Social media platforms reinforce preexisting beliefs. Platform algorithms curate content with opinion-confirming content so users spend more time online (Kitchens et al., 2020). Information on social media is often opinion-based, largely unfiltered, and easily shareable by users with an account (Kumar and Shah, 2018). Researchers have found fake news spreads 70% more likely than accurate news on Twitter (Vosoughi et al., 2018). Higher instances of sharing negative posts applied to COVID-19 vaccine related content. For example, a review of 5,000 COVID-19 vaccine related tweets found 182 tweets to have a negative behavioral intent compared to 97 positive behavioral intent tweets (Liu and Liu, 2021). Users search, consume, follow, and believe what suits their goals. Skepticism and anti-vaccine narratives fit individuals’ motivated reasoning goals to share vaccination misinformation and resist COVID-19 vaccination. Because of widespread COVID-19 vaccine misinformation we recognized the prevalence of these misbeliefs and negative impact on public inoculations. Hence, this study designed and validated the COVID-19 vaccine misinformation scale (CVMS). The purpose was to provide researchers a psychometric tool to study those with the shared misbeliefs and relevant behavioral outcomes.
1.3 Pervasive COVID-19 vaccine hesitancy
Vaccine hesitancy stems from distrust in government and pharmaceutical reports of vaccine safety and efficacy (Wagner et al., 2020). For example, individuals falsely believed hurtful metals are in vaccines despite medical institutions stating their absence in vaccines (Center for Disease Control and Prevention, 2018). This distrust in the medical institutions and concerns about vaccine safety has spurred individuals to avoid COVID-19 inoculation. Some individuals falsely believe getting sick is a safer way for the immune system to naturally develop resistance for diseases (Wagner et al., 2020). For instance, people have claimed most children recover from illnesses like the common cold as evidence that proves children will develop immunity to other diseases. However, debilitating (e.g., Polio, Hib, Rotavirus) and life-threatening (e.g., Hepatitis B, Whooping Cough/Pertussis, Tetanus) diseases are unsafe for natural exposure. Vaccines have saved countless lives by activating one’s natural immune system to develop resistance (Henao-Restrepo et al., 2015, Koirala et al., 2020, World Health Organization, 2020).
Regrettably, vaccine misinformation was notably prevalent online (Steffens et al., 2019, Waszak et al., 2018). Narratives created by the anti-vaccination movement posed a problem for health care providers and organizations that must first untangle this misinformation (Steffens et al., 2019). Unfortunately, vaccine misinformation captivated the public compared to factual information (Loomba et al., 2020). This is partly explained by unsubstantiated case studies (e.g., a sewage system blamed for a Hong Kong high-rise housing outbreak) (Han et al., 2020) and social media connections sharing distressing fake news (e.g., Columbia health care COVID cartel received cash for deaths) (Taylor, 2020). Misinformation grabbed attention with flashy headlines compared to technical facts like biomedical statistics on vaccine effectiveness rates. This environment of misinformation created mistrust in medicine and the health care institutions dedicated to saving lives (Sanfilippo et al., 2020). Thereby, we hypothesize COVID-19 vaccine misinformation beliefs will have a positive relationship to vaccine hesitancy, where higher COVID-19 vaccine misinformation beliefs will relate to higher vaccine hesitancy. Figure 1 illustrates the hypothesized relationship of CVMS with vaccine hesitancy.Figure 1 Hypothesized CVMS effects on vaccine hesitancy model.
2 Methodology
2.1 Overview of Studies
The creation of the CVMS followed recommended scale development processes using two studies and two samples (Carpenter, 2018, Worthington and Whittaker, 2006). The first step formulated and revised scale items. The authors compiled COVID-19 vaccine misbeliefs from online sources (e.g., Center for Disease Control and Prevention) and published work studying debunked beliefs. The authors constructed construct items that succinctly represented misbeliefs. The second step examined reliability, discriminant validity, and convergent validity. The authors utilized IBM SPSS V26 scale reliability and bivariate correlation functions to perform this analysis. By following this standard practice, statistical analysis formed the new scale. The third step evaluated predictive validity and the proposed hypothesis (Hinkin, 2005). Predictive validity analysis assessed utility of the new scale by testing the CVMS with vaccine hesitancy. We conducted two large surveys with U.S. participants to follow standard psychometric scale development practice. The studies met the institution's guidelines for the protection of human subjects concerning safety and privacy. Two studies provided several benefits: collect a large array of measures for discriminant and convergent validity analysis without over burdening participants, assess a larger sample, and replicate predictive validity results across two samples (Donnellan et al., 2006, Schmidt et al., 2003). After psychometric development, we conducted regression analysis to assess for predictive validity.
2.2 Construction of items
We started with a comprehensive list of twenty-seven items from inductive and deductive reasoning (Hinkin, 2005). The exhaustive list of COVID-19 vaccine misbeliefs came from thoroughly debunked and dismissed misbeliefs reported by the Center for Disease Control and Prevention (CDC) (Center for Disease Control and Prevention, 2020). The list included a review of literature and revived vaccine misconceptions applied to the COVID-19 vaccine (Center for Disease Control and Prevention, 2018, Grech et al., 2020, Jacobson et al., 2015, Loomba et al., 2020, May and Silverman, 2003). For example, some misbelieve vaccines contain harmful chemicals and the researchers designed statements to capture this idea applied to COVID-19 vaccines (e.g., “COVID-19 vaccines contain unsafe toxins”). A 5-point semantic differential scale suitable for true/false items was utilized (1 – Definitely not true, 2 – Probably not true, 3 – Not sure/Cannot decide, 4 – Probably true; 5 – Definitely true) (Brotherton et al., 2013). This type of scale allotted for variation in responses while identifying believers and nonbelievers of COVID-19 vaccine misinformation.
2.3 Participants
A total of 1,103 individuals across the U.S. participated on Amazon Mechanical Turk. The researchers followed standard practices for sampling participants on this platform (e.g., completed over 100 previous assignments with an approval rating greater than 97%) (Buhrmester et al., 2016, Keith et al., 2017). Fifteen dropped out without completing. Eight failed attention checks (e.g., selecting ‘disagree’ between questions in the survey). Analysis was conducted with the remaining participants (N = 1,080, Study 1 n = 508, Study 2 n = 572). The age of participants ranged from 18 to 78 years old (M = 41.2, SD = 12.5) with 60% identifying as female. Median household income was between $50,000 to $59,999. Participants viewed approximately 3.62 hours of news each week. Sixteen percent of participants worked in healthcare or a science profession familiar with basic epidemiology (e.g., medical or healthcare professional). 834 participants identified as Caucasian, 122 as African American, 41 as Hispanic/Latino, 12 as Native American/American Indian, 54 as Asian/Pacific Islander, and 17 as other.
2.4 Regression variables
Independent variable. Based on the psychometric scale development process, the researchers used the validated COVID-19 vaccine misinformation scale (CVMS) in this analysis to assess for predictive validity.
Dependent variable. The Vaccine Hesitancy Scale (VHS) measured parental and personal beliefs towards vaccination (Domek et al., 2018, Larson et al., 2015, Shapiro et al., 2018). The scale was adapted with a U.K. population to a 9-item generic version focused principally on personal vaccine hesitancy beliefs (e.g., “I am concerned about serious adverse effects of vaccines.”) (Luyten et al., 2019).
Control variables. The researchers controlled for gender, age, household size, household income, and college degree in the regression analysis.
3 Results
3.1 Inter-item correlations and reduction
The authors performed the SPSS dimension reduction factor function to access inter-item correlations. Based on inter-item correlations, thirteen of the twenty-seven items demonstrated adequate values (|r|s < .30) (Tabachnick et al., 2007). There was a removal of fourteen items below the threshold from the constructed list (Worthington and Whittaker, 2006). For instance, despite false claims “Those with the COVID-19 vaccine shed a protein linked to reproductive complications” it inadequately correlated with other items. Impartial statistical results determined reduction of the list (Boateng et al., 2018). Casting a large pool of initial items and reducing to beneath half decreases the odds of missing an item suitable for the new measure (Flight et al., 2011, Pommer et al., 2013). Kaiser-Meyer-Olkin’s measure of sampling adequacy (.972) and Bartlett's test of sphericity [χ2(78) = 12,274.44, p < .001] indicated the items as suitable for factor analysis (Cerny and Kaiser, 1977, Kaiser, 1981, Tobias and Carlson, 1969).
3.2 Factor loadings and descriptive statistics
The recommended threshold for factor loadings are at least between .40 to .70, with higher scores better (Hulland, 1999, Peterson, 2000). Auspicious factor loadings toped .70 and suited the criteria used in this study for the selected ten CVMS items (Chyung et al., 2017; Hair Jr Joseph and F., Black William, C., Babin Barry, J., Anderson Rolph, E., , 2010, Yong and Pearce, 2013) (see Table 1 ). Higher factor loadings indicate an item explains more variance of the variable and this threshold was considered appropriate in previous pathway analysis literature (Lin, 2012). Despite COVID-19 vaccine misconceptions that appear acceptable for the CVMS (e.g., “COVID-19 vaccination can infect someone with the disease they are trying to prevent”) they did not meet the objective statistical analysis for inclusion. We followed these standards to evaluate items for inclusion based on SPSS dimension reduction factor analysis. Therefore, we proceeded with 10-items that met these standards.Table 1 Item-factor loadings and item-level descriptive statistics for the 10-item COVID-19 Vaccine Misinformation Scale (CVMS)
Study 1 (N = 508) Study 2 (N = 572)
Item M (SD) Factor loading M (SD) Factor loading
1) A COVID-19 vaccine will cause someone to be more susceptible to other diseases 2.41 (1.25) .867 2.69 (1.32) .853
2) Vitamin and mineral supplements are just as effective as a COVID-19 vaccine 2.41 (1.33) .824 2.71 (1.40) .851
3) Microchips are inserted during COVID-19 vaccination 2.06 (1.33) .854 2.44 (1.41) .840
4) A COVID-19 vaccine alters someone's DNA 2.33 (1.34) .835 2.53 (1.39) .849
5) COVID-19 vaccines cause autism 2.28 (1.31) .876 2.48 (1.31) .867
6) Herbs like thyme are a natural COVID-19 vaccine 2.26 (1.30) .855 2.54 (1.36) .849
7) COVID-19 vaccines cause neurological damage 2.46 (1.28) .859 2.64 (1.31) .858
8) Elderberry is a natural COVID-19 vaccine 2.25 (1.23) .822 2.63 (1.34) .853
9) People COVID-19 vaccinated endanger the lives of others 2.20 (1.34) .862 2.49 (1.39) .856
10) COVID-19 vaccines will damage someone's spinal cord 2.24 (1.27) .843 2.67 (1.29) .850
Scree plot and parallel analyses showed the CVMS appropriate as one factor. The first item explained 73.04% of the common variance. Direct oblimin rotated analysis with two principal component factor loadings generated scores underneath .20 for the second factor. Forced two factor analyses produced unsatisfactory scores (Dunn et al., 1994, Gibbons et al., 1985). Hence, one factor suited the new COVID-19 vaccine misinformation belief measure.
Over 70% of participants agreed, each of the ten items to be untrue (see Table 2 ). This followed a high recommended consensus for continuous true/false scales (Clark and Watson, 1995). The 10-item CVMS was composed of varying misbeliefs about COVID-19 vaccines. For example, there was no evidence microchips are inserted during COVID-19 vaccination despite widely spread U.S. conspiratorial accusations of former CEO of Microsoft, Bill Gates (Gu et al., 2021) (Study 1: M = 2.06, SD = 1.33; Study 2: M = 2.44, SD = 1.41). The CVMS encompassed incorrect conspiratorial beliefs and inaccurate health effects propagated at the onset of pandemic when less information was known about COVID-19.Table 2 Frequency statistics of COVID-19 Vaccine Misinformation Scale items
Frequency Believe Not True Percent Believe Not True
Item 1 794 73.52%
Item 2 765 70.83%
Item 3 805 74.54%
Item 4 796 73.70%
Item 5 830 76.85%
Item 6 810 75.00%
Item 7 788 72.96%
Item 8 823 76.20%
Item 9 810 75.00%
Item 10 800 74.07%
Notes: Responses 1 – Definitely false, 2 – Probably false, and 3 – Not sure/cannot decide counted as believe as not true. Responses 4 – Probably true and 5 – Definitely true counted as believe as true. Percent out of 1,080 COVID-19 vaccine misinformation item responses. All misinformation statements had responses that ranged from 1-5. Each statement was unproven at the time of the study.
3.3 Convergent and discriminant validity
Correlation analysis of the new scale with existing validated scales examined uniqueness (Maloney et al., 2012, Mathieu and Farr, 1991). Positive correlations indicated degree of convergent validity. Negative correlations indicated a degree of discriminant validity. A correlation value of one would indicate measures as no different and not unique. Correlation results quantified the degree of relatedness and difference between scales (Lucas et al., 1996). Correlations with the 10-item CVMS was illustrated in Table 3 . The CVMS was correlated with the subsequent scales: vaccine conspiracy beliefs (e.g., “Vaccine safety data is often fabricated”) (i.e., general mistrust of inoculations) (alpha = .94) (Shapiro et al., 2016), locus of control (rational scale) (e.g., “Believe in the power of fate”) (i.e., belief in external influences on outcomes) (alpha = .61) (Levenson, 1981), peculiarity (e.g., “Am odd”) (i.e., belief in being eccentric) (alpha = .86) (Simms et al., 2011), emotionally detached (e.g., “Am emotionally reserved”) (i.e., expressiveness of emotions) (alpha = .82) (Simms et al., 2011), calmness (e.g., “Remain calm under pressure”) (i.e., degree of levelheadedness) (alpha = .75) (Hogan and Hogan, 1992), extroversion (e.g., “Extraverted, enthusiastic”) (i.e., outgoingness) (alpha = .77) (Gosling et al., 2003), ability to handle stress (i.e., coping ability) (alpha = .77) (Littman et al., 2006), self-esteem (e.g., “I have high self-esteem”) (i.e., confidence in oneself) (alpha = .64) (Robins et al., 2001), openness (e.g., “Open to new experiences, complex”) (i.e., experience seeking) (alpha = .62) (Gosling et al., 2003). The tabled results evinced expected relationships between constructs. Two studies enabled analysis on a gamut of varying measures which provided support for convergent and discriminant validity.Table 3 CVMS bivariate correlations with variables and demographics
Study 1 Study 2
Variables M (SD) r M (SD) r
Spontaneous 3.72 (1.21) -.430 *** 3.61 (1.30) -.556 ***
Vaccine hesitancy scale (VHS) 2.99 (1.25) .556 *** 3.01 (1.07) .463 ***
Vaccine conspiracy belief scale (VCBS) 3.67 (1.85) .817 *** 4.02 (1.79) .825 ***
Locus of control 3.93 (1.02) -.422 ***
Peculiarity 3.65 (1.43) .343 ***
Emotionally detached 3.92 (1.17) .284 ***
Calmness 4.62 (.82) -.079
Extroversion 3.72 (1.34) .185 ***
Ability to handle stress 4.14 (1.74) .284 ***
Self-esteem 4.70 (1.66) .329 ***
Openness 4.61 (1.14) -.298 ***
Demographic characteristics
Gender (Female) 1.63 (.48) -.077 1.59 .492 -.175 ***
Age 40.38 (12.32) -.109 * 40.54 12.336 -.109 **
Household size 3.13 (1.41) .342 *** 3.30 1.507 .431 ***
Household income 6.00 (2.86) -.141 ** 5.99 2.755 -.045
College Degree .92 (.28) .006 .91 .284 .882
Note: *p < .05, **p < .01, ***p < .001.
COVID-19 vaccine misinformation beliefs was measured using the 10-items to form the COVID-19 Vaccine Misinformation Scale (CVMS). Variables and demographics are correlated with the CVMS. Gender was dummy coded with males as 1 and females as 2. College degree was dummy coded with those with an associate degree or higher as 1 and those without as 0.
3.4 Reliability
The 10-item CVMS demonstrated high reliability (Study 1: alpha = .957; Study 2: alpha = .958). There was a high level of precision among the items in measuring the construct (Avalos et al., 2005, Kwon et al., 2013, Robbins et al., 2010).
4 Regression Analysis
4.1 Predictive validity results
IBM SPSS V26 was used to perform regression analysis (George and Mallery, 2019, Park, 2009). To test the predictive validity hypothesis, we regressed COVID-19 vaccine misinformation beliefs with vaccine hesitancy [F(6, 1,073) = 69.059, R2 = .279, p < .001]. The regression results showed higher COVID-19 vaccine misinformation beliefs was associated with higher vaccine hesitancy (b = .533, t = 18.494, p < .001) while holding gender, age, household size, household income, and college degree constant (see Table 4 ). The model with and without control variables followed the same directional results of statistically significant relationships.Table 4 Regression CVMS effects with vaccine hesitancy
Variables b SE p
Independent variable
CVMS .533 .029 < .001
Control variables
Gender .065 .062 < .05
Age -.042 .002 .113
Household size -.065 .023 < .05
Household income -.019 .011 .473
College degree -.111 .109 < .001
Notes: COVID-19 vaccine misinformation beliefs was measured using the 10-items COVID-19 vaccine misinformation scale (CVMS). Gender was dummy coded with males as 1 and females as 2. College degree was dummy coded with those with an associate degree or higher as 1 and those without as 0.
5 Discussion
5.1 General discussion
Results demonstrated the ten-item CVMS to have high reliability across two studies. The CVMS also displayed adequate convergent, discriminant, and predictive validity as a new measure. The CVMS positively correlated with vaccine hesitancy as expected based on misinformation discrediting COVID-19 vaccines. The correlation values did not equal to one, evincing suitable convergent validity. The CVMS demonstrated uniqueness to various constructs and suitable discriminant validity. True/false scale analysis showed between 30-20% of participants believed in each of the COVID-19 vaccine misinformation statements. COVID-19 vaccine misinformation was widespread in the U.S. during the time of the data collection (i.e., during the pandemic) and among the sampled participants.
Public health policies are designed to reduce the spread of diseases and mitigate risks (Bundgaard et al., 2021, Qualls, N., Levitt, A., Kanade, N., Wright-Jegede, N., Dopson, S., Biggerstaff, M., Reed, C., Uzicanin, A., Group, C.C.M.G.W., Group, C.C.M.G.W., 2017). Predictive validity results found those with higher COVID-19 vaccine misinformation beliefs associated with higher vaccine hesitancy. This contributes to motivated reasoning theory within the context of public health messaging. Misinformation was widely available, shared, and seen by the public. These pieces of misinformation contributed to distrust in vaccines, highlighted by the public institutions as a main way to combat the COVID-19 pandemic. Addressing negative and unfounded claims spread online has become a necessary part of disease prevention because it can impede cooperation to scientifically supported preventative measures. The CVMS provides healthcare professionals a tool to identify misbelievers and opportunity to present convincing fact-based educational materials to dispel false beliefs. Reaching those with vaccine misbeliefs is crucial to reduce the spread of COVID-19 and future disease outbreaks.
5.2 Limitations
Major events will remain in the collective memory of those alive at the time (Zelizer, 1995). However, as with most major events (e.g., Sino–Japanese conflicts), societal memory will fade with time (Gustafsson, 2020). Memory recall can reconstruct understanding of the past (Mena et al., 2016). COVID-19 vaccine misinformation beliefs took hold during the pandemic. While false COVID-19 vaccine narratives spread during the pandemic, the strength of false beliefs may decrease overtime. For example, the vaccine microchip narrative may grow more preposterous as people learn about the enormous amounts of data smartphone devices track from users. Technology advancements may change how society perceives the world and grow more skeptical of opinion shared on social media. Reconstruction of COVID-19 memories may focus on facts and fiction may fade with time.
With incentives to gain followers and views on social media, misinformation will continue to grow online. The human imagination can take wild ideas like 5G network towers and connect them to unfounded COVID-19 transmission (Gu et al., 2021). With the COVID-19 virus mutating, vaccine misinformation can also change with new events. New false claims could gain traction since most have been debunked (Caulfield, 2020, Hakim, 2021, Khalid et al., 2020). Therefore, changes in time and new outbreaks could add to the list of COVID-19 vaccine misconceptions. The CVMS demonstrated reliability and validity in the recent years after the initial outbreak. However, with time and new variants, it is likely new misbeliefs will spawn and create new public concerns. For example, previous vaccine false narratives transferred to COVID-19 vaccines like unfounded changes to someone’s DNA (Center for Disease Control and Prevention, 2022). People with these false beliefs are likely to evaluate new information through this motivated reasoning lens.
5.3 Future Research
Mandates are a consequential form of initiating behaviors. Governments and private businesses implemented vaccine mandates for the safety of employees and operations to continue with fewer COVID-19 related complications (Leask et al., 2021). Kaiser Family Foundation conducted found in November 2021 that 14% of U.S. participants indicated they will definitely not get the COVID-19 vaccine (Kirzinger et al., 2021). This was a one percent decrease from December 2020 from their first vaccine monitor surveys. This polling found uninsured persons under 65 years old (25%), Evangelical White Christians (25%), and Republicans (26%), the three highest groups to indicate they will definitely not get the COVID-19 vaccine. Three percent of the participants indicated they would vaccinate if it was required. This suggests mandates may not reach those ardently vaccine resistant, especially when multiple vaccine injections are recommended. Future research can explore underlying reasons for COVID-19 vaccine resistance. Research suggests isolation, cohesion, and conformity as factors to groupthink (Forsyth, 2020). Belonging to a group may supersede any one COVID-19 vaccine misconception stated as a reason to not vaccinate.
6 Conclusion
The CVMS demonstrated suitable psychometric properties to measure a unique construct among large U.S. samples. Higher CVMS scores related to greater vaccine hesitancy, where immunization became a key preventive health care action to reduce COVID-19 infections. These findings advance our understanding of how quickly misinformation can be spread and acquired by the public about a disease. Health care providers can utilize the brief measure to identify the strength and specificity of misbeliefs to better address patient concerns.
7 Financial Disclosure
Funding was provided by California State University, East Bay from College of Business and Economics professional development funds.
8 Funding Statement
Funding was provided by California State University, East Bay from College of Business and Economics professional development funds.
9 Ethics Statement
The project was reviewed and approved by the California State University, East Bay Institutional Review Board (CSUEB-IRB-2020-176).
The authors whose names are associated with the manuscript certify having no affiliations with or involvement in any organization or entity with any financial interests. This includes no educational grants; participation in speakers’ bureaus; honoraria; employment; memberships; consultancies; equity interests; stock ownerships; patent-licensing arrangements; or expert testimony. Further there are no non-financial interests. This includes no affiliations, knowledge, beliefs, personal relationships, or professional relationships in the subject matter or materials discussed in the manuscript.
Conflict of Interest
None reported
CRediT authorship contribution statement
Stephen Bok: Conceptualization, Software, Data curation, Methodology, Validation, Writing – original draft, Supervision. Daniel Martin: Methodology, Validation, Writing – original draft. Erik Acosta: Conceptualization, Methodology, Writing – original draft. James Shum: Conceptualization, Writing – original draft, Validation. Jason Harvie: Writing – original draft, Writing – review & editing. Maria Lee: Conceptualization, Visualization, 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.
Data availability
The data that has been used is confidential.
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| 36505271 | PMC9722623 | NO-CC CODE | 2022-12-09 23:15:05 | no | Prev Med Rep. 2023 Feb 6; 31:102087 | utf-8 | Prev Med Rep | 2,022 | 10.1016/j.pmedr.2022.102087 | oa_other |
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
The Author(s). Published by Elsevier Ltd.
S2405-8440(22)03268-6
10.1016/j.heliyon.2022.e11980
e11980
Research Article
Study of working from home: the impact of ICT anxiety and smartphone addiction on lecturers at NIPA School of Administration on job performance☆
Suryanto Adi a
Fitriati Rachma b∗
Natalia Sela Inike b
Oktariani Andina c
Munawaroh M. d
Nurdin Nurliah a
AHN Young-hoon e
a National Institute of Public Administration, Indonesia
b Universitas Indonesia, Faculty of Administrative Science, Indonesia
c IPB University, School of Business, Indonesia
d Jakarta State University, Faculty of Economy, Indonesia
e The Presidential Committee on Autonomy and Decentralization, Republic of Korea
∗ Corresponding author.
6 12 2022
12 2022
6 12 2022
8 12 e11980e11980
7 1 2022
16 2 2022
22 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.
The COVID-19 pandemic has significantly impacted on the working system, shifting working from office (WFO) into working from home (WFH) practice that requires employees to be skillful in using technology to support their work activities. However, this condition can affect job performance. This study aims to analyze the impact of ICT anxiety and smartphone addiction on job performance of all lecturers at NIPA School of Administration (Jakarta, Bandung, and Makassar). This study applied a quantitative method with a total sampling technique and conducted a survey on 135 respondents using an online questionnaire. Furthermore, this study employed job demands and resources theory as well as PLS-SEM to analyze five variables (ICT anxiety, smartphone addiction, interruption, job efficacy, and job performance) and to test seven hypotheses. The findings show that there is a positive relationship between ICT anxiety and interruption while interruption has negative influences on job efficacy and job performance. Therefore, this study recommends the facilitation of knowledge sharing related to ICT competence or literacy. In addition, NIPA should improve the security guarantees of the intellectual rights of the lecturers in relation to the choice of technology and integrate the demands of ICT needs with administrative-technical procedures.
ICT anxiety; Smartphone addiction; Job performance; COVID-19; Work from home; PLS-SEM.
Keywords
ICT anxiety
Smartphone addiction
Job performance
COVID-19
Work from home
PLS-SEM
==== Body
pmc1 Introduction
Job Demands and Resources (JD-R) theory examines the former that can trigger stress and the latter that can increase work motivation (Prodanova and Kocarev, 2021). In this regard, the JD-R theory is related to the use of technology that is currently a significant component in work activities, considering that technology can be a demand or even a resource that influences employees.
The COVID-19 pandemic has significantly impacted on the working system, shifting working from office (WFO) into working from home (WFH) practice which poses a major challenge for employees. According to Cabinet Secretariat of the Republic of Indonesia in 2020, the WFH system has been implemented in Indonesia since March 2020.
Furthermore, the Minister of Administrative and Bureaucratic Reform has issued Circular Letter Number 19 of 2020 on Adjustment of State Civil Apparatus (Civil Service) Working System to Prevent the Surge of COVID-19 in Government Agencies. Conforming to this policy, all civil servants have to carry out their official duties from home. The WFH policy in Indonesia is dynamic in coherence with the restrictions on community activities in response to the COVID-19 surge.
Shen et al. (2020) reveal that the COVID-19 pandemic hinders organizational goals and negatively affects organizational performance. The COVID-19 pandemic triggers the emergence of fear, anxiety, and stress that are part of interruption, leading to the decrease in task completion and eventually low employee performance (Li and Lin, 2019; Vo-Thanh et al., 2020). Stress can also cause low employee contribution and productivity, resulting in low organizational performance (De Clercq et al., 2017).
Another factor closely related to job performance is efficacy, particularly in task completion that involves a large-scale working environment (Liang et al., 2020). In this regard, task completion time, implementation schedule, and task completion actions can explain the success level of employee job performance (Andreassen, 2015; Taylor et al., 2013).
Employees who work from home face a great challenge that requires self-preparation and technology (Rachmawati et al., 2021). The use of technology aims to facilitate work activities and increase productivity even though the work activities are conducted from home. Belzunegui-Eraso and Erro-Garcés (2020) reinforce that technology supports the implementation of a flexible working system with faster working time, efficiency, and mobile working activities.
However, other studies have shown that use of technology can negatively affect employee productivity (Mazidi et al., 2020). Turel et al. (2011) state that constant use of technology can lead to high workload, addiction to use of technology, and particular problems for employees such as fear or discomfort. It strengthens the hypothesis that use of technology at work has negative impacts, for instance, fear, discomfort, and addiction.
Fear or discomfort experienced by employees regarding technology is referred to as ICT anxiety (Barbeite and Weiss, 2004). ICT anxiety can arise when job demands require employees to optimally use technological tools to improve work results and efficiency (Alahakoon and Somaratne, 2018; Chhabra et al., 2020; Van Steenbergen et al., 2018). ICT anxiety can be an obstacle in the implementation of the WFH system since WFH activities are only possible with technological assistance (Khan et al., 2021; Mac Callum et al., 2014).
Besides ICT anxiety, another negative impact is an addiction to smartphone use. Smartphone addiction can be defined as a form of individual dependency due to excessive use of smartphones (Richard et al., 2020) that can negatively affect individual behavior, social relationships, and time management (Hsieh et al., 2020). Continuous use of smartphones can cause addiction (Brem et al., 2021; Li and Lin, 2019; Turel et al., 2011) which influences the ability of employees to carry out and control the implementation of their work tasks, affecting the achievement of company performance and goals. Smartphone addiction also affects the level of concentration and collaboration of the employees (Li and Lin, 2019).
This study aims to analyze the impact of ICT anxiety and smartphone addiction on job performance of all lecturers at NIPA School of Administration with intervening variables, namely interruption and work efficacy. NIPA School of Administration is selected as the unit of analysis because this Polytechnic is a government-affiliated college outside the Ministry of Education, Culture, Research, and Technology of the Republic of Indonesia. NIPA School of Administration was firstly established as the State Administrative Sciences College, whose students were Civil Servants with associate and bachelor degree. To meet the needs of employees with associate degree of administration, the Academy of Administrative Sciences was established in 1964 in Jakarta and accepted civil servants with high school degree or equivalent. The State Administrative Sciences College and the Academy of Administrative Sciences were then integrated into the Graduate School of NIPA School of Administration as specified by Presidential Decree Number 5 of 1971. NIPA School of Administration was then designed to organize academic and professional education programs in administrative science for government employees in compliance with Presidential Decree Number 10 of 1999. Nowadays, three NIPA School of Administration in Indonesia are located in Jakarta, Bandung, and Makassar.
The impact of ICT anxiety and smartphone addiction becomes a challenge for the learning system at NIPA School of Administration, whose entire academic community consists of government employees (such as State Civil Apparatus and employees of State and Local-Owned Enterprises), the Indonesian National Police, and the Indonesian National Armed Forces. The WFH policy forces lecturers and students to implement the studying from home system by utilizing ICT. It is a challenge because students of NIPA School of Administration are not familiar with the learning system using technology, such as video conferencing and online teaching materials.
This study provides an update of performance studies during the COVID-19 pandemic that has had a major impact on the implementation of distance learning systems. In fact, the selection of ICT anxiety and smartphone addiction as variables makes this study interesting. These two variables are unique, as they seem to have opposite meanings but can greatly affect the performance of the lecturers. Discussions about job performance amid the COVID-19 pandemic have also become renewable considering the increasing human activities and interactions with technology.
2 Literature review and theory
2.1 Conceptual review
The current use of technology influences work arrangements, which is referred to as the New Ways of Working or NWW (Van Steenbergen et al., 2018). The work system becomes more flexible and can be done anywhere, reducing workload and employee stress. Job demands can indeed decrease when a company uses technology in most activities. However, it should be underlined that employees may need time to adjust their work needs to technology (Van Steenbergen et al., 2018). In addition to its impact on job demands, the use of new ways of working using technology also affects job resources, by enabling more effective communication that strengthens the working relationship between superiors and subordinates. However, it apparently also results in a decline in career development since work flexibility renders employees lacking opportunities to learn and control overwork (Van Steenbergen et al., 2018).
The use of technology is also associated with job demands and job resources, as discussed by Prodanova and Kocarev (2021). Anxiety (job demands) and addiction (job resources) in the use of technology have an impact on employee work performance; therefore, technological tools (both hardware and software) must be used according to the provisions and capacities to control and reduce their negative impact on work performance. Job efficacy also influences job performance, but companies need to pay attention to interruption that can decrease job performance.
When discussing about technology tools that can support work activities, we can mention smartphones. Smartphones now allow more effective working without being limited by space and time, hence the chance for an individual who uses a smartphone at work to experience addiction. Li and Lin (2018) explain that dependence on smartphone is related to job performance because it can increase work efficiency and employee workplace social capital. Employee workplace social capital increases when employees interact with each other about work via smartphones. It will cause a social response to build a more significant social interaction (Li and Lin, 2018). However, it cannot increase the workplace social capital of the company because it is not considered an outcome of communication dependency.
Based on several aforementioned studies, it is evident that technology and job performance are interrelated. Employees who use technology tools effectively can increase work collaboration, self-confidence, and work knowledge which are the basis of work cooperation that can mediate work performance (Pitafi et al., 2018).
The use of technology is increasingly perceptible during the COVID-19 pandemic, where most work relationships are virtual, resulting in the need for companies to observe how employees build quality work that in return will affect job performance (Narayanamurthy and Tortorella, 2021). Narayanamurthy and Tortorella (2021) explain that, based on the Social Construction of Technology (SCOT) theory, the work environment when working from home can improve the quality of work and employee performance. In addition, implications of work during the pandemic directly influence employee work performance because work activities are carried out remotely. The use of technology during WFH cannot mediate the work environment of the employees to quality work and cannot mediate job insecurity to the quality of work results. However, the use of technology in work activities with the WFH system can mediate virtual connectedness to the quality of work results (Narayanamurthy and Tortorella, 2021).
2.2 Hypothesis development
2.2.1 Job demands and resources theory (JD-R)
The concept of job performance is related to job demands and resources. Referring to the opinion expressed by Bakker and Demerouti (2014), job demands and resources theory (JD-R) is a concept of development from job design and job stresses theory. The JD-R theory covers two aspects: job demands that can trigger employee stress and job resources owned by a company that can increase work motivation of employees. Job demands refer to the existing physical, psychological, and social aspects of an organization related to company costs (Demerouti et al., 2001).
Meanwhile, job resources refer to the physical, psychological, and social aspects owned by an organization that can functionally motivate employees to achieve work goals, reduce job demands and costs incurred, and encourage employee desire to develop themselves (Bakker, 2011; Bakker and Demerouti, 2007). This theory can also explain the environment and the implementation of work activities that can affect the results or achievements of the company.
Bakker and Demerouti (2014) explain several aspects of the JD-R theory, including flexibility and two processes that are related to achieving employee welfare and influencing employee performance. Employees with high job resources can minimize the effects of perceived job demands. Another aspect of the JD-R theory is personal resources, namely employee self-evaluation, which is related to the ability of the employees to succeed in work activities as it is related to motivation, performance, self-efficacy, and job satisfaction (Hobfoll et al., 2003). Another aspect is personal demands, namely the determination of the employees to carry out work activities and improve their performance, such as the level of perfectionism and expectations of performance standards (Bakker and Demerouti, 2017; Barbier et al., 2013). Based on Bakker and Demerouti (2007), the JD-R theory is used to project employee welfare; influence company commitment, job satisfaction, connectedness, and work engagement; and predict several consequences of work decisions taken by employees.
2.2.2 ICT anxiety
ICT anxiety can be defined as barriers or personal problems when using technology (Maican et al., 2019; Mitzner et al., 2010). Hsieh et al. (2020) define ICT anxiety as a feeling of discomfort when using technology and a reluctance to use new technology. Meanwhile, Saadé and Otrakji (2007) explain that the impact of using technology in the past and present and the decisions to use technology in the future will influence the anxiety of an individual in using technology.
North and Noyes (2002) provide another term for ICT anxiety, namely technophobia, defined as anxiety when interacting with technology either now or in the future. North and Noyes (2002) further explain that technophobia is an overall negative attitudes towards computer operations, both fear of social impacts and self-criticism that will result from using technological devices and fear of using technology in the future. According to Saadé and Kira (2009), there are three types of anxiety in which ICT anxiety is included in concept-specific anxiety because it is related to specific situations, namely when interacting or using technology. Saadé and Kira (2009) also explain that ICT anxiety is related to the belief of an individual in using technology.
Czaja et al. (2006) explain that an individual with anxiety over using technology tends to use technology rarely, or never. Ellis and Allaire (1999) also argue that an individual with ICT anxiety does not desire to use technology. These two opinions are relevant. After all, ICT anxiety influences individual decisions to use technology (Meuter et al., 2003) because it significantly influences individual attitudes (Celik and Yesilyurt, 2013). In addition, anxiety over using technology can prevent the individual from using other technologies (Balta-Ozkan et al., 2013; Venkatesh et al., 2012).
Saadé and Kira (2009) also explain that ICT anxiety will affect individual productivity, welfare, and social relationships. An individual with a high level of ICT anxiety can cause problems in daily work productivity due to ineffective and inefficient performance (Bai, 2019). ICT anxiety also affects work processes that will affect the performance results of the company (Belzunegui-Eraso and Erro-Garcés, 2020), especially when there is an increase in the use of technology during the COVID-19 pandemic (Elhai et al., 2020). When using technology at work, self-confidence will influence self-capacity to achieve efficient performance. This later can optimize the use of new technology to efficiently complete the given work and continuously integrate new technology in completing the work (Mac Callum et al., 2014).H.1 There is a positive relationship between ICT anxiety and interruption.
H.2 There is a negative relationship between ICT anxiety and job efficacy.
2.2.3 Smartphone addiction
Smartphone addiction is an addictive behavior with a negative connotation (Li and Lin, 2019). Kardefelt-Winther et al. (2017) describe the concept of smartphone addiction as the failure of an individual to control their behavior that causes addiction. Ting and Chen (2020) explain that smartphone addiction is a compulsive behavior that causes excessive interaction between humans and smartphones. Smartphone addiction will be different from one individual to another due to differences in smartphone usage. Several researchers refer smartphone addiction as excessive interaction between humans and technology, causing problems in psychological aspects in relation to behavioral changes, such as withdrawing from social activities and losing self-control when using smartphones (Park et al., 2013; Bian and Leung, 2015; Li and Lin, 2018).
Referring to the media system dependency (MSD) theory (Ball-Rokeach and DeFleur, 1976), smartphone addiction is the dependence of an individual on their smartphone to fulfill a goal. Obviously, the goal will differ between one individual and another (Li and Lin, 2016). The Mass Communication theory published in Li and Lin (2019) explains the two levels of dependence on smartphone use, namely the macro and micro levels. Macro-level dependence is related to various media as well as economic, political, and social systems connected via smartphones. Meanwhile, micro-level or individual-level dependence is related to the interaction between individuals.
Ting and Chen (2020) identify three factors causing smartphone addiction in an individual: environmental, psychological, and social factors. Derks and Bakker (2014) state that individuals using smartphones have a higher risk of addiction. Smartphone addiction can cause negative impacts physically, psychologically, and socially (Ting and Chen, 2020). The initial purpose of using smartphones is inseparable from the need of an organization or company to effectively and efficiently achieve its performance. However, the high level of smartphone use in daily activities can lead to the bad effects of smartphone use (i.e. addiction) which will later affect the performance and work results of the individual.H.3 There is a positive relationship between smartphone addiction and interruption.
H.4 There is a negative relationship between smartphone addiction and job efficacy.
2.2.4 Interruption
Interruption can be interpreted as demands that interfere with work and affect the emotional aspects of employees and the level of fatigue when working (Mazmanian et al., 2013). Another opinion explains that interruption can disrupt the flow and process of working on tasks, causing employees to make wrong decisions (Dieckmann et al., 2007). Jett and George (2003) explain that interruption is a condition that hinders or delays the achievement of a work goal. In addition, interruption arises from within an individual and can also be influenced by activities around them.
Interruption negatively influences employee welfare as it causes emotional problems that hinder work activities and the achievement of work goals of the company. Therefore, employees have to spend extra effort to adjust after experiencing interruption, including longer time to optimally complete each task. In addition, employees tend to be more focused on understanding and completing new work tasks, despite their attempt to adapt the tasks previously affected by distractions. Interruption results in the inability of employees to complete the work tasks on time due to procrastination on the given work tasks, rendering task completion ineffective and inefficient. Another impact of interruption is failure when working (Elfering et al., 2014), such as declining work involvement, lacking work control, reducing work focus, and disrupting work routines (Wang and Suh, 2018). The most significant impact of interruption in work is stress, frustration, and other types of negative emotions that affect employees. Interruption from higher education in Indonesia comes from changing into online teaching system and delaying or even eliminating some academic activities (Lestari et al., 2020).H.5 There is a negative relationship between interruption and job efficacy
H.6 There is a negative relationship between interruption and job performance.
2.2.5 Job efficacy
Job efficacy is the belief that employees can perform any given tasks (Bandura & National Institute of Mental Health, 1986). According to Bandura (1997), job efficacy is related to employee well-being, decreased stress levels, positive emotions, and better self-adaptation. Job efficacy is explicitly grouped based on individual behavior that aims to control the work environment and activities (Schaubroeck et al., 2000). Several researchers also have the same opinion regarding the definition of job efficacy, namely the assessment of a cognitive ability of an individual to perform a better job (Bozeman et al., 2001; Lubbers et al., 2005). The role of an individual with high job efficacy in work activities will undoubtedly be larger compared to employees with lower job efficacy (Lubbers et al., 2005). Lubbers et al. (2005) explain that job efficacy will influence job performance because employees with high job efficacy will optimize task completion and their abilities.
Fida et al. (2015) explain that job efficacy is the belief in the ability of an employee to deal with situations in an uncertain work environment. Based on the job demands-resources theory, job efficacy is included in the type of company resources that can help reduce the negative impact of job demands (Bakker and Demerouti, 2007). An individual with a high level of efficacy also has high self-confidence to overcome problems and changes in work situations (Zhang et al., 2021). According to Bandura (2001), job efficacy can increase individual control over work activities. In addition, several opinions state that job efficacy can trigger the formation of critical strategies by employees, better time management, and well utilization of both existing and new technologies (Miraglia et al., 2017; Schenkel et al., 2019; Zainab et al., 2017).
The impact of high efficacy in an employee is an increase in job performance, as job efficacy is a benchmark for assessing the ability of employees to manage and complete every work activity, aiming to achieve optimal company performance (Mosley et al., 2008). Lent et al. (1994) even state that job efficacy is an essential aspect of organizational commitment to improving company performance. Carter et al. (2018) explain that job efficacy will affect the competence and skill improvement and motivate employees to use their experience as an impetus to optimize performance. An increase in abilities surely will lead to the success of the company.H.7 There is a positive relationship between job efficacy and job performance.
2.2.6 Job performance
In simple terms, job performance can be defined as the work of the employees to produce good results or not (Winter, 1980), a nearly similar definition as productivity and work efficiency, profits, and the achievement of company goals (Downs and Moscinski, 1979). Job performance is one part of employee work behavior related to organizational goals, which is an achievement (Omotunde and Alegbeleye, 2021). In this regard, the achievement refers to the implementation of tasks according to the agreed policies and the ability of employees. The achievement of organizational goals is highly dependent on employee performance.
Murphy introduces four factors affecting job performance, namely task behaviors, interpersonal behaviors (communication and cooperation between employees), downtime behaviors (behaviors that avoid work), and destructive/hazardous behaviors (behaviors that can reduce work productivity) (Dillon and Pellegrino, 1989).
Furthermore, Campbell introduces eight factors that affect job performance, namely (1) job-specific task proficiency, (2) non-job-specific task proficiency, (3) written and oral communications, (4) demonstrating effort, (5) maintaining personal discipline, (6) facilitating peer and team performance, (7) supervision, and (8) management and administration. These eight factors are popularized by having different patterns with a relatively high degree of variation because they adapt to the type of work (Koopmans et al., 2011). The learning and exploration process in an organization have an effect in maintaining and improving organizational performance (Suryaatmaja et al., 2020).
Borman & Motowidlo (1993) also explain two dimensions of job performance in a company: task performance and contextual performance. Task performance is related to the technical aspects of the organization in supporting the achievement of company goals through several processes of production, both goods and services. Task performance is related to the work tasks of employees. In comparison, contextual performance is a pattern of employee behavior in work activities leading to psychological and social aspects. Although it does not lead to the main task of employees, contextual performance is significant as it can shape the social and psychological aspects of the company to help optimize the critical thinking processes of employees. This study examines the effect of several variables such as ICT anxiety, smartphone addiction, interruption, and job efficacy on job performance (Table 1 ).Table 1 Hypothesis.
Table 1No Hypothesis
H.1 There is a positive relationship between ICT anxiety and interruption
H.2 There is a negative relationship between ICT anxiety and job efficacy
H.3 There is a positive relationship between smartphone addiction and interruption.
H.4 There is a negative relationship between smartphone addiction and job efficacy
H.5 There is a negative relationship between interruption and job efficacy
H.6 There is a negative relationship between interruption and job performance
H.7 There is a positive relationship between job efficacy and job performance
3 Materials and methods
3.1 Research design
This study applied a quantitative approach, namely a deductive study, as it applied theory as the primary basis and later combined it with the results of processed data (Creswell and Creswell, 2018; Neuman, 2014). The quantitative approach combines and harmonizes deductive logic with an empirical study to find and explain the behavior and patterns (Neuman, 2014). This study employed theories related to the variables, namely the theory of job demands-resources, ICT anxiety, smartphone addiction, interruption, job efficacy, and job performance, which also corresponding to the primary reference journals, as depicted in Figure 1 .Figure 1 Research model.
Figure 1
This study applied a case study survey research as it discussed social phenomena that occur in the current period (the pandemic period), used the entire population, and distributed the questionnaire online (Yin, 2018). This study employed a cross-sectional method on a total population size of 135 people, namely all permanent lecturers at NIPA School of Administration in Indonesia.
Non-parametric statistics were employed on Likert-scale ordinal data obtained through categorization or classification, implying that the data are not normally distributed and thus eliminating the need for normality assumption and outlier testing (Kraska-MIller et al., 2013). The data were analyzed using Partial Least Square (PLS) method which does not assume a specific distribution for parameter estimation. Thus, tests for the significance of parameter are unnecessary as well (Albers, 2010).
This study also applied partial least squares structural equation modeling (PLS-SEM) to analyze data and provide evidence of reliability and validity. The application of PLS-SEM is highly recommended when the data have a limited number of samples while the model built is complex (Purwanto et al., 2021). PLS-SEM algorithms are generally claimed to perform particularly well with small samples and non-normal data (Hair et al., 2011). PLS-SEM works efficiently with small sample sizes and complex models and provides practically no assumptions about the underlying data (Cassel et al., 1999).
PLS-SEM can easily handle reflective and formative measurement models, as well as single-item constructions, without identification problems. In addition, PLS-SEM generally achieves a high level of statistical power with a small sample size. Therefore it can be applied in various research situations (Hair et al., 2017b). The advantage of PLS-SEM is that research can use very specific populations (Sander and Lee, 2014). PLS-SEM is a good alternative because it is more robust, meaning that the model parameters do not change much when a new sample is taken from the total population (Geladi and Kowalski, 1986).
The questionnaire consists of screening questions, the identities of respondents, and questions related to variables. The questionnaire used a five-point Likert-type scale as an appropriate assessment tool for studies using an interval scale to ensure that the same interval will apply among response categories (Creswell, 2015). The Likert rating scale is applied to measure psychometric indicators using a fixed-format rating scale (Robinson, 2018), which is the focus of this article. Scale is one of the most widely used instruments to measure opinions, preferences, and attitudes, which is also in accordance with this research (Likert, 1932). The Likert scale used is ordinal data that determine the rating of a perception indicator (Lee et al., 2010). This ranking considers the order of objects from the largest to the smallest or from the highest to the lowest. The Likert rating scale can be ranked form two to eleven response points, in which reliability, criterion validity, and the ability to discriminate between ratings of participants increase as the number of response points increases (Preston and Colman, 2000).
This study applied a five-point Likert-type scale based on the type scale that is a prerequisite of psychometric scales (Kline, 2000). A five-point Likert-type scale was selected because this scale can fulfill indicator reliability and allow the respondents to easily understand and distinguish each point on the scale, allowing easier information processing of the obtained data (Preston and Colman, 2000). The scale can be required to capture the richness of multidimensional variables. In addition, the determination of the scale is also based on the variables investigated, questionnaire space limits, or participant characteristics. The maximum number of items per scale will depend on the complexity of the variable being measured. A five-point Likert-type scale was selected by investigating cultural as well as other demographic differences such as gender and age in this study (Leung, 2011). Furthermore, a five-point Likert-type scale are preferable to the general public (Weijters et al., 2010) and able to express the statements of the respondents precisely and comfortably (Krosnick and Fabrigar, 1997). The Midpoint scale has shown good validity against equivalent full-scale versions (Nagy, 2002). Moreover, some researchers suggest that single-item measures may be preferred to multi-item scales (Postmes et al., 2013).
3.2 Participants
This study used a total sampling technique (Pitner et al., 2020), meaning all of the population that comprises a total of 135 lecturers of NIPA School of Administration in Jakarta, Bandung, and Makassar. No missing data were found since the respondents could answer all research questions.
All respondents have implemented online teaching method in response to the issuance of a public policy that enforces the WFH system in dealing with the pandemic, namely Circular Letter Number 19 of 2020 on Adjustment of State Civil Apparatus (Civil Service) Working System to Prevent the Surge of COVID-19 in Government Agencies by the Minister of Administrative and Bureaucratic Reform.
The respondents selected for this study were lecturers who have applied online teaching methods at NIPA School of Administration, comprising a total of 135 lecturers with nearly an equal proportion of female and male lecturers. Most of them are expert assistants with master's degrees. The respondent characteristics are shown in Table 2 .Table 2 Respondent characteristics.
Table 2Characteristic Frequency Percentage
Gender
Man 66 48.9
Woman 69 51.1
Academic Position
Expert Assistant 71 52.6
Lecturer 38 28.1
Head Lecturer 26 19.3
Education
Master (S2) 92 68.1
Doctoral (S3) 43 31.9
3.3 Instruments development
Constructs in the preparation of the questionnaire were adapted from previous studies with slight modifications according to the research conditions. The instruments used in this study consisted of 18 main questions representing five variables (Table 3 ) consisting of ICT anxiety, Smartphone addiction, interruption, job efficacy and job performance adapted from a study by Prodanova & Kocarev (2021), one screening question, and four questions regarding the identity of the respondents. Each construct will be measured using a five-point Likert scale, namely Very Dissatisfied (1), Dissatisfied (2), Neutral (3), Satisfied (4), and Very Satisfied (5).Table 3 Instruments.
Table 3No. Indicator Outer Loadings Cronbach Alpha
1 IA IA1 I feel uncomfortable with the electronic-based learning (e-learning) technology used/chosen by NIPA School of Administration. .78 .81
2 IA2 In distance learning, I feel worried about pressing the wrong button, which can cause damage or loss of data stored in IT systems, both private and institutional. .89
3 IA3 I doubt using digital learning media, worried that uncorrectable error will occur. .90
4 SA SA1 Using a smartphone is one of my main daily activities. .96 .66
5 SA2 I feel concerned (insecure) supposing the smartphone is damaged or not working. −.34 (deleted)
6 SA3 I feel lost without my smartphone. .72
7 I I1 I am easily distracted while teaching when it is conducted from home (distance learning). .86 .87
8 I2 I feel that the working environment at home is not significantly supportive of the teaching process in distance learning. .90
9 I3 My concentration is hampered when teaching from home (distance learning). .92
10 JE JE1 Work plans frequently change while teaching from home (distance learning). .73 .79
11 JE2 I spend more time teaching with a distance learning method from home than face-to-face learning on campus. .39 (deleted)
12 JE3 I face many distractions in my daily work, including teaching from home (distance learning). .87
13 JE4 I dedicate more time when working from home, but complete less work. .80
14 JE5 My co-workers and I spend much time talking about our personal lives while working from home. .74
15 JP JP1 Teaching from home (distance learning) helps me achieve my learning goals more efficiently than teaching on campus. .86 .88
16 JP2 Teaching from home (distance learning) is more beneficial in improving my performance at NIPA School of Administration. .83
17 JP3 Teaching from home (distance learning) is more beneficial in improving the performance of all lecturers at NIPA School of Administration. .91
18 JP4 Teaching from home (distance learning) is more beneficial in providing added value for NIPA School of Administration (organizational profits). .82
Source: Prodanova and Kocarev (2021).
To test the validity and reliability of all indicators, this study applied the Partial Least Square – Structural Equation Modeling (PLS-SEM) outer loadings with a minimum value of .7 (Hair et al., 2017b; Fitriati and Madu Siwi, 2022) and Cronbach's Alpha with a minimum value of .6 (Malhotra, 2020; Fitriati and Madu Siwi, 2022) respectively. All indicators obtain Cronbach's Alpha values of 0.7–0.8 (Table 3). By referring to convergent validates, an indicator is declared valid if the loading factor value is .5 (Hair et al., 2017a). The results of the validity test are presented in Table 3.
3.4 Questionnaire administration procedure
The questionnaire requires approximately 5–10 min to fill since it consists of 18 closed questions. The questionnaire link was shared to the coordinator and developed for all lecturers at NIPA School of Administration. Respondents can open the questionnaire on Google Forms via the link shared. The questionnaire consists of 18 questions as follows: three questions for ICT anxiety, three questions for smartphone addiction, three questions for interruption, five questions for job efficacy, and four questions for job performance. The 18 questions are validated through the PLS-SEM outer loadings test, resulting in the deletion of SA2 and JE2 items. Data collection was conducted from September to October 2021.
3.5 Measures and statistical analyses
The data collected will later go through a validity test using outer loadings with a minimum value of .7 (Hair et al., 2017b) as well as a reliability test using Cronbach's Alpha with a minimum value of .6 (Malhotra, 2020). Considering the small number of sample used in this study (Hair et al., 2017b), the statistical tests applied PLS-SEM using SMARTPLS3 software. PLS-SEM has unlimited algorithms for reflective and formative latent constructs and can estimate very complex path and study models. A very complex study model consists of many latent and manifest variables without experiencing problems when estimating data. Furthermore, PLS-SEM can be used when data distribution is not spread across the mean values. Hair et al., 2017b further explain that PLS-SEM is divided into two types: the outer and inner models.
The statements of the respondents in the questionnaire were summarized and described in the form of mean and standard deviation values. The indicators of the study obtained a mean of 3.11, implying the neutral opinion of the respondents regarding the influence of ICT anxiety and smartphone addiction on job performance. Meanwhile, the average value of standard deviation of all indicators is 1.07, concluding the mean score in the range of 3.11 ± 1.07. The descriptive statistic for each research indicator is presented in Table 4 .Table 4 Descriptive statistics of research indicators.
Table 4No Indicators N Mean Standard Deviation
1 I1 135 3.47 1.26
2 I2 135 3.71 1.15
3 I3 135 3.87 1.12
4 IA 1 135 3.96 1.13
5 IA 2 135 3.90 1.13
6 IA 3 135 4.14 .96
7 JE1 135 2.31 1.13
8 JE3 135 2.25 1.12
9 JE4 135 2.22 1.08
10 JE5 135 1.63 .86
11 JP1 135 3.57 1.06
12 JP2 135 3.53 1.12
13 JP3 135 3.46 1.05
14 JP4 135 3.69 .99
15 SA1 135 1.77 .91
16 SA3 135 2.36 1.11
Mean 3.11 1.07
4 Results
4.1 Assessment of the measurement or outer model
The assessment of the outer model can be grouped into two, namely validity and reliability tests, each of which will lead to several more tests. All values of the outer model tests are shown in Table 5 and Figure 2 . The first validity test is convergent validity, which proves that the respondents can understand all statements on the latent variables. Convergent validity refers to outer loadings of more than .7 (Hair et al., 2017b) and AVE.Table 5 Internal consistency measures for measurement model.
Table 5Variable Indicator Statement (λ) CR α AVE
ICT Anxiety (IA) IA1 I feel uncomfortable with the electronic-based learning (e-learning) technology used/chosen by NIPA School of Administration. .78 .89 .81 .73
IA2 In distance learning, I feel worried about pressing the wrong button, which can cause damage or loss of data stored in IT systems, both private and institutional. .88
IA3 I doubt using digital learning media, worried that uncorrectable error will occur. .89
Smartphone Addiction (SA) SA1 Using a smartphone is one of my main daily activities. .96 .83 .66 .72
SA3 I feel lost without my smartphone. .72
Interruption (I) I1 I am easily distracted while teaching when it is conducted from home (distance learning). .78 .99 .87 .80
I2 I feel that the working environment at home is not significantly supportive of the teaching process in distance learning. .88
I3 My concentration is hampered when teaching from home (distance learning). .89
Job Efficacy (JE) JE1 Work plans frequently change while teaching from home (distance learning). .73 .86 .79 .62
JE3 I face many distractions in my daily work, including teaching from home (distance learning). .87
JE4 I dedicate more time when working from home, but complete less work. .80
JE5 My co-workers and I spend much time talking about our personal lives while working from home. .74
Job Performance (JP) JP1 Teaching from home (distance learning) helps me achieve my learning goals more efficiently than teaching on campus. .86 .92 .88 .73
JP2 Teaching from home (distance learning) is more beneficial in improving my performance at NIPA School of Administration. .83
JP3 Teaching from home (distance learning) is more beneficial in improving the performance of all lecturers at NIPA School of Administration. .90
JP4 Teaching from home (distance learning) is more beneficial in providing added value for NIPA School of Administration (organizational profits). .82
Figure 2 Full model confirmatory factor analysis (CFA)
Figure 2
In the initial calculation, three indicators obtain outer loading values of less than 0.7, namely SA2 (−.34), SA3 (−.33) and JE2 (.39). Thus, SA2 indicator is removed. In the next measurement, SA3 obtains a value of .72 while JE2 is constant at .39. Thus, these indicators are also deleted. The negative outer loading value of SA2 shows a negative correlation of SA2 with smartphone addiction. Furthermore, removing SA2 from the model can increase the outer loadings value of SA1 and SA3. It is similar to the exclusion of JE2 that increases the reliability value of JE3, JE4, and JE5.
The AVE values obtained, which are more than 0.5, show that all variables fall into the valid category. The AVE value of each variable is as follows: .73 for ICT anxiety, .72 for smartphone addiction, .80 for interruption, .62 for job efficacy, and .73 for job performance. Thus, the lowest AVE value is obtained by job efficacy while the highest AVE value is obtained by interruption.
The second validity test is discriminant validity to prove that the respondents do not confuse the statement on the latent variable with questions on the other latent variables, particularly in terms of meaning. Discriminant validity is met supposing the HTMT value is less than .90. Referring to Table 6 , all HTMT values are less than .90, thus discriminant validity through HTMT is said to be valid (Henseler et al., 2015).Table 6 Results of HTMT measurement.
Table 6 ICT Anxiety Interruption Job Efficacy Job Performance Smartphone Addiction
ICT Anxiety
Interruption .34
Job Efficacy .32 .77
Job Performance .13 .42 .37
Smartphone Addiction .32 .12 .17 .42
The next assessment of the outer model is internal consistency reliability using two values, namely composite reliability and Cronbach's alpha. Most variables (ICT anxiety, interruption, job efficacy, and job performance) obtain a Cronbach's alpha value ranging from .70 to .90 while smartphone addiction obtains a Cronbach's alpha value ranging from .60 to .70. Thus, these variables are acceptable. In addition to Cronbach's alpha, this study also used a composite reliability value, in which all variables obtain a value of more than .70. In addition to measuring validity and reliability, the assessment of the outer model also pays attention to the multicollinearity test using the VIF value. Based on Tables 7 and 8 , it is obvious that the VIF value is less than five, indicating collinearity between constructs.Table 7 Results of outer VIF measurement.
Table 7Indicators VIF
I1 1.99
I2 2.64
I3 2.70
IA 1 1.49
IA 2 2.12
IA 3 2.25
JE1 1.54
JE3 2.11
JE4 1.57
JE5 1.47
JP1 2.15
JP2 2.06
JP3 2.89
JP4 2.00
SA1 1.31
SA3 1.31
Table 8 Inner VIF values.
Table 8 ICT Anxiety Interruption Job Efficacy Job Performance Smartphone Addiction
ICT Anxiety 1.07 1.15
Interruption 1.09 1.73
Job Efficacy 1.73
Job Performance
Smartphone Addiction 1.07 1.07
4.2 Assessment of the structural or inner model
The inner model analysis begins with the R-Square (R2) test, which aims to determine whether the endogenous latent variable has predictive power to the model or not (Handayati et al., 2020) or whether the R2 value indicates accuracy predictions or not (Hair et al., 2013). The rule of thumb for an acceptable R2 value is .67, .33, and .19, respectively explained as substantial, moderate, and weak (Chin, 2010).
As shown in Table 9 , ICT anxiety and smartphone addiction influence interruption by .08 or 8.0%. Then ICT anxiety, smartphone addiction, and interruption influence job efficacy by .43 or 43%. Lastly, interruption and job efficacy influence job performance by .15 or 15%. The second test of the inner model is confidence intervals, whose results are shown in Table 10 . The value of confidence intervals is 97.5%, thus the mean range of the population will fall between −.483 to .476.Table 9 Variance explained by the model.
Table 9 R-Square R Square Adjusted
Interruption .08 .07
Job Efficacy .43 .42
Job Performance .15 .13
Table 10 Values of predictive relevance from the model.
Table 10 SSO SSE Q2 (=1 − SSE/SSO)
ICT anxiety 405.000 405.000
Interruption 405.000 382.137 .06
Job Efficacy 540.000 408.350 .24
Job Performance 540.000 486.428 .10
Smartphone Addiction 270.000 270.000
The next test is the effect size (f2) with the rule of thumb refers to Cohen (2013) and Hair et al. (2014), namely the values of .02, .15, and .35 to show small, medium, and large effect sizes, respectively. The effect of a specific exogenous construct on the endogenous construct can be assessed by evaluating the f2 effect sizes. Eliminating the effect size of each exogenous variable on the explanatory power of the model reveals that eliminating the exogenous variables (ICT anxiety and smartphone addiction) that explain interruption has a small effect size (.08 and .00, respectively). Eliminating interruption and job efficacy that explain job performance has a small effect size (.05 and 0.01, respectively) while removing ICT anxiety and smartphone addiction that explain job efficacy has a small effect size (.00 and .01, respectively). Furthermore, removing interruption that explains job efficacy has a high effect size (.64).
Furthermore, predictive relevance (Q2) using blindfolding is carried out with the obtained value of .06 for interruption, .24 for job efficacy, and .10 for job performance (Table 10). It is evident that the value of Q2 > 0, indicating that the model has predictive relevance.
The last analysis in the inner model is the path coefficient with the following results (Figure 3 and Table 11 ): (a) the relationship between ICT anxiety and interruption is positive (.28); (b) the relationship between smartphone addiction and interruption is negative (−.02); (c) the relationship between ICT anxiety and job efficacy is negative (−.05); (d) the relationship between smartphone addiction and job efficacy is positive (.06); (e) the relationship between interruption and job efficacy is negative (.63); (f) the relationship between interruption and job performance is positive (.28); and (g) the relationship between job efficacy and job performance is negative (−.14).Figure 3 Bootstrap image for path analysis.
Figure 3
Table 11 Results of path analysis.
Table 11Hypotheses Β P Values f2 values Confidence interval Decision
LL UL
ICT anxiety–> Interruption .28 .01 .08 .07 .48 Accepted
ICT anxiety–> Job Efficacy −.05 .55 .00 −.24 .14 Rejected
Interruption–> Job Efficacy −.63 .00 .64 −.75 −.48 Accepted
Interruption–> Job Performance .28 .02 .05 .04 .54 Accepted
Job Efficacy–> Job Performance −.14 .32 .01 −.40 .16 Rejected
Smartphone Addiction–> Interruption −.02 .86 .00 −.25 .20 Rejected
Smartphone Addiction–> Job Efficacy .06 .48 .01 −.10 .21 Rejected
The next stage examines the p-values, from which it is concluded that three of seven hypotheses are accepted:1. The first hypothesis, stating that ICT anxiety has positive influences on interruption, is accepted (p-value: .01).
2. The second hypothesis, stating that ICT anxiety has negative influences on job efficacy, is rejected (p-value: .55).
3. The third hypothesis, stating that smartphone addiction has positive influences on interruption, is rejected (p-value: .86).
4. The fourth hypothesis, stating that smartphone addiction has negative influences on job efficacy, is rejected (p-value: .48).
5. The fifth hypothesis, stating that interruption has negative influences on job efficacy is accepted (p-value: .00).
6. The sixth hypothesis, stating that interruption has negative influences on job performance, is accepted (p-value: .02).
7. The seventh hypothesis, stating that job efficacy has positive influences on job performance, is rejected (p-value: .32).
This study also conducted bootstrapping, a non-parametric approach to test the accuracy/precision of PLS-SEM testing (Henseler et al., 2015). Bootstrapping allows testing the statistical significance of various PLS-SEM results such as path coefficients, Cronbach's alpha, HTMT, and R 2. Bootstrapping is carried out supposing the data already meet the criteria in the outer model.
5 Discussion
This section discusses the findings obtained by relating them to the theory that has been built. Despite the current digital era where technology is commonplace, the use of technology in the work environment is one of the challenging issues as technology can cause negative impacts. However, on the other hand, technology has a highly positive impact on work activities. These negative impacts include interference from the internal environment and even from technological tools. These two sides, namely the negative and positive impacts of technology, challenge workers to maintain and even improve their performance (Lowe-Calverley and Pontes, 2020). Following the explanation in the Introduction section, this study aims to analyze the impact of ICT anxiety and smartphone addiction on job performance of all lecturers at NIPA School of Administration with intervening variables, namely interruption and work efficacy. Between humans and technology, interactions have been created, one of which is in the world of work. Therefore, this study aims to observe the effect of technology described in ICT anxiety and smartphone addiction on work performance.
In this study, job demands are represented by ICT anxiety as the lecturers are demanded to understand technology in the era of distance learning, while resources are represented by smartphone addiction. Increased ICT anxiety can increase work disorders since the lack of individual self-confidence and increased fear and discomfort when using technology can hinder work activities (Celik and Yesilyurt, 2013; Meuter et al., 2003). The results of hypothesis testing using PLS-SEM show that there is a positive relationship between ICT anxiety and interruption while interruption has negative influences on job efficacy and job performance.
The results obtained from the questionnaire in terms of ICT anxiety show that only a small group of lecturers at NIPA School of Administration feel uncomfortable, worried, and doubtful when using technology in distance learning activities because they are accustomed to using technology in work activities. Therefore, low ICT anxiety of lecturers at NIPA School of Administration will also decrease work disturbances caused by the use of technology during distance learning. Technology is not an obstacle to the work of lecturers at NIPA School of Administration but can be a means of more efficient work activities (Mac Callum et al., 2014). Based on the answers of the respondents in the explanation section, it can be concluded that low ICT anxiety of the lecturers is also influenced by technology socialization and the readiness of the IT team to assist in distance learning activities.
Lecturers at NIPA School of Administration frequently experience interruption while teaching from home, for instance, distracted by family or bad internet connection. In this regard, interruption can decrease job efficacy and job performance. Technology use without preparation and evaluation, lack of digital skills, and the emergence of other technology use disorders will cause high interruption in the implementation of online learning systems (Oliveira et al., 2021).
However, despite having several obstacles, the lecturers at NIPA School of Administration consider that online teaching activities have become a habit that provides convenience and benefits, particularly during the pandemic. Preparation for the use of technology in learning activities carried out by permanent lecturers at NIPA School of Administration forms a process of adaptation and resilience to the use of technology to a large extent to minimize interruption (Latchem, 2014; Stein et al., 2007). With good ICT anxiety conditions, permanent lecturers at NIPA School of Administration tend to explore other technologies to balance and even increase distance learning activities (Balta-Ozkan et al., 2013; Venkatesh et al., 2012).
The significance of the relationship between interruption and a decrease in job efficacy and job performance is in line with the findings of Prodanova and Kocarev (2021), which explain that work disorders influence the efficacy and performance of work activities of an individual, hence the necessity for the individual to be able to control possible disturbances that can occur.
Another finding shows that smartphone addiction has no negative relationship with interruption or job efficacy. The use of smartphones by permanent lecturers at NIPA School of Administration is described as a form of work activity because it helps establish communication and uncomplicated data storage media and helps carry out work activities anywhere and anytime. This finding is in line with the opinion of Duke and Montag (2017) that using smartphones can increase work activities and provide motivation, allowing workers to carry out work activities more effectively and efficiently, despite the relatively small effect. The high use of smartphones to help work activities can produce much better output.
In terms of effectiveness and efficiency, the job performance of lecturers at the NIPA School of Administration tends to increase during distance learning, albeit not significant when compared to direct teaching. According to Murphy (Dillon and Pellegrino, 1989), the factors that influence job performance are the working behavior towards tasks. In this regard, distance learning activities carried out by lecturers are not much different from direct and interpersonal teaching activities. In relation to behavior, communication barriers can occur between lecturers and students due to increasingly limited interactions and the inability of lecturers to pay attention to students. Several disturbances that can hinder work activities of the lecturers also tend to occur from external factors, such as inadequate internet networks and home environmental factors. Organizations can benefit through the unique involvement and collaboration of each party for organizational governance. The benefits of increased company performance can be obtained through a collaborative process (Budiarso et al., 2021).
The permanent lecturers at NIPA School of Administration consider that the use of technology in the distance teaching system has been effective and supporting teaching activities. In this regard, it is evident that the role of the campus in terms of socialization is one of the keys to creating good relations between permanent lecturers at NIPA School of Administration and the application of technology.
6 Conclusion
The COVID-19 pandemic has a great impact on the implementation of the distance learning system for NIPA School of Administration. Although lecturers and students have started to get used to the online learning system, the communication and interaction between lecturers and students are not adequate. This study also shows a significant impact of ICT anxiety on interruption and interruption on job efficacy and job performance. However, the impact of other variables such as smartphone addiction on interruption and job efficacy, ICT anxiety on job efficacy, and job efficacy on job performance is not significant.
Therefore, this study recommends the facilitation of knowledge sharing related to ICT competence or literacy. For instance, personal sharing by young lecturers with senior lecturers in addition to mutual training and not generalizing the competence (digital literacy) of each lecturer. Second, improving the security guarantees of the intellectual rights of the lecturers in relation to the choice of technology provided by campus. Third, integrating the demands of ICT needs with administrative-technical procedures, particularly in financing/budgeting aspect. For instance, students demand the availability of video-based learning that they can repeatedly access, yet it is constrained by financial administration procedures.
This study has several limitations. First, the sample used is the lecturers at the NIPA School of Administration, rendering the sample unable to be replicated by lecturers with various levels of office in other areas or other universities or even by other professions. Thus, future studies can use a representative sample. Second, the sample size is limited due to the small size of the total population of this study. A large data size can show more statistical power. Third, the data were obtained only from questionnaire, thus future studies can include interviews to strengthen the analysis. Fourth, this study renders the impact of using technology on job performance only during the period of the study. Meanwhile, there have been numerous changes during the pandemic, which may lead to exciting findings. Future studies can apply a longitudinal study instead. Finally, several results of this study need to be further studied.
Declarations
Author contribution statement
Adi Suryanto, Rachma Fitriati, Sela Inike Natalia, Andina Oktariani, Munawaroh, Nurliah Nurdin, Young-hoon AHN: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.
Funding statement
This work was supported by Program Hibah Publikasi Terindeks Internasional (PUTI) Q1 Tahun Anggaran 2022-2023 Nomor: NKB-386/UN2.RST/HKP.05.00/2022.
Data availability statement
Data included in article/supp. material/referenced in article.
Declaration of interest’s statement
The authors declare no conflict of interest.
Additional information
No additional information is available for this paper.
☆ This article is a part of the "Business and Economics COVID-19" Special issue.
==== Refs
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| 36506378 | PMC9722629 | NO-CC CODE | 2022-12-14 23:52:16 | no | Heliyon. 2022 Dec 6; 8(12):e11980 | utf-8 | Heliyon | 2,022 | 10.1016/j.heliyon.2022.e11980 | oa_other |
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Asian J Surg
Asian J Surg
Asian Journal of Surgery
1015-9584
0219-3108
Asian Surgical Association and Taiwan Robotic Surgery Association. Publishing services by Elsevier B.V.
S1015-9584(22)01678-5
10.1016/j.asjsur.2022.11.118
Article
Nutritional recommendations for asymptomatic patients with novel coronavirus
Fan Chengwei ∗
Lu Ying
Wu Qian
Department of Clinical Nutrition, The People's Hospital of Jianyang City, Jianyang, 641400, China
∗ Corresponding author. Yiyuan Street NO.196, Jianyang City, Sichuan Province, 641400, China.
6 12 2022
6 12 2022
11 11 2022
24 11 2022
© 2022 Asian Surgical Association and Taiwan Robotic Surgery Association. Publishing services by Elsevier B.V.
2022
Asian Surgical Association and Taiwan Robotic Surgery Association
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
Nutrition
Recommendations
Coronavirus
==== Body
pmcTo the editor,
The coronavirus disease 2019 (Covid-19) pandemic continues, with recent estimates of more than 187 million cases diagnosed and more than 4 million deaths.1 With the lack of effective therapy, chemoprevention, and vaccination,focusing on the immediate repurposing of existing drugs gives hope of curbing the pandemic. By analyzing publicly available patient data from around the world, Minkyung Bae's2 team found a strong correlation between vitamin D levels and cytokine storm, and also found that vitamin D deficiency was associated with death. Cytokine storms are caused by an overreaction of the immune system. Cytokine storms can severely damage the lungs, leading to acute respiratory distress syndrome and patient death, which appears to be responsible for most deaths in patients with neocoronary pneumonia," the researchers noted. And vitamin D not only boosts one's innate immune system, it also prevents it from becoming overactive." Minkyung Bae said that moderate vitamin D levels, while not preventing patients from contracting the virus, can reduce complications and prevent death in those infected.3 And, this correlation may also help shed light on other mysteries related to neocrown pneumonia, such as why children have lower rates of illness and death. Children do not have a fully developed acquired immune system, the second line of defense for the immune system, and are more likely to overreact. But that doesn't mean everyone, especially those without known deficiencies, needs to start taking vitamin D supplements. While I think it's critical to let people know that vitamin D deficiency may play a role in the rate of death from new novel coronavirus, it's not marketing vitamin D to everyone," Minkyung Bae said, adding that more research is needed to understand how vitamin D can be most effectively used to prevent complications of new novel coronavirus.4
A schematic of the design is displayed in Fig. 1 , where vertical arrows show time sequences and horizontal arrows show randomization which will be done prior to COVID diagnostic testing for the patients, due to concerns regarding delays in receiving the contact's test results and the need to start treatment early.Fig. 1 Nutritional guidance process for novel coronavirus patients at different stages.
Fig. 1
The coronavirus disease 2019 (COVID-19) is an escalating global pandemic associated with the potential for severe respiratory complications. Vitamin D deficiency is strongly associated with the risk of developing novel coronavirus pneumonia, and patients with confirmed novel coronavirus pneumonia have significantly lower vitamin D levels compared with patients without novel coronavirus pneumonia. Vitamin D deficiency is common in patients with novel coronavirus pneumonia. There is a correlation between vitamin D deficiency and the prognostic outcome of novel coronavirus pneumonia including death, mechanical ventilation, and the need for ICU unit care. The diagnosis of vitamin D deficiency can help assess the likelihood of a patient developing severe novel coronavirus pneumonia. Whether vitamin D supplementation can prevent or treat novel coronavirus pneumonia needs to be further investigated. All clinical decisions should be based on strong evidence, and further validation of the results in more rigorous, large-sample, multicenter, high-quality randomized controlled clinical trials is needed in the future.5
Declaration of competing interest
Not applicable.
==== Refs
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3 Fernandes A.L. Murai I.H. Reis B.Z. Effect of a single high dose of vitamin D3 on cytokines, chemokines, and growth factor in patients with moderate to severe COVID-19 Am J Clin Nutr 115 3 2022 Mar 4 790 798 35020796
4 Bhutani S. vanDellen M.R. Cooper J.A. Longitudinal weight gain and related risk behaviors during the COVID-19 pandemic in adults in the US Nutrients 13 2 2021 Feb 19 671 33669622
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| 36517399 | PMC9722671 | NO-CC CODE | 2022-12-12 23:20:30 | no | Asian J Surg. 2022 Dec 6; doi: 10.1016/j.asjsur.2022.11.118 | utf-8 | Asian J Surg | 2,022 | 10.1016/j.asjsur.2022.11.118 | oa_other |
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Mar Policy
Mar Policy
Marine Policy
0308-597X
1872-9460
The Author(s). Published by Elsevier Ltd.
S0308-597X(22)00489-4
10.1016/j.marpol.2022.105442
105442
Full Length Article
Differential effect of fisheries to the COVID-19 pandemic in the region of Andalusia (Spain)
Cousido-Rocha Marta a
Carballo Marta González b
Pennino Maria Grazia a
Coll Marta cd
Báez José C. ef⁎
a Instituto Español de Oceanografía (IEO, CSIC), Centro Oceanográfico de Vigo, Subida a Radio Faro 50-52, 36390 Vigo, Pontevedra, Spain
b Instituto Español de Oceanografía (IEO, CSIC), Centro Oceanográfico de Canarias, C/ Farola del Mar, nº 22, 38180, Dársena Pesquera, San Andrés, Santa Cruz de Tenerife, Spain
c Institut de Ciències Del Mar (ICM-CSIC), P. Marítim de La Barceloneta, 37-49, 08003, Barcelona, Spain
d Ecopath International Initiative Research Association, 08172, Barcelona, Spain
e Instituto Español de Oceanografía (IEO, CSIC), Centro Oceanográfico de Málaga, Puerto pesquero de Fuengirola s/n, 29640, Málaga, Spain
f Instituto Iberoamericano de Desarrollo Sostenible, Universidad Autónoma de Chile, Temuco, Chile
⁎ Corresponding author at: Instituto Español de Oceanografía (IEO, CSIC), Centro Oceanográfico de Málaga, Puerto pesquero de Fuengirola s/n, 29640, Málaga, Spain.
6 12 2022
6 12 2022
10544211 6 2022
19 11 2022
1 12 2022
© 2022 The Authors
2022
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Fishing is one of the most widespread and important human activities in coastal ecosystems and it plays a fundamental role in employment and the economy of coastal communities. However, in the period 2020 to 2021, the global outbreak of COVID-19 negatively affected fishing economic activity. Against this background, Andalusia (South of Spain) is an important region in which the resilience of different fishing exploitation systems can be studied, but within the same social and economic framework. Therefore, the main study aim was to investigate the resilience of fishing activity to the COVID-19 pandemic in two Andalusian fishing grounds (i.e. Atlantic and Mediterranean). We analysed daily landings and the first-sale prices of fresh fish of the most caught species in both fishing grounds, while taking into account the different seasonal behaviour of the fisheries. Generalized Linear Models were used to compare the data, which were obtained during periods in which the COVID-19 severity levels differed. These levels were implemented according to political measures. The final objective was to understand how the degree of industrialization in the fleets can hinder or help maintain the economic activity of fisheries during major crises.
Keywords
Alboran Sea
Artisanal fisheries
Gulf of Cádiz
Industrial fisheries
Mediterranean Sea
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pmc1 Introduction
Currently, humanity is experiencing the effects of the COVID-19 pandemic [1] caused by a coronavirus (CoV), SARS-CoV-2. The global outbreak of COVID-19 in the period 2020 to 2021 led to a slow-down in the economic activity of all production sectors, which is without precedent in recent history [2], [3], [4]. The COVID-19 pandemic severely disrupted fisheries in many ways [5], [6], [7]. During the most difficult period of the pandemic, fresh seafood prices were in free fall due to the decrease in consumption caused by the mobility restrictions [8]. Border closures and the suspension of air travel prevented fishing companies from sending supplies to the vessels or changing their crews [9]. In addition, it has been observed that the basic reproduction number of the SARS-CoV-2 virus (R0) is unusually high on ships. For example, on the cruise ship Diamond Princess, R0 reached the value of 11, with almost 192 positive tests for COVID-19 per 1000 people, whereas in unconfined environments R0 has typically been between 2.2 and 5.7 [10], [11]. This situation was referred to by Báez and González Carballo [8] as the Diamond effect (i.e. the particular high contagion rate on boats, which made fishing on boats a high risk activity).
Since the declaration of the COVID-19 pandemic in 2020 by the World Health Organization, many countries opted for mobility restrictions, bar and restaurant closures, and other social distancing measures. The first cases of COVID-19 were linked to a marine shellfish and fish market in Wuhan (China) [12]. Subsequently, traces of virus were discovered on chopping boards used for imported salmon in Beijing (China) [13]. This led to a reduction in seafood consumption and social alarm, which affected fresh seafood consumption around the world leading to decreases in the economic activity of fisheries. In fact, the fall in demand was such that some fishermen chose to stay in the harbours [8], [14]. For example, in the western Mediterranean, the fishing effort was reduced by 34% during the most difficult months of the lockdown (March-May 2020), and landings and revenues fell by 34% and 49%, respectively, when compared to those from the period 2017 to 2019 [7].
Fishing is one of the most widespread and important human activities in coastal ecosystems and it plays a fundamental role in employment and the economy of coastal communities [15]. Therefore, any reduction in the economic activity of fisheries could be detrimental to the entire coastal community. After Galicia, the Region of Andalusia (Southern Spain) has the highest number of fishing landings in Spain [16]. Fishing activity in Andalusia has immense economic value and is a key element in its social and cultural image [17], [18]. This region is naturally divided by the Strait of Gibraltar into two different fishing grounds, one in Atlantic waters and the other in the Mediterranean Sea. According to Maya-Jariego [17], there is a clear differentiation between fleets in these two areas, one fleet with a considerably higher level of technology (mainly working in the Atlantic), and the other more coastal-based fleet (mainly working in the Mediterranean). Thus, in the Atlantic, the non-artisanal fishing fleet uses high-level technology for the extractive tasks, more stratified labour organization, and links with transformation industries and commercial networks [17]. On the other hand, in the Mediterranean basin, the non-artisanal fishing fleet is more traditional and has a lower level of technology [16], [17], [18]. In contrast, the artisanal fleet is very similar in both fishing grounds.
Due to the geographical and oceanographic separation of the fishing grounds of Andalusia, the international management of fishing in the Atlantic fishing ground is under the responsibility of the International Council for the Exploration of the Sea (corresponding to the ICES 9a division), whereas the Mediterranean fishing ground is managed by the General Fisheries Commission for the Mediterranean (GFCM, corresponding the Geographical Sub-Areas [GSAs] GSA1 and GSA2 [19]). In addition, the fisheries targeting tuna and tuna-like species in both fishing grounds are under the management of the International Commission for the Conservation of Atlantic Tunas (ICCAT).
Against this background, we highlight the fact that Andalusia is an important region in which the resilience of different fishing exploitation systems can be studied, but within the same social and economic framework. Therefore, our main study aim was to investigate the resilience of fishing activity to the COVID-19 pandemic in the two Andalusian fishing grounds. We analysed daily landings and first-sale prices of fresh fish (hereafter, first-sale prices) of the most caught species in different periods of the pandemic. Generalized Linear Models were used to assess the fitness of fishing activity and to understand how the degree of industrialization can hinder or help to maintain the economic activity of fisheries.
2 Material and methods
2.1 The fishing fleet in the Region of Andalusia
The Atlantic fishing ground of the Region of Andalusia mainly covers the Gulf of Cádiz, whereas the Mediterranean ground mainly runs from the port of Estepona to the port of Vera ( Fig. 1). From a geographical point of view, the ports of Algeciras and Atunara (La Linea de la Concepción; Cádiz) are in the Mediterranean Sea, but from a fisheries perspective they are included in the Atlantic fishing ground, because fleets from these ports mainly operate in this fishing ground. In the period 2020 to 2021, the total number of registered vessels in the entire area was 1,376 (including both artisanal and non-artisanal fishing boats), of which 761 were located in the Atlantic ground and 615 in the Mediterranean one. In 2020, total fisheries landings in Andalusia were 54,566 tons, with 35,993.5 tons from the Atlantic (66%) and 18,572.5 tons from the Mediterranean (34%)[20]. Total first-sale prices were €171,992,213.8, divided into €121,064,817.62 from the Atlantic fishing ground (70.4%) and €50,927,396.19 from the Mediterranean one (29.6%). Therefore, there was an economic ratio of €3.4/kg for the Atlantic fishing ground vs €2.7/kg for the Mediterranean one [21].Fig. 1 Andalusian region showing the (a) Atlantic and (b) Mediterranean fishing grounds and the main harbours.
Fig. 1
Regarding landings and economic value, in the period 2020 to 2021, the main fishing gear used in both fishing grounds were bottom trawls and purse seines ( Table 1). In terms of economic value, artisanal fisheries represented 9.4% and 8.77% of landings, and 16.74% and 16.61% from the Atlantic and Mediterranean fishing grounds, respectively. Therefore, the differential effect of the artisanal fleet on the Atlantic fishing ground vs the Mediterranean fishing ground should be considered to be nonsignificant when both fishing grounds are compared.Table 1 Total landings and total of first-sale prices in both fishing grounds.
Table 1 Atlantic Ocean Mediterranean Sea
Fishing mode Kilograms € Kilograms €
Bottom trawler 14.919.585 63.787.535,82 € 325,25 22.259.578,79 €
Artisanal fisheries 3.382.879 20.258.898,98 € 1.628.209 8.458.525,45 €
Tuna 155.136 1.326.980,24 € 101.314 1.022.968,95 €
Purse seine 14.695.472 25.716.090,54 € 11.784.967 13.168.897,51 €
Dredger 2.192.342 7.341.821,06 €
Longline 223.373 1.054.587,47 € 586.698 3.825.662,20 €
RASTRO 330.156 1.156.130,43 € 1.039.987 1.992.559,02 €
Shellfishing 94.399 517.038,09 €
Almadraba (Trap Net) 185 5.735,00 € 179.036 199.204,28 €
TOTAL 35.993.527 120.642.045 15.141.500 50.728.192
2.2 Landing data
The Junta de Andalucía (2021) provided the daily landings by species (in tons) and first-sale prices (in euros) datasets for 2020 and 2021 [20], [21]. We analysed the most abundant and common species from both fishing grounds. Table 2 shows the selected species according to their fisheries behaviour (i.e., seasonal or otherwise), daily total amount, and first-sale prices. We select species mostly caught by non-artisanal boats.Table 2 Numerical summary of the response variables (landings and first-sale prices) by species and fishing grounds (Atlantic and Mediterranean Sea). Species are also classified by the seasonality of their fishery.
Table 2Fishery (seasonality) Species name Common name Atlantic Mediterranean
Mean Landings Mean price Mean Landings Mean price
NonSEASONAL Trachurus mediterraneus Mediterranean horse mackerel 1871 1 3499 1.3
Trachurus picturatus Blue jack mackerel 90 2.3 1028 0.7
Trachurus trachurus Atlantic horse mackerel 2796 1.2 4012 1.1
Scomber scombrus Atlantic mackerel 172 1.5 37 2.7
Sardina pilchardus Sardine 15689 2.5 9675 2.3
Scomber colias Atlantic chub mackerel 20962 1 7187 0.7
Micromesistius poutassou Blue whiting 2862 1.8 326 3.9
Argyrosomus regius Meagre 842 8 17 5.2
Pagellus bogaraveo Blackspot seabream 105 24.4 41 17.2
Scomber japonicus Pacific chub mackerel 8 457 0.9
SEASONAL Octopus vulgaris Common octopus 6894 5.7 4124 6.3
Engraulis encrasicolus European anchovy 31209 2.6 7883 3.2
Sarda sarda Atlantic bonito 672 3.3 393 5
Dicologlossa cuneata Wedge sole 634 9.2 5 7.9
Parapenaeus longirostris Deep-water rose shrimp 12792 9.1 1449 12.9
Phycis phycis Forkbeard 84 5.7 31 7.4
Auxis thazard Frigate 108 1.8 4602 2.3
Auxis rochei Bullet tuna 86 1.8 2897 2
Euthynnus alletteratus Little tunny 113 2.6 743 2.6
Nephrops norvegicus Norway lobster 786 18.1 134 38
Scorpaena scrofa Red scorpion fish 9 19.1 59 12.6
Xiphias gladius Swordfish 3994 6.6 3227 7.2
Caranx rhonchus False scad 92 3.4 25 4
Boops boops Bogue 1168 0.5 1445 0.4
Aristeus antennatus Blue and red shrimp 340 36.7 528 37.6
Sardinella aurita Round sardinella 3661 2.4 10583 0.4
Thunnus thynnus Atlantic bluefin tuna 14559 12.9 1869 9.4
Note: Prices are given in Euros and landings in tons.
It should be noted that the same target species could be caught on different types of gear and by different boat strata. In addition, according to fishing ground, each species lives in a different biological setting and comes under a different management approach. Therefore, we tested possible trends by species and within the same fishing ground given that a) the effect of the artisanal fleet is considered to be nonsignificant between the two fishing grounds, and b) the target species could also differ between fishing grounds.
2.3 Statistical analysis
We created a statistical variable based on political measures, because the ones implemented to prevent the spread of the disease are representative of the different COVID-19 severity levels [7]. More specifically, to avoid confounding effects, we created a categorical variable—henceforth the COVID variable—which was defined in a slightly different way for species that have seasonal or nonseasonal fisheries behaviour ( Fig. 2). Thus, we considered two sets of species: seasonal or nonseasonal, depending on intra-annual presences observed in previous years, such that when there are no landings whatsoever in a single quarter, the fishery was considered to be seasonal. For nonseasonal fisheries, the COVID covariable could take the value No COVID, State of alarm 1, After State of alarm 1, and State of alarm 2 according to the different levels of severity. Hence, we analysed whether there were significant differences between the response variables (landings and first-sale prices) and the three latter categories in relation to the No COVID reference category. For seasonal fisheries, the No COVID level was divided into three categories, Reference State of alarm 1, Reference after State of alarm 1, and Reference State of alarm 2 (see Fig. 2). In this way, we analysed differences in landings and first-sale prices by comparing the same months of the year before and after the occurrence of COVID-19, thus avoiding any confounding effects due to the seasonal behaviour of the fishery.Fig. 2 Framework of the different levels of the COVID variable used in the Generalized Linear Models for species with a seasonal or nonseasonal fishery.
Fig. 2
To define the COVID covariable, the dates of the states of alarm were obtained from the official Spanish government website [22].
Generalized Linear Models [23] were used to investigate the influence of COVID levels on daily landings and first-sale price of the chosen species. GLMs are an extension of Linear Models (LMs) for which the distribution of the response variable can be other than Gaussian. For this reason, a link function g is required between the expected response and conditional response of the variable Y, μ(X)=E(Y|X), X = (X1, …., Xp) being the covariables and the linear predictor, formulating the GLM as(1) g(μ(X)) =β0 +β1X1 +.+βpXp
where β0, β1,.,βp are the unknown model parameters.
A GLM assumes that the response variable follows a distribution belonging to the exponential distribution family. This family includes distributions that are practical for modelling, such as Poisson, Binomial, Normal or Gamma distributions. Depending on the distribution of the response variable, different link functions can be applied that give rise to different models. For example, if the response follows a Gaussian distribution and the link is the identity function, the GLM becomes an LM. However, in our analysis, we assumed that our response variables (i.e. landings and first-sale prices) follow a Gamma distribution with a natural logarithm link due to their being strictly positive continuous variables.
In GLMs, the effects of categorical variables are considered to be fork-1 of the k factor levels, with the remaining one considered to be the base level. Hence, the estimated coefficient of each factor level will indicate the deviation in relation to the value of the base level. In our case, for nonseasonal fisheries, the COVID variable generates a coefficient for each of the levels—State of alarm 1, After State of alarm 1, and State of alarm 2—which indicates the deviation in relation to the value of the No COVID level. To avoid the confounding effects of seasonal patterns, the same approach was taken in relation to seasonal fisheries such that each level was compared to the corresponding year in which there was no COVID disease.
GLMs were performed using R software [24]. All the R code used in this study can be found as an open-access source in the GitHub repository (link).
3 Results
This study investigated the resilience of fishing activity to the COVID-19 pandemic by analysing 27 different species in the Andalusian fishing grounds (Atlantic Ocean and Mediterranean Sea). For each of the species, we conducted two GLMs using the response variable landings and first-sale prices, respectively. Table 3 shows the results of the GLM models in relation to the effects of the COVID levels by price and landings.Table 3 Summary of the GLM results.
Table 3Image 1
Image 2
Due to the small sample size, we were unable to detect significant effects in either of the two response variables for some species. This was the case of Scomber japonicus, for which there were insufficient data on any of the COVID levels in both fishing grounds. In addition, there were not enough data on Auxis thazard, Auxis rochei, Xiphias gladius, Sardinella aurita, and Thunnus thynnus to conduct some of the comparisons between the different COVID levels (Table 3).
Table 4 shows significant decreases/increases in the price or landings variables for several species for each corresponding COVID level in relation to the reference level. In particular, the percentage of significant increases or decreases is provided for each response variable (landings and first-sale prices), which were computed for the total of species for which a significant effect in the COVID level was detected.Table 4 The percentage of species for which a significant increase or decrease in price or landings was found during State of alarm 1, After State of alarm, and State of alarm 2 relative to the reference state. Note: The percentages are provided for each response variable (landings and first-sale prices). The percentage was computed for the total of species for which a significant effect was detected. D (%) denotes a decreasing percentage; I (%) denotes an increasing percentage.
Table 4 Landings Price
State of alarm 1 After state of alarm 1 State of alarm 2 State of alarm 1 After state or alarm 1 State of alarm 2
D (%) 81 50 67 67 56 32
I (%) 19 50 33 33 44 68
For all species, and relative to the no-COVID-19 period, a significant decrease was found in the price variable of 67% during State of alarm 1, whereas there were decreases of 56% and 32% during the after the State of alarm 1 and the State of alarm 2, respectively.
Also in relation to the no-COVID-19 period, a significant decrease of the 87% was found in the landings variable during State of alarm 1, and decreases of 50% during after the State of alarm 1 and 67% during State of alarm 2.
The results show that the pandemic had the greatest negative impact on the price variable during State of alarm 1—although this variable was already recovering during the following two pandemic levels—and had the greatest negative impact on the landings variable in States of alarm 1 and 2, although the effect was less during the latter state. Thus, the pandemic had a greater effect on landings than on prices.
For completeness, Table 5 shows significant increases or decreases in prices and landings by percentage of species and by fishing ground (Gulf of Cádiz and Mediterranean Sea). Relative to the no-COVID-19 period, we observed the following significant decreases in the price variable: 67% and 66% for all species in the Atlantic and in the Mediterranean Sea during State of alarm 1, respectively; 45% and 69% for all species in the Atlantic and Mediterranean Sea during the post-State of alarm 1, respectively; and 36% and 29% of all species during State of alarm 2 in the Atlantic and Mediterranean Sea, respectively.Table 5 The percentage of models for which a significant increase or decrease in price or landings was found during State of alarm 1, After State of alarm, and State of alarm 2 relative to the reference state. Note: The percentage are provided for each response variable (landings and first-sale prices) by fishing grounds (Atlantic and Mediterranean Sea). The percentage was computed for the total of species for which a significant effect was detected. D (%) denotes a decreasing percentage; I (%) denotes an increasing percentage.
Table 5 Landings Price
Atlantic Mediterranean Atlantic Mediterranean
State of alarm 1 After state or alarm 1 State of alarm 2 State of alarm 1 After state or alarm 1 State of alarm 2 State of alarm 1 After state or alarm 1 State of alarm 2 State of alarm 1 After state or alarm 1 State of alarm 2
D (%) 64 50 71 100 50 62 67 45 36 66 69 29
I (%) 36 50 29 0 50 38 33 55 64 33 31 71
Thus, the results show that the pandemic had a negative effect on prices in State of alarm 1 in both fishing grounds. However, during post-State of alarm 1, this effect was only observed in the Mediterranean, whereas the Atlantic was slowly recovering.
Relative to the no-COVID-19 period, we observed the following significant decreases in the landings variable: 64% and 100% for all species in the Atlantic and Mediterranean Sea during State of alarm 1, respectively; 50% for all species in both fishing grounds during the post-State of alarm 1; and 71% and 62% for all species in the Atlantic and the Mediterranean during State of alarm 2, respectively.
The results show that the pandemic had a negative effect on landings in State of alarm 1 in both fishing grounds, although the effect was greater in the Mediterranean Sea. During post-State of alarm 1, the decrease in landings was lower for all species.
Both fishing grounds showed a common pattern of decreases/increases in first-sale prices for the species Scombur scombrus, Sardina pilchardus, Micromesistius poutassou, Octopus vulgaris, Sarda sarda, Scorpaena scrofa, Boops boops, and Thunnus thynnus.
4 Discussion
In order to assess the fitness of fishing activity, we compared the impact of different periods of the pandemic on landings and the first-sale prices of fresh fish in two Andalusian fishing grounds by species and by fishing ground.
The results show that during State of alarm 1 there was a sharp fall in first-sale prices and landings in both Andalusian grounds. It is clear that the collateral economic effects of the market disturbances affected the ability of fishermen to make a living because of the reduced demand and consequent price collapse. Export-oriented fisheries faced greatly reduced demands, port closures, loss of access to cold storage, and the cessation of sea and air transport [25].
However, during subsequent periods the decreases were lower, which may have been due to the fact that the losses related to the first period were cushioned by reduced operating costs and the deployment of a wide network of public aid to the sector [6]. Indeed, after the first lockdown period, there was a major reduction in oil consumption worldwide, which led to decreased oil prices, thus benefiting fisheries [7].
The results show that the economic recovery from the COVID-19 lockdowns was temporary because it was reversed by successive restrictions related to mobility as well as fluctuations in demand for seafood during State of alarm 2. Uncertainty in food supplies and disruptions in traditional value chains meant that the resumption of fishing depended on the reopening of markets, restaurants, and other large-scale activities associated with the consumption of seafood, such as tourism [5], [26]. In fact, the interruptions caused by the COVID-19 restrictions completely changed market and eating habits [27]. Grocery stores saw an increased demand for typically inexpensive frozen or canned seafood, such as canned tuna [28]. Meanwhile, vessels oriented to the fresh fish market were not working due to the lack of demand [29]. Therefore, fisheries may remain vulnerable to any resurgence in COVID-19 infection rates and demands for fresh fish, especially higher value products.
The results showed that the Atlantic fishing ground was more resilient to the COVID-19 disruptions in terms of prices, whereas the Mediterranean one was resilient in terms of landings. This could be due to the fact that in operational terms, the Mediterranean fleet is mainly composed of small vessels with small crews, which facilitated their rapid return to work once the main health issues were resolved [7]. In contrast, the Atlantic fleet requires more crew members per vessel, which could hinder the return to work for operational and economic reasons in the face of any future severe crisis.
However, from an operational point of view, although the Mediterranean fisheries recovered quickly, they were more affected than the Atlantic fisheries by the decrease in the demand for local fresh fish. It should be noted that, in order to combat unemployment, several public support schemes were approved that partially compensated the sector for the decrease in fishing activity and enabled many fishermen to cease fishing until the health measures were implemented and minimum market conditions were ensured [30], [31].
Our results are in line with those of recent studies that have found that landings and prices have been disrupted by abrupt changes in demand and supply and limitations on the movement of people and goods [7], [32], [33]. In conclusion, our study suggests that the COVID-19 pandemic has been and continues to be a major challenge for the fisheries sector in Spanish waters and global ones. Although there have been some political initiatives to offset the negative consequences of the pandemic, the immediate impacts of the crisis were profound in relation to catches and market prices. Short-term responses need to be rapid and should target the most vulnerable sectors. As mentioned by Bennet et al. [14], a coordinated response needs to be developed to transform existing institutions, supply chains, and food systems in ways that improve the conditions and resilience of the fisheries sector to prepare for future unforeseen global crises.
Data availability
Data will be made available on request.
Acknowledgments
MCR and MGP would like to thank the project IMPRESS (RTI2018-099868-B-I00), ERDF, Ministry of Science, Innovation and Universities - State Research Agency. This study was supported by the project "Fostering the capacity of marine ecosystem models to PROject the cumulative effects of global change and plausible future OCEANS (PROOCEANS)", funded by the Ministerio de Ciencia e Innovación, Proyectos de I+D+I (RETOS-PID2020-118097RB-I00).
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26 Stoll J.S. Harrison H.L. Sousa E.D. Callaway D. Collier M. Harrel K. Jones B. Kastlunger J. Kramer E. Kurian S. Lovewell A. Strobel S. Sylvester T. Tolley B. Tonlinson A. Young T. Loring P.A. Alternative seafood networks during COVID-19: implications for resilience and sustainability. Front. Sustain Food Syst 5 2020 614368 10.3389/fsufs.2021.614368
27 Batlle-Bayer L. Aldaco R. Bala A. Puig R. Laso J. Margallo M. Vázquez-Rowe I. Antó J.M. Fullana-i-Palmer. P. Environmental and nutritional impacts of dietary changes in Spain during the COVID-19 lockdown Sci. Total Environ 748 2020 141410 10.1016/j.scitotenv.2020.141410
28 Havice E. Marschke M. Vandergeest P. Industrial seafood systems in the immobilizing COVID-19 moment Agric. Human Values 37 2020 655 656 10.1007/s10460-020-10117-6 32836756
29 Campling L. Havice. E. The global environmental politics and political economy of seafood systems Glob. Environ. Politics 18 2018 72 92 10.1162/glep_a_00453
30 Ministerio B.O.E. de la Presidencia España. Real Decreto 463/2020, de 14 de marzo, por el que se declara el estado de alarma para la gestión de la situación de crisis sanitaria ocasionada por el COVID-19 Boletín Oficial Del Estado 67 2020 25390 25400
31 Jefatura B.O.E. del Estado. Real Decreto-ley 8/2020, de 17 de marzo, de medidas urgentes extraordinarias para hacer frente al impacto económico y social del COVID-19 Boletín Oficial Del Estado 73 2020 1 53
32 Love D.C. Allison E.H. Asche F. Belton B. Cottrell R.S. Froehlich H.E. Gephart J.A. Hicks C.C. Little D.C. Nussbaumer E.M. Pinto da Silva P. Poulain F. Rubio A. Stoll J.S. Tlusty M.F. Thorne-Lyman A.L. Troell M. Zhang. W. Emerging COVID-19 impacts, responses, and lessons for building resilience in the seafood system Glob. Food Sec 28 2021 100494 10.1016/j.gfs.2021.100494
33 Villasante S. Tubío A. Ainsworth G. Pita P. Antelo M. Da-Rocha. J.M. Rapid Assessment of the COVID-19 Impacts on the Galician (NW Spain) Seafood Sector Front. Mar. Sci 8 2021 737395 10.3389/fmars.2021.737395
| 36506339 | PMC9722673 | NO-CC CODE | 2022-12-09 23:14:54 | no | Mar Policy. 2023 Feb 6; 148:105442 | utf-8 | Mar Policy | 2,022 | 10.1016/j.marpol.2022.105442 | oa_other |
==== Front
Enferm Infecc Microbiol Clin
Enferm Infecc Microbiol Clin
Enfermedades Infecciosas Y Microbiologia Clinica
0213-005X
1578-1852
Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. Published by Elsevier España, S.L.U.
S0213-005X(22)00282-8
10.1016/j.eimc.2022.11.004
Article
Infección leve por SARS-CoV-2 en pacientes vulnerables: implantación de una vía clínica de tratamiento precoz
Mild SARS-CoV-2 infection in vulnerable patients: implementation of a clinical pathway for early treatmentPinargote-Celorio Héctor MD PhD 1a
Otero-Rodríguez Silvia MD 2a
González-de-la-Aleja Pilar MD 3
Rodríguez-Díaz Juan-Carlos MD PhD 4
Climent Eduardo MD PhD 5
Chico-Sánchez Pablo MD MPH PhD 6
Riera Gerónima MD PhD 7
Llorens Pere MD PhD 8
Aparicio Marta MD 9
Montiel Inés MD PhD 10
Boix Vicente MD PhD 11
Moreno-Pérez Óscar MD PhD 12
Ramos-Rincón José-Manuel MD PhD 13b
Merino Esperanza MD PhD 14b⁎
1 Unidad de Enfermedades Infecciosas, Hospital General Universitario Dr. Balmis - Instituto Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España
2 Unidad de Enfermedades Infecciosas, Hospital General Universitario Dr. Balmis - Instituto Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España
3 Unidad de Enfermedades Infecciosas, Hospital General Universitario Dr. Balmis - Instituto Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España
4 Servicio de Microbiología, Hospital General Universitario Dr. Balmis - Instituto Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España, Departamento de Producción Vegetal y Microbiología, Universidad Miguel Hernández, Elche, España
5 Servicio de Farmacia. Hospital General Universitario Dr. Balmis, Alicante - Balmis - Instituto Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España, Área de Farmacia y Tecnología Farmacéutica. Universidad Miguel Hernández, Elche, España
6 Servicio Medicina Preventiva. Instituto de Investigación Sanitaria y Biomédica de Alicante ISABIAL. Hospital General Universitario Dr. Balmis, Alicante, España
7 Servicio de Farmacia. Hospital General Universitario Dr. Balmis - Instituto Investigación, Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España
8 Servicio de Urgencias, Unidad de Corta Estancia y Hospitalización a Domicilio, Hospital General Universitario Dr. Balmis, Alicante. Instituto Investigación Biomédica y Sanitaria de Alicante (ISABIAL), Alicante, España, Departamento de Medicina Clínica, Universidad Miguel Hernández, Elche, Alicante, España
9 Farmacia de Atención Primaria. Hospital General Universitario Dr. Balmis, Alicante, España
10 Dirección Atención Primaria Hospital General Universitario Dr. Balmis, Alicante. Instituto. Investigación Biomédica y Sanitaria de Alicante (ISABIAL), Alicante, España
11 Unidad de Enfermedades Infecciosas, Hospital General Universitario Dr. Balmis – Instituto, Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España, Departamento de Medicina Clínica, Universidad Miguel Hernández, Elche, Alicante, España
12 Sección Endocrinología, Hospital General Universitario Dr. Balmis-Instituto de Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España, Departamento de Medicina Clínica, Universidad Miguel Hernández, Elche, Alicante, España
13 Servicio de Medicina Interna, Hospital General Universitario Dr. Balmis - Instituto Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España, Departamento de Medicina Clínica, Universidad Miguel Hernández, Elche, España
14 Unidad de Enfermedades Infecciosas, Hospital General Universitario Dr. Balmis - Instituto Investigación Sanitaria y Biomédica de Alicante (ISABIAL), Alicante, España
⁎ Autora de correspondencia: Hospital General Universitario Dr. Balmis-ISABIAL, C/ Maestro Alonso s/n, CP 03010 Alicante, España
a Ambos autores comparten primera autoría.
b Ambos autores comparten autoría principal.
6 12 2022
6 12 2022
15 9 2022
20 11 2022
© 2022 Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. Published by Elsevier España, S.L.U. 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.
Introducción: El objetivo del manuscrito es describir la vía clínica de tratamiento precoz de pacientes con infección aguda por SARS-CoV-2 y evaluar los primeros resultados de su implementación
Métodos: Estudio descriptivo y retrospectivo de la implementación de una vía clínica de tratamiento en pacientes no-hospitalizados, (1 de enero al 30 de junio 2022). Elaboración de vía clínica: sistemas detección y derivación desde Atención Primaria, servicio de Urgencias, especialidades médicas y sistema de detección automatizada; evaluación clínica y administración de tratamiento en hospital de día COVID-19, y seguimiento clínico posterior. Variables explicativas: demográficas, comorbilidad, estado vacunal, vías de derivación y administración de tratamiento. Variables de resultado: hospitalización y muerte a los 30 días, toxicidad grado 2-3 relacionada con el tratamiento.
Resultados: Se administró tratamiento a 262 pacientes (53,4% mujeres, mediana de edad 60 años). Criterio indicación tratamiento: inmunosupresión (68,3%) y la combinación de edad, estado vacunal y comorbilidad en el resto. El 47,3% de los pacientes recibieron remdesivir, el 35,9% nirmatrelvir/ritonavir, el 13,4% sotrovimab y el 2,4% tratamiento combinado, con una mediana de 4 días tras inicio de síntomas. El 6.1% de los pacientes precisó ingreso hospitalario, 3,8% por progresión COVID-19. Ningún paciente falleció. El 18,7% presentaron toxicidad grado 2-3: 89,8% disgeusia y sabor metálico relacionado con nirmatrelvir/ritonavir. Siete pacientes interrumpieron tratamiento por toxicidad.
Conclusión: La creación e implementación de una vía clínica para pacientes no-hospitalizados con infección por SARS-CoV-2 es efectiva y permite la accesibilidad temprana y la equidad de los tratamientos actualmente disponibles.
Introduction: The objective of this report is to describe the clinical pathway for early treatment of patients with acute SARS-CoV-2 infection and to evaluate the first results of its implementation.
Methods: This is a descriptive and retrospective study of the implementation of a clinical pathway of treatment in outpatients (January 1 to June 30 2022). Clinical pathway: detection and referral systems from Primary Care, Emergency services, hospital specialities and an automated detection system; clinical evaluation and treatment administration in the COVID-19 day-hospital and subsequent clinical follow-up. Explanatory variables: demographics, comorbidity, vaccination status, referral pathways and treatment administration. Outcome variables: hospitalization and death with 30 days, grade 2-3 toxicity related to treatment.
Results: Treatment was administered to 262 patients (53,4% women, median age 60 years). The treatment indication criteria were immunosupression (68,3%), and the combination of age, vaccination status and comorbidity in the rest47,3% of the patients s received remdesivir, 35,9% nirmatrelvir/ritonavir, 13,4% sotrovimab and 2,4% combined treatment with a median of 4 days after symptom onset. Hospital admission was required for 6,1% of the patients, 3,8% related to progression COVID-19. No patient died. Toxicity grade 2-3 toxicity was reported in 18,7%, 89,8% dysgeusia and metallic tasted related nirmatrelvir/ritonavir. Seven patients discontinued treatment due to toxicity.
Conclusion: The creation and implementation of a clinical pathway for non-hospitalized patients with SARS-CoV-2 infection is effective and it allows early accessibility and equity of currently available treatments.
Palabras claves
SARS-CoV-2
COVID-19
vía clínica
remdesivir
nirmatrelvir/ritonavir
sotrovimab
tratamiento no-hospitalizados
Keywords
SARS-CoV-2
COVID-19
clinical pathway
remdesivir
nirmatrelvir/ritonavir
sotrovimab
non-hospitalized treatment COVID-19
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pmc
| 36506459 | PMC9722674 | NO-CC CODE | 2022-12-07 23:21:52 | no | Enferm Infecc Microbiol Clin. 2022 Dec 6; doi: 10.1016/j.eimc.2022.11.004 | utf-8 | Enferm Infecc Microbiol Clin | 2,022 | 10.1016/j.eimc.2022.11.004 | oa_other |
==== Front
Enferm Infecc Microbiol Clin
Enferm Infecc Microbiol Clin
Enfermedades Infecciosas Y Microbiologia Clinica
0213-005X
1578-1852
Published by Elsevier España, S.L.U. on behalf of Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica.
S0213-005X(22)00281-6
10.1016/j.eimc.2022.11.003
Article
Gravedad comparativa de los casos de COVID-19 causados por las variantes Alfa, Delta u Ómicron de SARS-CoV-2 y su asociación con la vacunación
Comparative severity of COVID-19 cases caused by Alpha, Delta or Omicron SARS-CoV-2 variants and its association with vaccinationVarea-Jiménez Elena 1
Cano Esteban Aznar 2
Vega-Piris Lorena 1
Sánchez Elena Vanessa Martínez 23
Mazagatos Clara 13
Rodríguez-Alarcón Lucía García San Miguel 2
Casas Inmaculada 43
Moros María José Sierra 25
Iglesias-Caballero Maria 4
Vazquez-Morón Sonia 4
Larrauri Amparo 13
Monge Susana 15⁎
1 National Centre of Epidemiology – Institute of Health Carlos III, Madrid, Spain
2 Centre for the Coordination of Alerts and Health Emergencies – Ministry of Health, Madrid, Spain
3 CIBER Epidemiology and Public Health, Spain
4 National Centre of Microbiology – Institute of Health Carlos III, Madrid, Spain
5 CIBER Infectious Diseases, Spain
⁎ Corresponding author: Department of Communicable Diseases, National Centre of Epidemiology, Institute of Health Carlos III, Calle de Melchor Fernández Almagro, 5, 28029 Madrid, Spain
6 12 2022
6 12 2022
22 9 2022
10 11 2022
© 2022 Published by Elsevier España, S.L.U. on behalf of Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica.
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.
Introducción: El objetivo es comprar la gravedad de las infecciones por las variantes Alfa, Delta y Ómicron del SARS-CoV-2 en periodos de co-circulación en España, y estimar la asociación entre vacunación y gravedad en cada variante.
Métodos: Las infecciones por SARS-CoV-2 notificadas a la red nacional de vigilancia epidemiológica con información sobre la variante viral y el estado de vacunación se clasificaron como casos si habían requerido hospitalización, o como controles en caso contrario. Alfa y Delta se compararon durante Junio-Julio de 2021; y Delta y Ómicron durante Diciembre 2021-Enero 2022. Se estimaron Odds Ratios ajustadas (ORa) mediante regresión logística, comparando la variante y el estado de vacunación entre casos y controles.
Resultados: Se incluyeron 5,345 infecciones por variante Alfa y 11,974 por Delta en Junio-Julio y 5,272 infecciones por Delta y 10,578 por Ómicron en Diciembre-Enero. Los casos no vacunados por Alfa (aOR: 0.57; 95% CI: 0.46-0.69) u Ómicron (0.28; 0.21-0.36) tuvieron menor probabilidad de hospitalización comparado con Delta. La vacunación completa se asoció a menor hospitalización de forma similar para Alfa (0.16; 0.13-0.21) y Delta (Junio-Julio: 0.16; 0.14-0.19; Diciembre-Enero: 0.36; 0.30-0.44) pero menor para Ómicron (0.63; 0.53-0.75) y para individuos con 65+ años.
Conclusion: Los resultados indican una mayor gravedad intrínseca de la variante Delta comparada con Alfa u Ómicron, con menor diferencia entre personas vacunadas. La vacunación se asoció a menor hospitalización en todos los grupos.
Background: This study compares the severity of SARS-CoV-2 infections caused by Alpha, Delta or Omicron variants in periods of co-circulation in Spain, and estimates the variant-specific association of vaccination with severe disease.
Methods: SARS-CoV-2 infections notified to the national epidemiological surveillance network with information on genetic variant and vaccination status were considered cases if they required hospitalisation or controls otherwise. Alpha and Delta were compared during June-July 2021; and Delta and Omicron during December 2021-January 2022. Adjusted Odds Ratios (aOR) were estimated using logistic regression, comparing variant and vaccination status between cases and controls.
Results: We included 5,345 Alpha and 11,974 Delta infections in June-July and, 5,272 Delta and 10,578 Omicron in December-January. Unvaccinated cases of Alpha (aOR: 0.57; 95% CI: 0.46-0.69) or Omicron (0.28; 0.21-0.36) had lower probability of hospitalisation vs. Delta. Complete vaccination reduced hospitalisation, similarly for Alpha (0.16; 0.13-0.21) and Delta (June-July: 0.16; 0.14-0.19; December-January: 0.36; 0.30-0.44) but lower from Omicron (0.63; 0.53-0.75) and individuals aged 65+ years.
Conclusion: Results indicate higher intrinsic severity of the Delta variant, compared with Alpha or Omicron, with smaller differences among vaccinated individuals. Nevertheless, vaccination was associated to reduced hospitalisation in all groups.
Palabras clave
COVID-19
SARS-CoV-2
Alfa
Delta
Omicron
SARS-CoV-2 variantes
hospitalización
vacuna
Keywords
COVID-19
SARS-CoV-2
Alpha
Delta
Omicron
VOC
SARS-CoV-2 variants
Hospitalisation
Vaccination
the Working group for the surveillance and control of COVID-19 in Spain* and RELECOV**
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pmc
| 36506460 | PMC9722675 | NO-CC CODE | 2022-12-13 23:16:45 | no | Enferm Infecc Microbiol Clin. 2022 Dec 6; doi: 10.1016/j.eimc.2022.11.003 | utf-8 | Enferm Infecc Microbiol Clin | 2,022 | 10.1016/j.eimc.2022.11.003 | oa_other |
==== Front
Enferm Infecc Microbiol Clin
Enferm Infecc Microbiol Clin
Enfermedades Infecciosas Y Microbiologia Clinica
0213-005X
1578-1852
Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. Published by Elsevier España, S.L.U.
S0213-005X(22)00280-4
10.1016/j.eimc.2022.11.002
Article
IMPACTO DE LA PANDEMIA COVID-19 EN EL DIAGNOSTICO DE TUBERCULOSIS EN UN HOSPITAL DE TERCER NIVEL
IMPACT ON TUBERCULOSIS DIAGNOSTIC DURING COVID-19 PANDEMIC IN A TERTIARY CARE HOSPITALRuiz-Bastián Mario 1⁎
Díaz-Pollán Beatriz MD PhD 234
Falces-Romero Iker 14
Toro-Rueda Carlos 1
García-Rodríguez Julio 1
SARS-CoV-2 Working Group1
1 Clinical Microbiology and Parasitology Department. Hospital Universitario La Paz, Madrid, Spain
2 Infectious Disease Unit, Hospital Universitario La Paz, Madrid, Spain
3 IdiPAZ (La Paz Institute for Health Research). La Paz University Hospital, Madrid, Spain
4 CIBERINFEC (Centre for Biomedical Research Network on Infectious Diseases). Instituto de Salud Carlos III, Madrid, Spain
⁎ Corresponding author: Clinical Microbiology and Parasitology Department, Hospital Universitario La Paz, Paseo de la Castellana 261, 28046, Madrid, Spain
6 12 2022
6 12 2022
15 8 2022
6 11 2022
© 2022 Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica. Published by Elsevier España, S.L.U. All rights reserved.
2022
Sociedad Española de Enfermedades Infecciosas y Microbiología Clínica
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.
Introducción: el objetivo de este estudio fue revisar cómo afectaron las primeras tres olas de la pandemia COVID-19 al diagnóstico de tuberculosis y describir el diagnóstico de las infecciones extrapulmonares causadas por Mycobacterium tuberculosis complex (TB).
Material y métodos: se realizó un estudio observacional y retrospectivo durante el periodo que incluye las tres primeras olas de la pandemia para valorar el impacto en las muestras de TB y para valorar el diagnóstico de las TB extrapulmonares se amplió el periodo de estudio para incluir los dos primeros años de la COVID-19. Todos los datos relevantes se extrajeron de la base de datos del hospital y del Servicio de Microbiología y Parasitología Clínica.
Resultados: en el periodo prepandémico se recibían una media de 44 muestras por semana para el estudio de TB; durante las tres primeras olas ese número cayó a 23,1 por semana. Se observó una reducción del 67,7% en el diagnóstico de la TB pulmonar y un aumento del 33,3% en el diagnóstico de la TB extrapulmonar cuando se comparó con los datos prepandemia.
Discusión: el número de casos declarados y el número de muestras para el diagnóstico de TB cayó durante las tres primeras olas del COVID-19 debido a la saturación del Sistema Nacional de Salud lo que podría llevar a un retraso en el diagnóstico, tratamiento y a un aumento de la transmisión en la población general. Los sistemas de vigilancia deberían reforzarse para evitar esto.
Introduction: The aim of this study is to review how did the first three COVID-19 waves affected the diagnostic of tuberculosis and to describe the extra-pulmonary Mycobacterium tuberculosis complex (TB) diagnosis.
Materials and Methods: A retrospective observational study was done during the first three waves of pandemic to ascertain the impact on TB samples and to recover the extra-pulmonary TB cases we included the first two years of COVID-19. All relevant data was recovered from hospital and Clinical Microbiology records.
Results: Prepandemic period showed an average of 44 samples per week for TB study; during the first three waves this number dropped to 23.1 per week. A reduction of 67.7% of pulmonary TB diagnosis was observed and an increase of 33.3% diagnosis of extra-pulmonary TB was noted when comparing pre-pandemic and pandemic period.
Discussion: The number of declared cases and samples for TB diagnosis dropped during the first three COVID-19 waves due to the overstretched Public Health System which could lead to a delay in diagnosis, treatment and to the spread of TB disease in the general population. Surveillance programs should be reinforced to avoid this.
Keywords
COVID-19
tuberculosis
respiratory samples
pneumonia
extra-pulmonary
Palabras clave
COVID-19
tuberculosis
muestras respiratorias
neumonía
extrapulmonar
==== Body
pmc
| 36506458 | PMC9722676 | NO-CC CODE | 2022-12-07 23:19:13 | no | Enferm Infecc Microbiol Clin. 2022 Dec 6; doi: 10.1016/j.eimc.2022.11.002 | utf-8 | Enferm Infecc Microbiol Clin | 2,022 | 10.1016/j.eimc.2022.11.002 | oa_other |
==== Front
J Adolesc Health
J Adolesc Health
The Journal of Adolescent Health
1054-139X
1879-1972
Published by Elsevier Inc. on behalf of Society for Adolescent Health and Medicine.
S1054-139X(22)01022-9
10.1016/j.jadohealth.2022.11.242
Original Article
Parental Perceptions Related to Co-Administration of Adolescent COVID-19 and Routine Vaccines
Gidengil Courtney A. MD, MPH ab∗
Parker Andrew M. PhD c
Gedlinske Amber M. MPH d
Askelson Natoshia M. PhD d
Petersen Christine A. DVM, PhD d
Lindley Megan C. MPH e
Woodworth Kate R. MD, MPH e
Scherer Aaron M. PhD d
a RAND Corporation, 20 Park Plaza, Suite 920, Boston, MA, USA, 02116
b Boston Children’s Hospital, 300 Longwood Avenue, Boston, MA, USA, 02115
c RAND Corporation, 4570 Fifth Ave #600, Pittsburgh, PA, USA, 15213
d University of Iowa, 200 Hawkins Drive, Iowa City, IA, USA, 52242
e Centers for Disease Control and Prevention, 1600 Clifton Road, Atlanta, GA, USA, 30329
∗ Corresponding author: Courtney Gidengil, MD MPH, RAND Corporation, 20 Park Plaza, Suite 920, Boston, MA 02116. Tel: (617) 338-2059, ext. 8637. Fax: (617) 546-7057.
6 12 2022
6 12 2022
13 4 2022
16 9 2022
22 11 2022
© 2022 Published by Elsevier Inc. on behalf of Society for Adolescent Health and Medicine.
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
Vaccinating adolescents against COVID-19 while avoiding delays in other routine vaccination is paramount to protecting their health. Our objective was to assess parental preferences to have their adolescents aged 12-17 years receive COVID-19 and other routine vaccines at the same time.
Methods
An online survey with a national, quota-based cross-sectional sample of US parents of youth ages 12-17 was fielded in April 2021 ahead of FDA’s Emergency Use Authorization of COVID-19 vaccine for age 12-15 years. Parents were asked about their willingness to have their adolescents aged 12-17 years receive both COVID-19 and routine vaccines at the same visit and/or to follow their provider’s recommendation. Predictors included demographic characteristics, being behind on routine vaccines, and perceived risks and benefits.
Results
Few parents were willing to have their adolescent receive COVID-19 and routine vaccines at the same visit (10.6%) or follow the healthcare provider’s recommendation (18.5%). In multivariate analyses, demographic characteristics had no effect on willingness; reporting that the adolescent was behind on routine vaccines correlated with decreased willingness (p=0.004). Greater concern about the adolescent getting COVID-19 (p=0.001), lower concern about the adolescent having side effects from the COVID-19 vaccine (p=0.013), and more positive feelings about vaccines in general (p=0.002) were associated with higher willingness.
Discussion
Few parents would prefer to have their adolescent receive COVID-19 and routine vaccines at the same visit. Understanding what drives willingness to receive all recommended vaccines in the context of the COVID-19 pandemic could inform policies to optimize adolescent vaccination.
Key words
COVID-19 vaccine
routine vaccines
vaccination
adolescent health
Abbreviations
CDC, Centers for Disease Control and Prevention
EUA, Emergency Use Authorization
FDA, Food and Drug Administration
HaPPI, Healthcare and Public Perceptions of Immunizations
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pmc Implications and Contribution
Few parents were willing to have their adolescent receive COVID-19 and routine vaccines at the same time. These findings have important implications for adolescents–who are already more behind on routine vaccines than prior to the pandemic– but also for younger children as they become eligible for COVID-19 vaccines.
The COVID-19 pandemic has had a significant impact on children and adolescents, both directly due to infection, and indirectly due to school closures and other societal impacts. Although outcomes such as hospitalization and death are more common in adults, adolescents can be severely affected as well.1 Of adolescents hospitalized for COVID-19, about one third require admission to the intensive care unit.2 School closures, whether full or partial, have been associated with an increase in mental health disorders, widened educational disparities, and increased medical and social vulnerability due to the loss of school-based health and other services.3 , 4 Protecting adolescents to the extent possible, including through vaccination, is important to protect their physical, mental, and social well-being.
On December 11, 2020, the Food and Drug Administration (FDA) issued an Emergency Use Authorization (EUA) for the Pfizer-BioNTech COVID-19 vaccine, including for adolescents aged 16–17 years. Younger adolescents became eligible on May 10, 2021, when the FDA expanded its EUA to include adolescents aged 12–15 years. Early on, the CDC initially recommended an interval of 14 days between administration of COVID-19 vaccine and other vaccines. However, this guidance was changed in mid-May 2021 based on substantial safety data and extensive experience with non-COVID-19 vaccines to allow coadministration of COVID-19 vaccine with other vaccines, in part to facilitate catch-up vaccination of adolescents.5 , 6 On August 23, 2021, the FDA fully approved the Pfizer-BioNTech COVID-19 vaccine for individuals 16 years of age and older.7
Despite the availability of authorized and approved COVID-19 vaccines, coverage among adolescents remains low;8 as of October 5, 2021, only 45% of adolescents aged 12-15 years and 52% of those aged 16-17 years had received at least one dose of a COVID-19 vaccine, and fewer than one third had completed the two-dose vaccination series.1 There was also a substantial drop in routine vaccination9 of adolescents in the early months of the COVID-19 pandemic, presumably due to stay-at-home orders.10 , 11 In the following months there was a rebound in vaccination rates, but not to the extent required to compensate for the prior drop.10 Even prior to the COVID-19 pandemic, adolescents were under-vaccinated12 due in part to lower rates of health care utilization. As a result, there have been calls to vaccinate adolescents at every opportunity to optimize vaccination rates.13 Ensuring that adolescents receive a COVID-19 vaccine while avoiding delays in other routine vaccinations is paramount to protecting their health. As one strategy, both the CDC14 and the American Academy of Pediatrics15 endorse coadministration of vaccines whenever possible as a best practice, including for COVID-19 vaccines.16 Understanding parents’ willingness for their adolescents to receive COVID-19 vaccine together with other routine vaccines will help to inform strategies to optimize adolescent vaccination.
This analysis uses data from a survey of beliefs, attitudes, and behaviors/behavioral intentions of parents of adolescents ages 12-17 years in advance of FDA expansion of the EUA for the Pfizer-BioNTech COVID-19 vaccine to age 12-15 years. The specific research objectives are to: (1) measure willingness of parents to have their adolescents aged 12-17 years receive COVID-19 and other routine vaccines at the same visit, or to follow their provider’s recommendation; and (2) identify predictors of willingness to receive COVID-19 and other routine vaccines at the same visit and/or follow their provider’s recommendation to inform future policy recommendations.
Methods
Sample and Procedure
An online survey on the acceptability of adolescent COVID-19 vaccination was developed by the Healthcare and Public Perceptions of Immunizations (HaPPI) Survey Collaborative, which is a cooperative agreement between CDC and researchers at the University of Iowa and the RAND Corporation to survey healthcare providers and the general public on important vaccine-related issues. The survey was administered to parents of adolescents aged 12-17 years from April 15 through April 23, 2021 (prior to both the FDA’s authorization of the Pfizer-BioNTech COVID-19 vaccine for adolescents, and CDC’s allowance of coadministration of vaccines).17 Respondents were recruited through Qualtrics Panels (Qualtrics, LLC; Provo, Utah) using sampling quotas for gender, and race and Hispanic ethnicity. For gender, the quota included: 40% identifying as male, 40% identifying as female, and 20% not specified. For race and Hispanic ethnicity, the quota included 62% non-Hispanic White, 12% non-Hispanic Black, 17% Hispanic, and 8% other race/ethnicity. All survey questions are publicly available online at: https://osf.io/nq94h/. This survey was approved by the Institutional Review Board at the University of Iowa, reviewed by CDC, and adhered to applicable federal law and CDC policy (45 C.F.R. part 46; 21 C.F.R. part 56).
The participation rate is not reported because the sampling frame was unknown, consistent with American Association for Public Opinion Research reporting guidelines for survey recruitment using an opt-in non-probability panel.18 , 19 The final sample included only those parents who passed a quality check, which means that they answered affirmatively to the question “Do you commit to thoughtfully provide your best answers to each question in this survey?”; did not speed through the survey (i.e., total response time was not shorter than two standard deviations below the mean duration for all survey respondents); completed the survey; and reported that their adolescent had not yet received a COVID-19 vaccine.
Measures
Parents were asked “If a healthcare provider recommended your child receive one or more routine vaccines in addition to a COVID-19 vaccine, how would you get your child vaccinated?” Only one response was allowed (see Table 1 for response options). Our main outcome was willingness of parents to have their adolescent receive COVID-19 and routine vaccines at the same visit, which was defined as endorsing either “Routine vaccine(s) and COVID-19 vaccine in the same visit” or “Whatever my child's healthcare provider recommended” in response to the coadministration question. This outcome was chosen to allow for parents to reasonably follow whatever guidance is given (from CDC to vaccinate and/or from their provider). Secondary outcomes were endorsing either of the above responses (COVID-19 and routine vaccines together or following provider recommendation) on their own. We explored each of these response options separately as secondary outcomes to assess internal consistency across these two constructs.Table 1 Preferences for timing of administration of COVID-19 and routine vaccines (N=765)∗
N (%)
Routine vaccine(s) and COVID-19 vaccine in the same visit 81 (10.6%)
Whatever my child's healthcare provider recommended 142 (18.6%)
COVID-19 vaccine first, then routine vaccine(s) at a separate visit 166 (21.7%)
Routine vaccine(s) first, then COVID-19 vaccine at a separate visit 142 (18.6%)
Only a COVID-19 vaccine; I do not want my child to receive routine vaccine(s) 9 (1.2%)
Only routine vaccine(s); I do not want my child to receive a COVID-19 vaccine 121 (15.8%)
No vaccines 58 (7.6%)
Don't know/Not sure 46 (6.0%)
∗ Response to the following question: “If a healthcare provider recommended your child receive one or more routine vaccines in addition to a COVID-19 vaccine, how would you get your child vaccinated?”
Statistical Analyses
We ran analyses with weights to correct for potential biases in the distribution of adolescents across U.S. Census regions. As unweighted and weighted analyses produced virtually identical results, we report the unweighted for simplicity. We used frequencies with 95% confidence intervals for demographic characteristics, primary and secondary measures, and predictors.
In our primary model of the main outcome, which was specified a priori, predictors of interest included demographics (parental gender, parental age, parental race/ethnicity, parental education level) and parental perception that the adolescent is behind on routine vaccines (which was measured by their response to the survey question “Have you been told by a health care provider or someone else that your child is behind on their routine vaccinations?”). Since respondents could have more than one adolescent, we included whether they had an older adolescent (age 16-17 years and already eligible for COVID-19 vaccine), a younger adolescent (age 12-15 years), or both; information on exact adolescent age and adolescent gender was not obtained in the parent survey. Respondents were not anchored to a particular child for the survey, so responses were presumed to be consistent across adolescent children in the family. We also included parental concern about their adolescent getting COVID-19 infection (on a 4-point scale from “Not concerned” to “Very concerned”, with an option to indicate that their adolescent had already had COVID-19 infection), parental concern about their adolescent having side effects from the COVID-19 vaccine (on a 4-point scale from “Not concerned” to “Very concerned”), and the parent’s feeling about vaccines in general (on a 7-point scale from “Very negative” to “Very positive).
The multivariate models were constructed using logistic regression. P-values ≤ 0.05 were considered statistically significant. All data analyses were conducted using Stata (v.14; StataCorp LLC).
Sensitivity Analyses
Sensitivity analyses assessed stability of the model to alternate predictor sets within two key constructs: confidence in vaccines (broadly similar to parental concern regarding vaccine side effects) and perceived risk of COVID-19 infection. In both cases, these alternate sets involved longer sets of questions than those used in our primary model, which were summed into a confidence in vaccines aggregate measure and a perceived risk of COVID-19 aggregate measure. One item in the set of perceived risk questions that formed the perceived risk of COVID-19 aggregate measure (“My child is at greater risk for getting very sick from COVID-19 than the average child”) did not strongly correlate positively with the other items in the set. In bivariate associations, this item also had an opposite relationship with willingness to have an adolescent received all needed vaccines at a visit. As a result, we analyzed it as its own predictor separately from the perceived risk of COVID-19 measure in our sensitivity analyses.
The sensitivity analyses included logistic regression models parallel to the primary model, but with the following predictors: (1) demographics, being behind on routine vaccines, mean vaccine confidence (for both routine vaccines and COVID-19 vaccine), the single risk item described above (“My child is at greater risk for getting very sick from COVID-19 than the average child”), and mean perceived risk from COVID-19 infection (excluding the single risk item about greater than average risk); and (2) identical to the primary model, but with the additional risk item related to greater risk than average of getting very sick from COVID-19 as described above. These two models were also run for each of the secondary outcomes.
Results
Sample characteristics
In total, 1,457 parents met eligibility criteria and accessed the survey, of whom 156 failed a quality check prior to consent and 172 did not agree to participate. Of the 1,129 who consented, 31 were identified by Qualtrics as “speeders” (completing the survey too quickly) and 74 closed the browser before completion (90.5% completion rate). An additional 256 parents reported that their adolescent had already received a COVID-19 vaccine and 3 did not disclose their child’s vaccination status, leaving a final sample of N=765 parents.
The majority of parents were White and non-Hispanic (60%), about half were female, and just over half had a Bachelor’s degree or higher (Table 2 ). Fifty-seven percent had an adolescent age child, 12-15 years only, 26% an adolescent age child, 16-17 years only, and 16% both. Thirteen percent reported that their adolescent was behind on receiving routine vaccines (an additional 2.6% were unsure).Table 2 Sample characteristics (N=765)
Characteristic N (%)
Age of parents’ adolescent child(ren)
12-15 years only 437 (57.1%)
16-17 years only 203 (26.5%)
Both 12-15 years and 16-17 years 125 (16.3%)
Parent gender
Female 349 (45.6%)
Male 413 (54.0%)
Transgender or other gender identity 3 (0.4%)
Parent age (mean + SD) 44.2 + 8.3
Parent race/ethnicity
White, non-Hispanic 455 (59.5%)
Black, non-Hispanic 106 (13.9%)
Hispanic 150 (19.6%)
Other, non-Hispanic 54 (7.1%)
Parent education
< High school 152 (19.9%)
Some college 228 (29.8%)
> Bachelor’s degree 385 (50.3%)
U.S. Census region
Northeast 139 (18.2%)
Midwest 148 (19.4%)
South 310 (40.5%)
West 168 (22.0%)
Willingness to receive COVID-19 and other routine vaccines at the same visit
A minority of parents (29%) were willing to have their adolescent receive COVID-19 and routine vaccines in the same visit [preference to receive both at the same visit (10.6%) or to follow a healthcare provider’s recommendation (18.6%)] (Table 1). The remainder said they would choose to have their adolescent receive COVID-19 and routine vaccines at separate visits (40%), routine vaccines only (16%), COVID-19 vaccine only (1%), or no vaccines (8%) (Table 1).
Predictors of parental willingness
In the primary multivariate model predicting willingness of parents to have their adolescents vaccinated for COVID-19 and routine vaccines at the same visit (Table 3 , Model 1), none of the demographic characteristics were significant. If a parent reported that their adolescent was behind on routine vaccines, they were significantly less willing to have their adolescent receive COVID-19 and routine vaccines together (OR 0.47; 95% CI 0.28, 0.79). Greater concern about side effects of the COVID-19 vaccine was associated with decreased willingness to receive the vaccine with other routine vaccines (OR 0.80; 95% CI 0.66, 0.95). Greater concern about the risk of COVID-19 infection to their adolescent (OR 1.33; 95% CI 1.12, 1.58) as well as more positive feelings towards vaccines in general (OR 1.23; 95% CI 1.08, 1.40) were both significantly associated with greater willingness to have their adolescents vaccinated for COVID-19 and routine vaccines at the same time.Table 3 Models predicting willingness to have adolescent receive COVID-19 and other routine vaccines at the same visit (N=735)∗
Predictor Model 1
Prefer coadministration of vaccines at same visit OR follow HCP recommendation OR (95% CI) Model 2
Prefer coadministration of vaccines at same visit
OR (95% CI) Model 3
Follow HCP recommendation
OR (95% CI)
Told behind on routine vaccines 0.47 (0.28, 0.79) 0.60 (0.28, 1.27) 0.49 (0.26, 0.90)
Greater concern about adolescent getting COVID-19 1.33 (1.12, 1.58) 1.28 (0.99, 1.65) 1.25 (1.03, 1.53)
Greater concern about side effects from COVID-19 vaccine 0.80 (0.66, 0.95) 0.69 (0.53, 0.89) 0.94 (0.76, 1.16)
More positive feelings about vaccines in general 1.23 (1.08, 1.40) 1.19 (0.97, 1.45) 1.19 (1.02, 1.39)
Abbreviations: HCP—Healthcare provider; OR—Odds ratio; CI—Confidence intervals
∗ Adjusted for parental gender, age, race/ethnicity, education, and adolescent(s’) age group(s) (12-15 years only, 16-17 years only, or both), of which none were significant; results in bold indicate statistical significance at p<0.05.
In analyses of the secondary outcomes, the predictors remained largely consistent. First, we looked separately at predictors of preference to receive COVID-19 and routine vaccines at the same visit (Table 3, Model 2). Demographic characteristics remained non-significant. The odds ratios for all other predictors were in the same direction as in the primary model and of similar magnitude; only higher concern about side effects of the COVID-19 vaccine remained significant (OR 0.69, 95% CI 0.53, 0.89). Next, we looked at willingness to follow their healthcare provider’s recommendation on timing of COVID-19 vaccine and routine vaccine administration (Table 3, Model 3). Again, all predictors were in the same direction and were of similar magnitude compared to the primary model; however, concern about side effects of COVID-19 vaccine was no longer significant.
Sensitivity analyses
Results from the sensitivity models were generally very similar in direction and magnitude to the primary model, including those for the secondary outcomes (Tables 4 and 5 ). An exception was that, although respondents’ perception that their adolescent was at higher risk of getting very sick from COVID-19 compared to the average child was significant in correlational analyses, it was not a significant predictor in any of the multivariate models in the sensitivity analyses.Table 4 Sensitivity analyses--model predicting willingness to have adolescent receive COVID-19 and other routine vaccines at the same visit with alternative predictors (N=760)∗
Predictor Model 1
Prefer coadministration of vaccines at same visit OR follow HCP recommendation OR (95% CI) Model 2
Prefer coadministration of vaccines at same visit
OR (95% CI) Model 3
Follow HCP recommendation
OR (95% CI)
Told behind on routine vaccines 0.48 (0.28, 0.80) 0.56 (0.26, 1.19) 0.53 (0.28, 0.99)
Perceived risk of COVID-19 aggregate measure 1.39 (1.10, 1.76) 1.13 (0.80, 1.59) 1.44 (1.10, 1.88)
Perceived higher than average risk that adolescent could get very sick from COVID-19 0.96 (0.79, 1.16) 1.13 (0.86, 1.48) 0.87 (0.70, 1.09)
Confidence in vaccines aggregate measure 2.38 (1.70, 3.35) 2.03 (1.23, 3.35) 2.12 (1.43, 3.15)
Abbreviations: HCP—Healthcare provider; OR—Odds ratio; CI—Confidence intervals
∗ Adjusted for parental gender, parental age, parental race/ethnicity, parental education, adolescent(s’) age group(s) (12-15 years only, 16-17 years only, or both), of which none were significant; results in bold indicate statistical significance at p<0.05.
Table 5 Sensitivity analyses--model predicting willingness to have adolescent receive COVID-19 and other routine vaccines at the same visit with additional risk item (N=735)∗
Predictor Model 1
Prefer coadministration of vaccines at same visit OR follow HCP recommendation OR (95% CI) Model 2
Prefer coadministration of vaccines at same visit
OR (95% CI) Model 3
Follow HCP recommendation
OR (95% CI)
Told behind on routine vaccines 0.48 (0.28, 0.81) 0.54 (0.25, 1.16) 0.53 (0.29, 1.00)
Higher concern about adolescent getting COVID-19 1.34 (1.12, 1.59) 1.24 (0.95, 1.60) 1.29 (1.05, 1.58)
Perceived higher than average risk that adolescent could get very sick from COVID-19 0.97 (0.81, 1.18) 1.22 (0.94, 1.58) 0.85 (0.68, 1.07)
Higher concern about side effects from COVID-19 vaccine 0.80 (0.67, 0.96) 0.67 (0.51, 0.87) 0.96 (0.78, 1.19)
More positive feelings about vaccines in general 1.23 (1.08, 1.40) 1.18 (0.97, 1.45) 1.20 (1.03, 1.40)
Abbreviations: HCP—Healthcare provider; OR—Odds ratio; CI—Confidence intervals
∗ Adjusted for parental gender, parental age, parental race/ethnicity, parental education, adolescent(s’) age group(s) (12-15 years only, 16-17 years only, or both), of which none were significant; results in bold indicate statistical significance at p<0.05.
Discussion
A national survey of parents of adolescents aged 12 to 17 years who were not yet vaccinated against COVID-19 found that only 29% would prefer for their adolescent to receive COVID-19 and other routine vaccines at the same visit or follow their healthcare provider’s recommendation with regard to coadministration, with the remainder opting for the vaccinations to occur at different visits (or not at all). No demographics characteristics were associated with greater willingness for coadministration but an adolescent being behind on routine vaccines was associated with lower willingness. Greater willingness was associated with higher perceived risk of COVID-19 infection, lower perceived risk of side effects from the vaccine, and more positive feelings about vaccines in general. Our sensitivity analyses confirmed the robustness of our findings to different measures of perceived risk and different outcomes related to willingness. To our knowledge this is the first national survey to specifically report on parental preferences for coadministration of COVID-19 with other routine vaccines.
The lack of demographic characteristics predicting willingness to have COVID-19 and other routine vaccines coadministered to adolescents serves as an important reminder that healthcare providers should not make assumptions about parent’s vaccination intentions based on any such characteristics. It also implies that if interventions are undertaken to encourage coadministration of vaccines, they should be implemented widely across populations. The relationships between perceived risks of COVID-19 infection and willingness to vaccinate imply that strategies that help parents to understand risks may be successful in helping adolescents consistently receive all recommended vaccines when indicated and on time. Such strategies have already been identified as particularly useful to increase COVID-19 vaccine acceptance in general, including boosting confidence in the safety and effectiveness of the COVID-19 vaccines and combating complacency about the pandemic.20
One potentially troubling finding was that parents of adolescents who were behind on their routine vaccines were less willing to have COVID-19 and other routine vaccines given at the same visit. The question we asked about willingness to coadminister vaccines was predicated on the adolescent needing vaccines. We expected that such parents might be more willing if they realized their adolescent was already late to receive other needed vaccines. However, it is possible that parents whose adolescents are behind on their routine vaccines differ from parents whose adolescents are up-to-date on vaccines in ways that were not measured in this survey. These unmeasured differences may also influence their acceptance of COVID-19 vaccines and willingness to have them coadministered with other vaccines. For example, adolescents who are behind on routine vaccines may have less consistent access to a trusted health care provider for their child.
Our primary outcome was respondents’ primary preference for how they would like their adolescent to receive COVID-19 and routine vaccines. We asked parents to endorse only their single, strongest preference (by question design). However, there is value in understanding what parents most strongly prefer, while recognizing that parents may also be willing to consider other vaccination options. We also included the option of doing whatever their child’s healthcare provider recommends as an indication of the parent’s willingness for vaccine coadministration, as it would be reasonable to expect parents to follow the healthcare provider’s advice. In fact, health care providers’ recommendation to get vaccinated is a significant factor in parental decision making.21 This highlights the importance of understanding whether providers are comfortable with and likely to recommend coadministration for adolescents. Although coadministration of vaccines is one means of getting adolescents who are in need of routine vaccines caught up, healthcare providers may be more reluctant to do so with COVID-19 vaccines. Strategies that rely on encouraging coadministration of COVID-19 vaccine with other routine vaccines may need to ensure that healthcare providers feel supported and are well equipped to make this recommendation.
In addition to considering healthcare providers’ comfort and implications for these settings, there are also important implications for policymakers. As the COVID-19 pandemic moves to a model of less frequent but still regular COVID-19 vaccination efforts, it will be important to understand the public’s willingness to receive COVID-19 vaccines together with other vaccines. The most common scenario for adolescents (who typically have a period without needing routine vaccinations between 11-12 and 16-18 years of age if otherwise up-to-date) is needing COVID-19 vaccine together with influenza vaccine. A strategy that relies on encouraging the public to get both vaccines at the same time may backfire and cause heightened concern, never mind a new vaccine that combines protection against both viruses in one injection. At the time of our survey, a majority of parents were willing to get both COVID-19 and other routine vaccines for their adolescents even if not simultaneously, which is encouraging overall. Our results suggest that policymakers may wish to ensure diversified strategies to continue to encourage vaccination regardless of timing. For those who prefer to receive all vaccines at the same time, the best strategy may to make doing so as easy as possible. Others who would decline to get COVID-19 and other vaccines together may need lots of different exposures and opportunities to get the vaccines they need (e.g., at school, in retail locations, and in all health care settings they might encounter).
The timing of this survey is important to consider, as it was conducted in the weeks preceding the FDA authorization of COVID-19 vaccine for younger adolescents (and full approval in those aged 16 years and older) and prior to the emergence of the Delta variant as a “variant of concern”; these developments may well have had an impact on parental willingness for adolescents to receive COVID-19 with other vaccines. It was also conducted prior to CDC stating that COVID-19 vaccines could be given at the same time as other vaccines, which occurred around the time of FDA’s authorization of the vaccine for adolescents. Thus it is entirely possible that the opinions held by parents at the time of the survey may have changed with new guidance from CDC explicitly endorsing coadministration. As a result, our findings may underestimate parents’ willingness to have their adolescents receive COVID-19 vaccines with other vaccines, and limits our certainty in interpreting our results. That being said, the relationships between willingness and other covariates (such as being behind on vaccines) may be less affected by changes in CDC policy. We also note that there are no other studies to our knowledge that have explicitly examined this construct. Updating our findings now that guidance on coadministration is clearer and with the evolution of COVID-19 boosters in this population would be an important next step.
Our findings are subject to other limitations. The surveys were administered online and only in English, which could yield underrepresentation of U.S. residents without Internet access or those who have limited English proficiency. We used a nonprobability, quota-based sample, which also has the potential to increase potential for bias and limit generalizability.22 Some respondents had more than one adolescent in their household, and we did not ask them to anchor to a particular adolescent. As a result, we were unable to draw firm conclusions on relationship between age of the adolescent and willingness, though we did control for having one age group or the other or both. We asked about routine vaccines, but not about influenza vaccine in particular. Finally, as noted above, this same survey conducted after authorization of COVID-19 vaccines for adolescents and allowance for coadministration with other vaccines might have yielded different findings.
Conclusions
We report findings from a national survey of parents in the United States that explored willingness to have adolescents receive COVID-19 vaccine together with other routine vaccines. Identifying factors associated with willingness to receive all recommended vaccines in the context of the COVID-19 pandemic is important not only for adolescents, but also for younger children as they become eligible for COVID-19 vaccines. Our findings are timely and urgently needed to inform strategies to optimize the administration of both COVID-19 and routine vaccines–including against influenza–to protect the health of the nation’s children.
Funding: This work was supported by the Centers for Disease Control and Prevention (CDC) [cooperative agreement U01IP001144].
Role ofsponsor: The CDC was involved in the design of the study, interpretation of the data, and review and approval of the manuscript. The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC.
Conflict of interest: No other conflicts of interest were disclosed.
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| 0 | PMC9722677 | NO-CC CODE | 2022-12-12 23:20:58 | no | J Adolesc Health. 2022 Dec 6; doi: 10.1016/j.jadohealth.2022.11.242 | utf-8 | J Adolesc Health | 2,022 | 10.1016/j.jadohealth.2022.11.242 | oa_other |
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Child Abuse Negl
Child Abuse Negl
Child Abuse & Neglect
0145-2134
1873-7757
Elsevier Ltd.
S0145-2134(22)00518-X
10.1016/j.chiabu.2022.105984
105984
Article
Global prevalence of physical and psychological child abuse during COVID-19: A systematic review and meta-analysis
Lee Hyun a
Kim EunKyung b⁎
a Yonsei University, Center for Social Welfare Research, 50 Yonsei-ro, Seodaemun-gu, Seoul, South Korea
b Yonsei University, Dept. of Social Welfare, 50 Yonsei-ro, Seodaemun-gu, Seoul, South Korea
⁎ Corresponding author.
6 12 2022
6 12 2022
10598414 8 2022
17 11 2022
27 11 2022
© 2022 Elsevier Ltd. All rights reserved.
2022
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Background
With the onset of COVID-19, most countries issued lockdowns to prevent the spread of the virus globally and child abuse was concerned under such a closed circumstance.
Objective
This study aims to estimate the prevalence of physical and psychological child abuse during COVID-19 and moderating variables for those abuses.
Participants and setting
The rates of child abuse reported in 10 studies encompassing 14,360 children were used, which were gathered through a systematic review.
Methods
We reviewed previous studies systematically for the appropriate data and conducted a meta-analysis.
Results
The prevalence of physical child abuse is estimated at 18 % and that of psychological abuse is estimated at 39 %. Regarding the unemployment rate, it reveals a high correlation with physical abuse (b = 0.09; p < 0.05) but not with psychological one (b = 0.03; no. sig).
Conclusions
To prevent child abuse during the pandemic, it is suggested to minimize COVID-19-related economic damage to families and explore factors for reducing the gap between low and high-income countries.
Keywords
Meta-analysis
Systematic review
COVID-19
Physical child abuse
Psychological child abuse
==== Body
pmc1 Introduction
COVID-19 was declared a pandemic on March 11, 2020 (World Health Organization, 2020). Globally, nearly all countries issued lockdowns including social distancing, school closures, movement restrictions, and telecommuting to prevent the spread of the virus. However, these measures had severe effects on economic activities (Fernandes, 2020) and all countries recorded negative growth rates in 2020 (Organization for Economic Cooperation and Development, 2022). The WHO (2020) had already announced that child abuse would increase during public health emergencies and thus expected that child abuse would increase with the onset of COVID-19. It is estimated that 1 out of 2 children aged 2–17, or over 1 billion children, experienced some form of abuse before COVID-19, although rates differed by continent (Hillis et al., 2016; Moody et al., 2018): pre-COVID-19 abuse rate estimates are 25 % for Africa, 34 % for Asia, 14 % for Latin America, 7 % for Europe, 30 % for Northern America, and 7 % for Oceania. The differences among continents are reflected in the regional economy and cultural traits (Ammerman & Hersen, 2000). Physical child abuse refers to kicking, hitting, and otherwise causing physical injury, and psychological abuse refers to harming self-esteem or emotional balance by threatening and withholding love. Each can cause death, injury, maladjustment, and behavior problems (Barnett et al., 2005). Given the need for social lockdowns and quarantines during COVID-19 and the WHO's anticipation that abuse rates would increase, it is now important to analyze physical and psychological child abuse under COVID-19 to compare.
Our aim with this study is to conduct a meta-analysis of the global prevalence of child physical and psychological abuse during the COVID-19 pandemic. It has been difficult to conduct surveys of child abuse during COVID-19 because of social distancing requirements including comparing the disproportionate abuse rates between high-and low-income countries. The COVID-19 pandemic began two years ago, so any meta-analysis of pandemic-related physical and psychological abuse would contain few studies. Nevertheless, our aims with this study are first to identify the global prevalence of child abuse by country or region and second to identify the factors that influenced child abuse during the COVID-19 pandemic.
2 Methods
2.1 Literature search strategy
We drafted a search strategy to identify original quantitative evidence from published original research and peer-reviewed articles on Scopus, Embase, PubMed, and Web of Science. Searching terminologies included mixing with two thematic areas: (a) COVID-19; (b) coronavirus; (c) child abuse; (d) child neglect; and (e) child maltreatment. The systematic review was registered as a protocol with Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines were as followed. 1. We specified COVID-19 to exclude other coronaviruses such as SARS and MERS (World Health Organization, 2021). Most studies were related to SARS, and we identified no articles using combinations of SARS-CoV-2, 2019nCoV, Wuhan coronavirus 2, and child abuse; to expand our results, we searched for both COVID-19 and coronavirus 2 and searched for articles on both child physical and psychological abuse. Firstly, we searched four violence types but could not find mixing terms with COVID-19 and child sexual abuse and neglect. The associated terms included mixing both COVID-19 and child physical and psychological abuse. 2. Children were defined as anyone below the age of 18. Even though risk and protective factors should precede the conducting analysis (Trickett et al., 2014), in the process of studying, we found an important comparison between high and low-income countries. That idea is rooted in the probability of child abuse being more likely to occur in the low-class groups than in high (Fang et al., 2015). Most studies of child abuse in low- and middle-income countries are revealed (UNICEF, 2014). We applied an inclusive definition to capture as broad a body of literature from these settings as possible. Past experiences of child abuse were excluded in analyses to compare before and after COVID-19. We used World Bank (2021) country criteria based on gross national income per capita to define countries as low, lower-middle-, or upper-middle-income (Fang et al., 2015) and then divided those into low or high income. We applied physical and psychological violence typologies found in UNICEF's Hidden in Plain Sight (2014) report to operationalize our definition of violence. To satisfy violence forms and reduce differences in our comparison of factors, we restricted the definition of physical and psychological violence to acts perpetrated by adults against children.
2.2 Inclusion and exclusion criteria
As noted above, we conducted this systematic search of the extant literature based on the PRISMA protocols (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols). First, we searched “COVID-19”, or “Coronavirus”, and “Child abuse”, or “Child neglect” or “Child maltreatment” across the following databases: Scopus, Embase, PubMed, and Web of Science. Second, we conducted hand searches of leading journals to identify additional sources. Third, we examined the reference lists of the identified and eligible studies. Fig. 1 illustrates the PRISMA flow chart (Moher et al., 2009) of how we filtered the identified the relevant studies included in the systematic review and meta-analysis.Table 1 Results of subgroup analysis and meta-regression for the prevalence of physical child abuse.
Table 1Categorical moderators No. of studies Prevalence (95 % CI) Subgroup differences Q P value
Continenta
North America 3 0.26 (0.10–0.51) 30.12 <0.001
Europe 2 0.07 (0.00–0.99)
Asia 7 0.16 (0.07–0.34)
Africa 1 0.43(0.40–0.46)
World Bank income level
Low income 2 0.54 (0.02–0.99) 30.24 <0.001
Middle Income 3 0.09 (0.02–0.30)
High Income 8 0.15 (0.08–0.26)
Sampling 1.86 0.17
Random 8 0.23 (0.12–0.38)
Non-random 5 0.12 (0.03–0.35)
Measure
PCCTS 5 0.13 (0.05–0.28) 3.44 0.18
BRFSS 4 0.31 (0.08–0.70)
Others 4 0.14 (0.03–0.44)
Continuous moderators Estimate Std. Error z value Pr(>|z|) Importance
Unemployment rate 0.09 0.04 2.36 0.02 0.92
Weeks since the onset of Corvid 19 0.01 0.02 0.14 0.89 0.50
a Due to the insufficient number of cases per continent, caution is required in analysis and interpretation.
Fig. 1 PRISMA flow Diagram Outlining the Funneling Identification of Relevant Studies.
Fig. 1
We selected the studies for this systematic review based on the following criteria (See Appendix 1): The study had to have a measurable and codable child abuse outcome that was assessed during the COVID-19 pandemic; the child abuse data must have been derived from parental self-report; although there was no geographic restriction to the location of the study, the study must have been published in English; only published peer-reviewed studies were considered; and we excluded qualitative studies and descriptive studies that were not empirical (literature reviews, letters to the editor, commentaries, calls-to-action, etc.) because they did not provide necessary information for our analyses. We obtained a total of 3446 results from a general search of the five databases, collecting the study titles, author names, and other reference details and saving them in Microsoft Excel spreadsheet.
2.3 Meta-analytic procedures
The research team searched the outline databases for articles from January 1, 2020, until February 13, 2022, because COVID-19 started at the end of 2019 and WHO declares the pandemic on March 11, 2020. Two members of research team examined the bibliographies of systematic reviews identified in the literature search for inclusion and exclusion decision making. To ensure comprehensive coverage, two members directly screened titles, methodologies, authors, and abstracts for reporting categories including demographic surveys; online surveys; studies with respondents who included parents, caregivers, and youth themselves; studies on child physical and psychological abuse; and studies identified from combining terms related to COVID-19 and terms related to child physical and psychological abuse.
The two research members conducted a double-blind screening using Mendeley Software of the titles and abstracts that remained after we removed duplicates and reconciled independent inclusion decisions jointly in consultation with the lead author. The research team subsequently read full article texts for independent inclusion decisions. Following consistent procedures, the research team reconciled choices and the lead author finalized the last articles. The research team conducted the corresponding authors of included publications if key information could not be deciphered from the text.
For this meta-analysis of global prevalence of child abuse during the COVID-19 pandemic, we computed effect sizes as follows. First, we estimated the pooled prevalence from child abuse rates reported in individual studies, using a forest plot to demonstrate the prevalence rate, with 95 % confidence intervals (CIs) in each study. Second, we calculated pooled odds ratio (ORs) of the impacts with 95 % CIs of the effects of specific individual and family factors. We used random-effects models to combine studies, used τ2 and I2 Q statistics to estimate the heterogeneity, and used I2 statistics to calculate the observed variance between studies. We examined publication bias with Egger's test and the aid of a funnel plot. Then, depending on the type of variable, we conducted comprehensive statistical analyses (Chan et al., 2021), specifically subgroup analysis and meta-regression, to identify and examine the moderating variables that cause differences in the global prevalence of child abuse (Cerna-Turoff et al., 2021).
2.4 Study characteristics
The penultimate analyses, above, yielded 10 empirical studies that met the general inclusion criteria, and Appendix 3 gives the details for these studies: author/s, publication year, study site, time frame, and the domestic violence outcome measurement. Given the short time frame that has occurred since the emergence of the COVID-19 pandemic (March 11, 2020, according to the World Health Organization), all studies were published between 2020 and 2022, specifically, nine in 2021 and (AboKresha et al., 2021; Augusti et al., 2021; Kurata et al., 2021; Lee et al., 2021; Rodriguez et al., 2021; Sari et al., 2021; Wong et al., 2021; Yamaoka et al., 2021; Zhang et al., 2021) one in 2022 (Selvi, 2022). However, although in effect we identified 10 empirical studies, one study (Kurata et al., 2021) included four studies, so that we actually used 13 empirical studies. There was wide geographic representation among the studies, with three conducted in the United States (Kurata et al., 2021; Lee et al., 2021; Rodriguez et al., 2021) and the remainders in Egypt (AboKresha et al., 2021), Norway (Augusti et al., 2021), Netherlands (Sari et al., 2021), China (Wong et al., 2021; Zhang et al., 2021), Japan (Kurata et al., 2021; Yamaoka et al., 2021), Indonesia (Selvi, 2022), India (Kurata et al., 2021), and Malaysia (Kurata et al., 2021). All the studies focused on short period such as weeks or months immediately after COVID-19 to compare the effect of restrictions on child abuse, although they varied for instances in terms of random vs. nonrandom sampling; additionally, nearly all studies used online surveys, with one of those on a mobile phone.
2.5 Quality assessment
To measure bias in the identified studies, we used a short six-item questionnaire which adapted from the National Institute of Health Quality Assessment Tool for Observation Cohort and Cross-Sectional Studies (National Institute of Health, 2022; Racine et al., 2021). Studies were given a score of 0 (no) or 1 (yes) for each of the six criteria (focused question, valid measure, dual review, eligible population, objective outcome, exposure time) and six scores were summed to give each study a total score of six points to indicate no bias (i.e., the highest score is six, the lowest is zero). When information was unclear or authors did not provide it, we marked that item as 0. After our quality assessment, four studies (AboKresha et al., 2021; Lee et al., 2021; Rodriguez et al., 2021; Sari et al., 2021) earned the maximum score of six points to indicate no bias. One study (Selvi, 2022) received a bias score of four points, the lowest of the ten studies, and the remainders (Augusti et al., 2021; Kurata et al., 2021; Wong et al., 2021; Yamaoka et al., 2021; Zhang et al., 2021) received five points.
3 Result
3.1 Physical child abuse
Fig. 2 provides a forest plot illustrating the distribution of the effect sizes with their corresponding 95 % CIs and related weights for the 10 studies (13 effect sizes) for physical child abuse. The overall mean effect size generated from a random effect restricted maximum likelihood model was 18 % (95 % CI: 10–29 %). Prevalence rates of physical child abuse were reported in 10 studies that included a total of 14,360 individuals; among those children, there were an estimation 1971 incidents reported in 10 studies. Estimates ranged from 3 % to 66 % with a pooled prevalence of 18 % but we also found high heterogeneity. The between-study heterogeneity variance was estimated at τ2 = 1.10, with an I2 of 99 % (95%CI: 98.8–99.2 %). Egger's test revealed no significant publication bias (intercept = −1.71; t = 0.26; P = 0.80).Table 2 Results of subgroup analysis and meta-regression for the prevalence of psychological child abuse.
Table 2Categorical moderators No. of studies Prevalence (95 % CI) Subgroup differences Q P value
Continent
North America 3 0.47 (0.13–0.83) 139.28 <0.001
Europe 2 0.18 (0.01–0.99)
Asia 7 0.35 (0.22–0.50)
Africa 1 0.88(0.86–0.90)
World Bank income level
Low income 2 0.77 (0.01–0.99) 8.71 0.01
Middle Income 3 0.31 (0.06–0.76)
High Income 8 0.33 (0.20–0.50)
Sampling 0.00 0.95
Random 8 0.39 (0.26–0.53)
Non-random 5 0.40 (0.09–0.81)
Measure
PCCTS 5 0.32 (0.12–0.60) 0.99 0.61
BRFSS 4 0.42 (0.19–0.68)
Others 4 0.48 (0.09–0.89)
Continuous moderators Estimate Std. Error z value Pr(>|z|) Importance
Unemployment rate 0.03 0.04 0.74 0.46 0.51
Weeks since the onset of Corvid 19 −0.02 0.04 0.49 0.62 0.61
Fig. 2 Forest Plot of Prevalence of Physical Child Abuse and Psychological Child Abuse (n = 10 studies; 13 effect sizes).
Note. Up (Forest Plot of Prevalence of Physical Child Abuse), Down (Forest Plot of Prevalence of Psychological Child Abuse).
Fig. 2
Fig. 2 provides a forest plot illustrating the distribution of the effect sizes with their corresponding 95 % CIs and related weights for the 10 studies (13 effect sizes) for psychological child abuse. The overall mean effect size generated from a random-effect restricted maximum likelihood model was 39 %. Prevalence rates of psychological child abuse were reported in 10 studies that included a total of 14,360 individuals; among those children, there were an estimated 4142 incidents reported in 10 studies. Estimates ranged from 8 % to 89 % with a pooled prevalence of 39 %, but there was evidence of high heterogeneity: Between-study heterogeneity was τ2 = 1.17 (I2 = 99.4 %). Egger's test again revealed no significant publication bias.
Considering that child abuse likely varied according to different countries and cultures, we performed a subgroup analysis by continent of physical child abuse rates during the COVID-19 pandemic and identified rates of 26 % in North America (95 % CI: 10–51 %), 7 % in Europe (95 % CI: 0–99 %), 16 % in Asia (95 % CI: 7–34 %), and 43 % in Africa (95 % CI: 40–46 %). Classified according to World Bank national income levels,1 the rates of physical child abuse were 54 % (95 % CI: 2–99 %) among low-income nations, 9 % in middle-income countries (95 % CI: 2–30 %), and 15 % in high-income countries (95 % CI: 8–26 %). There were differences in prevalence according to the sampling method and measure, but they were not statistically significant. Notably, physical child abuse prevalence increased significantly as unemployment rates at a very high correlation of 0.92 (b = 0.09; p < 0.05). The moderator weeks since the onset of COVID-19 had no significant effect on the prevalence of physical child abuse (b = 0.01; p = n.s).
3.2 Psychological child abuse
Fig. 2 provides a forest plot illustrating the distribution of the effect sizes with their corresponding 95 % CIs and related weights for the 10 studies (13 effect sizes) for psychological child abuse. The overall mean effect size generated from a random-effect restricted maximum likelihood model was 39 % (95 % CI: 25–56 %). Prevalence rates of the psychological child were in 10 studies that included a total of 14,360 individuals; among those children, there were an estimated 4142 incident reported in 10 studies. Estimates ranged from 8 % to 89 % with a pooled prevalence of 39 %, but there was evidence to suggest high heterogeneity (Fig. 2): Between-study heterogeneity was τ2 = 1.17 (I2 = 99.4 %). Egger's test again revealed no significant publication bias (intercept = −1.56; t = 1.57; p = 0.14).
We also performed a subgroup analysis by continent that revealed COVID-19 rates of psychological child abuse of 47 % in North America (95 % CI: 13–83 %), 18 % in Europe (95 % CI: 1–99 %), 35 % in Asia (95 % CI: 22–50 %), and 88 % in Africa (95 % CI: 86–90 %). By World Bank national income, the rate was 77 % in low-income countries (95 % CI: 1–99 %), 31 % in middle-income countries (95 % CI: 6–76 %), and 33 % in high-income countries (95 % CI: 20–50 %). Although there were variances in prevalence depending on the sampling method and measure, these differences were not statistically significant. Separately, the unemployment rate had a weak effect on the prevalence of psychological child abuse that was not statistically significant (b = 0.03; no. sig). Weeks since the onset of COVID-19 was not significant but contributed to a slight decrease in the prevalence of psychological child abuse (b = −0.02; p = n.s).
4 Discussion
This study aims to estimate the global prevalence of physical and psychological child abuse and conduct a sub-group analysis onset of COVID-19 with a systematic review and meta-analysis.
Regarding physical child abuse immediately after COVID-19, the global prevalence was 18 % (95 % CI: 10–29 %), while heterogeneity and I2 value were huge with no publication bias. For the prevalence in each continent, it was estimated at 26 % in North America, 7 % in Europe, 16 % in Asia, and 43 % in Africa respectively. In contrast with the above data for immediately after COVID-19, in the 168 pre-pandemic meta-analysis, the overall physical abuse prevalence was estimated 17.7 % (95 % CI: 13.0 % - 23.6 %) (Stoltenborgh et al., 2013). By continent, the estimates were 24 % in North America (54.8 % in southern North America), 22.9 % in Europe, 16.7 % in Asia, and 22.8 % in Africa. Caution is required before comparing the rates before COVID-19 pandemic and after its onset because there are so comparatively few post-pandemic studies. Despite this limitation, however, the prevalence of abuse pre-COVID-19 was similar to the prevalence of this study. Heterogeneity observed across studies highlights the need to examine demographical, geographical, and methodological moderators; moderator analyses can determine under what circumstances prevalence is higher or lower. Given our findings, in addition to deriving pooled prevalence estimates, we examined demographical, geographical, and methodological factors that could explain the subgroup differences, and found that physical child abuse occurred the most in Africa and the least in Europe. However, heterogeneity occurs the most in European countries. According to income level, physical child abuse occurs the most in low-income countries, followed by high-income and the least in middle-income countries. Physical child abuse was also documented more in random studies than in the nonrandom studies, but this difference was not significant. And there were also no significant differences between measurement methods.
Regarding psychological child abuse immediately after COVID-19, the overall prevalence was estimated at 39 % (95 % CI: 25–56 %), and by continent, the estimates were 47 % in North America, 18 % in Europe, 35 % in Asia, and 88 % in Africa respectively. Although there are limitations to interpretate owing to the small number of studies, the prevalence immediately after COVID-19 was significantly higher than pre COVID; notably, there was not a significant pre- versus post-onset difference in rates of physical child abuse. Researchers determined that the restrictions caused by COVID-19 increased family stress and in turn the incidence of psychological child abuse (Schneider et al., 2017; Xu et al., 2020). Psychological child abuse also occurred the most in low-income countries, followed by high-income and then middle-income countries. Psychological child abuse also occurred more in random studies than in nonrandom studies, but this was not statistically significant in this study. There are no significant differences between measurement methods, and the meta-regression analysis found no significant relationship between unemployment and psychological child abuse. Even though it is not significant in weeks since the onset of COVID-19, it is predicted that the removal of lockdown would lead to relieving stress so that psychological child abuse also be lessened.
Rather than specifically the period before versus immediately after the announcement of the COVID-19 pandemic, national cultural and social structures formed over time appear to explain the global differences in child abuse rates; child abuse in part reflects a society's perceptions of its children and their human rights. From a sociopsychological perspective, economic hardships increase stress and frustration, and poverty and unemployment correlate highly with physical child abuse (Ammerman & Hersen, 2000; Fang et al., 2015; Hillis et al., 2016; McCoy et al., 2020). The sudden COVID-19-related requirement to isolate kept all but essential workers unemployed and away from coping resources led to increased rates of child abuse after COVID-19 onset. Multiple telecommuting spouses who were also solely responsible for child care faced high stress as well, which sometimes led to increased rates of child abuse (Blundell et al., 2020; Wong et al., 2021). Researchers have consistently identified that child abuse occurs disproportionately more often in groups of low socio-economic status, especially violence against children (Cerna-Turoff et al., 2021; Department of Health and Human Services, 2021). The countries of Africa show among the most serious rates of child abuse in the world, and it seems necessary to conduct cross-cultural research across Africa to gain better understanding of abuse rates and causes across the continent (Stoltenborgh et al., 2013).
Overall, the COVID-19 pandemic lockdowns kept stressed, overworked parents from obtaining needs help from official welfare facilities. Additionally, once children were not going to school, entities who could have monitored abuse were no longer able to do so, such as teachers, social workers, school coaches, physicians, and nurses. Child abuse rates might have risen right after COVID-19 onset because preventive services were not available and monitors were not available to catch signs of abuse.
We should note that we conducted this study immediately after the onset of COVID-19, and findings from later in the pandemic—between waves or variants or between before and after widespread vaccinations—might offer valuable comparisons to the early data. One limitation of this meta-analysis is that the number of studies so far is relatively small compared with the components of other meta-analyses. Nevertheless, it is a meaningful try to attempt to know the global prevalence of physical and psychological child abuse in restricted surroundings. This study considers the diversities embedded in each country and the cultural and economic factors affect physical and psychological child abuse. It is also significant that we empirically identified economic factors (i.e., income level, unemployment rate) that had been mentioned in previous studies as relevant to global child abuse.
5 Conclusion
In this study, it is confirmed that environmental factors such as social structure, cultural characteristics, and economic factors—which were known to affect child abuse even before the outbreak of COVID-19—had the similar influence at the time of COVID-19 incidence. The social environment, which has been traditionally emphasized, is considered to be a more decisive factor in the occurrence of child abuse, rather than a new specific factor caused by the corona outbreak. Improving the social and living environment and providing support for the those who have been vulnerable to child abuse are still emphasized in periods of disaster such as COVID-19.
Nevertheless, one thing that should not be overlooked is that child abuse can become more severe when a nation's economic status, which grew worse during a disaster, makes households more unstable. In a disaster situation, the implementation of case management integrated with the local community to first alleviate the economic difficulties of poor families, along with policies for reducing poverty, may be considered.
This study identified the actual conditions and influence factors of child abuse in the new situation of the COVID-19 pandemic around the world. The results showed that there is a significant relationship between the rise in the unemployment rate due to the COVID-19 pandemic and physical child abuse. Follow-up studies are needed to figure out the size of the economic factors that affect child abuse before and after the disaster through marginal effects. It would also be possible to consider a study to find out how much improvement in environments that are vulnerable to child abuse before a disaster occurrence can alleviate child abuse in actual disaster situations.
Declaration of competing interest
No potential conflict of interest relevant to this article was reported.
Appendix 1 Search strategy
Search terms defined
Definitions of Child Abuses (May 2022, Child Welfare Information Gateway; https://www.childwelfare.gov/pubPDFs/define.pdf)
Physical Child Abuse: It is defined as “any nonaccidental physical injury to the child” and can include striking, kicking, burning, or biting the child, or any action that results in a physical impairment of the child.
Phychological Child Abuse: It is “injury to the psychological capacity or emotional stability of the child” as evidenced by an observable or substantial change in behavior, emotional response, or cognition and injury as evidenced by anxiety, depression, withdrawal or aggressive behavior.
Definitions of COVID-19 (WHO; https://www.who.int/health-topics/coronavirus#tab=tab_1)
COVID-19: Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment. The virus can spread from an infected person's mouth or nose in small liquid particles when they cough, sneeze, speak, sing or breathe. These particles range from larger respiratory droplets to smaller aerosols.
Definitions of Pandemic (WHO; https://www.who.int/europe/emergencies/situations/covid-19)
The COVID-19 pandemic is a global outbreak of coronavirus, an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. WHO declared a Public Health Emergency of International Concern on 30 January 2020, and to characterize the outbreak as a pandemic on 11 March 2020′.
Search terms list - keywords
COVID-19*
Coronavirus*
Child abuse*
Child neglect*
Child maltreatment*
Literature Sources • Published study literature from the below databases:
Scopus: https://www.scopus.com/search/form.uri?display=basic#basic
Embase: https://www.embase.com/search/quick?phase=continueToApp
PubMed: https://pubmed.ncbi.nlm.nih.gov/
Web of Science: https://www.webofscience.com/wos/woscc/basic-search • COVID-19 related policy databases:
WHO (World Health Organisation): https://www.who.int/
DHHS (Department of Health and Human Services): https://www.hhs.gov/
NIH (National Institute of Health): https://www.nih.gov/ • Children related policy databases:
UNICEF (United Nations Children's Fund): https://www.unicef.org/ • World Economic Review databases:
OECD (Organization for Economic Cooperation and Development): https://www.oecd.org/
WB (World Bank): https://www.worldbank.org/en/home
Inclusion and Exclusion Criteria Unlabelled Table Inclusion Criteria Exclusion Criteria
Key Words Child Abuse, child neglect, and child maltreatment during onset of COVID-19 or coronavirus Other child abuse types are excluded
Type of Article Original researches published or peer-reviewed from Jan. 1. 2020 to Feb. 13. 2022.
Searching journals are only Scopus, Embase, PubMed, and Web of Science Dissertation or other type-papers such as policy, review, comment and lesson
Research Method Quantitative research Other research methods such as qualitative, comment, review, meta, trial, and therapy
Language English language Others
Data Source Only survey data; on-line survey and/or face to face interview Reported data from polices, teachers, and social workers etc.
Respondents Respondents are parents of children under 18 years or children under 18 years Others
Search Strategy Unlabelled TableSearch Database/website Search terms used Date search performed Number of returns
Scopus ((Coronavirus*[Title/Abstract/Contents] OR COVID-19*[Title/Abstract/Contents] AND Child abuse*[Title/Abstract/Contents] OR Child neglect*[Title/Abstract/Contents] OR Child maltreatment*[Title/Abstract/Contents])) 29/11/2021–13/02/2022 686
Embase 954
PubMed 1258
Web of Science 548
Appendix 2 Study Quality assessment tool
Unlabelled Table# Criteria Yes No
1 Focused Question: Is the review based on focused question that is adequately formulated and described? 1 0
2 Valid Measure: Are the physical/psychological abuse measures validated questionnaires? 1 0
3 Peer Review: Was the study reviewed independently by two or more reviewers? 1 0
4 Eligible Population: Did at least 50 % of the eligible population participate? 1 0
5 Self-Report Outcome: Were the measures of physical/psychological abuse self-report as opposed to objective? 1 0
6 Exposure Time: Did short time elapse since COVID-19 for there to be an impact on child physical/psychological abuse? 1 0
Appendix 3 Study included in this review descriptions and quality assessment (10 studies; 13 effect sizes)
Unlabelled TableAuthor/s Location Survey time Respondent Sampling Method Survey Tool Measure ments Total # Child Physical Abuse (%) Child Psycho-logical abuse (%) Income Level* 2020 Unemployment Rate**
AboKresha et al., 2021 Egypt 9 to 13 April 2020 parents of children under 18 years snowball Online ICAST-P 1118 483 (43.2) 992 (88.7) Low 10.45 %
Augusti et al., 2021 Norway June 2020 adolescents who 13 to 16-year-old systematic sampling Online PCCTS 3545 101 (2.4) 295 (8.2) High 4.60 %
Lee et al., 2021 USA 24 March 2020 parents of children aged 0–12 years random Online PCCTS 283 56 (19.9) 175 (61.8) High 8.09 %
Rodriguez et al., 2021 USA 14 April 2020 parents of children aged 0–12 years random Online PCCTS 405 21(25.5) 101 (45.2) High 8.09 %
Sari et al., 2021 Nether lands 17 April-10 May 2020 parents of children aged 1–10 years snowball Online PCCTS 1156 196 (17.0) 427 (36.9) High 4.85 %
Wong et al., 2021 China 29 May-16 June 2020 parents of child under 10 years old random Online PCCTS 600 132 (22.0) 242 (40.3) High 5.8 %
Yamaoka et al., 2021 Japan 30 April-31 May 2020 parents of children aged 0–17 years random Online CMS 5344 603 (11.3) 1261 (23.6) High 2.77 %
Zhang et al., 2021 China (Hubei) July 2020 children who 12–16 years Multistage sampling Mobile phone UNICEF Items 1062 145 (13.7) 214 (20.2) Middle 5.0 %
Kurata et al., 2021 India 28 Sep. - 21 Oct., 2020 parents who 18–55 years random Online BRFSS 139 92 (66.2) 81 (58.3) Low 34.7 %
Kurata et al., 2021 Malaysia Sep. - Nov., 2020 parents who 18–55 years random Online BRFSS 39 5 (12.8) 8 (20.5) Middle 4.3 %
Kurata et al., 2021 Japan 28 Sep. - 21 Oct., 2020 parents who 18–55 years random Online BRFSS 155 26 (16.8) 49 (31.6) High 2.77 %
Kurata et al., 2021 USA 28 Sep. - 21 Oct., 2020 parents who 18–55 years random Online BRFSS 197 78 (39.6) 109 (55.3) High 8.09 %
Selvi, 2022 Indonesia during Pandemic parents of children aged 3–12 years random Online Index 317 128 (40.4) 231 (72.9) Middle 4.11 %
Note. It is not included in each study about the information of economic status (income level and 2020 unemployment rate) for a country. However, it is necessary to analyze the effect of economic status on child abuse in subgroup as above Table 1, Table 2, economic status for each country is added through web-site of World Bank (2021); http://data.worldbank.org/data-catalog/world-development-indicators Accessed 22.02.20.
Data availability
Data will be made available on request.
Acknowledgements
This work was not supported by anyone /any organization.
Disclosure statement
In accordance Journal of Child Abuse and Neglect policy and our ethical obligation as researchers, we are reporting that we do not receive fundings from anyone.
1 On the World Bank website (2021), there are information of economic status (income level and 2020 unemployment rate) for each country; http://data.worldbank.org/data-catalog/world-development-indicators Accessed 22.02.20
==== Refs
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| 0 | PMC9722678 | NO-CC CODE | 2022-12-07 23:21:58 | no | Child Abuse Negl. 2022 Dec 6;:105984 | utf-8 | Child Abuse Negl | 2,022 | 10.1016/j.chiabu.2022.105984 | oa_other |
==== Front
Vaccine
Vaccine
Vaccine
0264-410X
1873-2518
Elsevier Ltd.
S0264-410X(22)01510-9
10.1016/j.vaccine.2022.11.076
Article
Trusted information sources in the early months of the COVID-19 pandemic predict vaccination uptake over one year later
Latkin Carl ac⁎
Dayton Lauren a
Miller Jacob b
Eschliman Evan a
Yang Jingyan d
Jamison Amelia a
Kong Xiangrong e
a Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University
b Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University
c Division of Infectious Diseases, Johns Hopkins University School of Medicine
d Department of Political Science, Columbia University
e Wilmer Eye Institute, Johns Hopkins University School of Medicine
⁎ Corresponding author at: Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University
6 12 2022
6 12 2022
18 2 2022
28 9 2022
30 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.
Introduction
COVID-19 vaccine uptake has been a major barrier to stopping the pandemic in many countries with vaccine access. This longitudinal study examined the capability to predict vaccine uptake from data collected early in the pandemic before vaccines were available.
Methods
493 US respondents completed online surveys both at baseline (March 2020) and wave 6 (June 2021), while 390 respondents completed baseline and wave 7 (November 2021) surveys. The baseline survey assessed trust in sources of COVID-19 information, social norms, perceived risk of COVID-19, skepticism about the pandemic, prevention behaviors, and conspiracy beliefs. Multivariable logistic models examined factors associated with the receipt of at least one COVID-19 vaccine dose at the two follow-ups.
Results
In the adjusted model of vaccination uptake at wave 6, older age (aOR = 1.02, 95%CI = 1.00-1.04) and greater income (aOR = 1.69, 95%CI = 1.04-2.73) was associated with positive vaccination status. High trust in state health departments and mainstream news outlets at baseline were positively associated with vaccination at wave 6, while high trust in the Whitehouse (aOR = 0.42, 95%CI = 0.24-0.74) and belief that China purposely spread the virus (aOR = 0.66, 95%CI = 0.46-0.96) at baseline reduced the odds of vaccination. In the adjusted model of vaccination uptake at wave 7, increased age was associated with positive vaccination status, and Black race (compared to white) was associated with negative vaccination status. High trust in the CDC and mainstream news outlets at baseline were both associated with being vaccinated at wave 7, while high trust in the Whitehouse (aOR = 0.24, 95%CI = 0.11-0.51) and belief that the virus was spread purposefully by China (aOR = 0.60, 95%CI = 0.39-0.93) were negatively associated with vaccination.
Conclusions
These findings indicated that vaccine uptake could be predicted over a year earlier. Trust in specific sources of COVID-19 information were strong predictors, suggesting that future pandemic preparedness plans should include forums for news media, public health officials, and diverse political leaders to meet and develop coherent plans to communicate to the public early in a pandemic so that antivaccine attitudes do not flourish and become reinforced.
Keywords
Covid-19
SARS-CoV-2
Vaccine
Trust
Information Sources
Health Behaviors
==== Body
pmc1 Introduction
Vaccination uptake remains a critical issue. The WHO Working Group on Vaccine Hesitancy and other research has identified confidence and complacency as two key domains of vaccine uptake [1]. Vaccine complacency is viewed as the perceived risk of vaccine-preventable diseases, and confidence is defined as trust in 1) vaccine effectiveness and safety, 2) vaccine delivery system, and 3) policymakers who recommend the vaccines [2], [3]. Other research has identified domains such as perceived risk of vaccination, vaccine effectiveness, and social norms as predictors of COVID-19 vaccine hesitancy [4], [5], [6], [7], [8], [9], [10]. These domains align with theoretical models of vaccine uptake that show that vaccination decisions are based on an evaluation of risk and benefit information as well as being shaped by individuals’ social network norms [11], [12], [13], [14], [15]. These identified domains and theoretical models have an underlying assumption that people are using current information to drive their decision on vaccination uptake. However, there may be factors present before the vaccine was tested that predict uptake. The current study was guided by the question: Can we predict vaccine uptake using information collected before the vaccine was even available? Using longitudinal data, we assessed if sources of trusted information, as well as COVID-19-related social norms and beliefs, predict vaccine uptake approximately a year later.
Prior research suggests that trust is linked to vaccine hesitancy and may be important for COVID-19 vaccine uptake [7], [8]. Several reviews and multi-country studies have documented that medical mistrust related to COVID-19 and/or mistrust in vaccines are negatively associated with vaccine intentions, as is distrust in the health care system [16], [17], [18], [19], [20]. Larson et al., in a review of vaccine acceptance, conceptualize key domains of trust to include trust in policymakers, the health system, government, as well as trust in public health researchers and officials involved in approving and certifying vaccines as safe and effective [21]. Trust in COVID-19 information, however, may also be viewed as even more distal in the process and may have been established, in part, prior to the pandemic. These perceptions of trust earlier in the pandemic may guide the choice of sources of information and attention paid to these sources. Cross-sectional studies have found a negative association between greater trust in mainstream sources of COVID-19 vaccine information and vaccine hesitancy [12]. Prior research also suggests that early in the pandemic, there was a precipitous decline in perceived trust in sources of COVID-19 vaccine information [22]. In their review highlighting the importance of trust in vaccine decisions, Larson et al. also note that there is little longitudinal data on trust and vaccine uptake [21].
Sources of trusted information may drive different vaccination uptake decisions, instill confidence or confusion, and influence the level of trust in other sources of information. For example, the Trump administration’s pronouncements promoting unproven and potentially dangerous treatments for COVID-19 were at times at odds with scientific agencies such as the U.S. Food and Drug Administration (FDA) and Centers for Disease Control and Prevention (CDC), which may have led to distrusting the Trump administration and/or FDA and CDC. Additionally, a study conducted by Ananyev and colleagues found that increased exposure to Fox News led to reduced compliance with social distancing during the pandemic, suggesting that sources of news influenced COVID-19 behaviors [23]. Information sources also did not always stay consistent in their messaging; for example, the Whitehouse COVID-19 Response Team ran the program to develop and disseminate COVID-19 vaccines, and Dr. Fauci consistently emphasized the severity of the pandemic. Yet many in the Trump Whitehouse downplayed the severity of the pandemic, which may have led to reduced vaccine uptake [24], [25]. Altogether, these contradictory dynamics may have also reduced trust in governmental public health agencies and may have led individuals to be unsure about which news media sources to trust about the pandemic. This, in turn, could have also led to accessing other news sources, especially social media, that have provided misinformation about the COVID-19 pandemic and vaccinations [26].
One factor that may be correlated with trust is beliefs in conspiracy theories. Often conspiracy beliefs implicitly or explicitly indicate that mainstream sources of information cannot be trusted. Conspiracy beliefs have also been found to be linked to vaccine hesitancy [27], [28]. Early endorsements of conspiracy theories may lead to lower vaccine uptake. When people make public proclamations for or against an issue, they are less likely to change their attitudes [29]. Hence, individuals who may have promoted the idea early in the pandemic that it was not serious or was a hoax may have had difficulty changing their attitudes to viewing COVID-19 as sufficiently serious to require vaccination.
Vaccination uptake may also be influenced by social identity [30], [31]. Attitudes about the COVID-19 pandemic and the COVID-19 vaccines can also be viewed as a source of social identity. Participating in early COVID-19 prevention behaviors, such as social distancing, may foster a social identity of engaging in COVID-19 prevention behaviors; these individuals may then be more likely to get vaccinated as it aligns with their identity of participating in behaviors that aim to mitigate COVID-19. Social norms can also influence an individual’s social identity [32]. For example, having peers who support COVID-19 prevention behaviors early in the pandemic can foster an individual’s identity of engaging in behaviors that prevent COVID-19 and thus be associated with later vaccination uptake decisions. Political orientation is an additional factor that may shape an individual’s social identity of engaging, or not, in COVID-19 prevention behaviors. As the response to the pandemic became politically polarized, individuals may have viewed their COVID-19 prevention behaviors such as mask-wearing and later vaccines as a public indicator of their political identity. One experimental study found that after the vaccines became available, COVID-19 vaccine messages from politically liberal sources led conservatives to be less likely to encourage other people to become vaccinated [33].
In this study, we used a longitudinal sample of US residents, which allows us the unique opportunity to use measures of these factors collected in March 2020 (i.e., prior to vaccine availability). In March 2020, the COVID-19 clinical vaccine trials were in the early phases, with the initial enrollment of the first participants in mRNA clinical trials. At this time, Moderna was launching its first COVID-19 vaccine clinical trials in the US. Soon after, in May 2020, Pfizer began its US clinical trials. There was no information in March 2020 on potential side effects or vaccine effectiveness, nor was there clear information on when the outcomes of the clinical trials, let alone the vaccines themselves, would be available. We then assessed if this data collected in March 2020 could predict vaccine uptake by June and November 2021, when vaccines were widely available to US residents. We anticipated that trust in news sources, conservatism, behavioral prevention measures, conspiracy beliefs, skepticism, and social norms about the pandemic would be correlated, and these factors would predict COVID-19 vaccine uptake. Identifying such predictors may help identify factors that may be antecedents to vaccine hesitancy and improve methods to improve vaccine uptake.
2 Methods
2.1 Study population
Study participants were drawn from the online longitudinal COVID-19 and Well-Being Study that began in March 2020. This study aimed to examine individual, social, and societal-level fluctuations amid the rapidly changing landscape of the pandemic. Study participants were recruited through Amazon's Mechanical Turk (MTurk). This platform is regularly used by health researchers, as it allows for a diverse sample to be collected in a rapid and timely fashion [34]. Study populations recruited through MTurk are not nationally representative but have been documented to outperform other opinion samples on several dimensions [35]. Studies using MTurk have also demonstrated good reliability [36]. The study protocols followed MTurk's best practices, including ensuring participant confidentiality, protecting study integrity, generating unique completion codes, integrating attention and validity checks throughout the survey, repeating study-specific qualification questions, and removing ineligible participants [37], [38], [39]. Moreover, despite COVID-19, the demographic characteristics of MTurk appear to be stable [40]. Eligibility was determined by the following criteria: being age 18 or older, living in the United States, being able to speak and read English, having heard of the coronavirus or COVID-19, and providing written informed consent. Additionally, to enhance reliability, eligible participants had to pass attention and validity checks embedded in the survey [41]. Following recommendations by Rouse, we embedded checks to mitigate inattentive and random responding [41]. These checks included survey questions with exceedingly low probabilities, such as deep-sea fishing in Alaska and having appendages removed. We also repeated questions to ensure consistency. Finally, we examined the time participants took to complete the survey and verified the completeness of the data. Participants were compensated $2.50 for the first wave (March 24th-27th, 2020) and $4.25 for the sixth wave (June 14th -23rd, 2021), and $4.25 for the seventh wave data (November 16th -29th, 2021), which was equivalent to approximately $12 per hour. The study protocols were approved by the Johns Hopkins Bloomberg School of Public Health Institutional Review Board.
The first survey was administered beginning on March 24th, 2020, which was a week after 4 individuals started the Phase I clinical trial of the Modera mRNA-1273. At this time, there was no information on the vaccine’s safety or efficacy. By the time of the sixth and seventh survey in June and November 2021, respectively, vaccines were readily available for US adults. All eligible participants from wave 1, were invited to participate in subsequent waves. In total, 809 people participated in wave 1, with 493 respondents completing both waves 1 and 6, and due to attrition, there were 390 respondents who completed both waves 1 and 7.
2.2 Measures
The primary outcome question was the response to the question, “How many doses of the coronavirus vaccine have you received?” To assess trust in sources of information, a set of questions asked participants, “How much do you trust information from [….] about coronavirus?” The following were the five sources of information: (1) the CDC, (2) the Whitehouse, (3) Johns Hopkins University, (4) major news outlets such as CNN, (5) your State Health Department. Response options were “(1) A great deal,” “(2) Quite a bit,” “(3) Some,” “(4) Very little or none.” These sources were chosen based on popularity, prestige, and information sources anticipated to provide accurate information. Johns Hopkins University was chosen due to its major role in disseminating COVID-19 data on case rates and deaths. As the first two response categories indicated high trust ratings, responses to trust in information sources were dichotomized as high (a great deal or quite a bit) versus low (some, very little, or none).
Two variables, which were added together, assessed social norms, “My friends would laugh at me if I wore a mask to protect myself from the coronavirus” and “My friends would think it was rude if I didn't hang out with them because of the coronavirus” The response categories were “Strongly agree,” “Agree,” “Neither agree nor disagree,” “Disagree,” and “Strongly disagree ” (range 2-10). Prevention behavior was assessed with the item, “Are you trying to spend less time around other people to prevent getting the coronavirus?” (yes/no). The item “I am very worried about getting the coronavirus” was used to assess perceived risk/vaccine complacency, and the item “China purposely spread the coronavirus” was used to assess a conspiracy belief. All these items had response categories of a Likert scale with response categories were “Strongly agree,” “Agree,” “Neither agree nor disagree,” “Disagree,” and “Strongly disagree.” Based on the distribution, the item worried about getting the coronavirus was dichotomized (Strongly agree or Agree vs. other responses), and the item China purposely spread the coronavirus into three categories (strongly agree/agree, neither agree or disagree, and disagree/strongly disagree
Skepticism of the COVID-19 pandemic was assessed by the three items, “The health risks from coronavirus has been exaggerated,” “The coronavirus is a hoax,” and “The coronavirus isn’t any worse than the flu.” The response categories were “Strongly agree,” “Agree,” “Neither agree nor disagree,” “Disagree,” and “Strongly disagree.” These three items were summed as a scale and had a Cronbach’s alpha of 0.76, (range 3-15), with lower scores indicating greater skepticism.
Political ideology was assessed with the question, “Where would you place yourself on a scale running from ”Very liberal“ to ”Very conservative?“ The response categories were (1) “Very liberal,” (2) “Liberal,“ (3) “Slightly liberal,” (4) “Moderate,“ (5) “Slightly conservative,” (6) “Conservative,“ (7) “Very conservative,” and. (8) “Not applicable.” There were six respondents who reported “not applicable” and were recoded to the median. The categories for race/ethnicity were White, Non-Hispanic Black, Hispanic, Asian, Mixed, and Other. Due to small sample sizes, mixed-race and “other” categories were collapsed into one category. Gender, education, and income were also assessed. Level of education was collapsed to reflect some college or less, associate degree, or technical degree or less, and bachelor’s degree or higher. Income was dichotomized at the median of $60,000 or below.
2.3 Analysis
The goal of this study was to assess whether information collected on trust in COVID-19 information sources, social norms, social identity, COVID-19 beliefs, and demographics collected in March 2020 (wave 1) predicted receiving at least one dose of a COVID-19 vaccine uptake by June 2021 (wave 6) and November 2021 (wave 7). The sample was restricted to participants who completed wave 1 and waves 6 and/or 7. Descriptive statics were assessed for the 493 adults who completed both waves 1 and 6 and the 390 adults who completed waves 1 and 7. Unadjusted and adjusted logistics regression was used to model wave 1 predictors with vaccination uptake in June and November 2021. Only wave 1 measures with p-values less than 0.20 in the bivariate models were included in the fully adjusted models. In the adjusted models, all the variables were adjusted for the other variables to assess the independent contribution of each variable. For both waves, this cutoff was met by all wave 1 measures. A Spearman correlation matrix (N=493) modeled the correlation coefficients between the predictor variables.
3 Results
At wave 6, approximately two-thirds of the respondents had received at least one dose of a COVID-19 vaccine (68.4%), and by wave 7, slightly over three-quarters had received at least one dose (76.9%). The study population at wave 1 was predominately White with a bachelor’s or higher level of education (Table 1 ). The mean age was 40.5 years. There were high levels of trust in COVID-19 information from the CDC (81.9%), state health departments (76.7%), and Johns Hopkins University (JHU) (85.4%); moderate levels from major news outlets (48.9%), and low levels from the Whitehouse (28.2%). Approximately half of the respondents identified as liberal (50.5%), 21.9% as moderate, and 26.4% as conservative. A small portion of respondents reported feeling pressure not to adhere to preventative measures (16.8%), and most of the sample reported attempting to spend less time around others (96.4%). Roughly half of the respondents were worried about becoming infected at baseline (54.6%), and a small portion of the sample believed that it was spread purposely by China (8.7%).Table 1 Baseline characteristics among participants of waves 6 and 7
Variable Wave 6(N = 493)n (%) Wave 7(N = 390)n (%)
Received at least one COVID-19 vaccine dose 337 (68.4) 300 (76.9)
Age M (SD) 40.5 (12.0) 41.9 (11.9)
Female 274 (55.6) 214 (54.9)
Household income > $60,000 223 (45.2) 184 (47.2)
Completed bachelor’s degree or higher 286 (58.1) 223 (59.7)
Trusted COVID-19 information source
CDC 404 (81.9) 324 (83.1)
Whitehouse 339 (28.2) 111 (28.5)
Johns Hopkins University 421 (85.4) 338 (86.7)
State Health Department 378 (76.7) 304 (77.9)
Major news outlets 241 (48.9) 195 (50.0)
Social norms for mask wearing or social distancing M (SD) (range 2-10). 83 (16.8) 67 (17.2)
Political affiliation*
Liberal 249 (50.5) 197 (50.5)
Moderate 108 (21.9) 87 (22.3)
Conservative 130 (26.4) 103 (26.4)
Spending less time around people 475 (96.4) 376 (96.4)
Worried about becoming infected with COVID-19 269 (54.6) 211 (54.1)
Believes that China purposely spread COVID-19 43 (8.7) 35 (8.9)
COVID-19 skepticism scale M (SD) (range 3-15) 65 (13.2) 54 (13.9)
Race
White 396 (80.3) 321 (82.3)
Black 32 (6.5) 23 (5.9)
Hispanic 13 (3.1) 10 (2.6)
Asian 34 (6.9) 24 (6.2)
Other/Mixed 16 (3.3) 12 (3.1)
* six respondents answered “not applicable”
For wave 6 bivariate models (Table 2 ), neither age nor sex was related to vaccination status. Black race (compared to white) was associated with negative vaccination status (OR = 0.46, 95% CI = 0.22-0.94), but other race categories were not related to vaccination status. High trust in different sources of information was associated significantly with vaccination. High trust in the CDC, state health departments, JHU, and news outlets were positively associated with having received at least 1 vaccine dose, while high trust in the Whitehouse was negatively associated with vaccination status (OR = 0.41, 95% CI = 0.27-0.62). Conservatism was significantly associated with a negative vaccine status (OR = 0.76, 95% CI = 0.68-0.85), while social distancing (OR = 3.58, 95% CI = 1.36-9.41), worry about becoming infected (OR = 1.64, 95% CI = 1.12-2.40), and not having skepticism about COVID-19 (OR = 1.23, 95% CI = 1.12-1.34) were associated with positive vaccine status.Table 2 Bivariate and adjusted logistic regression models for having at least one COVID-19 vaccine dose by wave 6 (N = 493)
Variable OR (95% CI) aOR (95% CI)
Age in years 1.01 (0.99, 1.03) 1.02 (1.00, 1.04)
Sex assigned at birth (ref: male) 0.93 (0.70, 1.25) 1.08 (0.68, 1.71)
Income > $60,000 in last year 1.83 (1.23, 2.70) 1.69 (1.04, 2.73)
Bachelor’s degree completed 2.11 (1.44, 3.10) 1.29 (0.81, 2.06)
Trusted COVID-19 information sources
CDC 4.05 (2.52, 6.53) 1.76 (0.91, 3.40)
Whitehouse 0.41 (0.27, 0.62) 0.42 (0.24, 0.74)
Johns Hopkins University 4.34 (2.58, 7.30) 1.60 (0.80, 3.20)
State Health Departments 3.53 (2.29, 5.46) 2.41 (1.37, 4.25)
Major news outlets 3.43 (2.28, 5.16) 2.03 (1.26, 3.27)
Social norms: social distance or mask usage 1.12 (0.99, 1.25) 1.11 (0.97, 1.28)
Political ideology (liberal to conservative) 0.76 (0.68, 0.85) 0.88 (0.76, 1.03)
Spending less time around people to prevent COVID-19 3.58 (1.36, 9.41) 1.73 (0.52, 5.76)
Worried about getting COVID-19 1.64 (1.12, 2.40) 1.07 (0.65, 1.73)
Believes that China purposely spread COVID-19 0.41(0.30, 0.56) 0.66 (0.46, 0.96)
COVID-19 skepticism scale 1.23 (1.12, 1.34) 0.96 (0.85, 1.09)
Race (ref: White)
Non-Hispanic Black 0.46 (0.22, 0.94) 0.48 (0.20, 1.14)
Hispanic 0.91 (0.31, 2.72) 1.26 (0.34, 4.70)
Asian 1.48 (0.65, 3.37) 1.47 (0.58, 3.74)
Other / Mixed 1.98 (0.55, 7.06) 3.68 (0.83, 16.38)
In the fully adjusted models, increased age was associated with positive vaccination status (aOR = 1.02, 95% CI = 1.00-1.04), as was greater income (aOR = 1.69, 95% CI = 1.04-2.73). High trust in state health departments and mainstream news outlets were positively associated with having at least one vaccination dose, while high trust in the Whitehouse reduced odds of vaccination (aOR = 0.42, 95% CI = 0.24-0.74). Most COVID-19-related beliefs or behaviors were not significantly associated with vaccination status at wave 6, but the belief that China purposely spread the virus was associated with negative vaccination status (aOR = 0.66, 95% CI = 0.46-0.96).
Results from bivariate models in wave 7 (Table 3 ) demonstrate that increased age (OR = 1.03, 95% CI = 1.01-1.05) and having received a bachelor’s degree (OR = 2.25, 95% CI = 1.40-3.64) are associated with positive vaccination status. High trust in the CDC, JHU, state health departments, and news outlets at baseline were associated with positive vaccination status at wave 7, while high trust in the Whitehouse was associated with not having received a vaccination (OR = 0.28, 95% CI = 0.17-0.47). Black race (compared to white) was associated with a decreased likelihood of being vaccinated (OR = 0.38, 95% CI = 0.16-0.89). Social distancing, worry about becoming infected with COVID-19, and not having skepticism about COVID-19 were all positively associated with positive vaccination status, while conservativism (OR = 0.72, 95% CI = 0.63-0.83) and belief that the virus was spread purposely by China (OR = 0.36, 95% CI = 0.25-0.51) were associated with negative vaccination status.Table 3 Bivariate and adjusted logistic regression models for having at least one COVID-19 vaccine dose by wave 7 (N = 390)
Variable OR (95% CI) aOR (95% CI)
Age in years 1.03 (1.01, 1.05) 1.04 (1.02, 1.07)
Sex assigned at birth (ref: male) 0.91 (0.57, 1.46) 0.72 (0.39, 1.33)
Income > $60,000 in last year 1.46 (0.91, 2.36) 1.16 (0.61, 2.19)
Bachelor’s degree completed 2.25 (1.40, 3.64) 1.70 (0.92, 3.18)
Trusted COVID-19 information sources
CDC 4.32 (2.47, 7.56) 2.69 (1.12, 6.49)
Whitehouse 0.28 (0.17, 0.47) 0.24 (0.11, 0.51)
Johns Hopkins University 5.19 (2.81, 9.57) 1.85 (0.78, 4.35)
State Health Departments 3.11 (1.85, 5.23) 1.52 (0.71, 3.26)
Major news outlets 5.76 (3.27, 10.13) 3.54 (1.82, 6.92)
Social norms: social distance or mask usage 1.11 (0.96, 1.27) 1.06 (0.88, 1.28)
Political ideology (liberal to conservative) 0.72 (0.63, 0.83) 0.93 (0.75, 1.15)
Spending less time around people to prevent COVID-19 4.78 (1.61, 14.17) 2.87 (0.71, 2.97)
Worried about getting COVID-19 2.10 (1.30, 3.39) 1.56 (0.82, 2.97)
Believes that China purposely spread COVID-19 0.36 (0.25, 0.51) 0.60 (0.39, 0.93)
COVID-19 skepticism scale 1.30 (1.16, 1.44) 0.97 (0.83, 1.13)
Race (ref: White)
Non-Hispanic Black 0.38 (0.16, 0.89) 0.29 (0.09, 0.92)
Hispanic 0.67 (0.17, 2.68) 0.90 (0.15, 5.51)
Asian 2.02 (0.59, 6.98) 2.09 (0.50, 8.72)
Other / Mixed 1.45 (0.31, 6.75) 3.29 (0.39, 27.44)
In the fully adjusted models, increased age was associated with positive vaccination status, and Black race (compared to white) was associated with negative vaccination status, but other demographic variables were not significantly associated. High trust in the CDC and news outlets at baseline were both associated with being vaccinated at least once at wave 7, while high trust in the Whitehouse was associated with negative vaccination status (aOR = 0.24, 95% CI = 0.11-0.51). The belief that the virus was spread purposefully by China at baseline was also negatively associated with vaccination at wave 7 (aOR = 0.60, 95% CI = 0.39-0.93). Sex, income, and social norms were not significantly associated with vaccine status in bivariate or multivariate models.
In a final analysis, we examined the association between the key covariates, excluding the demographic factors, using Spearman's correlation coefficients. As seen in Table 4 , with the exception of the social norms and the wearing of masks outside, the majority of other variables were correlated. Though high trust in the Whitehouse was positively correlated with only one variable (trust in COVID-19 information from state health departments) and negatively associated with trust from major news outlets.Table 4 Spearman’s correlation matrix of covariates
High trust for COVID-19 information: CDC High trust for COVID-19 information: Whitehouse High trust for COVID-19 information: Johns Hopkins University High trust for COVID-19 information: Health Departments High trust for COVID-19 information: Major news outlets Political conservativism Spending less time around people Worried about getting COVID-19 Belief China purposely spread COVID-19 High COVID-19 skepticism Social norms
High trust for COVID-19 information: CDC 1.00 .118** .463** .439** .259** -0.034 .190** .130** -.200** -.164** 0.036
High trust for COVID-19 information: Whitehouse 1.00 0.017 .111* -.099* .491** -0.002 -0.051 -.318** .230** -0.004
High trust for COVID-19 information: JHU 1.00 .424** .267** -.119** .164** .188** -.171** -.251** 0.037
High trust for COVID-19 information: Health Department 1.00 .280** -0.073 .148** -.099* -.117** -.144** -0.055
High trust for COVID-19 information: major news outlets 1.00 -.203** .147** .161** -.236** -.207** 0.022
Political conservatism 1.00 0.073 -.221** .425** .309** -0.017
Spending less time around other people 1.00 .199** 0.025 -.194** -.123**
Worried about getting COVID-19 1.00 -.162** -.407** 0.022
Belief China purposely spread COVID-19 1.00 .394** .124**
High COVID-19 skepticism 1.00 .257**
Social norms 1.00
4 Discussion
With data collected before there were results or data about side effects from human vaccines trials for COVID-19, we predicted vaccine uptake over a year later among a sample of US residents. Some of the strongest predictors of vaccine uptake one year later were sources of trusted information about COVID-19 in the early months of the pandemic. Study findings identified that individuals who have low trust in state health departments, mainstream news media, and high trust in the Whitehouse had lower odds of being vaccinated over a year later. The findings are consistent the WHO Working Group on Vaccine Hesitancy and with Pew Research Center findings. The Pew Research Center initially surveyed respondents in April 2020 about their COVID-19 information sources as part of the American Trends survey. Those who reported relying on “Public health organizations and officials” and “National news outlets” most for news about the COVID-19 outbreak had the highest vaccination rates when assessed in August 2021 [42]. In comparison, those who reported relying most on “Donald Trump and his coronavirus task force” for news about the pandemic were more than 20% less likely to have had at least one dose of a COVID-19 vaccine.
It is interesting that high trust in the Trump administration, as assessed by trust in the Trump Whitehouse, was negatively associated with vaccine uptake. Those respondents who had low trust in the Trump Whitehouse had four times greater odds of being vaccinated by late November 2021, compared to those with high trust. There are a few possible explanations for this relationship between trust in the Trump Whitehouse and reduced vaccination uptake. First, it may be believed that the Trump Whitehouse did not support vaccinations. Although the Trump administration funded and supported the COVID-19 vaccine development, public health leaders in the administration who were encouraging vaccination, such as Dr. Anthony Fauci, were often seen as at odds with Trump’s pronouncements about the pandemic. Additionally, the conservative media and leaders frequently pilloried public health officials for their encouragement of behavioral COVID-19 prevention efforts, support for lockdowns, and vaccine mandates. These factors may have led some people to believe that the Trump Whitehouse did not support vaccination. Another explanation of the relationship between trust in the Trump Whitehouse and lower levels of vaccine uptake is social identity, with high trust in the Trump Whitehouse indicating a stronger social identity of a Trump supporter, which may include a lower likelihood of viewing the pandemic as a serious threat or vaccine complacency and conflating individual rights with vaccination refusal. Not being vaccinated can also be viewed as behaviorally consistent with high COVID-19 skepticism, which was significantly correlated with trust in COVID-19 information from the Trump Whitehouse in the current study. It is likely that sources of information and a range of beliefs about COVID-19, science literacy, public health, and conspiracies are mutually reinforcing. These beliefs can provide a barrier that excludes or distorts critical public health information about the pandemic. These findings suggest the important role of conservative leaders and conservative media in promoting vaccine uptake. They have a greater responsibility as many of their followers may not trust scientific sources of COVID-19 information.
We also found that the belief that China purposefully spread COVID-19 was strongly associated with lower vaccine uptake. At face value, it makes little sense that if one believes that China was purposefully spreading COVID-19, one would be less likely to become vaccinated. However, it may be that this belief is a marker of a constellation of beliefs, attitudes, values associated with vaccine behaviors. The survey items of trust in the Trump Whitehouse, China purposefully spreading COVID-19, and COVID-19 skepticism were all highly positively correlated. Except for trust in the Trump Whitehouse, these items were also associated with trying to spend less time around others to prevent COVID-19 and worried about becoming infected with the virus. Trust in all the other sources of COVID-19 information, except the Trump Whitehouse, was negatively correlated with the belief that China purposefully spread COVID-19 and COVID-19 skepticism.
Social norms of support for COVID-19 prevention behaviors early in the pandemic was not found to be an independent predictor of vaccine uptake a year later. In this study, we used an injunctive social norms measure that assessed perceptions of potential negative reactions by friends to COVID-19 prevention behaviors. This variable tended not to be strongly associated with the other independent variables. There are several plausible explanations for this finding. First, we did not ask directly about the friends' attitudes or behaviors (descriptive social norms) regarding either the pandemic or prevention behaviors, which may have had more predictive power than the injunctive social norms. Second, as the pandemic was in the early phase, people may not have interacted with their friends sufficiently to gauge their friends’ attitudes and behaviors. However, the significant association between perceived reactions by friends to COVID-19 prevention behaviors and COVID-19 skepticism suggests an association between one’s beliefs about the pandemic and anticipated reactions by friends for engaging in COVID-19 prevention behaviors. Furthermore, the social norms of COVID-19 prevention behaviors may not have been as strongly established in March 2020, as they were only variably beginning to be supported by laws and policies (e.g., mask mandates) and other prevention-geared measures (e.g., stickers denoting six feet apart placed on the ground for queueing, plexiglass screens in front of cashiers).
It could be argued that conservative ideologies with an emphasis on individual freedom and distrust of government would lead to low vaccine uptake. Yet, prior to COVID-19, vaccination decisions were not often discussed in the context of individuals' freedoms. Vaccination debates before COVID-19 were much more focused on vaccination exemptions based on religious views. Study findings on the relationship between political ideology and anti-vaccination beliefs prior to COVID-19 were mixed, with no clear indication that vaccine uptake was viewed as a political act [43], [44]. One explanation for the finding of the bivariate association between greater political conservatism and lower vaccination uptake, which was attenuated in the multivariate model, is that conservatism fostered higher trust in information on COVID-19 from the Trump Whitehouse, which provided ambiguous messages about vaccination, and led to decreased trust in COVID-19 information from public health entities that encouraged vaccination.
We also found that age and income were associated with vaccination status, which has also been found in prior studies [9]. COVID-19 skepticism was statistically significant in bivariate models but not the multivariable model. COVID-19 skepticism was strongly associated with trust in the Whitehouse, being more conservative, and belief that China purposefully spread COVID-19 and strongly negatively associated with concern about COVID-19 infection and trust in COVID-19 information from the CDC, state health departments, mainstream news media, and JHU. COVID-19 skepticism, which assessed denial of the pandemic severity, is consistent with some conservative leaders' downplaying of the pandemic. A longitudinal study in the US prior to vaccine availability found that conservative news media attracted individuals susceptible to conspiratorial thinking, and those with conservative political views were less exposed to mainstream news. Continued exposure to conservative new media reduced support for vaccination and decreased trust in the CDC [45].
The findings from this study, especially the correlation matrix in Table 4, fit well with Young and Bleakley’s (2020) ideological health spirals model (IHSM) [31]. In their model, political orientation, demographic, cultural, and individual factors lead to social identities that motivate media and social network exposures, which, in turn, influences attitudes toward COVID-19 behaviors, subjective norms regarding COVID-19, and self-efficacy surrounding COVID-19 behaviors. The IHSM model hypothesizes that attitudes and norms influence media exposure and interpersonal discussions that include a feedback loop, which makes it difficult to alter attitudes and behaviors. The IHSM highlights the role of social identity, which guides and is reinforced by media exposure.
Study limitations should be noted. This was a longitudinal study with attrition. Moreover, the sample was not random, which limits generalizability. We also did not assess all sources of COVID-19 information and how much information about the pandemic was gathered through social media and informal social networks. The measure of trust did not differentiate between less conservative news outlets such as CNN and more conservative new sources such as Fox News. The numerous waves of the pandemic, effects of nonpharmaceutical interventions, changes in recommendations for boosters, availability of vaccinations for children, emerging real-world evidence on vaccine effectiveness and safety, and other factors are all likely to influence vaccine attitudes and behaviors, making it more remarkable that baseline information could predict later vaccination uptake. These findings do not negate the importance of access to vaccines to facilitate uptake [46], [47]. The results also do not address the role of vaccine hesitancy based on the perceived safety and efficacy of the COVID-19 vaccines. We did not assess these domains at the baseline as there was no information about them before the vaccines were tested in rigorous randomized control trials. There are also likely bidirectional associations among news sources, trust in news sources, and political orientation, with each mutually reinforcing the other.
The results suggest that political leaders and news outlets have a critical role in shaping their followers’ attitudes about vaccines and trust in sources of information about the pandemic. However, once attitudes about the pandemic and vaccines are established, conservative leaders may believe that promoting social distancing and vaccines may lose support among their base. Emphasizing the role of altruism in vaccination among conservatives might allow them to encourage vaccines while not contradicting the sentiments of their supporters. An experimental study on vaccine intentions conducted in March of 2021 found that when Republicans were exposed to vaccine endorsements by prominent Republicans, including Trump, vaccine intentions increased by 7% compared to endorsements by prominent Democrats [33]. Moreover, Republicans who viewed the Democratic endorsements were significantly less likely to encourage others to become vaccinated and had more negative attitudes toward the vaccine than those who viewed the Republican elite endorsement. These findings provide direction for increasing vaccine uptake among Republicans but require conservative news sources to provide a platform for conservative leaders to disseminate messages to encourage vaccine uptake. Conservative leaders should also highlight accurate sources of scientific information and encourage followers to utilize these sources. The influence of social identity, especially identities linked to political orientation, in COVID-19 vaccine uptake suggests that it is essential to utilize media and public figures that are seen as trustworthy and credible among a range of individuals with diverse social identities to promote vaccine uptake.
Future pandemic preparedness plans should include forums for news media, public health officials, and political leaders to meet and develop coherent plans to communicate to the public early in a pandemic so that antivaccine attitudes do not flourish and become reinforced. Given that level of trust and sources of news information differ based on social identity resulting from political ideology and party affiliation, individuals may not trust information from sources of information about COVID-19 vaccinations outside their own political party. Consequently, groups that provide public health recommendations should consider including prominent political and media figures with scientific literacy. Often health advisory groups strive to be apolitical. However, this approach may not be effective in a highly polarized political climate. Therefore, it may be advisable that advisory groups include members who are viewed as credible across the political spectrum.
Funding sources
R01 DA040488, Alliance for a Healthier World
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
Data will be made available on request.
Acknowledgment
Study participants
==== Refs
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| 36513535 | PMC9722679 | NO-CC CODE | 2022-12-10 23:15:25 | no | Vaccine. 2022 Dec 6; doi: 10.1016/j.vaccine.2022.11.076 | utf-8 | Vaccine | 2,022 | 10.1016/j.vaccine.2022.11.076 | oa_other |
==== Front
Prim Care Diabetes
Prim Care Diabetes
Primary Care Diabetes
1751-9918
1878-0210
Primary Care Diabetes Europe. Published by Elsevier Ltd.
S1751-9918(22)00216-9
10.1016/j.pcd.2022.12.001
Article
The Effect of Education of Patients With Type 2 Diabetes At Risk of Covid-19 on Symptoms and Some Metabolic Outcomes: A Randomized Controlled Study☆
Tülüce Derya a⁎1
Dikici İbrahim Caner a2
Kaplan Serin Emine b3
a Faculty of Health Sciences Nursing Department, Harran University, Şanlıurfa, Turkey
b Faculty of Health Sciences Nursing Department, Gaziantep University, Gaziantep, Turkey
⁎ Corresponding author.
1 ORCID ID: https://orcid.org/0000-0002-1340-013X
2 ORCID ID: https://orcid.org/0000-0002-9838-4502
3 https://orcid.org/0000-0002-7327-9167
6 12 2022
6 12 2022
© 2022 Primary Care Diabetes Europe. 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.
Objective
Type 2 diabetes is one of the most common chronic diseases worldwide. It also has a high risk of morbidity and mortality in the covid 19 pandemic. Due to pandemic measures, disruptions have emerged in the care treatments of patients with type 2 diabetes. The present study aimed to determine the effects of telehealth monitoring and patient training on the symptoms and metabolic outcomes in the patients with type 2 diabetes who are at risk of COVID-19.
Methodology
The current study is in the design of a single-blind randomized controlled trial. Patients were randomized into intervention group (n:45) and control group (n=45). The patients in the intervention group received diabetes training once a week for the first 4 weeks and every other week for weeks 5 to 12. No training was given to the control group. The data was collected using the socio-demographic information form, the questionnaire of diabetes treatment, the form of metabolic control variables, and the Diabetes Symptoms Checklist. The data was analyzed with Chi-square, independent samples t-test, and paired sample t-test.
Results
The mean age of the patients in the control group was 56.86±9.40, and the mean age of those in the intervention group was 54.12±8.32. After the training, a statistically significant difference was found between the checklist averages of the groups in the subscale of hyperglycemia. However, a statistically significant difference was found between the subscales of neurology, cardiology, cognition, hyperglycemia, and the total checklist averages in the intervention group before and after the training (p<0.05). In the control group, there was a statistically significant difference between the subscale of hyperglycemia and the total checklist averages at the beginning and 3 months later (p<0.05).
Conclusion
It has been determined that the disease training given to the patients with diabetes via telehealth monitoring during the COVID-19 process has a positive effect on the diabetes control of the patients. Health education through telehealth methods can be an effective and cost-effective strategy to support patients with diabetes.
Keywords
Diabetes
patient training
telehealth
symptom control
COVID-19
==== Body
pmc1 Introduction
Diabetes is a serious chronic condition recognized as a major cause of premature death and disability worldwide. There are approximately 422 million people with diabetes all over the world and 1.6 million people die from this disease every year [1], [2].
The most important step in diabetes treatment is patient training [3], [4]. The content of disease education consists of nutrition management, physical activity and exercise, insulin injection techniques, oral antidiabetics and administration forms, self-monitoring of glucose, foot care, prevention from acute and chronic complications, psychosocial adaptation and the rights of the diabetic, and social support resources [5]. Therefore, planning and maintaining patient training at regular intervals increases patients' compliance with the disease, controls symptoms, prevents complications, improves quality of life, and reduces morbidity and mortality [1], [6], [7]. It is known that the training and telehealth monitoring given to the patients about the disease have a positive effect on controlling their metabolic variables. In a study, one-year telehealth monitoring shows that there is a significant difference in the HbA1C level and blood sugar regulation of the patients [8]. Thanks to the developing and changing technological infrastructure, it is possible to follow up the patients before they come to the hospital. There is growing evidence to support the use of advanced and innovative technologies such as telehealth to monitor and manage people with diabetes remotely and as often as they need to. Telehealth is generally defined as the exchange of medical information from one location to another using electronic communication or digital technologies such as desktop, laptop computers, mobile phones, and other wireless devices [9], [10]. Telehealth application has benefits such as better diagnosis and treatment, healthy individuals who can maintain their own health, increased preventive health practices, more effective follow-up of chronic diseases, a sustainable health system, time savings for health workers, less hospitalization and cost reduction [5].
Strong evidence demonstrates the beneficial effects of patient monitoring and the training focused on the important role of individual self-care with the support of healthcare professionals. Telehealth may be a strategy for closer monitoring and intervention to not only achieve better metabolic control but also assist in the global care of individuals with multiple chronic diseases. In the last decade, several studies have addressed the feasibility and effectiveness of telehealth strategies for the management of diabetes patients [11]. The studies have shown that the continuity of monitoring diabetes patients by telephone increases the patient's ability to manage their own care and positive behavioral changes have been observed in patients to prevent complications of diabetes [8], [12].
Patients with Type 2 Diabetes are at high risk in the COVID-19 pandemic [13], [14], [15]. During the COVID-19 process, patients are exposed to physical, psychological, and social changes due to being at home for a long time. This situation negatively affects the symptom and disease management of patients [16]. It is known that both patients and healthcare professionals have difficulties during the pandemic period. It is thought that monitoring by telephone becomes more important in eliminating these problems. In the pandemic, there is no data on the level of patients' control of their disease and their self-management. Therefore, the current study was conducted as a randomized controlled study to determine the effects of telehealth monitoring and patient training on the symptoms and metabolic outcomes in the patients with type 2 diabetes who are at risk of COVID-19.
1.1 Hypotheses
H0: Telehealth monitoring and patient training have no effect on the symptoms and metabolic outcomes in the patients with type 2 diabetes who are at risk of COVID-19.
HA: Telehealth monitoring and patient training have an impact on the symptoms and metabolic outcomes in the patients with type 2 diabetes who are at risk of COVID-19.
2 Methods
2.1 Study Design
The study was carried out as a single-blind randomized controlled study to examine the effect of training on the symptoms and metabolic outcomes of the patients who are at risk of COVID-19, and who received Type 2 DM treatment.
2.2 Participants
For the randomization of the study, the number of patients who applied to the XXX University Hospital Endocrinology Polyclinic between January-June 2020 was determined. The patients diagnosed with type 2 diabetes were randomized to intervention and control groups. The patients included in the intervention group were given training about the disease once a week for the first 4 weeks, and every other week for the next 8 weeks. The scales were re-administered to the control group at baseline and 12 weeks later, and no training was given to them. The sample group of the population consisted of a total of 90 patients, 45 for the control group and 45 for the intervention group. The study was planned in accordance with the Declaration of Helsinki and ethical approval for the study was obtained from XXX University Clinical Research Ethics Committee on 03/13/2020. (HRU/20.13.25). In addition, written consent was obtained from the patients.
2.3 Sample Size
The population of the study consisted of patients who applied to XXX Hospital, Endocrinology Outpatient Clinic between the dates of January and June 2020. The sample was determined by utilizing the G-Power version 3.17 program and using the known universe sampling method. Odd numbers were randomly selected for the control group, and even numbers were randomly selected for the intervention group using the Microsoft Excel program in the sample distribution. In the power analysis, the effect size was 0.7, the bias level was 0.05, and the representativeness was 0.92.
2.4 Randomization of the sample
The patients were reached through the registry system of the relevant hospital. Between the dates of January and June 2020, 1032 patients applied to the endocrinology outpatient clinic of the hospital with the diagnosis of type 2 DM. Of these patients, 158 patients did not meet the inclusion criteria, and 12 did not speak Turkish. The sample of 862 patients was determined with the G. Power version 3.17 program as 42 patients for the intervention group and 42 patients for the control group. Patients who met the inclusion criteria and accepted to participate in the study according to the list of names participated in the study until the sample number was reached. With the help of randomization of the patients by utilizing the Microsoft Excel program, 90 patients (45 patients for the intervention group and 45 patients for the control group) were randomly selected, with odd numbers in the control group and even numbers in the intervention group. 1 patient in the control group and 3 patients in the intervention group left the study voluntarily, and 1 patient voluntarily left the study due to COVID-19 infection during the training process. Therefore, the study was completed with 85 patients. Since the trainings were given to the patients by tele-monitoring method, the interaction of the patients with each other was prevented.
2.5 Inclusion Criteria and Exclusion Criteria
The criteria for inclusion in the study were (a) being a type 2 DM patient who was diagnosed for at least 6 months longer, (b) having no communication barriers, and (c) being able to use telehealth applications.
The criteria for exclusion in the study were (a) being a patient with a known psychiatric illness and/or using psychiatric medication, (b) not being a volunteer to the study, and (c) not being able to use the telephone.
2.6 Measures
The data was collected by the researchers by interviewing patients through telehealth applications. In data collection, a form containing socio-demographic variables and questions about the disease, the form of metabolic control variables form prepared by the researchers, and the Diabetes Symptoms Checklist were used.
The Socio-demographic Information Form, the Questionnaire for Diabetes Treatment, the Form of Metabolic Control Variables, 31 questions related to the disease and the socio-demographic variables such as age, gender, marital status, place of residence, educational level, occupation, level of income, smoking status, the status of being in a risk environment for COVID-19, use of protective equipment in the environment; and the disease-related questions such as the duration of diabetes, insulin use, measurement of the preprandial and postprandial blood glucose, blood pressure measurement, OAD use, the presence of other diabetes mellitus in the family, meal planning and use of change lists, difficulty in using oral pills, the frequency of insulin use, self-administration of the insulin injections and having difficulty in administering the insulin injections, exercise status, disability in having adequate and regular exercise, the status of controlling the blood sugar, dose changes in blood sugar when injecting insulin at home by the healthcare worker or the patients themselves, the status of hospitalization due to high blood sugar, and getting training about diabetes were asked by the researchers in line with the literature.
The Diabetes Symptoms Checklist, It was developed by Grootenhuis et al., and its Turkish validity and reliability study was performed by Terkes and Bektas (2012). The checklist assesses physical and psychological symptoms and the perceived burden of both type 2 diabetes and complications. The 33-item checklist includes six subscales: neurology, psychology/fatigue, cardiology, ophthalmology, psychology/cognition, and hyperglycemia. Each item on the scale is numbered from 0 to 5. If the person with diabetes says that he/she experiences the related symptom, that is if he/she answers "yes", he/she chooses the perceived discomfort level of the symptom on a scale from 1 to 5. If the person with diabetes says that there are no symptoms, the item is evaluated as “0”. The total score and all subscales' scores in the checklist range from 0 to 5, with higher scores indicating greater symptom burden. In the study, the Cronbach's alpha value of the checklist was found to be 0.91 [17], [18].
2.7 Data collection
The study data were collected between January 2020 and June 2020. The Socio-demographic Information Form, the Questionnaire for Diabetes Treatment, the Form of Metabolic Control Variables Survey and Diabetes Symptoms Checklist analysis were performed for pretest data upon admission in both intervention and control group patients.
Individualized patient education was given to the intervention group following the guide created. İnterview schedule was created with each patient. The patients were given training over the tele-health (Phone, SMS) once a week for the first 4 weeks and every other week for the next 8 weeks. During the interviews, the questions of the patients were answered and planning was made so that training about a topic could be given in each interview.
No training was provided to the patients in the control group. The posttest was performed 12 weeks after the pretest.
2.8 Training
The content of patient education was planned according to the patient education model in Fig. 1 [19]. The educational guideline for patients with type 2 diabetes was prepared by the researchers and reviewed by five experts. Each training given in line with the created guide lasted at least 15-20 minutes on average. The content of the education given to patients with type 2 diabetes is compatible with the literature and covers topics such as disease information, symptom management, effective drug use, nutrition, and physical activity. Diabetes Mellitus (DM) education content is given in Fig. 2.Fig. 1 Patient Education Models.
Fig. 1
Fig. 2 Diabetes Mellitus (DM) Education Content.
Fig. 2
2.9 Data Analysis
In the statistical evaluation of the data obtained as a result of the study, the conformity to the normal distribution was tested with the Shapiro-Wilk test, and it showed a normal distribution. Descriptive statistics such as percentage, mean, and standard deviation (SD) were used to evaluate the demographic profile of individuals. The distribution of individuals according to their socio-demographic information was evaluated with independent samples t-test and chi-square. Comparisons of the Diabetes Symptoms Checklist's scores of the individuals in the intervention and control groups before and after the training were measured with the independent sample t-test. Paired sample t-test analysis was used to compare the Diabetes Symptoms Checklist's scores of the groups before and after the training. Pre–post changes within groups were estimated via the standardized response mean, with mean differences between post-test means and pre-test means divided by the standard deviation of the difference scores. ANCOVA with post-test values as outcomes and intervention group (intervention group/control group) as predictor was used to estimate treatment effects. Adjusted mean differences (AMDs) with 95% confidence intervals and Cohen’s d were calculated to quantify the between-group effects. We computed two models for each outcome [20]. Cohen’s D, or standardized mean difference, is one of the most common ways to measure effect size. The effect size tells us how large the effect of the intervention is. Therefore, Cohens D was calculated as suggested in the literature [21], [22]. SPSS Windows version 24.0 package program was used for statistical analysis, and p<.05 was considered statistically significant.
3 Results
The mean age of the control group was 56.86±9.40, the mean age of the intervention group was 54.12±8.32, and the total mean age was 55.54±8.95. When the socio-demographic characteristics of the intervention and control groups included in the study were examined, there was no significant difference between the groups except for BMI (p> 0.05) ( Table 1).Table 1 Socio-demographic Characteristics of the Participants.
Table 1Characteristics Intervention (41) Control (44) Statistics
Mean±SD Mean±SD t/p
Age 54.12±8.32 56.86±9.40 1.419/0.160
BMI 88.19±14.56 77.88±15.66 -3.105/0.003
Number of people living in the house 4.70±2.52 4.54±2.45 -.300/ 0.765
n (%) n (%)
Gender
Female 23 (56.1) 28 (63.6) X=0.503
Male 18 (43.9) 16 (36.4) p=0.513
Marital status
Married 38 (92.7) 32 (72.7) X=5.816
Single 3 (7.3) 12 (27.3) p=0.022
Place of residence
Village/town 6 (14.6) 4 (9.1) X=1.356
District center 3 (7.3) 6 (13.6) p=0.508
Provincial center 32 (78.0) 34 (77.3)
Educational level
Illiterate 15 (36.0) 16 (36.4) X=4.537
Primary School 15 (36.0) 23 (52.3) p=0.209
High School 7 (17.1) 2 (4.5)
University 4 (9.8) 3 (6.8)
Occupation
Government officer 4 (9.8) 3 (6.8) X=0.507
Housewife 22 (53.7) 26 (59.1) p=0.917
Freelancer 9 (22.0) 8 (18.2)
Retired 6 (14.6) 7 (15.9)
Income level
Income is equal to expenses 39 (95.1) 36 (81.8) X=3.619
Income is less than expenses 2 (4.9) 8 (18.2) p=0.091
Smoking status
Yes 11 (26.8) 7 (15.9) X=5.635
No 26 (63.4) 24 (54.5) p=0.060
Quitted 4 (9.8) 13 (29.5)
Being in a COVID-19 risk environment before
Yes 12 (29.3) 9 (20.5) X=0.886
No 29 (70.7) 35 (79.5) p=0.452
Do you pay attention to the use of personal protective equipment in your environment?
Yes 36 (87.8) 43 (97.7) X=3.185
No 5 (12.2) 1 (2.3) p=0.102
The average duration of diabetes diagnosis of the participants was 8.37±5.73, and the duration of insulin use was 6.22±5.01. When the data of the participants about diabetes was examined, it was determined that there was no statistically significant difference between the groups, but only between the mean blood pressure and diastole ( Table 2).Table 2 The data on diabetes.
Table 2Characteristics Intervention Control Statistics
Mean±SD Mean±SD t/p
How long have you been diabetic? 9.31±6.17 7.50±5.18 -1.473/ 0.145
How long have you been using insulin? 8.36±4.17 5.05±5.13 -1.829/0.078
The first measurement of preprandial blood glucose 163.25±55.94 146.67±47.69 -1.387/ 0.170
The last measurement of preprandial blood glucose 168.02±55.51 150.06±37.95 -1.537/0.129
Blood pressure (systole) 139.37±24.89 131.08±17.44 -1.391/0.170
Blood pressure (diastole) 89.06±12.93 81.89±11.26 -2.034/0.047
The first measurement of postprandial blood glucose 245.37±106.94 218.47±74.47 -1.234/0.221
The last measurement of postprandial blood glucose 237.53±91.12 217.93±68.58 -0.979/0.331
n (%) n(%)
Use of OAD
Yes 32 (78) 41 (93.2) X=4.009
No 9 (22) 3 (6.8) p=0.062
Use of Insulin
Yes 9 (22) 17 (38.6) X=2.783
No 32 (78) 27 (61.4) p=0.106
Is there any other diabetes patient in the family?
Yes 23 (56.1) 31 (70.5) X=2.551
No 19 (43.9) 13 (29.5) p=0.123
Do you use exchange lists or food group lists to plan your meals?
Yes 3 (7.3) 4 (9.1) X=0.088
No 38 (92.7) 40 (90.9) p=1.000
Do you have any difficulties when taking your diabetes pills?
Yes 7 (20) 4 (9.3) X=1.823
No 28 (80) 39(90.7) p=0.206
How often do you use insulin?
Once a day 6 (54.6) 11 (55) X=0.027
Twice a day 3 (27.3) 5 (25) p=0.987
Three times a day or more 2 (18.2) 4 (20)
Do you inject your insulin yourself?
Yes 9 (81.8) 14 (70) X=0.518
No 2 (18.2) 6 (30) p=0.679
Do you experience any difficulties/challenges while injecting insulin?
Yes 8 (72.7) 13 (65) X=0.194
No 3 (27.3) 7 (35) p=1.000
Would you change the dose and/or timing of your insulin or pills?
Yes 9 (22) 16(36.4) X=2.123
No 32 (78) 28 (63.6) p=0.161
Do you move/exercise?
Yes 20 (48.8) 18 (40.9) X=0.532
No 21 (51.2) 26 (59.1) p=0.517
What reasons prevent you from getting enough and regular exercise?
I can't find enough time
Yes 9 (22) 10 (22.7) X=0.007
No 32 (78) 34 (77.3) p=1.000
I can't spend too much effort
Yes 2 (4.9) 8 (18.2) X=3.619
No 39 (95.1) 36 (81.8) p=0.091
I can't do it when I have another health problem
Yes 1 (2.4) 5 (11.4) X=2.577
No 40 (97.6) 39 (88.6) p=0.204
Have you been told that you need to take tests to monitor your sugar?
Yes 37(90.2) 36 (81.8) X=1.243
No 4 (9.8) 8 (18.2) p=0.355
Do you control your blood sugar?
Yes 29 (70.7) 34 (77.3) X=0.473
No 12(29.3) 10 (22.7) p=0.621
Has the blood glucose dose been changed at home by the healthcare professional before?
No 22 (53.7) 30 (68.2) X=3.244
Yes 19 (46.3) 14 (31.8) p=0.198
Has the blood glucose dose been changed at home by yourself?
No 34 (82.9) 29 (65.9) X=3.204
Yes 7 (17.1) 15 (34.1) p=0.087
Have you made any changes in the food content according to the blood glucose test at home?
No 32 (78) 29 (65.9) X=1.544
Yes 9 (22) 15 (34.1) p=0.238
Have you ever been hospitalized due to high blood sugar?
Yes 6 (14.6) 6 (13.6) X=0.017
No 35 (85.4) 38 (86.4) p=1.000
Have you received any diabetes training before?
Yes 1 (2.4) 6 (13.6) X=3.521
No 40 (97.6) 38 (86.4) p=2.195
Retinopathy
Yes 5 (12.2) 8 (18.2) X=0.587
No 36 (87.8) 36 (81.8) p=0.552
Hypertension
Yes 14 (34.1) 17 (38.6) X=0.185
No 27(65.9) 27 (61.4) p=0.822
When the mean scores of the participants from the total and subscales of the checklist were examined, it was determined 0.88±1.12 for the subscale of neurology, 1.09±0.93 for the subscale of psychology/fatigue, 0.72±0.77 for the subscale of cardiology, 0.41±0.81 for the subscale of ophthalmology, 0.96±0.88 for the subscale of psychology/cognition, 2.49±1.30 for the subscale of hyperglycemia, and the total checklist for 1.03±0.76. There was no statistically significant difference between the mean scores of the pre-intervention groups, except for the hyperglycemia subscale (p>0.05) ( Table 3). After the intervention, there was a statistically significant difference between the checklist's averages between the groups only in the hyperglycemia subscale. However, a statistically significant difference was found between the neurology, cardiology, cognitive, hyperglycemia subscales and the total checklist's averages after the intervention in the intervention group (p<0.05) (Table 3). In the control group, a statistically significant difference was found between the post-intervention hyperglycemia subscale and the total checklist averages (p<0.05) (Table 3). In the current study, it was determined that training had a positive effect on diabetes control in the intervention group compared to the control group.Table 3 The distribution and comparison of the scores of the groups from the Diabetes Symptoms Checklist's pre-test and post-test.
Table 3 Intervention Control Intervention-Before Control Intervention-After Control
Subscales Pre-test Post-test Pre-test Post-test t*; p t*; p
Neurology 0.88±1.19 0.48±0.66 0.88±1.07 0.76±0.90 -.015; 0.988 1.646; 0.104
t;p 2.839; 0.007 1.900; 0.064
Psychology/fatigue 1.23±1.04 1.05±0.87 0.96±0.80 1.00±0.80 -1.359; 0.718 -.311; 0.757
t;p 1.809; 0.078 -0.760; 0.452
Cardiology 0.80±0.96 0.51±0.70 0.65±0.55 0.65±0.58 -.934; 0.353 .986; 0.327
t;p 3.367; 0.002 -0.062; 0.951
Ophthalmology 0.56±0.99 0.41±0.61 0.27±0.56 0.31±0.53 -1.655; 0.102 -.770; 0.443
t;p 1.685; 0.100 -1.071; 0.290
Psychology/cognition 1.05±0.97 0.82±0.68 0.88±0.79 0.88±0.72 -.921; 0.360 .342; 0.733
t;p 2.544; 0.015 0.000; 1.000
Hyperglycemia 1.91±1.28 1.56±1.20 3.03±1.09 2.42±1.09 4.334; 0.000 3.427; 0.001
t;p 2.040; 0.048 4.532; 0.000
Total 1.04±089 0.77±0.60 1.02±0.62 0.94±0.60 -0.088; 0.930 1.333; 0.189
t;p 3.531; 0.001 2.308; 0.026
t*=independent samples t-test.
t=paired sample t-test.
4 Discussion
In type 2 diabetes patients, it was aimed to improve the self-care levels of the patients with the training given over the phone. In the COVID-19 pandemic, telehealth monitoring is important in terms of both providing diabetes management and protecting themselves from COVID-19 infection [23]. During the pandemic process, infection prevention policies have been developed in Turkey, such as quarantine practices, the importance given to social distancing rules, and extending the medication reports of patients with chronic diseases such as diabetes (thus reducing the admissions of patients to the hospital). It is an indispensable part of nursing both to maintain these practices and to continue the training of diabetic patients by telephone and to control the symptoms.
As a result of the present study, it was found that telehealth monitoring and patient training in the patients with type 2 DM who are at risk of COVID-19 were effective in the neurological, cardiological, psychological, hyperglycemia, and the total symptom control of the patients. Hyperglycemia and total symptom control were significantly decreased when compared with the first measurements in both groups. Diabetes patients are in the high-risk group in terms of both disease complication, morbidity and mortality in the COVID-19 pandemic. Therefore, the protection of diabetic patients from infection is closely related to the prognosis of diabetes [14], [24].
In the current study, when the mean scores of the diabetes symptoms checklist were examined, it was determined 0.88±1.12 for the subscale of neurology, 1.09±0.93 for the subscale of psychology/fatigue, 0.72±0.77 for the subscale of cardiology, 0.41±0.81 for the subscale of ophthalmology, 0.96±0.88 for the subscale of psychology/cognition, 2.49±1.30 for the subscale of hyperglycemia, 1.03±0.76 for the total checklist. In the study of Terkeş (2016), individuals' psychology/fatigue subscale mean score was 1.51, neurology subscale mean score was 1.91, cardiology subscale mean score was 0.84, ophthalmology subscale mean score was 1.65, psychology/cognition subscale mean score was 1.75, hyperglycemia subscale mean score was 1.48, and the mean score of the total checklist was 1.47.22 Kumsar et al., in their study to determine the effect of perceived symptoms on HbA1c level in the individuals with type 2 diabetes, found that neurological, ophthalmological, and hyperglycemia subscales increased significantly, and the highest score was found in the hyperglycemia subscale [25]. This finding is similar to the results of the current study.
Telehealth monitoring in patients with diabetes improves the self-management skills of patients, reduces their symptoms, and increases their quality of life [26], [27]. Especially during the pandemic process, telephone follow-up of patients contributed significantly to their self-monitoring [28]. In the study of Alanyalı and Arslan, it was found that as the self-management of the patients increased, the symptoms of diabetes significantly decreased. In a study, it was shown that hyperglycemia and hypoglycemia subscales of the patients improved after 6 months of rehabilitation applied to patients with type 2 DM [29]. A meta-analysis study showed that telehealth monitoring has a positive effect on the HbA1c level of patients in the short and long term [30]. However, in a systematic review study by Tilsdey et al. (2015), it was stated that a 6-month follow-up by telephone reduced HbA1c level, but had no effect in the long-term (12 months) follow-up [31]. As a result of the current study, it has been thought that controlling the symptoms of the patients is effective in the management of the disease, and the most important indicator of this can be associated with the decrease in the average hyperglycemia scores of the patients.
Negative cognitive mood, self-criticism, self-judgment, and over-identification tendency are observed more frequently in the patients with type 2 diabetes. These symptoms cause an increase in the physical symptoms of the patients. In controlling these symptoms, digital interventions are accepted as a fast, easy, and effective method in the evaluation of anxiety, depression, and stress symptoms of patients [26]. In a study, it was found that telerehabilitation can be an alternative method in the management of DM and contributes to the metabolic outcomes, physical exercise capacity, muscle strength, and depression level of the patients [32]. In their study, Scott et al. stated that 75% of the patients with T1DM during the COVID-19 pandemic would continue to make appointments with telehealth monitoring after the pandemic [33]. In another study, it was found that more than 80% of the patients with diabetes had appointments via telehealth in a clinic during the COVID-19 process, so that the problem of patients missing their appointments was quite low [34]. It was thought that the follow-up of patients by phone during the Covid 19 process has provided great convenience.
The patients have been exposed to some restrictions during the COVID-19 pandemic process. With these limitations, there was a decrease in the daily living activities and social interactions of the patients. During this period, patients experienced nutritional and psychological problems such as loss of appetite, malnutrition, depression, anxiety, and anger. The problems experienced caused the patients to control their symptoms, to manage their treatments effectively, to have insufficiencies in physical activity, and to eat irregularly. Both the presence of chronic disease and the difficulties in managing the disease have made patients be at high risk for COVID-19 infection. Due to the risk of COVID-19, the frequency of patients receiving service from the hospital has decreased. Thus, it has been determined that the patients have difficulty in reaching healthcare professionals when they have problems [35].
5 Conclusion
As a result of the current study, telehealth monitoring enabled patients to manage their symptoms and to continue their treatments effectively and adequately. Result of the post-intervention analysis revealed no statistical differences over the subscales of neurology, cardiology, cognition, hyperglycemia, and the total checklist averages between the study groups, while and both groups showed a statistically significant difference between the subscales of hyperglycemia after intervention.
Also a result of the telephone follow-up of the intervention group during the pandemic process, it was found that positive outcomes were obtained in terms of neurological, cardiological, cognitive, and hyperglycemic controls. In addition, it was aimed to maintain the quality of care of the patients with telehealth monitoring, while at the same time, holistic care principles were met. In the COVID-19 pandemic, telehealth monitoring of the patients with diabetes, who are worried about going to a health institution, has improved the health care of patients. {{{ Chart 1}}}Chart 1 Flow chart of the process.
Chart 1
Funding
This research did not receive any specific grant from any kind of funding agencies.
Authorship statement
We assure you that the authors of this article are Derya TÜLÜCE, İbrahim Caner DİKİCİ, Emine KAPLAN SERİN, respectively. The considered research is original and has not previously been published elsewhere (either partly or totally), and is not in the process of being considered for publication in another journal. All authors listed meet the authorship criteria according to the latest guidelines of the International Committee of Medical Journal Editors. All authors are in agreement with the latest version of manuscript. The authors declare that they have no competing interests.
Literature search: DT, ICD, Data collection: DT, ICD, Study design: DT, ICD, EKS, Analysis of data: EKS, Manuscript preparation: DT, ICD, Review of manuscript: DT, ICD
Conflicts of Interest
No conflict of interest has been declared by the authors.
Appendix A Supplementary material
Supplementary material
Supplementary material
☆ This study was performed in Harran University Research and Application Hospital, Şanlıurfa, Turkey.
Appendix A Supplementary data associated with this article can be found in the online version at doi:10.1016/j.pcd.2022.12.001.
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| 36509658 | PMC9722680 | NO-CC CODE | 2022-12-07 23:19:13 | no | Prim Care Diabetes. 2022 Dec 6; doi: 10.1016/j.pcd.2022.12.001 | utf-8 | Prim Care Diabetes | 2,022 | 10.1016/j.pcd.2022.12.001 | oa_other |
==== Front
J Funct Foods
J Funct Foods
Journal of Functional Foods
1756-4646
2214-9414
The Author(s). Published by Elsevier Ltd.
S1756-4646(22)00436-4
10.1016/j.jff.2022.105366
105366
Article
Maternal Fructose Boosts the Effects of a Western-Type Diet Increasing SARS-COV-2 Cell Entry Factors in Male Offspring
Fauste Elena a
Donis Cristina a
Pérez-Armas Madelín a
Rodríguez Lourdes a
Rodrigo Silvia a
Álvarez-Millán Juan J. b
Otero Paola a
Panadero María I. a
Bocos Carlos a⁎
a Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Montepríncipe, Boadilla del Monte, Madrid, Spain
b CQS Lab, Madrid, Spain
⁎ Corresponding author at: Facultad de Farmacia, Universidad San Pablo-CEU, Urbanización Montepríncipe, 28668 Boadilla del Monte, Madrid, Spain
6 12 2022
6 12 2022
10536610 6 2021
2 12 2022
3 12 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.
Graphical abstract
Fructose-rich beverages and foods consumption correlates with the epidemic rise in cardiovascular disease, diabetes and obesity. Severity of COVID-19 has been related to these metabolic diseases. Fructose-rich foods could place people at an increased risk for severe COVID-19. We investigated whether maternal fructose intake in offspring affects hepatic and ileal gene expression of proteins that permit SARS-CoV2 entry to the cell. Carbohydrates were supplied to pregnant rats in drinking water. Adult and young male descendants subjected to water, liquid fructose alone or as a part of a Western diet, were studied. Maternal fructose reduced hepatic SARS-CoV2 entry factors expression in older offspring. On the contrary, maternal fructose boosted the Western diet-induced increase in viral entry factors expression in ileum of young descendants. Maternal fructose intake produced a fetal programming that increases hepatic viral protection and, in contrast, exacerbates fructose plus cholesterol-induced diminution in SARS-CoV2 protection in small intestine of progeny.
Keywords
Fructose
Fetal programming
SARS-CoV-2
Liver
Ileum
Cholesterol
Abbreviations
ADAM17, ADAM metallopeptidase domain 17
ACE2, angiotensin-converting enzyme 2
HDL, high-density lipoprotein
SRB1, HDL-scavenger receptor B type 1
HFCS, high fructose corn syrup
MetS, metabolic syndrome
NAFLD, non-alcoholic fatty liver disease
SARS-CoV-2, severe acute respiratory syndrome coronavirus-2
SSB, sugar-sweetened beverages
TMPRSS2, transmembrane protease serine 2
T2DM, type 2 diabetes
==== Body
pmc1 Introduction
Food patterns and diet have greatly changed during recent decades in both industrialized and developing countries together with a sedentary lifestyle resulting in dramatic increases of obesity, metabolic syndrome (MetS), non-alcoholic fatty liver disease (NAFLD), and type 2 diabetes (T2DM) (Taskinen et al., 2019). MetS increases the risk of developing hypertension, cardiovascular diseases (CVD), T2DM, NAFLD, hyperuricemia, gout and chronic kidney disease (CKD)(Zhang et al., 2017).
Fructose is a monosaccharide found naturally in fruits, vegetables and honey. Fructose is also used as added sugar in the form of sucrose or high fructose corn syrup (HFCS) to sweeten a wide variety of processed foods and sugary drinks. High fructose diets and extensive commercial use of HFCS have been associated with the rising prevalence of MetS worldwide (Zhang et al., 2017). Experimental studies have shown that fructose can induce many features of MetS in rats, whereas glucose intake does not (Johnson et al., 2009). Thus, diets containing 10% wt/vol fructose in drinking water cause hypertriglyceridemia and fatty liver (Roglans et al., 2002). In fact, both obesity, MetS and T2DM are often called “diet-related diseases”. The term “processed food-related disease” refers to diseases where diet is one of the essential causative factors, and includes T2DM, hypertension, heart disease, obesity, dementia, fatty liver disease, and cancer. Thus, the healthcare community is increasingly aware that the global pandemic of these non-communicable diseases has its origins in our Western processed food diet, which should be extensively and urgently regulated (Lustig, 2020).
Interestingly, it has recently been found that 88% of adults in the USA are metabolically unhealthy. This means that only 12% of Americans, even those at “normal weight”, have safe levels of blood sugar, triglycerides, high-density lipoprotein (HDL), and blood pressure. The prevalence of metabolic health in adults from the USA and probably most Western countries is alarmingly low, and this situation has serious implications for public health(Araújo et al., 2019). From the 1970s, there has been both an increase in overall sugar consumption in the United States and several other westernized countries and the replacement of sucrose with HFCS in beverages and other processed foods. Although the consumption of HFCS has been rising in parallel to the increase in obesity and diabetes, there is currently no conclusive scientific information demonstrating a clear association between consumption of fructose and metabolic diseases. In fact, a paradox arises when HFCS intake has been declining over the last two decades, whereas rates of overweight among adults and diabetes have continued to increase in the same timeframe(Laughlin et al., 2014). Therefore, since it is well-established that metabolic events during pre- and postnatal development modulate metabolic disease risk in later life(Koletzko et al., 2005), the maternal diet being the most important event(Vickers et al., 2005) and fructose has frequently been linked to obesity, MetS and CVD(Johnson et al., 2009), maternal fructose intake could serve to explain the paradox. In fact, we and others have previously shown that maternal fructose intake provokes many features of MetS in adult male offspring(Alzamendi et al., 2016, Rodríguez et al., 2016, Saad et al., 2016). Moreover, maternal fructose intake can modulate how male progeny respond to a liquid fructose supplementation when adults(Fauste et al., 2020). However, although a connection between a high maternal consumption of fructose-containing beverages and the global epidemic of obesity and MetS could exist(Rodríguez et al., 2013, Vilà et al., 2011), ingestion of these beverages and fruit juices is still permitted and not regulated during gestation.
COVID-19 is a respiratory disease caused by the novel coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has reached pandemic status. Canonical SARS-CoV-2 host cell entry occurs by binding to the cell surface receptor angiotensin-converting enzyme 2 (ACE2) and then, the transmembrane protease serine 2 (TMPRSS2) cleaves the viral spike (S) protein, allowing fusion of cellular and viral membranes (Figure 1 ). However, since the expression of these two molecules is negligible in many tissues, the possibility of viral entry via non-canonical pathways cannot be discarded. Thus, these alternative pathways would involve both other proteases, such as Cathepsin L, ADAM metallopeptidase domain 17 (ADAM17) or furin(Coate et al., 2020, Yeung et al., 2021) as well as other putative receptors, such as neuropilin-1(Daly et al., 2020) or HDL-scavenger receptor B type 1 (SRB1)(Wei et al., 2020) (Figure 1).Fig. 1 Cell surface receptors and cofactors facilitating SARS-CoV-2 entry. Canonical pathway occurs by binding to the cell surface receptor angiotensin-converting enzyme 2 (ACE2) and then, the transmembrane protease serine 2 (TMPRSS2) cleaves the viral spike protein, allowing fusion of cellular and viral membranes. Non-canonical pathways would involve other proteases: Cathepsin L, ADAM metallopeptidase domain 17 (ADAM17) or furin; and receptors: neuropilin-1 or HDL-scavenger receptor B type 1 (SRB1). SRB1 alone or in collaboration with HDL augments SARS-CoV-2 attachment and then, host cell entry is completed through ACE2 interaction. The neuropilin and furin collaboration would be an alternative entry to SARS-CoV-2 to that made by the ACE2 and TMPRSS2 cooperation. ADAM17 does cleave ACE2, and releases a soluble form of ACE2 that can interact with SARS-CoV-2 and mediate its entry to the cell. This image has been created using Servier Medical Art (https://smart.servier.com).
While COVID-19 affects all population groups, severe pathology and mortality is disproportionately highest in the elderly and/or in those patients with underlying conditions, such as T2DM, obesity and other chronic diseases(Coate et al., 2020). Moreover, since the Western-style diet is known to contribute to the prevalence of these metabolic diseases, fructose-rich processed foods and sugary drinks and/or high-fat diets could place these populations at an increased risk for severe COVID-19 pathology and mortality(Butler & Barrientos, 2020).
Nevertheless, controversy exists about whether pre-existing abnormalities related to metabolic diseases determine the severity of COVID-19. Thus, one study showed that patients with metabolic-associated fatty liver disease (MAFLD) have a higher risk of COVID-19 disease progression and liver blood test abnormalities than patients without MAFLD(Ji et al., 2020). In contrast, another study did not find a higher susceptibility of fatty liver to SARS-CoV-2 infection. None of the genes necessary for SARS-CoV-2 infection was differentially expressed between lean or obese controls and patients with simple steatosis or non-alcoholic steatohepatitis (NASH). Moreover, no increase in liver gene expression of SARS-CoV-2 critical entry proteins was found between MAFLD and control mice(Biquard et al., 2020). Nevertheless, a more recent study indicated that SARS-CoV-2 entry factors in liver are differently affected by T2DM and NAFLD in obese patients. While obese women with T2D have unexpectedly lower levels of ACE2 and TMPRSS2 than obese normoglycemic women, obese patients with NASH showed a markedly higher expression of these genes, suggesting that advanced stages of NAFLD might predispose individuals to COVID-19(Fondevila et al., 2021).
In the present study, we examined in rats if maternal fructose, in comparison to glucose, affects the hepatic gene expression of SARS-CoV-2 critical cell entry factors in adult male descendants. Then, we studied if maternal fructose determined how liquid fructose affects the gene expression of key factors of SARS-CoV-2 entry to the hepatocyte in adult male rats. Finally, we determined in young males how maternal fructose is able to modulate the effects of liquid fructose, tagatose (an epimer of fructose) and fructose plus cholesterol (as an example of a Western-style diet) on hepatic and ileal gene expression of SARS-CoV-2 entry-dependent factors.
2 Materials and Methods
2.1 Animals and experimental design
An animal model of maternal liquid fructose intake was developed as previously described (Fauste et al., 2020, Rodrigo et al., 2018, Rodríguez et al., 2013). Female Sprague-Dawley rats weighing 200-240 g were fed ad libitum, a standard rat chow diet (Teklad Global 14% Protein Rodent Maintenance Diet, Envigo, USA), and housed under controlled light and temperature conditions (12-h light-dark cycle; 22 ± 1°C). The experimental protocol was approved by the Animal Research Committee of the University San Pablo-CEU, Madrid, Spain (ref. numbers 10/206458.9/13 and 10/042445.9/19). The experimental design was separated into two protocols.
In the first protocol, pregnant animals were randomly separated into a control group, a fructose-supplemented group (Fructose), and a glucose-supplemented group (Glucose) (five to six rats per group)(Rodríguez et al., 2013) . Fructose and glucose were supplied as a 10% (wt/vol) solution in drinking water throughout gestation. The concentration used here (10% wt/vol) is very close to that of sugar-sweetened beverages (SSB). Control animals received no supplementary sugar. Pregnant rats were allowed to deliver and on the day of birth, each suckling litter was reduced to nine pups per mother. After delivery, both mothers and their pups were maintained with water and food ad libitum. At 21 days of age, pups were separated by gender and male progeny were kept fed on a standard rat chow diet (Teklad Global 14% Protein Rodent Maintenance Diet, Envigo, USA) and water without additives. Animals within each experimental group were born to different dams to minimize the “litter effect”. In order to know the effects in adult progeny at 240 days of age, one half of the male progeny were randomly separated. When the progeny were 261-days-old, they were sacrificed and blood and livers were collected. Remarkably, these animals had received no subsequent additive in the drinking water for their entire lives(Rodrigo et al., 2018) (Figure 2 A). The other half of the male progeny were subjected to the next protocol: independently from the experimental group of mothers to which they had been born, they were maintained on solid pellets and supplied with drinking water containing 10% (wt/vol) fructose. Thus, three experimental groups were formed: C/F, F/F, G/F, the first letter indicating whether the mothers had been supplied with tap water during pregnancy (C: control), or water containing a carbohydrate (F: fructose; G: glucose); and the second letter indicating the period with fructose (F), when they were adults. When the progeny were 261-days-old, they were sacrificed and livers were immediately removed, placed in liquid nitrogen and kept at -80 °C until analysis. In parallel, a fourth experimental group was used, C/C: male progeny from control mothers supplied with water without any additives when adult. The period with fructose was selected to last 21 days (from 240 to 261 days of age) (Fauste et al., 2020) (Figure 3 A).Fig. 2 Fructose in pregnancy affects hepatic cell surface receptors and cofactors facilitating SARS-CoV-2 entry in adult male progeny. (A) Experimental design. Hepatic levels of specific mRNA for (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes of 261-day-old male progeny from control (empty bar), fructose-fed (black bar) and glucose-fed (grey bar) pregnant rats. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 5-6 litters. Values not sharing a common letter are significantly different (P < 0.05). TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1. E: embryonic/fetal days (E21: delivery); P: postnatal days.
Fig. 3 Liquid fructose in gestation exacerbates fructose-induced augmentation of hepatic TMPRSS2 expression in adult male progeny. (A) Experimental design. Hepatic levels of specific mRNA for (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes. Liver (mRNA) expression of fructose-fed male adult progeny from control (C/F, light grey bar), fructose- (F/F, black bar), and glucose-supplemented (G/F, dark grey bar) mothers. C/C: Control 261-day-old male offspring from control pregnant rats (empty bar, C/C). Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 5-6 litters. Values not sharing a common letter are significantly different (P < 0.05). TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1. E: embryonic/fetal days (E21: delivery); P: postnatal days.
In the second protocol, pregnant rats were randomly separated into a control group (no supplementary sugar) and a fructose-supplemented group (fructose 10% wt/vol in drinking water) (seven to eight rats per group) throughout gestation (Rodríguez et al., 2013). Pregnant rats were allowed to deliver and on the day of birth, each suckling litter was reduced to nine pups per mother. After delivery, both mothers and their pups were maintained with water and food ad libitum. At 21 days of age, pups were separated by gender and male progeny were kept fed on a standard rat chow diet (Teklad Global 14% Protein Rodent Maintenance Diet, Envigo, USA) and water without additives. When the offspring were 3 months old, they were subjected to a new treatment for 21 days regardless of the group of mothers they were born. Male progeny from Control or Fructose-fed mothers were randomly separated into four experimental groups (animals within each experimental group were born to different dams to minimize the “litter effect”): control (C, tap water), fructose (F, fructose 10% wt/vol in drinking water), fructose and cholesterol diet (FCho, fructose 10% wt/vol in drinking water and solid food with 2% added cholesterol; Tecklad Custome Diets TD.07841, Envigo, USA) and tagatose (T, tagatose 10% wt/vol in drinking water). After 21 days, they were sacrificed and liver and ileum were immediately removed, placed in liquid nitrogen and kept at -80 °C until analysis (Figures 4 A and 6A).Fig. 4 Fructose and fructose plus cholesterol affect hepatic cell surface receptors and cofactors facilitating SARS-CoV-2 entry in young male progeny. (A) Experimental design. Hepatic levels of specific mRNA for (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes. Liver (mRNA) expression from control (C, empty bar), fructose- (F, light grey bar), and fructose plus cholesterol-supplemented (FCho, dark grey bar) young male progeny from Control (left panel) or Fructose-fed (right panel) mothers. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 7-8 litters. Asterisks denote a significant difference (*, P < 0.05; **, P < 0.01; ***, P < 0.001) between the groups under the crossbar (groups with a different diet but the same motheŕs diet). Hash symbols denote a significant difference (#, P < 0.05; ##, P < 0.01; ###, P < 0.001) as compared to the Control mothers (groups with the same diet but different motheŕs diet). TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1. E: embryonic/fetal days (E21: delivery); P: postnatal days.
2.2 RNA extraction and gene expression by qPCR
Total RNA was isolated from liver or ileum using Ribopure (Invitrogen, ThermoFisher Scientific, USA). Total RNA was subjected to DNase I treatment using Turbo DNA-free (Invitrogen, ThermoFisher Scientific, USA), and RNA integrity was confirmed by agarose gel electrophoresis. Afterwards, cDNA was synthesized by oligo(dT)-primed reverse transcription with Superscript II (Invitrogen, ThermoFisher Scientific, USA). qPCRs were performed using a CXF96® Touch (Bio-Rad, California, USA). The reaction solution was carried out in a volume of 20 μl, containing 10 pmol of both forward and reverse primers, 10x SYBR Premix Ex Taq (Takara Bio Inc., Japan) and the appropriate nanograms of the cDNA stock. Rps29 was used as a reference gene for qPCR. The primer sequences were obtained either from the Atlas RT-PCR Primer Sequences (Clontech, CA, USA) or designed using Primer3 software (University of Massachusetts Medical School, MA, USA)(Rozen & Skaletsky, 2000).
Samples were analysed in duplicate on each assay. Amplification of non-specific targets was discarded using the melting curve analysis method for each amplicon. qPCR efficiency and linearity were assessed by optimization of the standard curves for each target. The transcription was quantified with CFX Maestro 2.0 software (Bio-Rad, California, USA) using the efficiency correction method (Pfaffl, 2001).
2.3 Statistical Analysis
Results were expressed as means ± S.E. On the first protocol, treatment effects were analyzed by one-way analysis of variance (ANOVA). When treatment effects were significantly different (P < 0.05), means were tested by Tukeýs multiple range test. When the variance was not homogeneous, a post hoc Tamhane test was performed. Significant differences were indicated with different letters.
On the second protocol, treatment effects were analyzed by two-way analysis of variance (ANOVA). Data that were not normally distributed were log transformed to achieve data normality. Then, the Bonferroni test was used for post hoc analysis to identify the source of significant variance. Significant differences (P<0.05) were indicated either with asterisks (*) between groups of animals receiving different treatments but belonging to the same dietary group of mothers or hash symbols (#) between groups of rats with the same treatment but coming from different dietary group of mothers. All statistical valuation was performed using SPSS version 25 computer program.
3 Results
3.1 Maternal fructose decreases hepatic SARS-CoV-2 cell entry factors in adult male progeny
We have taken advantage of our well-stablished animal model of MetS in which fetal programming is achieved by maternal fructose intake(Fauste et al., 2020, Rodrigo et al., 2018, Rodríguez et al., 2013). Thus, male rats born of fructose-fed mothers exhibited impaired insulin signalling and hyperinsulinemia(Rodríguez et al., 2016). However, surprisingly, hepatic cell surface receptors and cofactors facilitating SARS-CoV-2 entry showed a reduced gene expression (Figure 2). Although ACE2 gene expression was not detected in liver and TMPRSS2 mRNA hepatic levels (Figure 2B) showed no differences between descendants from control, fructose-fed and glucose-fed mothers, furin and neuropilin 1 (protease and receptor, respectively, known to collaborate together in the virus entry (Drucker, 2021)) gene expression displayed a reduction in progeny born of carbohydrate-fed mothers, the fructose group being more deeply affected (Figure 2C and 2D). ADAM17 (another factor belonging to the non-canonical pathway viral entry, as are furin and cathepsin) produced the same results (Figure 2E) as those described above for furin (Figure 2D). However, cathepsin L showed no differences between the three groups (Figure 2F).
Cholesterol is critical for viral entry and replication. In fact, SARS-CoV-2 spike protein interacts with HDL, indirectly facilitating viral entry through the SR-B1 cell surface receptor(Drucker, 2021). In accordance with findings here observed for other receptors and cofactors involved in the SARS-CoV-2 cell access, SR-B1 gene expression was diminished in progeny from fructose-fed dams becoming significant in comparison to males born of glucose-fed mothers (Figure 2G).
Therefore, maternal fructose intake seems to provoke a clear reduction in the hepatic gene expression of viral entry factors in adult male progeny and, possibly, this would protect the organ from SARS-CoV-2 cell infection.
3.2 Maternal fructose exacerbates fructose-induced augmentation of hepatic TMPRSS2 gene expression in adult male progeny
Bearing in mind the unexpected changes observed in the gene expression of male progeny born from fructose-supplemented mothers and in order to discover if this phenotype was conserved or reversed by a short liquid fructose-feeding period (3 weeks), we subjected male progeny from control, fructose- and glucose-fed mothers to a fructose liquid solution and determined if the maternal fructose intake influences the response of the adult offspring to fructose on hepatic receptors and cofactors that enable SARS-CoV-2 entry to the cell (Figure 3A).
However, as shown in Figure 3, whereas hepatic gene expression of the majority of key factors determined here for permitting SARS-CoV-2 entrance to the cell was not affected by fructose ingestion (Figure 3C-3G), TMPRSS2 expression was induced in fructose-fed rats and this augmentation was clearly maternal-intake dependent (Figure 3B). Thus, fructose-induced increase in TMPRSS2 expression was more pronounced in progeny from fructose-fed mothers, the effect becoming significantly different in comparison to rats from the control (C/C) group. Unfortunately, ACE2 gene expression, the necessary collaborator of this protease for the virus to enter to the cell, was not detected.
3.3 Maternal fructose exacerbates Western-type diet-induced increase of SARS-CoV-2 cell entry factors in ileum but not in liver of young male offspring.
Once we found that liquid fructose could influence the gene expression of a key molecule in SARS-CoV-2 entry to the cell, TMPRSS2, in adult rats and, moreover, that this effect was dependent of the motheŕs diet, we checked if this situation could be also found in liver of younger rats. We were also interested in studying the effect of a Western-style diet and extend the analysis to another interesting tissue that has also been involved in SARS-CoV-2 infection, that is, ileum.
First, in liver, few changes were observed. Thus, gene expression of TMPRSS2 (Figure 4B), neuropilin (Figure 4C), and ADAM17 (Figure 4E) was affected neither by maternal fructose nor by the diet consumed when descendants were adolescent. Furin and cathepsin L mRNA levels were increased by fructose plus cholesterol diet (FCho) (but not by fructose alone, F) in progeny from Control mothers but, curiously, this effect was lost for furin (Figure 4D), or even reverted for cathepsin (Figure 4F), in descendants from Fructose-fed mothers. For SRB1, in accordance with the effects observed in adult rats (Figure 2G), maternal fructose significantly decreased the mRNA levels of this gene (Figure 4G).
Interestingly, the effects found in ileum were more evident than in liver. Thus, liquid fructose did seem to generate less protection against viral infection in ileum since ACE2 and TMPRSS2 (Figures 5 A and 5B) trended to increase and neuropilin (Figure 5C), ADAM17 (Figure 5E), cathepsin (Figure 5F) and SRB1 (Figure 5G) gene expression were significantly augmented in descendants from Control mothers after fructose intake (F) in comparison to those rats that ingested only water (C). Furin gene expression was not affected (Figure 5D). Surprisingly, all these differences in gene expression between fructose (F) and controls (C) disappeared when descendants were born from Fructose-fed mothers.Fig. 5 Maternal fructose exacerbates Western-type diet-induced increase of SARS-CoV-2 cell entry factors in ileum of young male offspring. Experimental design shown in Figure 4. Ileal levels of specific mRNA for (A) ACE2, (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes. Ileum (mRNA) expression from control (C, empty bar), fructose- (F, light grey bar), and fructose plus cholesterol-supplemented (FCho, dark grey bar) young male progeny from Control (left panel) or Fructose-fed (right panel) mothers. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 7-8 litters. Asterisks denote a significant difference (*, P < 0.05; **, P < 0.01; ***, P < 0.001) between the groups under the crossbar (groups with a different diet but the same motheŕs diet). Hash symbols denote a significant difference (#, P < 0.05; ##, P < 0.01; ###, P < 0.001) as compared to the Control mothers (groups with the same diet but different motheŕs diet). ACE2: angiotensin-converting enzyme 2; TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1.
However, this protection against SARS-CoV-2 infection that apparently maternal fructose provoked in ileum when progeny ingested liquid fructose, was the opposite when descendants received a Western-style diet. Thus, whereas the fructose plus cholesterol (FCho) diet hardly produced any increase in gene expression (except for cathepsin (Figure 5F) and SRB1 (Figure 5G)) in progeny from Control mothers, descendants from Fructose-fed mothers were markedly affected. Although there were no observed differences for ACE2, TMPRSS2 and furin, gene expression was significantly increased for neuropilin (Figure 5C), ADAM17, cathepsin L, and SRB1 (Figures 5E-5G) when descendants from Fructose-fed mothers had ingested a Western-type diet (FCho) (in comparison to those that received fructose alone, F, or only water, C), indicating that these animals would be more prone to a SARS-CoV-2 infection, at least, at the level of small intestine.
3.4 Maternal fructose modulates how the sweetener tagatose affects SARS-CoV-2 cell entry factors in young male offspring.
In order to demonstrate that the effects here observed for direct liquid fructose intake were specific for this carbohydrate, tagatose was used instead (Figure 6 A).Fig. 6 Tagatose affects hepatic cell surface receptors and cofactors facilitating SARS-CoV-2 entry in liver of young male progeny. (A) Experimental design. Hepatic levels of specific mRNA for (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes. Liver (mRNA) expression from control (C, empty bar), fructose- (F, light grey bar), and tagatose-supplemented (T, dark bar) young male progeny from Control (left panel) or Fructose-fed (right panel) mothers. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 7-8 litters. Asterisks denote a significant difference (*, P < 0.05; **, P < 0.01; ***, P < 0.001) between the groups under the crossbar (groups with a different diet but the same motheŕs diet). Hash symbols denote a significant difference (#, P < 0.05; ##, P < 0.01; ###, P < 0.001) as compared to the Control mothers (groups with the same diet but different motheŕs diet). TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1. E: embryonic/fetal days (E21: delivery); P: postnatal days.
Curiously, although fructose intake did not have any effect in the liver gene expression of adolescent descendants regardless of the motheŕs diet (Figure 4B), TMPRSS2 gene expression (Figure 6B) was significantly increased by tagatose consumption, although this effect was less evident when progeny were born from fructose-fed mothers. The other genes here measured were not affected by tagatose consumption (Figure 6C-6G).
Again, the effects observed in ileum were more marked than in liver. Thus, whereas the effects that liquid fructose (F) provoked in ACE2, TMPRSS2 and ADAM17 in descendants from Control mothers were not observed after tagatose consumption (T) (Figure 7 A, 7B and 7E, respectively), the effects of fructose intake in neuropilin (Figure 7C), cathepsin (Figure 7F) and SRB1 (Figure 7G) gene expression not only were resembled by tagatose intake, but were even more pronounced, in comparison to those rats that ingested only water (C). Furin gene expression was not affected (Figure 7D). Once again, as already observed for fructose, all these differences in gene expression (except for SRB1, Figure 7G) between tagatose (T) and controls (C) found in the progeny from Control mothers, were mitigated when descendants were born from Fructose-fed mothers (Figure 7C and 7F). On the contrary, it was precisely in the progeny of Fructose-fed mothers where tagatose intake (T) provoked clear increases in ACE2 and TMPRSS2 (Figures 7A and 7B) gene expression which became significant versus those rats that consumed water without additives (Control, C).Fig. 7 Maternal fructose modulates how the sweetener tagatose affects SARS-CoV-2 cell entry factors in ileum of young male offspring. Experimental design shown in Figure 6. Ileal levels of specific mRNA for (A) ACE2, (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17 (F) cathepsin L and (G) SRB1 genes. Ileum (mRNA) expression from control (C, empty bar), fructose- (F, light grey bar), and tagatose-supplemented (T, dark bar) young male progeny from Control (left panel) or Fructose-fed (right panel) mothers. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 7-8 litters. Asterisks denote a significant difference (*, P < 0.05; **, P < 0.01; ***, P < 0.001) between the groups under the crossbar (groups with a different diet but the same motheŕs diet). Hash symbols denote a significant difference (#, P < 0.05; ##, P < 0.01; ###, P < 0.001) as compared to the Control mothers (groups with the same diet but different motheŕs diet). ACE2: angiotensin-converting enzyme 2; TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1.
4 Discussion
Whether people with metabolic diseases such as T2DM, CVD or obesity do exhibit increased susceptibility to SARS-CoV-2 infection continues to be debated and uncertain. However, COVID-19 infection clearly results in increased rates of hospitalization, greater severity of illness and mortality in patients with diabetes, CVD or obesity. Thus, knowing if the ACE2 expression and its cofactors facilitating SARS-CoV-2 infectivity is dysregulated in cells and tissues of patients with diabetes, CVD or obesity, and contributes to the pathophysiology of SARS-CoV-2 infection, seems to be crucial to understanding and preventing COVID-19 disease (Drucker, 2021).
Since a Western-style diet is known to contribute to the prevalence of these metabolic diseases, and therefore all of them have been encompassed within the term “processed food-related diseases”, the possibility that fructose-enriched foods, sugary drinks, high-fat and/or high-cholesterol diets could place these patients at an increased risk for severe COVID-19 pathology and mortality should not be discarded(Butler & Barrientos, 2020).
Animal models are critical for understanding viral pathogenesis, vaccine development, and drug screening. Small animal models are essential for research and antiviral therapeutic development. Rodent models are popular because of their affordability, availability, and clear genetic backgrounds and they have been widely used for studying the pathogenesis of human coronaviruses (Jiang et al., 2020).
To elucidate these mechanisms, we have used our rat model of fetal programming provoked by maternal fructose intake in which typical features of MetS appear, directly(Rodrigo et al., 2018, Rodríguez et al., 2016) or after fructose supplementation, in adult male progeny (Fauste et al., 2020). This animal model of MetS has been previously used by us and others and has been useful in demonstrating that maternal fructose intake causes clear dysregulations in progeny with possible clinical implications (Alzamendi et al., 2016). Interestingly, although male descendants of fructose-fed mothers exhibited impaired insulin signalling and hyperinsulinemia (Rodríguez et al., 2016), viral entry to the hepatocyte via non-canonical pathways did not seem to be facilitated. Thus, gene expression both of proteases such as ADAM17 and furin, and receptors such as neuropilin and SRB1, was decreased in males from fructose-fed mothers in comparison to those from control dams. These findings are interesting since neuropilin has been described as an alternative receptor to ACE2 for the viral entry (Daly et al., 2020), and moreover, has been reported to be assisted by furin. Furthermore, the likelihood that SARS-CoV-2 can bind and enter liver cells via the canonical pathway involving ACE2 and TMPRSS2 seems to be reduced since ACE2, in our hands, was not detected. Nevertheless, it could possibly be due to the heterogeneity of the liver cell composition. In fact, a similar situation has already been demonstrated in human pancreas where ACE2 and its protease are present in pancreatic ducts but not in β cells (Coate et al., 2020).
Surprisingly, although maternal fructose intake did seem to protect the liver from SARS-CoV-2 cell infection in adult male progeny, the findings turned out to be different when these descendants from control, fructose- or glucose-fed mothers, were themselves subjected to liquid fructose. Whereas most of the molecules here determined did not show any differences between the four groups studied, TMPRSS2 gene expression was induced by fructose intake and the effect became significant in male born of fructose-fed mothers versus control males. Interestingly, this protease has been proposed as one of the molecules responsible for the relative protection of infants and children against severe COVID19 illness (Schuler et al., 2021). In fact, TMPRSS2 expression has been demonstrated to increase with aging in mice and humans.
On the other hand, reports studying the pathophysiology of SARS-CoV-2 infection in people with diabetes, ECV and obesity, have found that gastrointestinal tract, liver, islets and adipose tissue are also affected, raising uncertainty about their implication in the severity of COVID-19 (Drucker, 2021). For example, ACE2 receptors are abundant in the small intestine and they have been related to the abdominal pain and diarrhea that COVID 19 patients frequently report (Ji et al., 2020).
Interestingly, it has been demonstrated that younger rats can be more greatly affected than older ones by the introduction of sugary drinks and, therefore, it is logical to think that more marked effects of the mothers’ diets could be detected if the offspring were introduced to sugar at a younger age (Kendig et al., 2015). Although young people seems to be less affected by COVID19 infection and also with less severity than older individuals, adolescents have a more frequent ingestion of processed food and beverages containing fructose than older people.
Therefore, we determine if maternal fructose in young males is able to modulate the effects of liquid fructose alone or as a part of a Western-style diet (along with a cholesterol-rich food) on hepatic and ileal gene expression of SARS-CoV-2 entry-dependent factors. In contrast to adult rats, both maternal fructose intake and direct fructose consumption in adolescent rats did not affect the hepatic gene expression of viral entry factors. The only effect that was coincident to those found in older rats was that maternal fructose intake diminished SRB1 mRNA expression in the progeny in comparison to descendants from control dams. These findings would indicate that young animals seem to be refractory to show changes in the expression of viral entrance molecules in response to diet modifications. It was only when cholesterol was included in the diet along with liquid fructose, that an increase in the expression of two proteases of the non-canonical pathway (furin and cathepsin) was observed in the progeny from control dams which, however, was not observed in descendants from fructose-fed mothers.
Whereas direct intake of liquid fructose by young males did not produce any effect in hepatic viral entry factors, fructose-induced changes were clearly observed in ileum. Although the canonical pathway (ACE2 and TMPRSS2) only showed a trend to increase, alternative proteases such as ADAM17 and cathepsin L gene expression were significantly induced by fructose intake. Moreover, the other receptor that has been demonstrated to collaborate with ACE2 to facilitate SARS-CoV-2 passage to the cell, SRB1 (Wei et al., 2020), also displayed a fructose-induced increase. Further, the alternative receptor to ACE2, neuropilin-1, was also increased in descendants from control mothers that had consumed liquid fructose. Curiously, it was surprising not to observe these fructose-provoked changes in descendants from fructose-fed mothers. These findings, in consonance to those found in liver, would again indicate that maternal fructose intake could be protecting against the harmful effects that direct liquid fructose intake produces in the viral entry factors to the cell in these adolescent rats.
Interestingly, the opposite was found when the progenies received a Western-style diet. Maternal fructose intake exacerbated the detrimental fructose plus cholesterol-induced effects in SARS-CoV-2 cell entry molecules found in descendants from control mothers. Although ACE2 and TMPRSS2 were not affected, the non-canonical pathway (cathepsin and ADAM17) gene expression was augmented in males from fructose-fed mothers after receiving the Western-type diet versus descendants consuming water or liquid fructose. Furthermore, the alternative ACE2 receptor, neuropilin, and the ACE2 collaborating receptor, SRB1, also exhibited a Western-style diet-induced increase. Thus, as Coate and col have proposed (Coate et al., 2020) for human β cells, although gene expression findings found here for ACE2 and TMPRSS2 do not suggest that SARS-CoV-2 can enter ileal cells via the canonical pathway, they do not exclude the possibility of viral entry via non-canonical pathways involving other suggested effector proteases and receptors of SARS-CoV2. Furthermore, these findings would reinforce those studies in humans showing that patients with MAFLD or advanced stages of NAFLD displayed higher expression of SARS-CoV-2 entry factors and, therefore, higher risk of COVID-19 disease progression than patients without these metabolic diseases (Fondevila et al., 2021, Ji et al., 2020).
Moreover, though ACE2 has been reported not to participate in SARS-CoV-2 cell entry in rats, it has been demonstrated in rodents expressing human recombinant ACE2 (hACE2) that the other factors facilitating the entrance of SARS-CoV-2 to the cell must be implicated (Jiang et al., 2020). It remains to be elucidated in humans (or in the hACE2 mice) whether ACE2 receptor gene expression is also affected by a Western-diet intake and, moreover, if the motheŕs diet could be involved.
Interestingly, during the preparation of the present report, an interesting study showing that some human cell lines were unable to support SARS-CoV-2 infection despite displaying a strong expression of ACE2 has been published(Yeung et al., 2021). Moreover, since it was observed that inhibition of ADAM17 diminished SARS-CoV-2 infection, the authors proposed that complexes formed by SARS-CoV-2 and soluble ACE2 (released by ADAM17 protease activity from the surface of the cells), might be able to attach and enter the tissues where ACE2 is poorly expressed (Yeung et al., 2021) (Figure 1).
Our previous reports reveal the importance of studying the effects of fructose in comparison to other sweeteners, in order to be sure that the effects provoked by fructose are specific to this carbohydrate. For this reason, in the present study we used an epimer of fructose called tagatose to compare to fructose. This epimer is increasingly used as a low-caloric sugar alternative and modestly improves glycemic control in individuals with and without diabetes (Noronha et al., 2018). In fact, it has been shown that chronic overconsumption of tagatose does not exert the same deleterious metabolic derangements observed after fructose administration (Collotta et al., 2018). In the present study, hepatic gene expression of most of the factors showed no response to tagatose, as had already been observed with fructose, except for TMPRSS2. The expression of TMPRSS2 was significantly increased after receiving tagatose in comparison to progeny having drunk fructose or water. This finding is important since this protease has been proposed as one of the molecules responsible for the severity of COVID19 (Schuler et al., 2021). Interestingly, it was observed in ileum that those effects found in descendants from control mothers after fructose intake were specific to this carbohydrate in ACE2, TMPRSS2 and ADAM17, but not for neuropilin, cathepsin or SRB1 gene expression since they were also observed after tagatose intake. Curiously, again, these effects disappeared or were mitigated by maternal fructose consumption. Interestingly, the specific effects of tagatose, ileal ACE2 and TMPRSS2 gene expression were significantly augmented in young males from fructose-fed mothers. These results found here with fructose and tagatose should make us reconsider the possibility of reducing or avoiding the wide and enormous use of carbohydrates as components of a great variety of foods as sweeteners, preservatives and other functions.
As limitations of the present study, apart from difficulties in extrapolating results from experimental animals to humans: a) although Bellamine et al (Bellamine et al., 2021) have shown that protein levels of key factors for viral entry mimic the mRNA levels and, moreover, parallel to the susceptibility of cells to SARS-CoV-2 infection , we have not directly measured SARS-CoV-2 binding or entry into the cells but have instead assessed (mRNA) gene expression of receptors and proteases required for the entry of this virus in liver or ileum; b) we have used simple sugar solutions instead of sucrose (table sugar) or high fructose corn syrup (HFCS), and thus more evident effects could have been observed since it is known that fructose absorption is improved by the presence of glucose; c) we have used fructose plus cholesterol instead other Western-type diets that also include saturated fat (Rajcic et al., 2021) and thus, possibly, more harmful effects could have been observed; d) determinations have not been carried out in other tissues susceptible to SARS-CoV-2 infection, such as lung, serum and adipose tissue. Thus, to confirm the applicability of our findings it would be necessary to address in the future the limitations of the present study.
In summary, we have demonstrated that maternal fructose intake provided some protection against SARS-CoV-2 infection to descendants . Curiously, however, one of the most prominent results found here is that maternal fructose intake exacerbates the fructose plus cholesterol-induced augmentation in the ileal SARS-CoV-2 cell entry factors gene expression in young male progeny. . Thus, bearing in mind the potential influence of processed foods and fructose-rich beverages in the development of many common non-communicable diseases such as atherosclerosis, metabolic syndrome, etc, and the more and more evident relationship of these metabolic diseases with the COVID disease, we would like to suggest that a reduction in the consumption of fructose-sweetened beverages, especially during gestation, all over the world could be beneficial.
Grant Support: Ministerio de Ciencia e Innovación (MICIN): SAF2017-89537-R/MCIN/AEI/10.13039/501100011033 and ERDF “A way of making Europe”, and PID2020-118054RB-I00/MCIN/AEI /10.13039/501100011033.
Disclosure
None of the authors have any conflicts of interest to report.
Data Transparency
All data will be made available to other researchers under request.
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
Data will be made available on request.
Acknowledgements
The authors thank Jose M. Garrido and his team for their help in handling the rats, and Brian Crilly for his editorial help. This work was supported by grants from Ministerio de Ciencia e Innovación (MICIN): SAF2017-89537-R/MCIN/AEI/10.13039/501100011033 and ERDF “A way of making Europe”, and PID2020-118054RB-I00/MCIN/AEI /10.13039/501100011033. Silvia Rodrigo was supported with a FUSP-CEU fellowship. Elena Fauste was supported with a FPU fellowship from MICIN. C.B. conceived and designed the study. E.F., C.D., M.P., P.O., S.R., and M.I.P. contributed reagents/materials/analysis tools for gene expression studies and parameter analysis. L.R., E.F., and M.I.P. handled the animals. M.I.P. and J.J.A-M analyzed the data. C.B. wrote the paper. None of the authors have any conflicts of interest to report.
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| 36506002 | PMC9722681 | NO-CC CODE | 2022-12-08 23:16:13 | no | J Funct Foods. 2023 Jan 6; 100:105366 | utf-8 | J Funct Foods | 2,022 | 10.1016/j.jff.2022.105366 | oa_other |
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Journal of Functional Foods
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10.1016/j.jff.2022.105366
105366
Article
Maternal Fructose Boosts the Effects of a Western-Type Diet Increasing SARS-COV-2 Cell Entry Factors in Male Offspring
Fauste Elena a
Donis Cristina a
Pérez-Armas Madelín a
Rodríguez Lourdes a
Rodrigo Silvia a
Álvarez-Millán Juan J. b
Otero Paola a
Panadero María I. a
Bocos Carlos a⁎
a Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Montepríncipe, Boadilla del Monte, Madrid, Spain
b CQS Lab, Madrid, Spain
⁎ Corresponding author at: Facultad de Farmacia, Universidad San Pablo-CEU, Urbanización Montepríncipe, 28668 Boadilla del Monte, Madrid, Spain
6 12 2022
6 12 2022
10536610 6 2021
2 12 2022
3 12 2022
© 2022 The Author(s)
2022
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Graphical abstract
Fructose-rich beverages and foods consumption correlates with the epidemic rise in cardiovascular disease, diabetes and obesity. Severity of COVID-19 has been related to these metabolic diseases. Fructose-rich foods could place people at an increased risk for severe COVID-19. We investigated whether maternal fructose intake in offspring affects hepatic and ileal gene expression of proteins that permit SARS-CoV2 entry to the cell. Carbohydrates were supplied to pregnant rats in drinking water. Adult and young male descendants subjected to water, liquid fructose alone or as a part of a Western diet, were studied. Maternal fructose reduced hepatic SARS-CoV2 entry factors expression in older offspring. On the contrary, maternal fructose boosted the Western diet-induced increase in viral entry factors expression in ileum of young descendants. Maternal fructose intake produced a fetal programming that increases hepatic viral protection and, in contrast, exacerbates fructose plus cholesterol-induced diminution in SARS-CoV2 protection in small intestine of progeny.
Keywords
Fructose
Fetal programming
SARS-CoV-2
Liver
Ileum
Cholesterol
Abbreviations
ADAM17, ADAM metallopeptidase domain 17
ACE2, angiotensin-converting enzyme 2
HDL, high-density lipoprotein
SRB1, HDL-scavenger receptor B type 1
HFCS, high fructose corn syrup
MetS, metabolic syndrome
NAFLD, non-alcoholic fatty liver disease
SARS-CoV-2, severe acute respiratory syndrome coronavirus-2
SSB, sugar-sweetened beverages
TMPRSS2, transmembrane protease serine 2
T2DM, type 2 diabetes
==== Body
pmc1 Introduction
Food patterns and diet have greatly changed during recent decades in both industrialized and developing countries together with a sedentary lifestyle resulting in dramatic increases of obesity, metabolic syndrome (MetS), non-alcoholic fatty liver disease (NAFLD), and type 2 diabetes (T2DM) (Taskinen et al., 2019). MetS increases the risk of developing hypertension, cardiovascular diseases (CVD), T2DM, NAFLD, hyperuricemia, gout and chronic kidney disease (CKD)(Zhang et al., 2017).
Fructose is a monosaccharide found naturally in fruits, vegetables and honey. Fructose is also used as added sugar in the form of sucrose or high fructose corn syrup (HFCS) to sweeten a wide variety of processed foods and sugary drinks. High fructose diets and extensive commercial use of HFCS have been associated with the rising prevalence of MetS worldwide (Zhang et al., 2017). Experimental studies have shown that fructose can induce many features of MetS in rats, whereas glucose intake does not (Johnson et al., 2009). Thus, diets containing 10% wt/vol fructose in drinking water cause hypertriglyceridemia and fatty liver (Roglans et al., 2002). In fact, both obesity, MetS and T2DM are often called “diet-related diseases”. The term “processed food-related disease” refers to diseases where diet is one of the essential causative factors, and includes T2DM, hypertension, heart disease, obesity, dementia, fatty liver disease, and cancer. Thus, the healthcare community is increasingly aware that the global pandemic of these non-communicable diseases has its origins in our Western processed food diet, which should be extensively and urgently regulated (Lustig, 2020).
Interestingly, it has recently been found that 88% of adults in the USA are metabolically unhealthy. This means that only 12% of Americans, even those at “normal weight”, have safe levels of blood sugar, triglycerides, high-density lipoprotein (HDL), and blood pressure. The prevalence of metabolic health in adults from the USA and probably most Western countries is alarmingly low, and this situation has serious implications for public health(Araújo et al., 2019). From the 1970s, there has been both an increase in overall sugar consumption in the United States and several other westernized countries and the replacement of sucrose with HFCS in beverages and other processed foods. Although the consumption of HFCS has been rising in parallel to the increase in obesity and diabetes, there is currently no conclusive scientific information demonstrating a clear association between consumption of fructose and metabolic diseases. In fact, a paradox arises when HFCS intake has been declining over the last two decades, whereas rates of overweight among adults and diabetes have continued to increase in the same timeframe(Laughlin et al., 2014). Therefore, since it is well-established that metabolic events during pre- and postnatal development modulate metabolic disease risk in later life(Koletzko et al., 2005), the maternal diet being the most important event(Vickers et al., 2005) and fructose has frequently been linked to obesity, MetS and CVD(Johnson et al., 2009), maternal fructose intake could serve to explain the paradox. In fact, we and others have previously shown that maternal fructose intake provokes many features of MetS in adult male offspring(Alzamendi et al., 2016, Rodríguez et al., 2016, Saad et al., 2016). Moreover, maternal fructose intake can modulate how male progeny respond to a liquid fructose supplementation when adults(Fauste et al., 2020). However, although a connection between a high maternal consumption of fructose-containing beverages and the global epidemic of obesity and MetS could exist(Rodríguez et al., 2013, Vilà et al., 2011), ingestion of these beverages and fruit juices is still permitted and not regulated during gestation.
COVID-19 is a respiratory disease caused by the novel coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), which has reached pandemic status. Canonical SARS-CoV-2 host cell entry occurs by binding to the cell surface receptor angiotensin-converting enzyme 2 (ACE2) and then, the transmembrane protease serine 2 (TMPRSS2) cleaves the viral spike (S) protein, allowing fusion of cellular and viral membranes (Figure 1 ). However, since the expression of these two molecules is negligible in many tissues, the possibility of viral entry via non-canonical pathways cannot be discarded. Thus, these alternative pathways would involve both other proteases, such as Cathepsin L, ADAM metallopeptidase domain 17 (ADAM17) or furin(Coate et al., 2020, Yeung et al., 2021) as well as other putative receptors, such as neuropilin-1(Daly et al., 2020) or HDL-scavenger receptor B type 1 (SRB1)(Wei et al., 2020) (Figure 1).Fig. 1 Cell surface receptors and cofactors facilitating SARS-CoV-2 entry. Canonical pathway occurs by binding to the cell surface receptor angiotensin-converting enzyme 2 (ACE2) and then, the transmembrane protease serine 2 (TMPRSS2) cleaves the viral spike protein, allowing fusion of cellular and viral membranes. Non-canonical pathways would involve other proteases: Cathepsin L, ADAM metallopeptidase domain 17 (ADAM17) or furin; and receptors: neuropilin-1 or HDL-scavenger receptor B type 1 (SRB1). SRB1 alone or in collaboration with HDL augments SARS-CoV-2 attachment and then, host cell entry is completed through ACE2 interaction. The neuropilin and furin collaboration would be an alternative entry to SARS-CoV-2 to that made by the ACE2 and TMPRSS2 cooperation. ADAM17 does cleave ACE2, and releases a soluble form of ACE2 that can interact with SARS-CoV-2 and mediate its entry to the cell. This image has been created using Servier Medical Art (https://smart.servier.com).
While COVID-19 affects all population groups, severe pathology and mortality is disproportionately highest in the elderly and/or in those patients with underlying conditions, such as T2DM, obesity and other chronic diseases(Coate et al., 2020). Moreover, since the Western-style diet is known to contribute to the prevalence of these metabolic diseases, fructose-rich processed foods and sugary drinks and/or high-fat diets could place these populations at an increased risk for severe COVID-19 pathology and mortality(Butler & Barrientos, 2020).
Nevertheless, controversy exists about whether pre-existing abnormalities related to metabolic diseases determine the severity of COVID-19. Thus, one study showed that patients with metabolic-associated fatty liver disease (MAFLD) have a higher risk of COVID-19 disease progression and liver blood test abnormalities than patients without MAFLD(Ji et al., 2020). In contrast, another study did not find a higher susceptibility of fatty liver to SARS-CoV-2 infection. None of the genes necessary for SARS-CoV-2 infection was differentially expressed between lean or obese controls and patients with simple steatosis or non-alcoholic steatohepatitis (NASH). Moreover, no increase in liver gene expression of SARS-CoV-2 critical entry proteins was found between MAFLD and control mice(Biquard et al., 2020). Nevertheless, a more recent study indicated that SARS-CoV-2 entry factors in liver are differently affected by T2DM and NAFLD in obese patients. While obese women with T2D have unexpectedly lower levels of ACE2 and TMPRSS2 than obese normoglycemic women, obese patients with NASH showed a markedly higher expression of these genes, suggesting that advanced stages of NAFLD might predispose individuals to COVID-19(Fondevila et al., 2021).
In the present study, we examined in rats if maternal fructose, in comparison to glucose, affects the hepatic gene expression of SARS-CoV-2 critical cell entry factors in adult male descendants. Then, we studied if maternal fructose determined how liquid fructose affects the gene expression of key factors of SARS-CoV-2 entry to the hepatocyte in adult male rats. Finally, we determined in young males how maternal fructose is able to modulate the effects of liquid fructose, tagatose (an epimer of fructose) and fructose plus cholesterol (as an example of a Western-style diet) on hepatic and ileal gene expression of SARS-CoV-2 entry-dependent factors.
2 Materials and Methods
2.1 Animals and experimental design
An animal model of maternal liquid fructose intake was developed as previously described (Fauste et al., 2020, Rodrigo et al., 2018, Rodríguez et al., 2013). Female Sprague-Dawley rats weighing 200-240 g were fed ad libitum, a standard rat chow diet (Teklad Global 14% Protein Rodent Maintenance Diet, Envigo, USA), and housed under controlled light and temperature conditions (12-h light-dark cycle; 22 ± 1°C). The experimental protocol was approved by the Animal Research Committee of the University San Pablo-CEU, Madrid, Spain (ref. numbers 10/206458.9/13 and 10/042445.9/19). The experimental design was separated into two protocols.
In the first protocol, pregnant animals were randomly separated into a control group, a fructose-supplemented group (Fructose), and a glucose-supplemented group (Glucose) (five to six rats per group)(Rodríguez et al., 2013) . Fructose and glucose were supplied as a 10% (wt/vol) solution in drinking water throughout gestation. The concentration used here (10% wt/vol) is very close to that of sugar-sweetened beverages (SSB). Control animals received no supplementary sugar. Pregnant rats were allowed to deliver and on the day of birth, each suckling litter was reduced to nine pups per mother. After delivery, both mothers and their pups were maintained with water and food ad libitum. At 21 days of age, pups were separated by gender and male progeny were kept fed on a standard rat chow diet (Teklad Global 14% Protein Rodent Maintenance Diet, Envigo, USA) and water without additives. Animals within each experimental group were born to different dams to minimize the “litter effect”. In order to know the effects in adult progeny at 240 days of age, one half of the male progeny were randomly separated. When the progeny were 261-days-old, they were sacrificed and blood and livers were collected. Remarkably, these animals had received no subsequent additive in the drinking water for their entire lives(Rodrigo et al., 2018) (Figure 2 A). The other half of the male progeny were subjected to the next protocol: independently from the experimental group of mothers to which they had been born, they were maintained on solid pellets and supplied with drinking water containing 10% (wt/vol) fructose. Thus, three experimental groups were formed: C/F, F/F, G/F, the first letter indicating whether the mothers had been supplied with tap water during pregnancy (C: control), or water containing a carbohydrate (F: fructose; G: glucose); and the second letter indicating the period with fructose (F), when they were adults. When the progeny were 261-days-old, they were sacrificed and livers were immediately removed, placed in liquid nitrogen and kept at -80 °C until analysis. In parallel, a fourth experimental group was used, C/C: male progeny from control mothers supplied with water without any additives when adult. The period with fructose was selected to last 21 days (from 240 to 261 days of age) (Fauste et al., 2020) (Figure 3 A).Fig. 2 Fructose in pregnancy affects hepatic cell surface receptors and cofactors facilitating SARS-CoV-2 entry in adult male progeny. (A) Experimental design. Hepatic levels of specific mRNA for (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes of 261-day-old male progeny from control (empty bar), fructose-fed (black bar) and glucose-fed (grey bar) pregnant rats. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 5-6 litters. Values not sharing a common letter are significantly different (P < 0.05). TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1. E: embryonic/fetal days (E21: delivery); P: postnatal days.
Fig. 3 Liquid fructose in gestation exacerbates fructose-induced augmentation of hepatic TMPRSS2 expression in adult male progeny. (A) Experimental design. Hepatic levels of specific mRNA for (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes. Liver (mRNA) expression of fructose-fed male adult progeny from control (C/F, light grey bar), fructose- (F/F, black bar), and glucose-supplemented (G/F, dark grey bar) mothers. C/C: Control 261-day-old male offspring from control pregnant rats (empty bar, C/C). Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 5-6 litters. Values not sharing a common letter are significantly different (P < 0.05). TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1. E: embryonic/fetal days (E21: delivery); P: postnatal days.
In the second protocol, pregnant rats were randomly separated into a control group (no supplementary sugar) and a fructose-supplemented group (fructose 10% wt/vol in drinking water) (seven to eight rats per group) throughout gestation (Rodríguez et al., 2013). Pregnant rats were allowed to deliver and on the day of birth, each suckling litter was reduced to nine pups per mother. After delivery, both mothers and their pups were maintained with water and food ad libitum. At 21 days of age, pups were separated by gender and male progeny were kept fed on a standard rat chow diet (Teklad Global 14% Protein Rodent Maintenance Diet, Envigo, USA) and water without additives. When the offspring were 3 months old, they were subjected to a new treatment for 21 days regardless of the group of mothers they were born. Male progeny from Control or Fructose-fed mothers were randomly separated into four experimental groups (animals within each experimental group were born to different dams to minimize the “litter effect”): control (C, tap water), fructose (F, fructose 10% wt/vol in drinking water), fructose and cholesterol diet (FCho, fructose 10% wt/vol in drinking water and solid food with 2% added cholesterol; Tecklad Custome Diets TD.07841, Envigo, USA) and tagatose (T, tagatose 10% wt/vol in drinking water). After 21 days, they were sacrificed and liver and ileum were immediately removed, placed in liquid nitrogen and kept at -80 °C until analysis (Figures 4 A and 6A).Fig. 4 Fructose and fructose plus cholesterol affect hepatic cell surface receptors and cofactors facilitating SARS-CoV-2 entry in young male progeny. (A) Experimental design. Hepatic levels of specific mRNA for (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes. Liver (mRNA) expression from control (C, empty bar), fructose- (F, light grey bar), and fructose plus cholesterol-supplemented (FCho, dark grey bar) young male progeny from Control (left panel) or Fructose-fed (right panel) mothers. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 7-8 litters. Asterisks denote a significant difference (*, P < 0.05; **, P < 0.01; ***, P < 0.001) between the groups under the crossbar (groups with a different diet but the same motheŕs diet). Hash symbols denote a significant difference (#, P < 0.05; ##, P < 0.01; ###, P < 0.001) as compared to the Control mothers (groups with the same diet but different motheŕs diet). TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1. E: embryonic/fetal days (E21: delivery); P: postnatal days.
2.2 RNA extraction and gene expression by qPCR
Total RNA was isolated from liver or ileum using Ribopure (Invitrogen, ThermoFisher Scientific, USA). Total RNA was subjected to DNase I treatment using Turbo DNA-free (Invitrogen, ThermoFisher Scientific, USA), and RNA integrity was confirmed by agarose gel electrophoresis. Afterwards, cDNA was synthesized by oligo(dT)-primed reverse transcription with Superscript II (Invitrogen, ThermoFisher Scientific, USA). qPCRs were performed using a CXF96® Touch (Bio-Rad, California, USA). The reaction solution was carried out in a volume of 20 μl, containing 10 pmol of both forward and reverse primers, 10x SYBR Premix Ex Taq (Takara Bio Inc., Japan) and the appropriate nanograms of the cDNA stock. Rps29 was used as a reference gene for qPCR. The primer sequences were obtained either from the Atlas RT-PCR Primer Sequences (Clontech, CA, USA) or designed using Primer3 software (University of Massachusetts Medical School, MA, USA)(Rozen & Skaletsky, 2000).
Samples were analysed in duplicate on each assay. Amplification of non-specific targets was discarded using the melting curve analysis method for each amplicon. qPCR efficiency and linearity were assessed by optimization of the standard curves for each target. The transcription was quantified with CFX Maestro 2.0 software (Bio-Rad, California, USA) using the efficiency correction method (Pfaffl, 2001).
2.3 Statistical Analysis
Results were expressed as means ± S.E. On the first protocol, treatment effects were analyzed by one-way analysis of variance (ANOVA). When treatment effects were significantly different (P < 0.05), means were tested by Tukeýs multiple range test. When the variance was not homogeneous, a post hoc Tamhane test was performed. Significant differences were indicated with different letters.
On the second protocol, treatment effects were analyzed by two-way analysis of variance (ANOVA). Data that were not normally distributed were log transformed to achieve data normality. Then, the Bonferroni test was used for post hoc analysis to identify the source of significant variance. Significant differences (P<0.05) were indicated either with asterisks (*) between groups of animals receiving different treatments but belonging to the same dietary group of mothers or hash symbols (#) between groups of rats with the same treatment but coming from different dietary group of mothers. All statistical valuation was performed using SPSS version 25 computer program.
3 Results
3.1 Maternal fructose decreases hepatic SARS-CoV-2 cell entry factors in adult male progeny
We have taken advantage of our well-stablished animal model of MetS in which fetal programming is achieved by maternal fructose intake(Fauste et al., 2020, Rodrigo et al., 2018, Rodríguez et al., 2013). Thus, male rats born of fructose-fed mothers exhibited impaired insulin signalling and hyperinsulinemia(Rodríguez et al., 2016). However, surprisingly, hepatic cell surface receptors and cofactors facilitating SARS-CoV-2 entry showed a reduced gene expression (Figure 2). Although ACE2 gene expression was not detected in liver and TMPRSS2 mRNA hepatic levels (Figure 2B) showed no differences between descendants from control, fructose-fed and glucose-fed mothers, furin and neuropilin 1 (protease and receptor, respectively, known to collaborate together in the virus entry (Drucker, 2021)) gene expression displayed a reduction in progeny born of carbohydrate-fed mothers, the fructose group being more deeply affected (Figure 2C and 2D). ADAM17 (another factor belonging to the non-canonical pathway viral entry, as are furin and cathepsin) produced the same results (Figure 2E) as those described above for furin (Figure 2D). However, cathepsin L showed no differences between the three groups (Figure 2F).
Cholesterol is critical for viral entry and replication. In fact, SARS-CoV-2 spike protein interacts with HDL, indirectly facilitating viral entry through the SR-B1 cell surface receptor(Drucker, 2021). In accordance with findings here observed for other receptors and cofactors involved in the SARS-CoV-2 cell access, SR-B1 gene expression was diminished in progeny from fructose-fed dams becoming significant in comparison to males born of glucose-fed mothers (Figure 2G).
Therefore, maternal fructose intake seems to provoke a clear reduction in the hepatic gene expression of viral entry factors in adult male progeny and, possibly, this would protect the organ from SARS-CoV-2 cell infection.
3.2 Maternal fructose exacerbates fructose-induced augmentation of hepatic TMPRSS2 gene expression in adult male progeny
Bearing in mind the unexpected changes observed in the gene expression of male progeny born from fructose-supplemented mothers and in order to discover if this phenotype was conserved or reversed by a short liquid fructose-feeding period (3 weeks), we subjected male progeny from control, fructose- and glucose-fed mothers to a fructose liquid solution and determined if the maternal fructose intake influences the response of the adult offspring to fructose on hepatic receptors and cofactors that enable SARS-CoV-2 entry to the cell (Figure 3A).
However, as shown in Figure 3, whereas hepatic gene expression of the majority of key factors determined here for permitting SARS-CoV-2 entrance to the cell was not affected by fructose ingestion (Figure 3C-3G), TMPRSS2 expression was induced in fructose-fed rats and this augmentation was clearly maternal-intake dependent (Figure 3B). Thus, fructose-induced increase in TMPRSS2 expression was more pronounced in progeny from fructose-fed mothers, the effect becoming significantly different in comparison to rats from the control (C/C) group. Unfortunately, ACE2 gene expression, the necessary collaborator of this protease for the virus to enter to the cell, was not detected.
3.3 Maternal fructose exacerbates Western-type diet-induced increase of SARS-CoV-2 cell entry factors in ileum but not in liver of young male offspring.
Once we found that liquid fructose could influence the gene expression of a key molecule in SARS-CoV-2 entry to the cell, TMPRSS2, in adult rats and, moreover, that this effect was dependent of the motheŕs diet, we checked if this situation could be also found in liver of younger rats. We were also interested in studying the effect of a Western-style diet and extend the analysis to another interesting tissue that has also been involved in SARS-CoV-2 infection, that is, ileum.
First, in liver, few changes were observed. Thus, gene expression of TMPRSS2 (Figure 4B), neuropilin (Figure 4C), and ADAM17 (Figure 4E) was affected neither by maternal fructose nor by the diet consumed when descendants were adolescent. Furin and cathepsin L mRNA levels were increased by fructose plus cholesterol diet (FCho) (but not by fructose alone, F) in progeny from Control mothers but, curiously, this effect was lost for furin (Figure 4D), or even reverted for cathepsin (Figure 4F), in descendants from Fructose-fed mothers. For SRB1, in accordance with the effects observed in adult rats (Figure 2G), maternal fructose significantly decreased the mRNA levels of this gene (Figure 4G).
Interestingly, the effects found in ileum were more evident than in liver. Thus, liquid fructose did seem to generate less protection against viral infection in ileum since ACE2 and TMPRSS2 (Figures 5 A and 5B) trended to increase and neuropilin (Figure 5C), ADAM17 (Figure 5E), cathepsin (Figure 5F) and SRB1 (Figure 5G) gene expression were significantly augmented in descendants from Control mothers after fructose intake (F) in comparison to those rats that ingested only water (C). Furin gene expression was not affected (Figure 5D). Surprisingly, all these differences in gene expression between fructose (F) and controls (C) disappeared when descendants were born from Fructose-fed mothers.Fig. 5 Maternal fructose exacerbates Western-type diet-induced increase of SARS-CoV-2 cell entry factors in ileum of young male offspring. Experimental design shown in Figure 4. Ileal levels of specific mRNA for (A) ACE2, (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes. Ileum (mRNA) expression from control (C, empty bar), fructose- (F, light grey bar), and fructose plus cholesterol-supplemented (FCho, dark grey bar) young male progeny from Control (left panel) or Fructose-fed (right panel) mothers. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 7-8 litters. Asterisks denote a significant difference (*, P < 0.05; **, P < 0.01; ***, P < 0.001) between the groups under the crossbar (groups with a different diet but the same motheŕs diet). Hash symbols denote a significant difference (#, P < 0.05; ##, P < 0.01; ###, P < 0.001) as compared to the Control mothers (groups with the same diet but different motheŕs diet). ACE2: angiotensin-converting enzyme 2; TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1.
However, this protection against SARS-CoV-2 infection that apparently maternal fructose provoked in ileum when progeny ingested liquid fructose, was the opposite when descendants received a Western-style diet. Thus, whereas the fructose plus cholesterol (FCho) diet hardly produced any increase in gene expression (except for cathepsin (Figure 5F) and SRB1 (Figure 5G)) in progeny from Control mothers, descendants from Fructose-fed mothers were markedly affected. Although there were no observed differences for ACE2, TMPRSS2 and furin, gene expression was significantly increased for neuropilin (Figure 5C), ADAM17, cathepsin L, and SRB1 (Figures 5E-5G) when descendants from Fructose-fed mothers had ingested a Western-type diet (FCho) (in comparison to those that received fructose alone, F, or only water, C), indicating that these animals would be more prone to a SARS-CoV-2 infection, at least, at the level of small intestine.
3.4 Maternal fructose modulates how the sweetener tagatose affects SARS-CoV-2 cell entry factors in young male offspring.
In order to demonstrate that the effects here observed for direct liquid fructose intake were specific for this carbohydrate, tagatose was used instead (Figure 6 A).Fig. 6 Tagatose affects hepatic cell surface receptors and cofactors facilitating SARS-CoV-2 entry in liver of young male progeny. (A) Experimental design. Hepatic levels of specific mRNA for (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17, (F) cathepsin L, and (G) SRB1 genes. Liver (mRNA) expression from control (C, empty bar), fructose- (F, light grey bar), and tagatose-supplemented (T, dark bar) young male progeny from Control (left panel) or Fructose-fed (right panel) mothers. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 7-8 litters. Asterisks denote a significant difference (*, P < 0.05; **, P < 0.01; ***, P < 0.001) between the groups under the crossbar (groups with a different diet but the same motheŕs diet). Hash symbols denote a significant difference (#, P < 0.05; ##, P < 0.01; ###, P < 0.001) as compared to the Control mothers (groups with the same diet but different motheŕs diet). TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1. E: embryonic/fetal days (E21: delivery); P: postnatal days.
Curiously, although fructose intake did not have any effect in the liver gene expression of adolescent descendants regardless of the motheŕs diet (Figure 4B), TMPRSS2 gene expression (Figure 6B) was significantly increased by tagatose consumption, although this effect was less evident when progeny were born from fructose-fed mothers. The other genes here measured were not affected by tagatose consumption (Figure 6C-6G).
Again, the effects observed in ileum were more marked than in liver. Thus, whereas the effects that liquid fructose (F) provoked in ACE2, TMPRSS2 and ADAM17 in descendants from Control mothers were not observed after tagatose consumption (T) (Figure 7 A, 7B and 7E, respectively), the effects of fructose intake in neuropilin (Figure 7C), cathepsin (Figure 7F) and SRB1 (Figure 7G) gene expression not only were resembled by tagatose intake, but were even more pronounced, in comparison to those rats that ingested only water (C). Furin gene expression was not affected (Figure 7D). Once again, as already observed for fructose, all these differences in gene expression (except for SRB1, Figure 7G) between tagatose (T) and controls (C) found in the progeny from Control mothers, were mitigated when descendants were born from Fructose-fed mothers (Figure 7C and 7F). On the contrary, it was precisely in the progeny of Fructose-fed mothers where tagatose intake (T) provoked clear increases in ACE2 and TMPRSS2 (Figures 7A and 7B) gene expression which became significant versus those rats that consumed water without additives (Control, C).Fig. 7 Maternal fructose modulates how the sweetener tagatose affects SARS-CoV-2 cell entry factors in ileum of young male offspring. Experimental design shown in Figure 6. Ileal levels of specific mRNA for (A) ACE2, (B) TMPRSS2, (C) neuropilin-1, (D) furin, (E) ADAM17 (F) cathepsin L and (G) SRB1 genes. Ileum (mRNA) expression from control (C, empty bar), fructose- (F, light grey bar), and tagatose-supplemented (T, dark bar) young male progeny from Control (left panel) or Fructose-fed (right panel) mothers. Relative target gene mRNA levels were measured by Real Time PCR as explained in Materials and Methods, normalized to Rps29 levels and expressed in arbitrary units (a.u.). Data are means ± S.E. from 7-8 litters. Asterisks denote a significant difference (*, P < 0.05; **, P < 0.01; ***, P < 0.001) between the groups under the crossbar (groups with a different diet but the same motheŕs diet). Hash symbols denote a significant difference (#, P < 0.05; ##, P < 0.01; ###, P < 0.001) as compared to the Control mothers (groups with the same diet but different motheŕs diet). ACE2: angiotensin-converting enzyme 2; TMPRSS2: transmembrane protease serine 2; ADAM17: ADAM metallopeptidase domain 17; SRB1: HDL-scavenger receptor B type 1.
4 Discussion
Whether people with metabolic diseases such as T2DM, CVD or obesity do exhibit increased susceptibility to SARS-CoV-2 infection continues to be debated and uncertain. However, COVID-19 infection clearly results in increased rates of hospitalization, greater severity of illness and mortality in patients with diabetes, CVD or obesity. Thus, knowing if the ACE2 expression and its cofactors facilitating SARS-CoV-2 infectivity is dysregulated in cells and tissues of patients with diabetes, CVD or obesity, and contributes to the pathophysiology of SARS-CoV-2 infection, seems to be crucial to understanding and preventing COVID-19 disease (Drucker, 2021).
Since a Western-style diet is known to contribute to the prevalence of these metabolic diseases, and therefore all of them have been encompassed within the term “processed food-related diseases”, the possibility that fructose-enriched foods, sugary drinks, high-fat and/or high-cholesterol diets could place these patients at an increased risk for severe COVID-19 pathology and mortality should not be discarded(Butler & Barrientos, 2020).
Animal models are critical for understanding viral pathogenesis, vaccine development, and drug screening. Small animal models are essential for research and antiviral therapeutic development. Rodent models are popular because of their affordability, availability, and clear genetic backgrounds and they have been widely used for studying the pathogenesis of human coronaviruses (Jiang et al., 2020).
To elucidate these mechanisms, we have used our rat model of fetal programming provoked by maternal fructose intake in which typical features of MetS appear, directly(Rodrigo et al., 2018, Rodríguez et al., 2016) or after fructose supplementation, in adult male progeny (Fauste et al., 2020). This animal model of MetS has been previously used by us and others and has been useful in demonstrating that maternal fructose intake causes clear dysregulations in progeny with possible clinical implications (Alzamendi et al., 2016). Interestingly, although male descendants of fructose-fed mothers exhibited impaired insulin signalling and hyperinsulinemia (Rodríguez et al., 2016), viral entry to the hepatocyte via non-canonical pathways did not seem to be facilitated. Thus, gene expression both of proteases such as ADAM17 and furin, and receptors such as neuropilin and SRB1, was decreased in males from fructose-fed mothers in comparison to those from control dams. These findings are interesting since neuropilin has been described as an alternative receptor to ACE2 for the viral entry (Daly et al., 2020), and moreover, has been reported to be assisted by furin. Furthermore, the likelihood that SARS-CoV-2 can bind and enter liver cells via the canonical pathway involving ACE2 and TMPRSS2 seems to be reduced since ACE2, in our hands, was not detected. Nevertheless, it could possibly be due to the heterogeneity of the liver cell composition. In fact, a similar situation has already been demonstrated in human pancreas where ACE2 and its protease are present in pancreatic ducts but not in β cells (Coate et al., 2020).
Surprisingly, although maternal fructose intake did seem to protect the liver from SARS-CoV-2 cell infection in adult male progeny, the findings turned out to be different when these descendants from control, fructose- or glucose-fed mothers, were themselves subjected to liquid fructose. Whereas most of the molecules here determined did not show any differences between the four groups studied, TMPRSS2 gene expression was induced by fructose intake and the effect became significant in male born of fructose-fed mothers versus control males. Interestingly, this protease has been proposed as one of the molecules responsible for the relative protection of infants and children against severe COVID19 illness (Schuler et al., 2021). In fact, TMPRSS2 expression has been demonstrated to increase with aging in mice and humans.
On the other hand, reports studying the pathophysiology of SARS-CoV-2 infection in people with diabetes, ECV and obesity, have found that gastrointestinal tract, liver, islets and adipose tissue are also affected, raising uncertainty about their implication in the severity of COVID-19 (Drucker, 2021). For example, ACE2 receptors are abundant in the small intestine and they have been related to the abdominal pain and diarrhea that COVID 19 patients frequently report (Ji et al., 2020).
Interestingly, it has been demonstrated that younger rats can be more greatly affected than older ones by the introduction of sugary drinks and, therefore, it is logical to think that more marked effects of the mothers’ diets could be detected if the offspring were introduced to sugar at a younger age (Kendig et al., 2015). Although young people seems to be less affected by COVID19 infection and also with less severity than older individuals, adolescents have a more frequent ingestion of processed food and beverages containing fructose than older people.
Therefore, we determine if maternal fructose in young males is able to modulate the effects of liquid fructose alone or as a part of a Western-style diet (along with a cholesterol-rich food) on hepatic and ileal gene expression of SARS-CoV-2 entry-dependent factors. In contrast to adult rats, both maternal fructose intake and direct fructose consumption in adolescent rats did not affect the hepatic gene expression of viral entry factors. The only effect that was coincident to those found in older rats was that maternal fructose intake diminished SRB1 mRNA expression in the progeny in comparison to descendants from control dams. These findings would indicate that young animals seem to be refractory to show changes in the expression of viral entrance molecules in response to diet modifications. It was only when cholesterol was included in the diet along with liquid fructose, that an increase in the expression of two proteases of the non-canonical pathway (furin and cathepsin) was observed in the progeny from control dams which, however, was not observed in descendants from fructose-fed mothers.
Whereas direct intake of liquid fructose by young males did not produce any effect in hepatic viral entry factors, fructose-induced changes were clearly observed in ileum. Although the canonical pathway (ACE2 and TMPRSS2) only showed a trend to increase, alternative proteases such as ADAM17 and cathepsin L gene expression were significantly induced by fructose intake. Moreover, the other receptor that has been demonstrated to collaborate with ACE2 to facilitate SARS-CoV-2 passage to the cell, SRB1 (Wei et al., 2020), also displayed a fructose-induced increase. Further, the alternative receptor to ACE2, neuropilin-1, was also increased in descendants from control mothers that had consumed liquid fructose. Curiously, it was surprising not to observe these fructose-provoked changes in descendants from fructose-fed mothers. These findings, in consonance to those found in liver, would again indicate that maternal fructose intake could be protecting against the harmful effects that direct liquid fructose intake produces in the viral entry factors to the cell in these adolescent rats.
Interestingly, the opposite was found when the progenies received a Western-style diet. Maternal fructose intake exacerbated the detrimental fructose plus cholesterol-induced effects in SARS-CoV-2 cell entry molecules found in descendants from control mothers. Although ACE2 and TMPRSS2 were not affected, the non-canonical pathway (cathepsin and ADAM17) gene expression was augmented in males from fructose-fed mothers after receiving the Western-type diet versus descendants consuming water or liquid fructose. Furthermore, the alternative ACE2 receptor, neuropilin, and the ACE2 collaborating receptor, SRB1, also exhibited a Western-style diet-induced increase. Thus, as Coate and col have proposed (Coate et al., 2020) for human β cells, although gene expression findings found here for ACE2 and TMPRSS2 do not suggest that SARS-CoV-2 can enter ileal cells via the canonical pathway, they do not exclude the possibility of viral entry via non-canonical pathways involving other suggested effector proteases and receptors of SARS-CoV2. Furthermore, these findings would reinforce those studies in humans showing that patients with MAFLD or advanced stages of NAFLD displayed higher expression of SARS-CoV-2 entry factors and, therefore, higher risk of COVID-19 disease progression than patients without these metabolic diseases (Fondevila et al., 2021, Ji et al., 2020).
Moreover, though ACE2 has been reported not to participate in SARS-CoV-2 cell entry in rats, it has been demonstrated in rodents expressing human recombinant ACE2 (hACE2) that the other factors facilitating the entrance of SARS-CoV-2 to the cell must be implicated (Jiang et al., 2020). It remains to be elucidated in humans (or in the hACE2 mice) whether ACE2 receptor gene expression is also affected by a Western-diet intake and, moreover, if the motheŕs diet could be involved.
Interestingly, during the preparation of the present report, an interesting study showing that some human cell lines were unable to support SARS-CoV-2 infection despite displaying a strong expression of ACE2 has been published(Yeung et al., 2021). Moreover, since it was observed that inhibition of ADAM17 diminished SARS-CoV-2 infection, the authors proposed that complexes formed by SARS-CoV-2 and soluble ACE2 (released by ADAM17 protease activity from the surface of the cells), might be able to attach and enter the tissues where ACE2 is poorly expressed (Yeung et al., 2021) (Figure 1).
Our previous reports reveal the importance of studying the effects of fructose in comparison to other sweeteners, in order to be sure that the effects provoked by fructose are specific to this carbohydrate. For this reason, in the present study we used an epimer of fructose called tagatose to compare to fructose. This epimer is increasingly used as a low-caloric sugar alternative and modestly improves glycemic control in individuals with and without diabetes (Noronha et al., 2018). In fact, it has been shown that chronic overconsumption of tagatose does not exert the same deleterious metabolic derangements observed after fructose administration (Collotta et al., 2018). In the present study, hepatic gene expression of most of the factors showed no response to tagatose, as had already been observed with fructose, except for TMPRSS2. The expression of TMPRSS2 was significantly increased after receiving tagatose in comparison to progeny having drunk fructose or water. This finding is important since this protease has been proposed as one of the molecules responsible for the severity of COVID19 (Schuler et al., 2021). Interestingly, it was observed in ileum that those effects found in descendants from control mothers after fructose intake were specific to this carbohydrate in ACE2, TMPRSS2 and ADAM17, but not for neuropilin, cathepsin or SRB1 gene expression since they were also observed after tagatose intake. Curiously, again, these effects disappeared or were mitigated by maternal fructose consumption. Interestingly, the specific effects of tagatose, ileal ACE2 and TMPRSS2 gene expression were significantly augmented in young males from fructose-fed mothers. These results found here with fructose and tagatose should make us reconsider the possibility of reducing or avoiding the wide and enormous use of carbohydrates as components of a great variety of foods as sweeteners, preservatives and other functions.
As limitations of the present study, apart from difficulties in extrapolating results from experimental animals to humans: a) although Bellamine et al (Bellamine et al., 2021) have shown that protein levels of key factors for viral entry mimic the mRNA levels and, moreover, parallel to the susceptibility of cells to SARS-CoV-2 infection , we have not directly measured SARS-CoV-2 binding or entry into the cells but have instead assessed (mRNA) gene expression of receptors and proteases required for the entry of this virus in liver or ileum; b) we have used simple sugar solutions instead of sucrose (table sugar) or high fructose corn syrup (HFCS), and thus more evident effects could have been observed since it is known that fructose absorption is improved by the presence of glucose; c) we have used fructose plus cholesterol instead other Western-type diets that also include saturated fat (Rajcic et al., 2021) and thus, possibly, more harmful effects could have been observed; d) determinations have not been carried out in other tissues susceptible to SARS-CoV-2 infection, such as lung, serum and adipose tissue. Thus, to confirm the applicability of our findings it would be necessary to address in the future the limitations of the present study.
In summary, we have demonstrated that maternal fructose intake provided some protection against SARS-CoV-2 infection to descendants . Curiously, however, one of the most prominent results found here is that maternal fructose intake exacerbates the fructose plus cholesterol-induced augmentation in the ileal SARS-CoV-2 cell entry factors gene expression in young male progeny. . Thus, bearing in mind the potential influence of processed foods and fructose-rich beverages in the development of many common non-communicable diseases such as atherosclerosis, metabolic syndrome, etc, and the more and more evident relationship of these metabolic diseases with the COVID disease, we would like to suggest that a reduction in the consumption of fructose-sweetened beverages, especially during gestation, all over the world could be beneficial.
Grant Support: Ministerio de Ciencia e Innovación (MICIN): SAF2017-89537-R/MCIN/AEI/10.13039/501100011033 and ERDF “A way of making Europe”, and PID2020-118054RB-I00/MCIN/AEI /10.13039/501100011033.
Disclosure
None of the authors have any conflicts of interest to report.
Data Transparency
All data will be made available to other researchers under request.
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
Data will be made available on request.
Acknowledgements
The authors thank Jose M. Garrido and his team for their help in handling the rats, and Brian Crilly for his editorial help. This work was supported by grants from Ministerio de Ciencia e Innovación (MICIN): SAF2017-89537-R/MCIN/AEI/10.13039/501100011033 and ERDF “A way of making Europe”, and PID2020-118054RB-I00/MCIN/AEI /10.13039/501100011033. Silvia Rodrigo was supported with a FUSP-CEU fellowship. Elena Fauste was supported with a FPU fellowship from MICIN. C.B. conceived and designed the study. E.F., C.D., M.P., P.O., S.R., and M.I.P. contributed reagents/materials/analysis tools for gene expression studies and parameter analysis. L.R., E.F., and M.I.P. handled the animals. M.I.P. and J.J.A-M analyzed the data. C.B. wrote the paper. None of the authors have any conflicts of interest to report.
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| 0 | PMC9723251 | NO-CC CODE | 2022-12-07 23:19:58 | no | Am Heart J. 2022 Dec 6; 254:246 | latin-1 | Am Heart J | 2,022 | 10.1016/j.ahj.2022.10.039 | oa_other |
==== Front
Am Heart J
Am Heart J
American Heart Journal
0002-8703
1097-6744
Published by Mosby, Inc.
S0002-8703(22)00235-6
10.1016/j.ahj.2022.10.040
0042
Lactate Dehydrogenase can be Considered a Predictive Marker of Severity and Mortality of Covid-19 in Diabetic and Non-Diabetic Patients. A Case Series
Motawea Karam R. 1
Varney Joseph 2
Talat Nesreen E. 1
Rozan Samah S. 1
Chébl Pensée 1
Reyad Sarraa M. 1
1 Faculty of Medicine, Alexandria University, Alexandria, Egypt
2 American University of the Caribbean, School of Medicine, St Maarten, SXM
6 12 2022
12 2022
6 12 2022
254 246247
Copyright © 2022 Published by Mosby, 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.
Case Series Presentation
Eight elderly confirmed SARS-CoV-2 patients who had severe course of COVID-19 and admitted to ICU expressed high lactate dehydrogenase (LDH) above normal level. The mean value of LDH was 440.40 U/L with 84.52 standard deviation (normal range = 100 – 190 U/L). The mean age of patients was 73.63 years (standard deviation = 3.34). The patients were 4 males (50%) and 4 females (50%). The median of stay duration at ICU was 2 days (range = 1-32 days). Four patients died (50%) and four patients survived (50%). All the patients were at the same ICU and received the same treatment course for COVID-19.
Discussion
It has been shown that LDH is a potential marker of vascular permeability in immune-mediated lung injury. Areas within the body where LDH are most active include the liver, striated muscles, heart, kidneys, lungs, brain, and red blood cells. LDH is a known marker for different inflammatory states, sepsis, myocardial infarctions, infections, and malignancies. One study showed that LDH elevation was associated with a 6-fold increase in the odds of developing a severe COVID-19 disease. Furthermore elevated LDH was associated with a 16 fold increase in patient mortality. Elevated LDH levels seem to reflect that the multiple organ injury and failure may play a more prominent role in influencing the clinical outcomes in patients with COVID-19. This study is a report of 8 elderly critically ill COVID-19 patients who expressed high lactate dehydrogenase above normal level. This indicates that lactate dehydrogenase can predict the outcome of elderly COVID-19 patients. All the eight patients developed severe course of COVID-19, four of them died.
Conclusion
High levels of lactate dehydrogenase can predict the severity and mortality of COVID-19 in elderly patients. LDH levels could be considered for inclusion in future risk stratification models for COVID-19 severity and mortality. More observational studies
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pmc
| 0 | PMC9723252 | NO-CC CODE | 2022-12-07 23:19:58 | no | Am Heart J. 2022 Dec 6; 254:246-247 | utf-8 | Am Heart J | 2,022 | 10.1016/j.ahj.2022.10.040 | oa_other |
==== Front
Am Heart J
Am Heart J
American Heart Journal
0002-8703
1097-6744
Published by Mosby, Inc.
S0002-8703(22)00208-3
10.1016/j.ahj.2022.10.013
0006
Prevalence of Dyslipidaemia, Obesity and Vitamin d Insufficiency Among Patients Attending the Diabetic Clinic at Queen Elizabeth Central Hospital in Malawi
Katundu Kondwani 12
Mukhula Victoria 2
Kumwenda Johnstone 3
Mipando Mwapatsa 1
Muula Adamson 5
Phiri Tamara 34
Phiri Chimota 4
Lampiao Fanuel 1
Mwandumba Henry 12
1 Department of Biomedical Sciences, Kamuzu University of Health Sciences, Blantyre, Malawi
2 Malawi-Liverpool Wellcome Trust, Blantyre, Malawi
3 Department of Medicine, Kamuzu University of Health Sciences, Blantyre, Malawi
4 Department of Medicine, Queen Elizabeth Central Hospital, Blantyre, Malawi
5 Department of Community and Environmental Health, Kamuzu University of Health Sciences, Blantyre, Malawi
6 12 2022
12 2022
6 12 2022
254 235235
Copyright © 2022 Published by Mosby, 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.
Background
Dyslipidaemia, obesity and vitamin D insufficiency are important risk factors for cardiovascular disease and may increase the risk for severe COVID-19 among individuals with Diabetes mellitus.
Objective
To investigate the prevalence of dyslipidaemia, obesity and vitamin D insufficiency among patients attending the diabetes clinic at Queen Elizabeth Central Hospital in Blantyre, Malawi.
Methods
A total of 102 participants were included in the cross-sectional study. Medical data were collected, and anthropometric measurements were performed. Blood samples were collected for HbA1C, serum lipogram and vitamin D analyses. Associated risk factors for dyslipidaemia and vitamin D insufficiency were assessed.
Results
A proportion of 74% of the participants had dyslipidaemia. Low-density lipoprotein-cholesterol dyslipidaemia was the most common form of dyslipidaemia (52%). Overweight and obesity was prevalent in 58% of the participants. The median (IQR) HBA1C level was 11% (9-14 %). Overweight or obesity and age over 30 years were risks for dyslipidaemia (RR 1.3 (95% CI 1.1 – 1.6), p=0.04, and RR 2.2 (95% CI 1.2 – 4.7) p=0.003, respectively. The prevalence of vitamin D insufficiency in the study group was 76%. HBA1C of > 7% was positively associated with vitamin D insufficiency (RR 1.6 (CI 1.0 – 2.8), p=0.02).
Conclusions
Dyslipidaemia, obesity, and vitamin D insufficiency were highly prevalent in the study group. Poorly controlled blood glucose was associated with vitamin D insufficiency. The high prevalence of dyslipidaemia, obesity and vitamin D deficiency are the possible precipitating factors for the increasing rates of cardiovascular events and COVID-19 severity among patients with diabetes in Malawi.
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pmcFunding and Conflicts of Interest
| 0 | PMC9723262 | NO-CC CODE | 2022-12-07 23:19:58 | no | Am Heart J. 2022 Dec 6; 254:235 | utf-8 | Am Heart J | 2,022 | 10.1016/j.ahj.2022.10.013 | oa_other |
==== Front
Am Heart J
Am Heart J
American Heart Journal
0002-8703
1097-6744
Published by Mosby, Inc.
S0002-8703(22)00208-3
10.1016/j.ahj.2022.10.013
0006
Prevalence of Dyslipidaemia, Obesity and Vitamin d Insufficiency Among Patients Attending the Diabetic Clinic at Queen Elizabeth Central Hospital in Malawi
Katundu Kondwani 12
Mukhula Victoria 2
Kumwenda Johnstone 3
Mipando Mwapatsa 1
Muula Adamson 5
Phiri Tamara 34
Phiri Chimota 4
Lampiao Fanuel 1
Mwandumba Henry 12
1 Department of Biomedical Sciences, Kamuzu University of Health Sciences, Blantyre, Malawi
2 Malawi-Liverpool Wellcome Trust, Blantyre, Malawi
3 Department of Medicine, Kamuzu University of Health Sciences, Blantyre, Malawi
4 Department of Medicine, Queen Elizabeth Central Hospital, Blantyre, Malawi
5 Department of Community and Environmental Health, Kamuzu University of Health Sciences, Blantyre, Malawi
6 12 2022
12 2022
6 12 2022
254 235235
Copyright © 2022 Published by Mosby, 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.
Background
Dyslipidaemia, obesity and vitamin D insufficiency are important risk factors for cardiovascular disease and may increase the risk for severe COVID-19 among individuals with Diabetes mellitus.
Objective
To investigate the prevalence of dyslipidaemia, obesity and vitamin D insufficiency among patients attending the diabetes clinic at Queen Elizabeth Central Hospital in Blantyre, Malawi.
Methods
A total of 102 participants were included in the cross-sectional study. Medical data were collected, and anthropometric measurements were performed. Blood samples were collected for HbA1C, serum lipogram and vitamin D analyses. Associated risk factors for dyslipidaemia and vitamin D insufficiency were assessed.
Results
A proportion of 74% of the participants had dyslipidaemia. Low-density lipoprotein-cholesterol dyslipidaemia was the most common form of dyslipidaemia (52%). Overweight and obesity was prevalent in 58% of the participants. The median (IQR) HBA1C level was 11% (9-14 %). Overweight or obesity and age over 30 years were risks for dyslipidaemia (RR 1.3 (95% CI 1.1 – 1.6), p=0.04, and RR 2.2 (95% CI 1.2 – 4.7) p=0.003, respectively. The prevalence of vitamin D insufficiency in the study group was 76%. HBA1C of > 7% was positively associated with vitamin D insufficiency (RR 1.6 (CI 1.0 – 2.8), p=0.02).
Conclusions
Dyslipidaemia, obesity, and vitamin D insufficiency were highly prevalent in the study group. Poorly controlled blood glucose was associated with vitamin D insufficiency. The high prevalence of dyslipidaemia, obesity and vitamin D deficiency are the possible precipitating factors for the increasing rates of cardiovascular events and COVID-19 severity among patients with diabetes in Malawi.
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pmcFunding and Conflicts of Interest
| 0 | PMC9723263 | NO-CC CODE | 2022-12-07 23:19:58 | no | Am Heart J. 2022 Dec 6; 254:253 | latin-1 | Am Heart J | 2,022 | 10.1016/j.ahj.2022.10.053 | oa_other |
==== Front
Am Heart J
Am Heart J
American Heart Journal
0002-8703
1097-6744
Published by Mosby, Inc.
S0002-8703(22)00208-3
10.1016/j.ahj.2022.10.013
0006
Prevalence of Dyslipidaemia, Obesity and Vitamin d Insufficiency Among Patients Attending the Diabetic Clinic at Queen Elizabeth Central Hospital in Malawi
Katundu Kondwani 12
Mukhula Victoria 2
Kumwenda Johnstone 3
Mipando Mwapatsa 1
Muula Adamson 5
Phiri Tamara 34
Phiri Chimota 4
Lampiao Fanuel 1
Mwandumba Henry 12
1 Department of Biomedical Sciences, Kamuzu University of Health Sciences, Blantyre, Malawi
2 Malawi-Liverpool Wellcome Trust, Blantyre, Malawi
3 Department of Medicine, Kamuzu University of Health Sciences, Blantyre, Malawi
4 Department of Medicine, Queen Elizabeth Central Hospital, Blantyre, Malawi
5 Department of Community and Environmental Health, Kamuzu University of Health Sciences, Blantyre, Malawi
6 12 2022
12 2022
6 12 2022
254 235235
Copyright © 2022 Published by Mosby, 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.
Background
Dyslipidaemia, obesity and vitamin D insufficiency are important risk factors for cardiovascular disease and may increase the risk for severe COVID-19 among individuals with Diabetes mellitus.
Objective
To investigate the prevalence of dyslipidaemia, obesity and vitamin D insufficiency among patients attending the diabetes clinic at Queen Elizabeth Central Hospital in Blantyre, Malawi.
Methods
A total of 102 participants were included in the cross-sectional study. Medical data were collected, and anthropometric measurements were performed. Blood samples were collected for HbA1C, serum lipogram and vitamin D analyses. Associated risk factors for dyslipidaemia and vitamin D insufficiency were assessed.
Results
A proportion of 74% of the participants had dyslipidaemia. Low-density lipoprotein-cholesterol dyslipidaemia was the most common form of dyslipidaemia (52%). Overweight and obesity was prevalent in 58% of the participants. The median (IQR) HBA1C level was 11% (9-14 %). Overweight or obesity and age over 30 years were risks for dyslipidaemia (RR 1.3 (95% CI 1.1 – 1.6), p=0.04, and RR 2.2 (95% CI 1.2 – 4.7) p=0.003, respectively. The prevalence of vitamin D insufficiency in the study group was 76%. HBA1C of > 7% was positively associated with vitamin D insufficiency (RR 1.6 (CI 1.0 – 2.8), p=0.02).
Conclusions
Dyslipidaemia, obesity, and vitamin D insufficiency were highly prevalent in the study group. Poorly controlled blood glucose was associated with vitamin D insufficiency. The high prevalence of dyslipidaemia, obesity and vitamin D deficiency are the possible precipitating factors for the increasing rates of cardiovascular events and COVID-19 severity among patients with diabetes in Malawi.
==== Body
pmcFunding and Conflicts of Interest
| 0 | PMC9723264 | NO-CC CODE | 2022-12-07 23:19:58 | no | Am Heart J. 2022 Dec 6; 254:243 | latin-1 | Am Heart J | 2,022 | 10.1016/j.ahj.2022.10.032 | oa_other |
==== Front
Am Heart J
Am Heart J
American Heart Journal
0002-8703
1097-6744
Published by Mosby, Inc.
S0002-8703(22)00229-0
10.1016/j.ahj.2022.10.034
0036
Comorbidity Burden in Patients with Covid-19 Treated with Molnupiravir in the United States
Prajapati Girish
Das Amar
Sun Yezhou
Fonseca Eileen
Merck & Co., Inc., Rahway, NJ, USA
6 12 2022
12 2022
6 12 2022
254 244244
Copyright © 2022 Published by Mosby, 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.
Individuals with certain medical conditions, e.g., diabetes, heart, and/or lung disease, are at higher risk of severe COVID-19. Molnupiravir, an oral antiviral drug for treatment of mild-to-moderate COVID-19 in certain adults, was granted US FDA emergency use authorization. Retrospective analyses of US patient-level medical and pharmacy claims, and hospital chargemaster data, aggregated by HealthVerity, was conducted. Adults (≥18 years) were indexed to their first outpatient pharmacy fill for molnupiravir between Dec-24-2021 and May-02-2022. Comorbidities were identified using ICD-10 diagnosis, CPT, and/or HCPCS codes during pre-index period (back to Dec-01-2018) and comedications by generic name (from NDCs) ≤90 days before index. Demographic, comorbidity, and comedication characteristics were reported using descriptive statistics. The analyses included 26,191 patients: mean age 58.7 (SD 16.3) years, 59.0% female and 75.9% resided in the South. Presence of ≥1 comorbidity associated with severe COVID-19 was observed in 87.0%: hypertension (52.5%), overweight/obesity (37.4%), mood disorder (30.7%) and cardiovascular disease (18.9%). Diabetes mellitus was observed in 6,944 (26.5%) patients: mean age 62.5 (SD 14.3) years and 54.4% female. Polypharmacy (≥5 comedications) within the last 90 days was also prevalent in both the overall (49.7%) and patients with diabetes (66.1%). Concomitant use of comedications contraindicated with ritonavir-based COVID-19 treatment was noted in 33.7% of all patients and 45.8% of patients with diabetes. Majority of COVID-19 patients treated with molnupiravir in clinical practice were at high risk of severe COVID-19. Future research needs to assess impact of molnupiravir on clinical outcomes in real-world practice, including in patients with comorbid conditions.
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pmc
| 0 | PMC9723265 | NO-CC CODE | 2022-12-07 23:19:58 | no | Am Heart J. 2022 Dec 6; 254:244 | utf-8 | Am Heart J | 2,022 | 10.1016/j.ahj.2022.10.034 | oa_other |
==== Front
Am Heart J
Am Heart J
American Heart Journal
0002-8703
1097-6744
Published by Mosby, Inc.
S0002-8703(22)00229-0
10.1016/j.ahj.2022.10.034
0036
Comorbidity Burden in Patients with Covid-19 Treated with Molnupiravir in the United States
Prajapati Girish
Das Amar
Sun Yezhou
Fonseca Eileen
Merck & Co., Inc., Rahway, NJ, USA
6 12 2022
12 2022
6 12 2022
254 244244
Copyright © 2022 Published by Mosby, 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.
Individuals with certain medical conditions, e.g., diabetes, heart, and/or lung disease, are at higher risk of severe COVID-19. Molnupiravir, an oral antiviral drug for treatment of mild-to-moderate COVID-19 in certain adults, was granted US FDA emergency use authorization. Retrospective analyses of US patient-level medical and pharmacy claims, and hospital chargemaster data, aggregated by HealthVerity, was conducted. Adults (≥18 years) were indexed to their first outpatient pharmacy fill for molnupiravir between Dec-24-2021 and May-02-2022. Comorbidities were identified using ICD-10 diagnosis, CPT, and/or HCPCS codes during pre-index period (back to Dec-01-2018) and comedications by generic name (from NDCs) ≤90 days before index. Demographic, comorbidity, and comedication characteristics were reported using descriptive statistics. The analyses included 26,191 patients: mean age 58.7 (SD 16.3) years, 59.0% female and 75.9% resided in the South. Presence of ≥1 comorbidity associated with severe COVID-19 was observed in 87.0%: hypertension (52.5%), overweight/obesity (37.4%), mood disorder (30.7%) and cardiovascular disease (18.9%). Diabetes mellitus was observed in 6,944 (26.5%) patients: mean age 62.5 (SD 14.3) years and 54.4% female. Polypharmacy (≥5 comedications) within the last 90 days was also prevalent in both the overall (49.7%) and patients with diabetes (66.1%). Concomitant use of comedications contraindicated with ritonavir-based COVID-19 treatment was noted in 33.7% of all patients and 45.8% of patients with diabetes. Majority of COVID-19 patients treated with molnupiravir in clinical practice were at high risk of severe COVID-19. Future research needs to assess impact of molnupiravir on clinical outcomes in real-world practice, including in patients with comorbid conditions.
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pmc
| 0 | PMC9723266 | NO-CC CODE | 2022-12-07 23:19:59 | no | Am Heart J. 2022 Dec 6; 254:249 | latin-1 | Am Heart J | 2,022 | 10.1016/j.ahj.2022.10.046 | oa_other |
==== Front
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0012-3692
1931-3543
American College of Chest Physicians
S0012-3692(22)03934-4
10.1016/S0012-3692(22)03934-4
Article
Table of Contents
6 12 2022
12 2022
6 12 2022
162 6 A9A18
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.
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pmc
| 0 | PMC9723268 | NO-CC CODE | 2022-12-07 23:19:59 | no | Chest. 2022 Dec 6; 162(6):A9-A18 | utf-8 | Chest | 2,022 | 10.1016/S0012-3692(22)03934-4 | oa_other |
==== Front
Chest
Chest
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0012-3692
1931-3543
American College of Chest Physicians. Published by Elsevier Inc.
S0012-3692(22)03734-5
10.1016/j.chest.2022.09.013
Correspondence
Response
Moskowitz Ari MD, MPH a
Self Wesley H. MD, MPH c∗
Mohamed Amira MD a
Shotwell Matthew S. PhD b
Semler Matthew W. MD d
a Department of Medicine, Montefiore Medical Center, The Bronx, NY
b Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN
c Department of Emergency Medicine and Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN
d Department of Medicine, Vanderbilt University Medical Center, Nashville, TN
∗ CORRESPONDENCE TO: Wesley H. Self, MD, MPH
6 12 2022
12 2022
6 12 2022
162 6 e332e333
© 2022 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.
2022
American College of Chest Physicians
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.
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pmcTo the Editor:
We thank Dr Lellouche and colleagues for their comments regarding our manuscript.1 Selecting an outcome measure for clinical trials in COVID-19 and other causes of hypoxemia is complex. We believe that oxygen-free days (OFD) is an important addition to the available clinical trial outcomes, but appreciate the considerations outlined by Dr Lellouche.
First, Dr Lellouche notes that variation in oxygen saturation targets could lead to differences in oxygen weaning and, therefore, differences in OFDs caused by practice patterns rather than pathophysiology. This limitation is correct but applies to all free day outcomes (eg, ventilator-free days, vasopressor-free days, and so forth), which are widely accepted in critical care research. Although practice variation may impact raw values for OFD, randomization in clinical trials would balance this variation between groups and preserve the validity of between-group comparisons.
Second, Dr Lellouche and colleagues highlight that skin pigmentation may affect the accuracy of oxygen saturation measurements using pulse oximetry (Spo 2). If pulse oximeters systematically overestimate Spo 2 compared with Sao 2 in patients with darker skin pigmentation, this could result in supplemental oxygen being weaned more quickly. Addressing this issue is critically important—more for the millions of patients affected in clinical care than for the use of OFDs as a clinical trial outcome, where randomization balances baseline patient characteristics such as skin pigmentation.
The statement by Lellouche et al that “…Spo 2 overestimates Sao 2 by 2% to 3%” in patients with darker skin may be overly simplistic. Measurement error has two components: directional bias and variability. The magnitude of the directional bias in Spo 2 measurement may differ based on the oximeter type and on the patient’s Spo 2 value. In data collected by the authors, at Spo 2 values < 92% and > 98%, no directional bias was present, and at values between 92% and 98%, Sao 2 values relative to Spo 2 were approximately 1% lower for Black patients.2 Variability is also an important, and underappreciated, contributor to error in Spo 2 measurement.2, 3, 4 At a given Spo 2 value, patients with darker skin are more likely to have either lower or high er Sao 2 values. Although directional bias could be corrected with a simple equation (eg, subtract 2% to 3% from the Spo 2), variability must be corrected by improving the device itself.
In summary, we agree with Lellouche et al that harmonizing the approach to supplemental oxygen use across participants and minimizing Spo 2 measurement error for patients of all skin pigmentations would strengthen trials using OFDs as an outcome.
Acknowledgments
Author contributions: W. H. S. is the guarantor of this work. A. Moskowitz, W. H. S., A. Mohamed, M. S. S., and M. W. S. provided essential intellectual input into the crafting of this letter response. A. Moskowitz and M. W. S. authored the first draft of the work. All the authors provided critical input into the final version and approved of the final product.
Funding/support: This research was, in part, funded by the National Heart, Lung and Blood Institute (NHLBI) of the National Institutes of Health (NIH) through awards 42-312-0217571-66406L and 1OT2HL156812.
Financial/nonfinancial disclosures: See earlier cited article for author conflicts of interest.
Role ofsponsors: The sponsor had no role in the design of the study, the collection and analysis of the data, or the preparation of the manuscript.
Disclaimer: The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the NIH, the National Heart, Lung, and Blood Institute, or the U.S. Department of Health and Human Services.
==== Refs
References
1 Moskowitz A. Shotwell M.S. Gibbs K.W. Oxygen-free days as an outcome measure in clinical trials of therapies for COVID-19 and other causes of new-onset hypoxemia Chest 162 4 2022 804 814 35504307
2 Semler M.W. Casey J.D. Lloyd B.D. Protocol and statistical analysis plan for the Pragmatic Investigation of optimaL Oxygen Targets (PILOT) clinical trial BMJ Open 11 10 2021 e052013
3 Chesley C.F. Lane-Fall M.B. Panchanadam V. Racial disparities in occult hypoxemia and clinically based mitigation strategies to apply in advance of technological advancements Respir Care 2022 10.4187/respcare.09769 [Published online ahead of print June 3, 2022]
4 Valbuena V.S.M. Barbaro R.P. Claar D. Racial bias in pulse oximetry measurement among patients about to undergo extracorporeal membrane oxygenation in 2019-2020: a retrospective cohort study Chest 161 4 2022 971 978 34592317
| 36494136 | PMC9723269 | NO-CC CODE | 2022-12-07 23:20:02 | no | Chest. 2022 Dec 6; 162(6):e332-e333 | utf-8 | Chest | 2,022 | 10.1016/j.chest.2022.09.013 | oa_other |
==== Front
Chest
Chest
Chest
0012-3692
1931-3543
American College of Chest Physicians. Published by Elsevier Inc.
S0012-3692(22)03733-3
10.1016/j.chest.2022.09.012
Correspondence
Oxygen-Free Days and the Confounders of Clinical Practice
Lellouche François MD, PhD a∗
Blanchet Marie-Anne a
Branson Richard D. MSc RRT b
a Centre de recherche, Département de médecine, Institut Universitaire de Cardiologie et de Pneumologie de Québec Université Laval, Québec, QC, Canada
b Department of Surgery, Division of Trauma & Critical Care, University of Cincinnati, Cincinnati, OH
∗ CORRESPONDENCE TO: François Lellouche, MD, PhD
6 12 2022
12 2022
6 12 2022
162 6 e331e332
© 2022 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.
2022
American College of Chest Physicians
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.
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pmcTo the Editor:
In CHEST (October 2022), Moskowitz et al1 describe a new outcome measure for clinical trials focusing on patients with acute hypoxemic respiratory failure, the oxygen-free days,1 which are related to hospital discharge, an important patient outcome, and hospital length of stay, an important financial outcome. The authors acknowledged several limitations to this end point but may have missed several important confounders. These include the oxygenation target, skin pigmentation, and the type of oximeter used (Fig 1 ).Figure 1 Impact of several parameters on oxygen needs: Spo2 target, skin pigmentation, and type of oximeter used have an impact on the oxygen flow required, and potentially on oxygen therapy duration and associated factors, such as oxygen-free days and hospital length of stay. Extreme situations are presented: ① Spo2 target recommended by the British Thoracic Society (96% ± 2%)1 or the one in the rapid BMJ recommendations (92% ± 2%)2; ② Skin pigmentation for most pigmented (Fitzpatrick scale 5-6) and less pigmented subjects (Fitzpatrick scale 1-2); ③ Type of oximeter used, with those that overestimated Sao2 the most and those that underestimated Sao2 the most (https://openoximetry.org/oximeters). Sao2 = arterial oxygen saturation; Spo2 = oxygen saturation.
Knowledge regarding oxygen therapy has advanced markedly over the last 20 years, with risks associated with hyperoxemia in addition to those more intuitive risks associated with hypoxemia identified. Current recommendations in acutely ill patients, except COPD, suggest targeting an oxygenation range with a low and a high oxygen saturation (Spo 2) limit. In different guidelines, the recommended Spo 2 targets range from 96% ± 2% proposed by the British Thoracic Society2 to 92% ± 2% proposed by a Canadian group.3
In 36 hospitalized patients receiving oxygen therapy, targeting 96% rather than 92% Spo 2 resulted in a twofold increase in oxygen flow.4 Consequently, clearly for the same patient managed in London, England (recommended Spo 2 between 94% and 98%) or in London, Ontario (recommended Spo 2 between 90% and 94%, if Canadian suggestions are followed), the duration of oxygen therapy and likely hospital length of stay will increase, whereas oxygen-free days will be reduced in London, England.
Similarly, if the recommendation for the Spo 2 target is not adjusted for skin pigmentation, the same issue will present. In a patient with dark skin pigmentation, oxygen will be weaned more quickly, as pulse oximetry (Spo 2) overestimates arterial oxygen saturation (Sao2) by 2% to 3% compared with light pigmented subjects (Fig 1).5
Finally, recent data show that the type of oximeter used may even have a higher influence than skin pigmentation (Fig 1).
It is time to harmonize practices for oxygen management worldwide, and more complex guidelines should be used that take into account the skin pigmentation as well as the type of oximeter used. This is also true if oxygen-free days are used as a marker in clinical trials evaluating new treatments. Ignoring these parameters would have an important impact on this new proposed outcome.
Acknowledgments
Financial/nonfinancial disclosures: The authors have reported to CHEST the following: F. L. is co-founder, shareholder and director of Oxynov. This company has designed and marketed the automated oxygen adjustment system (FreeO2), but this device is not evoked in the letter. None declared (M. A. B., R. B.).
Editor's Note: Authors are invited to respond to Correspondence that cites their previously published work. Those responses appear after the related letter. In cases where there is no response, the author of the original article declined to respond or did not reply to our invitation.
==== Refs
References
1 Moskowitz A. Shotwell M.S. Gibbs K.W. Oxygen-free days as an outcome measure in clinical trials of therapies for COVID-19 and other causes of new-onset hypoxemia Chest 162 4 2022 804 814 35504307
2 O’Driscoll B.R. Howard L.S. Earis J. Mak V. British Thoracic Society Emergency Oxygen Guideline G, Group BTSEOGD BTS guideline for oxygen use in adults in healthcare and emergency settings Thorax 72 Suppl 1 2017 ii1 ii90
3 Siemieniuk R.A.C. Chu D.K. Kim L.H. Oxygen therapy for acutely ill medical patients: a clinical practice guideline BMJ 363 2018 k4169 30355567
4 Bourassa S. Bouchard P.A. Dauphin M. Lellouche F. Oxygen conservation methods with automated titration Respir Care 65 10 2020 1433 1442 32071135
5 Sjoding M.W. Dickson R.P. Iwashyna T.J. Gay S.E. Valley T.S. Racial bias in pulse oximetry measurement N Engl J Med 383 25 2020 2477 2478 33326721
| 36494135 | PMC9723270 | NO-CC CODE | 2022-12-07 23:20:02 | no | Chest. 2022 Dec 6; 162(6):e331-e332 | utf-8 | Chest | 2,022 | 10.1016/j.chest.2022.09.012 | oa_other |
==== Front
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0012-3692
1931-3543
American College of Chest Physicians. Published by Elsevier Inc.
S0012-3692(22)03681-9
10.1016/j.chest.2022.08.2213
Chest Imaging and Pathology for Clinicians
Acute Exacerbation of Interstitial Lung Disease After SARS-CoV-2 Vaccination
A Case Series
Ishioka Yoshiko MD a
Makiguchi Tomonori MD, PhD a
Itoga Masamichi MD, PhD b
Tanaka Hisashi MD, PhD a
Taima Kageaki MD, PhD a
Goto Shintaro MD, PhD c
Tasaka Sadatomo MD, FCCP a∗
a Department of Respiratory Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
b Department of Clinical Laboratory Medicine, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
c Department of Pathology and Bioscience, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
∗ CORRESPONDENCE TO: Sadatomo Tasaka, MD, FCCP
6 12 2022
12 2022
6 12 2022
162 6 e311e316
© 2022 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.
2022
American College of Chest Physicians
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.
Case Presentation
An acute exacerbation of interstitial lung disease (ILD) is an acute deterioration that can occur at any time and is associated with significant morbidity and mortality rates. We herein report three patients with ILD who experienced acute respiratory failure after SARS-CoV-2 messenger RNA vaccination. All the patients were male; the mean age was 77 years. They had a smoking history that ranged from 10 to 30 pack-years. Duration from the vaccination to the onset of respiratory failure was 1 day in two patients and 9 days in one patient. In an autopsied case, lung pathologic evidence indicated diffuse alveolar damage superimposed on usual interstitial pneumonia. In the other two cases, CT scans showed diffuse ground-glass opacities and subpleural reticulation, which suggests acute exacerbation of ILD. Two patients were treated successfully with high-dose methylprednisolone. Although benefits of vaccination outweigh the risks associated with uncommon adverse events, patients with chronic lung diseases should be observed carefully after SARS-CoV-2 vaccination.
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pmcCOVID-19 remains a global health concern, and several SARS-CoV-2 vaccines have been developed for the prevention of infection or aggravation and lowering mortality rates.1 However, some concerns about adverse effects that are related to SARS-CoV-2 vaccination have been raised. Recently, various adverse effects have been reported, but there are little data on adverse effects on the lungs, such as exacerbation of preexisting lung disease. In this report, we describe three cases of acute respiratory failure subsequent to SARS-CoV-2 messenger RNA (mRNA) vaccination, which include an autopsied case.
Case Reports
Case 1
A 67-year-old man who had a previous diagnosis of metastatic colon cancer and who received chemotherapy with S-1 plus oxaliplatin experienced the development of interstitial lung disease (ILD) in July 2021. He had a former smoking history of 30 pack-years. Because his ILD was mild and nonprogressive, no corticosteroids or antifibrotic agents were introduced. In August 2021, he received his first dose of BNT162b2 vaccine (Comirnaty; BioNTech/Pfizer). From the next day, he had fever and dyspnea on exertion. Because of the worsening symptoms, he visited the ED and was admitted to our hospital 3 days after the vaccination. His chest high-resolution CT (HRCT) scan revealed diffuse ground-glass opacities (GGO) superimposed on preexisting subpleural reticulation and traction bronchiectasis (Fig 1 ).Figure 1 A-C, High resolution CT scan findings of Case 1. High-resolution CT scan taken B, 1 month before the exacerbation showed subpleural reticulation and traction bronchiectasis A, that had not been observed 15 months before. On admission, C, high-resolution CT scan revealed diffuse ground-glass opacities superimposed on the preexisting interstitial changes.
On admission, his vital signs were a temperature of 38.0°C, pulse rate of 150 beats/min, respiratory rate of 30 breaths/min, and peripheral oxygen saturation of 52% in ambient air. Physical examination revealed bilateral fine crackles and no heart murmur, leg edema, or clubbed fingers. Arterial blood gas analysis on admission revealed hypoxemia and respiratory alkalosis (pH 7.493; Paco 2, 15.8 mm Hg; Pao 2, 51.5 mm Hg; HCO3 –, 9.2 mM) with 8 L/min of supplemental oxygen via face mask. Blood tests revealed elevated levels of C-reactive protein (CRP) (5.17 mg/dL), lactate dehydrogenase (LDH) (607 units/L), Krebs von den Lungen-6 (506 units/mL), and procalcitonin (0.25 ng/mL). Before the vaccination, the levels of CRP (0.32 mg/dL) and LDH (271 units/mL) were within normal range. Antinuclear antibody and other autoantibodies were negative. WBC count was 12,550/μL, and the differential count was neutrophils 78.5%, lymphocytes 12.1%, and eosinophils 0.3%. SARS-CoV-2 antigen test and polymerase chain reaction were both negative. Cultures of his sputum and tracheal secretions were also negative. During the transfer to the ICU, he suddenly had cardiopulmonary arrest and died.
The autopsy results revealed the following data: The lungs had subpleural dense fibrosis with alternating areas of normal lung, which suggests temporal heterogeneity of fibrosis. At the boundary between normal lung and dense fibrosis, fibroblastic foci were observed, which suggests usual interstitial pneumonia (UIP) (Fig 2A). The lung pathologic examination also showed diffuse alveolar damage that was characterized by infiltration of inflammatory cells and hyaline membranes with protein-rich edema fluid (Fig 2B). These findings were consistent with the clinical diagnosis of acute exacerbation of UIP. Although atherosclerosis was observed, there was no pathologic changes in the cardiovascular system that could be responsible for sudden death.Figure 2 A and B, Autopsy findings from Case 1. A, The lungs had subpleural dense fibrosis with alternating areas of normal lung suggest temporal heterogeneity of fibrosis. As shown in the inset, scattered fibroblastic foci were also observed, which was suggestive of usual interstitial pneumonia. B, The lung pathology report also showed diffuse alveolar damage that was characterized by infiltration of inflammatory cells and hyaline membranes with protein-rich edema fluid.
Case 2
An 82-year-old man who had received his third dose of BNT162b2 vaccine 16 days before was referred to our hospital because of severe respiratory failure. He presented with fever, nonproductive cough, and progressive dyspnea over a week. He had a former smoking history of 10 pack-years. When he visited the ED of a local hospital, his peripheral oxygen saturation was as low as 75%, and the chest roentgenogram showed bilateral pulmonary infiltrates. On admission, his vital signs were a body temperature of 37.5°C, pulse rate of 108 beats/min, respiratory rate of 30 breaths/min, and peripheral oxygen saturation of 87% with 10 L/min of supplemental oxygen through a rebreather mask. Physical examination revealed fine crackles on the lungs and clubbed fingers. Arterial blood gas analysis on admission revealed hypoxemia and respiratory alkalosis (pH 7.456; Paco 2, 31.6 mm Hg; Pao 2, 53.2 mm Hg; HCO3 –, 21.9 mM) with 10 L/min of supplemental oxygen via face mask. Laboratory findings revealed the following data: WBCs 15,080/μL (neutrophils, 81.2%; lymphocytes, 11.3%); CRP, 20.66 mg/dL; LDH, 669 units/L; Krebs von den Lungen-6, 1,069 units/mL, and procalcitonin, 0.25 ng/mL. None of autoantibodies were positive. SARS-CoV-2 polymerase chain reaction was negative.
Because HRCT on admission showed diffuse GGO with subpleural reticulation and traction bronchiectasis (Fig 3 ), we considered acute exacerbation of preexisting interstitial pneumonia and introduced high-dose steroid therapy with 500 mg of methylprednisolone for 3 days followed by 1 mg/kg of prednisolone that was slowly tapered. After 5 days of mechanical ventilation, the patient was extubated and received high-flow nasal cannula oxygen therapy. HRCT revealed resolution of GGO, but subpleural reticulation and traction bronchiectasis remained. The reticular shadow persisted on the ventral subpleural region where minimal GGO were observed. Considering this HRCT findings and the presence of clubbing, it was suggested that ILD might have been present before the vaccination. On the day 29 from admission, he was discharged home while receiving 25 mg of oral prednisolone and supplemental oxygen.Figure 3 A-C, High-resolution CT scan findings from Case 2. A and B, High-resolution CT scan on admission show diffuse ground-glass opacities with subpleural reticulation and traction bronchiectasis. C, After high-dose steroid therapy, high-resolution CT scan on the day 27 from admission revealed resolution of ground-glass opacities; however, subpleural reticulation and traction bronchiectasis remained.
Case 3
An 81-year-old man, who had been diagnosed with interstitial pneumonia 13 months before received his third dose of BNT162b2 vaccine. He had a former smoking history of 20 pack-years. HRCT revealed honeycombing with subpleural reticulation predominantly in the lung bases, which indicated a clinical diagnosis of idiopathic pulmonary fibrosis (IPF). Because a significant decline in vital capacity was observed, antifibrotic treatment with 300 mg/d of nintedanib had been introduced 7 months before. On the next day of the vaccination, he had fever and dyspnea and was transferred to our hospital. His HRCT revealed wide-spread GGO, which was superimposed on preexisting honeycombing and subpleural (Fig 4 ).Figure 4 A and B, High-resolution CT scan findings from Case 3. A, High-resolution CT scan 4 months before the admission shows honeycombing with subpleural reticulation predominantly in the lung bases, which indicates a clinical diagnosis of idiopathic pulmonary fibrosis. B, On admission, his high-resolution CT scan showed wide-spread ground-glass opacity, which was superimposed on preexisting honeycombing and subpleural reticulation.
On admission, his vital signs were a temperature of 38.9°C, pulse rate of 104 beats/min, respiratory rate of 33 breaths/min, and peripheral oxygen saturation of 80% in ambient air. Laboratory findings revealed the following data: WBCs 9,010/μL (neutrophils, 81.7%; lymphocytes, 9.7%); CRP, 11.41 mg/dL; LDH, 465 U/L; Krebs von den Lungen-6, 1,579 units/mL, and procalcitonin, 0.03 ng/mL. No autoantibodies were detected. SARS-CoV-2 polymerase chain reaction was negative. Because we considered acute exacerbation of IPF (AE-IPF), we introduced high-dose steroid therapy with 1 g of methylprednisolone for 3 days followed by 1 mg/kg of prednisolone that was slowly tapered. His respiratory status improved gradually.
Discussion
We experienced three cases of acute respiratory failure subsequent to SARS-CoV-2 mRNA vaccination, including an autopsied case. Two cases had received a diagnosis of ILD, and one case was considered to have preexisting ILD based on the HRCT finding. We speculated that acute respiratory failure in these patients might be associated with SARS-CoV-2 vaccination.
AE-IPF has been defined as an acute, clinically significant deterioration that develops within <1 month without obvious clinical cause such as fluid overload, left heart failure, or pulmonary embolism.2 Diffuse alveolar damage is the histopathologic feature of AE-IPF, which is characterized by diffuse, bilateral GGO on HRCT. A growing body of evidence now focuses on acute exacerbations of ILD (AE-ILD) other than IPF.3 Based on a shared pathophysiologic evidence, it is accepted generally that AE-ILD can affect various types of ILD but apparently occurs more frequently in patients with an underlying UIP pattern.3 , 4 A recent meta-analysis showed that the UIP pattern was observed predominantly in elderly men with a history of smoking, whereas nonspecific interstitial pneumonia occurred in a younger patient population.5 All the patients were elderly men with a history of smoking. In two cases, the UIP pattern of ILD was indicated pathologically in Case 1 and radiologically in Case 3. We considered that the UIP pattern might be a risk factor of AE-ILD subsequent to SARS-CoV-2 vaccination.
The cause and pathogenesis of AE-ILD remains unclear, but there are distinct risk factors such as infection, mechanical stress, aspiration, and drug toxicity that may trigger the development of acute exacerbation.2 There have been some reports of AE-IPF after influenza A vaccination,6 which suggests that vaccinations may serve as a trigger of acute exacerbation. According to the World Health Organization global pharmacovigilance database, 678 cases of suspected ILD, 159 cases of suspected organizing pneumonia, 12 cases of acute lung injury, and two cases of acute interstitial pneumonitis after BNT162b2 vaccine were reported, whereas no case of AE-ILD was included.7 In addition, a clinical trial of BNT162b2 vaccine showed no case of AE-ILD.1 To the best of our knowledge, four cases of AE-ILD subsequent to SARS-CoV-2 vaccination have been reported.8, 9, 10 All the patients were men who had a smoking history and in whom AE-ILD developed within 9 days after the vaccination, which coincides with the cases described in this report. AE-ILD may develop over weeks.2 Development of respiratory failure over the course of several days may be a hallmark of the postvaccination exacerbation of ILD.
Amiya et al10 reported two cases of AE-ILD after SARS-CoV-2 vaccination. In their report, previous CT scan images showed only slight fibrosis of the lung base in one case and no significant abnormalities in the other. The two cases might not have been exacerbation of chronic fibrotic ILD, which is different from these patients.
In general, AE-ILD has a poor prognosis and is associated with a high mortality rate within 6 to 12 months.2 Although there has been no sufficient evidence-based data, in clinical practice, AE-ILD is often treated with a high-dose corticosteroid therapy, which was administered to each of the four patients previously reported.8, 9, 10 Although the mechanism of mRNA vaccine-induced exacerbation of ILD remains unclear, recent reports suggest that an innate inflammatory response could be induced by the mRNA or by SARS-CoV-2 spike protein.11 , 12 It has also been reported that cross-reactivity of spike proteins with lung surfactants and related proteins may induce pulmonary inflammation.13 Two of the patients received high-dose corticosteroid therapy, and clinical improvement was observed in both, which indicated that the excessive activation of immune system might contribute to the exacerbation.
In two patients, we could not perform lung pathologic testing or BAL, which made the exclusion of various causes of acute respiratory failure such as viral or bacterial infection difficult. In Case 2, the patient became symptomatic 9 days after receiving the vaccination. In addition, the possibility of drug-induced ILD because of oxaliplatin should be considered in Case 1. Drug-induced ILD can also develop acute exacerbation.14 Because CRP and LDH levels were not elevated even after the last dose of oxaliplatin, at least it is unlikely that active oxaliplatin-induced pneumonia was underlying. Taken together, although the vaccination might result in AE-ILD, it cannot be denied that infection or other factors may have been involved.
In conclusion, we experienced three cases of acute respiratory failure in patients with ILD, which could be associated with SARS-CoV-2 mRNA vaccination. Because ILD is considered to be a risk factor for severe COVID-19,15 the benefits of vaccination should outweigh the risks associated with uncommon adverse events. However, clinicians should note the risk and carefully observe patients with ILD after SARS-CoV-2 mRNA vaccination.
Acknowledgments
Financial/nonfinancial disclosures: None declared.
Other contributions:CHEST worked with the authors to ensure that the Journal policies on patient consent to report information were met.
==== Refs
References
1 Polack F.P. Thomas S.J. Kitchin N. Safety and efficacy of the BNT162b2 mRNA Covid-19 vaccine N Engl J Med 383 27 2020 2603 2615 33301246
2 Collard H.R. Ryerson C.J. Corte T.J. Acute exacerbation of idiopathic pulmonary fibrosis: an international working group report Am J Respir Crit Care Med 194 3 2016 265 275 27299520
3 Park I.N. Kim D.S. Shim T.S. Acute exacerbation of interstitial pneumonia other than idiopathic pulmonary fibrosis Chest 132 1 2007 214 220 17400667
4 Miyazaki Y. Tateishi T. Akashi T. Clinical predictors and histologic appearance of acute exacerbations in chronic hypersensitivity pneumonitis Chest 134 6 2008 1265 1270 18689595
5 Ebner L. Christodoulidis S. Stathopoulou T. Meta-analysis of the radiological and clinical features of usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP) PLoS One 15 1 2020 e0226084
6 Umeda Y. Morikawa M. Anzai M. Acute exacerbation of idiopathic pulmonary fibrosis after pandemic influenza A (H1N1) vaccination Intern Med 49 21 2010 2333 2336 21048370
7 VigiAccess. WHO Collaborating Centre for International Drug Monitoring http://www.vigiaccess.org
8 Bando T. Takei R. Mutoh Y. Acute exacerbation of idiopathic pulmonary fibrosis after SARS-CoV-2 vaccination Eur Respir J 59 3 2022 2102806
9 Ghincea A. Ryu C. Herzog E.L. An acute exacerbation of idiopathic pulmonary fibrosis after BNT162b2 mRNA COVID-19 vaccination: a case report Chest 161 2 2022 e71 e73 35131075
10 Amiya S. Fujimoto J. Matsumoto K. Case report: acute exacerbation of interstitial pneumonia related to messenger RNA COVID-19 vaccination Int J Infect Dis 116 2022 255 257 35065256
11 Lu Q. Liu J. Zhao S. SARS-CoV- 2 exacerbates proinflammatory responses in myeloid cells through C-type lectin receptors and Tweety family member 2 Immunity 54 6 2021 1304 1319 34048708
12 Zhao Y. Kuang M. Li J. SARS-CoV-2 spike protein interacts with and activates TLR41 Cell Res 31 7 2021 818 820 33742149
13 Kanduc D. Shoenfeld Y. On the molecular determinants of the SARS-CoV-2 attack Clin Immunol 215 2020 108426 32311462
14 Usui Y. Kaga A. Sakai F. A cohort study of mortality predictors in patients with acute exacerbation of chronic fibrosing interstitial pneumonia BMJ Open 3 7 2013 e002971
15 Drake T.M. Docherty A.B. Harrison E.M. Outcome of hospitalization for COVID-19 in patients with interstitial lung disease: an international multicenter study Am J Respir Crit Care Med 202 12 2020 1656 1665 33007173
| 36494131 | PMC9723271 | NO-CC CODE | 2022-12-07 23:19:59 | no | Chest. 2022 Dec 6; 162(6):e311-e316 | utf-8 | Chest | 2,022 | 10.1016/j.chest.2022.08.2213 | oa_other |
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Chest
Chest
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0012-3692
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American College of Chest Physicians. Published by Elsevier Inc.
S0012-3692(22)01228-4
10.1016/j.chest.2021.12.676
Ultrasound Corner
A 55-Year-Old Man With Progressive Shortness of Breath
Pandompatam Govind MD, RDCS ∗
Qaseem Muhammad MD
Department of Critical Care, HSHS Saint John's Hospital, Springfield, IL
∗ CORRESPONDENCE TO: Govind Pandompatam, MD, RDCS
6 12 2022
12 2022
6 12 2022
162 6 e321e323
© 2022 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.
2022
American College of Chest Physicians
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.
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pmcA 55-year-old man with non-insulin-dependent diabetes, hypertension, and obesity (BMI, 35) presented with 5 days of progressive shortness of breath with exertion and bilateral lower leg pain. He had received a single-shot COVID-19 vaccine 1 week before presentation.
On arrival, his vital signs were temperature, 36.1 °C; pulse, 80 beats/min; BP, 160/101 mm Hg; respiratory rate, 28 breaths/min; oxygen saturation, 82% on room air. He was placed on 4 L nasal cannula. Relevant laboratory analysis included hemoglobin, 15.0 g/dL; creatinine, 1.1 mg/dL; troponin, 0.91 ng/mL; platelets, 176 (× 103/mL).
On examination, the patient appeared ill. He was tachypneic. He had clear lungs bilaterally and mild bilateral lower extremity edema. Point-of-care ultrasound (POCUS) examination of the heart was performed (Video 1).
Question: Based on the findings of the POCUS examination, what is the most likely diagnosis?
Answer: A mobile saddle pulmonary embolus (PE) is seen on the parasternal short axis (PSAX) view at the bifurcation of the main pulmonary artery (PA). A D-shaped interventricular septum along with RV (right ventricle) enlargement and dysfunction in PSAX and four-chamber views is seen. Tricuspid annular plane systolic excursion (TAPSE) is reduced.
The patient’s saddle PE was subsequently confirmed on CT angiography imaging with extension into lobar and segmental branches of all pulmonary lobes and evidence of RV strain (Fig 1 ). Further POCUS 2-point deep venous thrombosis examination showed a right popliteal deep venous thrombosis.Figure 1 CT angiography of the chest, showing patient’s saddle pulmonary embolus with extension into right and left pulmonary arteries.
Given the patient’s low normal platelet count and recent COVID-19 vaccine, initial suspicion for vaccine-induced immune thrombotic thrombocytopenia was considered. He was started on argatroban infusion, and heparin products were avoided. Given his relatively low oxygen requirement and hemodynamic stability, a conservative approach was undertaken, and thrombolytics or catheter-directed approaches were not pursued.
The patient improved clinically over the next 72 h and was weaned off oxygen. Repeat POCUS at 72 h showed resolution of the saddle embolism at the bifurcation of the main PA, along with some improvement in RV function (TAPSE, 1.21 to > 1.41) (Video 2). Platelet count remained stable throughout hospitalization, and associated vaccine-induced immune thrombotic thrombocytopenia testing was negative. The patient was transitioned to apixaban and discharged on hospital day 3.
Discussion
Saddle PE visualization on transthoracic ultrasound is an infrequent finding, and only a small number of cases have been described.1, 2, 3, 4 A saddle PE is defined as a PE occurring at the bifurcation of the main pulmonary artery. Approximately 5% of patients with PE present with a saddle PE.5 Although most patients with saddle PE have RV dysfunction, overall mortality appears to be low and maybe not as deadly as traditionally thought.6 In a retrospective analysis, there was no significant difference in mortality or hospital length of stay between saddle PE and non-saddle PE groups; however, patients with saddle PE did have increased use of systemic thrombolysis for late (> 6 h after admission) decompensation.7 Patients with saddle PE can have a large clot burden, and thus early detection and treatment along with close clinical monitoring for decompensation is essential.
The PSAX view of the pulmonic valve and main pulmonary artery can sometimes be difficult to obtain, particularly in obese and tachypneic patients in a point-of-care setting. However, with practice and optimal patient positioning, the pulmonic valve and main PA bifurcation can be visualized in most patients with a POCUS examination. In patients with suboptimal parasternal views because of lung hyperinflation or body habitus, the main PA can be visualized from a subcostal short axis view as well. Although finding a saddle PE on POCUS is rare, we believe that rigorous evaluation with 2D (two-dimensional) and Doppler imaging of the right ventricular outflow tract (RVOT), pulmonic valve, and main PA up to and including the bifurcation should be attempted in all suspected patients with PE in addition to classic RV dysfunction parameters.
Video 1, along with showing the large saddle PE, illustrates several of these classic RV dysfunction parameters seen in PE. A D-shaped interventricular septum predominantly in systole, reflecting RV pressure overload, is visualized in PSAX. An increased RV:LV ratio (seen here increased approximately to 1:1), occurs because of a thin-walled RV that is unable to cope with the sudden increase in RV afterload caused by a PE. In addition, the RV longitudinal contraction is reduced, characterized by a decreased TAPSE, measured in M-mode from the apical four-chamber view.
Other indirect echocardiographic signs of PE using 2D and Doppler imaging such as McConnell’s sign and 60/60 sign are often relied on during POCUS to help diagnose PE, but no one sign has shown adequate diagnostic accuracy for a definitive diagnosis. Renewed interest has been seen in notching patterns of the RVOT pulse wave (PW) profile as another potential clue for PE. “Notching” of the PW signal is often found in pulmonary hypertension and can give insight into the hemodynamics of pulmonary vascular disease.8 Early systolic notching is an RVOT PW Doppler pattern that has been shown to be helpful in identifying massive and submassive PE.9 The early systolic notching “spike and dome” appearance may reflect the early arrival of pressure wave reflection caused by submassive and massive PE in the pulmonary vasculature.9 , 10 The patient exhibited this disturbed RVOT ejection pattern (Fig 2 ), and this was helpful to confirm our 2D findings. Qualitative RVOT PW Doppler waveforms can be interrogated in patients suspected of having submassive or massive PE.Figure 2 Early systolic notching (red arrow) pulse wave Doppler pattern seen in right ventricular outflow tract with “spike and dome” appearance.
We repeated the POCUS examination 72 h after initial presentation on the patient, which showed resolution of the saddle embolus. D-shaped septum and RV enlargement had not yet resolved, but TAPSE was improving (Video 2). Such follow-up is not routine or necessary with CT imaging, given risks associated with recurrent radiation and contrast exposure. The noninvasive nature of POCUS allows repeat examinations and, in this case, illustrates the effectiveness of anticoagulation for the treatment of PE. Repeating POCUS examinations also can be helpful in PE cases that are initially stable on admission, but then become acutely unstable later in the hospitalization. Rapid detection of worsening RV failure or other new causes of hemodynamic instability can be detected and treated early. See Narration Video for a detailed explanation of Videos 1 and 2.
Reverberations
1. Saddle PE can occasionally be directly visualized with POCUS .
2. Saddle PE is not always a massive PE or life-threatening .
3. RVOT Doppler waveforms can be evaluated during POCUS for PE .
4. Follow-up POCUS examinations in patients with PE may be helpful to monitor response to treatment or to detect new clinical changes .
Supplementary Data
Video 1
Video 2
Video 3
Acknowledgments
Financial/nonfinancial disclosures: None declared.
Other contributions:CHEST worked with the authors to ensure that the Journal policies on patient consent to report information were met.
Additional information: Videos for this case are available under "Supplementary Data".
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References
1 Secko M. Legome E. Rinnert S. Saddle embolism diagnosed by point-of-care transthoracic echocardiography before computed tomography angiogram of the chest Am J Emerg Med 34 12 2016 2467
2 Kanjanauthai S. Couture L.A. Fissha M. Gentry M. Sharma G.K. Saddle pulmonary embolism visualized by transthoracic echocardiography J Am Coll Cardiol 56 11 2010 e21 20813279
3 Baran J. Kabłak-Ziembicka A. Sobczyk D. Przewłocki T. Gackowski A. Saddle pulmonary embolism diagnosed by bedside transthoracic echocardiography Pol Arch Intern Med 129 5 2019 346 347 30733428
4 Letourneau M.M. Wilczynski S. Rao S. Osman A. Two-for-one saddles: a case of mobile double pulmonary embolism CASE (Phila) 2 2 2018 66 68 30062313
5 Sardi A. Gluskin J. Guttentag A. Kotler M.N. Braitman L.E. Lippmann M. Saddle pulmonary embolism: is it as bad as it looks? A community hospital experience Crit Care Med 39 11 2011 2413 2418 21705903
6 Ryu J.H. Pellikka P.A. Froehling D.A. Peters S.G. Aughenbaugh G.L. Saddle pulmonary embolism diagnosed by CT angiography: frequency, clinical features and outcome Respir Med 101 7 2007 1537 1542 17254761
7 Alkinj B. Pannu B.S. Apala D.R. Kotecha A. Kashyap R. Iyer V.N. Saddle vs nonsaddle pulmonary embolism: clinical presentation, hemodynamics, management, and outcomes Mayo Clin Proc 92 10 2017 1511 1518 28890217
8 Arkles J.S. Opotowsky A.R. Ojeda J. Shape of the right ventricular Doppler envelope predicts hemodynamics and right heart function in pulmonary hypertension Am J Respir Crit Care Med 183 2 2011 268 276 20709819
9 Afonso L. Sood A. Akintoye E. A Doppler echocardiographic pulmonary flow marker of massive or submassive acute pulmonary embolus J Am Soc Echocardiogr 32 7 2019 799 806 31056367
10 Bernard S. Namasivayam M. Dudzinski D.M. Reflections on echocardiography in pulmonary embolism—literally and figuratively J Am Soc Echocardiogr 32 7 2019 807 810 31272591
| 36494133 | PMC9723272 | NO-CC CODE | 2022-12-07 23:19:59 | no | Chest. 2022 Dec 6; 162(6):e321-e323 | utf-8 | Chest | 2,022 | 10.1016/j.chest.2021.12.676 | oa_other |
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S0992-5945(22)00229-X
10.1016/S0992-5945(22)00229-X
Article
Quelle compensation de l’État aux communes pour les centres de vaccination contre la Covid-19 ?
Dalmat Yann-Mickael
6 12 2022
November-December 2022
6 12 2022
32 661 1010
Copyright © 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.
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pmc La députée Valérie Rabault interroge le ministre de la Santé et de la Prévention sur une éventuelle compensation financière de l’État pour les communes, compte tenu des dépenses engagées pour le fonctionnement des centres de vaccination contre la Covid-19.
© Léna Constantin/stock.Adobe.com
Valérie Rabault [1] rappelle qu’au plus fort de la crise sanitaire les communes ont joué un rôle essentiel dans l’accélération de la vaccination de la population, en permettant, à la demande de l’État, l’ouverture de centres de vaccination avec une rapidité et une efficacité remarquables ! En effet, le déploiement de ces centres de vaccination a demandé une forte mobilisation financière de la part des communes, que l’État s’est engagé à compenser. Début 2021, une enveloppe de 60 millions d’euros a ainsi été débloquée, mobilisable par le biais des agences régionales de santé (ARS) et du fonds d’intervention régionale (FIR). Cette enveloppe, qui représente un montant moyen de 46 000 euros pour chacun des 1 300 centres de vaccination ouverts sur le territoire, s’est révélée insuffisante pour compenser l’intégralité des coûts de fonctionnement supportés par les communes. Le 10 mars 2021, Olivier Véran, alors ministre des Solidarités et de la Santé, s’est donc engagé à ce que le « quoi qu’il en coûte s’applique aussi au fonctionnement des centres », confirmant que « ces 60 millions d’euros ne correspondent pas à un solde de tout compte et seront réabondés autant que nécessaire » [2]. Dans une circulaire n° 2021-50, diffusée le 2 avril 2021 [3], le ministère des Solidarités et de la Santé a par ailleurs enjoint les ARS, « afin d’éviter toute tension sur la trésorerie des partenaires portant les centres de vaccination, […] à apporter de la visibilité sur les délais des premiers versements de subvention, dès signature des conventions et à les réduire autant que possible ».
Or, déplore la députée,« à ce jour [question fin septembre 2022], de nombreuses communes n’ont perçu qu’une très faible partie de la compensation financière qu’elles ont sollicitée au regard des coûts réellement supportés pour le fonctionnement de ces centres de vaccination : plus d’un an et demi après le début de la campagne de vaccination, cette situation est difficilement compréhensible ».
Aussi, la parlementaire souhaite connaître le montant de compensation financière engagé par l’État, au niveau national et par les ARS, ainsi que le montant de la compensation sollicité par les communes, et le prie de bien vouloir faire respecter la parole de l’État en compensant intégralement les dépenses que les communes ont engagées, à la demande de l’État, pour garantir le fonctionnement efficace des centres de vaccination.
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Références
1 https://questions.assemblee-nationale.fr/q16/16-1313QE.htm
2 www.senat.fr/cra/s20210310/s20210310_0.html
3 www.apvf.asso.fr/wp-contenl/uploads/2021/04/MINSANTE-50-Remunerations-et-financement-de-la-vaccination_.pdf
| 0 | PMC9723506 | NO-CC CODE | 2022-12-07 23:20:04 | no | 2022 Dec 6 November-December; 32(661):10 | utf-8 | null | null | null | oa_other |
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Elsevier Masson SAS.
S0992-5945(22)00238-0
10.1016/S0992-5945(22)00238-0
Article
Couvertures vaccinales des adultes à risque, prévisions post-Covid-19
Bertholom Chantal Professeur de microbiologie
École nationale de physique-chimie-biologie – Paris
6 12 2022
November-December 2022
6 12 2022
32 661 2325
Copyright © 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.
Si la vaccination contre le Sars-CoV-2 est actuellement une priorité pour lutter efficacement contre cette épidémie, les vaccins hors Covid-19 sont également des objectifs majeurs de santé publique (60 millions de personnes adultes à vacciner) et particulièrement pour les adultes vivant avec une maladie chronique (10 % de la population adulte) à risque accru d’infections.
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pmc © CHERRYANDBEES / STOCK.ADOBE.COM
Vaccination contre la grippe
Des recommandations vaccinales spécifiques existent depuis de nombreuses années en France mais les couvertures vaccinales restent faibles (environ 50 %) avec des disparités régionales importantes (tableau 1). Tableau 1 Données de couverture vaccinale contre la grippe entre 2016 et 2020, par groupe d’âge.
Saison grippale 2016–2017 2017–2018 2018–2019 2019–2020
Âge inférieur à 65 ans à risque 18,7 % 28,9 % 29,7 % 38,7 %
Âge supérieur ou égal à 65 ans 50,0 % 49,7 % 51,0 % 59,9 %
Total 45,7 % 45,6 % 46,8 % 55,8 %
D’après B. Wyplos.
Vaccination contre le pneumocoque
Les infections invasives à pneumocoque de l’adulte font l’objet d’une surveillance. L’âge moyen des patients concernés par ces infections est de 70 ans, la majorité d’entre eux ayant des comorbidités (84 %) et le taux de mortalité est de 21 % (délai médian de décès à cinq jours).
Malgré la disponibilité des vaccins, le taux de vaccination contre les infections à pneumocoque est faible, autour de 10 %, traduisant un problème d’offre vaccinale.
Chez les patients immunodéprimés, une étude prospective effectuée en consultation d’infectiologie au Centre hospitalier universitaire de Saint-Etienne entre 2015 et 2017 a montré que le taux de couverture vaccinale chez ces patients était également faible [1] (tableau 2) .Tableau 2 Taux de vaccination chez les malades immunodéprimés.
Malades immunodéprimés Diphtérie, tétanos, poliomyélite Grippe Pneumocoque (VPC13-VPP23)
Rhumatologie n = 57 93 % 56,1 % 33,3 %
Gastro-entérologie n = 103 85,4 % 19,4 % 1,9 %
Néphrologie n = 137 43,1 % 43,1 % 1,5 %
Infectiologie n = 561 46 % 46 % 6,2 %
Total n = 858 53,4 % 39,7 % 6,2 %
D’après [1].
Depuis 2017, le schéma de vaccination est le même pour tous les adultes à risque, comprenant une injection du vaccin VPC-13, suivi à deux mois d’une injection du vaccin VPP-23 avec un rappel à cinq ans avec le vaccin VPP-23.
Encadré 1. Identification des patients inclus dans l’étude Covarisq.
Comorbidités
• Bronchite chronique obstructive
• Emphysème/asthme grave
• Cardiopathie congénitale
• Insuffisance cardiaque
• Insuffisance rénale chronique
• Hépatopathie chronique
• Diabète non équilibré
• Brèche ostéo-méningée
• Implant cochléaire
Immunodéprimés
Aspléniques
Déficits immunitaires primitifs
Insuffisance rénale chronique
VIH
Chimiothérapie
Transplantés d’organe
Greffe de cellules souches Hématopoïétiques
Traitement par Immunosuppresseurs Syndrome néphrotique
Ces recommandations sont-elles appliquées chez les adultes à risque ?
L’étude Couverture vaccinale des adultes à risque (Covarisq) [2] est une étude rétrospective dont l’objectif principal a été d’estimer la couverture vaccinale nationale annuelle contre les infections à pneumocoque chez les adultes à risque (immunodéprimés ou atteints de comorbidité) de 2014 à 2018.
Cette étude rétrospective transversale a été effectuée à partir des bases de données du Système national des données de santé de patients de plus de 18 ans, présentant un état d’immunosuppression et/ou porteurs de maladies prédisposant la survenue d’une infection à pneumocoque entre le 1er janvier 2014 et le 31 décembre 2018 (encadré 1).
Les résultats de cette étude ont montré qu’en France, en 2018, presque 4 millions de personnes présentaient des comorbidités et 570 000 personnes étaient immunodéprimées, l’ensemble de ces sujets étant à risque de développer des infections sévères à pneumocoque (tableaux 3 et 4). Tableau 3 Patients inclus dans l’étude Covarisq atteints de comorbidités.
Année 2014 2018
Patients atteints de comorbidités 3 299 963 3 634 584
Diabète 2 350 347 (71,21 %) 2 617 003 (72,03 %)
Maladies respiratoires chroniques 603 257 (18,18 %) 616 003 (16,95 %)
Insuffisance cardiaque chronique 356 795 (10,81 %) 424 223 (11,67 %)
Hépatopathie chronique 263 506 (7,99 %) 285 214 (7,85 %)
Insuffisance rénale chronique terminale 50 578 (1,53 %) 58 155 (1,60 %)
Cardiopathie cyanogène 24 698 (0,75 %) 28 467 (0,78 %)
Implant cochléaire 3 683 (0,11 %) 5 482 (0,15 %)
D’après B. Wyplos.
Tableau 4 Patients immunodéprimés inclus dans l’étude Covarisq.
Année 2014 2018
Immunodéprimés 490 556 570 035
Maladies auto-immunes traitées 147 832 (30,14 %) 191 527 (33,60 %)
Cancer et hémopathie maligne 143 371 (29,23 %) 152 255 (26,71 %)
VIH 95 196 (19,41 %) 100 604 (17,69 %)
Transplantés 46 068 (9,39 %) 53 971 (9,47 %)
Déficits immunitaires innés 27 697 (5,65 %) 34 999 (6,14 %)
Asplénie, hyposplénie 29 511 (6,02 %) 33 429 (5,86 %)
Syndrome néphrotique 15 059 (3,02 %) 18 848 (3,27 %)
Greffe de cellules souches 9 771 (1,99 %) 11 381 (2,00 %)
D’après B. Wyplos.
Alors que tous ces patients devraient pouvoir bénéficier de la vaccination anti-pneumococcique (PCV13 + PPV23), la couverture vaccinale est insuffisante et estimée, en 2018, à 2,9 % pour les personnes à risque et à 18,8 % pour les immunodéprimés.
La couverture vaccinale contre le pneumocoque est inférieure à celle de la grippe (45 % en moyenne) pour laquelle des efforts sont encore à faire également, alors que l’offre vaccinale existe et est satisfaisante (médecins traitants, spécialistes) (figures 1 et 2). Figure 1 Couverture vaccinale en 2018 contre la grippe et les infections à pneumocoque des patients avec comorbidités.
D’après B. Wyplos.
Figure 2 Couverture vaccinale en 2018 contre la grippe et les infections à pneumocoque des patients immunodéprimés.
D’après B. Wyplos.
Quelle va être l’importance pour l’année à venir des infections respiratoires hors Covid-19?
Après une quasi-disparition des bronchiolites de l’enfant en 2019–2020, une reprise des infections à virus respiratoire syncytial (VRS) est observée actuellement dans toute la France, onze régions étant déjà en phase épidémique en France métropolitaine.
Concernant la grippe, en 2020–2021, grâce à la fermeture des frontières, aux mesures barrière et à une meilleure protection vaccinale avec une couverture à 55,8 %, il n’y a pas eu d’épidémie de grippe.
En 2021–2022, l’ouverture des frontières, la diminution des mesures barrières et la diminution de la circulation du Sars- CoV-2, une épidémie de grippe est évidemment à craindre, deux virus grippaux (type A) circulant actuellement en ville et 23 virus de type A et un virus de type B circulant déjà à l’hôpital. Concernant les infections bactériennes, il a été observé pendant la Covid-19 une diminution des infections invasives à pneumocoque, H. influenzae et N. meningitidis, probablement liée aux mesures barrière et il est à craindre en 2021–2022 un risque de rebond post-pandémique pour ces infections.
Conclusion
Les couvertures vaccinales contre la grippe et les infections à pneumocoque sont, malgré l’offre de soins, insuffisantes chez les sujets à risque pourtant faciles à identifier (âge supérieur à 65 ans, comorbidités, immunocompromis).
Si les gestes barrières ont été compris et plutôt bien respectés en début d’épidémie de Covid-19, seront-ils pérennes en 2021–2022 pour contrôler le risque de rebond des infections respiratoires et des infections invasives ?
Déclaration de liens d’intérêts : l’auteur déclare ne pas avoir de liens d’intérêts.
source
D’après une communication de B.Wyplosz – Bicêtre, Webinaire « les jeudis de la SFI ». Immunosuppresseurs : risques et vaccinations, 28 octobre 2021.
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Références
1 Pouvaret A. Maillard N. Roblin X. Couverture vaccinale des patients immunodéprimés : des disparités selon les spécialités 2019 CHU de Saint-Étienne Saint- Étienne, France
2 Goussiaume G. Moulin B. Wyplosz B. Covarisq (estimation de la COuverture VAccinale des adultes à RISQues) : taux de vaccination des personnes en insuffisance rénale chronique terminale ou hospitalisées suite à un syndrome néphrotique à l’échelle nationale en 2017 Néphrologie & Thérapeutique 16 5 2020 299 300
| 0 | PMC9723507 | NO-CC CODE | 2022-12-07 23:20:04 | no | 2022 Dec 6 November-December; 32(661):23-25 | utf-8 | null | null | null | oa_other |
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J Environ Radioact
J Environ Radioact
Journal of Environmental Radioactivity
0265-931X
1879-1700
Elsevier Ltd.
S0265-931X(22)00267-3
10.1016/j.jenvrad.2022.107076
107076
Article
Development of a “222Rn incremented method” for the rapid determination of air exchange rate using soil gas
Kumara K. Sudeep a∗
Karunakara N. a
Mayya Y.S. b
a Centre for Advanced Research in Environmental Radioactivity, Mangalore University, Mangalagangothri, 574199, Mangalore, India
b Department of Chemical Engineering, IIT-Bombay, Mumbai, 400 076, India
∗ Corresponding author.
6 12 2022
2 2023
6 12 2022
257 107076107076
1 7 2022
12 11 2022
16 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 air exchange rate (AER) is a critical parameter that governs the levels of exposure to indoor pollutants impacting occupants’ health. It has been recognized as a crucial metric in spreading COVID-19 disease through airborne routes in shared indoor spaces. Assessing the AER in various human habitations is essential to combat such detrimental exposures. In this context, the development of techniques for the rapid determination of the AER has assumed importance. AER is generally determined using CO2 concentration decay data or other trace gas injection methods. We have developed a new method, referred to as the “222Rn incremented method”, in which 222Rn from naturally available soil gas was injected into the workplace for a short duration (∼30 min), homogenized and the profile of decrease of 222Rn concentration was monitored for about 2 h to evaluate AER. The method was validated against the established 222Rn time-series method. After ascertaining the suitability of the method, several experiments were performed to measure the AER under different indoor conditions. The AER values, thus determined, varied in a wide range of 0.36–4.8 h−1 depending upon the ventilation rate. The potential advantages of the technique developed in this study over conventional methods are discussed.
Keywords
Soil gas
Incremented 222Rn
Indoor workplace
AER
Diurnal
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pmc1 Introduction
The air exchange rate (AER) is an important parameter closely related to the health of populations exposed to indoor pollutants. With the unfolding of the recent outbreak of the COVID-19 pandemic, the importance of understanding the airborne infection transfer mechanism has become increasingly apparent. The importance of indoor ventilation was stressed to minimize the transfer of infections in shared spaces (Anand et al., 2021). The AER is defined as the ratio of volumetric air supply rate Q(t) into a zone (i.e. a room or space) to the volume (V R) of this zone and is generally expressed as air change per hour (h−1) or (ACH or AER). The concentration of a pollutant species in the indoor space varies inversely with the AER. Therefore it is a crucial determinant of indoor air quality. Comfortable and healthy indoor climate conditions can only be achieved by a constant supply of fresh air. AER is one of the parameters considered in several countries while designing buildings for public occupation. However, AER has not been given adequate importance in tropical climatic regions and data on this parameter is virtually non-existent. With rapid industrialization resulting in the degradation of the air quality in the indoor environment, there is an urgent need to establish database on indoor air pollution and AER for different climatic regions. Such studies are expected to serve a vital role in understanding and modelling the transmission of viral-related diseases such as the COVID-19 pandemic presently affecting the world.
In the past, uses of gases such as helium, hydrogen, oxygen, carbon monoxide, methane, acetone, and the radioactive noble gases 41Ar and 85Kr have been examined for the determination of AER. Excellent reviews on these are given by Grimsrud et al. (1980), Shaw (1984), and Sherman (1990). Until the early 1990s, 85Kr was used for AER measurement (Schulze and Schuschke, 1990) but was later discontinued due to safety and economic reasons. Subsequently, the application of Nitrous oxide (N2O) (Abu-Jarad et al., 1982), Sulfar hexafluoride (SF6) (Nero et al., 1983), Hexafluorobenzene (C6F6) (Hirsch et al., 2000), Perfluorocarbons (PFC) (Raatschen, 1995), and Carbon dioxide (CO2) (Munzenberg., 2004) have been explored. However, these methods have several disadvantages such as (i) the need for injecting an external tracer in occupied buildings, (ii) adsorption on surface or wall deposition of the injected chemical tracer, (iii) environmentally hazardous and expensive, and (iv) the tracer concentration is influenced by the number of occupants in the rooms being examined for AER.
Nitrous oxide (N2O) was widely used for AER measurement in buildings, primarily in Europe (Heidt and Werner, 1986; Keller and Beckert, 1994; Salthammer, 1994; Wegner, 1983, 1984) and in the U S A (Lagus and Grot, 1997). Often injection of this gas above 100 ppm is necessary for determining the AER with reasonable accuracy, but this concentration is above the permissible limit of 50 ppm for the living environment (Detlef et al., 2011; Raatschen, 1995). Other disadvantages of N2O are the ease of adsorption on the surface of the walls and its high solubility in water, which leads to an overestimation of the AER (Schulze and Schuschke, 1990). The SF6 has been widely used for air infiltration measurement in buildings since the early 1970s (Drivas et al., 1972; Hunt and Burch, 1975) as it can be measured reliably on the nanogram scale with electron capture detection (ECD) (Gregory, 1962). On the other hand, due to its high stability, degradation of SF6 in the atmosphere is prolonged, and it should be used with caution, sparingly, and at low concentrations. CF6 and PFT have also been used as tracer gases for the determination of the AER with the constant injection method. However, the disadvantage of these compounds is their attachment to room surfaces and the emission rates of these gases are strongly temperature-dependent (Hill et al., 2000). CO2 is often used to determine the indoor AER because it can be easily measured and quantified (Detlef et al., 2011).
Although CO2 fulfils a number of the specifications of good tracer gas; Dols et al. (1992), Nabinger et al. (1994), and Persily (1997) have illustrated that AER cannot be consistently determined with this method since its concentration is highly influenced by the number of inhabitants in the rooms, their time of stay, and other factors, and, hence, the incessantly changing CO2 supply rates. The metabolically generated CO2 overlaps with the externally injected CO2 leading to uncertainty in AER measurement. Furthermore, depending on the extent of natural ventilation, the AER may be overestimated up to 2-fold (Detlef et al., 2011). Hence, efforts are constantly made to develop new methods for the estimation of AER.
Radon (222Rn) has the potential to be used as a tracer to study indoor AER with several advantages. They are: (i) it is a naturally occurring radioactive gas and ubiquitous in indoor as well as the outdoor environment, (ii) easily measurable even at very low concentration levels by passive and active monitors, (iii) the mechanism of its transportation in the indoor and outdoor atmosphere is very well understood, (iv) its concentration is independent of human presence. Although the concentration of 222Rn undergoes diurnal and seasonal variation, and also depends on ambient temperature, humidity, pressure, etc., apart from the ventilation rate of the building, these can be adequately addressed by time-series analysis (TSA) methods. There are four possible methods through which 222Rn can be used for determining AER: (i) by examining the monthly average equilibrium factor between 222Rn and its decay products, which would yield a pseudo-ventilation rate (Shaikh et al., 1992), and from this wall loss rate can be subtracted to yield air exchange rate, and (ii) measurement of wall exhalation rate using active 222Rn monitors (from the knowledge of surface averaged wall exhalation rate, the ratio of indoor surface area to the volume, and the difference between the averaged indoor 222Rn concentration to the outdoor concentration, one can estimate the AER), and (iii) analysis of long term time-series data of 222Rn concentration in indoor air (established using online continues 222Rn monitors) in which the spectral component and fundamental frequency of the time-series is related to mean ventilation rate.
These methods have known disadvantages. The 222Rn progeny measurements and the use of equilibrium factors have significant uncertainties as the progeny concentrations are influenced by their deposition rates on walls and aerosol concentrations. Besides, the method requires long-term monitoring (typically more than a month). The 222Rn wall exhalation measurements method is experimentally challenging and often involves significant uncertainties. We have developed a method which we refer to as the “222Rn incremented method” for rapid determination of AER. In this method, 222Rn is injected into the indoor environment to be investigated and concentration is monitored continuously for ∼2 h. The decay rate (loss of 222Rn due to the exchange with atmospheric air) would directly yield AER. The above method overcomes several disadvantages of the previously explored methods and is simple, user-friendly, and uses 222Rn from the natural soil gas as a tracer.
2 Materials and methods
2.1 Extraction of soil gas
As discussed just above, 222Rn present in soil gas is used as a tracer for the determination of AER. Soil gas is a non-depleting reservoir of 222Rn and is capable of providing steady concentration flux even when drawn continuously for a long time duration, as demonstrated in the previous publications (Karunakara et al., 2015, 2020; Shetty et al., 2020). The details of soil gas 222Rn extraction were described in our earlier publications (Karunakara et al., 2015, 2020; Sudeep et al., 2012; Shetty et al., 2020). A soil gas probe (STITZ, Genitron Instruments, Germany) was inserted inside the ground to a depth of 0.8–1 m since the 222Rn concentration in soil gas increases exponentially with depth and saturates to an equilibrium concentration at a depth greater than about 0.8–1 m (Nazaroff and Nero, 1988; Al-Azmi and Karunakara., 2007; Al-Azmi, 2009; Sudeep et al., 2012; Katalin et al., 2013; Shetty et al., 2019; 2020; Karunakara et al., 2015, 2020). A pump with adjustable flow rates (10–80 L min−1, depending upon the experimental requirements) was connected to the probe to draw the soil gas and inject it into the workplace as shown in Fig. 1 . The outlet of the soil gas probe was connected to an air pump through a progeny filter, moisture filter, and a flow meter with a controller. Soil gas was drawn through this arrangement and injected into the indoor workplace. The use of soil gas as a source of 222Rn has several advantages and was discussed in Karunakara et al. (2020) and in brief, they are (i) 222Rn is naturally available in the soil gas as a source, (ii) soil gas is capable of supplying steady 222Rn concentration over a long period without any dilution in the concentration, (iii) there is no need of regulatory approvals from national regulatory bodies for the use of soil gas, and (iv) the health risk associated with a short-term injection of 222Rn into the dwelling is insignificant.Fig. 1 Experimental set-up for harvesting soil gas as a natural source of 222Rn for the determination of AER in the indoor workplace.
Fig. 1
2.2 Selection of indoor workplaces and dwellings
Different indoor workplaces such as classrooms, staff rooms, and laboratories within the Mangalore University campus (12.82° N, 74.92° E) were chosen for this study based on the size, type of occupancy, and extent of ventilation. The volume of the workplaces was in the range of 28.9–146.2 m3. In addition to this, some residential buildings were investigated in the urban and rural regions of Mangalore, West Coast of India. The building materials used in investigated buildings were laterite brick walls, cement plastering, and concrete roofs. The floorings were laid either with tiles, granite or marble.
2.3 Instruments for 222Rn measurement
The 222Rn concentration was continuously monitored using two different types of monitors: (i) scintillation cell-based monitors (Smart RnDuo, AQTEK SYSTEMS, India) and (ii) ionization chamber-based monitors (AlphaGuard PQ2000PRO and AlphaGuard Professional, Saphymo, Germany). These instruments have the capability to simultaneously record ambient humidity (%), temperature (◦C), and pressure (mbar). The 222Rn detection range of the scintillation cell-based monitor was 8 Bq m−3 to 50 MBq m−3, while the ionization chamber-based monitor was 2 Bq m−3 to 20 MBq m−3. The detailed specification of these instruments was published previously (Al-Azmi and Karunakara, 2007; Karunakara and Al-Azmi, 2010; Karunakara et al., 2015; Sudeep et al., 2017; Sahoo et al., 2017; Shetty et al., 2019).
Periodic calibration of these instruments was performed at Bhabha Atomic Research Centre (BARC), Mumbai, using a standard solid flow-through type 226Ra source of activity 110.6 kBq (model RN -1025, Pylon electronics, Ottawa, Canada). The readings provided by the scintillation-based 222Rn monitor and ionization chamber-based monitors were in good agreement (the deviation between the 222Rn data recorded by the two types of monitors was <2%). The scintillation cell-based monitor was operated in flow mode with an operational cycle interval of 15 min, and the ionization chamber-based monitor was in 10 min flow mode.
The detection of 222Rn by these instruments is unaffected by humidity and other trace gases (CO2, CH4, etc.) that may be present in the soil gas, unlike the electrostatic collection-based 222Rn and 220Rn monitors (Karunakara et al., 2015).
2.4 Experimental setup for determination of AER
Two sets of experiments were performed in this study. In the first set, experiments were performed to evaluate the AER by the 222Rn time-series method by continuously recording the indoor ambient 222Rn concentration for the long-term to generate the time-series of concentration values. In the second set of experiments, the application of the 222Rn incremented method was examined for the determination of AER. These are discussed in the following sections.
2.5 Application of indoor ambient 222Rn for determination of AER
In this method, long-term and continuous measurements of 222Rn were performed using an ionization chamber based-monitor to record variations in the ambient background concentrations. Meteorological parameters such as air temperature (◦C), pressure (mbar), and relative humidity (%) were also recorded simultaneously. The time-series database on 222Rn and its periodicity were subjected to autocorrelation analysis. Autocorrelation analysis is a mathematical technique for detecting repeating patterns, such as the presence of a periodic signal disguised by noise or identifying a signal's missing fundamental frequency inferred by its harmonic frequencies. It is an excellent method of getting smooth periodicity. The inverse of the decay constant can be interpreted as AER by performing the exponential decay fit for the detrended 222Rn data with respect to time. The use of the 222Rn time-series technique for evaluating AER was applied by several investigators previously (Bobby, 1991, 1993; Hobbs, 1999; Antonio et al., 2008; Perrier et al., 2004; Michael et al., 2018; Yarmoshenko et al., 2015, 2016, 2021) and is one of the proven methods.
2.6 Determination of AER by 222Rn incremented method
In the 222Rn incremented method, developed in this study, soil gas was drawn, as explained in section 2.1, and injected into the indoor workplace under investigation for nearly 30 min. The injected 222Rn was allowed to mix uniformly with the indoor air using a circulating fan. Through the injection of soil gas, the 222Rn concentration in the indoor air was increased to slightly elevated levels (1–2 kBq m−3) when compared to its concentration in ambient air. After thorough mixing for ∼15 min, the concentration in the indoor workplace was monitored continuously until it decreased to the ambient background level. The rise in the 222Rn concentration due to the injection of soil gas and its subsequent decrease was monitored continuously (for every 1-min cycle) along with meteorological parameters until the concentration values reached the ambient background level, which was about 2 h.
The measured 222Rn concentration values were plotted as a function of time, and the exponential decay fit to the data was performed to yield residence time (t1) of 222Rn in the indoor air. The inverse of residence time (1/t1) is the AER. It is important to note that the loss of 222Rn injected into the indoor air due to radioactive decay (half-life = 3.82 d, decay constant = 0.0075 h−1) is negligible when compared to that due to the exchange with atmospheric air or AER.
The equation used to calculate the AER is:(1) y=A*exp(−xt1)+y0
where A, t1 and y0 are the parameters to be obtained by best fit, t1 represents the mean residence time, and its inverse 1/t1 represents the AER.
2.6.1 Monitoring spatial distribution of injected 222Rn
Experiments were carried out to check the homogeneous mixing of injected 222Rn by simultaneous measurements at five different locations within the workplace environment. For this, 222Rn was injected into the room for nearly 30 min and was thoroughly mixed with ambient air of the room using a circulating fan for about 15 min (2000 rpm). Subsequently, the 222Rn concentration was measured simultaneously at five different locations within the indoor workplace using scintillation and ionization-based monitors.
2.6.2 Evaluation of thoron (220Rn) interference in 222Rn measurement
Although the 220Rn interference in the 222Rn measurements is not expected due to its very short radioactive half-life (55.6 s), experiments were carried out to confirm this as well. This was achieved by simultaneous monitoring of both radioactive gases during the experiment. Both scintillation and ionization based monitors were deployed to measure 222Rn concentration, and the 220Rn concentration was measured using a scintillation cell-based thoron monitor (Model: STM, manufacturer: Electronic Enterprises Limited, India). The details of the instrument and technique of 220Rn measurements were presented in a previous publication (Gaware et al., 2011, 2013; Karunakara et al., 2015; Sudeep et al., 2017). The STM is a continuous thoron monitor that uses the principle of coincidence counting between the alpha particles produced from the 220Rn and 216Po combined with gross alpha counting in a scintillation cell. It has a minimum detection limit (MDL) of 5 Bq m−3 for 1 h counting time. It was calibrated against a standard concentration generated in a calibration chamber using a standard source of 220Rn (Pylon, Canada, Model TH -1025) as well as inter-compared with reference equipment - RAD-7 (Model: DURRIDGE RAD7).
2.6.3 Measurement of diurnal variation of AER
The 222Rn incremented method was explored to evaluate the variations in the AER during day and night. For this, experiments were performed during different time slots (5–8 h, 8–11 h, 11–14 h, 14–17 h, 17–21 h, 21-2 h, and 2–5 h) in the 24 h duration of a day. In this case, 222Rn gas was injected, and the experiment was performed to determine the AER, as explained in the previous section.
3 Results and discussion
3.1 AER evaluation by measurement of ambient 222Rn concentration (time-series method)
The 222Rn concentration, temperature, relative humidity, and pressure in both indoor and outdoor locations were measured as outlined in sections 2.3-2.5. The results are presented in Table 1 . Measurements were performed in 15 workplaces and 11 dwellings using either scintillation or ionization-based monitors, or both instruments simultaneously depending on the availability. The duration of measurement in each location varied from 20 to 30 d. For these measurements, the instruments were operated in diffusion mode in which the 220Rn does not interfere with 222Rn measurements since the former is discriminated from entering the detector active volume (Karunakara et al., 2015, 2020; Sudeep et al., 2012; Shetty et al., 2020). A good agreement between the measurements recorded using these two types of instruments was ascertained by subjecting the 222Rn concentration data sets obtained from the instruments to statistical analysis. The ANOVA and F- tests confirmed that the mean values of the two data sets were not significantly different at 95% confidence level (for α = 0.05, Fobserved = 0.003 and F0.05 (2, 72) = 3.123).Table 1 Radon concentration and meteorological parameters at indoor and outdoor.
Table 1Measurement location 222Rn concentration(Bq m−3) Relative humidity (%) Temperature (°C)
Range mean Range mean Range mean
Indoor 2–84 38.5 ± 20.9 69–75.5 72.5 ± 1.8 27.1–30 29.9 ± 1.4
Outdoor 1.6–35.7 12.5 ± 7.6 60.8–78.7 69.5 ± 5.6 26.3–30.9 28.9 ± 1.7
The time-series dataset for one of the workplaces is presented in Fig. 2 as a representative result. The variation of 222Rn concentration, relative humidity, and temperature are also presented in the same figure. As expected, the time-series data exhibited the dependence of 222Rn concentration values with temperature and humidity. These are the most important parameters which affect the 222Rn concentration (Hubbard et al., 1992; Singh et al., 2005; Ju et al., 2018; Pranas et al., 2020), and it indirectly influences the AER. A sine fit (of the form Y <svg xmlns="http://www.w3.org/2000/svg" version="1.0" width="20.666667pt" height="16.000000pt" viewBox="0 0 20.666667 16.000000" preserveAspectRatio="xMidYMid meet"><metadata> Created by potrace 1.16, written by Peter Selinger 2001-2019 </metadata><g transform="translate(1.000000,15.000000) scale(0.019444,-0.019444)" fill="currentColor" stroke="none"><path d="M0 440 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z M0 280 l0 -40 480 0 480 0 0 40 0 40 -480 0 -480 0 0 -40z"/></g></svg> Y0+A*Sin [π*(x-xc)/w], where xc is the phase shift, w is the period, A is the amplitude, and Y0 is the offset) was used to extract the underlying periodicity in the dataset. It depicts the existence of a semi-sinusoidal diurnal variation trend in the indoor 222Rn concentration with a periodicity of ∼24 h. After detrending the data with respect to periodic variations, the information on AER was extracted by performing autocorrelation. The data set was first smoothened by Fast Fourier Transform (FFT), using Mathematica computer software (Wolfram Mathematica 7, USA). Basically, detrending eliminates a time-series trend component (removing the non-stationary time-series trend), where a trend refers to a change in the mean over time (a continuous decrease or increase over time). Since air exchange is a first-order process one can extract the 1/t value (which is nothing but AER) from the exponential fit to the plot of detrended 222Rn data as shown in Fig. 3 . The AER values, thus obtained, are presented in Table 2 . Depending upon the type of indoor workplace and dwellings, the AER values varied in a wide range of 0.36–4.8 h−1. The values reported by previous investigators for indoor workplaces, dwellings, and caves by the 222Rn time-series method vary in the range of 0.1–2.0 h−1 (Bobby, 1991, 1993; Hobbs, 1999; Antonio et al., 2008; Perrier et al., 2004; Yarmoshenko et al., 2014, 2021; Vasilyev et al., 2015, 2016).Fig. 2 Time series data of 222Rn concentration, relative humidity, and temperature.
Fig. 2
Fig. 3 Exponential decay fit for the detrended 222Rn time-series to extract AER.
Fig. 3
Table 2 AER values derived from222Rn time-series method for different workplaces and dwellings.
Table 2Sl No Measurement location Rooftop Number of windows Number of doors Volume of the indoor (m3) AER(h−1)
1 Workplaces concrete 2 (C) 2 41.5 0.97 ± 0.06
2 concrete 4 (C) 1 113.4 1.15 ± 0.06
3 concrete – 3 27.5 3.40 ± 0.17
4 concrete 2 (C) 2 124.3 0.78 ± 0.04
5 False ceiling 2 (C) 1 92.0 0.72 ± 0.04
6 False ceiling 2 1 94.0 3.02 ± 0.16
7 False ceiling 2 (C) 1 94.0 0.38 ± 0.02
8 False ceiling 2 (C) 1 95.5 1.90 ± 0.10
9 concrete 2 1 29.8 2.20 ± 0.10
10 False ceiling 2 1 71.8 4.00 ± 0.10
11 False ceiling – 3 88.5 0.54 ± 0.03
12 False ceiling 4 (C) 1 141.0 0.63 ± 0.03
13 False ceiling 4 (C) 1 146.2 0.40 ± 0.02
14 False ceiling 4 (C) 1 138.7 0.36 ± 0.01
15 False ceiling 4 (C) 2 118.5 0.86 ± 0.05
16 Dwellings False ceiling 2 1 56.0 0.73 ± 0.04
17 concrete 2 (C) 1 52.9 0.62 ± 0.03
18 concrete 2 1 43.7 2.40 ± 0.10
19 concrete 1 (C) 1 42.6 1.15 ± 0.06
20 concrete 2 1 39.5 4.80 ± 0.30
21 Tiles (mud) 1 1 33.4 2.40 ± 0.10
22 concrete 2 1 42.5 4.60 ± 0.30
23 concrete 1 1 34.4 1.51 ± 0.07
24 Tiles (mud) 2 1 29.5 0.74 ± 0.04
25
26 concrete
concrete –
2 (C) 1
1 28.9
32.3 0.66 ± 0.03
0.40 ± 0.02
(C): windows closed all the time.
3.2 Radon incremented method for AER measurements –the new method
Determination of the AER through 222Rn time-series data has associated disadvantages due to its necessity to perform measurements for a long duration, typically for more than 30 d. However, in situations where the AER is to be determined quickly, methods capable of yielding accurate results in a rapid time are essential. The 222Rn incremented method, developed in this study, fulfils this requirement and is hence advantageous. Before conducting experiments on AER, it is essential to ensure that the 222Rn injected from the soil gas is distributed uniformly within the workplace volume. It is also important to ensure that the 220Rn interference is minimal in 222Rn measurements. Hence experiments were conducted to study these aspects.
As explained in section 2.6.1, the soil gas was injected and mixed with indoor air using the circulating fan and the 222Rn concentration was monitored at five different locations within the indoor workplace to check the homogeneous distribution of the injected 222Rn using two ionization-based monitors, and three scintillation-based monitors. The results of these measurements are presented in Fig. 4 . The 222Rn concentration data sets obtained from the instruments were subjected to statistical analysis. The ANOVA and F- tests confirmed that the mean values of the five data sets were not significantly different at a 95% confidence level (for α = 0.05, Fobserved = 0.004 and F0.05 (4, 120) = 2.447). This confirmed a homogeneous distribution of 222Rn in the indoor air of the workplace.Fig. 4 Variation of 222Rn concentration at five different locations within a workplace. Simultaneous monitoring to check the homogeneous mixing of 222Rn injected from soil gas.
Fig. 4
It is important to ascertain that 220Rn interference in the 222Rn measurements is negligible in the incremented method because (i) instruments are operated in flow mode without 220Rn discriminator, and (ii) 220Rn is also present in soil gas in higher concentration when compared to that in ambient air. The experimental arrangements were such that the transit time for the soil gas in the conduit from the point of extraction to the injection point in the room was ∼10 s (Karunakara et al., 2015, 2020; Sudeep et al., 2012; Shetty et al., 2020). But, the important aspect is the delay volume offered by the indoor workplace which ensures that 220Rn concentration is decayed to a negligibly small value. Moreover, after the injection of the soil gas the injected soil gas was allowed to mix homogenously for 10–15 min using mixing fans. This allowed sufficient time for the decay of 220Rn present in the injected air. To confirm this, several experiments were performed as explained in section 2.6.2. The results for one of the experiments are presented in Fig. 5 as a representative one. After the injection of the soil gas, the 222Rn concentration reached a maximum value of 1.7 kBq m−3. On the other hand, the average 220Rn concentration remained negligibly low (54 ± 24 Bq m−3) when compared to 222Rn. Therefore, the contribution of 220Rn interference on 222Rn measurement can be ignored.Fig. 5 Comparison of 222Rn and 220Rn concentrations within a workplace (simultaneous monitoring to quantify 220Rn interference in the 222Rn measurements).
Fig. 5
Upon confirming good uniformity of injected 222Rn within the workplace volume and negligible influence of 220Rn on 222Rn measurements, several experiments were conducted to check the suitability of incremented method in eight indoor workplaces that are expected to have different AER depending upon the number of windows and doors (section 2.6). As a representative result, the 222Rn concentration profile for one of the workplaces is presented in Fig. 6 . Depending upon the type of the workplace (size, number of open doors and windows in the workplace), the AER varied in a wide range of 0.37–3.47 h−1.Fig. 6 Exponential decay fit for the measured 222Rn concentration data (incremented method) to extract AER. The corresponding AER value, thus determined, was 3.47 h−1.
Fig. 6
3.3 Validation of 222Rn incremented method and advantages
The validation of the incremented method was achieved through the comparison of the AER results with those obtained from the 222Rn time-series method. As enumerated in sections 2.5 and 3.1, the 222Rn time-series method is considered to be a proven method (Yarmoshenko et al., 2014, 2021; Vasilyev and Zhukovsky, 2013; 2015, 2016). Fig. 7 presents the correlation between the AER values obtained from the two methods. The linear fit to the dataset yielded a significant positive correlation between the two methods with a correlation coefficient r = 0.998 (p = 0.0001, slope = 1.025). Also, the ANOVA and F- tests confirmed that the mean values of the two data sets were not significantly different at a 95% confidence level (for α = 0.05, Fobserved = 0.002 and F0.05 (1, 14) = 4.6). These observations confirm that the 222Rn incremented method is capable of yielding accurate results and hence gainfully applied for the rapid determination of the AER in indoor workplaces. The added advantages of this method is that the naturally available 222Rn from soil gas was utilized as a tracer, which is very convenient to use.Fig. 7 Correlation between AER values obtained from 222Rn time-series (horizontal axis) and 222Rn incremented method (vertical axis).
Fig. 7
3.4 Possible radiation dose due to 222Rn incremented method to the indoor occupants
In the present study, the experiments were performed when the workplaces were not occupied by the individuals. However, it is worth noting that the additional radiation dose to the occupant due to the use of 222Rn for the experiments would be negligibly small even in situations in which experiments are performed when the workplace is occupied. For an indoor 222Rn concentration of 2 kBq m−3 due to the injection of soil gas, as the case in the present study, the resulting additional radiation dose was computed to be 25 μSv. This was arrived at by considering an exposure duration of 2 h, an equilibrium factor of 0.4 (UNSCEAR, 2000), and using the dose coefficient of 16.8 nSvh−1(Bqm−3)−1 (ICRP-137, 2017). It may be noted that the value of 25 μSv is a higher estimate since the average concentration corresponding to the experiment duration was 300–400 Bq m−3 (the average concentration is calculated for the experimental duration τ = 2 h, with mean residence time = 1/λ, a typical ventilation rate of 0.5 h−1). The one-time exposure of 25 μSv is about two orders of magnitude lower than a typical annual baseline exposure of 2.34 mSv to the population in normal background radiation areas (UNSCEAR, 2000). Moreover, it is well within the variability of dose to an individual from natural background radiation, which varies from 1 to 10 mSv (UNSCEAR, 2000). It is also within the regulatory framework which permits the use of radiation at very low levels of exposure, such as the one presented here, for applications which are of benefit to society.
However, we recommend that the experiments to measure AER are conducted when the workplaces or dwellings are not occupied to ensure that there is no additional radiation burden to the individuals. As shown in Fig. 6, the incremented 222Rn concentration level due to the injection of soil gas reduced to the ambient level in ∼120 min. The individuals may be allowed to return to the workplace or dwelling after confirming that the 222Rn concentration level has reduced to ambient levels. By exercising this precaution one can avoid additional radiation burden to the inhabitant due to the AER measurements.
3.5 Evaluation of diurnal variation of AER in the indoor workplace
The 222Rn incremented method based AER measurements were performed continuously for 20 d in one of the indoor workplaces to evaluate the diurnal variation in the AER. In these measurements, the daytime was divided into four time slots (5–8 h, 8–11 h, 11–14 h, and 14–17 h), and similarly, nighttime was divided into three time slots (17–21 h, 21-2 h, and 2–5 h). The typical variations of the 222Rn concentration profiles for different time slots are presented in Fig. 8 , along with corresponding AER values. As expected the AER values were higher during daytime when compared to those recorded for nighttime. The mean value of AER for the daytime was 0.87 h−1, and that for nighttime was 0.46 h−1. The two-fold higher AER values during daytime can be attributed to the higher ventilation rates since the doors and windows were kept open during the daytime.Fig. 8 Variation of AER values determined from the 222Rn incremented method for different time slots of a day. (a) For the daytime (5–8 h, 8–11 h, 11–14 h, and 14–17 h), and (b) for the nighttime (17–21 h, 21-2 h, and 2–5 h).
Fig. 8
3.6 Comparison of AER values with previously reported data
Several earlier studies have reported the AER values, and these values are listed and compared with the values observed in the present study in Table 3 . The reported data are grouped based on the methods adopted (i) 222Rn time-series method, (ii) 222Rn decay products method, (iii) gas sensing technique, (iv) constant injection method, (v) concentration decay and build-up methods, (vi) steady-state injection method and (vii) decay method (non-radioactive gas). The reported values of the AER have a wide range from 0.02 h−1 to 84.1 h−1. The values observed in the present study are similar to those reported by Abu-Jarad et al. (1982), Nero et al. (1983), Christopher et al. (1997), Giesbrecht et al. (1999), Salthammer (1994), Shaikh et al. (1992), Gabriel et al., 2010, Gabriel et al., 2016. On the other hand, the values recorded in the present study are lower when compared to those reported by Samer et al. (2014) and higher when compared to those reported by Giesbrecht et al. (1999), Helm et al., (2011), Fronka et al. (2011), Munzenberg., (2004), Benedettelli et al. (2015), Men et al., (2020).Table 3 Comparison of AER values obtained in the present study with those reported in the literature.
Table 3Country Method Tracer gas AER(h−1) Reference
Mangalore India 222Rn incremented method 222Rn 0.37–3.47 Present study
Mangalore India 222Rn time-series method 222Rn 0.36–4.8 Present study
Russia 222Rn 0.1–0.6 Yarmoshenko et al. (2014)
Russia 222Rn 0.26–0.52 Vasilyev et al. (2016)
USA 222Rn 0.35–2.0 Hobbs (1999)
France 222Rn 0.96–5.28 Perrier et al., 2004
India 222Rn decay products method 222Rn decay products 0.42–4.46 Shaikh et al. (1992)
UK Gas sensing technique Freon11
Freon 12
N20
Halothane 0.93–2.89 Abu-Jarad et al., 1982
Norway Constant injection PFT 0.15 Oie et al., 1998
Denmark PFT 0.16–0.96 Andersen et al. (1997)
Germany C6F6 0.73 Hirsch et al. (2000)
Denmark PFT,
PMCP, and PMCH 0.1–2.70 Beko et al., 2016
China CO2 0.84 Pok et al., 2014
Denmark Concentration decay and build-up CO2 0.1–1.0 Beko et al., 2010
China Steady-state injection method CO2 0.12–11.2 Yan et al. (2012)
China decay method CO2 0.29 Jing et al. (2017)
Germany CO2 0.02–1.98 Helm, 2011
Germany CO2 0.05–1.5 Munzenberg., 2004
China CO2 0.11–10.4 Yan et al. (2012)
Hong Kong CO2 0.2–5.4 (day)
0.2–9.0 (night) Chao et al., 1997
Italy CO2 0.15–0.16 Benedettelli et al. (2015)
China CO2 0.78–1.35 Men et al., 2020
China CO2 0.90 Pok et al., 2014
China CO2 0.41 Cheng and Li. (2018)
Portugal CO2 0.17–0.33 Almeida et al., 2020
Portugal CH4 and CO2 0.13–7.59 Clito (2013)
China SO2 0.81–1.45 Men et al., 2020
USA SF6 0.039–4.10 Wallace et al. (2002)
Czech Republic N20 0.049–0.061 Fronka et al. (2011)
Portugal N2O 0.92–1.01 Giesbrecht et al. (1999)
Portugal H2O 0.22–0.78 Giesbrecht et al. (1999)
Denmark TEE 0.1–1.5 Harving et al. (1992)
Israel SF6 0.03–0.25 Rachel et al., 2014
USA SF6 0.03–1.1 Nero et al. (1983)
Portugal SF6 0.17–0.98 Almeida et al., 2020
Germany NO2 0.1–1.7 Salthammer, 1994
Egypt Krypton-85 17.8–84.1 Samer et al. (2014)
3.7 Applicability of the technique in dense urban environments
Finally, we comment on the applicability of the technique in high-rise buildings in dense urban environments where arrangements to draw soil gas and injection into the indoor environment under investigation would be difficult. In such situations, the soil gas can be transported from a designated point of extraction to the experiment site through leak-proof inflatable airbag arrangements for injection as desired. Such arrangements have been demonstrated previously for calibration experiments for 222Rn measuring devices using soil gas (Al-Azmi, 2009).
4 Conclusion
The technique demonstrated in this study to determine indoor AER using the 222Rn incremented method, utilizing 222Rn from natural soil gas, has yielded encouraging results. The advantages of this technique over conventional methods are: (i) it allows rapid determination of AER, (ii) the use of naturally available soil gas as a source of 222Rn, (iii) the concentration of 222Rn can be measured in trace levels by active measuring systems, and its distribution and transit mechanisms in the atmosphere are very well understood and (iv) the measurement is unaffected due to human presence unlike CO2.
Although in the present study the experiments were performed when the workplaces were not occupied by the individuals, it is worth noting that the additional radiation dose to the occupant due to the use of 222Rn would be negligibly small (<25 μSv) even in situations in which experiments are performed when the workplace is occupied. However, we recommend that the experiments are conducted when the workplaces or dwellings are not occupied to ensure that there is no additional radiation burden to the inhabitants. The individuals may return to the workplace or dwelling after confirming that the 222Rn concentration level has reduced to ambient levels. The results of the present study have confirmed that for a typical AER of 3.47 h−1 the incremented 222Rn concentration level due to the injection of soil gas reduced to the ambient level in ∼2 h.
The AER in typical dwellings and workplaces of the tropical region of the West Coast of India ranged from 0.36 to 4.8 h−1. The technique is sensitive enough to quantify small variations in the AER values, as demonstrated by the diurnal variation studies. A large-scale study based on the new method in the actual indoor environment of both urban and rural regions of the West Coast of India and the comparison of the results with tracer gas techniques is now progressing. The limitations of the technique in the dense urban environment, where drawing soil gas and injecting it into the indoor environment in a high-rise building is difficult, may be overcome by the use of inflatable airbags for transporting the soil gas from the point of extraction to the site under investigation.
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
Data will be made available on request.
Acknowledgement
One of the authors (Dr Sudeep Kumara K) is grateful to the Council of Scientific & Industrial Research (10.13039/501100001410 CSIR-HRDG ), Government of India, for providing financial support. Also, the author would like to thank Mangalore University for its administrative support.
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Ricardo M.S.F. Almeida Eva Barreira Pedro Moreira A discussion regarding the measurement of ventilation rates using tracer gas and decay technique Infrastructure 5 2020 85 10.3390/infrastructures5100085
Sahoo B.K. Sudeep Kumara K. Karunakara N. Gaware J.J. Sapra B.K. Mayya Y.S. Thoron Mitigation System Based on Charcoal Bed for Applications in Thorium Fuel Cycle Facilities (Part 1): Development of Theoretical Models for Design Considerations vol. 172 2017 237 248
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Sherman M.H. Tracer-gas techniques for measurement ventilation in a single zone Build. Environ. 25 4 1990 365374 0360-1323
Sudeep Kumara K. Sahoo B.K. Gaware J.J. Sapra B.K. Mayya Y.S. Karunakara N. Thoron Mitigation System based on charcoal bed for applications in thorium fuel cycle facilities (part 2) Development, characterization, and performance evaluation 172 2017 249 260
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| 36493636 | PMC9723537 | NO-CC CODE | 2022-12-07 23:20:04 | no | J Environ Radioact. 2023 Feb 6; 257:107076 | utf-8 | J Environ Radioact | 2,022 | 10.1016/j.jenvrad.2022.107076 | oa_other |
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BMJ Case Rep
BMJ Case Rep
bmjcr
bmjcasereports
BMJ Case Reports
1757-790X
BMJ Publishing Group BMA House, Tavistock Square, London, WC1H 9JR
bcr-2022-250749
10.1136/bcr-2022-250749
Case Reports: Unusual association of diseases/symptoms
Newly diagnosed autoimmune Addison’s disease in a patient with COVID-19 with autoimmune disseminated encephalomyelitis
Beshay Lauren 1
Wei Kevin 2
Yang Qin 1
1 Endocrinology, University of California Irvine, Irvine, California, USA
2 Endocrinology, University of California Irvine, Orange, California, USA
Correspondence to Dr Qin Yang; [email protected]
2022
5 12 2022
5 12 2022
15 12 e25074928 11 2022
© BMJ Publishing Group Limited 2022. No commercial re-use. See rights and permissions. Published by BMJ.
2022
A man in his 20s with a history of acute disseminated encephalomyelitis (ADEM) was brought into the emergency department (ED) after his family found him at home collapsed on the floor unresponsive with a blood glucose of 28 mg/dL at the field. In the ED, the patient was tachycardic, tachypnoeic and hypotensive, requiring pressors and intubation at 9 hours and 12 hours after arrival, respectively. Laboratory results revealed a positive COVID-19 test, serum sodium of 125 mmol/L and persistent hypoglycaemia. The patient was given a high dose of dexamethasone for COVID-19 treatment 1 hour before pressors were started. He was then continued on a stress dose of intravenous hydrocortisone with rapid clinical improvement leading to his extubation, and discontinuation of vasopressors and glucose on day 2 of admission. The patient received his last dose of intravenous hydrocortisone on day 4 in the early afternoon with the plan to order adrenal testing the following morning prior to discharge. On day 5, the aldosterone <3.0 ng/dL, adrenocorticotropic hormone (ACTH) level >1250 pg/mL, and ACTH stimulation test showed cortisol levels of 3 and 3 µg/dL at 30 and 60 min, respectively. The anti-21-hydroxylase antibody was positive. The patient was discharged on hydrocortisone and fludrocortisone. The patient’s symptoms, elevated ACTH, low cortisol and presence of 21-hydroxylase antibodies are consistent with autoimmune Addison’s disease. This is the first case reporting autoimmune Addison’s disease in a patient with COVID-19 with a history of ADEM. The case highlights the importance of considering adrenal insufficiency as a diagnostic differential in haemodynamically unstable patients with COVID-19.
Adrenal disorders
COVID-19
NIH DK121146
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pmcBackground
The COVID-19 pandemic has resulted in nearly 500 million confirmed cases and over 6 million deaths globally. COVID-19 is primarily a respiratory disease, but SARS-CoV-2 may affect multiple organs, contributing to severe disease and also post-COVID-19 syndrome.1 An accumulating body of evidence is uncovering the link between COVID-19 and endocrinopathies such as new-onset diabetes, thyroiditis, diabetes insipidus, pituitary apoplexy, hypogonadism and adrenal insufficiency.2–7 The mechanisms for COVID-19-related endocrinopathies are not fully elucidated but are likely multifactorial, including proinflammatory status, impaired immune function and thrombogenic responses to pathogens.
Several cases of primary adrenal insufficiency in the setting of COVID-19 have been reported (table 1), mostly due to adrenal haemorrhage or infarct.8 9 One report has suggested that COVID-19, in conjunction with another prothrombotic condition (primary antiphospholipid syndrome), may precipitate these causes of adrenal insufficiency.9 Furthermore, there is a wide variety of laboratory findings in patients with COVID-19-related adrenal insufficiency, and a majority of cases have been shown to affect patients in their mid to late adulthood, possibly related to the increased prothrombotic risk with age (table 1).10 Here we describe a novel case of a young adult with a history of multiple childhood episodes of acute disseminated encephalomyelitis (ADEM), an autoimmune demyelinating disorder of the central nervous system, who developed acute primary adrenal insufficiency after presenting with COVID-19 infection.
Table 1 Compilation of various case reports’ patient characteristics and laboratory values
Case report Age (years) Sex COVID-19 vaccination status Required pressors? Required intubation? Sodium (mmol/L) Glucose (mg/dL) TSH (µIU/mL) Cortisol (µg/dL) ACTH (pg/mL)
Present case 20s M No Yes Yes 125 28 1.89 4 >1250
Asano et al12 70s F n/a No No 142 174 n/a 29.7 176.6
Machado et al8 40s F n/a No No Hypo n/a n/a <1.0 807
Elkhouly et al14 50s M n/a No Yes 135 n/a n/a 26.6 n/a
Sheikh et al5 40s F n/a No No 139 n/a 1.83 1.1 56
Hashim et al15 50s M n/a No No 108 95 2.12 2.03 n/a
Chua et al13 40s M n/a No No 136 126 2.99 <1.0 7.1
Heidarpour et al16 60s M n/a Yes Yes 135 192 n/a 13 n/a
Frankel et al9 60s F n/a No No 129 n/a n/a <1.0 207
Kumar et al17 70s F n/a No No 112 n/a n/a 16 Normal
Alvarez et al11 70s M n/a No No 127 n/a n/a 2.1 n/a
Sanchez et al20 60s F n/a No No 117 n/a 0.33 2.6 1944
Bhattarai et al10 Late adolescence F n/a Yes Yes 110 63 22.4 1.0 26
Case report Aldosterone (ng/dL) Presence of anti-21-hydroxylase antibody Associated conditions Imaging findings Treatment
Present case <3.0 Positive ADEM Mild atrophy of bilateral adrenal glands IV hydrocortisone, followed by PO hydrocortisone and fludrocortisone
Asano et al12 n/a n/a Sjogren’s syndrome Bilateral adrenal infarction IV hydrocortisone, followed by PO hydrocortisone
Machado et al8 <3.0 Negative Positive APL antibodies Bilateral non-haemorrhagic infarction IV hydrocortisone, followed by PO hydrocortisone and fludrocortisone
Elkhouly et al14 n/a n/a HTN, right adrenal adenoma Bilateral adrenal haemorrhages IV hydrocortisone prior to cardiac arrest and subsequent death
Sheikh et al5 n/a n/a T2DM n/a Hydrocortisone
Hashim et al15 n/a n/a None n/a Prednisolone
Chua et al13 n/a n/a T2DM n/a PO hydrocortisone
Heidarpour et al16 n/a n/a HTN n/a IV hydrocortisone, followed by PO prednisolone
Frankel et al9 n/a n/a APLS Bilateral enlarged adrenal glands w/ surrounding haziness IV hydrocortisone, followed by PO prednisone and fludrocortisone
Kumar et al17 n/a n/a HTN, HLD Bilateral non-haemorrhagic adrenal infarction IV hydrocortisone, followed by PO hydrocortisone
Alvarez et al11 n/a n/a Psoriasis Bilateral adrenal haemorrhage IV hydrocortisone, followed by PO hydrocortisone
Sanchez et al20 <0.3 Positive Hypothyroidism, T2DM Unremarkable IV hydrocortisone, followed by PO hydrocortisone and fludrocortisone
Bhattarai et al10 n/a Positive Raynaud’s phenomenon, possible autoimmune thyroiditis Bilateral adrenals diminutive w/o nodularity or haemorrhage IV hydrocortisone, followed by PO hydrocortisone and fludrocortisone
ACTH, adrenocorticotropic hormone; ADEM, acute disseminated encephalomyelitis; APL(S), antiphospholipid (syndrome); HLD, hyperlipidaemia; HTN, hypertension; IV, intravenous; n/a, not available; PO, oral; T2DM, type 2 diabetes mellitus; TSH, thyroid-stimulating hormone.
Case presentation
A man in his 20s with a history of three episodes of ADEM in childhood was brought into the emergency department (ED) by emergency medical service after his family found him collapsed on the floor unresponsive at home early morning with a blood glucose of 28 mg/dL at the field. Prior to the presentation, he reported 3 days of sore throat, nausea, vomiting, fatigue, chills and muscle aches, for which he initially tested negative for COVID-19 at an urgent care. At the time of presentation, his mother reported that his only medical history included three admissions at ages 4, 10 and 11 years for ADEM flares and possible optic neuritis. ADEM is an autoimmune demyelinating disease of the central nervous system that typically follows an infection and is more common in childhood. At the time of his diagnosis, our patient was prescribed prolonged courses of prednisone, the longest being 1 year. He never received immunosuppressants and had not been on any other home medications since the resolution of the last flare at age 11 years. He was not vaccinated against COVID-19 because of the family’s belief that his ADEM episodes were triggered by various childhood vaccinations. Otherwise, he remained healthy and did not have any additional medical history, surgical history or family history of autoimmune conditions.
In the ED, the physical examination was notable for temperature 39°C, pulse 145/min, blood pressure (BP) 80/42 mm Hg, respiratory rate 39/min with initially normal oxygen saturation on room air, somnolence and no signs of mucocutaneous hyperpigmentation.
Investigations
Laboratory results on admission revealed a positive COVID-19 PCR test, serum sodium 125 mmol/L (reference range 135–145), potassium 3.1 mmol/L (reference range 3.5–4.1) and persistent hypoglycaemia requiring a dextrose drip. Thyroid-stimulating hormone was 1.89 μIU/mL (reference range 0.45–4.12). Pancultures were sent, and the patient was started on broad-spectrum antibiotics (intravenous vancomycin and piperacillin–tazobactam). The patient remained hypotensive despite receiving 4 L of intravenous fluid boluses and was started on norepinephrine 7 µg/min after 9 hours of his presentation. A dose of dexamethasone 6 mg was given per COVID-19 protocol shortly prior to initiation of pressors. His oxygen requirements steadily increased to a simple face mask of 6 L/min, and he was subsequently intubated with the addition of vasopressin for BP support 3 hours after norepinephrine was initiated. The patient was transferred to the intensive care unit for the management of acute hypoxic respiratory failure and undifferentiated shock.
Differential diagnosis
Work-up for cardiogenic aetiologies of shock was unremarkable (ECG showed sinus tachycardia and troponins were within normal limits). A stat echocardiogram and CT angiography of the chest with contrast were ordered to assess for a thromboembolic cause of obstructive shock, though both studies were unrevealing for right heart strain or pulmonary embolism, respectively. The CT of the chest demonstrated pneumonia involving the entire left lung associated with segmental/subsegmental atelectasis in the posteromedial aspects of the left lower lobe. Contrast-enhanced CT of the abdomen and pelvis demonstrated suboptimal visualised adrenal glands, with no thickening, lesions or calcifications. Blood, sputum and urine cultures were drawn to evaluate for other superimposed bacterial or fungal infections leading to septic shock. Given also high clinical suspicion for an adrenal crisis, a 05:00 cortisol was drawn the next morning which resulted at 1 µg/dL (reference range >18). A second dose of dexamethasone 6 mg was given before endocrine was consulted to help guide the steroid treatment.
Treatment
It was noted that the 05:00 cortisol was drawn 14 hours after the first dexamethasone dose. Despite this, there was still high suspicion of possible adrenal insufficiency, and the patient was switched to intravenous hydrocortisone 100 mg every 8 hours in addition to fludrocortisone 0.1 mg daily. The hypotension and hyponatraemia quickly resolved, and vasopressors along with the glucose were discontinued before the patient was extubated on day 2 of his hospitalisation. He remained on intravenous steroids and antibiotics. On day 4, the sputum culture returned positive for methicillin-susceptible Staphylococcus aureus (MSSA) (thought to be a contaminant), with no growth seen on the blood and urine cultures. Given clinical stability, all antibiotics were discontinued. He received a final dose of intravenous hydrocortisone in the afternoon with the plan to repeat confirmatory testing for adrenal insufficiency the following morning to guide the management of steroids and mineralocorticoids at discharge.
On day 5—the last day of his admission—a baseline 08:00 cortisol was 4 µg/dL (reference range >18; unknown patient baseline) with adrenocorticotropic hormone (ACTH) level of >1250 pg/mL (reference range 0–45; unknown patient baseline). A high-dose 250 µg ACTH stimulation test followed; cortisol levels were 3 and 3 µg/dL (reference range >18) at 30 and 60 min, respectively. Aldosterone was <3.0 ng/dL (reference range 4.0–31.0), and anti-21-hydroxylase antibody was positive (reference: negative).
Outcome and follow-up
The patient was then diagnosed with primary adrenal insufficiency and was discharged on oral hydrocortisone 20 mg in the morning and 10 mg in the afternoon and oral fludrocortisone 0.1 mg daily. On the follow-up phone call, the patient endorsed taking his medications consistently. The patient established care with endocrinology at his primary institution upon discharge from the hospital.
Discussion
Here we report a case of new-onset adrenal insufficiency in a COVID-19-positive patient with a background of ADEM. The patient was an otherwise healthy young man, who presented with symptoms consistent with adrenal crisis (hypoglycaemia, hypotension and hyponatraemia) shortly before the progression of his acute hypoxic respiratory failure due to COVID-19 pneumonia. Work-up for multiple causes of shock, including cardiogenic, obstructive and septic, was unrevealing except for lung findings of bronchiolitis and pneumonia involving the entire left lung along with a positive COVID-19 PCR test (sputum culture returned positive for MSSA, which was a contaminant in the context of negative blood cultures). Since the patient was started on dexamethasone per COVID-19 treatment protocol in the emergency room, primary adrenal insufficiency was diagnosed after his recovery from shock based on his elevated ACTH and reduced cortisol, positive cosyntropin stimulation test and positive anti-21-hydroxylase antibodies, which were collected on day 5 of his admission prior to discharge. Although adrenal insufficiency has been reported in patients with COVID-19, this case has several distinct features.
From previous case reports of COVID-19-related adrenal insufficiency we have compiled (table 1),5 8–17 the majority of cases are caused by adrenal infarct or haemorrhage, likely because SARS-CoV-2 infection is known to elicit a hypercoagulable state leading to acute thrombotic complications including ischaemic stroke.18
In addition to the case reports, a retrospective study of 219 patients with COVID-19 with severe or critical lung disease on CT scan showed that 51 patients with an average age 67 years old exhibited adrenal infarct on imaging.19 However, the actual number of patients who presented clinically with adrenal insufficiency was only a fraction of that seen on imaging (8%). Of all our compiled cases, two have reported on autoimmune Addison’s disease complicated by COVID-19.10 20 The first case is a woman in her 60s who was diagnosed with primary adrenal insufficiency 5 months after asymptomatic COVID-19 infection. The direct connection of adrenal insufficiency to COVID-19 is less clear. The second case is a woman in late adolescence who presents with adrenal crisis 4 days after a positive COVID-19 test. However, the patient’s abdominal MRI showed diminutive adrenal glands, consistent with a chronic pre-existing adrenal insufficiency. Both cases of adrenal insufficiency with positive 21-hydroxylase antibodies have or possibly have Hashimoto’s thyroiditis, suggesting some autoimmune background.
A unique feature of our case is our patient’s childhood history of ADEM, a rare condition with an average age onset of 3–7 years, and annual incidence of 0.2–0.5 per 100 000 children.21–23 ADEM is characterised by demyelination in the brain, spinal cord and occasionally the optic nerve, resulting from autoimmune inflammation that occurs in response to a preceding infection or immunisation.21–23 Most patients affected by ADEM recover completely from the initial acute illness, although a minority of patients may experience relapsing episodes.24 Our patient had three documented episodes of ADEM. Whether he has a stronger autoimmune background that may have rendered him more susceptible to developing other autoimmune conditions is unclear. Approximately 50%–65% of patients with autoimmune adrenal insufficiency have one or more other autoimmune endocrine disorders (most commonly thyroid disease and type 1 diabetes).25–29 It is worth noting, however, that ADEM is mostly monophasic and not generally associated with other autoimmune diseases.24 A literature search did not yield any report of adrenal insufficiency in patients with ADEM. Although many studies suggest an increasing prevalence of Addison’s disease across all ages, the annual incidence is estimated to be 0.6 per 100 000.30–33 Given the similar but very low incidence of ADEM and Addison’s disease (especially that ADEM is very rare in adulthood with unknown incidence rates), the co-occurrence of both diseases in the population would be extremely rare. Therefore, although it is still possible that our patient with both conditions may represent a chance association, some intrinsic autoimmune mechanism could have linked his ADEM and adrenal insufficiency. Nevertheless, our case is the first to report the autoimmune Addison’s disease in a patient with a history of ADEM.
COVID-19 clearly plays a major role in the patient’s presentation of adrenal crisis. It remains speculative whether SARS-CoV-2 infection contributes to the autoimmunity of the patient’s adrenal insufficiency. COVID-19 has been associated with multiple autoimmune diseases, including ADEM.34–41 However, it is usually difficult to determine the causality in most cases. Our patient had detectable adrenal autoimmunity around 8 days after the onset of symptoms (with an additional incubation period of 3–5 days). The time is considered too short for antibody production during SARS-CoV-2 infection, which usually takes 1–3 weeks after an antigenic stimulus.42 43 We therefore cannot deduce that the COVID-19 infection alone caused the patient to develop autoimmune adrenalitis. More plausible is that the patient has a history of positive anti-21 hydroxylase antibody with subclinical adrenal insufficiency (not yet apparent as radiological adrenal shrinkage), which was precipitated by his COVID-19 infection. Coco et al evaluated 48 patients with adrenal cortex autoantibodies, 21% of which developed overt adrenal insufficiency in 4 years.44 The same investigators reported that of 100 patients with adrenal cortex autoantibodies, the estimated cumulative risk (using a life-table analysis) of developing overt adrenal insufficiency during a mean follow-up period of 6 years was 100% in children and 32% in adults.44 These data provide plausible evidence that our patient likely had antecedent subclinical adrenal insufficiency, which was precipitated by COVID-19 infection, leading to the development of adrenal crisis.
Hypoglycaemia, especially severe hypoglycaemia, can be seen in children but is uncommon in adult patients with adrenal insufficiency. Our patient experienced severe and refractory hypoglycaemia requiring a dextrose drip to maintain glucose levels. Several mechanisms may contribute to the patient’s severe hypoglycaemia. The poor appetite and oral intake due to the combination of adrenal insufficiency and SARS-CoV-2 infection are the apparent factors. As discussed above, the patient likely had a background of adrenal autoimmunity, with an acute precipitation of the adrenal crisis, and the compensatory mechanisms for increasing glycaemic levels may have not been established yet.
As dexamethasone is a standard therapy on the protocol for treating COVID-19 infection, the diagnosis of adrenal insufficiency can be complicated and could be delayed since dexamethasone suppresses endogenous cortisol production. Although cosyntropin-stimulation test in non-critically ill patients who received high-dose dexamethasone treatment within 72 hours can still be informative, data are lacking in critically ill patients treated with high-dose dexamethasone.32 45 It is crucially important to treat suspected adrenal insufficiency first before the diagnosis is established. Since dexamethasone has minimal mineralocorticoid effects, fludrocortisone should be added. Alternatively, stress doses of hydrocortisone can be used.
It is worth noting that the age of our case’s patient (early 20s) and those of previously reported autoimmune Addison’s disease with COVID-19 (late adolescence) are significantly younger compared with the median age in the 60s in the other case reports of adrenal insufficiency caused by infarct or haemorrhage. SARS-CoV-2 infection is known to elicit a hypercoagulable state. The risk is likely further increased by the advanced age. Autoimmune adrenalitis, on the other hand, usually occurs at younger ages, with an average of 40 years old (30–50 years).28 46–48 Therefore, in younger patients with COVID-19 presenting with adrenal insufficiency, it is pertinent to consider autoimmune adrenalitis as an aetiology.
In summary, this case is the first to report autoimmune Addison’s disease in a patient with a history of ADEM, although an association with adrenal insufficiency is yet to be proven. Importantly, COVID-19 infection triggers adrenal crisis, which leads to the diagnosis of Addison’s disease. The case highlights the importance of the recognition and management of adrenal crisis in haemodynamically unstable patients with COVID-19. Infarcts and haemorrhage are more common aetiologies for adrenal insufficiency, although autoimmune Addison’s disease should be considered, especially in younger patients. The standard therapy of administering dexamethasone for COVID-19 may complicate the diagnosis of adrenal insufficiency, but early treatment is crucial. Future case series studies may provide insights into the incidence, aetiology, clinical course and outcome of adrenal insufficiency in patients with COVID-19.
Learning points
It is important to consider adrenal insufficiency as a diagnostic differential in haemodynamically unstable patients with COVID-19.
Young patients with an autoimmune history who develop haemodynamic instability during an infection should warrant a work-up for possible autoimmune Addison’s disease.
The standard therapy of administering dexamethasone for COVID-19 may complicate the diagnosis of adrenal insufficiency, but early treatment is crucial.
Ethics statements
Patient consent for publication
Parental/guardian consent obtained.
Contributors: QY and LB evaluated the patient. KW is a fourth-year medical student who helped with the literature review. All three together wrote and revised the manuscript.
Funding: The authors received no financial support for the research, authorship, and/or publication of this manuscript.
Case reports provide a valuable learning resource for the scientific community and can indicate areas of interest for future research. They should not be used in isolation to guide treatment choices or public health policy.
Competing interests: None declared.
Provenance and peer review: Not commissioned; externally peer reviewed.
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21 Bhatt P, Bray L, Raju S, et al . Temporal trends of pediatric hospitalizations with acute disseminated encephalomyelitis in the United States: an analysis from 2006 to 2014 using national inpatient sample. J Pediatr 2019;206 :26–32. 10.1016/j.jpeds.2018.10.044 30528761
22 Stonehouse M, Gupte G, Wassmer E, et al . Acute disseminated encephalomyelitis: recognition in the hands of general paediatricians. Arch Dis Child 2003;88 :122–4. 10.1136/adc.88.2.122 12538312
23 Tenembaum S, Chitnis T, Ness J, et al . Acute disseminated encephalomyelitis. Neurology 2007;68 :S23–36. 10.1212/01.wnl.0000259404.51352.7f 17438235
24 Krupp LB, Tardieu M, Amato MP, et al . International pediatric multiple sclerosis Study Group criteria for pediatric multiple sclerosis and immune-mediated central nervous system demyelinating disorders: revisions to the 2007 definitions. Mult Scler 2013;19 :1261–7. 10.1177/1352458513484547 23572237
25 Devon I, Rubin M. Hashimoto encephalopathy, 2022.
26 Betterle C, Scarpa R, Garelli S, et al . Addison's disease: a survey on 633 patients in Padova. Eur J Endocrinol 2013;169 :773–84. 10.1530/EJE-13-0528 24014553
27 Dalin F, Nordling Eriksson G, Dahlqvist P, et al . Clinical and immunological characteristics of autoimmune Addison disease: a nationwide Swedish multicenter study. J Clin Endocrinol Metab 2017;102 :379–89. 10.1210/jc.2016-2522 27870550
28 Erichsen MM, Løvås K, Skinningsrud B, et al . Clinical, immunological, and genetic features of autoimmune primary adrenal insufficiency: observations from a Norwegian registry. J Clin Endocrinol Metab 2009;94 :4882–90. 10.1210/jc.2009-1368 19858318
29 Husebye ES, Pearce SH, Krone NP, et al . Adrenal insufficiency. Lancet 2021;397 :613–29. 10.1016/S0140-6736(21)00136-7 33484633
30 Barthel A, Benker G, Berens K, et al . An update on Addison's disease. Exp Clin Endocrinol Diabetes 2019;127 :165–75. 10.1055/a-0804-2715 30562824
31 Betterle C, Presotto F, Furmaniak J. Epidemiology, pathogenesis, and diagnosis of Addison's disease in adults. J Endocrinol Invest 2019;42 :1407–33. 10.1007/s40618-019-01079-6 31321757
32 Munir S, Quintanilla Rodriguez BS, Waseem M. Addison Disease. In: StatPearls. Treasure Island (FL), 2022.
33 Olafsson AS, Sigurjonsdottir HA. Increasing prevalence of Addison disease: results from a nationwide study. Endocr Pract 2016;22 :30–5. 10.4158/EP15754.OR 26437215
34 Altowyan E, Alnujeidi O, Alhujilan A, et al . COVID-19 presenting as thrombotic thrombocytopenic purpura (TTP). BMJ Case Rep 2020;13 . 10.1136/bcr-2020-238026. [Epub ahead of print: 17 Dec 2020].
35 Ariño H, Heartshorne R, Michael BD, et al . Neuroimmune disorders in COVID-19. J Neurol 2022;269 :2827–39. 10.1007/s00415-022-11050-w 35353232
36 Gavkare AM, Nanaware N, Rayate AS, et al . COVID-19 associated diabetes mellitus: a review. World J Diabetes 2022;13 :729–37. 10.4239/wjd.v13.i9.729 36188145
37 Hussein O, Abd Elazim A, Torbey MT. Covid-19 systemic infection exacerbates pre-existing acute disseminated encephalomyelitis (ADEM). J Neuroimmunol 2020;349 :577405. 10.1016/j.jneuroim.2020.577405 33002725
38 Langley L, Zeicu C, Whitton L, et al . Acute disseminated encephalomyelitis (ADEM) associated with COVID-19. BMJ Case Rep 2020;13 . 10.1136/bcr-2020-239597. [Epub ahead of print: 13 Dec 2020].
39 Muralidhar Reddy Y, B SK, Osman S, et al . Temporal association between SARS-CoV-2 and new-onset myasthenia gravis: is it causal or coincidental? BMJ Case Rep 2021;14 . 10.1136/bcr-2021-244146. [Epub ahead of print: 21 Jul 2021].
40 Parsons T, Banks S, Bae C, et al . COVID-19-associated acute disseminated encephalomyelitis (ADEM). J Neurol 2020;267 :2799–802. 10.1007/s00415-020-09951-9 32474657
41 Patrizio A, Ferrari SM, Elia G, et al . Graves' disease following SARS-CoV-2 vaccination: a systematic review. Vaccines 2022;10 . 10.3390/vaccines10091445. [Epub ahead of print: 01 09 2022].
42 Qu J, Wu C, Li X, et al . Profile of immunoglobulin G and IgM antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Clin Infect Dis 2020;71 :2255–8. 10.1093/cid/ciaa489 32337590
43 Wölfel R, Corman VM, Guggemos W, et al . Virological assessment of hospitalized patients with COVID-2019. Nature 2020;581 :465–9. 10.1038/s41586-020-2196-x 32235945
44 Coco G, Dal Pra C, Presotto F, et al . Estimated risk for developing autoimmune Addison's disease in patients with adrenal cortex autoantibodies. J Clin Endocrinol Metab 2006;91 :1637–45. 10.1210/jc.2005-0860 16522688
45 Bower AN, Oyen LJ. Interaction between dexamethasone treatment and the corticotropin stimulation test in septic shock. Ann Pharmacother 2005;39 :335–8. 10.1345/aph.1E353 15644480
46 Kong MF, Jeffcoate W. Eighty-six cases of Addison's disease. Clin Endocrinol 1994;41 :757–61. 10.1111/j.1365-2265.1994.tb02790.x
47 Oelkers W. Adrenal insufficiency. N Engl J Med 1996;335 :1206–12. 10.1056/NEJM199610173351607 8815944
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| 0 | PMC9723877 | NO-CC CODE | 2022-12-07 23:20:05 | no | BMJ Case Rep. 2022 Dec 5; 15(12):e250749 | utf-8 | BMJ Case Rep | 2,022 | 10.1136/bcr-2022-250749 | oa_other |
==== Front
Eur Heart J
Eur Heart J
eurheartj
European Heart Journal
0195-668X
1522-9645
Oxford University Press
36044988
10.1093/eurheartj/ehac227
ehac227
Meta-Analysis
AcademicSubjects/MED00200
The collateral damage of COVID-19 to cardiovascular services: a meta-analysis
https://orcid.org/0000-0001-9895-9356
Nadarajah Ramesh Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, 6 Clarendon Way, Leeds LS2 9DA, UK
Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
https://orcid.org/0000-0001-6093-599X
Wu Jianhua Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
School of Dentistry, University of Leeds, Leeds, UK
https://orcid.org/0000-0001-8149-3449
Hurdus Ben Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
Asma Samira Division of Data, Analytics and Delivery for Impact, World Health Organization, Geneva, Switzerland
https://orcid.org/0000-0002-1278-6245
Bhatt Deepak L. Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
https://orcid.org/0000-0001-6103-8510
Biondi-Zoccai Giuseppe Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina, Italy
Mediterranea Cardiocentro, Napoli, Italy
https://orcid.org/0000-0003-3493-1930
Mehta Laxmi S. Division of Cardiology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
Ram C. Venkata S. Apollo Hospitals and Medical College, Hyderabad, Telangana, India
University of Texas Southwestern Medical School, Dallas, TX, USA
Faculty of Medical and Health Sciences, Macquarie University, Sydney, Australia
https://orcid.org/0000-0002-2740-0042
Ribeiro Antonio Luiz P. Cardiology Service and Telehealth Center, Hospital das Clínicas, and Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
https://orcid.org/0000-0002-8370-4569
Van Spall Harriette G.C. Department of Medicine and Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Canada
Population Health Research Institute, Hamilton, Canada
https://orcid.org/0000-0001-8806-6052
Deanfield John E. National Institute for Cardiovascular Outcomes Research, Barts Health NHS Trust, London, UK
Institute of Cardiovascular Sciences, University College, London, UK
https://orcid.org/0000-0002-5259-538X
Lüscher Thomas F. Imperial College, National Heart and Lung Institute, London, UK
Royal Brompton & Harefield Hospital, Imperial College, London, UK
https://orcid.org/0000-0001-9241-8890
Mamas Mamas Keele Cardiovascular Research Group, Institute for Prognosis Research, University of Keele, Keele, UK
https://orcid.org/0000-0003-4732-382X
Gale Chris P. Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, 6 Clarendon Way, Leeds LS2 9DA, UK
Leeds Institute of Data Analytics, University of Leeds, Leeds, UK
Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
Corresponding author. Tel: +44 113 343 3241, Email: [email protected], Twitter @Dr_R_Nadarajah
30 5 2022
30 5 2022
ehac22710 9 2021
02 4 2022
20 4 2022
© The Author(s) 2022. Published by Oxford University Press on behalf of European Society of Cardiology. All rights reserved. For permissions, please e-mail: [email protected]
2022
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Abstract
Aims
The effect of the COVID-19 pandemic on care and outcomes across non-COVID-19 cardiovascular (CV) diseases is unknown. A systematic review and meta-analysis was performed to quantify the effect and investigate for variation by CV disease, geographic region, country income classification and the time course of the pandemic.
Methods and results
From January 2019 to December 2021, Medline and Embase databases were searched for observational studies comparing a pandemic and pre-pandemic period with relation to CV disease hospitalisations, diagnostic and interventional procedures, outpatient consultations, and mortality. Observational data were synthesised by incidence rate ratios (IRR) and risk ratios (RR) for binary outcomes and weighted mean differences for continuous outcomes with 95% confidence intervals. The study was registered with PROSPERO (CRD42021265930). A total of 158 studies, covering 49 countries and 6 continents, were used for quantitative synthesis. Most studies (80%) reported information for high-income countries (HICs). Across all CV disease and geographies there were fewer hospitalisations, diagnostic and interventional procedures, and outpatient consultations during the pandemic. By meta-regression, in low-middle income countries (LMICs) compared to HICs the decline in ST-segment elevation myocardial infarction (STEMI) hospitalisations (RR 0.79, 95% confidence interval [CI] 0.66–0.94) and revascularisation (RR 0.73, 95% CI 0.62–0.87) was more severe. In LMICs, but not HICs, in-hospital mortality increased for STEMI (RR 1.22, 95% CI 1.10–1.37) and heart failure (RR 1.08, 95% CI 1.04–1.12). The magnitude of decline in hospitalisations for CV diseases did not differ between the first and second wave.
Conclusions
There was substantial global collateral CV damage during the COVID-19 pandemic with disparity in severity by country income classification.
Structured Graphical Abstract
Structured Graphical Abstract Major findings of the collateral damage of the COVID-19 pandemic on cardiovascular services. Abbreviations in text.
Cardiovascular
COVID-19
Hospitalization
Mortality
Treatment
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Introduction
During the coronavirus disease 2019 (COVID-19) pandemic, reports described fewer hospitalizations, procedures, and consultations for non-COVID-19 cardiovascular (CV) diseases.1–3 After a short period of ‘recovery’, the emergence and rapid spread of the Omicron variant triggered the re-introduction of ‘lockdown’ restrictions,4,5 portending a future of preparing for and coping with waves of the contagion.
Previous systematic reviews of the impact of the COVID-19 pandemic on CV services have provided an incomplete overview. Some studies focused on hospitalizations,6,7 others were restricted to specific conditions,8–16 and one investigated only a specific outcome.17 Only one report has considered the impact of the pandemic across different geographic territories, and was limited to one CV care pathway.9 None has considered whether the effect of the pandemic on CV services has varied over time. A quantitative understanding of the global impact of the COVID-19 pandemic on the breadth of CV services and health of individuals with CV disease could facilitate better preparation for future waves.
We therefore provide a systematic review of the literature with a meta-analysis to quantify the effects of the pandemic on CV services in terms of access, treatment, and outcomes. We investigate the occurrence of variation across CV conditions, geographic region, country income classification, and the time course of the pandemic. Finally, we consider how to better manage CV services to minimize collateral CV damage.
Methods
We searched the Medline and Embase databases through the Ovid platform from 1 January 2019 through 15 December 2021 (because the earliest case was diagnosed in Wuhan, China in November 2019) for studies that reported a comparison of hospitalizations, diagnostic and interventional procedures, outpatient and community consultations, and mortality. The full search strategy is available in Supplementary material online, S1. We defined CV services as healthcare services provided by any CV practitioner (cardiologist, cardiac surgeon, cardiac physiologist, cardiac nurse, or trainee) relating to CV diseases specified in the ESC Textbook of Cardiovascular Medicine.18 We excluded CV diseases where care would primarily be overseen by other medical and surgical specialities—venous thrombo-embolism and peripheral vascular diseases (including aortic, peripheral arterial, and cerebrovascular disease)—which have been summarized elsewhere.6,19 This review was registered on PROSPERO (CRD42021265930) and informed by the PRISMA statement (see Supplementary material online, Table S63).20 The risk of bias for each report for each outcome was assessed using the ROBINS-I tool.21 Reports with critical risk of bias were excluded.
We undertook quantitative syntheses of cohort studies that compared the COVID-19 pandemic period and a pre-pandemic period (all definitions in Supplementary material online, S1). A meta-analysis was performed to synthesize observational data for binary and continuous outcomes. Incidence rate ratios (IRRs, a comparison of incidence rates during each period) and risk ratios (RRs, a ratio of the probability of an event occurring in the intervention compared with the probability of the event occurring in the control, where each event is independent) were used for binary outcomes and counts data; weighted mean differences (WMDs) were used for continuous outcomes measured with the same scale. The DerSimonian and Laird random effects models were fitted in all analyses because of the variation amongst studies in population, intervention, comparator, timing, and setting.22 Funnel plots and Egger’s test were used to assess publication bias.23 Heterogeneity scores were measured by the I² statistic and Cochran’s Q test, with 40% or P < 0.10, respectively, indicative of substantial heterogeneity.24 Where quantitative synthesis could not be undertaken, we have provided a narrative synthesis.
To explore for differences in effect of the pandemic across geographic boundaries, country wealth, and time course, we performed meta-regression by geographic region, country-level income, and wave of pandemic covered by each report. Geographic regions were defined as Europe, North America, and other countries, and country-level income as high income (HIC) vs. low–middle income (LMIC) using the World Bank classification of income.25 We also investigated for sources of heterogeneity by meta-regression of a range of study characteristics: sample size, data source, duration of study period during the pandemic, presence or absence of matched comparator periods, study definition of pandemic period, and whether or not patients with co-existent COVID-19 diagnosis were included. Detailed methods are available in Supplementary material online, S2.
Results
We identified 4613 unique records, reviewed 497 full-text reports, and included 189 studies,158 of which were used in quantitative synthesis (Supplementary material online, S4 Tables S38–S61). Figure 1 shows the PRISMA flow diagram. In total, 49 countries were covered across six continents. There was geographic and economic disparity in the number of available studies; the majority were from Europe (n = 111, 59%; of which the UK n = 25, 13%, and Italy n = 21, 11%) and North America (n = 34, 18%) (Figure 2). Most studies provided information exclusively relating to HICs (n = 151, 80%). Over half of studies described acute coronary syndromes (ACS) (n = 96, 51%), followed by heart failure (HF) (n = 16, 8%) and arrhythmias (n = 15, 8%). The vast majority of studies reported data from the first wave of the pandemic (n = 152, 80%). A minority of studies (n = 19, 10%) excluded patients diagnosed with concurrent SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. We classified 26% of studies across all outcomes as being at severe risk of bias, with 57% at moderate risk of bias (Figure 3; Supplementary material online, S3 Tables S1–S37). Confounding was the most common source of elevated risk of bias (26% severe, 56% moderate). Studies reporting mortality outcomes were the most likely to be classified as being at severe risk of bias (51%), partly due to incomplete reporting of concurrent SARS-CoV-2 infection. Egger’s test did not identify any significant publication bias (Supplementary material online, S6 Figures S19–S22; all P-values were non-significant).
Figure 1 Flowchart of selected studies. Flowchart based on the Preferred Reported Items for Systematic Review and Meta-Analysis (PRISMA) statement.
Figure 2 The origin of included studies demonstrated on a global choropleth (A), and a chart including the number of studies per country for the 20 most commonly represented countries (B).
Figure 3 Summary of overall risk of bias scores assessed using the ROBINS-I tool for all studies across all outcomes (A) and subdivided by categories of outcomes (B–E). AMI, acute myocardial infarction.
Acute cardiovascular disease hospitalizations
Hospitalizations declined across the breadth of CV disease during the pandemic. Hospitalization rates for each subtype of ACS declined; ST-elevation myocardial infarction (STEMI) [IRR 0.78, 95% confidence interval (CI) 0.72–0.85, I2 = 97.4%], non-STEMI (NSTEMI) (IRR 0.66, 95% CI 0.60–0.72, I2 = 98.3%), and unstable angina (IRR 0.80, 95% CI 0.66–0.98, I2 = 85.8%) (Figure 4; Supplementary material online, S1–S3). Hospitalizations for HF declined during the pandemic (IRR 0.66, 95% CI 0.59–0.73, I2 = 99.9%) (Supplementary material online, Figure S4), reflective of a decline in admissions with both decompensated chronic HF and de novo presentations.26
Figure 4 Summary estimates for analyses across hospitalizations, in-hospital management, diagnostic and interventional procedures, and mortality. The full forest plots for each analysis are available in Supplementary material online, Figures S1–S18. EP, electrophysiology.
The total number of hospitalizations for arrhythmias also declined (IRR 0.70, 95% CI 0.57–0.85, I2 = 95.2%) (Supplementary material online, Figure S5), an effect consistently reported for each of bradyarrhythmias,27–29 atrial fibrillation/flutter,30–32 and ventricular arrhythmias (VAs).28 However, studies reporting arrhythmias detected by remote monitoring of cardiac implantable electronic devices (CIEDs) painted a different picture of arrhythmia incidence in the community in individuals with CV disease. Three studies reported increases in episodes of atrial fibrillation during the pandemic, which correlated with areas of high COVID-19 prevalence.33–35 During the peak COVID-19 incidence in New York City, New Orleans, and Boston, an increase in implantable cardioverter defibrillator (ICD) shock burden was observed,36 whilst two large studies found a reduction in VA incidence amongst individuals with ICDs after major public health restrictions.37,38
On meta-regression, we found that the decline in hospitalizations for CV disease was consistent across different geographical regions (Supplementary material online, Table S62). However, there was a greater decline in STEMI hospitalizations during the pandemic in LMICs (RR = 0.79, 95% CI 0.66–0.94). Notably, between the first and second wave, we found no difference in decline of hospitalizations for STEMI, NSTEMI, and HF. However, studies that reported data pertaining to a longer time span during the pandemic demonstrated a less extreme effect size for decline in hospitalizations for STEMI and NSTEMI compared with studies that reported a shorter time span (STEMI hospitalizations RR 1.17, 95% CI 1.00–1.38; NSTEMI hospitalizations RR 1.30, 95% CI 1.09–1.57).
For other acute CV presentations, there is limited evidence for the impact of the pandemic. A single-centre study reported that the number of hospitalizations with pericarditis and hypertensive crisis did not increase during the pandemic.39 A Danish nationwide study of infective endocarditis (IE) hospitalizations found no difference during the pandemic, whereas a Mexican single-centre study showed a 93% reduction.40,41 One single-centre study reported a decline in hospitalizations with adult congenital heart disease (ACHD) during the pandemic,42 and two studies demonstrated a significant increase in the incidence of stress cardiomyopathy.43,44
Invasive management of acute myocardial infarction
The number of percutaneous coronary intervention (PCI) procedures for STEMI and NSTEMI declined during the pandemic to a similar extent to the decline in hospitalizations (PCI for STEMI, IRR 0.72, 95% CI 0.67–0.77, I2 = 92.5%; PCI for NSTEMI, IRR 0.70, 95% CI 0.61–0.80, I2 = 88.1%) (Figure 4; Supplementary material online, S6 and S7). However, amongst patients hospitalized for STEMI and NSTEMI, the proportion who received revascularization did not change during the pandemic (PCI for STEMI hospitalizations, RR 0.98, 95% CI 0.96–1.01, I2 = 82.3%; PCI for NSTEMI hospitalizations, RR 1.05, 95% CI 0.93–1.17, I2 = 88.3%) (Supplementary material online, Figures S8 and S9).
The detrimental effect of the pandemic is evident in system delays related to the STEMI care pathway. Whilst door-to-balloon times (D2B) did not increase significantly during the pandemic (WMD 3.33 min, 95% CI −0.32 to 6.98 min, I2 = 94.2%) we estimated that there was over an hour greater delay between symptoms to first medical contact (S-FMC) during the pandemic (WMD 69.45 min, 95% CI 11.00–127.89 min, I2 = 99.4%) (Supplementary material online, Figure S10).
There was divergence by geographic region and country-level income in the management of acute myocardial infarction during the pandemic. Meta-regression demonstrated that the decline in revascularization was greater in LMICs compared with HICs (PCI for STEMI, RR 0.73, 95% CI 0.62–0.87; PCI for NSTEMI, RR 0.69, 95% CI 0.48–0.99) (Supplementary material online, Table S62). Increases in D2B and S-FMC time were only found to be significant in countries outside of Europe and North America (Table 1). Finally, the proportion of patients treated for STEMI with thrombolysis increased during the pandemic (RR 1.41, 95% CI 1.08–1.84, I2 = 55.3%) (Supplementary material online, Figure S8), driven by increased use of thrombolysis in LMICs and countries outside of Europe and North America (Table 1).
Table 1 Summary estimates for all outcomes by subgroups of geographical region and country-level income classification
Geographic region Country-level income
All data Europe North America Other countries High-income countries Low–middle income countries
Study Estimate (95% CI) Study Estimate (95% CI) Study Estimate (95% CI) Study Estimate (95% CI) Study Estimate (95% CI) Study Estimate (95% CI)
Hospitalization (IRR)
STEMI 65 0.78 (0.72–0.85) 39 0.80 (0.74–0.87) 7 0.83 (0.73–0.94) 19 0.72 (0.58–0.89) 48 0.82 (0.77–0.88) 17 0.68 (0.54–0.85)
NSTEMI 43 0.66 (0.60–0.72) 29 0.68 (0.60–0.76) 3 0.71 (0.66–0.75) 11 0.60 (0.51–0.71) 32 0.68 (0.61–0.76) 11 0.58 (0.49–0.69)
Unstable angina 9 0.80 (0.66–0.98) 6 0.77 (0.63–0.95) 0 3 0.88 (0.53–1.49) 7 0.78 (0.60–1.02) 2 0.85 (0.69–1.03)
Heart failure 19 0.66 (0.59–0.73) 12 0.70 (0.65–0.74) 5 0.58 (0.41–0.84) 2 0.65 (0.39–1.08) 17 0.66 (0.58–0.74) 2 0.65 (0.39–1.08)
Arrhythmias 6 0.70 (0.57–0.85) 5 0.73 (0.59–0.90) 0 1 0.51 (0.36–0.72) 4 0.75 (0.58–0.99) 2 0.61 (0.49–0.77)
AMI management (IRR or RR)
STEMI (PCI procedures, IRR) 47 0.72 (0.67–0.77) 28 0.75 (0.70–0.80) 3 0.75 (0.58–0.97) 16 0.66 (0.54–0.79) 37 0.76 (0.71–0.81) 10 0.60 (0.49–0.72)
NSTEMI (PCI procedures, IRR) 15 0.70 (0.61–0.80) 10 0.72 (0.66–0.78) 2 0.75 (0.67–0.83) 3 0.59 (0.31–1.13) 12 0.72 (0.67–0.77) 3 0.59 (0.31–1.13)
STEMI (thrombolysis rate, RR) 13 1.41 (1.08–1.84) 6 1.02 (0.80–1.29) 0 7 2.18 (1.10–4.31) 7 1.07 (0.87–1.33) 6 2.70 (1.07–6.86)
STEMI (PCI rate, RR) 17 0.98 (0.96–1.01) 9 0.99 (0.96–1.02) 1 1.04 (1.00–1.09) 7 0.96 (0.93–1.00) 12 0.99 (0.97–1.02) 5 0.89 (0.72–1.10)
NSTEMI (PCI rate, RR) 10 1.05 (0.93–1.17) 4 1.06 (0.88–1.29) 2 1.15 (0.91–1.46) 4 0.99 (0.80–1.22) 6 1.02 (0.95–1.10) 4 1.12 (0.83–1.52)
Delays in STEMI care (WMD, minutes)
Symptom to first medical contact time 12 69.5 (11.0 to 127.9) 5 14.6 (–11.1 to 40.3) 2 225.1 (–23.1–473.2) 5 48.0 (7.1– 88.9) 7 85.4 (14.5–185.3) 5 48.0 (7.1– 88.9)
Door-to-balloon time 22 3.3 (–0.3 to 7.0) 11 0.9 (–2.8 to 4.8) 2 –1.7 (–4.5 to 1.2) 9 8.4 (0.6– 16.2) 17 0.9 (–2.2 to 4.0) 5 9.5 (–0.5 to 19.4)
Cardiac surgery and TAVI (IRR)
Cardiac surgery 11 0.66 (0.55–0.79) 5 0.59 (0.41–0.84) 2 0.66 (0.61–0.72) 4 0.87 (0.85–0.88) 9 0.64 (0.53–0.79) 2 0.76 (0.49–1.18)
CABG 9 0.58 (0.44–0.76) 4 0.45 (0.33–0.61) 2 0.78 (0.39–1.55) 3 0.62 (0.39–1.00) 7 0.59 (0.42–0.82) 2 0.54 (0.29–1.03)
Valve surgery 5 0.57 (0.34–0.96) 3 0.53 (0.29–0.96) 2 0.59 (0.17–2.02) 0 5 0.57 (0.34–0.96) 0
Aortic valve surgery 7 0.59 (0.48–0.73) 6 0.56 (0.47–0.67) 0 1 1.13 (0.57–2.27) 7 0.59 (0.48–0.73) 0
TAVI 7 0.76 (0.43–1.33) 6 0.65 (0.37–1.14) 1 1.83 (1.67–2.00) 0 7 0.76 (0.43–1.33) 0
EP procedures (IRR)
PPM 8 0.55 (0.44–0.69) 6 0.54 (0.45–0.64) 0 2 0.58 (0.23–1.44) 6 0.54 (0.45–0.64) 2 0.58 (0.23–1.44)
All CIED 5 0.51 (0.44–0.59) 5 0.51 (0.44–0.59) 0 0 5 0.51 (0.44–0.59) 0
Percutaneous catheter ablation 5 0.42 (0.24–0.75) 3 0.47 (0.22–0.97) 1 0.20 (0.17–0.24) 1 0.68 (0.46–1.03) 5 0.42 (0.24–0.75) 0
Diagnostic procedures (IRR)
Transthoracic echo 6 0.29 (0.19–0.46) 4 0.28 (0.16–0.47) 0 2 0.33 (0.11–1.00) 4 0.28 (0.16–0.47) 2 0.33 (0.11–1.00)
ECG 4 0.21 (0.08–0.57) 2 0.22 (0.08–0.60) 0 2 0.19 (0.02–1.82) 2 0.22 (0.08–0.60) 2 0.19 (0.02–1.82)
ABPM 3 0.12 (0.03–0.50) 1 0.22 (0.14–0.33) 0 2 0.08 (0.01–0.93) 1 0.22 (0.14–0.33) 2 0.08 (0.01–0.93)
Ambulatory ECG monitoring 4 0.25 (0.12–0.51) 2 0.28 (0.23–0.34) 0 2 0.19 (0.03–1.39) 2 0.28 (0.23–0.34) 2 0.19 (0.03–1.39)
Exercise tolerance tests 3 0.32 (0.17–0.61) 1 0.47 (0.32–0.69) 0 2 0.26 (0.10–0.66) 1 0.47 (0.32–0.69) 2 0.26 (0.10–0.66)
Mortality (RR)
STEMI 37 1.17 (1.07–1.28) 18 1.20 (1.04–1.38) 3 0.97 (0.56–1.69) 16 1.14 (1.04–1.26) 23 1.11 (0.97–1.28) 14 1.22 (1.10–1.37)
NSTEMI 8 0.94 (0.83–1.07) 5 0.94 (0.82–1.07) 0 3 1.12 (0.44–2.86) 4 0.94 (0.82–1.07) 4 1.06 (0.55–2.05)
Heart failure 6 1.11 (1.03–1.20) 4 1.13 (0.99–1.29) 0 2 1.08 (1.04–1.12) 4 1.13 (0.99–1.29) 2 1.08 (1.04–1.12)
OHCA medical cause 4 0.78 (0.58–1.04) 3 0.70 (0.52–0.95) 1 1.03 (0.92–1.15) 0 4 0.78 (0.58–1.04) 0
OHCA cardiac cause 6 1.04 (0.76–1.40) 2 0.91 (0.36–2.27) 2 1.27 (0.79–2.03) 2 0.95 (0.78–1.17) 6 1.04 (0.76–1.40) 0
ABPM, ambulatory blood pressure monitoring; AMI, acute myocardial infarction; CABG, coronary artery bypass graft; CI, confidence interval; CIED, cardiac implantable electronic device; ECG, electrocardiogram; EP, electrophysiology; IRR; incidence rate ratio; NSTEMI, non-ST-elevation myocardial infarction; OHCA, out-of-hospital cardiac arrest; PCI, percutaneous coronary intervention; PPM, permanent pacemaker; RR, relative risk; STEMI, ST-elevation myocardial infarction, TAVI, transcatheter aortic valve implantation; WMD, weighted mean difference.
Interventional procedures
Nationwide data from the UK and the USA found that elective PCI decreased by >50% during the pandemic,45,46 and disproportionately affected older ages and Black, Asian, and minority ethnic (BAME) groups.45 During the pandemic, we observed a reduction in implantations of permanent pacemakers (IRR 0.55, 95% CI 0.44–0.69, I2 = 98.3%), implantations of all CIEDs (IRR 0.51, 95% CI 0.44–0.59, I2 = 86.0%), and the overall number of percutaneous catheter ablations performed (IRR 0.42, 95% CI 0.24–0.75, I2 = 99.4%) (Figure 4; Supplementary material online, Figure S11). In contrast, we found conflicting reports for rates of transcatheter aortic valve implantations (TAVIs) during the pandemic compared with pre-pandemic (IRR 0.76, 95% CI 0.43–1.33, I2 = 99.2%) (Supplementary material online, Figure S12). Whilst reports from most of Europe showed a decline in TAVI rates,1,47–50 there was an increase in the number of TAVI procedures performed during the pandemic in Poland and Ontario, Canada.51,52
The total number of cardiac surgical operations fell during the pandemic (IRR 0.66; 95% CI 0.55–0.79, I2 = 99.6%) (Supplementary material online, Figure S12). There were clear declines in coronary artery bypass graft (CABG) operations (IRR 0.58, 95% CI 0.44–0.76, I2 = 99.0%) and surgical interventions for the aortic valve (IRR 0.59, 95% CI 0.48–0.73, I2 = 85.6%).
Diagnostic procedures
Observational studies reporting a comparison of the number of diagnostic CV procedures during and pre-pandemic were infrequent. Available studies reported declines in exercise tolerance tests (IRR 0.32, 95% CI 0.17–0.61, I2 = 92.9%), ambulatory ECG monitoring (IRR 0.25, 95% CI 0.12–0.51, I2 = 96.6%), ambulatory blood pressure monitoring (IRR 0.12, 95% CI 0.03–0.50, I2 = 97.1%), 12-lead ECGs (IRR 0.21, 95% CI 0.08–0.57, I2 = 99.3%), and transthoracic echocardiograms (IRR 0.29, 95% CI 0.19–0.46, I2 = 98.1%) during the pandemic (Figure 4; Supplementary material online, S13). The use of diagnostic invasive coronary angiography has been reported to fall by as much as 74%.53 Single-centre studies demonstrated that transoesophageal echocardiograms, computed tomography coronary angiograms, and myocardial perfusion scans either ceased or sharply declined.27,54,55
Outpatient and community consultations
During the pandemic, we found a marked decline in in-person outpatient consultations (IRR 0.27, 95% CI 0.09–0.75, I2 = 100%) (see Supplementary material online, Figure S14). Five studies reported an increase in telemedicine cardiology outpatient appointments in both HICs and LMICs during the pandemic.54,56–59 However, multicentre reports from the USA and Germany suggested overall deficits of 61%, 33%, and 5% in outpatient CV consultations even after including telemedicine appointments.56,58,60 Surveys showed that almost half of all exercise-based cardiac rehabilitation programmes closed during the pandemic,61–63 and of programmes that continued many used technology to provide virtual consultations.62–64
Mortality
In-hospital all-cause mortality
For patients hospitalized with acute CV disease, in-hospital all-cause mortality was reported frequently and 30-day all-cause mortality rarely. For both STEMI and HF, in-hospital mortality increased during the pandemic (STEMI, RR 1.17, 95% CI 1.07–1.28, I2 = 23.3%; HF, RR 1.11, 95% CI 1.03–1.20, I2 = 63.9%) and did not differ for NSTEMI (RR 0.94, 95% CI 0.83–1.07, I2 = 0.0%) (Figure 4; Supplementary material online, S15 and S16). For both STEMI and HF, in-hospital mortality increased during the pandemic in LMICs but not in HICs (Table 1).
30-day all-cause mortality
Only six studies reported 30-day all-cause mortality for NSTEMI, STEMI, or HF.65–70 Three studies showed that 30-day mortality increased during the pandemic for NSTEMI but not STEMI.65–67 In one report, higher 30-day mortality for NSTEMI was correlated with concurrent SARS-CoV-2 infection.67 For the other two studies, infection status was not reported but primary PCI (PPCI) was ‘protected’ during the pandemic whilst patients admitted for NSTEMI received lower rates of and a greater delay to angiography.65,66 An analysis of nationwide health records described increased odds of 30-day mortality following admission with HF.70 Notably, studies of mortality in the mid- to long term suggest that these trends may continue. One-year cardiac-related mortality for patients admitted for STEMI during the pandemic was reported to be no different from a historical control group, in spite of worse in-hospital outcomes.71 Patients admitted for NSTEMI during the pandemic, who on average waited longer for revascularization, have been reported to have over twice as high a risk of all-cause mortality and a 20-fold increased risk of hospitalization with HF at 6 months compared with historical controls.72 Patients surviving hospitalization for HF during the pandemic also have higher all-cause mortality at 1 year compared with patients hospitalized in 2019, correlated with fewer receiving their inpatient care on specialist cardiology wards.73
Out-of-hospital cardiac arrest
We found no evidence for an increase during the pandemic period of out-of-hospital cardiac arrest (OHCA) of presumed medical or cardiac cause—as defined by attending emergency medical service personnel (OHCA medical cause, IRR 0.78, 95% CI 0.58–1.04, I2 = 95.1%; OHCA cardiac cause, IRR 1.04, 95% CI 0.76–1.40, I2 = 98.6%) (Figure 4; Supplementary material online, S17 and S18).
Population-level cardiovascular mortality
Four studies using UK nationwide data reported increased non-COVID-19 acute CV mortality compared with the historical average in the early months of the pandemic,74–77 with a ‘displacement of death’ occurring in homes (30.9% vs. 23.5%) and care homes (15.7% vs. 13.5%).77 In the USA, two studies demonstrated increased deaths from heart disease during the pandemic compared with previous years,78,79 with a greater excess in areas of higher density of COVID-19 infection.78 This pattern was also noted in LMICs, with the greatest excess CV mortality reported in the most deprived cities.80,81
Discussion
This systematic review and meta-analysis of the effect of the COVID-19 pandemic on CV services has identified a number of important points. First, the COVID-19 pandemic witnessed a substantial global decline in hospitalizations with acute CV disease, fewer diagnostic and interventional procedures, and fewer outpatient and community consultations. Second, we found no difference in the decline in hospitalizations for STEMI, NSTEMI, and HF during the second wave compared with the first wave. Third, there is disparity in the severity of collateral CV damage across geographic and economic boundaries. Across LMICs and countries outside of Europe and North America, we observed a more severe decline in hospitalizations and revascularization for STEMI, greater delays in STEMI care pathways with more frequent use of thrombolysis, and elevated in-hospital mortality for both STEMI and HF (Structured Graphical Abstract).
Previous reviews have observed a decline in hospitalizations for ACS during the pandemic,8–10 but here we extend the quantitative analysis of hospitalization rates to HF and arrhythmias, and demonstrate similar patterns. Other authors have shown that in-hospital mortality rose during the pandemic when studies reporting different CV diseases are combined,17 and specifically in patients who underwent PPCI for STEMI.9 In this analysis, we are able to demonstrate elevated in-hospital mortality during the pandemic for both STEMI and HF, and demonstrate variation across geographic regions and by country economic development. Finally, we provide the first estimates of the detrimental effect of the pandemic on interventional procedures, diagnostic procedures, and outpatient consultations.
We found that the decline in hospitalization for acute CV disease occurred across the breadth of CV diseases, and reports suggest that reductions occurred irrespective of formal restrictions on movement,65,82,83 or the extent of COVID-19 diagnoses within the local population.84 We observed delays to seeking help and receiving medical attention, independent reports of increased CV deaths in homes and care homes, and reports of increased case severity amongst those who did reach hospital.3,42,85–87 One may infer that fear of the contagion, ‘stay at home campaigns’, and overwhelmed emergency medical services prevented and delayed hospitalization of unwell patients. The scale of disruption to public interaction with CV services was not fully anticipated before the pandemic. In response, information campaigns, such as ‘You can’t pause a heart’ by the European Society of Cardiology (ESC),88 aimed to equilibrate public health messaging by accentuating the importance of expediently seeking medical attention for symptoms of acute CV disease. Whilst some studies reported that information campaigns quickened recovery in rates of hospitalization for acute myocardial infarction,82,83,89,90 we did not find a significant difference in the decline of hospitalization rates between the first and second wave across STEMI, NSTEMI, and HF. However, we did observe that studies reporting a longer time span of the pandemic period, and thus better reflecting both ‘decline’ and ‘recovery’ phases of hospitalization rates related to public health restrictions,65 evidenced a less extreme decline in hospitalizations for acute CV disease. Initial evidence on the Omicron variant suggests that it is more easily spread, but generally causes less severe disease, than previous SARS-CoV-2 variants.91 As the public and healthcare services become more familiar with ‘living with’ COVID-19 and widespread vaccination in HICs limits morbidity and mortality directly related to SARS-CoV-2 infection,92 it remains to be seen if hospitalization rates for acute CV disease will be robust to future waves.
There were comparatively few available data for the effect of the pandemic on CV services in LMICs. Only for hospitalizations, STEMI care pathways, and in-hospital mortality were we able to investigate for disparities compared with HICs, and we consistently found more severe collateral CV damage. The 143 LMICs constitute 80% of the world’s population—approximately 6 billion people—and the World Health Organization (WHO) estimates that 80% of all CV deaths now occur in LMICs.93 Whilst guideline-based therapy for STEMI has dramatically improved outcomes in HICs, regional systems of care for STEMI in LMICs are sparse. There are few emergency medical services, catheterization labs tend to be clustered in urban centres, and poor insurance coverage for the majority of the population limits the applicability of expensive procedures, leaving fibrinolysis as the most common treatment of STEMI.94 Historically, inpatients with acute HF in North America and Europe have had lower mortality rates than patients in South America and Asia,95 and 6-month mortality rates of almost 20% after HF hospitalization have been reported in sub-Saharan Africa.96 Access to diagnostic and interventional cardiac procedures is limited in LMICs,97 as is the ability to be able to provide guideline-directed management for other CV diseases.98 The pandemic exacerbated established challenges to the delivery of STEMI and HF care in LMICs. We are concerned that the gap in CV care and outcomes between HICs and LMICs may have widened during the pandemic across the breadth of CV diseases and services, yet data are not available to evidence this notion.
Collateral CV damage from missed diagnoses and delayed treatments will continue to accrue unless mitigation strategies are speedily implemented (Figure 5). The deferral of interventional procedures, especially for structural heart disease, leaves many patients at high risk of adverse outcomes.99 Risk stratification and prioritization will be needed to avert substantial excess mortality,100,101 and the pragmatic use of percutaneous over surgical options should be considered.102–104 A digital transformation in the healthcare model could cut the deficit in outpatient care and improve risk factor control. During the pandemic, there have been fewer contacts for CV diagnoses and risk factor monitoring,105,106 and lockdowns led to a significant decline in physical activity, weight gain, and worsening psychological health.107,108 Virtual consultations and tele-rehabilitation can provide better patient engagement with similar outcomes to in-person interactions, and patients can be empowered to manage their CV health by integrating home health equipment into routine clinical practice.59,109,110 Nonetheless, inequitable access to telemedicine and digital technology has been described for female, non-English-speaking, older, and poorer patients, and we must guard against reinforcing such inequities in healthcare.111
Figure 5 Potential collateral damage of the COVID-19 pandemic to cardiovascular services. The height and time scale of the three peaks depicted are not certain or to scale. We do expect the disruption to cardiovascular services to accumulate over time unless mitigation strategies are utilized.
As this review reveals, there is limited information about CV health and care from LMICs (data gaps exist in the African, South American, and Western Pacific regions). There are a few nationwide initiatives to systematically collect and report data on CV health in LMICs,112 and the WHO is engaging with member states and technology partners to strengthen their local health information systems.113 The ESC Atlas of Cardiology provides an enviable resource for data relating to population health in Europe.114 A global living collaborative network focusing on CV care during the pandemic at an institutional level could be established,115 and internationally harmonized CV data available in a responsive fashion could enable a ‘global barometer’ of the consequences of the pandemic as well as the opportunity to prepare for future major health crises.116
There are limitations to our analysis. The evidence base is skewed to HICs in Europe and North America, the earlier part of the pandemic, certain CV diseases, and short-term outcome measures, which limit quantitative insights. We classified most studies as being at severe or moderate risk of bias across all outcomes, which is in agreement with previous reports of the methodological quality of publications during the COVID-19 pandemic.17,117 Many studies did not report the number or proportion of included patients that had co-existent COVID-19 infection, which introduces bias and prohibits detailed analysis of what contribution the direct effect of COVID-19 on the CV system may have had on our estimates for in-hospital mortality and hospitalizations. Nonetheless, a meta-analysis including >27 000 patients demonstrated that in-hospital mortality in CV disease was increased during the pandemic independent of co-infection with COVID-19, and the direction of effect was consistent between studies at moderate and severe risk of bias.17 Furthermore, the direct CV consequences of COVID-19 include myocarditis, HF, arrhythmias, and acute myocardial injury,118 so the number of hospitalizations for acute CV disease would probably increase if direct COVID-19 pathology was the predominant factor, in contrast to our findings.
Heterogeneity was high in most analyses, which we investigated through meta-regression for a range of factors in outcomes of hospitalizations, invasive management of acute myocardial infarction, and in-hospital mortality. We found that geographic region, income classification, and whether the first or second wave was reported introduced variability in effect size, as did study characteristics such as the data source, presence of a matched comparator period, the length of the pandemic study period, and the time point at which data collection started during the pandemic period (Supplementary material online, Table S62). Significance was often not reached for individual factors due to the small number of studies. The smaller number of studies reporting procedures and outpatient consultations precluded meta-regression to investigate heterogeneity. Nevertheless, the direction of association is consistent across outcomes (Supplementary material online, Figures S1–S18), suggesting that the conclusions we draw for trends during the pandemic are reliable.
Conclusions
This systematic review with a meta-analysis provides, to date, the most comprehensive summary of the effect of the COVID-19 pandemic on CV services and individuals with CV disease. From 189 articles, we show evidence of fewer hospitalizations, procedures, and consultations with increased mortality amongst in-hospital and community populations. We identified disparity by geographical region and country income classification in the availability of data and the severity of the detrimental effect of the pandemic on CV services, and presently there are insufficient data to fully characterize the effects to CV services in LMICs. Notwithstanding this, we provide synthesized evidence that the COVID-19 pandemic resulted in substantial global collateral CV damage.
Author contributions
C.P.G. conceived the idea of the study. R.N. and B.H. screened the studies and reviewed the selected articles. R.N. and B.H. undertook data extraction. J.W. carried out the statistical analysis. R.N., J.W., and C.P.G. interpreted the findings, and R.N. drafted the manuscript. J.W., B.H., S.A., D.L.B., G.B.Z., L.S.M., C.V.S.R., A.P.L.R., H.G.C.V.S., J.E.D., T.F.L., M.M., and C.P.G. critically reviewed the manuscript, and R.N. revised the manuscript for final submission. All authors have approved the final draft of the manuscript. R.N. is the guarantor. R.N. accepts full responsibility for the work and the conduct of the review, had access to the data, and controlled the decision to publish. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.
Supplementary material
Supplementary material is available at European Heart Journal online.
Supplementary Material
ehac227_Supplementary_Data Click here for additional data file.
Acknowledgements
We acknowledge the tremendous help of Katerina Davidson in formatting the manuscript, organizing records, and designing tables, Keerthenan Raveendra for screening articles and discussing data extraction strategies, and Karen Abel, library research support and data advisor at the University of Leeds, in developing the initial search terms and strategy.
Conflict of interest: none declared.
Ethical approval
Ethical approval was not required.
Data sharing
Data are available on reasonable request. The technical appendix, statistical code, and dataset are available from the corresponding author at [email protected].
Abbreviations
ACHD adult congenital heart disease
ACS acute coronary syndrome
CABG coronary artery bypass graft
CIED cardiac implantable electronic device
COVID-19 coronavirus disease 2019
CV cardiovascular
D2B door-to-balloon time
ECG electrocardiogram
ESC European Society of Cardiology
HF heart failure
HIC high-income country
ICD implantable cardioverter defibrillator
IE infective endocarditis
IRR incidence rate ratio
LMIC low–middle income country
NSTEMI non-ST-elevation myocardial infarction
OHCA out-of-hospital cardiac arrest
PCI percutaneous coronary intervention
PPCI primary PCI
RR risk ratio
S-FMC symptom to first medical contact
STEMI ST-elevation myocardial infarction
TAVI transcatheter aortic valve implantation
VA ventricular arrhythmia
WHO World Health Organization
WMD weighted mean difference
==== Refs
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| 36044988 | PMC9724453 | NO-CC CODE | 2022-12-09 23:25:57 | no | Eur Heart J. 2022 May 30;:ehac227 | utf-8 | Eur Heart J | 2,022 | 10.1093/eurheartj/ehac227 | oa_other |
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Rev Esp Cir Ortop Traumatol
Rev Esp Cir Ortop Traumatol
Revista Espanola De Cirugia Ortopedica Y Traumatologia
1888-4415
1988-8856
SECOT. Published by Elsevier España, S.L.U.
S1888-4415(22)00348-4
10.1016/j.recot.2022.11.007
Article
Impacto de la pandemia COVID-19 en la cirugía de columna en un centro de segundo nivel
Impact of COVID-19 pandemia on spine surgery in 2nd level hospitalFlorensa Pau Solé ⁎
Sanchez Jacob González
Torrano Adrián Gil
Garcia Jaume Peroy
Talavera Ramon Jové
Atance Jaume Mas
Servicio de Cirugía Ortopédica y Traumatología, Hospital Universitari Arnau de Vilanova de Lleida, Spain
⁎ Autor correspondencia
6 12 2022
6 12 2022
10 10 2022
28 11 2022
© 2022 SECOT. 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.
Introducción/Objetivo: Las consecuencias de la pandemia COVID-19, como en otros aspectos de la medicina, se han visto reflejadas también en la actividad quirúrgica de columna vertebral.
El objetivo principal del presente estudio es cuantificar el número de intervenciones realizadas entre los años 2016 y 2021 y analizar el tiempo de espera en los pacientes intervenidos como medida indirecta del volumen de la lista de espera. Como objetivos secundarios se realiza un análisis del tiempo de estancia hospitalaria y el tiempo quirúrgico a lo largo de la serie.
Métodos: Se ha realizado un estudio descriptivo retrospectivo en relación con el volumen de intervenciones y diagnósticos durante un periodo que incluye desde la etapa previa a la pandemia (2016) hasta finales del año 2021 en que la situación global llegó a una cuasi normalización de la actividad. Se han identificado un total de 1039 registros. Se incluyen las variables edad, género, días en lista de espera antes de la intervención, diagnóstico, tiempo de estancia hospitalaria y tiempo quirúrgico.
Resultados: Se objetiva una disminución en el número total de intervenciones durante la pandemia respecto al año 2019 (32.15% menos el año 2020 y 23.5% menos el 2021). Tras el análisis de los datos, se observa un aumento en la dispersión y la mediana del tiempo de espera global y por patologías a partir del 2020, sin observarse diferencias significativas en el tiempo de hospitalización ni en tiempo quirúrgico.
Conclusión: Durante la pandemia se ha producido una disminución del número de intervenciones debido a la necesidad de redistribuir recursos humanos y materiales para hacer frente al incremento de pacientes críticos afectados por COVID-19. El aumento de la dispersión y de la mediana global y por patologías de la variable tiempo de espera, se traduce como un aumento del tiempo de espera en las cirugías diferibles realizadas durante los años de la pandemia y un aumento de las intervenciones realizadas de manera urgente, éstas con un tiempo de espera mucho menor.
Introduction/Objectives: The consequences of COVID-19 pandemic, like in any other field of medicine, had such a massive effect in the activity of spine surgeons.
The main purpouse of the study is quantifying the number of interventions done between 2016 and 2021 and analyze the time between the indication and the intervention as an indirect measurement of the waiting list. As secondary objectives we focused on variations of the length of stay and duration of the surgeries during this specific period.
Methods: We performed a descriptive retrospective study including all the interventions and diagnosis made during a period including pre-pandemic data (starting on 2016) until 2021, when we considered the normalization of surgical activity was achieved.
1039 registers were compiled. The data collected included age, gender, days in waiting list before the intervention, diagnosis, time of hospitalization and surgery duration.
Results: We found that the total number of interventions during the pandemic has significantly decreased compared to 2019 (32.15% less in 2020 and 23.5% less in 2021). After de data analysis, we found an increase of data dispersion, average waiting list time and for diagnosis after 2020. No differences were found regarding hospitalization time or surgical time.
Conclusion: The number of surgeries decreased during pandemic due to the redistribution of human and material resources to face the raising of critical COVID-19 patients. The increase of data dispersion and median of waiting time, is the consequence of a growing waiting list for non-urgent surgeries during the pandemic as the urgent interventions also raised, those with a shorter waiting time.
Palabras clave
Cirugía de columna
Spine surgery
Covid-19
Keywords
Spine surgery
Covid-19
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pmc
| 36494012 | PMC9724500 | NO-CC CODE | 2022-12-07 23:20:13 | no | Rev Esp Cir Ortop Traumatol. 2022 Dec 6; doi: 10.1016/j.recot.2022.11.007 | utf-8 | Rev Esp Cir Ortop Traumatol | 2,022 | 10.1016/j.recot.2022.11.007 | oa_other |
==== Front
Prev Med Rep
Prev Med Rep
Preventive Medicine Reports
2211-3355
Published by Elsevier Inc.
S2211-3355(22)00397-7
10.1016/j.pmedr.2022.102090
102090
Article
The United States Public Health Services Failure to Control the Coronavirus Epidemic
Simoes Eduardo J. ⁎
Jackson-Thompson Jeannette
University of Missouri (MU) School of Medicine Department of Health Management and Informatics and MU Institute for Data Science and Informatics
⁎ Corresponding author at: Director of the Health and Behavioral Risk Research Center, Department of Health Management, and Informatics, School of Medicine, University of Missouri, CE 707 CS&E Bldg. 1 Hospital Drive, Columbia, MO 65212.
6 12 2022
6 12 2022
10209028 9 2022
3 12 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.
The unprecedented COVID-19 epidemic in the United States (US) and worldwide, caused by a new type of coronavirus (SARS-CoV-2), occurred mostly because of higher-than-expected transmission speed and degree of virulence compared with previous respiratory virus outbreaks, especially earlier Coronaviruses with person-to-person transmission (e.g., MERS, SARS). The epidemic’s size and duration, however, are mostly a function of failure of public health systems to prevent/control the epidemic. In the US, this failure was due to historical disinvestment in public health services, key players equivocating on decisions, and political interference in public health actions. In this communication, we present a summary of these failures, discuss root causes, and make recommendations for improvement with focus on public health decisions.
Keywords
public
health
prevention
control
COVID-19
epidemic
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pmc1 Introduction
The unprecedented COVID-19 epidemic in the United States (US) and worldwide, caused by a new type of coronavirus (SARS-CoV-2), occurred mostly because of higher-than-expected transmission speed and degree of virulence compared with previous respiratory virus outbreaks, especially earlier Coronaviruses with person-to-person transmission (e.g., MERS, SARS). 1 , 2The epidemic’s size and duration, however, are mostly a function of failure of public health systems to prevent/control the epidemic. In the US, this failure was due to historical disinvestment in public health services, key players equivocating on decisions, and political interference in public health actions. 3 , 4 , 5In this short communication, focus is on public health decisions.
2 Background
The United States Public Health Service (USPHS) along with health agencies in 50 states and District of Columbia (DC) plus about 3,000 local health agencies are legally and politically responsible for public health services and protecting population health. The USPHS is composed of nine agencies, including Centers for Disease Control and Prevention (CDC) and Food and Drug Administration (FDA) (Figure 1 ). 6 CDC’s mandate is to protect the public health of the nation and states, to protect the health of their state’s population. CDC works directly with state and local health agencies for public health actions contracted by the federal government to states, localities, and private organizations. In most localities, funding of essential public health functions (e.g., health surveillance and infectious disease outbreak investigations) is dependent on state guidance and funding through both state and federal programs.Figure 1 The Department of Health and Human Services Organization Chart representing the different agencies that compose it.
In the COVID-19 epidemic that started in the winter of 2019-2020, CDC and the network of state and local health agencies failed to protect the US population’s health due to inaction or ineffective actions before and during the epidemic. Decentralization of actions and CDC’s lateness issuing guidelines and providing additional personnel/other resources contributed to uncoordinated contact tracing and follow up measures among different actors. In this communication, we present a summary of these failures, discuss root causes, and make recommendations for improvement.
3 Public Health Inactions or Ineffective Actions “During the COVID-19 Epidemic”
In this epidemic, CDC and its network of state, local, and private organization partners failed in four critical actions that, if implemented early and effectively, were key to controlling this outbreak and reducing its burden (infection, complications, deaths). Needed actions were: 1) effective contact tracing; 2) effective, available diagnostic screening; 3) promoting mask use and other prevention/control measures; and 4) protecting targeted high-risk populations.
3.1 Contact Tracing – delayed, insufficient, ineffective, and uncoordinated
Contact tracing early in an epidemic is one of the most effective actions needed to control infectious disease epidemics. Researchers have identified that COVID-19 contact tracing was less effective in identifying secondary cases than in other infectious diseases, but a reasonable level of efficiency was reported that justified its use. However, initiation of contact tracing was delayed beyond expected in most areas. CDC took too long to coordinate contact tracing and early preventive actions with local health agencies and state health departments for five reasons.
First, due to political reasons, the federal government failed to alert the American people early in February 2020 when CDC's National Center for Immunization and Respiratory Diseases was ready to launch a planned campaign. 7 , 8Second, contact tracing/outbreak investigation depend on availability of minimally sensitive, specific, and reliable diagnostic tests to screen and/or confirm cases; these were not available for many months. In early 2020, due to faulty reagents and bureaucracy CDC failed to develop reliable laboratory tests for COVID-19. 9 Roll out of these tests to most state/local labs was slow, with samples sent to CDC for testing, and highly criticized by all users. Third, decentralization of public health actions, characteristic of the US public health network, provides agility for local-level action if resources are available locally. This decentralization, however, can hinder timing and coordination of efforts for problems with nationwide impact if agreed upon guidelines for engagement/intervention or key resources are not available. In the COVID-19 epidemic, state and local health agencies needed CDC’s leadership, guidance, and rapid training early in 2020, but CDC only started working more closely with states and aimed to increase contact tracing and other epidemic control measures with the October 1, 2020, $2.59 trillion Coronavirus Aid, Relief, and Economic Security (CARES) Act. Fourth, in February 2020, state and local health agencies needed a coordinated and massive effort from CDC to train/deploy thousands of additional contact tracers needed to control the epidemic; these agencies did not have the necessary workforce ready to implement contact tracing at the scale needed. As of January 2022, seven state health departments had “0” contact tracers on their roster; the remaining 43 states and DC had between 5 (Arizona) and 7,498 (California). Thus, there are 0 – 73 contact tracers per 100,000 population across 50 states and DC. For example, North Dakota had 73.2 contact tracers per 100K population, Nebraska 51 per 100K, California 19 per 100K, Pennsylvania 10.2 per 100K while Florida had zero contact tracers. 10 In addition, there are wide variations in models, processes, levels of centralization, and uses of technology and deployment among states. Finally, lower than expected efficacy and effectiveness have been reported. A June-October 2020 study with data from 13 states and one territory found that 2 of 3 individuals with COVID-19 were not reached for interview or named no contacts when interviewed; a mean of 0.7 contacts were reached by telephone by public health authorities; and only 0.5 contacts per case were monitored, a lower rate than needed to overcome the estimated global SARS-CoV-2 reproductive number. 11 An August-October 2020 study in Washington state found the proportion of individuals disclosing contacts remained below 50%, with minimal differences by demographic characteristics. The longest time interval occurred between symptom onset and test result notification. 12 Similar findings were reported for a study in North Carolina. 13
3.2 Diagnostic screening for infection – Ineffective or Unavailable
By February 2020, CDC had done only 459 tests. Also, FDA delayed approval of good test kits already made available by private sector. Despite a joint decision by CDC and FDA to create a workaround process, approval was delayed and staggered until May 2020. 14 First antigen test was approved on May 9, 2020; first antigen test where results could be read directly from testing card, on Aug 26, 2020; 15 and first antigen test for self-testing at home, on November 17, 2020. 16
3.3 Promoting mask use and other prevention and control measures – Confusing and Insufficient
Until the second half of 2020, due in part to flawed research on SARS-CoV-2’s aerosolizing potential, CDC continued to emphasize hand washing and surface cleaning as key strategies to prevent infection/disease, despite evidence that appropriate wearing of masks was more effective in reducing SARS-CoV-2 transmission. In addition, CDC flip-flopped on mask use. The original recommendation for wearing a cloth mask in July 2020 (https://www.cdc.gov/media/releases/2020/p0714-americans-to-wear-masks.html) was followed by modified recommendations for fully vaccinated people (visit others fully vaccinated without masking or distancing) in March 2021 (https://www.cdc.gov/media/releases/2021/p0308-vaccinated-guidelines.html). 17 , 18 In late July 2021, CDC recommended that fully vaccinated people should again mask indoors in high transmission areas (https://content.govdelivery.com/accounts/USFSIS/bulletins/2eaacee). 19 A consequence of this lack of clear signaling was a low uptake of mask wearing that has persisted to this day. 20 , 21
3.4 Protecting targeted high-risk populations - Insufficient
Early in the epidemic, studies from CDC and academic researchers identified high-risk groups for COVID-19, its complications and death: older adults; people with medical conditions, especially chronic diseases, and obesity; pregnant women; and jailed/imprisoned people. 22 , 23 , 24 , 25 In addition, researchers had recommended a high-risk population strategy for prevention and control of COVID-19. 26 Although CDC created health information material targeting high-risk groups, CDC and other public health agencies did little in the epidemic’s first year to guide the healthcare sector and communities on strategies to reduce COVID-19 burden in this population group. 27 The number of COVID-19 related deaths among nursing home residents as of December 20, 2020accounted for 38% of all COVID-19 deaths in the US and these numbers appears to be underestimated. 28 , 29 There is also evidence that in the first year of the epidemic, lower quality nursing homes had worse outcomes than those with higher quality of care. 30
A CDC guideline for strategies and interventions only became available on July 30, 2021, when vaccination had already been established for six months and community transmission levels were better known. 31 However, in the document, recommendations and guidelines were very general; strategies provided were nonspecific and not targeted, regardless of health care facility or high-risk group.
4 Root Causes of USPHS failure to control the COVID-19 epidemic
Two key reasons exist for less than effective action to control the US COVID-19 epidemic. First, before the epidemic’s start, CDC and state health agencies failed to fully assess, raise necessary awareness, and address continued and relentless defunding and reducing of essential public health functions at the state level. 32 Currently, the US spends approximately $3.6 trillion on health, but less than $100 billion on public health/prevention (<3%); CDC’s funding is at its 2008 level. The combination of nearly two decades of lower funding and reorganization substantially reduced the public health workforce at the state and, more significantly, local public health agency level. It reduced the number of contact tracers, health monitors and “shoe leather epidemiologists” essential for prevention and control of epidemics. 33 By 2020, there were 26,000 fewer employees at state, county, and municipal health agencies than in 2009. In an outbreak investigation, the shoe leather epidemiologist approach, refined by CDC’s Epidemic Intelligence Services (EIS), entails walking door-to-door (wearing out shoe leather in the process) asking patients and their contacts direct questions. (CDC. “CDC’s Epidemic Intelligence Service (EIS).” Centers for Disease Control and Prevention, August 15, 2022. https://www.cdc.gov/eis/index.html). 34
Second, USPHS was unable to deploy information and digital technology to prevention/control activities before and during the epidemic. Local, state, and federal health agencies were unable to deploy digital technologies during contact tracing on the scale necessary to reverse personnel deficiencies. For example, mobile apps with geolocation and associated technologies (used in Taiwan) should have been added to US contact tracing actions years ago. 35 CDC’s lead public health surveillance system in approximately half of states has organizational/functional structure and technological applications developed in the late 1980s. For example, the US National Notifiable Disease Surveillance System (NNDSS) still relies mainly on National Electronic Telecommunications System for Surveillance (NETSS) developed in the late 1980s-early 1990s. 36 Although a CDC-led data modernization initiative (DMI) is on the way, NETSS has yet to fully transition to a system that takes advantage of technological innovations in digital communication/transmission and 21st century informatics architecture. 37 , 38 DMI allows for uses of mobile informatic technology to support early disease detection, outbreak investigation, and detection of population epidemics. However, only a handful of states have fully implemented process and structured surveillance functions. 39
5 Conclusions – Way Forward
The USPHS, especially CDC and its network of federal, state, and local partners, has worked to build a robust public health surveillance system that effectively protected the health of Americans for over 40 years. This system’s weaknesses and partial failures appear to have contributed to a less than effective management of the COVID-19 epidemic, resulting in over 76.4 million cases, 4.4 million new hospitalizations (August 01, 2020 – February 05, 2022) and 900,000 deaths in the US between January 1, 2020, and February 05, 2022. 40
CDC’s public health surveillance improvement strategy launched in 2014, its updated DMI and experience gained to control the COVID-19 epidemic promise to bring changes that could address problems identified with the USPHS response to population health threats. With DMI, CDC is making progress in automation of data sources, data interoperability, transparency, and shared governance with partners, retaining and training health IT and informatics workforce and investing in decentralized informatics innovation. 41 These measures should increase speed of actions necessary to control and prevent epidemics. In addition, strengthening size and training of public health workforce at state and local levels is necessary. Finally, USPHS must readdress CDC’s governance/hierarchical structure to guarantee independence and agility of action to control modern epidemics, with their ever-increasing potential to become pandemics due to rapid means of transportation and economic interdependence of countries.
Uncited references
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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.
1 Rando et al., “Pathogenesis, Symptomatology, and Transmission of SARS-CoV-2 through Analysis of Viral Genomics and Structure.”
2 Abdelrahman, Li, and Wang, “Comparative Review of SARS-CoV-2, SARS-CoV, MERS-CoV, and Influenza A Respiratory Viruses.”
3 Viglione, “Four Ways Trump Has Meddled in Pandemic Science — and Why It Matters.”
4 Tollefson, “How Trump Damaged Science — and Why It Could Take Decades to Recover.”
5 Berg, “Inside the Fall of the CDC.”
6 Affairs (ASPA), “HHS Agencies & Offices.”
7 Piller, “The inside Story of How Trump’s COVID-19 Coordinator Undermined the World’s Top Health Agency.”
8 Tollefson, “How Trump Damaged Science — and Why It Could Take Decades to Recover.”
9 “The United States Badly Bungled Coronavirus Testing—but Things May Soon Improve.”
10 “State Approaches to Contact Tracing during the COVID-19 Pandemic.”
11 Lash et al., “COVID-19 Case Investigation and Contact Tracing in the US, 2020.”
12 Bonacci et al., “COVID-19 Contact Tracing Outcomes in Washington State, August and October 2020.”
13 Lash, “COVID-19 Contact Tracing in Two Counties — North Carolina, June–July 2020.”
14 Commissioner, “Coronavirus (COVID-19) Update,” May 12, 2020.
15 Commissioner, “COVID-19 Update.”
16 Commissioner, “Coronavirus (COVID-19) Update,” November 18, 2020.
17 “Coronavirus Disease 2019. CDC Calls on Americans to Wear Masks to Prevent COVID-19 Spread.”
18 “Coronavirus Disease 2019. CDC Guidelines on How Fully Vaccinated Can Visit Safely with Others.”
19 “Interim Public Health Recommendations for Fully Vaccinated People.”
20 Diamond, “Mask Mandates Make a Return — along with Controversy.”
21 Whelan, “CDC’s Covid-19 Mask Guidance Clouded by Flawed Data - WSJ.”
22 CDC, “Cases, Data, and Surveillance.”
23 Levin et al., “Assessing the Age Specificity of Infection Fatality Rates for COVID-19.”
24 Emami et al., “Prevalence of Underlying Diseases in Hospitalized Patients with COVID-19.”
25 Laires et al., “The Association Between Chronic Disease and Serious COVID-19 Outcomes and Its Influence on Risk Perception.”
26 Govindarajan, “Targeted Prevention of COVID-19, a Strategy to Focus on Protecting Potential Victims, Instead of Focusing on Viral Transmission.”
27 CDC, “COVID-19 and Your Health.”
28 Girvan, “38% of COVID-19 Deaths in Nursing Homes & Assisted Living Facilities.”
29 Shen et al., “Estimates of COVID-19 Cases and Deaths Among Nursing Home Residents Not Reported in Federal Data.”
30 Gupta et al., “Interpreting COVID-19 Deaths among Nursing Home Residents in the US.”
31 Christie, “Guidance for Implementing COVID-19 Prevention Strategies in the Context of Varying Community Transmission Levels and Vaccination Coverage.”
32 Trust for America’s Health, “The Impact of Chronic Underfunding on America’s Public Health System.”
33 Last, A Dictionary of Public Health (1 Ed.).
34 CDC, “CDC’s Epidemic Intelligence Service (EIS).”
35 Wang, Ng, and Brook, “Response to COVID-19 in Taiwan.”
36 “Case Surveillance History and Modernization | CDC.”
37 Richards, Iademarco, and Anderson, “A New Strategy for Public Health Surveillance at CDC.”
38 CDC, “Data Modernization Initiative.”
39 “CDC Data Modernization Initiative – Notable Milestones: 2019-2022.”
40 CDC, “COVID-19 Cases, Deaths, and Trends in the US | CDC COVID Data Tracker.”
41 “CDC Data Modernization Initiative – Notable Milestones: 2019-2022.”
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| 36507303 | PMC9724501 | NO-CC CODE | 2022-12-12 23:20:48 | no | Prev Med Rep. 2022 Dec 6;:102090 | utf-8 | Prev Med Rep | 2,022 | 10.1016/j.pmedr.2022.102090 | oa_other |
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